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The Mediating Effects of Problematic Internet Usage on Social Phobia and Psychosocial Well-being

Permanent Link: http://ufdc.ufl.edu/UFE0044780/00001

Material Information

Title: The Mediating Effects of Problematic Internet Usage on Social Phobia and Psychosocial Well-being
Physical Description: 1 online resource (61 p.)
Language: english
Creator: Freedland, Alana
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: internet -- phobia -- psychosocial -- social -- well-being
Human Development and Organizational Studies in Education -- Dissertations, Academic -- UF
Genre: Mental Health Counseling thesis, M.A.E.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The increasing expansion andpopularity of the Internet and other media activities have led to concern among researchers as to the implications of this phenomenon on the user’s mental health and well being. The purpose of this thesis was to explore the effects of Internet usage as well as other media activities on social phobia symptoms,anxiety, depression, and loneliness.  Participants were comprised of  undergraduate students recruited at the University of Florida. Participants completed a packet of the following questionnaires and self-reported assessments that measured media usage, problematic Internet use, social phobia symptoms, anxiety, depression, and loneliness. Preliminary analysis revealed that the Media Usage Scale was not reliable(.064); therefore the GPIUS was used as a mediator instead. The analysis revealed significant direct effects for the various c paths  (LAS to loneliness, depression, and anxiety). GPIUS total score partially mediated the relationship between LAS total scoreand UCLA loneliness total score (R2 = .2114, F (5,296)= 15.87, p Social phobia symptoms were positively related to problematic Internet use, anxiety, depression, and loneliness. Additionally, problematic Internet usage partially mediated the relationships between social phobia symptoms andloneliness, depression, and anxiety. This study implicates the importance ofscreening and monitoring of problematic Internet prior to using the Internet for therapeutic reasons.
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.
Statement of Responsibility: by Alana Freedland.
Thesis: Thesis (M.A.E.)--University of Florida, 2012.
Local: Adviser: Smith, Sondra.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044780:00001

Permanent Link: http://ufdc.ufl.edu/UFE0044780/00001

Material Information

Title: The Mediating Effects of Problematic Internet Usage on Social Phobia and Psychosocial Well-being
Physical Description: 1 online resource (61 p.)
Language: english
Creator: Freedland, Alana
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: internet -- phobia -- psychosocial -- social -- well-being
Human Development and Organizational Studies in Education -- Dissertations, Academic -- UF
Genre: Mental Health Counseling thesis, M.A.E.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The increasing expansion andpopularity of the Internet and other media activities have led to concern among researchers as to the implications of this phenomenon on the user’s mental health and well being. The purpose of this thesis was to explore the effects of Internet usage as well as other media activities on social phobia symptoms,anxiety, depression, and loneliness.  Participants were comprised of  undergraduate students recruited at the University of Florida. Participants completed a packet of the following questionnaires and self-reported assessments that measured media usage, problematic Internet use, social phobia symptoms, anxiety, depression, and loneliness. Preliminary analysis revealed that the Media Usage Scale was not reliable(.064); therefore the GPIUS was used as a mediator instead. The analysis revealed significant direct effects for the various c paths  (LAS to loneliness, depression, and anxiety). GPIUS total score partially mediated the relationship between LAS total scoreand UCLA loneliness total score (R2 = .2114, F (5,296)= 15.87, p Social phobia symptoms were positively related to problematic Internet use, anxiety, depression, and loneliness. Additionally, problematic Internet usage partially mediated the relationships between social phobia symptoms andloneliness, depression, and anxiety. This study implicates the importance ofscreening and monitoring of problematic Internet prior to using the Internet for therapeutic reasons.
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.
Statement of Responsibility: by Alana Freedland.
Thesis: Thesis (M.A.E.)--University of Florida, 2012.
Local: Adviser: Smith, Sondra.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044780:00001


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1 THE MEDIATING EFFECTS OF PROBLEMATIC INTERNET USAGE ON SOCIAL PHOBIA AND PSYCHOSOCIAL WELL BEING By ALANA FREEDLAND A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQU IREMENTS FOR THE DEGREE OF MASTER OF ARTS IN EDUCATION UNIVERSITY OF FLORIDA 2012

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2 2012 Alana Freedland

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3 To my family, supervisors, mentors, professors, the students in the Counselor Education program, and all who have influenced my education

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4 ACKNOWLEDGMENTS I thank my parents for all of their support in my education. I thank my mentors, Dr. Smith, Dr. McNamara, and Dr. West Olantunji, and the graduate students for their help with the study. I thank the participa nts who volunteered to be a part of my study and the undergraduate research assistants who helped in the collection and entry of the data.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ...... 4 LIST OF TABLES ................................ ................................ ................................ ................ 7 LIST OF FIGURES ................................ ................................ ................................ .............. 8 LIST OF ABBREVIATIONS ................................ ................................ ................................ 9 ABSTRACT ................................ ................................ ................................ ........................ 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ ........ 12 Overview ................................ ................................ ................................ ..................... 12 Media Usage ................................ ................................ ................................ ............... 12 Interactive Media ................................ ................................ ................................ .. 13 Non interactive Media ................................ ................................ .......................... 14 Social Phobia and Media Activities ................................ ................................ ............ 15 Psychosocial Wellbeing ................................ ................................ .............................. 16 Purpose of the Study ................................ ................................ ................................ .. 16 Aims and Hypotheses ................................ ................................ ................................ 17 2 METHOD ................................ ................................ ................................ ..................... 19 Participants ................................ ................................ ................................ ................. 19 Measures ................................ ................................ ................................ .................... 19 3 RESULTS ................................ ................................ ................................ .................... 24 4 DISCUSSION ................................ ................................ ................................ .............. 32 Implicat ions and Limitations ................................ ................................ ....................... 34 Conclusion ................................ ................................ ................................ .................. 37 APPENDIX A INFORMED CONSENT ................................ ................................ .............................. 39 B MEDIA USAGE QUESTIONNAIRE ................................ ................................ ........... 41 C STATE TRAIT ANXIETY INVENTORY ................................ ................................ ..... 46 D LIEBOWITZ SOCIAL ANXIETY SCALE ................................ ................................ .... 47

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6 E UCLA LONELINESS SCALE (VERSION 3) ................................ .............................. 48 F BECK DEPRESSION INVENTORY (REVISED EDITION) ................................ ....... 49 G GENERAL PROBLEMATIC INTERNET USAGE SCALE ................................ ......... 51 H DEMOGRAPHIC QUESTIONNAIRE ................................ ................................ ......... 54 LIST OF REFERENCES ................................ ................................ ................................ ... 55 BIOGRAPHICAL SKETCH ................................ ................................ ............................... 61

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7 LIST OF TABLES Table page 3 1 Demographics ................................ ................................ ................................ ........ 28 3 2 Reliability of measures. ................................ ................................ .......................... 28 3 3 Measures of means ................................ ................................ ................................ 28 3 4 Correlations of Measures ................................ ................................ ....................... 29

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8 LIST OF FIGURES Figure page 2 1 Mediation model with loneliness, depression, and anxiety as the outcome variables. ................................ ................................ ................................ ................ 23 3 1 Mediation model with loneliness as the outcome variable. ................................ ... 30 3 2 Mediation model with depression as the outcome variable. ................................ 30 3 3 Mediation model with anxiety as the outcome variable. ................................ ....... 31

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9 LIST OF ABBREVIATION S BDI II Beck Depression Inventory (Revised Edition) GPIUS General Problematic Internet Usage Scale LSAS Liebowitz Soc ial Anxiety Scale p significance r Pearson product correlation R 2 Variance accounted for STAI Trait State Trait Anxiety Inventory Trait

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10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillme nt of the Requirements for the Degree of Master of Arts in Education THE MEDIATING EFFECTS OF PROBLEMATIC INTERNET USAGE ON SOCIAL PHOBIA AND PSYCHOSOCIAL WELL BEING By Alana Freedland August 2012 Chair: Sondra Smith Adcock Major: Mental Health Counseling The increasing expansion and popularity of the Internet and other media activities have led to concern among researchers as to the implications of this ph to explore the effects of Internet usage as well as other media activities on social phobia symptoms, anxiety, depression, and loneliness. Pa rticipants were comprised of undergraduate students recruited at the University of Florida. Participants completed a packet of questionnaires and self reported assessments that measured media usage, problematic Internet use, social phobia symptoms, anxiety, depression, and loneliness. Preliminary analysis revealed that the Media Usage Scale was not reliable (.064); therefore the GPIUS was used as a mediator instead. The analysis revealed significant direct effects fo r the various c paths LAS to loneliness, depression, and anxiety GPIUS total score partially mediated the relationship between LAS total score and UCLA loneliness total score ( R 2 = .2114, F (5,296) = 15.87, p <.000)), LAS total score and BDI II total score ( R 2 = .2558, F (5,324) = 22.27, p <.000), and LAS and STAI total score ( R 2 = 3907, F (5,342) = 43.85, p <.000).

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11 Social phobia symptoms were positively related to problematic Interne t us age anxiety, depression, and loneliness. Additional ly, problematic Internet usage partially mediated the relationships between social phobia symptoms and loneliness, depression, and anxiety. This study implicates the importance of screening and monitoring of problematic Internet use prior to using the Inte rnet for therapeutic reasons.

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12 CHAPTER 1 INTRODUCTION Overview The increasing popularity and expansion of the Internet and other technological media such as cellular phones has led to increased usage in recent years. Social networking on popular sites like Facebook has grown exponentially, with a total of 500 million users and counting, as of July 2010 (Zuckerberg, 2010). Additionally, cellular phone ownership has continued to rise, especially among younger populations. According to a survey at a large uni versity, 93% of students owned a cellular phone (Beaver, Knox, & Zusman, 2010). As technology users continue to grow, researchers have become more concerned about the implications of this expanding phenomenon (Mitchell, Lebow, Uribe, Grathouse, Shoger, 201 1). Th e goal of this thesis study is to examine the potential benefits and disadvantages of the growth of different media activities, the possible mental health implications for users with social phobia symptoms, and the effects of these media activities on psychosocial wellbeing for users with social phobia symptoms. Media Usage An increase in media activities has led to a multitude of research concerning the impact of these activities, specifically the use of the Internet (Dimitri, Moreno, Jelenchik, M yaing & Zhou, 2011; Caplan & High, 2011). With the increasing popularization of the Internet among college students, research on the addictive nature of online activity and the potential negative impact of Internet use has become a growing topic (Greenfiel d, 2011). For example, among a sample of college students, using the Internet for longer than five hours a day was significantly associated with problematic Internet use (Odaci

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13 & Kalkan, 2010). The researchers considered problematic Internet use to include using the Internet to avoid unpleasant emotions, addictive properties such as being unable to to amount of Internet use, the researchers also found p roblematic Intern et use to be significantly correlated to loneliness and dating anxiety. Odaci and Kalkan (2010) concluded that the Internet may be supplementing or replacing offline social activity and support. In contrast to Odaci and Kalkan ; Valenzuela, Park, and Kee ( 2009) found a positive relationship between the intensity of Facebook use (a social networking site), emotional attachment to the site, and life satisfaction among college students. These findi ngs suggest that online social activities may be positively related to well being. These contradictory findings suggest that Internet usage should not be examined as a single universal construct but should be divided into different constructs. Interactiv e M edia In addition to overall usage, particular Internet activities have become a hot topic among researchers. Liu and Larose (2008) found that the type of usage was a better predictor of perceived social support among college students than the quantity of hours spent online Online social self efficacy, or the ability to effectively communicate with others on the Internet, was positively related to satisfaction of social well being at school Communication through the use of chat rooms provides user s with a quick and instantaneous mode of online contact. R esearch ers ha ve found a link between chat room usage and personality types with conflicting results. According to Campbell, Cumming and Hughes (2006) and Peris and colleagues (2002), soci ally oriented people

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14 were more likely to participate in chat rooms than those who were introverted. Chat room users also reported lower symptoms of social phobia than nonusers. In contrast to these findings, Anolli, Villani and Riva (2005) found that chat room users tended to be introverted as defined by the Eysenck Personality Short Scale (EPQ R). Contradictions in these studies findings may be explained by the types of chat room usage examined. Anolli et al. (2005) specifically investigated chat room usag e for the purpose of relationship development, while Peris and colleagues (2002) examined total chat room use regardless of the purpose of use Another example of online social self efficacy includes the use of social networking sites. According to Hansen, Childress, and Trujillo (2010), social networking had a ion to other students. In addition to connect ion social networking users have also reported using these sites for social and informa tional support (Chung, 2011). Non interactive Media With research indicating that communication through various media activities may increase social self efficacy, the effects of non interactive types of media activities or activities not used for the p urpose of communication, such as Internet surfing have caused some researchers to be concerned over the potential social replacement effects that these activities may serve In a study examining the effects of Internet use, non communica tive online activity predicted higher levels of depression and social anxiety among participants with perceived low friendship quality (Selfhout, Branje, Delsing, ter Bogt, & Meeus, 2009). The findings of Selfhout et al. (2009) highlight the importance of differentiating between interactive and non interactive media usage when examining the effects of these types of activities on mental health. While the research has indicated a

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15 positive relationship between social well being and interactive media usage as well as the negative impact of non interactive media, studies have not examined the mediational relationship between these activities on social phobia symptoms and social well being. Social Phobia and Media Activities With evidence supporting the social advantages of interactive media and negative effects of non interactive media, current research examining social phobia and the Internet has begun to emerge. Researchers have found positive relationships between social phobia symptoms and Internet usage as well as other socially interactive technologies including text messaging and web cams (Rosenthal, 2010; Stevens & Morris, 2007). Similarly, Madell and Muncer (2006) found chat room usage in participants with social phobia to be slightly higher than those without the disorder. The that the group was achieving social gratification through the Internet. The authors suggested that the social phobia group may have preferre d chat room communication due to the lower threat of scrutiny compared to face to face communication. In addition to interactive media, researchers have also found a positive relationship between non interactive activities including Internet browsing for p ersonal use and social phobia (Mazalin & Moore, 2003). While these studies indicate a relationship between media usage and social phobia, the studies failed to examine the effects of these activities on well being. Examining if these activities are supplem enting or decreasing social well being in individuals with higher social anxiety is an important next step

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16 Psychosocial Wellbeing Although research indicates a clear positive relationship between social phobia and media usage, a consensus does not exist concerning the effects of media usage on psychosocial well being. According to Kavanaugh, Carroll, and Rosson (2005) the use being including the strengthening of offline relationship contrast to these findings, Stepanikova, Nie and He (2010) concluded that time spent on the Internet positively correlated to loneliness and resulted in lower life satisfaction. To further examine the impact of online activities, Kim and colleagues (2009) found that using the Internet for entertainment purposes had a negative impact on psychosocial wellbeing. Similarly, Smyth (2007) found that video game usage, specifically massively multiplayer online role pl wellness. The inconsistencies of the research related to psychosocial well being and Internet usage emphasize the importance of differentiating between interactive and non interactive media Purpos e of the Stud y The purpose of this study is to further examine the effects of interactive and non interactive media usage and social phobia symptoms on psychosocial wellbeing. Additionally, there are very few valid and reliable measurements that examine In ternet various sources of media such as cell phone usage and gaming consoles Most of these scales, including the General Problematic Internet Usage Scale ( Caplan, 2002 ) fail to differentiate betwe en interactive and non interactive Internet activities. Therefore, a questionnaire constructed by the researcher will be utilized to fully assess a variety of media activities.

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17 To further examine interactive social media activities, the questionnaire will break these down into two categories: immediate and delayed. Prior research has indicated a faster response, higher incentive, and preference for immediate over delayed rewards (Luo, Ainslie, Giragosian, & Monterosso, 2009; Wittmann, Lovero, Lane, & Paulus, 2010). Therefore, examining the differential impact of immediate versus delayed social media activities on psychosocial well being is important. The researcher predicts that the immediacy of the activity may have a n impact on social reward. The researcher designed the study to examine differences between delayed and immediate interactive online usage and whether non interactive media activities mediate the relationship between social phobia and psychosocial well being. Aims and Hypotheses Speci fic aim 1. To establish internal consistency, con struc t validity, and con vergen t validity of the Media Usage Scale with the General Problematic Internet Usage Scale. Hypothesis 1. It is hypothesized that the Media Usage Scale will have int ernal consistency, construct validity, and convergent validity with the General Problematic Internet Usage Scale Specific aim 2. To examine the relationship between social phobia symptoms and the total frequency and hours of total media usage in a sample of non clinical college aged participants. If the Media Usage Scale is invalid, the relationship between the General Problematic Internet Usage Scale (GPIUS) total and social phobia symptoms will be examined.

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18 Hypothesis 2. It is hypothesized that so cial phobia symptoms will have a significant positive correlation to the total frequency and hours of total media usage or GPIUS total. Specific aim 3. To examine the relationship between social phobia symptoms and trait anxiety, depression, and loneliness in a sample of college aged participants. Hypothesis 3. It is hypothesized that social phobia symptoms will have a significant positive correlation to trait anxiety, depression, and loneliness. Specific aim 4. To examine how the relationship between soci al phobia symptoms and trait anxiety, depression, and loneliness is mediated by immediate and delayed interactive and non interactive media frequency and duration or GPIUS total. Hypothesis 4. It is hypothesized that immediate and dela yed interactive and non interactive media frequency and duration or GPIUS total will mediate the relationship between social phobia symptoms and trait anxiety, depression, and loneliness. Immediate interactive media activities will have a stronger effect on the relationship between social phobia symptoms and trait anxiety, depression, and loneliness than delayed interactive media activities. Both interactive activities will influence the relationship in a positive direction. On the other hand, it is hypothesized that non interactive media activities will influence the relationship in a negative direction

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19 CHAPTER 2 METHOD Participants Participants were comprised of 427 undergraduate students recruited at a large u niversity (276 female, 151 male). 63.7% were White 14.9% Latino, 9.1% African American, 5.8% Asian American, .5% Asian East Indian, and 5.3% other. The average year in college was 2.28 and the mean age of the participants was 19.63. Measures Participants completed a packet of the following questionnaires and self reported assessments; the Media Usage Scale (MUS) the State trait Anxiety Inventory (STAI Y) the Liebowitz Social Anxiety Scale (LSAS) the UCLA Loneliness Scale (Version 3), the Beck Depression Inventory (Revised Edition) (BDI II) and the General Problematic Internet Usage Scale (GPIUS) The Media Usage Scale is a survey constructed by the researcher that examines the frequency, duration, and type of media ac tivities used by the sample population. The questionnaire consists of 38 questions that assess the frequency and duration of use of media activities including emailing, chatting online, texting on the cell phone, online shopping, et c. The last six questions are pulled from the General Problematic Internet Usage Scale (Caplan, 2002) examining constructs such as loneliness, addiction, social phobia, and isolation. I feel safer relating to others onli ne rather than face to face I seek others online when I feel isolated I have gotten in trouble at work/school because I was online The State trait Anxiety Inventory (STAI Y) ( Speilberger, Gorsuch, & Lushene, 1970) is a self report questionnair e that is used to measure anxiety. The questionnaire

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20 includes forty questions split into two sections. The first half of the report measures state anxiety, a temporary condition of perceived anxiety. The second half measures trait anxiety, the stable state For the purposes of this study, the second half of the inventory was used alone. The STAI Y is both a reliable and valid measure. The measure exhibits convergent validity with the IPAT Anxiety Scale, Manifest Anx iety Scale, and Affect Adjective Check List (r=.75, .80, and .52). The Liebowitz Social Anxiety Scale (LSAS SR) (Liebowitz, 1987) is a twenty four item self report questionnaire used to measure fear and avoidance in social interaction and performance sit uations. According to Rytwinski (2009) the LSAS SR is a cost effective and accurate psychometric determinant of the presence of social anxiety disorder, with a classification accuracy of 93.9% in a sample of 291 participants previously assessed with the St ructured Clinical Interview. The UCLA Loneliness Scale (Version 3) (Russell, Peplau, & Cutrona, 1980) is a self report survey consisting of twenty questions that examine perceived loneliness. Russell (1996) found the measure to contain high internal con sistency (r=.94), high convergent validity with the previous UCLA scale (r=.91), and the Beck Depression Inventory (r=.62). The Beck Depression Inventory (Revised Edition) (BDI II) (Beck, Steer, Ball, & Ranieri, 1996) consists of twenty one self reported q uestions used to measure depressive symptoms. The inventory displays high internal consistency (r=.73 .95) and test retest reliability (r=.60 .83). The General Problematic Internet Usage Scale ( G PIUS) ( Caplan, 2002 ) consists of 29 items examining negative consequences of the Internet, social benefit/social

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21 Online Cognitive Scale (r=0.61), high internal consistency (r=0.94), and high test re test reliability (r=0.81). Proced ure Approval of the study was obtained from the Institutional Review Board. Participants were recruited from undergraduate courses at a large Southeastern public university and received extra credit for participating in the study. Students were consented b y trained research assistants and completed the packets in front of the research assistants. The surveys took approximately thirty minutes for participants to complete. After completion, participants received a copy of the consent form. Data Analytic Stra tegy Preliminary analysis was conducted to test for reliability among the measures and normative distribution of each variable to test for skewness and kurtosis. Using a blom transformation, non normative variables were corrected for skewness and kurtosis. A factor analysis was conducted to test for factor loadings. To examine the first aim of the study, which was to establish internal consistency, convergent validity, and construct validity of the Media Usage Scale with the General Problematic Internet Usa ge Scale a C ronbach alpha score was obtained for the measure Since the Media Usage Scale ( MUS ) yielded a poor alpha score, a validity score was not necessary. Aims 2 and 3, which were t o examine the relationship s between social phobia symptoms and th e total frequency and duration of media usage or GPIUS total and to examine the relationship s between social phobia symptoms and trait anxiety, depression, and loneliness, were examined using Pearson product correlations, a method used to look at the linear relationships between variables (Kornbrot, 2005) To examine the fourth aim of the

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22 study, which was to examine how the relation ship between social phobia symptoms and trait anxiety, depression, and loneliness is mediated by immediate and delayed interactive and non interactive media frequency and duration or GPIUS total, a m ediation analysis was conducted to ex amine the relationship between L S AS total score and UCLA loneliness total score, BDI II total score, and STAI Y trait total score with GPIUS total score as the proposed mediator. The analysis was conducted using a method used to study indirect effects in ordinary least squares (OLS) regression

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23 Figure 2 1. Mediation model with loneliness, depression, and anxiety as the outcome variables. Loneliness, Depression, Anxiety Social Phobia Problematic Internet Usage

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24 CHAPTER 3 RESULTS Preliminary analysis revealed skewness in the UCLA Loneliness total score (5.38), GPIUS total score (2.18), the Liebowitz Social Anxiety total score (5.22), the STAI trait total score (5.21), and both skewness and kurtosis in the BDI total score (14.86, 24.81). These variables were normalized using a blom transformation. Specific Aim 1 The first aim of the study was to establish internal consistency, convergent validity, and construct validity of the Media Usage Scale with the General Problematic Internet Usage Scale. To examine reliability, e ach measure was tested and yielded adequate Since a scale cannot be valid if it is unreliable it was determined that the Media Usage Scale was both an invalid and unreliable measure, thus this scale was not used as a mediator in the analysis. Specific Aim 2 The second aim of the study was to exa mine the relationship between social phobia symptoms and the total frequency and duration of total media usage in a sample of non clinical college aged participants If the Media Usage Scale were determined to be invalid, the relationship between the GPIUS total and social phobia symptoms would be examined instead Since the Media Usage Scale was determined to be invalid and unreliable, the GPIUS was substituted for the MUS and correlated to LSAS total score while control ling for g ender, body mass in dex ( BMI ), and i ncome Prior research findings have revealed significant relationships between gender (Johnson, 2011; Yang, Chiu, & Chen, 2011), BMI (Cheng Fang et al., 2010), socio economic status (Hargittai,

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25 2010) and the frequency of specific patte rns of technology use and activities Additionally, data analysis of this study revealed significant relationships between gender and STAI Y trait total ( r = .123) and LSAS total ( r = .204), BMI and UCLA Loneliness total ( r = .129), and income and UCLA Lon eliness total ( r = .130). Therefore these variables were controlled for. The GPIUS total score was significantly positively correlated to L S AS total score ( r = .347), Additionally, GPIUS was correlated with the UCLA Loneliness total score, BDI total score and STAI trait total score while controlling for g ender, BMI and i ncome. The GPIUS total score was significantly positively correlated to UCLA loneliness total score ( r = .297), BDI II total score ( r = .369), and the STAI Y trait total score ( r = .440) Specific Aim 3 The third aim of the study was to examine the relationship between social phobia symptoms and trait anxiety, depression, and loneliness. Social phobia was positively related to trait anxiety ( r = .586), depression ( r = .440), and lonelines s ( r = .372). Specific Aim 4 The fourth aim of the study was to examine how the relationship between social phobia symptoms and trait anxiety, depression, and loneliness was mediated by immediate and delayed interactive and non interactive media frequency and duration or GPIUS total. GPIUS total was analyzed as the mediator because the MUS was determined to be invalid and unreliable. A preliminary factor analysis revealed low factor loadings between loneliness, anxiety, and depressio n; therefore these variables were examined separately. Using squares (OLS) regression, the researcher tested the strength of the relationship

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26 between the independent variable and the assigned mediator (Liebowitz Social Anxiety Scale (L S AS) and GPIUS score, a path ), the total effect of the independent on the dependent variable (L S AS total score and UCLA Loneliness total score BDI II total score, and STAI Y total score respecti vely; c path ), after examining the indirect effect of the independent variable on the dependent variable through the mediator ( ab path ). estimate of this indirect effect (1000 re samples generated with bias correction and acceleration). The analysis revealed significant direct effects for the various c paths ( social phobia to loneliness, depression, and anxiety) GPIUS total score partially mediated the relationship between L S AS total score and UCLA loneliness total score ( R 2 = .2114, F (5,296) = 15.87, p <.000)), L S AS total score and BDI II total score ( R 2 = .2558, F (5,324) = 22.27, p <.000), and L S AS and STAI Y total score ( R 2 = 3907, F (5,342) = 43.85, p <.000). Add itional Findings Individual items of the Media Usage S cale were correlated to GPIUS total score, L S AS total score, UCLA loneliness total score, BDI II total score, and STAI Y total score while controlling for g ender, BMI, and income. After controlling fo r these variables, individual MUS items were not significantly correlated to GPIUS, L S AS, UCLA, BDI II and STAI Y total scores. Females participated in significantly more social networking than males. Males participated in significantly more non interacti ve and interactive computer and video gaming than females. Higher BMI was negatively associated with frequency and duration of time spent on a social networking site for non interactive purposes ( r = .14, .12), sending instant messa ges ( r = .14), and entering a chat room

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27 ( r = .13 ). Higher BMI was positively correlated with time spent sending and responding to email ( r = .10), time spent playing non interactive computer or video games ( r = .15), selling an item online ( r = .14), tal king on the phone or video webcam ( r = .17), and blogging ( r = .13). Income was negatively correlated with the frequency and duration of time spent updating a status on a social networking site ( r = .12, .11), and frequency and duration of time spent selling items online ( r = .13, .11). Income was positively associated with going on the Internet for non interactive purposes such as surfing the web and researching ( r = .14). Gender, BMI, and i ncom e were not significantly correlated with GPIUS total score.

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28 Table 3 1. Demographics Variable Means Gender Males Females 151 276 Age 19.63 Income 70,000 79.999 Table 3 2. Reliability of measures. Measure Cronbach Alpha STAI Trait .912 BDI .879 UCLA Fear .902 UCLA Avoidance .874 GPIUS .925 Media Usage Scale .064 Table 3 3. Measures of means Measure N Mean Std. Dev. UCLA Loneliness Scale_Total 361 34.83 8.96 GPIUS_Total 424 60.41 16.80 LSAS_Total 395 37.55 20.60 BDI_Total 393 7.38 6.66 STAI_trait_Total 416 37.45 9.84

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29 Table 3 4. Correlations of Measures GPIUS_Total UCLA_Loneliness _Total BDI_Total STAI_trait _Total LSAS_Total GPIUS_Total 1.00** .297** .369** .440** .347** UCLA_Loneliness_Total .297** 1.00** .455** .577** .372** BDI _Total .369** .455** 1.00** .728** .440** STAI_trait_Total .440** .577** .728** 1.00** .586** LSAS_Total .347** .372** .440** .586** 1.00** **p<.001

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30 R 2 = .2114, F (5,296) = 15.87, p <.001 Figure 3 1. Mediation model with loneliness as the outcome variable. R 2 = .2558, F (5,324) = 22.27, p <.001 Figure 3 2. Mediation model with depression as the outcome variable. Social Phobia Loneliness Problematic Internet Usage Social Phobia Pro blematic Internet Usage Depression

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31 R 2 = .3907, F (5,342) = 43.85, p< .001 Figure 3 3. Mediation model with anxiety as the outcome variable. Anxiety Social Phobia Problematic Internet Usage

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32 C HAPTER 4 DISCUSSION The results of the study revealed significant positive relationships between social phobia and lonelin ess, depression, and anxiety. Problematic Internet u sage was significantly related to these variables as well. Additionally, problematic Internet usage partially mediated the relationships between social phobia and loneliness, depression, and anxiety. Pro blematic Internet usage was determined to be a partial mediator since the relationships between social phobia and the outcome variables (loneliness, depression, and anxiety) were also significant. Therefore, although problematic Internet usage did not full y account for these relationships, it was found to significantly strengthen the mediation model. Although the Media Usage Scale was determined to be unreliable, individual items of the scale were analyzed for significant relationships to the following var iables; problematic Internet usage, social phobia, loneliness, depression, and anxiety. However, a fter controlling for gender, BMI, and income, individual items on the MUS were not correlated to GPIUS, L S AS, UCLA Loneliness BDI II and STAI Y total scores While some individual items on the MUS correlated with gender, BMI, and income, these variables did not reveal significant relationships with problematic Internet usage. Data analysis also revealed that individual items on the Media Usage Scale were insi gnificant mediators. Despite these insignificant findings, it is important to note that while participation in specific media activities and hours spent participating in these activities did not mediate the relationship between social phobia symptoms and psychosocial well being, problematic Internet usage proved to be significant. Problematic Internet use included

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33 using the Internet when feeling isolated or lonely, replacing the Internet with face to face activity, and Internet addiction (Caplan, 2002). Pr oblematic Internet usage was significantly correlated to social phobia symptoms, trait anxiety, depression, and loneliness. Additionally, problematic usage significantly increased the relationships between social phobia symptoms and anxiety, depression, an d loneliness. These findings are similar to other studies that suggest that time spent on the Internet does not correlate to psychosocial well being (Campbell, Cumming, & Hughes, in which hours of Internet use was positively related to anxiety and depressive symptoms. However, in accordance with the present study, the researchers also found a positive correlation between using the Internet to replace face to face interaction and both anxiety and depressive symptoms. Additionally, other studies have found that using the Internet for problematic purposes, such as using the Internet to replace face to face activity or using the Internet when feeling isolated or lonely, is negatively related to psychosocial well being (Odaci & Kalkan, 2010; Caplan, 2002). Similarly, Weidman, Fernandez, Levinson, Augustine, Larsen, and Rodebaugh (2012) found that participants with social phobia who used the Internet to replace face to face interaction h ad a lower quality of life and higher depression than those who did not use the Internet to replace face to face interaction. The results of the current study suggest that it is important to examine purpose of use rather than specific activities and hours spent on the Internet when determining unhealthy Internet habits. The insignificance of the individual media activities and hours spent participating in the activities offers an expla nation of why the Media Usage Scale was found to unreliable. While the sc ale can be used for qualitative

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34 purposes as an analysis of individual items it should not be used for measuring or diagnosing unhealthy media usage. In summary, the results of this study showed that problematic Internet usage was positively related to al l of the constructs including social phobia symptoms, loneliness, depression, and anxiety. Social phobia symptoms were positively related to loneliness, depression, and anxiety. Finally, problematic Internet usage strengthened the relationships be tween social phobia symptoms and loneliness, depression, and anxiety. These findings show that problematic Internet usage partially mediated the relationship between social phobia symptoms and loneliness, depression, and anxiety. Implications and Limitat ions The present study findings suggest that it is important for counselor s to examine the purpose of Internet usage and other media activities when determining problematic behavior and effects on psychosocial well being with clients with social phobia sym ptoms. Research suggests a high frequency of Internet use among young adults (Loan, 2011; Assael, 2005) as well as other media forms such as cellular phone usage (Hanson, Drumheller, Mallard, McKee, & Schiegel, 2011). With the particularly high prevalence of media usage within this population, counselors should be aware of problematic Internet usage when working with college students, especially college students who present social phobia symptoms. Additionally, college students are often presented with uniq ue social situations that can be particularly difficult for students who suffer from social phobia symptoms such as large classroom sizes, social events, and public speaking. Seim and Spates (2010), for example, found public speaking to be the second most common fear among a sample of college students. This has a high implication for college counselors who are likely to see students who present with social

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35 phobia symptoms. The results of the present study suggest that problematic Internet usage should be ad dressed and treated, if necessary for students suffering from symptoms of social phobia. With the advancement of technology such as the Internet, the popularity of the utilization of the Internet for therapeutic purposes has increased (Diggins, 2012; Pax ling et. al., 2011; Moritz, Wittekind, Hauschildt, & Timpano, 2011). However, t he present study suggests that counselor s should screen for problematic Internet behavior prior to using the Internet as an alternative or adjunct to face to face therapy. Couns elor s can utilize the GPIUS prior to conducting therapeutic services over the Internet to help determine the utility and appropriateness of an online intervention. Additionally, counselor reening and monitoring of Internet behavior may prevent further anxiety, depressive, and loneliness symptoms. Counselors should consider avoiding the use of the Internet for therapeutic interventions with clients with high problematic Internet usage scores Additionally, counselors should limit or conclude use of the Internet for therapeutic interventions if the client begins to show problematic usage symptoms during treatment. Further implications include the importance of treating problematic Internet us e to help in treating anxiety, depression, and loneliness in clients with social phobia symptoms. Because Internet addi ction is a fairly new ly identified phenomenon and currently unrecognized in the Diagnostic and Statistical Manual, Fourth Edition, Text Revision (American Psychiatric Association [DSM IV TR], 2000), very few interventions have been explored for this disord er. One treatment model that has been created specifically for Internet addiction, a characteristic of problematic Internet use, is

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36 Cognitive Behavioral Therapy for Internet addiction (CBT IA; Young, 2011). This model includes techniques that utilize cogni tive and behavioral modification through daily Internet logs, cognitive restructuring for maladaptive beliefs such as worthlessness without the Internet, and harm reduction therapy. The findings of the present study suggest that therapeutic interventions b e explored for the use of treating problematic Internet use While Internet addiction is one aspect of problematic Internet usage, according to Caplan (2002) other factors of problematic Internet usage include Internet use to alter mood, for social benefit and for social control. Interventions aimed at treating problematic Internet usage should be explored for these factors in addition to the treatment of the addictive behaviors. Additionally, counselors should assess for social phobia, loneliness, depres sive, and anxiety symptoms when treating problematic Internet use. According to the findings of this study, problematic Internet use is positively related to these constructs. Therefore, it is important for counselors to assess for these symptoms when clie nts present symptoms of problematic Internet use and treat if necessary. The limitations of this study include the use of non clinical participants; thus the results cannot be applied to a clinical population. However, the use of a non clinical population has further implications for the field of counseling since counselors often work with non clinical clients. Another limitation includes the use of only college aged participants from one university, which limited the diversity of the sample. While this li mited the diversity of the sample of the study, the sample selection may have decreased the variability of the results thus increasing significance for the selected population.

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37 the surveys in person rather than online. Online administration could have provided the clinical population deemed the method of the study to be appropriate for the desired samp le. From the present study, problematic Internet usage can be determined to be a partial mediator, however, the direction of the relationship cannot be assumed. Therefore, a future direction of the study could further explore this relationship through the use of a longitudinal design. Other f uture directions for the study include examination with a clinical population and more diverse sample Internet based treatment for social phobia has raised special interest since many sufferers have difficulty attendi ng face to face therapy and fear seeking treatment (Kessler, 2003). Internet based treatment for social phobia has been proven to be effective both with and without clinical assistance (Berger, Caspar, Richardson, Kneubhuler, Sutter, & Andersson, 2011; Ay dos, Titov, & Andrews, 2009). It is important to examine problematic Internet usage within this clinical population as the popularity of Internet based treatment increases. Additionally, it will be important to examine problematic Internet usage in a wide range of clinical populations as the use of the Internet increases for the treatment of various disorders including anxiety disorders, post traumatic stress disorder, alcohol abuse, and depression (Cartbring et al., 2011; Wagner, Schulz, & Knaevelsrud, 201 2; Blankers, Koeter, & Schippers, 2011; Berger, Hammeril, Gubser, Andersson, & Caspar, 2011). Conclusion In conclusion, the findings of the present study impl y that when examining problematic Internet behavior it is imperative that counselor s look at the purpose of use

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38 rather than the frequency and duration of use. Counselors should assess for problematic Internet usage when working with clients who present social phobia symptoms. Additionally, therapeutic interventions for the treatment of problematic Int ernet usage should be explored. Future directions of this study include examining Internet use in clinical populations and other demographic groups.

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39 A PPENDIX A INFORMED CONSENT Protocol Title: Social Phobia and Media Usage Please read this consent docu ment carefully before you decide to participate in this study. Purpose of the research study: The purpose of this study is to examine the mediating effects of interactive and non interactive media usage on the relationship between social phobia and psycho social well being. What you will be asked to do in the study: You will be asked to fill out a packet of questionnaires measuring media usage, mood, social phobia symptoms, and personality. Time required: 15 30 minutes Risks and Benefits: Minimal risk is involved in this study. You may find the questions to be monotonous. You may also feel uncomfortable about the personal nature of the questions. You will not benefit directly or personally from this study. The data collected in this study may be benefi cial in developing and/or adjusting treatments for social phobia in the future. There are no financial benefits for participating in this study. Participants will be awarded extra credit for completing the study. Compensation: You will be awarded extra cr edit that will be worth less than 2% of your grade for completing this study. Confidentiality: Your identity will be kept confidential to the extent provided by law. Your packet will be assigned a participant ID number No identifying information will be connected to your responses Your consent form will be locked in a room in the Behavioral Health Unit. Your name will not be used should the study get published. Voluntary participation: Your participation in this study is completely voluntary. There is no penalty for not participating. Right to withdraw from the study: You have the right to withdraw from the study at anytime without consequence. Whom to contact if you have questions about the study:

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40 Sondra Smith, Ph. D.; Department of Counselor Educ ation; P.O. Box 117046, Gainesville, Fl 32610; phone: 273 4328 Alachua County Crisis Center; 218 Southeast 24th Street, Gainesville, FL 32641 7516 ; (352) 264 6789 Whom to contact about your rights as a research participant in the study: IRB02 Office, Box 112250, University of Florida, Gainesville, FL 32611 2250; phone 392 0433. Agreement: I have read the procedure described above. I voluntarily agree to participate in the procedure and I have received a copy of this description. Participant: __________ _________________________________ Date: _________________ Person Obtaining Consent : ___________________________________ Date: _________________

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41 APPENDIX B MEDIA USAGE QUESTION NAIRE Instructions: Indicate how many times and how long you engage in the fo llowing media activities. 1. How many times on average have you logged into a social networking site for non interactive purposes (without the intent of communication, e.g. browsing, playing games) in the past week? ____ times 2. How much time on average have you spent on a social networking site for non interactive purposes (without the intent of communication, e.g. browsing, playing games) in the past week? Just include the average time for each instance. For example: I spend an average of 30 minutes browsing a social networking site each time I log in. _____hours ___ minutes ____seconds 3. friend on a social networking site in the past week? _____times 4. How much time on average have you messaging a friend on a social networking site in the past week? Just include the average time for each instance. For example: I spend an average of 30 seconds ____hours ___ _minutes ____seconds 5. How many times on average have you updated your status on a social networking site this past week? _____times 6. How much time on average have you spent updating your status on a social networking site this past week? Just include the av erage time for each instance. For example: I spend an average of 30 seconds updating my status. _____ hours____minutes ____seconds 7. How many times on average have you sent or responded to an email this week? _____times 8. How much time on average have you sp ent sending or responding to your email this week? Just include the average time for each instance. For example: I spend an average of 3 minutes sending or responding to an email. ____minutes ____seconds 9. How many times on average have you read your email without responding this week? _____times

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42 10. ______times 11. How much time on average have you spent reading your email without responding this week? Just include the average time for each instance. For example: I spend an average o f 3 minutes checking an email. ____hours _____minutes ____seconds 12. How many times on average have you sent or responded to an instant message online this week? _____times 13. How much time on average have you spent sending instant messages online this week? J ust include the average time for each person. For example: I spend an average of 30 seconds instant messaging a friend online. ____hours_____minutes ____seconds 14. How many times on average have you entered a chat room online this week? _____times 15. How much time on average have you spent sending a message in a chat room online this week? Just include the average time for each instance. For example: I spend an average of 20 seconds sending a message in a chat room. _____hours _____minutes ____seconds 16. How many times on average have you played a non interactive (i.e. not involving other players) computer or video game this week? _____times 17. How much time have you spent on average playing a non interactive (i.e. not involving other players) computer or video game this week? Just include the average time for each instance. For example: I spend an average of 2 hours playing a non interactive computer or video game. _____hours ____minutes ____seconds 18. How many times on average have you played a interactive (i.e. invo lving other players) computer or video game this week? _____times 19. How much time on average have you spent playing an interactive (i.e. involving other people) computer or video game this week? Just include the average time for each instance. For example: I spend an average of 2 hours playing an interactive computer or video game. _____hours ___minutes ____seconds 20. How many times on average have you gone on any other interactive online site (e.g., virtual world) this past week? ____times

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43 21. How much time on a verage have you spent on any other interactive online site (e.g., virtual worlds) this past week? Just include the average time for each instance. For example: I spend an average of 2 hours in a virtual world each time I enter the site. ______hours ____mi nutes ____seconds 22. I am a Florida gator. 23. How many times on average have you messaged someone on a dating site this week? _____times 24. How much time on average have you spent messaging someone on a dating site t his week? Just include the average time for each instance. For example: I spend an average of 30 seconds sending a message to someone on a dating site. ____hours ____minutes ___seconds 25. How many times on average have you searched on a dating site without m essaging someone this week? _____times 26. How much time on average have you spent searching on a dating site without messaging someone this week? Just include the average time for each instance. For example: I spend an average of 1 hour searching on a dating site each time I log in. _____hours ____minutes ____seconds 27. How many times on average have shopped online this week? _____times 28. How much time on average have you spent shopping online this week? Just include the average time for each instance. For examp le: I spend an average of 1 hour shopping on one online website. _____hours ____minutes ____seconds 29. How many times on average have you engaged in online banking this week? ______times 30. How much time have you spent online banking this week? Just include th e average time for each instance. For example: I spend an average of 20 minutes on an online banking website. ______hours ____minutes ____seconds 31. How many times on average have you sold an item online this week? _____times 32. How much time on average have y ou spent selling items online this week? Just include the average time for each instance. For example: I spend an average of 20 minutes selling an item online. _____hours ____minutes ____seconds 33. How many times on average have you talked on the phone or vi deo webcam this week?

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44 _____times 34. How much time on average have you spent talking on the phone or video webcam this week? Just include the average time for each instance. For example: I spend an average of 30 minutes talking to one person on the phone or v ideo webcam. _____hours ___minutes ____seconds 35. How many times on average have you sent or responded to a text message this week? _____times 36. How much time on average have you spent sending or responding to a text message this week? Just include the averag e time for each instance. For example: I spend an average of 20 seconds sending or responding to a text message. ____hours _____minutes ____seconds 37. How many times on average have you gone on the Internet for non interactive (without the intent of communic ation) purposes (e.g. surfing the web, research, school work, etc.,) this past week? _____times 38. How much time on average have you spent on the Internet for non interactive (without the intent of communication) purposes (e.g. surfing the web, research, sch ool work, etc.,) this past week? Just include the average time for each instance. For example: I spend an average of 2 hours on the Internet each time I open up my browser. _____hours ____minutes ____seconds 39. ____hours ____min utes ____seconds 40. How many times on average have you blogged this past week? _____times 41. How much time on average have you spent blogging this past week? Just include the average time for each instance. For example: I spend an average of 30 minutes blogg ing each time I update my website. _____hours ___minutes ____seconds Instructions: Indicate how you feel about the following statements. 42. I communicate on the Internet when I am lonely (1)____ (2)____ (3)__ ___ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 43. I try to find people onl ine when I feel isolated (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree

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45 44. I feel safer communicating with others online rather than face to face (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 45. I am more confident interacting with people online than offline (1)___ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 46. I am more comfortable being on a computer than being with people (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 47. (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree

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46 APP ENDIX C STATE TRAIT ANXIETY INVENTORY

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47 APPENDIX D LIEBOWITZ SOCIAL ANXIETY SCALE

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48 APPENDIX E UCLA LONELINESS SCALE (VERSION 3)

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49 APPENDIX F BECK DEPRESSION INVENTORY (REVISED EDITION)

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50

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51 APPENDIX G GENERAL PROBLEMATIC INTERNET USAGE SCALE Instructi ons: Indicate how you feel about the following statements. 1. I use the Internet to talk with others when I feel isolated (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 2. I seek others online when I feel isolated (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 3. I us e the (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 4. (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 5. I am treated better online than in face to face relations hips (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 6. I feel safer relating to others online rather than face to face (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 7. I am more confident socializing online than offline (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 8. I am more comfortable with computers than people (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neit her Agree nor Disagree Agree Strongly Agree 9. I am treated better online than offline (1)____ (2)____ (3)_____ (4 )_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 10. I have gotten in trouble at work/school because I was online (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 11. I missed class or work because I was online (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree

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52 12. I feel worthless offline but I am someone online (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 13. online (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 14. I have had unsuccessful attempts to control my Internet use (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 15. I am unable to reduce my time online (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 16. I feel guilt abo ut time online (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree A gree Strongly Agree 17. I tried to stop using the Internet for long periods of time. (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 18. I lose track of time online (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 19. I spent a good de al of time online (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 20. I go online for longer time than I intended (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 21. Place a check above Disagree (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 22. I am preoccupied with the Inte (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree n or Disagree Agree Strongly Agree 23. (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 24. When not online, I wonder what is happening online

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53 (1)____ (2 )____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 25. (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agre e nor Disagree Agree Strongly Agree 26. (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 27. (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 28. (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 29. I have control over how others perceive me online (1)____ (2)____ (3)_____ (4)_____ (5)_____ Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree

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54 APPENDIX H DEMOGRAPHIC QUESTIONNAIRE

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55 LIST OF REFERENCES American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (Revised 4th ed.). Washington, DC: Author. Anolli, L., Villani, D., & Riva, G. (2005). Personality of people us ing chat: An on line research. CyberPsychology & Behavior, 8 (1), 89 95. doi:10.1089/cpb.2005.8.89 Assael, H. (2005). A demographic and psychographic profile of heavy internet users and users by type of internet usage. Journal of Advertising Research, 45 ( 1), 93 123. doi:10.1017/S0021849905050014 Aydos, L., Titov, N., & Andrews, G. (2009). Shyness 5: The clinical effectiveness of internet based clinician assisted treatment of social phobia. Australasian Psychiatry, 17 (6), 488 492. doi:10.1080/1039856090328 4943 Beaver, T., Knox, D., & Zusman, M. E. (2010). 'Hold the phone!': Cell phone use and partner reaction among university students. College Student Journal, 44 (3), 629 632. Retrieved from https://search.ebscohost. com.lp.hscl.ufl.edu/login.aspx?direct=true&db=psyh&AN =2010 21257 004&site=ehost live Beck, A. T., & Steer, R. A. (1987). Beck depression inventory [revised edition]. Retrieved from http://search.ebscohost.com.lp.hscl.ufl.edu/login.aspx?direct=true&db=loh&AN=11 %3a31&site=ehost live Berger, T., Caspar, F., Richardson, R., Kneubhler, B., Sutter, D., & Andersson, G. (2011). Internet based treatment of social phobia: A randomized controlled trial comparing unguided with two types of guided self help. Behaviour Research and Therapy, 49 ( 3), 158 169. doi:DOI: 10.1016/j.brat.2010.12.007 Berger, T., Hmmerli, K., Gubser, N., Andersson, G., & Caspar, F. (2011). Internet based treatment of depression: A randomized controlled trial comparing guided with unguided self help. Cognitive Behaviour Therapy, 40 (4), 251 266. doi:10.1080/16506073.2011.616531 Blankers, M., Koeter, M. W. J., & Schippers, G. M. (2011). Internet therapy versus internet self help versus no treatment for problematic alcohol use: A randomized controlled trial. Journal of Co nsulting and Clinical Psychology, 79 (3), 330 341. Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?dir ect=true&db=eric&AN=EJ934323&site=ehost live; http://dx.doi.org.lp.hscl.ufl.edu/10.1037/a0023498

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56 Campbell, A. J., Cumming, S. R., & Hughes, I. (2006). Internet use by the soc ially fearful: Addiction or therapy? CyberPsychology & Behavior, 9 (1), 69 81. doi:10.1089/cpb.2006.9.69 Caplan, S. E. (2002). Problematic internet use and psychosocial well being: Development of a theory based cognitive behavioral measurement instrument. Computers in Human Behavior, 18 (5), 553 575. doi:DOI: 10.1016/S0747 5632(02)00004 3 Caplan, S. E., & High, A. C. (2011). Online social interaction, psychosocial well being, and problematic internet use. In K. S. Young, C. N. de Abreu, K. S. Young & C. N de Abreu (Eds.), Internet addiction: A handbook and guide to evaluation and treatment. (pp. 35 53). Hoboken, NJ US: John Wiley & Sons Inc. Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?direct=true&db=psyh&AN=2010 22949 003&site=ehost live Carlbring, P., Maurin, L., Trngren, C., Linna, E., Eriksson, T., Sparthan, E., Andersson, G. (2011). Individually tailored, internet based treatment for anxiety disorders: A randomized controlled trial. Behaviour Research & Therapy, 49 (1), 18 24. doi:10.1016/j.brat.2010.10.002 Cheng Fang Yen, Hsiao, R. C., Chih Hung Ko, Ju Yu Yen, Chi Fen Huang, Shu Chu n Liu, & Shing Yaw Wang. (2010). The relationships between body mass index and television viewing, internet use and cellular phone use: The moderating effects of socio demographic characteristics and exercise. International Journal of Eating Disorders, 43 ( 6), 565 571. Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?direct=true&db=a2h&AN=53856248&site=ehost live Chung, J. E. (2011). Benefits of social networking in online social support groups. ProQuest Information & Learning). Dissertation Abstracts International Section A: Humanities and Social Sciences, 71 (9 ) Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?direct=true&db=psyh&AN=2011 99050 380&site=ehost live (2011 99050 380) Diggins, K. (201 2). Internet based therapy for depression. Journal for Nurse Practitioners, 8 (1), 79 81. doi:10.1016/j.nurpra.2011.09.011 Englander, F., Terregrossa, R. A., & Wang, Z. (2010). Internet use among college students: Tool or toy? Educational Review, 62 (1), 85 96. doi:10.1080/00131910903519793 Eysenck, H. J., & Eysenck, S. B. G. (1969). Eysenck personality inventory.

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57 Greenfield, D. (2011). The addictive properties of internet usage. In K. S. Young, C. N. de Abreu, K. S. Young & C. N. de Abreu (Eds.), Internet addiction: A handbook and guide to evaluation and treatment. (pp. 135 153). Hoboken, NJ US: John Wiley & Sons Inc. Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?direct=true&db=psyh&AN=2010 22949 008&site=ehost live Hansen, M. J., Childress, J. E., Trujillo, D. J., & Association for, I. R. (2010). Exploring the effects of social networking on st udents' perceptions of social connectedness, adjustment, academic engagement, and institutional commitment. ().Association for Institutional Research. Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?direct=true&db=eric&AN=ED520480&site=ehost live Hanson, T. L., Drumheller, K., Mallard, J., McKee, C., & Schlegel, P. (2011). Cell phones, text messaging, an d facebook: Competing time demands of today's college students. College Teaching, 59 (1), 23 30. doi:10.1080/87567555.2010.489078 H argittai, E. [. (. (2010). Digital natives variation in internet skills and uses among members of the net generation : Toward a sociology of inequality in the digital century (english). Sociol.Inq., 80 (1), 92 113. Retrieved from https://search ebscoho st com.lp.hscl.ufl.edu/login.aspx?direct=true&db=fcs&AN=22389534&site=ehost live Johnson, G. M. (2011). Internet activities and developmental predictors: Gender differences among digital natives. Journal of Interactive Online Learning, 10 (2), 64 76. Retri eved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?direct=true&db=eric&AN=EJ938851&site=ehost live; http://www.ncolr.org/jiol/issues/pdf/10.2.1.pdf Kavanaugh, A., Carroll, J. M., & Rosson, M. B. (2005). Community networks: Where offline communities meet online. Journal of Computer Medi ated Communication, 10 (4), 1 1. Kessler, R. C. (2003). The impairments caused by social phobia in the general population: Implications for intervention. Acta Psychiatrica Scandinavica, 108 19 27. doi:10.1034/j.1600 0447.108.s417.2.x Kornbrot, D. (2005 ). Pearson product moment correlation John Wiley & Sons, Ltd doi:10.1002/0470013192.bsa473 Liu, X., & LaRose, R. (2008). Does using the internet make people more satisfied with their lives? the effects of the internet on college students' school life sati sfaction. CyberPsychology & Behavior, 11 (3), 310 320. doi:10.1089/cpb.2007.0040 Loan, F. A., fayazlib@yahoo.co.in (2011). Internet use by rural and urban college students: A comparative study. DESI DOC Journal of Library & Information

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58 Technology, 31 (6), 431 438. Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/ login.aspx?direct=true&db=ofm&AN=71835966&site=ehost live Luo, S., Ainslie, G., Giragosian, L., & Monterosso, J. R. (2009). Behavioral and neural evidence of incentive bias for immediate rewards relative to preference matched delayed rewards. Journal of N euroscience, 29 (47), 14820 14827. doi:10.1523/JNEUROSCI.4261 09.2009 Madell, D., & Muncer, S. (2006). Internet communication: An activity that appeals to shy and socially phobic people? CyberPsychology & Behavior, 9 (5), 618 622. doi:10.1089/cpb.2006.9.618 Mazalin, D., & Moore, S. (2003). 'You have MALE': Identity development, social anxiety and internet use Wiley Blackwell. Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?direct=true&db=aph&AN=11892913&site=ehost live Mitchell, M. E., Lebow, J. R., Uribe, R., Grathouse, H., & Shoger, W. (2011). Internet use, happiness, social support and introversion: A more fine grained analysis of person variables and internet activity. Computers in Human Behavior, 27 (5), 1857 1861. doi:10.1016/j.chb.2011.04.008 Mitchell, M. E., Lebow, J. R., Uribe, R., Grathouse, H., & Shoger, W. (2011). Internet use, happiness, socia l support and introversion: A more fine grained analysis of person variables and internet activity. Computers in Human Behavior, 27 (5), 1857 1861. doi:10.1016/j.chb.2011.04.008 Moritz, S., Wittekind, C. E., Hauschildt, M., & Timpano, K. R. (2011). Do it y ourself? self help and online therapy for people with obsessive compulsive disorder. Current Opinion in Psychiatry, 24 (6), 541 548. Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?direct=true&db=cin20&AN=2011297153&site=ehost live Ochieng, B .M.N. (2011). The effect of kin, social network and neighbourhood support on individual well being. Health & S ocial Care in the Community, 19 (4), 429 437. doi:10.1111/j.1365 2524.2011.00992.x among young adult university students. Computers & Education, 55 (3), 1091 1097. doi:1 0.1016/j.compedu.2010.05.006 Paxling, B., Almlov, J., Dahlin, M., Carlbring, P., Breitholtz, E., Eriksson, T., & Andersson, G. (2011). Guided internet delivered cognitive behavior therapy for generalized anxiety disorder: A randomized controlled trial. Co gnitive Behaviour Therapy, 40 (3), 159 173. doi:10.1080/16506073.2011.576699

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59 Peris, R., Gimeno, M. A., Pinazo, D., Ortet, G., Carrero, V., Sanchiz, M., & Ibez, I. (2002). Online chat rooms: Virtual spaces of interaction for socially oriented people. Cybe rPsychology & Behavior, 5 (1), 43 51. doi:10.1089/109493102753685872 Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40 (3), 87 9 891. doi:10.3758/BRM.40.3.879 Rosenthal, J. H. (2010). The effect of internet use and treatment sought in individuals diagnosed with social phobia. ProQuest Information & Learning). Dissertation Abstracts International: Section B: The Sciences and Engine ering, 70 (7 ). (2010 99020 232). Russell, D., Peplau, L. A., & Cutrona, C. E. (1980). The revised UCLA loneliness scale: Concurrent and discriminant validity evidence. Journal of Personality and Social Psychology, 39 (3), 472 480. doi:10.1037/0022 3514.39. 3.472 Russell, D. W. (1996). UCLA loneliness scale (version 3): Reliability, validity, and factor structure. Journal of Personality Assessment, 66 (1), 20. Retrieved from https://search.ebscohost.com.lp.hscl.ufl.edu/login.aspx?direct=true&db=s3h&AN=6 380089&site=ehost live Rytwinski, N. K., Fresco, D. M., Heimberg, R. G., Coles, M. E., Liebowitz, M. R., Cissell, S., Hofmann, S. G. (200 9). Screening for social anxiety disorder with the self report version of the liebowitz social anxiety scale. Depression & Anxiety (1091 4269), 26 (1), 34 38. doi:10.1002/da.20503 Seim, R. W., & Spates, C. R. (2010). The prevalence and comorbidity of speci fic phobias in college students and their interest in receiving treatment. Journal of College Student Psychotherapy, 24 (1), 49 58. doi: 10.1080/87568220903400302 Selfhout, M. H. W., Branje, S. J. T., Delsing, M., ter Bogt, Tom F. M., & Meeus, W. H. J. (200 9). Different types of internet use, depression, and social anxiety: The role of perceived friendship quality. Journal of Adolescence, 32 (4), 819 833. Retrieved from https://search.ebscohost.com.lp.hscl.ufl.edu/login.aspx?direct=true&db=eric&AN=E J869719&site=ehost live; http://dx.doi.org.lp.hscl .ufl.edu/10.1016/j.adolescence.2008.10.011 Smyth, J. M. (2007). Beyond self selection in video game play: An experimental examination of the consequences of massively multiplayer online role playing game play. CyberPsychology & Behavior, 10 (5), 717 721. doi:10.1089/cpb.2007.9963 Spielberger, C. D., Gorsuch, R. L., & Lushene, R. (1970). State trait anxiety inventory.

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60 Stepanikova, I., Nie, N. H., & He, X. (2010). Time on the internet at home, loneliness, and life satisfaction: Evidence from panel time dia ry data. Computers in Human Behavior, 26 (3), 329 338. doi:10.1016/j.chb.2009.11.002 Stevens, S. B., & Morris, T. L. (2007). College dating and social anxiety: Using the internet as a means of connecting to others. CyberPsychology & Behavior, 10 (5), 680 68 8. doi:10.1089/cpb.2007.9970 Tonioni, F., D'Alessandris, L., Lai, C., Martinelli, D., Corvino, S., Vasale, M., Bria, P. (2012). Internet addiction: Hours spent online, behaviors and psychological symptoms (english). Gen.Hosp.Psych., 34 (1), 80 87. Re trieved from https://search ebscohost.com.lp.hscl.ufl.edu/login.aspx?direct=true&db=fcs&AN=25618074&site= ehost live Valenzuela, S., Park N., & Kee, K. F. (2009). Is there social capital in a social network site?: Facebook use and college students' life satisfaction, trust, and participation. Journal of Computer Mediated Communication, 14 (4), 875 901. doi:10.1111/j.1083 6101.2009.01474.x W agner B., S chulz W. & K naevelsrud C. (2012). Efficacy of an internet based intervention for posttraumatic stress disorder in iraq: A pilot study (english). Psychiatry Res., 195 (1 2), 85 88. Retrieved from https://search ebscohost.com.lp.hscl.ufl.edu/login.aspx?direct=true&db=fcs&AN=25498276&site= ehost live Wittmann, M., Lovero, K. L., Lane, S. D., & Paulus, M. P. (2010). Now or later? striatum and insula activation to immediate versus delayed rewards. Journal of Neuroscience, Psychology, and Economics, 3 (1), 15 26. doi:10.1037/a0017252 Yang, D., Chiu, J., & Chen, Y. (2011). Examining the social influence on college students for playi ng online game: Gender differences and implications. Turkish Online Journal of Educational Technology TOJET, 10 (3), 115 122. Retrieved from https://search ebscohost com.lp.hscl.ufl.edu/login.aspx?direct=true&db=eric&AN=EJ944947&site=ehost live

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61 BIOGRAPHICAL SKETCH Alana Freedland was born in Oak Park, Michigan. Alana grew up mostly in Tampa, Florida and graduated fr om Tampa Preparatory School in 2005. She earned her B.S. in p sychology and a minor in d ance at the University of Florida in 2009 and began graduate school in the Counselor Education program at the University of Florida as a master /specialist candidate i n 2010. Alana works as a research assistant in the departments of Psychiatry and Clinical and Health Psychology at the University of Florida. She has presented at national and international conferences including the 39 th and 40 th Annual Meetings of the In ternational Neuropsychology Society, the 22 nd Annual International Conference for ADHD, the Georgia State University Multicultural Competence Conference, and the 2010 Annual Meeting of the Florida Society of Neurology. Alana has been published as an author in Type 1 Diabetes/Book 3 and the Journal of Dementia and Geriatric Cognitive Disorders. Alana will complete her degree in Mental Health Counseling at the University of Florida in Summer 2012. She looks forward to her future career as a counselor.