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Student Success and Its Relationship to Occupational Status Score in the Los Angeles Community College District

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Title: Student Success and Its Relationship to Occupational Status Score in the Los Angeles Community College District
Physical Description: 1 online resource (121 p.)
Language: english
Creator: Lester, Ronald C
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: bourdieu, capital, class, college, community, cultural, influences, occupational, parent, retention, socioeconomics, status
Educational Administration and Policy -- Dissertations, Academic -- UF
Genre: Higher Education Administration thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The purpose of this study was to determine if there is a significant relationship between parent socioeconomic status and (1) community college student college grade point average and (2) course completion ratio. The data for this study were obtained through the Transfer and Retention of Urban Community College Students (TRUCCS) project in the Los Angeles Community College District. This study examined the variables of parent occupational status score, which served as a proxy for socioeconomic status, demographics, high school grade point average, and psychosocial variables, and their relationship to college grade point average and course completion ratio. The statistical analyses of factor analysis and forward block entry regression were conducted to determine significance. Analysis of the statistical tests suggested that there is no significant relationship between parent socioeconomic status and community college student grade point average and course completion ratio. Implications of the research to community college administration were also presented.
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 Ronald C Lester.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Hagedorn, Linda.

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Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021431:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021431/00001

Material Information

Title: Student Success and Its Relationship to Occupational Status Score in the Los Angeles Community College District
Physical Description: 1 online resource (121 p.)
Language: english
Creator: Lester, Ronald C
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: bourdieu, capital, class, college, community, cultural, influences, occupational, parent, retention, socioeconomics, status
Educational Administration and Policy -- Dissertations, Academic -- UF
Genre: Higher Education Administration thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The purpose of this study was to determine if there is a significant relationship between parent socioeconomic status and (1) community college student college grade point average and (2) course completion ratio. The data for this study were obtained through the Transfer and Retention of Urban Community College Students (TRUCCS) project in the Los Angeles Community College District. This study examined the variables of parent occupational status score, which served as a proxy for socioeconomic status, demographics, high school grade point average, and psychosocial variables, and their relationship to college grade point average and course completion ratio. The statistical analyses of factor analysis and forward block entry regression were conducted to determine significance. Analysis of the statistical tests suggested that there is no significant relationship between parent socioeconomic status and community college student grade point average and course completion ratio. Implications of the research to community college administration were also presented.
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 Ronald C Lester.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Hagedorn, Linda.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021431:00001


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STUDENT SUCCESS AND ITS RELATIONSHIP TO OCCUPATIONAL STATUS SCORE
IN THE LOS ANGELES COMMUNITY COLLEGE DISTRICT




















By

RONALD C. LESTER


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2007































2007 Ronald C. Lester









ACKNOWLEDGMENTS

I thank my friends and family members who have provided support and encouragement

during the lengthy process of finishing my dissertation.

I also owe a very special thank you to my tremendous committee chair, Dr. Linda Serra

Hagedorn. I thank her for the encouragement, support and advice she provided during this

process.









TABLE OF CONTENTS

page

A CK N O W LED G M EN TS ................................................................. ........... ............. 3

L IST O F TA B L E S ....................... ....... ............................................................... 6

D E F IN IT IO N O F T E R M S ....................................................... .......................................8

ABSTRAC T .........................................................................................

CHAPTER

1 INTRODUCTION ............... .............................. ............................. 10

Statem ent of the Problem ................. ...................................... ............ .. ................ 11
N am -Powers-Terrie Occupational Status Score......................................... .................12
Los Angeles County and the Los Angeles Community College District........................12
P purpose of the Stu dy ....................................................................................... 13
B background of the P problem .......................................................................... .. ............. 14
Significance of the Problem .............................. ...................... ............ ............... 15
T heoretical F ram ew ork ........... ...................................................................... ........ .. 15
L im itatio n s ....................... ... ............. ... ..........................................................1 6
S u m m ary ........................... ............. ... ........................................................16

2 REVIEW OF THE LITERATURE .............. ..............................................................18

S o cial C lass......... .................................................................2 1
Student Preparedness ............................. ..... .............. ........................32
Stu dent T ran sfer ......... ............................................................................... ................ 37
Student R detention ......... .............................................................................. ..................... 39
Stu d ent Su access ................................................................4 1
S u m m ary ........................... ............. ... ........................................................4 5

3 M E T H O D O L O G Y ........................................................................................................... 4 6

Research Questions and Hypotheses .............................................. ............... 46
R e se a rc h D e sig n .....................................................................................................................4 6
R research P population ................................................................47
D ata Collection ......... ...... ....... ........................... .................. 47
D ata A n a ly sis ...................................................................................................................4 8
In strum entation ........................................................................... 50
V validity and R liability ........................................................................................... ..5 1
Summary ..................................................... 51






4












4 ANALYSIS AND PRESENTATION OF THE DATA .....................................................54

P o p u latio n P ro fi le ....................................................................................... .................... 5 4
V alidity and R liability .......... ...................................................................... ...... .. ... 55
Regression Analyses ............... ................. ....................................... 56
Grade Point Average .................................. .. .. .. ...... .. ............57
C course C om pletion R atio ....................................................................... ................... 58
S u m m a ry ................... ............................................................ ................ 6 0

5 CONCLUSIONS AND RECOMMENDATIONS...................................... ............... 71

C conclusions and Im plications........................................................................... .............72
Impact on College Grade Point Average .............................. ..................... .............. 74
Impact on Course Completion Ratio (Success Rate) ............................................... 76
D discussion ......................................................................................... ...... .... ................ 77
Lim stations of the Study .......................... .......... .. .......... ....... ..... 79
Suggestions for Further R esearch............. ................................ ................... ............... 80

APPENDIX

A THE TRANSFER AND RETENTION OF URBAN COMMUNITY COLLEGE
STUDENT (TRUCCS) QUESTIONNAIRE.................................................. ...............83

B SPSS PRINTOUT -- FULL MODEL -- GRADE POINT AVERAGE..............................90

C SPSS PRINTOUT FULL MODEL SUCCESS RATE................................................101

R E F E R E N C E S ................... ......................................................... ................ 1 12

B IO G R A PH IC A L SK E T C H ......................................................................... ... ..................... 12 1









LIST OF TABLES

Table page

3-1 List of Variables ............... ................. ........... ................. .......... .. 52

3-2 R ecoded ethnic groups............ .............................................................. ....... .............. 53

4-1 Students in the LACCD. Distribution by gender. ....................................................... 61

4-2 Students in the LACCD. Distribution by age. ...................................... ............... 61

4-3 Students in the LACCD. Distribution by ethnic origin. ............................................. 62

4-4 Students in the LACCD. Distribution by high school. ................... ............................. 62

4-5 Factor analysis: determination, academic integration, aspire to transfer...........................63

4-6 Zero order correlations for the model grade point average.....................................66

4-7 Zero order correlations for the model success rate .................................... .................67

4-8 Distribution of parent occupational status score (SES) ...................................................68

4-9 Distribution of student course completion ratio...................................... ............... 68

4-10 M odel summ ary grade point average ...................................................... ............. 69

4-11 Regression analysis summary for grade point average.....................................................69

4-12 M odel summary course completion ratio ............................................ ............... 70

4-13 Regression analysis summary for course completion ratio.................................... 70

B -1 G rade point average notes......................................................................... ...................90

B-2 Grade point average descriptive statistics................................. ........................ .. ......... 91

B -3 G rade point average correlations ............................................... ............................ 92

B-4 Grade point average variables entered/removed(b)....................................................95

B-5 Grade point average m odel sum m ary ........................................ .......................... 96

B-6 Grade point average AN OV A (e) ............................................... ............................ 97

B-7 Grade point average coefficients(a) ..................................................... ...................98

B-8 Grade point average excluded variables(d).............................................................100









C-l Success rate notes ................................ .. ... ..... .................. 101

C -2 Success rate descriptive statistics............................................. .................................. 102

C -3 Success rate correlations ....................................................... ........ ............. 103

C-4 Success rate variables entered/removed(b).................... .........................................106

C-5 Success rate m odel sum m ary .................................................. ............................. 107

C -6 Success rate A N O V A (e) ..................................................................... .......................108

C-7 Success rate coefficients(a) ........................................................................ .. 109

C-8 Success rate excluded variables(d) .......................................................... ............... 111









DEFINITION OF TERMS


Community college






Course completion ratio




Cultural capital





Diversity


Grade point average


Los Angeles Community
College District (LACCD)

Occupational status score



Retention


Socioeconomic status


Ann institution of higher education that provides education to
students mainly from the area surrounding the community college.
Community colleges provide educational opportunities from
Associates and certificate degree programs. Community colleges
also typically offer vocational programs and provide transfer
opportunities.

The value obtained by dividing the number of classes that a student
successfully passes (A, B, C or P for pass/no pass scales) by the
number of classes the student attempted. Course completion ratio is
a measure of success rate.

The theory proposed by Pierre Bourdieu (Bourdieu & Passeron,
1977, p. 30) that states that parents provide their children with
attitudes and knowledge. With respect to education, parents with
more experience with the educational system have more of these
attitudes and knowledge to their children.

The range of students who are attending community colleges.
Diversity in educational institutions includes students with different
ages, ethnic origins, language, sexual orientation, and those with
disabilities.

The value obtained by dividing the total number of grade points by
the number of credits attempted.

The system of nine community college campuses situated throughout
Los Angeles County, California.

A measure of socioeconomic status that incorporates occupation,
education, and income into a numerical value ranging from 0 to 100
as detailed by Nam, Powers, and Terrie (Terrie & Nam, 1994).

A measure of students remaining enrolled in an educational
institution. Generally, the period of enrollment is measured from fall
through spring semesters or fall through the following fall.

For purposes of this study, a measurement by a proxy of the highest
occupational status score between mother and father. In general it is
a measure of social/economic class. In the literature, socioeconomic
status is used as a common term for social class.









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

STUDENT SUCCESS AND ITS RELATIONSHIP TO
OCCUPATIONAL STATUS SCORE IN THE
LOS ANGELES COMMUNITY COLLEGE DISTRICT

By

Ronald C. Lester

December 2007

Chair: Linda Serra Hagedorn
Major: Higher Education Administration

The purpose of this study was to determine if there is a significant relationship between

parent socioeconomic status and (1) community college student college grade point average and

(2) course completion ratio. The data for this study were obtained through the Transfer and

Retention of Urban Community College Students (TRUCCS) project in the Los Angeles

Community College District (Hagedorn, Maxwell, & Moon, 2001).

This study examined the variables of parent occupational status score, which served as a

proxy for socioeconomic status, demographics, high school grade point average, and

psychosocial variables, and their relationship to college grade point average and course

completion ratio. The statistical analyses of factor analysis and forward block entry regression

were conducted to determine significance.

Analysis of the statistical tests suggested that there is no significant relationship between

parent socioeconomic status and community college student grade point average and course

completion ratio. Implications of the research to community college administration were also

presented.









CHAPTER 1
INTRODUCTION

Until the passage of the Morrill Act of 1862 establishing land-grant colleges, access to

higher education was not widely available. In addition to changes in student access to college,

the limited funding for higher education and changing student interests has created challenges for

the higher education system (Key, 1996). During the post-war period, higher education changed

from a system of the privileged to a system that served the masses. Every social class is now

represented in higher education systems, so that a large portion of students come from lower

class families, and those with the greatest familiarity with higher education have performed the

best (Hansen & Mastekaasa, 2006). Individuals from all socioeconomic groups, including those

with poor English speaking ability, now have the opportunity to pursue higher education. This

has created challenges for community colleges and universities to help students advance through

college in a successful manner (Ellis & Stebbins, 1996).

A large group of individuals attending institutions of higher education are students that

have had no other family member attend college before them, referred to as first-generation

college students. First-generation college students have additional challenges with their

backgrounds of inadequate knowledge of the college experience and attitudes towards college.

Further, institutions of higher education have had to deal not only with a diverse student

population, but they have also been made accountable for student success of first-generation

college students (McConnell, 2000). Additionally, the community college system has allowed

for a wide variety of individuals to pursue higher education despite their impaired college

readiness. Many students have concentrated too much on memorization rather than learning and

understanding concepts (Diaz-Lefebvre, 2004). Research has been done in many different ways

regarding students and their backgrounds and their success in the higher education system.









Student success has been a particularly important area of research with numerous factors

contributing to particular levels of success. Factors studied have included family socioeconomic

status, educational background of the family, and student high school grade point average.

Statement of the Problem

The major shifts in types of students in higher education, especially in the community

college system, have made it critical for colleges to be aware of the background of students in

order to be better prepared to serve their needs and help the students to be successful in their

educational and career endeavors (Pennington, Williams & Karvonen, 2006; Person, Rosenbaum

& Deil-Amen, 2006; Miller, Pope & Steinmann, 2005). Many students who have entered the

community college system today are first generation college students or come from a type of

background or financial status which may have made college particularly difficult for them to

maneuver, including the admissions and financial aid process, lack of family support, and poor

college preparation (Educational Resources Institute & Institute for Higher Education Policy,

1997). Retention of students is particularly challenging and is the focus of this research project.

The Transfer and Retention of Urban Community College Students (TRUCCS) project

(Lee, Sax, Kim, & Hagedorn, 2004) collected and analyzed data on student characteristics, as

well as outcome measures in the Los Angeles Community College District. The TRUCCS

project revealed many aspects of student retention and success. One factor, however, that has

not been studied thoroughly within the TRUCCS project is the influence of parental occupational

status score, a proxy for socioeconomic status. The present study used the Nan-Powers-Terrie

occupational status score to determine the effect of parental socioeconomic status on different

student success outcome measures including college grade point average and course completion

ratio.









Nam-Powers-Terrie Occupational Status Score

The Nam-Powers-Terrie occupational status score measures the socio-economic status of

occupations found in census data. The score represents the percentage of individuals that are in

occupations having lower average education and income (Terrie & Nam, 1994). The scores are

calculated by taking the sum of (1) the number of individuals in occupations at lower median

educational levels, (2) the number of individuals in occupations at lower median income levels,

and (3) the number of individuals in the particular occupation of interest, then dividing this sum

by the total number of individuals in these groups, and multiplying by 100 (Terrie & Nam,

1994). The scores range from 0-100 on a ranked scale, which has been widely used in social

science research involving social stratification.

Los Angeles County and the Los Angeles Community College District

Los Angeles County is an urban area of California with a land area of 2,000 square miles.

The 2005 population estimate of the county was 9,935,475, up 4.4% from April 2000 census data

(U.S. Census Bureau, 2007). Females comprised 50.6% of this population. The ethnic make up

of the county in 2005 was White, 74.1%, Black 9.7%, American Indian and Alaska Native 1.1%,

Asian/Hawaiian/Other Pacific Islander 13.4%, and Hispanic 29.5%. Of this group, 36.2% were

foreign born, 54.1% spoke a language other than English at their home, and 69.9% were high

school graduates. (Los Angeles Community College District, 2007).

The Los Angeles Community College District (LACCD) is made up of nine colleges

spread throughout Los Angeles County. In 2005, over half of the students were older than 25

years old and over 25% were 35 or older. Females made up 60.5% of enrollment and males

39.5%. The ethnic make up of the LACCD was 16.6% African American, 15.0% Asian, 46.8%

Hispanic, and 18.9% White. Of these students, 40% were non-native English speaking, 40%

were below the poverty line, and 25% came from homes where parents obtained education only









through the elementary level. Occupational and educational goals of the students included

35.5% seeking vocational training, 30.8% intending to transfer to a four-year college, 10.2%

wanting general education, 6.9% in transitional situations (including those who wanted to

improve basic skills or complete a high school diploma, and 16.6% with unknown or undecided

goals (Los Angeles Community College District, 2007)

Purpose of the Study

Information about the knowledge base of students and their career outcomes can be very

useful to community colleges making current and future plans. To help satisfy this need, the

present study sought to determine if there is a relationship between parental socioeconomic

status and academic outcomes of students in higher education. Specifically, occupational status

of parents, which served as a proxy for socioeconomic status in this project, was targeted due to

the lack of literature addressing this area. The outcome measures of particular interest were

college grade point average and course completion ratio. Since not everyone attending

community college plans to graduate, the course completion ratio was a better measure of

student success.

This research study sought to answer the following questions:

1. Is there a significant relationship between socioeconomic status and student
college grade point average?

2. Is there a significant relationship between socioeconomic status and student
course completion ratio?

The hypotheses used to test these research questions are:

1. There is no significant relationship between the socioeconomic status and student
college grade point average.

2. There is no significant relationship between socioeconomic status and student
course completion ratio.









Background of the Problem

Community colleges provide an open door to practically anyone wishing to pursue higher

education (Hendrick, Hightower, & Gregory, 2006). This policy has allowed individuals into

higher education who may benefit from different types of treatments due to their backgrounds.

Shortly after the large increase in numbers of community colleges in the 1960s, community

colleges had to adjust to many different groups in the higher education system and had to

become familiar with the backgrounds of these students in order to adapt to their special needs

and characteristics (Gleazer, 2000).

Parental influences, including the influence of parent educational levels on the child's

education, have been well documented in the literature (Davis-Kean, 2005). The influence of

parent educational levels has been particularly well documented, while influence of parent

occupational status has not been as prevalent in the literature. Children of immigrant parents

have had a particularly difficult transition to higher education due to the background of their

parents who did not have experience in the higher education system (Kim & Schneider, 2005).

Despite educational levels, parents have had varying degrees of interaction with their children

when discussing college (Kim & Schneider, 2005).

One of the functions of the community college is to provide a transfer gateway to a four-

year institution, but not every student who has entered the community college has a desire to

transfer (Dougherty & Kienzl, 2006). Nevertheless, successful transfer and the attainment of a

bachelor's degree are considered by many as a successful college outcome. While graduation

within a specified time, usually a total of six years to obtain a bachelor's degree in a four-year

institution, has been a benchmark oftentimes used as a measure of student success, graduation

rate does not capture the students who may leave the higher education system temporarily and

then return later to complete their degree (Zwick & Sklar, 2005).









Significance of the Problem

Community colleges have faced growing pressures with the ever-increasing number of

students entering the system (Walker, 2001). With these demands came the difficulties in

making sure that the diverse student population achieved success (Ellis & Stebbins, 1996). One

of the main challenges facing community colleges has been that of students entering the system

poorly trained and prepared for college (Grimes & David, 1999). Community colleges have

continued to look for ways to improve outcomes. By better understanding the background of the

students in the system community colleges could have attempted to make improvements based

on the information they obtained (Grimes & David, 1999).

Theoretical Framework

The present study uses Bourdieu's theory of cultural capital as the basis for determining

parental influences on educational outcomes (Bourdieu, 1984, p. 12). Bourdieu theorized that

different social groups differ in terms of educational practices and cultural capital. This study

uses this theory to determine whether the level of student grade point average, course completion

ratio, and successful transfer to a four year institution differs among families of different

socioeconomic levels. This study used the theoretical framework of Lee and Bowen (2006), who

applied Bourdieu's theory of cultural capital to test for effects of parent involvement on child

school achievement. In keeping with Bourdieu's theory of cultural capital, Lee and Bowen

(2006) stated that, "although cultural capital is possessed by an individual or a family, it is more

a function of the concordance of the educational aspects of the family's habitus with the values

and practices of the educational system with which the family interacts." Thus, families could

have obtained more cultural capital given their experiences with the educational system. In

addition, the findings of Lee and Bowen (2006) were consistent with Bourdieu's theory that

proposed that some families have inherited cultural capital that may give them an advantage.









However, families with a smaller amount of cultural capital may be at a disadvantage, as they did

not have the same access to resources (Lareau, 2001, p. 78). Lee and Bowen (2006)

hypothesized that families having different amounts of cultural capital, in this case educational

knowledge, would have wide variations in parental educational involvement and student success.

This was also reflected by DiMaggio and Mohr (1985), who stated that cultural capital has had a

positive influence on educational achievement.

Limitations

1. This study was limited to a sample of students who were enrolled in the Los
Angeles Community College District during the Spring 2001 semester.

2. This study was limited to individuals who agreed to complete the questionnaire
and provided permission for investigators to obtain their transcript information.

3. The validity of the study was limited to the reliability of the research instrument
used.

Summary

Parental influences have been the subject of numerous research studies examining various

facets of that interaction with student success in college. Influences such as parent education and

income, particularly socioeconomic status, have been the focus of various studies indicating

significant relationships of these influences with outcomes of their children in higher education.

This research study examined the impact of parent occupational status as a different measure of

socioeconomic status on student outcomes in the Los Angeles Community College District.

Specifically, this study examined the impact that parent socioeconomic status, as determined by

occupational status score, had on student grade point average and course completion ratio. It is

hypothesized that parental socioeconomic status (independent variable) does not have a

significant relationship with student college grade point average (dependent variable). It is also









hypothesized that parental socioeconomic status (independent variable) does not have a

significant relationship with student course completion ratio (dependent variable).









CHAPTER 2
REVIEW OF THE LITERATURE

The American community college has allowed individuals from all backgrounds to have an

opportunity to obtain higher education, including those linguistically and academically

challenged. This created challenges for community colleges to be effective for these students

(Ellis & Stebbins, 1996; Closson, 1996). The flexible programming and emphasis on needs of

the community have allowed community colleges to meet these new challenges and special

student needs. This has included helping students to become self-directed both at college and in

their personal lives (Closson, 1996). With the major changes occurring throughout the

community college system, some have proposed that the mission of community colleges has

greatly changed from that of providing a transfer mechanism to four-year institutions to also

providing technical, vocational and community education. These changes could continue into

the future to help make sure that the national work force will be competitive globally.

Changes also occurred in community colleges wanting to move in the direction of offering

baccalaureate degrees as a baccalaureate degree which have increasingly become necessary for

entry level positions (Walker, 2001). However, others have maintained that community colleges

have been meeting their original function to be flexible and responsive to community needs

(Wattenbarger & Witt, 1995). Wattenbarger and Witt (1995) held that vocational education was

a part of the original plan of community colleges, which provided an opportunity for students to

earn an associate's degree and possibly go on to work as technicians or in businesses.

Despite the theoretical perspective of the origin of community colleges, there is no doubt

that the student body being served has changed in regards to student backgrounds, characteristics

and needs (Pennington, et al., 2006; Person, Rosenbaum & Deil-Amen, 2006; Miller, et al.,

2005). Issues faced by community colleges differ among institutions, for example rural









community college versus larger, urban community colleges (Pennington, et al., 2006). With the

increase in the number of community colleges and the growth of the student population, students

have also faced difficulties, such as obtaining the correct information to move effectively

through the community college system (Person, Rosenbaum & Deil-Amen, 2006). The changes

of the community college student population have included increases in the number of students

from single-parent homes, as well as those requiring more counseling services. In addition,

today's students have a better understanding of technology and have greater expectations of the

effect of higher education on their careers (Miller, et al., 2005). Walker (2001) stipulated that in

order to meet these changes, community colleges must understand the conditions surrounding

them.

While research studies have investigated some of these factors, additional research is still

needed to better understand current issues of the American community colleges, especially

regarding children from ethnic minorities and the effectiveness of classroom teaching in high

school and college (Zhou, 2003; Foster, Lewis & Onafowora, 2003). The size of ethnic

minorities has grown especially rapidly in large urban communities (Zhou, 2003). Poor minority

youth in urban areas have experienced numerous challenges, such as isolation from the

mainstream of the community, being surrounded by ghettos while seeing and wanting

materialistic items, poor living conditions and overcrowded schools (Zhou, 2003).

Administrators and faculty must become cognizant of the cultural background of students and

incorporate that knowledge into teaching (Foster, et al., 2003).

While previously most community college students, especially those from families with

high socioeconomic status (Dougherty & Kienzl, 2006) have intended to transfer to a four-year

institution, some recent research has suggested that many students enter a community college to









obtain only a two-year or vocational degree (Anderson, Alfonso, & Sun, 2006). Anderson and

colleagues (2006) discussed the trend that increasing numbers of two- and four-year institutions

have been making system-to-system agreements, allowing easy access of students in the two-

year institution to enter the associated four-year institution. The agreements have become

especially important because of reduced appropriations for higher education in the presence of

higher tuition costs and a high demand for higher education (Anderson, et al., 2006). Coley

(2000) pointed out that the understanding of these agreements became difficult for students who

attended several community colleges and who therefore met some problems when transferring.

In their discussion, Anderson and colleagues (2006) noted four theoretical models of the

functions of the community college (Anderson, et al., 2006): functionalism, neo-Marxism,

institutionalism, and statism. Functionalists look at the community college as a place for

women, minorities and the working class to obtain higher education and as a means to provide

vocational-technical skills and provide an entryway for transfer to a four-year institution. Neo-

Marxists look at community colleges as institutions that keep women, minorities and the working

class in vocational-technical programs. Institutionalists look at community colleges as a way to

keep the universities from losing academic status by shifting the demand for higher education

towards two-year institutions. Statists have seen community colleges with multiple goals and

different influences. Statists have viewed local and state governments as wanting to increase

employment and job training opportunities for their constituents, since most of their funding for

schools has come from the local area (Anderson, et al., 2006). Anderson and colleagues (2006)

further state that statewide agreements between universities and community colleges have been

on the rise to contend with rapidly increasing tuition rates and the high demand for affordable

higher education.









Despite the challenges, there have been certain successes. For example, a recent study

found that the success rates of students who transferred from a two-year institution to a four-year

institution depended on the number of community college credits, the type of institution, and the

students' academic level (Koker & Hendel, 2003). Interestingly, Koker and Hendel (2003)

found no significant relationship between age, gender, and completion rates, although they did

find that there was a significant relationship between ethnic background and graduation, and that

white students had higher graduation rates than non-white students. Characteristics studied

included demographics, transfer behavior, and completion of the bachelor's degree at an urban

university. Of particular note was the researchers' recommendation that research look at the

academic backgrounds of the students who transferred from a community college to a four-year

institution to see which students were most likely to leave before obtaining a bachelor's degree

(Koker & Hendel, 2003).

Social Class

Social class was the subject of a research study performed by Hogan (2005) who provided

an update reinvestigation of an earlier study by Wright and Perrone (1977) regarding social

layers. Hogan's (2005) research focused on professionals in higher-end jobs to see who had the

greatest financial return on education. He found that, although individuals with MBA, MD and

JD degrees had a high return on their education, it was only when these individuals became

business owners that they became particularly successful (Hogan, 2005). Previously, Wright and

Perrone (1977) indicated that there were substantial differences in class when they looked at

income and education. They found this to be true even when they controlled for occupational

status, length of time on the job, and demographics (Wright & Perrone, 1977). Wright and

Perrone (1977) found that the benefits of education were higher for managers than those in the

working class, and that class differences between managers and the working class were less









apparent among white women and blacks than among white men. They also postulated that

members of particular races or genders in the same class had comparable returns on their

educational investments, and that in particular class categories, the gap in income was smaller

between races than between genders (Wright & Perrone, 1977). Yet other research indicated no

significant relationship in the areas of occupational standing, income, or cognitive ability

(Hauser & Huang, 1997). Hauser and Huang were following up on research conducted by

Herrnstein and Murray (1994) and reported in their book The Bell Curve: Intelligence and Class

Structure in American Life.

According to Berliner (2006), social class has been a long-standing issue and one that

influences individuals for a lifetime. In particular, Berliner's (2006) analysis found that (1) those

in the United States living in poverty remained in poverty for a longer period of time than

individuals in other affluent countries, (2) those who were minorities and lived in urban areas

had poor academic performance in a number of areas on international standards, (3) the

academic performance of those in the lowest socioeconomic levels was more influenced by

family and living environment than by genetics, (4) children in poverty exhibited major medical

difficulties that created problems for academic and life success, and (5) a small reduction in the

level of poverty in a family had a constructive influence on academics. Individuals who lived in

these areas had major disadvantages that caused problems not only academically but also with

daily non-academic activities (Berliner, 2006).

Social class has led to advantages for some and disadvantages for others (Massey, 1996;

Bickel & Howley, 2003). Massey (1996) found that one of the changes was the concentration of

wealth and poverty in separate areas, thus causing a segregation of classes in different

geographic areas. With the increased concentration of poverty in particular areas, especially in









urban regions, the problems of poverty became more visible to members of society, and the

wealthy became more distanced from the problems of poverty (Massey, 1996). However, Bickel

and Howley (2003) found that poverty rates in the United States were still highest in rural areas.

Bickel and Howley (2003) also found that students in rural areas were improving in math

achievement. Nesbit (2006) found that embedded in social class were factors that varied

depending upon one's own concept of class, including beliefs, occupations, money, lifestyle and

power (Nesbit, 2006). Nesbit (2006) focused on the effect of social class on adult education and

found that adult education provided the opportunity for those in lower classes to move upward in

society, even in the presence of social inequities.

Parental educational background has played a particularly strong influence on children,

with children of parents who attended college being more likely to go to college also (Nakhaie &

Curtis, 1998). Nakhaie and Curtis (1998) found that the educational level of mothers had the

strongest influence on the educational of their daughters rather than their sons, and that the

educational level of fathers had the strongest influence on the educational level of their sons

rather than their daughters. However, the same relationship between mothers and daughters and

fathers and sons did not exist when researchers looked at the class level of the parents, which the

researchers construed to be a function of the cultural capital held by the parents (Nakhaie &

Curtis, 1998). Children whose parents went to college had an advantage over children whose

parents did not go to college, including the capability to educate their children about college

planning and course requirements (Cabrera & La Nasa, 2001). Cabrera and La Nasa (2001)

found that family, school and individual characteristics had as much or greater importance on

student success than socioeconomic status. They also asserted that helping individuals become

college qualified was a high determinant of student success in college, that parental involvement









enhanced the likelihood that the child would become college qualified, and that if parents in

lower socioeconomic levels saw the benefits of a college degree their involvement in the child's

education would be increased (Cabrera & La Nasa, 2001).

It has also been demonstrated that children have chosen similar paths of education and

occupations as the education and occupation of their parents (Dryler, 1998). Interestingly,

Dryler's (1998) study indicated that the influence on occupational choice and education was

greater between fathers and sons than between mothers and daughters. Additionally, Dryler

(1998) found that parents with high levels of education and those in service-level occupations

have had a particularly important influence when it came to their children assuming an

educational path that was uncharacteristic of their gender. However, this was not found for

working class families and those who had less education. Other research suggested that the

mother's educational level was particularly important. Leinberger-Jabari, Parker and Oberg

(2005) found that the mother's education had a significant influence on the growth of her child's

capabilities, and that the children of better educated mothers tended to stay home and in school

longer. They also reported that the health of the mother affected the health and nutrition of the

child, and the nutritional level of the child had a great impact on the child's educational and

occupational success (Leinberger-Jabari, et al., 2005). In other research regarding parental

influences on child health issues, it was found that, as socioeconomic status improved in poor

families, health risks decreased for the children in families where the mothers had a better

education (Hatt & Waters, 2006). They proposed that mothers that were better educated to

implement more sanitary habits around the home, and that families with a higher socioeconomic

status may have a healthier living atmosphere. Hatt and Waters (2006) also found that mothers









that had a better education but lived in a lower socioeconomic status were still able to implement

more sanitary habits around the home despite living in poorer economic conditions.

Students who were the first in their families to attend college (first-generation) have had

different issues leading to their success, including preparation, self-confidence, and family

involvement (McConnell, 2000). However, McConnell (2000) also pointed out that the first-

generation students in community colleges differed from their counterparts at four-year

institutions, in that the community college students had a lower persistence level and thus started

out at a higher risk of dropping out of college. Consistent with other researchers, McConnell

(2000) brought up the issue that studies on first-generation college students often included data

on students at both two-year and four-year institutions, but due to differences between these

groups, research results could not be generalized over both groups. The mainly traditional age

and associated characteristics of students at four-institutions differed greatly from the student

population at two-year institutions. Some of the differences in characteristics between the two

student populations included demographics, conceptions of importance of college, personal

aspirations, family support, self-assurance levels and academic level (McConnell, 2000).

McConnell (2000) also found that many of the first-generation college students who dropped out

of college were doing well academically when they dropped out, and suggested that the

combined demands of going to school, working, and being involved with their families caused

these students to leave college. This finding presented an alternate look at student preparedness

in terms of personal demands rather than academic background.

An additional impact of parents on their children was shown in research on children with

separated parents (Smith, 1995). Smith's research showed that, after correction for parental

education and occupation, there were no significant effects on academic achievement test scores.









However, in the same study it was found that grades were significantly affected in this situation.

Smith (1995) suggested that this difference indicated the importance of the parameters used in

measuring academic success. Biblarz and Raftery (1999) postulated as well that children in

families with single mothers, single fathers and stepfamilies had poorer educational outcomes.

Those with the background of a single father or stepfamilies tended to have poorer educational

outcomes than families with both parents or a single mother (Biblarz & Raftery, 1999). Biblarz

and Raftery (1999) explained the better outcomes of children with single-mother families as due

to the mother's time investment in the children. They also found that the amount of time parents

invested in their children had a direct effect upon the outcomes of the children (Biblarz &

Raftery, 1999).

In research conducted by Gibson and Jefferson (2006) it was found that adolescent self-

concept is influenced by family and close friends, including teachers and peers. Parental

involvement was the subject of interest for a study that involved students in a college-preparatory

program (Gibson & Jefferson, 2006). The researchers' finding of a significant relationship

between parental involvement and adolescent self-concept should be helpful information to

schools in assisting with student development (Gibson & Jefferson, 2006). Gibson and Jefferson

(2006) also found that the perception parents had of their involvement with their children and the

corresponding perception the children had of their parents' involvement differed in a significant

manner, and they suggested that programs addressing parental involvement should be developed

to reduce those perceived differences. This was important because the manner in which

adolescents have seen themselves has greatly affected their decisions (Gibson & Jefferson,

2006).









Occupational prestige has been well documented in the literature (Goyder, 2005; Melchoir

et al., 2005). Some studies indicated that levels of occupational prestige have shifted over time

commensurate with changes in societal needs and viewpoints (Goyder, 2005). There are several

reasons for this observation, including changing occupation dynamics, specifically job task and

occupational changes due to new technologies (Goyder, 2005). It has been hypothesized that

prestige attributed to different occupations shifted during the third quarter of the 20th century.

According to Goyder's (2005) data, the prestige of some professions such as lawyers had a

decline, while service occupations such as firefighters and policeman had high increases.

Curiously, Goyder (2005) found that occupations that required less formal education, such as

auto and textile mill workers, had an increase in occupational prestige, but not as high as the

previous group. Because of the changes that had occurred in occupational prestige, Goyder

(2005) recommended looking at the spread of occupational scores over time to determine

occupational prestige. Additionally, a study by Melchoir and colleagues (2005) looked at the

inequality of health risks and occupational class level and found that those individuals working

in manual or lower occupational classes had a higher rate of absences due to illness. The

researchers focused on class differences between occupations and found that the lower

occupational classes had the worst working conditions, with a concomitant negative effect on

work performance (Melchoir et al., 2005).

Demographic risk factors have been found to have particular impact on course completion

for students in certain groups (Abell, 2003; Coley 2000). Abell (2003) reported in her study that

the students who were at particular risk of dropping were those who worked full-time, enrolled

part-time, and had issues with maintaining their financial independence. Successful students

were also found to study more and reviewed course materials prior to exams more frequently









than students who withdrew. Further, these individuals did not integrate into the college

environment or become involved in study groups or meeting with faculty and peers outside of

class (Abell, 2003). Abell (2003) found that students who integrated into the college mode of

living were the most likely to finish college at either two- or four-year institutions. The time

needed to finish college was particularly important due to the investment that students could

make towards their education and have a direct effect on their success (Abell, 2003).

Coley (2000) also found that students who did not perform well in high school and had low

levels of motivation would be well counseled to attend community college first and then to

transfer to a four-year institution, because they would learn the same material at a lower cost. He

also cautioned that students who went to community college as a "second chance" if they did not

do well in high school may think they can be successful in higher education without good

performance in high school (Coley, 2000). In yet another study, Conklin (1997) found that

students dropped courses most frequently because of work conflicts or personal problems,

neither of which the college could control, but through which intervention in the form of

counseling and advisement may have provided assistance to students. Conklin (1997) also found

that looking at the level at which students drop particular courses was important in assessing

problems that the college may need to address with an individual course. In the 1997 study by

Conklin, the reasons given by students for dropping high attrition classes were reported at a

higher frequency that that for all classes, and the reasons included the course difficulty,

dissatisfaction with the instructor, a heavy course load, and a general dislike for the course

material.

One student group that has been integral to the community college is the adult population

(Donaldson & Graham, 1999). Donaldson and Graham (1999) found that this group learned









differently than traditionally aged students, and that, since older students had more life

experiences, they approached college differently than younger students. The older students also

had a better idea of their goals in higher education and made careful decisions based on those

goals, and they also integrated into college life according to their views and understandings. Due

mainly to time constraints with work schedules, adult students have had to make difficult choices

about how to spend their time. Because of their personal experiences, they were able to put

meaning to the information they learned in classes right away, and in that way they were able to

have similar college success outcomes as traditional age students (Donaldson & Graham, 1999).

Donaldson and Graham (1999) also suggested that adults had a better idea of what they wanted

out of college which aided in their success rate.

Studies have continued to be performed on older adults in the community college system

(Laanan, 2003). Laanan (2003) identified several unique characteristics of the older community

college student: the importance they place on their families and financial situations, the various

backgrounds older community college students may have including previous college education,

and their motivation for higher education. The older students may also be enrolled to train for a

different job or simply for lifelong learning, including taking both credit and noncredit classes,

whereas the younger students were trying to gain upward mobility in society in terms of income

and occupational status (Laanan, 2003). Further, with the diverse backgrounds of community

college students, the choice of courses has been different and includes both credit and non-credit

courses (Laanan, 2003). According to Laanan (2003), over one-fourth of students in community

colleges have wanted to obtain a college degree at the bachelor's level or higher. Further,

Laanan (2003) found that high school preparation was a significant variable when she looked at

why students are in college.









Older adults have been included in the classification of nontraditional in research, although

"nontraditional" may not be a useful label and may cause some limitations (Kim, 2002). Kim

(2002) suggested that nontraditional has been a term used that encompasses a very diverse group,

not just older students, and the term is applicable to the majority of community college students

and thus needs to be broken down into groups that are meaningful to study. Further, Kim (2002)

advised that in some studies there may be factors that caused an interaction that were shared by

traditional and non-traditional students that were unrelated to which group they belonged.

Rather, the focus should be on different groups of students in the community college such as

first-generation college students, adult students, and reentry students (Kim, 2002).

Another group served by community colleges has included students from lower

socioeconomic backgrounds (Romano & Millard, 2006). Romano and Millard (2006) reported

that one measure of the service provided to lower income students was the number of students

receiving Pell grants. The researchers selected Pell grants since there was no national database

containing that information (Romano & Millard, 2006). However, they also pointed out that

there several reasons why Pell grant rates may not give an accurate indication to the lower

income community: ineligibility of part-time students (a major fraction of lower-income

constituents), the difficulty of the application process, and overall incorrect measures, among

other reasons (Romano & Millard, 2006). Regardless of these limitations, Romano & Millard

(2006) used Pell grant data and found that community colleges pulled in a high rate of students

with low family income. They also found that students in community colleges tended to be

ethnic minorities, first-generation college students, and progeny of single parent families,

particularly families that did not speak English. Older students tended to have different









characteristics, which included higher incomes than students who had just finished high school

(Romano & Millard, 2006).

The diverse student population has created a challenge for today's community colleges

(Lau, 2003). Consistent with other research, Lau (2003) found that, in order to retain students,

several obstacles must be confronted on the parts of both the students and the college. The

college should help students obtain adequate funding, provide appropriate academic support, and

maintain diversity. Faculty members need to be inventive with instruction and encourage each

other to work individually with students to improve the learning environment. For their part, the

students need to have the motivation to play an active role in their education (Lau, 2003). Lau

(2003) pointed out that students who developed a sense of belonging in the college environment

have been more successful and had a greater chance of staying in college. This has been

especially important during the freshman year, as students who did not perform well at the start

had a high likelihood of leaving college (Lau, 2003).

Research has indicated that one way community colleges can serve their student population

and help students avoid future job-hunting obstacles is by teaching social skills, which have

become increasingly important to employers (Deil-Amen, 2006). Deil-Amen (2006) went

further to state that neglect of social skill education worsens the problems many individuals face

in the workplace. This has especial importance because many community college students come

from homes that do not promote social skills and have few positive role models. By learning

social skills, disadvantaged students can improve their chances of moving upward in society by

qualifying for more prestigious jobs than they may otherwise hold (Deil-Amen, 2006). Deil-

Amen (2006) also suggested that community colleges need to work with employers to provide









students with job opportunities once they finish college, because many of these students are not

connected with individuals working in professional settings.

Student Preparedness

The traditionally open-admission policy of community colleges has provided easier student

access, but it has also created problems such as the necessity of providing appropriate student

remediation (Perin, 2006). Hadden (2000) pointed out that open access allowed a number of

students to fail, and thus indicated great need for remedial help. Some colleges have made

developmental classes mandatory for students who perform poorly on placement tests, while

others have allowed students the choice of whether or not to take the developmental class

(Hadden, 2000). Hadden (2000) also stated that students who attended the suggested

developmental classes had a better chance of success than those who did not take the

developmental classes. A complication, however, was that community colleges generally

allowed open access and opportunities in college, allowing students to attend the college who

may not be helped by developmental classes and ultimately fail (Hadden, 2000).

With so many individuals withdrawing from community colleges, Cohen and Brawer

(1996, p. 62) found that many community colleges have made retention a priority. Other studies

have found that an orientation course had positive impact regarding student enrollment,

persistence, retention, and degree completion, especially if it increased student involvement

(Derby & Smith, 2004). This was particularly important for students who pursued associate

degrees. Further, Derby & Smith (2004) found that most programs in place to improve retention

were developed primarily for students who were unprepared for college. They asserted that

retention programs should be for all students, but they also said that the usual measure of grade

point average may not be an accurate predictor of programmatic success.









Some community colleges have moved beyond remedial classes and have begun to offer

on-line learning assistance to students in order to help them complete course assignments and

learn basic skills (Perin, 2004). However, Perin (2004) also found that at times this assistance

provided too much help rather than simply guiding students to finish class assignments. Perin

(2004) also found that, as institutions incorporated more remedial resources, some of the

available remedial sources were found to be underutilized, and perhaps there was possibly some

duplication of services offered in some institutions. He also said that the reported successes

should be scrutinized to see if these services were really making a difference or if the services

were primarily used by the better students, thus skewing the rate of effectiveness (Perin, 2004).

In another study, Perin (2006), consistent with other research, found that students who planned to

enter higher education institutions often did not have the reading and math skills needed for

success, but they still attended community college, thus putting pressure on the institution to

provide effective remediation. Further, as a result of taking remedial courses, students required

more time to complete their degrees or programs of study (Perin, 2006). The problem has been

made more complex by the variation of remediation standards among different state systems, and

the tendency of some colleges to disregard the established standards (Perin, 2006).

Research on student academic preparation has shown that students who took a demanding

mathematics course load in high school had better success rates in college mathematics courses

above the level of than Algebra 2 (Berry, 2003). Berry (2003) also found that only 25% of high

school students completed this level of math preparedness, even when the courses were available

in the high school. Schools may have offered demanding courses, but students needed to be

encouraged to take them (Berry, 2003). Further, Berry (2003) emphasized that high schools and

colleges must work together to move students effectively from high school to college, and that









high school faculty and personnel need to work early with students and parents about college and

how to prepare for it. Another study looked at developmental math courses and student retention

in those courses (Umoh & Eddy, 1994). Umoh and Eddy (1994) did not find significant results

with traditional factors such as grade point average, gender, parental education, or student

academic and social integration, but rather they found that retention differences occurred with

instructors and students' motivation to succeed. One commonality found among students with

the highest success rates in this research was regular class attendance (Umoh & Eddy, 1994).

Umoh & Eddy (1994) suggested that more research be done to determine the significance of

student intent to succeed.

In a different study it was found that underprepared students without remedial work had a

lower course completion rate, greater attrition rate, more test anxiety and less sense of control

than students who were prepared for college (Grimes, 1997). Grimes (1997) found that those

students who had high persistence levels with their educations had greater academic success,

while those with low persistence levels did poorly, despite their level of college preparedness.

Grimes (1997) also suggested that educational institutions need to help students develop a sense

of personal responsibility for their success in college. Further, by determining whether or not

students felt they were in control of events, institutions could better focus on the most effective

programs to improve student achievement in preparatory courses and lead to overall student

success. In an earlier study, several student characteristics were found to predict college

withdrawal (Grimes & Antworth, 1996). Grimes and Antworth (1996) found that women had a

greater tendency to withdraw due to health or family issues, while men dropped because of the

lack of challenging coursework, and minority students dropped due to various academic and

social issues. Grimes and Antworth (1996) also found that better prepared students had higher









course completion rates and grade point averages, but that grade point averages were generally

lower for men, compared to women, as well as for students with financial problems.

Degree aspirations have differed among some community college students depending on

whether they attended a public or private two-year institution (Laanan, 2003). Students at

private two-year institutions were more likely to have the highest academic aspirations, although

the majority of students of both public and private two-year institutions had aspirations of

earning a bachelor's degree or above (Laanan, 2003). Simmons (1995) examined student

persistence among a group of dislocated workers who were retrained in the community college

system. She concluded that those with greatest persistence were those with low skills and less

previous education. She also found that the greatest predictor for students to persevere with

community college was the progress the students made in completing courses successfully, while

students who did not complete or failed courses were the most likely to drop out of college. An

interesting finding of Simmons (1995) was that attending college full time had a much stronger

positive relationship with persistence than part-time enrollment.

Regarding preparedness, students with General Educational Development certificates

(GED) often have more difficulty than those with traditional high school diplomas. Soltz (1996)

found that those with GED certificates had high drop out rates and that community colleges

should try to develop ways to help these individuals achieve success. The same research study

also found that students who did continue to pursue higher education, and who had received a

GED rather than a traditional high school diploma, had graduation rates similar to those of the

overall college student group, thus indicating that the "open door" policy of community colleges

was beneficial to GED holders (Soltz, 1996).









Student integration as a predictor of success has been another theme in the literature. For

example, Mlynarczyk and Babbitt (2002) studied a program of English as a Second Language

(ESL) and found that students who integrated into the learning community, particularly in the

classroom, and also in social environments outside the classroom, had a better chance of success

in college than those that did not integrate (Mlynarczyk & Babbit, 2002). The researchers found

that students' confidence in learning was increased by hearing their peers describe and solve a

particular problem. This finding indicated that involvement in a learning community early in

their college experience helped students to succeed while in college (Mlynarczyk & Babbitt,

2002).

A study by Hyers and Zimmerman (2002) identified subgroups and looked at how each of

the subgroups could be helped to achieve higher graduation rates. The researchers concluded

that the subgroups with higher graduation rates had higher ACT scores, high school rank,

orientation grades and first-quarter grade point averages. However, the researchers also found

that segmentation analysis revealed differences within these groups were occurring that showed

differences within groups that were not found otherwise. Segmentation analysis showed

relationships between subgroups that were in the mid range of variables rather than at the high or

low ends.

Pierre Bourdieu coined the term cultural capital (Bourdieu & Passeron, 1977, p. 30) and

applied it to the observation that students who did the best in school were those who were the

most familiar with individuals of the leading culture and were therefore most aware of how to

navigate the educational system. This was consistent with other research that demonstrated that

students who did the best in higher education were those that who "knew the system" (Cabrera &

La Nasa, 2001; Dryler, 1998). Cabrera and La Nasa (2001) found that parents with college









educations were more involved in overseeing the college preparation of their children than

parents without degrees. They also observed that, if parents in lower socioeconomic levels could

see the relationship between obtaining a college degree and the related economic and social

outcomes, their involvement in their children's school endeavors would increase (Cabrera & La

Nasa, 2001). Further, Dryler's (1998) research suggested that children followed career paths

similar to their parents' because they thought they would obtain more parental help by this route.

Student Transfer

One of the missions of the community college has been to provide an opportunity for

students to have access to a four-year institution (Bragg, 2001; Townsend, 2001). Bragg (2001)

further stated that community colleges needed to ensure that the entire curriculum provided

transfer opportunities for students, while still offering programs that fulfilled the other missions

that have evolved for community colleges. This could be enhanced by developing a meaningful

set of outcomes and effectively communicating them in the community (Bragg, 2001). While

transfer had been a main mission of community college, critics of the transfer mission pointed to

research showing that students who started at four-year institutions had a higher rate of obtaining

the four-year degree than students who transferred from community college to a four-year

institution (Townsend, 2001).

Community colleges have provided an opportunity for students who would not otherwise

be able to obtain a bachelor's degree, especially considering that the cost of community college

is less than that of four-year institutions (Townsend, 2001). Studies have found that students

from high social backgrounds had higher transfer rates than students at other levels, and that

older students had less tendency to transfer than their younger counterparts (Dougherty &

Kienzl, 2006). Interestingly, the same study indicated that black students had higher educational

goals than white students from the same social and economic backgrounds (Dougherty & Kienzl,









2006). Another factor that influenced transfer rate was faculty involvement at the community

college level (Tatum, Hayward, & Monzon, 2006). While the researchers did not look at what

effect this involvement would have on student transfer, they found that faculty involvement was

very important and that incentives should be provided to increase this involvement and to entice

more faculty to encourage students to persist with their educations beyond community college

(Tatum, Hayward, & Monzon, 2006).

A study by Hagedorn, Moon, Cypers, Maxwell and Lester (2006) looked at successful

transfer of students. Hagedorn and colleagues (2006) found the success rate was poor, noting in

particular the weak success rate of students in remedial programs. They also pointed out that

since many of the students who entered community college were not well prepared, they required

more time to reach and complete transfer level courses, thus requiring much more persistence

(Hagedorn, et al., 2006). Hagedorn and colleagues (2006) further stated that many community

college students had less cultural capital than students in four-year institutions, and that their

understanding of what it would take to get to the four-year institution may not have been correct,

including the wrong impression that at the end of two years they would be finished with

community college and would have successfully transferred to a four-year institution.

Successful transfer to a four-year institution was also the focus of a study by McMillan and

Parke (1994). In this study conducted in the Illinois community college system, the researchers

found that program of study and student intent impacted the successful transfer of students from

the two-year institution to the four-year institution (McMillan & Parke, 1994). McMillan &

Parke (1994) also pointed out that in order for two-year institutions to help students transfer

successfully to four-year institutions, the two-year institutions needed to look at a variety of

parameters determining transfer rates, including job placement and retention rates, and retention









rates of courses, programs, institutions, and transfer. Research by Dougherty and Kienzl (2006)

reconfirmed previous research that social background affected successful transfer from a two-

year to a four-year institution. The researchers found, consistent with other literature, that

students on the high end of socioeconomic status had considerably higher transfer rates. They

determined that part of this could be attributed to better preparation for college and higher

educational goals (Dougherty & Kienzl, 2006). Better high school preparation was a key area

that the researchers suggested should be examined to provide better opportunities for students

who would otherwise be at a disadvantage (Dougherty & Kienzl, 2006).

Completing community college has been shown to have a positive impact on earning

power (Gillum & Davies, 2003). In addition, Gillum and Davies (2003) found that, even though

wage records indicated a definite validation that completing college increased earning power,

legislators in the area they studied did not have hard facts and relied on unverified ideas about

earnings of community college students. Thus, community colleges need to better project the

benefits of their institutions in order to obtain necessary governmental funding (Gillum &

Davies, 2003). This is of further interest, since research has shown that students were relatively

uninformed about occupational wages, even in their own field of study, until their fourth year in

college (Betts, 1996). Betts (1996) also found that, consistent with other research, it was

important for parents and students to be aware of future earnings and the availability of financial

aid, as well as the cost of education in order to make informed decisions.

Student Retention

The problems with retention and graduation rates due to the widely varying issues and

backgrounds of the community college students have been exacerbated by federal policies

mandating certain outcomes order to keep their Title IV programs, especially with fraud and

abuse found in audits of the programs (Riggs & Goodwin, 1997). This could hurt students from









low-income families if the Title IV programs were to become more restrictive (Riggs &

Goodwin, 1997). A study conducted by Hoyt (1999) provided even deeper analysis of student

retention. After studying several years worth of college retention data Hoyt (1999) found that, as

in other studies, academic success, ethnicity, work, and outside interests had a significant impact

on retention. However, Hoyt (1999) also found significant positive impact of conditions not

studied at other institutions, including living at home and early exposure to college.

With dual missions of the community college to provide vocational training as well as to

prepare students to transfer to a four year institution, Ko (2005) found that the retention rates of

vocational students were lower than those of other students in both two-year and four-year

institutions. According to Ko (2005), community colleges have not looked at vocational students

in particular when studying retention, and he proposed that perhaps community colleges should

look at vocational students to see what can be done to improve their generally lower retention

rates. Further, by staying focused on special groups of students, community colleges could

better develop effective programs to retain students in college (Ko, 2005).

Tinto (1975) presented a thorough analysis of the college student dropout process. He

pointed out that students who dropped out of college for different reasons were oftentimes

grouped together inappropriately by researchers. Among dropout reasons, temporary and

permanent withdrawal and transfer to a different institution have sometimes been grouped

together in research (Tinto, 1975). Further, students may drop out voluntarily or be dismissed

for academic performance, and these differences should also be taken into account in the

research (Tinto, 1975). The failure to group drop-out behavior correctly has led colleges and

state planners to miscalculate drop out rates when projecting student enrollments (Tinto, 1975).









In later research Tinto and Goodsell-Love (1993) found that students had a higher

persistence level when they participated in peer support groups, which seemed to encourage

them to attend classes and participate. They found that students in these learning communities

were able to fulfill both the social and academic needs of students new to college. In another

study, Tinto and Russo (1994) found that students involved in a support network outside of the

college setting were able to increase their involvement and achievement needs and had a higher

rate of continuing their education, but that it was difficult to achieve this involvement. Tinto

(1997) also pointed out that, since the classroom was the center of education, then group

involvement must occur in the classroom as well. Further, he found that this involvement

extended beyond the classroom to social and study groups (Tinto, 1997). Tinto (1998) further

emphasized that student involvement is most important while in the first year of college, and

stated that colleges need to encourage faculty to work together across disciplines to promote

shared experiences and to encourage faculty-student interaction.

Student Success

Research studies have compared occupational outcomes of individuals who began their

higher education at two-year colleges versus those who started at four-year institutions, with the

general assumption that students who started in a two-year institution were less likely to

complete a bachelor's degree (Dougherty, 1992; Whitaker & Pascarella, 1994). Dougherty

(1992) found that students who started work towards a bachelor's degree at a community college

had lower persistence levels than those who started directly at a four-year institution, and he

suggested that community colleges should work to improve transfer education. Whitaker and

Pascarella (1994) went further to conclude that students who completed their bachelor's degree

entirely at a four-year institution had higher earnings outside of college than students who started

out at two-year institutions. The researchers suggested that this may be due to individuals who









began at two-year institutions went into occupations of lower status than individuals who began

college at four-year institutions (Whitaker & Pascarella, 1994). Lin and Vogt (1996) also

compared occupational outcomes of students in community college versus students who attended

only four-year institutions and concluded that students earning their bachelor's degree solely at a

four-year school obtained higher earnings and job status than those that first attended a

community college. They suggested that the wide range of quality of two-year institutions may

be a factor in their research findings.

Additional predictors of student success have been found to be college entrance exam

scores and rank in high school (Smith & Schumacher, 2005). High school class rank and grades

in high school calculus (if taken) was particularly significant among males, although high school

calculus grade was a strong predictor for both males and females. Interestingly, the researchers

also found that verbal SAT scores were a strong predictor of success for males and math SAT

scores were a strong predictor of success for females (Smith & Schumacher, 2005). Other

studies have looked at even more factors that predict student success, such as gender, ethnicity,

parental education, parent marital status, attitude, and involvement in different learning

communities (Zheng, Saunders, Shelley, & Whalen, 2002). Zheng and colleagues (2002)

suggested that the groups identified as having special needs should receive more attention at the

individual level and should be persuaded to participate in group meetings in order to be more

successful in college.

Course grades are of particular interest in the literature. Placement test scores as predictors

of success in grades and retention in English and math courses was the subject of study by

Armstrong (2000), who found that student activities and other personal traits and demographics

were better predictors than placement test scores. Armstrong also determined that variances of









test scores were affected in part by how the different instructors scored the placement tests.

Armstrong's (2000) research indicated personal characteristics of students such as high school

preparation, the conception of the importance of a college education, past experiences, high

school grade point average, and involvement in school activities were more useful than

placement scores in predicting success in the community college system he studied.

Course completion has been a major factor to consider when researchers looked at

academic success. They noted a high drop out rate among distance learners (Visser, Plomp,

Amirault & Kuiper, 2002; Nash, 2005). Visser and colleagues (2002) found that motivation to

persist in distance education courses could be increased by enabling students in distance

education classes to meet with others in their cohort and by having instructors send motivational

messages, even when those messages were sent to the group rather than to individual students

(Visser, et al., 2002). In his study of drop-out factors, Nash (2005) found that students would

use, if provided, free tutoring and orientation sessions. Topics for these sessions could include

setting goals, managing time, studying effectively, and test taking skills, instead of specific

courses. Further, the researchers found that most of the students in their study enrolled in

distance learning courses due to time restrictions and learning preferences, as well as the

perception that distance education courses would be less demanding than traditional on-campus

courses (Nash, 2005).

A study by DeTure (2004) found that learning styles and student confidence levels did not

predict the level of student success in on-line courses. Further, DeTure (2004) found that

students in distance-education classes who were more independent had a higher degree of

confidence in their abilities; however, she also found that students with high independence and

self confidence did not have higher grades than other students in the same courses. Another









study by Higgins (2005) looked at drop-out rates of students in a community college nursing

program. She looked at prerequisite courses, preadmission tests, demographics, exit exam scores

and nursing skills laboratory scores. Demographic indicators, including age, ethnicity, and

gender were not found to be predictors of student success in this particular program, possibly due

to a tutoring program that was in place (Higgins, 2005). Rather, Higgins (2005) found that

prerequisite testing and coursework had a significant relationship with program completion.

Faculty mentoring also provided a higher success rate in this program (Higgins, 2005).

The literature indicates that socioeconomic status is one predictor of academic success,

although the strength of the relationship varied between students (Pearce, 2006; Sirin, 2005).

Pearce's research (2006) looked at the Chinese-American minority group, which generally

performed well. The factors of parental expectation and parental involvement were found to be a

significant factor in the success of Chinese-American students (Pearce, 2006). Because the

structure of Chinese-American families may have an important influence on performance of

students in this group, the results of Pearce's study cannot be applied easily to other minority

groups (Pearce, 2006). However, Pearce (2006) did suggest that working to change the home

environment of certain groups may help student success. Sirin (2005) found that changes in

society, economics and research methodologies have over time made research on socioeconomic

status and student success more complicated, and that newer research has to incorporate those

changes. But Sirin (2005) still found a significant effect on student success due not only to

resources at home but also to social capital. While students with low family socioeconomic

status had a higher risk of going to schools with limited funds, there were programs in place to

help assist these students with varying results (Sirin, 2005).









The present study is based on the research of Lee and Bowen (2006), who applied

Bourdieu's theory of cultural capital. Lee and Bowen studied how parents' involvement with

their children affected success in school. They included ethnicity, socioeconomic level, and

parent educational level in the study and used factor analysis to analyze five types of

involvement that parents had with their children both at home and at school. Lee and Bowen

(2006) used demographics as proxies for social status in testing Bourdieu's theory of cultural

capital. The researchers found that poverty and ethnic origin were more significant than parental

involvement factors in predicting the academic achievement of children. However, an

achievement gap was found that could be partially explained by parental involvement and the

interaction of parental involvement and different demographic backgrounds (Lee & Bowen,

2006).

Summary

Dramatic changes in the role of the community college and backgrounds of the students it

serves have occurred including increased diversity of the student body and the need to provide

remedial education. This has led to numerous challenges to the community colleges that must be

addressed in order to help students achieve educational success. According to Bourdieu's theory

of cultural capital, families may or may not possess certain pieces of information, depending on

their class status, and this capital may be an important component of individual success

(Bourdieu, 1977, p. 488; Bourdieu & Passeron, 1977, p. 32).









CHAPTER 3
METHODOLOGY

The purpose of this study was to determine if there is a significant relationship between

socioeconomic status and community college student college grade point average and course

completion ratio, using parent occupational status score as a proxy for socioeconomic status.

The data for this study were obtained through the Transfer and Retention of Urban Community

College Students (TRUCCS) project in the Los Angeles Community College District (Hagedorn

et al., 2001).

Research Questions and Hypotheses

1. What is the relationship between socioeconomic status and student college grade
point average?

2. What is the relationship between socioeconomic status and student course completion
ratio?

Research Design

As a theoretical framework for this analysis, the study used the research of Lee and Bowen

(2006), who applied Bourdieu's theory of cultural capital for their study of parent involvement

and student achievement. Lee and Bowen's research served as a model to determine whether the

level of student grade point average and course completion ratio (the dependent variables)

differed between families at different socioeconomic status levels. The independent variables

were demographics (gender, age, and ethnicity), high school grade point average, psychosocial

factors (determination, academic integration and aspiration to transfer to a four-year institution),

and socioeconomic status with parent occupational status score as a proxy for socioeconomic

status. The occupational status scores were based and coded on that the scheme developed and

described by Terrie and Nam (1994) using the occupations students provided on the research

instrument.









Research Population

Students from the nine campuses in the Los Angeles Community College District enrolled

during the Spring 2001 semester were the subjects of this study. A total of 4968 students

answered the survey instrument consisting of 47 questions. The sample was 37.9% male, 58.9%

female with 3.2% not indicating gender. Also, 2.9% of the students were 18 years old or

younger, 53.4% were 19-24 years old, 30.1% were 25-39 years old, 9.9% were 40-54 years old,

and 1.7% were 55 years old or older. Further, 83.8% of the sample belonged to ethnic

minorities, with Hispanics at 51.8%, African-American/Blacks at 15.6% and Asian/Pacific

Islanders at 14.0% making up the largest minority groups. Caucasian/White individuals made up

14.6% of the sample population. Non-native English speakers comprised 53.5% of the sample

and 43.8% were the primary wage earners in their families. Working students included 32.2%

working full time and 36.5% working part time. In addition, 31.3% of the sample had their

children living with them. Family education levels included 17.5% of students with fathers and

20.4% with mothers who had a sixth grade education or less.

Data Collection

The data for this study were originally collected through the Transfer and Retention of

Urban Community College Students (TRUCCS) project, as reported in Hagedorn's unpublished

report from the University of Southern California titled, "TRUCCS sampling plan: Representing

the Los Angeles Community College District." Hagedorn's sampling plan went further to

describe how information regarding demographics and transfer aspirations were obtained from

answers to survey questions. Data regarding the courses students took, course completion ratios,

and grade point averages were calculated from student transcript records, obtained for those

students signing the appropriate records release. The TRUCCS researchers originally examined

information in the Right-to-Know database to determine student transfer information and course-









taking patterns from the Los Angeles Community College District, and to help determine the

strategy for administering the questionnaire. Since no meaningful course-taking patterns could

be determined from that investigation, the researchers examined college catalogs and class

schedules for the nine Los Angeles Community College District campuses. The researchers

found three levels of English courses from which they determined the sample population: non-

district credit courses, first-year transfer courses, and second year transfer courses. In this

manner the researchers were able to sample individuals across a broad spectrum of interests,

while not including those individuals attending community college for casual or entertainment

purposes.

Hagedorn's questionnaire was distributed to students in English courses throughout the

Los Angeles Community College District in Spring 2001 in proportion to enrollments at each of

the nine campuses. In addition, the researchers looked at the three levels of English courses and

included that proportion in their sampling plan. English-as-a-second language courses were also

included, but only those taught at advanced levels, to ensure that students could understand the

questionnaire. Some other English courses, such as those for cooperative education, were

eliminated from the sample population as they did not fit the purposes of the study.

Data Analysis

In the present study, factor analysis was used to reduce data from multiple items to a

smaller number of more reliable scales, to provide a more valid measure of latent constructs.

Factor analysis looks for significant patterns between different variables. That is, factor analysis

can group together multiple related variables, thereby reducing the variables into smaller groups

called factors. Even though some variables may not be directly observed, factor analysis can

detect similar characteristics of a group of variables and establish a new factor (Darlington, n.d.).

The new independent variables defined through factor analysis can then be used in the blocks of









variables when forward block entry regression was used in the analysis. A confirmatory

reliability analysis was performed on the scales using Cronbach's Alpha of 0.70 or greater as the

minimum. Scales measuring student determination, academic integration, and aspiration to

transfer had the appropriate reliability Cronbach's Alpha coefficients and were therefore retained

in the model.

Forward block entry regression was used in the analysis of the independent variables. The

Statistical Package for the Social Sciences was used to calculate course completion ratios, as

well as to select the higher occupational status score of either the father or mother of each

student to serve as a proxy for student socioeconomic status. For forward block entry regression

college grade point average was used as the dependent variable, and the independent variables

were ethnic origin, gender, age, high school grade point average, aspiration to transfer to a four-

year institution, determination, academic integration, English language proficiency, job-related

responsibilities, and socioeconomic status. The forward block entry regression was then

repeated using course completion ratio as the dependent variable with the same independent

variables of ethnic origin, gender, age, high school grade point average, aspiration to transfer to a

four-year institution, determination, academic integration, English language proficiency, job-

related responsibilities, and socioeconomic status (see Table 3-1). Multiple levels of blocking

were used to control for covariance. The first level included the independent variables of

demographics. The second level used high school grade point average as a block. The third

level had psychosocial variables as the block. The fourth level used socioeconomic status as the

block.

By using forward block entry regression, changes in R2 can be detected after each factor is

added to determine its significance. The amount of R2 change between the different models can









then be studied to see what factors have the greatest influence and associated significance level.

By entering the variables in blocks, comparisons can be made between models. Thus the

significance of combinations of different variables can be seen to provide a breakdown of the

outcomes of the statistical tests (Stockburger, n.d.).

Instrumentation

The research instrument used in this study was a 47-item questionnaire given to 5000

registered students during the Spring 2001 semester in the Los Angeles Community College

District. This instrument was developed and written by the TRUCCS research team and

underwent a pilot study prior to being administered to the students (see Appendix A for the

TRUCCS survey instrument). The questionnaire included items regarding demographics,

socioeconomic status, college expectations, and barriers the students saw to achieving success.

The questionnaire was designed for a diverse student population, with many individuals not

having English as their first language (Hagedorn, Chi, Cepeda & McLain, 2007) In Summer

2001 the transcript data was collected for those students who signed the appropriate consent

form. The questionnaire was posted on the internet so that students from the original sample

could update their information and provide feedback on their experiences from the past year

(Hagedorn, et al., 2001).

To measure ethnicity, the questionnaire was designed to have students enter as many

ethnic groups as best described them. There were 22 questions that allowed students to enter

their ethnicity. Of those, the values for African-American/Black and Caucasian/White

ethnicities were assigned the dichotomous values of either "1" if not marked and "2" if marked.

In order to measure the ethnicity variable, the remaining ethnicities used in this study were

recorded into commonly-accepted ethnically-related groups. Table 3-2 is a summary of the









groups of recorded ethnicities. The Caucasian/White ethnic group was used as the comparison

group in this dissertation.

Validity and Reliability

Internal validity is been defined as the degree to which the independent variables being

manipulated actually have an effect on the dependent variable (Shavelson, 1996, p. 20), while

reliability is defined as the extent to which a measure is consistent or dependable and free from

random error (Shavelson, 1996, p. 473). The questionnaire was pre-tested for reliability and

then used by the TRUCCS team, whose original task was to study retention and transfer behavior

of students in the Los Angeles Community College District. Since that study looked at

explaining retention and transfer behavior, neither random sampling nor random assignment was

performed, but rather the sample was obtained in a "quasi-experimental" manner. Further, the

TRUCCS team concentrated on the internal validity of the research design. The sampling

method ensured variation of the original independent variables and enabled the researchers to be

confident of having internal validity when comparing sub-groups. Further, since TRUCCS was

focusing on transfer and retention factors, the sampling method ensured that younger students

were over-sampled and older students were under-sampled (Hagedorn, et al., 2006). This

dissertation examined correlations, and both standardized and unstandardized regression weights

were analyzed to specifically address the research questions.

Summary

Parental influences have been the subject of numerous studies for many years. The

variable of parent occupational status as a measure of socioeconomic status has not been as

extensively researched and was the variable of interest in this research study. The influence of

parental socioeconomic status was measured for college grade point average and course

completion ratio.









Table 3-1. List of Variables
Independent Variables
Gender
Age
Ethnicity
African American/Black
Caucasian/White
Hispanic
Asian/Pacific Islander
American Indian/Native Alaskan
High School GPA
Psychosocial Factors
Determination
Academic Integration
Aspire to Transfer
Socioeconomic Status


Dependent Variables
College Grade Point Average
Course Completion Ratio









Table 3-2. Recoded ethnic groups
American Indian/Alaskan Native Recoded Ethnic Group
Alaskan Native
American Indian

Asian Recoded Ethnic Group
Chinese
Filipino
Japanese
Korean
Thai
Laotian
Cambodian
Vietnamese
South Asian (Indian Subcontinent)
Pacific Islander/Samoan, Hawaiian, or Guamanian
Other Pacific Islander

Hispanic Recoded Ethnic Group
Mexican
Mexican-American/Chicano
South American
Central American
Other Latino/Hispanic









CHAPTER 4
ANALYSIS AND PRESENTATION OF THE DATA

The purpose of this study was to determine if there is a significant relationship between

parent socioeconomic status and community college student grade point average and course

completion ratio, using parent occupational status score as a proxy. The data for this study were

obtained through the Transfer and Retention of Urban Community College Students (TRUCCS)

project in the Los Angeles Community College District (Hagedorn, et al., 2001). The research

questions were:

1. What is the relationship between socioeconomic status and student college grade
point average?

2. What is the relationship between socioeconomic status and student course completion
ratio?

Population Profile

Table 4-1 provides a summary of demographic variables describing the gender distribution

of students participating in the TRUCCS project. Females comprised 58.9% of the sample.

Table 4-2 provides data on the age distribution, determined by the age of the student on

December 31 of the year the survey was completed. The largest category was the 21-24 year old

age group, comprised of 1297 individuals (26.1%).

Table 4-3 provides a summary of demographic variables that describe the ethnic origin of

students participating in the TRUCCS project. The largest ethnic category represented was the

Hispanic group comprised of 2571 individuals (51.8%). Overall, 4159 individuals (83.8%) who

took the survey identified themselves as a member of one or more minority group.

Table 4-4 provides a summary of the high school grade point average variable. The mean

was approximately halfway between a B- and B grade point average (mean = 5.47, s.d. 1.85,

with a value of 5 being equal to a B- GPA and a value of 6 equal to a B).









Validity and Reliability

Table 4-5 provides the reliability of the new psychosocial factors of determination,

academic integration, and aspiration to transfer. The Alpha values of 0.7807 for determination,

0.8005 for academic integration, and 0.7244 for aspiration to transfer are high coefficients of

reliability and demonstrate that the items combined together are related and that the new latent

construct of determination has high internal consistency.

For both the model with grade point average as the dependent variable and the model with

course completion ratio as the dependent variable, one-tailed Pearson Product Moment

Correlation Coefficients were examined to identify zero-order correlations that were statistically

significant. Table 4-6 shows the zero order correlations (Pearson r) for all variables used in the

model with grade point average as the dependent variable. As the table shows, most of the

relationships between variables were significant. As expected, the highest relationship was

between high school grade point average and college grade point average (r = 0.280). The next

highest relationship was between student age and college grade point average (r = 0.182). The

variable of determination was also significant (r = 0.178). Noteworthy is that the ethnic

background of being Hispanic had a significant negative relationship with college grade point

average (r = -0.135). Other relationships meaningful to this study included a positive

relationship between the ability to speak English (r = 0.267), and a large negative relationship

between the Hispanic ethnic origin and socioeconomic status (r = -0.341). As expected there

were large positive relationships between aspiration to transfer and determination (r = 0.223) and

between academic integration and determination (r =0.221).

Table 4-7 shows the zero-order correlations for all variables used in the model with course

completion ratio as the dependent variable. As the table shows, most correlations were

significant. The highest correlation was, again as expected, between high school grade point









average and success rate (r = 0.260). Determination also had a significant relationship with

success rate (r = 0.159) and ability to speak English (r = 0.113). The African American/Black

ethnic origin variable had a positive relationship with academic integration (r = 0.123) and a

negative relationship with the ability to speak English (r = -0.116). On the other hand, the

Asian/Pacific Islander ethnic origin variable had a positive relationship with the ability to speak

English (r = 0.267). There was also a large, significant positive relationship between

socioeconomic status and the African American/Black (r = 0.129) and the Asian/Pacific Islander

(r = 0.119) ethnic origins, and a large, significant negative relationship between socioeconomic

status and the Hispanic ethnic origin (r = -0.340). Again, a positive relationship was found

between aspiration to transfer and determination (r = 0.224), and between academic integration

and determination (r = 0.222).

Table 4-8 presents a summary of the distribution of parent occupational status scores. As

previously noted, the parent occupational status score served as a proxy for socioeconomic status

in this study. The scores ranged from a low of 0.70 to a high of 99.80 (mean = 53.08, s.d. =

26.52, N = 4122). The group with occupational scores with the highest frequency had scores

ranging from 60.1-70.0 (N = 863, 20.94% of sample population). Overall, 44.88% of

occupational status scores were 50.0 or below and 55.12% of scores were 50.1 or higher.

Table 4-9 presents a summary of the distribution of student course completion ratios. The

course completion ratio was the ratio of the number of courses for which a student received a

passing grade divided by the number of courses for which the student registered. On average,

students completed 68.7% (s.d. = 0.24%, N = 4654) of the courses in which they enrolled.

Regression Analyses

Multiple regression is a statistical process to examine the relationship between one

dependent variable and two or more independent variables (Shavelson, 1996, p. 528). The









purpose of this analysis was to ascertain the influence of each of the independent variables on the

dependent variables. A forward block entry regression analysis was performed first with grade

point average as the dependent variable and demographics, high school grade point average,

psychosocial factors and socioeconomic status as the independent variables, and then with course

completion ratio as the dependent variable and demographics, high school grade point average,

psychosocial factors and socioeconomic status as the independent variables.

Grade Point Average

Table 4-10 presents the results of the forward block entry regression analysis for the

dependent variable of grade point average and the demographic, high school grade point average,

psychosocial, and socioeconomic status independent variables. Once the first block

(demographic) was entered, only 8.0% of the variance was explained. Adding the second block

(high school grade point average) increased the R square and explained 13.9% of the variance,

and explained the most variance of all the blocks. The addition of the third block (psychosocial)

explained a total of 15.9% of the variance. The addition of the fourth block of socioeconomic

status was not statistically significant (F = 56.74, df =2, p = 0.390) which means that the model

does not change with a change in socioeconomic status.

Table 4-11 presents the unstandardized regression coefficient (b), the standardized

regression coefficient (B), and R2 for the dependent variable grade point average and the

demographic, high school, grade point average, psychosocial, and socioeconomic status

independent variables. The R2 value of 0.159 was statistically significant, with F(12,3601) =

56.74, and p = 0.00. All independent variables except being Asian/Pacific Islander ethnic origin,

academic integration and SES were found to contribute significantly to the prediction of grade

point average.









The unstandardized b of -0.394 for the African-American/Black ethnic group is the largest,

but negative, relationship and indicates that being African-American/Black is a negative

predictor of college grade point average. It may be interpreted that all things equal, being

African-American/Black predicts grade point average to be 0.394 lower than the white control

group. Being Hispanic has an unstandardized b of -0.282 indicating that being Hispanic is also a

negative predictor of college grade point average. Accordingly, being Hispanic predicts grade

point average to be 0.282 lower than the white control group. However, determination (b =

0.149) and average grade in high school (b = 0.100) are positive predictors of college grade point

average. For every increase of 1.0 unit in determination, college grade point average increases

by 0.149 grade points. For every increase of 1.0 unit in average grade in high school, college

grade point average increases by 0.100 grade points. Thus, ethnicity is the largest predictor of

college grade point average in this study, with African-American/Black and Hispanic students

being negatively impacted as compared to their white counterparts. When comparing the

standardized regression coefficients (B), average grade in high school (B = 0.225) is the

strongest and most important predictor of college grade point average when comparing variables

in the same metric. This is followed in strength by the negative predictors of being African

American/Black (B = -0.170) or Hispanic (B = -0.172) which are approximately 75 percent the

strength of average grade in high school. Determination (B = 0.129) closely follows in

prediction strength and is a positive predictor of college grade point average with a strength just

over half that of average grade in high school. Socioeconomic status, with a significance level of

0.390, was found to not have a significant relationship with college grade point average.

Course Completion Ratio

Table 4-12 presents the results of the forward block entry regression analysis for the

dependent variable of course completion ratio and the demographic, high school grade point









average, psychosocial and socioeconomic status independent variables. Once the first block

(demographic) was entered, only 3.7% of the variance was explained. Adding the second block

(high school grade point average) increased the R square and explained 9.1% of the variance,

and explained the most variance of all the blocks. The addition of the third block (psychosocial)

explained 11.4% of the variance. The addition of the fourth block of socioeconomic status was

not significant (F = 38.899, df = 12, 3260, p = 0.249) which means that the model does not

change with a change in socioeconomic status with all controls in place.

Table 4-13 presents the unstandardized regression coefficient (b), the standardized

regression coefficient (B), and R2 for the dependent variable student course completion ratio and

the demographic, high school grade point average, psychosocial, and socioeconomic independent

variables. R2 = 0.114 was statistically significant, F(12,3620) = 38.899, p = 0.00. All

independent variables except Asian/Pacific Islander ethnic origin, aspiration to transfer,

academic integration and SES were found to contribute significantly to the prediction of course

completion ratio.

The unstandardized b of -0.089 for the African-American/Black ethnic group is the largest,

but negative, relationship and indicates that being African-American/Black is a negative

predictor of course completion ratio. It may be interpreted that all things being equal, the course

completion ratio decreases by 0.089 for African Americans in the sample. Being Hispanic has

an unstandardized b of -0.058 indicating that being Hispanic is also a negative predictor of

course completion ratio. Accordingly, the course completion ratio decreases by 0.058 for

Hispanics in the sample. However, determination (b = 0.042), average grade in high school (b =

0.028), and understanding the English language (b = 0.023) are positive predictors of course

completion ratio. For every increase of 1.0 unit in determination, course completion ratio









increases by 0.042 points. For every increase of 1.0 unit in average grade in high school, course

completion ratio increases by 0.028 points. For every increase of 1.0 unit in understanding the

English language, course completion ratio increases by 0.023 points. Thus, ethnicity is the

largest predictor of course completion ratio in this study, with African-American/Black and

Hispanic students being negatively impacted as compared to their white counterparts. When

comparing the standardized regression coefficients (B), average grade in high school (B = 0.211)

is again the strongest and most important predictor of course completion ratio. This is followed

in strength by the negative predictor of being African American/Black (B = -0.130) which is 62

percent the strength of average grade in high school. The positive predictor of determination (B

= 0.124) and the negative predictor of being Hispanic (B = -0.120) follow in strength and have

approximately 58 percent the strength of average grade in high school. Socioeconomic status,

with a significance level of 0.390, was found to not have a significant relationship with success

rate. These observations showed that socioeconomic status had no direct effect on student grade

point average or course completion ratio.

Summary

A total of 4968 questionnaires were completed by students in the Los Angeles Community

College District during the Spring 2001 semester. The questionnaire was administered by the

research team comprising the Transfer and Retention of Urban Community College Students.

The analyses showed no significant relationship between socioeconomic status and student grade

point average or course completion ratio.










Table 4-1. Students in the LACCD. Distribution by gender.
Gender N %

Male 1884 37.9
Female 2927 58.9
No Response 157 3.2

Total 4968 100.0



Table 4-2. Students in the LACCD. Distribution by age.
Age N %

16 or less 31 0.6
17 27 0.5
18 87 1.8
19 678 13.6
20 683 13.7
21-24 1297 26.1
25-29 707 14.2
30-39 788 15.9
40-54 491 9.9
55 or more 82 1.7
No response 97 2.0

Total 4968 100.0









Table 4-3. Students in the LACCD. Distribution by ethnic origin.
Ethnic Origin N %

Asian/Pacific Islander 695 14.0
African-American/Black 776 15.6
Hispanic 2571 51.8
Alaskan Native/American Indian 117 2.4
Caucasian/White 727 14.6
All other/no response 82 1.6

Total 4968 100.0



Table 4-4. Students in the LACCD. Distribution by high school.
High School GPA N %

1 D or lower 66 1.3
2 C- 137 2.8
3C 575 11.6
4 C+ 779 15.7
5 B- 836 16.8
6B 890 17.9
7B+ 839 16.9
8 A- 425 8.6
9 A 250 5.0
Missing or no response 97 3.4

Total 4968 100.0

Mean = 5.47, S.D. = 1.85









Table 4-5. Factor analysis: determination, academic integration, aspire to transfer.
Item Description Determination (Items in question 37 of TRUCCS)

For the following items, please indicate the extent to which you agree or disagree
with the following statements.
1 I am very determined to reach my goals
2 It is important for me to finish the courses in my program of studies
3 I feel most satisfied when I work hard to achieve something
4 I expect to do well and earn good grades in college
5 I keep trying even when I am frustrated by a task


Scale
Mean
if Item
Deleted


24.7628
24.7344
24.8227
24.8455
25.1818


Scale
Variance
if Item
Deleted


9.0976
9.4655
9.2732
9.6165
8.8409


Corrected
Item-
Total
Correlation


0.5886
0.5840
0.5568
0.5597
0.5064


Alpha
if Item
Deleted


0.7287
0.7318
0.7394
0.7395
0.7619


N of cases = 4642, N of items :


5, Alpha = 0.7807









Table 4-5 (continued)
Item Description Academic Integration


(Items in questions 13 and 14 in TRUCCS)


For this course only, approximately how many times in the past 7 days, did you:
Work in small groups during class time
Telephone or email another student to ask a question about your studies
Ask the instructor questions
Speak up during class discussion

Approximately how many times in the past 7 days, did you:
Talk with an instructor before or after class
Talk with an instructor during office hours
Help another student understand homework
Study in small groups outside of class
Speak with an academic counselor


Scale
Mean
if Item


16.4145
16.7679
15.7327
15.6562
15.8749
16.8454
15.8707
16.6559
16.8467


Corrected
Variance
if Item


45.8573
47.9065
40.8903
41.6669
42.8940
47.3423
42.6000
45.6315
47.8157


Item-
Total


0.4303
0.4123
0.6190
0.4758
0.5642
0.4918
0.5371
0.4941
0.4467


Alpha
if Item


0.7887
0.7906
0.7621
0.7874
0.7708
0.7831
0.7747
0.7809
0.7874


N of cases = 4572, N of items 9


9, Alpha = 0.8005









Table 4-5 (continued)
Item Description Aspire to Transfer


(Items in question 10 in TRUCCS)


As things stand today, do you think you will ...?
Get a bachelor's degree
Transfer to a 4-year college/university


Scale
Mean
if Item
Deleted


4.1357
4.1321


Scale
Variance
if Item
Deleted


1.3690
1.1930


Corrected
Item-
Total
Correlation


Alpha
if Item
Deleted


0.5692
0.5692


N of cases = 4619, N of items :


Item


2, Alpha = 0.7244










Table 4-6. Zero order correlations for the model grade point average.
1 2 3 4 5 6 7 8 9 10 11 12 13
1 GPA 1.000
2 Afr/Ameri -0.070* 1.000
3 Asian/Pac 0.087* -0.151* 1.000
4 Hispanic -0.135* -0.411* -0.379* 1.000
5 Gender 0.099* 0.068* -0.033* 0.006 1.000
6 Age 0.182* 0.137* -0.014 -0.114* 0.079* 1.000
7 HS Grade 0.280* -0.041* 0.109 -0.080* 0.155* 0.021 1.000
8 Aspire 0.041* 0.040* 0.015* -0.007 -0.027* -0.160* 0.084* 1.000
9 Determ 0.178* 0.087* -0.079* 0.015 0.105* 0.155* 0.151* 0.223* 1.000
10 Integrat 0.046* 0.123* -0.033* -0.068* 0.000 0.110* 0.106* 0.079* 0.221* 1.000
11 Eng Lang 0.092* -0.115* 0.267* -0.075* 0.015 0.092* 0.118* -0.079* -0.087* 0.051* 1.000
12 Job Resp -0.030* -0.049* -0.011* 0.065* -0.050* 0.072* -0.014 0.026 -0.037* 0.013 0.106* 1.000
13 SES 0.060* 0.129* 0.121* -0.341 -0.026 0.011 0.056* 0.032* -0.005 0.005 -0.031* -0.015 1.000
* p<0.05










Table 4-7. Zero order correlations for the model success rate
1 2 3 4 5


6 7 8 9 10 11 12 13


1 Success Ratio 1.000
2 Afr/Ameri -0.073* 1.000
3 Asian/Pac 0.075* -0.151* 1.000
4 Hispanic -0.083* -0.409* -0.379* 1.000
5 Gender 0.079* 0.069* -0.032* 0.006 1.000
6 Age 0.093* 0.136* -0.013 -0.112* 0.079* 1.000
7 HS Grade 0.260* -0.041* 0.106* -0.080* 0.154* 0.021 1.000
8 Aspire 0.049* 0.040* 0.013 -0.004 -0.027 -0.158* 0.084* 1.000
9 Determ 0.159* 0.088* -0.075* 0.016 0.109* 0.153* 0.150* 0.224* 1.000
10 Integrat 0.047* 0.123* -0.034* -0.067* 0.000 0.109* 0.107* 0.082* 0.222* 1.000
11 EngLang 0.113* -0.116* 0.267* -0.075* 0.013 0.093* 0.117* -0.079* -0.088* 0.050* 1.000
12 Job Resp -0.047* -0.049* 0.014 0.065* -0.053* 0.070* -0.015 0.027 -0.042* 0.014 0.105* 1.000
13 SES 0.012 0.129* 0.119* -0.340* -0.028* 0.011 0.056* 0.032* -0.008 0.005 -0.029* -0.013 1.000
p<0.05










Table 4-8. Distribution of parent occupational status score (socioeconomic status)
Occupational Status Score N %

00.0-10.0 297 7.21
10.1-20.0 240 5.82
20.1-30.0 458 11.11
30.1-40.0 446 10.82
40.1-50.0 409 9.92
50.1-60.0 186 4.51
60.1-70.0 863 20.94
70.1-80.0 377 9.15
80.1-90.0 527 12.79
90.1-100.0 319 7.74

Total 100 100.00


Table 4-9. Distribution of student course completion ratio
Characteristic Low High M SD

Course Completion Ratio 0.0 1.0 0.6869 0.24

N of cases = 4654









Table 4-10. Model summary grade point average


Model
1
2
3
4
ANOV
Model
1


R
R R Square C
0.283 0.080 0
0.373 0.139 0
0.399 0.159 0
0.399 0.159 0
A Grade Point Average
Sum of Squares
Regression 192.247
Residual 2213.827
Total 2406.074


2 Regression
Residual
Total

3 Regression
Residual
Total

4 Regression
Residual
Total


335.436
2070.638
2406.074

382.187
2023.887
2406.074

382.602
2023.472
2406.074


Square
change
.080
.060
.019
.000


df
5
3608
3613

6
3607
3613

11
3602
3613

12
3601
3613


Table 4-11. Regression analysis summary for grade
Variable b
African-American/Black -0.394
Asian/Pacific Islander -0.071
Hispanic -0.282
Gender -0.080
Age 0.081
Average Grade in High School 0.100
Aspiration to Transfer 0.026
Determination 0.149
Academic Integration -0.026
Understanding English Language 0.043
Job-Related Responsibilities -0.023
SES 0.043


R2= 0.159


F Change
62.663
249.431
16.641
0.738


Sig. F Change
0.000
0.000
0.000
0.390


Mean Square F


36.449
0.614


55.906
0.574


34.744
0.562


31.884
0.562


point average
SEB B
0.042 -0.170
0.043 -0.030
0.033 -0.172
0.026 0.048
0.008 0.162
0.007 0.225
0.013 0.032
0.019 0.129
0.016 -0.021
0.016 0.045
0.010 -0.034
0.001 0.014


Sig.
0.000
0.101
0.000
0.002
0.000
0.000
0.050
0.000
0.197
0.006
0.027
0.390


62.663


0.000


97.387



61.836



56.740


0.000



0.000



0.000


Block 1


Block 2
Block 3





Block 4









Table 4-12. Model summary course completion ratio
R Square
Model R R Square Change F Change dfl Sig. F Change
1 0.192 0.037 0.037 27.663 5 0.000
2 0.302 0.091 0.054 216.350 1 0.000
3 0.337 0.114 0.023 18.733 5 0.000
4 0.338 0.114 0.000 1.327 1 0.249
ANOVA Course Completion Ratio
Model Sum of Squares df Mean Square F Sig.
1 Regression 7.710 5 1.542 27.663 0.000
Residual 202.176 3627 0.056
Total 209.886 3632

2 Regression 19.094 6 3.182 60.479 0.000
Residual 190.792 3626 0.053
Total 209.886 3632

3 Regression 23.905 1 2.173 42.310 0.000
Residual 185.981 3621 0.051
Total 209.886 3632

4 Regression 23.973 12 1.998 38.899 0.000
Residual 185.913 3620 0.051
Total 209.886 3632



Table 4-13. Regression analysis summary for course completion ratio
Variable b SEB B Sig.
African-American/Black -0.089 0.013 -0.130 0.000 Block 1
Asian/Pacific Islander -0.160 0.013 -0.022 0.229
Hispanic -0.058 0.010 -0.120 0.000
Gender 0.016 0.008 0.032 0.044
Age 0.011 0.002 0.073 0.000
Average Grade in High School 0.028 0.002 0.211 0.000 Block 2
Aspiration to Transfer 0.007 0.004 0.030 0.066 Block 3
Determination 0.042 0.006 0.124 0.000
Academic Integration -0.003 0.005 -0.009 0.564
Understanding English Language 0.023 0.005 0.082 0.000
Job-Related Responsibilities -0.010 0.003 -0.051 0.001
SES 0.043 0.001 0.014 0.249 Block 4
R2 = 0.114 (N = 3633, p = 0.000).









CHAPTER 5
CONCLUSIONS AND RECOMMENDATIONS

The purpose of this study was to determine if there is a significant relationship between

parent socioeconomic status and community college student college grade point average and

course completion ratio. The data for this study was obtained from results of a 47-question

survey administered through the Transfer and Retention of Urban Community College Students

(TRUCCS) project in the Los Angeles Community College District (Hagedorn, et al., 2001).

The present research sought to answer two questions not addressed by Hagedorn's statistical

analysis.

1. What is the relationship between socioeconomic status and student college grade
point average?

2. What is the relationship between socioeconomic status and student course completion
ratio?

To achieve this goal, four independent variables were used: demographics (gender, age,

and ethnicity), average grade in high school, the psychosocial concerns (determination, academic

integration and desire to transfer to a four-year institution), and parent occupational status score

(higher of the two parents), which served as a proxy for socioeconomic status. The statistical

analyses were conducted using SPSS 10.0. Factor analysis was used to detect latent variables

that may be present (the psychosocial variables). Forward block entry regression was conducted

to determine whether there was a significant relationship between the dependent and independent

variables being studied.

Previous research has shown that a large urban community college system has unique

characteristics that have to be addressed when looking at levels and predictors of student success.

Since community colleges are directly answerable to their local community, changes in the local

political climate may alter directions that the community colleges may move towards. Changes









in the community college focus need to consider student backgrounds, even though the

considerations may conflict with the local political climate. If Bourdieu's cultural capital theory

(Bourdieu & Passeron, 1977, p. 30) is correct, it is essential that today's students be successful in

college to gain cultural capital for their children.

The results of this research study showed that socioeconomic status does not provide a

significant influence on student success. Rather, other factors have greater weight on the student

outcomes measured. While previous studies (Berliner, 2006; Dougherty & Kienzl, 2006) among

different populations have shown socioeconomic status to be significant, the unique

characteristics of the population in this research study show no significant relationship, and thus

need to be examined to determine what causes the differences. Otherwise, policies may be

implemented with an inadequate administrative understanding of expectations of the outcomes,

or with an inadequate staff

Conclusions and Implications

This study examined several factors that may or may not influence student college grade

point average and course completion ratio. Analysis of demographic information showed a high

ethnic minority percentage (83.8%). Aside from ethnicity, the subjects in this study have a high

level of homogeneity, in that most are first generation college students, live in a large urban

environment, attend a large urban community college, and have lower socioeconomic status

backgrounds. However, the population differs greatly from those of studies of four-year

institutions, not only in ethnic combination, but also in age, socioeconomic background, and

family responsibilities. Thus, policies based on studies of four-year institutions do not directly

apply to community colleges. Large urban community college districts such as those in Los

Angeles, Houston, Chicago, and Maricopa County, Arizona should be compared with each other,

because issues in large urban community college systems have more similarities among









themselves, such as student background and financial resource management, than with four-year

institutions.

This study finds socioeconomic status not to be significant in determining student success,

in contrast to previous research (Berliner, 2006; Dougherty & Kienzl, 2006) showing a

significant influence of socioeconomic status. Aspects that may help explain these results are the

characteristics of the population being studied. The presence of a large minority representation

in the Los Angeles Community College District population is very different than found in most

other studies.

Analysis of the data revealed that understanding the English language has a positive

relationship with both college grade point average and course completion ratio. Thus, the better

the student is able to understand the English language, the higher the college grade point average

and course completion ratio. However, this was not true for African-Americans and Hispanics,

who often live in ghetto areas and are not exposed to standard English in the formative years.

The ability to understand the English language is not necessarily a function of socioeconomic

status but rather opportunities to interact in an English-speaking environment and the support to

speak English. Programs for English as a second language may not be as effective as they need

to be, or perhaps individuals' access to English as a second language program may be

problematic. Individuals who work may not have the time to attend these programs at the times

offered.

Job-related responsibilities have a significant negative relationship with both college grade

point average and course completion ratio. Individuals in the study sample have a lot of things

going on in their lives that may be overriding the effect of college preparation and remediation.









Working while in college, whether part-time or full-time, is difficult for even the best of

students. The impediments are even higher for those who already start out with disadvantages.

Impact on College Grade Point Average

Controlling for the independent variables used in the analysis, no significant relationship

between parent occupational status, a proxy for socioeconomic status, and student college grade

point average was found in the analysis. In the analysis of the independent variables of

demographics (gender, age, and ethnicity), high school grade point average, psychosocial factors

(determination, academic integration and aspiration to transfer to a four-year institution), and

parent occupational status score as a proxy for socioeconomic status, the greatest positive

variable was determination (Beta = 0.149, standardized beta = 0.129), followed by average grade

in high school (Beta = 0.100, standardized beta = 0.225), age (Beta = 0.081, standardized beta =

0.162), and understanding the English language (Beta = 0.043, standardized beta = 0.045).

Student aspiration to transfer also had an impact (Beta = 0.026, standardized beta = 0.032). The

relationship between high school grade point average and college grade point average (with high

school grade point average having the strongest standardized beta of 0.225) is logical and

conforms with findings in other research studies (Zwick & Sklar, 2005). This shows that one of

the best predictors of college grade point average is high school grade point average, which is an

obvious result and supported in the literature.

Although this study and other research have found that a high level of high school grade

point average is an indicator of college preparation, the competency of the high schools must

also be considered. If the school is of poor quality but the student grade point average is high,

then the student may still be poorly prepared for college. College administrators must be aware

of the quality of the high schools attended when evaluating students for placement in their

institutions. While traditional four-year institutions weigh the total background of students









before accepting them, including personal interviews, essays, and extracurricular activities, the

community college mission of open enrollment allows a channel into the higher education

system for students who may not have come from a high quality high school. This issue may be

further exasperated when primary and secondary schools decide to allow students to progress to

the next grade level rather than holding them back for poor performance.

This study also showed that older students tend to perform better than younger students,

possibly because older students have learned to better manage life activities, including

adjustments to attending college along with working or raising children. The influence of

student determination was also reasonable when college grade point average was considered, as

students who are determined to succeed will work harder to meet their educational goals.

Determination can be the effect of family culture and nurturing, as well as educational

background of the family.

The high negative relationship between college grade point average and the ethnicities of

African-American/Black (Beta = -0.394, standardized beta = -0.170) and Hispanics (Beta = -

0.082, standardized beta = -0.172) at first glance indicates that ethnicity is an important

consideration. However, the large percentage of Blacks and Hispanics in the sample may be

masking other issues such as socioeconomic status. Further, many of the students may not have

had access to resources that would better prepare them for college.

As described in Chapter 2, much research has been performed in the area of helping

minority students increase their achievement, with some successes. However, students in certain

ethnic groups still continue to be negatively impacted. This finding demonstrates that even with

programs in place to help minorities succeed in college, problems still exist. One of the major

tasks of community colleges is providing remedial courses (Hadden, 2000; Perin, 2006). But









with the poor college success rates of ethnic minorities, community colleges need to reevaluate

their remediation programs. Most of the programs focus on fundamental skills such as reading

and math. However, in this author's opinion, teaching students matter-of-fact self disciplines,

such as class and daily studying, should be reinforced consistently in all remediation programs.

This would also go far in increasing the cultural capital that these students will pass on. As

mentioned above, adequate preparation for college also depends on the high school. College

course work is very demanding, and students that are already poorly prepared will have an even

more difficult time adjusting to college life. This is consistent with Bourdieu's theory of cultural

capital. Even with a portion of the study population having high socioeconomic status, the

cultural capital of the family may still be low depending upon how long the family has been

living in the area and where parental education was obtained.

Impact on Course Completion Ratio (Success Rate)

Controlling for the independent variables used in the analysis, no significant relationship

between parent socioeconomic status and student course completion ratio was found in the

analysis. Rather, other factors were found to be significant in predicting the course completion

ratio. In the analysis of the independent variables of demographics (gender, age, and ethnicity),

high school grade point average, psychosocial factors (determination, academic integration and

aspiration to transfer to a four-year institution), and parent occupational status score as a proxy

for socioeconomic status, the greatest positive variable was again determination (Beta = 0.042,

standardized beta = 0.124), followed by average grade in high school (Beta = 0.028, standardized

beta = 0.211), and understanding the English language (Beta = 0.023, standardized beta = 0.082).

As demonstrated with college grade point average, high school grade point average is a

significant predictor of course completion ratio, demonstrated by having the strongest

standardized beta of 0.211. This is consistent with numerous other research studies. The









students with high high school grade point averages were either better prepared or overall better

students, as is reflected in the higher predicted course completion ratio in this study.

Interestingly, aspiration to transfer did not have as much influence on course completion ratio as

it did on college grade point average. Further, students planning on transferring to a four-year

institution may have better pre-college preparation and determination leading to better college

success. The significant positive factor of ability to speak English indicates that students with

better English-speaking ability will perform better and thus be more successful in completing

courses. As discussed above, the data indicate that the Los Angeles Community College District

needs to address English-speaking ability and work to improve student use of the English

language.

Age also had a significant relationship (Beta = 0.011, standardized beta = 0.073) with

course completion ratio. As discussed above, this may be a function of the maturity of these

students and their ability to focus and make appropriate college-related decisions. Older students

may also be better able to understand and interpret course parameters, keep up better with

assignment due dates, and otherwise be more aware of requirements. They may also be more

adjusted to their lifestyles and the issues they face, and thus may be better equipped to handle the

college experience. This would be especially true for students with children.

Discussion

The results found in this study, while showing no significant relationship of socioeconomic

status to the dependent variables, are enlightening. Students are born into a particular

socioeconomic status, and there is little one can do to change it while they are growing up. If

socioeconomic status were found to be significant in this study then that would mean that not

much could be done to improve student success. Beyond socioeconomic status, students have









other characteristics that may be able to be changed and may be factors not looked at in other

research studies.

The good news is that there are personal and learned factors than can better facilitate

college outcomes. In this author's opinion, it is these additional factors that community colleges

need to look at when examining how to improve student success. It is important for community

colleges to be completely aware of the cultural background and traits of students and to try to

account for these in the educational experience. Policy makers and researchers need to look at

the characteristics of students in the community college, looking further than obscure reasons

that seem like easy answers to complex situations.

Confounding the data is the change through the years in overall national student population

versus the unique characteristics of the group in this sample in the Los Angeles Community

College District. Today, with the programs available and financial aid offerings, more students

are going to college regardless of socioeconomic status. Students from affluent families that

may have the resources to attend four year institutions also opt on occasion to attend community

colleges at times when a required or desired class may not be available. These students may also

temporarily attend the community college to better prepare for a class they may take in the future

at the four year institution. Those students that float in and out of the community college system

make data from studies performed at four-year institutions even less applicable.

Of particular note in this study was the high percentage of minority students. This is

especially important since the ethnic make up and backgrounds of individuals in this sample

differ from samples made in traditional four-year institutions. As previously shown, even though

there are some issues with the findings, ethnicity does play a role in student success. The

analysis of the data indicates this for Hispanic and Black students but not for Asian students.









This may be accounted for by the differences between Asian family units and other ethnic

minorities (Zhou & Kim, 2006). The Asian family structure promotes learning and individual

achievement. The family members are driven to succeed and exhibit high levels of self-

discipline. Further, the culture of poverty theory postulated by Oscar Lewis (1966, p. xlv), could

explain how individuals who are prone to poverty develop a set of values that keeps them from

being successful. Lewis' (1966) theory showed that individuals who lived in poverty until the

age of six or seven were not able to get out of the mindset of poverty and were not mentally

prepared to take the benefits and opportunities that they would come across during their lives.

The disadvantages of one generation tended to transfer to the next generation. This is consistent

with Bourdieu's theory of cultural capital whereby these individuals born into poverty did not

have the cultural capital they needed in order to improve their lives.

The negative relationship found between the ethnicities of Black and Hispanic students and

college grade point average and success rate demonstrates the need for finding new ways to

address the problems that these groups face in being successful in college. Educators should

possibly look at what is being taught prior to entering college in order to prepare these students

for the college experience. Access to quality programs is once again an issue. With the growing

numbers of individuals passing through the community college system, remedial programs are

being required to expand, perhaps beyond reasonable limits and the expectations of available

staff and financial resources. The population in this study had a very high ethnic minority make

up, and the associated negative success rate is something the Los Angeles Community College

District needs to address further.

Limitations of the Study

As stated in Chapter 1, following were limitations of this study:









1. This study was limited to a sample of students who were enrolled in the Los
Angeles Community College District during the Spring 2001 semester.

2. This study was limited to individuals who agreed to complete the questionnaire
and provided permission for investigators to obtain their transcript information.

3. The validity of the study was limited to the reliability of the research instrument
used.

These limitations may prohibit the generalization of results to other community college

districts, but could be useful when studying community college systems in similar urban

environments. The suggestions for further research take these limitations into consideration in

order to find ways to generalize research data between different institutions and students.

Suggestions for Further Research

This study was limited to multiple campuses of one large, urban community college

system. The backgrounds of the individuals were diverse, although with an exceptionally high

Hispanic ethnic representation. Limited research exists that is comparable to this sample's large

urban setting. A survey similar to that in this study could be developed and administered in other

urban community colleges to test for similarities. Of particular interest would be looking at the

ethnicity and any other characteristics of the students in the different community colleges in

order to determine whether or not one could compare or generalize the results to other areas.

Also, it would be interesting to study the role of socioeconomic status in other urban settings to

determine if the results of this study demonstrate a trend.

Lewis' study of poverty showed that those in poor communities did not plan for the future

or had tendencies towards delayed gratification, but instead they had "a strong present-time

orientation" (Harris, 1997, p. 310). This could be studied further to find out if certain students

possess these characteristics. And if they do have these characteristics, what can be done to

provide them with the tools, interest or ability to succeed in college? Are some of these tools









already available but not being used effectively? The culture of poverty extends through the

generations and ways to break that cycle need to be continually investigated. Knowledge of

what motivates the students to work hard or what keeps them from achieving would be valuable

for community college administrators.

Additionally, language should be studied further to determine if improvements of students'

English proficiency could impact their success. Specifically, a study could be done to develop

better programs to assist students with English as a second language to develop skills that will

help them improve their success rate. Programs of English as a second language are popular but

with the difficulties that non-native English speakers have in college, more work can be done to

have programs that really help the individuals rather than fulfilling a legal mandate to simply

provide assistance with learning the English language. As shown in this study, the grade point

averages and course completion ratios of Blacks are also negatively impacted by language,

indicating a need for a greater emphasis on English skills in general. Also, English language

ability of both faculty and students should be studied. With non-native English speakers going

into the teaching profession institutions need to make sure that the future teachers are

linguistically prepared.

As previously discussed, despite high high school grade point averages, high school

preparation could be an issue in the research. The fitness of high schools from which the

students graduate should be studied in order to find any weaknesses. Even though studies of

high schools have been performed in the past, it would be worthwhile to analyze the relationship

between high schools and the particular community colleges their graduates attend. The reasons

behind the growth of remediation should be further researched to determine fixes that can be

applied prior to reaching the college level. Research needs to look at community colleges which









have remedial programs in place but which still show poor student outcomes. This could be a

wake-up call for community colleges, and researchers need to look at what the community

colleges are doing and how programs can be improved.













APPENDIX A
THE TRANSFER AND RETENTION OF URBAN COMMUNITY COLLEGE STUDENT

(TRUCCS) QUESTIONNAIRE




Community College Student Survey


l''ar Srt irenr:

'Ihis InTIfrrruk4llikT i 1h l:i31g c,] llc d ; l y r iat dLRi2i lJoill I iI U11iliveLr y of 0So tliicn CllJf
Unijiw.rty f CulLorvnia all IAugAy. i i cujulwicLii i[ll t$ LOs Aagclcs COLn.LU utily
a p r uof a large stil.y of .irarniunniLy otllegt: sLuLI..LLN iii Lu. All lt.Li. "'Iu liLt~e Leit It ;
participant in a mulnli-ycar pn j reI. Y'un eioopeiarion will assist researchers to help LosR
Cammunin Cdllcge sluin irl to be t.uccessfUll in rheir educaiianal puiuits. Yo' r arssisian
the Fprolcct;. we tank yvu fo'r yor ptlwifipaihri n in his imnpotain Ieearch.


DIRECTIONS
Plane antswr all quoetloan as comp4lesly ard ci nrtEly ap possible. BOeause your raspons
a macwlrne, your careful obserunpe of thns few Blispl nitas will be most appr~ciatld.
Use only bladk lead percil (N.2 is Ideaj},
EXAMPLEB:
Make haewy lack rrarks that! In i tlih alS
(da raotlrde or cheke ovate). Conel Mart: Incom
.* Eie clanl any araw you wilh to change. DO aO ECB
Make no stQ iray iaring D any kind.


Your primaEy mail address:


(nur phnna niimhRr:


Social





:i
EC
3iA3
ai.:6'1


We want to follow$ your r0otieem for the next two years. yet w realize that many students wl
time. Please provide the nerme of two people who are Ilksly to know your address evern I y
the nme, address, and phone krnmber 01 ttwo persons


Crotact 1: A flalive or frlOrI who doBs not li olithli
yJu aic who is liklyI to kflnv your address at all limes:

Name,

AIaaCres:

City, Stae, Zip:

Phnnp Ilumher.

Email address-


Cnntat 2' Another relai'.e or frJend
you and who is likely to know vcur a


Name.


AddlrsE: ________

Ciy, State, Zip


Phina Number


Errailaddress: .. ..


I i oo IoIgoIoIocooooooo o
Du Iu I WMM h I-I IMIIS Ja

-1 e me


oniia and tlhe
SCollcgs Dis.icr
:LcCOI ai a
nriele~
ce is ciiucial m




SwIIIll be read by -



let Mar:




Security Number


i T cn al Ti -










II move Irom tlme to
Wu meve. We request-


%who does mrt live til
dress al all time o
I-




















11699 -

Smeama am


-'--





















1. BElowt 4e1 sime reas 1hart. IbMlgh
hero InrfluencI your clbi niu 1
attend Ihis p;rt lrulPr :olcgc.
How Imlprrtei t vw each Teason
In your declion Io cian here?
IfMnrlf.ne c lr Ec staBTar.-.1


M y pWrcrr.s ant: rrc ra morn hr:rc.

Mly ponl per.-rr c- hr-- fvrrily
nnmhrnlrrwrlnrd mrre n rnr: b~r.. .
Pile college ie agcd reputat oi....

I averted Ib ;o to a :l earfnt. c7llge
Ulan rraiyVl ~V'y ereB .... .......
T'ii sollesrJ Iat yald cu:ial ucaiuilLW; .
I cuuij diL rind jub... .............
i"li curelle yge i f'da............

A hg'l !:::IIUD c r uluier L .LuriIli
ERW Fre-.rre
liscollp g r cFptorr nhors ...
slcoyI 311p's J-agr Ig r ig W* ;niy.

- ia...u.lle'a aljd lla Lro-a rul I 1
ygm^ 4-year -risl .............
I 7cu dr'L 5ind rly 'U ing elte. Lo u. .. .
I wart I gel a bettlr io .............
My friends are atlennaye e i .
"ils *allege ca sto vhere I we .

-ile -lles- aiteraeduAtlo Ie
prLaurrc v fpeial rdrerel :o. rn
Viml llihr lullUStCe L,,NOThme. ....
I Ni rlt lu grl Lul e:e degree ........
-o k lur Engilh fi, cr< ..........

PFy a~lp'L-r Aliiillra.l ireAi Aqrin
he'se........... ..........

-il's lleg clterse-r prcglian or
erUifcul I ree or k .. ......


i i,












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*J*" ':**.'*':
. C








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II


- .110 @ 1 .


d. Whirh of ihe liolhwing sblat enets be-t dErcrieLs ,Iur oll0ge
pma.e Pr ne.1 4.water? :;ir.k rne.1

I nil s or al Ii i ............. ... ........ ...'
I il altered his lee r d -' ;lh Ln ull ... ...
I wil aLj -r :his oo e rde 2 I iLrt Y}i*i l. ..l:: ...... ... :
I wil nol ra1ond h ra., .Jt I will t:=na I llh:r =o I .1 .. .. -.
I .il nnlm -nrr hIr, h ,ir I wil' etlf6 2 or irr-7 orer oll&ele .. _
I wilnnir ;!Trnd Tyrlc ... .. ......




5. WhYer did you attenri school? niled Annaher
(:Ek al 1haapphm Ineasoh olur mn. Sale Country

Elul 'rrie v i hr 'c r re tqu r t ir 111 ... .......
,.un ir liiti :,hix:l inr !; 1? -::> 141... ...... ... :... '.
Slight l :l, x.l .gr.: lfi l 1) .................. .......
College ........ ................... .. ...... ..




1. No4 including Ihis cdlege, how rrany cthcr colleges ,r
ellverbelles luha ry1u ever atlendd'? f'ulark oi-.I

Na' e '(I Ia al'lbidteC dlih llioiou llegSl.........
Sl L e r .. .... ... .... .... .... ... .... ... .. ..


or Tr7 .OtrS .... ......... .. C




7. Hoe many cedil hew- Y.u Aerned 4 thjl o-49ge ir
previous srenesErs? (iMark tairn.
1 -r1! . ... .. ... .. .. ... .. .. ... r.
... ............ ... ........ .

-3.. .................................



37-130 .. .... .. .... ................. "
19-27 ..

126-3. I;n .. ........................ .....
37-3r,.
NIowr I li;ln .. .. ........... .




8. Sinre leaving hiig scIod hrve you eVer
lakJeno ursps al en ea ar IilrtlItlton?7 F.- tc.t fr
1Mkha1'.l laapily.) Crealt Credit

Y' at rrot lre-caml n -,ullor c lle ...... ..........
's at s v'-V&r' lleap r nlverlts ............. ........ c

ns l atBniele-atwoo aeOBmovyemlr,1d
lrul de xa'ple. LeL lic:l. l,.::li ;:', Ill :i:; !:: ... .......




S. In ddll torI t tiles EPlge, pr yutj lIking cows-es
at anolhcr slaiao Or collage hi4s Sinquri'?
Itar al 1atapalv.i

Is, a[ar r o[i c3n .1il c0t Ig ... ... .....
:l, j i ru.-y::;- x k:[:: .r.. rsil V .......
'Y !!;. : l ,l I h i ll r n i .. .. .. .. .. .. .... .... .. .
i:i. lJ .j :ulinr l :- Ir;:d:: :h :l ..
r ,i i d lit c:i l .... ... .. .. .. .


i


2. HI, ew ller uf yur ulr.u aI paursuell Irinde aer ul. rtrantly
attenmllng msI college? lIMarS one.'

NonE o 1 y c0 oE itleid ........... ..
One 1 rri Iaoislli s ..... ......
Af~u af my clossf rinds ........... 1
Atbcri hap.F.f rryoll s f-. .. .. fi
Moot of rrv Oclosestl ienred ......
All ul Il' clr Alse lie 'Js .. ..... .. .





o,'.
3. In gemmral, whal do IhiTflowrig people
m11nn aboittnhs parlculBar DolIMe9'7
I|Ml one bor each uIatserart. / i


Y u .... ........ ... ... .......... .
biur .k l 'ii- .;dc .................... r
.ur r -a.n u- :.irin ..r .......... ........ ;
Vtur arr.rt3 c-gu rrnls. ...... ....... C '
our 1ine-relaltva ........... ..........
tur ilpl EocIol tS.01if.S.... ..... .. .
re .. ... .............. ......


so
















1.A. tilnga tslan today. do You
IPlik yYe will -..
(Mark ne for -ach slaleieniL

Clai,-ge your ;re r c1h10ie .............
Gradwi u with halOrs ............ ...
Play varey ~eflriod ale .;L'r Helis .......
Geta bechealrT dR res ................
Perrrnrnndly slop at.efln iclslege .......
Le, I lh rca lEfe terflporarily and
ieJrrn Ialer .. .................
Tnrsfier L asroU.li' o mun' vity liK. -...
Trur'er to a 4-/ear co lee or uni chty ...
DEVilp cklke rn.w laliaino.hips tlIth
s1. denrits a Nl r :ule ................
Tak 'egJlaf' it'i l" .r-n rut r; l-. 1
1h ,r llr.g ........................
Charge ,'u:jr WIeg&e rlajo: .............


01 0 0


.z "00




)0
O .CLOO
O!:^

r3-C-:C


: a i_ IL;L;


11. IJndalle all college dagrisearned Inlted Aeth&t
(1llily), helrkilllt haIppri Stalsa CwUtry
Ass& 6ate degree (Ai.. or cqui'.rlei ...... '......... .
Bachicnor's ri.guee ([.A., B.3.. et.) ,, .. 0 .... ... '
Graiale dcg.rec (M.A. M.S., FP.D.,
Ed.D., J.D., M.D., 5 .......... ) ........ r.-
e- rtl hic a .t .. ............... ........ '-
c.r.a.at.........



1L II Iher wrre ran obalBelees, what is t lhe i huil cademii
dlgme you wuld Ilk Io si tahl In Vour Ilfalim'e? (Markone.)
W ill ;i| ak c iriN;e biul da rnul ii I rlt I 1.0 eil'ia (IrI irc: .. .. :_)
Vua lio na r tl lica .. ........ ..........
Aseou cai (AA. or n-vales nt............ ........
Bachelor srkrce (.A-., B.., etc.; ..... ........._0
Al l~slt a BahelorE, rrybe marc .. .. ........ .
MLirs'a deias :.M.A., .S, rt ) ...
Da lorial degree (Ph.C., Ed...D.. eld I .......... ... .
Medicral de.gre (M.D., D.DS.. VV.4 ].. ..........(


13. Approirnueleiy hew many Hlmat
In the aR k7 d ie, dki y;u:
(frtari ore or lich ilaiLerfli ..i

S'p a lass........ .... .....
Talktilh Bn ii lri.-.ir rcctor.e Cr alter
u i-rus .............. ....
T-ilk wilh ral iislrlnbtr urlIil Ch c
ho u rs .. ... .. .. .... .. .. ... ... ..
u1.au -llail ul 11e Irl1s I for hrccrl'trk
I-Ilp;innrr sludenL Jnderslend
h r newm rk ......................
Study In smell grou.Ss culidE,' Ctlass.
Spsla wit .'an acader.li courelo' ....


/ w





C., 00 CD o:)C
.0 C C; C Ci 1


.( .' 0 ,


14. For lhlia cui cU nly, appreshraOly how
man tliy ki the pst 7 da.did you: /
(Mak onf iVr h l;vraI ni/nt.i




W ok 'ri sTiall y o s ring cl ss lirn r ... ..... '
Telelph.ne or enall anlhir sruc~l nt lo
ska iq ii.fl i about. tyouf l .... ....
AkErh Inntr'lar quornne ..... .. ..... : -: '
SpEek up dur l t qin de.i.n ............






1S. Inthed as 7 iyi ,appracrlmntaly 1! '
low mrrny hDurs did you:
:Mk cse tore ach gtalerrrn1.j 1
-, / LLic kI '
W ork 1 inr ............... .... i l
Db ious iolk cr childarA ........ O.
alcl1TV ....... ... 0 ; : .
SpEnd on 1hri cnrrm.pu linCluwinK
lime in rJ ss| ...... ............ L- : :-! -I l
Sel:d lalking wih n students a.cut
thhgar.Elreialcd lu a ccurse. .. (3
StriJy al ur. ihirn ............ L-)- t C
SLuJ liy loe i lthe oli~e library 3 .. -
SluJy will stluderla Irom thin
corse ........................0 -- I
Sriyv lith ESJrCrltiramrn oltir
course lnctlB 1acoLne ,,... 0 D--



1I. How largw;e problenlda you xpBea each
al iwtaIillUwin-gto b4r%, Vr4 ll g0Ilirgyour
education at TilB coller e
(Mark one'lr cin slaterne'm.) /.



Irnspt *lalrin. ,h;:rrr ca s. eSt.)........... -
F;anily (ispcn asilllidea e.g.. chid care:
pRrent cara) ........... .. .
Jb-"'BI ) op,-lrws tiies ................
P;yirn t oi lage ....... .. .. ..... ..
'i-.du.i Claeseff ner aemrestr. ....... 0 .
Ur eitandfn lh. Enr lisli ianCuage......... I
Difliidty oasse ........... ..




17- Honw, rlen do you use Engllsn I
Wfi hite folwing psopil /
(Maiek onlr as3n slterieni .)I

Wllh rrmN pret I'
'.Vllh hnlendE .... ....... .......... '-
Wih achcirs r olasors at ?il collegE 0C 'C C|


O Goes .-
















1& Floartea do you use a language alher I
than Enrlleh with lhe lollolng people? i / ,/
(Cark "ne for ea cfr slerienl.) ./1 4 /



WWirnyl erEns ................. .......I
Wilh rrircs:. ............................. i.. -O .c31
Wl tleaC:hes cr protesiv-et hiE college .. C cl ji


19- How well aru you able In do
Ihea allowing in En!ijsh? I.
iMa- ue fur eac! il. / 1

Read ..................................... O. O
Wrie. ........ .. .. ..... ..... ... O
Under tan'd a collage I-dure .................. C: 0 I
Rnm d ,r c.all Ir b:x:k ................... .
Wrine ar' eey s arn ........................ C0
Wrihro r rrm pipr ................... ......
Part otet In dess dilectus is................ CI
Ccrnmmiir.rlcl it i iict.jr.tar; ................. C

20. be English your mllve language?
.. .. CJ G ori i tinsl kn 22
No .....0 Coltnualo quealon 21


21. How well are you table ldott i
following ln your native lgiage? / [ I
(Malk one for earci il&ii.)

Ra.ad... .......... .................... L 0
Wrilc ........... .................. .. 3 C
UiIdertaLnd acol ege leolue ................ 0 C
Wrea ar clegssay ~eicr ................ ....

Wrile a terin paper .......................... 0 ; Cj
artopRate In clasE diECUEEtkicE .I.............. I ; 0
Cuirnrinunicala wih inslr l tudl i ................. ,I C

22. Hw long doea II talo you lo travel 1 thola college
(MaIrk oie.)
Lcp; thylr 15 rririnrliJ; .............. .. .... ,
110o S InilUteb ...........................
q1 In 4 mhn uijin ............................ ;.
4t lo 61 D niiulea ............................ C :
Eetw een ~1 a-d 2 ho rE ..................... .
.Mpl r Iln P h.irs ........................... .

2L& Da you have disability? .M;rk .rill ux'il .ippyi.
Healn ............... .............. ............. _
nre.. ..... .................. ..........
M iillty irpaled ............... .... .................. :
Atlteri i d liiti 1isorC r .................. ...... ........
Paycrdig oda dlaor a .................. ............
I nrring dlis-bilil ............. ..... ............ .
Von prcoein tMalatcainl, De ccrncLed ty kiaseec
p-o tac t "nsr s ............................. ...
Olher .. ..... .................
ao disabililiee ................... ................... ....


24. What w4a your arffage grade in high school? (hi.rk ann.)
A or A (Exlraordnly: ................................. .
A- (S .iprriar .:ioli y ..... .. .. .. .. :
9- (E ce'an ..........................................
B .:ic rr G ::xoo ........... ....... ........
C- i Ab' rnoom n ............... ....... ....... ..
C i (Avb:e ) g. ................. ............
C (a4e ragJ)C.
C [ ,r c v .AvtErag ............ ........... .......... _
2 o' o er I or. ........ ................... ..........


25. Belare this sermneler, wh at mrathcrnllac co rssr havu
yau ltken? Include courts In high school or previoum
ealtegk'Iark. [lark dll Litl app )
;ii; ,rnih, Rlu ir ES rr'Eah. or Pnre-i. eOr .... ............ C
Alygbra I....................... .................
Gn .rn lry ................. .. ........ .... ..
Alg bra II ............. .. ......................... .
Tr.gu'iarinlr ............................... ,
Pre-calIcul ...... .... ....................... .. ...
C ilrul. ............ ........... ... ........ .


26. Blefre Ihl S elneiler, what s9l let ear uter hateyou
takeli? Nclkldecwuaae In high achiol ar prevlaut
college worL I(ark all tL acplyI
G erer l Bio ... ........ .
Clan lslry ..................................... ...... .
Physi;s ... ................. '
BiDolN y spetiE.:y ,.i.e., 1ICro2iok:C y. gei ll, e bL:L. iy,
coll bhikloq -rrinr.h bino ag.et ; .. ....... .......
Other Eat aSlerc (I.e., gelogy. imeala lgy. Et.) .........



27. WIth whoenid you llve while attlndina Ihis college?
'Mark all ih l t ph ly.;
W th nfhl spoJ.ie or ..r":er ... .................... ....... .
W tlh :rri' parer.'9 0or Iqviar ImTiE ......... .. .............. :
VAIth :rw CdikJri-.'slF 'iirldre' ............................. :
Wth alilr;s I croirer( i RnaoralsterIli .................
WV L!i ::4w r r-rlivr .................................... "
V1tn a rooTrnnor i orr a dlad! sj ....................... .
I liui ;


2B.Yourgenrder:
'Male ..........




2B. Haw old will you be an Decainer01 o Ihila year?
1 fi yprirr n. *urgnr.. ..
17 ................. .................... .............

1B .................. ................................. .
W . .. .. .. .. .. .. . ..
1-4 ................. .............................

!, .. . .. .

4,-54, ................. .................. ...... .... -
55 cldr ......... ...................


-I 0060


0 0


-4-

















30. What Ie your ethnic growp(si? (Mrk Uil 1hal aprJy.;


C hin c .... ... .. ...... .. .....
Fillinq .... ...............
jas lies e..... ...... ..
l(oreonr...................
KTIrpon ....... ... .
Thif ..
Lac ia ..... .
C rmindiran ...............
Vk' tnlrnwnse ................
South Asiar. Illrndn Slcn rn n I entl
A rA i .. ... .. .. .. .... .. .. .
AMlrcii-~rurer;miild h .........
M GMIa n .. .. .. ..


. .. .. .. .. .. .. .. -
. .. .. . :

. .. .. .. .. .

. . .. .. .. .. ,''


. . .. .. . .. '"
. . . . .
. . .. . .. i
. . .

. .. . .


frdexi~.i-Arnercal.'ChIcai1o ........... .... C
So;i .Arr e ..ar ................ ..
C0enrrt l A rnrr.un ................. ............
Olhe L.1ilo.'HlEspri ....................
Al alan NEllve .. ....... ............ ... '.
Ann i;iri n Inrlian ................ ......... ....
Fac Il alEides'Sanrlon, -La,'%ailin, r OlarGIlrlrn ... c'
OUie FPacdh Isnrlder. .... ... .............
Cl .ic1ini"hr i ............. ...........
OUie .......... ............... ... .... .

31. AFre v currently mandrd?
; ... ....................... .
N .... ....... ....... ...... ..... ...


32. Who r [aire) iht prhnrary w ea~ lrnrft() rIn yOurt
lInuehaboM? (MarkalthRal eappl)
VtbLurse .l ... ... ... ................... .
naflf[r .,"Sp. a . . .
PaiM1R. sLarndllsg ........... .. ....
Chl dernpi1r2s e-i dr .. ............ ........
CAte ............ ....................


31. Hlow anrrt F your clldIr'aenlatpchldr n :ase IMrng
in your housvIeNl? IMarklrie.)

N :P 4 ....................................... .

5 n.nr!: ..... ..............................
...............L



34. Excludlng yo e elf, how many people -
(-ilfrI-n, grandchilldren, brthers.
$iJ.:f paMehwit et.)] a you
financially supp htling?
(mIartorake1in 'T9, j

Ui. Ir5 year o e ... .. ..s. o, a e
? o l r yt rn ;a e .......... ... ..... .
OQer 18 years of RFe ..... .. -


as.Which owioT lhe lollkuing beal descrteo your
employment elftuals Ihle lme? (lre ofle I
Empiyedlulk tne (Indcudlng eHl-Perip5E) .. ....
Em in"%rd p;irt.linil includingg seln-eipkiyel .......
NoL smplo ed but ot klna for ter ...
NoL LmnployrEd s.ld no prenety Iktying triv ,x .


3B. How dn you Ihlmk of yoursell? flark ore.)
Irlnm rit Rs a sI lderi 'no is ernoploed ...... "
Prinmarily at in Sr1pLr^yS .h: in gar erc<. .:leIe
Prilnmrily aNs parEL wl' iis goiig to college .................
Solely s.nl ......................--


27. For the ollowrlng Items, please Ildlciate
tih elentto witch you agrie or
dleagrea wvtt Vte fllo~ing set|emnprit.
irark oie Mr onch srtter6mnr I/


My t;.in lnrs hr '- gw% rrm ;i k:t cd
cn::l:!IJrr lrrent Ir rrry ; slr .............
I en1' dlrig challenglg clase
=ar i l nir i e ........... ............ .
IWA'hliolh ar l:.u3 Illilk Ti 'e 's A
,-T.'r i t . -. .. . . ... 0
I :;liarl I: ;;llr:y atl ent 2 Cr 3 ays :;cr
Iu L l ..... ........ ..... ............. I
I eqec' to do ewll.\ ar. .-n .Cd lrades
ii! cullirge ............................ 0


tJnrli!r.qla;iidirig hn"h in; wu.cjhl is in .lrla [E
to nE ...... ......... .... .....
I a lwly cnmlclrci lyinirurk assignenls ....
k: kop Iring I. rI whuen I wn Iruslr e!d
t l 1Bsk ..... .... .. ... ........
L nirrirni i::an Ie iilxled be v tr the grade
nru ; ............. ......... ...
nli in Impinalt tr me to lnih 1lhe ho.irae
in rry pr~ry:alrid It dief .................
Tninr:; arr hFirrer rur ine IlcuSd 01 rmy
I15e or elhilcdl .. ..... ...
I f cr'cnrl y hiu:! dilfcJilLyrraBlirn
rlrm l lin -.' .. ... .... .. .. .... .. ... ...
I an vqry deterniled to rea~h rrf' goin ...
I or S init illf .y nc .r..r ils .ib .ib t slti nd -ig
college .................. ...........
I felIrosi salisileI 'aier I eras1 ha rio
a ch e S 3lr I .n . .. .. .......
'XE ITErily a nfre Important tha. T'
r. i . .. . .. ... .. .. .. . .
bucceaa In ollag Ie rs Lasolr dua lo effort
;hs IL *~., h '.- tera you rI l ........
I l bI L 1, aJ 'ie :colea e .............
I wan unt: 1hEt c.i tefors Br assirnrreni
is ;i ::eu ruie s drt ri il......... .........
I knor I cLin Irnirn a-l -1I skills laug.L
In co ... .. ., .....
I w.,rL lu bt-c iil invilvuje in piroCrms
I): C eil up Lhe auirOll~enl .............
I have declared B oll.leerrpaor ....


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IB. I have-alllnded an orlantallan isaon at thl Ncollae.


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FacLory rwrk-r Iridnulacimur g, warciw.ouinr,
shlpoing. opentlones telephone operator. e:.)... ..... :

SkHllIIe rara rnan (mirhinlet, piLnbr. tie ealtet,
nktir.rir Lin, mriDl mrnrh;sinr rirs ;: s5 rta ry,
chaB. tBcl canO ............................ ..... -
SupervlEor rrr na9sr ;pro etnESc all ........... .....

Sm;i l n iIi',r !; c serl e, et .) ............. ................. ...

Prefesrloral, hihe c.aal (eale, lirna ne,
leImc'ing., ci:n ullin :ng r:im r. acr.runti' ,
Actcr, lEfrer- c. ................ ........... .
HbnaewurL (l UnI Irrplyed c;r Do n t ckn ................... ............. C .... L_


a


wtm a


tse .. ... ... C
No ...................... C. )




3. Are you reclivlng ftie Iollowmtn types ol IlnBIda as aeBlanc?7
(Fark Ilhn9l appy,'.

Lo- n ................ C.

Sc~i-laralip grarnt........ C




40, Do you own your ow. -. ? ilKarK one in each clumnn.)


te rio

Horme iol reitna; ..................... ... 0 ....
Computer fI ilh lillerr' aleat j ......... .....- .. ..

Compltitr (cwhull t Irr.Brnt r cenl| ........ .C.. ..
Car........ .......... .. .. .. 0



41. WhEt Is Ite ilghest leM ol fainrP aldumction obtilned by
your parents allier hk the U.S. at irn mratarc mrry?
(Mark Mouqe Flhar

B1h rae: *or leo .......... ,, .. ,.. )

Junior hgll ia rriddle acho l ............ .....'- ..... i

mrne hig school .................. ... ..... 0
Fiilnead rign st il OGED .......... .. ... ..... iC
srne cnnunily college ......... ...... .....

Comndlted Conlurnit ccile9e ............... 0 .....
Srrnea rour-y ar college ........................ ..... 3.
Cu:Il ulld (uui-y'i al oulle degree .........) .....

Sorne mgradtialesci oDl l ... ....... ... C ..... 0
Graduale degree ................ ..... ... .....' "
I nnnl krmv .... ... .... 0 0



42 Whlle ou were growing up, mark Ihel ob tea beBt
descrlbes uwr parent's rielor ocupallo,.
[Mark one In each cau'n.j
Mother Father

retired .................................... .....

Day abcrle: Ic ea.ning Conirmlfiliin, lafl.
fhl o ery.ec..... ..... ... ....... ... ... .. 0

Wurker c hourly emrpli l i(!;nr.i. I~hml.
hcpptal. 3gro ulblrc.irurl dr.-r. cilc Il, rclnl
sales E ser.5ic, laurdry or rnalntlerinae, tc.. .....


43. Wrile in your father' r.nrin MK (ir, nf not working IoW
his emost recent~ ob).










44. Wrlle In vour n other's mainr loabir, ti nal wearing r~k.
hpr proa Femiot iobl.









45. Describe your pLeSEit wOrkfl cr.









4,. Dqoerl1W t type of wofl/C Lfre y1OU plan ia be Involaed
in 7 or B year from ,w,.












47. F nr much educetdon do you Ihink Is needed For te eboe
typc of work yo P re plinnng? (l^ark one.

Hllh ~chn l dlpornm ~' Fn .. ..... ....... L

Sonle comnmuriy Cllege ................................ .

^-A.xplc'-in a' As.c- a, ce 'eep ic..A : r e .l.s.enl ..........
Hnmet rn.ir-wvap r tI .. .. .

ornMplie-lr V a four-year ci C lege d jr- .'B.A.. .S.) ......... C'
Cornpl;rind rflre tlon a four-year college ceg'e ......... I
nIplt"mna C a pJreFliduunI le c e :r r-.rlcntal .. .... ,.. ,.
rlphE'r i cf a f in l;uaL e ,le.ld-ii M iSrd; r Dnr. rc .... .... C

Crcnplet on af 5. aNdvrced piole Eloral degre
iDc:lunr e, P iL.n M.D., clc..... .......... ..... ...


as=















CIodce:


I SC RECORDS RELEASE A I TIOR [ZATI ON
ROi)lE R
9'HO)L OF
Dn I: r T[' [


Dclr Sluden.

We: rrcuel ouLr pliicipatt[olin in lri rl. .p'rLtL scidy. Th e i]ll0rlna1ii. w. are Eat ertletig Y0i oill ],i piaject .- ill be
uscl 1w imnproive cllegc teaching and leam I ng :Lird inlprTvc 1he sud !nl e4pUencnLK in corTmmuml L:-Lllccge. It
would bic helpful it we coUld CX.amine record., pertalmmg t)o :ducaihional preparai;n., de m(clrapti.c character; rtic
and Luurse enroll I irlliL i ritlurcMliki.:n it orilg with :ur re sponles to this Na Il ey. The FFraily EducatiotiIl Rihll; and
Priit,'y Act of' 1 474 (1-FRPAj. pFrvLdcs that an cdruc:Ltonal inst] Ltion may not re eUS 4~nt'iLLCnlial inl'orrntiorn
abouTt a Sttdent without the sttirenfr' crin gnt.- Flcxs prnvrle LLt with permi3siaon Ato iccL" these pnrT lins f'I yo iur
records wilh ther L.AS Aingel!e CL.rctriLuniLy CollCIes. YavUr ciiStIi will ili sO aOlluI LI' tL cOtLLILuc yeCu [iU IollonW-up
r1 eCSLJL'.


Th",jni vOi.


Lpr.de Skje Iryutie JI rrr Ph.D.
A z*ciatc I1rofsKor .& :'h:tir, {CnmmrTn nity (oll evc I .caidership
213-740-721 N


I hcreby nauthoiiz the rcsci.Ml'll tcoi hciLdcd "y Dr. I.inda Sirri': H'Ictldom to h-ltiin f rnm the I ..s An elet
(ComMnmiity Cille Ie.i the rec rd :f cti ..-Tr registritionrt. Lth rLail course grade: 1 :!Ltice iv, trilorumLtton fruolt rny
cnlltye ;pplic;:ion., sc.rces from my sasim riciLtstet, aind other records dirCCedl pwrtatinin.g to my acadctuit
experirice at Lhe Los Anlelcs Commnilily Co llegds This pcrmir ion is valid only tor the purposc.ii of thle r scarclh
described hcrepr.

! undcrtanid that my namr aJnd olihr irLforrrtalion iLtil ]]Imay idenlif ruce individuallyJ wiLt [101 bi frleased by the
iesecachers. I provide my pcniT.ssion freely wnlloLtt coercion no threar.


Studeili's S iill it LL



Y or t fll n ime pleasee pnrnc





I.1; : l>[I H -CJ.5-1 1


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a a









APPENDIX B
SPSS PRINTOUT -- FULL MODEL -- GRADE POINT AVERAGE

Table B-1. Grade point average notes


Output Created
Comments
Input Data


Missing
Value
Handling


Filter
Weight
Split File
N of Rows in
Working Data
File
Definition of
Missing

Cases Used


Syntax


Resources Memory
Required
Additional
Memory
Required for
Residual Plots
Elapsed Time


14-APR-2007 12:43:34


C:\Documents and Settings\ron\Desktop\FINAL
DATASET.sav




4968


User-defined missing values are treated as missing.

Statistics are based on cases with no missing values for any
variable used.

REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE
/CRITERIA=PIN(.05) POUT(. 10)
/NOORIGIN
/DEPENDENT gpa
/METHOD=ENTER q30_ 1 ASPACIS HISPANIC q28
q29 /METHOD=ENTER q24
/METHOD=ENTER ASPIRE DETERMINE INTEGRAT
q16_7 q164 /METHOD=ENTER ses


11540 bytes


0 bytes


0:00:00.23











Table B-2. Grade point average descriptive statistics
Mean Std. Deviation N
GPA 2.4983 .8161 3614
Q30_ 1 African-
American/Black 1.15 .35 3614
ASPACIS .1372 .3442 3614
HISPANIC .5437 .4982 3614
Q28 Your gender 1.59 .49 3614
Q29 Age on December 31
of this year 6.17 1.64 3614
Q24 Average grade in high
school 5.43 1.84 3614
ASPIRE 4.1828 .9828 3614
DETERMIN 6.2330 .7069 3614
INTEGRAT 2.0300 .8150 3614
Q16_7 Understanding the
English language 1.39 .84 3614
Q16 4 Job-related
responsibilities 2.21 1.23 3614
SES 52.9300 26.3518 3614











Table B-3. Grade point average correlations


Pearson GPA
Correlation Q30_11 African-
American/Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December
31 of this year
Q24 Average grade in
high school
ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding
the English language
Q16_4 Job-related
responsibilities


-< <
0



I


Ci


1.000 -0.070


-0.070
0.087
-0.135
0.099


1.000
-0.151
-0.411
0.068


0











0.151 -0.411 0.068 0.137



1.000 -0.379 -0.033 -0.014
-0.379 1.000 0.006 -0.114
-0.033 0.006 1.000 0.079
C &

Cl

0.087 -0.135 0.099 0.182

-0.151 -0.411 0.068 0.137
1.000 -0.379 -0.033 -0.014
-0.379 1.000 0.006 -0.114
-0.033 0.006 1.000 0.079


0.182 0.137 -0.014 -0.114 0.079 1.000


0.280
0.041
0.178
0.046


-0.041
0.040
0.087
0.123


0.109 -0.080 0.155 0.021
0.015 -0.007 -0.027 -0.160
-0.079 0.015 0.105 0.155
-0.033 -0.068 0.000 0.110


0.092 -0.115 0.267 -0.075 0.015 0.092

-0.030 -0.049 -0.011 0.065 -0.050 0.072
0.060 0.129 0.121 -0.341 -0.026 0.011


o 2













0.155 -0.027
Ci






0.280 0.041

-.041 0.040
0.109 0.015
0.080 -0.007
0.155 -0.027

0.021 -0.160
1.000 0.084
0.084 1.000
0.151 0.223
0.106 0.079

0.118 -0.079

-0.014 0.026
0.056 0.032


4


g HCz








0.178 0.046 0.092 -0.030 0.060


0.087
-0.079
0.015
0.105


0.123 -0.115 -0.049 0.129
-0.033 0.267 -0.011 0.121
-0.068 -0.075 0.065 -0.341
0.000 0.015 -0.050 -0.026


0.155 0.110 0.092 0.072 0.011


0.151
0.223
1.000
0.221


0.106 0.118 -0.014 0.056
0.079 -0.079 0.026 0.032
0.221 -0.087 -0.037 -0.005
1.000 0.051 0.013 0.005


-0.087 0.051 1.000 0.106 -0.031

-0.037 0.013 0.106 1.000 -0.015
-0.005 0.005 -0.031 -0.015 1.000











Table B-3. Continued


GPA
Q30_11 African-
American/Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31
of this year
Q24 Average grade in high
school
ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language
Q16_4 Job-related
responsibilities
SES


0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.000 0.003 0.000 0.034 0.000


0.000
0.000
0.000
0.000


0.000
0.000
0.000


0.000 0.000
0.000
0.000
0.024 0.367


0.000
0.024
0.367


0.000
0.207
0.000
0.000


0.000 0.000 0.207 0.000 0.000


0.000
0.007
0.000
0.003


0.006
0.009
0.000
0.000


0.000
0.177
0.000
0.023


0.000
0.334
0.177
0.000


0.000
0.049
0.000
0.488


0.006
0.000
0.000
0.000


0.009
0.177
0.334
0.049


0.000
0.000
0.177
0.000


0.000
0.023
0.000
0.488


0.000
0.000
0.000
0.190


0.002
0.254
0.000
0.001


0.000
0.000
0.000
0.059


0.107 0.000 0.000 0.000 0.000 0.000 0.251


0.107
0.000
0.000
0.000


0.000
0.000
0.000


0.000 0.000 0.000
0.000 0.000
0.000 0.000


0.000


0.000


0.000
0.000
0.000
0.001


0.000 0.000 0.000 0.000 0.190 0.000 0.000 0.000 0.000 0.001

0.034 0.002 0.254 0.000 0.001 0.000 0.203 0.056 0.013 0.217 0.000
0.000 0.000 0.000 0.000 0.059 0.251 0.000 0.026 0.387 0.377 0.033


0.203
0.056
0.013
0.217


0.000
0.026
0.387
0.377


Sig. (1-
tailed)


o
0
c^
4^

_o
O)




0

hi


.r-

I



\i


0.000 0.033

0.186
0.186











Table B-3. Continued


Co



o o
;-

4O



00
<
".


Ci


GPA
Q30_11 African-
American/Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31
of this year
Q24 Average grade in high
school
ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language
Q16_4 Job-related
responsibilities


3614 3614

3614 3614
3614 3614
3614 3614
3614 3614

3614 3614

3614 3614
3614 3614
3614 3614
3614 3614

3614 3614

3614 3614
3614 3614


3614 3614 3614 3614

3614 3614 3614 3614
3614 3614 3614 3614
3614 3614 3614 3614
3614 3614 3614 3614

3614 3614 3614 3614

3614 3614 3614 3614
3614 3614 3614 3614
3614 3614 3614 3614
3614 3614 3614 3614

3614 3614 3614 3614

3614 3614 3614 3614
3614 3614 3614 3614


3614 3614 3614 3614 3614

3614 3614 3614 3614 3614
3614 3614 3614 3614 3614
3614 3614 3614 3614 3614
3614 3614 3614 3614 3614

3614 3614 3614 3614 3614

3614 3614 3614 3614 3614
3614 3614 3614 3614 3614
3614 3614 3614 3614 3614
3614 3614 3614 3614 3614

3614 3614 3614 3614 3614

3614 3614 3614 3614 3614
3614 3614 3614 3614 3614


H


"




3,


3614 3614

3614 3614
3614 3614
3614 3614
3614 3614

3614 3614

3614 3614
3614 3614
3614 3614
3614 3614

3614 3614

3614 3614
3614 3614











Table B-4. Grade point average variables entered/removed(b)
Model Variables Entered Variables Method
Removed
1 Q29 Age on December 31 of this year, ASPACIS, Q28 Your Enter
gender, Q30_11 African-American/Black, HISPANIC(a)
2 Q24 Average grade in high school(a) Enter
3 Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 .Enter
Understanding the English language, DETERMIN(a)
4 SES(a) Enter
a All requested variables entered. b Dependent Variable: GPA











Table B-5. Grade point average model summary

Change Statistics
Adjusted R Std. Error of R Square
Model R R Square Square the Estimate Change F Change dfl df2 Sig. F Change
1 .283(a) .080 .079 .7833 .080 62.663 5 3608 .000


.060 249.431
.019 16.641


1 3607
5 3602


4 .399(d) .159 .156 .7496 .000 .738 1 3601 .390
a Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_I 1 African-American/Black, HISPANIC. b Predictors: (Constant), Q29
Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_ 11 African-American/Black, HISPANIC, Q24 Average grade in high school. c Predictors: (Constant),
Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_ 11 African-American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related
responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN. d Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS,
Q28 Your gender, Q30_ 11 African-American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7
Understanding the English language, DETERMIN, SES


.373(b)
.399(c)


.7577
.7496











Table B-6. Grade point average ANOVA(e)
Sum of
Model Squares df Mean Square F Sig.
1 Regression 192.247 5 38.449 62.663 .000(a)


Residual
Total
2 Regression
Residual
Total
3 Regression
Residual
Total
4 Regression
Residual
Total


2213.827
2406.074
335.436
2070.638
2406.074
382.187
2023.887
2406.074
382.602
2023.472
2406.074


3608
3613
6
3607
3613
11
3602
3613
12
3601
3613


.614


55.906
.574

34.744
.562

31.884
.562


97.387 .000(b)



61.836 .000(c)



56.740 .000(d)


a Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-
American/Black, HISPANIC. b Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28
Your gender, Q30_11 African-American/Black, HISPANIC, Q24 Average grade in high school. c Predictors:
(Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_ 1 African-
American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT,
ASPIRE, Q16_7 Understanding the English language, DETERMIN. d Predictors: (Constant), Q29 Age on
December 31 of this year, ASPACIS, Q28 Your gender, Q30_ 1 African-American/Black, HISPANIC, Q24
Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the
English language, DETERMIN, SES. e Dependent Variable: GPA










Table B-7. Grade point average coefficients(a)


Model Unstandardized Standardized t Sig.
Coefficients Coefficients

B Std. Error Beta


1 (Constant)
Q30 11 African-
American/Black


ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year
2 (Constant)
Q30 11 African-
American/Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year

Q24 Average grade in high
school

3 (Constant)
Q30 11 African-
American/Black

ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year

Q24 Average grade in high
school
ASPIRE
DETERMINE
INTEGRAT


2.350
-0.420


-0.019
-0.316
0.163
0.088

1.799
-0.375

-0.061
-0.280
0.095
0.087


0.110


0.945
-0.395


-0.071
-0.290
0.079
0.081


0.100

0.027
0.149
-0.021


0.088
0.044


0.044
0.033
0.027
0.008

0.092
0.042

0.043
0.032
0.026
0.008


0.007


0.139
0.042


0.043
0.032
0.026
0.008


0.007

0.013
0.019
0.016


26.817
-0.182 -9.607


-0.008
-0.193
0.098
0.177


-0.441
-9.564
6.089
10.932


19.627
-0.162 -8.841


-0.026
-0.171
0.057
0.175


-1.422
-8.765
3.642
11.149


0.249 15.793 0.000


6.781
-0.170 -9.308


-0.030
-0.177
0.048
0.162


-1.647
-9.152
3.037
10.015


0.225 14.101 0.000


0.032
0.129
-0.021


1.979
7.756
-1.305


0.000
0.000


0.659
0.000
0.000
0.000

0.000
0.000

0.155
0.000
0.000
0.000


0.000
0.000


0.100
0.000
0.002
0.000


0.048
0.000
0.192









Table B-7. Continued
Model


Q16_7 Understanding the
English language

Q16_4 Job-related
responsibilities

4 (Constant)
Q30 11 African-
American/Black


Unstandardized
Coefficients

B Std.
Error
0.042 0.016


-0.023 0.010


0.916 0.143
-0.394 0.042


Standardized
Coefficients


Beta


0.044


-0.034


2.688 0.007


-2.201 0.028


6.386 0.000


-0.170 -9.294 0.000


ASPACIS
HISPANIC
Q28 Your gender


Q29 Age on December 31 of
this year

Q24 Average grade in high
school

ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language


Q16 4 Job-related
responsibilities
SES
a Dependent Variable: GPA


0.081 0.008


0.100 0.007


0.026
0.149
-0.021
0.043


0.013
0.019
0.016
0.016


-0.023 0.010


0.162 10.027 0.000


0.225 14.053 0.000


0.032
0.129
-0.021
0.045


-0.034


0.000 0.001 0.014


1.960
7.759
-1.289
2.734


0.050
0.000
0.197
0.006


-2.213 0.027

0.859 0.390


-0.071
-0.282
0.080


0.043
0.033
0.026


-0.030
-0.172
0.048


-1.642
-8.522
3.059


0.101
0.000
0.002










Table B-8. Grade point average excluded variables(d)


Model
1


Q24 Average grade in high school
ASPIRE


DETERMINE
INTEGRAT
Q16_7 Understanding the English
language
Q16_4 Job-related responsibilities

SES
2 ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the English
language
Q16_4 Job-related responsibilities

SES
3 SES


Beta In
.249(a)
.080(a)
.165(a)
.036(a)


t
15.793
4.942
10.289
2.251


Sig.
0.000
0.000
0.000
0.024


.046(a) 2.726 0.006


.035(a)
.022(a)
.057(b)
.130(b)
.008(b)

.024(b)


.034(b)
.012(b)
.014(c)


-2.190 0.029


1.278
3.614
8.240
0.508


0.201
0.000
0.000
0.611


1.465 0.143

-2.201 0.028


0.759
0.859


0.448
0.390


Partial
Correlation
0.254
0.082
0.169
0.037

0.045

-0.036

0.021
0.060
0.136
0.008

0.024

-0.037

0.013
0.014


Collinearity
Statistics
Tolerance
0.957
0.970
0.958
0.975

0.911

0.985

0.882
0.961
0.936
0.962

0.904

0.985

0.881
0.877


a Predictors in the Model: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender,
Q30_11 African-American/Black, HISPANIC. b Predictors in the Model: (Constant), Q29 Age on December 31
of this year, ASPACIS, Q28 Your gender, Q30_ 1 African-American/Black, HISPANIC, Q24 Average grade in
high school. c Predictors in the Model: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your
gender, Q30_11 African-American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related
responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN. d Dependent
Variable: GPA










SPSS PRINTOUT


APPENDIX C
FULL MODEL


SUCCESS RATE


Table C-1. Success rate notes
Output Created


14-APR-2007 12:47:57


Comments
Input


Missing
Value
Handling


Data


Filter
Weight
Split File
N of Rows in
Working Data
File
Definition of
Missing

Cases Used


Syntax


Resources Memory
Required
Additional
Memory
Required for
Residual Plots
Elapsed Time


C:\Documents and Settings\ron\Desktop\FINAL
DATASET.say




4968


User-defined missing values are treated as missing.

Statistics are based on cases with no missing values
for any variable used.

REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG
N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
CHANGE
/CRITERIA=PIN(.05) POUT(. 10)
/NOORIGIN
/DEPENDENT sucr
/METHOD=ENTER q30_ 1 ASPACIS
HISPANIC q28 q29 /METHOD=ENTER q24
/METHOD=ENTER ASPIRE DETERMINE
INTEGRAT q16_7 q16_4 /METHOD=ENTER ses


11540 bytes


0 bytes


0:00:00.35











Table C-2. Success rate descriptive statistics


SUCR

Q30 11 African-American/Black

ASPACIS

HISPANIC
Q28 Your gender

Q29 Age on December 31 of this year

Q24 Average grade in high school

ASPIRE
DETERMINE

INTEGRAT
Q16_7 Understanding the English language

Q16_4 Job-related responsibilities

SES


Mean

.6888

1.14

.1374

.5431
1.59

6.17

5.43

4.1803
6.2308

2.0290
1.39

2.21


Std.
Deviation
.2404

.35

.3443

.4982
.49

1.64

1.84

.9851
.7111

.8141
.84

1.23


52.9404 26.3467


N

3633

3633

3633

3633
3633

3633

3633

3633
3633

3633
3633

3633

3633











Table C-3. Success rate correlations


Pearson SUCR
Correlation Q30_11 African-
American/Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year
Q24 Average grade in high
school
ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language
Q16_4 Job-related
responsibilities
SES


1.000

-0.073
0.075
-0.083
0.079


<









-0.073

1.000
-0.151
-0.409
0.069


0.075 -0.083

-0.151 -0.409
1.000 -0.379
-0.379 1.000
-0.032 0.006


0.093 0.136 -0.013 -0.112


0.260
0.049
0.159
0.047


-0.041
0.040
0.088
0.123


0.106 -0.080
0.013 -0.004
-0.075 0.016
-0.034 -0.067


0.113 -0.116 0.267 -0.075

-0.047 -0.049 -0.014 0.065
0.012 0.129 0.119 -0.340


.o









Cl Cl -

0.079 0.093

0.069 0.136
-0.032 -0.013
0.006 -0.112
1.000 0.079

0.079 1.000

0.154 0.021
-0.027 -0.158
0.109 0.153
0.000 0.109

0.013 0.093

-0.053 0.070
-0.028 0.011


Z








a




0.260 0.049 0.159

-0.041 0.040 0.088
0.106 0.013 -0.075
-0.080 -0.004 0.016
0.154 -0.027 0.109

0.021 -0.158 0.153

1.000 0.084 0.150
0.084 1.000 0.224
0.150 0.224 1.000
0.107 0.082 0.222

0.117 -0.079 -0.088

-0.015 0.027 -0.042
0.056 0.032 -0.008


0.047 0.113

0.123 -0.116
-0.034 0.267
-0.067 -0.075
0.000 0.013

0.109 0.093

0.107 0.117
0.082 -0.079
0.222 -0.088
1.000 0.050

0.050 1.000

0.014 0.105
0.005 -0.029


-0.047 0.012

-0.049 0.129
-0.014 0.119
0.065 -0.340
-0.053 -0.028

0.070 0.011

-0.015 0.056
0.027 0.032
-0.042 -0.008
0.014 0.005

0.105 -0.029

1.000 -0.013
-0.013 1.000
.-s






























-0.013 1.000











Table C-3. Continued


Sig. (1-tailed) SUCR
Q30_11 African-
American/Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year
Q24 Average grade in high
school
ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language
Q16_4 Job-related
responsibilities
SES


-I

0.000


0.000
0.000
0.000
0.000


0.000
0.000
0.000


<



0.000

0.000

0.000
0.026


0.000


0

00
M


C"l
0.000
ou
hs


0000


0.000 0.000
0.000 0.026
0.357
0.357


0.000

0.000
C-l






0.000

0.000
0.218
0.000
0.000


0.000 0.000 0.218 0.000 0.000


0.000
0.001
0.000
0.002


0.007
0.008
0.000
0.000


0.000
0.216
0.000
0.022


0.000
0.404
0.163
0.000


0.000
0.051
0.000
0.499


0.000

0.007
0.000
0.000
0.000


0.001

0.008
0.216
0.404
0.051


E-i
H

0.000

0.000
0.000
0.163
0.000


H




0.002

0.000
0.022
0.000
0.499


0.000

0.000
0.000
0.000
0.222


0


a






c3

0.002

0.002
0.204
0.000
0.001


0.233

0.000
0.000
0.000
0.043


0.102 0.000 0.000 0.000 0.000 0.000 0.251


0.102
0.000
0.000
0.000


0.000
0.000
0.000


0.000

0.000
0.000


0.000
0.000

0.000


0.000
0.000
0.000


0.000
0.000
0.000
0.001


0.000 0.000 0.000 0.000 0.222 0.000 0.000 0.000 0.000 0.001

0.002 0.002 0.204 0.000 0.001 0.000 0.191 0.055 0.005 0.200 0.000
0.233 0.000 0.000 0.000 0.043 0.251 0.000 0.027 0.305 0.383 0.039


0.191
0.055
0.005
0.200


0.000
0.027
0.305
0.383


0.000 0.039

0.210
0.210











Table C-3. Continued


SUCR
Q30_11 African-
American/Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year
Q24 Average grade in high
school
ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language
Q16_4 Job-related
responsibilities
SES


3633

3633
3633
3633
3633


3

3633

3633
3633
3633
3633


<



3633

3633
3633
3633
3633


3633

3633
3633
3633
3633


Ci

3633

3633
3633
3633
3633


Cl
o




3633
Oi-













3633
3633
3633
3633
363
363


Ci

3633

3633
3633
3633
3633


3633 3633
3633 3633

3633 3633
3633 3633
3633 3633
3633 3633


3633 3633 3633 3633 3633 3633 3633 3633 3633


3633
3633
3633
3633


3633
3633
3633
3633


3633
3633
3633
3633


3633
3633
3633
3633


3633
3633
3633
3633


3633
3633
3633
3633


3633
3633
3633
3633


3633 3633
3633 3633
3633 3633
3633 3633


3633 3633 3633 3633 3633 3633 3633 3633 3633

3633 3633 3633 3633 3633 3633 3633 3633 3633
3633 3633 3633 3633 3633 3633 3633 3633 3633


3633

3633
3633
3633
3633


3633

3633
3633
3633
3633
i63
0-13
363
363


0


a





c3


3633

3633
3633
3633
3633


3633

3633
3633
3633
3633


3633 3633 3633 3633


3633
3633
3633
3633


3633
3633
3633
3633


3633
3633
3633
3633


3633
3633
3633
3633


3633 3633 3633 3633

3633 3633 3633 3633
3633 3633 3633 3633










Table C-4. Success rate variables entered/removed(b)
Model Variables Entered Variables Method
Removed
1 Q29 Age on December 31 of this year, ASPACIS, Enter
Q28 Your gender, Q30_11 African-American/Black,
HISPANIC(a)
2 Q24 Average grade in high school(a) Enter
3 Q16_4 Job-related responsibilities, INTEGRAT, Enter
ASPIRE, Q16_7 Understanding the English language,
DETERMIN(a)

4 SES(a) Enter
a All requested variables entered. b Dependent Variable: SUCR










Table C-5. Success rate model summary

Change Statistics
Adjusted R Std. Error of R Square
Model R R Square Square the Estimate Change F Change dfl df2 Sig. F Change
1 .192(a) .037 .035 .2361 .037 27.663 5 3627 .000
2 .302(b) .091 .089 .2294 .054 216.350 1 3626 .000
3 .337(c) .114 .111 .2266 .023 18.733 5 3621 .000
4 .338(d) .114 .111 .2266 .000 1.327 1 3620 .249
a Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC. b Predictors:
(Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_ 1 African-American/Black, HISPANIC, Q24 Average grade in high
school. c Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC, Q24
Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN. d
Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC, Q24 Average
grade in high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN, SES










Table C-6. Success rate ANOVA(e)
Sum of Mean
Model Squares df Square F Sig.


1


Regression
Residual


Total
2 Regression
Residual
Total
3 Regression
Residual
Total
4 Regression
Residual
Total


7.710
202.176
209.886
19.094
190.792
209.886
23.905
185.981
209.886
23.973
185.913
209.886


5
3,627
3,632
6
3,626
3,632
11
3,621
3,632
12
3,620
3,632


1.542
0.056

3.182
0.053


27.663


.000(a)


60.479 .000(b)


2.173 42.310 .000(c)
0.051

1.998 38.899 .000(d)
0.051


a Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-
American/Black, HISPANIC. b Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28
Your gender, Q30_11 African-American/Black, HISPANIC, Q24 Average grade in high school. c Predictors:
(Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_ 1 African-
American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT,
ASPIRE, Q16_7 Understanding the English language, DETERMIN. d Predictors: (Constant), Q29 Age on
December 31 of this year, ASPACIS, Q28 Your gender, Q30_ 1 African-American/Black, HISPANIC, Q24
Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the
English language, DETERMIN, SES. e Dependent Variable: SUCR










Table C-7. Success rate coefficients(a)


Model
1 (Constant)
Q30 11 African-American/
Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year
2 (Constant)
Q30 11 African-American/
Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year
Q24 Average grade in high
school
3 (Constant)
Q30 11 African-American/
Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year
Q24 Average grade in high
school
ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language
Q16 4 Job-related
responsibilities


Unstandardized
Coefficients
Std.
B Error
0.686 0.026

-0.097 0.013

0.006 0.013
-0.062 0.010
0.040 0.008

0.013 0.002

0.531 0.028

-0.084 0.013

-0.005 0.013
-0.052 0.010
0.021 0.008

0.013 0.002


0.031

0.283

-0.089

-0.016
-0.055
0.016

0.011

0.027

0.007
0.042
-0.003

0.024


0.002

0.042

0.013

0.013
0.010
0.008

0.002

0.002

0.004
0.006
0.005

0.005


Standardized
Coefficients


Beta


-0.142

0.008
-0.129
0.082

0.091


-0.124

-0.008
-0.108
0.044

0.089


t
26.073

-7.396

0.432
-6.282
5.016

5.534

19.197

-6.589

-0.408
-5.408
2.713

5.546


0.238 14.709

6.766

-0.130 -6.949

-0.022 -1.194
-0.113 -5.720
0.033 2.049

0.073 4.412


0.210

0.030
0.125
-0.009

0.083


12.867

1.812
7.311
-0.556

4.978


-0.051 -3.203 0.001


Sig.
0.000

0.000

0.666
0.000
0.000

0.000

0.000

0.000

0.683
0.000
0.007

0.000

0.000

0.000

0.000

0.233
0.000
0.041

0.000

0.000

0.070
0.000
0.578

0.000


-0.010 0.003










Table C-7. Continued


Model
4


Unstandardized
Coefficients
Std.
B Error
0.295 0.043

-0.089 0.013


-0.016
-0.058
0.016


0.013
0.010
0.008


0.011 0.002

0.028 0.002


(Constant)
Q30 11 African-American/
Black
ASPACIS
HISPANIC
Q28 Your gender
Q29 Age on December 31 of
this year
Q24 Average grade in high
school
ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language
Q16 4 Job-related
responsibilities
SES


0.004
0.006
0.005


0.023 0.005

-0.010 0.003

0.000 0.000


Standardized
Coefficients


Beta


t
6.846


Sig.
0.000


-0.130 -6.965 0.000


-0.022
-0.120
0.032


-1.203
-5.811
2.015


0.229
0.000
0.044


0.073 4.396 0.000

0.211 12.905 0.000


0.030
0.124
-0.009


1.839
7.302
-0.577


0.066
0.000
0.564


0.082 4.903 0.000

-0.051 -3.186 0.001

-0.019 -1.152 0.249


a Dependent Variable: SUCR


0.007
0.042
-0.003











Table C-8. Success rate excluded variables(d)


Model
1


Beta In


Q24 Average grade in high
school


ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language
Q16 4 Job-related
responsibilities
SES

2 ASPIRE
DETERMINE
INTEGRAT
Q16_7 Understanding the
English language
Q16 4 Job-related
responsibilities
SES

3 SES


Partial
t Sig. Correlation


.238(a) 14.709 0.000

.073(a) 4.444 0.000


.158(a)


9.593 0.000


.048(a) 2.883 0.004


.083(a)


.048(a)

.015(a)
.051(b)
.125(b)
.021(b)

.062(b)


.047(b)

.024(b)

.019(c)


4.878 0.000

-2.951 0.003

-0.853 0.394


3.184
7.688
1.276


0.001
0.000
0.202


3.748 0.000

-2.977 0.003

-1.415 0.157

-1.152 0.249


0.237

0.074
0.157
0.048

0.081

-0.049

-0.014

0.053
0.127
0.021

0.062

-0.049

-0.023

-0.019


Collinearity
Statistics
Tolerance

0.958

0.970
0.958
0.975

0.911

0.985

0.883

0.961
0.936
0.962

0.904

0.985

0.881

0.877


a Predictors in the Model: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender,
Q30_11 African-American/Black, HISPANIC. b Predictors in the Model: (Constant), Q29 Age on December 31
of this year, ASPACIS, Q28 Your gender, Q30_ 1 African-American/Black, HISPANIC, Q24 Average grade in
high school. c Predictors in the Model: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your
gender, Q30_11 African-American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related
responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN. d Dependent
Variable: SUCR









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BIOGRAPHICAL SKETCH

Ronald C. Lester was born in Fort Smith, Arkansas. He attended Southside High School in

Fort Smith and upon graduation attended Westark Community College in Fort Smith and then

the University of Arkansas at Little Rock. He graduated with a Bachelor of Arts in

anthropology, with high honors, and a minor in biology. He worked at the University of

Cincinnati, finally serving as Assistant to the Associate Dean for Student Services in the College

of Medicine. In 1997 he decided to move back to a warmer climate and relocated to Gainesville,

Florida, where he briefly worked at the Alachua County Board of County Commissioners, and

then obtained employment at the University of Florida.

He has worked at the University of Florida since March, 1988 in the Department of

Pediatrics. During his tenure in Pediatrics he obtained a Masters in Business Administration

degree in April, 1994. He decided to again return to graduate school, this time at the University

of Florida, and completed his doctorate in higher education administration in December, 2007.

He is currently a divisional administrator in the Department of Pediatrics and manages the

administrative and budgetary responsibilities for several divisions within the department.

He also keeps active on campus. He has been active in the Academic and Professional

Assembly (APA) which has over 2400 university professional members. The APA has been

empowered to serve as an advisory body to university administration regarding issues and

policies affecting and of concern to this group of university employees. He has served as

president elect, president and past president of the APA.





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1 STUDENT SUCCESS AND ITS RELATIONSHIP TO OCCUPATIONAL STATUS SCORE IN THE LOS ANGELES COMMU NITY COLLEGE DISTRICT By RONALD C. LESTER 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 2007

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2 Ronald C. Lester

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3 ACKNOWLEDGMENTS I thank my friends and family members who have provided support and encouragement during the lengthy process of finishing my dissertation. I also owe a very special thank you to my tremendous committee chair, Dr. Linda Serra Hagedorn. I thank her for the encouragement, support and advice she provided during this process.

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4 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................3 LIST OF TABLES................................................................................................................. ..........6 DEFINITION OF TERMS..............................................................................................................8 ABSTRACT.....................................................................................................................................9 CHAP TER 1 INTRODUCTION..................................................................................................................10 Statement of the Problem....................................................................................................... .11 Nam-Powers-Terrie Occupational Status Score.............................................................. 12 Los Angeles County and the Los Angeles Community College District........................12 Purpose of the Study........................................................................................................... ....13 Background of the Problem....................................................................................................14 Significance of the Problem....................................................................................................15 Theoretical Framework.......................................................................................................... .15 Limitations.................................................................................................................... ..........16 Summary.................................................................................................................................16 2 REVIEW OF THE LITERATURE........................................................................................ 18 Social Class.............................................................................................................................21 Student Preparedness........................................................................................................... ...32 Student Transfer......................................................................................................................37 Student Retention.............................................................................................................. ......39 Student Success......................................................................................................................41 Summary.................................................................................................................................45 3 METHODOLOGY................................................................................................................. 46 Research Questions and Hypotheses...................................................................................... 46 Research Design.....................................................................................................................46 Research Population........................................................................................................47 Data Collection................................................................................................................47 Data Analysis...................................................................................................................48 Instrumentation................................................................................................................ 50 Validity and Reliability...................................................................................................51 Summary.................................................................................................................................51

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5 4 ANALYSIS AND PRESENTATION OF THE DATA......................................................... 54 Population Profile............................................................................................................. ......54 Validity and Reliability...........................................................................................................55 Regression Analyses............................................................................................................ ...56 Grade Point Average....................................................................................................... 57 Course Completion Ratio................................................................................................58 Summary.................................................................................................................................60 5 CONCLUSIONS AND RECOMME NDATIONS................................................................. 71 Conclusions and Implications.................................................................................................72 Impact on College Grade Point Average......................................................................... 74 Impact on Course Completion Ratio (Success Rate)......................................................76 Discussion...............................................................................................................................77 Limitations of the Study....................................................................................................... ..79 Suggestions for Further Research........................................................................................... 80 APPENDIX A THE TRANSFER AND RETENTION OF URBAN COMMUNITY COLLEGE STUDENT (TRUCCS) QUESTIONNAIRE .......................................................................... 83 B SPSS PRINTOUT -FULL MODEL GRADE POINT AVERAGE................................. 90 C SPSS PRINTOUT FULL MODEL SUCCESS RATE................................................... 101 REFERENCES............................................................................................................................112 BIOGRAPHICAL SKETCH.......................................................................................................121

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6 LIST OF TABLES Table page 3-1 List of Variables.................................................................................................................52 3-2 Recoded ethnic groups...................................................................................................... .53 4-1 Students in the LACCD. Distribution by gender............................................................... 61 4-2 Students in the LACC D. Distribution by age.................................................................... 61 4-3 Students in the LACCD. Di stribution by ethnic origin. ....................................................62 4-4 Students in the LACCD. Distribution by high school. ...................................................... 62 4-5 Factor analysis: determ i nation, academic integrati on, aspire to transfer........................... 63 4-6 Zero order correlations for the m odel grade point average................................................ 66 4-7 Zero order correlations for the model success rate............................................................ 67 4-8 Distribution of parent occupational status score (S ES)..................................................... 68 4-9 Distribution of student course com pletion ratio................................................................. 68 4-10 Model summary grade point average.............................................................................. 69 4-11 Regression analysis summary for grade point average ...................................................... 69 4-12 Model summary course completion ratio....................................................................... 70 4-13 Regression analysis summary for course com pletion ratio................................................ 70 B-1 Grade point average notes.................................................................................................. 90 B-2 Grade point average descriptive statistics ..........................................................................91 B-3 Grade point average correlations....................................................................................... 92 B-4 Grade point average vari ables entered/rem oved(b)........................................................... 95 B-5 Grade point average model summary................................................................................ 96 B-6 Grade point average ANOVA(e)....................................................................................... 97 B-7 Grade point average coefficients(a)................................................................................... 98 B-8 Grade point average excluded variables(d)...................................................................... 100

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7 C-1 Success rate notes............................................................................................................101 C-2 Success rate desc riptive statistics..................................................................................... 102 C-3 Success rate correlations..................................................................................................103 C-4 Success rate variables entered/removed(b)...................................................................... 106 C-5 Success rate model summary........................................................................................... 107 C-6 Success rate ANOVA(e)..................................................................................................108 C-7 Success rate coefficients(a)..............................................................................................109 C-8 Success rate excluded variables(d)..................................................................................111

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8 DEFINITION OF TERMS Community college Ann institution of highe r education that provides education to students mainly from the area su rrounding the community college. Community colleges provide educational opportunities from Associates and certificate degree programs. Community colleges also typically offer vocational programs and provide transfer opportunities. Course completion ratio The value obtained by di viding the number of cl asses that a student successfully passes (A, B, C or P for pass/no pass scales) by the number of classes the student attempted. Course completion ratio is a measure of success rate. Cultural capital The theory proposed by Pi erre Bourdieu (Bourdieu & Passeron, 1977, p. 30) that states that parents provide their children with attitudes and knowledge. With resp ect to education, parents with more experience with the educati onal system have more of these attitudes and knowledge to their children. Diversity The range of students who are attending community colleges. Diversity in educational institutions includes students with different ages, ethnic origins, language, sexual orientation, and those with disabilities. Grade point average The value obtained by dividing the total number of grade points by the number of credits attempted. Los Angeles Community The system of nine community college campuses situated throughout College District (LACCD) Los Angeles County, California. Occupational status score A measure of socio economic status that in corporates occupation, education, and income into a numerical value ranging from 0 to 100 as detailed by Nam, Powers, a nd Terrie (Terrie & Nam, 1994). Retention A measure of students rema ining enrolled in an educational institution. Generally, the period of enrollment is measured from fall through spring semesters or fall through the following fall. Socioeconomic status For purposes of this study, a measurement by a proxy of the highest occupational status score between moth er and father. In general it is a measure of social/economic class. In the literature, socioeconomic status is used as a common term for social class.

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9 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 STUDENT SUCCESS AND ITS RELATIONSHIP TO OCCUPATIONAL STATUS SCORE IN THE LOS ANGELES COMMUNITY COLLEGE DISTRICT By Ronald C. Lester December 2007 Chair: Linda Serra Hagedorn Major: Higher Education Administration The purpose of this study was to determine if there is a significant relationship between parent socioeconomic status and (1) community college student college grade point average and (2) course completion ratio. The data for this study were obtained through the Transfer and Retention of Urban Community College Stude nts (TRUCCS) project in the Los Angeles Community College District (Hag edorn, Maxwell, & Moon, 2001). This study examined the variables of parent occupational status score, which served as a proxy for socioeconomic status, demographics, high school grade point average, and psychosocial variables, and th eir relationship to college grade point average and course completion ratio. The statistical analyses of factor analysis an d forward block entry regression were conducted to determine significance. Analysis of the statistical tests suggested that there is no significant relationship between parent socioeconomic status and community co llege student grade point average and course completion ratio. Implications of the research to community college administration were also presented.

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10 CHAPTER 1 INTRODUCTION Until th e passage of the Morri ll Act of 1862 establishing land -grant colleges, access to higher education was not widely available. In addition to changes in student access to college, the limited funding for higher education and changi ng student interests has created challenges for the higher education system (Key, 1996). Duri ng the post-war period, higher education changed from a system of the privileged to a system that served the masses. Every social class is now represented in higher education systems, so that a large porti on of students come from lower class families, and those with the greatest familiarity with higher education have performed the best (Hansen & Mastekaasa, 2006). Individuals from all soci oeconomic groups, including those with poor English speaking ability, now have th e opportunity to pursue higher education. This has created challenges for community colleges a nd universities to help students advance through college in a successful manne r (Ellis & Stebbins, 1996). A large group of individuals a ttending institutions of highe r education are students that have had no other family member attend college before them, referred to as first-generation college students. First-generation college st udents have additional ch allenges with their backgrounds of inadequate knowledg e of the college experience a nd attitudes towards college. Further, institutions of higher education have had to deal not only with a diverse student population, but they have also been made accountable for student success of first-generation college students (McConnell, 2000). Additionally, the community college system has allowed for a wide variety of individuals to pursue higher education despite their impaired college readiness. Many students have concentrated too much on memorization rather than learning and understanding concepts (Diaz-Lefe bvre, 2004). Research has been done in many different ways regarding students and their bac kgrounds and their success in the higher education system.

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11 Student success has been a particularly important area of research with numerous factors contributing to particular levels of success. Fa ctors studied have includ ed family socioeconomic status, educational background of the family, and student high school grade point average. Statement of the Problem The m ajor shifts in types of students in hi gher education, especia lly in the community college system, have made it critical for college s to be aware of the background of students in order to be better prepared to se rve their needs and help the stude nts to be successful in their educational and career endeavors (Pennington, Williams & Karvonen, 2006; Person, Rosenbaum & Deil-Amen, 2006; Miller, Pope & Steinmann, 2005). Many students who have entered the community college system today are first genera tion college students or come from a type of background or financial status which may have made college particularly difficult for them to maneuver, including the admissions and financial ai d process, lack of family support, and poor college preparation (Educational Resources Inst itute & Institute for Higher Education Policy, 1997). Retention of students is par ticularly challenging and is the focu s of this research project. The Transfer and Retention of Urban Comm unity College Students (TRUCCS) project (Lee, Sax, Kim, & Hagedorn, 2004) collected and an alyzed data on student characteristics, as well as outcome measures in the Los Angele s Community College District. The TRUCCS project revealed many aspects of student retent ion and success. One factor, however, that has not been studied thoroughly within the TRUCCS pr oject is the influence of parental occupational status score, a proxy for socioeconomic status. The present study used the Nan-Powers-Terrie occupational status score to determine the effect of parental socioeconom ic status on different student success outcome measures including college grade point average and course completion ratio.

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12 Nam-Powers-Terrie Occupa tional Status Score The Nam -Powers-Terrie occupational status sc ore measures the socio-economic status of occupations found in census data. The score represen ts the percentage of i ndividuals that are in occupations having lower average education and income (Terrie & Nam, 1994). The scores are calculated by taking the sum of (1) the number of individuals in occupations at lower median educational levels, (2) the number of individuals in occupations at lower median income levels, and (3) the number of individuals in the particular occupation of in terest, then dividing this sum by the total number of individuals in these groups, and multiplying by 100 (Terrie & Nam, 1994). The scores range from 0-100 on a ranked scal e, which has been wide ly used in social science research involvi ng social stratification. Los Angeles County and the Los Angeles Community College District Los Angeles County is an urban area of Califor nia with a land area of 2,000 square miles. The 2005 population estim ate of the county was 9,935,475, up 4.4% from April 2000 census data (U.S. Census Bureau, 2007). Females comprise d 50.6% of this population. The ethnic make up of the county in 2005 was White, 74.1%, Black 9.7% American Indian and Alaska Native 1.1%, Asian/Hawaiian/Other Pacific Islander 13.4%, and Hispanic 29.5%. Of this group, 36.2% were foreign born, 54.1% spoke a language other than English at their home, and 69.9% were high school graduates. (Los Angeles Community College District, 2007). The Los Angeles Community College District (LACCD) is made up of nine colleges spread throughout Los Angeles C ounty. In 2005, over half of the students were older than 25 years old and over 25% were 35 or older. Females made up 60.5% of enrollment and males 39.5%. The ethnic make up of the LACCD was 16.6% African American, 15.0% Asian, 46.8% Hispanic, and 18.9% White. Of these students, 40% were non-native English speaking, 40% were below the poverty line, and 25% came from homes where parents obta ined education only

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13 through the elementary level. Occupational a nd educational goals of the students included 35.5% seeking vocational training, 30.8% intending to transfer to a f our-year college, 10.2% wanting general education, 6.9% in transitional situations (i ncluding those who wanted to improve basic skills or complete a high sc hool diploma, and 16.6% with unknown or undecided goals (Los Angeles Commun ity College District, 2007) Purpose of the Study Inform ation about the knowledge base of stude nts and their career outcomes can be very useful to community colleges making current and fu ture plans. To help satisfy this need, the present study sought to determin e if there is a relationship be tween parental socioeconomic status and academic outcomes of students in higher education. Sp ecifically, occupational status of parents, which served as a pr oxy for socioeconomic status in this project, was targeted due to the lack of literature addressing this area. The outcome measures of pa rticular interest were college grade point average and course comp letion ratio. Since not everyone attending community college plans to graduate, the cour se completion ratio was a better measure of student success. This research study sought to answer the following questions: 1. Is there a significant relationship betw een socioeconomic status and student college grade point average? 2. Is there a significant relationship betw een socioeconomic status and student course completion ratio? The hypotheses used to test th ese research questions are: 1. There is no significant relationship between the socioeconomic status and student college grade point average. 2. There is no significant relationship betw een socioeconomic status and student course completion ratio.

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14 Background of the Problem Community colleges provide an open door to pr actically anyone wishing to pursue higher education (Hendrick, Hightower, & Gregory, 2006). This policy has allowed individuals into higher education who may benefit from different t ypes of treatments due to their backgrounds. Shortly after the large increas e in numbers of community co lleges in the 1960s, community colleges had to adjust to many different groups in the higher education system and had to become familiar with the backgrounds of these stude nts in order to adapt to their special needs and characteristics (Gleazer, 2000). Parental influences, including the influence of parent educational levels on the childs education, have been well documented in the lite rature (Davis-Kean, 2005). The influence of parent educational levels has been particularly well documented, while influence of parent occupational status has not been as prevalent in the literature. Child ren of immigrant parents have had a particularly difficult transition to higher education due to the background of their parents who did not have experience in the hi gher education system (K im & Schneider, 2005). Despite educational levels, parents have had vary ing degrees of interaction with their children when discussing college (K im & Schneider, 2005). One of the functions of the community college is to provide a transfer gateway to a fouryear institution, but not every student who has entered the community co llege has a desire to transfer (Dougherty & Kienzl, 2006). Nevertheless, successful tran sfer and the attainment of a bachelors degree are considered by many as a successful college outcome. While graduation within a specified time, usually a total of six ye ars to obtain a bachelors degree in a four-year institution, has been a benchmark oftentimes used as a measure of student success, graduation rate does not capture the students who may leav e the higher education system temporarily and then return later to complete th eir degree (Zwick & Sklar, 2005).

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15 Significance of the Problem Community colleg es have faced growing pre ssures with the ever-increasing number of students entering the system (Walker, 2001). W ith these demands came the difficulties in making sure that the diverse st udent population achieved success (Ellis & Stebbins, 1996). One of the main challenges facing community colleges has been that of students entering the system poorly trained and prepared for college (Grimes & David, 1999). Community colleges have continued to look for ways to improve outcomes. By better understand ing the background of the students in the system community colleges coul d have attempted to make improvements based on the information they obtained (Grimes & David, 1999). Theoretical Framework The present study uses B ourdieus theory of cultural capital as the basis for determining parental influences on educational outcomes (B ourdieu, 1984, p. 12). Bourdieu theorized that different social groups differ in terms of educa tional practices and cultural capital. This study uses this theory to determine whether the level of student grade point av erage, course completion ratio, and successful transfer to a four year institution differs among families of different socioeconomic levels. This study used the theo retical framework of Lee and Bowen (2006), who applied Bourdieus theory of cultural capital to test for effects of parent involvement on child school achievement. In keeping with Bourdieu s theory of cultural capital, Lee and Bowen (2006) stated that, although cultura l capital is possessed by an indi vidual or a family, it is more a function of the concordance of the educational as pects of the familys habitus with the values and practices of the educational system with which the family interacts. Thus, families could have obtained more cultural capital given their experiences with the educational system. In addition, the findings of Lee and Bowen (2006) were consistent w ith Bourdieus theory that proposed that some families have inherited cultural capital that may give them an advantage.

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16 However, families with a smaller amount of cultural capital may be at a disadvantage, as they did not have the same access to resources (Lareau, 2001, p. 78). Lee and Bowen (2006) hypothesized that families having different amounts of cultural capital, in this case educational knowledge, would have wide variations in parental educational involvement and student success. This was also reflected by DiMaggio and Mohr (1 985), who stated that cult ural capital has had a positive influence on educational achievement. Limitations 1. This study was lim ited to a sample of st udents who were enrolled in the Los Angeles Community College District during the Spring 2001 semester. 2. This study was limited to individuals who agreed to complete the questionnaire and provided permission for investigators to obtain their transcript information. 3. The validity of the study was limited to the reliability of the re search instrument used. Summary Parental influences have been the subject of num erous research studies examining various facets of that interaction with st udent success in college. Influences such as parent education and income, particularly socioeconomic status, have been the focus of va rious studies indicating significant relationships of these influences with outcomes of thei r children in higher education. This research study examined the impact of parent occupational status as a different measure of socioeconomic status on student outcomes in the Los Angeles Community College District. Specifically, this study examined the impact that parent socioeconomic status, as determined by occupational status score, had on student grade point average and c ourse completion ratio. It is hypothesized that parental socioeconomic stat us (independent variab le) does not have a significant relationship with student college grade point average (depen dent variable). It is also

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17 hypothesized that parental socioeconomic stat us (independent variab le) does not have a significant relationship with student course completion ra tio (dependent variable).

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18 CHAPTER 2 REVIEW OF THE LITERATURE The Am erican community college has allowed in dividuals from all backgrounds to have an opportunity to obtain higher e ducation, including those linguistically and academically challenged. This created challenges for community colleges to be effective for these students (Ellis & Stebbins, 1996; Closson, 1996). The flexible programming and emphasis on needs of the community have allowed community college s to meet these new challenges and special student needs. This has included helping students to become self-directed both at college and in their personal lives (Closs on, 1996). With the major cha nges occurring throughout the community college system, some have proposed that the mission of community colleges has greatly changed from that of providing a transf er mechanism to four-year institutions to also providing technical, voca tional and community education. These changes could continue into the future to help make sure that the nati onal work force will be competitive globally. Changes also occurred in community colleges wa nting to move in the direction of offering baccalaureate degrees as a baccalaureate degree which have increasingly become necessary for entry level positions (Walker, 2001). However, ot hers have maintained that community colleges have been meeting their origin al function to be flexible a nd responsive to community needs (Wattenbarger & Witt, 1995). Wattenbarger and Witt (1995) held that vocational education was a part of the original plan of community colleges, which pr ovided an opportunity for students to earn an associates degree and possibly go on to work as technicians or in businesses. Despite the theoretical perspective of the orig in of community colleges, there is no doubt that the student body being served has changed in regards to student backgrounds, characteristics and needs (Pennington, et al., 2006; Person, Rose nbaum & Deil-Amen, 2006; Miller, et al., 2005). Issues faced by community colleges differ among institutions, for example rural

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19 community college versus larger, urban community colleges (Pennington, et al., 2006). With the increase in the number of community colleges a nd the growth of the student population, students have also faced difficulties, such as obtaining the correct in formation to move effectively through the community college system (Pers on, Rosenbaum & Deil-Amen, 2006). The changes of the community college student population have included increases in the number of students from single-parent homes, as well as those requ iring more counseling services. In addition, todays students have a better understanding of technology and have greater expectations of the effect of higher education on their careers (Miller, et al., 2005). Wa lker (2001) stipulated that in order to meet these changes, community co lleges must understand the conditions surrounding them. While research studies have investigated some of these factors, additi onal research is still needed to better understand current issues of the American co mmunity colleges, especially regarding children from ethnic minorities and th e effectiveness of classroom teaching in high school and college (Zhou, 2003; Foster, Lewis & Onafowora, 2003). The size of ethnic minorities has grown especially rapidly in larg e urban communities (Zhou, 2003). Poor minority youth in urban areas have experienced numerous challenges, such as isolation from the mainstream of the community, being su rrounded by ghettos while seeing and wanting materialistic items, poor living conditions and overcrowded schools (Zhou, 2003). Administrators and faculty must become cogniza nt of the cultural background of students and incorporate that knowledge into teaching (Foster, et al., 2003). While previously most community college students, especially those from families with high socioeconomic status (Doughert y & Kienzl, 2006) have intended to transfer to a four-year institution, some recent research has suggested that many students enter a community college to

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20 obtain only a two-year or vocational degree (Anderson, Alf onso, & Sun, 2006). Anderson and colleagues (2006) discussed the trend that increa sing numbers of twoand four-year institutions have been making system-to-system agreements, allowing easy access of students in the twoyear institution to enter the associated fou r-year institution. The agreements have become especially important because of reduced appropriations for higher education in the presence of higher tuition costs and a high demand for hi gher education (Anderson, et al., 2006). Coley (2000) pointed out that the understanding of thes e agreements became difficult for students who attended several community co lleges and who therefore met some problems when transferring. In their discussion, Anderson and colleagues (20 06) noted four theoretical models of the functions of the community college (Anderson, et al., 2006): functionalism, neo-Marxism, institutionalism, and statism. Functionalists look at the community college as a place for women, minorities and the worki ng class to obtain higher educati on and as a means to provide vocational-technical skills and provide an entryway for transfer to a four-year institution. NeoMarxists look at community colleges as institutio ns that keep women, mino rities and the working class in vocational-technical prog rams. Institutionalists look at community colleges as a way to keep the universities from losing academic stat us by shifting the demand for higher education towards two-year institutions. Statists have seen community colleges with multiple goals and different influences. Statists have viewed lo cal and state governments as wanting to increase employment and job training opportunities for thei r constituents, since mo st of their funding for schools has come from the local area (Anderson, et al., 2006). Anders on and colleagues (2006) further state that statewide agr eements between universities and community colleges have been on the rise to contend with rapidly increasing tuition rates and the high demand for affordable higher education.

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21 Despite the challenges, there have been cer tain successes. For example, a recent study found that the success rates of stude nts who transferred from a twoyear institution to a four-year institution depended on the number of community co llege credits, the type of institution, and the students academic level (Koker & Hendel, 2003). Interestingly, Koker and Hendel (2003) found no significant relationship between age, gende r, and completion rates, although they did find that there was a significant relationship between ethnic background a nd graduation, and that white students had higher gra duation rates than non-white stud ents. Characteristics studied included demographics, transfer behavior, and completion of the bachelors degree at an urban university. Of particular note wa s the researchers recommendation that research look at the academic backgrounds of the students who transfe rred from a community college to a four-year institution to see which students were most likely to leave before obtaining a bachelors degree (Koker & Hendel, 2003). Social Class Social class was the subject of a research study perform ed by Hogan (2005) who provided an update reinvestigatio n of an earlier study by Wright and Perrone (1977) regarding social layers. Hogans (2005) research focused on profe ssionals in higher-end jobs to see who had the greatest financial return on education. He found that, although individuals with MBA, MD and JD degrees had a high return on their education, it was only when thes e individuals became business owners that they became particularly successful (Hogan, 2005). Previously, Wright and Perrone (1977) indicated that th ere were substantial differences in class when they looked at income and education. They found this to be true even when they controlled for occupational status, length of time on the job, and demogr aphics (Wright & Perrone, 1977). Wright and Perrone (1977) found that the benefits of educati on were higher for managers than those in the working class, and that class differences betw een managers and the working class were less

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22 apparent among white women and blacks than am ong white men. They also postulated that members of particular races or genders in the same class ha d comparable returns on their educational investments, and that in particular class categories, the gap in income was smaller between races than between genders (Wright & Pe rrone, 1977). Yet other research indicated no significant relationship in the areas of occ upational standing, income, or cognitive ability (Hauser & Huang, 1997). Hauser and Huang were following up on research conducted by Herrnstein and Murray (1994) and reported in their book The Bell Curve: Intelligence and Class Structure in American Life According to Berliner (2006), social class has been a long-standing issue and one that influences individuals for a lifetime. In particul ar, Berliners (2006) anal ysis found that (1) those in the United States living in poverty remain ed in poverty for a longer period of time than individuals in other affluent c ountries, (2) those who were minor ities and lived in urban areas had poor academic performance in a number of areas on international standards, (3) the academic performance of those in the lowest socioeconomic levels was more influenced by family and living environment than by genetics, (4) children in poverty exhibited major medical difficulties that created problems for academic a nd life success, and (5) a small reduction in the level of poverty in a family ha d a constructive influence on academ ics. Individuals who lived in these areas had major disadvantages that caused problems not only academically but also with daily non-academic activiti es (Berliner, 2006). Social class has led to advantages for some and disadvantages for others (Massey, 1996; Bickel & Howley, 2003). Massey (1996) found that one of the changes was the concentration of wealth and poverty in separate areas, thus causing a segregation of classes in different geographic areas. With the increased concentration of poverty in particular areas, especially in

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23 urban regions, the problems of poverty became more visible to members of society, and the wealthy became more distanced from the probl ems of poverty (Massey, 1996). However, Bickel and Howley (2003) found that poverty rates in the United States were still highest in rural areas. Bickel and Howley (2003) also found that st udents in rural areas were improving in math achievement. Nesbit (2006) found that embedded in social class were factors that varied depending upon ones own concept of class, includi ng beliefs, occupations, money, lifestyle and power (Nesbit, 2006). Nesbit (2006) focused on the effect of social cla ss on adult education and found that adult education provide d the opportunity for t hose in lower classes to move upward in society, even in the presence of social inequities. Parental educational background has played a particularly strong influence on children, with children of parents who attended college bei ng more likely to go to co llege also (Nakhaie & Curtis, 1998). Nakhaie and Curtis (1998) found th at the educational level of mothers had the strongest influence on the educat ional of their daughter s rather than their sons, and that the educational level of fathers had the strongest influence on the e ducational level of their sons rather than their daughters. However, the sa me relationship between mothers and daughters and fathers and sons did not exist when researchers l ooked at the class level of the parents, which the researchers construed to be a f unction of the cultural capital held by the parents (Nakhaie & Curtis, 1998). Children whose parents went to college had an advantag e over children whose parents did not go to college, including the capab ility to educate their children about college planning and course requirements (Cabrera & La Nasa, 2001). Cabrera and La Nasa (2001) found that family, school and indi vidual characteristics had as much or greater importance on student success than socioeconomic status. They also asserted that help ing individuals become college qualified was a high determinant of student success in college, that parental involvement

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24 enhanced the likelihood that the child would become college qualified, and that if parents in lower socioeconomic levels saw the benefits of a college degree their involvement in the childs education would be increased (C abrera & La Nasa, 2001). It has also been demonstrated that children have chosen similar paths of education and occupations as the education and occupation of their parents (Dryler, 1998). Interestingly, Drylers (1998) study indicated that the infl uence on occupational choice and education was greater between fathers and sons than between mothers and daughters. Additionally, Dryler (1998) found that parents with hi gh levels of education and those in service-level occupations have had a particularly important influen ce when it came to their children assuming an educational path that was uncharacteristic of their gender. However, this was not found for working class families and those who had less education. Other research suggested that the mothers educational level was particularly im portant. Leinberger-Jabari, Parker and Oberg (2005) found that the mothers e ducation had a significant influen ce on the growth of her childs capabilities, and that the childre n of better educated mothers tende d to stay home and in school longer. They also reported that the health of the mother affect ed the health and nutrition of the child, and the nutritional level of the child had a great impact on the childs educational and occupational success (Leinberger-Jabari, et al., 20 05). In other research regarding parental influences on child health issues, it was found that, as socioeconomic status improved in poor families, health risks decreased for the children in families where the mothers had a better education (Hatt & Waters, 2006). They proposed that mothers that were better educated to implement more sanitary habits around the home, and that families with a higher socioeconomic status may have a healthier living atmosphere. Hatt and Waters (2006) also found that mothers

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25 that had a better educatio n but lived in a lower socioeconomic st atus were still able to implement more sanitary habits around the home de spite living in poorer economic conditions. Students who were the first in their families to attend college (first-generation) have had different issues leading to their success, incl uding preparation, self-confidence, and family involvement (McConnell, 2000). However, McConne ll (2000) also pointed out that the firstgeneration students in community colleges diffe red from their counterparts at four-year institutions, in that the commun ity college students had a lower pe rsistence level and thus started out at a higher risk of dropping out of college. Consistent with other researchers, McConnell (2000) brought up the issue that studies on first-generation college students often included data on students at both two-year and four-year institutions, but due to differences between these groups, research results could not be generalized over both groups. The mainly traditional age and associated characteristics of students at four-institutions differed greatly from the student population at two-year institutions. Some of the differences in characte ristics between the two student populations included demographics, co nceptions of importance of college, personal aspirations, family support, self-assurance le vels and academic level (McConnell, 2000). McConnell (2000) also found that many of the firs t-generation college students who dropped out of college were doing well academically when they dropped out, and suggested that the combined demands of going to school, worki ng, and being involved with their families caused these students to leave college. This finding pres ented an alternate look at student preparedness in terms of personal demands rather than academic background. An additional impact of parents on their children was shown in research on children with separated parents (Smith, 1995). Smiths research showed that, after correction for parental education and occupation, there were no significant effects on academic achievement test scores.

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26 However, in the same study it was found that grades were significantly affected in this situation. Smith (1995) suggested that this difference indicated the importance of the parameters used in measuring academic success. Biblarz and Rafter y (1999) postulated as well that children in families with single mothers, single fathers a nd stepfamilies had poorer educational outcomes. Those with the background of a single father or stepfamilies tended to have poorer educational outcomes than families with both parents or a si ngle mother (Biblarz & Raftery, 1999). Biblarz and Raftery (1999) explained the better outcomes of children with single-mother families as due to the mothers time investment in the children. They also found that the amount of time parents invested in their children had a direct effect upon the outcomes of the children (Biblarz & Raftery, 1999). In research conducted by Gibson and Jeffers on (2006) it was found that adolescent selfconcept is influenced by family and close frie nds, including teachers a nd peers. Parental involvement was the subject of interest for a stud y that involved students in a college-preparatory program (Gibson & Jefferson, 2006). The research ers finding of a significant relationship between parental involvement and adolescent self-concept should be helpful information to schools in assisting with stude nt development (Gibson & Jeffe rson, 2006). Gibson and Jefferson (2006) also found that the perception parents had of their involv ement with their children and the corresponding perception the children had of their parents invol vement differed in a significant manner, and they suggested that programs addressing parental involveme nt should be developed to reduce those perceived differences. This was important because the manner in which adolescents have seen themselves has greatly affected their decisions (Gibson & Jefferson, 2006).

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27 Occupational prestige has been well documented in the liter ature (Goyder, 2005; Melchoir et al., 2005). Some studies indicated that levels of occupational prestige have shifted over time commensurate with changes in so cietal needs and viewpoints (Goyde r, 2005). There are several reasons for this observation, including changing occupation dynamics, specifically job task and occupational changes due to new technologies (Goyder, 2005). It has been hypothesized that prestige attributed to different occupations shifted during the third quarter of the 20th century. According to Goyders (2005) data, the prestige of some professions such as lawyers had a decline, while service occupations such as fi refighters and policeman had high increases. Curiously, Goyder (2005) found that occupations that required less formal education, such as auto and textile mill workers, had an increase in occupational prestige, but not as high as the previous group. Because of the changes that had occurred in occupational prestige, Goyder (2005) recommended looking at the spread of occupational scores over time to determine occupational prestige. Additionally, a study by Me lchoir and colleagues (2005) looked at the inequality of health risks and occupational class level and found that those individuals working in manual or lower occupational classes had a higher rate of absences due to illness. The researchers focused on class differences be tween occupations and found that the lower occupational classes had the worst working condi tions, with a concomitant negative effect on work performance (Melchoir et al., 2005). Demographic risk factors have been found to have particular impact on course completion for students in certain groups (Abell, 2003; Cole y 2000). Abell (2003) repor ted in her study that the students who were at partic ular risk of dropping were those who worked full-time, enrolled part-time, and had issues with maintaining thei r financial independence. Successful students were also found to study more a nd reviewed course materials prior to exams more frequently

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28 than students who withdrew. Further, these individuals did not inte grate into the college environment or become involved in study groups or meeting with faculty and peers outside of class (Abell, 2003). Abell (2003) found that students who integrated into the college mode of living were the most likely to fi nish college at either twoor four-year institutions. The time needed to finish college was particularly impor tant due to the investme nt that students could make towards their education and have a di rect effect on their success (Abell, 2003). Coley (2000) also found that students who did not perform well in high school and had low levels of motivation would be well counseled to attend community college first and then to transfer to a four-year institution, because they would learn the same material at a lower cost. He also cautioned that students who went to commun ity college as a second chance if they did not do well in high school may think they can be successful in higher education without good performance in high school (Coley, 2000). In yet another study, C onklin (1997) found that students dropped courses most frequently becau se of work conflicts or personal problems, neither of which the college could control, but through which interven tion in the form of counseling and advisement may have provided assistance to students. C onklin (1997) also found that looking at the level at wh ich students drop particular courses was important in assessing problems that the college may need to address with an individual c ourse. In the 1997 study by Conklin, the reasons given by students for droppi ng high attrition classe s were reported at a higher frequency that that for all classes, and the reasons included the course difficulty, dissatisfaction with the instructor, a heavy cour se load, and a general dislike for the course material. One student group that has been integral to the community college is the adult population (Donaldson & Graham, 1999). Donaldson and Grah am (1999) found that this group learned

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29 differently than traditionally aged students and that, since older students had more life experiences, they approached college differently than younger students. The older students also had a better idea of their goals in higher educ ation and made careful de cisions based on those goals, and they also integrated into college life according to th eir views and understandings. Due mainly to time constraints with work schedules, adult students have had to make difficult choices about how to spend their time. Because of thei r personal experiences, they were able to put meaning to the information they learned in classes right away, and in that way they were able to have similar college success outcomes as tradi tional age students (Donaldson & Graham, 1999). Donaldson and Graham (1999) also suggested that a dults had a better idea of what they wanted out of college which aided in their success rate. Studies have continued to be performed on ol der adults in the community college system (Laanan, 2003). Laanan (2003) id entified several unique characteri stics of the older community college student: the importance they place on thei r families and financial situations, the various backgrounds older community college students may have including previo us college education, and their motivation for higher education. The olde r students may also be enrolled to train for a different job or simply for lifelong learning, in cluding taking both credit and noncredit classes, whereas the younger students were tr ying to gain upward mobility in society in terms of income and occupational status (Laanan, 2003). Further, with the diverse bac kgrounds of community college students, the choice of courses has been different and includes both credit and non-credit courses (Laanan, 2003). According to Laanan (2 003), over one-fourth of students in community colleges have wanted to obtain a co llege degree at the bachelors level or higher. Further, Laanan (2003) found that high school preparation was a significant variable when she looked at why students are in college.

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30 Older adults have been included in the clas sification of nontraditional in research, although nontraditional may not be a us eful label and may cause some limitations (Kim, 2002). Kim (2002) suggested that nontraditional has been a te rm used that encompasses a very diverse group, not just older students, and the term is applicab le to the majority of community college students and thus needs to be broken down into groups that are meaningful to study. Further, Kim (2002) advised that in some studies there may be factors that caused an interaction that were shared by traditional and non-traditional students that were unrelated to which group they belonged. Rather, the focus should be on different groups of students in the community college such as first-generation college stude nts, adult students, and reentry students (Kim, 2002). Another group served by community colle ges has included students from lower socioeconomic backgrounds (Romano & Millard, 2006). Romano and Millard (2006) reported that one measure of the service provided to lo wer income students was the number of students receiving Pell grants. The resear chers selected Pell grants sinc e there was no national database containing that information (Romano & Millard, 2006). However, they also pointed out that there several reasons why Pell grant rates may not give an accurate i ndication to the lower income community: ineligibility of part-time students (a major fraction of lower-income constituents), the difficulty of the application process, and overall incorrect measures, among other reasons (Romano & Millard, 2006). Rega rdless of these limitations, Romano & Millard (2006) used Pell grant data and found that commun ity colleges pulled in a high rate of students with low family income. They also found that students in community colleges tended to be ethnic minorities, first-generation college students, and progeny of single parent families, particularly families that did not speak Englis h. Older students tended to have different

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31 characteristics, which included higher incomes than students who had just finished high school (Romano & Millard, 2006). The diverse student populati on has created a challenge for todays community colleges (Lau, 2003). Consistent with other research, Lau (2003) found that, in order to retain students, several obstacles must be confronted on the pa rts of both the students and the college. The college should help students obtain adequate f unding, provide appropriate academic support, and maintain diversity. Faculty members need to be inventive with instruction and encourage each other to work individually with students to impr ove the learning environmen t. For their part, the students need to have the motivation to play an active role in their e ducation (Lau, 2003). Lau (2003) pointed out that students who developed a sense of belonging in the college environment have been more successful and had a greater ch ance of staying in college. This has been especially important during the freshman year, as students who did not pe rform well at the start had a high likelihood of leavi ng college (Lau, 2003). Research has indicated that one way community colleges can serve their student population and help students avoid future job-hunting obsta cles is by teaching social skills, which have become increasingly important to employers (Deil-Amen, 2006). Deil-Amen (2006) went further to state that neglect of social skill education worsens the problems many individuals face in the workplace. This has especial importance because many community college students come from homes that do not promote social skills and have few positive role models. By learning social skills, disadvantaged students can improve their chances of moving upward in society by qualifying for more prestigious jobs than they may otherwise hold (Deil-Amen, 2006). DeilAmen (2006) also suggested that community colleges need to work with employers to provide

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32 students with job opportunities once they finish college, because many of these students are not connected with individuals working in professional settings. Student Preparedness The trad itionally open-admission policy of community colleges has provided easier student access, but it has also created problems such as the necessity of providing appropriate student remediation (Perin, 2006). Hadden (2000) pointed out that open access allowed a number of students to fail, and thus indica ted great need for remedial help. Some colleges have made developmental classes mandatory for students wh o perform poorly on placement tests, while others have allowed students the choice of wh ether or not to take the developmental class (Hadden, 2000). Hadden (2000) also stated th at students who attended the suggested developmental classes had a better chance of success than those who did not take the developmental classes. A co mplication, however, was that community colleges generally allowed open access and opportunities in college, allowing students to attend the college who may not be helped by developmental cla sses and ultimately fail (Hadden, 2000). With so many individuals withdrawing from community colleges, Cohen and Brawer (1996, p. 62) found that many community colleges have made retention a priority. Other studies have found that an orientation course had pos itive impact regarding student enrollment, persistence, retention, and degree completion, esp ecially if it increase d student involvement (Derby & Smith, 2004). This was particularly important for students who pursued associate degrees. Further, Derby & Smith (2004) found that most programs in place to improve retention were developed primarily for students who were unprepared for college. They asserted that retention programs should be for al l students, but they also said that the usual measure of grade point average may not be an accurate predictor of programmatic success.

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33 Some community colleges have moved beyond remedial classes and have begun to offer on-line learning assistance to stud ents in order to help them co mplete course assignments and learn basic skills (Perin, 2004). Ho wever, Perin (2004) also found that at times this assistance provided too much help rather th an simply guiding students to finish class assignments. Perin (2004) also found that, as institutions incorpor ated more remedial resources, some of the available remedial sources were found to be und erutilized, and perhaps there was possibly some duplication of services offered in some institutio ns. He also said that the reported successes should be scrutinized to see if th ese services were really making a difference or if the services were primarily used by the better students, thus skewing the rate of effectiveness (Perin, 2004). In another study, Perin (2006), consistent with ot her research, found that students who planned to enter higher education institutions often did not have the reading and math skills needed for success, but they still attended community colleg e, thus putting pressure on the institution to provide effective remediation. Fu rther, as a result of taking remedial courses, students required more time to complete their degrees or programs of study (Perin, 2006). The problem has been made more complex by the variation of remediat ion standards among different state systems, and the tendency of some college s to disregard the establishe d standards (Perin, 2006). Research on student academic preparation ha s shown that students who took a demanding mathematics course load in high school had bett er success rates in college mathematics courses above the level of than Algebra 2 (Berry, 2003). Berry (2003) also found that only 25% of high school students completed this le vel of math preparedness, even when the courses were available in the high school. Schools may have offered demanding courses, but students needed to be encouraged to take them (Berry, 2003). Furt her, Berry (2003) emphasized that high schools and colleges must work together to move students ef fectively from high school to college, and that

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34 high school faculty and personnel need to work earl y with students and parents about college and how to prepare for it. Another study looked at developmental math courses and student retention in those courses (Umoh & Eddy, 1994). Umoh and Eddy (1994) did not find significant results with traditional factors such as grade point average, gender, parental education, or student academic and social integration, but rather they found that retention differences occurred with instructors and students motivation to succee d. One commonality found among students with the highest success rates in th is research was regular cla ss attendance (Umoh & Eddy, 1994). Umoh & Eddy (1994) suggested that more research be done to determine the significance of student intent to succeed. In a different study it was found that underpre pared students without remedial work had a lower course completion rate, greater attrition rate more test anxiety and less sense of control than students who were prepared for college (Grimes, 1997). Grimes (1997) found that those students who had high persistence levels with th eir educations had greater academic success, while those with low persistence levels did poorly, despite their level of college preparedness. Grimes (1997) also suggested that educational institutions need to help students develop a sense of personal responsibility for their success in co llege. Further, by determining whether or not students felt they were in contro l of events, institutions could better focus on the most effective programs to improve student achievement in pr eparatory courses and lead to overall student success. In an earlier study, se veral student characteristics we re found to predict college withdrawal (Grimes & Antwort h, 1996). Grimes and Antworth (1996) found that women had a greater tendency to withdraw due to health or family issues, while men dropped because of the lack of challenging coursework, and minority st udents dropped due to various academic and social issues. Grimes and Antworth (1996) also found that better prep ared students had higher

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35 course completion rates and grade point averages, but that grade point averages were generally lower for men, compared to women, as well as for students with financial problems. Degree aspirations have di ffered among some community college students depending on whether they attended a public or private twoyear institution (Laanan, 2003). Students at private two-year institutions were more likely to have the highest academic aspirations, although the majority of students of both public and priv ate two-year institutions had aspirations of earning a bachelors degree or above (Laana n, 2003). Simmons (1995) examined student persistence among a group of disl ocated workers who were retrai ned in the community college system. She concluded that those with greatest persistence were those with low skills and less previous education. She also found that the grea test predictor for students to persevere with community college was the progress the students made in completing courses successfully, while students who did not complete or failed courses were the most lik ely to drop out of college. An interesting finding of Simmons (1995) was that attending college full time had a much stronger positive relationship with persistence than part-time enrollment. Regarding preparedness, students with Gene ral Educational Development certificates (GED) often have more difficulty than those with traditional high school diplomas. Soltz (1996) found that those with GED certificates had high drop out rates and that community colleges should try to develop ways to help these indivi duals achieve success. The same research study also found that students who di d continue to pursue higher education, and who had received a GED rather than a traditional high school diploma, had graduation rates si milar to those of the overall college student group, thus indicating that the open door policy of community colleges was beneficial to GE D holders (Soltz, 1996).

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36 Student integration as a predic tor of success has been another theme in the literature. For example, Mlynarczyk and Babbitt (2002) studied a program of English as a Second Language (ESL) and found that students who integrated into the learning community, particularly in the classroom, and also in social environments outsi de the classroom, had a better chance of success in college than those that di d not integrate (Mlynarczyk & Ba bbit, 2002). The researchers found that students confidence in le arning was increased by hearing th eir peers describe and solve a particular problem. This finding indicated that involvement in a learning community early in their college experience helped students to su cceed while in college (Mlynarczyk & Babbitt, 2002). A study by Hyers and Zimmerman (2002) identified subgroups and looked at how each of the subgroups could be helped to achieve higher graduation rates. The researchers concluded that the subgroups with higher graduation rates had higher ACT scores, high school rank, orientation grades and first-quart er grade point averages. Howe ver, the researchers also found that segmentation analysis revealed differences within these groups were occurring that showed differences within groups that were not found otherwise. Segmentation analysis showed relationships between subgroups that were in the mid range of variab les rather than at the high or low ends. Pierre Bourdieu coined the term cultural capital (Bourdi eu & Passeron, 1977, p. 30) and applied it to the observation that students who did the best in school were those who were the most familiar with individuals of the leading cu lture and were therefore most aware of how to navigate the educational system. This was consiste nt with other research that demonstrated that students who did the best in higher education we re those that who knew the system (Cabrera & La Nasa, 2001; Dryler, 1998). Ca brera and La Nasa (2001) found that parents with college

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37 educations were more involved in overseeing th e college preparation of their children than parents without degrees. They also observed that if parents in lower socioeconomic levels could see the relationship between obtaining a colleg e degree and the related economic and social outcomes, their involvement in their childrens school endeavors would in crease (Cabrera & La Nasa, 2001). Further, Drylers (1998) research suggested that children followed career paths similar to their parents because they thought they would obtain more parental help by this route. Student Transfer One of the m issions of the community colleg e has been to provide an opportunity for students to have access to a f our-year institution (Bragg, 2001; Townsend, 2001). Bragg (2001) further stated that community colleges needed to ensure that the entire curriculum provided transfer opportunities for students, while still offering programs th at fulfilled the other missions that have evolved for community colleges. This could be enhanced by developing a meaningful set of outcomes and effectively communicating them in the community (Bragg, 2001). While transfer had been a main mission of community co llege, critics of the tran sfer mission pointed to research showing that students who started at four-year institutions had a higher rate of obtaining the four-year degree than students who transf erred from community college to a four-year institution (Townsend, 2001). Community colleges have provided an opportunity for student s who would not otherwise be able to obtain a bachelors degree, especially considering that the cost of community college is less than that of four-year institutions (Townsend, 2001). Studies have found that students from high social backgrounds had higher transfer rates than students at other levels, and that older students had less tendency to transfer than their younger counterparts (Dougherty & Kienzl, 2006). Interestingly, the same study indicat ed that black students had higher educational goals than white students from the same soci al and economic backgrounds (Dougherty & Kienzl,

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38 2006). Another factor that influe nced transfer rate was facult y involvement at the community college level (Tatum, Hayward, & Monzon, 2006). While the researchers did not look at what effect this involvement would ha ve on student transfer, they f ound that faculty involvement was very important and that incentive s should be provided to increase this involvement and to entice more faculty to encourage stude nts to persist with their educations beyond community college (Tatum, Hayward, & Monzon, 2006). A study by Hagedorn, Moon, Cypers, Maxwell and Lester (2006) looked at successful transfer of students. Hagedorn and colleague s (2006) found the success rate was poor, noting in particular the weak success rate of students in remedial programs. They also pointed out that since many of the students who entered community college were not well pr epared, they required more time to reach and complete transfer level courses, thus requiring much more persistence (Hagedorn, et al., 2006). Hagedorn and colleague s (2006) further stated that many community college students had less cultural capital than st udents in four-year inst itutions, and that their understanding of what it would take to get to the four-year institu tion may not have been correct, including the wrong impression that at the end of two years they would be finished with community college and would have successfully transferred to a f our-year institution. Successful transfer to a four-y ear institution was also the focus of a study by McMillan and Parke (1994). In this study c onducted in the Illinois community college system, the researchers found that program of study and student intent imp acted the successful transfer of students from the two-year institution to the four-year inst itution (McMillan & Parke, 1994). McMillan & Parke (1994) also pointed out that in order for two-year institu tions to help students transfer successfully to four-year institutions, the two-y ear institutions needed to look at a variety of parameters determining transfer rates, including job placement and retenti on rates, and retention

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39 rates of courses, programs, in stitutions, and transfer. Resear ch by Dougherty and Kienzl (2006) reconfirmed previous research th at social background affected successful transfer from a twoyear to a four-year institution. The researcher s found, consistent with other literature, that students on the high end of socioeco nomic status had considerably higher transfer rates. They determined that part of this could be attributed to better pr eparation for college and higher educational goals (Dougherty & Kienzl, 2006). Better high school preparation was a key area that the researchers suggested should be examined to provide better opportunities for students who would otherwise be at a disa dvantage (Dougherty & Kienzl, 2006). Completing community college has been show n to have a positive impact on earning power (Gillum & Davies, 2003). In addition, Gi llum and Davies (2003) found that, even though wage records indicated a definite validation th at completing college in creased earning power, legislators in the area they studied did not have hard facts and relied on unverified ideas about earnings of community college students. Thus, community colleges need to better project the benefits of their institutions in order to obtain necessary governmental funding (Gillum & Davies, 2003). This is of further interest, sin ce research has shown that students were relatively uninformed about occupational wages, even in thei r own field of study, until their fourth year in college (Betts, 1996). Betts (1996) also found th at, consistent with ot her research, it was important for parents and students to be aware of future earnings a nd the availability of financial aid, as well as the cost of education in order to make informed decisions. Student Retention The problem s with retention and graduation ra tes due to the widely varying issues and backgrounds of the community college students have been exacerbated by federal policies mandating certain outcomes order to keep their T itle IV programs, especially with fraud and abuse found in audits of the programs (Riggs & Goodwin, 1997). This could hurt students from

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40 low-income families if the Title IV programs were to become more restrictive (Riggs & Goodwin, 1997). A study conducted by Hoyt (1999) provided even deeper analysis of student retention. After studying several years worth of college retention data Hoyt (1999) found that, as in other studies, academic succe ss, ethnicity, work, and outside in terests had a significant impact on retention. However, Hoyt (1999) also found significant positive impact of conditions not studied at other institutions including living at home and early exposure to college. With dual missions of the community college to provide vocational training as well as to prepare students to transf er to a four year institution, Ko ( 2005) found that the re tention rates of vocational students were lower than those of other students in both two-year and four-year institutions. According to Ko (2005), community colleges have not looked at vocational students in particular when studying rete ntion, and he proposed that perhaps community colleges should look at vocational students to see what can be d one to improve their generally lower retention rates. Further, by staying focused on special groups of students, community colleges could better develop effective pr ograms to retain students in college (Ko, 2005). Tinto (1975) presented a thorough analysis of the college st udent dropout process. He pointed out that students who dropped out of college for differe nt reasons were oftentimes grouped together inappropriately by researcher s. Among dropout reasons, temporary and permanent withdrawal and transfer to a diffe rent institution have sometimes been grouped together in research (Tinto, 1975). Further, students may drop out voluntarily or be dismissed for academic performance, and these differences should also be taken into account in the research (Tinto, 1975). The failu re to group drop-out behavior correctly has led colleges and state planners to miscalculate drop out rates wh en projecting student enro llments (Tinto, 1975).

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41 In later research Tinto and Goodsell-L ove (1993) found that students had a higher persistence level when they participated in peer support groups which seemed to encourage them to attend classes and participate. They found that students in these learning communities were able to fulfill both the social and academic needs of students new to college. In another study, Tinto and Russo (1994) found that students involved in a support network outside of the college setting were able to increase their i nvolvement and achievement needs and had a higher rate of continuing their education, but that it wa s difficult to achieve th is involvement. Tinto (1997) also pointed out that, since the classroom was the cen ter of education, then group involvement must occur in the classroom as we ll. Further, he found that this involvement extended beyond the classroom to social and st udy groups (Tinto, 1997). Tinto (1998) further emphasized that student involvement is most impo rtant while in the first year of college, and stated that colleges need to encourage faculty to work together across disciplines to promote shared experiences and to encour age faculty-stude nt interaction. Student Success Research studies have compared occupationa l outcom es of individuals who began their higher education at two-year colle ges versus those who started at four-year institutions, with the general assumption that students who started in a two-year institution were less likely to complete a bachelors degree (Dougherty, 1992 ; Whitaker & Pascarella, 1994). Dougherty (1992) found that students who started work toward s a bachelors degree at a community college had lower persistence levels than those who started directly at a four-year institution, and he suggested that community colleges should work to improve transfer education. Whitaker and Pascarella (1994) went further to conclude that students who co mpleted their bachelors degree entirely at a four-year institution had higher earni ngs outside of college than students who started out at two-year institutions. Th e researchers suggested that this may be due to individuals who

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42 began at two-year institutions we nt into occupations of lower st atus than individuals who began college at four-year institutions (Whitaker & Pascarella, 1994). Lin and Vogt (1996) also compared occupational outcomes of students in community college versus students who attended only four-year institutions and concluded that stud ents earning their bachelors degree solely at a four-year school obtained higher earnings and jo b status than those th at first attended a community college. They suggested that the wide range of quality of twoyear institutions may be a factor in thei r research findings. Additional predictors of stude nt success have been found to be college entrance exam scores and rank in high school (Smith & Schumac her, 2005). High school class rank and grades in high school calculus (if take n) was particularly significant among males, although high school calculus grade was a strong predictor for both male s and females. Intere stingly, the researchers also found that verbal SAT scores were a str ong predictor of success for males and math SAT scores were a strong predictor of success fo r females (Smith & Schumacher, 2005). Other studies have looked at even more factors that predict student su ccess, such as gender, ethnicity, parental education, parent marital status, attitude, and involvement in different learning communities (Zheng, Saunders, Shelley, & Whal en, 2002). Zheng and colleagues (2002) suggested that the groups identifi ed as having special needs should receive more attention at the individual level and should be persuaded to par ticipate in group meetings in order to be more successful in college. Course grades are of particular interest in the literature. Placement test scores as predictors of success in grades and reten tion in English and math course s was the subject of study by Armstrong (2000), who found that student activities and other personal traits and demographics were better predictors than placement test scores Armstrong also determined that variances of

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43 test scores were affected in part by how the di fferent instructors scored the placement tests. Armstrongs (2000) research indicated personal char acteristics of students such as high school preparation, the conception of th e importance of a college edu cation, past experiences, high school grade point average, and involvement in school activities were more useful than placement scores in predicting success in the community college system he studied. Course completion has been a major factor to consider when researchers looked at academic success. They noted a high drop out rate among distance learners (Visser, Plomp, Amirault & Kuiper, 2002; Nash, 2005). Visser a nd colleagues (2002) found that motivation to persist in distance education courses could be increased by enabling students in distance education classes to meet with others in thei r cohort and by having instructors send motivational messages, even when those messages were sent to the group rather than to individual students (Visser, et al., 2002). In his study of drop-out factors, Nash (2005) found that students would use, if provided, free tutoring and orientation sess ions. Topics for these sessions could include setting goals, managing time, studying effectively, and test taking skills, instead of specific courses. Further, the resear chers found that most of the st udents in their study enrolled in distance learning courses due to time restrictions and learning preferences, as well as the perception that distance educati on courses would be less demandi ng than traditional on-campus courses (Nash, 2005). A study by DeTure (2004) found that learning st yles and student confidence levels did not predict the level of student su ccess in on-line courses. Further, DeTure (2004) found that students in distance-education classes who were more indepe ndent had a higher degree of confidence in their abilities; however, she also found that students with high independence and self confidence did not have highe r grades than other students in the same courses. Another

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44 study by Higgins (2005) looked at drop-out rates of students in a community college nursing program. She looked at prerequisite courses, pr eadmission tests, demographics, exit exam scores and nursing skills laboratory scores. Demogra phic indicators, including age, ethnicity, and gender were not found to be predictors of student success in this particul ar program, possibly due to a tutoring program that wa s in place (Higgins, 2005). Ra ther, Higgins (2005) found that prerequisite testing and course work had a significant relations hip with program completion. Faculty mentoring also provided a higher succ ess rate in this program (Higgins, 2005). The literature indicates that socioeconomic st atus is one predictor of academic success, although the strength of the relationship varied between students (Pearce, 2006; Sirin, 2005). Pearces research (2006) looke d at the Chinese-American minority group, which generally performed well. The factors of parental expectation and parental involvement were found to be a significant factor in the success of Chinese-Am erican students (Pearce, 2006). Because the structure of Chinese-American families may have an important influence on performance of students in this group, the result s of Pearces study cannot be applied easily to other minority groups (Pearce, 2006). However, Pearce (2006) did suggest that working to change the home environment of certain groups may help student success. Sirin (2005) found that changes in society, economics and research methodologies have over time made research on socioeconomic status and student success more complicated, and that newer research has to incorporate those changes. But Sirin (2005) st ill found a significant effect on student success due not only to resources at home but also to social capital. While students with low family socioeconomic status had a higher risk of going to schools with limited funds, there we re programs in place to help assist these students with varying results (Sirin, 2005).

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45 The present study is based on the research of Lee and Bowen (2006), who applied Bourdieus theory of cultural capital. Lee and Bowen studied how pare nts involvement with their children affected success in school. They included ethnicity, so cioeconomic level, and parent educational level in th e study and used factor analysis to analyze five types of involvement that parents had w ith their children both at home and at school. Lee and Bowen (2006) used demographics as prox ies for social status in testin g Bourdieus theory of cultural capital. The researchers found that poverty and ethnic origin were more significant than parental involvement factors in predicting the academic achievement of children. However, an achievement gap was found that could be partia lly explained by parental involvement and the interaction of parental invol vement and different demogra phic backgrounds (Lee & Bowen, 2006). Summary Dram atic changes in the role of the commun ity college and backgr ounds of the students it serves have occurred including increased divers ity of the student body and the need to provide remedial education. This has led to numerous challenges to the community colleges that must be addressed in order to help student s achieve educational success. According to Bourdieus theory of cultural capital, families may or may not possess certain pieces of information, depending on their class status, and this capital may be an important component of individual success (Bourdieu, 1977, p. 488; Bourdi eu & Passeron, 1977, p. 32).

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46 CHAPTER 3 METHODOLOGY The purpose of this study was to determ ine if there is a significant relationship between socioeconomic status and community college student college grade point average and course completion ratio, using parent occupational status score as a proxy for socioeconomic status. The data for this study were obtained through th e Transfer and Retenti on of Urban Community College Students (TRUCCS) project in the Los Angeles Community College District (Hagedorn et al., 2001). Research Questions and Hypotheses 1. What is the relationship between socioec onom ic status and student college grade point average? 2. What is the relationship between socioeconom ic status and student course completion ratio? Research Design As a theoretical fram ework for this analysis, the study used the research of Lee and Bowen (2006), who applied Bourdieus theory of cultural capital for their study of parent involvement and student achievement. Lee and Bowens research served as a model to determine whether the level of student grade point average and course completion ratio (the dependent variables) differed between families at different socioecono mic status levels. The independent variables were demographics (gender, age, and ethnicity ), high school grade point average, psychosocial factors (determination, academic in tegration and aspiration to transf er to a four-year institution), and socioeconomic status with parent occupatio nal status score as a proxy for socioeconomic status. The occupational status scores were based and coded on that the scheme developed and described by Terrie and Nam (1994) using the oc cupations students provided on the research instrument.

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47 Research Population Students from the nine cam puses in the Los Angeles Community College District enrolled during the Spring 2001 semester were the subject s of this study. A total of 4968 students answered the survey instrument consisting of 47 questions. The sample was 37.9% male, 58.9% female with 3.2% not indicating gender. Als o, 2.9% of the students were 18 years old or younger, 53.4% were 19-24 years old, 30.1% were 25-39 years old, 9.9% were 40-54 years old, and 1.7% were 55 years old or ol der. Further, 83.8% of the sample belonged to ethnic minorities, with Hispanics at 51.8%, African-A merican/Blacks at 15.6% and Asian/Pacific Islanders at 14.0% making up the largest minority groups. Caucasian/White individuals made up 14.6% of the sample population. Non-native English speakers comprised 53.5% of the sample and 43.8% were the primary wage earners in thei r families. Working students included 32.2% working full time and 36.5% working part time. In addition, 31.3% of the sample had their children living with them. Family education le vels included 17.5% of students with fathers and 20.4% with mothers who had a si xth grade education or less. Data Collection The data for this study were originally colle cted through the Transfer and Retention of Urban Community College Students (TRUCCS) pr oject, as reported in Hagedorns unpublished report from the University of Southern Calif ornia titled, TRUCCS sampling plan: Representing the Los Angeles Community College District. Hagedorns sampling plan went further to describe how information regarding demographics and transfer aspirations were obtained from answers to survey questions. Data regarding th e courses students took, co urse completion ratios, and grade point averages were calculated from student transcript reco rds, obtained for those students signing the appropriate records release. The TRUCCS researchers originally examined information in the Right-to-Know database to determine student transfer information and course-

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48 taking patterns from the Los Angeles Community College District, and to help determine the strategy for administering the que stionnaire. Since no meaningful course-taking patterns could be determined from that inve stigation, the researchers examin ed college catalogs and class schedules for the nine Los Angeles Community College District campuses. The researchers found three levels of English courses from whic h they determined the sample population: nondistrict credit courses, first-year transfer course s, and second year transf er courses. In this manner the researchers were able to sample i ndividuals across a broad spectrum of interests, while not including those individuals attending community colle ge for casual or entertainment purposes. Hagedorns questionnaire was distributed to students in English courses throughout the Los Angeles Community College District in Spri ng 2001 in proportion to enrollments at each of the nine campuses. In addition, th e researchers looked at the three levels of English courses and included that proportion in their sampling plan. English-asa-second language courses were also included, but only those taught at advanced levels, to ensure that students could understand the questionnaire. Some other English courses, such as those for coope rative education, were eliminated from the sample population as they did not fit the purposes of the study. Data Analysis In the present study, factor analysis was used to reduce data from multiple items to a smaller number of more reliable scales, to provide a more valid measure of latent constructs. Factor analysis looks for significan t patterns between different variable s. That is, factor analysis can group together multiple related variables, thereby reducing th e variables into smaller groups called factors. Even though some variables may not be directly observed, factor analysis can detect similar characteristics of a group of variables and establis h a new factor (Darlington, n.d.). The new independent variables defi ned through factor analysis can th en be used in the blocks of

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49 variables when forward block entry regression was used in the analysis. A confirmatory reliability analysis was performed on the scales using Cronbachs Alpha of 0.70 or greater as the minimum. Scales measuring student determin ation, academic integration, and aspiration to transfer had the appropriate reli ability Cronbachs Alpha coefficien ts and were therefore retained in the model. Forward block entry regression was used in the analysis of the independent variables. The Statistical Package for the Social Sciences was used to calculate course completion ratios, as well as to select the higher occ upational status score of either the father or mother of each student to serve as a proxy for student socioeconomic status. Fo r forward block entry regression college grade point average was used as the de pendent variable, and th e independent variables were ethnic origin, gender, age, high school grade point average, as piration to transfer to a fouryear institution, determination, academic integr ation, English language proficiency, job-related responsibilities, and socioeconomic status. The forward block entry regression was then repeated using course completion ratio as the dependent variable with the same independent variables of ethnic origin gender, age, high school grade point av erage, aspiration to transfer to a four-year institution, determination, academic in tegration, English language proficiency, jobrelated responsibilities, and soci oeconomic status (see Table 3-1). Multiple levels of blocking were used to control for covariance. The fi rst level included the independent variables of demographics. The second level used high school grade point average as a block. The third level had psychosocial variables as the block. The fourth level used socioeconomic status as the block. By using forward block entry regression, changes in R2 can be detected after each factor is added to determine its significance. The amount of R2 change between the different models can

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50 then be studied to see what factors have the gr eatest influence and associated significance level. By entering the variables in bl ocks, comparisons can be made between models. Thus the significance of combinations of different variables can be seen to provide a breakdown of the outcomes of the statistical tests (Stockburger, n.d.). Instrumentation The research instrum ent used in this study was a 47-item questionnaire given to 5000 registered students during the Spring 2001 seme ster in the Los Angeles Community College District. This instrument was developed and written by the TRUCCS research team and underwent a pilot study prior to being administer ed to the students (see Appendix A for the TRUCCS survey instrument). The questionnaire included items regarding demographics, socioeconomic status, college expe ctations, and barriers the students saw to achieving success. The questionnaire was designed for a diverse student population, with many individuals not having English as their first language (Hagedor n, Chi, Cepeda & McLain, 2007) In Summer 2001 the transcript data was collected for thos e students who signed th e appropriate consent form. The questionnaire was poste d on the internet so that students from the original sample could update their information and provide feedb ack on their experiences from the past year (Hagedorn, et al., 2001). To measure ethnicity, the que stionnaire was designed to have students enter as many ethnic groups as best described them. There we re 22 questions that a llowed students to enter their ethnicity. Of those, the values fo r African-American/Black and Caucasian/White ethnicities were assigned the dichotomous values of either if not marked and if marked. In order to measure the ethnicity variable, the remaining ethnici ties used in this study were recoded into commonly-accepted ethnically-relat ed groups. Table 3-2 is a summary of the

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51 groups of recoded ethnicities. The Caucasian/ White ethnic group was used as the comparison group in this dissertation. Validity and Reliability Intern al validity is been de fined as the degree to which the independent variables being manipulated actually have an effect on the dependent variable (Shavelson, 1996, p. 20), while reliability is defined as the extent to which a measure is consistent or dependable and free from random error (Shavelson, 1996, p. 473). The ques tionnaire was pre-tested for reliability and then used by the TRUCCS team, whose original task was to study retention and transfer behavior of students in the Los Angeles Community Co llege District. Sin ce that study looked at explaining retention and transfer behavior, neither random samp ling nor random assignment was performed, but rather the sample was obtained in a quasi-experim ental manner. Further, the TRUCCS team concentrated on the internal vali dity of the research design. The sampling method ensured variation of the original independ ent variables and enabled the researchers to be confident of having internal validity when comp aring sub-groups. Further, since TRUCCS was focusing on transfer and reten tion factors, the sampling method ensured that younger students were over-sampled and older students were unde r-sampled (Hagedorn, et al., 2006). This dissertation examined correlations, and both stan dardized and unstandardized regression weights were analyzed to specifically address the research questions. Summary Parental influences have been the subject of num erous studies for many years. The variable of parent occ upational status as a measure of socioeconomic status has not been as extensively researched and was the variable of interest in this re search study. The influence of parental socioeconomic status was measured for college grade point average and course completion ratio.

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52 Table 3-1. List of Variables Independent Variables Dependent Variables Gender College Grade Point Average Age Course Completion Ratio Ethnicity African American/Black Caucasian/White Hispanic Asian/Pacific Islander American Indian/Native Alaskan High School GPA Psychosocial Factors Determination Academic Integration Aspire to Transfer Socioeconomic Status

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53 Table 3-2. Recoded ethnic groups American Indian/Alaskan Native Recoded Ethnic Group Alaskan Native American Indian Asian Recoded Ethnic Group Chinese Filipino Japanese Korean Thai Laotian Cambodian Vietnamese South Asian (Indian Subcontinent) Pacific Islander/Samoan, Hawaiian, or Guamanian Other Pacific Islander Hispanic Recoded Ethnic Group Mexican Mexican-American/Chicano South American Central American Other Latino/Hispanic

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54 CHAPTER 4 ANALYSIS AND PRESENTATION OF THE DATA The purpose of this study was to determ ine if there is a significant relationship between parent socioeconomic status and community co llege student grade point average and course completion ratio, using parent occ upational status score as a proxy. The data for this study were obtained through the Transfer and Retention of Urban Community College Students (TRUCCS) project in the Los Angeles Community College Di strict (Hagedorn, et al ., 2001). The research questions were: 1. What is the relationship between socioec onomic status and student college grade point average? 2. What is the relationship between socioeconom ic status and student course completion ratio? Population Profile Table 4-1 provides a summary of de mographic variables describing th e gender distribution of students participating in the TRUCCS project Females comprised 58. 9% of the sample. Table 4-2 provides data on the age distributi on, determined by the age of the student on December 31 of the year the surv ey was completed. The largest category was the 21-24 year old age group, comprised of 1297 individuals (26.1%). Table 4-3 provides a summary of demographic va riables that describe the ethnic origin of students participating in the TRUCCS project. The largest ethnic category represented was the Hispanic group comprised of 2571 individuals (51.8%). Overall, 4159 individuals (83.8%) who took the survey identified themselves as a member of one or more minority group. Table 4-4 provides a summary of the high schoo l grade point average variable. The mean was approximately halfway between a Ba nd B grade point average (mean = 5.47, s.d. 1.85, with a value of 5 being equal to a BGPA and a value of 6 equal to a B).

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55 Validity and Reliability Table 4-5 provides the reliability of the new psychosocial factors of determ ination, academic integration, and aspirati on to transfer. The Alpha values of 0.7807 for determination, 0.8005 for academic integration, and 0.7244 for aspirati on to transfer are high coefficients of reliability and demonstrate that the items combined together are related and that the new latent construct of determination has high internal consistency. For both the model with grade point average as the dependent variable and the model with course completion ratio as the dependent va riable, one-tailed Pearson Product Moment Correlation Coefficients were examined to identify zero-order correlations that were statistically significant. Table 4-6 shows the zero order correlations (Pearson r ) for all variables used in the model with grade point average as the dependent variable. As the table shows, most of the relationships between variables were significant. As expected the highest relationship was between high school grade point average and college grade point average ( r = 0.280). The next highest relationship was between student age and college grade point average ( r = 0.182). The variable of determination was also significant (r = 0.178). Noteworthy is that the ethnic background of being Hispanic had a significant negative relations hip with college grade point average (r = -0.135). Other relationships meani ngful to this study included a positive relationship between the ab ility to speak English ( r = 0.267), and a large negative relationship between the Hispanic ethnic origin and socioeconomic status ( r = -0.341). As expected there were large positive relationships between aspiration to transfer and determination ( r = 0.223) and between academic integra tion and determination ( r =0.221). Table 4-7 shows the zero-order correlations for all variables used in the model with course completion ratio as the dependent variable. As the table shows, most correlations were significant. The highest correla tion was, again as expected, be tween high school grade point

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56 average and success rate ( r = 0.260). Determination also had a significant relationship with success rate ( r = 0.159) and ability to speak English ( r = 0.113). The African American/Black ethnic origin variable had a positive re lationship with academic integration ( r = 0.123) and a negative relationship with the ability to speak English ( r = -0.116). On the other hand, the Asian/Pacific Islander ethnic origin variable had a positive relationship with the ability to speak English ( r = 0.267). There was also a large, si gnificant positive relationship between socioeconomic status and the African American/Black ( r = 0.129) and the Asian/Pacific Islander ( r = 0.119) ethnic origins, and a large, significan t negative relationship between socioeconomic status and the Hispanic ethnic origin ( r = -0.340). Again, a positive relationship was found between aspiration to tran sfer and determination (r = 0.224), and between academic integration and determination ( r = 0.222). Table 4-8 presents a summary of the distributi on of parent occupationa l status scores. As previously noted, the parent occupational status score served as a proxy fo r socioeconomic status in this study. The scores ranged from a low of 0.70 to a high of 99.80 (mean = 53.08, s.d. = 26.52, N = 4122). The group with occupational scores with the highest fr equency had scores ranging from 60.1-70.0 (N = 863, 20.94% of samp le population). Overall, 44.88% of occupational status scores we re 50.0 or below and 55.12% of scores were 50.1 or higher. Table 4-9 presents a summary of the distributi on of student course co mpletion ratios. The course completion ratio was the ratio of the number of courses for which a student received a passing grade divided by the number of courses fo r which the student registered. On average, students completed 68.7% (s.d. = 0.24%, N = 4654) of the courses in which they enrolled. Regression Analyses Multip le regression is a statistical proce ss to examine the relationship between one dependent variable and two or more indepe ndent variables (Shavelson, 1996, p. 528). The

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57 purpose of this analysis was to ascertain the infl uence of each of the independent variables on the dependent variables. A forward block entry regression analysis was performed first with grade point average as the dependent variable and demographics, high school grade point average, psychosocial factors and socioecono mic status as the independent variables, and then with course completion ratio as the dependent variable and demographics, high school grade point average, psychosocial factors and socioeconomic st atus as the independent variables. Grade Point Average Table 4-10 presents the results of the forw ard block entry regression analysis for the dependent variable of grade point av erage and the demographic, high school grade point average, psychosocial, and socioeconomic status independent variables. Once the first block (demographic) was entered, only 8.0% of the va riance was explained. Adding the second block (high school grade point average) increased the R square and e xplained 13.9% of the variance, and explained the most variance of all the blocks. The addition of the third block (psychosocial) explained a total of 15.9% of the variance. The addition of the fourth block of socioeconomic status was not statistically significant (F = 56.74, df =2, p = 0.390) which means that the model does not change with a change in socioeconomic status. Table 4-11 presents the unstandardized regression coefficient (b), the standardized regression coefficient (B), and R2 for the dependent variable grade point average and the demographic, high school, grade point averag e, psychosocial, and socioeconomic status independent variables. The R2 value of 0.159 was st atistically significan t, with F(12,3601) = 56.74, and p = 0.00. All independent variables except being Asian/Pa cific Islander ethnic origin, academic integration and SES were found to contri bute significantly to the prediction of grade point average.

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58 The unstandardized b of -0.394 for the African-A merican/Black ethnic gr oup is the largest, but negative, relationship and indicates that being African-A merican/Black is a negative predictor of college grade point average. It ma y be interpreted that a ll things equal, being African-American/Black predicts grade point average to be 0.394 lower than the white control group. Being Hispanic has an unstandardized b of -0.282 indicating that bei ng Hispanic is also a negative predictor of college grade point average. Accordingly, being Hispanic predicts grade point average to be 0.282 lower than the white control group. However, determination (b = 0.149) and average grade in high school (b = 0.100) are positive predictors of college grade point average. For every increase of 1.0 unit in dete rmination, college grade point average increases by 0.149 grade points. For every increase of 1.0 unit in average grade in high school, college grade point average increases by 0.100 grade points. Thus, ethnicity is th e largest predictor of college grade point average in this study, with Afri can-American/Black and Hispanic students being negatively impacted as compared to th eir white counterparts. When comparing the standardized regression coefficients (B), av erage grade in high sc hool (B = 0.225) is the strongest and most important pred ictor of college grade point average when comparing variables in the same metric. This is followed in strength by the negative predictors of being African American/Black (B = -0.170) or Hispanic (B = -0.172) which are approximately 75 percent the strength of average grade in high school. Determination (B = 0.129) closely follows in prediction strength and is a positiv e predictor of college grade point average with a strength just over half that of average grade in high school. Socioeconomic status, with a significance level of 0.390, was found to not have a significant relati onship with college grade point average. Course Completion Ratio Table 4-12 presents the results of the forw ard block entry regression analysis for the dependent variable of course com pletion ratio and the demographic, high school grade point

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59 average, psychosocial and socioeconomic status independent variables. Once the first block (demographic) was entered, only 3.7% of the va riance was explained. Adding the second block (high school grade point average) increased the R square and explained 9.1% of the variance, and explained the most variance of all the blocks. The addition of the third block (psychosocial) explained 11.4% of the variance. The addition of the fourth block of socioeconomic status was not significant (F = 38.899, df = 12, 3260, p = 0.249) which means that the model does not change with a change in socioeconomi c status with all controls in place. Table 4-13 presents the unstandardized regression coefficient (b), the standardized regression coefficient (B), and R2 for the dependent variable stude nt course completion ratio and the demographic, high school grade point average, psychosocial, and socioeconomic independent variables. R2 = 0.114 was statistically signifi cant, F(12,3620) = 38.899, p = 0.00. All independent variables except Asian/Pacific Isla nder ethnic origin, aspiration to transfer, academic integration and SES were found to contri bute significantly to the prediction of course completion ratio. The unstandardized b of -0.089 for the African-A merican/Black ethnic gr oup is the largest, but negative, relationship and indicates that being African-A merican/Black is a negative predictor of course completion ratio. It may be interpreted that a ll things being equal, the course completion ratio decreases by 0.089 for African Americans in the sample. Being Hispanic has an unstandardized b of -0.058 indicating that being Hispanic is also a negative predictor of course completion ratio. Accordingly, the course completion ratio decreases by 0.058 for Hispanics in the sample. However, determina tion (b = 0.042), average grade in high school (b = 0.028), and understanding the English language (b = 0.023) are positive pred ictors of course completion ratio. For every in crease of 1.0 unit in determinat ion, course completion ratio

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60 increases by 0.042 points. For every increase of 1.0 unit in average grade in high school, course completion ratio increases by 0.028 points. For every increase of 1.0 unit in understanding the English language, course completion ratio incr eases by 0.023 points. Thus, ethnicity is the largest predictor of course completion ratio in this study, with African-American/Black and Hispanic students being negatively impacted as compared to their white counterparts. When comparing the standardized regression coefficien ts (B), average grade in high school (B = 0.211) is again the strongest and most important predictor of course co mpletion ratio. This is followed in strength by the negative predictor of being African American/Black (B = -0.130) which is 62 percent the strength of average grade in high scho ol. The positive predicto r of determination (B = 0.124) and the negative predictor of being Hispan ic (B = -0.120) follow in strength and have approximately 58 percent the strength of averag e grade in high school. Socioeconomic status, with a significance leve l of 0.390, was found to not have a si gnificant relationship with success rate. These observations showed that socioecono mic status had no direct effect on student grade point average or cour se completion ratio. Summary A total of 4968 questionnaires were complete d by students in the Los Angeles Community College District during the Sp ring 2001 semester. The questionnaire was administered by the research team comprising the Transfer and Retention of Urban Community College Students. The analyses showed no significant relationship be tween socioeconomic status and student grade point average or cour se completion ratio.

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61 Table 4-1. Students in the LA CCD. Distribution by gender. Gender N % Male 1884 37.9 Female 2927 58.9 No Response 157 3.2 Total 4968 100.0 Table 4-2. Students in the LACCD. Distribution by age. Age N % 16 or less 31 0.6 17 27 0.5 18 87 1.8 19 678 13.6 20 683 13.7 21-24 1297 26.1 25-29 707 14.2 30-39 788 15.9 40-54 491 9.9 55 or more 82 1.7 No response 97 2.0 Total 4968 100.0

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62 Table 4-3. Students in the LACC D. Distribution by ethnic origin. Ethnic Origin N % Asian/Pacific Islander 695 14.0 African-American/Black 776 15.6 Hispanic 2571 51.8 Alaskan Native/American Indian 117 2.4 Caucasian/White 727 14.6 All other/no response 82 1.6 Total 4968 100.0 Table 4-4. Students in the LACC D. Distribution by high school. High School GPA N % 1 D or lower 66 1.3 2 C137 2.8 3 C 575 11.6 4 C+ 779 15.7 5 B836 16.8 6 B 890 17.9 7 B+ 839 16.9 8 A425 8.6 9 A 250 5.0 Missing or no response 97 3.4 Total 4968 100.0 Mean = 5.47, S.D. = 1.85

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63 Table 4-5. Factor analysis: determination, academic integration, aspire to transfer. Item Description Determination (Items in question 37 of TRUCCS) For the following items, please indicate the extent to which you agree or disagree with the following statements. 1 I am very determined to reach my goals 2 It is important for me to finish the courses in my program of studies 3 I feel most satisfied when I work hard to achieve something 4 I expect to do well and earn good grades in college 5 I keep trying even when I am frustrated by a task Scale Scale Corrected Mean Variance ItemAlpha if Item if Item Total if Item Deleted Deleted Correlation Deleted 1 24.7628 9.0976 0.5886 0.7287 2 24.7344 9.4655 0.5840 0.7318 3 24.8227 9.2732 0.5568 0.7394 4 24.8455 9.6165 0.5597 0.7395 5 25.1818 8.8409 0.5064 0.7619 N of cases = 4642, N of items = 5, Alpha = 0.7807

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64 Table 4-5 (continued) Item Description Academic Integration (Items in questions 13 and 14 in TRUCCS) For this course only, approximately how many times in the past 7 days, did you: 1 Work in small groups during class time 2 Telephone or email another student to ask a question about your studies 3 Ask the instructor questions 4 Speak up during class discussion Approximately how many times in the past 7 days, did you: 5 Talk with an instructor before or after class 6 Talk with an instructor during office hours 7 Help another student understand homework 8 Study in small groups outside of class 9 Speak with an academic counselor Scale Corrected Mean Variance ItemAlpha if Item if Item Total if Item 1 16.4145 45.8573 0.4303 0.7887 2 16.7679 47.9065 0.4123 0.7906 3 15.7327 40.8903 0.6190 0.7621 4 15.6562 41.6669 0.4758 0.7874 5 15.8749 42.8940 0.5642 0.7708 6 16.8454 47.3423 0.4918 0.7831 7 15.8707 42.6000 0.5371 0.7747 8 16.6559 45.6315 0.4941 0.7809 9 16.8467 47.8157 0.4467 0.7874 N of cases = 4572, N of items = 9, Alpha = 0.8005

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65 Table 4-5 (continued) Item Description Aspire to Transf er (Items in question 10 in TRUCCS) As things stand today, do you think you will ? 1 Get a bachelors degree 2 Transfer to a 4-year college/university Scale Scale Corrected Mean Variance ItemAlpha if Item if Item Total if Item Item Deleted Deleted Correlation Deleted 1 4.1357 1.3690 0.5692 2 4.1321 1.1930 0.5692 N of cases = 4619, N of items = 2, Alpha = 0.7244

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66Table 4-6. Zero order correlations for the model grade point average. 1 2 3 4 5 6 7 8 9 10 11 12 13 1 GPA 1.000 2 Afr/Ameri -0.070* 1.000 3 Asian/Pac 0.087* -0.151* 1.000 4 Hispanic -0.135* -0.411* -0.379* 1.000 5 Gender 0.099* 0.068* -0.033* 0.006 1.000 6 Age 0.182* 0.137* -0.014 -0.114* 0.079* 1.000 7 HS Grade 0.280* -0.041* 0.109 -0.080* 0.155* 0.021 1.000 8 Aspire 0.041* 0.040* 0.015* -0.007 -0.027* -0.160* 0.084* 1.000 9 Determ 0.178* 0.087* -0.079* 0.015 0.105* 0.155* 0.151* 0.223* 1.000 10 Integrat 0.046* 0.123* -0.033* -0.068* 0.000 0.110* 0.106* 0.079* 0.221* 1.000 11 Eng Lang 0.092* -0.115* 0.267* -0.075* 0.015 0.092* 0.118* -0.079* -0.087* 0.051* 1.000 12 Job Resp -0.030* -0.049* -0.011* 0.065* -0.050* 0.072* -0.014 0.026 -0.037* 0.013 0.106* 1.000 13 SES 0.060* 0.129* 0.121* -0.341 -0.026 0.011 0.056* 0.032* -0.005 0.005 -0.031* -0.015 1.000 p<0.05

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67Table 4-7. Zero order correlati ons for the model success rate 1 2 3 4 5 6 7 8 9 10 11 12 13 1 Success Ratio 1.000 2 Afr/Ameri -0.073* 1.000 3 Asian/Pac 0.075* -0.151* 1.000 4 Hispanic -0.083* -0.409* -0.379* 1.000 5 Gender 0.079* 0.069* -0.032* 0.006 1.000 6 Age 0.093* 0.136* -0.013 -0.112* 0.079* 1.000 7 HS Grade 0.260* -0.041* 0.106* -0.080* 0.154* 0.021 1.000 8 Aspire 0.049* 0.040* 0.013 -0.004 -0.027 -0.158* 0.084* 1.000 9 Determ 0.159* 0.088* -0.075* 0.016 0.109* 0.153* 0.150* 0.224* 1.000 10 Integrat 0.047* 0.123* -0.034* -0.067* 0.000 0.109* 0.107* 0.082* 0.222* 1.000 11 Eng Lang 0.113* -0.116* 0.267* -0.075* 0.013 0.093* 0.117* -0.079* -0.088* 0.050* 1.000 12 Job Resp -0.047* -0.049* 0.014 0.065* -0.053* 0.070* -0.015 0.027 -0.042* 0.014 0.105* 1.000 13 SES 0.012 0.129* 0.119* -0.340* -0.028* 0.011 0.056* 0.032* -0.008 0.005 -0.029* -0.013 1.000 p<0.05

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68 Table 4-8. Distribution of parent occupational status score (socioeconomic status) Occupational Status Score N % 00.0-10.0 297 7.21 10.1-20.0 240 5.82 20.1-30.0 458 11.11 30.1-40.0 446 10.82 40.1-50.0 409 9.92 50.1-60.0 186 4.51 60.1-70.0 863 20.94 70.1-80.0 377 9.15 80.1-90.0 527 12.79 90.1-100.0 319 7.74 Total 100 100.00 Table 4-9. Distribution of st udent course completion ratio Characteristic Low High M SD Course Completion Ratio 0.0 1.0 0.6869 0.24 N of cases = 4654

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69 Table 4-10. Model summary grade point average R Square Model R R Square Change F Change dfl Sig. F Change 1 0.283 0.080 0.080 62.663 5 0.000 2 0.373 0.139 0.060 249.431 1 0.000 3 0.399 0.159 0.019 16.641 5 0.000 4 0.399 0.159 0.000 0.738 1 0.390 ANOVA Grade Point Average Model Sum of Squares df Mean Square F Sig. 1 Regression 192.247 5 36.449 62.663 0.000 Residual 2213.827 3608 0.614 Total 2406.074 3613 2 Regression 335.436 6 55.906 97.387 0.000 Residual 2070.638 3607 0.574 Total 2406.074 3613 3 Regression 382.187 11 34.744 61.836 0.000 Residual 2023.887 3602 0.562 Total 2406.074 3613 4 Regression 382.602 12 31.884 56.740 0.000 Residual 2023.472 3601 0.562 Total 2406.074 3613 Table 4-11. Regression analysis su mmary for grade point average Variable b SEB B Sig. African-American/Black -0.394 0.042 -0.170 0.000 Block 1 Asian/Pacific Islander -0.071 0.043 -0.030 0.101 Hispanic -0.282 0.033 -0.172 0.000 Gender -0.080 0.026 0.048 0.002 Age 0.081 0.008 0.162 0.000 Average Grade in High School 0.100 0.007 0.225 0.000 Block 2 Aspiration to Transfer 0.026 0.013 0.032 0.050 Block 3 Determination 0.149 0.019 0.129 0.000 Academic Integration -0.026 0.016 -0.021 0.197 Understanding English Language 0.043 0.016 0.045 0.006 Job-Related Responsibilities -0.023 0.010 -0.034 0.027 SES 0.043 0.001 0.014 0.390 Block 4 R2 = 0.159

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70 Table 4-12. Model summary course completion ratio R Square Model R R Square Change F Change dfl Sig. F Change 1 0.192 0.037 0.037 27.663 5 0.000 2 0.302 0.091 0.054 216.350 1 0.000 3 0.337 0.114 0.023 18.733 5 0.000 4 0.338 0.114 0.000 1.327 1 0.249 ANOVA Course Co mpletion Ratio Model Sum of Squares df Mean Square F Sig. 1 Regression 7.710 5 1.542 27.663 0.000 Residual 202.176 3627 0.056 Total 209.886 3632 2 Regression 19.094 6 3.182 60.479 0.000 Residual 190.792 3626 0.053 Total 209.886 3632 3 Regression 23.905 1 2.173 42.310 0.000 Residual 185.981 3621 0.051 Total 209.886 3632 4 Regression 23.973 12 1.998 38.899 0.000 Residual 185.913 3620 0.051 Total 209.886 3632 Table 4-13. Regression analysis su mmary for course completion ratio Variable b SEB B Sig. African-American/Black -0.089 0.013 -0.130 0.000 Block 1 Asian/Pacific Islander -0.160 0.013 -0.022 0.229 Hispanic -0.058 0.010 -0.120 0.000 Gender 0.016 0.008 0.032 0.044 Age 0.011 0.002 0.073 0.000 Average Grade in High School 0.028 0.002 0.211 0.000 Block 2 Aspiration to Transfer 0.007 0.004 0.030 0.066 Block 3 Determination 0.042 0.006 0.124 0.000 Academic Integration -0.003 0.005 -0.009 0.564 Understanding English Language 0.023 0.005 0.082 0.000 Job-Related Responsibilities -0.010 0.003 -0.051 0.001 SES 0.043 0.001 0.014 0.249 Block 4 R2 = 0.114 (N = 3633, p = 0.000).

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71 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS The purpose of this study was to determ ine if there is a significant relationship between parent socioeconomic status and community co llege student college grade point average and course completion ratio. The data for this study was obtained from re sults of a 47-question survey administered through the Transfer and Retention of Urban Community College Students (TRUCCS) project in the Los Angeles Community College District (Hagedorn, et al., 2001). The present research sought to answer two questions not addr essed by Hagedorns statistical analysis. 1. What is the relationship between socioec onomic status and student college grade point average? 2. What is the relationship between socioeconom ic status and student course completion ratio? To achieve this goal, four independent variables were used: demographics (gender, age, and ethnicity), average grade in high school, th e psychosocial concerns (determination, academic integration and desire to transfer to a four-year institution), and parent occupational status score (higher of the two parents), which served as a proxy for socioeconomic status. The statistical analyses were conducted using SPSS 10.0. Factor anal ysis was used to dete ct latent variables that may be present (the psychosocial variable s). Forward block entry regression was conducted to determine whether there was a significant re lationship between the de pendent and independent variables being studied. Previous research has shown that a large urban community college system has unique characteristics that have to be addressed when looking at levels and predictors of student success. Since community colleges are directly answerable to their local community, changes in the local political climate may alter directions that the community colleges may m ove towards. Changes

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72 in the community college focus need to consider student backgrounds, even though the considerations may conflict with th e local political climate. If B ourdieus cultural capital theory (Bourdieu & Passeron, 1977, p. 30) is correct, it is essentia l that todays students be successful in college to gain cultural capital for their children. The results of this research study showed th at socioeconomic status does not provide a significant influence on student succ ess. Rather, other factors have greater weight on the student outcomes measured. While previous studies (Berliner, 2006; Dougherty & Kienzl, 2006) among different populations have shown socioeconom ic status to be si gnificant, the unique characteristics of the population in this research study show no significant relationship, and thus need to be examined to determine what cause s the differences. Otherwise, policies may be implemented with an inadequate administrative understa nding of expectations of the outcomes, or with an inadequate staff. Conclusions and Implications This study exam ined several factors that may or may not influence student college grade point average and course completion ratio. Anal ysis of demographic information showed a high ethnic minority percentage (83.8%). Aside from et hnicity, the subjects in this study have a high level of homogeneity, in that most are first ge neration college students, live in a large urban environment, attend a large urban community co llege, and have lower socioeconomic status backgrounds. However, the population differs gr eatly from those of studies of four-year institutions, not only in ethnic combination, but also in age, socioeconomic background, and family responsibilities. Thus, po licies based on studies of four-yea r institutions do not directly apply to community colleges. Large urban comm unity college districts such as those in Los Angeles, Houston, Chicago, and Maricopa County, Arizona should be compared with each other, because issues in large urban community co llege systems have more similarities among

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73 themselves, such as student background and fina ncial resource management, than with four-year institutions. This study finds socioeconomic status not to be significant in determin ing student success, in contrast to previous research (Berlin er, 2006; Dougherty & Kien zl, 2006) showing a significant influence of socioeconomic status. Aspects that may he lp explain these results are the characteristics of the population being studied. The presence of a large minority representation in the Los Angeles Community College District popul ation is very different than found in most other studies. Analysis of the data reveal ed that understanding the E nglish language has a positive relationship with both college grad e point average and course comp letion ratio. Thus, the better the student is able to understa nd the English language, the higher the college grade point average and course completion rati o. However, this was not true fo r African-Americans and Hispanics, who often live in ghetto areas a nd are not exposed to standard English in the formative years. The ability to understand the English language is not necessarily a function of socioeconomic status but rather opportunities to interact in an En glish-speaking environm ent and the support to speak English. Programs for English as a second language may not be as effective as they need to be, or perhaps individuals access to English as a second language program may be problematic. Individuals who work may not have the time to attend these programs at the times offered. Job-related responsibilities have a significant negative relationship with both college grade point average and course completi on ratio. Individuals in the study sample have a lot of things going on in their lives that may be overriding the effect of college preparation and remediation.

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74 Working while in college, whether part-time or full-time, is difficult for even the best of students. The impediments are even higher for t hose who already start out with disadvantages. Impact on College Grade Point Average Controlling for the independent variables used in the ana lysis, no si gnificant relationship between parent occupational status, a proxy for so cioeconomic status, and student college grade point average was found in the analysis. In th e analysis of the inde pendent variables of demographics (gender, age, and ethnicity), high school grade point averag e, psychosocial factors (determination, academic integration and aspiration to transfer to a four-year institution), and parent occupational status score as a proxy for socioeconomic status, the greatest positive variable was determination (Beta = 0.149, standa rdized beta = 0.129), followed by average grade in high school (Beta = 0.100, sta ndardized beta = 0.225), age (Beta = 0.081, standardized beta = 0.162), and understanding the English language (Beta = 0.043, standardized beta = 0.045). Student aspiration to transfer also had an imp act (Beta = 0.026, standardiz ed beta = 0.032). The relationship between high school grade point average and college grade point average (with high school grade point average havi ng the strongest standardized beta of 0.225) is logical and conforms with findings in other research studies (Zwick & Sklar, 2005). This shows that one of the best predictors of college grade point averag e is high school grade poin t average, which is an obvious result and supported in the literature. Although this study and other research have f ound that a high level of high school grade point average is an indicator of college preparation, the competency of the high schools must also be considered. If the sc hool is of poor quality but the stud ent grade point average is high, then the student may still be poorly prepared for college. College administrators must be aware of the quality of the high schools attended wh en evaluating students for placement in their institutions. While traditiona l four-year institutions weigh the total background of students

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75 before accepting them, including personal interviews essays, and extracurricular activities, the community college mission of open enrollment allows a channel into the higher education system for students who may not have come from a high quality high school. This issue may be further exasperated when primary and secondary schools decide to allow students to progress to the next grade level rather than holdi ng them back for poor performance. This study also showed that older students tend to perform better than younger students, possibly because older students have learned to better mana ge life activities, including adjustments to attending college along with wo rking or raising children. The influence of student determination was also reasonable when college grade point average was considered, as students who are determined to succeed will work harder to meet their educational goals. Determination can be the effect of family culture and nurturing, as well as educational background of the family. The high negative relationship between college grade point average and the ethnicities of African-American/Black (Beta = -0.394, standardized beta = 0.170) and Hispanics (Beta = 0.082, standardized beta = -0.172) at first glance indicates that ethnicity is an important consideration. However, the la rge percentage of Blacks and Hi spanics in the sample may be masking other issues such as socioeconomic stat us. Further, many of the students may not have had access to resources that would better prepare them for college. As described in Chapter 2, much research ha s been performed in the area of helping minority students increase their achievement, with some successes. However, students in certain ethnic groups still continue to be negatively impacted. This findi ng demonstrates that even with programs in place to help minorities succeed in co llege, problems still exist. One of the major tasks of community colleges is providing re medial courses (Hadden, 2000; Perin, 2006). But

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76 with the poor college success rates of ethnic minor ities, community colleges need to reevaluate their remediation programs. Most of the progr ams focus on fundamental skills such as reading and math. However, in this authors opinion, teaching students matter-of-fact self disciplines, such as class and daily studying, should be reinfor ced consistently in all remediation programs. This would also go far in increasing the cultural capital that these students will pass on. As mentioned above, adequate preparation for colle ge also depends on the high school. College course work is very demanding, and students that are already poorly prepared will have an even more difficult time adjusting to college life. This is consistent with Bourdieus theory of cultural capital. Even with a portion of the study popul ation having high socioeconomic status, the cultural capital of the family may still be low depending upon how long the family has been living in the area and where pare ntal education was obtained. Impact on Course Completion Ratio (Success Rate) Controlling for the independent variables used in the ana lysis, no si gnificant relationship between parent socioeconomic status and student course completion ratio was found in the analysis. Rather, other factors were found to be significant in predicti ng the course completion ratio. In the analysis of the independent variable s of demographics (gender, age, and ethnicity), high school grade point average, psychosocial f actors (determination, acad emic integration and aspiration to transfer to a fou r-year institution), and parent occ upational status score as a proxy for socioeconomic status, the greatest positive variable was again determination (Beta = 0.042, standardized beta = 0.124), followed by average grade in high school (Beta = 0.028, standardized beta = 0.211), and understanding the English langua ge (Beta = 0.023, standardized beta = 0.082). As demonstrated with college grade point av erage, high school grad e point average is a significant predictor of course completion ratio, demonstrated by having the strongest standardized beta of 0.211. This is consistent with numerous other research studies. The

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77 students with high high school grade point averages were either bett er prepared or overall better students, as is reflected in the higher predicted course co mpletion ratio in this study. Interestingly, aspiration to transf er did not have as much influence on course completion ratio as it did on college grade point average. Further, students planning on transferring to a four-year institution may have better precollege preparation and determin ation leading to better college success. The significant positive factor of ability to speak English indicates that students with better English-speaking ability wi ll perform better and thus be more successful in completing courses. As discussed above, th e data indicate that the Los Angeles Community College District needs to address English-speaking ability and work to improve student use of the English language. Age also had a significant relationship (Bet a = 0.011, standardized beta = 0.073) with course completion ratio. As discussed above, this may be a function of the maturity of these students and their ability to focus and make appropriate college-related decisions. Older students may also be better able to understand and inte rpret course parameters, keep up better with assignment due dates, and otherwise be more awar e of requirements. They may also be more adjusted to their lifestyles and the issues they face, and thus may be be tter equipped to handle the college experience. This would be especi ally true for students with children. Discussion The results found in this study, while showing no significant relationship of socioeconom ic status to the dependent variab les, are enlightening. Student s are born into a particular socioeconomic status, and there is little one can do to change it while they are growing up. If socioeconomic status were found to be significant in this study th en that would mean that not much could be done to improve student succe ss. Beyond socioeconomic status, students have

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78 other characteristics that may be able to be changed and ma y be factors not looked at in other research studies. The good news is that there are personal and learned factors than can better facilitate college outcomes. In this authors opinion, it is these additional factor s that community colleges need to look at when examining how to improve student success. It is important for community colleges to be completely aware of the cultural background and traits of students and to try to account for these in the educational experience. Policy makers and researchers need to look at the characteristics of students in the community college, looking further than obscure reasons that seem like easy answers to complex situations. Confounding the data is the change through th e years in overall national student population versus the unique characteristics of the group in this sample in the Los Angeles Community College District. Today, with the programs av ailable and financial aid offerings, more students are going to college regardless of socioeconomic st atus. Students from affluent families that may have the resources to attend four year inst itutions also opt on occasion to attend community colleges at times when a required or desired class may not be available. These students may also temporarily attend the community college to better prepare for a class they may take in the future at the four year institution. Those students that float in and ou t of the community college system make data from studies performed at four-year institutions even less applicable. Of particular note in this study was the high percentage of minority students. This is especially important since the ethnic make up and backgrounds of individuals in this sample differ from samples made in traditional four-yea r institutions. As previously shown, even though there are some issues with the findings, ethnicity does play a role in student success. The analysis of the data indicates this for Hispanic and Black students but not for Asian students.

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79 This may be accounted for by the differences between Asian family units and other ethnic minorities (Zhou & Kim, 2006). The Asian family structure promotes learning and individual achievement. The family members are driven to succeed and exhibit high levels of selfdiscipline. Further, the culture of poverty theory postulated by Oscar Lewis (1966, p. xlv), could explain how individuals who are pr one to poverty develop a set of values that keeps them from being successful. Lewis (1966) theory showed that individuals who lived in poverty until the age of six or seven were not ab le to get out of the mindset of poverty and were not mentally prepared to take the benefits and opportunities that th ey would come across during their lives. The disadvantages of one generation tended to transf er to the next generati on. This is consistent with Bourdieus theory of cultu ral capital whereby th ese individuals born into poverty did not have the cultural capital they need ed in order to improve their lives. The negative relationship found between the ethn icities of Black and Hispanic students and college grade point average and success rate de monstrates the need for finding new ways to address the problems that these groups face in being successful in colle ge. Educators should possibly look at what is being taught prior to entering college in order to prepare these students for the college experience. Access to quality prog rams is once again an issue. With the growing numbers of individuals passing through the comm unity college system, remedial programs are being required to expand, perh aps beyond reasonable limits and the expectations of available staff and financial resources. The population in this study had a very high ethnic minority make up, and the associated negative success rate is something the Los Angeles Community College District needs to address further. Limitations of the Study As stated in Chapter 1, f ollowing we re limitations of this study:

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80 1. This study was limited to a sample of st udents who were enrolled in the Los Angeles Community College District during the Spring 2001 semester. 2. This study was limited to individuals who agreed to complete the questionnaire and provided permission for investigators to obtain their transcript information. 3. The validity of the study was limited to the reliability of the re search instrument used. These limitations may prohibit the generalizati on of results to other community college districts, but could be useful when studying community college systems in similar urban environments. The suggestions for further resear ch take these limitations into consideration in order to find ways to generali ze research data between differe nt institutions and students. Suggestions for Further Research This study was lim ited to multiple campuses of one large, urban community college system. The backgrounds of the individuals were diverse, although with an exceptionally high Hispanic ethnic representation. Li mited research exists that is co mparable to this samples large urban setting. A survey similar to that in this study could be developed a nd administered in other urban community colleges to test for similarities. Of particular interest would be looking at the ethnicity and any other characteri stics of the students in the different community colleges in order to determine whether or not one could comp are or generalize the results to other areas. Also, it would be interesting to st udy the role of socioeconomic stat us in other urban settings to determine if the results of this study demonstrate a trend. Lewis study of poverty showed that those in poor communities did not plan for the future or had tendencies towards delayed gratifica tion, but instead they had a strong present-time orientation (Harris, 1997, p. 310). This could be studied further to find out if certain students possess these characteristics. A nd if they do have these characteristics, what can be done to provide them with the tools, inte rest or ability to su cceed in college? Are some of these tools

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81 already available but not being used effectively? The culture of poverty extends through the generations and ways to break th at cycle need to be continuall y investigated. Knowledge of what motivates the students to work hard or wh at keeps them from achieving would be valuable for community college administrators. Additionally, language should be studied further to determine if improvements of students English proficiency could impact their success. Specifically, a study could be done to develop better programs to assist students with English as a second language to develop skills that will help them improve their success rate. Programs of English as a second language are popular but with the difficulties that non-nati ve English speakers have in colle ge, more work can be done to have programs that really help the individuals ra ther than fulfilling a legal mandate to simply provide assistance with learning the English language. As shown in this study, the grade point averages and course completion ratios of Bl acks are also negatively impacted by language, indicating a need for a greater emphasis on Englis h skills in general. Also, English language ability of both faculty and students should be studied. With non-native English speakers going into the teaching profession institutions need to make sure that th e future teachers are linguistically prepared. As previously discussed, despite high hi gh school grade point averages, high school preparation could be an issue in the researc h. The fitness of high schools from which the students graduate should be studied in order to find any weaknesses. Even though studies of high schools have been performed in the past, it would be worthwhile to analyze the relationship between high schools and the particular community colleges their graduates attend. The reasons behind the growth of remediation should be further researched to determine fixes that can be applied prior to reaching the college level. Rese arch needs to look at community colleges which

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82 have remedial programs in place but which still show poor student outcomes. This could be a wake-up call for community colleges, and resear chers need to look at what the community colleges are doing and how programs can be improved.

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83 APPENDIX A THE TRANSFER AND RETENTION OF URBAN COMMUNITY COLLEGE STUDENT (TRUCC S) QUESTIONNAIRE

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90 APPENDIX B SPSS PRINTOUT -FULL MODEL GRADE POINT AVERAGE Table B-1. Grade point average no tes Output Created 14-APR-2007 12:43:34 Comments Input Data C:\Documents and Set tings\ron\Desktop\FINAL DATASET.sav Filter Weight Split File N of Rows in Working Data File 4968 Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics are based on cases with no missing values for any variable used. Syntax REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT gpa /METHOD=ENTER q30_11 ASPACIS HISPANIC q28 q29 /METHOD=ENTER q24 /METHOD=ENTER ASPIRE DETERMIN INTEGRAT q16_7 q16_4 /METHOD=ENTER ses Resources Memory Required 11540 bytes Additional Memory Required for Residual Plots 0 bytes Elapsed Time 0:00:00.23

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91 Table B-2. Grade point average descriptive statistics Mean Std. Deviation N GPA 2.4983 .8161 3614 Q30_11 AfricanAmerican/Black 1.15 .35 3614 ASPACIS .1372 .3442 3614 HISPANIC .5437 .4982 3614 Q28 Your gender 1.59 .49 3614 Q29 Age on December 31 of this year 6.17 1.64 3614 Q24 Average grade in high school 5.43 1.84 3614 ASPIRE 4.1828 .9828 3614 DETERMIN 6.2330 .7069 3614 INTEGRAT 2.0300 .8150 3614 Q16_7 Understanding the English language 1.39 .84 3614 Q16_4 Job-related responsibilities 2.21 1.23 3614 SES 52.9300 26.3518 3614

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92Table B-3. Grade point average correlations GPA Q30_11 African-American/Black ASPACIS HISPANIC Q28 Your gender Q29 Age on December 31 of this year Q24 Average grade in high school ASPIRE DETERMIN INTEGRAT Q16_7 Understanding the English language Q16_4 Job-related responsibilities SES GPA 1.000 -0.070 0.087 -0.135 0.099 0.182 0.280 0.041 0.178 0.046 0.092 -0.030 0.060 Q30_11 AfricanAmerican/Black -0.070 1.000 -0.151 -0.411 0.068 0.137 -0.041 0.040 0.087 0.123 -0.115 -0.049 0.129 ASPACIS 0.087 -0. 151 1.000 -0.379 -0.033 -0.014 0.109 0.015 -0.079 -0.033 0.267 -0.011 0.121 HISPANIC -0.135 -0.411 -0.379 1.000 0.006 -0.114 -0.080 -0.007 0.015 -0.068 -0.075 0.065 -0.341 Q28 Your gender 0.099 0.068 -0. 033 0.006 1.000 0.079 0.155 -0.027 0.105 0.000 0.015 -0.050 -0.026 Q29 Age on December 31 of this year 0.182 0.137 -0. 014 -0.114 0.079 1.000 0.021 -0.160 0.155 0.110 0.092 0.072 0.011 Q24 Average grade in high school 0.280 -0. 041 0.109 -0.080 0.155 0.021 1.000 0.084 0.151 0.106 0.118 -0.014 0.056 ASPIRE 0.041 0.040 0.015 -0.007 -0.027 -0.160 0.084 1.000 0.223 0.079 -0.079 0.026 0.032 DETERMIN 0.178 0.087 -0. 079 0.015 0.105 0.155 0.151 0.223 1.000 0.221 -0.087 -0.037 -0.005 Pearson Correlation INTEGRAT 0.046 0.123 -0. 033 -0.068 0.000 0.110 0.106 0.079 0.221 1.000 0.051 0.013 0.005 Q16_7 Understanding the English language 0.092 -0.115 0.267 -0.075 0.015 0.092 0.118 -0.079 -0.087 0.051 1.000 0.106 -0.031 Q16_4 Job-related responsibilities -0.030 -0.049 -0.011 0.065 -0.050 0.072 -0.014 0.026 -0.037 0.013 0.106 1.000 -0.015 SES 0.060 0.129 0.121 -0.341 -0.026 0.011 0.056 0.032 -0.005 0.005 -0.031 -0.015 1.000

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93Table B-3. Continued GPA Q30_11 African-American/Black ASPACIS HISPANIC Q28 Your gender Q29 Age on December 31 of this year Q24 Average grade in high school ASPIRE DETERMIN INTEGRAT Q16_7 Understanding the English language Q16_4 Job-related responsibilities SES GPA 0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.000 0.003 0.000 0.034 0.000 Q30_11 AfricanAmerican/Black 0.000 0.000 0.000 0.000 0.000 0.006 0.009 0.000 0.000 0.000 0.002 0.000 ASPACIS 0.000 0.000 0.000 0.024 0.207 0.000 0.177 0.000 0.023 0.000 0.254 0.000 HISPANIC 0.000 0.000 0.000 0.367 0.000 0.000 0.334 0.177 0.000 0.000 0.000 0.000 Q28 Your gender 0.000 0.000 0.024 0.367 0.000 0.000 0.049 0.000 0.488 0.190 0.001 0.059 Q29 Age on December 31 of this year 0.000 0.000 0.207 0.000 0.000 0.107 0.000 0.000 0.000 0.000 0.000 0.251 Q24 Average grade in high school 0.000 0.006 0.000 0.000 0.000 0.107 0.000 0.000 0.000 0.000 0.203 0.000 ASPIRE 0.007 0.009 0.177 0.334 0.049 0.000 0.000 0.000 0.000 0.000 0.056 0.026 DETERMIN 0.000 0.000 0.000 0.177 0.000 0.000 0.000 0.000 0.000 0.000 0.013 0.387 INTEGRAT 0.003 0.000 0.023 0.000 0.488 0.000 0.000 0.000 0.000 0.001 0.217 0.377 Sig. (1tailed) Q16_7 Understanding the English language 0.000 0.000 0.000 0.000 0.190 0.000 0.000 0.000 0.000 0.001 0.000 0.033 Q16_4 Job-related responsibilities 0.034 0.002 0.254 0.000 0. 001 0.000 0.203 0.056 0. 013 0.217 0.000 0.186 SES 0.000 0.000 0.000 0.000 0.059 0.251 0.000 0.026 0.387 0.377 0.033 0.186

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94Table B-3. Continued GPA Q30_11 African-American/Black ASPACIS HISPANIC Q28 Your gender Q29 Age on December 31 of this year Q24 Average grade in high school ASPIRE DETERMIN INTEGRAT Q16_7 Understanding the English language Q16_4 Job-related responsibilities SES GPA 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 Q30_11 AfricanAmerican/Black 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 ASPACIS 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 HISPANIC 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 Q28 Your gender 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 Q29 Age on December 31 of this year 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 Q24 Average grade in high school 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 ASPIRE 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 DETERMIN 3614 3614 3 614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 INTEGRAT 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 Q16_7 Understanding the English language 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 Q16_4 Job-related responsibilities 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 N SES 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614 3614

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95 Table B-4. Grade point averag e variables entered/removed(b) Model Variables Entered Variables Removed Method 1 Q29 Age on December 31 of th is year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC(a) Enter 2 Q24 Average grade in high school(a) Enter 3 Q16_4 Job-related responsibilitie s, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN(a) Enter 4 SES(a) Enter a All requested variables entere d. b Dependent Variable: GPA

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96Table B-5. Grade point average model summary Change Statistics Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change 1 .283(a) .080 .079 .7833 .080 62.663 5 3608 .000 2 .373(b) .139 .138 .7577 .060 249.431 1 3607 .000 3 .399(c) .159 .156 .7496 .019 16.641 5 3602 .000 4 .399(d) .159 .156 .7496 .000 .738 1 3601 .390 a Predictors: (Constant), Q29 Age on Decem ber 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HIS PANIC. b Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q 28 Your gender, Q30_11 African-American/Bla ck, HISPANIC, Q24 Average grade in hig h school. c Predictors: (Constant), Q29 Age on December 31 of this year, ASPA CIS, Q28 Your gender, Q30_11 African-Ame rican/Black, HISPANIC, Q24 Average grade i n high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE Q16_7 Understanding the English language, DETER MIN. d Predictors: (Constant), Q29 Age o n December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-America n/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related responsibilit ies, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN, SES

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97 Table B-6. Grade point average ANOVA(e) Model Sum of Squares df Mean Square F Sig. Regression 192.247 5 38.449 62.663 .000(a) Residual 2213.827 3608 .614 1 Total 2406.074 3613 Regression 335.436 6 55.906 97.387 .000(b) Residual 2070.638 3607 .574 2 Total 2406.074 3613 Regression 382.187 11 34.744 61.836 .000(c) Residual 2023.887 3602 .562 3 Total 2406.074 3613 Regression 382.602 12 31.884 56.740 .000(d) Residual 2023.472 3601 .562 4 Total 2406.074 3613 a Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 AfricanAmerican/Black, HISPANIC. b Predictors: (Constant), Q29 Age on Dece mber 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC, Q24 Average grade in high school. c Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 AfricanAmerican/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN. d Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 Afri can-American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related respon sibilities, INTEGRAT, ASPIRE Q16_7 Understanding the English language, DETERMIN, SES. e Dependent Variable: GPA

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98 Table B-7. Grade point average coefficients(a) Unstandardized Coefficients Standardized Coefficients Model B Std. ErrorBeta t Sig. (Constant) 2.3500.088 26.8170.000 Q30_11 AfricanAmerican/Black -0.4200.044-0.182 -9.6070.000 ASPACIS -0.0190.044-0.008 -0.4410.659 HISPANIC -0.3160.033-0.193 -9.5640.000 Q28 Your gender 0.1630.0270.098 6.0890.000 1 Q29 Age on December 31 of this year 0.0880.0080.177 10.9320.000 (Constant) 1.7990.092 19.6270.000 Q30_11 AfricanAmerican/Black -0.3750.042-0.162 -8.8410.000 ASPACIS -0.0610.043-0.026 -1.4220.155 HISPANIC -0.2800.032-0.171 -8.7650.000 Q28 Your gender 0.0950.0260.057 3.6420.000 Q29 Age on December 31 of this year 0.0870.0080.175 11.1490.000 2 Q24 Average grade in high school 0.1100.0070.249 15.7930.000 (Constant) 0.9450.139 6.7810.000 Q30_11 AfricanAmerican/Black -0.3950.042-0.170 -9.3080.000 ASPACIS -0.0710.043-0.030 -1.6470.100 HISPANIC -0.2900.032-0.177 -9.1520.000 Q28 Your gender 0.0790.0260.048 3.0370.002 Q29 Age on December 31 of this year 0.0810.0080.162 10.0150.000 Q24 Average grade in high school 0.1000.0070.225 14.1010.000 ASPIRE 0.0270.0130.032 1.9790.048 DETERMIN 0.1490.0190.129 7.7560.000 3 INTEGRAT -0.0210.016-0.021 -1.3050.192

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99 Table B-7. Continued Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. Q16_7 Understanding the English language 0.0420.0160.044 2.688 0.007 Q16_4 Job-related responsibilities -0.0230.010-0.034 -2.201 0.028 (Constant) 0.9160.143 6.386 0.000 Q30_11 AfricanAmerican/Black -0.3940.042-0.170 -9.294 0.000 ASPACIS -0.0710.043-0.030 -1.642 0.101 HISPANIC -0.2820.033-0.172 -8.522 0.000 Q28 Your gender 0.0800.0260.048 3.059 0.002 Q29 Age on December 31 of this year 0.0810.0080.162 10.027 0.000 Q24 Average grade in high school 0.1000.0070.225 14.053 0.000 ASPIRE 0.0260.0130.032 1.960 0.050 DETERMIN 0.1490.0190.129 7.759 0.000 INTEGRAT -0.0210.016-0.021 -1.289 0.197 Q16_7 Understanding the English language 0.0430.0160.045 2.734 0.006 Q16_4 Job-related responsibilities -0.0230.010-0.034 -2.213 0.027 4 SES 0.0000.0010.014 0.859 0.390 a Dependent Variable: GPA

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100 Table B-8. Grade point aver age excluded variables(d) Collinearity Statistics Model Beta Int Sig. Partial Correlation Tolerance Q24 Average grade in high school .249(a)15.7930.0000.254 0.957 ASPIRE .080(a)4.9420.0000.082 0.970 DETERMIN .165(a)10.2890.0000.169 0.958 INTEGRAT .036(a)2.2510.0240.037 0.975 Q16_7 Understanding the English language .046(a)2.7260.0060.045 0.911 Q16_4 Job-related responsibilities .035(a) -2.1900.029-0.036 0.985 1 SES .022(a)1.2780.2010.021 0.882 ASPIRE .057(b)3.6140.0000.060 0.961 DETERMIN .130(b)8.2400.0000.136 0.936 INTEGRAT .008(b)0.5080.6110.008 0.962 Q16_7 Understanding the English language .024(b)1.4650.1430.024 0.904 Q16_4 Job-related responsibilities .034(b) -2.2010.028-0.037 0.985 2 SES .012(b)0.7590.4480.013 0.881 3 SES .014(c)0.8590.3900.014 0.877 a Predictors in the Model: (Constant), Q29 Age on D ecember 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Bl ack, HISPANIC. b Predictors in the Mode l: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 A frican-American/Black, HISPAN IC, Q24 Average grade in high school. c Predictors in the M odel: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 Understandi ng the English language, DETERMIN. d Dependent Variable: GPA

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101 APPENDIX C SPSS PRINTOUT FULL MODEL SUCCESS RATE Table C-1. Success rate notes Output Created 14-APR-2007 12:47:57 Comments Input Data C:\Documents and Set tings\ron\Desktop\FINAL DATASET.sav Filter Weight Split File N of Rows in Working Data File 4968 Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics are based on ca ses with no missing values for any variable used. Syntax REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT sucr /METHOD=ENTER q30_11 ASPACIS HISPANIC q28 q29 /METHOD=ENTER q24 /METHOD=ENTER ASPIRE DETERMIN INTEGRAT q16_7 q16_4 /M ETHOD=ENTER ses Resources Memory Required 11540 bytes Additional Memory Required for Residual Plots 0 bytes Elapsed Time 0:00:00.35

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102 Table C-2. Success rate descriptive statistics Mean Std. Deviation N SUCR .6888 .2404 3633 Q30_11 African-American/Black 1.14 .35 3633 ASPACIS .1374 .3443 3633 HISPANIC .5431 .4982 3633 Q28 Your gender 1.59 .49 3633 Q29 Age on December 31 of this year 6.17 1.64 3633 Q24 Average grade in high school 5.43 1.84 3633 ASPIRE 4.1803 .9851 3633 DETERMIN 6.2308 .7111 3633 INTEGRAT 2.0290 .8141 3633 Q16_7 Understanding the English language 1.39 .84 3633 Q16_4 Job-related responsibilities 2.21 1.23 3633 SES 52.9404 26.3467 3633

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103Table C-3. Success rate correlations SUCR Q30_11 African-American/Black ASPACIS HISPANIC Q28 Your gender Q29 Age on December 31 of this yea r Q24 Average grade in high school ASPIRE DETERMIN INTEGRAT Q16_7 Understanding the English language Q16_4 Job-related responsibilities SES SUCR 1.000 -0.073 0.075 -0.083 0.079 0.093 0.260 0.049 0.159 0.047 0.113 -0.047 0.012 Q30_11 AfricanAmerican/Black -0.073 1.000 -0.151 -0.409 0.069 0.136 -0.041 0.040 0.088 0.123 -0.116 -0.049 0.129 ASPACIS 0.075 -0.151 1.000 -0.379 -0.032 -0.013 0.106 0.013 -0.075 -0.034 0.267 -0.014 0.119 HISPANIC -0.083 -0.409 -0.379 1.000 0.006 -0.112 -0.080 -0.004 0.016 -0.067 -0.075 0.065 -0.340 Q28 Your gender 0.079 0.069 -0.032 0.006 1.000 0.079 0.154 -0.027 0.109 0.000 0.013 -0.053 -0.028 Q29 Age on December 31 of this year 0.093 0.136 -0.013 -0.112 0.079 1.000 0.021 -0.158 0.153 0.109 0.093 0.070 0.011 Q24 Average grade in high school 0.260 -0.041 0.106 -0.080 0.154 0.021 1.000 0.084 0.150 0.107 0.117 -0.015 0.056 ASPIRE 0.049 0.040 0.013 -0.004 -0.027 -0.158 0.084 1.000 0.224 0.082 -0.079 0.027 0.032 DETERMIN 0.159 0.088 -0.075 0.016 0.109 0.153 0.150 0.224 1.000 0.222 -0.088 -0.042 -0.008 INTEGRAT 0.047 0.123 -0.034 -0.067 0.000 0.109 0.107 0.082 0.222 1.000 0.050 0.014 0.005 Q16_7 Understanding the English language 0.113 -0.116 0.267 -0.075 0.013 0.093 0.117 -0.079 -0.088 0.050 1.000 0.105 -0.029 Q16_4 Job-related responsibilities -0.047 -0.049 -0.014 0.065 -0.053 0.070 -0.015 0.027 -0.042 0.014 0.105 1.000 -0.013 Pearson Correlation SES 0.012 0.129 0.119 -0.340 -0.028 0.011 0.056 0.032 -0.008 0.005 -0.029 -0.013 1.000

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104Table C-3. Continued SUCR Q30_11 African-American/Black ASPACIS HISPANIC Q28 Your gender Q29 Age on December 31 of this yea r Q24 Average grade in high school ASPIRE DETERMIN INTEGRAT Q16_7 Understanding the English language Q16_4 Job-related responsibilities SES SUCR 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.002 0.000 0.002 0.233 Q30_11 AfricanAmerican/Black 0.000 0.000 0.000 0.000 0.000 0.007 0.008 0.000 0.000 0.000 0.002 0.000 ASPACIS 0.000 0.000 0.000 0.026 0.218 0.000 0.216 0.000 0.022 0.000 0.204 0.000 HISPANIC 0.000 0.000 0.000 0.357 0.000 0.000 0.404 0.163 0.000 0.000 0.000 0.000 Q28 Your gender 0.000 0.000 0.026 0.357 0.000 0.000 0.051 0.000 0.499 0.222 0.001 0.043 Q29 Age on December 31 of this year 0.000 0.000 0.218 0.000 0.000 0.102 0.000 0.000 0.000 0.000 0.000 0.251 Q24 Average grade in high school 0.000 0.007 0.000 0.000 0.000 0.102 0.000 0.000 0.000 0.000 0.191 0.000 ASPIRE 0.001 0.008 0.216 0.404 0.051 0.000 0.000 0.000 0.000 0.000 0.055 0.027 DETERMIN 0.000 0.000 0.000 0. 163 0.000 0.000 0. 000 0.000 0.000 0.000 0.005 0.305 INTEGRAT 0.002 0.000 0.022 0.000 0.499 0.000 0.000 0.000 0.000 0.001 0.200 0.383 Q16_7 Understanding the English language 0.000 0.000 0.000 0.000 0.222 0.000 0.000 0.000 0.000 0.001 0.000 0.039 Q16_4 Job-related responsibilities 0.002 0.002 0.204 0.000 0.001 0.000 0.191 0.055 0.005 0.200 0.000 0.210 Sig. (1-tailed) SES 0.233 0.000 0.000 0.000 0.043 0.251 0.000 0.027 0.305 0.383 0.039 0.210

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105Table C-3. Continued SUCR Q30_11 African-American/Black ASPACIS HISPANIC Q28 Your gender Q29 Age on December 31 of this yea r Q24 Average grade in high school ASPIRE DETERMIN INTEGRAT Q16_7 Understanding the English language Q16_4 Job-related responsibilities SES SUCR 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 Q30_11 AfricanAmerican/Black 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 ASPACIS 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 HISPANIC 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 Q28 Your gender 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 Q29 Age on December 31 of this year 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 Q24 Average grade in high school 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 ASPIRE 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 DETERMIN 3633 3633 3 633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 INTEGRAT 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 Q16_7 Understanding the English language 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 Q16_4 Job-related responsibilities 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 N SES 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633 3633

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106 Table C-4. Success rate vari ables entered/removed(b) Model Variables Entered Variables Removed Method 1 Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC(a) Enter 2 Q24 Average grade in high school(a) Enter 3 Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN(a) Enter 4 SES(a) Enter a All requested variables entered. b Dependent Variable: SUCR

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107Table C-5. Success rate model summary Change Statistics Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change 1 .192(a) .037 .035 .2361 .037 27.663 5 3627 .000 2 .302(b) .091 .089 .2294 .054 216.350 1 3626 .000 3 .337(c) .114 .111 .2266 .023 18.733 5 3621 .000 4 .338(d) .114 .111 .2266 .000 1.327 1 3620 .249 a Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HIS PANIC. b Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC, Q24 Ave rage grade in high school. c Predictors: (Constant), Q29 Ag e on December 31 of this year, ASPACIS, Q2 8 Your gender, Q30 _11 African-American/B lack, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related responsibilitie s, INTEGRAT, AS PIRE, Q16_7 Understanding the English language, DETERMIN. d Predictors: (Constant), Q29 Age on December 31 of this year, AS PACIS, Q28 Your gender, Q30_11 African-American/Black, HISPAN IC, Q24 Average grade in high school, Q16_4 Job-related responsibilities, INTEGR AT, ASPIRE, Q16_7 Understanding the English language, DETERMI N, SES

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108 Table C-6. Success rate ANOVA(e) Model Sum of Squares df Mean Square F Sig. Regression 7.710 51.54227.663.000(a) Residual 202.176 3,6270.056 1 Total 209.886 3,632 Regression 19.094 63.18260.479.000(b) Residual 190.792 3,6260.053 2 Total 209.886 3,632 Regression 23.905 112.17342.310.000(c) Residual 185.981 3,6210.051 3 Total 209.886 3,632 Regression 23.973 121.99838.899.000(d) Residual 185.913 3,6200.051 4 Total 209.886 3,632 a Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 AfricanAmerican/Black, HISPANIC. b Predictors: (Constant), Q29 Age on Dece mber 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC, Q24 Average grade in high school. c Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 AfricanAmerican/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 Understanding the English language, DETERMIN. d Predictors: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 Afri can-American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related respon sibilities, INTEGRAT, ASPIRE Q16_7 Understanding the English language, DETERMIN, SES. e Dependent Variable: SUCR

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109 Table C-7. Success rate coefficients(a) Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. (Constant) 0.6860.026 26.073 0.000 Q30_11 African-American/ Black -0.0970.013-0.142 -7.396 0.000 ASPACIS 0.0060.0130.008 0.432 0.666 HISPANIC -0.0620.010-0.129 -6.282 0.000 Q28 Your gender 0.0400.0080.082 5.016 0.000 1 Q29 Age on December 31 of this year 0.0130.0020.091 5.534 0.000 (Constant) 0.5310.028 19.197 0.000 Q30_11 African-American/ Black -0.0840.013-0.124 -6.589 0.000 ASPACIS -0.0050.013-0.008 -0.408 0.683 HISPANIC -0.0520.010-0.108 -5.408 0.000 Q28 Your gender 0.0210.0080.044 2.713 0.007 Q29 Age on December 31 of this year 0.0130.0020.089 5.546 0.000 2 Q24 Average grade in high school 0.0310.0020.238 14.709 0.000 (Constant) 0.2830.042 6.766 0.000 Q30_11 African-American/ Black -0.0890.013-0.130 -6.949 0.000 ASPACIS -0.0160.013-0.022 -1.194 0.233 HISPANIC -0.0550.010-0.113 -5.720 0.000 Q28 Your gender 0.0160.0080.033 2.049 0.041 Q29 Age on December 31 of this year 0.0110.0020.073 4.412 0.000 Q24 Average grade in high school 0.0270.0020.210 12.867 0.000 ASPIRE 0.0070.0040.030 1.812 0.070 DETERMIN 0.0420.0060.125 7.311 0.000 INTEGRAT -0.0030.005-0.009 -0.556 0.578 Q16_7 Understanding the English language 0.0240.0050.083 4.978 0.000 3 Q16_4 Job-related responsibilities -0.0100.003-0.051 -3.203 0.001

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110 Table C-7. Continued Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. (Constant) 0.2950.043 6.846 0.000 Q30_11 African-American/ Black -0.0890.013-0.130 -6.965 0.000 ASPACIS -0.0160.013-0.022 -1.203 0.229 HISPANIC -0.0580.010-0.120 -5.811 0.000 Q28 Your gender 0.0160.0080.032 2.015 0.044 Q29 Age on December 31 of this year 0.0110.0020.073 4.396 0.000 Q24 Average grade in high school 0.0280.0020.211 12.905 0.000 ASPIRE 0.0070.0040.030 1.839 0.066 DETERMIN 0.0420.0060.124 7.302 0.000 INTEGRAT -0.0030.005-0.009 -0.577 0.564 Q16_7 Understanding the English language 0.0230.0050.082 4.903 0.000 Q16_4 Job-related responsibilities -0.0100.003-0.051 -3.186 0.001 4 SES 0.0000.000-0.019 -1.152 0.249 a Dependent Variable: SUCR

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111 Table C-8. Success rate excluded variables(d) Collinearity Statistics Model Beta Int Sig. Partial Correlation Tolerance Q24 Average grade in high school .238(a)14.7090.0000.237 0.958 ASPIRE .073(a)4.4440.0000.074 0.970 DETERMIN .158(a)9.5930.0000.157 0.958 INTEGRAT .048(a)2.8830.0040.048 0.975 Q16_7 Understanding the English language .083(a)4.8780.0000.081 0.911 Q16_4 Job-related responsibilities .048(a) -2.9510.003-0.049 0.985 1 SES .015(a) -0.8530.394-0.014 0.883 ASPIRE .051(b)3.1840.0010.053 0.961 DETERMIN .125(b)7.6880.0000.127 0.936 INTEGRAT .021(b)1.2760.2020.021 0.962 Q16_7 Understanding the English language .062(b)3.7480.0000.062 0.904 Q16_4 Job-related responsibilities .047(b) -2.9770.003-0.049 0.985 2 SES .024(b) -1.4150.157-0.023 0.881 3 SES .019(c) -1.1520.249-0.019 0.877 a Predictors in the Model: (Constant), Q29 Age on D ecember 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Bl ack, HISPANIC. b Predictors in the Mode l: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 A frican-American/Black, HISPAN IC, Q24 Average grade in high school. c Predictors in the M odel: (Constant), Q29 Age on December 31 of this year, ASPACIS, Q28 Your gender, Q30_11 African-American/Black, HISPANIC, Q24 Average grade in high school, Q16_4 Job-related responsibilities, INTEGRAT, ASPIRE, Q16_7 Understandi ng the English language, DETERMIN. d Dependent Variable: SUCR

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121 BIOGRAPHICAL SKETCH Ronald C. Lester was born in Fort S mith, Ar kansas. He attended Southside High School in Fort Smith and upon graduation attended Westark Community College in Fort Smith and then the University of Arkansas at Little Rock. He graduated with a Bachelor of Arts in anthropology, with high honors, and a minor in biology. He wo rked at the University of Cincinnati, finally serving as Assistant to the A ssociate Dean for Student Services in the College of Medicine. In 1997 he decided to move back to a warmer clim ate and relocated to Gainesville, Florida, where he briefly worked at the Al achua County Board of C ounty Commissioners, and then obtained employment at the University of Florida. He has worked at the University of Florida since March, 1988 in the Department of Pediatrics. During his tenure in Pediatrics he obtained a Masters in Business Administration degree in April, 1994. He decided to again return to graduate school, this time at the University of Florida, and completed his doctorate in higher education administration in December, 2007. He is currently a divisional administrator in the Department of Pedi atrics and manages the administrative and budgetary respon sibilities for several divisi ons within the department. He also keeps active on campus. He has b een active in the Academic and Professional Assembly (APA) which has over 2400 university professional members. The APA has been empowered to serve as an advisory body to university administration regarding issues and policies affecting and of concern to this group of university employees. He has served as president elect, president and past president of the APA.