Citation
Internet Use and its Effect on Sexual Behavior in Traditional College-Age Students

Material Information

Title:
Internet Use and its Effect on Sexual Behavior in Traditional College-Age Students
Creator:
Pritchard, Paula Courtney
Place of Publication:
[Gainesville, Fla.]
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (195 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Nursing Sciences
Nursing
Committee Chair:
Simpson, Sharleen H.
Committee Members:
Seymour, Sandra F.
Neimeyer, Greg J.
Schmitt, Karla
Graduation Date:
5/1/2008

Subjects

Subjects / Keywords:
Adolescents ( jstor )
African Americans ( jstor )
College students ( jstor )
Email ( jstor )
Hispanics ( jstor )
Human sexual behavior ( jstor )
Internet ( jstor )
Sexually transmitted diseases ( jstor )
Universities ( jstor )
Womens studies ( jstor )
Nursing -- Dissertations, Academic -- UF
Genre:
Electronic Thesis or Dissertation
bibliography ( marcgt )
theses ( marcgt )
Nursing Sciences thesis, Ph.D.

Notes

Abstract:
Our purpose was to explore how technology, specifically internet use, gender and ethnicity may affect decision making, and influence sexual decision making in traditional college students. Using a 'paper-survey' designed by using Goodson and colleagues' (2000) survey tool 'Survey Instrument to Assess College Students' Behavior and Attitudes' to document demographic information as well as college student's perceptions and behaviors while using the internet (specifically for sexually related information) was modified into an actual internet survey document. Using mass emails, participants in this study were solicited from two university campuses in Florida. Due to their divergent populations, a small private Historically Black College/University (HBCU) as well as a large Florida public university (PFU) were chosen. Overall, 1202 students participated in the study. Results of this study demonstrate differences in gender, age, race and university of record. These differences appear in developing relationships, online sexual behaviors, email use, and offline sexual behaviors. The results indicate that students who are 23 and older are more likely to value the importance of the internet, and also value the opportunity to engage in developing online partners for different relationships that include online sexual activity when compared to their younger counterparts. Gender differences are also identified as an inverse relationship when combined with age. Females are less likely to use the internet as they age, and the opposite is true for males within this sample population. This information may provide insight into program changes that would target individuals based upon gender. The study also demonstrated racial differences in internet use. It is important to note that this area needs to be replicated specifically because socioeconomic factors were not taken into account, and therefore may have a significant impact upon the validity of the results. Therefore any future reports of racial differences using results of this study should be used with that cautionary note. The evidence of data obtained by this preliminary study of internet use and sexual behaviors of traditional college age students demonstrates the immediate need for future studies that target internet use and sexual behaviors by race, gender, and age. These studies should specifically look at the aspect of heightened sexual behavior as a result of frequency of internet use through the emerging adult years. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2008.
Local:
Adviser: Simpson, Sharleen H.
Statement of Responsibility:
by Paula Courtney Pritchard

Record Information

Source Institution:
UFRGP
Rights Management:
Copyright Paula Courtney Pritchard. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
7/11/2008
Classification:
LD1780 2008 ( lcc )

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Table 4-127. Correlations: While viewing sexually explicit materials I have masturbated.
Viewing
explicit
web sites I


School


Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N


Gender


Age


1 -.101(**)


1212


-.101(**)

.000
1210

-.025

.391
1211

-.662(**)

.000
1209


.000
1210


Race


have
masturbated


-.025 -.662(**) -.187(**)


.391
1211


1 .114(**)


1210


School





Gender





Age





Race


viewing
explicit
web sites I
have Pearson
masturbated Correlation -.187(**) .587(**)
Sig. (2-
tailed) .000 .000
N 1208 1206
** Correlation is significant at the 0.01 level (2-tailed).


.000
1209


.000
1209


.103(**)


.000
1208


.587(**)


.000
1207


1 .118(**)


1211

.118(**)

.000
1208


.000
1206


.093(**)


.000
1208


.001
1207


1 .136(**)


.000
1205


1209


.093(**)


.001
1207


.000
1205


1208


Table 4-128. Mean: While viewing sexually explicit web sites I have masturbated: School
School Mean Std. Deviation N
PFU 2.20 1.245 1045
HBCU 1.52 .893 155
Total 2.11 1.227 1200


.114(**)

.000
1209

.103(**)

.000
1207










Table 4-17. Mean: How often do you use email: Age
Age Mean Std. Deviation N
18-19 1.11 .354 412
20-22 1.07 .283 520
23-and above 1.09 .330 268
Total 1.09 .320 1200

Table 4-18. Mean: How often do you use email: Race
Race Mean Std. Deviation N
AAB 1.24 .530 210
AAPI 1.01 .119 71
MAL 1.06 .240 131
NHW 1.06 .253 728
O 1.06 .231 54
Total 1.09 .320 1199


Table 4-19. Tests of between subjects. How often do you use email: Age, Gender,
Year


Type III


D


Independent Sum of ol
Variable Squares Fi
Gender .266
Age .386
Race .867
School 1.081
Gender/Age .058
Gender/Race .744
Age/Race .846
Gender Age
Race .326
Gender/School .288
Age/School .220
Gender/Age/School .052
Race/School .818
Gender
Race/School .844
Age/Race/School .914
Gender/Age/Race
School .063
Error 106.102
Total 1535.000
Corrected Total 122.566
R squared= .134 (adjusted R squared


degrees
f
freedom
1
2
4
1
2
4
8

8
1
2
2
4

1
4

1
1145
1191
1190
.100)


Mean
Square
.266
.193
.217
1.081
.029
.186
.106

.041
.288
.110
.026
.204

.844
.229

.063
.093


F value
2.872
2.084
2.338
11.664
.313
2.006
1.141


.440
3.106
1.190
.280
2.207

9.105
2.467

.675


Level of
Significan
.0
.1
.0
.0
.7
.0
.3


.8
.0
.3
.7
.0

.0
.0

.4


Race, School

Partial
Eta
ce Squared
90 ..003
25 ..004
154 .008
101 .010
'31 .001
191 .007
33 .008

,97 .003
78 .003
05 .002
'56 .000
66 .008

03 .008
143 .009

11 .001









CHAPTER FOUR
RESULTS

Chapter Overview

The purpose of this chapter is to discuss the results of the analysis of specific questions as

they relate to the overarching research question. The primary purpose of this study is to better

understand how technology and culture affect sexual decision making influence sexual behaviors

in traditional college age students.

In order to determine this, the variables of gender, race, age, and school are analyzed

using the amount of internet use and how sexual activity, sexual behaviors, and relationships,

both online and offline.

Survey Aims

In Goodson and colleagues (2000) modified survey used in this research project there

were approximately 134 variables/questions and as such provided a plethora of data. However,

this particular study was only attempting to address specific issues based upon the results of the

literature review. The aims of the study addressed the following research questions:

* Is age a factor in Internet Use and Sexual Behaviors?

* Is gender a factor in Internet Use and Sexual Behaviors

* Does internet use have an effect on established relationships?

* Is ethnicity a factor in Internet Use and Sexual Behaviors?

* Is the location (university of record a factor in Internet Use?

Some of the data variables analyzed to attempt to answer these questions included:

* I have made new friends over the internet.

* How long have you been using the internet

* How often do you use the internet?









Table 4-148. Mean: I like to use stimulants while having cybersex with an online partner: School
School Mean Std. Deviation N
PFU 1.03 .233 1037
HBCU 1.02 .181 152
Total 1.03 .227 1189

Table 4-149. Mean: I like to use stimulants while having cybersex with an online partner: Gender
Gender Mean Std. Deviation N


Females
males
Total


1.06
1.03


.163
.328
.227


827
360
1187


Table 4-150. Mean: I like to use stimulants while having cybersex with an online partner: Age
Age Mean Std. Deviation N
18-19 1.02 .149 403
20-22 1.04 .272 519
23-and above 1.03 .220 266
Total 1.03 .225 1188


Table 4-151. Mean: I like to use stimulants while having cybersex with an online partner: Race
Race Mean Std. Deviation N
AAB 1.01 .155 2(
AAPI 1.04 .204
MAL 1.04 .228 13
NHW 1.03 .253 72


O
Total


1.00
1.03


.000
.227


55
1189


2
25









4-47 I have made new friends over the internet. Mean: Females, Age, Race, School ..............96

4-48 I have made new friends over the internet. Mean: Males, Age, Race, School ..................97

4-49 Tukey HSD.I have made new friends over the internet: Age....... ...... ...................98

4-50 Tukey HSD. I have made new friends over the internet: African American Blacks.........98

4-51 Correlations: Have you ever met anyone in person that you met on line........................99

4-52 Have you ever met anyone in person that you met on line. School...............................99

4-53 Have you ever met anyone in person that you met on line. Gender .............................100

4-54 Have you ever met anyone in person that you met on line. Age ...................................100

4-55 Have you ever met anyone in person that you met on line. Race...............................100

4-56 Tests of between subjects. Have you ever in person someone that you met online. .......101

4-57 Correlations: I have posted online personal messages in an attempt to find a potential
partner ........................................................ ...................................102

4-58 Mean: I have posted on line personal messages in an attempt to meet a potential
p artn er: S ch o o l ................................................... ................ ................ 10 2

4-59 Mean: I have posted on line personal messages in an attempt to meet a potential
p artn er: G en d er ..................................................................... ............... 10 3

4-60 Mean: I have posted on line personal messages in an attempt to meet a potential
p artn er: A g e .............................................................................103

4-61 Mean: I have posted on line personal messages in an attempt to meet a potential
partner: R ace .............. ................................................. ... ....... ...... 103

4-62 Tests of between subjects. I have posted online personal messages in order to meet a
potential partner. ........................................................................ 104

4-63 I have posted online personal messages in order to meet a potential partner. Mean:
G en d e r, A g e ................................................... ................... ................ 10 4

4-64 I have posted online personal messages in order to meet a potential partner. Mean:
Gender, Age, School ......... ...................................... ....... ...... ............... 105

4-65 I have posted online personal messages in order to meet a potential partner. Mean:
Fem ales, A ge, R ace, School ................................................. ............................... 106

4-66 I have posted online personal messages in order to meet a potential partner. Mean:
M ales, A ge, R ace, School ......... ........................................................ ......................... 107









In the United States, almost 4 million cases of STIs are diagnosed in adolescents each

year. The most common reported STI in the United States is Chlamydia, and it is most prevalent

among adolescents. Chlamydia prevalence reported by the CDC demonstrated that sexually

active adolescent females were six times more likely to be infected than the general population

(CDC, 2002).

In the United States, between the years 1990 to 1995, the incidence rate of Acquired

Immune Deficiency Syndrome (AIDS) in the population between the ages of 13 to 25 years rose

nearly 20 %. In 2003, the CDC reported that approximately 50 % of all new HIV cases were

found among individuals less than 25 years of age. In 2004, HIV disease was named the tenth

leading cause of death in people aged 15 to 24. At the end of 2004, 12 % of all new HIV cases

reported in Florida were among individuals with ages ranging were from 13 to 29 years

(National Vital Statistics Reports, 2005).

There are many factors that place adolescents at risk for early sexual debut, multiple

partners, and subsequent STI. These risk factors include race, socioeconomics, drug and alcohol

use, and peer pressure. Exposure to sexual content in media may also be a contributing factor to

the increases in sexual activity and STI of adolescent as well as traditional college aged students.

There have been many studies that discuss sexual behaviors as they relate directly to the

internet; however there are virtually no studies that explore whether viewing sexual content or

participating in chat rooms (i.e. cybersex ) on the internet leads to offline sexual activity in

adolescents and traditional college age students (Escobar-Chaves et al., 2005). Therefore, the

purpose of this study is to explore how technology, specifically the amount of internet use may

affect sexual decision-making and influence sexual behaviors in traditional college age students.









4-22 Correlations: How long have you been using the internet? ............................................85

4-23 Mean: How long have you been using the internet: School ...........................................85

4-24 Mean: How long have you been using the internet: Gender.........................................85

4-25 Mean: How long have you been using the internet: Age................ ...................86

4-26 Mean: How long have you been using the internet: Race .............................................86

4-27 Test of between subjects: How long have you been using the internet? .........................86

4-28 Correlations: How often do you use the internet? .................................. ............... 87

4-29 Mean: How often do you use the internet: School............ ................................. 87

4-30 Mean: How often do you use internet: Gender............... .................. .............. 87

4-31 Mean: How often do you use the internet; Age.......................... .....................88

4-32 Mean: How often do you use the internet: Race................................. .............. 88

4-33 Test of between subjects: How often do you use the internet?....................... ....... 88

4-34 How often do you use the internet: Mean: Race, School..................... ............... 89

4-35 How often do you use the internet: Mean: Gender, Age ................................................89

4-36 How often do you use the internet: Mean: Gender, Age, School ....................................89

4-37 How often do you use the internet: Mean: Females, Age, Race, School.........................90

4-38 How often do you use the internet: Mean: Males, Age, Race, School ...........................91

4-39 Tukey HSD. How often do you use internet: Age....... ... ....................................... 92

4-40 Tukey HSD. How often do you use internet: African American Blacks.........................92

4-41 Correlations: I have made friends over the internet................................... ... ..................93

4-42 M ean: I have made friends over the internet: School ................................ ............... 93

4-43 Mean: I have made friends over the internet: Gender..................................................94

4-44 M ean: I have made friends over the internet: Age..................... .......... ............... 94

4-45 M ean: I have made friends over the internet: Race ................................. ............... 94

4-46 Tests of between subjects. I have made new friends over the internet............................95









CHAPTER 1
THE PROBLEM

As technological advancement continues to advance at such a rapid rate, much of the

technology that was a luxury a few years ago is now considered a necessity. The internet is

deemed such an item (Weiser, 2000). It is used in business, academics, information technology,

communication and entertainment. As communication and entertainment are instantaneous as a

result of internet technology, the process of human socialization and relationship roles are

changing. As such, the internet has the capability to impact socialization and relationship roles of

adolescents and young adults (Bryant & Bryant, 2005). Gender roles, sexual attitudes, and

behaviors are in a critical stage of development, and as such the internet provides a forum for

sensation seeking in chat rooms, interactive gaming, and viewing sexually explicit material

which may have an effect on sexual behavior (Weisskirch & Murphy, 2004).

Sexually active adolescents are absolutely at risk for pregnancy and sexually transmitted

infections (STI). Annually, approximately 900,000 adolescent females become pregnant and

over one third of these young women are less than 17 years. Incredibly, 35 % of all young

women in the United States will have been pregnant at least one time before they reach the age

of 20 (Escobar-Chaves et al., 2005).

According to the Centers for Disease Control and Prevention (CDC) (2006), adolescents

aged 15-19 have the highest rates of STI of all age groups, and of the 19 million new infections

that occur every year, one half of those will take place in adolescents age 15 to 24 (CDC, 2006).

According to the Morbidity and Mortality Weekly Report (MMWR) (2006), 7.4% of adolescents

had their first sexual encounter prior to the age of thirteen. 14.4 % of all high school students

have engaged in sexual activity with four or more partners.















IF IVERSITYo College Students'Perceptions and Behavior When Using the Internetfor s r' PR',nl. "..r .r.An
FLORIDA exploratory? study.


H. Public access to sexually explicit materials on the Internet

This section contains several questions related to regulation and'or public access to ie'u ai. ,. bi: r ii are i i s on the Internet. Please
select whether you are in favor of or against the following practices:

Are you in favor or against... Infavor Against
I. The government regulatingicontrolling all sexually explicit materials on the Internet- 0 0
H2. The government blocking ::::e ~ r: i; \u e'g ::r web sites whose primary purpose is
sexual'entertainment arousal.
H3. Universities blocking access to sexually explicit web-sites whose primary purpose is O O
sexual informationteducation.
H4. Universities blocking access to sexually explicit web-sites whose primary purpose is O O
sexual entertainmentarousal.
H5. Public libraries blocking access to sexually .'. b:: i -. iir whose primary purpose is 0 0
sexual information/education.
M6. Public libraries blocking access to sexually explicit web-sites whose primary purpose is O O
sexual entertainmentiarousal.
H7. Adults having the right to access any -.ie ilf iiU,. '.pl, :ir iS, tar.i i, on the Internet O O
H8. ChildrenAdolescents having access to any type of sexually explicit materials on the Internet. O O



Page 7 of 7
Page 7 of 7









17 or less, between 18 and 19, between 20 and 22, and 23 or older. There were also

specifications for class level (i.e. freshman, sophomore, junior, senior, graduate, and other).

Students also needed to be able to read and comprehend English, as well as have access to a

computer with internet capabilities. The survey asked where the participant was most likely to

access the internet, however, did not specify from where the survey was accessed for

participation.

Survey Procedures

A mass mailing was sent to students attending both universities (Appendix C). This

email explained the study and invited the student to participate. In the email, students were

informed of the study, including the title of the study with a statement of the type of information

being solicited (i.e. demographic information, frequency and duration of internet use, using if the

internet to surf for sexually related content, establish personal relationships, sexual

entertainment, arousal, perceptions, and public access to sexually explicit material). A URL link

specific to each university was embedded into the mass email. Students who wished to

voluntarily participate would click on the URL link embedded into the mass email and were sent

to the approved informed consent page.

Upon reading and agreeing to consent and acceptance (by clicking on agree)

Students were hyperlinked to start page of the actual survey site hosted by the PI. If the student

clicked on the disagree button, they were hyperlinked to a page that stated 'thank you for your

time'.

The first page of the study provided demographic data. As the survey was designed for

consenting adults, any student who identified themselves as 17 or under would be hyperlinked

from the study to a page stating ineligibility. As this study is anonymous, and did not collect IP

addresses, there were no safeguards in place to prevent an under aged individual from accessing









The limitations of this particular study included a convenience sample, and as such it

would be difficult to compare it with the general population. The authors also noted that as only

college students were the focus of the research, a different cohort might answer questions

differently. As with all sexual surveys, there is always a risk of under reporting or exaggerating

sexual encounters/compulsivity. As with the majority of research on human sexual behavior, the

use of self-report questionnaires may create a source of bias in that participants may exaggerate

the frequency of sexual activities, underreport the frequency of sexual activities, misunderstand a

question due to lack of knowledge and respond inaccurately, or answer questions in ways that

they feel are socially desirable.

Internet

McFarlane and associates (2000) used a cross sectional design study conducted in

Colorado from September 1999 through April 2000 compared the risk of STI transmission of

persons seeking sex partners on the internet versus those not seeking sex partners on the internet.

Of the 856 participants interviewed, 135 of those participating had sought sex partners on the

internet. Eighty-eight of those who sought sex partners reported having sex with someone they

met online. Thirty-four of those people had had four or more partners in a six month period.

Condom use was reported in only 44 % of those with four or more contacts. In this study only

adults were asked to participate, and 70 % reported themselves to be White and homosexual.

However, most of the participants who used the internet as a method of obtaining partners were

more likely to report previous STIs (McFarlane et al., 2000).

Mitchell and colleagues (2001) conducted a study to assess the risk factors surrounding

online sexual solicitations of youth. Telephone surveys were performed from August 1999 to

February 2000 which captured a random sample of 1501 adolescents from 10 through 17 years

of age who regularly used the internet. Nineteen % of those interviewed experienced at least one









Table 4-67. Tukey HSD. I have posted online personal messages in order to meet a potential
partner: Age
95% Confidence Interval
Age Mean Standard Level of Lower Upper
Age Comparison Difference Error Significance Bound Bound
18-19 20-22 .32 .044 .164 -.18 .02
23-up -.32 .052 .000 -.44 -.19
20-22 18-19 .08 .044 .164 -.02 .18
23-up -.24 .050 .000 -.35 -.12
23-up 18-19 .32 .052 .000 .19 .44
20-22 .24 .050 .000 .12 .35
*Based upon a modified population marginal mean. The mean difference is significant at the .05
level

Table 4-68. Tukey HSD. I have posted online personal messages in order to meet a potential
partner: African American Blacks
95% Confidence Interval
Mean Standard Level of Lower Upper
Race Race Difference Error Significance Bound Bound
AAB AAPI .05 .090 .980 -.20 .30
AAB MAL .17 .073 .161 -.03 .37
AAB NHW .13 .052 .090 -.01 .27
AAB O -.09 .100 .886 -.37 .18
Based upon observed means. AAB= African American Black, AAPI=Asian American/Pacific.24
Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.









income communities to report having their own television, VCR and video games (Roberts,

2000). This may become a factor of potentially harmful internet exposure without parental

intervention of children especially those from lower income families.

Gruber and Grube(2000), reviewed recent scientific literature on adolescents and

sexuality in the media. Findings indicated that the available research did not address adolescent

exposure to sexual content in the media and its effects on beliefs, knowledge, intentions, and

behaviors. The literature review also identified lack of research on sexual content of the internet,

video games or handheld devices, (Gruber & Grube, 2000).

Collins and colleagues (2004) conducted a national longitudinal survey of 1792

adolescents approximately 12 to 17 years of age. Participating adolescents described their media

viewing habits and sexual experience using initial and yearly follow-up interviews. Multivariate

regression analysis determined that adolescents who viewed more sexual content at initial

interview were more likely to initiate intercourse and participate in non-coital sexual activities

during the following year. This study also noted that adolescents in the 90th percentile of TV sex

viewing had a predicted probability of intercourse initiation that was twice that of adolescents in

the 10th percentile, for all ages studied. Interestingly, Black adolescents who watched more

representations of sexual risks or safety in an educational format were less likely to initiate

intercourse in the subsequent year (Collins et al., 2004).

The purpose of Morrison and colleagues (2004) research was to investigate variables that

may be associated with exposure to sexually explicit material (SEM). Participants for this study

were psychology students enrolled at a Canadian University.

Results revealed a weak correlation between exposure to SEM via media (internet, TV,

and video) and sexual esteem and sexual anxiety. Pornography exposure was greater for the









result, further studies are needed in order to replicate and validate these preliminary results and

specify this information in both quantitative and qualitative inquiry.

As previously stated, the idea of guilt or shame associated with using the internet for

these purposes may be a factor in students from HBCU exhibiting these behaviors. Seidman's

work (2004) also demonstrated that earlier exposure to sexually explicit material may be a

predictor of frequency of current pornography use. Therefore, the need for further research in

this particular area becomes extremely important in attempting to predict online sexual behaviors

and offline behaviors.

I Have Had Cybersex With An Online Partner

Using ANOVA statistics, the question "I have had cybersex with an online partner" was

analyzed. This question was analyzed as the dependent variable with age, gender, race and

university of record (school). The results were insignificant.

I Like To Drink Alcohol While Having Cybersex With An Online Partner

Using ANOVA statistics, the question 'I like to drink alcohol while having cybersex with

an online partner' was analyzed. This question was analyzed as the dependent variable with age,

gender, race and university of record (school). The results were insignificant.

I Like To Use Stimulants While Having Cybersex With An Online Partner

Using ANOVA statistics, the question 'I like to use stimulants (drugs) while having

cybersex with an online partner' was analyzed. This question was analyzed as the dependent

variable with age, gender, race and university of record (school). The results were insignificant

with all effects.

Summary

This study had a very large and controlled sample size that was extremely representative

of the U.S. population according to race. There was greater representation of African American









gender, and university of record. The results included in table 4-7 are the main effects and

interaction effects of those variables. The Wilks Lambda statistics were chosen because they are

the most widely used test that will measure the proportion of the variance in the dependent

variable that is unaccounted for in the independent variable (Polit, 1996). The results identify

significant main effects for school (p=.031), gender (p=.000), age (p=000), and race (p=.041).

Interaction effects include gender and age (p=.000), school, gender and age (p=.014), school,

gender, and race (p=.007), and school, age, gender and race (p=.000).

Tables Within the Study

All questions within the study will have a correlation table with Pearson R scores and

levels of significance. ANOVA analysis tables will follow. After the ANOVA table, means

tables will be provided with standard deviations and number of participants.

In the event that the interaction of the ANOVA is significant as well as the MANOVA,

means tables will be present to determine the different means and confidence intervals of those

specific means. In the event that there at least three variables, TUKEY Honestly Significantly

different (HSD) tables will also be displayed. An overall description of the interactions will

follow these groupings of tables.

In the event that the ANOVA is significant and the MANOVA proved not to be in the

initial analysis, it will be treated as an insignificant interaction. This is an effort to create clarity

within all of the significant interactions that will be discussed.

How Long Have You Been Using Email?

Email is considered an important part of communication and has become a seemingly

necessary part of life. Email usage was measured specifically as it may provide data on the

number of students using email and the length of time it has actually been used. The survey

asked participants the specific question "How long have you been using email?" The participant









PFU 1.96 .202
HBCU 1.92 .270
Total 1.95 .213 1


941
153
094


Table 4-71. Mean: Does your partner object to the amount of time you spend on line. Gender
Gender Mean Std. Deviation N
Females 1.96 .201 760
Males 1.94 .238 332
Total 1.95 .213 1092

Table 4-72. Mean: Does your partner object to the amount of time you spend on line. Age
Age Mean Std. Deviation N
18-19 1.96 .196 377
20-22 1.96 .206 475
23-and above 1.94 .242 241
Total 1.95 .211 1093

Table 4-73. Mean: Does your partner object to the amount of time you spend on line? Race
Race Mean Std. Deviation N
AAB 1.92 .270 203
AAPI 1.97 .174 65
MAL 1.98 .157 120
NHW 1.96 .199 654
0 1.92 .269 52
Total 1.95 .213 1094









4-152 Tests of between subjects. I like to use stimulants while having cybersex with an
online: partner ...................................................... ................. .. ...... ......... 152









Survey Information

Using the web based survey program previously described, this survey document and

approved consent would be available and accessible to currently enrolled college age students

via a hyperlink sent by mass email with an invitation to participate in the survey.

Population

Sample Participants

Undergraduate students who attended a specific HBCU in Florida as well as those

students who attended a large public university in Florida (PFU) were the targeted population.

As previously stated, traditional age college students by far have had more technology

experience, and by this experience are best suited to participate in this study.

The rationale for using the HBCU and PFU student populations has to do with specific

demographics. PFU has a predominantly white population (66%). It also has the designated title

as being one of the largest universities in the United States. PFU has approximately 30,000

students registered as undergraduates enrolled.

The HBCU selected is a private university and the student population is predominantly

black (91%). This particular HBCU is also a Christian/Methodist University. In contrast to PFU,

the HBCU is a small university with an approximate undergraduate population of 3000 students.

According to Fall 2007 enrollment data for both schools, the approximate number of

undergraduate students attending both of these universities is 33100. Using a confidence level of

.95, a confidence interval of 3.1, the total number of students needed for the sample would be

approximately 1000.

Eligibility

In order to be eligible for this study, students must be enrolled at either university.

Undergraduate Students were the target population, and therefore, age ranges were identified as









use and as such more studies of ethnic differences in computer use may be warranted (Dickerson,

Reinhart, Feeley, Bidani, & al, 2004).

As there are multiple reasons for the increasing amount of internet use, there is little

documentation of an increase in pathologies related to increased usage. There is speculation that

some internet activities are more likely to produce an environment to encourage addictive

behaviors, however, that has not been documented in the general population. Some of the

activities that may promote already identified addictive behaviors, or potential developing of

addictive behaviors related to excessive use include e-mail, chat rooms and multiple players

gaming (Griffiths, 2001; Shapira et al., 2003).

Age, level of education, and gender have an effect on the type of internet activity that is

elicited. Men are more likely, and have been the dominant users of online sexual activities such

as viewing pornographic websites. Women are more likely to visit sexually oriented chat rooms

which provide interaction and socialization as well as anonymity (Cooper, Marahan-Martin,

Mathy, & Maheu, 2002). Finally, the typical educational profile suggests that excessive internet

users have levels of education of fifteen years or greater (Hall & Parsons, 2001; Kraut, Patterson,

Lundmark et al., 1998).

Original theories regarding internet use for sexuality purposes were very polarized in

their findings. One group of researchers emphasized that internet use specifically as it relates to

sexuality is pathological. These studies asserted that individuals would be able to effectively

engage in sexual fantasies, and elicit sex conversations that could range from simple flirtations to

downright naughty communication. These researchers thought the internet would be a breeding

ground for illegal sexual activities, including child pornography and pedophilia (Cooper,

Scherer, Boies, & Gordon, 1999). In contrast, the second group of researchers asserted that


































2008 Paula Courtney Pritchard









well as a large Florida public university (PFU) were chosen. Overall, 1202 students participated

in the study.

Results of this study demonstrate differences in gender, age, race and university of record.

These differences appear in developing relationships, online sexual behaviors, email use, and

offline sexual behaviors. The results indicate that students who are 23 and older are more likely

to value the importance of the internet, and also value the opportunity to engage in developing

online partners for different relationships that include online sexual activity when compared to

their younger counterparts.

Gender differences are also identified as an inverse relationship when combined with age.

Females are less likely to use the internet as they age, and the opposite is true for males within

this sample population. This information may provide insight into program changes that would

target individuals based upon gender.

The study also demonstrated racial differences in internet use. It is important to note that

this area needs to be replicated specifically because socioeconomic factors were not taken into

account, and therefore may have a significant impact upon the validity of the results. Therefore

any future reports of racial differences using results of this study should be used with that

cautionary note.

The evidence of data obtained by this preliminary study of internet use and sexual

behaviors of traditional college age students demonstrates the immediate need for future studies

that target internet use and sexual behaviors by race, gender, and age. These studies should

specifically look at the aspect of heightened sexual behavior as a result of frequency of internet

use through the emerging adult years. .









Table 4-22. Correlations: How long have you been using the internet?
How long
have you
been using
School Gender Age Race the internet


Pearson
Correlation
Sig. (2-tailed)


121


Pearson
Gender Correlation
Sig. (2-tailed)
N


Age


Race


How long
have you
been using
the


Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)


-.101(**)
.000
1210


-.025
.391
1211


-.662(**)
.000
1209


1 -.101(**)
.000
2 1210


-.025
.391
1211


1 .114(**)
.000
1210 1209


.114(**)
.000
1209

.103(**)
.000
1207


Pearson


internet Correlation -.078(**) -.009
Sig. (2-tailed) .007 .762
N 1207 1205
** Correlation is significant at the 0.01 level (2-tailed).


.662(**)
.000
1209

.103(**)
.000
1207


-.078(**)
.007
1207

-.009
.762
1205


1 .118(**)
.000
1211 1208


.118(**)
.000
1208





.022
.450
1206


1 .095(**)
.001
1209 1204


.095(**)
.001
1204


Table 4-23. Mean: How long have you been using the internet: School
School Mean Std. Deviation N
PFU 3.98 .205 1044
HBCU 3.93 .362 155
Total 3.98 .232 1199

Table 4-24. Mean: How long have you been using the internet: Gender
Gender Mean Std. Deviation N
females 3.98 .231 834
Males 3.97 .233 363
Total 3.98 .232 1197


School


.022
.450
1206


1

1207









were more likely to experience partner objection when compared to their PFU counterparts.

When comparing males by race and school in table 4-76, MAL, O, and AAPI could not be

compared as there was no representation at HBCU among this grouping. However, when looking

specifically at AAB and NWH; AAB attending HBCU were less likely than their PFU

counterparts to experience partner objection. On the other hand, NHW attending PFU were less

likely than their male counterparts at HBCU to experience partner objection. It is important to

note that both Tukey HSD tests for age and race found in tables 4-81 and 4-82 demonstrated

insignificance among the interaction effects in multiple comparisons. Therefore, the overall

results show significance but based upon the sample there is no way to determine the relevance

of the interaction as it pertains to this grouping.

The overall interaction found in table 4-74 demonstrates that there is a difference in

partner objection between school, age, gender and race. When comparing race, age, gender, and

school as factors in a four way interaction in this sample illustrated in tables 4-79 and 4-80, many

of the races do not have a single individual in the sample in certain age groups when separated

for age and gender. As previously stated with the other significant interactions of the four

independent variables, it would be difficult to interpret the interaction in between and as a result

of these very specific groups.

Does Your Offline Partner Know About The Friends/Relationships That You Have Online

The next dependent variable analyzed was the question 'Does your offline partner know

about the friends that you have online?' There were two categories in which respondents could

answer the question. Yes had a value of one (1), and no had a value of no (2). ANOVA statistics

were used to analyze this variable with the independent variables of race, age, gender and

university of record. The data in table 4-88 demonstrated only significant result of the interaction

effect of age and school on the dependent variable.









Table 4-85. Mean: Does your partner know about your online friends: Gender
Gender Mean Std. Deviation N
Females 1.51 .500 741
Males 1.60 .491 331
Total 1.54 .499 1072

Table 4-86. Mean: Does your partner know about your online friends: Age
Age Mean Std. Deviation N
18-19 1.54 .499 370
20-22 1.58 .494 467
23-and above 1.45 .498 236
Total 1.54 .499 1073

Table 4-87. Mean: Does your partner know about your online friends: Race
Race Mean Std. Deviation N
AAB 1.66 .474 201
AAPI 1.55 .501 65
MAL 1.52 .502 120
NHW 1.50 .500 636
0 1.58 .499 52
Total 1.54 .499 1074

































To Dr. Sharleen Simpson









than that of their HBCU counterparts, and there also was a trend in the means that suggested that

as they age they more frequently post messages in order to meet a potential partner. However,

those females attending HBCU had a lower overall mean, and as they aged, they were less likely

to post messages. Males attending HBCU had a higher overall average mean than their PFU

counterparts. The same phenomena occurred in males attending PFU, as the means increased

with age. Males attending HBCU had an increase in age and use among those students 18-19,

and decreased use from 20-22. Then the age group of 23 and older students jumped almost

disproportionately with an average mean of 2.750.

When comparing race, age, gender, and school as factors in a four way interaction in this

sample, many of the races do not have a single individual in the sample in certain age groups

when separated for age and gender (see tables 4-65 and 4-66). Therefore, it would be difficult to

attempt to describe the interaction in between and as a result of these very specific areas.

Does Your Partner Object To The Amount Of Time You Spend Online

One of the key factors in internet use may be its effect on personal relationships. A few of

the questions in the survey alluded to these types of relationship questions. The questions were

Boolean style with yes (1) or no responses (2). The first question to be analyzed is Does your

partners) object to the amount of time you spend online? Using ANOVA statistics, the question

'Does your partner object to the amount of time you spend on line' (as the dependent variable)

was compared to gender, race, age, and university of record (school). The results in table 4-74

demonstrated a tremendous amount of significant interactions. Gender (p=.002) and age (p=.041)

were both significant as main effect independent variables. Gender and age proved significant as

the interaction of both variables combined with the dependent variable. Gender, age and race

also proved significant as a combined interaction effect when compared to the dependent

variable (p=.011). However this was not significant on the MANOVA (table 4-7), therefore it









Table 4-135. Correlations: I have had sex with online partners.
I have had
sex with on
line
School Gender Age Race partners)


Pearson
School Correlation
Sig. (2-
tailed)
N
Pearson
Gender Correlation
Sig. (2-
tailed)
N
Pearson
Age Correlation
Sig. (2-
tailed)
N
Pearson
Race Correlation
Sig. (2-
tailed)
N


1 -.101(**)


1212


-.101(**)

.000
1210

-.025

.391
1211

-.662(**)

.000
1209


.000
1210


-.025 -.662(**)


.391
1211


1 .114(**)


1210

.114(**)

.000
1209

.103(**)

.000
1207


I have had
sex with
on line Pearson
partners) Correlation -.057(*) .143(**)
Sig. (2-
tailed) .049 .000
N 1205 1203
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).


.000
1209


.000
1209


.103(**)


.000
1207


1 .118(**)


1211

.118(**)

.000
1208



.027

.349
1204


.000
1208


1209


.014

.623
1202


Table 4-136. Mean: I have had sex with on line partnerss: School
School Mean Std. Deviation


PFU
HBCU
Total


1.30
1.19
1.29


-.057(*)


.049
1205


143(**)


.000
1203

.027

.349
1204

.014

.623
1202


1205


.644
.537
.632


1043
154
1197









Through these unique and creative attempts to solicit a reasonable sample size over 1500

participants responded. This sample size provided more than an adequate number of responses to

validate the findings of the study. Email correspondence with the PI from participants regarding

questions and comments were extremely valuable and will be included in the discussion section

of this dissertation.









CHAPTER FIVE
DISCUSSION


The information provided in this chapter will attempt to provide insight and interpretation

of the results of the data analyzed for this study. After this information is presented, limitations

as to the sample population, questionnaire, and recommendations for further studies will be

brought forth.

With this particular research interest, there are more questions than answers and that will

be a focus of this discussion. To begin this discussion, revisiting the specific research questions

is necessary. Also, to respond to these questions, a review of the analyses is in order.

Length of Internet Use And Email Use

Based upon the results of this research study and the question of length of internet use,

neither age, race, gender, nor university of record proved to be significant in length of email or

internet use. Over 95% of all participants in the study have used the internet for least three years

consecutively. As the results are insignificant, it does demonstrate that the population of both

schools has been exposed to and has been using the internet and email for at least three years.

This is important because it indicates that almost all of the participants have enough experience

to be able to navigate the specific areas of the internet. This finding also suggests that despite

age, race, gender and university of record, all of the participants have a basis for understanding

how to use the internet for information, entertainment and/or any other purposes.

Frequency Of Email Use

The next area of interest within this group of questions (identified in the survey) was how

often the internet was used. It appears as though overall, all students who attend HBCU use their

emails less frequently than their racial counterparts at PFU. There is a gender difference as well.

In comparison to all other students, Black males and females attending HBCU use email less









Internet Use

The World Wide Web has been providing a novel type of communication that enables the

user to access large amounts of information with a touch of a finger. The inception of the

Internet began in approximately 1969 by the United States government. In 1969, the government

funded a program to determine the feasibility of networking computers together. ARAPANET

developed out of the government's effort to connect computers together throughout the country.

The main purpose of ARAPANET was to secure communication between military

organizations and safely store large amounts of critical information in the event of a nuclear

holocaust (Bogard, 1996).

In 1989, the World Wide Web (WWW) went global, and brought about the instantaneous

access of information to every comer of the planet. The number of internet users started to

increase in 1993 and has steadily increased since that time. The greatest increase in the number

of users accessing the internet began in 1999. In 1999, approximately 36 % of American

households had internet access (Anderson, 2001). According to Neilson Ratings (2005), for the

month of June 2005 more than 140 million Americans surfed the WWW. Sex is the most

frequently searched topic on the internet (Goodson, McCormick, & Evans, 2001; Griffiths,

2001). Compared to the general population, college students are the heaviest internet users

spending much of their time using and surfing the internet. In 2002, 59 % of all Americans had

visited the Internet at least one time; conversely, 86 % of all college students were regular users

of the Internet. According to Hoffman and associates (2004), the internet is all-encompassing in

the lives of college students, and has become a staple in college life. Internet use for college

students is primarily for social activities(Hoffman, Novak, & Venkatesh, 2004).









Table 4-146. Tests of between subjects. I like to drink alcohol while having cybersex with an
online partner.
Type III
Independent Sum of Degrees of Mean Level of Partial Eta
Variable Squares Freedom Square F value Significance Squared
Gender .044 1 .044 .576 .448 .001
Age .017 2 .009 .113 .893 .000
Race .042 4 .011 .139 .968 .000
School .043 1 .043 .571 .450 .000
Gender/Age .011 2 .006 .075 .927 .000
Gender/Race .310 4 .078 1.027 .392 .004
Age/Race .663 8 .083 1.096 .363 .008


Gender Age
Race
Gender/
School
Age/School
Gender/Age
/School
Race/School
Gender
Race/School
Age/Race
/School
Gender/Age/
Race School
Error
Total
R squared=.938


.279

.002
.062

.175
.065

.034

.010

.018
86.611
1406.000
(Adjusted R


.884

.867
.662

.315
.930


8

1
2

2
4

1

4

1
1146
1192
squared-


.035

.002
.031

.087
.016

.034

.002

.018
.076


.461

.028
.413

1.157
.215

.448

.033

.239


.003

.000
.001

.002
.001

.000

.000

.000


.998


.936)









APPENDIX A
INFORMED CONSENT


Sexual Behaviors of nternet Use of Traditional College Age Students: An
exploratory study.


U Institutional Review Board
U. UNIVERSITY of FLORIDA

Informed Consent Form
to Participant in Research

INTRODUCTION

Name of person seeking your consent: Paula C. Pritchard

Place of employment & position: University of Florida Doctoral Student.

This is a research study of sexual behaviors and internet use of traditional college age students is
to explore how technology, specifically the internet may affect sexual decision making and
influence sexual behaviors in traditional college age students.

Could participating in this study offer any direct benefits to you? No, as described on question
lla.

Could participating cause you any discomforts or are there any risks to you? No, as described on
question 10.

Please read this form which describes the study in some detail. I or one of my co-workers will
also describe this study to you and answer all of your questions. Your participation is entirely
voluntary. If you choose to participate you can change your mind at any time and withdraw from
the study. You will not be penalized in any way or lose any benefits to which you would
otherwise be entitled if you choose not to participate in this study or to withdraw. If you have
questions about your rights as a research subject, please call the University of Florida
Institutional Review Board (IRB) office at (352) 846-1494. If you decide to take part in this
study, please click agree at the end of this consent.

GENERAL INFORMATION ABOUT THIS STUDY


1. What is the Title of this research study?









4-108 Mean: Important alternative way to meet potential on line sexual partners: School.......130

4-109 Mean: Important alternative way to meet potential on line sexual partners: Gender......131

4-110 Mean: Important alternative way to meet potential on line sexual partners: Age ...........31

4-111 Mean: Important alternative way to meet potential on line sexual partners: Race..........131

4-112 Tests of between subjects. An alternative way/place for meeting potential sexual
partners: G ender, R ace, A ge, School .................................................................... .... 132

4-113 Tukey HSD. An alternative way/place for meeting potential sexual partners: Age........132

4-114 Tukey HSD. An alternative way/place for meeting potential sexual partners: Race.......133

4-115 Correlations: I have accessed sexually explicit materials on the internet.......................134

4-116 Mean: I have accessed sexually explicit materials on the internet: School ...................134

4-117 Mean: I have accessed sexually explicit materials on the internet: Gender ..................135

4-118 Mean: I have accessed sexually explicit materials on the internet: Age........................135

4-119 Mean: I have accessed sexually explicit materials on the internet: Race ......................135

4-120 Tests of between subjects. I have accessed sexually explicit materials on the internet... 136

4-121 Correlations: I have accessed sexually explicit materials to become sexually aroused. .137

4-122 Mean: I have accessed sexually explicit materials to become sexually aroused:
S ch o o l .............. ............................................................................ 13 8

4-123 Mean: I have accessed sexually explicit materials to become sexually aroused:
G en d er ................... ............................ ........................... ................ 13 8

4-124 Mean: I have accessed sexually explicit materials to become sexually aroused: Age .... 138

4-125 Mean: I have accessed sexually explicit materials to become sexually aroused: Race...138

4-126 Tests of between subjects. I have accessed sexually explicit materials on the internet
to become sexually aroused: Gender, Race, Age, School...............................................139

4-127 Correlations: While viewing sexually explicit materials I have masturbated................140

4-128 Mean: While viewing sexually explicit web sites I have masturbated: School.............140

4-129 Mean: While viewing sexually explicit web sites I have masturbate: Gender ..............141

4-130 Mean: While viewing sexually explicit web sites I have masturbated: Age .................141









Table 4-53. Have you ever met anyone in person that you met on line. Gender
Gender Mean Std. Deviation N
Females 1.72 .451 836
Males 1.57 .495 362
Total 1.67 .469 1198

Table 4-54. Have you ever met anyone in person that you met on line. Age
Age Mean Std. Deviation N
18-19 1.72 .450 413
20-22 1.70 .458 520
23-and above 1.55 .499 266
Total 1.67 .469 1199

Table 4-55. Have you ever met anyone in person that you met on line. Race
Race Mean Std. Deviation N
AAB 1.63 .483 210
AAPI 1.76 .428 72
MAL 1.71 .453 133
NHW 1.68 .468 730
0 1.56 .501 55
Total 1.67 .469 1200









responded using specific phrases. The responses included 'less than six months' (1), six to

twelve months' (2), 'two to three years' (3), 'greater than three years' (4) and 'I don't use email'

(5).

Univariate ANOVA analysis on the question 'How long have you been using email?' as

the dependent variable was compared with gender, race, university of record, and age as the

independent variables. All tests of between subjects proved to be non significant. Tables 4-11

through 4-14 demonstrate the analysis and means. .

How Often Do You Use Email?

The next dependent variable to be tested was the question "How often do you use email?

The responses that the sample participants could chose from were: daily (1), 2 to 3 times per

week (2), and less than once a week (3). Using ANOVA, the dependent variables described were

compared to the four independent variables: age, race, gender and university of record (school).

There was a significant main interaction for school as the independent variable compared

with frequency of email use as a main effect (p=.001) in both ANOVA (table 4-19) and

MANOVA (table 4-7). The mean demonstrated in table 4-15 that students from HBCU use their

emails less frequently than those from PFU. There was also a significant interaction effect for

gender, race and school (table 4-20) as compared with frequency of internet use (p=.003) in both

the ANOVA (table 4-19) and MANOVA (table 4-7).

Overall, students who attend HBCU use their emails less frequently than their racial

counterparts at PFU (table 4-20). There is also both gender and racial differences as well. In

comparison to all other students, Black males and females attending HBCU use email less

frequently than their racially like counterparts at PFU. AA/PI females (overall) use email more

than their female counterparts. In contrast to AAB male students attending HBCU, AAB

students attending PFU are the most frequent of all male email users, and AAB males attending









* How long have you been using email?

* How often do you use email?

* I have posted online messages to meet a potential partner.

* Met anyone in person that you met online

* Does your partner object to the amount of time you spend online?

* Does your partner know about the friends/relationships that you have online?

* Has your partner ever expressed jealousy over the relationships you have developed online?

* How important is it to you that an alternate way/place for meeting new people?

* How important is it to you that an alternative way/place for meeting potential on-line sexual
partners is available?

* I have accessed sexually explicit materials on the internet

* I have accessed sexually explicit materials on the internet to become sexually
aroused/excited?

* While viewing sexually explicit web sites, I have masturbated.

* I have had cybersex with an online partner.

* I like to drink alcohol while having cybersex with an online partner.

* I like to use stimulants (drugs) while having cybersex with an online partner

Exclusion Criteria for Surveys

As the survey had 134 separate questions, criteria had to be set to determine what

constituted a complete survey. Many participants would get through at least four or five of the

eight page survey and then would abandon the process. Based upon this information a precedent

needed to be set. Some of the question information was set within the design and unless

answered the individual would not be able to progress through the survey. The two information

items were school and age. In one section of the survey (section C-4) there were seventeen









Table 4-88. Tests of between subjects. Does your offline partner know about the
friends/relationships that you have online.
Type III
Independent Sum of Degrees of Mean Level of Partial Eta
Variable Squares Freedom Square F value Significance Squared
Gender .747 1 .747 3.088 .079 .003
Age .121 2 .061 .251 .778 .000
Race .573 4 .143 .592 .668 .002
School .007 1 .007 .029 .866 .000
Gender/Age .719 2 .360 1.429 .227 .003
Gender/Race .915 4 .229 .946 .436 .004
Age/Race 1.661 8 .208 .859 .551 .007


Gender Age
Race
Gender/
School
Age/School
Gender/Age
/School
Race/School
Gender
Race/School
Age/Race
/School
Gender/Age/
Race School
Error
Total
R squared = .069


.646

.336
1.486

.059
.529

.006

1.042


.254 1
247.912 1025
2793.000 1071
(adjusted R squared=.028)


Table 4-89. Does your offline partner know about the friends/relationships that you have online.
Mean: Age, School


Std. Error


.046
.158
.046
.164
.081
.138


95% Confidence Interval
Lower Upper
Bound Bound
1.521 1.700
1.235 1.856
1.574 1.755
1.243 1.885
1.204 1.523
1.387 1.930


.081

.336
.743

.029
.132

.006

.260

.254
.242


.334

1.391
3.071

.122
.547

.023

1.077

1.051


.953

.239
.047

.885
.701

.880

.367


.003

.001
.006

.000
.002

.000

.004


Age
18-19

20-22

23-up


Mean


School
PFU
HBCU
PFU
HBCU
PFU
HBCU


1.610
1.546
1.665
1.564
1.363
1.658









Table 4-62. Tests of between subjects. I have posted online personal messages in order to meet a
potential partner.
Type III
Independent Sum of Degrees of Mean Level of Partial Eta
Variable Squares Freedom Square F value Significance Squared
Gender 4.948 1 4.948 113.19 .001 .010
Age 5.849 2 2.925 6.690 .001 .011
Race .291 4 .073 .166 .955 .001
School .011 1 .011 .025 .875 .000
Gender/Age 7.927 2 3.964 9.067 .000 .016
Gender/Race 1.036 4 .259 .592 .668 .002
Age/Race 2.864 8 .358 .819 .586 .006


Gender Age
Race
Gender/
School
Age/School
Gender/Age
/School
Race/School
Gender
Race/School
Age/Race
/School


5.207

.058
.907

3.217
1.654

1.099

1.666


Gender/Age/
Race School 4.874
Error 503.154
Total 2597.000
R squared=.806. (adjusted R


8

1
2

2
4

1

4

1
1151
1197
Squared=.799)


Table 4-63. I have posted online personal messages in order to meet a potential partner. Mean:
Gender, Age
Gender Age Mean Standard 95 % Confidence Interval
Error Lower Bound Upper Bound
Females 18-19 1.253 .124 1.011 1.496
20-22 1.227 .108 1.016 1.439
23-up 1.209 .129 .955 1.463
Males 18-19 1.261 .103 1.059 1.463
20-22 1.618 .098 1.425 1.810
23-up 2.180 .134 1.917 2.442


.651

.058
.453

1.609
.414

1.099

.416

4.874
.437


1.489

.133
1.037

3.680
.946

2.515

.953

11.149


.157

.716
.355

.026
.436

.113

.433

.001


.010

.000
.002

.006
.003

.002

.003

.010









Table 4-83. Correlations: Does your partner know about your online friends.


Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N


Does your partner
know about the
friends/relationships
you have over the
internet?


School Gender

1 .101(**)


1212

.101(**)

.000
1210


.000
1210


Age


Does your partner
know about the
friends/relationships
you have over the
Race internet?


-.025 .662(**)


.391
1211


.104(**)


.000
1209


1 .114(**) .103(**)


.000
1210 1209


-.025 .114(**)


School




Gender




Age




Race


Pearson
Correlation .104(**)
Sig. (2-


.000
1209


.084(**)


.000
1207


1 .118(**)


1211


.103(**) .118(**)


.000
1207


.084(**)


.000
1208


.000
1208

1


1209


-.115(**)

.000
1079


-.050 .115(**)


tailed) .001 .006
N 1081 1079
** Correlation is significant at the 0.01 level (2-tailed).


.098
1080


.000
1079


1081


Table 4-84. Mean: Does your partner know about your online friends: School
School Mean Std. Deviation N
PFU 1.52 .500 922
HBCU 1.66 .474 152
Total 1.54 .499 1074


.391
1211

.662(**)

.000
1209


.001
1081


.006
1079

-.050

.098
1080









When comparing school to partner knowledge of online relationships over the internet,

table 4-84 illustrates the findings that students attending PFU are more likely to share that

information with their offline partners than those students attending HBCU. Regarding age, and

partner knowledge, the Tukey HSD in table 4-90 demonstrated that there is an interaction

between younger participants when compared to their older counterparts. Overall, students over

the age of 23 are more likely to disclose online relationships to their partners when compared to

their younger counterparts. Students who are between the ages of 20-22 are less likely to disclose

that information when compared to their older or younger counterparts, and students are more

likely to share their information when compared to students 20-22. Interestingly, they are less

likely to share information with regarding relationships (online) with their partners than their 23

year old and older counterparts.

Partner Jealousy Over Relationships Developed Online

The next question analyzed stated: Has your partner ever expressed jealousy over the

relationships you have developed online? The subjects had the response choices of yes which

had a value of one (1), and no, which had a value of two (2). Using ANOVA, the results in table

4-95 demonstrated significance in the interactions between age and race as a combined

interaction, as well as school and age and race as another interaction when compared with the

dependent variable of jealousy. The overall MANOVA in table 4-7 demonstrated insignificance

in these groups. Tables 4-91 through 4-99 illustrate the data.

An Alternate Way/Place For Meeting New People

The next question to be analyzed was 'How important is it to you that an alternate

way/place for meeting new people is available?' This statement was scaled with the following

numeric responses: Extremely important was valued at five (5), Important was valued at four (4),

Uncertain was valued at three (3), Not important was valued at two (2) and Not important at all









I would like to thank my dear friend and colleague Dr. Alma Yearwood-Dixon, Dean of

the School of Nursing, at Bethune-Cookman University. Her dedication to following her dream

has been a guiding force for me following mine. Her tender care of my spirit has allowed me the

time and energy to complete this daunting task. Her vision of a better world and a belief that one

person can bring about change has been inspirational to me.

I would also like to acknowledge Dr. Patricia Goodson for allowing me to use her survey,

and modify it for this research. Her initial development and testing of this tool has allowed most

of the energy consumed in this project in actual data collection and analysis.

I would like to recognize DCova Technologies, the software company that was used in this

dissertation. Software for this project was specifically designed, tested and utilized for the survey

as well as all data collection in this dissertation. Many late nights were spent designing testing

and revamping the software so that it would do exactly what I needed it to do to get the job done.

The motto of the founder is 'We have the technology' and what they didn't have, they invented

on the spot. The 'tech' support provided for all of my many computers, hard-drives, backups,

and desire to keep my technology 'cutting-edge' has preempted many computer crashes and

brought about peace of mind.

Finally, I would also like to thank my very dear friend and partner, Jorge Valentin

DeLaCova. Without his support and presence, this would have never been possible. He is solely

responsible for helping me recognize the happiness that has replenished my life and made me

strong enough to get here. He has helped me realize all of the joy that I might have missed

otherwise throughout this journey.









Table 4-109. Mean: Important alternative way to meet potential on line sexual partners: Gender
Gender Mean Std. Deviation N


Females
males
Total


1.88
2.52
2.08


.963
1.124
1.056


832
364
1196


Table 4-110. Mean: Important alternative way to meet potential on line sexual partners: Age
Age Mean Std. Deviation N
18-19 2.04 1.012 412
20-22 1.99 1.047 518
23-and above 2.30 1.108 267
Total 2.08 1.056 1197

Table 4-111. Mean: Important alternative way to meet potential on line sexual partners: Race
Race Mean Std. Deviation N
AAB 1.95 1.080 209
AAPI 2.29 1.067 72
MAL 2.14 1.072 133
NHW 2.08 1.036 730
0 2.11 1.144 54
Total 2.08 1.056 1198









ACKNOWLEDGMENTS

I would like to thank my supervisory committee for their tireless patience and continued

support throughout this effort. Most significantly, I would like to thank my supervisory

committee chair Sharleen Simpson. Dr. Simpson has proven to be the epitome of a true mentor.

Her tireless efforts to intercede on my behalf seemingly at every turn have provided me

guidance, support, collegiality, and friendship. Her passion for research and her students can

only be outdone by her wonderful spirit and presence. It has been the blessing of this dissertation

to have met such an excellent individual as Dr. Simpson.

Dr. Sandra Seymour, an esteemed professor, and another member of my committee that

has also been monumental in assisting me to complete this task. She was motivational at moving

me forward, listening to my troubles, and pushing me in the right direction. Her nurturing

persona has had an enormous affect on my perception of the world. Her light hearted spirit has

turned many a half empty cup into an over-flowing one.

Dr. Greg Neimeyer, who is my minor committee member, has been a Godsend. When I

needed information, he was there. When I needed participants, he was there. When I needed a

minor committee member, he was there. I would become discouraged as we were not getting the

numbers to complete the survey; he was there with his favorite saying 'I think we can get more'

and always a suggestion to meet the task at hand. Through his guidance, wisdom and very unique

perspective of the world, he gave me ideas and knowledge. Dr. Neimeyer helped me move out of

my boxed way of thinking to understand behavior in this new realm of surrealistic technology

that until I met him, I would not have dreamed possible.

Dr. Karla Schmitt, another member of my committee is the keystone of this project. It was

a casual conversation of the notion of adolescents 'hooking up' and an article in a New York

magazine that brought about the initial idea and complete fruition of this dissertation.









APPENDIX C
MASS EMAIL

Mass Email Letter

Dear Student,



As a doctoral student at the University of Florida, I am cordially inviting you to participate in a

survey that is investigating Sexual Behaviors and Internet Use in Traditional College Aged

Students (aged 18-22). This survey is completely anonymous and will take approximately 30-45

minutes of your time to complete.

Your participation in this research may increase understanding of how the World Wide Web

plays a part in sexual decision making. Questions asked on the survey include basic demographic

information, questions about frequency and duration of internet use, use if the internet to surf for

sexually related content, use of the internet to establish personal relationships i.e. friends), use of

the internet for sexual entertainment, emotional arousal and perception, and finally, public access

to sexually explicit material on the internet.

Should you decide to participate in this ground breaking research, please complete the entire

survey by allowing yourself an appropriate amount of time to complete this survey.

Again, your participation is completely voluntary and your results remain anonymous. Data

collected will be in aggregate form, and information will only be published for this dissertation

in aggregate (grouped) delimited (numbers) data. The results of the data collected from this

survey will not include the name of the university that you attend, nor any of your particular

demographic data.
















UF I ftb^ College Students' Perceptions and Behavior When Using the Internet for Sexuality-Related Information. An
Ur F FDor A exploratory st-.v.


D. Use of InternetfEmail to establish personal connections/relationships

In the next section we would like to ask questions related to your use of the InternetEmail for developing and maintaining personal (not professional)
relationships

Frequently Sometimes Rarel Never
1 I use e-mail to keep in touch with family an3d friends O O O O
D2 I have made new friends iver the email/internet O O O O
3. I have discussed sexuaality-reated issues with my finds 0 0 I I
ve the InternetEmail
04. I have participated in chat-groups on the Internet. O O O
5 I have en a silent observer on chatgroups on the Interet. [[ o
06. I have said things on line that I would not say in person. O o O
07. I have disclosed personal information on-line that I would F o T o
ot disclose in person
58. I have posted on-line personal messages in attempts to meet O O O O
a potential partner (for example, on web sites for singles)


any times people wish to participate in chat group discussion forums,
t want to remain anonymous. A common strategy is to create a persona"
r a fictional character and present oneself as being that character Yes No
09. Have you ever created a new iden-tty or new 'persona"
lrr yourself on-line? HWhich aspets ofyour identity to you re-create?
)10. Did you change your gender? O O
1i1. Did you change your age and present yourself as someone older?
D12 Did you change your ag and present yourself as someone younger? O O
)513 Did you change your sexual orientation? O O
ID14 Did you change your appearance? O
115 Did you change your "availabiity status? C
resented yourself as unattached, when you have an ofline relationships) or vic versa)?


Yes No
S16. Have you ever met in person anyone you fast met on line? O O
P17. Whmen you met in person was your first meeting in a public "safe" place I I
Such as a restaurant coffee house campus, party)?
118. Are you currently in an ofiine relationship I
fo instance dating, dating exclusively engaged, married living with a significant other)
1 9. Does your offline partner know about the friends/relationships ) I
you have over the Intrnet Emai?
D20. Does your partner object to the amount of time you spend on line? O
521. Has your partner ever expressed jealousy over the relationships
you have developed on-line? F o


22 If"cybe-relationships" are defined as any kind of personal relationship you maintain with other people over the InternettEmail how do you
perceive the impact ofyour cyber-relationships on the relationships you have when you're not on-line? Please select the statement that best reflects
your opiio
I. My cyber relationships have a positive impact on my off-line relationshipss. O 0
[2 My cyber relationships have a negative on my off-line relationship's). [
13. My cyber relationships have no impact on my off-line relationshipss. O 0
4 My cyber relationships have no impact on my off-line relationships)
because my partners) doesn't know about them.
5 I.m not sure whether my cybe relationships impact my off-line relationships) O
6 Not applicable I don't have any cyber relationships- O


Strongly Strongly
__Disagree Disagree Uncerain Agree Ag
23. Te Iteet is an alternative way/place to meet new people. O O O O O
D24. People can develop meaningful relationships over the Internet'Email. O0 0 0 0 O
P25. The Internet provides a way of developing emotionally I I I I I
support w relationships
26. The InternetEmail is an alternative way/place for 0 O 0 0 )
meeting potential sexual partners


Not
ow Important is it to you that Extremely Not Important
important Importnt Uncertain Important tall
D27 An alternative way/place for meeting new people is available? O O O O O
D28 People can develop meaningEul relationships over- the Internet/e-mail7 O O O 0 O
V29A way of developing emotionally supportive relationships O O o
is available?
530 An alternative way/place for meeting potential on-line
sexual partners is available? ( c




Page 4 of 7









knowledge, the Tukey HSD demonstrated significance in the multiple comparisons of age. There

is an interaction between younger participants when compared to their older counterparts.

Overall, students over the age of 23 are more likely to disclose online relationships to their

partners when compared to their younger counterparts. In comparing age and university, the only

age group that is less likely to disclose information to their offline partners is HBCU students

who are 23 or older. All other age ranges demonstrate that PFU students are more likely to

provide information to their offline partners.

This may be related to the type of university (private, Christian based) in comparison to a

public university, as race proved to be insignificant. Based upon the results, again further in-

depth follow up about this behavior should be initiated within this population.

The Expression Of Jealousy Over Online Relationships

The next question analyzed stated: Has your partner ever expressed jealousy over the

relationships you have developed online? Using ANOVA, the results demonstrated significance

in the interactions between age, race and school and age and race as grouped interaction

variables, (age, race, and school, as one group interaction and race and school as the other).

Unfortunately, these variable groupings proved insignificant on the MANOVA. Therefore, the

results cannot be considered significant and/or reportable as such.

The Importance Of Alternative Ways/Place For Meeting Potential Online Sexual Partners

The next variable to be tested was 'the internet/email is an alternative way/place for

meeting potential sexual partners'. This dependent variable was again tested comparing age,

gender, university of record (school), and race as independent variables. The analysis proved

significant for the main effect of gender as well as age when compared to the dependent variable.

There was also a significant interaction of age, race and school; however this combination was









systems. Both studies showed that increased internet use increased social isolation and created

weaker social ties, but could not be positively linked to depression (Kraut, Patterson, Landmark

et al., 1998; Sanders, Field, Diego, & Kaplan, 2000).

Rotosky, Regnerus, and Wright (2003) also used the National Longitudinal Study of

Adolescent Health to determine if religiosity was associated with the delay of adolescent coital

debut. Using data from 3,691 adolescents, Rotosky and colleagues (2003), wanted to determine

if adolescent religiosity and sexual attitudes predicted sexual debut in gender and ethnicity.

Again, there were two contact points approximately one year apart. The researchers selected a

sample of 80 high schools, which were stratified by region, urban location, school type, ethnic

diversity, and enrollment size. The sample of schools ranged in size from fewer than 100

students to more than 3,000 students. Approximately 80% of the schools contacted agreed to

participate. The schools and students proved to be representative samples of the population and

provided generalizability to the study. As with Longmore and colleagues (2004) study, only

those adolescents who indicated that they had not engaged in sexual intercourse by the first wave

of the study were selected to comprise the sample for the analysis of predicting coital debut.

Adolescents ages 15 and older were asked sex attitude questions exclusively. Correlational

analyses with a Bonferroni correction were used to determine associations between independent

and dependent variables for males and females. 23% of the males and 25% of the females who

reported no sexual intercourse at the first wave had engaged in sexual intercourse by the second

wave. Each year of age of the adolescent determined an approximate increase of 21 % in sexual

debut between the first and second wave of the study. In Black males, earlier sexual debut was

twice that of White males regardless of religiosity and virginity pledging. In adolescent boys, the

odds of sexual debut decreased by approximately 36 % when their mothers had at least a college









Table 4-7. Multivariate Test: Wilks Lambda: Independent Variables Main and Interaction
Effects.


Effect

School

Gender

Age

Race
School/
Gender

School/Age

Gender/Age
School/
Gender/ Age

School/Race
Gender
/Race
School/Race
Gender

Age/Race
School/Age/
Race
Age/Gender
/Race
School/Age/
Gender/Race


Test
Wilks
Lambda
Lambda
Wilks
Lambda
Wilks
Lambda
Wilks
Lambda
Wilks
Wilks
Lambda
Wilks
Lambda
Wilks
Lambda
Wilks
Lambda
Wilks
Lambda
Wilks
Lambda
Wilks
Lambda
Wilks
Lambda
Wilks
Lambda
Wilks
Lambda


Value


.965

.917

.922

.896

.977

.969

.919

.936

.917

.926

.959

.869

.905

.845

.948


1.680

4.217

1.942

1.295

1.074

.750

2.005

1.563

1.029

.910

1.975

.827

1.186

.992

2.539


Degree of
Freedom

20.000

20.000

40.000

80.000

20.000

40.000

40.000

40.000

80.000

80.000

20.000

160.000

80.000

160.000

20.000


Error

933.000

933.000

1866.000

3682.99

933.000

1866.000

1866.000

1866.000

3682.990

3682.990

933.000

6981.594

3682.990

9681.594

933.000


Level of
Significance

.031

.000

.000

.041

.371

.873

.000

.014

.410

.702

.007

.945

.126

.513

.000









It is important to note that both Tukey HSD tests for age and race demonstrated

insignificance among the interaction effects in multiple comparisons. Therefore, the results of the

ANOVA show significance but based upon the sample (as evidenced by the Tukey HSD) there is

no way to determine the relevance of the interaction as it pertains to this grouping. There is no

clear relationship between race and age as it relates to partner objection except that there may be

racial differences that cannot be identified based upon the sample. A larger sample of minority

populations (i.e. AAPI and MAL) may provide an interpretable interaction between race and age.

The overall interaction demonstrates that there is a difference in partner objection

between school, age, gender and race. As mentioned earlier, uneven distribution of the sample

population make it difficult to interpret the significance of these interactions.

The interaction of the variables of gender and age when compared to partner objection

proved to be most interesting. The inverse relationship that was indicated in frequency of internet

use was replicated in this question. Females, as they age are less likely to experience partner

objection, and males are more likely to experience partner objection. This may be a direct result

of the specific use of the internet. According to O'Reilly and colleagues (2007), males are more

likely to view sexually explicit material online, and women are more likely to feel threatened by,

or object the use of the internet for this specific behavior. Although it was not specified on the

current survey, the work of O'Reilly and colleagues (2007) suggests that a similar effect may be

present in this population.

Does Your Offline Partner Know About The Friends/Relationships That You Have Online

In the analysis of this particular variable, partner knowledge of online relationships

demonstrated significant results in the interaction effect of age and school on the dependent

variable. Overall, students attending PFU are more likely to share information with their offline

partners about relationships than those students attending HBCU. Regarding age, and partner










I have made new friends over the internet. Mean: Males, Age, Race, School


Gender
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males
Males


Age
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up


Race
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0


* This level combination of factors is not observed, thus the corresponding marginal mean is not
estimable
AAB= African American Black, AAPI=Asian American/Pacific Islander, MAL=Mexican
American Latino, NWH=Non White Hispanic, O=Other.


School
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU


Mean
2.375
3.000
2.500
*
2.111
*
2.642
2.500
2.200
*
2.333
2.400
2.778
*
2.362
*
2.280
*
3.250
*
2.000
2.833
2.500
2.167

2.167
*
2.267
4.000
2.667
*


Standard
Error
.348
.254
.402
*
.329
*
.120
.697
.441
*
.402
.441
.328
*
.226
*
.102
*
.493
*
.697
.402
.402
*
.285
*
.114
.986
.285
*


95% Confidence Interval
Lower Upper
Bound Bound
1.691 3.059
2.501 3.499
1.711 3.289
*
1.467 2.756
*
2.406 2.878
1.133 3.867
1.335 3.065
*
1.544 3.123
1.535 3.265
2.133 3.422
* *
2.188 3.075
*
2.079 2.480
*
2.283 4.217
*
.633 3.367
2.044 3.623
1.711 3.289
*
1.608 2.725
*
2.043 5.490
2.066 5.934
2.108 3.225
*


Table 4-48.









sexual solicitation while using the internet in the previous year and 3 % reported an aggressive

solicitation. Only 10 % of sexual solicitations were reported to any authority. Approximately 75

% of parents and adolescents had no idea of where to report this type of behavior. Adolescent

females were more likely to be solicited as well as those who used the internet more frequently.

Solicitation occurred more frequently in those adolescents who participated in chat rooms,

engaged in risky behavior online, talked to strangers online, or used the internet at households

other than their own (Mitchell, Finkelhor, & Wolak, 2001).

The objectives in Koch & Praterelli's (2004) study 'Effects ofIntroversion and

Extroversion on Social Internet Use' research the social significance of the internet. They used

Likert scores to rate the negative effects of internet use (i.e. addiction). Participants were asked

to rate the internet based upon reading a passage to determine different responses based upon

gender. They hypothesized that individuals who read a passage about negative effects of the

internet would respond negatively. They also hypothesized that Introverts would be more

comfortable with the anonymity that the internet provides than those individuals that were

described as extroverts. Participants were recruited from freshmen level courses (n=240). Results

of the statistical analysis proved not to be statistically significant regarding reading a negative

passage about the internet. Koch & Praterelli (2004) determined that the reason why it might not

be statistically significant because addicts don't always identify themselves as addicts. This

study demonstrated that there was a large difference between men and women's perceptions and

use of the internet which is consistent with previous research. Again, college age males reported

viewing more sexually explicit material for social purposes (Koch & Pratarelli, 2004).

Hightow et al (2005) reviewed state surveillance records as part of a retrospective study

that examined new diagnoses and risk behaviors in men 18 to 30 years old living in North









reported that media is used as an information source about sexuality, drug abuse, and violent

behaviors (Morris, 2004).

High amounts of exposure to portrayals of sex may have an effect upon adolescents'

beliefs and cultural norms. Media creates the illusion that sex is central to daily life and as a

result may promote earlier sexual debut. Behaviors that are observed in media sources may alter

beliefs about outcomes of engaging in sexual activity. Adolescents who view media and see

characters having casual sex without negative consequences may be more likely to adopt those

portrayed behaviors (Brener, Lowry, Kann, Kolbe, & al, 2002).

Correlational studies indicate that adolescent females choose television programs with

sexual content more often than do adolescent males. Interestingly, adolescent females are more

likely to observe it in the company of parents. To the contrary, older adolescent males view

media with greater hardcore sexual content, and are more likely to listen to more sexually

explicit music. Adolescent males use the internet, computer and or online games more often than

the female adolescents(Gruber & Grube, 2000).

Women are portrayed as sex objects in approximately 25 % of the 33 most popular video

games. Violence against women is portrayed in approximately 21 %, and in over 40 % of these

games, female characters do not have names (Rich, 2003). Adolescents who use these kinds of

media are more likely to believe these stereotypic sex roles versus reality than their counterparts

that do not use these games (Gruber & Grube, 2000; Morris, 2004).

The mainstream mass media (television, movies, music, and internet) provide

increasingly frequent portrayals of sexuality. Studies suggest that interactive visual media that

are interactive do have an impact on sexual behavior (Boies, 2002; Escobar-Chaves et al., 2005;

Griffiths, 2001; Hollander, 2004). Media portrayals of men and women reinforce relatively









Blacks (17.4%) than reflected in the U.S. population (12.3%). There was a greater percentage of

Asian American/Pacific Islanders (6.0%) than in the U.S. total population (3.7%). Mexican

Americans/Latinos represented 11. % of the study population and demonstrate 12.5 % of the

U.S. population. Non Hispanic Whites were under represented in the study as that group only

represented 60.5% of the study, and 75.1 % of the U.S. population according to the year 2000

census data (http://www.census.gov/prod/2001pubs/c2kbr01-9.pdf).

In the data analyzed for this study, gender continues to be the major factor in internet

usage. Age is also very noteworthy when looking at trends of decisions that may likely influence

behaviors. Race, specifically AAB appear to be a determinant of behaviors that involve internet

use overall. This is very significant as it may provide some insight as how to effectively use the

internet for educative and health related topics. This study collected data related to internet use

and education/information, but was not analyzed for this specific study. This study specifically

looked at internet use differences in age, race, gender and school. Future analysis of gathered

data may lead to further research within this topic.

As for the idea of race as a determinant for efficacy of use; there have been studies that

discuss how internet knowledge and frequency of use is a determinant of efficacy (Potosky,

2006). Race was not identified as a determinant of efficacy of use. It is important to note that as

this study compared a small private black university to a major public university, there is a

significant difference in internet use and interest overall. As the study did not ask specific

information regarding socioeconomic status (current or overall economic status history), there is

no way to determine if the differences in internet use are related to race or socioeconomic status.

This is an imperative area of further research, specifically because it is foundational to the notion

of whether or not race is an indicator of internet use. Finally, there needs to be research









BIOGRAPHICAL SKETCH

My name is Paula C. Pritchard. I have been a nurse for over 20 years. I have practiced in

the state of California as well as the state of Florida. My area of expertise is in maternal child

nursing. I have worked in both the acute care settings as well as the public sector caring for

infants, children, adolescents and emerging adults. I have worked for the predominant portion of

my career with the individuals and families that have been exposed to HIV disease, drug

addiction, STI and those who had no or little access to appropriate healthcare. During the last

five years, I have had the opportunity to teach as both as an adjunct and assistant professor at the

'Great Bethune-Cookman University'.

I am a graduate from California state university at Fullerton where I received a

baccalaureate degree in nursing science (BSN). Prior to the BSN, I received a associate of

science in nursing from San Jacinto Junior College in San Jacinto, California. I completed my

master of science in adult health and administration for the University of Phoenix in Maitland,

Florida. My utilization review project involved African American Black Women in Volusia

County with HIV disease and barriers to health care access. This research developed my personal

desire to improve health care access and preventable diseases from impacting this population to

total annihilation.

As this area of research has been one that is very close to my heart, during my doctoral

studies, I determined that qualitative inquiry was the very best method to use to gain insight into

this pandemic. I developed this eclectic minor that encompassed psychology, anthropology,

internet technology, and a deeper understanding of adolescent and emerging adults within this

framework.









will not be discussed. The interaction of gender, race and school proved significant. The

combined interaction of the variables when compared to the dependent variable produced a p

value of (.001). This comparison was also significant on the MANOVA (table 4-7). Race, age

and school as an interaction proved significant on the ANOVA (table 4-74), however the

interaction was not significant on the MANOVA (table 4-7) and as such will not be discussed.

Finally the four way interaction again proved to be significant with the combined interaction

producing a p value of (.000) when compared to the dependent variable of partner objection to

the amount of time an individual spends on line.

Gender proved to be interesting. Based upon the population represented, females are less

likely to have a partner object to the amount of time that they spend online. The average female

overall mean was higher (mean= 1.974) than the male average mean (1.881) as demonstrated in

table 4-71. The average mean for age found in table 4-72 indicated that people age 20 to 22 are

more likely to have partner objection to the amount of time they spend on line. Individuals age

23 and older were less likely to experience partner objection when compared to their younger

counterparts. When gender and age were combined in table 4-75, the results indicated that

females grouped by ages 20 and older were less likely than their same age male counterparts to

experience partner objection to online use. Males who identified themselves as 18-19 did not

experience the same level of partners objecting to their overall time spent using the internet.

Although the results demonstrate a difference, there is not an explanation that may account for

this difference.

When comparing race, gender and school, the results in table 4-76 demonstrated that

females attending HBCU were less likely than their racially similar counter parts at PFU to

experience partner objection unless they were AAB. Those students matching that demography









Table 4-31. Mean: How often do you use the internet; Age
Age Mean Std. Deviation N
18-19 1.01 .140 406
20-22 1.01 .077 509
23-and above 1.05 .229 258
Total 1.02 .145 1173

Table 4-32. Mean: How often do you use the internet: Race
Race Mean Std. Deviation N
AAB 1.06 .263 206
AAPI 1.00 .000 71
MAL 1.00 .000 125
NHW 1.01 .117 719
0 1.00 .000 53
Total 1.02 .145 1174

Table 4-33. Test of between subjects: How often do you use the internet?
Type III Degrees Partial
Independent Sum of of Mean Level of Eta
Variable Squares Freedom Square F value Significance Squared


Gender
Age
Race
School
Gender/Age
Gender/Race
Age/Race
Gender Age
Race
Gender/School
Age/School
Gender/Age/School
Race/School
Gender
Race/School
Age/Race/School
Gender/Age/Race
School
Error
Total
R squared=.983


.008
.129
.266
.044
.537
.014
.186


1.0
2.0
4.0
1.0
2.0
4.0
8.0


.229
.009
.105
.545
.223

.012
.155


.165 1.0
20.501 1125
1238.000 1171
(Adjusted R squared=.983)


.509
.029
.006
.122
.000
.941
.252


.000
.006
.013
.002
.026
.001
.009


.008
.065
.066
.044
.268
.004
.023


.029
.009
.052
.273
.056

.012
.039

.165
.018


.436
3.540
3.648
2.401
14.732
.196
1.275

1.570
.470
2.878
14.965
3.066

.680
2.123

9.079


.129
.493
.057
.000
.016

.410
.076

.003


.011
.000
.005
.026
.011

.001
.007

.008










Table 4-112. Tests of between subjects. An alternative way/place for meeting potential sexual
partners: Gender, Race, Age, School
Type III
Independent Sum of Degrees of Mean Level of Partial Eta
Variable Squares Freedom Square F value Significance Squared
Gender 7.901 1 7.901 5.995 .014 .005
Age 33.439 2 16.719 12.686 .000 .022
Race 6.724 4 1.681 1.275 .278 .004
School 2.126 1 2.126 1.613 .204 .001
Gender/Age 1.662 2 .831 .630 .533 .001
Gender/Race 6.544 4 1.636 1.241 .292 .004
Age/Race 7.109 8 .889 .674 .715 .005


Gender Age
Race
Gender/
School
Age/School
Gender/Age
/School
Race/School
Gender
Race/School
Age/Race
/School


11.697

2.880
3.986

3.113
1.035

1.334

13.882


Gender/Age/
Race School 1.356
Error 1511.710
Total 10253.000
R squared=.806. (adjusted R


8

1
2

2
4

1

4

1
1147
1193
Squared=.799)


Table 4-113. Tukey HSD. An alternative way/place for meeting potential sexual partners: Age
95% Confidence Interval
Age Mean Standard Level of Lower Upper
Age Comparison Difference Error Significance Bound Bound


18-19 20-22
23-up
20-22 18-19
23-up
23-up 18-19
20-22


-.16
-.77
.16
-.61
.77
.61


.076
.090
.076
.087
.090
.087


.081
.000
.081
.000
.000
.000


-.34
-.99
-.01
-.81
.56
.41


.01
-.56
.34
-.41
.99
.81


.354

.140
.221

.307
.940


1.462

2.880
1.993

1.557
.259

1.334

3.470

1.356
1.318


.008

.002
.003

.002
.001


1.109

2.185
1.512

1.181
.196

1.012

2.633

1.029


.033


.009









An additional problem is meta-tagging. Meta tagging is the practice of embedding of

certain words that can be indexed through basic internet searches. An example of this is that an

individual may 'Google' a certain word or phrase, if a survey has that meta-tag within it, it is

likely to come up within that particular search. Using this type of code embedding can increase

the number of participants by publicizing a survey; however, the downside is the resultant breach

of sampling to uncontrolled participants outside of the intended population (Rhodes, Bowie, &

Hergenrather, 2003).

Adding the URL link of the survey to a published website will also be picked up by many

search engines, thus causing publishing the location of a web based survey. This is an effective

tool if the researcher is attempting to obtain a large sample. However, in the case of attempting

to control the sampling body, a URL link to a published web site can be very detrimental.

There are a few ways that researchers may be able to protect the data pool. One way is to

minimize the publicity of the actual web survey. This can successfully be completed by using

private domains to house the web survey. Usually, private web based surveys are difficult to

search for, which reduces the risk of unintended participants.

Confidentiality

Procedures for the Protection of Human Subjects

Before beginning data collection, approval from the HBCU and the PFU Institutional

Review Boards (IRB) were obtained. This became a difficult process as the original study

protocol proposed was a mixed methodology design. The IRB at the PFU had some difficulty

with the data collection in the qualitative portion, and after some discussion, this portion of the

study was scrapped. The same study and all protocols were approved completely at the HBCU.

This study was an anonymous study. Data collected had no identifiers that could

distinguish participants individually. The only demographics obtained in this study were gender,









Table 4-107. Correlations: An alternative way to meet online sex partners.


Gender Age


Race


An
alternative
way/place
for meeting
potential
online
sexual
partners is
available


Pearson
Correlation
Sig. (2-tailed)


Pearson
Gender Correlation
Sig. (2-tailed)
N


Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N


An
alternative
way/place
for
meeting
potential
online
sexual
partners is


1

1212

-.101(**)
.000
1210


-.025
.391
1211


-.662(**)
.000
1209


-.101(**)
.000
1210


-.025
.391
1211


1 .114(**)
.000
1210 1209


.114(**)
.000
1209

.103(**)
.000
1207


-.662(**)
.000
1209

.103(**)
.000
1207


1 .118(**)
.000
1211 1208


.118(**)
.000
1208


1 .076(**)
.009
1209 1201


Pearson


available Correlation -.057(*) .344(**)
Sig. (2-tailed) .047 .000
N 1204 1202
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).


.218(**)
.000
1203


.076(**)
.009
1201


Table 4-108. Mean: Important alternative way to meet potential on line sexual partners: School
School Mean Std. Deviation N


PFU
HBCU
Total


2.09
1.99
2.08


1.056
1.051
1.056


1044
154
1198


School


School


Age


Race


-.057(*)
.047
1204

.344(**)
.000
1202

.218(**)
.000
1203


1204









Table 4-69. Correlations: Does your partner object to the amount of time you spend on line:
Does your
partner


School Gender


Age


Race


object to
the
amount of
time you
spend on
line?


Pearson
School Correlation
Sig. (2-
tailed)
N
Pearson
Gender Correlation
Sig. (2-
tailed)
N
Pearson
Age Correlation
Sig. (2-
tailed)
N


Race


Pearson
Correlation
Sig. (2-
tailed)
N


1 -.101(**)


1212

-.101(**)

.000
1210

-.025

.391
1211

-.662(**)

.000
1209


.000
1210

1


1210

.114(**)

.000
1209

.103(**)

.000
1207


Does
your
partner
object to


amount
of time
you
spend on Pearson
line? Correlation -.057 -.044
Sig. (2-
tailed) .059 .147
N 1101 1099
** Correlation is significant at the 0.01 level (2-tailed).


Table 4-70. Mean: Does your partner object to the amount of time you spend on line. School
School Mean Std. Deviation N


-.662(**)

.000
1209

.103(**)

.000
1207

.118(**)

.000
1208


-.025

.391
1211

.114(**)

.000
1209

1


1211

.118(**)


.000
1208


-.057

.059
1101

-.044

.147
1099

-.043

.156
1100

.055

.068
1099


1209


-.043

.156
1100


.055

.068
1099


1101









sexually aroused was analyzed. The responses for this specific question included frequently

which was given a value of four, sometimes which was given a response of three, rarely, which

was given a response of two, and never, which was given a value of one. The dependent variable

was the question: 'I have accessed sexually explicit materials on the internet to become sexually

aroused.' The independent variables were gender age race and university of record (school). The

only significant analyses in the ANOVA in table 4-126 proved to be gender and university of

record (school) in these analyses (separately). Gender and school were also significant as a main

affect on the MANOVA analysis (table 4-7).

The mean in table 4-123 demonstrate the differences between males and females on

accessing sexually explicit materials on the internet. The results indicate that accessing sexually

explicit material was almost 1 12 times higher in males than in females. This finding although

significant is not a surprise. Males are very visual creatures in the nature of sex and arousal

(Canli, &Gabrieli, 2004), and it is not surprising that this would occur in this population.

According to the results in table4-122, students who attend HBCU were much less likely

to access the internet than their counterparts at PFU in the pursuit of accessing sexually explicit

materials for sexual arousal. This may directly relate to the number of males from HBCU that are

represented in this population as well as the availability of computers and internet access as well.

Therefore, although this is an interesting revelation, it should be viewed with caution.

While Viewing Sexually Explicit Web Sites, I Have Masturbated

The question "while viewing sexually explicit material I have masturbated" was analyzed

with ANOVA. The responses for this specific question included frequently (4), sometimes (3),

rarely (2), and never (1). This question was analyzed as the dependent variable with age, gender

and race as the independent variables.









use the internet more frequently. It appears in this interaction there is an inverse relationship in

relation to gender and aging. This relationship is demonstrated in table 4-35.

The interaction of age gender and school also demonstrated a significant difference.

Students regardless of gender or age (overall) who attended PFU had higher rates of internet use

than their HBCU counterparts (tables 4-29 through 4-31). Females at both PFU and HBCU

demonstrated the same tendencies as they did as a whole. Regardless of school, their overall

means decreased as they aged. Males who attended these universities did not demonstrate a

similar pattern. HBCU male students who were age 18-19 or 23 and older used the internet less

frequently than their PFU counterparts. Interestingly as demonstrated in table 4-36, both

populations of male students demonstrated an increase in internet use during the ages of 20-22.

The significant difference in internet use in males occurred in the HBCU population. Males

demonstrated more frequent use as they aged (as previously stated), however the use of the

internet in HBCU males who were age 18-19 had a much lower mean than those male students

age 18-19 at PFU. The difference may be accounted for in the racial differences in the sample.

In the four way interaction effect of gender, race, school and age, females overall used

the internet less frequently than their male counterparts (table 4-37). AAB females from HBCU

age 18 and older who participated in the sample demonstrated less frequent internet use than

their age and race similar counterparts from PFU. All AAB from HBCU who were age 18-20

and 23 and older demonstrated the least frequent use of their entire male and racially different

counterparts (table 4-38).

This interaction demonstrates that there is a difference in frequency of internet use

between school, age, gender and race. When comparing race, age, gender, and school as factors

in a four way interaction in this sample, many of the races do not have a single individual in the









Table 4-152. Tests of between subjects. I like to use stimulants while having cybersex with an
online: partner
Type III
Independent Sum of Degrees of Mean Level of Partial Eta
Variable Squares Freedom Square F value Significance Squared
Gender .026 1 .026 .499 .480 .000
Age .010 2 .005 .094 .910 .000
Race .020 4 .005 .096 .984 .000
School 9.88 E- 1 988-E .002 .965 .000
Gender/Age .068 2 .034 .663 .515 .001
Gender/Race .045 4 .011 .220 .927 .001
Age/Race .054 8 .007 .131 .998 .001


Gender Age
Race
Gender/
School
Age/School
Gender/Age
/School
Race/School
Gender
Race/School
Age/Race
/School


.180

.001
.019

.034
.026

.019

.008


Gender/Age/
Race School .009 1
Error 58.563 1140
Total 1313.000 1186
R squared= .955 (Adjusted R squared=.954)


.023

.001
.009

.017
.006

.019

.002

.009
.051


.439

.018
.184

.332
.126

.370

.040

.179


.898

.894
.832

.717
.973


.003

.000
.000

.001
.000

.000

.000

.000


.997









Table 4-120. Tests of between subjects. I have accessed sexually explicit materials on the
internet.
Type III
Independent Sum of Degrees of Mean Level of Partial Eta
Variable Squares Freedom Square F value Significance Squared
Gender 26.888 1 26.888 38.981 .000 .033
Age 1.527 2 .763 1.107 .331 .002
Race 2.890 4 .722 1.047 .381 .004
School 3.796 1 3.796 5.503 .019 .005
Gender/Age .140 2 .070 .101 .904 .000
Gender/Race 2.592 4 .648 .939 .440 .003
Age/Race 3.533 8 .442 .640 .744 .004


Gender Age
Race
Gender/
School
Age/School
Gender/Age
/School
Race/School
Gender
Race/School
Age/Race
/School


1.953

.959
.852

.317
2.000

2.264

1.868


Gender/Age/
Race School .045 1
Error 795.983 1154
Total 9248.000 1200
R squared=.914 (adjusted R Squared=.910)


.244

.959
.426

.159
.500

2.264

.467

.045
.690


.354

1.390
.617

.230
.725

3.282

.677

.065


.944

.239
.540

.795
.575

.070

.608


.002


.000
.003

.003

.002

.000









Table 4-141. Correlations: I like to drink alcoholic beverages while having cybersex.
I like to
drink
alcoholic
beverages
while
having
cybersex
with an
online
School Gender Age Race partner


Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N


1 -.101(**)


1212


-.101(**)

.000
1210

-.025

.391
1211

-.662(**)

.000
1209


.000
1210


-.025 -.662(**)


.391
1211


1 .114(**)


1210

.114(**)

.000
1209

.103(**)

.000
1207


I like to
drink
alcoholic
beverages
while
having
cybersex
with an
online Pearson
partner Correlation -.044 .140(**)
Sig. (2-
tailed) .124 .000
N 1203 1201
** Correlation is significant at the 0.01 level (2-tailed).


.000
1209


.000
1209

.103(**)


.000
1207


1 .118(**)


1211

.118(**)

.000
1208


.083(**)

.004
1202


.000
1208


1209


.015

.610
1200


School




Gender





Age





Race


-.044

.124
1203

.140(**)


.000
1201


.083(**)


.004
1202

.015

.610
1200


1203









Table 4-56. Tests of between subjects. Have you ever in person someone that you met online.
Type III
Independent Sum of Degrees of Mean Level of Partial Eta
Variable Squares Freedom Square F value Significance Squared
Gender .455 1 .455 2.141 .144 .002
Age 1.208 2 .604 2.844 .059 .005
Race 1.024 4 .256 1.205 .307 .004
School .075 1 .075 .355 .552 .000
Gender/Age .175 2 .087 .411 .663 .001
Gender/Race 1.432 4 .358 1.685 .151 .006
Age/Race .416 8 .052 .245 .982 .002


Gender Age
Race
Gender/
School
Age/School
Gender/Age
/School
Race/School
Gender
Race/School
Age/Race
/School


1.197

.093
.333

.928
.437

.143

.419


Gender/Age/
Race School .058
Error 244.503
Total 3612.000
R squared=.932. (adjusted R


1
1151
1197
Squared=.930)


.688

.508
.457

.113
.725

.512


.150

.093
.166

.464
.109

.143

.105

.058
.212


.005

.000
.001

.004
.002


.704

.439
.783

2.183
.515

.675

.493

.274


.601


.002

.000










Table 4-65. I have posted online personal messages in order to meet a potential partner. Mean:
Females, Age, Race, School


Standard
Error
.132
.099
.141
.105
.105


95% Confidence Interval
Lower Upper
Bound Bound


Gender
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females


* This level combination of factors is not observed, thus the corresponding marginal
estimable.
AAB= African American Black, AAPI=Asian American/Pacific Islander, MAL=Mel
American Latino, NWH=Non White Hispanic, O=Other.


Age
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up


Race
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0


School
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU


Mean
1.240
13.56
1.091
*
1.025
*
1.116
2.000
1.200
1.000
1.286
1.413
1.261
*
1.103
*
1.157
1.00
1.600
1.000
1.500
1.435
1.200
1.000
1.154
1.000
1.465
1.000
1.333
1.000


.981
1.162
.814
*
.820
*
1.012
.703
.790
-.297
1.041
1.222
.990

.895
*
1.071
-.297
1.265
.083
.583
1.164
620
-.297
.794
-.297
1.334
.251
.584
.251


1.499
1.549
1.367
*
1.230
*
1.220
3.297
1.610
2.297
1.531
1.604
1.531
*
1.310
*
1.242
2.297
1.935
1.917
2.417
1.705
1.780
2.297
1.514
2.297
1.595
1.749
2.082
1.749
mean is not

xican


.053
.661
.209
.661
.125
.097
.138
*
.106
*
.044
.661
.171
.468
.468
.138
.296
.661
1.83
.661
.066
.382
.382
.382










Table 4-37. How often do you use the internet: Mean: Females, Age, Race, School


Gender
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females


95% Confidence Interval
Lower Upper
Bound Bound


Age
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up


Race
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0


AAB= African American Black, AAPI=Asian American/Pacific Islander, MAL=Mexican
American Latino, NWH=Non White Hispanic, O=Other.
* This level combination of factors is not observed, thus the corresponding marginal mean is not
estimable


School
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU


Mean
1.000
1.023
1.000
1.000

1.000
1.000
1.000
1.000
1.000
1.000
1.000

1.043

1.000
1.000
1.000
1.000
1.004
1.000
1.000
1.000
1.000
1.286

1.000
1.000
1.000
1.000
1.021
1.667
1.000
1.000


Standard
Error
.027
.020
.029
*
.021
*
.011
.135
.043
.135
.026
.020
.028
*
.023
*
.009
.135
.035
.095
.095
.029
.067
.135
.037
.135
.014
.078
.095
.078


.947
.983
.944
*
.958
*
.978
.735
.916
.735
.949
1.004
.945
*
.955
*
.987
.735
.932
.813
.813
1.228
.868
.735
.927
.735
.994
1.514
.813
.847


1.053
1.063
1.056
*
1.042
*
1.022
1.265
1.084
1.265
1.051
1.083
1.055
*
1.045
*
1.022
1.265
1.068
1.187
1.187
1.344
1.132
1.265
1.073
1.265
1.048
1.820
1.187
1.153









4-88 Tests of between subjects. Does your offline partner know about the
friends/relationships that you have online. ........................................... ............... 120

4-89 Does your offline partner know about the friends/relationships that you have online.
M ean: Age, School............ ........ ... ............. ...... ......... 120

4-90 Tukey HSD. Does your offline partner know about the friends/relationships that you
h av e on lin e? A g e..................................................................... 12 1

4-91 Correlations: Has your partner express jealousy over the relationships you have
o n lin e ................. .. .......... ................. ............................................... 12 2

4-92 Mean: Has your partner ever expressed jealousy over online relationships: School.......123

4-93 Mean: Has your partner ever expressed jealousy over online relationships: Gender......123

4-94 Mean: Has your partner ever expressed jealousy over online relationships: Age ..........123

4-95 Mean: Has your partner ever expressed jealousy over online relationships: Race..........123

4-96 Tests of between subjects. Has your partner ever expressed jealousy over the
relationships you have developed online. ............................................. ............... 124

4-97 Mean: Has your partner ever expressed jealousy over the relationships you have
developed online: A ge, R ace ........................................... .......................................... 125

4-98 Tukey HSD. Has your partner ever expressed jealousy over the relationships you
have developed online: A ge.................................................. ............................... 125

4-99 Has your partner ever expressed jealousy over the relationships you have developed
online. M ean: A ge R ace and School..................................................................... ..... 126

4-100 Correlations: It is important that an alternative place for meeting people is available.... 127

4-101 Mean: It is important that an alternative place for meeting people is available: School.127

4-102 Mean: It is important that an alternative place for meeting people is available ............128

4-103 Mean: It is important that an alternative place for meeting people is available: Age .....128

4-104 Mean: It is important that an alternative place for meeting people is available: Race ....128

4-105 Tests of between subjects. It is important that an alternate place for meeting people is
available: A ge, G ender, R ace, School ........................................ ........................ 129

4-106 Tukey HSD. How important is it to you that an alternate way/place for meeting new
people is available. A ge .................. ............................. ........ .. ........ .... 129

4-107 Correlations: An alternative way to meet online sex partners. ......................................130









Hightow, L., MacDonald, P., Pilcher, C., Kaplan, A., Foust, E., Nguyen, T., et al. (2005). The
unexpected movement of the HIV epidemic in the southeastern United States:
Transmission among college students. JAIDS, 38(5), 531-537.

Hoffman, D., Novak, T. P., & Venkatesh, A. (2004). Has the internet become indespensible?
Communications of the ACM, 47(7).

Hollander, D. (2004). Changes in teenagers' sexual behaviors stall. Perspectives on Sexual and
Reproductive Health, 36(4), 141.

Holowaty, P., Harvey, B., Feldman, L., Rannie, K., Shortt, L., & Jamal, A. (1997). A comparison
of the demographic, lifestyle and sexual behaviour characteristics of virgin and non-
virgin adolescents. The Canadian Journal ofHuman Sexuality, 6(3), 197.

Howard, D., & Wang, M. (2004). Multiple Sexual-Partner Behavior Among Sexually Active US
Adolescent Girls. American Journal of Health Behavior, 28(1), 3.

Huang, Y. (2006). Identity and intimacy crisis and their relationship to internet dependence
among college students. Cyberpsychology & Behavior, 9(5), 571-576.

Koch, W., & Pratarelli, M. (2004). Effects of intro/extraversion and sex on social internet use.
North American Journal of Psychology, 6(3), 371-382.

Kraut, R., Patterson, J., Landmark, V., Kiesler, K., Mukopadhay, T., & Scherlis, W. (1998).
Internet paradox: A social technology that reduces social involvement and psychological
well being? American Psychologist, 539, 1017-1031.

LaRose, R., & Eastin, M. (2004). A social cognitive theory of internet uses and gratifications:
Toward a new model of media attendance. Journal ofBroadcasting & Electronic Media,
48(3), 358.

Leiblum, S. (2001). Women, sex and the internet. Sexual and Relationship Therapry, 16(4), 390-
405.

Longmore, M. A., Manning, W. D., Giordano, P. C., & Rudolph, J. L. (2004). Self esteem,
depressive symptoms and adolescent sexual onset. SocialPsychology, 67(3q), 279.

Malone, R. (2000). Research, the internet, and the way things are. Health Education and
Behavior (27).697.

McCown, J., Fischer, D., Page, R., & Homant, M. (2001). Internet relationships: People who
meet people. Cyberpsychology & Behavior, 4(5), 593-596.

McFarlane, M., Bull, S., & Rietmeijer, C. (2000). The internet as a newly emerging risk
environment. JAMA, 284(4).









Table 4-137. Mean.: I have had cybersex with on line partnerss: Gender
Gender Mean Std. Deviation N
Females 1.23 .576 831
Males 1.41 .732 364
Total 1.29 .633 1195

Table 4-138. Mean: I have had sex with on line partnerss: Age
Age Mean Std. Deviation N
18-19 1.29 .630 408
20-22 1.26 .614 521
23-and above 1.33 .670 267
Total 1.29 .632 1196

Table 4-139. Mean: I have had sex with on line partnerss: Race
Race Mean Std. Deviation N
AAB 1.24 .589 209
AAPI 1.38 .721 72
MAL 1.33 .695 132
NHW 1.28 .629 729
0 1.25 .552 55
Total 1.29 .632 1197









Table 4-115. Correlations: I have accessed sexually explicit materials on the internet.


School


Gender


Age


Race


Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N


1 -.101(**)


1212


.000
1210


-.101(**)


.000
1210


-.025 -.662(**) -.154(**)


.391
1211


1 .114(**)


1210


.000
1209


-.025 .114(**)


.391
1211


-.662(**)

.000
1209


.000
1209


.103(**)

.000
1207


on the Pearson
Internet Correlation -.154(**) .516(**)
Sig. (2-
tailed) .000 .000
N 1211 1209
** Correlation is significant at the 0.01 level (2-tailed).


1211


.118(**)


.000
1208


.000
1209


.103(**)


.000
1207


1 .118(**)


.000
1208


1 .106(**)


1209


.046 .106(**)


.113
1210


.000
1208


Table 4-116. Mean: I have accessed sexually explicit materials on the internet: School
School Mean Std. Deviation N
PFU 2.66 .972 1047
HBCU 2.21 .930 156
Total 2.60 .978 1203


I have
accessed
sexually
explicit
materials
on the
Internet


.000
1211


School


Gender


Age


Race



I have
accessed
sexually
explicit
materials


.516(**)


.000
1209

.046

.113
1210


.000
1208


1211









Table 4-76. Does your partner object to the amount of time you spend online. Mean: Females,
Race and School
95% Confidence Interval
Lower Upper
Gender Race School Mean Std. Error Bound Bound
Females AAB PFU 1.949 .072 1.807 2.090
Females AAB HBCU 1.926 .021 1.885 1.966
Females AAPI PFU 1.985 .041 1.905 2.065
Females AAPI HBCU 2.000 .208 1.592 2.408
Females MAL PFU 1.981 .027 1.927 2.035
Females MAL HBCU 2.000 .208 1.592 2.408
Females NHW PFU 1.962 .011 1.940 1.983
Females NHW HBCU 2.000 .106 1.792 2.208
Females 0 PFU 1.974 .057 1.862 2.086
Females 0 HBCU 2.000 .094 1.816 2.184
Males AAB PFU 1.833 .063 1.170 1.957
Males AAB HBCU 1.933 .048 1.839 2.028
Males AAPI PFU 1.944 .051 1.845 2.044
Males MAL HBCU *
Males MAL PFU 1.952 .037 1.879 2.026
Males MAL HBCU *
Males NHW PFU 1.959 .014 1.930 1.987
Males NHW HBCU 1.500 .127 1.250 1.750
Males 0 PFU 1.917 .051 1.817 2.016
Males O HBCU *
This level of mean is not observed therefore the corresponding population marginal mean is
not estimable. AAB= African American Black, AAPI=Asian American/Pacific.24 Islander,
MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.









Table 4-114. Tukey HSD. An alternative way/place for meeting potential sexual partners: Race
95% Level of Confidence
Mean Standard Level of Lower Upper
Race Race Difference Error Significance Bound Bound
AAB AAPI -.25 .157 .486 -.68 .17
AAB MAL -.11 .128 .921 -.45 .24
AAB NHW -.27 .090 .026 -.51 .02
AAB O -.24 .177 .641 -.73 .24
AAB= African American Black, AAPI=Asian American/Pacific Islander, MAL=Mexican
American Latino, NWH=Non White Hispanic, O=Other.









Table 4-129. Mean: While viewing sexually explicit web sites I have masturbate: Gender
Gender Mean Std. Deviation N
Females 1.64 .982 833
Males 3.19 1.029 365
Total 2.11 1.227 1198

Table 4-130. Mean: While viewing sexually explicit web sites I have masturbated: Age
Age Mean Std. Deviation N
18-19 2.00 1.204 409
20-22 2.10 1.223 522
23-and above 2.30 1.252 268
Total 2.11 1.227 1199

Table 4-131. Mean: While viewing sexually explicit web sites I have masturbated: Race
Race Mean Std. Deviation N
AAB 1.70 1.039 210
AAPI 2.26 1.267 72
MAL 2.11 1.252 132
NHW 2.21 1.248 731
0 2.13 1.203 55
Total 2.11 1.227 1200









Future plans include immediately pursuing the second phase of this study using one of

the populations within the sample. I also plan to attempt to develop a survey tool that will elicit

clearer responses from participants in these vital areas. After developing that tool, I will

investigate the internet phenomenon within the HBCU system and replicate parts of the study for

clarification and deeper understanding. I hope to make a difference in this at risk population.









Table 4-126. Tests of between subjects. I have accessed sexually explicit materials on the
internet to become sexually aroused: Gender, Race, Age, School
Type III
Independent Sum of Degrees of Mean Level of Partial Eta
Variable Squares Freedom Square F value Significance Squared
Gender 41.807 1 41.807 47.036 .000 .039
Age .818 2 .409 .460 .631 .001
Race 3.169 4 .792 .891 .468 .003
School 7.666 1 7.666 8.625 .003 .007
Gender/Age .707 2 .354 .398 .672 .001
Gender/Race 3.747 4 .937 1.054 .378 .004
Age/Race 2.957 8 .370 .416 .912 .003


Gender Age
Race
Gender/
School
Age/School
Gender/Age
/School
Race/School
Gender
Race/School
Age/Race
/School
Gender/Age/
Race School
Error
Total


3.779


2.698
.109

.005
1.819

2.824

.272

.023
1023.927
7639.000


8

1
2

2
4

1

4

1
1152
1198


.472

2.698
.054

.003
.455

4.824

.068

.023
.889


.531

3.035
.061

.003
.512

3.178

.076

.026


.833


.997
.727


.004

.003
.000

.000
.002

.003

.000

.000


.989









consistent sets of sexual and relationship norms; however in many cases sexually responsible

models are not portrayed. According to Taylor (2005), people who watch television shows that

have increased sexual content are more likely to make elevated estimates of the frequency of

actual sexual behaviors based upon the media observed. Examples of overestimated sexual

behaviors include using sex for favors, sexual activity without love, or a romantic interest and

boasting sexual conquests. Sexual television content has also been linked to beliefs and

expectations about one's own sexual experiences. Increased exposure to visual sexual media are

correlated to decreased sexual inhibition (Taylor, 2005).

Over the past few decades, nationally representative surveys have accumulated a wealth

of data on levels of sexual activity in adolescents. According to one such survey, approximately

47 % of adolescents in grades 9 through 12 have had four or more sexual partners (Hollander,

2004). According to Remez (2000) most adolescents fear pregnancy over sexually transmitted

infections (STI). Adolescents growing up and educated in the era of HIV disease have the

misconception that oral sex is considered to be a risk free sexual behavior in comparison to

vaginal and/or anal intercourse. Many adolescents also believe that oral and anal sex are not

considered to be coital sex so engaging in them maintains the concept of virginity (Holowaty et

al., 1997; Remez, 2000).

The internet has begun to play an increasing role in the sex lives of young people. In one

particular survey, individuals with an age range of 18-24 reported meeting an average often

partners through the internet in the last two years. Many stated that seven of the ten partners were

obtained via the internet in the previous year. Participants within this age range were more

willing than their older counter parts to seek potential partners in chat rooms (anonymous) and

then elevate the interaction to exchange of addresses. Respondents to the anonymous survey









Table 4-66. I have posted online personal messages in order to meet a potential partner. Mean:
Males, Age, Race, School
95% Confidence Interval
Standard Lower Upper
Gender Age Race School Mean Error Bound Bound
Males 18-19 AAB PFU 1.500 .234 1.041 1.959
Males 18-19 AAB HBCU 1.667 .171 1.332 2.002
Males 18-19 AAPI PFU 1.167 .270 .637 1.696
Males 18-19 AAPI HBCU *
Males 18-19 MAL PFU 1.222 .220 .790 1.655
Males 18-19 MAL HBCU *
Males 18-19 NHW PFU 1.273 .081 1.13 1.432
Males 18-19 NHW HBCU 1.000 .146 .083 1.917
Males 18-19 O PFU 1.000 .296 .420 1.580
Males 18-19 O HBCU *
Males 20-22 AAB PFU 1.833 .270 1.304 2.363
Males 20-22 AAB HBCU 1.00 .296 .420 1.580
Males 20-22 AAPI PFU 1.667 .220 1.234 2.099
Males 20-22 AAPI HBCU *
Males 20-22 MAL PFU 1.579 .152 1.281 1.877
Males 20-22 MAL HBCU *
Males 20-22 NHW PFU 13.76 .069 1.242 1.511
Males 20-22 NHW HBCU *
Males 20-22 O PFU 2.250 .331 1.601 2.899
Males 20-22 O HBCU *
Males 23-up AAB PFU 2.00 .468 1.083 2.917
Males 23-up AAB HBCU 15.00 .270 .970 2.030
Males 23-up AAPI PFU 2.500 .270 1.970 3.030
Males 23-up AAPI HBCU *
Males 23-up MAL PFU 1.917 .191 1.542 2.291
Males 23-up MAL HBCU *
Males 23-up NHW PFU 1.507 .076 1.357 1.656
Males 23-up NHW HBCU 4.000 .661 2.703 5.297
Males 23-up O PFU 1.833 .191 1.459 2.208
Males 23-up O HBCU *
This level combination of factors is not observed, thus the corresponding marginal mean is not
estimable
AAB= African American Black, AAPI=Asian American/Pacific Islander, MAL=Mexican
American Latino, NWH=Non White Hispanic, O=Other.









Gender and School proved to be significant as a main effect interaction with gender, race

and school as a combined interaction when compared with the dependent variable in table 4-132.

All three tests were significant on the MANOVA (table 4-7). Based upon the means of these

analyses found in table 4-129, males are far more likely than their female counterparts to view

sexually explicit materials for the purpose of masturbation. Based upon these mean, males are

twice as likely to masturbate to sexually explicit web sites versus their female counterparts.

Table 4-127 identifies that students attending PFU are far more likely to view these materials for

the purpose of masturbation. Although it was not significant on the ANOVA table (4-132), with

the exception of AAPI females, females at PFU of any other race are more likely to view this

material for the purpose of self gratification than their racial counterparts at HBCU.

In table 4-133, males who are AAB attending PFU are twice as likely to view sexually

explicit material on the internet for the purpose of masturbation as that of their HBCU

counterparts. The Tukey HSD in table 4-134 demonstrated that AAB were less likely to

masturbate while viewing sexually explicit material than all other races sampled in this study.

I Have Had Cybersex With An Online Partner.

Using ANOVA statistics, the question "I have had cybersex with an online partner" was

analyzed. The responses for this specific question included frequently which was given a value

of four, sometimes which was given a response of three, rarely, which was given a response of

two, and never, which was given a value of one. This question was analyzed as the dependent

variable with age, gender, race and university of record (school). The results found in table 4-140

proved to be insignificant with all effects.

I Like To Drink Alcohol While Having Cybersex With An Online Partner

Using ANOVA statistics, the question 'I like to drink alcohol while having cybersex with an

online partner' was analyzed. The responses for this specific question included frequently which









in table 4-50 demonstrated a level of significance for the interaction between AAB and MAL and

NHW both (p=.000). Age was not significant in the Tukey HSD (table 49), so the interaction

effects of groups within the groups (multiple comparison) was not significant. It is interesting to

note that the means of all groups who identified themselves as 23 or older were significantly

higher (more likely to make friends over the internet) when compared to other age groups.

This analysis identified that race, gender, and university of record (school) are factors in

making new friends on the internet. There was a significant interaction between gender, race and

school, demonstrating that AAB, MAL, and NHW (male or female) who attend HBCU are more

likely to make friends over the internet. However, as previously stated, the representation of

these groups within the sample does not demonstrate representation in all groups, and should be

viewed with cautious suspicion.

Have You Met Anyone In Person That You Met Online?

The online survey asked specific questions regarding relationships. Two of the questions

were used in this analysis. These questions were Boolean Style (yes-no) responses. The

questions were posed in this fashion: "Have you ever met in person anyone you first met online".

These questions were again analyzed using the variables of gender, race, university of record,

and age. Univariate ANOVA tests were performed and there was no significant interaction

among any of the variables as represented in table 4-56. The results demonstrate that in this

particular sample, meeting a person online may not equate into meeting them in person in an

offline situation. Tables 4-51 through 4-56 illustrate the results.

I Have Posted Online Messages To Meet A Potential Partner

One of the questions asked in the survey was phrased "I have posted online personal

messages in attempts to meet a potential partner (for example, in web sites for singles)". The

answers were given numeric values that ranged from the number four for frequently, three for









education. Finally, in the presence of at least one or more romantic partners present at the first

wave, sexual debut increased by approximately 90 % by the second wave.(Rostosky, Regnerus,

& Wright, 2003).

Multiple Sex Partners

Howard and Wang (2004) examined the relationship of multiple sexual-partners to other

risk behaviors among adolescent girls. Using the National Youth Risk Behavior Survey, 3288

sexually active adolescent females participated in this study. This study attempted to correlate

relative risk of engaging in risk taking behaviors, age, ethnicity, and number of sexual partners.

Results showed that having one sexual partner was associated with lack of condom use and being

in 12th grade. Two or more partners was associated with being Black, fighting, binge drinking,

and cigarette use (Howard & Wang, 2004).

Welch and colleagues (1998) found a surprising number of gonorrhea cases in lower

income cases in Syracuse, New York. A study was conducted to provide information for public

health interventions regarding gonorrhea and other STIs in the identified high risk population.

The descriptive study provided information from individual and focus group interviews in an

attempt to develop effective health education messages and services for the teen population. A

major portion of high-risk behavior occurred in conjunction with the use of alcohol and

marijuana. Coercion and possibly violence are a part of sexual activity for at least some teens.

Concerns included violence as a health problem, rape in jail as an STI risk, and sex with a female

partner who is unconscious from intoxication. Female adolescents reported that males would

pressure them to have sex without a condom. They concluded that early sexual debut can be the

result of coercion specifically in the early sexual experience of adolescents in this group.

Interestingly, participants described the practice of exchanging sex for consumer items (food,

















ULFYjivijj ad College Stdents' Perceptions and Behavior When Using the Internet for Sexuality-Related Information. An
Iexploratory study.


B. 1User Characteristics

The questions below are intended to assess your use of the Internet and Email. Please click the circle underneath the number that best
represents your use of access to the services listed.


Less than
2-3 times once a
Daily a week week
l. How often do you use email? I 0 0 0
B2. How often do you use the Internet? 0 | 0 0


B3. How long have you been using e-mail?
0 1 < than 6 months
02. 6- 12 months
03. 2 3 years
04. > 3 years
0 5. I don't use email



5. Where do _.u u uil. log on to your e-mail?
,: 1,:,: e mie r ir ri eque ly used site)
0 1. campus computer lab
02. personal computer at home
0 3. personal computer at work
0 4. a friend'relative' s computer
0 5. other


B4- How long have you been using the Internet?
0 1. < than 6 months
02. 6 12 months
03.2 3 years
04. > 3 years
0 5. I don't use e-mail



B6. Where do you usually log on to the Internet?
(choose most frequently used site)
0 1. campus computer lab
0 2. personal computer at home
03. personal computer at work
0 4. a friendrelative's computer
05. other


Page 2 of 7









Table 4-105. Tests of between subjects. It is important that an alternate place for meeting people
is available: Age, Gender, Race, School
Type III Degrees
Independent Sum of of Mean F Level of Partial Eta
Variable Squares Freedom Square value Significance Squared
Gender .403 1 .403 .378 .539 .919
Age 11.049 2 5.524 5.177 .006 .009
Race 7.996 4 1.999 1.873 .113 .006
School .138 1 .138 .129 .720 .000
Gender/Age 2.383 2 1.192 1.117 .328 .002
Gender/Race 3.888 4 .972 .911 .457 .003
Age/Race 5.718 8 .715 .670 .718 .005
Gender/Age 6.178 8 .772 .724 .671 .005


/Race
Gender/Schoo
1
Age/School
Gender/Age
/School
Race/School
Gender
/Race/School
Age/Race
/School
Gender/Age
/Race/School
Error
Total


1.019

1.327
1.729

2.688
.017

5.416

.865


1225.082
15710.000


1 1.019 .954


.663 .622
.865 .810

.672 .630
.017 .016


4 1.354 1.269

1 .865 .810


1148
1194


.329

.537
.445

.641
.900

.280

.368


.001
.001

.002
.000

.004

.001


1.067


Table 4-106. Tukey HSD. How important is it to you that an alternate way/place for meeting new
people is available. Age


Standard L


Age Age Difference Error S
18-19 20-22 -.15 .068
23-up -.51 .081
20-22 18-19 .15 .068
23-up -.37 .078
23-up 18-19 .51 .081
20-22 .37 .078
(Mean difference is significant at the .05 level)


,evel of
significance
.78
.000
.078
.000
.000
.000


95% Confidence Interval
Lower Upper
Bound Bound
-.31 .01
-.70 -.32
-.01 .31
-.55 -.18
.32 .70
.18 .55


Mean









Table 4-132. Tests of between subjects. While viewing sexually explicit web sites on the
internet, I have masturbated.
Type III
Independent Sum of Degrees of Mean Level of Partial Eta
Variable Squares Freedom Square F value Significance Squared
Gender 56.277 1 560277 59.528 .000 .049
Age 2.044 2 1.022 1.081 .340 .002
Race 3.487 4 .872 .922 .450 .003
School 6.249 1 6.249 6.610 .010 .006
Gender/Age 2.971 2 1.486 1.571 .208 .003
Gender/Race 6.395 4 1.599 1.691 .150 .006
Age/Race 3.104 8 .388 .410 .915 .003


Gender Age
Race
Gender/
School
Age/School
Gender/Age
/School
Race/School
Gender
Race/School
Age/Race
/School


4.477

.941
.011

.207
2.562

5.441

1.519


Gender/Age/
Race School .846 1
Error 1088.145 1151
Total 7140.000 1197
R squared=.848 (adjusted R Squared=.842)


.560

.941
.006

.104
.641

5.441

.380


.592

.995
.006.110

.678
5.755

.402

.895


.846
.945


.785

.319
.994

.896
.608


.004

.001
.000

.000
.002

.005


.808

.344









Table 4-92. Mean: Has your partner ever expressed jealousy over online relationships: School
School Mean Std. Deviation N
PFU 1.94 .240 926
HBCU 1.89 .308 152
Total 1.93 .251 1078

Table 4-93. Mean: Has your partner ever expressed jealousy over online relationships: Gender
Gender Mean Std. Deviation N


Females
Males
Total


1.92
1.93


.240
.276
.252


749
327
1076


Table 4-94. Mean: Has your partner ever expressed jealousy over online relationships: Age
Age Mean Std. Deviation N
18-19 1.93 .251 370
20-22 1.94 .230 467
23-and above 1.91 .283 240
Total 1.93 .250 1077

Table 4-95. Mean: Has your partner ever expressed jealousy over online relationships: Race
Race Mean Std. Deviation N
AAB 1.89 .313 201
AAPI 1.94 .242 65
MAL 1.93 .264 120
NHW 1.95 .221 640
0 1.90 .298 52
Total 1.93 .251 1078










Table 4-49. Tukey HSD.I have made new friends over the internet: Age
95% Confidence Interval


Age
Age Comparison
18-19 20-22
23-up
20-22 18-19
23-up
23-up 18-19
20-22


Mean
Difference
.04
-.03
-.04
-.07
.03
.07


Standard
Error
.065
.078
.065
.074
.078
.074


Level of
Significance
.811
.938
.811
.643
.938
.643


Table 4-50. Tukey HSD. I have made new friends over the internet:


African American Blacks
95% Confidence Interval


Mean Standard Level of Lower Upper
Race Race Difference Error Significance Bound Bound
AAB AAPI .26 .135 .315 -.11 .62
AAB MAL .53* .109 .000 .23 .83
AAB NHW .45* .077 .000 .24 .66
AAB O .14 .149 .872 -.26 .55
Based upon observed means. *The mean is significant at the .05 level. AAB= African American
Black, AAPI=Asian American/Pacific Islander, MAL=Mexican American Latino, NWH=Non
White Hispanic, O=Other.


Lower
Bound


Upper
Bound


-.11
-.21
-.19
-.24
-.16
-.11









Table 4-59. Mean: I have posted on line personal messages in an attempt to meet a potential
partner: Gender


Mean


Gender
Females
Males
Total


Std. Deviation


1.23
1.48
1.30


836
362
1198


.598
.836
.688


Table 4-60. Mean: I have posted on line personal messages in an attempt to meet a potential
partner: Age
Age Mean Std. Deviation N
18-19 1.20 .530 410
20-22 1.28 .677 521
23-and above 1.51 .859 268
Total 1.30 .688 1199

Table 4-61. Mean: I have posted on line personal messages in an attempt to meet a potential
partner: Race
Race Mean Std. Deviation N
AAB 1.40 .770 211
AAPI 1.35 .772 72
MAL 1.23 .638 133
NHW 1.27 .641 729
0 1.49 .879 55
Total 1.30 .688 1200









Table 4-57. Correlations: I have posted online personal messages in an attempt to find a potential
partner.
I have


School Gender


Age


Race


posted
personal
messages


Pearson
School Correlation
Sig. (2-
tailed)
N
Pearson
Gender Correlation
Sig. (2-
tailed)
N
Pearson
Age Correlation
Sig. (2-
tailed)
N
Pearson
Race Correlation
Sig. (2-
tailed)
N


I have
posted
personal
messages


Pearson
Correlation
Sig. (2-
tailed)
N


1 -.101(**)


1212

-.101(**)

.000
1210

-.025

.391
1211

-.662(**)

.000
1209


.053

.067
1208


.000
1210


-.025

.391
1211


1 .114(**)


1210

.114(**)

.000
1209

.103(**)

.000
1207


.171(**)

.000
1206


.000
1209


1 .118(**)


1211

.118(**)

.000
1208


.166(**)

.000
1207


Table 4-58. Mean: I have posted on line personal messages in an attempt to meet a potential
partner: School


Mean


Std. Deviation


1.29
1.40
1.30


1044
156
1200


.784
.688


-.662(**)


.000
1209


.053

.067
1208


.103(**)


.171(**)


.000
1207


.000
1206


.000
1208


1209


.166(**)


.000
1207

-.054

.062
1205


-.054

.062
1205


School
PFU
HBCU
Total


1208









Table 4-41. Correlations: I have made friends over the internet.


School


Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N
Pearson
Correlation
Sig. (2-
tailed)
N


Gender


Age


1 -.101(**)


1212


-.101(**)

.000
1210

-.025

.391
1211

-.662(**)

.000
1209


.000
1210


Race


-.025 -.662(**)


.391
1211


1 .114(**)


School





Gender





Age





Race


I have
made new
friends
over the Pearson
internet Correlation .149(**) .153(**)
Sig. (2-
tailed) .000 .000
N 1208 1206
** Correlation is significant at the 0.01 level (2-tailed).


.000
1209

.103(**)


.000
1209


I have
made new
friends
over the
internet

.149(**)


.000
1208


.153(**)


.000
1207


1 .118(**)


1211


.000
1208


.118(**)


.000
1208


.000
1206

.005

.850
1207


1 -.144(**)


.000
1205


1209


.005 -.144(**)


.850
1207


.000
1205


1208


Table 4-42. Mean: I have made friends over the internet: School
School Mean Std. Deviation


PFU
HBCU
Total


2.15
2.60
2.21


1210

.114(**)

.000
1209

.103(**)

.000
1207


.986
1.126
1.016


1045
155
1200









Table 4-4. Gender cross tabulated with race from both universities combined.
Gender Race Race Race Race Race Other Total
Asian
African American Mexican Non
American Native Pacific American Hispanic Identified
Black American Islander Latino White Other
Female 169 1 51 93 490 34 838
Male 42 4 21 40 241 21 369
Missing 5
Total 211 5 72 133 731 55 1212

Table 4-5. Gender cross tabulated with university and age differentiated.
Gender School Age 18-19 Age 20-22 Age 23- up Total
Females HBCU 47 49 31 127
Females PFU 254 301 122 677
Total Total Females 301 350 154 744
Males HBCU 17 5 7 29
Males PFU 98 115 111 292
Total Total Males 115 120 118 353
Missing
Data 3
Totals 416 522 271 1212









had a higher average mean than that of their HBCU counterparts, and a trend in the means

suggests that as they age they more frequently post messages in order to meet a potential partner.

This may be directly related to the racial mix from both schools. Again, the variation of ethnic

population between HBCU and PFU is very polarized and the significant ANOVAs may be a

direct result of this.

Males attending HBCU had a higher overall average mean than their PFU counterparts

in attempting to meet potential online partners. However, the phenomenon with increases in the

male populations between the universities was the same phenomena that occurred in the female

population. Males attending PFU demonstrated an increase in means related to age and posting

messages in order to meet a potential partner. Remarkably, there was a jump in the mean of

HBCU males 23 and older, which is almost twice of that of their age 20-22 year old counter

parts. This should definitely be studied further as there was reasonable representation in that

grouping variable (N=29).

As previously stated, in comparing race, age, gender, and school as factors in a four way

interaction in this sample, many of the races do not have a single individual in the sample in

certain age groups when separated for age and gender. Therefore, it would be difficult to attempt

to describe the interaction in between and as a result of these very specific areas. There is

significance in the relationship, and as such further studies should attempt to include larger

numbers within these categories.

The question about meeting potential partners did not specifically ask the sample

population what the purpose of the posting or the relationship of the anticipated partner might be.

It would be interesting to do a follow up survey with questions about the specific purposes of the

posting to meet a potential partner (i.e. friendship, romantic relationship, and/or etc.).











Table 4-102. Mean: It is important that an alternative place for meeting people is available
Gender Mean Std. Deviation N


Females
Males
Total


2.58
2.86
2.66


1.074
1.135
1.100


836
364
1200


Table 4-103. Mean: It is important that an alternative place for meeting people is available: Age
Age Mean Std. Deviation N


18-19
20-22
23-and above
Total


2.65
2.60
2.82
2.67


1.048
1.114
1.138
1.100


413
520
268
1201


Table 4-104. Mean: It is important that an alternative place for meeting people is available: Race
Race Mean Std. Deviation N


AAB
AAPI
MAL
NHW
0
Total


2.64
2.89
2.70
2.62
2.98
2.66


1.130
1.120
1.128
1.065
1.296
1.100


133
732
54
1202









I Have Accessed Sexually Explicit Materials On The Internet.................................. 164
I Have Accessed Sexually Explicit Materials To Become Sexually Aroused....................165
While Viewing Sexually Explicit Web Sites I Have Masturbated ............. ... ................ 166
I Have Had Cybersex With An Online Partner ......................... ................. 167
I Like To Drink Alcohol While Having Cybersex With An Online Partner.....................167
I Like To Use Stimulants While Having Cybersex With An Online Partner....................167
S u m m a ry ......... ................................................................................................1 6 7
L im stations ................. .. ... ...... ...........................................170
Suggestions For Further Investigation............. .. ............... ................... 172

APPENDIX

A IN F O R M E D C O N SE N T ......................................................................... .. .....................174

B IN TE R N E T SU R V E Y .............................................................................. ......................179

C MASS EMAIL........... ...... .... .......... .............. ..... .........186

L IST O F R E F E R E N C E S ..................................................................................... ..................184

B IO G R A PH IC A L SK E T C H ......................................................................... ........................ 189









results virtually eliminated errors in coding and recording when compared to human coding. In

this study, hand coding resulted in a 15 % error rate per case and computerized coding provided

a data pool free of errors. Results from computer based surveys that are compatible with

statistical software can be automatically downloaded into software products such as Excel, and

uploaded into other statistical software products without data-entry errors and with time

efficiency (Rogelberg & Stanton 2007).

Disadvantages

Unfortunately, there are always pitfalls to this type of data collection. The major problem

with gathering this type of data is determining validity. There is always a possibility of

respondents not being completely honest when answering. Another issue is the possibility of

repeated submission from the same respondent. According to Rhodes et al (2003) the potential

for multiple submissions can be minimized using specific designs embedded into the software

program. Predominantly, questions such as asking the respondent if they have previously

completed the survey or having the participant provide some unique identifiable information

such as date of birth have also proven to be effective techniques. However, the major drawback

to multiple submissions may be the length of the survey as an inhibitor for multiple submissions

in itself.

Another problem with internet surveys is attempting to maintain the integrity of the data

pool. With public internet surveys, it is difficult to maintain a specified populace. Even though

there are many ways to protect surveys and target specific groups, it is not infallible. It is not

unusual for a legitimate respondent to participate, and then share the URL link with a friend who

may not meet the eligibility requirement initially intended by the researcher. This snow ball

effect can be detrimental to the validity of the study, and the researcher may be entirely unaware

of this issue (Rhodes, Bowie, & Hergenrather, 2003).














r i C L' vR? i College Students' Perceptions and Behavior When Using the Internet for Sexuality-RelatedInformation: An
UFI F LOR IDA exploratory stuca,


E. Use of the Internet for sexual entertainment/arousal

Many people use the Intenet'Email for sexual gratification of various kinds. Below are questions related to your personal use of the
Internet for this purpose and your opinions about the availability of sexual entertainment on the Internet. In this section- we are defining
sexually explicit materials" as those that either show clear pictures of, or talk/write about sexuality using sexual vocabulary (language).

Freq-ntly Sometmmes Rarely Never
E1. I have accessed sexual explicit materials on the Internet
(either accidentally or intentionally)
2. I have accessed sexually explicit materials on the Internet because
Sam cious about sex I i
3 I have accessed sexually explicit materials on the Internet to become
sexually aroused'excited.0 0
E4. I have accessed sexual explicit materials on the Internet to enhance T
my sex life with my off line partnerss.
E5 When I access sexualy explicit web-sites I do it alone O O O
E6 When I access sexually explicit web-sites I do it with my offlin partner(s)- 0 0 0
E7. When I access sexually explicit web-sites I do it with a group ofpeople. O O O O
E. I have posted messages to sexually explicit chat groups on the Internet. 0 0 0 0
9. I have been a si-nt observer in sexual explicitchat rooms on the Internet. O O I O
lD. I have posted objections to sexually explicit chat-group conversations when
Sound them distasteful._
11 I have felt sexually harassed during certain cyber conversations 0 0 0
E12 I have subscrbed to sexuallyexplicit web ites (paidfor registeing) 0 i O 0 O


The next two questions will ask you about searching for andior purchasing sexual paraphernalia on-line By sexual paraphernalia we are
referring to sex toys; video tapes, DVDs, clothing lingerie, condoms= oils etc Please select the statements that most fits your use of the
Internet for these activities

Fruently Sometimes Rarel Never
l13 I have used the Internet to window shop'browse~iew sexual paraphenalia- O O O O
14. I have used the Internet to order.purchasesexual paraphernalia O O O O


The next set of questions has the purpose of assessing sexual behavior while on line Some people, whie viewing sexually explicit
materials on-ine wil masturbate and/or have cyber-sex ("viral sex with a cyber partners) while on line)

Frequent Sometimes Rarely Never
15. While viewing sexual expect web- sites. I have masturbated- O O
16. I have had cyber sex with an online partnerss. O0 0 0
17. Like to drink alcoholic beverages while having cyber sex with an [ 0 0 0
online partnerss.
18 I like to use stimulants (drugs) while having cyber sex with an online partner(s)- O O O 0
l 9 I like to view other types of sexually explicit material such as adult magazines 0 0 O 0
2D I like to view other types of sexually explicit material such as 0
adult video tapes/q VDs 0


Sexually explicit materials on the Internet: Stony Uneta A Strongly
21 Are a way of learning new sexual techniques- 0 0 0 O 0
r22 HeTp improve my sexual relationships off-line- O O O O O
23 Are a way of fills ng my sexual fantasies O O O O O0
24 Stimulatemy sexualfantasies- or 0 0 0 0 o
E25 MaToe me seualy aroused. O O O O O
E26 Tatis l y curiosity about sex-? 0 0 0 0 [


Not
how important is it to you to use the Internet Extremely Not Important
mportat Importat Uncertain Importat at all
E27 To learn new sexual techniques O O O O O
2 To improve your seual relationships off- line? O O O O O
E29To fil your sexua] fantasies? O O O O O
E3D. To stimulate your sexual fantasies? 0 0 O O 0
E31. To becomes sexually aroused? O O O O O
E32. To satisfy your cRioSty about sex? O O O O O


I Cont nue-

Page 5 of 7









Table 4-80. Does your partner object to the amount of time you spend online. Mean: Males, Age,
Race, School
95% Confidence Interval
Standard Lower Upper
Gender Age Race School Mean Error Bound Bound
Males 18-19 AAB PFU 2.000 .074 1.856 2.144
Males 18-19 AAB HBCU 1.800 .054 1.695 1.905
Males 18-19 AAPI PFU 2.000 .085 1.833 2.167
Males 18-19 AAPI HBCU *
Males 18-19 MAL PFU 1.857 .079 1.703 2.011
Males 18-19 MAL HBCU *
Males 18-19 NHW PFU 1.966 .027 1.912 2.019
Males 18-19 NHW HBCU 2.000 .147 1.711 2.289
Males 18-19 O PFU 2.000 .093 1.817 2.183
Males 18-19 O HBCU *
Males 20-22 AAB PFU 2.000 .093 1.817 2.183
Males 20-22 AAB HBCU 2.000 .104 1.796 2.204
Males 20-22 AAPI PFU 2.000 .093 1.817 2.183
Males 20-22 AAPI HBCU *
Males 20-22 MAL PFU 2.000 .049 1.904 2.096
Males 20-22 MAL HBCU *
Males 20-22 NHW PFU 1.953 .022 1.909 1.998
Males 20-22 NHW HBCU *
Males 20-22 O PFU 2.000 .104 1.796 2.204
Males 20-22 O HBCU *
Males 23-up AAB PFU 1.500 .147 1.211 1.789
Males 23-up AAB HBCU 2.000 .085 1.833 2.167
Males 23-up AAPI PFU 1.833 .085 1.667 2.000
Males 23-up AAPI HBCU *
Males 23-up MAL PFU 2.000 .063 1.877 2.123
Males 23-up MAL HBCU *
Males 23-up NHW PFU 1.957 .025 1.908 2.006
Males 23-up NHW HBCU 1.000 .208 .592 1.408
Males 23-up O PFU 1.750 .060 1.632 1.868
Males 23-up O HBCU *
This level combination of factors is not observed, thus the corresponding marginal mean
is not estimable
AAB= African American Black, AAPI=Asian American/Pacific Islander, MAL=Mexican
American Latino, NWH=Non White Hispanic, O=Other.









INFORMED CONSENT
8. How many people are expected to take part in this research study?

2500 people are expected to participate in this research study

WHAT ARE THE RISKS AND BENEFITS OF THIS STUDY AND WHAT ARE YOUR
OPTIONS?

9. What are the possible discomforts and risks from taking part in this research study?

There are no possible discomforts or risk involved with this study. Some of the questions may
make you feel slightly uncomfortable because of the subject matter. Your identity will remain
anonymous, and therefore it is impossible to link your responses to yourself in any way. There
are no physical or psychological risks to you by your participation in this study.

Other possible risks to you may include: not applicable. This study may include risks that are
unknown at this time.

Participation in more than one research study or project may further increase the risks to you. If
you are already enrolled in another research study, please inform Paula C. Pritchard (listed in
question 2 of this consent form) or the person reviewing this consent with you before enrolling in
this or any other research study or project.

Throughout the study, the researchers will notify you of new information that may become
available and might affect your decision to remain in the study.

If you wish to discuss the information above or any discomforts you may experience, please ask
questions now or call the name of PI or contact person listed on the front page of this form.

10a. What are the potential benefits to you for taking part in this research study ?

There is no direct benefit to you for participating in this research study

10b. How could others possibly benefit from this study?

Potential benefits include the opportunity to improve understanding of how traditional age
college students utilize the internet and explore the possibilities of its influence on sexual
behaviors and sexual decision making.

10c. How could the researchers benefit from this study?

In general, presenting research results helps the career of a scientist. Therefore, Paula C.
Pritchard may benefit if the results of this study are presented at scientific meetings or in
scientific journals. There is no conflict of interest associated with the principal or any other
associated with this study.










Table 4-79. Does your partner object to the amount of time you spend online. Mean: Females,
Age, Race, School


95% Confidence Interval
Lower Upper
Bound Bound


Gender
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females
Females


* This level combination of factors is not observed, thus the corresponding marginal
estimable.
AAB= African American Black, AAPI=Asian American/Pacific Islander, MAL=Mel
American Latino, NWH=Non White Hispanic, O=Other.


Age
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
18-19
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
20-22
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up
23-up


Race
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0
AAB
AAB
AAPI
AAPI
MAL
MAL
NHW
NHW
0
0


School
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU
PFU
HBCU


Mean
2.000
1.886
2.000

2.000
*
1.971
2.000
2.000
2.000
1.846
1.978
1.955
*
1.943
*
1.962
2.000
1.923
2.000
2.000
1.913
2.000
2.000
2.000
2.000
1.962
2.000
2.000
2.000


Std. Error
.042
.031
.045

.034
*
.018
.208
.066
.208
.041
.031
.044
*
.035
*
.014
.208
.058
.147
.208
.043
.104
.208
.066
.208
.023
.120
.147
.120


1.917
1.825
1.911

1.934
*
1.936
1.592
1.871
1.592
1.766
1.917
1.868
*
1.874
*
1.933
1.592
1.810
1.711
1.592
1.828
1.796
1.592
1.871
1.592
1.908
1.764
1.711
1.764


2.083
1.948
2.089

2.066
*
2.006
2.408
2.129
2.408
1.926
2.039
2.042
*
2.012
*
1.990
2.408
2.036
2.289
2.408
1.998
2.204
2.408
2.129
2.408
1.997
2.236
2.144
1.905
mean is not

xican




Full Text

PAGE 1

1 INTERNET USE AND ITS EFFECT ON SE XUAL BEHAVIOR IN TRADITIONAL COLLEGE-AGE STUDENTS By PAULA COURTNEY PRITCHARD A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 Paula Courtney Pritchard

PAGE 3

3 To Dr. Sharleen Simpson

PAGE 4

4 ACKNOWLEDGMENTS I would like to thank m y supervisory committ ee for their tireless patience and continued support throughout this effort. Most significantly, I would lik e to thank my supervisory committee chair Sharleen Simpson. Dr. Simpson has proven to be the epitome of a true mentor. Her tireless efforts to intercede on my behalf seemingly at every turn have provided me guidance, support, collegiality, and friendship. Her passion for research and her students can only be outdone by her wonderful spirit and presence. It has been the blessi ng of this dissertation to have met such an excellent individual as Dr. Simpson. Dr. Sandra Seymour, an esteemed professor, and another member of my committee that has also been monumental in a ssisting me to complete this task. She was motivational at moving me forward, listening to my tr oubles, and pushing me in the right direction. Her nurturing persona has had an enormous affect on my perception of the world. Her li ght hearted spirit has turned many a half empty cup into an over-flowing one. Dr. Greg Neimeyer, who is my minor committee member, has been a Godsend. When I needed information, he was there. When I needed participants, he was there. When I needed a minor committee member, he was there. I would b ecome discouraged as we were not getting the numbers to complete the survey; he was there wi th his favorite saying I think we can get more and always a suggestion to meet the task at ha nd. Through his guidance, wisdom and very unique perspective of the world, he gave me ideas and knowledge. Dr. Neimeyer helped me move out of my boxed way of thinking to understand behavior in this new realm of surrealistic technology that until I met him, I would not have dreamed possible. Dr. Karla Schmitt, another member of my committ ee is the keystone of this project. It was a casual conversation of the notion of adolesce nts hooking up and an article in a New York magazine that brought about the initial idea a nd complete fruition of this dissertation.

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5 I would like to thank my d ear friend and colleague Dr. Alma Yearwood-Dixon, Dean of the School of Nursing, at Bethune-Cookman Univ ersity. Her dedication to following her dream has been a guiding force for me following mine. He r tender care of my spir it has allowed me the time and energy to complete this daunting task. He r vision of a better world and a belief that one person can bring about change has been inspirational to me. I would also like to acknowledge Dr. Patricia Goodson for allowing me to use her survey, and modify it for this research. Her initial development and testing of this tool has allowed most of the energy consumed in this project in actual data collection and analysis. I would like to recognize DCova Technologies, the software comp any that was used in this dissertation. Software for this pr oject was specifically designed, test ed and utilized for the survey as well as all data collection in this dissertation. Ma ny late nights were spent designing testing and revamping the software so that it would do ex actly what I needed it to do to get the job done. The motto of the founder is We have the technol ogy and what they didnt have, they invented on the spot. The tech support pr ovided for all of my many com puters, hard-drives, backups, and desire to keep my technology cutting-e dge has preempted many computer crashes and brought about peace of mind. Finally, I would also like to thank my very dear friend and partner, Jorge Valentin DeLaCova. Without his support and presence, this w ould have never been possible. He is solely responsible for helping me rec ognize the happiness that has repl enished my life and made me strong enough to get here. He has helped me r ealize all of the joy that I might have missed otherwise throughout this journey.

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6 TABLE OF CONTENTS Page ACKNOWLEDGMENTS ............................................................................................................... 4LIST OF TABLES ...........................................................................................................................9ABSTRACT ...................................................................................................................... .............17 CHAP TER 1 THE PROBLEM .....................................................................................................................18Internet Use .............................................................................................................................20Developmental Factors ......................................................................................................... ..23Influential Media ............................................................................................................. .......25Theoretical Perspectives ...................................................................................................... ...28Study Purpose .........................................................................................................................302 REVIEW OF LITERATURE .................................................................................................31Media Influence on Sex ..........................................................................................................31Depression, Religiosity, and Sexual Debut ............................................................................ 34Multiple Sex Partners ......................................................................................................... ....37Internet ....................................................................................................................................39Sex Defined ............................................................................................................................423 METHODOLOGY ................................................................................................................. 44Internet Data Collection ..........................................................................................................44Advantages .................................................................................................................... ..44Disadvantages ................................................................................................................. .46Confidentiality ............................................................................................................... .........47Procedures for the Protection of Human Subjects ........................................................... 47Data Protection ................................................................................................................48Survey Tool ............................................................................................................................48Web Based Surveys ................................................................................................................49Online Survey Design .............................................................................................................50Survey Information ..........................................................................................................51Population .................................................................................................................... ...........51Sample Participants .........................................................................................................51Eligibility ................................................................................................................... ......51Survey Procedures ...........................................................................................................52Sampling Techniques and Difficulties .................................................................................... 53Alternative Methods ........................................................................................................... ....53

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7 Personal Contacts ............................................................................................................53Project Spam ....................................................................................................................54Project Get-email ............................................................................................................. 544 RESULTS ....................................................................................................................... ........56Chapter Overview .............................................................................................................. .....56Survey Aims ...........................................................................................................................56Exclusion Criteria for Surveys ................................................................................................57Demographics .................................................................................................................. .......58Multivariate Analysis of Variance .......................................................................................... 58Tables Within the Study ....................................................................................................... ..59How Long Have You Been Using Email? .............................................................................. 59How Often Do You Use Email? .............................................................................................60How Long Have You Been Using The Internet? .................................................................... 61How Often Do You Use The Internet? ................................................................................... 62I Have Made New Friends Over The Internet ........................................................................64Have You Met Anyone In Person That You Met Online? .....................................................65I Have Posted Online Messages To Meet A Potential Partner ...............................................65Does Your Partner Object To The Amount Of Time You Spend Online .............................. 67Does Your Offline Partner Know About Th e Friends/Relationships That You Have Online ........................................................................................................................ ..........69Partner Jealousy Over Relati onships Developed Online ........................................................ 70An Alternate Way/Place For Meeting New People ................................................................70An Alternative Way/Place For Meeting Potential Online Sexual Partners ............................ 71I Have Accessed Sexually Explicit Materials On The Internet ..............................................72I Have Accessed Sexually Explicit Materials To Become Sexually Aroused ........................ 72While Viewing Sexually Explicit We b Sites, I Have Masturbated ........................................ 73I Have Had Cybersex With An Online Partner. .....................................................................74I Like To Drink Alcohol While Having Cybersex With An Online Partner .......................... 74I Like To Use Stimulants While Havi ng Cybersex With An Online Partner ......................... 75Summary ....................................................................................................................... ..........755 DISCUSSION .................................................................................................................... ...153Length of Internet Use And Email Use ................................................................................ 153Frequency Of Email Use ...................................................................................................... 153Frequency Of Internet Use .................................................................................................... 155Making Friends Online .........................................................................................................157Meeting Friends Offline ......................................................................................................158I Have Posted Online Messages To Meet A Potential Partner .............................................159Does Your Partner Object To The Amount Of Time You Spend Online? ........................... 161Does Your Offline Partner Know About Th e Friends/Relationships That You Have Online ........................................................................................................................ ........162The Expression Of Jealousy Over Online Relationships ......................................................163The Importance Of Alternative Ways/Pl ace For Meeting Potential Online Sexual Partners ...................................................................................................................... ........163

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8 I Have Accessed Sexually Explicit Materials On The Internet ............................................164I Have Accessed Sexually Explicit Materials To Become Sexually Aroused ...................... 165While Viewing Sexually Explicit Web Sites I Have Masturbated ....................................... 166I Have Had Cybersex With An Online Partner ....................................................................167I Like To Drink Alcohol While Having Cybersex With An Online Partner ........................ 167I Like To Use Stimulants While Havi ng Cybersex With An Online Partner ....................... 167Summary ....................................................................................................................... ........167Limitations ................................................................................................................... .........170Suggestions For Further Investigation .................................................................................. 172APPENDIX A INFORMED CONSENT ......................................................................................................174B INTERNET SURVEY ..........................................................................................................179C MASS EMAIL .................................................................................................................... ..186LIST OF REFERENCES .............................................................................................................184BIOGRAPHICAL SKETCH .......................................................................................................189

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9 LIST OF TABLES Table Page 4-1 Gender from both universities combined. ..........................................................................76 4-2 Age from both universities combined. ............................................................................... 76 4-3 Race from both universities combined. .............................................................................76 4-4 Gender cross tabulated with race from both universities combined. ................................. 77 4-5 Gender cross tabulated with uni versity and age differentiated. ......................................... 77 4-6 Race cross tabulated with unive rsity and age differentiated. ............................................. 78 4-7 Multivariate Test: Wilks Lambda: Independent Variables Main and Interaction Effects. ...................................................................................................................... .........79 4-8 Correlations: How long have you been using email? ........................................................ 80 4-9 Mean: How long have you been using email: School........................................................ 80 4-10 Mean: How long have you been using email: Gender ....................................................... 80 4-11 Mean: How long have you been using email: Age ............................................................ 81 4-12 Mean: How long have you been using email: Race ........................................................... 81 4-13 Univariate ANOVA. How long have you been u sing email: Age, Gender, Race, School Year ........................................................................................................................81 4-14 Correlations: How often have you been using email? ....................................................... 82 4-15 Mean: How often do you use email: School ...................................................................... 82 4-16 Mean: How often do you use email: Gender ..................................................................... 82 4-17 Mean: How often do you use email: Age .......................................................................... 83 4-18 Mean: How often do you use email: Race ......................................................................... 83 4-19 Tests of between subjects. How often do you use email: Age, Gender, Race, School Year .......................................................................................................................... ..........83 4-20 How often do you use email: Mean, Standard Error and Confidence Interval .................. 84 4-21 Tukey HSD. How often do you use email: Race. .............................................................. 84

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10 4-22 Correlations: How long have you been using the internet? ............................................... 85 4-23 Mean: How long have you been using the internet: School .............................................. 85 4-24 Mean: How long have you been using the internet: Gender .............................................. 85 4-25 Mean: How long have you been using the internet: Age ...................................................86 4-26 Mean: How long have you been using the inte rnet: Race .................................................86 4-27 Test of between subjects: How long have you been using the internet? ........................... 86 4-28 Correlations: How often do you use the internet? .............................................................87 4-29 Mean: How often do you use the internet: School ............................................................. 87 4-30 Mean: How often do you use internet: Gender ..................................................................87 4-31 Mean: How often do you use the internet; Age ................................................................. 88 4-32 Mean: How often do you us e the internet: Race ................................................................ 88 4-33 Test of between subjects: How often do you use the internet? ..........................................88 4-34 How often do you use the internet: Mean: Race, School ................................................... 89 4-35 How often do you use the in ternet: Mean: Gender, Age ................................................... 89 4-36 How often do you use the intern et: Mean: Gender, Age, School ...................................... 89 4-37 How often do you use the internet: Mean: Fem ales, Age, Race, School ........................... 90 4-38 How often do you use the internet : Mean: Males, Age, Race, School .............................. 91 4-39 Tukey HSD. How often do you use internet: Age ............................................................. 92 4-40 Tukey HSD. How often do you use in ternet: African Am erican Blacks ........................... 92 4-41 Correlations: I have made friends over the internet. .......................................................... 93 4-42 Mean: I have made friends over the internet: School ........................................................ 93 4-43 Mean: I have made friends over the internet: Gender ........................................................ 94 4-44 Mean: I have made friends over the internet: Age ............................................................. 94 4-45 Mean: I have made friends over the internet: Race ........................................................... 94 4-46 Tests of between subjects. I have m ade new friends over the internet. ............................. 95

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11 4-47 I have made new friends over the inte rnet. Mean: Fe males, Age, Race, School ...............96 4-48 I have made new friends over the inte rnet. Mean: Males, Age, Race, School .................. 97 4-49 Tukey HSD.I have made new friends over the internet: Age ............................................ 98 4-50 Tukey HSD. I have made new friends ove r the internet: African Am erican Blacks ......... 98 4-51 Correlations: Have you ever met anyone in person that you met on line. ......................... 99 4-52 Have you ever met anyone in person that you met on line. School ................................... 99 4-53 Have you ever met anyone in person that you met on line. Gender ................................ 100 4-54 Have you ever met anyone in person that you met on line. Age ..................................... 100 4-55 Have you ever met anyone in person that you met on line. Race .................................... 100 4-56 Tests of between subjects. Have you ev er in person som eone that you met online. ....... 101 4-57 Correlations: I have posted online personal m essages in an attempt to find a potential partner. ...................................................................................................................... .......102 4-58 Mean: I have posted on line personal me ssages in an attem pt to meet a potential partner: School .................................................................................................................102 4-59 Mean: I have posted on line personal me ssages in an attem pt to meet a potential partner: Gender ................................................................................................................103 4-60 Mean: I have posted on line personal me ssages in an attem pt to meet a potential partner: Age .....................................................................................................................103 4-61 Mean: I have posted on line personal me ssages in an attem pt to meet a potential partner: Race ....................................................................................................................103 4-62 Tests of between subjects. I have posted online personal m essages in order to meet a potential partner. ..............................................................................................................104 4-63 I have posted online personal messages in order to m eet a potential partner. Mean: Gender, Age .....................................................................................................................104 4-64 I have posted online personal messages in order to m eet a potential partner. Mean: Gender, Age, School ........................................................................................................105 4-65 I have posted online personal messages in order to m eet a potential partner. Mean: Females, Age, Race, School ............................................................................................106 4-66 I have posted online personal messages in order to m eet a potential partner. Mean: Males, Age, Race, School ................................................................................................ 107

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12 4-67 Tukey HSD. I have posted online persona l m essages in order to meet a potential partner: Age .....................................................................................................................108 4-68 Tukey HSD. I have posted online persona l m essages in order to meet a potential partner: African American Blacks ...................................................................................108 4-69 Correlations: Does your partner object to the amount of tim e you spend on line: ..........109 4-70 Mean: Does your partner object to th e am ount of time you spend on line. School ......... 109 4-71 Mean: Does your partner object to th e am ount of time you spend on line. Gender ........ 110 4-72 Mean: Does your partner object to th e am ount of time you spend on line. Age .............110 4-73 Mean: Does your partner object to th e am ount of time you spend on line? Race ........... 110 4-74 Tests of between subjects. Does your pa rtner object to the amount of tim e you spend online................................................................................................................................111 4-75 Does your partner object to the amount of tim e you spend online. Gender and Age ......111 4-76 Does your partner object to the amount of time you spend online. Mean: Females, Race and School ...............................................................................................................112 4-77 Does your partner object to the amount of tim e you spend online. Mean: Gender, Age, Race .........................................................................................................................113 4-78 Does your partner object to the amount of tim e you spend online. Mean: Age, Race, School ........................................................................................................................ ......114 4-79 Does your partner object to the amount of time you spend online. Mean: Females, Age, Race, School ............................................................................................................115 4-80 Does your partner object to the amount of tim e you spend online. Mean: Males, Age, Race, School ....................................................................................................................116 4-81 Tukey HSD. Does your partner object to the amount of tim e you spend online. Age .... 117 4-82 Tukey HSD. Does your partner object to the amount of tim e you spend online. Race ... 117 4-83 Correlations: Does your partne r know about your online friends. ...................................118 4-84 Mean: Does your partner know a bout your online friends: School ................................. 118 4-85 Mean: Does your partner know a bout your online friends: Gender ................................ 119 4-86 Mean: Does your partner know about your online friends: Age ..................................... 119 4-87 Mean: Does your partner know about your online friends: Race .................................... 119

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13 4-88 Tests of between subjects. Does your offline partner know about the friends/relationships th at you have online. ......................................................................120 4-89 Does your offline partner know about the friends/relationships that you have online. Mean: Age, School........................................................................................................... 120 4-90 Tukey HSD. Does your offline partner know about the friends/relationships that you have online? Age ..............................................................................................................121 4-91 Correlations: Has your partner express jealousy over the relationships you have online................................................................................................................................122 4-92 Mean: Has your partner ever expressed jealousy over online relationships: School ....... 123 4-93 Mean: Has your partner ever expressed jealousy over online relationships: Gender ...... 123 4-94 Mean: Has your partner ever expressed jealousy over online relationships: Age ........... 123 4-95 Mean: Has your partner ever expressed jealousy over online relationships: Race .......... 123 4-96 Tests of between subjects. Has your partner ever expressed jealousy over the relationships you have developed online. ........................................................................124 4-97 Mean: Has your partner ever expressed jealousy over the relationships you have developed o nline: Age, Race ........................................................................................... 125 4-98 Tukey HSD. Has your part ner ever expressed jealous y over the relationships you have developed online: Age ............................................................................................. 125 4-99 Has your partner ever ex pressed jealousy over the relationships you have developed online. Mean: Age Race and School ................................................................................126 4-100 Correlations: It is important that an alte rnative place for m eeting people is available. ... 127 4-101 Mean: It is important that an alternativ e place for m eeting people is available: School 127 4-102 Mean: It is important that an altern ative place for m eeting people is available .............. 128 4-103 Mean: It is important that an alternativ e place for m eeting people is available: Age ..... 128 4-104 Mean: It is important that an alternativ e place for m eeting people is available: Race .... 128 4-105 Tests of between subjects. It is important that an alternate place for m eeting people is available: Age, Gender, Race, School ............................................................................. 129 4-106 Tukey HSD. How important is it to you that an alternate way/place for m eeting new people is available. Age ...................................................................................................129 4-107 Correlations: An alternative wa y to m eet online sex partners. ........................................ 130

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14 4-108 Mean: Important alternative way to meet potential on line sexual partners: School ....... 130 4-109 Mean: Important alternative way to meet potential on line sexual partners: Gender ...... 131 4-110 Mean: Important alternative way to meet potential on line sexual partners: Age ........... 131 4-111 Mean: Important alternative way to meet potential on line sexual partners: Race .......... 131 4-112 Tests of between subjects. An alternative way/place for meeting potential sexual partners: Gender, Race, Age, School ............................................................................... 132 4-113 Tukey HSD. An alternative way/place fo r m eeting potential sexual partners: Age ........ 132 4-114 Tukey HSD. An alternative way/place fo r m eeting potential se xual partners: Race ....... 133 4-115 Correlations: I have acce ssed sexually explicit m aterials on the internet. ....................... 134 4-116 Mean: I have accessed sexually explic it m aterials on the internet: School ..................... 134 4-117 Mean: I have accessed sexually explic it m aterials on the internet: Gender .................... 135 4-118 Mean: I have accessed sexually explic it m aterials on the internet: Age .......................... 135 4-119 Mean: I have accessed sexually explic it m aterials on the internet: Race ........................ 135 4-120 Tests of between subjects. I have accessed sexually explicit m ateri als on the internet. .. 136 4-121 Correlations: I have accessed sexually explicit m aterials to become sexually aroused. 137 4-122 Mean: I have accessed sexually explicit materials to become sexually aroused: School ........................................................................................................................ ......138 4-123 Mean: I have accessed sexually explicit materials to become sexually aroused: Gender ........................................................................................................................ ......138 4-124 Mean: I have accessed sexually explicit ma terials to becom e sexually aroused: Age .... 138 4-125 Mean: I have accessed sexually explicit mate rials to becom e sexually aroused: Race ... 138 4-126 Tests of between subjects. I have accessed sexually explicit m ateri als on the internet to become sexually aroused: Gender, Race, Age, School ................................................ 139 4-127 Correlations: While viewing sexually explicit m aterials I have masturbated. ................. 140 4-128 Mean: While viewing sexually explicit we b sites I have m asturbated: School ............... 140 4-129 Mean: While viewing sexually explicit web sites I have masturbate: Gender ................ 141 4-130 Mean: While viewing sexually explicit web sites I have m asturbated: Age ...................141

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15 4-131 Mean: While viewing sexually explicit web sites I have m asturbated: Race ..................141 4-132 Tests of between subjects. While viewing sexually explicit w eb sites on the internet, I have masturbated. .......................................................................................................... 142 4-133 While viewing sexually explicit web sites on the internet, I have m asturbated. Mean: Gender Females, Race and School ................................................................................... 143 4-134 Tukey HSD. While viewing sexually e xplicit web sites on th e internet, I have m asturbated: Race ............................................................................................................143 4-135 Correlations: I have had sex with online partners. ........................................................... 144 4-136 Mean: I have had sex with on line partner(s): School .....................................................144 4-137 Mean.: I have had cybersex with on line partner(s): Gender ...........................................145 4-138 Mean: I have had sex with on line partner(s): Age .......................................................... 145 4-139 Mean: I have had sex with on line partner(s): Race ......................................................... 145 4-140 Tests of between subjects. I have ha d cybersex with an online partner. ......................... 146 4-141 Correlations: I like to drink alcohol ic beverages while having cybersex. ....................... 147 4-142 Mean: I like to drink alcoholic bevera ges while having cybersex with an online partner: School .................................................................................................................148 4-143 Mean: I like to drink alcoholic bevera ges while having cybersex with an online partner: Gender ................................................................................................................148 4-144 Mean: I like to drink alcoholic bevera ges while having cybersex with an online partner: Age .....................................................................................................................148 4-145 Mean: I like to drink alcoholic bevera ges while having cybersex with an online partner: Race ....................................................................................................................148 4-146 Tests of between subjects. I like to dr ink alcohol while having cybersex with an online partner. ..................................................................................................................149 4-147 Correlations: I like to use stim ulants while having online sex. ....................................... 150 4-148 Mean: I like to use stimulants while having cybersex with an online partner: School .... 151 4-149 Mean: I like to use stimulants while having cybersex with an online partner: Gender ... 151 4-150 Mean: I like to use stimul ants while having cybersex w ith an online partner: Age ....... 151 4-151 Mean: I like to use stimulants while having cybersex with an on line partner: Race .......151

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16 4-152 Tests of between subjects. I like to us e stim ulants while having cybersex with an online: partner ............................................................................................................... ...152

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17 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy INTERNET USE AND ITS EFFECT ON SE XUAL BEHAVIORS IN TRADITIONAL COLLEGE AGE STUDENTS By Paula Courtney Pritchard May 2008 Chair: Sharleen Simpson Major: Nursing Sciences As the internet has revolutionized communica tions and business prac tices worldwide, it has been integrated into everything that hum ans as a population engage in. It provides instantaneous access. This ubiquitous presence may appear innocent at the surface; however there may be some sinister characteristics within this medium that requi re further study. One of the issues that exists may be the effect s of the internet on behavior, specifically in the area of sexual activity in adolescents and traditional college age students. At least one third of internet visits are intended to surf sexually or iented web sites, chat rooms, and news groups. Our purpose was to explore how technology, specifically internet use, gender and ethnicity may affect decision making, and influence sexual d ecision making in traditional college students. Using a paper-survey designed by usi ng Goodson et. Al (2000) survey tool Survey Instrument to Assess College Students Behavior and Attitudes to document demographic information as well as college students percep tions and behaviors while using the internet (specifically for sexually related information) was modified into an actual internet survey document. Using mass emails, participants in this study were solicited from two university campuses in Florida. Due to their divergent populations, a small private Hist orically Black College/University (HBCU) as

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18 well as a large Florida public university (PFU) were chosen. Overall, 1202 students participated in the study. Results of this study demonstrate differences in gender, age, race and university of record. These differences appear in developing relations hips, online sexual behaviors, email use, and offline sexual behaviors. The results indicate th at students who are 23 and older are more likely to value the importance of the internet, and also value the opportunity to engage in developing online partners for different relationships that include online sexual activity when compared to their younger counterparts. Gender differences are also identified as an inverse relationship when combined with age. Females are less likely to use the internet as they age, and the opposite is true for males within this sample population. This information may provi de insight into program changes that would target individuals based upon gender. The study also demonstrated racial differences in internet use. It is important to note that this area needs to be replicated specifically because socioeconomic factors were not taken into account, and therefore may have a significant imp act upon the validity of th e results. Therefore any future reports of racial differences using re sults of this study should be used with that cautionary note. The evidence of data obtained by this pr eliminary study of internet use and sexual behaviors of traditional college age students dem onstrates the immediate need for future studies that target internet use and sexual behaviors by race, gender, and age. These studies should specifically look at the aspect of heightened sexual behavior as a result of frequency of internet use through the emerging adult years. .

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18 CHAPTER 1 THE PROBLEM As technological advancem ent continues to a dvance at such a rapi d rate, much of the technology that was a luxury a few years ago is now considered a necessity. The internet is deemed such an item (Weiser, 2000). It is us ed in business, academics, information technology, communication and entertainment. As communicati on and entertainment are instantaneous as a result of internet technology, th e process of human socializat ion and relationship roles are changing. As such, the internet has the capability to impact socialization and relationship roles of adolescents and young adults (Bryant & Bryant, 2005). Gender roles, sexual attitudes, and behaviors are in a critical stag e of development, and as such the internet provides a forum for sensation seeking in chat rooms, interactiv e gaming, and viewing sexually explicit material which may have an effect on sexual be havior (Weisskirch & Murphy, 2004). Sexually active adolescents are absolutely at risk for pregnancy and sexually transmitted infections (STI). Annually, approximately 900,00 0 adolescent females become pregnant and over one third of these young women are less than 17 years. Incredibly, 35 % of all young women in the United States will have been pregna nt at least one time before they reach the age of 20 (Escobar-Chaves et al., 2005). According to the Centers for Disease Contro l and Prevention (CDC ) (2006), adolescents aged 15-19 have the highest rate s of STI of all age groups, and of the 19 million new infections that occur every year, one half of those will take place in adolescents age 15 to 24 (CDC, 2006). According to the Morbidity and Mortality Week ly Report (MMWR) (2006), 7.4% of adolescents had their first sexual encounter prior to the age of thirteen. 14.4 % of all high school students have engaged in sexual activity with four or more partners.

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19 In the United States, almost 4 million cases of STIs are diagnosed in adolescents each year. The most common reported STI in the United States is Chlamydia, and it is most prevalent among adolescents. Chlamydia prevalence reporte d by the CDC demonstrated that sexually active adolescent females were six times more like ly to be infected than the general population (CDC, 2002). In the United States, between the year s 1990 to 1995, the incidence rate of Acquired Immune Deficiency Syndrome (AIDS) in the popul ation between the ages of 13 to 25 years rose nearly 20 %. In 2003, the CDC reported that a pproximately 50 % of all new HIV cases were found among individuals less than 25 years of age. In 2004, HIV disease was named the tenth leading cause of death in people aged 15 to 24. At the end of 2004, 12 % of all new HIV cases reported in Florida were among individuals with ages rangin g were from 13 to 29 years (National Vital Statis tics Reports, 2005). There are many factors that place adolescents at risk for early sexual debut, multiple partners, and subsequent STI. These risk factor s include race, socioeconomics, drug and alcohol use, and peer pressure. Exposure to sexual content in media may also be a contributing factor to the increases in sexual activity a nd STI of adolescent as well as tr aditional college aged students. There have been many studies that discuss sexua l behaviors as they relate directly to the internet; however there are virt ually no studies that explore wh ether viewing sexual content or participating in chat rooms (i.e. cybersex ) on the internet leads to offline sexual activity in adolescents and traditional college age student s (Escobar-Chaves et al., 2005). Therefore, the purpose of this study is to explore how technolog y, specifically the amount of internet use may affect sexual decision-making and in fluence sexual behaviors in tr aditional college age students.

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20 Internet Use The W orld Wide Web has been providing a nove l type of communication that enables the user to access large amounts of information with a touch of a finger. The inception of the Internet began in approximately 1969 by the Unit ed States government. In 1969, the government funded a program to determine the feasibilit y of networking computers together. ARAPANET developed out of the governments effort to c onnect computers together throughout the country. The main purpose of ARAPANET was to secure communication between military organizations and safely store la rge amounts of critical informa tion in the event of a nuclear holocaust (Bogard, 1996). In 1989, the World Wide Web (WWW) went globa l, and brought about the instantaneous access of information to every corner of the planet. The number of internet users started to increase in 1993 and has steadily increased since that time. The gr eatest increase in the number of users accessing the internet began in 1999. In 1999, approximately 36 % of American households had internet access (Anderson, 2001). A ccording to Neilson Ratings (2005), for the month of June 2005 more than 140 million Ameri cans surfed the WWW. Sex is the most frequently searched topic on the internet (Goodson, McCormic k, & Evans, 2001; Griffiths, 2001). Compared to the general population, college students are the heav iest internet users spending much of their time using and surfing the internet. In 2002, 59 % of all Americans had visited the Internet at least one time; conversely, 86 % of all colle ge students were regular users of the Internet. Acco rding to Hoffman and associates (2004), the internet is all-encompassing in the lives of college students, and has become a st aple in college life. Internet use for college students is primarily for social activ ities(Hoffman, Novak, & Venkatesh, 2004).

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21 According to Anderson (2001), the most popular internet activities reflect social behaviors. They include instant messaging, chat rooms, and email. Over 85 % of college students participate in instant messaging. Se venty-Six % of all college stude nts surf the web daily, and the number of web pages is projected to increase to over 50 billion pages in 2005 (Anderson, 2001). As the number of Internet users have grown, computer use has become ingrained in the daily lives of American society. According to Hoffman and colleagues (2004) who studied internet use, between the years of 2000 to 2003, more people reported that computers had become part of their daily rou tines. Respondents in this study stated that computers increase personal communication of family and friends through e-mail and has become the preferred mode of communication (versus telephone and le tter writing). This technological phenomenon has changed certain normal daily activities such as banking and shopping for goods in person to electronic banking and shopping via th e internet. Personal satisfacti on in computer use has also increased from 64 to 72 % over a period of two years. According to Hoffman and colleagues (2004), individuals who use the inte rnet are much more satisfied w ith computer use as a result of computer design, ease of use, webpage designs, a nd overall increase in efficiency of computers and speed of internet connections. As internet use has increased astronomicall y, it is uncertain how the amount of internet usage affects certain portions of the populations. Minority p opulations, lower socioeconomic populations, as well as the elderl y are now accessing the Internet in greater numbers than has ever been documented. Many studies have shown that there is little diffe rence in internet use between ethnicities, however the samples used in these studies may not have been reflective of the general population as well as the increase in computer accessibility. The shift in internet use with these populations may be due in part to improved accessibility, a ffordability, and ease of

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22 use and as such more studies of ethnic differenc es in computer use may be warranted (Dickerson, Reinhart, Feeley, Bidani, & al, 2004). As there are multiple reasons for the increasi ng amount of internet use, there is little documentation of an increase in pathologies related to increased usage. There is speculation that some internet activities are more likely to produce an environment to encourage addictive behaviors, however, that has not been docum ented in the general population. Some of the activities that may promote already identified ad dictive behaviors, or potential developing of addictive behaviors related to excessive use in clude e-mail, chat rooms and multiple players gaming (Griffiths, 2001; Shapira et al., 2003). Age, level of education, and gender have an e ffect on the type of inte rnet activity that is elicited. Men are more likely, and have been the dominant users of online sexual activities such as viewing pornographic websites. Women are more likely to vis it sexually oriented chat rooms which provide interaction and socialization as well as anonymity (C ooper, Marahan-Martin, Mathy, & Maheu, 2002). Finally, the t ypical educational profile sugge sts that excessive internet users have levels of education of fifteen year s or greater (Hall & Pars ons, 2001; Kraut, Patterson, Lundmark et al., 1998). Original theories regarding internet use fo r sexuality purposes were very polarized in their findings. One group of researchers emphasized that internet use specifically as it relates to sexuality is pathological. These studies asserted that individuals would be able to effectively engage in sexual fantasies, and el icit sex conversations that could range from simple flirtations to downright naughty communication. These researcher s thought the internet would be a breeding ground for illegal sexual activ ities, including child pornogr aphy and pedophilia (Cooper, Scherer, Boies, & Gordon, 1999). In contrast, th e second group of researchers asserted that

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23 internet use for sexual purposes may be adaptive versus pathological. According to this scientific camp, internet use for adaptive sexual purposes created an opportunity to normalize sexual desire to create another avenue of information sharing about sexuality and intimacy that is void of physical attributes (Cooper et al., 1999). As more individuals become technologically lite rate, and excessive use of the internet is being reported, the notion of in ternet addiction as well as internet use for sexually related recreation has become of interest to researchers. However, the topic of sexual addiction as it relates to the internet seems to be controversia l. Griffiths (1999) suggests that many excessive internet users are not internet addicts, but use the internet to engage in their part icular addiction (i.e. gambling, sex, and etc.). Griffiths (1999) de termined that the internet has the inherent capability to be used for sexually motivated behavi ors, either online or offline. Offline internet uses include the purchase of sexually relevant material ( books, objects, and educational purposes) or trolling for offline se xual partners. Griffiths (2001) suggests that very few of the activities listed lead to sexual a ddiction, and/or sexual internet addiction. He argues that research regarding internet use has overes timated the number of internet addicts (due to poor research design). He does however admit that some beha viors are more likely to promote internet addiction citing examples of email, and chat ro oms. However, he alleges that sexual addiction using the internet is possible, ci ting that behaviors most likely to promote sexual addiction within this venue include online pornography for masturba tory purposes, and online sexual relationships (Griffiths, 2001). Developmental Factors Adolescence is defined as the period of transition between childhood and adulthood. During this transitiona l time, skills are developed in preparation for the roles and responsibilities of adulthood. Biologic, cognitive and psychosocial changes are also occurring in preparation for

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24 impending adulthood. This transitional phase ca n also prove to be a vulnerable time for adolescents. Psychosocial interactions such as peer group acceptance can exert undue pressure and affect decision making skills during these susceptible years (Delamat er & Friedrich, 2002).. During adolescence many patterns of behavior are established. These behaviors can be influenced by psychosocial development. A ccording to Erikson as cited by Calvert and colleagues (2002), adolescents are in the process of developing pers onal identity. They are in the psychosocial process of moving away from identifying with family and moving toward identifying with a peer group. Adolescents are also in the process of managing physical and emotional intimacy in personal relationships (D elamater & Friedrich, 2002;Calvert Jordan, & Cocking, 2002). As they attain this physical a nd emotional intimacy, gende r and sexual identity is established. Heterosexuality, homosexuality and bi-sexuality lifestyles are determined in this phase of development (Bancroft, 2002; Calvert, Jordan, & Cocking,2002). This is extremely significant for college st udents as the psychological and environmental changes that occur initially in the life of a colle ge student may create ex traneous stress and may become a factor in developing addictive behavi ors related to specific internet use (Hall & Parsons, 2001). In addition to developmental f actors, internet accessibility on college campuses is a free service as most colleges and universities have user domains. This affordable, accessible, and unmonitored use of the internet can increase the amount of computer time students use and allow them to circumvent necessary tasks of development. Online relationships provide an environment of anonymity which allows the user to engage in fantasies and create an entirely different self. This provides an escape from real world relationships, usurping developmental tasks such as identity formation and buildi ng intimate relationships (Hall & Parsons, 2001). Cognitive skills that allow the critical ability to make decisions are not fully developed which

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25 places adolescents at a greater risk of influen ces that can affect decision-making. One of the major influences that affect adolescent behavior is technology based media, specifically sexual oriented media (Escobar-Chaves et al., 2005). The obsession with se x and sexuality is pervasive in all areas of the media. It is estimated that on average, adolescents view 143 incidents of sexual behavior on prime time television each week. Approximately 80 % of all movies shown on network or cable television stations have sexual content in some fo rm or another. At some point in time during a 24 hour period, adolescents will tune in and watch music television (MTV) (Brown & Newcomer, 2002). Approximately 60 % of the videos aired on MTV portray sexual feelings and display provocativ e clothing, and sexually suggestive body movements. Analysis of sexual messages on television indicate that th ey are predominantly presented with little discussion of the potential risk s of unprotected sexua l intercourse, and only a small number of messages include adverse consequences of th is risk taking behavior (Gruber & Grube, 2000; Rich, 2003). Development also influences comprehensi on and interpretation of sexual content. Younger children are less likely to understand the subtlety of suggestive ma terial observed in the media. Adolescents at an earlier stage of biol ogic development were less interested in sexual content media than more mature adolescents. More mature adolescents sought out sexual content as a way of learning about romance and relatio nships. These adolescent s believed that media (internet, television, movies) provides visual in formation on how to become popular and attract the opposite sex (Collins et al., 2004). Influential Media A national study reported that the m ajority of American children live in homes with two or more televisions; approximately 75 % of thos e homes have access to cable television. Over half of the adolescents surveyed had a televisi on set in their own room. These same adolescents

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26 reported that media is used as an informati on source about sexuality, drug abuse, and violent behaviors (Morris, 2004). High amounts of exposure to portrayals of sex may have an effect upon adolescents beliefs and cultural norms. Media creates the illusion that sex is central to daily life and as a result may promote earlier sexual debut. Behaviors that are observed in media sources may alter beliefs about outcomes of engaging in sexual activity. Adolescen ts who view media and see characters having casual sex without negative consequences may be more likely to adopt those portrayed behaviors (Brener, Lowr y, Kann, Kolbe, & al, 2002). Correlational studies indicate that adolescent females choose television programs with sexual content more often than do adolescent males. Interestingly, adolescent females are more likely to observe it in the company of parents. To the contrary, older adolescent males view media with greater hardcore se xual content, and are more likely to listen to more sexually explicit music. Adolescent males use the internet, computer and or online games more often than the female adolescents(Gruber & Grube, 2000). Women are portrayed as sex objects in appr oximately 25 % of the 33 most popular video games. Violence against women is portrayed in approximately 21 %, and in over 40 % of these games, female characters do not have names (R ich, 2003). Adolescents who use these kinds of media are more likely to believe these stereotypi c sex roles versus realit y than their counterparts that do not use these games (Gr uber & Grube, 2000; Morris, 2004). The mainstream mass media (television, movies, music, and internet) provide increasingly frequent portrayals of sexuality. Studies suggest that interactive visual media that are interactive do have an impact on sexual be havior (Boies, 2002; Esco bar-Chaves et al., 2005; Griffiths, 2001; Hollander, 2004). Media portray als of men and women reinforce relatively

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27 consistent sets of sexual and relationship norms; however in many cases sexually responsible models are not portrayed. According to Taylor (2005), people who watch television shows that have increased sexual content are more likely to make elevated estimates of the frequency of actual sexual behaviors based upon the media obs erved. Examples of overestimated sexual behaviors include using sex for fa vors, sexual activity without love, or a romantic interest and boasting sexual conquests. Sexual television content has also been linked to beliefs and expectations about ones own sexual experiences. Increased exposur e to visual sexual media are correlated to decreased sexual inhibition (Taylor, 2005). Over the past few decades, nationally representative surveys have accumulated a wealth of data on levels of sexual activity in adolescents. According to one such survey, approximately 47 % of adolescents in grades 9 through 12 have had four or more sexua l partners (Hollander, 2004). According to Remez (2000) most adolescents fear pregnancy over sexually transmitted infections (STI). Adolescents growing up and educated in the era of HIV disease have the misconception that oral sex is considered to be a risk free sexual behavior in comparison to vaginal and/or anal intercourse. Many adolescents also believe th at oral and anal sex are not considered to be coital sex so engaging in them maintains the concept of virginity (Holowaty et al., 1997; Remez, 2000). The internet has begun to pl ay an increasing role in the sex lives of young people. In one particular survey, individuals with an age ra nge of 18-24 reported meeting an average of ten partners through the internet in the last two years. Many stated that seven of the ten partners were obtained via the internet in the previous year. Participants within this age range were more willing than their older c ounter parts to seek potential part ners in chat rooms (anonymous) and then elevate the intera ction to exchange of addresses. Respondents to the anonymous survey

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28 were recruited through chat rooms, electronic bu lletin boards and list se rves. This particular survey revealed that of 40 % of the respondents ha d engaged in sex with a partner that they met online, and males were 10 % more likely to engage in sex with a partner met online (McFarlane, Bull, & Rietmeijer, 2002). In the presence of a growing technological society, adolescents are observing sexual activity in all sources of media; from televi sion to the internet (R ich, 2003). These same adolescents who are bombarded by this type of medi a are more likely to act out these behaviors, and increase their risk of cont racting STIs. Anecdotal informa tional sources have documented a trend in adolescent sexual behaviors that use tech nology to locate partners to participate in these activities. Decline in da ting trends and romantic relationships on college campuses have brought forth a different kind of relationship; an acronym known as frie nds with benefits (DenizetLewis, 2004). A friend with benefits removes the romance component in relationships and allows for the benefit of sex among and between friends. This phenomena has trickled down to middle school and high school adol escents (DenizetLewis, 2004). Persons who engage in sexual activity with multiple and/or anonymous sex partners are more likely to become infected with STIs. Hist orically in the HIV er a, sex with anonymous partners had been initiated in bars, bathhouses, cl ubs, or parks. Research has documented that the internet may be another venue fo r the initiation of sexual contact. Due to the anonymity of chat rooms and other internet sites, communication of sexual fantasies can be completed without reproach. These anonymous meetings may lead to in person meetings and sexual contact (McFarlane, Bull, & Rietmeijer, 2000). Theoretical Perspectives In much of the literature re viewed, Social Cognitive Learning Theory (SCT) is used as a framework for studying internet use and sexual behaviors. The basic tenets underlying this

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29 theory assume that individuals ar e more inclined to imitate a behavior if they are rewarded. Secondly, individuals are more likely to model a behavior when they can actually identify with the model. Finally, if the model is attractive and th e behavior is possible then it is more likely to be performed (Boies, 2002; Goodson et al., 2001; LaRose & Eastin, 2004). According to LaRose and Easton (2004), behavior is determined by outcome. If the outcome is desirable, then the behavior is eith er experienced in a physical sense or mediated through observation. Sexually oriented media on the internet provides an e xpected gratification. The user can observe a desired behavior, and vi cariously experience it through observation. If the user is satisfied with the outcome of the behavior (i.e. viewing se xually explicit material), then in all likelihood (according to SCT) the user woul d again be compelled to repeat the behavior (LaRose & Eastin, 2004). In concert with Social Cognitive Learning Theory there is a behavioral pattern that can lead to addiction. Griffiths (2000) states that repeated use of technology may cause a behavioral addiction. He describes this technical addi ction in six steps. Addiction can occur through saliency (when the behavior monopolizes the life of the user), mood modification (users experience a sense of euphoria with each encounter of the behavior), to lerance (users are no longer feeling a sense of euphoria, and spend larger amounts of time engaging in the behavior), withdrawal symptoms (physiological, and psychological occurrences as a result of discontinuing or decreasing the behavior), conflict (disa ccord with interpersonal relationship and the self regarding concern over engaging in the behavior), and fi nally relapse, which can occur even years after the behavior has been extinguished. Goodson, McCormick and Evans (2000) discuss th ree categories of sexual internet use as it relates to SCT. Through a framework based upon SCT, they made th e assertion that the

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30 internet is used for obtaining information of a sexual nature, establis hing and/or maintaining relationships, and sexual entertainment. G oodson and colleagues (2000) designed a survey instrument to assess students perceptions and beha viors when surfing the net for sexually related reasons based upon the three categories of use. Their study supported th e hypothesis that those who use the internet frequently have significantly di fferent attitudes regarding information seeking and sexual entertainment (i.e. pornography) than those who did not use the internet as frequently. The obvious concern for adolescent sexuality is the risk involved with sexual behaviors, specifically STIs and adolescen t pregnancy. Decreasing adoles cent sexual behavior and its associated risk is a top priority of the United Stat es national health objectiv es. It is apparent that there are other factors involved with the decision making process of adolescents to engage in sexual activities that have yet to be determined. Given this information, it is essential to review prevalent literature to gain insight into areas of current research and areas warranting further research. Study Purpose The purpose of this study is to better unders tand how technology, age, race and university of record (w here these emerging adults attend college) may affect sexual decision making and sexual behaviors in traditional college age students. A quantitative study design was chosen for data collection. A modified version of Goodson et al ( 2000) tool entitled Survey Instrument to Assess College Students Behavior and Attitudes was redesigned to be used as a web based survey. This survey tool used both Boolean and Likert style questions. Information obtained in the introduction of the survey provided demographic informa tion which included age range, ethnicity, religious affiliation, as well as information regard ing students perceptions and behaviors while using the internet for sexuall y related information, and entertainment.

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31 CHAPTER TWO REVIEW OF LITERATURE The objectiv e of this literature review is to explore the available evidence of adolescent sexual behaviors, risk factors, and how they relate to internet use. This re view examines research studies selected from the literature for the ye ars between 1997 and 2008. These research studies were identified by a search of PubMed, Proquest and Ebsco-host databases for the specified years. The key search terms used were adolescen ts, college age students, sexuality, sexual debut, STI, internet, media and technology. Studies selected were those that reported findings related to adolescents sexual behavior, sexua l debut, risk factors and internet use. Refereed journals were used almost exclusively. Media Influence on Sex Durham s (1999) study identified the medias influence in the form of television and video upon sexuality and feminine roles in adol escent females. This qualitative inquiry used participant observation and in-depth interviewing of adolescent females. The participants were recruited from two middle schools in the southwestern United Stat es. The schools were selected because of the diverse racial a nd socioeconomic status of the st udents. This diversity allowed more generalizability of the results of the project. This study attempted to gain an understanding of the social processes of adolescent peer groups, specifically peer group activity and the medi as effect on sexuality and the social context of its formation. The findings indicated that al though media (defined as television, internet, or video) is an important factor in determining individual behavi ors, peer group effect and mass media together have a profound effect on indivi dual behaviors. Group behaviors affected by media access became the accepted norm of participants despite individual differences, activities and lifestyle choices. Interesti ngly, Durhams (1999) study identif ied the idea that compulsory

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32 heterosexuality was foundational in group acceptance of individuals, despite race, ethnicity, and socioeconomic factors. As heterosexuality is the dominant theme in media, these findings demonstrate the pervasiveness of the mass medi a effect on adolescent females, sexuality and group behavior (Durham, 1999). Roberts (2000) described adolescent access and exposure to all forms of media, as well as the contexts where media exposure occurs. In this study, a cross sectional national random sample of 2065 adolescents, 8 through 18 years of age completed questi onnaires about personal use of media, including television, videos, comp uters, video games, radio, compact discs, tape players, books, newspapers, and magazines. The results showed that most households contain most media, and the majority of adolescents have their own personal access to the media. The average daily use of varying forms of media is approximately six to eight hours per day. Television remains the dominant medium and appr oximately one-half of the adolescents sampled use a computer daily. A substantial proportion of a dolescents media use occu rs in the absence of parents. An interesting demographic outcome occurred in household media based upon gender. Availability of video games in male adolescent homes was 27 % higher than that of their female counterparts. Media availability in adoles cent bedrooms proved to have many variables. Differences between computers present in White and Black a dolescents bedrooms proved to be negligible. However, Blacks and Hispanics were more likel y than their White counterparts to have televisions, video cassette recorders (VCR) or video games in th eir bedrooms. White adolescents were more likely to have audio systems in th eir rooms. Televisions, VCRs, and video game systems in adolescent bedrooms are inversely related to household income. Adolescents who attend schools in lower income communities ar e more likely than those who attend in high

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33 income communities to report having their ow n television, VCR and video games (Roberts, 2000). This may become a factor of potentially harmful internet expos ure without parental intervention of children especially those from lower income families. Gruber and Grube(2000), reviewed recent sc ientific literature on adolescents and sexuality in the media. Findings indicated that the available res earch did not address adolescent exposure to sexual content in the media and it s effects on beliefs, know ledge, intentions, and behaviors. The literature review also identified lack of research on sexual content of the internet, video games or handheld devi ces, (Gruber & Grube, 2000). Collins and colleagues (2004) conducted a national longitudinal survey of 1792 adolescents approximately 12 to 17 years of age. Participating adolescent s described their media viewing habits and sexual experience using initial and yearly follow-up interviews. Multivariate regression analysis determined that adolescents who viewed more sexua l content at initial interview were more likely to initiate intercourse and participate in non-coital sexual activities during the following year. This study al so noted that adolescents in the 90th percentile of TV sex viewing had a predicted probability of intercourse initiati on that was twice that of adolescents in the 10th percentile, for all ages studied. Interestingly, Black adolescents who watched more representations of sexual risks or safety in an educational format were less likely to initiate intercourse in the subsequent year (Collins et al., 2004). The purpose of Morrison and colleagues (2004) re search was to investigate variables that may be associated with exposure to sexually explicit material (SEM). Participants for this study were psychology students enrolled at a Canadian University. Results revealed a weak correlation between exposure to SEM via media (internet, TV, and video) and sexual esteem and sexual anxiety. Pornography exposure was greater for the

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34 non-virgin participants. The au thors examined the results within the SCT framework, with unexpected results. According to Morrison a nd company (2004) the presumption was that exposure to SEM presents viewers with an unrealistic view of se x. This exposure would result in a decreased sexual self esteem, diminished ge nital self image and le ssen the likelihood of practicing safe sex. However, th at was not the case. SCT did not support Morrisons hypothesis. Morrison and company (2004) predicted that male and female participan ts levels of sexual anxiety would be inversely correlated in self re ported exposure to SEM. They concluded that due to technology and accessibility, studies of ex plicit material should move from a harms based framework (sexual pathology) to a more adaptiv e role and develop frameworks in which to capture the complexity of SEM and its eff ect on the viewer (Morrison, Bearden, Harriman, Morrison, & Ellis, 2004). Depression, Religiosity, and Sexual Debut Longm ore and colleagues (2004) using a restricted use sample of the National Longitudinal Study of Adolescent H ealth, examined the influence of self esteem and depressive symptoms on sexual debut. Adolescents were in terviewed initially and at one year intervals. These intervals were identified as wave one a nd wave two. Criteria fo r inclusion to the study required that all adolescents participating must not have previously e ngaged in sexual activity prior to the first wave. The dependent variable measured in the study was vaginal intercourse. Independent variables included se lf esteem, depressive sympto ms, dating patterns, ethnicity, socioeconomic status, mothers education, and family makeup (i.e. single parent, biologic family, or step family). In the first wave of this study, a total of 18,924 students enrolled in grades 7 through 12 were interviewed. In wa ve two, a total of 13,570 students were reinterviewed. The final sample of this study included 7,965 studen ts (4,300 girls and 3,665 boys). Using logistic regression, this study demonstrated that depressive symptoms would increase the

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35 likelihood of sexual debut at a younger age. Race ethnicity and socioeconomic status were moderating factors on self esteem and depressi ve symptoms. Results showed that both self esteem and depressive symptoms affect sexual debut in adolescen t males. Depressive symptoms, however, exert the greatest effect on sexual debut in adolescent ma les. In older adolescent males a more positive self-esteem correlates with later sexual debut. Male adolescents that live with both biological parents are more likely to have a later sexual debut than those of single or step parent families. This study suggested that self esteem and depressive symptoms have no correlational relationship to race or ethnicity in adolescent males. In female adolescents, age, race and ethnicity proved to be a predictor of sexual debut. Frequency of depressive symptoms in White female adolescents aged 13 proved to be a predictor of earlier sexual debut in contrast to Black females in the same age range. Age and dating also influence sexual onset in older adolescent female s. One date or consecutive dating during wave one and wave two increases the odds of sexual debut. For female adolescents, living with a single parent or other alternative family types (i.e gay or lesbian family structures) increases the frequency of sexual debut during the time between wave one and wave two. Step parent families do not have an effect on sexual debut of females. Finally, female adolescents who have mothers with greater than 16 years of education have decreased odds of sexual debut during wave one and wave two of the study (Longm ore, Manning, Giordano, & Rudolph, 2004). Internet use has also been linked to soci al isolation and depression among adolescents. Sanders and colleagues (2000) investigated whether levels of internet use were associated with depression and social isolation. The study looked at length of daily internet use and how it correlated to social isolation and depression. Th is study demonstrated si milar findings to those of Kraut and colleagues (1998) research that looked at internet use and family communication

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36 systems. Both studies showed that increased inte rnet use increased social isolation and created weaker social ties, but could not be positively linked to depre ssion (Kraut, Patterson, Landmark et al., 1998; Sanders, Field, Diego, & Kaplan, 2000). Rotosky, Regnerus, and Wright (2003) also used the National Longitudinal Study of Adolescent Health to determine if religiosity was associated with the dela y of adolescent coital debut. Using data from 3,691 adolescents, Roto sky and colleagues (2003), wanted to determine if adolescent religiosity and sexual attitudes pr edicted sexual debut in gender and ethnicity. Again, there were two contact points approximately one year apart. The researchers selected a sample of 80 high schools, which were strati fied by region, urban location, school type, ethnic diversity, and enrollment size. The sample of schools ranged in size from fewer than 100 students to more than 3,000 stude nts. Approximately 80% of the schools contacted agreed to participate. The schools and stude nts proved to be representative samples of the population and provided generalizability to the study. As w ith Longmore and colleagues (2004) study, only those adolescents who indicated that they had not engaged in sexual intercourse by the first wave of the study were selected to comprise the samp le for the analysis of predicting coital debut. Adolescents ages 15 and older were asked sex attitude questions ex clusively. Correlational analyses with a Bonferroni correction were used to determine associations between independent and dependent variables for males and females. 23% of the males and 25% of the females who reported no sexual intercourse at the first wave had engaged in sexual intercourse by the second wave. Each year of age of the adolescent determ ined an approximate increase of 21 % in sexual debut between the first and second wave of the st udy. In Black males, earlier sexual debut was twice that of White males regard less of religiosity and virginit y pledging. In adolescent boys, the odds of sexual debut decreased by approximately 36 % when their mothers had at least a college

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37 education. Finally, in the presence of at least one or more romantic partne rs present at the first wave, sexual debut increased by approximately 90 % by the second wave.(Rostosky, Regnerus, & Wright, 2003). Multiple Sex Partners Howard and Wang (2004) examined the relation ship of multiple sexua l-partners to other risk behaviors among adolescent girls. Using the National Youth Risk Behavior Survey, 3288 sexually active adolescent females participated in this study. This study attempted to correlate relative risk of engaging in risk taking behaviors, age, ethnic ity, and number of sexual partners. Results showed that having one sexual partner was associated with lack of condom use and being in 12th grade. Two or more partne rs was associated with bein g Black, fighting, binge drinking, and cigarette use (Howard & Wang, 2004). Welch and colleagues (1998) found a surpri sing number of gonorrhea cases in lower income cases in Syracuse, New York. A study was conducted to provide information for public health interventions regarding gonorrhea and othe r STIs in the identified high risk population. The descriptive study provided information from individual and focus group interviews in an attempt to develop effective health education messages and services for the teen population. A major portion of high-risk behavior occurred in conjunction with th e use of alcohol and marijuana. Coercion and possibly violence are a part of sexual activity for at least some teens. Concerns included violence as a health problem, rape in jail as an STI risk, and sex with a female partner who is unconscious from intoxication. Female adolescents reported that males would pressure them to have sex without a condom. They concluded that early sexual debut can be the result of coercion specifically in the early sexual experience of adolescents in this group. Interestingly, participants described the practice of exchanging sex for consumer items (food,

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38 brand name clothing, and sneakers) although they did not consid er this to be prostitution. (Welych, Laws, Fiorito, Durham, & al, 1998). Dodge and colleagues (2004) suggest that an association exists between sexual compulsivity and participation in sexual beha viors that are high risk in terms of HIV/STD infection. Dodge and colleagues (2004) studied 876 heterosexual college students using the Sexual Compulsivity Scale (SCS). Predominant studies using the SCS were done with members of high risk groups (i.e. HIV at risk, and or HIV infected). Dodge and colleagues were able to support reliability and construct validity of the SCS in a hete rosexual population. Dodge et al (2004), found a significant relationship (p<0.001) be tween sexual compulsivity and partner sex activities. Participants who re ported a broader range of partne r sex activities with higher frequencies had higher sexual compulsivity scores Additionally, a relationship between solo sex activities (masturbation) and sexual compulsivity emerged from the statistical analysis (p < 0.001). Participants who reported higher frequenc ies were more likely to have higher sexual compulsivity scores. Analysis of the data al so divulged a relationship between public sex activities and sexual com pulsivity (p < 0.001). Those who part icipated in public sex activities with higher frequencies were more likely to have higher sexual compulsivity scores. Finally, post hoc analysis of this data demonstrated that pa rticipants who had non exclusive relationships had higher sexual compulsivity scores. There was a significant difference between men and women in terms of sexual compulsivity scores. Mean scores for se xual compulsivity were higher in male population f (876) = 12.63, p < 0.001, d = .81. Frighteningly, th ere was a relationship present in sexual compulsivity and age. Younger participants ha d higher sexual compulsivity scores (Dodge, Reece, Cole, & Sandfort, 2004).

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39 The limitations of this particular study in cluded a convenience sample, and as such it would be difficult to compare it with the genera l population. The authors al so noted that as only college students were the focus of the resear ch, a different cohort might answer questions differently. As with all sexual su rveys, there is always a risk of under reporting or exaggerating sexual encounters/compulsivity. As with the majority of resear ch on human sexual behavior, the use of self-report questionnaires ma y create a source of bias in th at participants may exaggerate the frequency of sexual activities underreport the fre quency of sexual activities, misunderstand a question due to lack of knowledge and respond in accurately, or answer questions in ways that they feel are socially desirable. Internet McFarlane and associates (2000) used a cr oss sectional design study conducted in Colorado from September 1999 through April 2000 co mpared the risk of STI transmission of persons seeking sex partners on th e internet versus those not seeking sex partners on the internet. Of the 856 participants intervie wed, 135 of those participating had sought sex partners on the internet. Eighty-eight of those who sought sex partners reported having sex with someone they met online. Thirty-four of those people had had f our or more partners in a six month period. Condom use was reported in only 44 % of those with four or more contacts. In this study only adults were asked to participate, and 70 % re ported themselves to be White and homosexual. However, most of the participants who used the internet as a me thod of obtaining partners were more likely to report previous STIs (McFarlane et al., 2000). Mitchell and colleagues (2001) conducted a st udy to assess the risk factors surrounding online sexual solicitations of youth. Telephone surveys were performed from August 1999 to February 2000 which captured a random samp le of 1501 adolescents from 10 through 17 years of age who regularly used the internet. Nineteen % of those interviewed e xperienced at least one

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40 sexual solicitation while using the internet in th e previous year and 3 % reported an aggressive solicitation. Only 10 % of sexual so licitations were reported to any authority. Approximately 75 % of parents and adolescents had no idea of where to report this type of behavior. Adolescent females were more likely to be solicited as well as those who used the internet more frequently. Solicitation occurred more frequently in those adolescents who particip ated in chat rooms, engaged in risky behavior online, talked to strangers online, or used the internet at households other than their ow n (Mitchell, Finkelhor, & Wolak, 2001). The objectives in Koch & Praterellis (2004) study Effects of Introversion and Extroversion on Social Internet Use research the social significance of the internet. They used Likert scores to rate the negativ e effects of internet use (i.e. a ddiction). Participants were asked to rate the internet based upon reading a passage to determine different responses based upon gender. They hypothesized that individuals who read a passage about negative effects of the internet would respond negatively. They also hypothesized that Intr overts would be more comfortable with the anonymity that the intern et provides than those individuals that were described as extroverts. Participants were recru ited from freshmen level courses (n=240). Results of the statistical analysis prove d not to be statistically signifi cant regarding reading a negative passage about the internet. Koch & Praterelli (2004) de termined that the reason why it might not be statistically significant because addicts dont always identify themselves as addicts. This study demonstrated that there was a large difference between me n and womens perceptions and use of the internet which is consistent with pr evious research. Again, college age males reported viewing more sexually explicit material for social purposes (Koch & Pratarelli, 2004). Hightow et al (2005) reviewed state surveillance records as pa rt of a retrospective study that examined new diagnoses and risk behavi ors in men 18 to 30 years old living in North

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41 Carolina. They reviewed 735 r ecords and of the 735 records, approximately, 11 % (84) were male college students. Eighty seven % of the 84 college students were Black Americans. Approximately 92 % of those stud ents were either men who had sex with men, or men who had sex with women. Hightow and co mpany (2005) described an epidemic that was previously unknown. Even more disconcerting is that the collegiate sexual ne twork of this cohort extends into five other states in the Southeast United States. Hightow and colleagues (2005) found limited re search on the phenomenon (one study in 1990) and concluded that although the rates of HIV infection in college age black men are lower than found in the general popula tion, it does demonstrate that a higher prevalence in a small cohort can have a significant effect on transmission. Additionally, Hightow and colleagues (2005) determined upon a literature search that college students (a gain) are more likely to meet sex partners over the internet, a nd report higher rates of risky sex behaviors (Hightow et al., 2005). Sanger and associates (2004) studied 62 a dolescent females residing in a correctional facility and their use of the inte rnet to participate in chat room conversations. Findings indicated that 87 % participated in chat rooms interact ions and spent an average of 10 hours per week interacting in the chat rooms. Seventy % of those had been approached in chat rooms to participate in sexual behaviors. Findings identi fied the high incidence of participants, and the implications of chat room interactions. Implications of chat room interactions were identified as a potential for language problems (based upon vocabulary, meanings of certain words, processing information conveyed, recognition of hidden messages and/or in words, or understanding attempts of others to use manipulative language). This raised questions about whether the seriousness of possibl e harmful or manipulative intera ctions is actually understood

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42 by adolescents. The absence of physical, verbal, a nd nonverbal cues that exist within the chat room arena has been well documented in the lite rature. This absence may make the chat room environment dangerous to adolescents as they ma y not be able to discern whether a message is truthful, sincere, or accurate. A dolescents do not have the luxury of experience and therefore they are at an increased risk to sexual predators that may lurk within this environment. This study also suggested that more research in these addressed areas is warranted. Sex Defined This very interesting study assessed exactly what respondents considered to be the definition of sex. Bogart and colleagues (2000) used an interesting design to determine how respondents interpret actual sex by assuming whether or not hypothe tical actors would deem the act to be defined as actual sex. The participants of this study had to make this determination based upon gender of the particip ant, gender of the actor, the act, and whether an orgasm influenced their labeling of behaviors. The sample consisted of 223 undergrad student s who were concurrently enrolled in a human sexuality course at a unive rsity in the western United St ates. This particular study is extremely significant as it occurred not quite two m onths after President Clinton stated that he did not have sex with that woman. This study was conducted prior to specific details regarding the President s alleged indiscretion. Results for vaginal and anal sex were simila r to previous findings. The participants believed that the female actor would have a larger definition sex; meaning that more variety of sexual behaviors would be constr ued as actual sex. Interestingly, if the male actor achieved orgasm, the behavior would likely be perceived as sex. Whether the female did or did not orgasm had no bearing on the males definition of sex. In the case of oral interc ourse if the recipient climaxed, the sex behavior was more likely to be construed as sex. The performer was less likely

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43 to consider oral sex to be actua l sex. The authors determined that (in this study) vaginal and anal intercourse were more likely to be construed as sex, but oral in tercourse was less likely to be defined as actual sex. This demonstrates the importance of having participants define exactly what they believe is sex (Bogart, Ceci l, Wagstaff, Pinkerton, & Abramson, 2000) As new technologies, such as computer-m ediated communication, expand the potential for mass influence on society, adol escent sexual behaviors, the pe rvasiveness of media culture, earlier sexual debut, and risky behaviors will continue to become more problematic. The presence of sex and its co-star, violence saturate s all forms of media that adolescents have at easy access and have been, and continue to be, the focus of entertainment. Mass media producers are only concerned with the effects of sex and violence inasmuch as it affects their profits. Adolescent interest in the techno logical world allows them to view media without the ability to discern the information criticall y. In the substantial body of knowledge presen t in the literature; specific studies of adolescents are limited perhaps due to the nature of the topic. This literature review has identified the need for a more compre hensive look at risky sexual behaviors, chat room use and earlier sexual de but of exposed adolescents and traditional age college students with computer access.

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44 CHAPTER THREE METHODOLOGY The purpose of this study was to better understand how technology and culture affect sexual decision m aking and sexual behaviors in traditional college age students. In order to determine this, a web based quantitative study using su rvey methods to collect data was initiated. Internet Data Collection Advantages Data collection using in ternet surveys has multiple advantages. Economy is an initial factor. Web based surveys are abou t one half of the costs of traditional mailings (Duffy, 2002). Ease of recruitment and the potential to achieve larger samples in a reasonably short period of time is another. Depending on how the particip ants are notified, web based surveys are most successful in the first few days of recruitment. Respondents who are intere sted in participating will usually answer in the first two days or not at all (Duffy, 2002). According to Rhodes et al (2003), using web based surveys improves actual usable data versus more traditional methods. Rhodes (2003) s uggests that because of the explanatory design inherent in web based surveys there is an actual reduction of errors. Inst ructions are simple to follow. Respondents are directed to a site; th ey follow instructions supplied on menus, and follow the format of the survey. If built into the design, respondents can see how much of the survey they have actually completed (i.e. page one of eight) and may be more likely to complete the entire survey. Errors that occur in paper based surveys, such as skipped items or accidentally choosing multiple items can be avoi ded with web-based surveys. The interactive features that are built into th e survey may offer dialogue box es that pop up based upon the particular response a participant may choose. This allows the participant to skip questions that may not be relevant (Couper, Conrad, & Tourangeau, 2007)

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45 Participants have the luxury of responding in their own time frame as web based surveys are available twenty-four seven. This may be ve ry important specifically when attempting to solicit particular groups (i.e. college students). Survey participants also have the ability to disconnect their participation at any time based upon comfort leve l. This level of comfort may actually improve how honest online respondents are in comparison to face to face interviewees. This comfort level may be a direct result of the anonymity factor in web based surveys. Participants who access internet surveys believe th at there is anonymity in web based surveys. The anonymity factor may add to the respondents level of comfort, thus improving the honesty factor, especially when it rela tes to sexual behaviors. Level of embarrassment may be minimized for participants using web based surveys, and im prove the level of participant control in the process (Rhodes, Bowie, & Hergenrather, 2003). Individuals who partake in web based surveys make a personal decision to actively participate as they log on to the site where the survey is housed. According to Rhodes and colleagues (2003) this concept of activ e participation moves the respondent from the mindset of experimentati on to a partnership position with the research being accomplished. This partnership may improve completion of survey responses. Malone (2002) suggests that web based participation with subjects opens up a new way of thinking in research design and development. Respondents engaging in web based research are more likely to provide feedback, and have an interest in the out come of the research. Rhodes (2003) found this to be evident as respondents in their study supplied URL addresses to potential candidates of their own volition. Another advantage of web based surveys is mi nimizing the error rates in recording data. Depending on the survey design, many web based surveys can be compatible with statistical software. Weber, et al (2005) f ound that utilizing computer based research methods for recording

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46 results virtually eliminated erro rs in coding and recording when compared to human coding. In this study, hand coding resulted in a 15 % error ra te per case and computerized coding provided a data pool free of errors. Resu lts from computer based surveys that are compatible with statistical software can be automatically downloa ded into software products such as Excel, and uploaded into other statistical software produc ts without data-entry errors and with time efficiency (Rogelberg & Stanton 2007). Disadvantages Unfortunately, there are always pitfalls to th is type of data collection. The m ajor problem with gathering this type of data is determin ing validity. There is always a possibility of respondents not being completely honest when an swering. Another issue is the possibility of repeated submission from the same respondent. According to Rhodes et al (2003) the potential for multiple submissions can be minimized using specific designs embedded into the software program. Predominantly, questions such as asking the respondent if th ey have previously completed the survey or having the participan t provide some unique identifiable information such as date of birth have also proven to be effective techniques. However, the major drawback to multiple submissions may be the length of the survey as an inhibitor for multiple submissions in itself. Another problem with internet surveys is atte mpting to maintain the integrity of the data pool. With public internet surveys, it is diffi cult to maintain a specified populace. Even though there are many ways to protect surveys and target specific groups, it is not infallible. It is not unusual for a legitimate respondent to participate, and then share the UR L link with a friend who may not meet the eligibility re quirement initially intended by the researcher. This snow ball effect can be detrimental to the validity of the study, and the researcher may be entirely unaware of this issue (Rhodes, Bowi e, & Hergenrather, 2003).

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47 An additional problem is meta-tagging. Meta tagging is the prac tice of embedding of certain words that can be indexed through basic internet searches. An example of this is that an individual may Google a certain wo rd or phrase, if a survey has th at meta-tag within it, it is likely to come up within that pa rticular search. Using this type of code embedding can increase the number of participants by publicizing a survey ; however, the downside is the resultant breach of sampling to uncontrolled participants outside of the intended population (Rhodes, Bowie, & Hergenrather, 2003). Adding the URL link of the survey to a publis hed website will also be picked up by many search engines, thus causing publishing the location of a web based survey. This is an effective tool if the researcher is atte mpting to obtain a large sample. However, in the case of attempting to control the sampling body, a URL link to a published web site can be very detrimental. There are a few ways that researchers may be ab le to protect the data pool. One way is to minimize the publicity of the actual web survey. This can successfully be completed by using private domains to house the web survey. Usuall y, private web based surveys are difficult to search for, which reduces the ri sk of unintended participants. Confidentiality Procedures for the Prot ection of Human Subjects Before beginning data collection, approval fr om the HBCU and the PFU Institutional Review Boards (IRB) were obtained. This beca me a difficult process as the original study protocol proposed was a mixe d methodology design. The IRB at the PFU had some difficulty with the data collection in the qualitative portion, and after some discussion, this portion of the study was scrapped. The same study and all protoc ols were approved comp letely at the HBCU. This study was an anonymous study. Data collected had no iden tifiers that could distinguish participants individu ally. The only demographics obtai ned in this study were gender,

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48 race, age range, and school. The school value was embedded in the design of the web survey. This survey tool did not store or retain the user (student) source internet protocol (IP) address as an added protection method for participant anonymity. Data Protection As stated in the original protocol submitted, any data that will be presented or published would be presented as aggregate data and no iden tifiers to the participant universities would be included. All survey data that was collected was in comma delimited format. Survey results were immediately saved into a database hosted on the secure web server. The ra w data was stored as index values (i.e. numbers 0, 1, 2, 3), in the da tabase. A schema map for the web survey was developed from the original survey design of G oodson, et al (2000) and wa s required in order to decipher the data. All string, nominal and ordinal data variables were given index values. These index values could only be inte rpreted as meaningful information with the corresponding unique schema map. Survey Tool Goodson and colleagues (2000), developed a paper and pencil survey tool to docum ent college students use of the inte rnet. This survey tool entitled Survey Instrument to Assess College Students Behavior and Attitudes documented demographic information as well as college students perceptions and behaviors while using the internet (specifically for sexually related information). Goodson and colleagues (2000) goal was to validate the actual tool versus data collection. Using the construct of Social Cognitive Theory, this tool was developed to document students use of the in ternet to acquire information regarding sexuality, establishing and maintaining relationships, and/or sexual gratification. This tool was validated through reliability of internal consistencies, temporal st ability and factor analysis by the authors. The

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49 average Cronbach-Alpha coefficient for questions ranges from .78 to .95. Any question that yielded a result of less than .50 in th eir testing criteria was eliminated. In the paper survey, there were areas that respondents could fill in their area of study and the school that they attended as well as any comments or suggestions. The paper survey consisted of 134 separate questions where responses were documented by ascending or descending Likert type scales as well as Boolean answers co nsisting of yes or no responses. Web Based Surveys Creating a web based survey using the r ecommended SNAP software program became problematic given the size and le ngth of Goodsons (2000) paper survey. Initial work with the SNAP program revealed that it required the data to be individually entered into the statistical software that was to be used (SPSS-15). As there were 134 separate questions with at least five responses, it made the task of reentering data into SPSS quite form idable. Given the error rate in coding and reentering data, an alternative method was suggested. Rather than attempt to fit the survey into SNAP, a custom software package was designed to mimic the original design of the paper survey. In addition, the custom software package could download the survey data in a comma delimited file format to import directly into SPSS-15. DCova Technologies (2007), a software de velopment company out of Orlando, Florida was commissioned to develop the program. The so ftware programmer had intensive knowledge in Java, Hypertext Preprocessor (P HP), and Microsoft .Net platforms. The web server in which the survey application was hosted did not s upport Microsoft .Net so it was removed as a candidate. PHP and Java platforms were both su pported by the web server and either could be used. However, the skill set of the programmers was much greater in Java versus PHP, and

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50 therefore it was determined that the Java pl atform was best suited for the survey. DCova Technologies (2007), also regi stered the domain name where the survey was hosted. Online Survey Design After review of different online survey designs, a modified shopping cart design was developed around the requirements document (paper survey). A modified shopping cart design is used to collect user data c hoices throughout the web session. Th e results persis t throughout the session of the users survey activ ity. This means that as long as the users web browser is open during a single session, the user has the ability to go back to pr evious pages, change and over write answers without creating double entries. This minimized the ri sk of superfluous data being collected. In order to improve and accommodate ease of use within the design, both radio style push button and checkbox style buttons were impl emented based upon usability engineering principles. Radio style buttons differ greatly from checkbox st yle buttons. With radio style buttons, the user can only choose one item on a specific menu. In contrast, a checkbox style button offers the user one or more choices on a specific menu. As the requirements document had both styles of questions and responses, it was necessary to use both within the design model. As the survey was to be used at two univers ities, and the target audience was college age students, to define the separate student bodies, logos of differing universities were superimposed on the survey design. However, the actual su rvey was otherwise unchanged. Students accessing the web based survey would be able to identify with their correspond ing universities, and perhaps provide a sense of active participation in the process. In the event that the student did not complete the entire survey, re sponses were collected to the point where they completed the answers. If the answer to the responses to a specific survey question was blank, it was identified as such in Excel as well as SPSS.

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51 Survey Information Using the web based survey program previous ly described, this survey document and approved consent would be available and accessibl e to currently enrolled college age students via a hyperlink sent by mass email with an invitation to participate in the survey. Population Sample Participants Undergraduate students who attended a specifi c HBCU in Florida as well as those students who attended a large pub lic university in Florida (PFU) were the targeted population. As previously stated, tr aditional age college students by far have had more technology experience, and by this experience are best suited to participate in this study. The rationale for using the HBCU and PFU st udent populations has to do with specific demographics. PFU has a predominantly white popul ation (66%). It also has the designated title as being one of the largest universities in the United States. PFU has approximately 30,000 students registered as un dergraduates enrolled. The HBCU selected is a private university and the student population is predominantly black (91%). This particular HBCU is also a Christian/Methodist University. In contrast to PFU, the HBCU is a small university with an approxi mate undergraduate populati on of 3000 students. According to Fall 2007 enrollment data for both schools, the approximate number of undergraduate students attending both of these universities is 33100. Using a confidence level of .95, a confidence interval of 3.1, the total number of students needed for the sample would be approximately 1000. Eligibility In order to be eligible for this study, students m ust be enrolled at either university. Undergraduate Students were the target population, and therefore, age ranges were identified as

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52 17 or less, between 18 and 19, between 20 and 22, and 23 or older. There were also specifications for class level (i.e. freshman, s ophomore, junior, senior, graduate, and other). Students also needed to be able to read and comprehend English, as well as have access to a computer with internet capabilities. The survey asked where the participant was most likely to access the internet, however, did not specify from where the survey was accessed for participation. Survey Procedures A m ass emailing was sent to students atte nding both universities (Appendix C). This email explained the study and invi ted the student to participate. In the email, students were informed of the study, incl uding the title of the study with a statement of the type of information being solicited (i.e. demographic information, frequency and duration of internet use, using if the internet to surf for sexually related content, establish personal relationships, sexual entertainment, arousal, perceptions, and public access to sexually explicit material). A URL link specific to each university was embedded into the mass email. Students who wished to voluntarily participate would click on the URL link embedded into the mass email and were sent to the approved informed consent page. Upon reading and agreeing to consent and acceptance (by clicking on agree) Students were hyperlinked to start page of the actu al survey site hosted by the PI. If the student clicked on the disagree button, they were hyperlinked to a page th at stated thank you for your time. The first page of the study provided demogr aphic data. As the survey was designed for consenting adults, any student who identified th emselves as 17 or under would be hyperlinked from the study to a page stating ineligibility. As this study is anonymous, and did not collect IP addresses, there were no safeguards in place to prevent an under aged individual from accessing

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53 the survey again and identifying themselves as older in order to participate. This is an inherent problem in all surveys (whether online, telephonic or in paper format), and this factor is dependent on the honesty and/or moral ity of the individual respondent. Sampling Techniques and Difficulties After approval from the various IRB were obtained, it was necessary to send out the mass emails. At the HBCU, a copy of the mass email was forwarded to the Chief Information Officer (CIO) as well as the Informa tion Technology (IT) department. This was provided so an email could be sent to each and every student in atte ndance at the HBCU. The cu rrent enrollment of the HBCU is 3093 undergraduate students as well as 47 gra duate students. At PFU, the same principles were followed. Ho wever, at PFU, the IT department stated that they did not have a comple te email list for undergra duate or graduate students in attendance. As this presented a significant problem to this survey, alternative methods were employed to capture the population in order to successf ully meet the sample size required. Alternative Methods Personal Contacts One of the alternate avenues to accessing the student body was by directly contacting departments through known faculty. A database of approximately 450 students from the Psychology department was solicited through faculty emails. Approximately 600 students from the Nursing department were solicited th rough faculty emails. Faculty from both the Anthropology and Communications de partments were also contacted, but they were unavailable to assist in this process. A separate contact was made through the housing department, and a list of 2500 emails was sent to the PI. In order to send out the emails efficiently and attempt not to be perceived as

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54 spam by the simple mail transfer protocol (SMT P) email server through PFU, another program had to be developed. Project Spam This program designed and developed by DCova technology was nam ed the SPAM project. This program used Java platform a nd made a list of the 2500 email recipients and automatically sent out the intended email to each individual email. This significantly decreased the required time completing this arduous process from days and weeks to hours using this program. The program sent out an email every 5 seconds for the duration of the initial list received. Subsequent emails were sent out every two seconds without any difficulty experienced by either PFU web server, or the residential server used to send out the large number of emails. Project Get-email Another avenue used to improve and increase the sam ple number and response from PFU was to harvest emails using the published dire ctory on the main website. As there are no software programs readily available that harves t emails directly, again, another program had to be developed. This program was developed with Java platforms and named GETEMAIL harvested email addresses by directly connectin g to the server site pages using alphabet components. Approximately 20,919 emails and six harvesting s later, emails were sent out to PFU students. The program could identify student s (from employees and faculty through the published listings); however there was no specifica tion whether the student was classified as undergraduate or graduate. Therefore mailings were not specifically sent out to undergraduates. Of the 20,919 emails sent out approximately 300 we re returned as either mailer-daemon failure related to mailboxes over quot a, no longer valid email, or no such individual.

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55 Through these unique and creative attempts to solicit a reasonable sample size over 1500 participants responded. This sample size provided more than an ad equate number of responses to validate the findings of the study. Email correspondence with the PI from participants regarding questions and comments were extremely valuable and will be included in the discussion section of this dissertation.

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56 CHAPTER FOUR RESULTS Chapter Overview The purpose of this chapter is to discuss the resu lts of the analysis of specific questions as they relate to the overarching research questi on. The prim ary purpose of this study is to better understand how technology and cultur e affect sexual decision maki ng influence sexual behaviors in traditional college age students. In order to determine this, the variables of gender, race, age, and school are analyzed using the amount of internet use and how sexual activity, sexual behaviors, and relationships, both online and offline. Survey Aims In Goodson and colleagues (2000) modified surv ey used in this re search project there were approximately 134 variables/qu estions and as such provided a plethora of data. However, this particular study was only attempting to addre ss specific issues based upon the results of the literature review. The aims of the study addressed the following research questions: Is age a factor in Internet Use and Sexual Behaviors? Is gender a factor in Intern et Use and Sexual Behaviors Does internet use have an effect on established relationships? Is ethnicity a factor in Inte rnet Use and Sexual Behaviors? Is the location (university of reco rd a factor in Internet Use? Some of the data variables analyzed to at tempt to answer these questions included: I have made new friends over the internet. How long have you been using the internet How often do you use the internet?

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57 How long have you been using email? How often do you use email? I have posted online messages to meet a poten tial partner. Met anyone in person that you met online Does your partner object to the amount of time you spend online? Does your partner know about the friends/relationships that you have online? Has your partner ever expresse d jealousy over the relationships you have developed online? How important is it to you that an al ternate way/place for meeting new people? How important is it to you that an alterna tive way/place for meeti ng potential on-line sexual partners is available? I have accessed sexually explic it materials on the internet I have accessed sexually explicit material s on the internet to become sexually aroused/excited? While viewing sexually explicit we b sites, I have masturbated. I have had cybersex with an online partner. I like to drink alcohol while having cybersex with an online partner. I like to use stimulants (drugs) while having cybersex with an online partner Exclusion Criteria for Surveys As the survey had 134 separate questions, cr iteria had to be set to determine what constituted a complete survey. Many participants would get through at least four or five of the eight page survey and then would abandon the pr ocess. Based upon this in formation a precedent needed to be set. Some of the question information was set within the design and unless answered the individual would not be able to progress through the surv ey. The two information items were school and age. In one section of the survey (section C-4) there were seventeen

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58 checkboxes that could be left bl ank as a response to the ques tion being asked, and also were excluded from the number of questions that coul d be counted in determining percentage of survey completion. It was determined that st udents completing at least 50 % of the survey (excluding section C-4, school a nd age), would have to answer at least 58 questions on the survey in order to be included in the final sample. Approximately 1512 surveys were submitted by participants via email. Of those 1512 surveys, 1212 were completed with over 50 % of all questions answered, and were kept in the data sample. The response rate of completed surv eys was 78 %. Of all surveys collected 87 % of surveys collected were from PFU students (n=10 56). At the HBCU, students submitted 13 % of surveys (n=156). Demographics The tables that are presented in 4-1 th rough 4-3 give an overall description of the demographics of the population sampled. The information identifies gender, age, race, and classification (school year). In tables 4-4 through 4-6, specific grouped data are displayed that includes differences in the specific university population that was sampled. Of the 1064 surveys accepted, 69.9 % were female (n= 839), and 29.9 % were males (n= 371). Of the 1212 respondents, 60.6 % of the survey participants were Non-Hispanic White (NHW) (n=733), 17.5 % were African American Black (AAB) (n=211) 11 % were Mexican American Latino (MAL) (n=133), 5.9 % were Asian American/Pacifi c Islander (AAPI) (n=72), 4.5 % identified themselves as Other (O) (n=55) and Native Ameri cans (NA) were 5 in number and as such will be excluded from the data to avoid confounders. Multivariate Analysis of Variance Using multivariate analysis of variance (MANOVA), overall analysis was completed on the independent variables. The independent variab les included in this study were age, race,

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59 gender, and university of record. The results in cluded in table 4-7 are the main effects and interaction effects of t hose variables. The Wilks Lambda statis tics were chosen because they are the most widely used test that will measure the proportion of the variance in the dependent variable that is unaccounted for in the independe nt variable (Polit, 1996). The results identify significant main effects for sc hool (p=.031), gender (p=.000), age (p=000), and race (p=.041). Interaction effects include gender and age (p =.000), school, gender and age (p=.014), school, gender, and race (p=.007), and school, age, gender and race (p=.000). Tables Within the Study All questions within the study will have a co rrelation table with Pearson R scores and levels of significance. ANOVA analysis ta bles will follow. After the ANOVA table, means tables will be provided with standard deviations and number of participants. In the event that the interaction of th e ANOVA is significant as well as the MANOVA, means tables will be present to determine the different means and confidence intervals of those specific means. In the event that there at leas t three variables, TUKEY Honestly Significantly different (HSD) tables will also be displayed. An overall description of the interactions will follow these groupings of tables. In the event that the ANOVA is significant and the MANOVA proved not to be in the initial analysis, it will be treated as an insignifica nt interaction. This is an effort to create clarity within all of the significant interactions that will be discussed. How Long Have You Been Using Email? Em ail is considered an important part of communication and has become a seemingly necessary part of life. Email usage was measured specifically as it may provide data on the number of students using email and the length of time it has actually been used. The survey asked participants the specific question How long have you been using email? The participant

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60 responded using specific phrases. The responses included less than six months (1), six to twelve months (2), two to thr ee years (3), greater than three years (4 ) and I dont use email (5). Univariate ANOVA analysis on the question How long have you been using email? as the dependent variable was compared with gender race, university of record, and age as the independent variables. All tests of between subj ects proved to be non significant. Tables 4-11 through 4-14 demonstrate the analysis and means. How Often Do You Use Email? The next dependent variable to be tested was the question How often do you use em ail? The responses that the sample participants coul d chose from were: daily (1), 2 to 3 times per week (2), and less than once a week (3). Usi ng ANOVA, the dependent variables described were compared to the four independent variables: age, race, gender and university of record (school). There was a significant main interaction for sc hool as the independent variable compared with frequency of email use as a main e ffect (p=.001) in both ANOVA (table 4-19) and MANOVA (table 4-7). The mean demonstrated in table 4-15 that students from HBCU use their emails less frequently than those from PFU. Ther e was also a significant interaction effect for gender, race and school (table 4-20 ) as compared with frequency of internet use (p=.003) in both the ANOVA (table 4-19) and MANOVA (table 4-7). Overall, students who attend HBCU use their emails less frequently than their racial counterparts at PFU (table 4-20). There is also both gender and ra cial differences as well. In comparison to all other students, Black males and females attending HBCU use email less frequently than their racially like counterparts at PFU. AA/PI females (overall) use email more than their female counterparts. In contrast to AAB male students attending HBCU, AAB students attending PFU are the most frequent of all male email users, and AAB males attending

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61 HBCU are the least frequent of all email users. When compari ng MAL attending both universities, there was no MAL representation from HBCU. School interactions could not be determined by the study within this group. Howe ver, MAL females used email more frequently than their male counte rparts (table 4-20). The mean for gender is very small and in this interaction is not the driving force. School and race prove to be the most significant in determ ining this interaction. In table 4-21, the Tukey HSD demonstrated that AAB use email less frequently than all of their raci al counterparts, with levels of significance (p=.001) or less among a ll races represented. Over all, this particular analysis demonstrates that the in teraction of school, as well as gender and race proved to make a difference in how often email is used within this sample. How Long Have You Been Using The Internet? Internet use is different iated from email use as there are diffe rent qualities for its use. Internet use has other applications for use versus just communication. The internet can be used for education, recreation, as well as numerous other r easons. Therefore, it is important to determine whether length of internet use demonstrates di fferences among the variables. ANOVA statistics were used to analyze this data Participants who answered the question How long have you been using the internet had response choi ces of less than six months (1), six to twelve months (2), two to three years(3), and greater than th ree years (4) and I dont use the internet (5). The ANOVA (table 4-27) demonstrated that a ll tests of between subj ects proved to be non significant. The means in tables 4-23 through 4-27 suggest that all of the participants in the study have been using the internet for at least 3 to 4 years, and the mean differences within the different groups are very small. These re sults identify and acknowledge that students participating in the study have at least the same or similar skill levels.

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62 How Often Do You Use The Internet? One of the variables that may influence behavior is the frequency with which an individual participates in that particular behavior. Therefore it wa s important to determine if age, race, gender, university of record had an effect on the frequency th at an individual spends online. This variable was stated in a question format How often do you use the internet? There were three choices for a response with values from one to three. Participants who chose daily use were given the value of one. Those who chose two to th ree times per week were given a value of two and those who described the frequency of internet use as less than once per week were given a numeric value of three The variables analyzed were the universities of record. Using ANOVA, the dependent variable described in table 4-33 was compared to th e four independent variables: age, race, gender and university of record (school). The main effect variable age demonstrated significance (p=.029) when compared to the variable of frequency of internet use. Individuals who identified themselves as older did not use the internet as frequently as those who identi fied themselves as younger. Race demonstrated a significant main effect (p=.006). Ov erall, AAB use the in ternet less frequently than their racial counterparts. Interestingly, with th is variable, gender proved to be insignificant in this interaction as a main effect variable. In comparing the independent variables as in teraction variables, gender age and school proved significant (p=.000) on both ANOVA (table 4-33) and MANOVA (table 4-7). The four way interaction between gender, age, race a nd school (p=.003) also proved significant on ANOVA (table 4-33) and MANOVA (table4-7). The interaction of gender and age proved to be significant as the findings demonstrated that in this sample, females use the internet less frequently as they age, and in comparison, males

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63 use the internet more frequently. It appears in this interaction th ere is an inverse relationship in relation to gender and aging. This relati onship is demonstrated in table 4-35. The interaction of age gender and school al so demonstrated a significant difference. Students regardless of gender or age (overall) wh o attended PFU had higher rates of internet use than their HBCU counterparts (tables 4-29 th rough 4-31). Females at both PFU and HBCU demonstrated the same tendencies as they did as a whole. Regardless of school, their overall means decreased as they aged. Males who atte nded these universities did not demonstrate a similar pattern. HBCU male students who were ag e 18-19 or 23 and older us ed the internet less frequently than their PFU counterparts. Intere stingly as demonstrated in table 4-36, both populations of male students demonstrated an increase in internet use during the ages of 20-22. The significant difference in in ternet use in males occurred in the HBCU population. Males demonstrated more frequent use as they aged (as previously stated), however the use of the internet in HBCU males who were age 18-19 had a much lower mean than those male students age 18-19 at PFU. The difference may be accounted fo r in the racial differences in the sample. In the four way interaction effect of gende r, race, school and age, females overall used the internet less frequently than their male c ounterparts (table 4-37). AAB females from HBCU age 18 and older who participated in the sample demonstrated less frequent internet use than their age and race similar counterparts from PFU. All AAB from HB CU who were age 18-20 and 23 and older demonstrated the least frequent us e of their entire male and racially different counterparts (table 4-38). This interaction demonstrates that there is a difference in frequency of internet use between school, age, gender and race. When comp aring race, age, gender, and school as factors in a four way interaction in this sample, many of the races do not have a single individual in the

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64 sample in certain age groups when separated fo r age and gender. Therefore, it would be difficult to attempt to describe, define, or interpret the inte raction in between and as a result of these very specific areas. These four way interactions must be viewed with some skepticism based upon the very specific population representation. I Have Made New Friends Over The Internet The next variable to be analyzed was que stion: I have made new friends over the internet. Sample participants were able to c hoose from four response categories. The responses ranged from a numeric value of one for never, tw o for rarely, three for sometimes and four for frequently. Univariate ANOVA statistics were used to analyze this data. The question I have made new friends over the internet was the depende nt variable, and age, race, gender and school were used as the independent variables. The ANOVA in table 4-46 demonstrated signi ficance between the independent variables when compared to making new friends over the internet. The only main effect that was demonstrated proved to be gender. The leve l of significance was measured at p=.013. There was also a four way interaction betw een gender, race, age and school (p=.016). The Tukey HSD in table 4-49 did not identify any significance within the groups for age. Race proved to be significant in the groups. This interaction demonstrated that th ere is a difference in the way that AAB, MAL, and NHW ma ke friends over the internet. According to the means represented in tabl e 4-43, based upon those means, males who participated in the survey were more likely to make new friends over the internet. In comparison to university of record (school), students atte nding HBCU versus PFU (table 4-42) were more likely to make friends over the internet. In race, both AAB males and females attending HBCU were more likely to make friends over the in ternet. When comparing races; AAB were more likely to make friends over the internet in comparison to both MAL and NHW. The Tukey HSD

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65 in table 4-50 demonstrated a level of significan ce for the interaction between AAB and MAL and NHW both (p=.000). Age was not significant in the Tukey HSD (table 49), so the interaction effects of groups within the groups (multiple comparison) was not sign ificant. It is interesting to note that the means of all groups who identified themselves as 23 or older were significantly higher (more likely to make friends over the in ternet) when compared to other age groups. This analysis identified that race, gender, a nd university of record (school) are factors in making new friends on the internet. There was a si gnificant interaction betw een gender, race and school, demonstrating that AAB, MAL, and NHW (male or female) who attend HBCU are more likely to make friends over the internet. Howeve r, as previously stated, the representation of these groups within the sample does not demonstr ate representation in al l groups, and should be viewed with cautious suspicion. Have You Met Anyone In Person That You Met Online? The online survey asked specific questions rega rding relationships. Two of the questions were used in this analysis. These questions were Boolean Style (yes-no) responses. T he questions were posed in this fashion: Have you ever met in person anyone you first met online. These questions were again analyzed using the va riables of gender, race, university of record, and age. Univariate ANOVA tests were perfor med and there was no significant interaction among any of the variables as re presented in table 4-56. The resu lts demonstrate that in this particular sample, meeting a person online may not equate into meeting them in person in an offline situation. Tables 4-51 thr ough 4-56 illustrate the results. I Have Posted Online Messages To Meet A Potential Partner One of the questions asked in the survey was phrased I have posted online personal m essages in attempts to meet a potential partne r (for example, in web sites for singles). The answers were given numeric values that ranged from the number four for frequently, three for

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66 sometimes, two for rarely, and one for never. Using ANOVA, the question was compared with gender, age, race, and university of record (school). Tables 4-57 through 4-68 demonstrate the findings. The ANOVA proved to be significant (table 462). There were significant main effect interactions between gender a nd age with the dependent variable (separately). Gender and age also proved significant as an in teraction effect of both indepe ndent variables when compared with the dependent variable (p=.000). In table 4-6 4, gender, age and school also proved to have a significant interaction as indepe ndent variables when combined and then compared with the dependent variable (p=.026). Fina lly there were four way interact ions between gender, age, race, and school as four independent variables combined and compared with the dependent variable proved significant as well (p=.001). All of the independent variab les also proved significant in the initial MANOVA (table 4-7). The result for the means for gender in tabl e 4-59 identify that males (mean=1.690) are more likely than their female (mean=1.228) counte rparts to post a message in order to meet a potential partner. Age also prove d significant (table 4-60). Male s who participated in the study more frequently posted messages as they aged. The means between the ages of males increased in each of the groups. Females, on the other hand, did the exact opposite. Their average means decreased as they got older (table 4-63). Tabl e 4-58 demonstrated that the overall mean PFU (mean=1.445) was higher than that of HBCU (mean=1.398). Non Hispanic Whites had higher overall means than their racial counterparts. However, African American Blacks means were second highest among the groups (table 4-61). Table 4-64 demonstrates that males attending HBCU who identified th emselves as 23 or older had the highest means of all groups, Fe males attending PFU had a higher average mean

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67 than that of their HBCU counterparts, and there al so was a trend in the means that suggested that as they age they more frequently post messages in order to meet a potential partner. However, those females attending HBCU had a lower overall m ean, and as they aged, they were less likely to post messages. Males attending HBCU had a higher overall average mean than their PFU counterparts. The same phenomena occurred in males attending PFU, as the means increased with age. Males attending HB CU had an increase in age and use among those students 18-19, and decreased use from 20-22. Then the age gr oup of 23 and older students jumped almost disproportionately with an average mean of 2.750. When comparing race, age, gender, and school as factors in a four wa y interaction in this sample, many of the races do not have a single individual in the sample in certain age groups when separated for age and gender (see tables 4-65 and 4-66). Therefore, it would be difficult to attempt to describe the interaction in between and as a result of these very specific areas. Does Your Partner Object To The Amount Of Time You Spend Online One of the key factors in internet use m ay be its effect on personal relationships. A few of the questions in the survey alluded to these type s of relationship questio ns. The questions were Boolean style with yes (1) or no responses (2). The first question to be analyzed is Does your partner(s) object to the amount of time you spend online? Usi ng ANOVA statistics, the question Does your partner object to th e amount of time you spend on line (as the dependent variable) was compared to gender, race, age, and university of record (school). The results in table 4-74 demonstrated a tremendous amount of significant interactions. Gender (p=.002) and age (p=.041) were both significant as main effect independent variables. Gende r and age proved significant as the interaction of both variables combined with the dependent variable. Gender, age and race also proved significant as a combined interact ion effect when compared to the dependent variable (p=.011). Howeve r this was not significant on the MANOVA (table 4-7), therefore it

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68 will not be discussed. The interaction of gender, race and school proved significant. The combined interaction of the variables when co mpared to the dependent variable produced a p value of (.001). This comparison was also si gnificant on the MANOVA (t able 4-7). Race, age and school as an interaction proved signifi cant on the ANOVA (table 4-74), however the interaction was not sign ificant on the MANOVA (table 4-7) a nd as such will not be discussed. Finally the four way interaction again proved to be significant with the combined interaction producing a p value of (.000) when compared to the dependent variable of partner objection to the amount of time an individual spends on line. Gender proved to be interesting. Based upon the population represented, females are less likely to have a partner object to the amount of time that they spend online. The average female overall mean was higher (mean= 1.974) than the ma le average mean (1.881) as demonstrated in table 4-71. The average mean for age found in ta ble 4-72 indicated that people age 20 to 22 are more likely to have partner objection to the am ount of time they spend on line. Individuals age 23 and older were less likely to experience partner objection when co mpared to their younger counterparts. When gender and age were combin ed in table 4-75, the results indicated that females grouped by ages 20 and older were less lik ely than their same age male counterparts to experience partner objection to online use. Male s who identified themselves as 18-19 did not experience the same level of partners objecting to their overall time spent using the internet. Although the results demonstrate a difference, ther e is not an explanation that may account for this difference. When comparing race, gender and school, the results in table 4-76 demonstrated that females attending HBCU were less likely than their racially similar counter parts at PFU to experience partner objection unless they were AAB. Those students matching that demography

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69 were more likely to experience partner objecti on when compared to th eir PFU counterparts. When comparing males by race and school in table 4-76, MAL, O, and AAPI could not be compared as there was no representation at HBCU among this grouping. However, when looking specifically at AAB and NWH; AAB attendi ng HBCU were less lik ely than their PFU counterparts to experience part ner objection. On the other hand, NHW attending PFU were less likely than their male counterparts at HBCU to experi ence partner objection. It is important to note that both Tukey HSD tests for age and race found in tables 4-81 and 4-82 demonstrated insignificance among the interaction effects in multiple comparisons. Therefore, the overall results show significance but based upon the samp le there is no way to determine the relevance of the interaction as it pe rtains to this grouping. The overall interaction found in table 4-74 de monstrates that there is a difference in partner objection between school, age, gender and race. When comparing race, age, gender, and school as factors in a four way in teraction in this sample illustra ted in tables 4-79 and 4-80, many of the races do not have a single individual in the sample in certain age groups when separated for age and gender. As previously stated with the other significant inte ractions of the four independent variables, it would be difficult to interpret the intera ction in between and as a result of these very specific groups. Does Your Offline Partner Know About The Friends/Relationships That You Have Online The next dependent variable analyzed was the question Does your offline partner know about the friends that you have online? There were two categories in which respondents could answer the question. Yes had a value of one (1), and no had a value of no (2). ANOVA statistics were used to analyze this variable with the independent variables of race, age, gender and university of record. The data in table 4-88 demo nstrated only significant re sult of the interaction effect of age and school on the dependent variable.

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70 When comparing school to partner knowledge of online relationships over the internet, table 4-84 illustrates the findings that students attending PFU ar e more likely to share that information with their offline partners than t hose students attending HBCU Regarding age, and partner knowledge, the Tukey HSD in table 4-90 demonstrated that there is an interaction between younger participants when compared to their older counter parts. Overall, students over the age of 23 are more likely to disclose online relationships to their partners when compared to their younger counterparts. Students who are between the ages of 20-22 are less likely to disclose that information when compared to their olde r or younger counterparts, and students are more likely to share their information when compared to students 20-22. Inte restingly, they are less likely to share information with regarding relations hips (online) with their partners than their 23 year old and older counterparts. Partner Jealousy Over Relationships Developed Online The next question analyzed stated: Has your partner ever expre ssed jealousy over the relationships you have developed online? The s ubjects had the response choices of yes which had a value of one (1), and no, which had a value of two (2). Using ANOVA, the results in table 4-95 demonstrated significance in the interac tions between age and race as a combined interaction, as well as school a nd age and race as another interaction when compared with the dependent variable of jealousy. The overall M ANOVA in table 4-7 demonstrated insignificance in these groups. Tables 4-91 th rough 4-99 illust rate the data. An Alternate Way/Place For Meeting New People The next question to be analyzed was How im portant is it to you that an alternate way/place for meeting new people is available? This statement was scaled with the following numeric responses: Extremely important was valued at five (5), Important was valued at four (4), Uncertain was valued at three (3), Not important was valued at two (2) an d Not important at all

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71 was valued at one (1). ANOVA statistics were used to analyze this dependent variable compared with gender, race, age, and school as the depe ndent variables. The initial ANOVA in table 4-105 proved significant for the main effect of age only. The means of the three groups in table 4-103 de monstrated that as respondents increased in age, they were more likely to value the intern et is an important way to meet new people. The Tukey HSD in table 4-106 demonstrated si gnificance with the groups using multiple comparisons of age. The Tukey HSD identified that significant intera ction occurred between ages 23 and older and all other ag e groups. In comparing ages, as students aged, they valued the importance of the internet mu ch more than those who identified themselves as younger, An Alternative Way/Place For Meeting Poten tial Online Sexual Partners The next variable to be tested was How important is it to you that an alternative way/place for meeting potential sexual partners is available? The responses were valued through numerical values. How important is it to you th at an alternate way/place for meeting new people is available? This statement was scaled w ith the following numeri c responses: Extremely important (5), Important (4), Uncertain (3), Not im portant (2) and Not important at all (1). This dependent variable was again tested comparing age, gender, unive rsity of record (school), and race as independent variables. The analysis illu strated in table 4-112 proved significant for main effects of gender as well as age when compared to the dependent variable. In table 4 109, the means in males were much higher than that of their female counterparts. The overall male mean for gender wa s higher (2.52) as comp ared with the female sample in this group (1.88). This demonstrated that gender is a factor in determining the importance of the internet as a way to meet potential online sexual partners. Men participating in the sample believe that the internet as more impo rtant than females as an approach to meet online sexual partners.

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72 Age also had a significant interaction. Table 4-113 demonstrates th e significance of age in the Tukey HSD as well. Individuals who are ol der (in the sample) are more likely to value the internet as an alternate way to meet individu als when compared to t hose who were younger. The interaction appears to be insignificant when comparing students 18-20 with those who are 20-22. The significant interaction occurs at ages greater than 23. The ove rall results demonstrates that gender and age have a significant effect on the diffe rence in perceptions of the importance of the internet as a place to me et online sex partners. I Have Accessed Sexually Explic it Materials On The Internet The next variable to be tested was I ha ve access ed sexually explicit materials on the internet. The responses were valued through numerical values. Usi ng ANOVA statistics, the question posed to respondents read: I have ac cessed sexually explicit material on the internet was analyzed. The responses for this specific que stion included frequently which was given a value of four, sometimes which was given a re sponse of three, rare ly, which was given a response of two, and never, which was given a value of one. The dependent variable was the question: I have accessed sexually explicit materials on the internet? The independent variables were gend er age race and universit y of record (school). The only significant analyses demonstrated on table 4-120 proved to be gender and school. Gender was also significant as a main affect on the MANOVA analysis (t able 4-7). Table 4-116 and 4-117 identifies that Males a nd students from PFU were more likely (base upon means) to have accessed the internet for sexually explicit material than females and students from HBCU. I Have Accessed Sexually Explicit Ma terials T o Become Sexually Aroused The next variable to be tested was I have acce ssed sexually explicit materials on the internet to become sexually aroused. The responses were valued through numerical values. Using ANOVA statistics, the question I have accessed sexuall y explicit material on the internet to become

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73 sexually aroused was analyzed. The responses fo r this specific questi on included frequently which was given a value of four, sometimes whic h was given a response of three, rarely, which was given a response of two, and never, which wa s given a value of one. The dependent variable was the question: I have accessed sexually explicit materials on the internet to become sexually aroused. The independent variable s were gender age race and university of record (school). The only significant analyses in the ANOVA in table 4-126 proved to be gender and university of record (school) in these analyses (separately). Gender and school were also significant as a main affect on the MANOVA an alysis (table 4-7). The mean in table 4-123 demonstrate the differences between males and females on accessing sexually explicit materials on the internet. The results indicate that accessing sexually explicit material was almost 1 times higher in males than in females. This finding although significant is not a surprise. Males are very visu al creatures in the natu re of sex and arousal (Canli, &Gabrieli, 2004), and it is not surprising that this would occur in this population. According to the results in table4-122, students who attend HBCU were much less likely to access the internet than their counterparts at PFU in the pursuit of accessing sexually explicit materials for sexual arousal. This may directly rela te to the number of males from HBCU that are represented in this population as well as the availability of computers and internet access as well. Therefore, although this is an interesting revelation, it s hould be viewed with caution. While Viewing Sexually Explicit Web Sites, I Have Masturbated The question while viewing sexually explicit m a terial I have masturbated was analyzed with ANOVA. The responses for this specific ques tion included frequently (4), sometimes (3), rarely (2), and never (1). This question was analyzed as the depe ndent variable with age, gender and race as the independent variables.

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74 Gender and School proved to be significant as a main effect interac tion with gender, race and school as a combined interaction when compar ed with the dependent variable in table 4-132. All three tests were significant on the MANOVA (table 4-7). Based upon the means of these analyses found in table 4-129, males are far more likely than their female counterparts to view sexually explicit materials for the purpose of masturbation. Based upon these mean, males are twice as likely to masturbate to sexually exp licit web sites versus thei r female counterparts. Table 4-127 identifies that students attending PFU are far more like ly to view these materials for the purpose of masturbation. Although it was not significant on the ANOVA table (4-132), with the exception of AAPI females, females at PFU of any other race are more likely to view this material for the purpose of self gratificati on than their racial c ounterparts at HBCU. In table 4-133, males who are AAB attending PFU are twice as likely to view sexually explicit material on the internet for the purpos e of masturbation as that of their HBCU counterparts. The Tukey HSD in table 4-134 de monstrated that AAB were less likely to masturbate while viewing sexually explicit material than all other races sampled in this study. I Have Had Cybersex With An Online Partner. Using ANOVA statistics, the question I have had cybersex with an online partner was analyzed. T he responses for this specific question included frequently which was given a value of four, sometimes which was given a response of three, rarely, which was given a response of two, and never, which was given a value of one. This question was analyzed as the dependent variable with age, gender, race and university of record (school). The results found in table 4-140 proved to be insignifican t with all effects. I Like To Drink Alcohol While Having Cybersex With An Online Partner Using ANOVA statistics, the question I like to drink alcohol while having cybersex with an online partner was analyzed. The responses for th is specific question included frequently which

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75 was given a value of four, sometimes which was given a response of three, rarely, which was given a response of two, and never, which was given a value of one. This question was analyzed as the dependent variable with age, gender, r ace and university of record (school). Table 4-146 illustrates that results were insignificant with all effects. I Like To Use Stimulants While Havi ng Cybersex With A n Online Partner Using ANOVA statistics, the question I like to use stimulants (drugs) while having cybersex with an online partner was analyzed. The responses for this specific question included frequently which was given a value of four, so metimes which was given a response of three, rarely, which was given a response of two, and never, which was given a value of one. This question was analyzed as the dependent variable with age, gender, race a nd university of record (school). The results demonstrated in table 4152 were insignificant with all effects. Summary These variables do not represen t the entire cach et of variables within the study. However, they were chosen from the many different variable s in order to best res pond to the aims of the study. The results of chapter four demonstrate that gender, age, and race have an effect on internet use and some behaviors. As the result s of this study are quantitative, the results only address whether or not a sp ecific interaction exists. This study demonstrates that overall, men are more likely to use the internet for sexual behaviors. They are also more likely to attemp t to find an online partner for sexual activity as well as other relationships. Age is also a large factor in internet use as it relates to importance. In the sample population, as the respondents age in creased, so did their perceptions of the value of the internet. Interestingly, their frequency of internet use did not. This may be a result of more specific tasks while these older individuals ar e engaged in internet activity.

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76 This information concludes the summation of the results of this dissertation. Chapter five will discuss the results in context with interpretation and implications. This examination will occur in the same order as the results se ction when the questions that sp ecifically pertained to this study were initially analyzed Table 4-1. Gender from both universities combined. Label Frequency Percent Valid Percent Cumulative Percent Females 839 69.2 69.3 70.1 Male 371 30.6 30.7 100.0 Missing 2 .2 Total 1212 100.0 Table 4-2. Age from both universities combined. Label FrequencyPercentValid Percent Cumulative Percent 18-19 416 34.3 34.4 34.4 20-22 523 43.2 43.2 77.5 23-up 272 22.4 22.9 100.0 Missing 1 .1 0 Table 4-3. Race from both universities combined. Label Frequency Percent Valid Percent Cumulative Percent AAB 211 17.4 17.5 17.5 Native American 50.40.4 17.9 Asian American Pacific Islander 725.96.0 23.8 Mexican American/Latino 13311.011.0 34.8 Non-Hispanic White 73360.560.6 95.5 Self Designated Other 554.54.5 100.0 Missing 30.2 Total 1212100.0 100.0

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77 Table 4-4. Gender cross tabulated with race from both universities combined. Gender Race Race Race Race Race Other Total African American Black Native American Asian American Pacific Islander Mexican American Latino Non Hispanic White Identified Other Female 169 1 519349034 838 Male 42 4 214024121 369 Missing 5 Total 211 5 7213373155 1212 Table 4-5. Gender cross tabulated with university and age differentiated. Gender School Age 18-19 Age 20-22 Age 23up Total Females HBCU 47 49 31 127 Females PFU 254 301 122 677 Total Total Females 301 350 154 744 M a l e s H B C U 1 757 2 9 Males PFU 98 115 111 292 Total Total Males 115 120 118 353 Missing Data 3 Totals 416 522 271 1212

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78 Table 4-6. Race cross tabulated with university and age differentiated. Race School Age 18-19 Age 20-22 Age 23-up Total African American Black HBCU 605129 125 African American Black PFU 33344 65 African American Black Total 938533 190 Asian American Pacific Islander HBCU 001 1 Asian American Pacific Islander PFU 283211 65 Asian American Pacific Islander Total 283212 66 Mexican American Latino HBCU 001 1 Mexican American Latino PFU 495825 112 Mexican American Latino Total 495826 113 Non Hispanic White HBCU 314 7 Non Hispanic White PFU 224325175 637 Non Hispanic White Total 227326179 644 Other HBCU 123 4 Other PFU 151915 41 Other Total 162118 45 Missing Total 10 Total Population 413522267 1212

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79 Table 4-7. Multivariate Test: Wilks Lambda: Independent Variables Main and Interaction Effects. Effect Test Value F Degree of Freedom Error Level of Significance School Wilks Lambda .9651.68020.000933.000 .031 Gender Lambda Wilks .9174.21720.000933.000 .000 Age Lambda Wilks .9221.94240.0001866.000 .000 Race Lambda Wilks .8961.29580.0003682.99 .041 School/ Gender Lambda Wilks .9771.07420.000933.000 .371 School/Age Wilks Lambda .969.75040.0001866.000 .873 Gender/Age Wilks Lambda .9192.00540.0001866.000 .000 School/ Gender/ Age Wilks Lambda .9361.56340.0001866.000 .014 School/Race Wilks Lambda .9171.02980.0003682.990 .410 Gender /Race Wilks Lambda .926.91080.0003682.990 .702 School/Race Gender Wilks Lambda .9591.97520.000933.000 .007 Age/Race Wilks Lambda .869.827160.0006981.594 .945 School/Age/ Race Wilks Lambda .9051.18680.0003682.990 .126 Age/Gender /Race Wilks Lambda .845.992160.0009681.594 .513 School/Age/ Gender/Race Wilks Lambda .9482.53920.000933.000 .000

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80 Table 4-8. Correlations: How long have you been using email? School Gender Age Race How long have you been using email School Pearson Correlation 1-.101(**)-.025-.662(**) -.167(**) Sig. (2tailed) .000.391.000 .000 N 1212121012111209 1205 Gender Pearson Correlation -.101(**)1.114(**).103(**) -.014 Sig. (2tailed) .000 .000.000 .639 N 1210121012091207 1203 Age Pearson Correlation -.025.114(**)1.118(**) .123(**) Sig. (2tailed) .391.000 .000 .000 N 1211120912111208 1204 Race Pearson Correlation -.662(**).103(**).118(**)1 .121(**) Sig. (2tailed) .000.000.000 .000 N 1209120712081209 1202 How long have you been using email Pearson Correlation -.167(**)-.014.123(**).121(**) 1 Sig. (2tailed) .000.639.000.000 N 1205120312041202 1205 ** Correlation is significant at the 0.01 level (2-tailed) Table 4-9. Mean: How long have you been using email: School School Mean Std. Deviation N PFU 3.94 .319 1042 HBCU 3.75 .651 155 Total 3.91 .384 1197 Table 4-10. Mean: How long have you been using email: Gender Gender Mean Std. Deviation N females 3.92 .380 834 Males 3.90 .393 361 Total 3.91 .384 1195

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81 Table 4-11. Mean: How long have you been using email: Age Age Mean Std. Deviation N 18-19 3.84 .509 413 20-22 3.95 .283 520 23-and above 3.96 .306 263 Total 3.91 .384 1196 Table 4-12. Mean: How long have you been using email: Race Race Mean Std. Deviation N AAB 3.80 .588 210 AAPI 3.99 .119 71 MAL 3.92 .411 132 NHW 3.94 .304 730 O 3.93 .428 54 Total 3.91 .384 1197 Table 4-13. Univariate ANOVA. How long have you been using email: Age, Gender, Race, School Year Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .014 1.014.099.753 .000 Age .691 2.3462.493.083 .004 Race .554 4.138.999.407 .003 School .004 1.004.026.871 .000 Gender/Age .530 2.2651.913.148 .003 Gender/Race .368 4.092.664.617 .002 Age/Race 1.054 8.132.950.474 .007 Gender Age Race 1.084 8.136.978.451 .007 Gender/School .000 1.000.003.956 .000 Age/School .070 2.035.251.778 .000 Gender/Age/School .192 2.096.691.501 .001 Race/School .764 4.1911.378.239 .005 Gender Race/School .029 1.029.206.650 .000 Age/Race/School .211 4.053.381.822 .001 Gender/Age/Race School .131 1.131.948.330 .001 Error 159.157 1148.139 Total 18465.000 1194 Corrected Total 176.15 1193

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82 Table 4-14. Correlations: How ofte n have you been using email? School Gender Age Race How often do you use email School Pearson Correlation 1-.101(**)-.025-.662(**) .306(**) Sig. (2tailed) .000.391.000 .000 N 1212121012111209 1201 Gender Pearson Correlation -.101(**)1.114(**).103(**) -.015 Sig. (2tailed) .000 .000.000 .608 N 1210121012091207 1199 Age Pearson Correlation -.025.114(**)1.118(**) -.040 Sig. (2tailed) .391.000 .000 .171 N 1211120912111208 1200 Race Pearson Correlation -.662(**).103(**).118(**)1 -.194(**) Sig. (2tailed) .000.000.000 .000 N 1209120712081209 1199 How often do you use email Pearson Correlation .306(**)-.015-.040-.194(**) 1 Sig. (2tailed) .000.608.171.000 N 1201119912001199 1201 **Correlation is significant at the 0.01 level (2-tailed). Table 4-15. Mean: How often do you use email: School School Mean Std. Deviation N PFU 1.05 .232 1046 HBCU 1.34 .597 155 Total 1.09 .320 1201 Table 4-16. Mean: How often do you use email: Gender Gender Mean Std. Deviation N females 1.09 .320 830 Males 1.08 .319 369 Total 1.09 .320 1199

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83 Table 4-17. Mean: How often do you use email: Age Age Mean Std. Deviation N 18-19 1.11 .354 412 20-22 1.07 .283 520 23-and above 1.09 .330 268 Total 1.09 .320 1200 Table 4-18. Mean: How often do you use email: Race Race Mean Std. Deviation N AAB 1.24 .530 210 AAPI 1.01 .119 71 MAL 1.06 .240 131 NHW 1.06 .253 728 O 1.06 .231 54 Total 1.09 .320 1199 Table 4-19. Tests of between subjects. How of ten do you use email: Age, Gender, Race, School Year Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .266 1.2662.872.090 ..003 Age .386 2.1932.084.125 ..004 Race .867 4.2172.338.054 .008 School 1.081 11.08111.664.001 .010 Gender/Age .058 2.029.313.731 .001 Gender/Race .744 4.1862.006.091 .007 Age/Race .846 8.1061.141.333 .008 Gender Age Race .326 8.041.440.897 .003 Gender/School .288 1.2883.106.078 .003 Age/School .220 2.1101.190.305 .002 Gender/Age/School .052 2.026.280.756 .000 Race/School .818 4.2042.207.066 .008 Gender Race/School .844 1.8449.105.003 .008 Age/Race/School .914 4.2292.467.043 .009 Gender/Age/Race School .063 1.063.675.411 .001 Error 106.102 1145.093 Total 1535.000 1191 Corrected Total 122.566 1190 R squared= .134 (adjusted R squared=.100)

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84 Table 4-20. How often do you use email: Mean, Standard Error and Confidence Interval Gender Race University Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound Females AAB PFU1.065.077.914 1.216 AAB HBCU1.318.0301.259 1.378 AA/PI PFU1.000.059.884 1.116 AA/PI HBCU1.000.304.403 1.597 MAL PFU1.052.036.981 1.124 MAL HBCU2.000.3041.403 2.597 NHW PFU1.044.0151.015 1.073 NHW HBCU1.667.1551.363 1.971 O PFU1.033.072.893 1.174 O HBCU1.111.137.842 1.381 Males AAB PFU1.000.090.823 1.177 AAB HBCU1.465.0671.324 1.587 AA/PI PFU1.056.068.923 1.188 AA/PI HBCU MAL PFU1.037.050.938 1.136 MAL HBCU NHW PFU1.072.0201.003 1.111 NHW HBCU1.250.186.884 1.616 O PFU1.067.075.920 1.213 O HBCU AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other. Table 4-21. Tukey HSD. How often do you use email: Race. Race Race Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound AAB AAPI.23.042.000.11 .34 AAB MAL.18.034.000.09 .27 AAB NHW.18.024.000.12 .25 AAB O.19.046001.06 .31 AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

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85 Table 4-22. Correlations: How long ha ve you been using the internet? School Gender Age Race How long have you been using the internet School Pearson Correlation 1-.101(**)-.025 .662(**) -.078(**) Sig. (2-tailed) .000.391.000 .007 N 1212121012111209 1207 Gender Pearson Correlation -.101(**)1.114(**).103(**) -.009 Sig. (2-tailed) .000 .000.000 .762 N 1210121012091207 1205 Age Pearson Correlation -.025.114(**)1.118(**) .022 Sig. (2-tailed) .391.000 .000 .450 N 1211120912111208 1206 Race Pearson Correlation -.662(**).103(**).118(**)1 .095(**) Sig. (2-tailed) .000.000.000 .001 N 1209120712081209 1204 How long have you been using the internet Pearson Correlation -.078(**)-.009.022.095(**) 1 Sig. (2-tailed) .007.762.450.001 N 1207120512061204 1207 ** Correlation is significant at the 0.01 level (2-tailed). Table 4-23. Mean: How long have you been using the internet: School School Mean Std. Deviation N PFU 3.98 .205 1044 HBCU 3.93 .362 155 Total 3.98 .232 1199 Table 4-24. Mean: How long have you been using the internet: Gender Gender Mean Std. Deviation N females 3.98 .231 834 Males 3.97 .233 363 Total 3.98 .232 1197

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86 Table 4-25. Mean: How long have y ou been using the internet: Age Age Mean Std. Deviation N 18-19 3.97 .259 412 20-22 3.98 .210 520 23-and above 3.98 .229 266 Total 3.98 .232 1198 Table 4-26. Mean: How long have you been using the internet: Race Race Mean Std. Deviation N AAB 3.92 .396 210 AAPI 4.00 .000 71 MAL 3.98 .261 132 NHW 3.99 .173 731 O 4.00 .000 55 Total 3.98 .232 1199 Table 4-27. Test of between subjects: Ho w long have you been using the internet? Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .054 1.0.0541.023.312 .001 Age .057 2.0.029.543.581 .001 Race .093 4.0.023.442.778 .002 School .015 1.0.015.288.592 .000 Gender/Age .127 2.0.0631.198.302 .002 Gender/Race .304 4.0.0761.439.219 .005 Age/Race .456 8.0.0571.079.375 .007 Gender Age Race .543 8.0.0681.284.248 .009 Gender/ School .063 1.0.0631.186.276 .001 Age/School .006 2.0.003.056.945 .000 Gender/Age/School .094 2.0.047.890.411 .002 Race/School .030 4.0.008.143.966 .000 Gender Race/School .009 1.0.009.172.678 .000 Age/Race/School .041 4.0.010.196.940 .001 Gender/Age/ Race School .079 1.0.0791.503.221 .001 Error 60.744 1150.00.053 Total 18969.000 1196 R squared=.997 (adjusted R Squared=.997)

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87 Table 4-28. Correlations: How of ten do you use the internet? School Gender Age Race How often do you use the internet School Pearson Correlation 1-.101(**)-.025-.662(**) -.078(**) Sig. (2tailed) .000.391.000 .007 N 1212121012111209 1207 Gender Pearson Correlation -.101(**)1.114(**).103(**) -.009 Sig. (2tailed) .000 .000.000 .762 N 1210121012091207 1205 Age Pearson Correlation -.025.114(**)1.118(**) .022 Sig. (2tailed) .391.000 .000 .450 N 1211120912111208 1206 Race Pearson Correlation -.662(**).103(**).118(**)1 .095(**) Sig. (2tailed) .000.000.000 .001 N 1209120712081209 1204 How often do you use the internet Pearson Correlation -.078(**)-.009.022.095(**) 1 Sig. (2tailed) .007.762.450.001 N 1207120512061204 1207 ** Correlation is significant at the 0.01 level (2-tailed). Table 4-29. Mean: How often do you use the internet: School School Mean Std. Deviation N PFU 1.00 .070 1022 HBCU 1.11 .348 152 Total 1.02 .145 1174 Table 4-30. Mean: How often do you use internet: Gender Gender Mean Std. Deviation N Females 1.02 .139 813 Males 1.02 .157 359 Total 1.02 .145 1172

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88 Table 4-31. Mean: How often do you use the internet; Age Age Mean Std. Deviation N 18-19 1.01 .140 406 20-22 1.01 .077 509 23-and above 1.05 .229 258 Total 1.02 .145 1173 Table 4-32. Mean: How often do you use the internet: Race Race Mean Std. Deviation N AAB 1.06 .263 206 AAPI 1.00 .000 71 MAL 1.00 .000 125 NHW 1.01 .117 719 O 1.00 .000 53 Total 1.02 .145 1174 Table 4-33. Test of between subjects : How often do you use the internet? Independent Variable Type III Sum of Squares Degrees of Freedom Mean SquareF value Level of Significance Partial Eta Squared Gender .008 1.0.008.436.509 .000 Age .129 2.0.0653.540.029 .006 Race .266 4.0.0663.648.006 .013 School .044 1.0.0442.401.122 .002 Gender/Age .537 2.0.26814.732.000 .026 Gender/Race .014 4.0.004.196.941 .001 Age/Race .186 8.0.0231.275.252 .009 Gender Age Race .229 8.0.0291.570.129 .011 Gender/School .009 1.0.009.470.493 .000 Age/School .105 2.0.0522.878.057 .005 Gender/Age/School .545 2.0.27314.965.000 .026 Race/School .223 4.0.0563.066.016 .011 Gender Race/School .012 1.0.012.680.410 .001 Age/Race/School .155 4.0.0392.123.076 .007 Gender/Age/Race School .165 1.0.1659.079.003 .008 Error 20.501 1125.018 Total 1238.000 1171 R squared=.983 (Adjusted R squared=.983)

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89 Table 4-34. How often do you use th e internet: Mean: Race, School Race School Mean Standard Error 95% Confidence Level Lower Bound Upper Bound AAB PFU 1.000.026.948 1.052 AAB HBCU 1.120 .0171.086 1.153 AAPI PFU 1.000 .020.961 1.039 AAPI HBCU 1.000* .135.735 1.265 MAL PFU 1.000 .014.972 1.028 MAL HBCU 1.000* .135.735 1.265 NHW PFU 1.009 .006.998 1.020 NHW HBCU 1.233* .0531.130 1.337 O PFU 1.000 .025.951 1.049 O HBCU 1.000 .061.880 1.120 *Based on a modified population marginal mean AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other. Table 4-35. How often do you use th e internet: Mean: Gender, Age Gender Age Mean Standard Error 95 % Confidence Interval Lower Bound Upper Bound Females 18-19 1.003.025.953 1.052 20-22 1.006 .022 .963 1.049 23-up 1.097 .027 1.044 1.151 Males 18-19 1.102 .021 1.061 1.144 20-22 1.000 .021 .959 1.041 23-up 1.026 .027 .972 1.080 Table 4-36. How often do you use the in ternet: Mean: Gender, Age, School Gender Age School Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound Females 18-19 PFU 1.000.013.975 1.025 Females 18-19 HBCU 1.008.064.882 1.133 Females 20-22 PFU 1.001.011.978 1.023 Females 20-22 HBCU 1.014.056.906 1.123 Females 23-up PFU 1.004.031.943 1.065 Females 23-up HBCU 1.190.0441.103 1.278 Males 18-19 PFU 1.003.0211.961 1.045 Males 18-19 HBCU 1.350.0511.250 1.450 Males 20-22 PFU 1.000.021.959 1.041 Males 20-22 HBCU 1.000.067.868 1.132 Males 23-up PFU 1.003.025.954 1.052 Males 23-up HBCU 1.083.073.940 1.226

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90 Table 4-37. How often do you use the intern et: Mean: Females, Age, Race, School Gender Age Race School Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound Females 18-19 AAB PFU 1.000.027.947 1.053 Females 18-19 AAB HBCU 1.023.020.983 1.063 Females 18-19 AAPI PFU 1.000.029.944 1.056 Females 18-19 AAPI HBCU *** Females 18-19 MAL PFU 1.000.021.958 1.042 Females 18-19 MAL HBCU *** Females 18-19 NHW PFU 1.000.011.978 1.022 Females 18-19 NHW HBCU 1.000.135.735 1.265 Females 18-19 O PFU 1.000.043.916 1.084 Females 18-19 O HBCU 1.000.135.735 1.265 Females 20-22 AAB PFU 1.000.026.949 1.051 Females 20-22 AAB HBCU 1.043.0201.004 1.083 Females 20-22 AAPI PFU 1.000.028.945 1.055 Females 20-22 AAPI HBCU *** Females 20-22 MAL PFU 1.000.023.955 1.045 Females 20-22 MAL HBCU *** Females 20-22 NHW PFU 1.004.009.987 1.022 Females 20-22 NHW HBCU 1.000.135.735 1.265 Females 20-22 O PFU 1.000.035.932 1.068 Females 20-22 O HBCU 1.000.095.813 1.187 Females 23-up AAB PFU 1.000.095.813 1.187 Females 23-up AAB HBCU 1.286.0291.228 1.344 Females 23-up AAPI PFU 1.000.067.868 1.132 Females 23-up AAPI HBCU 1.000.135.735 1.265 Females 23-up MAL PFU 1.000.037.927 1.073 Females 23-up MAL HBCU 1.000.135.735 1.265 Females 23-up NHW PFU 1.021.014.994 1.048 Females 23-up NHW HBCU 1.667.0781.514 1.820 Females 23-up O PFU 1.000.095.813 1.187 Females 23-up O HBCU 1.000.078.847 1.153 AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other. This level combination of fact ors is not observed, thus the corre sponding marginal mean is not estimable

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91 Table 4-38. How often do you use the inte rnet: Mean: Males, Age, Race, School Gender Age Race School Mean Std. Error 95% Confidence Interval Males Lower Bound Upper Bound Males 18-19 AAB PFU 1.000.048.906 1.094 Males 18-19 AAB HBCU 1.200.0351.132 1.268 Males 18-19 AAPI PFU 1.000.055.892 1.108 Males 18-19 AAPI HBCU *** Males 18-19 MAL PFU 1.000.048.906 1.094 Males 18-19 MAL HBCU *** Males 18-19 NHW PFU 1.015.016.983 1.047 Males 18-19 NHW HBCU 1.500.0951.313 1.687 Males 18-19 O PFU 1.000.060.882 1.118 Males 18-19 O HBCU *** Males 20-22 AAB PFU 1.000.055.892 1.108 Males 20-22 AAB HBCU 1.000.067.868 1.132 Males 20-22 AAPI PFU 1.000.045.912 1.088 Males 20-22 AAPI HBCU *** Males 20-22 MAL PFU 1.000.033.936 1.064 Males 20-22 MAL HBCU *** Males 20-22 NHW PFU 1.000.014.973 1.027 Males 20-22 NHW HBCU *** Males 20-22 O PFU 1.000.067.868 1.132 Males 20-22 O HBCU *** Males 23-up AAB PFU 1.000.095.813 1.187 Males 23-up AAB HBCU 1.167.0551.059 1.275 Males 23-up AAPI PFU 1.000.055.892 1.108 Males 23-up AAPI HBCU *** Males 23-up MAL PFU 1.000.039.927 1.076 Males 23-up MAL HBCU *** Males 23-up NHW PFU 1.014.016.983 1.044 Males 23-up NHW HBCU 1.000.135.735 1.265 Males 23-up O PFU 1.000.041.920 1.080 Males 23-up O HBCU *** AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other. *This level combination of factor s is not observed, thus the corre sponding marginal mean is not estimable

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92 Table 4-39. Tukey HSD. How often do you use internet: Age Age Age Comparison Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound 18-19 20-22 .01.009.585-.01 .03 23-up -.04.011.009-.06 -.01 20-22 18-19 -.01.009.585-.03 .01 23-up -.05.010.000-.07 -.02 23-up 18-19 .04.011.009.01 .06 20-22 .05.010.000.02 .07 Table 4-40. Tukey HSD. How often do you us e internet: African American Blacks Race Race Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound AAB AAPI .06.019.006.01 .11 AAB MAL .06.015.000.02 .10 AAB NHW .05.011.000.02 .08 AAB O .06.021.021-.01 .12 AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

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93 Table 4-41. Correlations: I have ma de friends over the internet. School Gender Age Race I have made new friends over the internet School Pearson Correlation 1-.101(**)-.025-.662(**) .149(**) Sig. (2tailed) .000.391.000 .000 N 1212121012111209 1208 Gender Pearson Correlation -.101(**)1.114(**).103(**) .153(**) Sig. (2tailed) .000 .000.000 .000 N 1210121012091207 1206 Age Pearson Correlation -.025.114(**)1.118(**) .005 Sig. (2tailed) .391.000 .000 .850 N 1211120912111208 1207 Race Pearson Correlation -.662(**).103(**).118(**)1 -.144(**) Sig. (2tailed) .000.000.000 .000 N 1209120712081209 1205 I have made new friends over the internet Pearson Correlation .149(**).153(**).005-.144(**) 1 Sig. (2tailed) .000.000.850.000 N 1208120612071205 1208 ** Correlation is significant at the 0.01 level (2-tailed). Table 4-42. Mean: I have made fr iends over the internet: School School Mean Std. Deviation N PFU 2.15 .986 1045 HBCU 2.60 1.126 155 Total 2.21 1.016 1200

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94 Table 4-43. Mean: I have made fr iends over the internet: Gender Gender Mean Std. Deviation N Females 2.10 .998 835 Males 2.44 1.021 363 Total 2.21 1.017 1198 Table 4-44. Mean: I have made friends over the internet: Age Age Mean Std. Deviation N 18-19 2.22 1.011 410 20-22 2.18 1.021 521 23-and above 2.25 1.020 268 Total 2.21 1.017 1199 Table 4-45. Mean: I have made friends over the internet: Race Race Mean Std. Deviation N AAB 2.56 1.097 210 AAPI 2.31 1.043 72 MAL 2.03 .941 132 NHW 2.11 .979 731 O 2.42 .994 55 Total 2.21 1.016 1200

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95 Table 4-46. Tests of between subjects. I ha ve made new friends over the internet. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender 6.049 16.0496.228.013 .842 Age 1.132 2.566.583.559 .005 Race 2.727 4.682.702.591 .001 School 1.219 11.2191.255.263 .002 Gender/Age 2.487 21.2441.280.278 .001 Gender/Race 1.952 4.488.502.734 .002 Age/Race 3.007 8.376.387.928 .002 Gender Age Race 6.130 8.766.789.612 .003 Gender /School .582 1.582.599.439 .005 Age/School .402 2.201.207.813 .001 Gender/Age /School 2.559 21.2791.317.268 .000 Race/School .678 4.170.175.951 .002 Gender Race/School .576 1.576.593.442 .001 Age/Race/School 3.560 4.890.916.454 .003 Gender/Age/ Race School 5.619 15.6195.785.016 .005 Error 1117.948 1151.971 Total 7060.00 1197 R squared=.842 (adjusted R Squared=.835)

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96 Table 4-47. I have made new friends over the internet. Mean: Females, Age, Race, School Gender Age Race School Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound Females 18-19 AAB PFU 2.440.1972.053 2.827 Females 18-19 AAB HBCU 2.636.1492.345 2.928 Females 18-19 AAPI PFU 2.318.2101.906 2.730 Females 18-19 AAPI HBCU *** Females 18-19 MAL PFU 1.795.1581.485 2.105 Females 18-19 MAL HBCU *** Females 18-19 NHW PFU 1.891.0791.736 2.046 Females 18-19 NHW HBCU 3.000.9861.066 4.934 Females 18-19 O PFU 1.900.3121.289 2.511 Females 18-19 O HBCU 3.000.9861.066 4.934 Females 20-22 AAB PFU 2.393.1862.027 2.758 Females 20-22 AAB HBCU 2.543.1452.258 2.829 Females 20-22 AAPI PFU 2.043.2051.640 2.447 Females 20-22 AAPI HBCU *** Females 20-22 MAL PFU 1.949.1581.639 2.258 Females 20-22 MAL HBCU 1.974** Females 20-22 NHW PFU 2.000.0651.846 2.101 Females 20-22 NHW HBCU 2.467.986.066 3.934 Females 20-22 O PFU 3.000.2541.967 2.966 Females 20-22 O HBCU 1.500.6971.633 4.367 Females 23-up AAB PFU 2.739.697.133 2.867 Females 23-up AAB HBCU 2.200.2052.336 3.142 Females 23-up AAPI PFU 2.000.4411.335 3.065 Females 23-up AAPI HBCU 2.000.986.066 3.934 Females 23-up MAL PFU 1.000.2731.464 2.536 Females 23-up MAL HBCU 2.121.986-.934 2.934 Females 23-up NHW PFU 1.000.0991.927 2.316 Females 23-up NHW HBCU 2.667.569-.116 2.116 Females 23-up O PFU 1.333.5691.550 3.783 Females 23-up O HBCU .569.217 2.450 This level combination of fact ors is not observed, thus the corre sponding marginal mean is not estimable AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other

PAGE 98

97 Table 4-48. I have made new friends over the internet. Mean: Males, Age, Race, School Gender Age Race School Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound Males 18-19 AAB PFU 2.375.3481.691 3.059 Males 18-19 AAB HBCU 3.000.2542.501 3.499 Males 18-19 AAPI PFU 2.500.4021.711 3.289 Males 18-19 AAPI HBCU *** Males 18-19 MAL PFU 2.111.3291.467 2.756 Males 18-19 MAL HBCU *** Males 18-19 NHW PFU 2.642.1202.406 2.878 Males 18-19 NHW HBCU 2.500.6971.133 3.867 Males 18-19 O PFU 2.200.4411.335 3.065 Males 18-19 O HBCU *** Males 20-22 AAB PFU 2.333.4021.544 3.123 Males 20-22 AAB HBCU 2.400.4411.535 3.265 Males 20-22 AAPI PFU 2.778.3282.133 3.422 Males 20-22 AAPI HBCU *** Males 20-22 MAL PFU 2.362.2262.188 3.075 Males 20-22 MAL HBCU *** Males 20-22 NHW PFU 2.280.1022.079 2.480 Males 20-22 NHW HBCU *** Males 20-22 O PFU 3.250.4932.283 4.217 Males 20-22 O HBCU *** Males 23-up AAB PFU 2.000.697.633 3.367 Males 23-up AAB HBCU 2.833.4022.044 3.623 Males 23-up AAPI PFU 2.500.4021.711 3.289 Males 23-up AAPI HBCU *** Males 23-up MAL PFU 2.167.2851.608 2.725 Males 23-up MAL HBCU *** Males 23-up NHW PFU 2.267.1142.043 5.490 Males 23-up NHW HBCU 4.000.9862.066 5.934 Males 23-up O PFU 2.667.2852.108 3.225 Males 23-up O HBCU *** This level combination of fact ors is not observed, thus the corre sponding marginal mean is not estimable AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

PAGE 99

98 Table 4-49. Tukey HSD.I have made new friends over the internet: Age Age Age Comparison Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound 18-19 20-22 .04.065.811-.11 .19 23-up -.03.078.938-.21 .16 20-22 18-19 -.04.065.811-.19 .11 23-up -.07.074.643-.24 .11 23-up 18-19 .03.078.938-.16 .21 20-22 .07.074.643-.11 .24 Table 4-50. Tukey HSD. I have made new friends over the interne t: African American Blacks 95% Confidence Interval Race Race Mean Difference Standard Error Level of Significance Lower Bound Upper Bound AAB AAPI .26 .135 .315 -.11 .62 AAB MAL .53* .109 .000 .23 .83 AAB NHW .45* .077 .000 .24 .66 AAB O .14 .149 .872 -.26 .55 Based upon observed means. *The mean is signifi cant at the .05 level. AAB= African American Black, AAPI=Asian American/Pacific Island er, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

PAGE 100

99 Table 4-51. Correlations: Have you ever met anyone in person that you met on line. School Gender Age Race Have you ever met anyone in person that you met on line? School Pearson Correlation 1-.101(**)-.025-.662(**) -.044 Sig. (2tailed) .000.391.000 .127 N 1212121012111209 1208 Gender Pearson Correlation -.101(**)1.114(**).103(**) -.144(**) Sig. (2tailed) .000 .000.000 .000 N 1210121012091207 1206 Age Pearson Correlation -.025.114(**)1.118(**) -.128(**) Sig. (2tailed) .391.000 .000 .000 N 1211120912111208 1207 Race Pearson Correlation -.662(**).103(**).118(**)1 .009 Sig. (2tailed) .000.000.000 .760 N 1209120712081209 1205 Have you ever met anyone in person that you met on line? Pearson Correlation -.044-.144(**)-.128(**).009 1 Sig. (2tailed) .127.000.000.760 N 1208120612071205 1208 ** Correlation is significant at the 0.01 level (2-tailed). Table 4-52. Have you ever met anyone in person that you met on line. School School Mean Std. Deviation N PFU 1.68 .466 1045 HBCU 1.62 .487 155 Total 1.67 .469 1200

PAGE 101

100 Table 4-53. Have you ever met anyone in person that you met on line. Gender Gender Mean Std. Deviation N Females 1.72 .451 836 Males 1.57 .495 362 Total 1.67 .469 1198 Table 4-54. Have you ever met anyone in person that you met on line. Age Age Mean Std. Deviation N 18-19 1.72 .450 413 20-22 1.70 .458 520 23-and above 1.55 .499 266 Total 1.67 .469 1199 Table 4-55. Have you ever met anyone in person that you met on line. Race Race Mean Std. Deviation N AAB 1.63 .483 210 AAPI 1.76 .428 72 MAL 1.71 .453 133 NHW 1.68 .468 730 O 1.56 .501 55 Total 1.67 .469 1200

PAGE 102

101 Table 4-56. Tests of between subjects. Have you ever in person someone that you met online. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .455 1.4552.141.144 .002 Age 1.208 2.6042.844.059 .005 Race 1.024 4.2561.205.307 .004 School .075 1.075.355.552 .000 Gender/Age .175 2.087.411.663 .001 Gender/Race 1.432 4.3581.685.151 .006 Age/Race .416 8.052.245.982 .002 Gender Age Race 1.197 8.150.704.688 .005 Gender/ School .093 1.093.439.508 .000 Age/School .333 2.166.783.457 .001 Gender/Age /School .928 2.4642.183.113 .004 Race/School .437 4.109.515.725 .002 Gender Race/School .143 1.143.675.512 .001 Age/Race /School .419 4.105.493.741 .002 Gender/Age/ Race School .058 1.058.274.601 .000 Error 244.503 1151.212 Total 3612.000 1197 R squared=.932. (adjusted R Squared=.930)

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102 Table 4-57. Correlations: I have pos ted online personal messages in an attempt to find a potential partner. School Gender Age Race I have posted personal messages School Pearson Correlation 1 -.101(**) -.025 -.662(**) .053 Sig. (2tailed) .000 .391 .000 .067 N 1212 1210 1211 1209 1208 Gender Pearson Correlation -.101(**) 1 .114(**) .103(**) .171(**) Sig. (2tailed) .000 .000 .000 .000 N 1210 1210 1209 1207 1206 Age Pearson Correlation -.025 .114(**) 1 .118(**) .166(**) Sig. (2tailed) .391 .000 .000 .000 N 1211 1209 1211 1208 1207 Race Pearson Correlation -.662(**) .103(**) .118(**) 1 -.054 Sig. (2tailed) .000 .000 .000 .062 N 1209 1207 1208 1209 1205 I have posted personal messages Pearson Correlation .053 .171(**) .166(**) -.054 1 Sig. (2tailed) .067 .000 .000 .062 N 1208 1206 1207 1205 1208 Table 4-58. Mean: I have posted on line personal messages in an attempt to meet a potential partner: School School Mean Std. Deviation N PFU 1.29 .671 1044 HBCU 1.40 .784 156 Total 1.30 .688 1200

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103 Table 4-59. Mean: I have posted on line personal messages in an attempt to meet a potential partner: Gender Gender Mean Std. Deviation N Females 1.23 .598 836 Males 1.48 .836 362 Total 1.30 .688 1198 Table 4-60. Mean: I have posted on line personal messages in an attempt to meet a potential partner: Age Age Mean Std. Deviation N 18-19 1.20 .530 410 20-22 1.28 .677 521 23-and above 1.51 .859 268 Total 1.30 .688 1199 Table 4-61. Mean: I have posted on line personal messages in an attempt to meet a potential partner: Race Race Mean Std. Deviation N AAB 1.40 .770 211 AAPI 1.35 .772 72 MAL 1.23 .638 133 NHW 1.27 .641 729 O 1.49 .879 55 Total 1.30 .688 1200

PAGE 105

104 Table 4-62. Tests of between subjects. I have post ed online personal messages in order to meet a potential partner. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender 4.948 14.948113.19.001 .010 Age 5.849 22.9256.690.001 .011 Race .291 4.073.166.955 .001 School .011 1.011.025.875 .000 Gender/Age 7.927 23.9649.067.000 .016 Gender/Race 1.036 4.259.592.668 .002 Age/Race 2.864 8.358.819.586 .006 Gender Age Race 5.207 8.6511.489.157 .010 Gender/ School .058 1.058.133.716 .000 Age/School .907 2.4531.037.355 .002 Gender/Age /School 3.217 21.6093.680.026 .006 Race/School 1.654 4.414.946.436 .003 Gender Race/School 1.099 11.0992.515.113 .002 Age/Race /School 1.666 4.416.953.433 .003 Gender/Age/ Race School 4.874 14.87411.149.001 .010 Error 503.154 1151.437 Total 2597.000 1197 R squared=.806. (adjusted R Squared=.799) Table 4-63. I have posted online personal messages in order to meet a potential partner. Mean: Gender, Age Gender Age Mean Standard Error 95 % Confidence Interval Lower Bound Upper Bound Females 18-19 1.253 .1241.011 1.496 20-22 1.227 .1081.016 1.439 23-up 1.209 .129 .955 1.463 Males 18-19 1.261 .1031.059 1.463 20-22 1.618 .0981.425 1.810 23-up 2.180 .1341.917 2.442

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105 Table 4-64. I have posted online personal messages in order to meet a potential partner. Mean: Gender, Age, School Gender Age School Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound Females 18-19 PFU 1.134 .062 1.014 1.255 Females 18-19 HBCU 1.452* .313 .837 2.067 Females 20-22 PFU 1.281 .055 1.172 1.390 Females 20-22 HBCU 1.138* .272 .604 1.671 Females 23-up PFU 1.330 .140 1.056 1.605 Females 23-up HBCU 1.087 .218 .660 1.514 Males 18-19 PFU 1.232 .104 1.028 1.436 Males 18-19 HBCU 1.333* .249 .845 1.822 Males 20-22 PFU 1.741 .102 1.542 1.941 Males 20-22 HBCU 1.000* .296 .420 1.580 Males 23-up PFU 1.951 .122 1.713 2.190 Males 23-up HBCU 2.750* .357 2.049 3.451 *Based upon a modified population marginal mean

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106 Table 4-65. I have posted online personal messages in order to meet a potential partner. Mean: Females, Age, Race, School Gender Age Race School Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound Females 18-19 AAB PFU 1.240.132.981 1.499 Females 18-19 AAB HBCU 13.56.0991.162 1.549 Females 18-19 AAPI PFU 1.091.141.814 1.367 Females 18-19 AAPI HBCU *** Females 18-19 MAL PFU 1.025.105.820 1.230 Females 18-19 MAL HBCU ** Females 18-19 NHW PFU 1.116.0531.012 1.220 Females 18-19 NHW HBCU 2.000.661.703 3.297 Females 18-19 O PFU 1.200.209.790 1.610 Females 18-19 O HBCU 1.000.661-.297 2.297 Females 20-22 AAB PFU 1.286.1251.041 1.531 Females 20-22 AAB HBCU 1.413.0971.222 1.604 Females 20-22 AAPI PFU 1.261.138.990 1.531 Females 20-22 AAPI HBCU *** Females 20-22 MAL PFU 1.103.106.895 1.310 Females 20-22 MAL HBCU *** Females 20-22 NHW PFU 1.157.0441.071 1.242 Females 20-22 NHW HBCU 1.00.661-.297 2.297 Females 20-22 O PFU 1.600.1711.265 1.935 Females 20-22 O HBCU 1.000.468.083 1.917 Females 23-up AAB PFU 1.500.468.583 2.417 Females 23-up AAB HBCU 1.435.1381.164 1.705 Females 23-up AAPI PFU 1.200.296620 1.780 Females 23-up AAPI HBCU 1.000.661-.297 2.297 Females 23-up MAL PFU 1.1541.83.794 1.514 Females 23-up MAL HBCU 1.000.661-.297 2.297 Females 23-up NHW PFU 1.465.0661.334 1.595 Females 23-up NHW HBCU 1.000.382.251 1.749 Females 23-up O PFU 1.333.382.584 2.082 Females 23-up O HBCU 1.000.382.251 1.749 This level combination of fact ors is not observed, thus the corre sponding marginal mean is not estimable. AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

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107 Table 4-66. I have posted online personal messages in order to meet a potential partner. Mean: Males, Age, Race, School Gender Age Race School Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound Males 18-19 AAB PFU 1.500 .234 1.041 1.959 Males 18-19 AAB HBCU 1.667 .171 1.332 2.002 Males 18-19 AAPI PFU 1.167 .270 .637 1.696 Males 18-19 AAPI HBCU * Males 18-19 MAL PFU 1.222 .220 .790 1.655 Males 18-19 MAL HBCU * Males 18-19 NHW PFU 1.273 .081 1.13 1.432 Males 18-19 NHW HBCU 1.000 .146 .083 1.917 Males 18-19 O PFU 1.000 .296 .420 1.580 Males 18-19 O HBCU * Males 20-22 AAB PFU 1.833 .270 1.304 2.363 Males 20-22 AAB HBCU 1.00 .296 .420 1.580 Males 20-22 AAPI PFU 1.667 .220 1.234 2.099 Males 20-22 AAPI HBCU * Males 20-22 MAL PFU 1.579 .152 1.281 1.877 Males 20-22 MAL HBCU * Males 20-22 NHW PFU 13.76 .069 1.242 1.511 Males 20-22 NHW HBCU * Males 20-22 O PFU 2.250 .331 1.601 2.899 Males 20-22 O HBCU * Males 23-up AAB PFU 2.00 .468 1.083 2.917 Males 23-up AAB HBCU 15.00 .270 .970 2.030 Males 23-up AAPI PFU 2.500 .270 1.970 3.030 Males 23-up AAPI HBCU * Males 23-up MAL PFU 1.917 .191 1.542 2.291 Males 23-up MAL HBCU * Males 23-up NHW PFU 1.507 .076 1.357 1.656 Males 23-up NHW HBCU 4.000 .661 2.703 5.297 Males 23-up O PFU 1.833 .191 1.459 2.208 Males 23-up O HBCU * This level combination of fact ors is not observed, thus the corre sponding marginal mean is not estimable AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

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108 Table 4-67. Tukey HSD. I have posted online pers onal messages in order to meet a potential partner: Age Age Age Comparison Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound 18-19 20-22 .32.044.164-.18 .02 23-up -.32.052.000-.44 -.19 20-22 18-19 .08.044.164-.02 .18 23-up -.24.050.000-.35 -.12 23-up 18-19 .32.052.000 .19 .44 20-22 .24.050.000 .12 .35 *Based upon a modified population marginal mean. The mean difference is significant at the .05 level Table 4-68. Tukey HSD. I have posted online pers onal messages in order to meet a potential partner: African American Blacks 95% Confidence Interval Race Race Mean Difference Standard Error Level of Significance Lower Bound Upper Bound AAB AAPI .05.090.980-.20 .30 AAB MAL .17.073.161-.03 .37 AAB NHW .13.052.090-.01 .27 AAB O -.09.100.886-.37 .18 Based upon observed means. AAB= African Amer ican Black, AAPI=Asian American/Pacific.24 Islander, MAL=Mexican American Lati no, NWH=Non White Hispanic, O=Other.

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109 Table 4-69. Correlations: Does your partner object to the am ount of time you spend on line: School Gender Age Race Does your partner object to the amount of time you spend on line? School Pearson Correlation 1-.101(**)-.025-.662(**) -.057 Sig. (2tailed) .000.391.000 .059 N 1212121012111209 1101 Gender Pearson Correlation -.101(**)1.114(**).103(**) -.044 Sig. (2tailed) .000 .000.000 .147 N 1210121012091207 1099 Age Pearson Correlation -.025.114(**)1.118(**) -.043 Sig. (2tailed) .391.000 .000 .156 N 1211120912111208 1100 Race Pearson Correlation -.662(**).103(**).118(**)1 .055 Sig. (2tailed) .000.000.000 .068 N 1209120712081209 1099 Does your partner object to the amount of time you spend on line? Pearson Correlation -.057-.044-.043.055 1 Sig. (2tailed) .059.147.156.068 N 1101109911001099 1101 ** Correlation is significant at the 0.01 level (2-tailed). Table 4-70. Mean: Does your pa rtner object to the amount of time you spend on line. School School Mean Std. Deviation N

PAGE 111

110 PFU 1.96.202941 HBCU 1.92.270153 Total 1.95.2131094 Table 4-71. Mean: Does your pa rtner object to the amount of time you spend on line. Gender Gender Mean Std. Deviation N Females 1.96 .201 760 Males 1.94 .238 332 Total 1.95 .213 1092 Table 4-72. Mean: Does your pa rtner object to the amount of time you spend on line. Age Age Mean Std. Deviation N 18-19 1.96 .196 377 20-22 1.96 .206 475 23-and above 1.94 .242 241 Total 1.95 .211 1093 Table 4-73. Mean: Does your pa rtner object to the amount of time you spend on line? Race Race Mean Std. Deviation N AAB 1.92 .270 203 AAPI 1.97 .174 65 MAL 1.98 .157 120 NHW 1.96 .199 654 O 1.92 .269 52 Total 1.95 .213 1094

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111 Table 4-74. Tests of between subjects. Does your partner object to the amount of time you spend online. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .413 1.4139.544.002 .009 Age .278 2.1393.209.041 .006 Race .071 4.018.413.799 .002 School .058 1.0581.329.249 .001 Gender/Age .378 2.1894.363.013 .008 Gender/Race .250 4.0631.445.217 .006 Age/Race .545 8.0681.574.128 .012 Gender Age Race .866 8.1082.500.011 .019 Gender/ School .162 1.1623.745.053 .004 Age/School .013 2.006.146.864 .000 Gender/Age /School .233 2.1162.688.068 .005 Race/School .289 4.0721.669.155 .006 Gender Race/School .464 1.46410.720.001 .010 Age/Race /School .711 4.1784.107.003 .015 Gender/Age/ Race School .584 1.58413.500.000 .013 Error 45.226 1045.043 Total 4121.11 1091 R squared=.070 (adjusted r squared=.030) Table 4-75. Does your partner object to the amount of time you spend online. Gender and Age Gender Age Mean Standard Error 95 % Confidence Interval Lower Bound Upper Bound Females 18-19 1.982.0391.906 2.059 20-22 1.951 .034 1.884 2.018 23-up 1.987 0.044 1.889 2.074 Males 18-19 1.946 .033 1.882 2.010 20-22 1.992 .034 1.925 2.059 23-up 1.720 .042 1.637 1.803

PAGE 113

112 Table 4-76. Does your partner object to the amount of time you spend online. Mean: Females, Race and School Gender Race School Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound Females AAB PFU 1.949.0721.807 2.090 Females AAB HBCU 1.926.0211.885 1.966 Females AAPI PFU 1.985.0411.905 2.065 Females AAPI HBCU 2.000.2081.592 2.408 Females MAL PFU 1.981.0271.927 2.035 Females MAL HBCU 2.000.2081.592 2.408 Females NHW PFU 1.962.0111.940 1.983 Females NHW HBCU 2.000.1061.792 2.208 Females O PFU 1.974.0571.862 2.086 Females O HBCU 2.000.0941.816 2.184 Males AAB PFU 1.833.0631.170 1.957 Males AAB HBCU 1.933.0481.839 2.028 Males AAPI PFU 1.944.0511.845 2.044 Males MAL HBCU *** Males MAL PFU 1.952.0371.879 2.026 Males MAL HBCU *** Males NHW PFU 1.959.0141.930 1.987 Males NHW HBCU 1.500.1271.250 1.750 Males O PFU 1.917.0511.817 2.016 Males O HBCU *** This level of mean is not observed therefore the correspondi ng population marginal mean is not estimable. AAB= African American Blac k, AAPI=Asian American/Pacific.24 Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

PAGE 114

113 Table 4-77. Does your partner object to the am ount of time you spend online. Mean: Gender, Age, Race Gender Age Race Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound Females 18-19 AAB 1.943.0261.891 1.995 Females 18-19 AAPI 2.000.0451.911 2.089 Females 18-19 MAL 2.000.0341.934 2.066 Females 18-19 NHW 1.985.1041.781 2.190 Females 18-19 O 2.000.1091.786 2.214 Females 20-22 AAB 1.912.0261.862 1.962 Females 20-22 AAPI 1.955.0441.868 2.042 Females 20-22 MAL 1.943.0351.874 2.012 Females 20-22 NHW 1.981.1041.776 2.185 Females 20-22 O 1.962.0791.807 2.117 Females 23-up AAB 1.957.1061.748 2.165 Females 23-up AAPI 2.000.1161.772 2.228 Females 23-up MAL 2.0001.091.786 2.214 Females 23-up NHW 1.976.0611.856 2.096 Females 23-up O 2.000.0951.184 2.186 Males 18-19 AAB 1.900.0461.811 1.989 Males 18-19 AAPI 2.000.0851.833 2.167 Males 18-19 MAL 1.857.0791.703 2.011 Males 18-19 NHW 1.983.0751.836 2.130 Males 18-19 O 2.000.0931.817 2.183 Males 20-22 AAB 2.000.0701.863 2.137 Males 20-22 AAPI 2.000.0931.817 2.183 Males 20-22 MAL 2.000.0491.904 2.096 Males 20-22 NHW 1.953.0221.909 1.998 Males 20-22 O 2.000.1041.796 2.204 Males 23-up AAB 1.750.0851.583 1.917 Males 23-up AAPI 1.833.0851.667 2.000 Males 23-up MAL 2.000.0631.877 2.123 Males 23-up NHW 1.479.1051.273 1.684 Males 23-up O 1.750.0601.632 1.868 AAB= African American Black, AAPI=Asian Am erican/Pacific.24 Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

PAGE 115

114 Table 4-78. Does your partner object to the amount of time you spend onlin e. Mean: Age, Race, School Age Race School Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound 18-19 AAB PFU 2.000.0421.917 2.083 18-19 AAB HBCU 1843.0311.782 1.904 18-19 AAPI PFU 2.000.0481.906 2.094 18-19 AAPI HBCU 18-19 MAL PFU 1.929.0431.845 2.013 18-19 MAL HBCU *** 18-19 NHW PFU 1.968.0161.936 2.000 18-19 NHW HBCU 2.000.1271.750 2.250 18-19 O PFU 2.000.0571.888 2.112 18-19 O HBCU 2.000.2081.592 2.408 20-22 AAB PFU 1.923.0511.823 2.023 20-22 AAB HBCU 1.989.0541.882 2.095 20-22 AAPI PFU 1.977.0521.876 2.078 20-22 AAPI HBCU *** 20-22 MAL PFU 1.971.0301.912 2.031 20-22 MAL HBCU *** 20-22 NHW PFU 1.958.0131.931 1.984 20-22 NHW HBCU 2.000.2081.592 2.408 20-22 O PFU 1.962.0591.845 2.078 20-22 O HBCU 2.000.1471.711 2.289 23-up AAB PFU 1.750.1271.500 2.000 23-up AAB HBCU 1.957.0481.863 2.050 23-up AAPI PFU 1.917.0671.785 2.048 23-up AAPI HBCU 2.000.2081.592 2.408 23-up MAL PFU 2.000.0451.911 2.089 23-up MAL HBCU 2.000.2081.592 2.408 23-up NHW PFU 1.955.0171.922 1.988 23-up NHW HBCU 1.500.1201.264 1.736 23-up O PFU 1.875.0791.719 2.031 23-up O HBCU 2.000.1201.764 2.236 This level of mean is not observed therefore the correspondi ng population marginal mean is not estimable. AAB= African American Blac k, AAPI=Asian American/Pacific.24 Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

PAGE 116

115 Table 4-79. Does your partner object to the amount of time you spend online. Mean: Females, Age, Race, School Gender Age Race School Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound Females 18-19 AAB PFU 2.000.0421.917 2.083 Females 18-19 AAB HBCU 1.886.0311.825 1.948 Females 18-19 AAPI PFU 2.000.0451.911 2.089 Females 18-19 AAPI HBCU Females 18-19 MAL PFU 2.000.0341.934 2.066 Females 18-19 MAL HBCU *** Females 18-19 NHW PFU 1.971.0181.936 2.006 Females 18-19 NHW HBCU 2.000.2081.592 2.408 Females 18-19 O PFU 2.000.0661.871 2.129 Females 18-19 O HBCU 2.000.2081.592 2.408 Females 20-22 AAB PFU 1.846.0411.766 1.926 Females 20-22 AAB HBCU 1.978.0311.917 2.039 Females 20-22 AAPI PFU 1.955.0441.868 2.042 Females 20-22 AAPI HBCU *** Females 20-22 MAL PFU 1.943.0351.874 2.012 Females 20-22 MAL HBCU *** Females 20-22 NHW PFU 1.962.0141.933 1.990 Females 20-22 NHW HBCU 2.000.2081.592 2.408 Females 20-22 O PFU 1.923.0581.810 2.036 Females 20-22 O HBCU 2.000.1471.711 2.289 Females 23-up AAB PFU 2.000.2081.592 2.408 Females 23-up AAB HBCU 1.913.0431.828 1.998 Females 23-up AAPI PFU 2.000.1041.796 2.204 Females 23-up AAPI HBCU 2.000.2081.592 2.408 Females 23-up MAL PFU 2.000.0661.871 2.129 Females 23-up MAL HBCU 2.000.2081.592 2.408 Females 23-up NHW PFU 1.962.0231.908 1.997 Females 23-up NHW HBCU 2.000.1201.764 2.236 Females 23-up O PFU 2.000.1471.711 2.144 Females 23-up O HBCU 2.000.1201.764 1.905 This level combination of fact ors is not observed, thus the corre sponding marginal mean is not estimable. AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

PAGE 117

116 Table 4-80. Does your partner object to the amount of time you spend online. Mean: Males, Age, Race, School Gender Age Race School Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound Males 18-19 AAB PFU 2.000.0741.856 2.144 Males 18-19 AAB HBCU 1.800.0541.695 1.905 Males 18-19 AAPI PFU 2.000.0851.833 2.167 Males 18-19 AAPI HBCU *** Males 18-19 MAL PFU 1.857.0791.703 2.011 Males 18-19 MAL HBCU *** Males 18-19 NHW PFU 1.966.0271.912 2.019 Males 18-19 NHW HBCU 2.000.1471.711 2.289 Males 18-19 O PFU 2.000.0931.817 2.183 Males 18-19 O HBCU *** Males 20-22 AAB PFU 2.000.0931.817 2.183 Males 20-22 AAB HBCU 2.000.1041.796 2.204 Males 20-22 AAPI PFU 2.000.0931.817 2.183 Males 20-22 AAPI HBCU *** Males 20-22 MAL PFU 2.000.0491.904 2.096 Males 20-22 MAL HBCU *** Males 20-22 NHW PFU 1.953.0221.909 1.998 Males 20-22 NHW HBCU *** Males 20-22 O PFU 2.000.1041.796 2.204 Males 20-22 O HBCU *** Males 23-up AAB PFU 1.500.1471.211 1.789 Males 23-up AAB HBCU 2.000.0851.833 2.167 Males 23-up AAPI PFU 1.833.0851.667 2.000 Males 23-up AAPI HBCU *** Males 23-up MAL PFU 2.000.0631.877 2.123 Males 23-up MAL HBCU *** Males 23-up NHW PFU 1.957.0251.908 2.006 Males 23-up NHW HBCU 1.000.208.592 1.408 Males 23-up O PFU 1.750.0601.632 1.868 Males 23-up O HBCU *** This level combination of f actors is not observed, thus th e corresponding marginal mean is not estimable AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

PAGE 118

117 Table 4-81. Tukey HSD. Does your partner object to the amount of time you spend online. Age Age Age Comparison Mean Difference Std Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound 18-19 20-22 .00 .014 .947 -.03 .04 23-up .02 .017 .383 -.02 .06 20-22 18-19 .00 .014 .947 -.04 .03 23-up .02 .016 .512 -.02 .06 23-up 18-19 -.02 .017 .383 -.06 .02 20-22 -.02 .016 .512 -.06 .02. Table 4-82. Tukey HSD. Does your partner object to the amount of time you spend online. Race 95% Confidence Interval Race Race Mean Difference Standard Error Level of Significance Lower Bound Upper Bound AAB AAPI -.05 .030 .484 -.13 .03 AAB MAL -.05 .024 .164 -.12 .01 AAB NHW -.04 .017 .138 -.08 .01 AAB O .00 .032 1.000 -.09 .09

PAGE 119

118 Table 4-83. Correlations: Does your pa rtner know about your online friends. School Gender Age Race Does your partner know about the friends/relationships you have over the internet? School Pearson Correlation 1 .101(**)-.025 .662(**) .104(**) Sig. (2tailed) .000.391.000 .001 N 1212121012111209 1081 Gender Pearson Correlation .101(**)1.114(**).103(**) .084(**) Sig. (2tailed) .000 .000.000 .006 N 1210121012091207 1079 Age Pearson Correlation -.025.114(**)1.118(**) -.050 Sig. (2tailed) .391.000 .000 .098 N 1211120912111208 1080 Race Pearson Correlation .662(**).103(**).118(**)1 -.115(**) Sig. (2tailed) .000.000.000 .000 N 1209120712081209 1079 Does your partner know about the friends/relationships you have over the internet? Pearson Correlation .104(**).084(**)-.050 .115(**) 1 Sig. (2tailed) .001.006.098.000 N 1081107910801079 1081 ** Correlation is significant at the 0.01 level (2-tailed). Table 4-84. Mean: Does your partner know about your online friends: School School Mean Std. Deviation N PFU 1.52 .500 922 HBCU 1.66 .474 152 Total 1.54 .499 1074

PAGE 120

119 Table 4-85. Mean: Does your partner know about your online friends: Gender Gender Mean Std. Deviation N Females 1.51 .500 741 Males 1.60 .491 331 Total 1.54 .499 1072 Table 4-86. Mean: Does your partner know about your online friends: Age Age Mean Std. Deviation N 18-19 1.54 .499 370 20-22 1.58 .494 467 23-and above 1.45 .498 236 Total 1.54 .499 1073 Table 4-87. Mean: Does your partner know about your online friends: Race Race Mean Std. Deviation N AAB 1.66 .474 201 AAPI 1.55 .501 65 MAL 1.52 .502 120 NHW 1.50 .500 636 O 1.58 .499 52 Total 1.54 .499 1074

PAGE 121

120 Table 4-88. Tests of between subjects. Do es your offline partner know about the friends/relationships that you have online. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .747 1.7473.088.079 .003 Age .121 2.061.251.778 .000 Race .573 4.143.592.668 .002 School .007 1.007.029.866 .000 Gender/Age .719 2.3601.429.227 .003 Gender/Race .915 4.229.946.436 .004 Age/Race 1.661 8.208.859.551 .007 Gender Age Race .646 8.081.334.953 .003 Gender/ School .336 1.3361.391.239 .001 Age/School 1.486 2.7433.071.047 .006 Gender/Age /School .059 2.029.122.885 .000 Race/School .529 4.132.547.701 .002 Gender Race/School .006 1.006.023.880 .000 Age/Race /School 1.042 4.2601.077.367 .004 Gender/Age/ Race School .254 1.2541.051.305 .001 Error 247.912 1025.242 Total 2793.000 1071 R squared = .069 (adjus ted R squared=.028) Table 4-89. Does your offline partner know about th e friends/relationships that you have online. Mean: Age, School Age School Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound 18-19 PFU 1.610.0461.521 1.700 HBCU 1.546 .1581.235 1.856 20-22 PFU 1.665 .0461.574 1.755 HBCU 1.564 .1641.243 1.885 23-up PFU 1.363 .0811.204 1.523 HBCU 1.658 .1381.387 1.930

PAGE 122

121 Table 4-90. Tukey HSD. Does your offline partner know about the friends/relationships that you have online? Age Age Age Comparison Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound 18-19 20-22 -.04.034.445-.12 .04 23-up .09.041.068-.01 .19 20-22 18-19 .04.034.445-.04 .12 23-up .13.039.002.04 .22 23-up 18-19 -.09.041.068-.19 .01 20-22 -.13.039.002-.22 -.04 The mean difference is significant at the .05 level.

PAGE 123

122 Table 4-91. Correlations: Has your partner expre ss jealousy over the relationships you have online. School Gender Age Race Has your partner ever expressed jealousy over the relationships you have developed on line? School Pearson Correlation 1-.101(**)-.025-.662(**) -.058 Sig. (2tailed) .000.391.000 .058 N 1212121012111209 1085 Gender Pearson Correlation -.101(**)1.114(**).103(**) -.047 Sig. (2tailed) .000 .000.000 .119 N 1210121012091207 1083 Age Pearson Correlation -.025.114(**)1.118(**) -.024 Sig. (2tailed) .391.000 .000 .431 N 1211120912111208 1084 Race Pearson Correlation -.662(**).103(**).118(**)1 .077(*) Sig. (2tailed) .000.000.000 .011 N 1209120712081209 1083 Has your partner ever expressed jealousy over the relationships you have developed on line? Pearson Correlation -.058-.047-.024.077(*) 1 Sig. (2tailed) .058.119.431.011 N 1085108310841083 1085 ** Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed).

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123 Table 4-92. Mean: Has your partner ever expre ssed jealousy over onlin e relationships: School School Mean Std. Deviation N PFU 1.94 .240 926 HBCU 1.89 .308 152 Total 1.93 .251 1078 Table 4-93. Mean: Has your partner ever expre ssed jealousy over onlin e relationships: Gender Gender Mean Std. Deviation N Females 1.94 .240 749 Males 1.92 .276 327 Total 1.93 .252 1076 Table 4-94. Mean: Has your partner ever expre ssed jealousy over onl ine relationships: Age Age Mean Std. Deviation N 18-19 1.93 .251 370 20-22 1.94 .230 467 23-and above 1.91 .283 240 Total 1.93 .250 1077 Table 4-95. Mean: Has your partner ever expre ssed jealousy over onlin e relationships: Race Race Mean Std. Deviation N AAB 1.89 .313 201 AAPI 1.94 .242 65 MAL 1.93 .264 120 NHW 1.95 .221 640 O 1.90 .298 52 Total 1.93 .251 1078

PAGE 125

124 Table 4-96. Tests of between subjects. Has your partner ever expressed jealousy over the relationships you have developed online. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .055 1.055.895.344 .001 Age .160 2.0801.290.276 .003 Race .584 4.1462.362.052 .009 School .085 1.0851.370.242 .001 Gender/Age .116 2.058.937.392 .002 Gender/Race .311 4.0781.257.285 .005 Age/Race 1.041 8.1302.104.033 .016 Gender Age Race .263 8.033.530.833 .004 Gender/ School .003 1.003.048.826 .000 Age/School .113 2.056.913.402 .002 Gender/Age /School .021 2.011.172.842 .000 Race/School .169 4.042.684.603 .003 Gender Race/School .125 1.1252.016.156 .002 Age/Race /School .831 4.2083.360.010 .013 Gender/Age/ Race School .051 1.051.828.363 .001 Error 63.637 1027.062 Total 4084.000 1075 R squared=984. (Adjusted R Squared=.984)

PAGE 126

125 Table 4-97. Mean: Has your partner ever expre ssed jealousy over the relationships you have developed online: Age, Race Age Race Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound 18-19 AAB 1.933.0331.869 1.997 18-19 AAPI 1.893 .058 1.780 2.006 18-19 MAL 1.902 .051 1.802 2.003 18-19 NHW 1.963 .077 1.812 2.113 18-19 O 1.967 .095 1.781 2.152 20-22 AAB 1.939 .044 1.852 2.026 20-22 AAPI 1.877 .062 1.756 1.998 20-22 MAL 1.916 .036 1.845 1.987 20-22 NHW 1.974 .084 1.801 2.138 20-22 O 1.949 .075 1.810 2.097 23-up AAB 1.571 .081 1.411 1.730 23-up AAPI 2.000 .099 1.806 2.194 23-up MAL 1.936 .090 1.759 2.114 23-up NHW 1.887 .072 1.745 2.029 23-up O 1.944 .079 1.789 2.100 AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other Table 4-98. Tukey HSD. Has your partner ever expressed jeal ousy over the relationships you have developed online: Age Age Age Comparison Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound 18-19 20-22 -.01.017.775-.05 .03 23-up .02.21587-.03 .07 20-22 18-19 .01.017.775-.03 .05 23-up .03.020.237-.01 .08 23-up 18-19 -.02.021.587-.07 .03 20-22 -.03.020.237-.08 .01

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126 Table 4-99. Has your partner ever expressed jealousy over the relationships you have developed online. Mean: Age Race and School Age Race School Mean Standard Error 95% Confidence Interval Lower Bound Upper Bound 18-19 AAB PFU 1.979.0531.874 2.084 18-19 AAB HBCU 1.887.037 1.814 1.960 18-19 AAPI PFU 1.893.058 1.780 2.006 18-19 AAPI HBCU * 18-19 MAL PFU 1.902.051 1.802 2.003 18-19 MAL HBCU * 18-19 NHW PFU 1.926.020 1.887 1.964 18-19 NHW HBCU 2.00.152 1.701 2.299 18-19 O PFU 1.950.068 1.816 2.084 18-19 O HBCU 2.000.249 1.512 2.488 20-22 AAB PFU 1.923.061 1.804 2.042 20-22 AAB HBCU 1.956.065 1.828 2.083 20-22 AAPI PFU 1.877.062 1.756 1.998 20-22 AAPI HBCU * 20-22 MAL PFU 1.916.036 1.845 1.987 20-22 MAL HBCU * 20-22 NHW PFU 1.961.016 1.929 1.993 20-22 NHW HBCU 2.000.249 1.512 2.488 20-22 O PFU 1.923.071 1.784 2.063 20-22 O HBCU 2.000.176 1.655 2.345 23-up AAB PFU 1.250.152 .951 1.549 23-up AAB HBCU 1.891.057 1.779 2.003 23-up AAPI PFU 2.000.080 1.843 2.157 23-up AAPI HBCU 2.000.249 1.512 2.488 23-up MAL PFU 1.905.054 1.798 2.011 23-up MAL HBCU 2.000.249 1.512 2.488 23-up NHW PFU 1.940.020 1.901 1.980 23-up NHW HBCU 1.833.144 1.552 2.115 23-up O PFU 1.917.095 1.730 2.103 23-up O HBCU 2.000.144 1.718 2.282 This level combination of factors is not observed, thus the corresponding marginal mean is not estimable. AAB= African American Black, AAPI=Asian American/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

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127 Table 4-100. Correlations: It is important that an alternative place for meeting people is available. School Gender Age Race Important that an alternative place for meeting people is available School Pearson Correlation 1-.101(**)-.025-.662(**) -.001 Sig. (2tailed) .000.391.000 .967 N 1212121012111209 1210 Gender Pearson Correlation -.101(**)1.114(**).103(**) .117(**) Sig. (2tailed) .000 .000.000 .000 N 1210121012091207 1208 Age Pearson Correlation -.025.114(**)1.118(**) .050 Sig. (2tailed) .391.000 .000 .081 N 1211120912111208 1209 Race Pearson Correlation -.662(**).103(**).118(**)1 -.003 Sig. (2tailed) .000.000.000 .904 N 1209120712081209 1207 Important that an alternative place for meeting people is available Pearson Correlation -.001.117(**).050-.003 1 Sig. (2tailed) .967.000.081.904 N 1210120812091207 1210 Table 4-101. Mean: It is important that an al ternative place for meeting people is available: School School Mean Std. Deviation N PFU 2.66 1.092 1046 HBCU 2.66 1.161 156 Total 2.66 1.100 1202

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128 Table 4-102. Mean: It is important that an al ternative place for meeting people is available Gender Mean Std. Deviation N Females 2.58 1.074 836 Males 2.86 1.135 364 Total 2.66 1.100 1200 Table 4-103. Mean: It is important that an alte rnative place for meeting people is available: Age Age Mean Std. Deviation N 18-19 2.65 1.048 413 20-22 2.60 1.114 520 23-and above 2.82 1.138 268 Total 2.67 1.100 1201 Table 4-104. Mean: It is important that an altern ative place for meeting peopl e is available: Race Race Mean Std. Deviation N AAB 2.64 1.130 211 AAPI 2.89 1.120 72 MAL 2.70 1.128 133 NHW 2.62 1.065 732 O 2.98 1.296 54 Total 2.66 1.100 1202

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129 Table 4-105. Tests of between subjects. It is impor tant that an alternate place for meeting people is available: Age, Gender, Race, School Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .403 1.403.378.539 .919 Age 11.049 25.5245.177.006 .009 Race 7.996 41.9991.873.113 .006 School .138 1.138.129.720 .000 Gender/Age 2.383 21.1921.117.328 .002 Gender/Race 3.888 4.972.911.457 .003 Age/Race 5.718 8.715.670.718 .005 Gender/Age /Race 6.178 8.772.724.671 .005 Gender/Schoo l 1.019 11.019.954.329 .001 Age/School 1.327 2.663.622.537 .001 Gender/Age /School 1.729 2.865.810.445 .001 Race/School 2.688 4.672.630.641 .002 Gender /Race/School .017 1.017.016.900 .000 Age/Race /School 5.416 41.3541.269.280 .004 Gender/Age /Race/School .865 1.865.810.368 .001 Error 1225.082 11481.067 Total 15710.000 1194 Table 4-106. Tukey HSD. How important is it to you that an alternate way/place for meeting new people is available. Age Age Age Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound 18-19 20-22 -.15 .068.78-.31 .01 23-up -.51 .081.000 -.70 -.32 20-22 18-19 .15 .068.078 -.01 .31 23-up -.37 .078.000 -.55 -.18 23-up 18-19 .51 .081.000 .32 .70 20-22 .37 .078.000 .18 .55 (Mean difference is signi ficant at the .05 level)

PAGE 131

130 Table 4-107. Correlations: An alternativ e way to meet online sex partners. School Gender Age Race An alternative way/place for meeting potential online sexual partners is available School Pearson Correlation 1-.101(**)-.025-.662(**) -.057(*) Sig. (2-tailed) .000.391.000 .047 N 1212121012111209 1204 Gender Pearson Correlation -.101(**)1.114(**).103(**) .344(**) Sig. (2-tailed) .000 .000.000 .000 N 1210121012091207 1202 Age Pearson Correlation -.025.114(**)1.118(**) .218(**) Sig. (2-tailed) .391.000 .000 .000 N 1211120912111208 1203 Race Pearson Correlation -.662(**).103(**).118(**)1 .076(**) Sig. (2-tailed) .000.000.000 .009 N 1209120712081209 1201 An alternative way/place for meeting potential online sexual partners is available Pearson Correlation -.057(*).344(**).218(**).076(**) 1 Sig. (2-tailed) .047.000.000.009 N 1204120212031201 1204 ** Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed). Table 4-108. Mean: Important alternative way to meet potential on line sexual partners: School School Mean Std. Deviation N PFU 2.09 1.056 1044 HBCU 1.99 1.051 154 Total 2.08 1.056 1198

PAGE 132

131 Table 4-109. Mean: Important alternative way to meet potential on line sexual partners: Gender Gender Mean Std. Deviation N Females 1.88 .963 832 males 2.52 1.124 364 Total 2.08 1.056 1196 Table 4-110. Mean: Important alternative way to meet potential on line sexual partners: Age Age Mean Std. Deviation N 18-19 2.04 1.012 412 20-22 1.99 1.047 518 23-and above 2.30 1.108 267 Total 2.08 1.056 1197 Table 4-111. Mean: Important alternative way to meet potential on line sexual partners: Race Race Mean Std. Deviation N AAB 1.95 1.080 209 AAPI 2.29 1.067 72 MAL 2.14 1.072 133 NHW 2.08 1.036 730 O 2.11 1.144 54 Total 2.08 1.056 1198

PAGE 133

132 Table 4-112. Tests of between subjects. An al ternative way/place for meeting potential sexual partners: Gender, Race, Age, School Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender 7.901 17.9015.995.014 .005 Age 33.439 216.71912.686.000 .022 Race 6.724 41.6811.275.278 .004 School 2.126 12.1261.613.204 .001 Gender/Age 1.662 2.831.630.533 .001 Gender/Race 6.544 41.6361.241.292 .004 Age/Race 7.109 8.889.674.715 .005 Gender Age Race 11.697 81.4621.109.354 .008 Gender/ School 2.880 12.8802.185.140 .002 Age/School 3.986 21.9931.512.221 .003 Gender/Age /School 3.113 21.5571.181.307 .002 Race/School 1.035 4.259.196.940 .001 Gender Race/School 1.334 11.3341.012.315 .001 Age/Race /School 13.882 43.4702.633.033 .009 Gender/Age/ Race School 1.356 11.3561.029.311 .001 Error 1511.710 11471.318 Total 10253.000 1193 R squared=.806. (adjusted R Squared=.799) Table 4-113. Tukey HSD. An alternative way/pl ace for meeting potential sexual partners: Age Age Age Comparison Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound 18-19 20-22 -.16.076.081-.34 .01 23-up -.77.090.000-.99 -.56 20-22 18-19 .16.076.081-.01 .34 23-up -.61.087.000-.81 -.41 23-up 18-19 .77.090.000.56 .99 20-22 .61.087.000.41 .81

PAGE 134

133 Table 4-114. Tukey HSD. An alternative way/plac e for meeting potential sexual partners: Race Race Race Mean Difference Standard Error Level of Significance 95% Level of Confidence Lower Bound Upper Bound AAB AAPI -.25.157.486-.68 .17 AAB MAL -.11.128.921-.45 .24 AAB NHW -.27.090.026-.51 .02 AAB O -.24.177.641-.73 .24 AAB= African American Black, AAPI=Asian Am erican/Pacific Islander, MAL=Mexican American Latino, NWH=Non White Hispanic, O=Other.

PAGE 135

134 Table 4-115. Correlations: I have accessed se xually explicit materials on the internet. School Gender Age Race I have accessed sexually explicit materials on the Internet School Pearson Correlation 1-.101(**)-.025-.662(**) -.154(**) Sig. (2tailed) .000.391.000 .000 N 1212121012111209 1211 Gender Pearson Correlation -.101(**)1.114(**).103(**) .516(**) Sig. (2tailed) .000 .000.000 .000 N 1210121012091207 1209 Age Pearson Correlation -.025.114(**)1.118(**) .046 Sig. (2tailed) .391.000 .000 .113 N 1211120912111208 1210 Race Pearson Correlation -.662(**).103(**).118(**)1 .106(**) Sig. (2tailed) .000.000.000 .000 N 1209120712081209 1208 I have accessed sexually explicit materials on the Internet Pearson Correlation -.154(**).516(**).046.106(**) 1 Sig. (2tailed) .000.000.113.000 N 1211120912101208 1211 ** Correlation is significant at the 0.01 level (2-tailed). Table 4-116. Mean: I have accessed sexually e xplicit materials on the internet: School School Mean Std. Deviation N PFU 2.66 .972 1047 HBCU 2.21 .930 156 Total 2.60 .978 1203

PAGE 136

135 Table 4-117. Mean: I have accessed sexually e xplicit materials on the internet: Gender Gender Mean Std. Deviation N Females 2.27 .870 837 Males 3.36 .764 364 Total 2.60 .978 1201 Table 4-118. Mean: I have accessed sexually explicit materials on the internet: Age Age Mean Std. Deviation N 18-19 2.57 .998 412 20-22 2.57 .966 522 23-and above 2.69 .966 268 Total 2.60 .977 1202 Table 4-119. Mean: I have accessed sexually e xplicit materials on the internet: Race Race Mean Std. Deviation N AAB 2.35 .931 211 AAPI 2.69 .914 72 MAL 2.63 1.007 132 NHW 2.65 .984 733 O 2.69 .960 55 Total 2.60 .978 1203

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136 Table 4-120. Tests of between subjects. I have accessed sexually exp licit materials on the internet. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender 26.888 126.88838.981.000 .033 Age 1.527 2.7631.107.331 .002 Race 2.890 4.7221.047.381 .004 School 3.796 13.7965.503.019 .005 Gender/Age .140 2.070.101.904 .000 Gender/Race 2.592 4.648.939.440 .003 Age/Race 3.533 8.442.640.744 .004 Gender Age Race 1.953 8.244.354.944 .002 Gender/ School .959 1.9591.390.239 .001 Age/School .852 2.426.617.540 .001 Gender/Age /School .317 2.159.230.795 .000 Race/School 2.000 4.500.725.575 .003 Gender Race/School 2.264 12.2643.282.070 .003 Age/Race /School 1.868 4.467.677.608 .002 Gender/Age/ Race School .045 1.045.065.798 .000 Error 795.983 1154.690 Total 9248.000 1200 R squared=.914 (adjusted R Squared=.910)

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137 Table 4-121. Correlations: I have accessed sexua lly explicit materials to become sexually aroused. School Gender Age Race I have accessed sexually explicit materials to become sexually aroused School Pearson Correlation 1-.101(**)-.025-.662(**) -.177(**) Sig. (2tailed) .000.391.000 .000 N 1212121012111209 1209 Gender Pearson Correlation -.101(**)1.114(**).103(**) .577(**) Sig. (2tailed) .000 .000.000 .000 N 1210121012091207 1207 Age Pearson Correlation -.025.114(**)1.118(**) .077(**) Sig. (2tailed) .391.000 .000 .007 N 1211120912111208 1208 Race Pearson Correlation -.662(**).103(**).118(**)1 .134(**) Sig. (2tailed) .000.000.000 .000 N 1209120712081209 1206 I have accessed sexually explicit materials to become sexually aroused Pearson Correlation -.177(**).577(**).077(**).134(**) 1 Sig. (2tailed) .000.000.007.000 N 1209120712081206 1209 ** Correlation is significant at the 0.01 level (2-tailed).

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138 Table 4-122. Mean: I have accessed sexually explic it materials to become sexually aroused: School School Mean Std. Deviation N PFU 2.32 1.187 1045 HBCU 1.70 .953 156 Total 2.24 1.177 1201 Table 4-123. Mean: I have accessed sexually explic it materials to become sexually aroused: Gender Gender Mean Std. Deviation N Females 1.79 .986 836 Males 3.27 .903 363 Total 2.24 1.177 1199 Table 4-124. Mean: I have accessed sexually explicit materials to become sexually aroused: Age Age Mean Std. Deviation N 18-19 2.17 1.179 411 20-22 2.18 1.166 521 23-and above 2.44 1.174 268 Total 2.23 1.176 1200 Table 4-125. Mean: I have accessed sexually explicit materials to become sexually aroused: Race Race Mean Std. Deviation N AAB 1.86 1.021 211 AAPI 2.32 1.220 72 MAL 2.27 1.199 132 NHW 2.33 1.193 731 O 2.27 1.178 55 Total 2.24 1.177 1201

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139 Table 4-126. Tests of between subjects. I have accessed sexually exp licit materials on the internet to become sexually ar oused: Gender, Race, Age, School Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender 41.807 141.80747.036.000 .039 Age .818 2.409.460.631 .001 Race 3.169 4.792.891.468 .003 School 7.666 17.6668.625.003 .007 Gender/Age .707 2.354.398.672 .001 Gender/Race 3.747 4.9371.054.378 .004 Age/Race 2.957 8.370.416.912 .003 Gender Age Race 3.779 8.472.531.833 .004 Gender/ School 2.698 12.6983.035.082 .003 Age/School .109 2.054.061.941 .000 Gender/Age /School .005 2.003.003.997 .000 Race/School 1.819 4.455.512.727 .002 Gender Race/School 2.824 14.8243.178.075 .003 Age/Race /School .272 4.068.076.989 .000 Gender/Age/ Race School .023 1.023.026.873 .000 Error 1023.927 1152.889 Total 7639.000 1198

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140 Table 4-127. Correlations: While viewing sexually explicit materials I have masturbated. School Gender Age Race Viewing explicit web sites I have masturbated School Pearson Correlation 1-.101(**)-.025-.662(**) -.187(**) Sig. (2tailed) .000.391.000 .000 N 1212121012111209 1208 Gender Pearson Correlation -.101(**)1.114(**).103(**) .587(**) Sig. (2tailed) .000 .000.000 .000 N 1210121012091207 1206 Age Pearson Correlation -.025.114(**)1.118(**) .093(**) Sig. (2tailed) .391.000 .000 .001 N 1211120912111208 1207 Race Pearson Correlation -.662(**).103(**).118(**)1 .136(**) Sig. (2tailed) .000.000.000 .000 N 1209120712081209 1205 viewing explicit web sites I have masturbated Pearson Correlation -.187(**).587(**).093(**).136(**) 1 Sig. (2tailed) .000.000.001.000 N 1208120612071205 1208 ** Correlation is significant at the 0.01 level (2-tailed). Table 4-128. Mean: While viewing sexually expl icit web sites I have masturbated: School School Mean Std. Deviation N PFU 2.20 1.245 1045 HBCU 1.52 .893 155 Total 2.11 1.227 1200

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141 Table 4-129. Mean: While viewing sexually expl icit web sites I have masturbate: Gender Gender Mean Std. Deviation N Females 1.64 .982 833 Males 3.19 1.029 365 Total 2.11 1.227 1198 Table 4-130. Mean: While viewing sexually ex plicit web sites I have masturbated: Age Age Mean Std. Deviation N 18-19 2.00 1.204 409 20-22 2.10 1.223 522 23-and above 2.30 1.252 268 Total 2.11 1.227 1199 Table 4-131. Mean: While viewing sexually expl icit web sites I have masturbated: Race Race Mean Std. Deviation N AAB 1.70 1.039 210 AAPI 2.26 1.267 72 MAL 2.11 1.252 132 NHW 2.21 1.248 731 O 2.13 1.203 55 Total 2.11 1.227 1200

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142 Table 4-132. Tests of between subjects. While viewing sexually explicit web sites on the internet, I have masturbated. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender 56.277 156027759.528.000 .049 Age 2.044 21.0221.081.340 .002 Race 3.487 4.872.922.450 .003 School 6.249 16.2496.610.010 .006 Gender/Age 2.971 21.4861.571.208 .003 Gender/Race 6.395 41.5991.691.150 .006 Age/Race 3.104 8.388.410.915 .003 Gender Age Race 4.477 8.560.592.785 .004 Gender/ School .941 1.941.995.319 .001 Age/School .011 2.006.006.110.994 .000 Gender/Age /School .207 2.104.678.896 .000 Race/School 2.562 4.6415.755.608 .002 Gender Race/School 5.441 15.441.402.017 .005 Age/Race /School 1.519 4.380.895.808 .001 Gender/Age/ Race School .846 1.846.344 .001 Error 1088.145 1151.945 Total 7140.000 1197 R squared=.848 (adjusted R Squared=.842)

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143 Table 4-133. While viewing sexua lly explicit web sites on the in ternet, I have masturbated. Mean: Gender Females, Race and School Gender Race School Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound Females AAB PFU 1.643 .246 1.161 2.126 Females AAB HBCU 1.421 .096 1.232 1.610 Females AAPI PFU 1.945 .174 1.603 2.287 Females AAPI HBCU 2.000* .972 .092 3.908 Females MAL PFU 1.641 .116 1.413 1.869 Females MAL HBCU 1.000* .972 -.908 2.908 Females NHW PFU 1.689 .047 1.597 1.781 Females NHW HBCU 1.111 .495 .140 2.082 Females O PFU 1.633 .229 1.184 2.083 Females O HBCU 1.000 .439 .139 1.861 Males AAB PFU 3.486 .288 2.920 4.052 Males AAB HBCU 1.733 .213 1.315 2.152 Males AAPI PFU 3.407 .216 2.983 3.831 Males MAL HBCU ** ** ** ** Males MAL PFU 3.188 .161 2.872 3.504 Males MAL HBCU ** ** ** ** Males NHW PFU 3.315 .064 3.190 3.440 Males NHW HBCU 3.500* .595 2.332 4.668 Males O PFU 3.011 .237 2.547 3.476 Males O HBCU ** ** ** ** Based upon a modified population marginal mean ** This level of mean is not observed therefore the correspondi ng population marginal mean is not estimable Table 4-134. Tukey HSD. While viewing sexually explicit web sites on the internet, I have masturbated: Race Race Race Mean Difference Standard Error Level of Significance 95% Confidence Interval Lower Bound Upper Bound AAB AAPI -.56.133.000-.92 -.20 AAB MAL -.41.108.002-.70 -.11 AAB NHW -.51.076.000-.72 -.30 AAB O -.42.147.034-.82 -.02

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144 Table 4-135. Correlations: I have had sex with online partners. School Gender Age Race I have had sex with on line partner(s) School Pearson Correlation 1-.101(**)-.025-.662(**) -.057(*) Sig. (2tailed) .000.391.000 .049 N 1212121012111209 1205 Gender Pearson Correlation -.101(**)1.114(**).103(**) .143(**) Sig. (2tailed) .000 .000.000 .000 N 1210121012091207 1203 Age Pearson Correlation -.025.114(**)1.118(**) .027 Sig. (2tailed) .391.000 .000 .349 N 1211120912111208 1204 Race Pearson Correlation -.662(**).103(**).118(**)1 .014 Sig. (2tailed) .000.000.000 .623 N 1209120712081209 1202 I have had sex with on line partner(s) Pearson Correlation -.057(*).143(**).027.014 1 Sig. (2tailed) .049.000.349.623 N 1205120312041202 1205 ** Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed). Table 4-136. Mean: I have had sex with on line partner(s): School School Mean Std. Deviation N PFU 1.30 .644 1043 HBCU 1.19 .537 154 Total 1.29 .632 1197

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145 Table 4-137. Mean.: I have had cybe rsex with on line partner(s): Gender Gender Mean Std. Deviation N Females 1.23 .576 831 Males 1.41 .732 364 Total 1.29 .633 1195 Table 4-138. Mean: I have had se x with on line partner(s): Age Age Mean Std. Deviation N 18-19 1.29 .630 408 20-22 1.26 .614 521 23-and above 1.33 .670 267 Total 1.29 .632 1196 Table 4-139. Mean: I have had se x with on line partner(s): Race Race Mean Std. Deviation N AAB 1.24 .589 209 AAPI 1.38 .721 72 MAL 1.33 .695 132 NHW 1.28 .629 729 O 1.25 .552 55 Total 1.29 .632 1197

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146 Table 4-140. Tests of between subjects. I have had cybersex with an online partner. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender 1.457 11.4573.669.056 .003 Age .053 2.027.067.935 .000 Race .308 4.077.194.942 .001 School .421 1.4211.061.303 .001 Gender/Age .068 2.034.85.918 .000 Gender/Race .921 4.230.580.677 .002 Age/Race 1.016 8.127.320.959 .002 Gender Age Race 1.070 8.134.337.952 .002 Gender/ School .033 1.033.082.774 .000 Age/School .132 2.066.166.847 .000 Gender/Age /School .005 2.003.007.993 .000 Race/School .342 4.085.215.930 .001 Gender Race/School .729 1.7291.837.176 .002 Age/Race /School .167 4.042.105.981 .000 Gender/Age/ Race School .513 1.5131.293.256 .001 Error 455.834 1148.397 Total 2451.000 1194 R squared=.814 (adjusted R Squared=.807)

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147 Table 4-141. Correlations: I like to drink al coholic beverages while having cybersex. School Gender Age Race I like to drink alcoholic beverages while having cybersex with an online partner School Pearson Correlation 1-.101(**)-.025-.662(**) -.044 Sig. (2tailed) .000.391.000 .124 N 1212121012111209 1203 Gender Pearson Correlation -.101(**)1.114(**).103(**) .140(**) Sig. (2tailed) .000 .000.000 .000 N 1210121012091207 1201 Age Pearson Correlation -.025.114(**)1.118(**) .083(**) Sig. (2tailed) .391.000 .000 .004 N 1211120912111208 1202 Race Pearson Correlation -.662(**).103(**).118(**)1 .015 Sig. (2tailed) .000.000.000 .610 N 1209120712081209 1200 I like to drink alcoholic beverages while having cybersex with an online partner Pearson Correlation -.044.140(**).083(**).015 1 Sig. (2tailed) .124.000.004.610 N 1203120112021200 1203 ** Correlation is significant at the 0.01 level (2-tailed).

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148 Table 4-142. Mean: I like to dri nk alcoholic beverages while ha ving cybersex with an online partner: School School Mean Std. Deviation N PFU 1.06 .289 1042 HBCU 1.02 .180 153 Total 1.05 .277 1195 Table 4-143. Mean: I like to dri nk alcoholic beverages while ha ving cybersex with an online partner: Gender Gender Mean Std. Deviation N Females 1.03 .207 831 Males 1.11 .390 362 Total 1.05 .278 1193 Table 4-144. Mean: I like to dri nk alcoholic beverages while ha ving cybersex with an online partner: Age Age Mean Std. Deviation N 18-19 1.03 .190 407 20-22 1.05 .276 520 23-and above 1.08 .369 267 Total 1.05 .276 1194 Table 4-145. Mean: I like to dri nk alcoholic beverages while ha ving cybersex with an online partner: Race Race Mean Std. Deviation N AAB 1.03 .218 208 AAPI 1.07 .256 72 MAL 1.05 .285 132 NHW 1.06 .300 728 O 1.02 .135 55 Total 1.05 .277 1195

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149 Table 4-146. Tests of between subjects. I like to drink alcohol while havi ng cybersex with an online partner. Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .044 1.044.576.448 .001 Age .017 2.009.113.893 .000 Race .042 4.011.139.968 .000 School .043 1.043.571.450 .000 Gender/Age .011 2.006.075.927 .000 Gender/Race .310 4.0781.027.392 .004 Age/Race .663 8.0831.096.363 .008 Gender Age Race .279 8.035.461.884 .003 Gender/ School .002 1.002.028.867 .000 Age/School .062 2.031.413.662 .001 Gender/Age /School .175 2.0871.157.315 .002 Race/School .065 4.016.215.930 .001 Gender Race/School .034 1.034.448.504 .000 Age/Race /School .010 4.002.033.998 .000 Gender/Age/ Race School .018 1.018.239.625 .000 Error 86.611 1146.076 Total 1406.000 1192 R squared=.938 (Adjusted R squared=.936)

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150 Table 4-147. Correlations: I like to us e stimulants while having online sex. School Gender Age Race I like to use stimulants while having cybersex with an online partner School Pearson Correlation 1-.101(**)-.025-.662(**) -.018 Sig. (2tailed) .000.391.000 .542 N 1212121012111209 1197 Gender Pearson Correlation -.101(**)1.114(**).103(**) .103(**) Sig. (2tailed) .000 .000.000 .000 N 1210121012091207 1195 Age Pearson Correlation -.025.114(**)1.118(**) .033 Sig. (2tailed) .391.000 .000 .251 N 1211120912111208 1196 Race Pearson Correlation -.662(**).103(**).118(**)1 .002 Sig. (2tailed) .000.000.000 .948 N 1209120712081209 1194 I like to use stimulants while having cybersex with an online partner Pearson Correlation -.018.103(**).033.002 1 Sig. (2tailed) .542.000.251.948 N 1197119511961194 1197 ** Correlation is significant at the 0.01 level (2-tailed).

PAGE 152

151 Table 4-148. Mean: I like to use s timulants while having cybersex with an online partner: School School Mean Std. Deviation N PFU 1.03 .233 1037 HBCU 1.02 .181 152 Total 1.03 .227 1189 Table 4-149. Mean: I like to use s timulants while having cybersex with an online partner: Gender Gender Mean Std. Deviation N Females 1.01 .163 827 males 1.06 .328 360 Total 1.03 .227 1187 Table 4-150. Mean: I like to use stimulants while having cybersex with an online partner: Age Age Mean Std. Deviation N 18-19 1.02 .149 403 20-22 1.04 .272 519 23-and above 1.03 .220 266 Total 1.03 .225 1188 Table 4-151. Mean: I like to use s timulants while having cybersex with an online partner: Race Race Mean Std. Deviation N AAB 1.01 .155 207 AAPI 1.04 .204 70 MAL 1.04 .228 132 NHW 1.03 .253 725 O 1.00 .000 55 Total 1.03 .227 1189

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152 Table 4-152. Tests of between subjects. I like to use stimulants while having cybersex with an online: partner Independent Variable Type III Sum of Squares Degrees of Freedom Mean Square F value Level of Significance Partial Eta Squared Gender .026 1.026.499.480 .000 Age .010 2.005.094.910 .000 Race .020 4.005.096.984 .000 School 9.88 E1988-E.002.965 .000 Gender/Age .068 2.034.663.515 .001 Gender/Race .045 4.011.220.927 .001 Age/Race .054 8.007.131.998 .001 Gender Age Race .180 8.023.439.898 .003 Gender/ School .001 1.001.018.894 .000 Age/School .019 2.009.184.832 .000 Gender/Age /School .034 2.017.332.717 .001 Race/School .026 4.006.126.973 .000 Gender Race/School .019 1.019.370.543 .000 Age/Race /School .008 4.002.040.997 .000 Gender/Age/ Race School .009 1.009.179.672 .000 Error 58.563 1140.051 Total 1313.000 1186 R squared= .955 (Adjusted R squared=.954) .

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153 CHAPTER FIVE DISCUSSION The inf ormation provided in this chapter will attempt to provide insight and interpretation of the results of the data analyzed for this st udy. After this information is presented, limitations as to the sample population, questionnaire, and recommendations for further studies will be brought forth. With this particular research interest, there are more questions than answers and that will be a focus of this discussion. To begin this di scussion, revisiting the speci fic research questions is necessary. Also, to respond to these questions a review of the anal yses is in order. Length of Internet Use And Email Use Based upon the results of this research study a nd the question of length of internet use, neither age, race, gend er, nor university of record proved to be significant in length of email or internet use. Over 95% of all pa rticipants in the study have used the internet for least three years consecutively. As the results are insignificant, it does demonstrate that the population of both schools has been exposed to and has been using th e internet and email for at least three years. This is important because it indicates that almost all of the participants have enough experience to be able to navigate the specific areas of th e internet. This finding al so suggests that despite age, race, gender and university of record, all of the participants have a basis for understanding how to use the internet for information, entertainment and/or any other purposes. Frequency Of Email Use The next area of interest within this group of questions (identified in the survey) was how often the internet was used. It appears as though overall, all st udents who attend HBCU use their em ails less frequently than thei r racial counterparts at PFU. Ther e is a gender difference as well. In comparison to all other students, Black ma les and females attending HBCU use email less

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154 frequently than racially like counterparts at PFU. AA/PI females (overall) use email more than their male counterparts. There is a significant difference between AAB males frequency of email use as well. AAB males attending PFU are the most frequent of all male email users, and AAB males attending HBCU use email the least frequent of all email users. MAL females used email more frequently than their male counterparts. No interaction could be determined for MAL males, as there was no representation in the sample from HBCU. Gender differences demonstrate that males use email less frequently than females overall. Overall, the Tukey HSD proved significant on the interaction between AAB and all other race categories in the sample. In the case of gender, race and school, Af rican American Blacks use the internet less frequently than their racial counterparts. Th e Tukey HSD demonstrated that the interaction between races was significant for African American Blacks and all other races. AAB females from HBCU age 18 and older who participated in the sample demonstrated less frequent internet use than their age and race similar counterpar ts from PFU. AAB from HBCU who were age 18-20 and 23 a nd older demonstrated the least frequent use of all of their male and racially different counterparts. According to the sample population surveye d, AAB males and females attending HBCU use email less frequently than their PFU counterpa rts. There are a multitude of reasons for this difference. Perhaps one of the main reasons relates directly to socioeconomic factors. Unfortunately, African American Blacks (as a whole) have lower gross incomes overall, lower socioeconomic status, and this certainly affects availability and frequency of use of email and internet use directly related to availability of computers. In order to communicate, cellular telephones have gained popularity. In these current times (based on personal observation), everyone has a cellular telephone, and it is especially prevalen t in these age groups. Cellular

PAGE 156

155 telephones are cheap, making it a preferable met hod of communication. It is much quicker, and requires little skill or leve ls of literacy to use. Another difference that might be explained for less email use is the geography of the campuses. The campus at PFU is large and expansiv e, the population is large, more diverse and interactions because of computer availability and extensive lands cape may be more compatible with this population. Although this study did not address socioeconomic factors between the two institutions, this information would significantly help in addressing th ese issues. This finding may also be a direct result of mandatory computers at PFU Students who are enrolled at HBCU are not mandated to purc hase computers for school. Students at PFU are required to have these items upon admission. There are also larger computer work areas for the students at PFU, with tech support available nigh t and day. Students at HBCU, which is a private university, are at th e mercy of much smaller work areas and not having the availability of a computer when they need it. Approximately 80% of the students at HBCU have computers. Until last year (2007), wireless access was not available campus wide at HBCU. This has not been the case at PFU. St udents attending HBCU are primarily funded by financial aid, scholarships and student loans. Th ese students do not have an expendable income and are less likely to purchase a co mputer unless it becomes mandatory. Frequency Of Internet Use As previously stated, one of the variables th at m ay influence behavior is the frequency with which an individual spends pa rticipating in that particular behavior. With regard to internet use, outside of the aspect of communi cation (excluding www.myspace.com, and their contemporaries), the internet can be used fo r information seeking, entertainment, gaming, voyeurism, and education and because of this frequency of internet use is differentiated from email use.

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156 In this study, gender and age (together) as in teraction variables demonstrated the most striking differentiation in the frequency of internet use. Males and females who use the internet have an inverse relationship as th ey age. In this sample, females use the internet less frequently as they age, and males do the exact opposite. As males age, they tend to use the internet more. This difference may be demonstrative of the activities that are occu rring within the cyber environment of the internet or as a result of the internet. This finding demonstrates a need for further follow up investigation, as this may be significant in the development of programs in which internet use and gender might impact. Race was also a factor in fre quency of internet use. In an overall comparison, AAB used the internet less frequently than other races. Ba sed upon the means in this interaction, AAB used the internet less than NHW and MAL. This intera ction was also significa nt in the Tukey HSD. NHW used the internet more than AAB, but less than other races. These results may be a direct consequence of economics, and availability of th e internet in AAB. As suggested earlier, in follow up studies, it would be important to i nquire about socioeconom ic status, as this information may shed some light on crucial informa tion as it relates to fre quency of internet use and race. Finally, the four way interacti on effect of gender, race, school and age proved significant. Overall, females and males from HBCU used the internet less overall when compared to PFU, Females from HBCU used the internet less than their HBCU male counterparts. Black females from HBCU, regardless of age used the internet less than their Black female counterparts from PFU. Although there were other interactions fo r race, gender, school and age, many of the categories had either very few or no represen tation within these very specific groups. The specific information for AAB in this study is ci ted as there was adequa te representation in

PAGE 158

157 females. The representation of AAB males who attended HBCU were less in number in the older age groups, therefore the discussion is limited to females in the four way interaction. NHW females who attended HBCU also had less frequent internet use than their NHW counterparts at PFU. This finding may demonstrate that the signifi cance may be a result of availability versus racial differences as suggested earlier. In light of the lack of represen tation of some subgroups (age, race, and gender) within th e overall population, the interpreta tion of the results for these variables should be viewed with caution. The most important aspect of this question i nvolves the interaction of the variables, age and gender. It is revealing that the relationship of the frequency of internet use to age and gender may be significant in how and why it is used. Fu rther investigations may illuminate study areas that include such topics as inte rnet addictions and the impact of the internet on psychosocial development. Making Friends Online ANOVA statistics d emonstrated significance between the independent variables. Main effects were demonstrated in ge nder. Males were more likely to use the internet to make new friends. In this grouping, there was also a four way interaction between gender, race, age and school (p=.016). The Tukey HSD proved to be in significant for age, and significant for the interaction (multiple comparisons) of AAB when compared to MAL and NHW.. In comparison to university of record (sc hool), students attending HBCU were more likely to engage in making friends online. With regard to race both AAB males and females attending HBCU were more likely to make frie nds over the internet than those AAB attending PFU. Interestingly, when comparing all races; AAB were more likely to make friends over the internet than both MAL and NHW. Overall, these results demonstrat e that there is a difference in internet use in these populations.

PAGE 159

158 It is a little surprising that males in gene ral were more likely to make friends over the internet. This was a general purpose statement, and it did not specify the nature of the relationship. With that being said, the idea of relationships for on line games (i.e. XBOX), may be important as the technologica l aspect of the internet may be a driving factor in this relationship. It is more difficult to explain the trend in online friendship from di fferent universities. This may be a result of the cultural and demogra phic climate of the instit ution itself. This cannot be adequately explained by the current study, bu t would be an important aspect of further research. Although AAB (as a whole) in this study are mo re likely to make friends on the internet (as these results indicate), using these findings to gain insight into the relationships that are generated within the cyber realm and acted out in different scenarios may be premature without further information about the context of each situ ation. It may simply be a function of being further away from home and separated from friends Further investigation into the internet use patterns of all adolescents and emerging adults, pa rticularly as they pe ruse sexual topics may help us comprehend phenomena like the record leve l of STIs, earlier ages of sexual initiation and practice of serial monogamy among the adol escent and emerging adult populations of all races. These kinds of questions may be more appr opriately addressed in qualitative studies to allow understanding of the context of each situation. Meeting Friends Offline As previously stated, the idea of m aking frie nds online may influence the idea of meeting offline. The online survey asked specific questions regarding relationships. The questions were posed in this fashion: Hav e you ever met in person anyone you first met online. These questions were again analyzed using the variables of gender, race, university of record, and age.

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159 Univariate ANOVA tests were performed and th ere was no significant in teraction among any of the variables. According to the study completed by (McFar lane et al., 2002) th ere was a correlation between online relationships and offline meetings for sexual encounters. However, in this study, when this variable was compared with mee ting a person offline, there was no significant interaction. Therefore, although it has been examined in other st udies the results could not be replicated in this population. Ho wever, in the context of making friends on line, different questions need to be posed to have an understa nding of the offline re lationship. The questions that need to be asked include relationships for friendship, romance, gaming, and as such information may determine the nature of the offline relationship. I Have Posted Online Messages To Meet A Potential Partner The results f or this particular analysis dem onstrate that males are more likely than their female counterparts to post a message in order to meet a potential partner. Age also proved significant as males who participated in the st udy were more likely to post messages as they aged. The average means between ages and the male gender demonstrated an increase whereas females did the exact opposite. Their average mean s decreased as they got older. This may be influenced by the types of relationships that fema les are more likely to engage in as they age. These answers may also be respective of ongoing relationships. However, follow up questions as to the reasons for meeting a potential partner ar e in order to understand the relationship within the groups. The overall mean PFU (mean=1.445) was highe r than that of HBCU (mean=1.398). Non Hispanic Whites had higher overall means than thei r racial counterparts. However, the means of African American Blacks were second highest among the groups. Male s attending HBCU who identified themselves as 23 or older had the highest means of all groups, Females attending PFU

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160 had a higher average mean than that of their HBCU counterparts, and a trend in the means suggests that as they age they more frequently po st messages in order to meet a potential partner. This may be directly related to the racial mix from both schools. Again, the variation of ethnic population between HBCU and PFU is very po larized and the significant ANOVAs may be a direct result of this. Males attending HBCU had a higher overall average mean than their PFU counterparts in attempting to meet potential online partners However, the phenomenon with increases in the male populations between the unive rsities was the same phenomena that occurred in the female population. Males attending PFU de monstrated an increase in m eans related to age and posting messages in order to meet a pot ential partner. Remarkably, there was a jump in the mean of HBCU males 23 and older, which is almost twice of that of their age 20-22 year old counter parts. This should definitely be studied furthe r as there was reasonable representation in that grouping variable (N=29). As previously stated, in comparing race, age, gender, and school as factors in a four way interaction in this sample, many of the races do not have a single individual in the sample in certain age groups when separated for age and gende r. Therefore, it would be difficult to attempt to describe the interaction in between and as a result of these very specific areas. There is significance in the relatio nship, and as such further studies should attempt to include larger numbers within these categories. The question about meeting potential partne rs did not specifically ask the sample population what the purpose of the posting or the relationship of th e anticipated partner might be. It would be interesting to do a follow up survey with questions a bout the specific purposes of the posting to meet a potential partner (i.e. frie ndship, romantic relationship, and/or etc.).

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161 The importance of the internet as an a lternate way to meet new people was also significant to all independent vari ables. The means of age range rises subsequently with each advancing category. This finding co rrelates with many of the othe r data analyses discussed, and may be associated with the growing trend that the internet continues to become more subtly pervasive in daily lives of emerging adults (Weiser, 2000). Does Your Partner Object To The Amount Of Time You Spend Online? The first question analyzed had to do with whether or not a partner knew about an online friendships or relationshi ps (partner knowledge). This was a B oolean style question (yes or no). Gender and age were both significant as m ain effect independent variables. In the analysis of interactions gender and age proved significant as the interaction of both variables combined with the dependent variable. The interaction of gende r, race and school proved significant as well as the combined interaction of the variables when compared to the dependent variable. These comparisons were also significant on the MANOVA. The four way interaction of all independent and dependent variables again proved to be sign ificant with the combined interaction (p=.000) Based upon the sample population and the result s, Non White Hispanic s are less likely to disclose their on line relationships to their off line partners/rel ationships. Partner knowledge was also statistically significant for university of record. In this analysis, students who attended PFU were less likely to disclose their online relations hips than their HBCU co unterparts. This finding may be a direct result of the higher number of Non Hispanic Whites in attendance at PFU (n=637) versus HBCU (n=7). The results of uni versity of record should be discounted based upon the sample size of Non Hispanic Whites in attendance at HBCU. Gender proved to be interesting. Based upon the population represented, females are less likely to have a partner object to the amount of time that they spend online. The average female overall mean was higher (mean= 1.974) th an the male average mean (1.881).

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162 It is important to note that both Tukey HSD tests for age and race demonstrated insignificance among the interaction effects in multip le comparisons. Therefore, the results of the ANOVA show significance but based upon the sample (as evidenced by the Tukey HSD) there is no way to determine the relevance of the interact ion as it pertains to this grouping. There is no clear relationship between race a nd age as it relates to partner objection except that there may be racial differences that cannot be identified based upon the sample A larger sample of minority populations (i.e. AAPI and MAL) ma y provide an interpretable inte raction between race and age. The overall interaction demonstrates that there is a difference in partner objection between school, age, gender and race. As men tioned earlier, uneven dist ribution of the sample population make it difficult to interpret th e significance of th ese interactions. The interaction of the variables of gender and age when compared to partner objection proved to be most interesting. The inverse relationship that was indi cated in frequency of internet use was replicated in this question. Females, as they age are less likel y to experience partner objection, and males are more likely to experience partner objection. This may be a direct result of the specific use of the intern et. According to OReilly and colleagues (2007), males are more likely to view sexually explicit ma terial online, and women are more likely to feel threatened by, or object the use of the internet for this spec ific behavior. Although it was not specified on the current survey, the work of OReilly and colleagues (2007) suggests that a similar effect may be present in this population. Does Your Offline Partner Know About The Friends/Relationships That You Have Online In the analysis of this pa rticular variable, partner know ledge of online relationships demonstrated significant results in the interaction e ffect of age and school on the dependent variable. Overall, students atte nding PFU are more likely to shar e information with their offline partners about relationships th an those students attending HBCU. Regarding age, and partner

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163 knowledge, the Tukey HSD demonstrated significance in the multiple comparisons of age. There is an interaction between younger participants when compared to their older counterparts. Overall, students over the age of 23 are more lik ely to disclose online relationships to their partners when compared to their younger counter parts. In comparing ag e and university, the only age group that is less likely to di sclose information to their off line partners is HBCU students who are 23 or older. All other age ranges demo nstrate that PFU students are more likely to provide information to their offline partners. This may be related to the type of universit y (private, Christian base d) in comparison to a public university, as race proved to be insignificant. Based upon the results, again further indepth follow up about this behavior shoul d be initiated within this population. The Expression Of Jealousy Over Online Rela tionships The next question analyzed stated: Has your partner ever expre ssed jealousy over the relationships you have developed online? Using ANOVA, the results demonstrated significance in the interactions between ag e, race and school and age and race as grouped interaction variables, (age, race, and school, as one group interaction and race and school as the other). Unfortunately, these variable groupings proved insignificant on the MANOVA. Therefore, the results cannot be considered signif icant and/or reportable as such. The Importance Of Alternative Ways/Place Fo r Meeting Potentia l Online Sexual Partners The next variable to be tested was the in ternet/email is an alternative way/place for meeting potential sexual partners. This dependent variable was again tested comparing age, gender, university of record (school), and race as independent variables. The analysis proved significant for the main effect of gender as well as age when compared to the dependent variable. There was also a significant inte raction of age, race and schoo l; however this combination was

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164 not significant on the MANOVA. Both the main eff ects of gender as well as age were significant on the MANOVA. Age and race proved si gnificant on the Tukey HSD as well. In this grouping the means in males were much higher than that of their female counterparts. The overall male mean for gender wa s higher (3.520) as compared with the female sample in this group (2.524). This demonstrated a significant difference in the groups as to the importance of the internet as an important way to meet potential online sexual partners. Men in the sample believe that this is an important al ternative way to meet online sexual partners when compared to their female counterparts. Age also had a significant interaction. As i ndividuals are older (in the sample) they are more likely to value the internet as an alte rnate way to meet individuals. The Tukey HSD demonstrated that although the interaction is not significant between students 18-20 and those who are 20-22, there is a signifi cant interaction between those individuals who identified themselves as 23 and older when compared to th eir younger counterparts. Th is demonstrates that gender and age have a significant effect on the diffe rence in perceptions of the importance of the internet as a place to me et online sex partners. This is the third interaction among this grouping variable and identifies the continuing theme of how gender and age play a role in the importance as well as integration of the intern et as a component in life styles. I Have Accessed Sexually Explic it Materials On The Internet One of the questions ask ed of respondents wa s to assess their access to sexually explicit materials on the internet. The only significant an alyses proved to be gender and university of record in both ANOVA and MANOVA. Men were more likely than women to access sexually explicit materials on the internet. Students attending PFU were also more likely to access sexually explicit material. This demonstrates a significant difference in internet use from HBCU and PFU. This is another pattern that suggests th at further research is necessary when assessing

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165 sexual behaviors in this population. The differe nce in universities a nd the access of sexually explicit material may be a result of intern et accessibility, computer availability, and socioeconomics. One of the variables within the survey allu ded to religion and religiosity; however it was not analyzed in this study. The fact that HBCU is affiliated with a religious organization may have an impact on using the internet for this purpose. This relationship may require future investigation and analysis of data collected as a part of this study as it may provide elucidation of this specific area of interest I Have Accessed Sexually Explicit Ma terials T o Become Sexually Aroused I have accessed sexually explicit materials on the internet was anot her question posed to the sample population. The only significant anal yses proved to be gender and university of record (school) in these analyses (separately). Gender and school were also significant as a main affect on the MANOVA analysis. Not surprising, the means between males and females on accessing sexually explicit materials on the internet were almost 1 times higher in males than females. It is far more interesting that students who a ttend HBCU were much less likely to access the intern et than their counterparts at PFU in the pursuit of accessing sexually explicit materials for sexual arousal. This difference exists wit hout any connotation to race. As neither race nor age proved to be significant, this indicates that th e interaction of school perhaps has more to do with the different social environments, sociocultural history, a nd overall mission statement of the separate institutions. HBCU as previously stated is a Christian based organization, and this may be a factor in how this behavior is perceived and accepted by the overall popul ation. In contrast, PFU is a large public university with a huge overall student popula tion and the case for increased anonymity can be made when using the internet fo r these purposes. Another variable that was not

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166 analyzed in this study is the idea of guilt or sham e associated with using the internet for these purposes. Perhaps the interaction of religion/religio sity and guilt and/or shame may be associated as a deterrent in viewi ng erotica for arousal. This may be important in further studies when comparing students who attend religiously based universities to those who do not, or do not attend a university at all. In any case this is an area that requires further study. While Viewing Sexually Explicit Web Sites I Have Masturbated The question while viewing sexually explicit m a terial I have masturbated was analyzed with ANOVA. Based upon the results of these anal yses, males are far more likely than their female counterparts to view sexually explicit materials for the purpose of masturbation. The varying means demonstrated by males and females i ndicates that the male participants of this study were twice as likely to masturbate to sexually explicit web sites as their female counterparts. Seidman (2004) demonstrated that males are more likely to masturbate when viewing sexually explicit material and are more likely to view it alone. In cont rast, females who view sexually explicit material are more likely to view it with a partner withou t the act of self sexual gratification. The findings in this study support Seidmans (2004) findings. Students attending PFU are far more likely to view these materials for the purpose of masturbation. Coincidentally, (with the exception of AAPI females), all representatives at PFU of any other race are more likely to view this mate rial for the purpose of self gratification than their racial counter parts at HBCU. AAB males attending PFU are twice more likely to view sexually explicit material on the internet for the purpose of masturbation than th at of their AAB HBCU counterparts. The Tukey HSD analysis of multiple comparisons demonstrat ed within the groups that overall, AAB were less likely to masturbate while viewing sexually explicit material. To adequately interpret this

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167 result, further studies are needed in order to re plicate and validate these preliminary results and specify this information in both quantitative and qualitative inquiry. As previously stated, the idea of guilt or sh ame associated with using the internet for these purposes may be a factor in students fr om HBCU exhibiting these behaviors. Seidmans work (2004) also demonstrated that earlier expo sure to sexually explicit material may be a predictor of frequency of current pornography use. Therefore, the need for further research in this particular area becomes extr emely important in attempting to predict online sexual behaviors and offline behaviors. I Have Had Cybersex With An Online Partner Using ANOVA statistics, the question I have had cybersex with an online partner was analyzed. T his question was analyzed as the de pendent variable with age, gender, race and university of record (school). The results were insignificant. I Like To Drink Alcohol While Having Cybersex With An Online Partner Using ANOVA statistics, the question I like to drink alcohol wh ile having cybersex with an online partner was analyzed. This question w as analyzed as the dependent variable with age, gender, race and university of record (school). The results were insignificant. I Like To Use Stimulants While Havi ng Cybersex With A n Online Partner Using ANOVA statistics, the question I like to use stimulants (drugs) while having cybersex with an online partner was analyzed. This question wa s analyzed as the dependent variable with age, gender, race and university of record (school). The results were insignificant with all effects. Summary This study had a very large and controlled sam p le size that was extr emely representative of the U.S. population according to race. There was greater repres entation of African American

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168 Blacks (17.4%) than reflected in the U.S. populati on (12.3%). There was a gr eater percentage of Asian American/Pacific Islanders (6.0%) than in the U.S. total population (3.7%). Mexican Americans/Latinos represented 11. % of the study population and demo nstrate 12.5 % of the U.S. population. Non Hispanic Whites were under represented in the study as that group only represented 60.5% of the study, and 75.1 % of th e U.S. population according to the year 2000 census data (http://www.censu s.gov/prod/2001pubs/c2kbr01-9.pdf). In the data analyzed for this study, gender cont inues to be the major factor in internet usage. Age is also very noteworthy when looking at trends of decisions that may likely influence behaviors. Race, specifically AAB appear to be a determinant of behaviors that involve internet use overall. This is very signifi cant as it may provide some insight as how to effectively use the internet for educative and health related topics. This study collected data related to internet use and education/information, but was not analyzed for this specific study. This study specifically looked at internet use differences in age, race, gender and school. Future analysis of gathered data may lead to further research within this topic. As for the idea of race as a determinant for e fficacy of use; there have been studies that discuss how internet knowledge and frequency of use is a determinant of efficacy (Potosky, 2006). Race was not identified as a dete rminant of efficacy of use. It is important to note that as this study compared a small private black univers ity to a major public university, there is a significant difference in internet use and intere st overall. As the st udy did not ask specific information regarding socioeconomic status (current or overall economic status history), there is no way to determine if the differences in internet use are related to race or socioeconomic status. This is an imperative area of further research, sp ecifically because it is foundational to the notion of whether or not race is an indicator of internet use. Finally, there needs to be research

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169 conducted to investigate to determine if thes e specific internet be haviors among AAB are a phenomenon to these particular universitie s or AAB college students in general. One of the most outstanding revelations of this study is the notion that internet use, length and frequency have a definite increasing influence and value during the emerging adult years (generally identified as late teens and early twen ties). The data seems to demonstrate that the internet becomes more omnipresent as emerging adults go through this growth and development period. This relationship s upports previous findings regarding a ddictive behaviors and the idea of an increasing frequency and le vel of satiety (Griffiths, 2001; Shapira et al., 2003; Young, 1998). Griffith (2001), alleges that behaviors most lik ely to promote sexual addiction within this venue include online pornography for masturbato ry purposes, and online sexual relationships (Griffiths, 2001). It is po ssible that the increase in age and valuing the importance of the internet for potential partner (sexual or otherwise) as well as part ner objection incr easing with age demonstrates this effect occurring within this emerging adult populat ion. According to Young (1998) frequency of internet use is central to the notion of addiction. Th e levels of need for satiety are increased with each passing encounter and it is in this desire to quench this physiological/ psychological urge. Young (1998) empha sizes that individuals can get lost in the interaction and the socialization of the internet and lose track of all time and spatial dimensions in the cyber realm. In adolescents and emerging a dults, this pattern may prove to be detrimental to psychosocial behaviors. Huang (2006) demonstrated that internet use (greater than 10 hours per week) leads to a breakdown in the psychosocial development of i ndividuals during the sixth phase of Ericksons developmental framework (intimacy versus isola tion). The inability to develop intimacy relates directly to the incapability to develop a sens e of identity and crea tes an environment for

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170 gratification in order to satiate this internal desire (Huang, 2006). The trend that has been identified in this study demonstrat es that the longer an individu al uses the internet, the more important it becomes (even for sexual arousal). If the importance of intimate relationships is devalued, then there is an effect on psychosocial behavior that must be addressed. It is this lack of intimacy in favor of the internet which feed s the idea of isolation and creates crises. These crises can manifest in the form of self gratif ication, self loathing, or more isolated behaviors (Hall & Parsons, 2001). In essence, this behavior has a potential to escalate in adults who have the full capabilities of cognition. Emerging adults and adolescents who do not have full cognition are at even greater risk. They may become lost in the conundrum of th is technological medium and lose their identities, and ab ility to form intimate meaningful relationships. This is another area of study that requires furthe r understanding, especi ally in adolescents and emerging adults. The level of importance of meeting for the pur pose of online sex relationships that has been identified within this populat ion sample may very well identif y a pattern that exists in a generalized population of emerging a dults. Multiple studies have demonstrated this behavior in gay populations (Ross, Rosser, Coleman, & Mazin, 2006). This may play a factor in the development of offline sex partners as this behavior may escalate. The questions in the survey were not specific enough to elicit this type of information; howev er it does identify the need for further study in this area using this age population. Limitations Although this was a large sample size, th ere were limitations to the populations in general. Gender was the main i ssue within the sample. Females dominated the sample population by over 30 %. The gender sample was approximately 70 % female and 30 % male. Interestingly, this result demonstrates that despite the topic (which many may assume is of more interest to emerging adult males) wome n are more likely to participate in surveys and

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171 therefore are better represented in the study. This fact is profuse dem onstrated throughout the literature and this phenomenon does occur whether su rveys are online or offline, females perhaps by nature are more interested in partaking in these endeavors compared with their male counterparts (Rhodes, Bowie, & Hergenrather 2003) The lack of sufficiency in the number of male participants may be a factor in the results. Another limitation of the study is that the questionnaire may not have been specific enough, and may have resulted in confusion amo ng some of the respondents. As respondents had the opportunity to email the primary investigator, some of the co mments directly related to the questions for both internet use and meeting online partners. A fe w respondents wrote in concerns about the wording of a few of the questions and an interpretation of meaning (i.e. what was the purpose behind the use of the internet or the meeti ng of an individual). An example of the type of email has been included to give an example. The participant wrote: I participated in the "Sexual Behaviors a nd Internet Use" Survey and I am a little concerned. I have never looked at sexually ex plicit material over th e internet and during many of the questions there was no N/A selectio n that could be made. I'm afraid that it will make the data appear a certain way that is not necessarily true, not just for me but for many people taking this survey. To provide an example, one of the questions was "I feel embarrassed when looking at sexually explicit mate rial over the internet." Do I answer as "Frequently" or "Never"? Do I speculate about my emba rrassment had I looked at the material, or do I say never because I have never looked at it? I wasn't sure who to address about this, but I just believe that this is some thing to look at. I think this research is very important and I would hate for it to be affected by this confusion. This may be a limitation and may also have been a factor for some of th e insignificant findings. A very important factor in cr eating a follow up study would be to take this data and develop future qualitative studies based upon this observati on. These qualitative findings could then be used to refine the questionnaire before replicating it.

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172 Suggestions For Further Investigation The quantitative information presen ted in this study demands follow up qualitative inquiry. Utilizing a mi xed methods approach to data co llection allows the quantitative data analysis to be followed by a qualitative follow up study. This is clarified as an explanatory approach (Creswell, 2003). This explanatory model allows the narrative of individuals and groups to bring out the subtle in formation that may describe asp ects of a quantitative study that can be translated into findings that can cap ture a peoples ideas a nd thus yield a further understanding of this important quantitative study. According to Tasshakkori and Teddlie (1998), using a mixed method model with sequential quantitative qualitati ve analysis provides an enge ndering environment in which to develop further understanding. This environmental change occurs as a result of group dynamics that are exposed. This resultant exposure allows t hose participating to take into consideration the subtle differences in people and settings. It is within these unique encounters that enrich the original findings of that specifi c quantitative data anal ysis. When the qualitative narratives are analyzed there is a stronger de scription of the actual quantitative analysis with the melding of these research methods One of the best suited venues to venture in to the virtual reality of the internet is ethnography. A key feature of ethnographic method is that has the ability to be used without a design, and therefore makes it unequivocally useful in cyber space. This lack of design is the key feature as it allows an unfettere d flow of the information between the researcher, group and or individuals simultaneously. Fortunatel y, it also allows the researcher to be even more flexible in response to the ebb and flow of the venue in which individuals are pa rticipating (Patton, 2002; Prendergast, 2004).

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173 Ethnographic methods are used predominantly to study societal issues. Qualitative inquiry, specifically ethnography has been used to study culture occurring within the internet. The attraction of cyberspace as a research medium provides versatility that is not restricted by geography. It has the unique ability to interact wi th groups that would not normally be available to the researcher. The use of this web based su rvey demonstrates the potential to provide large accurate sample sizes, minimization of the risk of unintended partic ipants (through limited advertisement), as well as improve the ethnic ge neralizability of the population. However, it was limited as to the ability to find the nuances in beha vior that may occur in all of the participants that cannot be reflected in the data collected in the survey. Very few studies have engaged in demons trating differences am ong students attending HBCU and public universities. This study utilized this design in order to capture the significant differences that occur within thes e populations in other venues (i.e. learning, culture). The results also demonstrate the importance of studying popul ations within their ow n cultural environment to denote differences and develop interventions through those findings. A few suggestions for further study include focus groups that are not only gender based, but age restrictive and ethnically similar. In order to investigate specific topics such as internet use, internet surfing whether recreational or educ ational; groups need to be age specific to the age groups within this study. Another recommendation would be to replicate this study within the HBCU system. This would allow using a similar demographic population on a larger scale. A re plication like this may validate the findings in this study within a larger more specific population. It would be necessary to use a mixed methods study to el icit more complete and rich responses.

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174 APPENDIX A INFORMED CONSENT Sexual Behaviors of Internet Use of Traditional College Age Students: An exploratory study. Informed Consent Form to Participant in Research INTRODUCTION Name of person seeking your consent: Paula C. Pritchard Place of employment & position: University of Florida Doctoral Student This is a research study of sexual behaviors and internet use of traditional co llege age students is to explore how technology, specifically the in ternet may affect sexual decision making and influence sexual behaviors in tr aditional college age students. Could participating in this study offer any direct benefits to you? No, as described on question 11a. Could participating cause you any discomforts or ar e there any risks to yo u? No, as described on question 10. Please read this form which describes the study in some detail. I or one of my co-workers will also describe this study to you and answer all of your questions. Your par ticipation is entirely voluntary. If you choose to particip ate you can change your mind at any time and withdraw from the study. You will not be penalized in any way or lose any benefits to which you would otherwise be entitled if you choose not to participate in this study or to withdraw. If you have questions about your rights as a research subject, please cal l the University of Florida Institutional Review Board (IRB) office at (352) 846-1494. If you d ecide to take part in this study, please click agree at the end of this consent. GENERAL INFORMATION ABOUT THIS STUDY 1. What is the Title of this research study?

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175 INFORMED CONSENT Sexual Behaviors and Internet Use of Traditional College Age Students 2. Who do you call if you have que stions about this research study? Paula C. Pritchard (pri ncipal investigator) pcwrn@ufl.edu (386) 562-4533 3. Who is paying for this research study? The sponsor of this study is University of Florida 4. Why is this research study being done? The purpose of this research study is to look at sexual behaviors and internet use of traditional college age students. The purpose is to explore how technology, specifically the internet may affect sexual decision-making and in fluence sexual behaviors in tr aditional college age students. If the internet does influence sexual decision making in traditi onal aged college student, the results of this study could help better understand the interaction between internet use and sexual behaviors You are being asked to be in this research study because tr aditional age college students by far have had more technology experience than older adults, as well as use the internet more often. As a traditional age college student (18-22), you are best suited to participate in this study. WHAT CAN YOU EXPECT IF YO U PARTICIPATE IN THIS STUDY? 5. What will be done as part of your normal clin ical care (even if you did not participate in this research study)? This is not applicable 6. What will be done only because you are in this research study? You are agreeing to participate in an anonymous online survey where your responses will be correlated and analyzed in order to understand more fully, internet use and sexual behaviors. If you have any questions now or at any time during the study, please contact Paula C. Pritchard in question 2 of this form. 7. How long will you be in this research study? Your participation in the su rvey will take approximately 30 to 45 minutes of your time

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176 INFORMED CONSENT 8. How many people are expected to take part in this research study? 2500 people are expected to particip ate in this research study WHAT ARE THE RISKS AND BENEFITS OF THIS STUDY AND WHAT ARE YOUR OPTIONS? 9. What are the possible discomforts and risks from taking part in this research study? There are no possible discomforts or risk involv ed with this study. Some of the questions may make you feel slightly uncomforta ble because of the subject matter. Your identity will remain anonymous, and therefore it is impossible to link your responses to yourself in any way. There are no physical or psychological risks to you by your participation in this study. Other possible risks to you may include: not app licable. This study may include risks that are unknown at this time. Participation in more than one research study or project may further increase the risks to you. If you are already enrolled in another research stud y, please inform Paula C. Pritchard (listed in question 2 of this consent form) or the person reviewing this consent with you before enrolling in this or any other rese arch study or project. Throughout the study, the research ers will notify you of new information that may become available and might affect your de cision to remain in the study. If you wish to discuss the information above or any discomforts you may experience, please ask questions now or call the name of PI or contac t person listed on the front page of this form. 10a. What are the potential benefits to you fo r taking part in this research study ? There is no direct benefit to you for pa rticipating in this research study 10b. How could others possibl y benefit from this study? Potential benefits include th e opportunity to improve understa nding of how traditional age college students utilize the internet and expl ore the possibilities of its influence on sexual behaviors and sexual decision making. 10c. How could the researchers benefit from this study? In general, presenting research results helps th e career of a scientist. Therefore, Paula C. Pritchard may benefit if the resu lts of this study are presented at scientific meetings or in scientific journals. There is no conflict of interest associated with the principal or any other associated with this study.

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177 11. What other choices do you have if you do not want to be in this study? You have been invited to participate in this research project because you are a student and as previously stated college students utilize the internet more frequently, and have more experience using the internet, and this may influen ce sexual decision making and behavior. The investigators associated with this project may or may not teach in your college or be associated with courses for which you are enrolled or might be expected to register in the future. Your participation is voluntary and any decision to part or not participate wi ll in no way affect your grade or class standing. If you believe that your participation in this st udy or your decision to withdraw from or not to participate has improperly affected your grades(s), you should discuss this with the dean of your college or you may contact the IRB office. 12a. Can you withdraw from this study? You are free to withdraw your consent and to stop participating in this study at any time. If you do withdraw your consent, you will not be pena lized in any way and you will not lose any benefits to which you are entitled. If you decide to withdraw your consent to partic ipate in this study for a ny reason, please contact Paula C. Pritchard at (386) 562-4533. They will tell you how to stop your participation safely. If you have any questions regarding your rights as a research subject, plea se call the Institutional Review Board (IRB) office at (352) 846-1494. 12b. If you withdraw, can information about you still be used and/or collected? Information obtained in the survey data collectio n after it is submitted by you cannot be removed as there are no identifiers to your specific answers. Survey data is maintained in aggregate form from the time of survey submission. 12c. Can the Principal Investigator withdraw you from this study? You may be withdrawn from the study without yo ur consent for the following reasons: If you do not meet the inclusion criteria, you can be w ithdrawn from participating in this study. WHAT ARE THE FINANCIAL I SSUES IF YOU PARTICIPATE? 13. If you choose to take part in this research study, will it cost you anything? No.

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178 INFORMED CONSENT 14. Will you be paid for taking part in this study? No. 15. What if you are injured because of the study? If you are injured as a direct result of your participation in this study, only professional consultative care that you receive at the University of Florida Student Health Care Center will be provided without charge. Any hospital charges in curred would be paid by you or your insurance provider. You will be responsible for any de ductible, co-insurance, or copayments. Some insurance companies may not cover costs associat ed with research studies. Please contact your insurance company for additional information. No additional compensation is offered. The Principa l Investigator and other involved with this study may be University of Florida employees. As employees of the University, they are protected under state law, which limits financial recovery for negligence. Please contact the Principal Inves tigator listed in ques tion 3 of this form if you experience an injury or have questions about any discomforts that you experience while participating in this study. 16. How will your privacy and the confidential ity of your research records be protected? Information collected about you will be stored in locked filing cabinets or in computers with security passwords. Only certain people have the legal right to review thes e research records, and they will protect the secrecy (confidentiality) of these records as much as the law allows. These people include the researchers for this study, certain University of Florida officials, the hospital or clinic (if any) involved in this research, and the Institutional Review Board (IRB; an IRB is a group of people who are responsible for looking after the rights a nd welfare of people taking part in research). Otherwise your research records wi ll not be released wit hout your permission unless required by law or a court order. Researchers will take appropriate steps to protect any information they collect about you. However there is a slight risk that informati on about you could be reveal ed inappropriately or accidentally. Depending on the nature of the information such a release could upset or embarrass you, or possibly even affect your insurability or employability. If the results of this research are published or presented at scientific meetings, your identity will not be disclosed. 17. Authorization You have read about this studys purpose, procedures, possi ble benefits, and risks; the alternatives to being in the st udy; and how your privacy will be protected. You may download a copy of this form. You have been given the opportunity to ask questions before you agree and you have read that you can ask othe r questions at any time. By clic king on agree at the bottom of this form this form you are voluntarily agreeing to participate in this study. You are not waiving any of your legal rights. Agree Disagree

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179 APPENDIX B INTERNET SURVEY

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186 APPENDIX C MASS EMAIL Mass Email Letter Dear Student, As a doctoral student at the University of Florida, I am cordially inviting you to participate in a survey that is investigating Sexual Behaviors and Internet Use in Traditional College Aged Students (aged 18-22). This survey is complete ly anonymous and will take approximately 30-45 minutes of your time to complete. Your participation in this research may in crease understanding of how the World Wide Web plays a part in sexual decision making. Questions asked on the survey include basic demographic information, questions about frequenc y and duration of internet use, use if the internet to surf for sexually related content, use of the internet to establish personal re lationships i.e. friends), use of the internet for sexual entertainment, emotional arousal and perception, an d finally, public access to sexually explicit material on the internet. Should you decide to participate in this ground breaking researc h, please complete the entire survey by allowing yourself an appropriate am ount of time to complete this survey. Again, your participation is completely volunt ary and your results remain anonymous. Data collected will be in aggregate fo rm, and information will only be published for this dissertation in aggregate (grouped) delimited (numbers) data. The results of the data collected from this survey will not include the name of the unive rsity that you attend, nor any of your particular demographic data.

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187 Thank you for your participation! In you have any questions, please contact: Paula C. Pritchard pcwrn@ufl.edu If you are interestedplea se click on the link to http://paula.pritchard.name/uf http://paula.pritchard.name/bcu

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184 LIST OF REFERENCES Anderson, K. (2001). Internet use among co llege students: An exploratory study. Journal of American College Health, 50 (1), 21. Bancroft, J. (2002). Biological factors in hum an sexuality. The Journal of Sex Research, 39(1), 15. Bogard, W. (1996). The Simulation of Surveillance: Hypercontrol in Telematic Societies Cambridge: Cambridge University Press. Bogart, L., Cecil, H., Wagstaff, D., Pinkerton, S ., & Abramson, P. (2000). Is it "sex"?: College students' interpretations of sexual behavior terminology. The Journal of Sex Research., 37(2), 108-117. Boies, S. C. (2002). University students' uses of and reactions to online sexual information and entertainment: Links to online and offline sexual behaviour. The Canadian Journal of Human Sexuality, 11(2), 77. Brener, N., Lowry, R., Kann, L., Kolbe, L., & al e. (2002). Trends in sexual risk behaviors among high school students --United States, 1991-2001. JAMA, 288 (15), 1842. Brown, J., & Newcomer, S. (2002). Television viewing and adolescents' sexual behavior. Journal of homosexuality, 21 (1), 77-91. Bryant, P., & Bryant, J. A. (2005) Adolescents and the internet. Adolescent Medicine Clinics, 16(2), 413. Canli,T, & Gabrieli, J. (2004) Imagi ng gender differences in sexual arousal. Nature Neuroscience, 7, (4). 325. Calvert, S., Jordan, A, & Cocking, R. (2002). Children in the digital age: Influences of electronic media on development. Westport, CT. PraegerPublisher/Greenwood Publishing Group. CDC. (2006). Trends in reporta ble sexually transmitted diseases in the United States. Retrieved December 23, 2007 from: http://www.cdc.gov/stad/stats. Collins, R. E., Elliott, M., Berry, S., Kanouse, D., Kunkel, D., Hunter, S., et al. (2004). Watching sex on television predicts adolesce nt initiation of sexual behavior. Pediatrics, 114 (3), 280-289. Cooper, A., Marahan-Martin, J., Mathy, R., & Maheu, M. (2002). Towards an increased understanding of user demographics in online sexual activities. Journal of Sex and Marital Therapy, 28 105-109.

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185 Cooper, A., Scherer, C., Boies, S. C., & Gordon, B. L. (1999). Sexuality on the internet: From sexual exploration to pathological expression. Professional Psychology: Research and Practice, 30 (2), 154-164. Couper, M., Conrad, F., Tourangeau, R. (2007).Visual context in web surveys. Public Opinion Quarterly 17, (4), 623. Patton, M. Q. (2002). Qualitative research and evaluation methods (Third ed.). Thousand Oaks: Sage. Delamater, J., & Friedrich, W. (2002). Human sexual development. The Journal of Sex Research, 39 (1). DenizetLewis, B. (2004, May 30, 2004). Friends, frie nds with benefits and the benefits of the local mall. The New York Times. Dickerson, S., Reinhart, A. M., F eeley, T. H., Bidani, R., & al, e. (2004). Patient internet use for health information at three urban primary care clinics. Journal of the American Medical Informatics Association, 11 (6), 499. Dodge, B., Reece, M., Cole, S., & Sandfort, T. (2004). Sexual Compulsivity Among Heterosexual College Students. The Journal of Sex Research, 41 (4), 343. Duffy, M., (2002). Methodological i ssues in web-based research. Journal of Nursing Scholarship. 34 (1),83. Durham, M. G. (1999). Girls, media, and the ne gotiations of sexuality: A study of race, class, and gender in adolescent peer groups. Journalism and Mass Communication Quarterly, 76(2), 193. Escobar-Chaves, S. L., Tortolero, S., Markham, C., Low, B., Eitel, P., & Thickstun, P. (2005). Impact of the media on adolescent sexual attitudes and behaviors. Pediatrics [NLM MEDLINE], 116(1), 303. Goodson, P., McCormick, D., & Evans, A. (2001). Searching for sexually explicit materials on the Internet: An explorator y study of college students' behavior and attitudes. Archives of Sexual Behavior, 30(2), 101. Griffiths, M. (2001). Sex on the Internet: Obse rvations and implicati ons for Internet sex addiction. The Journal of Sex Research, 38 (4), 333. Gruber, E., & Grube, J. (2000). Adolescent sexu ality and the media: A review of current knowledge and implications. Western Journal of Medicine, 172 (3), 210. Hall, A., & Parsons, J. (2001). Inte rnet addiction: College student case study using best practices in cognitive behavior therapy. Journal of Mental Health Counseling, 23 (4), 312-328.

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186 Hightow, L., MacDonald, P., Pilcher, C., Kaplan, A., Foust, E., Nguyen, T., et al. (2005). The unexpected movement of the HIV epidemic in the southeastern United States: Transmission among college students. JAIDS, 38(5), 531-537. Hoffman, D., Novak, T. P., & Venkatesh, A. (20 04). Has the internet b ecome indespensible? Communications of the ACM, 47 (7). Hollander, D. (2004). Changes in teen agers' sexual behaviors stall. Perspectives on Sexual and Reproductive Health, 36 (4), 141. Holowaty, P., Harvey, B., Feldman, L., Rannie, K ., Shortt, L., & Jamal, A. (1997). A comparison of the demographic, lifestyle and sexual be haviour characteristics of virgin and nonvirgin adolescents. The Canadian Journal of Human Sexuality, 6 (3), 197. Howard, D., & Wang, M. (2004). Multiple SexualPartner Behavior Among Sexually Active US Adolescent Girls. American Journal of Health Behavior, 28 (1), 3. Huang, Y. (2006). Identity and intimacy crisis an d their relationship to internet dependence among college students. Cyberpsychology & Behavior, 9 (5), 571-576. Koch, W., & Pratarelli, M. (2004). Effects of intro/extraversion a nd sex on social internet use. North American Journal of Psychology, 6 (3), 371-382. Kraut, R., Patterson, J., Landmark, V., Kiesle r, K., Mukopadhay, T., & Scherlis, W. (1998). Internet paradox: A social technology that reduces social involvement and psychological well being? American Psychologist, 539 1017-1031. LaRose, R., & Eastin, M. (2004). A social cognitive theory of inte rnet uses and gratifications: Toward a new model of media attendance. Journal of Broadcasting & Electronic Media, 48(3), 358. Leiblum, S. (2001). Women, sex and the internet. Sexual and Relationship Therapry, 16 (4), 390405. Longmore, M. A., Manning, W. D., Giordano, P. C., & Rudolph, J. L. (2004). Self esteem, depressive symptoms and adolescent sexual onset. Social Psychology, 67 (3q), 279. Malone, R. (2000). Research, the internet and the way things are. Health Education and Behavior (27). 697. McCown, J., Fischer, D., Page, R., & Homant, M. (2001). Internet relationships: People who meet people. Cyberpsychology & Behavior, 4 (5), 593-596. McFarlane, M., Bull, S., & Rietmeijer, C. (2000). The internet as a newly emerging risk environment. JAMA, 284 (4).

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187 McFarlane, M., Bull, S. S., & Rietmeijer, C. (2 002). Young adults on the In ternet: risk behaviors for sexually transmitted diseases and HIV. The Journal of Adolescent Health Negro Education, 31 (1), 11-16. Mitchell, K., Finkelhor, D., & Wolak, J. (2001). Risk factors for and impactof online sexual solicitation of youth. JAMA, 285 (23). MMWR, (2006). Youth risk behavior surveillance-United States, 2005. Retrieved December 2, 2007 from: http://www.cdc.gov/mmwr/PDF/ss/ss5505.pdf Morris, P. (2004). Media pulse: Meas uring the media in kid's lives. Patient Care, 15 (7), 26. Morrison, T., Bearden, A., Harriman, R., Morri son, M., & Ellis, S. (2004). Correlates of exposure to sexually explicit material among Canadian post-secondary students. The Canadian Journal of Human Sexuality., 13 (3/4), 143-157. National Vital Statistics Repor ts. (2005). Young people at risk: HIV/AIDS among america's youth. Retrieved July 30, 2005, 2005, from www.cdc.gov/hiv/pubs/facts/youths.htm Neilson Ratings. (2005). Re trieved June 30, 2005, 2005, from http://www.nielsennetra tings.com/news.jsp?s ection=dat_to&country=us. O'Reilly, S., Knox, D., & Zusman, M. (2007). College students attitudes toward pornography use. College Student Journal. 41 (2), 402. Pendergast, C. (2004). The typical outline of an ethnographic research publication Teaching Sociology, 32 (3), 322. Polit, D. (1996). Data analysis and statistics for nursing research. Upper Saddle River, NJ. Prentice Hall Potosky, D. (2006). The internet knowedge (iKnow) measure. Computers in Human Behavior, 23, 2760-2777. Remez, L. (2000). Oral sex among adoles cents: Is it sex or is it abstinence? Family Planning Perspectives, 32 (6), 298. Rhodes, S., Bowie, & Hergenrather (2003). Coll ecting behavioural data using the world wide web: Considerations for researchers. Journal of Epidemiological Community Health,57, 1. 68. Rich, M. (2003). Boy, Mediated: Effects of En tertainment Media on Adolescent Male Health. Adolescent Medicine, 14 (3), 691. Roberts, D. (2000). Media and youth: Access, exposure and privatization. Journal of Adolescent Health, 27(2), 8-14.

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188 Rogelberg, S., Stanton, J. (2007) Introduction: Understanding and dealing with organizational survey nonresponse. Organizational Research Methods, 10 (2), 195. Ross, M., Rosser, B., Coleman, E. &Mazin, R. ( 2006). Misrepresentation on the internet and in real life about sex and HIV: A study of Latino men who have sex with men. Culture, Health and Sexuality, 8 Rostosky, S. S., Regnerus, M. D., & Wright, M. L. (2003). Coital debut: Th e role of religiosity and sex attitudes in th e add health survey. The Journal of Sex Research, 40(4), 358. Sanders, C. E., Field, T. M., Diego, M., & Kaplan, M. (2000). The relationship of internet use to depression and isolation. Adolescence, 35 (138), 237. Sanger, D., Long, A., Ritzman, M., Stofer, K., & Davis, C. (2004). Opinions of female juvenile delinquents about their intera ctions in chat rooms. Journal of Correction Education,55 (2),120. Seidman, E., (2004). The pornographic retr eat: Contemporary patterns of pornographyuse and the psychodynamic meaning of frequent pornography use for heterosexual men. Dissertation Abstract International: Sec tion B:. The Sciences and Engineering (64) (8B), 4063. Shapira, N., Lessig, M., Goldsmith, T., Szabo, S., Lazoritz, M., & Stein, D. (2003). Problematic internet use: proposed classif cation and diagnostic criteria. Depression and Anxiety (17), 207-216 Tashakkori, A., & Teddlie, C. (1998). Mixed Methodology: Combining Qualitative and Quantitative Approaches (Vol. 46). Thousand Oaks: Sage Taylor, L. D. (2005). Effects of visual and verb al sexual television conten t and perceived realism on attitudes and beliefs. The Journal of Sex Research, 42 (2), 130-138. Weber, B., Yarandi, H., Rowe, M., & Weber, J. (2005). A comparison study: Paper versus webbased data collection and management. Applied Nursing Research 18, 182. Weiser, E. (2000). Gender differences in intern et use patterns and internet application preferences: A two sample comparison. Cyberpsychology and Behavior, 3 (2), 167-178. Weisskirch, R. S., & Murphy, L. C. (2004).Frien ds, porn, punk: Sensatio n seeking in personal relationships, internet activities, and music preferences among college friends. Adolescence, 39 (54), 13. Welych, L. R., Laws, B., Fiorito, A., Durham, T., & al, e. (1998). Formative research for interventions among adolescents at hi gh risk for gonorrhea and other STDs. Journal of Public Health Management and Practice, 4 (6), 54. Young, K. (1998). Caught in the Net: How to recognize the signs of Intern et addiction and a winning strategy for recovery. New York.

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189 BIOGRAPHICAL SKETCH My name is Paula C. Pritchard. I have been a nurse for over 20 years. I have practiced in the state of California as well as the state of Florida. My area of expertise is in maternal child nursing. I have worked in both the acute care set tings as well as the public sector caring for infants, children, adolescents and emerging adults. I have worked for the predominant portion of my career with the individuals and families that have been exposed to HIV disease, drug addiction, STI and those who had no or little acce ss to appropriate healthcare. During the last five years, I have had the opportuni ty to teach as both as an adjunc t and assistant professor at the Great Bethune-Cookman University. I am a graduate from California state university at Fullerton where I received a baccalaureate degree in nursing sc ience (BSN). Prior to the BS N, I received a associate of science in nursing from San Jaci nto Junior College in San Jaci nto, California. I completed my master of science in adult health and administ ration for the University of Phoenix in Maitland, Florida. My utilization review project involved African Amer ican Black Women in Volusia County with HIV disease and barriers to health ca re access. This research developed my personal desire to improve health care a ccess and preventable diseases from impacting this population to total annihilation. As this area of research has been one that is very close to my heart, during my doctoral studies, I determined that qualitative inquiry was th e very best method to use to gain insight into this pandemic. I developed this eclectic mi nor that encompassed psychology, anthropology, internet technology, and a deeper understanding of adolescent and emerging adults within this framework.

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190 Future plans include immediately pursuing th e second phase of this study using one of the populations within the sample. I also plan to attempt to develop a survey tool that will elicit clearer responses from participants in these vital areas. After developing that tool, I will investigate the internet phenome non within the HBCU system and replicate parts of the study for clarification and deeper understanding. I hope to make a diff erence in this at risk population.