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What factors influence student persistence in the community college setting?

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What factors influence student persistence in the community college setting?

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WHAT FACTORS INFLUENCE STUDENT PERSISTENCE IN THE COMMUNITY
COLLEGE SETTING?

by
Mikiko Aoyagi Nakajima
________________________________________________________

A Dissertation Presented to the
FACULTY OF THE ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF EDUCATION

August 2008

Copyright 2008 Mikiko Aoyagi Nakajima

ii

DEDICATION
For my loving husband, Yosuke Nakajima,
my son, Mario Nakajima,
my loving father and mother, Takanobu and Yumiko Aoyagi,
and my loving grandparents, Katei and Kimi Shofu

iii
ACKNOWLEDGEMENT
I want to thank my chairperson, Dr. Myron Dembo, for his inspiration and guidance;
my dissertation committee Dr. Ron Mossler and Dr. Ginger Clark for their support and
time; my husband, family, and friends for their love and support that helped me
complete this study, I want to extend my deepest gratitude.

iv
TABLE OF CONTENTS

Dedication ii
Acknowledgements iii
List of Tables vii
Abstract viii
CHAPTER 1: INTRODUCTION 1
Purpose of the Study 15
Importance of the Study 16
Research Questions 17
Definition of Terms 18
Organization of the Study 20

CHAPTER 2: REVIEW OF THE LITERATURE 21
Persistence Research at Community Colleges 22
Community College Students 22
Student Retention/Persistence Models 23
Student Persistence/Retention Models for Community College Students 26
Persistence Research at the Community College Settings 28
Demographic Factors 29
Financial Factors 29
Academic Factors 30
Academic Integration Factors 34
Psychosocial Factors 40
Demographic Factors Associated with Student Persistence 46
Age 47
Ethnicity 49
Family Responsibility 51
Socioeconomic Status 52
Financial Factors Associated with Student Persistence 55
Employment 55
Tuition and Financial Aid 57
Academic Factors Associated with Student Persistence 59
Pre-entry Academic Characteristics 59
High School GPA 60
Placement Test and Standardized Tests 61
College Academic Ability 63

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Enrollment Status 64
Registration and Application Time 66
Academic Integration Associated with Student Persistence 68
Academic Integration 68
Faculty-Student Interaction 71
Faculty-Student Interaction and Academic Achievement 72
Faculty-Student Interaction and Persistence 75
Student Involvement and Support Services 78
Psychosocial Factors Associated with Student Persistence 81
Student Goals 82
Goal Setting Theory 82
Goals and Academic Performance 84
Goals and Persistence 86
Goals and Self-Efficacy 88
Self-Efficacy 91
Self-Efficacy Theory 91
Self-Efficacy and Achievement 94
Self-Efficacy and Academic Performance 96
Self-Efficacy and Persistence 99
Conclusions 102

CHAPTER 3: METHODS 106
Research Overview 106
Research Design 106
Participants 107
Instruments 107
Background Information 108
College Self-Efficacy Inventory (CSEI) 109
Institutional Integration Scale (IIS) 111
Career Decision Scale (CDS) 112
Procedures 113
Data Analysis 114

CHAPTER 4: RESULTS 116
Descriptive Statistics 116
Intercorrelations 117
Multivariate Analysis 127
Research Question 1 133
Research Question 2 134

CHAPTER 5: DISCUSSION 136
Relationship between Background Variables and Persistence 137
Relationship between Academic Integration Variables and Persistence 144

vi
Relationship between Psychosocial Variables and Persistence 147
Implications 152
Limitation 153
Recommendations 155

REFERENCES 162

APPENDICES 185
A. College Self-Efficacy Inventory 185
B. Institutional Integration Scale 186
C. Career Decision Scale 187
D. Informed Consent Form 188
E. Student Persistence Survey 192

vii
LIST OF TABLES

Table 1: Demographic, Academic, and Financial Variables and Student Persistence 31

Table 2: Academic integration variables and Student Persistence 35

Table 3: Psychosocial Variables and Student Persistence 41

Table 4: Descriptive Statistics and Estimates of Internal-Consistency Reliability 117

Table 5: Means, Standard Deviations, and Pearson Product Correlations for
Measured Variables 119

Table 6: t-scores for Predicting Variables and Student Persistence 128

Table 7: Logistic Regression Analysis for Variables Predicting Student Persistence 131

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ABSTRACT

The purpose of the current study was to extend the research on student
persistence in community college by investigating factors likely to influence student’s
decision to drop out or stay in school. Specifically, this study examined demographic,
financial, academic, academic integration (i.e., faculty-student interaction), and
psychosocial variables (i.e., goals and self-efficacy) and its relationship to student
persistence.
A sample of 427 community college students completed a 63-item survey
assessing psychosocial variables (i.e., self-efficacy and goals) and the academic
integration variable (i.e., student-faculty interaction), and number of background
variables (i.e., demographic, financial, and academic). In addition, student retention
was measured through college enrollment the following semester.
Results of this study revealed that cumulative GPA was the strongest predicting
variable for student persistence. Students who had higher cumulative GPA were twice
as likely to stay in college. In addition to cumulative GPA, both enrollment units and
English proficiency were predicting factors for students to persist in their education.
However, contrary to expectations, none of the academic integration or psychosocial
variables were predictors of student persistence among the students. Nonetheless, the
study also revealed that almost all of the variables interrelate with one another. Both
goals and self-efficacy were significantly correlated with cumulative GPA, which in
turn predicted student persistence. Faculty-student interaction was also significantly
correlated with enrollment units, which in turn predicted student persistence. Therefore,

ix
the results indicated the importance of investigating multiple factors in the effort to
solve the problem with student persistence in community college.

1

CHAPTER I

INTRODUCTION
Student enrollment in higher education has been increasing significantly during
the past two decades. According to the National Center for Educational Statistics
(NCES, 2005), enrollment increased by 17 percent between 1984 and 1994, and even a
larger increase of 21 percent was found between 1994 and 2004. Educational
attainment is strongly related to increased incomes and improved employment status
(Hoachlander, Sikora, Horn, & Carroll, 2003). Those students who completed their
college degree or those who transferred to a university have reported higher salaries,
improved employment, increased responsibility at their job sites, increased promotion
opportunities, and improved job performance. Obtaining college education also has
been found to have a great advantage in the job market (Kerckhoff, Raundenbush, &
Glennie, 2001). Thus, the nation’s colleges and universities are the democratic
pathways to achieve equal opportunity (Bissett, 1995). Attaining a college degree will
allow persons to climb higher on the socioeconomic status scale and to provide
improved support of the family (Phinney, Dennis, & Osorio, 2006).
Community colleges have played a central role in providing access to college
education for a wide variety of students from different background characteristics
(Bailey, Alfonso, Calcagno, Jenkins, Kienzl, & Leinbach, 2004). The major feature of
the community college’s mission is called the “open door” policy, which removed the
academic, financial, social, and geographic barriers for attaining college education,
especially for the disadvantaged students (Bissett, 1995). Thus, based on this mission,

2
most community colleges have low tuition, flexible scheduling, convenient location,
and programs and services that are intended to support at-risk students who have social
and academic barriers to college education (Bailey et al., 2004).
Since the emergence of community colleges in the early twentieth century, the
number of these institutions has increased tremendously. As of the year 2000, there
were over one-thousand community colleges in the United States with an estimated 11.6
million students attending them (Patton, 2000). They provide entry to the
postsecondary education system for low-income students, first generation students,
minority students, and students who had poor academic performance in high school.
These students have become the majority of the community college student population.
For example, in the school year of 1995-96, 55 percent of all first-time college students
enrolled in two-year public institutions were students from families with income that
were below average, while only 35 percent of the same student population were found
in four-year public institutions (Cohen & Brawer, 2003). In addition, 64 percent of high
school graduates in 1992, starting their postsecondary education in community colleges
by December 1994, were not qualified or were only minimally qualified to start their 4-
year college-level work (Hoachlander, et al., 2003).
However, access alone is insufficient. In order for the students to obtain the
benefit of college education, students must be successful after they have enrolled. Yet,
despite the widespread knowledge of the importance of having a college education, a
large number of students drop out of college without obtaining a college degree or
completing a certificate. This is more apparent at 2-year institutions compared to 4-year

3
institutions and particularly apparent during the first year of enrollment (Dougherty,
1992). In 2004, American College Testing (ACT) reported a 48 percent drop-out rate
for students enrolled in public two-year institutions and a 39 percent drop-out rate for
students in private two-year institutions (ACT, 2004). In addition, 29 percent of
students attending public schools and 51.9 percent of students attending private schools
graduated within 3 years. As can be seen with these numbers, low retention/high
attrition rate is a major problem for community colleges nation-wide.
Student retention is an important issue in community college administration
today. It is one of the significant factors that measure institutional effectiveness at the
time of accountability and budgetary constraints (Wild & Ebbers, 2002). In addition,
recruiting new students is associated with higher costs compared to retaining those
already enrolled (Brooks-Leonard, 1991), and more community colleges have become
enrollment- or tuition-driven enterprises in order to successfully survive in the college
marketplace (Grossett, 1991). Thus, high levels of attrition can have a large negative
impact on a college’s funding, facilities, planning, and long-term curriculum planning
(Gabriel, 2001a).
Researchers have been studying student retention for over seventy years
(Braxton, 2000). The most prominent and commonly referred model is Tinto’s
interactionalist theory (1993). Tinto indicated that students come to postsecondary
institutions with a variety of different background characteristics and secondary-school
experiences (e.g., race, gender, family, educational, financial context, high school
accomplishments). These background characteristics and high-school experiences

4
influence the student’s initial commitment to the institution as well as the initial goals of
the students. For example, students from higher socioeconomic households tend to
have increased commitment to the institution from the beginning. In addition, these
pre-college variables, as well as the initial commitments, influence students’ interaction
and integration into the institution’s academic and social systems. Students whose
parents have a college education have increased knowledge about the higher
educational system, thus are more easily integrated into the system. According to this
model, increased integration into the social and academic systems of the college will
lead to greater commitment to the college and development of goals, thus leading to
increased persistence. Tinto’s theory of student persistence is well supported among the
four-year college students (Cabrera, Nora, & Castafieda, 1993; Pascarella & Terenzini,
1991).
Another commonly referred retention model is Astin’s Theory of Involvement
(1984). According to Astin, college student persistence is associated with students’
involvement in college life. In his theory, he hypothesized that involvement means
investment of physical and psychological energy; involvement occurs along a
continuum; and it has quantitative and qualitative components. Quality and quantity of
involvement directly influences student learning and development, and effectiveness of
educational practice is directly related to a college’s ability to increase involvement.
Academic involvement, faculty involvement, peer involvement, work involvement, and
involvement elsewhere are the categories of involvement. Similar to Tinto, much of

5
Astin’s work has focused on traditional age, residential students attending four-year
colleges and universities (Chaves, 2006).
Since most of these theoretical concepts have been studied and developed
among four-year college students, their relevance to community college students has
been questioned (Braxton, Sullivan, & Johnson, 1997; Braxton & Lien, 2000; Rendon,
Romero, & Nora, 2000). Unlike community colleges, four-year colleges and
universities have selective admissions criteria and many are residential institutions. In
addition, academic and career goals for four-year and two-year college students may
differ. For example, many students at community colleges may not be interested in
earning a degree. Students may enroll with intent to learn a specific skill, to gain
promotion at their current job, or for personal enrichment (Bailey et al., 2004). On the
other hand, students may also enroll to test the post-secondary institution in an
inexpensive environment (Wild & Ebbers, 2002), or may have a clear goal to finish
lower level educational requirements and transfer to a four-year institution
(Hoachlander et al., 2003). Addition to having different goals, community college
students come from diverse background demographics, which complicate the student
retention problem. Many community college students are first-generation attendees
who have different characteristics compared to the traditional student (Terenzini,
Springer, Yaeger, Pascarella, & Nora, 1996). Traditional students are defined as
students who are between the ages of 18 and 24, entering college right after high school
graduation. On the other hand, first-generation college students are those whose parents
did not attend any type of higher education, are likely to be minority students, are likely

6
to be older, and may have a gap between high school graduation and college entrance.
Many of these students are also underprepared in at least one of the basic skills of
reading, writing, and math (McCabe, 2000). One of the most valuable aspects of
community colleges is attraction of these at-risk students to higher education. The
student characteristics of community colleges also have an effect on retention/attrition.
Since the characteristics are much different than the four-year students, traditional
models developed using four-year college students may not be as useful when
investigating community college students. Therefore, student retention for community
college students should be investigated via different lenses than that of four-year
institutions.
Although the majority of research on student persistence has been conducted at
the four-year college level, several researchers have investigated community college
student persistence. However, variables used in these studies are somewhat limited
compared to the four-year institutions and the majority of the studies investigated a
single variable instead of multiple-variables. Still, these variables can be categorized
into psychosocial factors, demographic risk factors, and environmental factors (e.g.,
Cofer & Somers, 2001; Hagedorn, Maxwell, & Hampton, 2002; Horn & Ethington,
2002; Perin, 2006; Schmid & Abell, 2003; Smith, Street, & Olivarez, 2002)
One of the most important predictor of student persistence for community
college students is having a clear goal for the future. As mentioned earlier, community
college students enroll in school for various reasons, and the more concrete the reasons
are, the more likely they will endeavor to achieve them. Researchers have indicated

7
that one of the strongest predictors of either academic achievement or failure is the
aspiration for success, thus having achievement goals (Kao & Tienda, 1995; Wentzel,
1991). As students develop these achievement goals, the goals act as cognitive
representations for what the students are striving for (Pintrich & Schunk, 2002).
Studies on goal theory state that having a goal will increase motivation (Bandura, 1997).
Students who have goals are more likely to experience a sense of self-efficacy when
attaining those goals, and are more likely to engage in activities that will help them
attain those goals (Pintrich & Schunk, 2002). For community college students,
researchers have demonstrated that educational objectives/intents were the most
powerful discriminating factor between persisters and non-persisters (Bers & Smith,
1991) and students who lacked academic and career goals were more likely to drop out
of community college (Fralick, 1993). Perin (2006) specifically investigated nursing
aspirants at a community college and discovered that these students had higher retention
rates compared to other students because they had specific goals.
Self-efficacy has also been proposed to be related to and predictive of
achievement (Bandura, 1997). It refers to the belief about one’s ability to be able to
organize thoughts, feelings, and actions in order to gain a desired outcome (Bandura,
1986). Researchers have demonstrated that self-efficacy is significantly related to
academic performance and persistence outcomes (Multon, Brown, & Lent, 1991).
Relationship between self-efficacy and student outcomes has been salient in the
community college setting as well. Grimes and David (1999) investigated
underprepared community college students and concluded that motivational factors (i.e.,

8
self-efficacy) influence student success and persistence. Similarly, Silver, Smith, and
Greene (2004) in their study found that self-efficacy was associated with improved
academic achievement and Hagedorn and her colleagues (2001) associated academic
self-confidence with higher rates of retention. Clearly, self-efficacy is related to
students’ persistence behaviors in community college settings.
The influence of these psychosocial factors on student persistence seems
intuitive since persistence is a behavior driven by motive, and is often used as an
indicator of motivation (Pintrich & Schunk, 2002). In the student retention models,
both Tinto (1993) and Astin (1984) indicated that institutional integration and student
involvement are associated with motivation and commitment, which ultimately
becomes the major causes of withdrawal from college. In their study of community
college students, Hyers and Zimmerman (2002) found that students who were most
likely to graduate based upon their high academic ability and low demographic risk
factors (non-minority, high SES, college entrance from high school), did not necessarily
persist in their college education. They had less than expected graduation rates from
college, indicating that factors other than academic ability and demographic variables
influence student retention. The researchers suggested the importance of personal
motivation and goals as alternative variables. However, literature on the relationship
between specific motivation variables and student retention at community college is
sparse. Several authors have acknowledged the importance of motivation in student
persistence, but did not specify what motivational variables were essential (Solis, 1995;
Voorhees, 1987). Nonetheless, despite the scarcities of literature, the above two

9
psychosocial factors have been identified that influence persistence among community
college students; goal setting and self-efficacy (Bers & Smith, 1991; Fralick, 1993;
Garardi, 1996; Hagedorn et al., 2002; Mohammadi, 1994; Perin, 2006; Silver et al.,
2001).
Demographic risk factors that influence community college student retention
include any student characteristics initially brought to the college. For example, several
studies have found that age can be a predictor of attrition. Older students are more
likely to drop out of community colleges compared to younger students (Feldman,
1993; Windham, 1995). Ethnicity was also found to be related to persistence in several
of the studies. Both Cofer and Somers (2001) and Zhao (1999) found that African-
American students were less likely to persist than White students. Academic ability
based on high school grade point average (Hagedorn et al., 2002), as well as cumulative
GPA at the community college (Campbell & Blakey, 1996; Makuakane-Drechsel &
Hagedorn, 2000), was also significantly associated with student retention.
The most prominent demographic risk factor that seems to influence student
retention is a student’s financial status. Since many community college students come
from a low SES status, tuition has a significant negative impact on student retention
(Cofer & Somers, 2000). Results from numerous studies also showed that students who
worked full time were more likely to drop out of community colleges compared to those
who worked only part time or not at all (Conklin, 1995; Lanni, 1997; Pascarella, Edison,
Nora, Hagedorn, & Terenzini, 1998; Schmid & Abell, 2003; Windham, 1995). Swager,
Campbell, and Orlowaki (1995) also found that conflict with work was the predominant

10
reason why community college students withdrew from their schools. On the other
hand, receipt of financial aid had a positive impact on community college student
retention (Makuakane-Drechsel & Hagedorn, 2000; Maryland Higher Education
Commission, 2004). Thus, it appears that economic standing has a major influence on a
student’s decision to persist in college education.
Lastly, several researchers have investigated student enrollment and registration
behaviors as a demographic risk factor. Students who register late in the semester to the
community college were much less likely to persist to the next semester compared to
those students who registered early or at the regular timeframe (Smith et al., 2002).
Freer-Weiss (2004) discovered in her study that students who register late have different
demographic, academic abilities, and goals that may influence the attrition rate, and that
the registration date may not have a direct impact on retention. Students with clear
goals and strong motivational factors may register early because they are more anxious
to start school. Therefore, registration behavior can be categorized into motivational
factors that influence student retention. Still, Summers (2000) found that students’
registration behaviors (early or late) had a significant influence on attrition, even after
students’ characteristics were matched.
Although demographic risk factors have a significant influence on student
retention, these variables cannot be used for intervention purposes. Community college
admission cannot be selective of certain ethnicities or high school GPAs, or limit a
student’s work hours. Thus, focusing on demographic risk factors does not benefit the
ultimate goal of raising student retention among community college students. Rather,

11
investigating environmental and psychosocial factors that influence student retention is
more advantageous.
Environmental factors are any external variables that may influence student
retention. Examples of environmental factors include student-faculty interaction,
student-student interaction, extracurricular activities, involvement in student
organizations, and student services. For community college students, faculty-related
experiences seem to have a major influence on student retention. Satisfaction with
faculty interaction (Heverly, 1999), having faculty with same ethnicity (Opp, 2002), and
regular faculty-student contact (Schmid & Abell, 2003) all were positive influences on
student persistence. Okun, Benin, and Brandt-Williams (1996) concluded that instead
of fixed variables such as gender, age, ethnicity, and work hours, institutional
commitment and encouragement from faculty were the significant predictor variables of
a student’s decision to stay in college. In addition, positive classroom experiences
(Grosset, 1991) and participation in study groups (Schmid & Abell, 2003; Tinto, 1997)
also influence student’s persistence. Increased student-faculty contact, participation in
study groups, and satisfaction with faculty contribute to increased student involvement
in the academic institution. Therefore, it can be assumed that student involvement is an
important aspect of the student retention process.
Another environmental factor commonly investigated among community
college student retention studies are types of services offered by the school. Increased
student retention and success were found among students who received matriculation
(Wolfe, 1998) and counseling services (Willett, 2001). Barr and Rasor (1999) also

12
found that community college freshmen involved in student services had increased
persistence even when demographic variables such as gender, ethnicity, and GPA were
matched. Enrolling in orientation courses also had a positive influence on student
retention at the community college level (Derby & Smith, 2004; Derby & Watson,
2006).
Student-faculty interaction and student involvement with college services can be
considered the major components of academic integration proposed by Tinto (1993) in
his model. He emphasized the importance of interaction between the student and the
college in order for integration to occur, which will influence persistence. Although
Tinto suggested that both academic and social integration is essential for student
retention, in general, academic integration has a stronger impact on student retention at
the community college level (Pascarella & Chapman, 1983; Voorhees, 1987). This
conclusion can also be drawn from a meta-analysis conducted by Napoli and Wortman
(1996). In their meta-analysis, they selected research using keywords such as
persistence, academic integration, and community colleges. They concluded from the
six studies that they have found that academic integration has a large and positive
impact on student persistence among community college students compared to social
integration. In addition, the interaction is what Astin (1999) called the “student-faculty
involvement”, which is the most important category for student retention. Thus,
although community college retention studies have not been based upon any particular
theoretical models, some of the variables fit the conceptual models used in four-year
institutions.

13
The majority of persistence research in community college settings has used
single variables in their studies. However, multivariable studies are more useful in the
practical setting since in reality, numerous variables interact with one another to create
an overall effect, each with direct and indirect effects on student persistence. For
example, when Halpin (1990) investigated Tinto’s model among community college
students, he found that demographic background variable sets accounted for 24 percent
of the variance between persisters and nonpersisters, with environmental variable sets
accounting for an additional 27.9 percent. Integration variable sets contributed another
30 percent to the explanation of variance. Napoli and Wortman (1998) developed a
model based on Tinto’s (1993) model, but included additional psychosocial measures of
conscientiousness, agreeableness, psychological well-being, self-esteem, social support,
student satisfaction ratings, negative life events, and negative school events. Results
showed educational goals were associated with socioeconomic status and self-esteem.
Both social support and academic integration were significantly influenced by a variety
of background and psychosocial factors, social integration was associated with social
support, and persistence was influenced by many factors as well. When all of these
factors were combined, 89 percent of the persistence/withdrawal outcomes were
identified. Thus, the results of this study imply the complexity of variables that
intertwine to influence the ultimate persistence behavior of students. When multiple
variables are investigated simultaneously, it allows the researcher to examine the
interrelationships between the variables which exist in real life.

14
Other than the studies mentioned above, only a few researchers have
investigated multiple variables simultaneously to explore the influence on student
persistence in the community college setting. Hawley and Harris (2005) used multiple
indicators, and classified the variables into three categories; barriers, motivations and
aspirations, and expectations. Each was found to have a distinct influence on student
persistence. Robbins and his colleagues also utilized multiple variables (Robbins, Allen,
Casillas, Peterson, & Le, 2006) while controlling for demographic and prior academic
achievements. They investigated the influence of motivational, skill, social, and self-
management scores on student retention. Although these studies are helpful in
providing information for college administrators, further research is necessary in order
to expand the comprehension of student persistence problems in community college
settings.
Research on persistence among community college students has been somewhat
skewed. Much of the attention has been given to demographic risk factors, such as age,
ethnicity, past academic performance, financial status, and registration behaviors.
However, recent developments show that environmental factors such as faculty-student
interaction and student services are also associated with student persistence.
Furthermore, although the number of studies that investigated psychosocial variables is
scarce, the importance of those factors should not be overlooked. Research has been
limited in providing adequate information of the interrelationships between the diverse
variables shown to influence student persistence. Many of the research has focused on
a part of the problem by examining a few variables at a time so the overall picture of

15
student retention at the community college level has not been clear. There is a need to
examine the whole picture by investigating the aforementioned variables
simultaneously in order to not only observe the direct effects of each variable, but to
observe the interrelated effects of the variables. Only then can we understand and
ultimately solve the retention problems of community college students.
Purpose of the study
The purpose of this study is to increase the understanding of persistence
behavior among community college students. More specifically, it is important to
determine why some students persist in their academic endeavors while others drop-out
even when demographic, academic, and financial variables are matched. Factors that
have been associated with persistence in community college settings are environmental
variables (e.g., faculty-student interaction and student services) and psychosocial
variables (e.g., goals and self-efficacy).
Although all of these variables have separately been associated with student
persistence, not all have been investigated together to examine the relations to each of
the other variables and the overall effect they have on student persistence. Thus, the
question to be answered is what demographic, environmental, and psychosocial factors
influence persistence at a community college? Implications for community college
educators will be discussed, including possible intervention strategies to enhance
student persistence and retention.

16
Importance of the Study
Community colleges provide higher education to many low-income, first-
generation, minority students, who without the access to community colleges may not
be able to obtain their post-secondary degree at all. However, access alone is not
sufficient for these students to actually attain their degree or certificate.
Retention/persistence of community college students has become a major problem in
the past several decades. Attrition not only affects the student who loses the chance for
academic and career advancement, but also affects the colleges themselves due to
financial consequences. Thus, this problem of student retention/persistence is a nation-
wide issue about which students, administrators, and legislators are and should be
concerned.
Research has already established that students’ demographic variables such as
ethnicity, employment status, financial status, and academic ability influence a student’s
persistence in community college. Data show that increasing selectivity for students
increases student retention and graduation rates at four-year institutions (ACT, 2004).
However, because of their open-door policy, community colleges cannot select the
“most likely to finish” students merely to raise their retention rates. Community
colleges cannot control the working hours of students or their family responsibilities.
Many of the variables researched in the past do not contribute to any interventions
strategies to increase student retention. If that is the case, what variables should
community colleges target in order to solve the problem of low retention rates among
their students?

17
Different students with similar demographic backgrounds (e.g., low SES,
minority, and low parent educational attainment) and the same responsibilities (e.g.,
work, family, and finance) may differ in their outcomes of college education. Some
may decide to stay in college, while the others may decide not to. This study will
endeavor to determine what differentiates these students by matching background
characteristics. Then, fleshing out the variables that differentiates the two groups of
students would help educators address this problem more effectively. It is hoped that
the results of this study will help community college educators to better understand
factors that influence student persistence and consequently implement an intervention
strategy to increase student retention.
Research Questions
The research questions addressed by this study are as follows:
1. What background variables, financial variables, academic variables
influence students’ persistence in community college education?
2. Does academic integration and psychosocial variables influence student
persistence?
Figure 1 shows the hypothesized relationships between the variables of the research
question.

18

Figure 1. Relationship among Variables

Definitions of Terms
Persistence – Continued enrollment of community college, measured from a semester to
semester timeframe.
Retention – Continued enrollment of community college, measured in an academic year
timeframe.
Pre-college characteristics – Includes students’ backgrounds variables such as age,
gender, ethnicity, marital status, high school grade point average, and admissions test
score.
Academic integration variables – According to Tinto’s model (1993), it is
conceptualized as interaction between the student and the college in order for students
to be integrated into the college. This includes faculty-student interactions, classroom
Background Variables
Age
Ethnicity
Family
responsibility
Parental education
Financial Variables
Financial aid
Employment status
Academic Variables
High school GPA
Enrollment status
Academic Integration
Student-faculty
interaction
Psychosocial Variables
Goals
Self-efficacy

Student
Persistence

19
experiences, and various student services available for the students (Strauss &
Volkwein, 2004).
Personal Goals – Achievement goals, also known as educational and career aspirations,
and as the students develop these goals, the goals act as cognitive representations for
what the students are striving for or trying to attain (Pintrich & Schunk, 2002).
Educational aspiration represents students’ educational goals (e.g., AA degree, transfer
to 4-year institution, BS degree) and career aspiration represents student’s occupational
goals.
Self-efficacy – Person’s belief about their capabilities to perform in specific
circumstances in order to attain desired consequences (Pintrich & Schunk, 2002). Self-
efficacy relates to expectations about oneself and will influence the choice, effort, and
persistence in face of obstacles and aversive experiences. In case of student persistence
in community college education, academic self-efficacy is especially important.

20
Organization of the Study
The dissertation is organized into five chapters that include the following:
Chapter I provides an overview of the extent of the problem of student persistence and
establishes a rationale for the topic of this dissertation study. It provides an overview of
the community college persistence problem and highlights the research to better
understand the whole picture of student persistence.
Chapter II presents the various persistence models introduced in the literature.
Strength and limitations of the theory will be discussed. The second section presents
the underlying theory of goals and self-efficacy and its relation to student persistence in
particular. The discussion goes beyond community colleges and includes research
conducted at the 4-year institutions as well.
Chapter III presents the research methodology that was used in this study. The
research site, procedures, and statistical measures are described. Chapter IV presents
the results of the study, followed by Chapter V that presents discussion and conclusions
derived from the findings of this research. Limitations and recommendations are
discussed, as well as implication for practice at community colleges.

21
CHAPTER II
REVIEW OF LITERATURE
The review of literature will examine previous research findings regarding
factors associated with student retention. First, unique characteristics of community
college students will be reviewed, followed by persistence research concerning the
specific student population. Subsequently, based upon the variables introduced from
the persistence research at community colleges, specific variables including goal setting,
self-efficacy, and student-faculty interaction will be discussed. Each construct will be
reviewed, followed by discussions regarding its influence on student retention. Lastly,
summary of the research findings will be presented.
This literature review was developed using articles obtained by conducting a
search of the online databases from PsychInfo, Proquest, and ERIC. The key words
used in the search include the following: persistence, student retention, attrition, drop-
outs, motivation, community college students, college students, self-efficacy,
educational aspiration, student goals, academic integration, student-faculty interaction,
and academic achievement.
Although community college students are the targeted population of this
literature review, research conducted concerning community college students are less
abundant. The majority of persistence research has been conducted with four-year
university students. Therefore, studies using the university population were also
utilized in this study. However, it is important to note that the results should be

22
interpreted with caution due to the differences between community college students and
four-year university students.
Another aspect of this literature review that needs to be mentioned is that some
of the variables are used interchangeably. For example, student retention, persistence,
drop-out, and attrition are used as the same construct that measures students’ decision to
stay of withdraw from school. Also, educational aspirations, student goals, and
educational objectives are used in a similar fashion as well.
Persistence Research at Community Colleges
Community College Students
Community college students are characterized by a wide variety of people with
different life experiences, cultures, ages, aspirations, languages, and beliefs. All of
these factors influence the students’ experience on the college campus as well as in the
classroom. More than half of the students at community colleges are enrolled part time,
are employed at least 20 hours per week (Cohen & Brawer, 2003; Martens, Lara,
Cordova, & Harris, 1995), and have families as well as additional responsibilities
(Cohen & Brawer, 2003).
Students’ educational aspirations in community college range from learning how
to read and write English, to earning vocational certificates such as Emergency Medical
Technician or Occupational Therapist, to learning a specific skill, to gaining promotion
at their current job, or for personal enrichment (Bailey et al., 2004). Students may also
be testing the post-secondary institution in an inexpensive environment (Wild & Ebbers,
2002), or may have a clear goal to finish lower level educational requirements in order

23
to transfer to a four-year institution (Hoachlander et al., 2003). Community colleges are
flexible, and offer students various courses in order to fulfill their aspirations, and thus
attract various people from 85 year-old senior citizens to 16 year old high-school
student.
Due to the open admissions policy of community colleges, a typical community
college student is quite different from a traditional student at the four-year institution.
Typically, community college students have experienced less academic success in high
school compared to their university attending peers (Grimes, 1997), and are also less
prepared in basic academic skills (McCabe, 2000). Many of the community college
students are also first-generation college students who have a very different
characteristics compared to the traditional student (Terenzini et al., 1996), and many
come from lower socioeconomic status groups as well (Martens et al., 1995).
Therefore, students at the community colleges have very different attributes
compared to students at the four-year institutions. Thus, even though both populations
are considered higher education, research cannot be generalized amongst the entire
population. The characteristics of the students must be considered when interpreting
the studies.
Student Retention/Persistence Models
There have been numerous persistence/retention models that have been
developed in the past. The model most commonly referred to is Tinto’s student
departure model (1993). Tinto theorizes that students enter the postsecondary
institutions with a variety of personal, family, and academic characteristics and skills,

24
including initial commitment to college attendance and personal goals. These
commitments and goals are later modified and reformulated on a continuing basis
through ongoing interactions between the individual and the academic and social
structures and members of the institutions. If students enter college without support
from their friends and family members, their commitment level to the institution may be
low, leading to early departure from the institution. On the other hand, the student may
enter college with a strong commitment for a certain degree. However, if student’s
values and beliefs are different from that of the institution, students may experience
isolation, again leading to departure from the institution. According to Tinto, full
integration into the social and academic life of the institutional culture will result in
decreasing the student departure decision.
Bean and Metzner’s (1985) student attrition model associated student behaviors
to the decision to persist. They identified behaviors as actions shaped by students’
attitudes and beliefs that result not only from the experience within the institution but
from external factors as well, such as the student’s financial situation or family support.
The behaviors are based upon the perception of institutional quality as well as students’
perception of their own fit with the institutions.
Cabrera and his colleagues (1993) combined Tinto’s student departure model
and Bean’s student attrition model in an attempt to further explain students’
persistence/withdrawal behaviors. They found that the effects of environmental factors
outside of the institutions, such as financial issues and family support, are more
complex and have increased impact on students’ departure decision. Thus, Carbrera et

25
al. concluded that the retention model needs to consider the relationship between the
individual, institution, and environmental variables to fully understand students’
decision making processes.
Psychological theories have also emerged that have focused on theories of
motivation (Pajares, 2002). This is understandable since persistence/retention and
attrition are all actions people take (behavior), and behavior is psychologically
motivated. Bean and Eaton (2000) advanced Bean and Metzner’s (1985) attrition
model and offer an integrated model that focuses on both Tinto’s integration and
psychological theories, specifically, self-efficacy, attributions, and coping styles.
According to their theory, the individual and his or her set of perceived strengths and
experiences from the past interact with the environmental variables of the present. For
example, students perform self-assessments regarding their efficacy toward writing a
paper and their experience with the faculty on campus in which the student just received
feedback from. Then, they will connect that particular experience to the how they
generally feel about the institution. In addition, since individuals from different cultures
and different genders perceive the world differently, the interactions of psychological
and environmental variables are different from person to person.
Finally, a social reproduction theory has been a perspective to examine student
persistence and attritions as well. Berger (2000) and McDonough (1997) suggested that
individual persistence rates may be attributed to the difference in individual’s
perception of their entitlements and the organizational habitus. Berger stated that the
congruence between cultural capital level of the student and that of the campus system

26
influence student persistence. For example, if the students feel they are out of place
because their peers come from high socioeconomic families whereas the student comes
from a low socioeconomic family, he or she may not persist with their education.
According to this framework, student attrition is a result of incongruence between
student and the institution, and its values and beliefs. Cabrera, Castaneda, Nora, and
Hengstler (1992) as well as St. John, Cabrera, Nora, and Asker (2000) also focused on
economic capital and its role in the persistence process, but their theory is not well
established.
Summary
Numerous retention models have been developed in the past. There are models
that emphasize students’ integration into the institution, environmental factors,
psychosocial factors, and economic factors. Although many models have been
developed, it does not seem that there is one model that explains the entire phenomenon
of student retention and withdrawal from college. Integration of various models is
suggested to investigate student retention as a whole.
Student Persistence/Retention Models for Community College Students
As portrayed in the previous section, numerous models for student
persistence/retention have been developed in the past. However, the majority of the
studies have been conducted among four-year university students (e.g., Cabrera et al.,
1993; Pascarella & Terenzini, 1991) and the use of these models among non-traditional
students or minority students have been problematic (Cabera, Nora, & Casteneda 1996;
Rendon et al., 2000; Tierney, 1992). Given that student characteristics are very

27
different at the community colleges and at the four-year institutions, it is easy to assume
that these models may not apply to the community college students. Furthermore,
factors influencing student persistence/withdrawal decisions may not be the same.
For example, Tinto’s (1993) student attrition model suggested that increased
integration into the social and academic systems of the college will lead to greater
commitment to the college and goals, thus leading to increased persistence. However,
studies on students attending community colleges have shown that only 20 percent of
students participate in school clubs as compared to 50 and 67 percent at public and
private four-year institutions (Coley, 2000). Students cannot participate in these clubs
because the majority of students attending community colleges balance academics with
commitments to family and off-campus employment. Moreover, a large percentage of
students and faculty members are part-time, thus generally leave campus after class.
Therefore, the traditional notion of student involvement as measured by social
integration seems unsuited when examining community college students (Hagedorn,
Maxwell, Rodriguez, Hocevar, & Fillpot, 2000).
Although many of the variables are thought to influence student persistence at
the community college level, there is no one traditional model that seems to fit
community college students, considering the unique population of students. Therefore,
it is important to investigate student retention at community college settings from a
different perspective. The following section will review all of the persistence research
conducted at the community college level. Common variables will be noted to guide
the structure of this literature review.

28
Persistence Research at the Community College Settings
Although literature on retention and persistence among community college
students are not as abundant as among the four-year institutions, several researchers
have investigated various factors that influence student retention. Upon searching the
database of PsychInfo and ERIC using the terms “student
retention/persistence/dropout/attrition” and “community college students”, a total of
forty-four studies have been found that investigated student retention, persistence, or
attrition at the community college level. Three of the studies were published in the
1980s, 23 were published in the 1990s, and 18 of the studies were published after 2000.
The summaries of the studies are listed in Tables 1 through 3. The columns included
the following: (a) author and date published; (b) constructs measured; (c) assessment
instruments used; and (d) main findings.
Among the 44 studies, numerous variables were found to be associated with
student retention/persistence among community college students. The variables can be
classified into several different categories, including demographic factors, financial
factors, academic factors, academic integration factors, and psychosocial factors.

29
Demographic Factors
Numerous demographic variables were associated with student
retention/persistence including gender, age, ethnicity, family responsibility, and
socioeconomic status. Among the 44 studies, two studies associated gender and student
persistence (Lanni, 1997; Voorhees, 1987), seven studies associated age (Brooks-
Leonard, 1991; Cofers & Somers, 2001; Feldman, 1993; Hagedorn et al., 2001; John &
Starkey, 1994; Lanni, 1997; Windham, 1995), six studies associated ethnicity (Cofers &
Somers, 2001; Feldman, 1993; Hawley & Harris, 2005; Hippensteel, John, & Starkey,
1996; John & Starkey, 1994; Lanni, 1997), two study associated family responsibility
(Fralick, 1993; Swager, Campbell, & Orlowski, 1995), and three studies associated
socioeconomic status with student persistence (Garardi, 1996; Hippensteel et al., 1996;
Pascarella, Smart, & Ethington, 1986).
Most of the studies included demographic variables in the research process.
Therefore, a list of summaries containing demographic variables can be found
throughout Tables 1 through 3. The studies are listed in alphabetical order of the author.
Financial Factors
Financial variables associated with student retention/persistence include
employment status and the effect of tuition and financial aid. Among the 44 studies, 10
studies associated students’ employment status and student persistence (Bers & Smith,
1991; Brooks-Leonard, 1991; Cofers & Somers, 2001; John & Starkey, 1994; Lanni,
1997; Schmid & Abell, 2003; Snell, Mekies, Green, & Tesar, 1993; Swagaer et al.,
1995; Windham, 1995) and six studies associated the influence of tuition and financial

30
aid with student persistence (Cofer & Somers, 2000; 2001; Hippensteel et al., 1996;
John & Starkey, 1994; Lanni, 1997; Makuakane-Drechsel & Hagedorn, 2000).
Studies including financial variables are listed and summarized in Table 1.
However, since other researchers used financial variables in their studies, several results
can be found in Table 2 and 3 as well. The studies are listed in alphabetical order of the
author.
Academic Factors
Several academic variables were associated with student retention/persistence
including standardized test scores, high school GPA, cumulative GPA, enrollment
status, and registration time. Among the 44 studies, four studies associated
standardized test scores to student persistence (Borglum & Kubala, 2000; Garardi,
1996; Lanni, 1997; Webb, 1988), five studies associated high school GPA (Feldman,
1993; Kirby & Sharpe, 2001; Pascarella et al., 1986; Snell et al., 1993; Webb, 1988), 11
studies associated cumulative GPA (Brooks-Leonard, 1991; Campbell & Blakey, 1996;
Cofers & Somers, 2001; Fralick, 1993; Hawley & Harris, 2005; John & Starkey, 1994;
Makuakane-Drechsel & Hagedorn, 2000; Mohammdi, 1994; Okun et al., 1996; Tinto,
1997; Windham, 1995), 12 studies associated enrollment status (Brooks-Leonard, 1991;
Cofer & Somers, 2000, 2001; Feldman, 1993; Hippensteel et al., 1996; Kirby & Sharpe,
2001; Lanni, 1997; Schmid & Abell, 2003; Windham, 1995) and three studies
associated registration time to student persistence (Freer-Weiss, 2004; Smith et al.,
2002; Summers, 2000).

31
Studies including academic variables are listed and summarized in Table 1.
However, since other researchers used academic variables in their studies, several
results can be found in Table 2 and 3 as well. The studies are listed in alphabetical
order of the author.

Table 1. Demographic, Academic, and Financial Variables and Student Persistence

Study Construct(s) Measured
Assessment
Instrument(s)
Main Findings
related to Persistence
Borglum
&
Kubala
(2000).
Student skills and
ability
Pre-entry attributes
Goals and intentions
Social & academic
integration
Withdrawal
Computerized
Placement Tests
(CPTs)
Enrolled Student
Satisfaction Survey

No correlation
between
academic/social
integration and
withdrawal rates.
+ CPT scores
Cofer &
Somers
(2000).
Demographic
variables
College experience
College
characteristics
Aspirations
Year price
Financial aid
variables
Within-year
persistence
The National
Postsecondary Student
Aid Surveys of 1995-
96 (NPSAS: 96)
database
No correlation
between ethnicity
& income-
persistence.
+ Full-time
students
- Tuition
- Low and middle
level debt
+ high levels of
debt

Feldman
(1993)
Demographic
variables
Academic variables
Goals
One-year retention
Student from Niagara
County Community
College
+ White student
- Students age 20-
24
- Part-time students
+ High school
GPA

32
Table 1 - Continued Demographic, Academic, and Financial Variables and Student Persistence

Study Construct(s) Measured Assessment Instrument(s) Main Findings related to Persistence
Freer-Weiss
(2004).
Demographic variables
HS experience
ASSET Placement test
scores
Degree objective
Application time
Attrition
Data obtained from
student’s file and the
University’s student
databases
Students who apply late have different
characteristics (age, sex, GED, and
degree objective) from students who
apply earlier.
- Late applicant students
Hippensteel,
John, & Starkey
(1996).
Demographic variables
High school experience
Aspirations
College experience
Price variables
Persistence
The National
Postsecondary Student
Aid Study (NPSAS-87)
database
Follow-up surveys in
spring.
+ African-American students
- Upper-middle-income adults
+ 2-year college freshmen
- Full-time students when their financial
needs were not met.
- Tuition charges
Student aids available to adults in 2-year
colleges insufficient to decrease the
negative effects of tuition on persistence.
Hippensteel, John,
& Starkey (1996).
Demographic variables
High school experience
Aspirations
College experience
Price variables
Persistence
The National Postsecondary
Student Aid Study
(NPSAS-87) database
Follow-up surveys in
spring.
+ African-American students
- Upper-middle-income adults
+ 2-year college freshmen
- Full-time students when their financial
needs were not met.
- Tuition charges
Student aids available to adults in 2-year
colleges insufficient to decrease the negative
effects of tuition on persistence.

33
Table 1 - Continued. Demographic, Academic, and Financial Variables and Student Persistence

Study Construct(s) Measured Assessment Instrument(s) Main Findings related to Persistence
Lanni (1997). Demographic variables
Academic variables
Financial aid information
Success level (graduation &
retention)
Administrative
information from
graduation & transcript
information
Follow-up survey
+/- Race, sex, age, full-time/part-time
enrollment status, and work status.
+ Math & English assessment
information.
+ Financial aid

Makuakane-
Drechsel &
Hagedorn
(2000).
Demographic variables
High school-related experience
College-related experience
Financial aid
Persistence
Data from UH
Community Colleges
Student Tracking System
(UHCCSTS)
+ Cumulative GPA
+ Average credit hours (full-time
enrollment)
+ Financial aid
Smith et al.
(2002).
Demographic variables
Academic variables
Registration period
Persistence
Registration form
College’s computerized
student database

- Late registrants
Summers
(2000).
Demographic variables
Academic intent
Financial aid eligibility
Enrollment and registration
behaviors
Course completion
Attrition
Survey developed by
researcher
- Enrollment and registration behaviors
(while controlling for student
characteristics)
34

Table 1 – Continued. Demographic, Academic, and Financial Variables and Student
Persistence

Study
Construct(s)
Measured
Assessment
Instrument(s)
Main Findings related to
Persistence
Swagaer et
al. (1995).
Demographic
variables
Course-related
variables
Personal and
situational variables
Withdrawal pattern
Data from the
Student
Withdrawal Form
Withdrawal patterns did
not differ between
gender, ethnicity and
GPA.
Most common reason
for “conflict with
work.”
“Childcare” and
“medical reasons” for
withdrawal among
females
Windham
(1995).
Demographic
variables
Academic variables
Financial aid
Student initial intent
Employment status
Attrition
College’s student
information
system
Florida Education
and Training
Placement
Information
Program
(PETPIP)
- Full-time employment
- College-prep courses
+ Young, entering
college directly from
high school, standard
high school diploma,
full-time enrollment,
and good grades.

Academic Integration Factors
Several academic integration variables were associated with student
retention/persistence including academic integration, student-faculty interaction, student
involvement, and orientation. Among the 44 studies, five studies associated academic
integration to student persistence (Bers & Smith, 1991; Buell, 1999; Napoli & Wortman,
1998; Pascarella et al., 1986; Snell et al., 1993), seven studies associated student-faculty
interaction (Grosset, 1991, 1997; Halpin, 1990; Heverly, 1999; LeSure-Lester, 2003;
Opp, 2002; Schmid & Abell, 2003; Tinto, 1997), three studies associated student
35

involvement (Barr & Rasor, 1999; Crawford, 1999; Tinto, 1997) and four studies
associated orientation with student persistence (Derby & Smith, 2004; Derby & Watson,
2006; Willett, 2001; Wolfe, 1998).
Studies including academic integration variables are listed and summarized in
Table 2. However, since other researchers used academic integration variables in their
studies, several results can be found in Table 3 as well. The studies are listed in
alphabetical order of the authors.
Table 2. Academic Integration Variables and Student Persistence

Study Construct(s) Measured
Assessment
Instrument(s)
Main Findings related
to Persistence
Barr &
Rasor
(1999).
Demographic variables
Academic achievement
benchmark
Persistence
Student services
+ Student services
on campus
Buell
(1999).
Demographic variables
Institutional integration
Commitment to earning
a degree
Family social support
Workplace social
support
Retention
Instructional
Integration
Questionnaire
(IIQ)
Perceived Social
Support Family
Scale (SSF)
Early Childhood
Job Satisfaction
Survey (ECJSS)
Integration in
college predicts
commitment to
earning a college
degree.
Family support
predicts
commitment to
earning a degree.
+ Family support
+ Committed to
earning a degree
Crawford
(1999).
Extended Opportunity
Programs and Services
program (EOP&S)
Cumulative GPA
Student involvement
Persistence
Management
Information
System (MIS)
Student Expenses
and Resources
survey (SEARS)
+ Involved in
college
+ EOP&S students

36
Table 2 - Continued. Academic Integration Variables and Student Persistence

Study Construct(s) Measured Assessment Instrument(s)
Main Findings related to
Persistence
Derby & Watson (2006). Demographic variables
Major-related variables
Academic variables
Orientation course
Retention

Database developed by the
department of Information
Technology
+ Orientation course
Derby & Smith (2004). Orientation course
Retention and persistence
+ Orientation program
Grosset (1997). Educational
intentions/commitment
Academic and social
integration
Persistence
Instruments from prior
research
Academic and Social
Integration Scale
+ Academic and social
engagement
Quality of
classroom/nonclassroom
experiences among faculty,
staff, and students had
positive effect on student
outcomes.
Halpin (1990). Background variable
Environmental variable
Integration variable
Persistence
Survey developed by
researcher
Instrument similar to Social
and Academic Integration
Scale
+ Integration (effects of
background and
environmental factors
controlled)
+ Academic integration
Heverly (1999). Process characteristics
Retention
Researcher developed survey
Comments obtained by
phone interview
+ Positive interaction with
faculty

37
Table 2 - Continued. Academic Integration Variables and Student Persistence

Study Construct(s) Measured Assessment Instrument(s)
Main Findings related to
Persistence
Napoli &
Wortman
(1998).
SES
Student goals
External commitment
Personality
Academic integration
Social integration
Positive and negative life
events
Social support
Self-esteem
Psychological adjustment to
college
Overall satisfaction with
college
Persistence
College Entrance Examination
Board’s (CEEB)
Student Involvement
Questionnaire (SIQ)
Institutional Attachment subscale
of Student Adaptation to College
Questionnaire (SACQ-IA)
NEO Personality Inventory
Student Involvement
Questionnaire –Academic
Integration (SIQ-AI) Student
Adaptation to College
Questionnaire (SACQ-AA)
Student Involvement
Questionnaire Social Integration
scale (SIQ-SI)
Social Adjustment subscale of the
SACQ (SACQ-SA)
Personal-Emotional Adjustment
subscale of SACQ (SACQ-PA)
Life Experience Survey (LES)
Self-Esteem scale (SE)
Student Opinion Survey (SOS)
+ Institutional commitment
+ Social integration
+ Academic integration
+ 1
st
semester GPA
+ Goal commitment
- Negative life events
- External commitment
- Negative school events
89% of the actual persistence
outcomes were correctly identified
by this model.
Academic integration directly
influenced by background,
cognitive, and psychosocial
factors.
Older students & females greater
initial institutional commitment
Negative school events inhibited
social integration.
Self-esteem and psychological
wellbeing positive effects on
academic integration.
Social support positive association
with academic integration.

38
Table 2 - Continued. Academic Integration Variables and Student Persistence

Study Construct(s) Measured Assessment Instrument(s) Main Findings related to Persistence
Opp
(2002).
Demographics of CSAO
Institutional characteristics
Barriers
Retention strategies
Program completion rate for
students of color
National Survey of Retention
Practices for Minority Students
National Center for Education
Statistics (NCES) Integrated
Postsecondary Education Data
System (IPEDS)
+ Colleges with CSAO of color
+ Having minorities on the board of
trustees
+ Having minority peer tutor
+ Percentage of faculty of color and
percentage of administrators of color
Pascarella
et al.
(1986).
Family background
Individual attributes
Precollege schooling
Educational aspiration
Institutional commitment
Academic & Social integration
Degree persistence
CIRP survey + Academic & social integration
+ Institutional commitment
None of the background
characteristics direct effect on
persistence.
Initial goal commitment no direct
influence on persistence.
+ High school academic achievement
(men)
+ Socioeconomic status and high
school social involvement (women)
Schmid &
Abell
(2003).
Demographic variables
Employment background
Educational background
Intention to return
Satisfaction with college
Informal interaction with faculty
Persistence
2001 Non-Returning Student
Survey
2001 Face of the Future Survey
2002 Graduate Exit Survey
College database for
demographic characteristics
- Full-time employment
- Part-time enrollment
+ Students who study consistently
+ Contact with faculty & interacting
with other students

39
Table 2 - Continued. Academic Integration Variables and Student Persistence

Study Construct(s) Measured Assessment Instrument(s)
Main Findings related to
Persistence
Snell,
Mekies,
Green, &
Tesar
(1993).
Demographic variables
Perception of college
Graduation
Researcher developed 16-item
questionnaire
+ High school GPA
+ Work 10 hours or less outside
classroom
+ Positive perception of college
Tinto
(1997).
Demographic variables
Coordinated Studies Program
(CSP)
Educational intentions
Perception of ability
GPA
Environment
Persistence
Researcher developed
questionnaire
Modified Pace’s Quality of
Student Effort Scale
+ Participation in the CSP
+ GPA
+ Hours studied per week
+ Perception of faculty
+ Factor score on involvement
with other students

Voorhees
(1987).
Demographic variables
Purpose of enrolling
Intent to return
Satisfaction with college
Academic integration
Persistence
American College Testing
programs’ Student Opinion
Survey to assess student attitudes
and opinions
+/- Gender, purpose for enrolling
and intent to return
Enrollment status, ethnicity,
satisfaction with institution had no
influence on persistence.
+ Academic integration (GPA,
number of informal contact with
faculty outside of class, number of
hours spent studying)

40

Table 2 - Continued. Academic Integration Variables and Student Persistence

Study
Construct(s)
Measured
Assessment
Instrument(s)
Main Findings related
to Persistence
Willett
(2001).
Follow-up
counseling
Persistence behavior
Course drop
Data extracted
from campus data
warehouse
+ Use of follow-up
counseling
Wolfe
(1998).
Matriculation
services
Retention
CAPP Database
Matriculation
extract
Student extract
Grade extract
+ Matriculation
services

Psychosocial Factors
Several psychosocial variables were associated with student
retention/persistence including student goals, family support, self-efficacy, and coping
styles. Among the 44 studies, studies associated student goals and student persistence
(Bers & Smith, 1991; Brooks-Leonard, 1991; Campbell & Blakey, 1996; Cofers &
Somers, 2001; Fralick, 1993; Grosset, 1991; Hagedorn et al., 2001; Hawley & Harris,
2005; John & Starkey, 1994; Kirby & Sharpe, 2001; Mohammdi, 1994; Okun et al.,
1996; Solis, 1995; Webb, 1988), studies associated family support (Buell, 1999; Okun
et al., 1996; Solis, 1995), studies associated self-efficacy (Garardi, 1996; Hagedorn et
al., 2001; Torres & Solberg, 2001), and one study associated coping styles with student
persistence (LeSure-Lester, 2003).
Studies including psychosocial variables are listed and summarized in Table 3.
The studies are listed in alphabetical order of the authors.

41
Table 3. Psychosocial Variables and Student Persistence
Study Construct(s) Measured Assessment Instrument(s) Main Findings related to Persistence
Bers &
Smith
(1991).
Demographic variables
Educational objective
Academic integration
Social integration
Institutional commitment
persistence
Current Student Survey
(CSS)

- Employment status
+ Students’ educational objectives
+ Academic and Social Integration
Brooks-
Leonard
(1991).
Demographic information
Educational objective
Academic information
Retention
Information obtained
from course registration
form
+ Educational objective
+ Full-time enrollment
- Full-time employment
- Age
+ First-term GPA

Campbell &
Blakey
(1996).

Demographic variables
Academic variables
ASSET scores
Degree goals
Persistence
ASSET data
Follow-up survey
+ Cumulative GPA
- Number of remedial courses
+ Degree seeking intent
Cofers &
Somers
(2001).
Demographic variables
College experience
Aspirations
Year price
Within-year persistence
The National
Postsecondary Student
Aid Surveys of 1992-93
(NPSAS: 93) and 1995-
96 (NPSAS: 96)
database
- African-American students
+ Students over the age of 30
+ Sophomore level students
- Tuition
- Low level of debt
+ High levels of debt.
+ Full-time students
+ Students with high or low grades compared to
students with “average” grades.
+ Higher degree aspirations

42
Table 3 - Continued. Psychosocial Variables and Student Persistence
Study Construct(s) Measured Assessment Instrument(s) Main Findings related to Persistence
Fralick
(1993).
Demographic variables
Employment status
High school experience
Goals
Persistence
Survey developed by
researcher
Gender and ethnicity had no relation to college
persistence.
- Academic, personal, financial, health, and
child-care problems
+ Definite goal or college major
Garardi
(1996).
Social background
Educational background
Standardized assessment
examinations
College performance
Self-concept of academic
ability
Graduation
Background data from
Official records of City
Technical college.
Brookover Self-concept
of Ability Scale

+ Family income of $20,000 or more
+ Father has some college education or more.
+ Assessment examination
+ Higher rating in the self-concept of ability
scale
Grosset
(1991).
Preentry attributes
Initial/subsequent goal and
institutional commitment
Academic and social
integration
External commitments
Persistence
Initial Questionnaire
and Follow-up
Questionnaire
developed by researcher
+ Clearly defined educational objectives &
definite educational plans for accomplishing the
goals
+ Classroom experiences and nonclassroom
interaction with faculty and advisors were
+ Value of college experiences (older students)
+ Institutional commitment (older students)
Hagedorn
et al.
(2001).
Demographic variables
Academic ability
Personal factors
Retention
Computerized
Assessment and
Placement Program
tests (CAPP)
+ Being younger
- Low assessment of academic ability
+ Full-time enrollment
+ Certainty of major

43
Table 3 - Continued. Psychosocial Variables and Student Persistence
Study Construct(s) Measured Assessment Instrument(s) Main Findings related to Persistence
Hawley
& Harris
(2005).
Demographic variables
Academic performance
Degree aspirations
Goals and values
Self-rating of abilities
Future activities
Personality characteristics
Behavioral characteristics
Persistence
CIRP Freshmen survey Student characteristics impacting
persistence: barrier, motivation and
aspirations, and expectations.
+/- Number of developmental classes; being
African-American or Latino; & cumulative
GPA
+ Intention to transfer to a four-year
institution
+ Time students plan to spend at the college
John &
Starkey
(1994).
Demographic variables
Financial background
High school experience
College experience
Educational aspirations
Cost variables
Persistence
The National
Postsecondary Student
Aid Survey Study
(NPSAS-87) database
Follow-up surveys in
spring.
+ African-American students
+ Age
+ Students with GED
+ Working students
+ Full-time enrollment
+ Freshman status
+ GPA
- Tuition charges and grant awards.
+ Vocational aspirations
Kirby &
Sharpe
(2001).
Demographic characteristics
Academic background
Support service needs
Educational goals
Academic and extracurricular
behaviors
Persistence
The Partners in
Education Inventory
from the Freshman
Integration and
Tracking System
Student Experience
Inventory
- Part-time students
Employment status had no influence on
persistence.
+ Higher math scores
+ Cumulative high school GPA
- Occupational

44
Table 3 - Continued. Psychosocial Variables and Student Persistence
Study Construct(s) Measured Assessment Instrument(s) Main Findings related to Persistence
LeSure-
Lester
(2003).
Peer group/ faculty interactions
Institutional & goal
commitment
Concern for student
development
Academic and intellectual
development
Coping styles
Persistence
Persistence/Voluntary
Dropout Decisions Scale
(P/VDD)
The COPE Inventory
+/- Coping styles (Latino student)
+ Active, dispositional style
- Denial and alcohol-drug
disengagement coping style
+ Academic development, faculty
concern, and faculty interest in
students
Mohammdi
(1994).
Demographic variables
Academic achievement
variables
Enrollment status
Student goals
Retention
Data from historical AKT
files (permanent data on
students, instructors, and
classes stored by Virginia
Community College
System)
+/- Enrollment status; credit hours;
GPA
+ Student goals

Okun et al.
(1996).
Demographic variables
Commitment
GPA
Credit load
Intention
Encouragement from others
Institutional departure
IBM response sheet Demographic variables such as gender,
age, ethnicity, marital status, number
of children, and working hours did not
influence institutional departure.
+ Semester GPA
+ Commitment to doing well in college
+/- Encouragement from others
- Students enrolled for three or fewer
credit hours

45
Table 3 - Continued. Psychosocial Variables and Student Persistence
Study Construct(s) Measured Assessment Instrument(s) Main Findings related to Persistence
Solis
(1995).
Commitment to attend
Motivation to persist
Satisfaction with academic
experience
Commitment to school work
Satisfaction with instruction
Family support
Future job prestige
Intent to persist
Researcher developed
questionnaire
+ Motivation to persist &
commitment to attend
+ Family support and future job
prestige
Torres &
Solberg
(2001).
Self-efficacy
Stress
Social integration
Family support
Health
Persistence
College Self-Efficacy
Inventory (CSEI)
College Stress Inventory
(CSI)
25-items from Social and
Academic Integration
Scale
Social Provisions Scale
(SPS)
College Distress
Inventory (CDI)
+ Self-efficacy
Self-efficacy important determinant
of educational outcomes.
Family support influenced the level
of academic self-efficacy.

Webb
(1988).
Demographic variables
ASSET test score
Educational goal
Certainty of academic major
Needs help factor
Freshman year retention
ASSET data tapes, which
ASSET Educational
Planning form
+ Being a day student; high school
graduate status; & ESL student
status.
+ ASSET test scores
+ Educational goal and certainty of
academic major
46

Summary
Community college students differ from four-year university students in terms
of educational aspirations, demographic backgrounds such as age, ethnicity, and
socioeconomic status, experience, and academic abilities. Due to these disparities,
reasons for students’ departure decisions differ among the two student populations.
Therefore, although numerous student retention models have been developed based on
four-year university students, it is likely that those models do not apply to community
college students.
A number of researchers have investigated student retention at the community
college level using multiple variables. The variables used in 44 studies can be
categorized into demographic factors, academic factors, financial factors, academic
integration factors, and psychosocial factors. In the following sections, each of the
variables will be discussed as it relates to community college student retention. Since
community colleges have open admission policies, intervention cannot be targeted
towards demographic, academic, or financial factors. However, it is still important to
have an understanding of how those factors influence students’ decision to persist or
withdraw. In addition, further understanding of academic integration and psychosocial
factors will help identify effective intervention strategies necessary to increase student
retention.
Demographic Factors Associated with Student Persistence
According to the persistence research conducted with community college
students, several demographic variables were focused to have significant associations
47

with student persistence. The variables that will be reviewed in the following section
are age, ethnicity, family responsibility, and socioeconomic status.
Age
Age of the students has been considered to focused various factors in the
academic realm such as grade point average (GPA) and the use of achievement goals.
Owens (2003) investigated how age influences community college students’ GPA. He
found a weak but significant relationship between the two variables, but cautioned that
additional variables such as finance, goals, family background, pre-college schooling
may also have an effect on the relationship. In another study, Brooks, May, and Morris
(2003) investigated the influence of age on students’ achievement goal orientation and
coping style. They found that as age increased, the use of learning goals increased
compared to the use of performance goals. Student who used learning goals were also
more likely to use a wide variety of coping behaviors.
In the study of college persistence, age seems to be a major factor. Numerous
studies conducted on community college students have demonstrated that college
persistence may vary with age. However, the studies showed contradicting results.
Several researchers found negative relationship between age and community college
persistence (Brooks-Leonard, 1991; Hagedorn et al., 2001; Lanni, 1997; & Windham
1995). Their studies demonstrated that as student age increased, persistence rates
reduced significantly. Therefore, the younger students were more likely to persist than
the older students. On the contrary, Wall (1996) found a positive relationship between
age and persistence. Her study demonstrated that older students (i.e., over 24) were
48

more successful (retention) than younger students. Lastly, Feldman (1993) found a
curvilinear relationship between age and community college persistence. According to
her logistic regression analysis, students at the highest risk of dropping out were those
in the age range of 20-24. Students in this age range were 1.77 times more likely to
drop out than the students aged 19 or younger. Furthermore, students in the age group
of 25 and older were less likely than the youngest students to drop out.
These contradictory results can be addressed by several reasons. Research has
consistently found that older students have more competing demands for time compared
to younger students (Home, 1998). Older students are more likely to have acquired
several different roles, such as being an employee, a spouse/life partner, or a parent.
The more competing roles the students have acquired, the less they will be able to focus
on the role of a - student (Jacobs & Berkowitz King, 2002). In addition, because of the
competing roles, older students are more likely to be enrolled part-time rather than full-
time resulting in longer time to finish compared to their younger counterparts (Horn &
Carroll, 1997). Therefore, older students tend to have lower retention rates compared to
younger students.
On the other hand, several researchers indicated potential advantages older
students may have. For example, Spannard (1990) suggested that older students tend to
be more vocationally oriented and see the benefits of schooling. Thus, clearer goals
may lead to better work habits, consequently leading to high retention rates. Elman and
O’Rand (1998) suggested that older students have greater financial resources, thus
providing support for tuition allowing them to continue their education.
49

Summary
Although the reasons for contradictory effects of age on student retention have
not come to a conclusion, it is clear that age has a significant association with
community college persistence. However, it is also important to acknowledge
additional variables that may be interacting with the age influence.
Ethnicity
Researchers have been investigating the relationship between academic
performance and demographic characteristics, especially student performance across
ethnic groups for a long time (Bankston & Caldas, 1997). Some studies investigated the
ethnic differences between beliefs and attitudes toward academic performance
(Linnehan, 2001) or the influence of work-related stress on overall academic
achievement (Hey, Calderon, & Seabert, 2003; Sy, 2006). The researchers found
significant differences between ethnicity.
In a study examining the influence of ethnicity on academic achievement, Strage
(1999) found that White students earned significantly better GPA compared to Hispanic
students among university students. Similarly, in a study of community college
students, both Zhao (1999) and Weissman, Bulakowski, and Jumisko (1998) found that
ethnicity and students’ GPA were significantly associated. White students had higher
GPA compared to non-White students.
Comparable results were found when investigating the influence of ethnicity on
student persistence. In a study of university and community college students in clinical
laboratory education programs, Laudicina (1999) found considerable difference
50

between the graduation rates among different ethnicities. Asian students had the
highest graduation rate while the African-American students had the lowest rate.
Native Americans, Hispanics, and white students were in the middle. Rowser (1997)
compared African-American and White university students and their drop-out rates.
She also found a significant difference between the two groups, even after societal
disadvantages that African-Americans have had were considered in the analysis.
Among research in persistence in community college settings, ethnicity was also
mentioned by several researchers. However, the association between ethnicity and
student persistence is mixed. Studies conducted by Cofer and Somer (2001) as well as
Feldman (1993) found that white students had higher retention rates than minority
students. Hawley and Harris (2005) also found that in their study of predominantly
Black community college (77% of the student population was Black), being Black and
Latino were strong predictors of retention while being a Mexican-American student was
a significant predictor of dropout. On the other hand, Voorhees (1987) and Brooks-
Leonard 1991) did not find ethnicity to be associated with persistence in their studies.
These mixed results may be attributed to how ethnicity related factors are
intimately related to socioeconomic factors. For example, Laudicina (1999) suggested
that the result of her study was consistent with studies that relate differences in
graduation rates among ethnic groups to socioeconomic status. African-American
students are more likely to have families with poorer socioeconomic background which
may have resulted in receiving low quality primary and secondary schoolings,
consequently putting them at a disadvantage in the higher educational setting (Leppel,
51

2002). Furthermore, Graham (1994) found that grade differences normally found
between African-American students and other groups were not replicated, when
socioeconomic status was taken into account.
Summary
The influence of ethnicity on student persistence needs to be interpreted with
caution. Although the impact of ethnicity and race seems to be evident, there may be
additional variables that are interacting with the results. Therefore, similar to the age
factor in the previous section, it is important to keep additional interacting variables in
mind when studying the effect of ethnicity and race.
Family Responsibility
Although not many researchers have included family responsibility as a variable
that influences retention, several studies do indicate its importance. For example,
Swager and her colleagues (1995) utilized information from the Student Withdrawal
Form to investigate reasons for withdrawal from the community college. The results
from the open-ended self-reported form revealed that a number of students indicated
“childcare” as their reasons for withdrawal. The percentage of this result was seen
more frequently among females students compared to males. Similarly, Fralick (1993)
conducted a randomized sampling telephone survey among students who did not return
to the community college. He also found that a number of students mentioned
difficulties with child-care as a reason for leaving school. Lastly, Gabriel (2001b) also
conducted a Non-Returning Student Survey to ask the main reasons for not returning to
college. His study replicated the previous two, indicating that family reasons were one
52

of the most frequently given response as to why they did not return to the community
college other than work and financial reasons.
Although none of the studies conducted a statistical analysis for their results, it
is apparent that family responsibilities (e.g., childcare) is an important variable for
student retention. Further study need to be conducted in order to confirm these results.
Socioeconomic Status
During the past years, many researchers have come to an agreement that social
background characteristics of the family can predict educational achievement (e.g.,
Benbow, Arjmand, & Walberg, 1991; Conell, Aber, & Spencer, 1994). Family
background characteristics typically include parental occupation, parental education,
and socioeconomic status (SES).
Low SES has been associated with several student outcomes. In group
interviews conducted with recent high school graduates, Lindholm (2006) found that
SES influenced students’ decision to forgo college after high school graduation.
Walpole (2003) investigated university students and found that students from low SES
backgrounds study less, report lower GPAs, and have lower levels of educational
aspirations compared to their peers from higher SES. Thus, SES appears to have an
effect on students’ decision to go to college, as well as outcomes after college entry.
In college students’ persistence research, Goldrick-Rab (2006) discovered that
university students from low SES are more likely to follow pathways characterized by
interrupted movement, such as stop-outs and transfer, compared to students from
economically advantaged background. The interrupted movement often resulted in
53

dropping out of college altogether. Studies conducted at community colleges show
similar influence of SES on student persistence. Garardi (1996) found that when family
income is $20,000 or more, the likelihood of the individual graduating increases.
Pascarella et al. (1986) also found that for women, the SES had a positive direct effect
on degree persistence.
Several explanations have been suggested by researchers regarding the influence
of SES on student outcomes. For example, Astone and McLanahan (1991) found that
parents of higher SES are more involved in their children’s education compared to
parents of lower SES, and the greater parental involvement promotes positive student
attitudes thus enhancing academic achievement. The socioeconomic status also predicts
parental expectations and definitions of success (Laureau, 1987; McDonough, Korn, &
Yamasaki, 1997). Parents of low SES tend to view high-school diploma as the norm
whereas high SES parents considers a bachelor’s or advanced degree a norm. Thus,
SES seems to influence parental involvement and expectations, resulting in different
student educational attainment. Furthermore, students from high SES are more likely to
have parents who attended college, and are more likely to have access to critical
information and financial resources necessary to complete the college education process
(Goldrick-Rab, 2006).
Summary
According to the previous research, socioeconomic status contributes to
students’ academic outcomes as well as their persistence decisions. Students who come
from low socioeconomic status may have decreased parental involvement in their
54

education or have decreased parental expectations. In addition, students from high
socioeconomic status may have parents who attended college, which helps students
transition into the college environment. Therefore, it is crucial to include the SES
variable into the study of students’ persistence, along with variables closely related to
SES such as parents’ educational level.
Section Summary
Several of the demographic factors identified in community college persistence
research were reviewed in this section. Those factors include age, ethnicity, family
responsibility, and socioeconomic status. Results of the literatures suggest that age is
associated with student retention. In general, the older the students, the less likely they
will persist. Older students tend to have increased responsibilities other than being a
student, which may influence this relationship. Ethnicity has also been associated with
student retention, but the results are mixed. Researchers suggest that socioeconomic
status rather than ethnicity may be the larger influence. Not many studies were
conducted regarding family responsibilities, but increased responsibility tends to relate
to increased drop outs. Lastly, the influence of socioeconomic factors was reviewed.
The SES related significantly to student retention, which can be explained by the
influence SES has on parental involvement, social capital, financial difficulties, and so
on.
In summary, all of these demographic variables are associated with student
retention. It is important to incorporate these variables into the study, by not only
examining the direct effects of each variable but also investigating the interrelationships
55

between the variables. For example, the interrelationship between ethnicity and SES
should not be overlooked. The following section will discuss financial factors that
influence student persistence. Although it is discussed in a separate section, financial
variables are closely associated with demographic variables as well.
Financial Factors Associated with Student Persistence
According to the persistence research conducted with community college
students, several financial variables have been found to have a significant association
with student persistence. The variables that will be reviewed are the influence of
employment, tuition, and financial aid.
Employment
With college tuition costs rising and the reductions in availability of financial
aid, the number of students working their way through college has increased greatly
(Hey, Calderon, & Seabert, 2003). However, studies have indicated negative effects of
employment during college (Broadbridge & Swanson, 2006). For example, Ross,
Niebling, and Heckert (1999) as well as Hey and his colleagues (2003) found that
having a job increases the overall stress level of students. Employment has also been
found to negatively affect academic outcomes. In a study of nursing students,
Salamonson and Andrew (2006) found that working more than 16 hours per week had a
detrimental effect on their academic performance. Lammers, Onwuegbuzie, and Slate
(2001) also found that students who spent more time working per week were less likely
to attend class regularly, to review the materials, be sleep deprived, thus effecting study
skills negatively resulting in decreased academic outcomes.
56

Employment has also been negatively associated with student retention. In a
study conducted by Swager and her colleagues (1995), conflict with work was one of
the prominent self-reported reasons for withdrawal among community college students.
Similarly, Gabriel (2001a) conducted a research at Northern Virginia Community
College to investigate the reasons of non-persistence. Results from the telephone
interviews showed that work and financial issues were reasons not to return to college.
Numerous quantitative studies also showed similar results. Conklin (1995) as well as
Bers and Smith (1991) found that employment significantly contributed to the
difference between persisters and nonpersisters in the community college.
Although negative impact of employment has been widely demonstrated in the
literature, some researchers suggest positive effects of employment. For example,
Hodgson and Spours (2001) proposed that when employment is related to their degrees,
students can utilize their work to collect data for assignments and dissertations. In
addition, contrary to studies showing negative influence of employment and persistence,
Beeson and Wessel (2002) found that students working on campus had increased
academic persistence compared to non-workers. The researchers attribute this positive
relationship between on-campus employment and persistence to increased integration
into the institutions. However, these positive effects were found in four-year university
students.
Summary
Overall, the results of the literature demonstrate a negative impact of
employment on student outcomes. Employment seems to be detrimental among
57

community college students and their decision to persist in their college life. Although
some positive effect of employment is found in the literature, in general, the negative
influence is attributed to students being pre-occupied by their work instead of their
academic tasks.
Tuition and Financial Aid
Considering that many college students support themselves during their college
life (Hey et al., 2003), it is easy to assume the influence tuition has on students’ college
persistence behaviors. In a qualitative study conducted at Del Mar College, students
indicated that lack of money was one of the main reasons they discontinued their
college education (Bonham & Luckie, 1993). Wessel, Bell, McPherson, Costello, and
Jones (2006) conducted a longitudinal study with a large sample size and also found
that students who have greater financial need were more likely to disqualify and less
likely to persist until graduation. These studies demonstrate how students’ financial
hardship can influence student persistence.
Additional studies at community colleges also demonstrated the effect of
financial expenses on student persistence. John and Starkey (1994) investigated a large
national sample of community college students using data from the National
Postsecondary Student Aid Survey of 1986-87 (NPSAS-87) and found that tuition
increases had a negative impact on student persistence. Cofer and Somers (2000, 2001)
replicated this study using the data from 1993 and 1996 NPSAS and also found the
same negative relationship. Hippensteel and his colleagues (1996) also conducted a
similar study, but specifically investigated non-traditional students enrolled in
58

community colleges. Consistent with the other results, they also found a negative
influence of tuition on student persistence.
On the other hand, presence of financial aid has the opposite influence on
college persistence. In a study of university students drawn from a national database,
Titus (2006) found that financial aid positively influenced students’ college completion
rates. Similarly, results from the studies conducted at community college settings
demonstrated comparable outcomes (Lanni, 1997; Makuakane-Drechsel & Hagedorn,
2000). The researchers found that students who received financial aid were more likely
to persist compared to those who did not receive the aid.
Several explanations were suggested on how financial aid increases students’
persistence behavior. St. John and his colleagues (1990) suggested that financial aid is
effective in compensating for the disadvantages students from low income families have.
Since low SES (Garardi, 1996; Goldrick-Rab, 2006) and lack of money (Wessel et al.,
2006) both have significant influence on student persistence, it is apparent that extra
funds will help students in need. In addition, receipt of financial aid will relieve
students from their stress of employment, thus allowing students to engage in social
activities or become integrated into the institution (Cabrera, Nora, & Castaneda, 1992).
The availability of financial aid will help eliminate the effect of income disparities that
influence student persistence.
Summary
The results from past literature suggest that financial issues influence student
retention. Since many community college students support themselves and pay their
59

own tuition, increase in tuition has a negative influence on student persistence. On the
other hand, financial aid which is intended to help students overcome their monetary
problems, have a positive influence on student persistence. Increased availability of
financial aid may help eliminate the socioeconomic barriers some student encounter.
Section Summary
Financial factors influence student persistence in community college settings.
Findings from the studies showed that in general, employment has a negative effect on
student persistence. Especially students who work full-time were less likely to persist
in their college education compared to students who work less. In addition, since most
college students are financially challenged, tuition increase also has a negative impact
on student persistence. On the other hand, receipt of financial aid helps improve student
retention because it aids students in their financial difficulties. This can be considered
one of the intervention possibilities in order to raise student retention.
Academic Factors Associated with Student Persistence
According to the persistence research conducted with community college
students, several academic variables have significant associations with student
persistence. The variables that will be reviewed are pre-entry academic characteristics,
college academic ability, enrollment status, and registration behaviors.
Pre-entry Academic Characteristics
Students’ pre-entry academic characteristics have been consistently linked to
various academic outcomes in the college setting (Gillespie, 1993; Harackiewiez,
Barron, Taner, and Elliot, 2002; Smittle, 1995). Pre-college academic characteristics
60

have been measured by high school GPA or by placement or assessment tests taken at
the early stages of college life.
High School GPA
Several researchers have associated high school GPA with academic
performance in college. Harackiewiez et al. (2002) conducted a longitudinal study on
university students and their academic performance using high school GPA as a
variable. They found that high school performance measures predicted both short and
long term academic performance among the university students. Jansen and Bruinsma
(2005) conducted a similar study in Netherlands and found that among university
students, high school GPA was the most important predictor of academic achievement.
In a community college setting, the results were comparable. In a correlational study,
Smittle (1995) found that the strongest relationship occurred between high school GPA
and college GPA.
High School GPA has also been associated with persistence behaviors in college.
In a study of university students, Allen (1999) found that high school GPA had a
significant effect on withdrawal decisions. Similar results have been demonstrated by
number of researchers with community college students. In a longitudinal study
conducted by Pascarella and his colleagues (1986), they found that for men, high school
GPA had a positive direct effect on degree completion. Feldman (1993), Kirby and
Sharpe (2001), as well as Hagedorn and her colleagues (2001) all discovered that high
school GPA is significantly associated with college persistence. When the persister
group was compared to the withdrawal group, they found that the persister group had
61

achieved significantly higher high school GPA compared to the withdrawal group.
Thus, the higher the students’ high school GPA, the less likely they will drop out.
Placement Tests and Standardized Tests
Although high school grades have been shown to be significantly associated
with students’ academic performance and persistence in college settings, there are
criticisms that high school grades do not take into account the differences between
schools in both expectations and performance (Bassiri & Schulz, 2003). Another
method of measuring students’ pre-entry academic characteristics proposed in the
literature is the use of standardized test scores. Similar to high school GPA,
standardized test scores have been associated with college academic performance
(Robbins, Lauver, Le, Davis, Langley, & Carlstrom, 2004).
There are several standardized tests used among colleges. For example, the
American College Test (ACT) Assessment is designed to measure academic
achievement within four areas: English, mathematics, science, and reading, while the
COMPASS is also designed to measure students’ mathematical, reading, and writing
skills. Both the ACT and COMPASS assessment scores are considered highly reliable
predictor of success in college (Robbins et al., 2006). Robbins and his colleagues found
that the standardized test scores best predicted first semester and first year GPA for the
students.
Other than the standardized tests, colleges use their own placement tests in order
to help determine the entering students’ starting academic level (Borglum & Kubala,
2000). Students who took the Scholastic Aptitude Test (SAT) or the ACT and placed
62

above the appropriate level are exempt from the tests, but the remaining students take
the placement test upon entry into the college. Similar to the standardized test scores,
placement test scores have also been associated with students’ academic performance in
the college setting, particularly their persistence behaviors.
In a study of community college students, Garardi (1996) found that reading and
writing assessment scores increase predicted the chances of student graduation.
Likewise, Lanni (1997) discovered that English assessment scores were associated with
retention. Windham (1995) on the other hand found that math placement scores
predicted student retention. From a slightly different view, Hawley and Harris (2005)
claimed that rather than the score itself, the amount of developmental coursework
students are required to complete due to the test scores were the highest predictors of
student dropouts. They proposed that the more developmental coursework a student is
required to take, the less likely student will persist with their college education.
However, Kreysa (2006) demonstrated contradicting results to the above study. In his
study of remedial and non-remedial students, he concluded that the students did not
differ in their graduation rates. He even suggested that enrolling in remedial courses
showed a positive relationship to persistence. Campbell and Blakey (1996) also found
similar results, relating the number of remedial courses to positive community college
student persistence. They also suggested that taking remedial course within the first
year increased student persistence. Nonetheless, Kreysa’s study was conducted with
university students while the other studies were at a community college. Therefore,
interpretation of these results should be done with caution.
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Summary
The results of the past literature show that students’ pre-college academic ability
measured by high school GPA and standardized test scores or placement scores predict
their college academic performance as well as their persistent/withdrawal decisions. In
general, those students who were successful in high school tend to succeed in college as
well. However, study from Hyers and Zimmerman (2002) discovered that some
students with the best pre-entry academic characteristics had less than expected
graduation rates. Therefore, even though past academic performance is important for
student success, their study implies that other variables do influence students’
persistence behaviors.
College Academic Ability
Another academic factor that is consistently associated with students’
persistence is college academic performance (Kirby & Sharpe, 2001; Leppel, 2002;
McGrath & Braunstein, 1997). Lufi, Parish-Plass, and Cohen (2003) studied college
students and found that academic persistence was positively correlated with college
GPA, when no other differences were found for personality and demographic variables.
However, the researchers caution that since the study was correlational, it is not clear
whether success in college contributed to persistence or vice versa. Another study
conducted by DeBerard, Spielmann, and Julka (2004) found that cumulative GPA was
significantly correlated with retention. However, they also found 10 predictors (e.g.,
health and psychosocial variables) that substantially correlated with the GPA, indicating
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that the relationship between GPA and retention may be moderated by the other
variables such as smoking, drinking, social support, and maladaptive coping strategies.
In the community college setting, numerous researchers have demonstrated the
association between college GPA and student retention. Brooks-Leonard (1991),
Windham (1995), and Mohammadi (1994) all concluded that high GPA was a
significant predictor of student retention among community college students. Campbell
and Blakey (1996) as well as Zhao (1999) replicated the results when specifically
investigating underprepared community college students. Makuakane-Drechsel and
Hagedorn (2000) also found consistent results when they examined Hawaiian students’
persistence behaviors.
Summary
Past literature clearly shows that college GPA is associated with student
persistence among college students. The higher the college GPA, students’ persistence
is increased. However, as Lufi and his colleagues (2003) have indicated, correlational
studies do not explain how the two variables relate to one another. In addition, as
DeBrard and his colleagues (2004) suggested, other variables may be influencing the
relationship between academic performance (GPA) and student persistence. Additional
research is necessary to investigate the GPA-retention relationship.
Enrollment Status
In addition to academic abilities, students’ college experiences are associated to
student persistence (Cofer & Somers, 2001). In particular, numerous researchers have
indicated that students’ enrollment status relates to student persistence (Brooks-Leonard,
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1991; Feldman, 1993; Lanni, 1997; Mohammadi, 1994; Voorhees & Zhou, 2000;
Windham, 1995). These researchers investigated community college students and
found that students who attend college on a full-time basis were more likely to have
higher retention rates compared to students who attend on a part-time basis. Feldman
found that part-time students are 2.23 times more likely to drop out compared to full-
time students. Makuakane-Drechsel and Hagedorn (2000) investigated specifically
Hawaiian community college students and Hagedorn et al. (2001) investigated
specifically African-American males and found identical results as the other studies.
Several explanations have been offered about why enrollment status may
influence student persistence. Makuakane-Drechsel and Hagedorn (2000) proposed
that full-time enrollment enhances academic integration of community college students.
Academic integration will be discussed in detail in the next section, but it has been
associated with student persistence. Thus, increased academic integration by attending
college full-time may have a positive effect on student persistence. Another
explanation can be considered from Horn and Ethington’s (2002) study. They found
that full-time community college students had significantly higher perceptions of gains
toward their development compared to part-time students. They proposed that full-time
enrollment contributed to increased feeling of involvement, which enhanced the
perception of gains. They too indicate that full-time enrollment promotes academic
integration, which associates with retention.
Lastly, it is more than likely that students who enroll part-time have other
external commitments such as work, family, or children. The external commitments
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allow students to have less time on the role of a student (Jacobs & Berkowitz-King,
2002). Review of demographic factors showed that age and employment had
significant negative influence on student persistence. Since part-time students are more
likely to be older and be employed, it is easy to assume that the negative influence of
the demographic factors also affect enrollment status-persistence relationship.
Summary
Although the reasons why enrollment status influences student persistence are
not definite, the negative relationship of part-time enrollment and student persistence
seem to be robust. Additional research is necessary in order to examine the
interrelationships between variables, and its influence on student persistence.
Registration and Application Time
Several researchers have indicated that the difference in registration time has an
association with student persistence in college education. Smith et al. (2002) conducted
a study on community college students and their registration behaviors. Within
stratified random sample of students, they found that late registrants were less likely to
persist to the following semester compared to early or regular registrants. They also
found that students who registered late had lower semester GPA compared to the other
two groups. Summers (2000) conducted a similar study and obtained comparable
results. He found that students’ registration behaviors had significant correlations with
academic outcomes, including GPA, course completion, and attrition. In addition, these
interrelationships were found even after holding other student characteristics (e.g., age,
gender, ethnicity, academic intent) constant.
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Freer-Weiss (2004) investigated application time of community college students.
Similar to the study conducted by Smith et al. (2002) and Summers (2000), he found a
significant relationship between application time and students’ persistence behavior.
Students who applied three weeks or less before the beginning of the term were less
likely to re-enroll the following term compared to students who applied earlier. He also
found that student characteristics were significantly different when early and late
applicants were compared. The early registrants tended to be traditional students
compared to non-traditional students, females compared to males, White compared to
African-American students, and transfer majors. The researchers suggested that late
applicants possess the common risk factors for high attrition such as being a non-
traditional student, being a minority student, and not receiving financial aid.
Summary
Although not many researchers have investigated registration time and its
influence on student retention, the few that did indicate that there is a relationship
between the two variables. Late registrants are more likely to withdraw, compared to
early and regular registrants. In addition, results indicated that students’ characteristics
differ between the two groups.
Section Summary
Several academic factors identified in community college persistence research
were reviewed in this section, including pre-entry academic characteristics, college
academic ability, enrollment status, and registration behaviors. Results of the studies
showed that both high school GPA and standardized test scores were associated with
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student persistence. In addition, GPA after entering college had a robust relationship
with student persistence. However, since all of the studies are correlational, it is unclear
whether high GPA led to persistence, or persistence led to high GPA. The findings
from the literature also indicated a significant difference between full-time and part-
time enrolled students. In general, students who attend full-time were more likely to
persist in their college education. Researchers have suggested that students who attend
part-time may have external commitments that hinder them from continuing. Further
investigation is necessary to explain the reason for this relationship. Lastly, registration
behaviors were reviewed. Students who register late were less likely to persist
compared to early and regular registrants. However, there is a possibility that
registration behavior is influenced by other student characteristics as well.
Academic Integration Factors Associated with Student Persistence
According to the persistence research conducted with community college
students, several academic integration variables were indicated to have significant
association with student persistence. The variables that will be reviewed are academic
integration, student-faculty interaction, and support services.
Academic Integration
Academic integration is one of the widely studied factors related to student
retention. Many of the student retention models indicate the importance of students’
engagement into the college community. Tinto’s (1993) model indicated that students’
persistence is determined by how well the student is integrated into college. Astin’s
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(1984) model emphasizes the importance of students’ involvement for increased
persistence.
Although academic integration is a widely used term in student retention studies,
a variety of variables have been used to measure the construct. Typical measures of
academic integration that are found in the persistence literature are self-reported student
assessment of the quality and quantity of interactions with faculty, administrators, other
college staff, and fellow students. In addition, GPA, hours per week spent on studies,
classroom involvement, university environment are some of the factors included
(Grosset, 1991).
For example, Pascarella et al. (1986) measured academic integration by average
grades and membership in a scholastic honor society. Among university students,
academic integration had a significant, positive, direct effect on student persistence. In
a study of transfer student, Johnson (1987) measured academic integration by an 11-
item Student Attitude Questionnaire which assessed students’ interest, motivation, and
confidence in being a student. The results revealed that academic integration was
significantly associated with academic satisfaction, which led to student persistence.
Napoli and Wortman (1998) also utilized the construct of academic integration
to examine first-time freshmen students enrolled in community colleges from three
different campuses. However, their construct of academic integration measured
educational demand characteristics, such as time allocated to study, attending class, and
enjoying the academic demands, rather than focusing on interactions between student
and faculty. The results indicated that academic integration was directly related to
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students’ persistence/withdrawal decisions. Grosset (1991) conducted a study at a
community college to investigate the influence of academic integration on student
retention among younger and older students. Academic integration measure included
variables such as number of hours spent in extracurricular activities, various classroom
involvements, and the number of times students met with faculty and staff outside the
classroom. Results revealed that for younger students, academic integration,
specifically classroom experiences were strongly associated with persistence. However,
the importance of integration diminished for older students.
Napoli and Wortman (1996) conducted a meta-analysis to assess the magnitude
of the effect of academic integration among community college students. They selected
literature using the keywords “persistence,” “attrition,” “academic integration,” “two-
year colleges,” and “community colleges,” and included six studies to measure the
effect. The meta-analysis results indicated that academic integration has a large and
positive impact on the persistence behaviors of community college students. However,
they did not indicate how academic integration was measured in each study, thus
making it hard to interpret these results.
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Summary
Academic integration measures have differed from study to study, so these
results cannot be generalized entirely. However, it is safe to conclude that some form
of students’ engagement in the academic community have a positive effect on student
persistence. Past research showed that the academic integration literature utilized a
variety of measurements, including faculty contact, satisfaction with faculty interaction,
classroom experiences, student effort, and intellectual growth reported by the students
(Strauss & Volkwein, 2004). The following section will discuss specific variables
identified in community college persistence literature used to assess academic
integration.
Faculty-Student Interaction
Faculty-student relationships have been considered one of the core factors
influencing students’ learning experiences as well as their overall educational
satisfaction (Astin, 1993; Pascarella & Terenzini, 1991). It has been investigated by
numerous researchers as one of the critical components of academic integration and has
been shown to have various influences on students. For example, informal faculty-
student interactions have been associated with students’ overall satisfaction with the
college (e.g., Astin, 1993; Pascarella, 1980), intellectual and personal development
(Halawah, 2006; Pascarella, Duby, Terenzini, & Iverson, 1983), self-efficacy and locus
of control (Bean & Eaton, 2001). In addition, it has been associated with academic
achievement (e.g., Lundberg & Schreiner, 2004; Terenzini & Pascarella, 1980) and
persistence in the higher education setting.
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There are number of reasons why student-faculty interaction has influence on
student outcomes. From Tinto’s (1993) theory of academic integration, some
researchers suggest that interaction with faculty encourages students to become more
involved in the academic aspects of college life, thus have an indirect effect on student
outcomes (Twale & Sander, 1999). Other researchers propose that faculty interaction
improves students’ critical thinking skills, by teaching them “to think like a professional
(Light, 2001, p. 117)” in one’s field. Yet, several researchers connect interaction with
particular learning styles of students (Zhang & Stemberg, 2001). They suggested that
faculty who interact with students on a personal level will facilitate learning of students
who have a more experiential learning style. Finally, interactions with faculty may
convey certain expectations to students about their ability to succeed, thus influencing
students to achieve in order to confirm those expectations (Tauber, 1997).
Summary
Although the exact mechanism of why faculty-student interaction influences
student academic outcome has not been identified, the literature clearly shows that it has
a positive impact. Increased student-faculty interaction is more likely to lead to
increased academic achievement among students. In the following sections, the effect
of faculty-student interaction on students’ academic achievement and persistence will
be discussed in detail.
Faculty-student Interaction and Academic Achievement
Literature on faculty-student interaction and its influence on academic
achievement is somewhat mixed. Several early studies concluded that faculty-student
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interactions were not associated with academic outcomes (Astin, 1977; Endo & Harpel,
1982). On the other hand, other researchers reported a positive association between
faculty-student interaction and academic performance (Pascarella, Terenzini, & Hibel,
1978; Terenzini & Pascarella, 1980; Terenzini & Wright, 1987). Pascarella and
Terenzini (1978) concluded that frequency and quality of student-faculty interaction
accounted for better freshman academic outcomes. However, a different study
indicated that not all types of student-faculty contact results in the same outcomes
(Terenzini & Pascarella, 1980). For example, discussion of intellectual matters had
increased impact on academic achievement compared to other topics such as personal
matters.
In a more recent studies, researchers investigated the differential effects of
student-faculty interaction on college outcomes based on the students’ background
characteristics (e.g., Lundberg & Schreiner, 2004; Mayo, Murguia, & Padilla, 1995; Sax,
Bryant, & Harper, 2005). Sax et al. (2005) examined whether the impact of student-
faculty interaction on student outcomes differed for women and men, while Lundberg
and Schreiner (2004) examined if it differed between students’ race and ethnicity. As a
result, although student-faculty interaction had positive influence on student outcomes,
there were differential effects both on gender and on ethnicity. Sax et al. conducted a
longitudinal study on university students and demonstrated that student-faculty
interaction influenced both men and women equally on the students’ scholarly self-
confidence, leadership ability, degree aspiration, and retention, but increased positive
effects were found among men and their college grades, critical thinking skills, and
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satisfaction with courses and instruction. Lundberg and Schreiner conducted a study on
undergraduate students from six racial/ethnicity groups. Their study found that for all
racial/ethnic groups, quality of relationship between student and faculty was the only
variable that significantly predicted student learning. However, student-faculty
interaction contributed more to student learning for students of color compared to White
students. In addition, Native-American and African-American student reported more
frequent interactions with faculty members compared to other groups, but also reported
less satisfying relationship with faculty members. These results indicate the importance
of the quality of faculty-student interactions rather than the frequency of interaction.
Other studies have found a significant relationship between student-faculty
interaction and student outcomes. In a study of Latina/o students, Anaya and Cole
(2001) found that frequency and quality of faculty relationships had positive influence
on students’ grade point average. Similarly, Anaya (2001) investigated outcome
performance of the Medical College Admission Test (MCAT) and the association
between student-faculty interactions and found that interaction with faculty outside the
classroom had positive influence on the outcome. Kezar and Moriarty (2000) also
found that student-faculty interaction had positive influence on students’ self-assessed
leadership abilities. National multi-institutional research also produced evidence that
faculty-student interactions enhanced academic performance when GPA was measured
(Anaya, 1999; Astin, 1993). Finally, a review of literature conducted by Zepke and
Leach (2005) found 20 studies that revealed positive influence of regular and
meaningful contact with teachers on academic outcomes.
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This section demonstrated how student-faculty interaction can influence student
outcomes in a positive way. It is important to note that the influence differs among
gender and ethnicity, and also that it is the quality rather than the frequency of
interaction that is most important. The following section will discuss how student-
faculty interaction influences students’ persistence decisions.
Faculty-student Interaction and Persistence
Theoretical models of student retention emphasize the level of student
integration into the academic and socialization components of the institution (Tinto,
1993). In other words, the more integrated the student is into the institutional
environment, the less likely the student is to drop out. Thus, numerous researchers have
concluded that the more informal interaction with faculty, the stronger institutional and
personal commitment one will have, leading to decreased probability of withdraw
(Pascarella, 1980).
Pascarella and Terenzini (1977, 1978, & 1979) conducted three similar studies
with entering freshmen and found that persistence/withdrawal decisions were associated
with 1) frequency of informal student-faculty non-classroom contact and 2) frequency
of interactions with faculty to discuss intellectual matters. Furthermore, a study
conducted in 1979 indicated that the quality of interaction is as important as the
frequency. These results were similar to the conclusion drawn from Lundberg and
Schreiner (2004) and their study of student outcomes.
In addition, numerous researchers have investigated the association between
student-faculty interaction and student persistence. Both Pascarella and Chapman
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(1983) as well as Stoecker, Pascarella, and Wolfe (1988) utilized the construct of
academic integration, measured by grades and informal contact with faculty, and found
that it significantly increased student persistence at the university level. In a similar
manner, Berger and Milem (1999) again used the construct of academic integration,
measure by informal contact with faculty as well as academic experiences, and found
that academic integration was an important predictor of institutional commitment, thus
leading to persistence.
Similar to the university setting, community college researchers have linked
faculty-student interaction to student persistence, as well. Halpin (1990) investigated
community college students and Grosset (1997) surveyed African-American community
college students to examine the relationship between student-faculty interaction and
student persistence. Both researchers used the construct of academic integration, as
represented by informal relationship with faculty, academic development, faculty
concern for teaching, and student development and their relationship to student
persistence. They found similar results, concluding that increased academic integration
resulted in a positive association with student persistence. Furthermore, Grosset
discovered that the positive effect of increased academic engagement on persistence
was significant regardless of the socioeconomic background of the students.
Schmid and Abell (2003) and Heverly (1999) specifically investigated student-
faculty interaction and its effect on student persistence among community college
students. Similar to the results indicated above, regular faculty-student contact was one
of the most important discriminating variables between returning and nonreturning
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students. Heverly also conducted a qualitative analysis based on students’ comments
via phone interview, and found that compared to nonreturning students, returning
students were more satisfied with several types of interactions involving the faculty.
Thus, these studies indicate the importance of student-faculty interaction on student
persistence among community college students.
Lastly, Tinto (1997) and Opp (2002) investigated positive perceptions of faculty
and its influence on student persistence. Tinto surveyed community college students
and their perceptions of the college environment. Results indicated that positive
perceptions of faculty were a significant predictor of persistence among those students.
Opp (2002) on the other hand investigated program completion rates among community
college students of color. He utilized data from 643 institutions to investigate the
percentage of faculty of color, administrators of color, and student of color, and how
that relates to student persistence. Results of the study showed that the percentage of
faculty of color and the percentage of administrators of color were the two strongest
positive predictors of program student retention for students of color. This may be
because studies have shown that among White, African-American and Mexican-
American students, all of the groups preferred to disclose more information to faculty of
their own race or ethnicity (Noel & Smith, 1996). Other research has shown that
African-American students have difficulty approaching faculty particularly those
faculty that are different from themselves in terms of race (Schwitzer, Griffin, Ancis, &
Thomas, 1999). Therefore, it can be assumed that for students of color, increase in the
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percentage of faculty of color allows increased interaction with the faculty, thus leading
to better student persistence.
Although the measurement of student-faculty interaction is somewhat mixed in
the literature, results of the studies have indicated how student-faculty interaction can
positively influence student persistence. The results were similar for both university
students as well as community college students. It is also important to note that the
positive perceptions of faculty may lead to increased student persistence as well.
Student Involvement and Support Services
In the previous section, the importance of student involvement, particularly,
engagement with faculty was discussed. Based on models such as Student Involvement
Theory (Astin, 1984, 1997) and Tinto’s (1993) student attrition theory, student
commitment, affect toward campus, involvement with faculty and administrators,
campus integration, connection to campus all effect retention.
Past studies have demonstrated that student involvement can be accomplished
not only by student-faculty interaction, but with other methods as well. For example,
enrollment in orientation courses has been found to promote retention through student
involvement. Orientation classes are intended to get students accustomed to the campus
environment and allow them the opportunity to meet with other students, faculty, and
administrators (Derby & Smith, 2004). In a study of community college students
enrolled in an orientation course, Derby and Smith found that enrollment in orientation
courses was significantly associated with student retention. They suggested that
participation in the courses help students understand the campus better, which allows
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them to make the physical and emotional investment in campus life. Similarly, Derby
and Watson (2006) investigated the effect of orientation courses on African-American
students enrolled in a community college. The orientation course was designed to
facilitate self-development through a variety of exercises and activities that are linked to
students’ personal and educational development. The aim was to assist students in their
transition into the college environment, encourage success and goal achievement, as
well as promote relationships that will help students achieve. Similar to the study by
Derby and Smith (2004), a significant correlation was found between students who
enrolled in the orientation course and retention.
In addition to orientation courses, several schools have implemented various
support programs with the purpose of increasing students’ feeling of connection to the
institution (Murtaugh, Burns, & Schuster, 1999). For example, Wolfe (1998)
investigated the impact of matriculation services (orientation, counseling, and
assessment) on student retention. Students who receive at least two out of three of the
services were more likely to be successful and have increased retention compared to
non-participating students. Grunder and Hellmich (1997) examined the effectiveness of
a College Success Program, designed to assist underprepared freshmen adjust to college
life. The program allows student to develop a close relationship with a mentor who
supports and guides the student throughout the college careers. The researchers found
that participation in the College Success Program was significantly associated with
retention rates among community college students. Another positive impact on student
retention was found in counseling services (Willett, 2001). He found that students who
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used the counseling service were statistically more likely to persist compared to other
students.
Barr and Rasor (1999) investigated students at the community college who
participated in various student services. The student service programs included Equal
Opportunity Program and Services (EOP&S), Disables Student Program and Services
(DSP&S), Athletes (college sports), Math Engineering Science Achievement (MESA),
Partnership to Assure College Entry (PACE), and Learning Disabilities (LD). Results
of the study revealed that freshmen affiliated with a student service on campus had
increased persistence compared to nonparticipants. The results were same across
gender, ethnicity, and age groups. In addition, the effects were maintained after
academic abilities were controlled.
Lastly, Tinto (1997) investigated students who participated in the Coordinated
Studies Program (CSP). The CSP was intended to provide an opportunity for the
students to share the curriculum and learn together by enrolling together in several
courses tied together by a common theme. The students participate in cooperative
learning activities which enhance involvement with their peers, and in addition, all of
the instructors are present and active in all class meetings. When Tinto compared
students in the CSP to those who did not participate, he found that students in the CSP
reported significantly more positive views of the college, students, faculty, classes, and
climate. They also reported positive involvement of themselves. In addition, students
in the CSP had significant persistent rate compared to non-CSP students.
Summary
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These results demonstrated that various support services and orientation courses
have a positive impact on student retention. It is assumed that these services enhance
student involvement with the campus, peers, and faculty members, which is associated
with persistence. Researchers suggest that one way to promote student retention is to
increase students’ usage of these services available for them.
Section Summary
Academic integration is characterized by students’ involvement into the
academic realm of college life. Studies in the past have investigated multiple factors,
such as students’ GPA, extracurricular activities, classroom involvements, perception of
the college, student- faculty interaction, and student services. In particular, the quantity
and quality of student-faculty interaction has a significant impact on student persistence.
In addition, results from the research showed that orientation courses and student
support services intended to integrate students into the college life also have positive
influence on student persistence.
Psychosocial Factors Associated with Student Persistence
According to the persistence research conducted with community college
students, two psychosocial variables were indicated to have significant association with
student persistence. Student goals and self-efficacy will be reviewed in the following
sections.
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Student Goals
Goal setting theory
A goal is a behavior outcome that a person is consciously trying to attain
(Pintrich & Schunk, 2000). In other words, it is a level of performance proficiency that
one wishes to attain, normally within a specific time period (Latham & Locke, 2006).
Goals have been shown to influence the direction of thought and behavior, the
regulation of thought and behavior, as well as the level of task performance (Ames,
1992; Locke & Latham, 2002).
One of the major theoretical bases of the effect of goals is Locke and Latham’s
(1990) goal setting theory. Goal setting refers to the process of creating quantitative or
qualitative standards of performance to serve as the aim of one’s actions (Pintrich &
Schunk, 2000). The theory is based upon a premise that human action is based upon
purposeful goals and that it is directed by conscious goals. In an effort to address the
question of why people perform differently on the same task in the same context even
after ability and prior knowledge is controlled, Locke and Latham (1994) claimed that
having different goals in the beginning results in different achievement outcomes in the
end. According to Locke and Latham, there are three mechanisms to why goals
influence the outcome. First, goals direct attention toward the action that is relevant to
the goal. Second, goals regulate the effort one puts forth in order to attain the desired
outcome, and third, goals affect the amount of persistence one puts forth. Thus, it is
apparent how goal setting is an important motivational process for human action
(Bandura, 1997).
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In the goal-setting theory, Locke and Latham (1990) proposed two important
aspects of goals; goal choice and goal commitment. Goal choice is the actual goal
individuals are trying to attain, and the level at which they are trying to accomplish it.
Research has indicated a linear relationship between the degree of goal difficulty a
person is committed to attain, and that person’s subsequent job performance (Latham &
Locke, 2006). High goals produce greater effort, focus, and persistence, and are thus
likely to increase the performance level of the person. Goal commitment refers to how
strongly the individuals are attached to the goal, how much they consider it important,
and how determined they are to attain it in face of obstacles (Locke & Latham, 1990).
Although higher goal commitment implies higher performance achievement, feeling of
commitment does not automatically lead to behavior (Locke & Latham, 1994). Thus,
goal commitment must ultimately be determined by the person actually taking action.
Goal setting is the process of creating standards of performance to serve as the
aim of one’s actions. Goal choice and goal commitment are both important aspects of
goals. Goal choice, which indicates the level of goal one determines, and goal
commitment, which indicates how much the individual considers the goal to be
important, both influence the effort and persistence one puts in to achieve the goal.
Numerous studies have investigated how goals influence human behavior and outcomes.
The following sections will discuss how goals are related to academic performance,
how goals are related to persistence, and how goals, self-efficacy, and self-regulatory
behaviors interact to obtain the desired outcomes.
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Goals and Academic Performance
Research has indicated that one of the strongest predictors of either academic
achievement or failure is the aspirations for success (Kao & Tienda, 1995; Wentzel,
1991). Aspirations are conceptualized as achievement goals, and as the students
develop these goals, the goals act as cognitive representations for what the students are
striving for or trying to attain (Pintrich & Schunk, 2002). Students who have goals are
more likely to experience a sense of self-efficacy when attaining those goals, and are
more likely to engage in activities that will help them attain those goals (Pintrich &
Schunk, 2002). Thus, academic aspirations and academic achievement are linked via
behaviors student engage in when they have strong aspirations (Ames, 1992; Locke &
Latham, 2002). As the students increase their aspiration and commit to achievement,
they regulate their behaviors in ways that will more likely achieve their goals
(McGregor & Elliot, 2002; Miller & Brickman, 2004).
Numerous studies have been conducted to study the relationship between
goals/aspirations and performance. For example, in a non-academic setting, Boyce,
Wayda, Johnston, Bunker, and Eliot (2001) investigated the effect of goals on tennis
serving performance among college students. They compared a group of “do-your-
best” control group to either self-set or instructor-set goal group and found that having a
goal significantly improved performance. In the academic setting, several studies have
linked goals with academic performance. In a study of students on academic probation,
Altmaier, Rapaport, and Seeman (1983) found that not having clear career goals
strongly influenced poor academic performance. On the other hand, Emerick (1992)
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reported that underachievers who developed future career goals significantly improved
their school performance. Thus, even for students who were not academically
successful, having a goal had a positive influence on their academic performance.
Studies among college students demonstrated similar results. For example, Ting (1997)
found that presence of long-term goals significantly predicted academic performance.
Researchers have investigated not only the presence of goals, but also goal
commitment and goal choice. For example, Houser-Marko and Sheldon (2006)
investigated “self-as-doer” individuals, who were conceptualized as individuals who
activated their goal attaining behaviors. In other words, these individuals can be
explained as those who are more committed to their goals. Results of the study showed
that individuals with stronger commitment to academic goals had better grades
throughout the college career, even after controlling for their base American College
Test (ACT) scores. Thus, importance of goal commitment is demonstrated by this
study. Latham and Brown (2006) investigated the influence of goal choice on academic
performance among students in MBA programs. The results showed that students who
set difficult learning goals had higher grade point average. Lathman and Locke (2006)
suggested that having a vague goal allows people to give themselves the benefit of the
doubt in evaluating their own performance. For example, if the goal is to do one’s best,
there is a wide range of performance levels that can be aligned with doing their best.
However, when a specific high goal is set, it provides an objective, unambiguous basis
for judging one’s performance.
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In addition to aspirations, career decision-making is also considered to be
important in one’s performance and achievement. Creed, Prindeaux and Patton (2005)
found that undecided students had poorer career, well-being, and social outcomes later
in their life. Germeijs and Verschueren (2007) demonstrated that high school students’
career decision-making is related to achievement when they enter higher education, and
Jones (1995) also found that career decision making predicted students’ GPA later in
their college years. Therefore, career decision-making is also an important aspect of
college students’ achievement.
Goals can influence individuals’ performance, especially in the academic realm.
In particular, goal commitment, goal choice, and career decision-making were
especially crucial to attain high academic performance. Although studies have been
conducted using a variety of populations, none of the studies targeted community
college students. Therefore, the results should be interpreted with caution, but since
numerous studies have been conducted using numerous populations, the generalizability
of the effect of goal setting is quite robust.
Goals and Persistence
In addition to the association between goals and academic performance, the
presence of goals is a prominent factor related to school retention. In the student
attrition model proposed by Tinto (1993), he suggests that students’ goals, both initially
and throughout the school years, are strongly associated with decisions to remain in
school. This relationship is well established in the literature. For example, Farmer,
Wardrop, Anderson, and Risinger (1995) found that early career aspirations were
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related to academic persistence among male students, and Blecher (2006) also found
that educational aspiration had a significant relationship with bachelor degree
attainment among family and consumer science major students. Similarly, Hull-Blanks
and her colleagues also found the association between career goals and student retention
among college freshmen (Hull-Blanks, Kurpius, Befort, Sollenberger, Niepon, & Huser,
2005). Furthermore, a qualitative study conducted by Zurita (2004) discovered
comparable results. The researcher interviewed five Latino undergraduate students who
persisted through graduation and five students who dropped out of the university. One
of the major differences between the groups was that the persistence group had
education and career goals while the drop-out group did not. Thus, the literature clearly
shows that having a goal (e.g., educational aspiration, career aspiration) has a positive
influence on retention among college students.
The importance of goals for students’ persistence is also crucial among
community college students. Numerous studies have investigated the influence on
goals among community college students and found results similar to that of four-year
institutions. Fralick (1993) found that when successful (persistence) and unsuccessful
(non-persistence) group of students were compared, the difference was found in
whether or not the students had definite goal or college major. Similarly, Perin (2006)
discovered that the retention rates were higher for students who were nursing aspirants
compared to other students in the community college who did not have a definite goal.
Furthermore, when several variables such as employment, number of credit hours,
demographic background (e.g., age, race, gender, socioeconomic status), and academic
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ability were investigated simultaneously as it relates to student retention, Mohammadi
(1994) found that student goals were, and Bers and Smith (1991) found that educational
objectives were the most significant factor contributing to student retention.
The literature clearly indicates the importance of having a goal on student
persistence. This relationship is especially imperative among community college
students. For students at the community college level, having a definite goal (either
educational aspiration or career aspiration) significantly influences their decision to stay
in college or drop out of college.
Goals and Self-efficacy
In addition to the academic achievement and persistence, goal setting also have
been linked to the perception of self-efficacy. Self-efficacy is an individual’s belief in
his or her ability to complete a set of tasks in order to obtain a specific outcome
(Bandura, 1986). Self-efficacy influences performance by affecting goal choice and
commitment. Results of research indicated that people with high self-efficacy are more
likely to choose difficult goals, and when self-efficacy is high, commitment to difficult
goals are also higher (Locke & Latham, 1994). High self-efficacy enables individuals
to face obstacles with confidence, feel motivated to master the challenges, and attribute
positive events to their effort and negative events to external circumstances (Bandura,
1997).
Several researchers have investigated the relationship between self-efficacy and
goals. According to the career self-efficacy theory (Hackett & Betz, 1981), women’s
career goals are strongly affected by the level of self-efficacy as well as self-confidence,
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and Lent, Brown, and Larkin (1986) also showed in their investigation that self-efficacy
contributed significantly to the prediction of career goals in the technical/science field
among undergraduate students.
Similar results were found in studies conducted by Mau (2003) and Nauta,
Epperson, and Kahn (1998). Mau conducted a longitudinal study on students’ career
aspiration in the science and engineering field from 8
th
grade to the second year in
college. The researchers included race, sex, and academic self-efficacy as the
predicting variable to persistence of career aspirations. Results of the study showed that
math self-efficacy was the strongest predictor of persistence in science career aspiration.
Nau et al. (1998) on the other hand investigated only female college students. They
investigated two groups of women with different career aspirations. One group was
undergraduate female students from mathematics, physical science, and engineering
majors, who were considered to have higher level career aspirations, and one group was
students from biological science majors. Results showed that students who had the non-
higher level career aspirations had significantly lower self-efficacy compared to the
other.
Self-efficacy not only is linked to career aspiration, but is also linked to
educational aspirations as well. Pinquart, Juang, and Silbereisen (2002) investigated the
role of academic self-efficacy on educational aspirations among German students.
Their longitudinal study showed that students with higher levels of academic self-
efficacy beliefs were more likely to increase their career aspirations and also switch
from non-college-bound vocational training to attending a university.
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Research showed that higher self-efficacy influences higher aspiration, thus
ultimately leading to increased performance. The relationship seems to be a reciprocal
relation, since some of the research also indicated that having higher career goals will
increase one’s self-efficacy and self-esteem (Chiu, 1990). According to Badura (1993),
performance that matches or exceeds the personal goals typically maintains or increases
self-efficacy, while performances that fail to meet the standards decrease self-efficacy.
Self-evaluation of goals and performance will eventually lead to changes in target goals,
effort, and persistence.
Results from the past literature demonstrate how goals and self-efficacy are
associated. The relationship is reciprocal, with achievement of difficult goals leading to
increased self-efficacy, and increased self-efficacy leading to increased level of goals.
Therefore, goal setting not only increases student retention by its direct effects, but also
important has an indirect effect by influencing self-efficacy.
Summary
In summary, goal setting affects the choices and directions of the behavior and
thoughts of individuals. Moreover, it increases the effort, prolongs persistence, and
cues individuals in order to search for appropriate strategies to attain those goals. The
results of past studies demonstrated that goal setting is a crucial component to student
retention. Students are less likely to persist in their education unless they have a goal to
graduate, obtain a job, or to transfer. In addition, the association between goal setting
and self-efficacy is pertinent. In the following section, self-efficacy theory will be
reviewed in detail, followed by discussions of its relationship to student retention.
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Self-Efficacy
Self-Efficacy Theory
Self-efficacy defined by Bandura (1997) is the “beliefs in one’s capabilities to
organize and execute the courses of action required to produce given attainments (p.3)”.
Bandura posits the construct of self-efficacy within the personal factor of the Triadic
Reciprocity model, which is the theoretical framework of the Social Cognitive Theory.
The model indicates that there is a reciprocal relationship among person, behavior, and
environment (Bandura, 1986). Behavioral factors are personal choices of action the
individual engages in, which influence both the person and the environment.
Environmental factors are the external conditions the person encounters, which
influence both the person and their behaviors. Personal factors are cognitions and
beliefs a person holds, which influence both behavior and the environment. The self-
efficacy theory proposes that the individual perceives himself as an acting agent in
exercising control over various course of action in order to achieve desired outcomes,
while being influenced by and influencing his/her environment (Bandura, 1997). Thus,
the person (self-efficacy) influences both the behavior and the environment, while at the
same time being influenced by those factors as well.
Self-efficacy differs both conceptually and psychometrically from related
constructs such as outcome expectations and self-concept (Zimmerman, 2000).
Outcome expectancies are assessed utilizing the value of the activities in attaining
various outcomes while self-efficacy is the perceived capability to perform those
activities. A study conducted by Shell, Murphy, and Bruning (1989) measured both
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self-efficacy and outcome expectancies on reading and writing achievement and found
discriminant validity of self-efficacy measures. Self-concept, which is one of the
closest constructs to self-efficacy, is a more general perception of oneself including
various forms of self-knowledge and evaluative feelings (Marsh & Shavelson, 1985).
Since global perception of oneself was not consistently related to outcomes such as
students’ academic performance, researchers have reconceptualized self-concept as a
hierarchical construct (Zimmerman, 2000). For example, they have created a
hierarchical triangle with global self-concept at the apex, subcategorical self-concept
(e.g., academic self-concept) in the middle, and domain-specific self-concept at the
bottom. Still, self-concept measures emphasize self-evaluative questions such as “How
good are you in Math?” while self-efficacy measures emphasize task-specific
performance expectations such as “How certain are you that you can determine the area
of this triangle?” Thus, self-concept and self-efficacy differs in the specificity and
correspondence to various performance tasks and contexts (Pajares, 1996).
Self-efficacy has been shown to have diverse effects on person’s cognitive,
motivational, affective, and selection processing (Bandura, 1994). It influences various
human actions such as choice of activities, the level of effort one puts into the task,
persistence, and emotional reactions. Bandura (1997) noted that self-efficacious
students participate more readily, work harder, persist longer, and experience fewer
unpleasant emotional reactions when they encounter hardships. One who has a strong
sense of self-efficacy approach difficult tasks without feeling stressed, and persists with
their effort when faced by challenges. Many attribute failure to lack of effort instead of
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ability and are genuinely intrinsically motivated. On the other hand, people with low
self-efficacy avoid difficult tasks perceiving them as personal threats and have low
aspirations and weak commitments to goals. They identify failure as an outcome of
personal deficiencies and are often times outcome oriented (Bandura, 1994).
Self-efficacy is especially pertinent on one’s achievement (Bandura, 1997). For
example, the Self-efficacy Model of School Learning, proposed by Schunk (1996)
portrays the role of self-efficacy in academic achievement. Students come to the
academic setting with individual differences regarding personal qualities, past
experiences, and social support. These three components influence one’s self-efficacy
beliefs. Self-efficacy then affects task engagement and management through personal
and situational influences. These influences impact motivation and self-efficacy for that
specific task, and self-efficacy further influences motivation. When one has low self-
efficacy, the person may give up easily or avoid the task altogether (Bandura, 1997,
Zimmerman, 1989). Persons with low self-efficacy may focus their cognitive resources
on personal shortcomings, previous failures, or lack of perceived support instead of
putting effort into problem solving skills and strategies for success (Zimmerman &
Risemberg, 1997). They may also be distracted by situational variables or debilitating
thoughts when engaging in the task, thus influencing action which ultimately impact
goal attainment (Bandura, 1997).
Self-efficacy is a belief one holds about his or her capability to perform a
specific task. It differs from general constructs such as self-concept because it is a very
task-specific construct. Self-efficacy influences various human behaviors, especially
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choice, effort, persistence, and emotional reaction. The level of self-efficacy is an
important factor of human motivation, and ultimately affects achievement of the task.
The following sections will discuss research of how self-efficacy is related to
achievement in general, achievement in the academic arena, and how self-efficacy
influences persistence behaviors.
Self-efficacy and Achievement
Research in self-efficacy and it’s relation to achievement goes beyond the
academic setting. Bandura (1977) first started his study of self-efficacy during
treatment of phobic individuals. Since then, self-efficacy has been associated with
various clinical issues such as predictive anxiety (Scheier & Botvin, 1997), depression
(Parsons, Owen, & Unger, 1994), immune system (Maddux, Brawley, & Boykin, 1995),
drinking problem treatments (DiClemente & Hughes, 1990), weight management
(Byrne, 2002), and breast cancer screening (Lechner, DeVries, & Offermans, 1997). In
a study of diabetic patients, Nelson and his colleagues found that self-efficacy played a
central role in the patient’s adherence to medications. They found that individuals with
higher self-efficacy scores were more likely to take their medications, follow the meal
plan for diabetic patients, adhere to low-fat diet, have higher levels of physical activity,
and monitor their blood glucose level (Nelson, McFarland, & Reiber, 2007).
Self-efficacy has also been found to be an important factor in pain management
and recovery from injury (Bandura, 1997; Milne, Hall, & Forwell, 2005) as well as
athletic performance (Barker & Jones, 2006). Milne and her colleagues found that in a
study of 270 athletes, those who scored higher on task and coping self-efficacy had
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increased adherence to rehabilitation. In a case study of cricket players, increased self-
efficacy was associated with increased bowling performance among the players (Barker
& Jones, 2006). Lastly, Gernigon and Delloye (2003) conducted an experimental study
of among national level competition sprinters regarding the effect of self-efficacy on
performance. They gave manipulated time feedback on the sprinters’ 60m trial to see if
the success versus failure feedback will result in a difference in their self-efficacy,
ultimately altering the sprinter’s performance. Result showed that success and failure
both increased and decreased self-efficacy, respectively. In addition, self-efficacy
influenced performance in the second trial.
Research regarding self-efficacy goes beyond the clinical and athletic field,
including music performance (McPherson & McCormick, 2006), work-related
performance (Judge, Jackson, Shawy, Scott, & Rich, 2007), and teachers’ classroom
management ability (Morris-Rothschild & Brassard, 2006). Research regarding self-
efficacy and its association with academic achievement is also abundant. These studies
have shown that self-efficacy not only measures intellectual aptitude (Schunk, 1991;
Zimmerman & Risemberg, 1997) but also mediates the actual performance through
regulation of cognitive resources (Braten, Samuelstuen, & Stromso, 2004; Walker,
Greene, & Mansell, 2006). These studies support the evidence that when learners have
same ability levels, those with higher self-efficacy consistently perform better than
those with lower self-efficacy (Zimmerman & Risemberg, 1997).
Research on self-efficacy has demonstrated its relationship to various factors of
human behaviors. It has been well documented to influence clinical issues, pain
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management, and injury rehabilitation. Self-efficacy also influences performance
outcomes in athletics, music, and work. The following section will review how self-
efficacy influences performance in the academic settings.
Self-efficacy and Academic Performance
Numerous studies have been conducted regarding the association between self-
efficacy and academic performance in the post-secondary setting. Hackett, Betz, and
Casas (1992) examined social cognitive factors among engineering students and
discovered that one’s degree of self-efficacy had a significant impact on reaching
academic milestones. Lee and Klein (2002) surveyed undergraduate students prior to
an exam to measure student self-efficacy, self-deception, conscientiousness, and basic
demographic information. Results showed that self-efficacy was significantly and
positively related to testing points. Gore (2006) addressed the relationship of self-
efficacy and persistence in 1
st
-year college students at a large public Midwestern
university. College self-efficacy as well as academic self-efficacy were measured twice
during the semester, the first 2 weeks of the fall semester and the last 2 weeks of the fall
semester. Students’ GPA and enrollment status was used as the outcome variables.
Results showed that although self-efficacy measured at the beginning and end of college
semester were both significantly associated with GPA, the strength of the association
increased at the end of semester.
Chemers, Hu, and Garcia (2001) conducted a longitudinal study of 1
st
-year
university students and examined the effects of academic self-efficacy and optimism on
students’ academic performance, stress, health, and commitment to remain in school.
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Structural equation modeling approach was used to assess the relationship between
several different variables. Results showed a significant direct effect of self-efficacy on
challenge-threat evaluations, academic expectations, and academic performance. Thus,
highly efficacious students had higher challenge-threat evaluations (i.e., they perceived
academic work demand as a challenge rather than a threat), greater academic
expectations, and better academic performance. Optimism also had a moderate effect
on self-efficacy and a small effect on challenge-threat evaluations and academic
expectation. Thus, highly optimistic student tended to be more efficacious and had
positive challenge-threat evaluation as well as higher academic expectations. Results of
the study support the role of self-efficacy on academic performance among college
students. In addition, self-efficacy was also related to students’ perceptions of their
capabilities to respond to demands of life.
There is also a number of meta-analyses that examined the relationship of self-
efficacy to achievement. Multon et al. (1991) used 36 separate studies to investigate the
relationship of self-efficacy to performance, as well as its relationship to persistence,
which will be discussed in detail in the next section. The samples varied in age, ability,
and measurement tools. The large majority of the samples consisted of elementary
school children and college students and the sample was approximately equally divided
between normal-achieving and low-achieving students. Academic performance was
measured using different methods including standardized achievement tests, classroom-
related measures, and basic skills tasks. Results of the study provided support for the
relationship between self-efficacy beliefs and academic performance. In addition to this
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general finding, the researchers also found that the relationship of self-efficacy to
performance varied depending on students’ achievement status, with stronger
relationships found among low-achieving students compared to normal-achieving
students. This result is important because it indicates that self-efficacy plays an
essential role especially for those students who are less likely to experience success.
Promoting self-efficacy among these low-achieving students may contribute to
academic success of these students. Lastly, instruments assessing basic skills revealed
the strongest effect sizes followed by classroom indicators and standardized test scores.
This shows the importance of the specificity of measurement in regards to self-efficacy
(Pajares, 1996).
Hysong and Quinones (1997) used 28 studies and examined the relationship of
self-efficacy to performance and task complexity. They found that the relationship
between self-efficacy and performance was strongest when the task was relatively
complex, but not when the task was simple. The researchers attributed the results to the
presence of uncertainty in the academic setting. They suggested that since not all
conditions are known regarding a task, individual uses efficacy judgment for task
accomplishment to estimate future outcome in a complex environment.
Robbins and his colleagues (Robbins et al., 2004) examined 109 studies that
included psychosocial and study skill factors (PSFs) and college outcomes (GPA). All
of the studies were conducted on full-time students enrolled at 4-year higher education
institutions in the United States. Results showed that academic self-efficacy was the
best PSF predictor of GPA.
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Results from these studies indicate a pertinent relationship between self-efficacy
and academic achievement. The higher the level of self-efficacy was associated with
increased academic outcomes. Unfortunately, all of these studies have been conducted
at the four-year institutions and studies conducted at the community college level are
scarce. Although additional research is necessary to generalize these results to
community college students, it is safe to conclude that self-efficacy has a significant
influence on academic performance based upon these numerous studies. The following
section will discuss another important factor of self-efficacy and its influence on student
persistence, as it relates to motivation and goal settings.
Self-efficacy and Persistence
In addition to the association between self-efficacy and academic performance,
self-efficacy has been associated with students’ persistence and retention behaviors in
post-secondary academic settings (Elias & Loomis, 2000; Kahn & Nauta, 2001; Lent et
al., 1984; Torres & Solberg, 2001). Persistence behaviors included factors such as
degree attainment, university major persistence, health maintenance.
Sandler (2000) investigated persistence behavior of nontraditional students (24
years of age and older) studying in two-year and four-year degree programs. Twelve
endogenous variables (career decision-making self-efficacy, family encouragement,
perceived stress, financial attitudes/satisfaction, financial attitudes/difficulty, academic
integration, social integration, cumulative GPA, institutional commitment, goal
commitment, and intent to persist) as well as eleven exogenous variables (gender,
race/ethnic affiliation, household income, relatives/dependents, financial aid, parents’
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educational level, academic degree aspirations, student type, degree program,
curriculum hours, and hours employed) were included in the study, with dependent
endogenous variable being persistence. Results of the study showed that career
decision-making self-efficacy, which measured the extent to which students are
confident about their ability to engage in educational and occupational information
gathering and decision making activities, had the strongest influence among all the
endogenous variables on persistence.
Gloria and Kurpius (2001) investigated nonpersistence decisions among
American-Indian undergraduates while Gloria, Castellanos, Lopez, and Rosales (2005)
investigated the influence of self-efficacy beliefs among Latino students/ academic
persistence. Both studies revealed that self-beliefs, social support, and comfort in the
university environment each significantly predicted academic nonpersistence among
American-Indian undergraduate students as well as Latino students. Social support was
the strongest predictor, but college self-efficacy and degree self-efficacy both had
significant correlation with nonpersistence decisions. Thus, believing in one’s own
ability to complete college and degree-related tasks resulted in fewer self-doubts about
obtaining an undergraduate degree. The results of this study reiterate the importance of
one’s sense of confidence in one’s college and degree-related behaviors in academic
persistence decisions.
In a meta-analysis study, research conducted by Multon et al. (1991) which was
reviewed earlier in this chapter also found relationship between self-efficacy beliefs and
persistence outcomes. They reviewed 18 studies that investigated the self-efficacy –
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persistence relationship. Results of the study showed significant relationship between
self-efficacy and persistence, but the effect size differed depending on the manner in
which persistence was operationalized. When persistence was measured as time spent
on task, the effect size was significantly smaller than when measured by number of
items completed or attempted. The researchers suggested that the differences may
occur because some of these studies have not tested their participants in maximally
challenging conditions. According to Bandura (1977), self-efficacy should be
positively related to persistence especially in the face of obstacles, thus non-challenging
situations may not result in the same self-efficacy – persistence relationship as
hypothesized. Although the results of this meta-analysis should be interpreted with
caution because the measurement of persistence varies between studies, it is still safe to
conclude that self-efficacy has a significant influence on student persistent behaviors.
Similar to the research on self-efficacy and academic achievement, not many
studies have been conducted at the community college level. Grimes and David (1999)
conducted an extensive study with entering community college students using a
freshman survey instrument developed by the Cooperative Institutional Research
Program (CIRP). Results of the study showed that motivational factors (i.e., self-
efficacy) influence student success and persistence among underprepared community
college students. Garardi (1996) also conducted a study with freshmen students at a
community college in New York. Demographic variables such as socioeconomic status,
academic skills, parents’ educational background, family income as well as self-concept
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of ability were measured. Results of the study indicated that self-concept of ability was
a significant predictor of college graduation.
This section reviewed the association between self-efficacy and student
persistence. However, similar to studies regarding self-efficacy and academic
achievement, the number of studies conducted at the community college level is scarce.
Only the two previous studies support the assumption that self-efficacy influence
student persistence at the community college level. Again, since the numbers of studies
are limited, additional research is necessary in order to clearly understand the
relationship between self-efficacy and persistence behaviors among community college
students. Lastly, association between self-efficacy and other motivational constructs
will be discussed in the next section.
Section Summary
Numerous research has investigated the influence of self-efficacy in a variety of
areas. Especially in the academic setting, self-efficacy has been associated with
academic performance, student persistence and retention, as well as motivation and self-
regulatory behaviors. Although much of the research were conducted at the four-year
institutions, it is likely that similar association will be found among students at the
community college level. Thus, self-efficacy is an essential psychosocial factor when
investigating student retention among community college students.
Conclusion
This literature review has been developed in order to review the variables
applicable to persistent research among community college students. Community
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college students, compared to four-year university students possess unique
characteristics of their own. Community college students are characterized by a wide
diversity of people from different cultures, ethnicities, ages, aspirations, languages, life
experiences, and beliefs. Many of these students have other commitments such as work,
family, and children. Therefore, when conducting a study with this population, it is
important to consider their background characteristics because those factors likely
influence the outcomes as well.
When investigating student retention, several models have been developed in the
past to examine the phenomenon. Although these models may be useful when
identifying the variables and interrelationships among them, researchers have not come
to a conclusion as to which is the best model to use when investigating student retention.
The lack of consensus is especially relevant in studies concerning community college
students. Since the majority of studies conducted to examine the relevance of each
model were done with four-year university students, the results cannot be generalized to
the community college population. Therefore, rather than focusing on one certain
model to examine student retention, this review of literature concentrated on
investigating variables introduced by past persistence literature of community college
students.
Based upon the summary of persistence literature at the community college
setting, numerous pertinent variables were found to influence student retention. The
variables were categorized into demographic variables, financial variables, academic
variables, academic integration variables, and psychosocial variables. The demographic
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variables included age, ethnicity, family responsibility, and socioeconomic status. The
financial variables included student employment, tuition, and financial aid. The
academic factors included pre-college entry academic characteristics, specifically high
school GPA as well as standardized and placement test scores. Another academic factor
was college GPA. In addition to academic ability of the students, enrollment status and
registration behaviors were reviewed. The main variables discussed within the
academic integration factors were student-faculty interaction and support services.
However, since numerous variables have been investigated under the construct of
“academic integration,” several other variables were introduced as well. Lastly, the
psychosocial factors included student goals and self-efficacy.
Each variable had a distinct relationship with student retention, indicating that
all of the variables mentioned above have significant influence on student retention at
the community college level. However, while the relationship between each variables
and student retention was robust, the interrelationships between variables were less
apparent. Although most of the variables had a direct correlation with student retention,
some of the variables seem to have indirect relationships as well. For example, age,
employment, enrollment status all seem to correlate to influence student retention.
Investigating the whole picture including all the variables will minimize the possibility
of the interrelationships being overlooked. Therefore, it was important to have all of
these variables investigated in this literature review.
One point to note in this literature review is that some of the variables studied
included research from both community college and four-year institutions. Furthermore,
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the variables introduced in this review overlaps with variables introduced in the
numerous models investigated with four-year college students. Therefore, this finding
indicates that there may be more similarities concerning persistence behaviors between
the two populations. However, unlike the four-year college level where multivariate
models have been investigated, multivariate study is scarce in the community college
setting. Therefore, further investigation is necessary to examine interrelationships
between variables.
This study integrates numerous variables introduced in student retention
research among community college students. The variables include demographic,
academic, financial, academic integration, and psychosocial factors. Results from past
literature have demonstrated a robust relationship between each of the variables and
student retention. The next step is to investigate the interrelationships between the
variables. For example, academic ability predicts student retention, but not among all
of the students. It is crucial to identify the additional variable that influences academic
ability- student retention relationship. In addition, students with low academic ability
may be influenced to persist by different variables than students with high academic
ability. Therefore, different interventions may have to be implemented based upon
those differences. As one of the few studies that will investigate multiple variables
simultaneously, there is a possibility to identify the interrelationships between the
variables. Identification of the interrelationships will not only provide a better
understanding of persistence among community college students, but will also promote
effective interventions strategies for college educators.
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CHAPTER III
METHODS
Research Overview
The purpose of this current study was to increase the understanding of
persistence behavior among community college students. More specifically, the paper
was intended to determine why some students persist in their academic endeavors while
others drop-out even when students’ demographic characteristic such as ethnicity,
employment, and financial aid were considered. Some of the determining factors that
have been associated with persistence in community college settings are background
variables such as ethnicity, age, socioeconomic status, employment, academic ability,
academic integration variables such as student-faculty interaction, and psychosocial
variables such as goal setting and self-efficacy.
The following research questions guided this study:
1. What background variables, financial variables, academic variables
influence students’ persistence in community college education?
2. Do academic integration and psychosocial variables influence student
persistence?
Research Design
Since the purpose of this research was to gain a general understanding of the
reasons why students decide to persist or withdraw from college, the research design
was explanatory in nature. In order to determine what the predicting variable is for
student retention, numerous variables introduced in the past literature was utilized in
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this study. Thus, quantitative data was collected via survey instrument to investigate
student retention. The survey included a questionnaire that asked a number of
background information. In addition to the background information collected, several
different scales were used to measure academic integration, career aspirations, and self-
efficacy.
Participants
Participants for this research were students enrolled in classes in Fall 2007
semester at a community college located in southern California. Fifty classes were
randomly selected by the SPSS software from the class schedule of Fall 2007. However,
physical education classes, short term classes, off-campus classes, internet classes, and
low-level English as Second Language (ESL) classes were eliminated from the samples.
Physical education classes were eliminated from the sample because many people in the
community enroll in a one-unit physical education for the purpose of exercising,
without any intentions of pursuing a college degree or certificate. Short term classes,
off-campus and internet classes were eliminated because those classes do not allow
opportunities for student-faculty interaction. Lastly, low-level ESL classes were
eliminated because of the possibility of comprehending the questionnaire. This
stratified random sampling ensured that the whole community college population was
represented in the study.
The participants consisted for 427 students who were enrolled in 19 courses (25
sections) during the Fall 2007 semester. Among the 427 surveys entered into the
analysis, 157 (36.8%) were male and 256 (60%) were female, and 14 (3.3%) did not
108

identify their gender. Of the total sample, 21 (4.9%) were African American, 43
(10.1%) Asian, 162 (37.9%) Hispanic, 26 (6.1%) Other Non-White, 136 (31.9%) White,
and 39 (9.1%) did not identify themselves in any category. The age ranged from 18 to
74 with a mean age of 24.64 (SD = 8.39). In addition, 56.1% of the students were
enrolled part-time during the Fall 2007 semester, and 43.9 % of the students were
enrolled full-time.
Instruments
Several instruments were used to collect data in order to examine the persistence
behaviors of community college students. These instruments were selected based upon
previous research in this domain. Several of the items in the survey were selected from
the Institutional Integration Scale (IIS) developed by Pascarella and Terenzini (1980).
Another scale that was utilized is the College Self-Efficacy Inventory (CSEI) developed
by Solberg, O’Brein, Villareal, Kennel, and Davis (1993). Lastly, the Career Decision
Scale (CDS) developed by Opisow, Carney, Winer, Yanico, and Koschier (1987) was
utilized. The first instrument was designed to examine student integration into the
college institution, the second instrument was designed to examine college students’
self-efficacy level, and the third instrument was designed to measure an individual’s
certainty and indecision regarding his or her choice of career and school major. In
addition to the three instruments, various background characteristics of the students
were asked in the survey. The following sections discuss the instruments in detail.
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Background Information
The participants were asked to complete various demographic, financial, and
academic information using questions derived from the Cooperative Institutional
Research Program (CIRP) Freshman Survey. The survey asked students’ ethnicity, age,
parental educational level, high school GPA, financial aid status, employment status,
and educational aspirations. Students’ units completed were provided by the institution.
In addition, items designed specifically for community college students were asked as
well (e.g., transfer plans). Furthermore, in addition to asking for educational aspirations
in the demographic section, the following open-ended question: “What is your career
goal?” was used to assess students’ goals. The open-ended approach is chosen to seek
free responses without bias of predetermined definitions of possible career goals (Hull-
Blanks et al., 2005).
College Self-Efficacy Inventory (CSEI)
The CSEI (see Appendix A) consists of 20-item scale designed to measure
students’ beliefs in their abilities to successfully engage in various academic related
tasks (Solberg et al., 1993). Each of the 20-items was phrased in the following
statement “How confident are you that you could successfully complete the following
tasks: …” The participants were asked to rate the items using a 10-point scale ranging
from 1 (not at all confident) to 10 (extremely confident). Sample items of the scale
include the following: “Research a term paper,” “Talk with your professors,” “Do well
on your exams.”
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Several subscales were created for the CSEI using a principal components
analysis of the 20 items (Solberg et al., 1993). The three factors solutions were found
including: course self-efficacy, social self-efficacy, and roommate self-efficacy. Factor
loadings of the course self-efficacy subscale items ranged from .91 to .59, social self-
efficacy subscale items ranged from .95 to .80, and roommate efficacy subscale items
ranged from .88 to .56. Second-order principal components analysis revealed that the
three subscales converged with other college adjustment indicators and discriminated
from other indicators such as acculturation and social support, implying adequate
construct validity (Solberg et al., 1993). With respect to reliability, the internal
consistency reliability measures (Cronbach alphas) for the total score and scores from
the course, social, and roommate self-efficacy belief subscales were .92, .88, .86 and .83
respectively (Gore, Leuwerke, & Turley, 2005).
Some studies in the past have averaged the total score of the 20 items to use as a
single index of college self-efficacy (Solberg & Villarreal, 1997; Torres & Solberg,
2001). However, in this current study, items within the roommate self-efficacy subscale
were eliminated from the questionnaire because the community college students are
commuter students who do not live in residential hall in the college. Gore and his
colleagues (2005) attempted to describe the differential concurrent and predictive
validity of the CSEI subscales and found that the subscales differentially correlated with
students’ college expectations (i.e., College Student Expectations Questionnaire),
academic performance (GPA), and academic persistence. Therefore, the results of this
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study validate to only include course and social self-efficacy subscales in the
questionnaire.
Institutional Integration Scale (IIS)
Measures of academic integration included several subscales derived from the
IIS developed by Pascarella and Terenzini (1980). The IIS (see Appendix B) consists
of 34-item scale was designed to assess five facets of institutional integration. The
scale contains items that comprise the subscales of (a) Peer-Group Interactions (7 items),
(b) Interaction with Faculty (5 items), (c) Faculty Concern for Student Development
and Teaching (5 items), (d) Academic and Intellectual Development (7 items), and (e)
Institutional and Goal Commitment (6 items). The questions have a 5-item Likert-type
response set that ranges from 1 (strongly disagree) to 5 (strongly agree). Sample items
of the scale included the following questions: “My nonclassroom interactions with
faculty have had a positive influence on my career goals and aspirations,” and “Few of
the faculty members I have had contact with are generally interested in students.” The
Chronbach alpha reliability for each of the subscales was as follows: .84, .83, .82, .74,
and .71, respectively. The intercorrelations among the five subscales ranged from .01
to .33 with a median correlation of .23. Thus, the subscales seem to assess dimensions
of institutional integration that are substantially independent of one another (Pascarella
& Terenzini, 1980).
French and Oakes (2004) revised the initial model of Pascarella and Terenzini
(1980) to increase the psychometric properties of the scale. The revision led to
increased internal consistency reliability for all five subscales (Peer-Group Interactions
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= .84, Interactions with Faculty = .89, Faculty Concern for Student Development and
Teaching = .88, Academic and Intellectual Development = .82, and Institutional and
Goal Commitment = .76). In addition, discriminant indices for the revised model also
increased, ranging from .26 to .64 (original model was .15 to .51). Finally, French and
Oakes proposed a first-order model with two latent factors (faculty and student
integration). They found that the Interactions with Faculty and Faculty Concern for
Student Development and Teaching indicated the one factor (faculty), while the other
subscales indicated the second factor (student).
This study only utilized the subscales intended to measure the academic
integration concerning the faculty. This is due to the results of past literatures that
showed that faculty interaction is essential in community college setting. Therefore,
Interactions with Faculty and Faculty Concern for Student Development and Teaching
subscales were utilized from the modified IIS.
Career Decision Scale (CDS)
The CDS (see Appendix C) consists of 18 self-rated items that indicate the
degree of certainty one feels about having made a career choice. The respondents
answer each statement according to a 5-point Likert-type scale ranging from 1 (not at all
like me) to 5 (exactly like me). Sample item includes, “I know I will have to go to work
eventually, but none of the careers I know about appeal to me.” Items 1 and 2 compose
the Decision scale and the sum of items 3-18 compose the Indecision scale. Higher
Decision scale indicates greater certainty of career choices, while higher Indecision
scale indicates greater indecisions regarding career choice. The internal consistency
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reliability for the CDS was found to be .90 (Guerra & Braungart-Rieker, 1999). Test-
retest reliability coefficient was reported to be between .70 and .90 (Osipow et al.,
1987). The CDS was adapted and reproduced by special permission of the Publisher,
Psychological Assessment Resources, Inc., 16204 North Florida Avenue, Lutz, Florida
33549, from the Career Decision Scale by Samuel H. Osipow, Ph.D., Copyright 1976,
1987, by PAR, Inc. Further reproduction is prohibited without permission of
Psychological Assessment Resources, Inc.
Procedures
Prior to the start of this study, the researcher obtained permission from the
Institutional Review Board (IRB) to conduct the study. Fifty classes were randomly
selected by the SPSS software from the Fall 2007 course schedule with the exception of
physical education classes, short term classes, and off-campus classes. Instructors of
each class were contacted by the researcher via email to ask permission to administer
the survey in their classes. If permission was obtained from the instructor, the students
enrolled in those classes were surveyed during the middle of the semester, which
allowed students to become familiar with the environment and allow opportunities for
faculty interactions.
The first set of data was collected by the researcher personally visiting the 25
separate courses during the middle of the semester. The researcher obtained student
consent (Appendix D) and administer the survey questionnaire (Appendix E). The
instructors were asked to leave the respective classrooms to reduce the sense of
coercion. The researcher briefly explained the purpose of the study (i.e., to gain
114

increased understanding of student retention behaviors, and to identify any interventions
that can be implemented to help the students) and notified them that participation was
both voluntary. In addition, it was emphasized that the collected data will be used for
this research study only and will not be used in any other way. Furthermore, students
were notified that participation of this study is voluntary and their answers will be kept
confidential in the strictest manner. After the brief orientation and the recruitment
speech, students were asked to read the consent form carefully and sign if they were
willing to participate in the study. The students completed the paper and pencil survey
by marking the appropriate circles on the scantron and providing short answers on the
survey. Finally, the students were instructed to complete the survey on their own and
submit the completed form in a box at the back of the room.
The second set of data was collected in the Spring 2008 semester in order to
identify which of the students who participated in the original survey returned to school.
The information was provided by the institution from the consensus based upon the
students’ identification numbers, and this second set of data measured the student
retention, which is the outcome variable for this study.
Data Analysis
All data collected in this study was coded and computerized using the SPSS
software. Descriptive statistics such as means, modes, medians, and standard deviations
were computed. Pearson correlations were conducted in order to examine if there are
relationships between the various independent variables, and whether any of the
independent variables correlate with the dependent variable, student persistence.
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Furthermore, logistic regression analysis was conducted to examine which independent
variables best predict student retention.
The results obtained from this research are reported in chapter 4. In chapter 5,
discussion and analysis of the findings, limitations of current study, practical
implications, as well as future research suggestions based on the findings from current
research will follow.
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CHAPTER IV
RESULTS
This chapter presents the statistical outcomes for the previously stated research
questions: 1) What background variables, financial variables, academic variables
influence students’ persistence in community college education? 2) Do academic
integration and psychosocial variables influence student persistence? Specifically,
descriptive data for the variables under study, including demographic data, means,
standard deviations, and intercorrelations are presented in the first section. In the latter
sections, results from logistic regression and MANOVA for the research questions are
presented.
Descriptive Statistics
A summary of means and standard deviations of demographic variables as well
as course statistics are listed in Table 4. Internal-consistency reliability using
Cronbach’s alpha was compued for these psychosocial variables, and were .87 for
course self-efficacy, .76 for social self-efficacy, .81 for faculty interaction, .44 for
faculty concern, .91 for career indecision, .88 for career decision, and .63 for
educational barriers.
117

Table 4
Descriptive Statistics and Estimates of Internal-Consistency Reliability (Coefficient
Alpha)
Variable MD SD Reliability
1. Course Self-Efficacy 6.91 1.50 .87
2. Social Self-Efficacy 7.11 1.89 .76
3. Faculty Interaction 3.27 .91 .81
4. Faculty Concern 3.23 .59 .44
5. Career Indecision 2.19 .88 .91
6. Career Decision 3.57 1.32 .88
7. Educational Barriers 3.68 .85 .63
8. Age 24.64 8.33 -
9. Units Completed 31.84 28.44 -
10. Units Attempted 33.38 27.71 -
11. Cumulative GPA 2.61 .95 -
12. On-campus Work Hours 2.47 6.36 -
13. Off-campus Work Hours 21.16 17.72 -

Intercorrelations
A summary of the means, standard deviations, and the Pearson correlation
coefficients with their respective levels of significance for correlations for the 28
predictor variables are presented in Table 5. It includes background variables (age,
ethnicity, number of children, financial responsibility, father education, mother
118

education), financial variables (financial aid, on-campus work hours, off-campus work
hours, total work hours), academic variables (high school GPA, units enrolled,
cumulative GPA, units attempted, units completed), educational barriers (transportation,
family, job, college payment, English proficiency), psychosocial variables (educational
goals, career goals, course self-efficacy, social self-efficacy, indecision scale, decision
scale), academic integration variables (faculty interaction, faculty concern), and student
persistence.
Student persistence measured by retention, was negatively related to students’
age (r = -.104, p < .05), off-campus work hours(r = -.161, p < .01), total work hours(r =
-.130, p < .01), and English proficiency (r = -.099, p < .05). It was also positively
related to units enrolled (r = .179, p < .01), receipt of financial aid (r = .122, p < .05),
and cumulative GPA(r = .125, p < .05). However, retention was not significantly
related to any of the psychosocial variables or the academic integration variable.
The correlational analysis also revealed that many of the predictor variables
were related to each other with intercorrelations ranging from -.42 to .47. The
significant correlations among the predictor variables indicate that numerous variables
intertwine and influence students as a whole.

119
Table 5

Means, Standard Deviations, and Pearson Product Correlations for Measured Variables (N=427)
Variable M SD 1. 2. 3. 4. 5. 6. 7. 8.
1. Age 24.64 8.38 -
2. Ethnicity 2.49 1.25 -.166** -
3. Number of
Children
1.27 .78 .400** .083 -
4. Financial 1.76 1.14 .409** .050 .468** -
5.Father Education 4.59 2.68 -.001 -.114* -.002 -.001 -
6. Mother Education 4.62 2.57 -.050 -.089 -.055 -.092 .597** -
7. Financial Aid 1.66 .55 .051 -.040 .070 .076 -.024 .023 -
8. On-campus Work 2.47 6.36 -.092 -.023 -.052 .001 -.062 -.098* .001 -
9. Off-campus Work 21.16 17.72 .127* -.054 -.005 .215** -.199** -.133** .078 -.045
10. Total Work 23.64 18.57 .089 -.060 -.023 .206** -.210** -.161** .075 .300**
11. HS GPA 2.39 1.04 -.105* .100 -.048 -.046 -.178** -.185** .015 .009
Note: *p < .05; ** p < .01; *** p < .001

120

Table 5 – Continued

Means, Standard Deviations, and Pearson Product Correlations for Measured Variables (N=427) - Continued
Variable M SD 1. 2. 3. 4. 5. 6. 7. 8.
12. Enrolled Units 9.68 4.50 -.337** .027 -.138** -.210** .025 .060 -.097* .082
13. Cumulative GPA 2.62 .96 .311** -.147** .098* .069 .133** .109* -.046 -.006
14. Units Attempted 33.38 27.71 .095 .012 .055 .092 .017 -.045 -.024 .004
15. Units Completed 31.84 28.44 .125* .014 .064 .084 .035 -.029 -.026 .006
16. Transportation 3.86 1.34 .114* -.039 .026 .007 -.030 -.052 .053 -.014
17. Family 3.76 1.36 -.044 .-.102* -.142** -.200** .074 .090 .025 -.019
18. Job 3.37 1.40 -.024 .006 -.009 -.171** .070 .071 .066 .038
19. College Payment 3.11 1.41 .055 -.129* .007 -.032 .104* .073 .053 -.009
20. English Proficiency 4.31 1.14 -.134* -.089 -.004 -.070 .056 .039 -.039 .092
21. Educational Goals 4.78 1.49 -.148* -.082 -.158** -.134** .048 .091 -.018 .047
Note: * p < .05; ** p < .01; *** p < .001

121

Table 5 – Continued

Means, Standard Deviations, and Pearson Product Correlations for Measured Variables (N=427) - Continued
Variable M SD 1. 2. 3. 4. 5. 6. 7. 8.
22. Course Self-Efficacy 6.91 1.49 .099* -.143** .022 .029 .088 .045 -.050 .052
23. Social Self-Efficacy 7.11 1.89 .083 .087 -.033 -.033 -.022 -.008 -.022 -.057
24. Faculty Interaction 3.28 .91 -.001 -.012 .017 .018 -.064 -.044 -.047 .054
25. Faculty Concern 3.25 .72 -.030 -.006 -.020 -.080 -.088 -.043 -.059 .019
26. Indecision 2.19 .87 -.130** .151* -.050 -.021 .000 .082 -.001 -.060
27. Decision 3.57 1.32 .203** -.033 .063 .115* .007 -.024 -.059 .031
28. Retention .70 .46 -.104* .041 .006 -.056 .051 .072 .122* .067
Note: * p < .05; ** p < .01; *** p < .001

122
Table 5 – Continued

Means, Standard Deviations, and Pearson Product Correlations for Measured Variables (N=427)
Variable 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
1. Age

2. Ethnicity

3. Number of
Children

4. Financial

5.Father Education

6. Mother Education

7. Financial Aid

8. On-campus Work
9. Off-campus Work -
10. Total Work .939** -
11. HS GPA .067 .066 -
Note: * p < .05; ** p < .01; *** p < .001

123
Table 5 – Continued

Means, Standard Deviations, and Pearson Product Correlations for Measured Variables (N=427)
Variable 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
12. Enrolled Units -.210** -.172** -.064 -
13. Cumulative GPA -.050 -.049 -.286** .007 -
14. Units Attempted -.092 -.086 .006 .047 .137** -
15. Units Completed -.107* -.100* -.028 .006 .233** .980** -
16. Transportation .141** .130** .019 -.088 .057 -.043 -.034 -
17. Family .004 -.003 -.023 .061 -.051 -.061 -.060 .277** -
18. Job -.252** -.228** -.035 .066 -.055 .026 .028 .210** .444** -
19. College Payment -.075 -.075 -.029 -.039 .004 .017 .023 .227** .359** .364**
20. English Proficiency -.001 .031 .041 .043 -.060 -.046 -.064 .147** .244** .152**
21. Educational Goals -.031 -.014 -.086 .145** .199** .028 .041 .040 -.023 -.029
Note: * p < .05; ** p < .01; *** p < .001

124

Table 5 – Continued

Means, Standard Deviations, and Pearson Product Correlations for Measured Variables (N=427)

Variable 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
22. Course Self-Efficacy -.060 -.039 -.239** .054 .223** -.003 .006 .102* .061 .040
23. Social Self-Efficacy .057 .035 .053 -.127** -.003 .139** -.031 -.030 .140** .068
24. Faculty Interaction -.106* -.082 -.072 .101* .049 .077 .089 .009 -.011 .010
25. Faculty Concern -.108* -.097* -.065 .144** .020 .027 .042 -.093 -.034 .061
26. Indecision -.039 -.058 .090 .132** -.222** -.076 -.099* -.031 -.088 -.129**
27. Decision -.027 -.015 -.093 -.129** .089 .091 .113* -.039 -.018 .063
28. Retention -.161** -.130** -.074 .179** .125* .078 .080 -.062 -.031 .087
Note: * p < .05; ** p < .01; *** p < .001

125
Table 5 – Continued

Means, Standard Deviations, and Pearson Product Correlations for Measured Variables (N=427)
Variable 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.
15. Units Completed
16. Transportation
17. Family
18. Job
19. College Payment -
20. English Proficiency .030 -
21. Educational Goals -.084 .073 -
22. Coarse Self-Efficacy -.071 .192** .218** -
23. Social Self-Efficacy -.079 .146** .151** .605** -
24. Faculty Interaction .006 -.024 .111* .266** .246** -
25. Faculty Concern -.027 .022 .019 .150** .137** .350** -
Note: * p < .05; ** p < .01; *** p < .001

126

Table 5 – Continued

Means, Standard Deviations, and Pearson Product Correlations for Measured Variables (N=427)
Variable 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.
26. Indecision .004 -.207** -.190** -.334** -.243** .052 .027 -
27. Decision -.052 .009 .060 .311** .279** .218** .150** -.423 -
28. Retention .065 -.099* .020 -.022 -.062 -.025 .071 .051 -.053 -
Note: * p < .05; ** p < .01; *** p < .001
127

Multivariate Analysis
A multivariate logistic regression analysis was used to determine the prediction
of student retention. Since persistence is a dichotomous variable, multiple regression
cannot be utilized for the analysis. Instead, it is recommended that logistic regression
be used, which estimates the effect of different independent variables (St. John, 1990).
Similar to the linear regression analysis, logistic regression calculates an equation
comprise of additive terms. Feldman (1993) found that logistic regression enables the
researcher to mix continuous and categorical variables and investigate the influence of
those variables on the dichotomous dependent variable. In the case of this study,
multiple variables are input into the regression to explore its effect on student
persistence.
First, the sample size was decreased from 427 to 383 by eliminating the 44
students who indicated that they were going to transfer from the college in Spring 2008.
Since these students had no intentions of coming back, they should not be considered as
non-persisters in the analysis even though the resulting data shows that they failed to
persist. A multivariate analysis of variance (MANOVA) was conducted to see whether
difference existed between the non-persisting students and students who transferred.
Results from the MANOVA indicated that the two groups do not differ on any of the
variables except for the Decision scale F(1, 128) = 10.237, p = .002.
Secondly, to assist in selecting the input parameters for the inclusion in and
exclusions from the models, results from the independent t-tests were used (see Table 6).
All parameters that had t-scores with p of 0.05 or smaller were used as input variables
128

in this model. Among the background, financial, academic variables tested for
associations with student retention, age (t = 2.127, p < .05), receipt of financial aid (t =
2.814, p. < 01), number of units attempted (t = -2.246, p. < 05), number of units
completed (t = -2.218, p. < .05), number of current enrolled units (t = -5.442, p < .001),
high school graduation year (t = 3.307, p. < 001), cumulative GPA (t = -2.559, p < .01),
English proficiency(t = 3.307, p < .001), off-campus work hours (t = 3.363, p < .001),
and total work hours (t = 3.097, p < .01) were significant. This selection method
reduced the potential input variables from 27 to 10.
Table 6
t-scores for Predicting Variables and Student Persistence

Persisters
(n = 295 )
Non-persisters
(n =77 )
t
Variable M SD M SD
Age 24.12 8.198 26.23 8.480 2.127*
Ethnicity 2.51 1.269 2.30 1.217 -1.276
Father Education 4.74 2.538 4.27 2.602 -1.048
Mother Education 4.80 1.474 4.57 1.583 -1.489
Financial Aid 1.61 .521 1.79 .511 2.814**
On-campus Work 2.77 6.763 2.24 5.875 -.647
Off-campus Work 19.41 17.467 27.43 17.936 3.363***
Total Work 22.187 18.055 29.667 19.883 3.097**
HS Graduation Year 3.06 1.274 3.55 1.175 3.307**
HS GPA 2.34 1.004 2.50 1.103 1.280
Note: * p < .05; ** p < .01; *** p < .001
129

Table 6 - Continued

t-scores for Predicting Variables and Student Persistence

Persisters
(n = 295 )
Non-persisters
(n =77 )
t
Variable M SD M SD
Number of Semester 2.72 1.492 2.51 1.868 -1.011
Units Attempt 34.82 26.977 26.72 29.899 -2.246*
Units Completed 33.35 28.868 25.15 29.900 -2.218*
Cum GPA 2.688 .901 2.312 1.164 -2.559**
F07 Units 10.18 4.502 7.31 3.941 -5.442***
Transportation 3.80 1.349 4.00 1.372 1.205
Family 3.74 1.369 3.79 1.406 .322
Job 3.45 1.406 3.16 1.396 -1.694
Paying 3.17 1.412 3.01 -.920 .182
English 4.24 1.224 4.52 .822 3.307***
Course SE 6.89 1.449 6.91 1.564 .100
Social SE 7.04 1.869 7.31 2.022 1.160
Faculty Interaction 3.25 .884 3.27 .918 .114
Faculty Concern 3.55 .710 3.36 .782 -2.168*
Indecison 2.22 .866 2.14 .908 -.671
Decision 3.52 1.33 3.50 1.36 -.140
Note: * p < .05; ** p < .01; *** p < .001

130

Lastly, logistic regression analysis was conducted. Academic integration
variables and psychosocial variables were inserted in Step one. Age, high school
graduation year, cumulative GPA, Fall 2007 enrollment units, English proficiency, were
inserted in Step two. The estimated betas (B) derived from this logistic regression
equation provides the magnitude of the effect of each predictor on the odds of
persistence. Thus, the odds ratios compared (1) the expected probability of a student
with a specific trait to achieve the parameterized outcome with (2) the expected
probability of a student without a specific trait to achieve the same outcome. In this
case, it compares the odds of persistence by the predictor variables.
Table 7 presents the results for the multivariate logistic regression for the
hypothesized persistence model including the exponentiated beta for each of the
variables and the significant level. Academic integration and the psychosocial variables
were entered into block one, but only faculty concern was a significant predictor of
persistence. The odds ration indicated that a standard deviation change in the faculty
concern scale improved the ratio of persistence by a factor of 1.51 (p = .046). The other
variables had no significant effect on student persistence. Furthermore, this model only
accounted for 2.9% of the total possible amount of variance.
The background, academic, and financial variables were entered in block two.
When block two was entered into the model, the model correctly predicted 24.9% of the
cases. Only 3 of the 10 variables included in the model had significant association with
persistence. In addition, the effect of faculty concern became insignificant (p = .088),
indicating that its effect was mediated by the other variables.
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Among the variables entered in block two, cumulative GPA, Fall 2007
enrollment units, and English proficiency was the only variables that predicted student
persistence. Amongst the three variables, cumulative GPA was the strongest predictor
with the odd ratio of 1.970 (p = .001). In other words, for each standard deviation
increase in the students’ cumulative GPA, the likelihood of the students persisting
almost doubled. Fall 2007 enrollment had an odds ratio of 1.093 (p = .027) and the
odds ratio of English proficiency was .606 (p = .007).
Table 7
Logistic Regression Analysis for Variables Predicting Student Persistence
Step 1
95.0% C.I. for Exp(B)
Variable p Exp(B) Lower Upper
CS .375 1.113 .879 1.410
SS .381 .923 .771 1.104
FI .893 .977 .697 1.370
FC .046 1.514 1.008 2.275
IND .836 .967 .700 1.334
DEC .631 1.060 .834 1.348
Note: CS = course self-efficacy, SS = social self-efficacy, FI = faculty interaction, FC =
faculty concern, IND = indecision, DEC = decision.
132

Table 7 – Continued

Logistic Regression Analysis for Variables Predicting Student Persistence
Step 2
95.0% C.I. for Exp(B)
Variable p Exp(B) Lower Upper
CS .776 1.040 .794 1.361
SS .620 .949 .770 1.169
FI .320 .818 .550 1.216
FC .060 1.559 .981 2.478
IND .808 1.0061 .658 1.711
DEC .345 1.145 .864 1.519
Age 629 .989 .945 1.035
Gender .723 1.121 .594 2.115
Ethnicity .410 1.115 .861 1.443
HS Grad Year .034 .702 .506 .973
Number of Semester .408 1.157 .819 1.632
Units Attempted .077 1.068 .993 1.149
Units Completed .083 .938 .873 1.008
Cumulative GPA .001 2.014 1.344 3.017
F07 Units .018 1.102 1.017 1.193
Off-campus Work .413 .977 .924 1.033
Total Work ..594 1.015 .962 1.070
Note: CS = course self-efficacy, SS = social self-efficacy, FI = faculty interaction, FC = faculty
concern, IND = indecision, DEC = decision.

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Table 7 – Continued

Logistic Regression Analysis for Variables Predicting Student Persistence
Step 2
95.0% C.I. for Exp(B)
Variable p Exp(B) Lower Upper
Financial Aid .183 .653 .349 1.222
English Proficiency .013 .622 .429 .904
Note: CS = course self-efficacy, SS = social self-efficacy, FI = faculty interaction, FC = faculty
concern, IND = indecision, DEC = decision.

Research Question 1
What background variables, financial variables, academic variables influence
students’ persistence in community college education? Analysis of the data revealed
that several variables influence student persistence in community college education.
Among the background variables, simple independent t-tests revealed that age
and high school graduation year influenced student persistence in community college
students. Student who persisted were more younger (M = 24.12, SD = 8.198) compared
to those who did not persist (M = 26.23, SD = 8.480), t(370) = 2.127, p = .046. In
addition, students who graduated from high school in 2004 or earlier (28.7%), students
who passed the G.E.D. test (33.3%), and students who never graduated from high
school (28.6%) had the most non-persisting rate compared to their students who
graduated in 2005 or later, X
2
= (5, N = 381) = 17.129, p = .004.
Among financial variables, receipt of financial aid, off-campus work hours, and
total work hours influenced student persistence. Eighty-five percent of students who
received financial aid persisted compared to 73% of those who did not receive any
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financial aid, X
2
= (2, N = 375) = 10.525, p = .003. In addition, those who worked more
hours did not persist (M = 27.43, SD = 17.936), compared to the persisters, who worked
less hours on campus (M = 19.41, SD = 17.467), t(371) = 3.689, p < .001. For total
work hours, persisters again worked significantly less (M = 29.667, SD = 19.883)
compared to the non-persisters (M = 22.186, SD = 18.054), t(371) = 3.265, p = .001.
Therefore, the more hours students worked off-campus, the less likely they were going
to persist.
Among academic variables, those who attempted more number of units (M =
34.82, SD = 26.977) persisted compared to the non-persisters, who attempted less units
(M = 26.72, SD = 29.899), t(366) = -2.246, p = .025. Similarly, those who completed
more number of units (M = 33.35, SD = 26.977) persisted compared to the non-
persisters, who completed less units (M = 25.15, SD = 29.900), t(366) = -2.218, p = .027.
Furthermore, hose who were enrolled in more number of units during the semester (M =
10.18, SD = 4.502) persisted compared to the non-persisters, who were enrolled in less
(M = 7.31, SD = 3.941), t(366) = -5.025, p = .001. Persisters also had higher
cumulative GPA (M = 2.69, SD = .900) compared to the non-persisters M = 2.31, SD =
1.164), t(365) = -2.559, p = .012. Lastly, students who did not persist indicated lack of
English proficiency (M = 4.24, SD = 1.224) compared to students who persisted M =
4.52, SD = .822), t(379) 2.455, p = .015.
Research Question 2
Does academic integration and psychosocial variables influence student
persistence? Simple univariate analysis of the data revealed that only faculty concern
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influenced student persistence in community college education. Students who persisted
had slightly higher score of faculty concern (M = 3.55, SD = .710) compared to the non-
persisting students (M = 3.36, SD = .782), t(379) = -2.168, p = .031. None of the other
variables were found to be significantly associated with student persistence.
Summary
Investigation revealed that cumulative GPA was the single strongest predictor of
student persistence at the community college. In addition to cumulative GPA,
enrollment status and English proficiency were predictors of student persistence.
However, other variables were correlated and may have demonstrated some influence
when studied as a single factor, but the effect diminished when multiple variables were
examined. Moreover, contrary to expectations, none of the psychosocial variables were
predicting factors for student persistence in this study. Further discussion and
interpretation of the results as well as limitations and recommendations are included in
Chapter 5.
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CHAPTER 5
DISCUSSION
The present study extended previous research on student persistence by
investigating multiple variables that may contribute to student persistence in community
college students. In particular, this study examined background variables, financial
variables, academic variables, academic integration variables, and psychosocial
variables and how it contributes to the students’ decision to persist in their college
education. The previous chapter provided a quantitative analysis of the relationship
between each variables and student persistence, including correlations and logistic
regression analysis.
The results revealed that cumulative GPA was the strongest predictor of student
persistence when all the variables were considered. In addition, enrollment units and
English proficiency were predicting factors for students to persist in their education.
Inconsistent with the expected hypotheses based upon previous studies, neither
academic integration nor psychosocial variables predicted student persistence.
However, the study also revealed that almost all of the variables interrelate with one
another, thus providing a unique picture on how complex the decision to stay in college
is for students.
This chapter presents a brief review of the findings from the current
investigations as it links to previous studies. Individual variables will be discussed as
how it relates to student persistence, as well as how each variable correlates with
different variables. It will then be followed by implications of those findings applicable
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to community college students. Limitations of the current study and recommendations
for future research and practice are also provided.
Relationship between Background Variables and Student Persistence
Numerous variables have been associated with student persistence in the past
(e.g., gender, age, family responsibility, socioeconomic status). However, much of the
research shows mixed results for those variables. In a similar fashion, this study
confirmed previous studies by showing both significant and non-significant relationship
between several of the variables and student persistence. Few of the background
variables had significant direct effects on student persistence while several of the other
variables (e.g.., age, work hours, financial aid) significantly influenced subsequent
variables, which in turn most likely influenced student persistence. These relationships
indicate that an extensive part of the decision to stay or drop out of school may be a
combination of multiple factors. The indirect effects of several of the variables are
transmitted through intervening variables such as the cumulative GPA.
For background variable, only age and high school graduation year was
significantly related to persistence in this current study. In this group of students,
younger students were more likely to persist compared to older students, which is
consistent with prior research (Brooks-Leonard, 1991; Hagedorn et al., 2001; Lanni,
1997; & Windham 1995). It can be assumed that older students have competing
demands that create difficulty to focus on their school. In addition, older students tend
to attend school part time. Average age for students who attended the college part-time
was 26.52 (SD = 9.15) while it was 22.24 (SD = 6.63) for full-time students. Among
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the age categories, 42.2 % of the students who did not persist came from the 20-25 age
group (X
2
= 18.512, df = 4, p = .001). This corresponds with the result that showed that
the more years passed since high school graduation, the less the students persisted.
Obviously, significantly more years have passed for older students since high school
graduation, thus explaining the relationship between high school graduation year and
persistence. However, both of these variables were significantly related to student
persistence when examined by itself, but when entered into the logistic regression
equation, the significance no longer existed.
In regards to other background variables such as gender, ethnicity, and parental
educational attainment, no significant relationship were found between those variables
and student persistence. Previous studies on these variables show mixed results, with
some showing significant relationship (Mohammadi, 1994; Zhao, 1999) while others
showing no significant relationship (Aquino, 1990, Brooks-Leonard, 1991). The results
of this current study add to the mound of literature that found both significant and
insignificant association between demographic variables and student persistence. It is
suggested that when variables such as ethnicity is considered as a factor related to
student persistence, the association with other factors such as socioeconomic status are
strongly interrelated, thus making it difficult to analyze the effect of the single variable
(Laudicina, 1999). In addition, it is important to note that although only two of the
background variables were directly related to student persistence (i.e., age and high
school graduation year), ethnicity and parental education level had indirect effects.
Ethnicity had a significant relationship with cumulative GPA, while parental education
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levels were significantly related to work hours. Results from the study revealed that
work hours influenced student retention, and cumulative GPA influenced student
retention. Therefore, it is possible that significant influences on student persistence via
background variables are indirect, and many of the variables interrelate with one
another.
Financial variables were also associated with student persistence. In particular,
parallel to previous studies on employment status (e.g., Cofers & Somers, 2001; Schmid
& Abell, 2003; Swagaer et al., 1995), work hours were directly related to student
persistence. Students who did not persist in their college education worked
significantly more hours compared to students who persisted. Among the students who
did not persist, 45% of them worked 40 hours per week or more (X
2
= 10.273, df = 3, p
= .016), indicating that having a full time job may cause difficulty in pursuing their
education. This is similar to past studies that found that students who worked full time
were more likely to drop out of college compared to those who worked part time or did
not work at all (Lanni, 1997; Swager et al., 1995; Windham, 1995). Students who
spend more time working per week have less time to study, are less likely to attend
class on a regular basis, and are more likely to be sleep deprived, effecting their college
education (Slate, 2001). In addition, students’ working hours were also significantly
related to family responsibilities and financial responsibilities, indicating that they had
increased barriers to overcome compared to those students who did not work. When
faced with a conflict between work and school, it is assumed that these students may not
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be able to simply quit work to pursue their education because of the responsibilities they
hold.
Financial aid was also associated with increased student persistence in this study,
which is similar to past research (e.g., Cofer & Somers, 2000; 2001; Hippensteel et al.,
1996; Lanni, 1997; Makuakane-Drechsel & Hagedorn, 2000). The current study
revealed that 85% of students who received financial aid persisted in their college
education compared to 70% of students who did not receive such aid (X
2
= 10.525, df =
2, p = .005). This is expected since lack of money can be one of the main reasons why
students discontinue their college education (Bonham & Luckie, 1993). In addition,
receipt of financial aid was also correlated with hours students work, indicating that it
allows students to work less, thus increasing the time spent for academic tasks. Since
work hours were also related to student persistence, receipt of financial aid also has an
indirect effect on persistence.
Although both number of employment hours and financial aid had significance
direct relationship with student persistence when examined by itself, the relationship
disappeared when input into the logistic regression equation. Therefore, the association
between work hours and student persistence as well as financial aid and student
persistence may have been a factor of other variables. Specifically, employment hours
were significantly related to number of units enrolled during the semester, which was a
significant predictor of student persistence. Thus, it may be that students who worked
more hours had less time available to enroll in classes, which negatively influences
persistence. Still, the importance of financial support should not be overlooked since
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other studies in the past have regularly found direct relationship between financial
barriers and student persistence (Cofer & Somers, 2001; Hawley & Harris, 2005).
Lastly, when academic factors were considered, cumulative GPA, enrollment
status, number of units attempted and completed, and English proficiency were
associated with student persistence. Contrary to previous studies (e.g., Cofer & Somers,
2000, 2001; Kirby & Sharpe, 2001), high school GPA was not related to student
persistence. In addition, numbers of units attempted and completed were significantly
associated with student persistence when examined alone, but the relationship did not
hold when entered into the logistic regression analysis.
According to this study, cumulative GPA was the strongest predictor of student
persistence and its effect did not diminish when other variables were entered into the
model. The results clearly showed that students were twice as likely to persist when
their cumulative GPA increased by one standard deviation. This corresponds to
previous studies that consistently showed association between students’ college
academic performance and persistence (e.g., Kirby & Sharpe, 2001; Leppel, 2002).
Furthermore, unlike studies that only found correlational association between
cumulative GPA and persistence (Debernard et al., 2004; Lufi et al., 2003), the results
of this current study is based upon logistic regression analysis, indicating that the GPA
was a predicting variable for student retention.
Cumulative GPA may be an indication of several factors. First, students with
higher cumulative GPA are obviously spending more time to study, thus are assumed to
be more involved with their college education. In addition, some students may perceive
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GPA as a reward for their academic experience. Higher level of GPA means higher
level of positive reinforcement, thus leading to increased likelihood of remaining in
school (Voohrees & Zhou, 2000). On the other hand, students with low GPA may be
indicative of the fact that they are underprepared for college education. Underprepared
students demonstrate significant difficulties in their college lives, showing lower self-
rating of ability and lower predictions of future accomplishments (Grimes & David,
1998). In addition, cumulative GPA was significantly associated with self-efficacy.
Although the relationship between self-efficacy and student persistence was not
significant in this study, previous researchers have demonstrated the large impact self-
efficacy has on the person’s choice of activities, the level of effort one puts into the task,
persistence, and emotional reactions (Bandura, 1994). Students who received lower
grades had lower self-efficacy, thus may have attributed their failure to their ability.
Therefore, instead of persisting on their college education, they may have simply
dropped out.
The number of units enrolled was also a significant predictor of student
persistence in this study. Results from this study showed that 70.8% of the students
who did not persist were enrolled in the college part-time, compared to 29.2% of
dropouts were enrolled full-time (X
2
= 13.685, df = 1, p < .001). This is consistent with
previous research that indicated significant relationship between students’ enrollment
status and student persistence (e,g,, Brooks-Leonard, 1991; Feldman, 1993; Lanni,
1997). Several explanations can be assumed why enrollment status may influence
student persistence. First of all, enrollment status was negatively related to number of
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children, financial responsibility, and off-campus work hours. It was also negatively
associated with age. Therefore, it is more likely that part-time students are older in age
and have other external commitments such as work, family, or children that prevent
them from enrolling full-time. This corresponds with previous research (Jacobs &
Berkowitz-King, 2002) that also found that external commitments do not allow students
to spend much time in their role as students. Secondly, it has been suggested that full-
time enrollment increases the academic integration of community college students
(Makukane-Drechsel & Hagedorn, 2000). This was confirmed in this current study by
the positive association between enrollment status, faculty interaction, and faculty
concern. It can be assumed that the more time students spend at the community college,
opportunities would increase for them to interact with faculty members. Therefore,
when students have increased enrollment units, it influences student persistence in a
positive manner. Lastly, since part-time students are assumed to have lesser freedom of
flexibility for scheduling, they may be negatively affected by the class schedules or by
the way programs and services are provided at the community college.
However, one factor to keep in mind when examining the relationship between
enrollment status and student persistence is that students attending community college
in a part-time basis may have different goals. For example, they may be taking a course
for job advancement or for personal interest, and once the short-term goal is attained,
they may not return to the college. Since the current study did not differentiate such
students who attained their goals to those who simply did not return due to other
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reasons, the relationship between enrollment status and persistence should be examined
with caution.
Lastly, English proficiency was one of the predicting elements of student
retention in this study, and the effect did not disappear when logistic regression analysis
was conducted. This is consistent with previous research that showed English
proficiency being a problem for student persistence (Hawley & Harris, 2005; NVCC,
2001). It can be assumed that students who answered that English would be a problem
with their college experience are placed in remedial classes. Remedial classes are
intended to bridge the gap for students unprepared for college-level English, and nearly
half of English classes taught at community college are at the remedial level (Cohen,
1990). Nevertheless, remedial classes tend to repeat materials from high school, which
can be discouraging and frustrating for some students. In addition, remedial classes
usually do not count towards transferable college classes, thus students placed in those
classes need to take increased units in order to qualify for transfer. In a study conducted
by NVCC (2001), researchers found that students’ decision whether to return to college
or not was directly related to their experiences with developmental English classes.
Placement in remedial classes and its effect was not examined in this study, so further
investigation is warranted to reveal the effect of English proficiency on student
persistence.
Relationship between Academic Integration Variables and Student Persistence
Academic integration is one of the most frequently studied factors in association
with student retention. Although it has been widely studied, there had been numerous
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variables used with the intention to measure the construct. In this study, academic
integration was measured via student’s self-reported assessment of their perception of
faculty interaction and faculty concern.
Contrary to previous research (e.g., Berger & Milem, 1999; Heverly, 1999;
Schmid & Abell, 2003), there was no direct relationship between faculty interaction and
student persistence. This means that students in this sample who had increased faculty
interaction did not actually persist in their college education than those who did not
have interactions. In other words, students who were academically integrated into the
community college were no more likely to persist in their education as students who
were not integrated. On the other hand, faculty concern was a significant predictor of
student persistence in this study. Students who felt that the faculty members genuinely
cared about them were more likely to persist in their college education compared to
students who did not feel that way.
One explanation that can be attributed to the lack of significant relationship
between faculty interaction and student persistence may be the increase of part-time
faculty at the community college setting. In the past three decades, the use of part-time
faculty at the community college settings has increased dramatically and it has been
suggested that the increase may have an ill effect on student outcomes (Benjamin, 2002).
Specifically, Jacoby (2006) found that as the percentage of part-time faculty on campus
increases, it was associated with lower graduation and retention rates. Since the
percentage of part-time faculty was not studied in this current research, further research
is needed to investigate this relationship. Still, it is important to consider the impact of
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part-time faculty on student retention. Since many part-time faculty are only on campus
when they teach their classes, they are relatively unavailable to interact with students
outside of the class time. Furthermore, part-time faculty are paid by the course or by
class-contact hour, and the institutions do not necessarily provide considerable
incentives for them to become involved with students outside of their classrooms. They
also may be lacking basic equipments such as phone, offices, computers to allow them
to meet with and advise the students outside of their classrooms, limiting the amount of
interaction between faculty and students (Jacoby, 2006). Given that the instrument used
in this study asked the quality of the interaction instead of the quantity, it makes sense
that the association could not be found since the students might not have had
opportunities to interact with the faculty. Although quality of the interaction has been
suggested to be as important as the quantity of interaction (Lundberg & Schreiner,
2004), frequency of interaction should also be investigated to further understand the
relationship between faculty interaction and student persistence.
On the other hand, faculty concern did have a significant relationship with
student persistence when examined alone. It makes sense that the more students feel
they are being cared for, the more likely they will return to that institution. In a large
scale study in Texas, the investigator found that helpful, understanding faculty
influenced student retention (Glenn, 2003). Helpful instructors were those who took the
time necessary to work with the students as well as be available for discussions,
questions, and requests outside of classes. It may only take one individual to convince
the student that he or she is important and will be missed if they decide to drop out.
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Therefore, it can be assumed that the implication of faculty concern is tremendous.
Although the effect of faculty concern on student persistence disappeared once other
variables were entered into the equation, it is still an important factor that all college
administrators and faculty members should be aware of to help with student persistence.
Relationship between Psychosocial Variables and Student Persistence
Contrary to predictions and prior research studies, the psychosocial variables did
not present direct association with student persistence for this study. In this study,
students who had careers goals did not necessarily persist in their college education
compared to students who did not have goals. Similarly, students who had higher levels
of self-efficacy did not necessarily persist compared to those students who had low
levels of self-efficacy. The results of this study contradicts to previous research that
found direct relationship between goals and student persistence, as well as self-efficacy
and student persistence (e.g., Campbell & Blakey, 1996; Garardi, 1996; Hagedorn et al.,
2001; Hawley & Harris, 2005; Torres & Solberg, 2001).
Mere presence of a career goal may not be sufficient for students to persist in
their college education. It is quite common that goals change over time. In particular,
Voorhees and Zhou (2000) found that more than two thirds of students at community
college changed their goals at least once after enrollment while 10% of students
changed their goals more than three times. Therefore, even though students may have a
goal at a particular time, it does not necessarily imply that students would endeavor to
attain those goals, thus leading to persistence. Researchers have also indicated that not
only the presence of goal is important, but also commitment to those goals (Hourser-
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Marko & Sheldon, 2006). Individuals who were more committed to their goals engaged
in behaviors that lead to goal attainment. Students with higher levels of goal
commitment were less likely to drop out of their college education (Hagedorn et al.,
2002). Furthermore, students who mapped their educational plans and established goals
and benchmarks had increased persistence towards their academics (Voorhees & Zhou,
2000).
This current study was a one-time only survey, thus only asked students’ present
goals and did not investigate the changes that may have occurred with time. A student
who had a career goal at the time of data-collection may have changed his or her goals
by the end of the semester, or may have become uncertain about his or her decision,
influencing the decision to stay in college or not. Therefore, longitudinal studies are
necessary to measure student goals as it changes over time and how it affects student
retention. In addition, this study did not measure goal commitment, which may have
had an influence on the relationship between goals and student persistence. Further
investigation is warranted with these factors in mind to reveal the true association
between goals and student persistence.
Although the psychosocial variable, career goals did not show significant
relationship with student persistence, several important implications from this study can
be found regarding this variable. First of all, it is important to note that although career
goals did not have a direct relationship with student persistence, it was significantly
correlated with cumulative GPA, which was the strongest predictor of student
persistence. It seems sensible that students who had career goals and knew how to
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achieve them would put in increased effort into their studies, thus increasing their
cumulative GPA. This assumption corresponds with past research that showed
relationship between goals/aspirations and performance (e.g., Emerick, 1992; Ting,
1997). Students who developed future goals significantly improved their academic
performance even if they were not academically successful from the start. In addition
to having goals, career decision-making was also considered to be important in one’s
achievement (e.g., Creed et al., 2005, Germeijs & Verschueren, 2007). Career decision
making was a predictor of students’ GPA later in their academic years. It is assumed
that students regulate their behaviors in ways they would enable them to achieve their
goals once they have set goals/aspirations or decided on their career (McGregor &
Elliot, 2002). Therefore, it can be assumed that presence of career goals will improve
one’s GPA, thus increase retention.
It is also important to note that the presence of career goals differed significantly
among age groups (X
2
= 28.478, df = 8, p < .001). Forty-two percent of student who
answered that they did not have a career goal were aged 20 and younger, while close to
80% of those who answered no were aged 24 and younger. It makes sense that younger
student are uncertain about their goals, since the possibilities and opportunities are
endless. Younger students also had decreased GPA, indicating the possibility that being
young and not having goals are red flags to a decreased GPA. Since the cumulative
GPA was the most predicting factor for student persistence, it is important to
acknowledge any factor that may influence the predicting variable. Therefore, even
though goals were not directly related to student persistence, it is still assumed to be a
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significant element that cannot be overlooked when examining factors associated with
student persistence.
Studies that investigated the association between self-efficacy and student
persistence is limited in the community college setting. Only two previous studies have
supported such assumption. Unfortunately, this current study did not find a significant
relationship between the two variables. It may be that at the community college setting,
variables other than self-efficacy have so much influence on student persistence that the
impact of self-efficacy is trivial. However, based upon numerous studies in the past that
showed significant influence of self-efficacy on academic performance and persistence
(e.g., Gore, 2006; Lee & Klein, 2002; Multon et al., 1991), it is difficult to conclude that
self-efficacy had no relationship with student persistence. Instead, the lack of
relationship may be because of how the construct was measured.
In this current study, self-efficacy was measured as academic self-efficacy,
which can be defined as the individual’s confidence in their ability to perform academic
tasks successfully at a certain level (Schunk, 1991). The range of specificity of
measurement varies depending upon what type of instrument is used to assess the
construct, ranging from measuring the individual’s confidence in the ability to master a
specific course or course content to measuring confidence in generalized academic
behaviors. The CSEI utilized in this study captures a broader conceptualization of
academic self-efficacy compared to instruments such as mathematics self-efficacy or
verbal self-efficacy (Zimmerman & Martinz-Pons, 1990). Furthermore, previous
studies showed that depending on the types of items included in the instrument, the
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instrument correlates differently with performance such as standardized test scores
(Lent, Brown, & Gore, 1997). It can be easily assumed that if the relationship between
the construct and performance is changed via items in the instruments, the relationship
between self-efficacy and persistence is likely to change due to items in the instruments
as well.
In addition, self-efficacy was measured close to the beginning of semester
instead of at the end of semester, which may not have been enough time for students to
develop efficacy toward their college experience. According to Bandura (1986), one of
the foundations for development of self-efficacy beliefs and the most important are
personal performance accomplishments. Previous studies provide empirical support for
this assumption (Lent et al., 1996). Since the CSEI is specific to measuring students’
confidence in their ability to complete college-specific academic tasks successfully, it is
assumed that students’ scores on this instrument will change as they gain college
experience. Therefore, since the CSEI was administered to them in October, students
may not have had sufficient time to develop their self-efficacy beliefs, thus the
relationship between self-efficacy and persistence may be skewed. This prediction is
confirmed by Gore (2006), who found that self-efficacy beliefs were relatively weak
predictors of academic performance and college persistence when measured at the
beginning of the first semester of college. The strength of its relationship increased
when measured at the end-of-semester. Kahn and Nauta (2001) also found stronger
relationship between students’ academic self-efficacy and college performance when
measured during the second semester of college.
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Although self-efficacy also did not have a significantly direct relationship with
student persistence, comparable to career goals, it was significantly correlated with
cumulative GPA which was the strongest predictor of student persistence. This is
consistent with numerous previous research that showed positive relationship between
self-efficacy and academic performance (e.g., Hysong & Quinones, 1997; Multon et al.,
1991). Self-efficacy is thought to influence choice of activities, level of effort,
persistence of that task, and emotional reactions (Bandura, 1994). Thus, it can be
assumed that students in this study who were highly efficacious exerted more effort into
their studies, thus increasing their cumulative GPA. Therefore, like career goals, lack
of direct significance between self-efficacy and student persistence should not be an
indication that self-efficacy ought to be neglected from student persistence studies.
Implications
In general, individuals who are most likely to remain enrolled at the community
college from this study are those who earn good grades, are attending college on a full-
time basis and have good English skills. In contrast, students who are most likely not to
continue their education have lower grades, attend college on a part-time basis, and
have trouble with English skills. When investigating further, the study revealed that
being young and not working full-time is an advantage, versus being old and working
on a full-time basis is a disadvantage toward persistence. These general results were
not unexpected, but these findings have important implications for community college
administrators.
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The fact that cumulative GPA was the strongest predictor of student persistence
regardless of other demographic, financial, or academic factors suggests that what
happens to the student after he or she enrolls in college may be more important than the
influence of pre-college variables. In other words, students’ experience in the college
may have a significant impact on student persistence beyond the differences in family
backgrounds, financial backgrounds, individual attributes, or commitments they may
have when he or she enters college. Therefore, it sheds light to the possibility of
enhancing student persistence at the community college through institutional policies
and practices intended to enhance cumulative GPA and its relational factors.
In addition, although there are limitations as to what community college can do
in order to help students with their background, financial, or even academic problems,
the results of this study provide a guideline to identify students at risk of dropping out
of the college. Administrators, faculty, and counselors at the community college should
be aware of these factors so that greater sensitivity can be offered and improved
services implemented for students who may be at risk. For example, younger students
were at risk because many of them lacked career goals, which correlated with low
cumulative GPA. Therefore, academic and career planning specifically for younger
students can be carried out by the institution, which would help young students establish
their career goals and subsequently improve their GPA, thus improving persistence.
Limitations
This study had several limitations that need to be taken into consideration when
interpreting the results. First of all, much of the data was based upon self-reports of the
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students. This makes the study vulnerable to social desirability effects. Although it was
emphasized that their answers are confidential, students may have thought that faculty
members would access the data, thus adjusting their responses. Second, although
random sampling was used to select the classes that were used in the survey, only half
of the instructors of those classes agreed to participate in the study. The willingness of
the instructor to participate in such a study may be indicative of their concern for
student persistence. Therefore, interaction between those instructors and students may
be different compared to the other instructors on campus.
Third, this study utilized single-time data collection instead of a longitudinal
study. Due to the one-shot nature of the study, measurement of retention was
challenging. Retention was only determined by enrollment status of the following
semester, making it difficult to distinguish whether students did not return because they
truly dropped out of college, accomplished their educational goals, or was just taking a
semester off. For example, Bonham and Luckie (1993) found that majority of students
(73%) who did not return to the community college considered themselves as stopouts
instead of dropouts. Stopouts are regarded as students who have not accomplished their
goals but plan to do so in the future. They may be just taking a semester off to handle
personal issues but are returning in the near future. The characteristics of stopouts
compared to dropouts may be different, thus the patterns and causes of retention may
need to be investigated differently (Mallette & Cabrera, 1991). This study did not
differentiate between the dropouts, stopouts, or those who successfully accomplished
155

their goals. In addition, it also did not distinguish students who registered at another a
community college.
Lastly, this study took place at a single, urban community college located in
Southern California. Therefore, the results may not be generalizable to students at other
community colleges, or at to the large body of students in higher education institutions.
Recommendations
Several recommendations for future research and practice can be derived from
this current study. For future research, replication of current study but utilizing a
longitudinal method is warranted. Variables used in this study such as goals and self-
efficacy may change over time as the students progress through their academic
education. Therefore, opportunities for multiple assessments of these variables would
likely show patterns that can explain student retention more comprehensively.
Longitudinal analysis would provide more comprehensive information about
community college students and their persistence.
In addition, variables used in this study should be measured differently. For
example, future studies need to differentiate between diverse retention measurements
(e.g., dropouts, stopouts). In most retention studies, all nonreturning students are
labeled dropouts thus leading to decreased retention scores. However, a different
pattern for the causes of nonreturn may appear if the retention measurement is altered.
Lastly, qualitative studies of actual returning and nonreturning students are
recommended to further gain insights toward variables that lead to student persistence.
156

There are also several practical recommendations for community colleges based
upon the results of this study and previous investigations. The following practices are
recommended:
1. Early identification of at-risk students
Since cumulative GPA was the most significant predictor variable for student
retention, it is obvious that colleges should focus on improvement of academic
performance among students. One of the problems is that college administrators are not
likely to notice students who are struggling academically until they start to fail. Beck
and Davidson (2001) indicated that when interventions were to be attempted, it usually
is after several failures, which reduces the likelihood of academic survival. Therefore,
in order to improve academic performance and thus improve student retention, it is
recommended to have an early warning system of high-risk students. Current study
showed that cumulative GPA is related to demographic factors (i.e., age, ethnicity,
parents’ educational attainment), academic factors (i.e., high school GPA), and non-
academic factors (i.e., faculty interaction, goals, self-efficacy). An assessment of these
variables at the time of entrance into the community college will allow administrators to
detect students who may be at-risk. Once those students are identified, they can be
closely advised and monitored to help them persist in their education.
Previous studies have shown successful attrition of at risk students using early
identification instruments such as the Stratil Counseling Inventory – College Form
(SCI-C: Bray, 1985) or the Survey of Academic Orientations (SAO: Beck & Davidson,
2001). The SCI-C is a six-scale measurement proposed to obtain information to
157

identify students in need of assistance, and also detected problem areas. The SCI-C is
administered within ten days after beginning of class in order to help the Students
Development personnel to identify students who needed help in the early stages of their
academic career. The SAO measures six factors, which are: Structure Dependence (S),
Creative Expression (C), Reading for Pleasure (R), Academic Efficacy (E), Academic
Apathy (A), and Mistrust of Instructors (M), arranged to for the acronym, “SCREAM.”
The score obtained by SAO were used to detect grades as well as student retention.
Such interventions are shown to be crucial institutional strategies to identify at-risk
students at the enrollment period (Glenn, 2003).
Moreover, it is recommended that college make faculty and staff aware of high-
risk students. Holding workshops on high risk students and introducing strategies to
help these students would be helpful. If the faculty and staff are aware of students at
high-risk, they may be able to take different approaches towards them, and integrate
intervention techniques. Once at-risk students are identified, counseling and support
services can be utilized to intervene with academically weak students, thus increasing
academic performance and persistence (Grubb, 2003; Summers, 2003).
2. Target the young, first-semester students for academic and career planning
As was found in this study, younger students were more likely to lack career
goals and have lower cumulative GPA. Therefore, helping them find their goals would
likely contribute to attaining higher GPA and eventually increased persistence.
Furthermore, previous research indicated the frequency of goal changes among students
who enter college. Services such as academic and career planning can help students
158

who are unsure of the requirements to attain their goals outline their educational plan
and establish objective goals and benchmark towards their ultimate goal (Voorhees &
Zhou, 2000). Based upon the goal-setting theory, it is proposed that goals would direct
attention toward action that is relevant to the goal, and allows individuals to regulate
effort one puts forth as well as the amount of persistence one puts forth (Locke &
Latham, 1994). Therefore, if the college can help students establish their goals and plan
their class schedules accordingly, students may likely stick to the plan. In addition, if
the college can provide more information regarding a specific career, students may
change their goals less frequently.
Career and academic planning can be offered in many ways, including
orientations, seminars, or part of a college course (Fralick, 1993). Special orientation
programs for new students to make them aware of the ranges of program possibilities,
course selection, registration processes, and available support services are important
(Grosett, 1991). In addition, individual counseling and advising sessions with a focus
on students’ short- and long-term goals would be helpful. If students have undefined
objectives, they should be guided and advised to translate nonspecific educational goals
into areas of program study where coursework and the desired outcome are directly
connected.
3. Use the classroom effectively
Previous researchers have demonstrated the importance of student faculty
interaction with student persistence (e.g., Heverly, 1999; Schmid & Abell, 2003). In
addition, although this current study could not find a direct relationship, considering the
159

correlation between student-faculty interaction and cumulative GPA, it is recommended
that college administers endeavor to increase such interactions. However, for many of
the students, classroom is the only place where students and faculty meet. It was noted
earlier that faculty-student interaction may be limited because of increase in the number
of part-time faculty in the community college. Even when the faculty may have time
available outside of the classrooms, students who attend college on a part-time basis do
not have the time to make appointments and interact with the faculty. Many of the
students who attend community colleges are commuters, working, older, and have other
obligations, limiting the opportunity to interact with faculty members. Therefore,
retention programs should be targeted to using the classroom setting.
One of the ways recommended for community colleges to enhance classroom
environment that promote integration and student-faculty interaction is the learning
community approach to instruction (Tinto, 1997). Promoting student-student
interaction around academic topics may render to increased interaction with faculty
members (Chang, 2005). Interacting with faculty in a group study setting is much less
intimidating than interacting alone, thus may be more appealing for the students. In
addition, encouraging students to participate in classroom discussions and creating
assignments in which students need to integrate knowledge and thoughts from various
disciplines were found to promote faculty-student interaction (Halawah, 2006).
A program such as the Coordinated Studies Program (CSP) has been designed
for use at the community college setting (Tinto, 1997). The CSP provided opportunities
for the students to share the curriculum and learn together by enrolling in several
160

courses constructed upon a common theme. The students participate in cooperative
learning activities which enhance involvement with their peers, and give students
opportunities to actively participate in knowledge creation. Students who participated
in the CSP had significant persistent rate compared to non-CSP students. Similarly,
freshman learning communities (FLC) is another intervention strategy for students
(Lichtenstein, 2005). The FLC provided students with smaller class-size and increased
collaboration amongst the students. The FLC provided a strong sense of community, in
which instructors were seen as engaged and approachable from the students. Students
who participate in smaller learning communities seem to have increased interaction
with peers and faculties, thus promoting the sense of community with the university
(Bailey et al., 2004). These strategies can be implemented by community college
administrators as ways to improve classroom environment.
4. English proficiency classes and advising
This current study demonstrated that inadequate English proficiency may
discourage a student to persist in their college education. Enrollment in developmental
classes is the norm and essential for these academically weak students, but previous
studies have also found that students who enroll in developmental classes actually have
lower graduation rates (Muraskin & Wilner, 2004). Therefore, it should be considered
that merely placing students in remedial classes are not sufficient in helping students
bridge their academic gap.
Instead, remedial courses should be linked to other support services, which can
provide students additional encouragement to view their outcomes and success. For
161

example, a College Success Program is designed to assist underprepared freshman,
which allows student to develop a close relationship with a mentor (Grunder &
Hellmich, 1997). Instead of being discouraged because of numerous remedial classes
that in itself provides little connection to the ultimate goal, a mentor can help students
see the importance of those classes and encourage them to persist. The researchers
found that participation in the College Success Program was significantly associated
with retention rate among community
5. Financial aid support
Even though direct relationship between financial variables and student
persistence disappeared when other variables were considered, previous research clearly
indicated the impact of financial support has on community college students’
persistence (e.g., Makuakane-Drechsel & Hagedorn, 2000; Titus, 2006). Therefore,
community college administrators should endeavor to maximize such support.
Retention studies in the past found that students’ complaint included lack of readily
available information, unhelpful offices, and unreasonable policies with regards to
financial services (Hevernly, 1999). Therefore, financial aid offices should be equipped
with sufficient staffs that are friendly, helpful, and knowledgeable in filling out the
necessary paperwork to file for financial aid.
162

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Zimmerman, B. J., & Risemberg, R. (1997). Becoming a self-regulated writer: A social
cognitive perspective. Contemporary Educational Psychology, 22, 73-122.

Zurita, M. (2004). Stopping out and persisting: Experiences of Latino undergraduates.
Journal of College Student Retention, 6, 301-324.

185

Appendix A
College Self-Efficacy Inventory (Solberg et al., 1993)
How confident are you that you could successfully complete the following tasks?
Please rate using the following 10-point scale:

Not at all confident Somewhat Confident Extremely confident
1 2 3 4 5 6 7 8 9 10

1. Research a term paper
2. Write a course paper
3. Do well on your exams
4. Manage your time effectively
5. Take good class notes
6. Keep up to date with your class work
7. Understand your textbooks
8. Participate in class discussions
9. Join a student organization
10. Ask a question in class
11. Talk to your professors/instructors
12. Get a date when you want one
13. Ask a professor a question outside of class
14. Talk with academic and support staff
15. Make new friends at college

186

Appendix B
Institutional Integration Scale (Pascarella & Terenzini, 1980)
Please respond to the statements below using the following rating scale:
Strongly Disagree Strongly Agree
1 2 3 4 5
1. My nonclassroom interactions with faculty members have positively
influenced my personal growth, values, and attitudes.
2. My nonclassroom interactions with faculty members have positively
influenced my intellectual growth and interest in ideas
3. My nonclassroom interactions with faculty members have positively
influenced my career goals and aspirations.
4. Since coming to this college, I have developed a close, personal relationship
with at least one faculty member.
5. I am satisfied with the opportunities to meet and interact informally with
faculty members.
6. Few of the faculty members I have had contact with are generally interested in
students.
7. Few of the faculty members I have had contact with are generally outstanding
or superior teachers.
8. Few of the faculty members I have had contact with are willing to spend time
outside of class to discuss issues of interest and importance to students
9. Most faculty members I have had contact with are interested in helping
students grow in more than just academic areas.
10. Most faculty members I have had contact with are genuinely interested in
teaching.
11. Most faculty members I have had contact with are genuinely interested in
students.

187

Appendix C

Career Decision Scale

This questionnaire contains some statements that people commonly make about their
educational and occupational plans. Some of the statements may apply to you; others
may not. Please read through them and indicate how closely each item describes you in
your thinking about a career or an educational choice by indicating the appropriate
number on the answer sheet.

Not at all like me Exactly like me
1 2 3 4 5

1. I have decided on a career and feel comfortable with it. I also know how to go
about implementing my choice.
2. I have decided on a major and feel comfortable with it. I also know how to go
about implementing my choice.

Due to copyright agreement with PAR, only two sample items from CDS were
permitted to use in this appendix section.

Adapted and reproduced by special permission of the Publisher, Psychological
Assessment Resources, Inc., 16204 North Florida Avenue, Lutz, Florida 33549, from
the Career Decision Scale by Samuel H. Osipow, Ph.D., Copyright 1976, 1987, by PAR,
Inc. Further reproduction is prohibited without permission of Psychological
Assessment Resources, Inc.
188

Appendix D

University of Southern California
Rossier School of Education
Waite Phillips Hall 600 C
Los Angeles, CA 90089-4036

INFORMED CONSENT FOR NON-MEDICAL RESEARCH

**********************************************************************

CONSENT TO PARTICIPATE IN RESEARCH

Factors influencing student persistence at the community college
setting

USC and Los Angeles Valley College (LAVC) are working together to learn more
about LAVC student success. The information you provide is extremely important in
helping students like yourself achieve educational goals. We thank you in advance for
sharing your perceptions and valuable input. You are asked to participate in a research
study conducted by Mikiko Aoyagi, M.S., a doctoral candidate from the Rossier School
of Education at the University of Southern California (Principal Investigator) and Dr.
Myron H. Dembo, PhD (Faulty Advisor). The results of this study will be contributed
to a dissertation and an executive summary. You were selected as a possible participant
in this study because you are a community college student at an urban community
college. You must be at least 18 years of age to participate. A total of between 600 and
700 participants will be selected from various classes offered by the college. Your
participation is voluntary. You should read the information below, and ask questions
about anything you do not understand, before deciding whether or not to participate.

PURPOSE OF THE STUDY
The purpose of this study is to better understand different factors that influence student
retention. This will be accomplished by assessing students’ perceptions of the college
environment, goals, and academic ability, in relation to family and work responsibilities,
enrollment status, gender, ethnicity, and retention.

PROCEDURES
If you volunteer to participate in this study, we would ask you to do the following
things:

189

You will be asked to complete a survey in class, which asks 63 questions about your
perceptions of the college environment, goals, and academic ability. This survey will
take approximately 30 minutes to complete in class. For example, you will be asked to
rate your opinion with items 1 – 44 using variations of the following scale “Strongly
disagree,” Somewhat,” and “Strongly agree.” A survey sample item will ask you to rate
your opinion with a statement such as: “I am satisfied with the opportunities to meet
and interact informally with faculty members.” Another example on the survey will ask
you to rate your opinion with the following statement: “Few of the faculty members I
have had contact with are generally interested in students.”
This study will also be looking at your enrollment for next semester in relation to the
surveys and demographic data (e.g., gender), which require your permission to access.
This type of analysis is normal and is part of the ongoing analyses performed at Los
Angeles Valley College. However, for the purposes of this study the data and your Los
Angeles Valley College ID number will only be viewed by the principal investigator
and faculty advisor administering this study. Your responses will be held in the strictest
professional confidence. Instructors will not have access to the information you
provide on this survey and your answers will not influence the grade you receive in this
course. You may still participate even if you do not grant permission for enrollment
status to be viewed as part of this study.

POTENTIAL RISKS AND DISCOMFORTS
This study does not pose any identifiable risks beyond minor discomfort. You may be
uncomfortable due to spending time away from your studies, from your enrollment
status being reviewed, or concerned with the confidentiality of your answers on the
survey. If you feel discomfort you may stop and withdraw from the study at any time.
Confidentiality will be protected at all times during data collection, analysis, and
presentation of the written research report.

POTENTIAL BENEFITS TO SUBJECTS AND/OR TO SOCIETY
There will be no direct benefit to you for participating in this study. However, the
information from this study will be used to help inform decisions and improve the
college environment.

PAYMENT/COMPENSATION FOR PARTICIPATION

You will not receive payment for your participation.

CONFIDENTIALITY
Any information that is obtained in connection with this study and that can be identified
with you will remain confidential and will be disclosed only with your permission or as
required by law.

190

Any personal information and data collected for the study will be coded to ensure
privacy. Only members of the research team will have access to the data associated
with this study. The data will be stored in the co-investigator’s office in a locked file
cabinet/password protected computer. The data will be stored for three years after the
study has been completed and then destroyed. Course instructors will not have access
to the information you provide on this survey and your answers will not influence the
grade you receive in this course. Responses will be held in the strictest professional
confidence and will only be viewed by the principal investigator and co-investigator.
The informed consent forms with your Los Angeles Valley College ID and name will
be stored separately from your completed survey so that no connection can be made to
them. In addition, the portion of the survey and the scantron that contains your student
ID will be cut out and destroyed after data transcription so the information will be
unidentifiable.

When the results of the research are published or discussed in conferences, no
information will be included that would reveal your identity.

PARTICIPATION AND WITHDRAWAL
You can choose whether to be in this study or not. If you volunteer to be in this study,
you may withdraw at any time without consequences of any kind. You may also refuse
to answer any questions you don’t want to answer and still remain in the study. The
investigator may withdraw you from this research if circumstances arise which warrant
doing so.

ALTERNATIVES TO PARTICIPATION
Your alternative is to not participate.

RIGHTS OF RESEARCH SUBJECTS
You may withdraw your consent at any time and discontinue participation without
penalty. You are not waiving any legal claims, rights or remedies because of your
participation in this research study. If you have questions regarding your rights as a
research subject, contact the University Park IRB, Office of the Vice Provost for
Research Advancement, Grace Ford Salvatori Hall, Room 306, Los Angeles, CA
90089-1695, (213) 821-5272 or upirb@usc.edu.

IDENTIFICATION OF INVESTIGATORS
If you have any questions or concerns about the research, please feel free to contact the
Principal Investigator, Mikiko Aoyagi via mail at 243 Glendora Ave. Apt B., Long
Beach, CA 90803; email at maoyagi@usc.edu; or phone at (562) 889-2798. You may
also contact the Faculty Advisor, Dr. Myron H. Dembo via mail at WPH 600C, Los
Angeles, CA 90089-4036; email at dembo@usc.edu; or phone at (213) 740-2364.

191

SIGNATURE OF RESEARCH SUBJECT

I have read (or someone has read to me) the information provided above. I have been
given a chance to ask questions. My questions have been answered to my satisfaction,
and I agree to participate in this study. I have been given a copy of this form.

□ I agree to have my demographic information accessed.

□ I do not agree to have my demographic information accessed.

□ I agree to have my enrollment status accessed.

□ I do not agree to have my enrollment status accessed.

Student ID #: (including all leading zeros, if applicable)

__________________________________________

Name of Subject

Signature of Subject Date

SIGNATURE OF INVESTIGATOR

I have explained the research to the subject and answered all of his/her questions. I
believe that he/she understands the information described in this document and freely
consents to participate.

Name of Investigator

Signature of Investigator Date (must be the same as
subject’s)

192

Appendix E

Student ID#: ___________________
Dear Student:

Your input is very valuable to this study. When responding to the following survey
items, please identify your thoughts and experiences about this college. When you are
finished, please turn in your surveys and signed consent form in the box provided at the
back of the room.

Below is a list of questions about you as a student at this college. Please use a #2 pencil
to fill in the answer bubbles on the scantron sheet.

The following questions ask about your confidence in completing academic-related
tasks. Please indicate on the scantron the number that best describes how confident
you are in successfully completing the following tasks: 1 = Not at all confident, 5 =
Somewhat confident, and 10 = Extremely confident. No one other than the
researcher will see your answers.

Not at all Somewhat Extremely
Confident Confident Confident
1 2 3 4 5 6 7 8 9 10

1. Research a term paper

2. Write a course paper

3. Do well on your exams

4. Manage your time effectively

5. Take good class notes

6. Keep up to date with your class work

7. Understand your textbooks

8. Participate in class discussions

9. Join a student organization
193

Not at all Somewhat Extremely
Confident Confident Confident
1 2 3 4 5 6 7 8 9 10

10. Ask a question in class

11. Talk to your professors/instructors

12. Get a date when you want one

13. Ask a professor a question outside of class

14. Talk with academic and support staff

15. Make new friends at college

The following questions are about your interactions with faculty on campus. Please
indicate on the scantron the number that best describes your opinion using the
following rating scale: 1 = Strongly Disagree, 3 = Neither Agree or Disagree, 5 =
Strongly Agree. No one other than the researcher will see your answers.

Strongly Neither Agree Strongly
Disagree or Disagree Agree

1 2 3 4 5

16. My nonclassroom interactions with faculty members have positively influenced
my personal growth, values, and attitudes.

17. My nonclassroom interactions with faculty members have positively influenced
my intellectual growth and interest in ideas.

18. My nonclassroom interactions with faculty members have positively influenced
my career goals and aspirations.

19. Since coming to this college, I have developed a close, personal relationship
with at least one faculty member.
194

Strongly Neither Agree Strongly
Disagree or Disagree Agree

1 2 3 4 5

20. I am satisfied with the opportunities to meet and interact informally with faculty
members.

21. Few of the faculty members I have had contact with are generally interested in
students.

22. Few of the faculty members I have had contact with are generally outstanding or
superior teachers.

23. Few of the faculty members I have had contact with are willing to spend time
outside of class to discuss issues of interest and importance to students.

24. Most faculty members I have had contact with are interested in helping students
grow in more than just academic areas.

25. Most faculty members I have had contact with are genuinely interested in
teaching.

26. Most faculty members I have had contact with are genuinely interested in
students.

195

The following questions are about your educational and occupational plans. Some
of the statements may apply to you; others may not. Please read through them and
indicated how closely each item describes you in your thinking about a career or an
educational choice (for #27 – 29, please put your ideal career in the parenthesis).
Please indicate on the scantron the number that best describes you using the
following rating scale: 1 = Not at all like me, 3 = Somewhat like me, and 5 =
Exactly like me. No one other than the researcher will see your answers.

Not at all like me Somewhat like me Exactly like me

1 2 3 4 5

27. ~ 44. Career Decision Scale (Opisow, Carney, Winer, Yanico, and Koschier,
1987)

Cannot be reproduced in this dissertation due to copyright reasons of the Publisher,
Psychological Assessment Resources, Inc., 16204 North Florida Avenue, Lutz,
Florida 33549, from the Career Decision Scale by Samuel H. Osipow, Ph.D.,
Copyright 1976, 1987, by PAR, Inc.
196

The following questions ask about how much of a problem you expect each of the
following to be during your education at this college. Please indicate on the
scantron the number that best describes the amount of problem you expect using the
following rating scale: 1 = Major Problem, 3 = Somewhat of a problem, and 5 =
Not a problem at all. No one other than the researcher will see your answers.

Major Problem Somewhat of a problem Not a problem at all
1 2 3 4 5

45. Transportation (parking, access to public transportation, etc)

46. Family responsibilities (e.g., child or parent care)

47. Job-related responsibilities

48. Paying for college

49. English proficiency

Lastly, the following questions are your background information. Please fill in on
the scantron the appropriate number for each question. No one other than the
researcher will see your answers.

50. In what year did you graduate from high school?
1) 2007
2) 2006
3) 2005
4) 2004 or earlier
5) Did not graduate but passed G.E.D. test
6) Never complete high school

51. What was your high school GPA?
1) 3.5-4.0
2) 3.0-3.4
3) 2.5-2.9
4) 2.0-2.4
5) 1.9 or lower

197

52. Do you receive any financial aid to support your financial needs?
1) Yes
2) No
53. How many semesters have you attended LAVC?
1) This is my first semester
2) 2
3) 3
4) 4
5) 5 or more

54. How many people are you responsible for financially?
1) 0
2) 1
3) 2
4) 3
5) 4
6) 5 or more

55. How many children do you have?
1) 0
2) 1
3) 2
4) 3
5) 4
6) 5 or more

56. What is the highest level of formal education obtained by your father?
1) Grammar school or less
2) Some high school
3) High school graduate
4) Postsecondary school other than college
5) Some college
6) College degree
7) Some graduate school
8) Graduate degree
9) Do not know

198

57. What is the highest level of formal education obtained by your mother?
1) Grammar school or less
2) Some high school
3) High school graduate
4) Postsecondary school other than college
5) Some college
6) College degree
7) Some graduate school
8) Graduate degree
9) Do not know

58. What is the highest academic degree that you intend to obtain?
1) None
2) Vocational certificate
3) Associate (A.A. or equivalent)
4) Bachelor’s degree (B.A., B.S., etc.)
5) Master’s degree (M.A., M.S., etc.)
6) Ph.D. or Ed.D.
7) M.D., D.O., D.D.S., or D.V.M.
8) J.D. (Law), B.D., or M.DIV. (Divinity)
9) Other

59. What is your main goal for enrolling at LAVC this semester? (Mark all that
apply)
1) To transfer to another 4-year college/university
2) To transfer to another 2-year college
3) To get a degree/certificate/license
4) To meet requirements for my job
5) To learn new skills to advance in my current job
6) To make a career change
7) To develop my computer/technology skills
8) For personal development/recreation

199

Please fill in the answers to each of the following questions.

60. What is your age? ____________

61. Do you have a career goal? If yes, what is it?

_______________________________________________________

62. How many hours do you work?
a. On Campus: __________ hours/week
b. Off Campus: __________ hours/week

63. Do you plan to return to LAVC next semester? Yes _____ No _____

Why? Or Why not?

______________________________________________________________________

______________________________________________________________________

Thank you for your participation.
Please place your completed survey and signed consent form in the box located at the back of
the room.
If you have any questions regarding the questions and/ or content of this survey, please
contact
Mikiko Aoyagi at (562) 889-2798 or Dr. Myron Dembo at (213) 740-2364.

Abstract (if available)

Abstract The purpose of the current study was to extend the research on student persistence in community college by investigating factors likely to influence student's decision to drop out or stay in school. Specifically, this study examined demographic, financial, academic, academic integration (i.e., faculty-student interaction), and psychosocial variables (i.e., goals and self-efficacy) and its relationship to student persistence.

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Asset Metadata

Creator Nakajima, Mikiko Aoyagi (author)

Core Title What factors influence student persistence in the community college setting?

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

Publication Date 07/11/2008

Defense Date 06/16/2008

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag community college,OAI-PMH Harvest,persistence,student retention

Language English

Advisor Dembo, Myron H. (committee chair), Clark, Ginger (committee member), Mossler, Ron (committee member)

Creator Email maoyagi@csulb.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1333

Unique identifier UC1221143

Identifier etd-Nakajima-20080711 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-83634 (legacy record id),usctheses-m1333 (legacy record id)

Legacy Identifier etd-Nakajima-20080711.pdf

Dmrecord 83634

Document Type Dissertation

Rights Nakajima, Mikiko Aoyagi

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu