AN EMPIRICAL TEST OF THE NONTRADITIONAL UNDERGRADUATE
STUDENT ATTRITION MODEL USING STRUCTURAL EQUATION MODELING
A dissertation presented to
the faculty of
the College of Education of Ohio University
In partial fulfillment
of the requirements for the degree
Doctor of Philosophy
Chad M. Brown
June 2007
© 2007
Chad M. Brown
All Rights Reserved.
This dissertation entitled
AN EMPIRICAL TEST OF THE NONTRADITIONAL UNDERGRADUATE
STUDENT ATTRITION MODEL USING STRUCTURAL EQUATION MODELING
by
CHAD M. BROWN
has been approved for
the Department of Counseling and Higher Education
and the College of Education by
Valerie Martin Conley
Associate Professor of Counseling and Higher Education
Renée A. Middleton
Dean, College of Education
Abstract
Brown, Chad M., Ph.D., June 2007, Higher Education
AN EMPIRICAL TEST OF THE NONTRADITIONAL UNDERGRADUATE
STUDENT ATTRITION MODEL USING STRUCTURAL EQUATION MODELING
(162 pp.)
Director of Thesis: Valerie Martin Conley
Nearly 50% of the students attending college for the first time this year will do so
at a two-year college, and nationally, there is an increasing call for accountability. This
means that two-year colleges, which have predominantly focused on access, now must
place equal importance on student success. Conceptual models traditionally used to study
persistence and success, do not adequately address the diverse needs and the unique
characteristics of two-year college students. Using structural equation modeling to
examine direct and indirect effects, this research was intended to test the nontraditional
undergraduate student attrition model as advanced by Bean and Metzner (1985) using
data from the Beginning Postsecondary Students Longitudinal Study 1996-98. Statistical
findings indicate that demographic and enrollment characteristics do not directly effect
persistence, but do so indirectly acting through what Bean and Metzner describe as
environmental pull factors. These include working full-time, having dependent children,
and having high levels of unmet financial need. Additionally, enrollment and
demographic characteristics also failed to have significant effects on student experiences,
overall satisfaction with college, and academic performance as measured by college
grade point average. Results also suggest that though the nontraditional undergraduate
student attrition model may be a good platform for future study of two-year college
persistence, its use is limited by the data available and the current definitions of both
student satisfaction and social integration as they pertain to the average two-year college
student.
Approved:
Valerie Martin Conley
Associate Professor of Counseling and Higher Education
Dedication
To my wife Susan and my son Joseph.
Acknowledgments
A number of people have contributed to the successful completion of this
dissertation.
To Dr. Mike Snider and Dr. Michael Mumper, thank you for your willingness to
serve and for your invaluable input during the proposal defense. Your expertise provided
a rich context to the entire process.
To Dr. Bob Young, your friendship and respect means more to me than words can
express. I understand that truth and Truth are not the same. You have taught me always
to consider carefully and to reserve judgment.
To Dr. Valerie Martin Conley, my dissertation advisor, thank you for encouraging
me to do more than I knew how. No matter where my career goes, you have infected me
with the research bug. I do so enjoy the process.
To Dr. Robin Menschenfreund, thank you for your support and encouragement.
You allowed me to spread my wings and fly. I have learned so much about myself and
about good leadership.
To my mother, Barb, and my father, Ray, thank you for teaching me the value of
hard work and always seeking to do my best.
To my son, Joseph Mattox, thank you for all the nights that we shared a laugh,
chocolate ice cream, and a bedtime story. You will always be my best little friend.
Finally, to my wife, Susan, thank you! You have tolerated a lifetime worth of bad
moods and self-doubt. Since the day we met, you have been the shining star that lights
my path. So much of this last year has been about me, it is your turn. I know you’ll do
great things.
viii
Table of Contents
Page
Abstract .............................................................................................................................. iv
Dedication .......................................................................................................................... vi
Acknowledgments............................................................................................................. vii
List of Tables ...................................................................................................................... x
List of Figures .................................................................................................................... xi
CHAPTER ONE ................................................................................................................. 1
INTRODUCTION .......................................................................................................... 1
Purpose of the Study ................................................................................................... 6
Significance of the Study ............................................................................................ 7
Limitations of the Study.............................................................................................. 8
Delimitations of the Study .......................................................................................... 9
Definitions................................................................................................................... 9
Dependent Variable ................................................................................................ 9
Independent Variables ............................................................................................ 9
Organization of the Study ......................................................................................... 10
CHAPTER TWO .............................................................................................................. 11
REVIEW OF THE LITERATURE .............................................................................. 11
Introduction............................................................................................................... 11
Who Attends Two-Year Colleges............................................................................. 12
Early Research .......................................................................................................... 16
Student Integration Model .................................................................................... 16
Nontraditional Undergraduate Student Attrition Model ....................................... 17
Defining the Nontraditional Student......................................................................... 20
Enrollment Patterns............................................................................................... 21
Financial and Family Status.................................................................................. 24
High School Graduation Status............................................................................. 27
Variables Affecting Student Persistence................................................................... 29
Student Experiences.............................................................................................. 37
Environmental Pull Variables ............................................................................... 40
Student Attitudes................................................................................................... 42
Student Academic Performance............................................................................ 44
Limitations of the Current Studies............................................................................ 45
Summary ................................................................................................................... 47
CHAPTER THREE .......................................................................................................... 49
METHODOLOGY ....................................................................................................... 49
Introduction............................................................................................................... 49
Research Questions............................................................................................... 50
Data Collection ......................................................................................................... 50
NCES Databases ................................................................................................... 50
The Conceptual Model.............................................................................................. 57
Dependent Variable .............................................................................................. 60
Independent Variables .......................................................................................... 61
ix
Dissertation Methodology......................................................................................... 70
Structural Equation Modeling............................................................................... 70
Summary ................................................................................................................... 86
CHAPTER FOUR............................................................................................................. 88
RESULTS ......................................................................................................................... 88
Descriptive Statistics................................................................................................. 88
Measurement Model ............................................................................................... 100
Structural Model ..................................................................................................... 102
Model Analysis ................................................................................................... 102
Model Modification ............................................................................................ 110
Summary ................................................................................................................. 117
CHAPTER FIVE ............................................................................................................ 119
DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE
RESEARCH................................................................................................................ 119
Introduction............................................................................................................. 119
Discussion ............................................................................................................... 120
Conclusions............................................................................................................. 125
Limitations .......................................................................................................... 132
Recommendations for Future Research .............................................................. 134
Summary ................................................................................................................. 137
References....................................................................................................................... 139
x
List of Tables
Table
Page
3.1: Variable Names and Definitions...........................................................................72
4.1: Missing Data for Predictor Variables ...................................................................89
4.2: Demographic Characteristics of Respondents ......................................................90
4.3: Demographic Characteristics of Respondents (Imputed Data).............................93
4.4: Intercorrelations of Observed Variables ...............................................................97
4.5: Measurement Model for Latent Constructs ........................................................101
4.6: Structural Model Analysis ..................................................................................103
4.7: Effect Decomposition for Structural Model .......................................................108
4.8: Fit Indices for Model Comparison......................................................................113
4.9: Structural Model Analysis – AL3.......................................................................115
4.10: Effect Decomposition for AL3 .........................................................................116
xi
List of Figures
Figure
Page
3.1: Conceptual Model....................................................................................................58
3.2: Structural Model ......................................................................................................59
4.1: Analysis of Structural Model.................................................................................106
4.2: Alternate Models: AL1, AL2, & AL3 ...................................................................112
5.1: Final Conceptual Model (AL3)..............................................................................126
1
CHAPTER ONE
INTRODUCTION
The two-year college structure in America is diverse and often varies from state to
state with some states providing strong governmental oversight and curricular continuity,
while other two-year systems are less cohesive. Despite these administrative differences,
most share similar missions: being responsive to community needs, providing
developmental education, and providing access to higher education by offering degree
and transfer programs at a cost significantly less than many four-year institutions. The
ability to meet the educational needs of a wide variety of students while at the same time
responding quickly and appropriately to the needs of the community at large, has positioned
two-year colleges at the forefront of many higher education initiatives and has allowed for
considerable growth. In 1998, 46% of all first-time students who enrolled at public
institutions of higher education elected to attend a two-year college, and 36% of all
undergraduate students were attending two-year degree granting institutions (Morgan, 2001).
By 2002, these numbers had risen to 48% and 40% respectively, with just over 6.2 million
enrolled at two-year public colleges (Snyder & Tan, 2005). This trend seems poised to
continue as nationally, four of the ten fastest growing occupations require an associate’s
degree (Bureau of Labor Statistics, 2004), and two-year college enrollment is expected to top
7.5 million by 2014 (Snyder & Tan, 2005).
For more than half a century, the primary focus of higher education, and more
specifically the two-year college sector, has arguably been increasing college access.
Though access can be defined in many ways (Heller, 2002), most believe access to mean
simply the ability to attend and pay for college. The proliferation of two-year colleges
2
and on-line educational opportunities along with state and federal grant, loan, and student
aid programs have opened doors to millions that otherwise might never have matriculated
into higher education. However, it is critical that student access not be confused with
student success. According to a recent National Center for Education Statistics (NCES)
report, over the last three decades college access has increased while rates of completion
have not (National Center for Education Statistics, 2004). Gladieux and Perna (2005)
point out that a system that creates access without persistence fails both society and the
very students it seeks to help. As more than 50% of those attending a postsecondary
institution borrow money and more than 20% of those borrowers stop out or stay out,
failure to persist may actually have a greater negative impact than simply never attending
college (Gladieux and Perna, 2005). Not only do these students not receive the financial
gain that comes with degree attainment, but at the same time, they become saddled with
additional debt.
In order to create policies that support student success, it is necessary to understand
the issues that both foster and impede persistence. Work ranging from the development and
testing of conceptual models to the examination of independent personal and institutional
characteristics has shown the explanations for differences in persistence to be both many
and varied. Unfortunately, a vast majority of the persistence research to date has focused on
traditional aged students attending four-year institutions (Cofer & Summer, 2000;
Ethington, 2000; Hippensteel, St. John & Starkey, 1996). Still many researchers and
policy makers have attempted to generalize these results to include all college students,
which has led to an incomplete understanding of persistence at two-year colleges and created
a considerable gap in the relevant research literature to date. According to Pascarella and
3
Terenzini (1998), “we are functioning in virtual ignorance of the education impact of one of
the nation’s most significant social institutions” (p. 155).
Persistence implies two things: an established goal, and continued progress
toward that goal. Conklin (1993) suggests that traditional measures of institutional
effectiveness and persistence, graduation rates and attainment of a bachelor’s degree
respectively, are not good standards against which to judge the performance of two-year
colleges. According to Berkner, Horn, and Clune (2000), only 30% of students who
began their postsecondary career at a two-year college indicated an original intent to
transfer to a four-year school, while an even lower number of first-time students, 21%,
entered a two-year college with the intent of receiving a degree or certificate. This
supports Conklin’s (1993) assertion that 50% of students enrolling annually at
community colleges are not degree seeking, but instead do so either to acquire or update
job skills.
Differences among students attending two-year and four-year colleges are not
simply limited to educational goals. According to Berkner, Horn, and Clune (2000), 23%
of first-time students enrolling at two-year colleges demonstrate four or more persistence
risk factors compared to 4% at four-year institutions. Risk factors include not having a
regular high school diploma, delaying postsecondary enrollment by more than one year
following the completion of high school, working full-time while enrolled, being
financially independent, having children, or being a single parent. In contrast, most firsttime students enrolling at four-year institutions have recently graduated from high school,
are financially dependent upon their parents, and are single (Berkner, Horn, & Clune,
2000).
4
Unlike four-year institutions where student peer groups often tend toward
homogeneity within the context of single institutions, two-year colleges often serve
diverse student bodies that have differing needs, experiences, and expectations.
Therefore, it is imperative that research models and methodology carefully consider the
impact of unique characteristics such as part-time enrollment, age, dependent children,
and having a GED.
A key factor in advancing understanding of student persistence is the use of a
conceptual model. As problems become more complex, the utility of the conceptual
model increases. According to Bean (1990) strong conceptual models can provide a
simplified, yet comprehensive explanation of the problem being studied by allowing
researchers to focus on variables with large impact while ignoring those without
significant statistical influence. Though a number of different models have been
advanced over the years to examine student persistence, one model, the student
integration model (Tinto, 1975, 1987, 1993), has served as the foundation for much of the
work on student persistence.
Studying the dropout behavior of college students, Tinto (1975) described what he
called “the theoretical model of dropout behavior” (p. 91). The student integration model
is grounded in the belief that long-term interactions between the individual and the
academic and social structures of the institution forces students to continually modify
their educational goals. The student integration model has been subjected to considerable
testing over the years and has been validated many times (Cabrera, Nora & Castaneda,
1993; Sandler, 2000). For more than thirty years this model has helped to advance
5
understanding of the longitudinal nature of the persistence process among students attending
four-year colleges, particularly traditional aged students on residential campuses.
Despite its impact, Tinto (1987, 1993) and others (Bean, 1990; Ethington, 2000;
Pascarella and Terenzini, 1998; Tierney, 1992) have suggested that the student integration
model does not equally explain persistence among different populations, particularly the
older, nontraditional student. In addition, Bean and Metzner (1985) suggest that the model is
less relevant where social interactions with peers and faculty are limited to time in class.
Citing the unique characteristics often associated with the nontraditional student, being older,
enrolling part-time, or commuting to school, Bean and Metzner proposed the nontraditional
undergraduate student attrition model. Distinctive from previous models in its treatment of
environmental influences, the nontraditional undergraduate student attrition model suggests
that issues such as family responsibility, hours employed, and ability to pay for college may
have direct, negative influences on student persistence.
Though a growing body of research on two-year college persistence does exist, most
of these works have used either single institution convenience samples, or have focused more
specifically on individual factors as they relate to persistence. In the same way that Tinto’s
(1975, 1987, 1993) conceptual model has helped to advance understanding of persistence
among more traditional students, a model that considers the unique characteristics and needs
of students electing to attend two-year colleges may similarly inform future research. It is the
absence of just such model that accounts for much of the gap in two-year college persistence
research when compared with the body of four-year literature. It is quite possible that the
nontraditional undergraduate student attrition model has the potential to serve this end.
However to date, the model remains relatively untested and unvalidated in the literature.
6
President George W. Bush and former President Bill Clinton have both spotlighted
the role that community colleges can play in economic development, and many state
governors have expressed similar views. As large companies continue to outsource many of
the manufacturing jobs, new jobs are demanding higher levels of general education and
technical skills. Traditionally, two-year colleges have prepared students for highly technical
careers in the science, technology, engineering, and math, while also providing the first two
years of a bachelor’s degree at significantly less cost. Most however, also offer unique
programs that can retrain displaced workers, help older adults learn new skills while
continuing to work, and bridge the gap in basic reading and mathematics skills that all too
often exists. For those who succeed, the two-year college is a vehicle for upward mobility.
At a time when Presidential commissions are considering the “Future of Higher Education,”
it is critical that two-year colleges consider student success as important as access.
Purpose of the Study
For more than two decades, the Bean and Metzner (1985) nontraditional
undergraduate student attrition model has remained relatively untested and unvalidated
in the literature. The purpose of this study is to test the theoretical underpinnings of the
Bean and Metzner model as operationalized here using national level data. Data from the
Beginning Postsecondary Students Longitudinal Study 1996-98 (BPS: 96/98) will be used to
examine the extent that student background characteristics, environmental pull
characteristics, college experiences, college GPA, and overall student satisfaction explain
student persistence among first-time beginners (FTB’s) enrolled at public two-year colleges
across the U.S. The following questions were considered during this research.
1.
Does the hypothesized model fit the observed data?
7
2.
Do student background characteristics directly and or indirectly affect
student persistence?
3.
Does academic performance as measured by college GPA directly affect
student persistence?
4.
Do student experiences have an indirect effect on student persistence?
5.
Do environmental pull factors have a direct and or indirect effect on
student persistence?
6.
Does student satisfaction directly affect student persistence?
Significance of the Study
This study is important in at least four ways. First, there is a tremendous gap in
the literature regarding student persistence at two-year colleges. Results of this study will
add to the growing body of work on student persistence and its longitudinal process as it
relates to two-year college students. A critical mass of research regarding the impact of
variables is key to determining how future policy changes might impact students and their
institutions, thereby allowing policy makers at both the federal and state levels to fund
initiatives likely to impact the greatest number of students.
Second, Bean (1990) posits that theoretical frameworks are important for simplifying
a complex problem, thus allowing statistical analysis to have greater utility. For more than
30 years, Tinto’s (1975, 1987, 1993) student integration model has been the foundation of
much of the research about student persistence, particularly as it relates to the traditionalaged, residential student attending a four-year institution. The repeated validation and
common acceptance of this model has significantly advanced understanding of student
persistence at four-year colleges, but does not appear to have the same utility for the two-year
sector (Bean, 1990; Ethington, 2000; Pascarella and Terenzini, 1998; Tierney, 1992).
8
Third, the nontraditional undergraduate student attrition model (Bean and Metzner,
1985) was intended to be tested using structural equation modeling. Though numerous
authors (Metzner & Bean, 1987; Stahl & Pavel, 1992; Zhai, Monzon & Grimes, 2005) have
tested the model, small sample sizes, issues of single institution homogeneity, and
inconsistent methodology have made testing of latent constructs and generalizability difficult.
By using national-level data with sufficient sample size to test latent constructs using proper
estimation methods, this study will have a unique contribution to previous work on the Bean
and Metzner model and two-year college persistence research overall.
Finally, this study uses data that already exist as part of a long term effort by the
National Center for Education Statistics to create research databases that allow for
examination of student success and other relevant topics. Therefore, identification of missing
data is critically important. The methodology used in this study relies heavily on sound
conceptual models grounded in previous research. Such models offer a comprehensive
explanation of the problem being studied, and expose crucial missing data allowing the
researcher to make suggestions for improving future efforts.
Limitations of the Study
This study is limited in five ways. First, the use of secondary data means that the
researcher is limited by the questions asked in the initial survey; however, every attempt
has been made to include only variables that are adequately addressed by the survey
questions. Second, the research is limited by the sample size available in BPS: 1996/98.
Third, the statistical software used for this analysis does not allow for the weighting of
ordinal data. Fourth, there are relatively few tests of the Bean and Metzner (1985) model
reported in the literature. Finally, the most recent data available as part of the BPS cohort
9
involves students that matriculated into higher education during the 1995-1996 academic
year; nearly a decade ago.
Delimitations of the Study
Secondary data sources like the Beginning Postsecondary Students (BPS)
Longitudinal Study, improve researchers ability to carry out large cross-sectional studies
where a diverse and sometimes over-sampled population is needed. It is not the intent of
this study to construct a new theoretical model, but instead to test the constructs of the
nontraditional undergraduate student attrition model (Bean & Metzner, 1985). It is also
not the intent of this study to try to explain persistence among all two-year college
students. Instead, this study seeks to examine the ability of the nontraditional
undergraduate student attrition model to explain individual persistence rates among
students within the sample while at the same time exploring the direct, indirect, and total
effects of the constructs on one another and on student persistence three years after
beginning postsecondary enrollment.
Definitions
Dependent Variable
Persistence- Continued enrollment or having completed either a degree or certificate
three years after beginning college.
Independent Variables
Background Characteristics- includes age, race, gender, socioeconomic status, having a
GED verses a high school diploma, enrollment status, and educational goals
Experiences- includes academic and social integration measures, and whether or not a
student has declared a major.
10
Environmental Pull- includes measures of family/dependent responsibility, unmet
financial need, and work patterns.
Student Satisfaction- measure of student satisfaction with identified college experiences.
Academic Performance- student performance as measured by college GPA.
Organization of the Study
This research is organized into five chapters. The first provides an introduction to
the study as well as basic background surrounding the issue of two-year college
persistence. Included within is a discussion of the purpose of the study, the significance,
as well as the limitations and delimitations. Chapter Two is an exploration of the
literature, examining research on college persistence within both the two-year and fouryear sectors. Chapter Three is an explanation of the research methodology, including
details regarding the national databases utilized as well as the statistical methods
employed by the researcher. Chapter Four contains findings of the research, while
Chapter Five includes not only the final conclusions of the research, but a discussion of
the implications for practice and recommendations for future research.
11
CHAPTER TWO
REVIEW OF THE LITERATURE
Introduction
This study examines the relationships between student characteristics and
individual rates of persistence of first-time undergraduate students enrolled at U.S. twoyear institutions. The main theoretical underpinnings of the study are based upon a
model of non-traditional student persistence first described by Bean and Metzner (1985).
Special attention has been given to the use of risk-factors related to student persistence as
originally identified by Horn (1996) in her analysis of 1990 BPS cohort.
This chapter begins with a discussion of both the two-year college and the typical
two-year college student. Highlighting the differences in background, educational goals,
and persistence between two-year cohorts and their four-year peers, this information
paints a picture of the unique nature of two-year college students nationally. Next a
historical review of drop-out research will focus on how a single theoretical model
proposed more than three decades ago, Tinto’s (1975) student integration model, has
shaped the lens through which student persistence has most often been studied.
However, the unique mission, broad range of educational goals, and high number of atrisk students at two-year colleges, means that this model may be less well suited to study
persistence among two-year students as compared to their four-year peers.
This review demonstrates the inadequacy of this model and advances instead the
Bean and Metzner (1985) model as most appropriate for understanding persistence at
two-year colleges. Bean and Metnzer’s supposition that environmental pull factors may
directly impact persistence, suggests that for some students, life situations may present
12
barriers that are insurmountable at a given point in time regardless of the student’s
educational goals or commitment to those goals. Discussion of the Bean and Metzner
model is followed by a review of the risk-factor index first described by Horn (1996) and
later validated by Berkner, Horn, and Clune (2000). This work is grounded in the Bean
and Metzner conceptual model, and expands the definition of the nontraditional student
by defining seven risk-factors believed to negatively influence student persistence.
Discussion of Horn’s risk-factor index is followed by a synthesis of previous research on
each of the variables believed important to predicting persistence among the
nontraditional student as advanced by Bean and Metzner. The chapter concludes with a
discussion of the limitations of the current research.
Who Attends Two-Year Colleges
Students attending two-year colleges are often very different from their four-year
counterparts in three ways; their overall characteristics (Bean & Metzner, 1985; Berkner,
Horn, & Clune , 2000), their reasons for enrollment (Conklin, 1993), and their overall
persistence and success (Berkner, He, Cataldi & Knepper, 2002). The following is
intended to highlight the unique nature and characteristics of this group that annually
accounts for nearly 50% of all students entering postsecondary education.
Over the last half-century, the two-year sector in America has seen continued and
sometimes rapid growth. Some two-year colleges boast multiple campuses enrolling as
many as 50,000 students in large urban areas while another may serve less than 1,000
students in some rural community. Some draw from around the country and even around
the world, while many remain relatively unknown outside their county or service district.
Despite these differences, most share a unique, unstated, but often assumed mission;
13
serving the underserved. Because two-year colleges often have open admissions policies
and provide access to higher education at a much lower cost than four-year public
institutions, those selecting this path to begin their postsecondary career often bring with
them very different backgrounds and experiences.
Students who attend two-year colleges are more likely to be older. Though the
average age has declined over the last two decades, researchers have learned that delayed
entry for even one year can have tremendous impact on educational outcomes (Adelman,
2006). Examining undergraduates who began their educational career in 1999-2000,
Horn, Cataldi, and Sikora (2005) found that 56% of those attending two-year colleges
had delayed their enrollment in postsecondary education by at least one year compared
with only 26% in four year colleges. Horn and Nevill (2006) suggest that delaying
enrollment also increases the likelihood of having dependent children, working full-time,
and attending college less than full-time. According to Berkner, Horn, and Clune (2000)
1 in 5 students attending public two-year institutions have children, compared to 1 in 20
attending four-year, not-for-profit institutions. In addition fully 49.3% of those students
attending two-year colleges work 35 or more hours per week compared with 25.1% of
four-year students (Horn & Berktold, 1998). Finally, only 52.6% of those attending
public, two-year colleges nationally attended full-time (Berkner, Horn, & Clune, 2000).
Nationally, there is overrepresentation of certain demographics among two-year
college enrollees. According to Horn and Nevill (2006) female students account for
59.1% of those attending two-year colleges compared with 54.9% at four year
institutions. Racial groups are also overrepresented among the two-year population with
Blacks accounting for 15.3% and Hispanics 14.4% of the student population compared
14
with 11.2% and 9.8% respectively (Horn & Nevill, 2006). Horn and Berger (2004) point
out that these rates have risen sharply over the last decade and that the gaps between
males and females as well as among minority groups continue to grow. Finally, those
attending two-year colleges are less likely than their four year peers to have parents who
have completed at least a bachelor’s degree, 30.8% and 53.4% respectively (Horn, Peter
& Rooney, 2002).
Students enrolling at four-year institutions can be expected to all share a single
goal, the eventual completion of a baccalaureate degree. Students selecting to enroll in
two-year institutions often do so for myriad reasons (Conklin, 1993). Boughan and
Clagett (1995) have suggested that students attending two-year institutions have a range
of end goals to choose from including 1) an associate’s degree, 2) a certificate, 3) transfer
to a four-year institution, 4) personal development and 5) improving job skills. Fully
50% of first-time students enrolling at two-year colleges in 1987-88 and 1995-96 did not
intend to complete a certificate or degree, but did so merely to acquire or update job skills
(Conklin, 1993; Berkner, Horn, & Clune, 2000). However, Horn and Nevill (2006),
building upon work by Hoachlander, Sikora and Horn (2003), found that even though
88% of two-year college students indicated degree completion as a goal, that when
respondents were allowed to select multiple reasons for attendance, the number of
students indicating degree completion as a goal decreased significantly.
Broad educational goals coupled with high levels of risk factors among students
attending two-year colleges have set the stage for “poor performance” relative to
traditional measures. Using longitudinal data from the 1995-96, Beginning
Postsecondary Survey, NCES reported that after five years, 70% of those students who
15
entered four-year institutions had completed or were persisting toward a bachelor’s
degree, while only 17% of those who entered two-year colleges were performing
similarly (National Center for Education Statistics, 2004). Conklin (1993), and Boughan
and Clagett (1995) both point out that this measure of accountability is not reasonable for
two-year institutions, even when an associate’s degree is held as the standard.
Considering a broad range of educational goals, Boughan and Clagett suggested a
typology for defining achievement within the two-year college sector. This typology
included eight categories; 1) received an award (degree or certificate) and transferred, 2)
transfer/no award, 3) sophomore status in good standing, 5) achievers (sum total of 1 –
4), 6) persisters yet to earn 30 credit hours, 7) non persisters, and 8) those with more
personal or special motives. Even considering this broad definition of success, Boughan
and Clagett found that of 2,387 entering freshmen at a single community college, only
28% could be classified as achievers four full years after beginning postsecondary
education.
In studying students who began at less-than-four-year institutions as part of BPS:
96/98, Berkner, Horn, and Clune (2000) also examined persistence using a more broad
definition that included both attainment and continued enrollment. Student who earned
either a degree or certificate were classified as attainers while, those who continued to be
enrolled, or had transferred, were considered persisters. According to their analysis
43% of first-time beginners (FTB’s) enrolling at two-year colleges had left higher
education all-together after only three years, compared to a 19% attrition rate among their
four-year peers. Despite new typologies and expanded definitions for persistence and
success, it still remains difficult to truly know a student’s real intention. Though Conklin
16
(1993) suggests that approximately 50% of two-year college attendees do not intend to
get a degree or certificate, the coupling of Federal and State financial aid with both
percent of full-time enrollment and a student’s intention to complete a degree or
certificate reduces the reliability of self reported intentions. Additionally students who
have families, are financially independent, and work full-time are far more likely to have
life events that simply create barriers to continued college enrollment.
Early Research
Student retention in higher education has been an area of extensive study.
Beginning in the 1920’s researchers became interested in graduation rates, and student
dropout. However, it was not until the 1960’s that researchers began to advance
comprehensive models that could be used to explore student persistence. Though a
number of models have been advanced, one model has been repeatedly tested and
validated in the literature.
Student Integration Model
Building on Spady’s (1970, 1971) Work, Vincent Tinto (1975) advanced what he
called a “A Longitudinal Model of Dropout” (p. 94). Tinto believed that student
persistence, at the most basic level, is a matter of institutional fit, where a lack of
congruency between student and institution is the primary cause of attrition.
Consequentially, the degree of social and academic integration positively increases
student retention (Spady, 1970, 1971; Tinto 1975, 1987, 1993). In short, a student’s
motivation and academic ability should match the academic and social characteristics of
the institution. This model has been referred to by a number of names including the
17
student integration model (Cabrera, Nora & Castaneda, 1993) and the student
interactionalist theory (Titus, 2004).
First tested by Terenzini and Pascarella (1977), it has been subject to repeated
testing over the years, validating nearly 70% of the constructs originally identified by
Tinto (Cabrera, Nora & Castaneda, 1993; Sandler, 2000). Despite it’s tremendous
contribution to the body of persistence literature, the student integration model is not
without its detractors. Tierney (1992) posits that Tinto’s (1975, 1987) model has been
“repeatedly validated with little regard to the epistemological framework upon which his
theory is built” (p. 607). Despite being shown to be valid across multiple institution
types (Halpin, 1990; Pascarella & Chapman, 1983; Williamson & Creamer, 1988) and
among students of varying age (St. John, Paulsen & Starkey, 1996), Tierney (1992)
believes that it fails to consider racial and ethnic minorities attending mainstream
institutions. In addition, Tinto (1987) himself suggests that adult students and students at
two-year colleges may not fit within the schema. Structurally, Bean (1980) found the
models non-recursive nature to make it unsuitable for path analysis and difficult to
determine the directionality of the effect of tested variables. Despite these criticisms of
the model, three decades of research have consistently validated two important
underpinnings of the student integration model: 1) student retention is a longitudinal
process that occurs over time and 2) the student-institution-fit, regardless of how it is
defined and labeled, is an important determinate of student persistence.
Nontraditional Undergraduate Student Attrition Model
Tinto’s (1975, 1987, 1993) student integration model has significantly advanced
understanding of the complex nature of student persistence (Goel, 2002). Despite this
18
most of the data that exists comes from studies of four-year institutions, and
consequentially is based on the “traditional” college student (Bean & Metzner, 1985;
Cofer & Summer, 2000; Ethington, 2000; Hippensteeel, St. John & Starkey, 1996;
Pascarella & Terenzini, 2005). Drawing on previous research experience and an
extensive review of the literature, Bean and Metzner (1985) presented a new conceptual
model, the nontraditional undergraduate student attrition model. This model was
intended to account for the unique characteristics associated with nontraditional students.
Considering three student characteristics, 1) age greater than 25, 2) attending
school part-time, and 3) residing off campus, Bean and Metzner advanced a model where
social integration is of lesser importance than academic integration and environmental
factors, such as family responsibilities and ability to pay, directly influence student
persistence. Metzner and Bean (1987) first tested this model on 624 nontraditional
freshmen students attending a Midwestern urban institution. The findings that students
were not leaving due to social factors did help validate the model, however, the direct
impact of environmental pull factors on student departure, though in the anticipated
direction, was not significant.
Stahl and Pavel (1992) tested the Bean and Metzner model on 597 students at an
urban community college. Using structural equation modeling (SEM), the authors did
not find the model to be a “good-fit” to the data. Using exploratory factor analysis the
authors constructed an alternate model. Though the grouping of the observations
changed, the new model still retained many of the same features and variables as
originally presented by Bean and Metzner (1985). Curiously however, Stahl and Pavel
(1992) did find environmental pull variables to have both indirect and direct effects on
19
student retention, but those effects were in the opposite direction of that predicted by
Bean and Metzner. More recently, Zhai, Monzon and Grimes (2005) tested the Bean and
Metzner model on 782 first-time freshmen enrolled at a large, urban community college
using path analysis. This work validated again at least portions of the nontraditional
undergraduate student attrition model. Though the statistical methodology used did not
allow the construct Environmental Pull to be specifically tested and validated, the
analysis did demonstrate that a number of variables believed by Bean and Metzner to be
part of this construct were found to directly impact student persistence. Some of these
impacts were in the anticipated direction, others were not. According to Zhai, Monzon
and Grimes both financial aid and income positively influenced student retention, but so
did an increasing number of hours worked.
The Bean and Metzner (1985) nontraditional undergraduate student attrition
model, is complex, recursive, and has been tested and reported in the literature only a few
times. Unfortunately, each of these analyses has used relatively small single-institution
or single-district convenience samples. In addition, the regression analysis performed by
Metzner and Bean (1987) along with the path analysis utilized by Zhai, Monzon, and
Grimes (2005) failed to test the Environmental Pull construct advanced by Bean and
Metzner. Only Stahl and Pavel (1992) utilizing structural equation modeling were able to
test the direct and indirect effects of the construct; however these effects were not
reported as the model was found to have poor fit with the data. Though not well tested or
validated, Bean and Metzner’s work has continued to provide a foundation for studying
persistence among the nontraditional student.
20
Defining the Nontraditional Student
Building upon previous work and research, Bean and Metzner (1985) advanced
the nontraditional undergraduate student attrition model. Stewart and Rue (1983)
defined the nontraditional student as being over the age of 25, however, Bean and
Metzner argued that age alone does not allow for a complete understanding of this
increasing population. Citing Chickering’s (1974) position that residing on campus was
the most important factor in determining socialization, Bean and Metzner determined that
any definition of a nontraditional student must include the student being a commuter.
Bean and Metzner, also included a third defining characteristic of the nontraditional
student, part-time attendance. Prior to 1985 most persistence work had not included
enrollment status as a variable for consideration, particularly since Tinto’s (1975) student
integration model, was constructed around the experiences of the full-time, traditional
student. Though this definition of the nontraditional student as proposed by Bean and
Metzner was not perfect, it was a first attempt at defining a large and increasing pool of
students that up until this point remained relatively unrepresented within the persistence
literature.
Using data from the National Postsecondary Student Aid Study 1990 (NPSAS:90)
and the Beginning Postsecondary Survey longitudinal survey 1990/1992 (BPS: 90/92),
Horn (1996) expanded upon the definition of a the nontraditional student. Grounding her
work in the Bean and Metzner (1985) conceptual model, Horn argued first that age itself
was not related to persistence, but that age “merely acts as a surrogate…that captures
students with family and work responsibilities” (p. 3). Instead of focusing simply on
background variables, Horn chose instead to examine characteristics that were likely to
21
facilitate attrition. By focusing her work on three sets of criteria; 1) student enrollment
patterns, 2) student financial and family situation, and 3) high school graduation status,
Horn was able to identify seven risk factors that significantly influenced student
persistence. This section presents these seven risk factors as identified by Horn (1996),
their relationship to the Bean and Metzner (1985) nontraditional undergraduate student
attrition model, and previous research on these factors among two-year college students.
Enrollment Patterns
Enrollment Status
Bean and Metzner (1985) suggested that part-time enrollment was negatively
associated with student persistence. Though Metzner and Bean (1987) failed to test this
premise, it was later supported in work by Stahl and Pavel (1992). Testing the
nontraditional undergraduate student attrition model, Stahl and Pavel found that students
attending full-time were more likely to persist at a higher rate than their part-time peers.
Mohammadi (1994) studied more than 3500 entering freshmen at a single urban
community college over a five year period. His work suggested that number of hours
enrolled (part-time v full-time) was the second best predictor of student persistence.
Horn’s (1996) work with longitudinal data from BPS: 90/92 supported this. Students
across all institution types persisted at an average rate of 48.6% when attending part-time,
compared with 71.6% for those attending full time.
Using NPSAS: 96, Cofer and Somers (2000) examined the persistence pattern of
7,507 students enrolled in two-year colleges. Their work supported Bean and Metzner
(1985) and Horn (1996). They found that being a full-time student was positively
associated with student persistence. Berkner, Horn, and Clune, (2000) examining BPS:
22
96/98 data for students who began their career at less-than-four-year institutions,
reported that three year persistence among those attending full-time (56.6%) verses those
attending part-time (39.7%) was significantly higher. Additionally they noted that firstyear attrition for these two groups was also quite different at 12.3% and 29.7%
respectively. Work by Goel (2002) using exploratory, stepwise multiple regression
analysis to study the entering cohort for two community college districts in 1997 also
supported these finding. Examining within institution persistence on a semester-bysemester basis, Goel found that for both colleges, enrollment status was a significant
predictor of persistence for the first term while for one; it continued to be a significant
predictor throughout the first year and a half.
Delaying Enrollment
Unlike Bean and Metzner (1985), Horn’s examination of enrollment did not
simply focus on the level of enrollment, but also on the delay of enrollment. Though
Bean (1980, 1983, 1990) has always advanced age as an important variable, Horn (1996)
envisioned a more complex and meaningful measure. Horn’s measure of age was a
comparative measure that examined student age in relation to his/her class peers in the
study cohort. This is an important distinction as data from NPSAS: 87, NPSAS: 90, and
NPSAS: 93 demonstrated that across different cohorts the definition of traditional age
changed. By selecting a set number to define nontraditional students, Bean and Metzner
had established for their model a variable that did not allow for an evolving student
demographic. In addition, students of all age ranges were treated the same. Horn’s
measure of delayed enrollment means that a student just beginning their postsecondary
career at the age of 20 could be classified as nontraditional, while the Bean and Metzner
23
model fails to recognize that delay as important. The idea of delayed enrollment as a
significant risk-factor for student persistence has recently been reinforced by longitudinal
work by Adelman (2006). Building upon Horn’s supposition that increasing age brings
with it other risk factors, Adelman stresses that delaying enrollment for even one year
following high school significantly impacts student persistence.
Horn, (1996) demonstrated that of students who delayed entry into postsecondary
education, 43.6% worked full-time, 46.5% were enrolled part-time, 40.7% had dependent
children, and 20.5% did not have a traditional high school diploma, compared with
20.0%, 10.9%, 1.1% , and 0.0% respectively. Additionally she found that of the students
who delayed enrollment by more than one year (were older than typical age), only 48.5%
had completed a certificate, a degree, or were still enrolled five years after beginning
postsecondary education, while those who had not delayed enrollment persisted at a
significantly higher rate (72.9%). After adjusting for the covariance among variables,
Horn found that even though the persistence gap between those who delayed entry and
those who did not narrowed, it remained significant at 59.0% and 68.1% respectively.
Similarly Adelman (2006) using the National Educational Longitudinal Study 1988
(NELS: 88) found that when considering entry characteristics, delaying enrollment was
as important as socioeconomic quartile and academic quartile ranks in determining longterm success in higher education. However, he also found that once sufficient other
variable were included, the impact of delayed enrollment ceased to be significant.
Smaller scale research has also demonstrated the negative effects of delayed
enrollment. Brooks-Leonard (1991) studied 796 first-time beginners in the Indiana
Technical and Vocational college district and found that with each advancing decade
24
beginning at age twenty, persistence rates decreased rapidly, and Mohammadi (1994)
found attrition rates to be highest among those 23-50 years of age. Using structural
equation modeling, Napoli and Wortman (1998) found that although age was not directly
related to persistence, it was indirectly and negatively related through level of perceived
social support for attending.
Financial and Family Status
Financial Independence
As much of the early work on student persistence was based on the traditional
aged student, it was also based on the student who was often financially dependent upon
his or her parents. In fact, the entire financial aid system is predicated on the parent’s
ability to pay for their child to attend college. Early models of student persistence
included financial impact as a background variable, often in the form of Socioeconomic
status (SES) (Bean, 1980; Tinto, 1975). Like Horn (1996), who recognized age as a
surrogate measure for other life experiences, commitments, and responsibilities, Bean
may have come to see SES in much the same way. The nontraditional undergraduate
student attrition model (Bean & Metzner, 1985) proposes that although SES is an
important background characteristic, when combined with other background and
environmental factors, the resulting ability to pay for college is more important than SES
alone. In fact, as an environmental pull variable, Bean and Metzner propose that it may
have a direct impact on student persistence.
Horn’s (1996) work revealed that students who were financially independent were
far less likely to have attained a degree or demonstrate continued enrollment when
compared to those who were financially dependent upon their parents; 50.9% and 74.2%
25
respectively. This held true for students attending two-year colleges as part of the
NPSAS: 96 cohort as well (Cofer & Somers, 2000). Hippensteel, St. John and Starkey
(1996) used data from NPSAS: 87, an earlier test, to examine the predictive ability of a
comprehensive model of student persistence. Their findings were not consistent with
Horn and Cofer and Somers. Using logistic regression, they examined within-year
persistence among 2,300 two-year college students enrolled in fall of 1986. A possible
explanation may include the studies exclusion of all students less than 24 years of age.
Considering Horn’s earlier argument that age transcends other variables, one could
reasonably presume that these classically defined nontraditional students were not likely
to be dependent upon their parents, and as such the number of cases that were dependent
may not have been sufficient to demonstrate significance.
Full-time Employment
Included in the nontraditional undergraduate attrition model is the variable,
hours of employment. Bean and Metzner (1985) postulate that student persistence is
directly and negatively affected by hours worked. In addition, age becomes a mitigating
factor; with advancing age, comes greater financial responsibilities and a greater
likelihood of full-time employment while enrolled. Metzner and Bean (1987) tested their
model using stepwise regression. Their findings suggested that number of hours worked
was positively associated with stress and absenteeism and negatively associated with fulltime enrollment. Horn’s (1996) analysis was structured in such a way that all variables
were dichotomous. As such she found that of students who reported working full-time
that 55.9% persisted compared to 69.2% for those not working full-time. However, after
26
considering the covariance among other variables this factor failed to maintain
significance.
Work by Cofer and Somers (2000, 2001) produced similar results to Horn’s.
Again using the dichotomous variable, a negative relationship did appear to exist between
working full-time and student persistence, but it was not significant. Hippensteel, St.
John and Starkey (1996) also used a dichotomous variable and found that work did not
effect persistence, however their analysis did not set a threshold level, but instead simply
compared students who reported working at all with students who did not report working.
Bean and Metzner (1985) suggest that working on campus, particularly in the form of
work-study, should be differentiated from work off-campus necessary to meet financial
obligations. It is quite possible that this single classification of working versus nonworking did not allow for complete analysis of the effect. Brooks-Leonard (1991)
studying 796 students at a single institution found employment status to be both directly
and indirectly related to student persistence. Other research on two-year persistence,
particularly that which was grounded in Tinto’s (1975) student integration model (Napoli
& Wortman, 1998; Williamson and Creamer, 1988) often failed to consider the affect of
working on student persistence.
Family Responsibilities
Bean and Metzner (1985) hypothesized that increased family responsibilities
might directly impact student persistence. Metzner and Bean (1987) however, found
family responsibilities, operationalized simply as number of dependents, to be
insignificant. Stahl and Pavel (1992) tested the Bean and Metzner model on 597
students and found that family responsibilities lent significance to the overall predictive
27
ability of the model. Horn’s (1996) work further specified the Bean and Metzner model
concerning the impact of family responsibilities by including two variables, 1) having
children and 2) being a single parent. Horn found overall that having dependent children
was negatively associated with student persistence regardless of marital status. However,
like full-time employment, once other variables were controlled for in the model, the
effects were no longer significant. The remainder of the work on two-year college
persistence discussed here-to-fore failed to test the impact of dependent children on
college persistence. Though surprising considering the increased likelihood of having
dependent children with advancing age, this gap is consistent with a lack of sources
regarding dependent children in the four-year sector as well.
High School Graduation Status
Though Bean and Metzner (1985) suggested that high school performance, as
measured by high school GPA, should be included as a background characteristic in
persistence models, they did note that research on this relationship, particularly among
nontraditional students is scant. Bean and Metzner also suggested that the impact of high
school GPA on college persistence was likely to be indirect through college GPA. In
testing the model on 624 non-traditional students at a four-year institution, Metzner and
Bean (1987) found high school GPA to be insignificant as a predictor of student
persistence. Using the NCES, National Longitudinal Studies Program – 1972,
Williamson and Creamer (1988) examined high school GPA for students at both fouryear and two-year institutions, also finding it to be insignificant in predicting student
persistence. Stahl and Pavel (1992) tested the nontraditional undergraduate student
attrition model on 597 students at a community college. Though they found high school
28
performance to be a significant predictor of student persistence, it was not
operationalized as suggested by Bean and Metzner, but in terms of high school class rank.
Horn (1996) advanced an alternative measurement for high school background
characteristics. Finding that postsecondary student’s who had a GED instead of
traditional high school diploma were more likely to be older students who elected to
attend two-year colleges rather than four-year institutions, Horn suggested the use of this
dichotomous measure. Working with data available through NPSAS: 87 and BPS: 90/92
Horn (1996) found that among students at two-year institutions with GED’s, persistence
rates were 40.9%, compared with 67.0% for those with a traditional high school diploma.
This was supported by Cofer and Somers (2000, 2001) who studying a number of
variables among students from both two-year and four-year institutions found that those
students who have a GED instead of a high school diploma were 7.25% less likely to
persist than their peers.
The seven risk-factors just described are unique among the persistence variables.
These factors have consistently demonstrated their negative affect on student persistence
and have done so across four different major research databases; NPSAS: 87, BPS:
90/92, BPS: 96/98, and NELS: 88/2000. In addition, only with very few exceptions,
other research has also shown these seven risk-factors to be negatively associated with
student persistence. Using these factors, Horn (1996) developed a risk-factor index,
describing students as minimally nontraditional, moderately nontraditional, or highly
nontraditional. As a student becomes increasingly nontraditional, the likelihood of
persistence decreases rapidly. Unfortunately, this approach assumes a strong
understanding of persistence among those students who are traditional. Though decades
29
of research have demonstrated the complex and longitudinal nature of student
persistence, much of this research is inconsistent.
The Bean and Metzner (1985) nontraditional undergraduate student attrition
model includes a number of the variables described by Horn. In addition, it also includes
a number of classic persistence variables such as gender, race, integration, college GPA,
and even student satisfaction. The next section explores each of the variables advanced
by Bean and Metzner offering a synthesis of the research across time and across
institution type.
Variables Affecting Student Persistence
Student retention has been a focus of academic researchers for a number of years.
This research has ranged from the validation of conceptual models to the simple testing
of one or two variables in relation to persistence. Most of the research however, stops
short of model validation representing simply an exploration of a number of variables.
The following is a summative examination of the literature on student persistence across
multiple institution types for the student variables suggested by Bean and Metzner (1985)
for inclusion in the nontraditional undergraduate student attrition model.
Background Characteristics
Age
Just as much of the research on student persistence has been in traditional fouryear institutions, most studies were conceptualized with the traditional student in mind.
As an example, Tinto (1975) did not include age as a background variable important to
student persistence when he first advanced the student integration model. Pascarella,
Duby, Miller and Rasher (1981), while testing Tinto’s model, electing to include age,
30
found it to be a strong predictor in persistence, withdrawal, and stopout behavior. Bean
and Metzner (1985) were the first to specifically identify age in a model of student
retention, identifying students older than 25 as “nontraditional.” However, Metzner and
Bean (1987), studied 624 nontraditional students at an urban, Midwestern university, and
did not find age to be a significant predictor of student persistence. Boughan & Clagett
(1995), Mohammadi (1994), and Wiliamson and Creamer (1988) all studying students at
two-year colleges, also found age to have no direct effect on college persistence. Despite
this, a number of authors have shown age to have a significant impact on student
persistence, both directly and indirectly, and negatively and positively.
Cofer and Somers (2000) found that students over 30 persisted at a higher rate
than their younger counterparts. Brooks-Leonard (1991) found that students between the
age of 21 and 54 had increasingly higher GPA, a factor which alone has been shown to be
a strong predictor of student persistence and retention. However, Goel (2002) found that
age was negatively associated with students earning a degree, certificate or transferring,
and Zhai, Monzon and Grimes (2005) found age to be a strong predictor of quarter to
quarter student retention. St. John, Paulsen and Starkey (1996) found that though age
was significant in the early stages of modeling, it ceased to be so, once college
aspirations were considered. Additionally As previously reported, work by Adelman
(2006), Berkner, Horn, and Clune (2000), and Horn (1996) all found age as measured by
delayed enrollment to be negatively associated with student persistence.
Gender
Unlike age, the effects of gender on student persistence have been examined
repeatedly. However, like age, results have been mixed. Two authors (Bean, 1980,
31
1982; Spady, 1971) examined men and women at a four-year institution independently
and found predictors of retention to be different for men and women. Other studies have
suggested that by controlling variables, persistence rates can be compared across genders.
Studies by Mohammadi (1994) and St. John, Paulsen and Starkey (1996) found men
more likely to persist, while work by Astin (1997), Chen and Thomas (2001), Halpin
(1990), and Perna (1998) found females more likely to persist. Other work (Allen, 1999;
Berger and Milem, 1999; Braxton, Milem and Sullivan, 2000; Braxton & Brier, 1989;
Williamson & Creamer, 1988; Zhai, Monzon & Grimes, 2005) found gender to have only
non-direct or no effects on student persistence. Berkner, Horn, and Clune (2000) in their
analysis of the BPS: 96/98 data found that although females are just as likely as males to
have one, two, or even three risk factors, females are twice as likely as males to be
classified as highly nontraditional; having four or more risk factors.
Race
The results of the impact of race on college persistence are surprisingly mixed as
well. Many authors have found that non-Caucasian and African American students
persist at a lower rate than do their Caucasian counterparts (Astin, 1997; Berger &
Milem, 1999; Boughan & Clagett, 1995; Braxton, Milem & Sullivan, 2000; Cofer and
Somers, 2000; Opp, 2002). Hippensteel, St. John and Starkey (1996) and St. John,
Paulsen and Starkey (1996) found that students of color, particularly African Americans
persist at a higher rate than do their counterparts. Braxton and Brier (1989), Liu and Liu
(2000), and Williamson and Creamer (1988) found race to have no effect on persistence,
while Nora, Cabrera, Hagedorn and Pascarella (1996) and Stoecker, Pascarella and
Wolfle (1988) found that race alone was not a significant predictor of persistence, but
32
when considered with other variables such as choice of major, responsibility for children,
and overall level of social integration, the inclusion of race did change the predictive
ability of the model being tested. Similarly though not identified as a risk factor by Horn
(1996), Berkner, Horn, and Clune (2000) found total risk factors to be associated with
race. Blacks were trice as likely to have four or more risk factors compared to Whites,
29.4% and 14.8% respectively. Also, Hispanics were more likely than their White peers
to be considered minimally nontraditional (24.9% v 18.3%) or moderately nontraditional
(24.4% v 18.2%).
Socio-Economic Status (SES)
Parental and total family income, though excluded from many studies on student
persistence, nevertheless has strong potential to impact college persistence. Several
authors (Braxton, Bray & Berger, 2000; Braxton, Milem & Sullivan, 2000; Hippensteel,
St. John & Starkey, 1996) report a positive direct relationship between SES and student
persistence. Other studies (Berger & Milem, 1999; Milem and Berger, 1997; Pascarella
& Chapman, 1983) suggest an unspecified indirect effect on student persistence. In a
study by Stoecker, Pascarella and Wolfle (1988), SES was shown to have differing
effects on student persistence when race and gender were considered. For, example both
direct and total effects were found to be significant for African American men, indirect
and total effects were significant for African American women and White men, and all
three effects were significant for White women. Leppel (2001) in her research on how
choice of major affects college persistence found both race and SES to be related to
college persistence through their impact on student’s choice of college major. Horn
(1996) found a statistically significant difference in three-year persistence rates between
33
those in the highest (74.1%) and lowest (49.1%) SES quartiles, however this variable
failed to have significance once levels of covariance were considered.
Parental Education Level
Allen (1999) found that parental education had significant indirect and total
effects on the prediction of student persistence. This supports the assertions of Pascarella
and Terenzini (1991, 2005) and St. John, Paulsen and Starkey (1996) that parental
education is an important factor to consider when constructing a model of student
persistence. Zhai, Monzon and Grimes (2005) reported the impact of parental education
on persistence within two-year colleges. Their two-year longitudinal study suggests that
parental education was both directly related to student persistence and indirectly through
the student’s need for remedial education. Berkner, Horn, and Clune (2000) found that
among those attending two-year colleges whose parents did not have any postsecondary
education that 23.5% demonstrated four or more risk factors. For those with parents that
had some postsecondary education – a bachelor’s degree, or an advanced degree – the
likelihood of four or more risk factors decreased significantly; 15.7%, 7.5%, and 4.0%
respectively.
Academic Ability
Both Bean and Metzner (1985) and Tinto (1975) believed academic ability as
defined by previous academic success, to be an essential component of a comprehensive
persistence model. Berger and Milem (1999) found high school GPA to be very
important, with three direct significant effects on measurements of early involvement and
a statistically significant total effect on subsequent institutional commitment (p. 657).
The positive relationship between previously expressed academic ability and college
34
persistence has been a recurring theme in persistence literature (Berger & Braxton, 1998;
Braxton and Brier, 1989; Ethington, 1990, Kahn & Nauta, 2001; Munro, 1981; St. John,
Paulsen & Starkey, 1996; Titus, 2004). Though demonstrated effects of high school
academic performance have been convincing among those attending four-year
institutions, these effects are less clear among the two-year college population. Chen and
Thomas (2001) and Goel (2002) both found high school GPA to be positively related to
student persistence. However, Stahl and Pavel (1992) and Williamson and Creamer
(1988) both found high school GPA to have no effect on student persistence.
Researchers examining persistence using national datasets such as BPS and
NPSAS are often unable to report high school GPA. High school GPA for both datasets
is gathered via student self-report when taking the SAT or ACT examination. Therefore,
these data are unavailable for those students not taking one of these two exams.
Unfortunately, the open admissions policies at many two-year colleges negate the need
for students to take these examination. Though only tangentially related to academic
ability, a number of researchers have chosen instead to focus on having a GED instead of
a traditional high school diploma. Horn (1996) advanced this as one of the seven-risk
factors that continued to be significant even after controlling for covariance. However,
results due to this variable have varied greatly. Berkner, Horn, and Clune (2000) found
having a GED to be a risk factor among those attending less-than-four-year institutions.
In like fashion, Cofer and Somers (2000) found that having a GED negatively impacted
persistence, while St. John, Paulsen and Starkey (1996) reported that students with a
GED persisted at a higher rate than their peers who had graduated from high school.
35
Educational Goals
Both the student integration model and the nontraditional undergraduate student
attrition model include education goals as a background characteristic, however, the
treatment of the variable is fundamentally different. Tinto (1975) posits that the student’s
original educational goals interact with the college structure through student experiences,
which ultimately changes and shapes the students educational goals. It is Tinto’s
treatment of educational goals along with institutional commitment, that makes this
model both non-recursive and susceptible to Bean’s (1980, 1982) criticism. Bean and
Metzner (1985) also believe that a student’s educational goals interact with the
environment shaping both the academic and social experiences of the student. However,
they do not believe that failing to persist should be viewed as a reevaluation of the
original student goals, particularly among nontraditional students who have significant
outside influences. Pascarella and Chapman (1983) found student educational goals to be
an important predictor of persistence across all institution types. Studies by Allen (1999),
Allen and Nora (1995), Mallette and Cabrera (1991), and Sandler (2000) found
educational goals to be an important predictor of persistence among those attending fouryear institutions, while Cofer and Somers (2000, 2001), Ethington (1990), and Goel
(2002) found the same for two-year persistence. Williamson and Creamer (1988) also
found educational goals to be an important predictor of student persistence for both
cohorts. However, works examining the BPS:90/92 (Horn, 1996), and BPS: 96/98
(Berkner, Horn, & Clune, 2000; Titus, 2004) failed to demonstrate this significance.
36
Enrollment Status
Much of the work on the impact of enrollment status on student persistence has
been among two-year colleges and four-year institutions that are primarily commuter
colleges. Nearly all of this early work (Alfred, 1973; Behrendt, 1974; Fetters, 1977)
found that part-time enrollment negatively affected student persistence. In a test of the
nontraditional undergraduate student attrition model, Metzner and Bean (1987) did not
include enrollment status as a variable as all students over the age of 25 attending fulltime were eliminated from the sample. Horn (1996) working with data from BPS: 90/92
found that students enrolled part-time were significantly less likely to persist than their
full-time peers. More recent work by St. John, Hu, Simmons, Carter and Weber (2004)
also found that students enrolled full-time persisted at a significantly greater rate, and
Zhai, Monzon and Grimes (2005) found enrollment level to be an important variable for
inclusion in a model to test longitudinal persistence among students in a large, urban
community college district. All of these researchers defined enrollment as a dichotomous
variable, either full or part-time. Stahl and Pavel (1992), using a continuous
measurement of enrollment and studying persistence at a single two-year college, had
mixed results, finding that part-time status did not negatively impact persistence.
Additionally, they found enrollment status to be related to age, whereby younger students
enrolling in a higher than average number of hours, were less likely to persist than those
taking a standard twelve credit hour load.
37
Student Experiences
Academic Integration
Tinto (1975) stated that a student’s level of academic integration was definable
“in terms of both his [her] grade performance and intellectual development in college” (p.
104). Terenzini and Pascarella (1977) were the first to test and validate this construct as
a reliable predictor of student persistence. Using measures of student satisfaction of their
perceived intellectual development along with college grades, Pascarella and Terenzini
(1980) again found academic integration to be an important predictor of persistence.
Though their earlier work found academic integration to be nearly equal to social
integration in the prediction of student persistence, this later work found academic
integration to be least important of all variables, including informal interactions with
faculty, peer group interactions, institutional commitment, and goal commitment.
Instead of viewing academic performance as measured by college GPA, as an
indicator of student integration, Bean (1980. 1983, 1990) believed it to be merely an
outcome variable resulting from a student’s academic experiences (Cabrera, Nora &
Castaneda, 1993). For Bean, academic integration was better captured through the
perceived quality of advising, the study skills and habits exhibited by the student, the
student’s certainty about a particular major, and student patterns of absenteeism. In later
work, Bean and Metzner (1985) even examined the perceived quality of student faculty
relationships as an additional measure of academic integration. In the two decades that
have followed, the construct of academic integration has been validated many times, both
at the four-year (Bean, 1983; Berger & Milem, 1999; Braxton & Brier, 1989; Donavan,
1984; Eaton & Bean, 1995; Munro, 1981; Sandler, 2000; Stoecker, Pascarella & Wolfle,
38
1988; Titus, 2004) and the two-year level (Goel, 2002; Halpin, 1990; Napoli & Wortman,
1998; Williamson and Creamer, 1988).
Social Integration
Tinto (1975) believed that social integration was at the very center of the studentinstitution fit that he described in his student integration model. Citing previous work
suggesting that social relationships with peers was an important factor in student
persistence, Tinto postulated that social integration directly affects a student’s level of
institutional commitment. Student experiences with social groups like clubs and
organizations and positive, informal contact with faculty combined with peer group
interactions could provide a student with a sense of belonging that reinforced the
student’s original level of institutional commitment. Though Bean (1980, 1983, 1990)
believed social integration to be important in the prediction of student persistence for the
traditional student as well, he also saw it as difficult to measure and less important in the
prediction of persistence among the nontraditional student (Bean & Metzner, 1985; Eaton
& Bean, 1995; Metzner & Bean, 1987).
As a predictor of student persistence, social integration has been validated many
times (Berger, 1997; Berger & Braxton, 1998; Braxton, Milem & Sullivan, 2000; Munro,
1981; Pascarella & Terenzini, 1991, 2005; Titus, 2004). In contrast, Donavan (1984),
Eaton and Bean (1995), Mallette and Cabrera (1991), Metzner (1989), and Metzner and
Bean (1985) all found measures of social integration to be insignificant in the prediction
of student persistence. All of this work has been in the four-year college sector. Work
within the two-year sector has been even less consistent. Chen and Thomas (2001) found
social integration to be significant, while academic integration was not. Goel (2002)
39
found just the opposite to be true, while Napoli and Wortman (1998) and Williamson and
Creamer (1988) found both to be significant in the prediction of student persistence.
However, Williamson and Creamer’s work also suggests that both academic and social
integration are important factors in predicting persistence only in the short run. When the
definition of persistence is extended beyond failure to enroll for a single semester to
include failure to re-enroll for a longer specified time frame, the ability of both academic
and social integration to explain student persistence weakens significantly.
Living on Campus
As much of the work on individual student persistence has been at traditional
four-year institutions, the variable of living on or off campus has not been thoroughly
studied. Bean (1980, 1990) suggests that living on campus should lead to improved
student persistence, particularly among those attending four-year residential campuses.
However, only a handful of authors (Berger, 1997; St. John, Paulsen & Starkey, 1996,
Titus, 2004) have shown this to be true. Among students at two-year institutions, Chen
and Thomas (2001) and Cofer and Somers (2000) both found that residing on campus
improves student persistence and Halpin (1990) demonstrated that student persistence
decreased as the distance a student commuted to college increased. This particular
variable is difficult to study among two-year college students as only 1 in 20 students
reside in college owned housing compared to 1 in 2 at four-year institutions (Berkner,
Horn, and Clune, 2000).
Declared a Major
Citing certainty about college major as a measure for academic integration, Bean
and Metzner (1985) believed this to be an important measure of student persistence.
40
Several authors (Chen & Thomas, 2001; Leppel, 2001; St. John, Hu, Simmons, Carter &
Weber, 2004, Sandler, 2000) have validated the choice of a student’s major as a
significant variable in the prediction of student persistence across multiple institution
types. Titus (2004) however, was unable to demonstrate the significance of having
selected a college major on individual student persistence while studying more than 5,000
students from four-year institutions across the nation.
Environmental Pull Variables
Family Responsibilities
The effects of family responsibilities, being married, having children or other
dependants, or being a single parent, is conspicuously missing in much of the research on
college persistence. This may be due to the fact that much of the research, particularly
among persistence at four-year institutions, is grounded in Tinto’s (1975) student
integration model. As such, focus is on the more traditionally defined student. However,
Bean and Metzner, (1985) suggested that family responsibilities could potentially have
direct effects on student persistence. Using number of dependents as a measure of family
responsibility and multiple regression analysis to test the nontraditional undergraduate
student attrition model, Metzner and Bean (1987), were unable to demonstrate this.
Work by Cabrera, Nora and Castaneda (1993), Napoli and Wortman (1998), St. John,
Paulsen and Starkey (1996), and Titus (2004), all found that family responsibilities,
measured either by having dependents, number of dependents or simply being married,
did not impact student persistence. In contrast, work by Leppel (2001) found that being
married negatively impacted student persistence, while having children had no effect.
Nora, Cabrera, Hagedorn and Pascarella (1996) found that having children had a negative
41
effect on the persistence of both women and minorities. Horn (1996), and Berkner, Horn,
and Clune (2000) found that having dependent children had a significant negative impact
on student persistence. In addition, both also found that being a single parent had an
equally negative impact on student persistence.
Student Finances
Bean and Metzner (1985) suggested that the student’s ability to pay for college
could directly impact student persistence, however testing the model, they (Metzner &
Bean,1987) were unable to demonstrate significance of any of the proposed
environmental pull variables on student persistence, including the student’s ability to pay
or college. St. John (1990), using data from the NCES High School and Beyond 1980
longitudinal study, found that student persistence was significantly related to both loan
and grant dollars received. Nora, Cabrera, Hagedorn and Pascarella (1996) examined
data from 3900 students from 29 different two-year and four-year institutions. This study
found that unmet financial need negatively impacted persistence for females and nonminority students. Using data from NPSAS: 87, St. John, Paulsen and Starkey (1996)
found that a variety of financial measures had substantial direct influence on student
persistence, and Hippensteel, St. John and Starkey (1996) found that among students at
two-year colleges, increasing tuition charges were negatively associated with year-to-year
persistence.
Working
Those researchers most concerned with examining the role of finances on student
persistence, also often considered the impact of student employment. Nora, Cabrera,
Hagedorn and Pascarella (1996) and St. John, Paulsen and Starkey (1996) found hours
42
worked to be significantly related to student persistence. Cofer and Somers (2000, 2001)
and Hippensteel, St. John, and Starkey (1996) both compared working full-time with
those students not reporting full-time work and found no significant impact among twoyear college students. In contrast, Titus (2004) used BPS: 96/98 data from more than
5,000 students attending four-year institutions across the US, and found that both work
study and working off campus had a net positive effect on student persistence.
Student Attitudes
Bean’s (1980, 1982, 1983) original work on student persistence was grounded in
Price’s (1977) research on worker turnover. As such, satisfaction with one’s experiences
was an important component. Bean’s emphasis on psychological aspects carried over
into his work on the nontraditional undergraduate student attrition model (Bean and
Metzner, 1985). Together they proposed a variety of psychological measures for possible
inclusion in a model of student persistence. Two of those measures have been tested and
validated by others.
Student Satisfaction
Bean (1983) examined 876 female freshmen at a four-year university and found
satisfaction to be significantly related to student dropout behavior. Bean (1980) selected
only females for testing the student attrition model as it was based on the Price/Mueller
worker turnover model, which was tested and validated using the rather homogenous
profession of nursing. In developing the nontraditional undergraduate student attrition
model, Bean and Metzner (1985) suggested consideration of student satisfaction with
organizational aspects such as governance structure, dissemination of information,
financial aid, admissions, and even variety and number of course offerings. However,
43
results for this variable have been mixed at best. Metzner (1989) testing a five item
measure of student satisfaction using the Bean and Metzner model to examine freshmen
retention among 1,033 freshmen at an urban four-year institution found that satisfaction
was not directly related to persistence, but it was indirectly related through the measure
intent to leave. Titus (2004) used a multi-measure construct to assess student satisfaction
with their college experiences and found that satisfaction indeed was highly related to
student persistence. Titus suggested a measure that included student satisfaction with
teachers, class size, institutional prestige, intellectual growth, campus climate and social
experiences.
Institutional Commitment
First described by Tinto (1975) as part of the student integration model,
institutional commitment has come to be synonymous with loyalty to the institution.
This variable has received more consideration among persistence research than other
student attitude measures. A number of authors, (Berger & Braxton, 1998; Berger &
Milem, 1999; Braxton & Brier (1989), Cabrera, Nora and Castaneda, 1993; Eaton &
Bean, 1995; Elkins, Braxton & James, 2000; Nora & Cabrera, 1993; Pascarella &
Chapman, 1983; Sandler, 2000; Titus, 2004) have all found institutional commitment to
be directly related to student persistence. Metzner (1989) and Metzner and Bean (1987)
found that institutional commitment had an indirect effect on student persistence through
assessing a student’s intent to re-enroll.
44
Student Academic Performance
Grade Point Average (GPA)
Student grade point average has been examined repeatedly as a measure for
predicting student retention. All students have some academic performance constraints
placed upon them, whether by themselves, a parent, or simply the minimum performance
standards imposed by the institution. As such, failure to meet these minimum standards
may result in a student’s failure to persist. Tinto (1975) originally proposed student GPA
as a proxy for academic integration. Bean (1980), Bean and Metzner (1985) and Cabrera,
Nora and Castaneda (1993) argued that as a measure of academic integration, college
GPA was insufficient. Each suggested that GPA was instead a mediating variable that
directly affected persistence and was itself shaped by a variety of other variables, such as
background characteristics, student experiences and even financial attitudes. A large
number of empirical studies have found GPA to be significantly related to student
persistence behavior (Bean, 1980, 1982, 1983; Boughan & Clagett, 1995; BrooksLeonard, 1991; Chen & Thomas, 2001; Cofer & Somers, 2000; Goel, 2002; Mallette &
Cabrera, 1991; Kahn & Nauta, 2001; Metzner, 1989; Metzner & Bean, 1987; Nora,
Cabrera, Hagedorn & Pascarella, 1996; Pascarella, Duby, Miller & Rasher, 1981; St.
John, Paulsen & Starkey, 1996; St. John, Hu, Simmmons, Carter & Weber. 2004;
Sandler, 2000; Titus, 2004).
According to the nontraditional undergraduate student attrition model as
proposed by Bean and Metzner (1985) student persistence is a complex problem, that
includes student background characteristics, experiences, attitudes and perceptions, and
external environmental factors that can directly impact student persistence, such as full-
45
time employment, dependent children and the ability to actually pay for college. Though
voluminous research exists on student persistence, much of that research has been carried
out at four-year institutions and conceptualized with the four-year student in mind. The
final portion of this chapter explores the limitations of the current research as it relates to
persistence among nontraditional students, particularly those enrolled at two-year
colleges.
Limitations of the Current Studies
Though Metzner and Bean (1987) were the first to test and validate some of the
constructs of the nontraditional undergraduate student attrition model, the sample
included only older commuter student’s attending part-time from a single four-year urban
university. Metzner and Bean failed to study the indirect effects of these measures,
which in previous work (Bean & Metzner, 1985) were considered key to furthering
understanding of this complicated process. Also, Metzner and Bean can not speak to the
effects of age or enrollment status based on the way they defined nontraditional students,
which excluded all students under the age of 25. In addition, the Bean and Metzner
definition of the nontraditional student uses a fixed age. Using this definition, students
less than 25 are not considered at-risk. However recent research by both Horn (1996) and
Adelman (2006) have shown that delaying enrollment for even one year following the
completion of high school can have serious consequences for student persistence through
the introduction of additional risk factors.
Stahl and Pavel (1992) tested the Bean and Metzner (1985) model on 597
students from an urban community college. Though operationalizing nearly every
variable proposed by Bean and Metzner and using structural equation modeling (SEM) to
46
examine both the goodness-of-fit and direct, indirect and total effects, they failed to
report these effects. The most recent test of the nontraditional undergraduate student
attrition model (Zhai, Monzon & Grimes, 2005) examined 782 first-time freshmen in a
large community-college district in southern California. Using path analysis the authors
did find that the model was not only a good fit to the data, but that at least one
environmental pull variable, hours worked, had a direct effect on student persistence.
Both Metzner and Bean, and Stahl and Pavel used single institution convenience samples,
while Zhai, Monzon and Grimes used a single community college district. No studies
operationalizing the nontraditional undergraduate student attrition model using data
from more than one institution were found. This is unfortunate as single institution data
fails to account for the likelihood of homogeneity that may exist among students from a
single institution sample (Titus, 2004).
Additionally, relatively few studies on two-year college persistence have been
grounded in a theoretical framework, and of those that have, most (Halpin, 1990; Napoli
& Wortman, 1998; Williamson and Creamer, 1988) have used Tinto’s (1975) student
integration model. As previously noted, Bean (1980), Tierney (1992), and even Tinto
(1987, 1993) have been critical of the student integration model’s ability to account for
the unique needs of the nontraditional student. Bean (1990) posits that the use of a
conceptual model is highly important as it can simplify a very complex problem
ultimately making the data more meaningful. Additionally, Bean and Metzner (1985)
suggested that the complexity of issues surrounding student persistence can be best
understood by exploring both the direct and indirect effects of variables and constructs as
they act on student persistence. Pascarella and Terenzini (2005) support this argument
47
suggesting that future research on student persistence should attempt to utilize newer
statistical methodologies such as hierarchical linear modeling and structural equation
modeling.
It is clear that students attending two-year colleges, are more diverse, have
different needs, different goals, and many more risk-factors. The constructs proposed by
Bean and Metzner (1985) and included as part of the nontraditional undergraduate
student attrition model, recognize and attempt to account for these differences. As such,
it is here advanced as a model better suited to studying persistence, given the unique
nature of two-year college students.
Summary
College persistence is a pressing issue in higher education today. Rising costs
coupled with a large number of older adults is forcing definitions of the “college student”
to evolve rapidly. The two-year college is playing an ever increasing role in the delivery
of higher education, particularly as it pertains to nontraditional students. It is clear from
the literature that persistence at two-year colleges and among the nontraditional student
has not been analyzed extensively. Researchers have an obligation to explore alternative
constructs and methodologies to help further understanding of the complex, longitudinal
nature of student persistence (Tierney, 1992; Pascarella & Terenzini, 2005).
A number of models and a variety of variables have been advanced to explain
student persistence. It is clear from the literature that generally these models have aided
understanding. In addition, most of the variables have been shown to be related to
student persistence, either directly or indirectly through mediating variables. Researchers
wishing to further enrich the knowledge regarding student persistence, must consider the
48
gaps in the literature particularly as they relate to the ever more common nontraditional
student. Nontraditional students are no longer those who are older, commute and attend
part-time, but also include those who have children, work full-time, are financially
independent, and have a GED instead of a traditional high school diploma.
49
CHAPTER THREE
METHODOLOGY
Introduction
Using Bean and Metzner’s (1985) conceptual model (nontraditional
undergraduate student attrition model), this study explores the relationships that exist
among student characteristics and individual rates of persistence of first-time
undergraduate students enrolled at U.S. two-year institutions three years after initial
enrollment in postsecondary education. Data are drawn from the 1996 Beginning
Postsecondary Students Longitudinal Study (BPS: 96/98) sponsored by the U.S.
Department of Education’s National Center for Education Statistics (NCES).
This study uses a statistical technique known as structural equation modeling.
This allows for: 1) simultaneous estimation of the measurement and structural models , 2)
examination of the direct, indirect and total effects among the constructs, 3) assessment
of the “goodness of fit” of the conceptual model, and 4) reporting of the total variance
explained by the model overall.
This chapter begins by restating the research questions. This is followed by an
explanation of the data collection procedures used by NCES to create the BPS data set
including discussion of the complex sample design, the use of weighting, and
questionnaire design, validity, and reliability. This is followed by presentation of the
conceptual model and explanation of the independent, dependent, and criterion variables.
This chapter concludes with a description of structural equation modeling (SEM), an
examination of the use of LISREL (a SEM software package), and the steps to be used by
the author to facilitate analysis of the data and report the results.
50
Research Questions
Using data available through the National Center for Education Statistics (NCES)
this study tests the extent that the Bean and Metzner (1985) nontraditional undergraduate
student attrition model, when operationalized using the data as described here-in,
explains student persistence among those enrolled at two-year colleges. The following
questions were considered during this research.
1.
Does the hypothesized model fit the observed data?
2.
Do student background characteristics directly and or indirectly affect
student persistence?
3.
Does academic performance as measured by college GPA directly affect
student persistence?
4.
Do student experiences have an indirect affect on student persistence?
5.
Do environmental pull factors have a direct and or indirect affect on
student persistence?
6.
Does student satisfaction directly affect student persistence?
Data Collection
NCES Databases
The Beginning Postsecondary Students (BPS) Longitudinal Study series was
intended to give researchers the ability to study issues of “enrollment, persistence,
progress, attainment, continuation into graduate/professional school, employment, and
rates of return to society” by studying cohorts of students during both their first year of
enrollment and at multiple subsequent points (Wine et al, 2000). The BPS: 96/01 follows
a cohort of students first beginning their postsecondary career during the 1995-1996
51
academic year. Originally interviewed in 1996 as part of the National Postsecondary
Student Aid Study (NPSAS: 96), the first follow-up interviews occurred during 1998
(BPS: 96/98), and the second in 2001 (BPS: 96/01). Follow-up interviews focused on
persistence and attainment among students still enrolled in institutions of higher
education and upon employment among students no longer enrolled.
National Postsecondary Student Aid Study
NPSAS: 96 data are derived from a two stage sampling frame that targeted
students who entered institutions of higher education in the United States and Puerto Rico
between May 1, 1995 and April 30, 1996.
These two stages included identifying first,
NPSAS eligible institutions, and second identifying from those institutions a population
of NPSAS eligible students. Using IPEDS 1993-94 institutional characteristics data, an
institutional sampling frame was constructed that included nine institutional strata based
on control and highest level of academic offering: 1) Public, less-than-2 year; 2) Public,
2-year; 3) Public, 4-year, non-doctorate-granting; 4) Public, 4-year, doctorate-granting; 5)
Private, not-for-profit, less-than-4-year; 6) Private, not-for-profit, 4-year, non-doctorategranting; 7) Private, not-for-profit, 4-year, doctorate-granting; 8) Private, for-profit, lessthan-2-year; and 9) Private, for-profit, 2-year or more. From the original sampling frame
consisting of 9,468 institutions 973 were randomly identified, of which 900 were
determined eligible to participate in NPSAS: 96 (Wine et al, 2000). For an institution to
have been identified as eligible, it must
1.
offer educational programs designed for individuals who have completed a
secondary education
2.
offer more than correspondence courses
52
3.
offer at least one academically, occupationally, or vocationally oriented
program of study requiring at least 3 months or 300 contact hours of
instruction
4.
offer courses open to the general public; and
5.
be located in the United States or Puerto Rico
Of those 900 eligible institutions, 836 provided lists of students from which
eligible students were drawn. In addition to being enrolled at a NPSAS eligible
institution, students who met the following criteria were considered NPSAS eligible
themselves if they:
1.
were enrolled in a term or course between May 1, 1995 and April 30,
1996;
2.
were enrolled in either an academic program, at least one course for credit,
or an occupation or vocational program requiring at least 3 months or 300
contact hours of instruction to receive a degree, certificate, or other formal
award;
3.
were not concurrently enrolled in high school; and
4.
were not enrolled solely in a GED or other high school completion
program.
As sample institutions provided the list of NPSAS eligible students, students were
randomly selected from among those lists at fixed rates to allow for the flow of the
information from the sampled NPSAS institutions. These sampling rates were set to meet
or exceed specific sample sizes desired among the student sample strata while accounting
for expected institutional misclassification among students identified as First-Time
53
Beginners (FTB’s), a group critical to BPS (Wine et al, 2000). Germaine to this effort
was the oversampling of potential FTB’s to ensure sufficient sample size for the BPS,
and as a result, 23,612 students were identified as potential FTB’s (Riccobono et al.,
1997). As part of NPSAS: 96, students were contacted using computer assisted telephone
interviews (CATI). Only with completion of the CATI were researchers able to
accurately determine student eligibility as an FTB.
Beginning Postsecondary Student Longitudinal Study
BPS interviews were attempted for 11,985 CATI respondents. Additionally, 425
NPSAS: 96 potential FTB non-respondents were selected for sampling in order to reduce
bias that may have resulted from non-response. Of those 425 non-respondents, 300
were selected for more intensive follow up. Of the 12,410 in the original sample,
interviews were conducted for 10,268 BPS eligible FTB’s. Those excluded include those
without access to a phone, those who were generally unavailable for interviews, those
who were mentally/physically incapacitated, or those who were found to not meet the
criteria as FTB’s. 9,812 sample members completed full interviews, while 113 and 343
respondents completed partial and abbreviated interviews, respectively.
As with NPSAS: 96, interviews were conducted via phone using CATI
technology. When necessary computer assisted personal interview (CAPI) strategies
were employed in field interviews. A total of 2,094 cases were referred for field
interviews. Field interviews were conducted for cases of Puerto Rico residency, inability
to locate in CATI, refusal to participate in CATI or when extensive work in CATI
resulted in failure to reach the subject (Wine et al., 2000).
54
Instrumentation. The instrument was designed using a multi-stage process. The
first set of data elements used for the field test data collection were developed and refined
by a Technical Review Panel (TRP) with input from NCES and additional U.S.
Department of Education Staff. The second set of data elements were revised by NCES
and the TRP prior to full-scale data collection. Using these data elements, individual
survey items were then constructed to maximize efficiency and consistency. Survey
items were arranged to partition the instrument into three main sections: 1) verification
of FTB status and collection of missing NPSAS: 96 data, 2) collection of information
regarding enrollment, employment, family structure, income, student financial aid,
educational experiences, and aspirations, and 3) updated/locating information for
subsequent follow-ups (Wine et al., 2000). Though utilizing different data collection
methods, the CATI and CAPI interviews were programmed identically utilizing the same
CASES 4.1 software.
Training and Interviewing. In February 1998, training began for telephone
interviewers, supervisors, and CAPI monitors. At the conclusion of training, each
interviewer completed a certification process to ensure their ability to facilitate accurate
and efficient interviews. Approximately four weeks after interviews had begun, those
interviewers deemed most affective by their supervisors and monitors were provided
additional training on refusal conversion techniques necessary to overcoming common
participation barriers and questions often encountered in the interview process (Wine et
al., 2000).
Phone interviewing began in spring of 1998, once training operations were
completed. Locating information as well as school and/or student provided contact
55
information was preloaded into the CATI software to facilitate ease of interviewing and
improve efficiency. Using automated call scheduling software, interviewers were
assigned sample subjects to contact. Field interviewing began approximately twelve
weeks after CATI had commenced. This time delay provided sufficient cases to create
workload in all 34 of the identified geographic regions. Using investigative measures
such as contact with sample subjects’ former neighbors, city and county offices, US
Postal and Department of Motor Vehicle services, and even directory assistance, every
effort was made to contact sample subjects.
Weighting. Most statistical methods used in analyzing data assume a simple
random sample. When gathering large amounts and types of data intended to have
multiple uses, it may be useful to make sure that underrepresented populations or highly
desired variables are oversampled. Oversampling helps ensure that key variables are
represented at a sufficiently high rate to allow for sound statistical analysis. This type of
data collection however, is not without its problems, particularly since it creates a set of
observations that misrepresent the population. By using sample weights, researchers can
correct for unequal probabilities of selection, making the data a more accurate
representation of the target population (Thomas & Heck, 2001). In addition, longitudinal
studies such as BPS: 96/98 introduce a second problem, nonresponse. Sample weights
can also be used to address gaps in the data created by nonresponse.
Since BPS: 96/98 is based on a complex sample design where institutions were
randomly selected from among categories, and students were then selected from among
those institutions, variable weights were constructed to account for the sampling design
as part of NPSAS: 96 (Wine et al, 2000). As part of the first follow-up in 1998, four
56
additional weight factors were considered: 1) adjustment for eligibility as FTB, 2)
adjustment for inability to locate the student, 3) adjustment for student refusal to be
interviewed, and 4) adjustment for other non-responses. The overall weighted response
rate for the main population of interest, students attending public two-year institutions,
was 78.6%.
Reliability and Validity. Reliability of data was evaluated through re-interviews
with a small subset of BPS: 96/98 respondents, n = 189 (Wine et al., 2000). Second
interviews were conducted approximately five to seven weeks after initial interview. By
comparing responses from the initial interview and subsequent re-interview, stability of
student responses was evaluated for the following areas: financial aid, parental support,
income, employment while enrolled, undergraduate experiences and distance education.
According to Riccobono et al. (1997) validity of certain data was assessed by asking
students to confirm or to self-report data previously reported by the student’s institution.
BPS: 96/98 was intended to allow researchers to study issues of “enrollment,
persistence, progress, attainment, continuation into graduate/professional school,
employment, and rates of return to society” by studying cohorts of students during both
their first year of enrollment and at multiple subsequent points (Wine et al, 2000). BPS:
96/98 has been used by a number of researchers, including many cited within this study.
Continued analysis of the data provided within may help to enrich understanding of the
complex issues facing higher education today. In this study, data from BPS: 96/98 are
used to operationalize the constructs within the conceptual model presented next.
57
The Conceptual Model
The Bean and Metzner (1985) model was considered superior to other models for
two reasons: 1) it recognized those characteristics unique to the nontraditional student
including enrollment status, age, and environmental pull characteristics such as work
responsibilities, actual ability to pay for college, and having dependent children, and 2)
environmental pull factors are theorized to directly affect continued enrollment without
speaking to the student’s satisfaction with or commitment to the institution and
postsecondary education in general. First, the nontraditional undergraduate student
attrition model was adapted to more adequately reflect the converging nature of the
academic and social experiences that in general are unique to the two-year college
experience. The conceptual model is presented in Figure 3.1. The conceptualized model
was then adapted to fit data available through BPS: 96/98. Particular attention was paid
to the variables described by Berkner, Horn, and Clune (2000) as significant predictors of
student persistence for two-year college students included in the BPS: 96/98 sample. The
structural model to be tested is presented in Figure 3.2, followed immediately by
discussion of each observed variable.
According to the conceptual model, persistence is directly influenced by student
background characteristics, environmental pull characteristics, academic performance
and student satisfaction. Persistence is also believed to be indirectly effected by student
experiences acting through both academic performance and student satisfaction. In
addition to direct effects, student background characteristics are believed to indirectly
affect student persistence through academic performance and the construct environmental
pull. Background is also posited to have direct effects on the construct Experiences.
58
Academic
Performance
Background
Characteristics
Student
Experiences
Satisfaction
Persistence
Environmental
Pull
Figure 3.1: Conceptual Model
Structural models include not only the constructs and construct relationships such
as those presented in Figure 3.1, but also the observed variables used to measure each of
those constructs. The construct Background has seven observations; age, gender, race,
socioeconomic status, educational goals, enrollment-status, and high school diploma
versus GED. The construct Experiences has three observations; academic integration,
social integration, and declared major status. The construct Environmental Pull is
measured using four observations; unmet financial need, having dependent children,
working off campus, and working full-time. The remaining three constructs, satisfaction,
academic performance and persistence, are all single observation constructs.
59
Figure 3.2: Hypothesized structural model.
60
The following section presents each observed variable and includes both relevant
descriptive data as well as treatment of the variables. Descriptive statistics were obtained
using the Data Analysis System available online through the National Center for
Education Statistics website. Variable means are reported for continuous data while
distributions are reported for all nominal/ordinal data. As these data represent the
weighted means and distributions for the entire population of two-year first-time
beginners for the 1995-96 academic year based upon the population, standard errors of
the estimate are also presented for all ordinal variables, along with the percentage
distribution of the sample. As all variables were recoded and renamed, treatment of each
is also described. In order to reduce confusion and comply with software specifications,
each variable has been recoded using consistent nomenclature methods so that all names
are alphabetical, lowercase and eight characters or less. All system missing data are
coded as -9.
Dependent Variable
The dependent (criterion) variable in this study, persistence, is defined as having
completed a certificate, an associate’s degree, or still being enrolled three years after
beginning postsecondary education. Persisters account for 56.4% (SE = 1.18), with
43.6% (SE = 1.18) failing to persist. The BPS variable PRENRLB1 was recoded as
“persist”. Respondents who were persisting (57.1%) or not persisting (42.9%) were
coded 1 and 2 respectively.
61
Independent Variables
Background
According to Bean and Metzner (1985) Background characteristics should include
the student’s age, gender, race, socioeconomic status, educational goals, student
enrollment-status and whether or not the student received a traditional high school
diploma or a GED.
A number of researchers have found age to be an important background variable
in predicting student persistence (Brooks-Leonard, 1991; Cofer & Somers, 2000; Goel,
2002, Horn, 1996; St. John, Paulsen & Starkey, 1996; Zhai, Monzon and Grimes, 2005).
The BPS variable (SBAGFM) is a derived, continuous variable that is calculated from the
student’s date of birth and the first month of reported enrollment for academic year 199596. The minimum reported student age was 16, the maximum was 72, and the weighted
mean age for all two-year FTB’s was 21.6 with SD = 7.2. Expanding upon Horn’s (1996)
concept of reporting age as traditional or nontraditional, the final variable (age) is ordinal
and uses a scale ranging from traditional to highly nontraditional. Respondents are
labeled traditional (age 16 – 19), slightly nontraditional (age 20-24), moderately
nontraditional (age 25 – 30), and highly nontraditional (age > 30) and are coded 1 thru 4
respectively.
Previous studies on persistence have produced mixed results of the effects of
gender. Studies by Mohammadi (1994) and St. John, Paulsen and Starkey (1996) found
men more likely to persist, while work by Astin (1997), Chen and Thomas (2001), Halpin
(1990), and Perna (1998) found females more likely to persist. Others still, have found
no effect on persistence (Allen, 1999; Braxton, Milem & Sullivan, 2000; Braxton &
62
Brier, 1989; Williamson & Creamer, 1988; Zhai, Monzon & Grimes, 2005). The BPS
variable SBGENDER was renamed gender and was coded 1 if the respondent was male
(45.8%) and 2 if the respondent is female (54.1%). Weighted estimates of gender differ
slightly and are as follows: male = 49.2% (SE = 1.41) and female = 50.8% (SE = 1.41).
Consistent with other background variables, the effects of race on student
persistence have also been mixed. Though many authors have found no relationship
between race and persistence (Braxton & Brier, 1989; Liu & Liu, 2000; Williamson &
Creamer, 1988), others have found that Caucasians persist at greater rates (Astin, 1997;
Berger & Milem, 1999; Boughan & Clagett, 1995; Braxton, Milem & Sullivan, 2000;
Cofer and Somers, 2000; Opp, 2002), and others still (Hippensteel, St. John & Starkey,
1996; St. John, Paulsen & Starkey, 1996) found that African-American students persist at
a higher rate than other students. The weighted distribution for the BPS variable
SBRACE is as follows: 69.4 % Caucasian (SE = 3.83), 11.3% African-American (SE =
1.37), 14.3 % Hispanic (SE = 3.29), 4.0% Asian/Pacific Islander (SE = 0.92), 0.8%
American Indian/Eskimo (SE = 0.31), and 0.2% other (SE = 0.12). The actual sample
distribution is 67.8 %, 13.6%, 14.0, 3.4%, 0.8% , and 0.4% respectively. The variable
was renamed “race” and labeled 1 thru 6 in the previously stated order.
Socioeconomic status has been demonstrated to have direct effects on student
persistence (Braxton, Bray & Berger, 2000; Braxton, Milem & Sullivan, 2000;
Hippensteel, St. John & Starkey, 1996). So too, has parental education level (Allen,
1999; Pascarella & Terenzini, 1991; St. John, Paulsen & Starkey, 1996). This study uses
the single variable Socioeconomic Diversity Index 1995-96 to operationalize both of these
variables. The BPS variable DISADVAN is a derived variable based on the status of the
63
student on three indicators of socioeconomic disadvantage: total family income as a
percentage of the 1994 federal poverty level, the highest educational level completed by
either parent, and the proportion of the student body in the student's high school eligible
for the free or reduced-price lunch program in 1994-95. Students are classified as Not
Disadvantaged (37.6%, SE = 2.38), Minimally Disadvantaged (47.3%, SE = 2.45), or
Moderately/Highly Disadvantaged (15.1%, SE = 1.59). The variable was renamed “ses”
with a respondent distribution of 43.8%, 39.5%, and 15.1% respectively. Responses were
coded 1, 2, and 3 beginning with the lowest levels of socioeconomic disadvantage.
Student educational goals were believed critical by Bean (1980), Bean and
Metzner (1985) and Tinto (1975, 187, 1993). Pascarella and Chapman (1983), Allen
(1999), Allen and Nora (1995) Cofer and Somers (2000, 2001), Ethington (1990), Goel
(2002), Mallette and Cabrera (1991), Sandler (2000) and Williamson and Creamer (1988)
all found student educational goals to be important predictors of student persistence.
Educational goals are measured in this study using the BPS variable EPHDEGY1. Each
student was asked to respond to the question: What is the highest level of education you
ever expect to complete? The variable was recoded and is called Educational Goals
(edgoals). Data will be coded as follows: 1 = no degree or certificate, (4.1%, SE = 1.03),
2 = certificate (5.6%, SE = 1.29), 3 = associate’s degree (11.1 %, SE = 1.30), 4 =
bachelor’s degree or completion of post baccalaureate program (42.1%, SE = 2.14), 5 =
master’s degree (28.5%, SE = 1.84), 6 = doctoral or first professional degree (8.6%, SE =
1.29). The actual sample distribution is 32.8%, 4.7%, 5.4%, 22.4%, 24.1%, and 10.5%
respectively.
64
Many researchers have found part-time attendance to be negatively associated
with student persistence when compared with those attending full-time (Berkner, Horn, &
Clune , 2000; Horn, 1996; Mohammadi, 1994; St. John, Hu, Simmons, Carter & Weber,
2004; Zhai, Monzon, & Grimes, 2005). The BPS variable First month enrolled –
intensity 1995-98 (ENINFM), was recoded as Enrollment Status (enroll) and has a
weighted distribution where 52.7% (SE = 3.02) of FTB’s are attending full-time while the
remaining 47.4% (SE = 3.02) are attending part-time. The actual distribution of the valid
responses is 55.6% and 44.2% respectively. Those attending full-time are coded 1 and
part-time 2.
Using BPS: 90/94 data, Horn (1996) found that having a GED instead of a
traditional high school diploma was negatively associated with student persistence. This
was also found to be true of the BPS: 96 cohort as well. Considering the open admissions
policy of most two-year institutions, students with GED’s are more likely to enter
postsecondary education at this level. According to Horn and Nevill (2006) the
likelihood of a student beginning college with something other than a traditional high
school diploma is twice as likely at public, two-year institutions than at four-year nondoctorate granting institutions, and more than four times as likely than at four-year,
doctorate granting institutions. As such it is important in adapting the nontraditional
undergraduate student attrition model to the two-year college sector that this be included
as a background variable. The BPS: 96/98 variable High school degree or equivalent
(HSDIPLOM), is used to construct a measurement of high school completion (diploma)
to be coded as follows: 1 = those who received a high school diploma , 2 = those who
received a GED or certificate of completion, and 3 = those who responded other. The
65
weighted distribution having received a diploma (88.1%, SE = 1.41), passed the GED
(8.7%, SE = 0.99), or other (3.3%, SE = 0.77) differs only slightly from the distribution of
valid responses at 89.2%, 7.3%, and 3.5% respectively.
Student Experiences
Both Tinto (1975, 1987, 1993) and Bean (1990) have suggested the importance of
student experiences, particularly the level of social and academic integration, in shaping
college commitment and continued persistence. A number of authors (Donavan,1984;
Eaton & Bean, 1995; Mallette & Cabrera; Metzner, 1989; Metzner & Bean, 1987) found
academic integration to be a strong predictor of persistence, while others (Berger &
Braxton,1998; Braxton, Milem & Sullivan, 2000; Pascarella & Terenzini, 1991, 2005)
advanced social integration as the strongest predictor. Both are included in this model.
Academic Integration is measured in this study using a derived BPS: 96/98
variable, Climate – Academic Integration 1995-96 (ACADINT). It is based on the
average of the responses indicating how often the respondent had participated in study
groups, had social contact with faculty, met with an academic advisor, or talked with
faculty about academic matters outside of class. Possible scores range from 100 – 300
with a mean of 159.33 with SD = 47.99. Social integration will be measured using
Climate – Social Integration 1995-96 (SOCINT), which is based on the average of the
responses indicating how often they had attended fine arts activities, participated in
intramural or non-varsity sports, participated in varsity or intercollegiate sports,
participated in school clubs, or gone places with friends from school. Possible scores
range from 100 – 300 with a mean of 135.61 with SD = 37.40. For both academic
integration (academic) and social integration (social) respondents answers are categorized
66
as little/no integration (100-167), moderately integrated (168-233) or highly integrated
(234-300). These values were coded 1, 2, and 3 respectively.
The third and final variable comprising the construct student experiences is
related to student major. Several authors (Chen & Thomas, 2001; Leppel, 2001; St. John,
Hu, Simmons, Carter & Weber, 2004, Sandler, 2000) have suggested that certainty about
academic major and even the major choice itself may impact student persistence. Titus
(2004) testing Bean’s (1990) model on BPS: 96/98 data for students attending four-year
institutions suggested using the BPS variable Major during first year 1995-96
(SEMAJ1Y1) to create a dichotomous variable. Following this suggestion the variable
“decmajor” is coded so that those students having declared a major (70.3%, SE = 4.24)
are coded as 1, while student who are undecided (29.7%, SE = 4.24) are coded as 2. The
actual distribution of valid responses was 74.6% and 25.4% respectively.
Environmental Pull
It is the theorized impact of the environmental pull variables that make the
nontraditional undergraduate student attrition model separate and distinct from both
Bean’s (1980, 1982, 1983) early models and all models presented by Tinto (1975, 1987,
1993). Environmental pull includes number of dependents, being a single parent, the
financial need of the student or student’s family, and the number of hours worked each
week while attending school. The model posits that the environmental pull variables
directly and negatively influence student persistence.
The work of Horn (1996) and Berkner, Horn, and Clune (2000) both found that
having dependent children significantly and negatively influenced student persistence
among BPS respondents for both the 1992 and 1996 cohorts. The variable family
67
responsibilities (family) was adapted from the BPS: 96/98 variable SBMRCHY1. Those
with dependent children (19.7%, SE = 0.82) were coded 1, while those without children
(80.3%, SE = 0.82) were coded 2. No distinction was made between married and
unmarried respondents. Of the valid respondents, 19.6% reported having dependent
children, while 80.4% did not.
Tremendous research exists on the relationship of both price and financial aid to
student persistence. Cofer and Somers (2000, 2001), Hippensteel, St. John and Starkey
(1996), Cabrera, Stampen and Hansen (1990), Nora, Cabrera, Hagedorn, and Pascarella
(1996), St John, Hu, Simmons, Carter and Weber (2004), and Sandler (2000) all found
that student persistence was negatively influenced by both increases in tuition costs and
the level of student need. Bean and Metzner (1985) suggested that instead of financial
need or cost of tuition, researchers might consider the student’s actual ability to pay the
college tuition bill. Applying this concept to the BPS: 96/98 data, Titus (2004) suggests
the variable Budget minus EFC and total aid, 1995-96 (SNEED2), which is considered
by NCES to be a measure of unmet financial need. This speaks directly to the student’s
ability to pay his or her college bill. Unmet student need for all two year respondents
ranged from $0 to $12,001 with the mean unmet need of $1480 (SD = 2055). In the final
analysis unmet need (needcat) is indexed based upon the raw score where $0 – $500 is
Low Need (1), $501 - $2500 is Moderate Need (2), $2501 - $5000 is Moderately High
Need (3), and > $5000 is Very High Need (4).
Student employment is thought to be negatively associated with student
persistence as it not only interferes with the time available for study, but can also keep
students from attending full-time, and even from attending classes for which they are
68
scheduled. Studying persistence among BPS: 96/98 respondents, Horn (1996) found that
working full-time is significantly and negatively correlated with student persistence.
Consistent with this work, the continuous BPS: 96/98 variable Hours per week while
enrolled 1998 (JEHOURB1), is converted to a dichotomous variable (fulltime). Students
who work full-time (> 35 hours per week) are coded 1, while those working less than
full-time are coded 2. Reported hours worked ranged from 0 to 60 with a mean of 23.79
(SD = 16.74).
Bean (1990) and Bean and Metzner (1985) both theorized that working offcampus would be negatively associated with student persistence. In addition, both also
believed that working on-campus might be positively associated with persistence.
Working off-campus (offcamp) is measured here using the BPS: 96/98 variable First yremployed on campus 95-96 (J1LOCAY1). Weighted estimates suggest that only 2.9%
(SE = 0.79) of FTB’s at two-year colleges who also report working actually work oncampus, while the remainder (97.2%, SE = 0.79) are employed off-campus. The
distribution of actual respondent was 3.6% and 96.4% respectively
Student Satisfaction
Bean (1980) was the first to propose satisfaction as a measure to predict student
retention. This measure has been validated several times (Bean, 1983; Metzner, 1989;
Titus, 2004), and is included in this study. It is operationalized here using the derived
variable Satisfied overall with first institution, 1995-96 (SATISALL). It is based on the
average scores indicating student satisfaction with the campus climate regarding students
of different racial or ethnic backgrounds, class sizes, cost of attendance, any counseling
services they had used, course availability, any cultural activities they had participated in,
69
instructors’ teaching ability, their intellectual growth, any job placement services they
had used, the prestige of the school, social life, and sports and recreational facilities. This
score has a possible range of 10 – 100 with a mean of 90.14 (SD = 15.21). Respondents
were recoded as Very Unsatisfied (10 – 29), Somewhat Unsatisfied (30 – 49), Neutral (50
– 69), Somewhat Satisfied (70 – 89), and Very Satisfied (90 – 100). They are coded 1 – 5
respectively.
Academic Performance
A large number of empirical works examining student persistence at both twoyear and four-year colleges have demonstrated the positive relationship between
academic performance as measured by college GPA and student persistence (Chen &
Thomas, 2001; Boughan & Clagett, 1995; Cabrera, Nora & Castaneda, 1993; Goel, 2002;
Milem & Berger, 1997; Pascarella & Chapman, 1983; Sandler, 2000; Titus, 2004).
Academic performance will be operationalized in this study using cumulative 1995-96
grade point average as reported by the student’s institution (SEGPAY1). Grades were
reported relative to a 4.0 scale multiplied by 100. Grades ranged from 2 to 400 with a
mean GPA 219.04 (SD = 128.59). Respondents scores were indexed and labeled as “F”
(0 – 99), “D” (100 – 199), “C” (200 – 267), “B” (267 – 333), and “A” (334 – 400). Each
is coded 1 – 5 respectively.
The Bean and Metzner (1985) nontraditional undergraduate student attrition
model considers the characteristics unique to the non-traditional student. The BPS: 96/98
data provides an excellent opportunity to test the models predictive ability on two-year
college persistence utilizing a national longitudinal sample. Horn’s (1996) previous work
on both the 1992 and 1996 BPS cohorts demonstrated a number of variables that can be
70
used to operationalize the Bean and Metzner model. The next section of this chapter
describes the processes used to extract, recode, test and analyze these data.
Dissertation Methodology
The population of interest includes all first-time college students enrolled in twoyear institutions in the U.S. and Puerto Rico during the 1995-96 academic year. Using
BPS: 96/98, students that possess the desired characteristics were identified for inclusion
in the study sample using the variable First Institution Type 1995-96 (ITNPSAS). The
researcher is authorized to access the restricted use files for BPS: 96/98/01.
Authorization was granted by completion of a notarized application. Using the
Electronic Code Book (ECB) to tag the desired variables, these data were then extracted
from the restricted-use research file CD and imported into SPSS 14.0. All variables
extracted for use are included in Table 3.1.
Using SPSS, the data were recoded as previously described, and data for
respondents who did not meet the criteria for inclusion were eliminated. SPSS was also
utilized for descriptive statistical analysis of the sample and its distribution. These results
are presented in Chapter Four. The SPSS file was then imported into PRELIS 2.0, a
companion software included in LISREL 8.80. Together, this software allows
researchers to screen and analyze data sets, impute missing data, and explore descriptive
data, in addition to evaluating structural equation models.
Structural Equation Modeling
The use of structural equation modeling (SEM) is appropriate for validating
longitudinal theoretical models like the one proposed here. This statistical procedure is
analogous to performing simultaneous factor analysis and path analysis, validating both
71
the structural and measurement components of the model (Cabrera, Nora & Castaneda,
1993). In addition, it allows for consideration of direct, indirect, and total effects when
exploring the relationships among the exogenous and endogenous variables and the
criterion variable.
Bean and Metzner (1985) in proposing the nontraditional undergraduate student
attrition model suggested that future research must consider the indirect effects of tested
variables in order to further enrich the overall understanding of student persistence
patterns. Not surprisingly, since then a number of researchers have used SEM to test
persistence models. Stahl and Pavel (1992) testing the Bean and Metzner model,
employed SEM techniques. Cabrera, Nora, and Castaneda (1992, 1993) used SEM to test
the role of finances in the student persistence process and also to advance and test their
own model of student persistence. Napoli and Wortman (1998) used SEM to examine
psychosocial factors related to student persistence at two-year colleges, and Zhai,
Monzon and Grimes (2005) used SEM to test the Bean and Metzner model on students in
an urban community college district.
Model Analysis
When considering the use of any statistical methodology, the researcher must first
be aware of statistical assumptions that must be met. For SEM these assumptions
include, 1) correct specification of the model, 2) multivariate normality, 3) independence
of exogenous variables, 4) sufficiently large sample size, and 5) no systematic missing
data. In addition to meeting the assumptions, researchers using data that is hierarchical or
has a complex sample design must decide how the data are to be treated. The following
72
Table 3.1
Variable Names and Definitions
BPS Variable
Name
Variable
Location
BPS Number
First enrollment at Public two-year
First institution type
1995-96
Restricted Data
File
ITNPSAS
Dependent Variables
Enrolled or having completed an undergraduate degree
program three years after first enrolling the same lessthan four-year institution
(1 = yes, 2 = no)
Independent Variables
Attainment or level
of enrollment 1998
Education:
Attainment
PRENRLB1
age
Age during first term of enrollment (1= 16-19, 2 = 2024, 3 = 25-30, 4 = >30)
Age during first
month enrolled
1995-96
Background:
Demographics
SBAGFM
gender
Nominal Variable (1 = male, 2 = female)
Gender
Background:
Demographics
SBGENDER
race
Ordinal Variable (1 = White, 2 = Black, 3 = Hispanic,
4 = Asian/Pacific Islancer, 5 = American
Indian/Alaskan Native, 6 = Other
Race-Ethnicity
(Including Hispanic)
Background:
Demographics
SBRACE
enroll
Enrolled full-time (1 = yes, 2 = no)
First month enrolled
intensity – 1998
Socioeconomic
Diversity Index
1995-96
Highest degree ever
expected 1996
High school degree
or equivalent
Education:
Attendance
ENINFM
Background:
Demographics
DISADVAN
Variable
Definition
Filter Variables
Institution Type
persist
Background Characteristics
ses
edgoals
diplom
Derived Variable (1 = Not Disadvantaged, 2 =
Minimally Disadvantage, 3 = Moderately/Highly
Disadvantaged)
Highest degree expected (1 = no degree…6 =
professional degree or doctorate)
Having a traditional high school diploma (1 = diploma,
2 = GED, 3 = other/none
Education:
Future
Background:
High School
EPHDEGY1
HSDIPLOM
73
Table 3.1 (continued)
Variable Names and Definitions
Variable
BPS Variable
Name
Variable
Location
BPS Number
Climate-Academic
Integration 1995-96
Education:
Experiences
ACADINT
Climate-Social
Integration 1995-96
Education:
Experiences
SOCINT
Major during first
year 1995-96
Education:
Program
SEMAJ1Y1
Background:
Demographics
SBMRCHY1
Unmet Financial Need = student budget minus
expected family contribution minus financial aid
(Recoded to Ordinal, scores from 1 to 4)
Whether the student worked full-time (> 35 hours per
week) while enrolled (1 = yes, 2 = no)
Whether the student worked off campus or not
(1 = yes, 2 = no)
Marital Status and
Children 1995-96
Budget minus EFE
and total aid 199596
Hours per week
while enrolled 1998
First yr-employed
on campus 95-96
Institution:
Net Price
SNEED2
Derived Variable – Continuous Scores from 10-100.
Recoded to Ordinal (1 = 10-29, 2= 30-49, 3 = 50-69, 4
= 70-89, 5 = 90-100)
Satisfied Overall
With First
Institution 1995-96
Education:
Experience
SATISALL
First year cumulative grade point average- Continuous
= GPA x 100 (1 = 0-99, 2 = 100-199, 3 = 200-267, 4 =
267-333, 5 = 334-400)
Grade point average
95-96 (continuous)
Education:
Performance
SEGPAY1
Definition
Experiences
academic
social
decmajor
Derived Variable – Continuous Scores from 100-300.
Recoded to Ordinal (1 = 100-167, 2= 168-233, 3 =
234-300)
Derived Variable – Continuous Scores from 100-300.
Recoded to Ordinal (1 = 100-167, 2= 168-233, 3 =
234-300)
Whether student declared a major (1 = yes, 2 = no)
Environmental Pull
family
needcat
fulltime
offcamp
Has Dependent Children (1 =yes, 2 = no)
Employment:
While Enrolled
Employment:
While Enrolled
JEHOURB1
J1LOCAY1
Student Satisfaction
satisfac
Academic Performance
gpa
74
section describes both how the assumptions were met as well as the considerations for
complex sample design.
Model Specification. According to both Kline (2005) and Schumacker and
Lomax (2004), model specification is the most difficult part of SEM. The author must
have sound reasoning for inclusion of specific variables and the specification of the
model paths. Therefore, structural models must be informed by previous research and
based upon a solid understanding of the issues surrounding the criterion variable. The
Bean and Metzner (1985) nontraditional undergraduate student attrition model is
grounded in extensive research. In addition, the model presented here builds upon this
research and sound theory by combining Bean and Metzner’s work with other work
relating specifically to the BPS: 96/98 database. Berkner, Horn, and Clune (2000)
identified a number of variables known to be significant predictors of student persistence
for the entire population of students included as part of BPS: 96/98. As described
previously, where possible and appropriate each of these variables were included among
the Background and Environmental Pull characteristics.
Multivariate Normality. There are a number of estimation methods for structural
models. The default method, Maximum Likelihood Estimation (ML), assumes a
multivariate normal distribution of data. As all data in this analysis are either ordinal or
dichotomous, multivariate normality can not be assumed. Joreskog and Sorbom (2001)
posit that the use of ML estimation is inappropriate for the analysis of all non-continuous
variables. To address this issue, model estimation is done using a procedure known as
Weighted Least Squares (WLS). Nora, Cabrera and Castaneda (1993) describe weighted
least squares as more robust than other estimation methods, and Kaplan (2000), Kline
75
(2005), and Schumacker and Lomax (2004) advance WLS estimation methods as most
appropriate for use in non-normal data. Foss, Troye and Howell (2000) found that WLS
estimation methods outperformed other estimation methods for non-normal data when
sample sizes were large (n > 1000). The WLS estimation procedure will be explained
more fully later in this chapter.
Independence of Exogenous Variables. Kline (2005) suggests that
multicollinearity, particularly within a single construct, can be addressed quickly and
simply by examining the correlation matrix. Correlations > .85 are suggestive of possible
multicollinearity. Where high correlations exist, indexed variables can be helpful. In this
study, the Socioeconomic Diversity Index variable includes a measure of both parent
education level and economic means. As educational attainment is directly related to and
often highly correlated with income, the use of this measure avoided confounding
variables and helped to assure independence of the exogenous variables. Descriptive
statistics, including examination of collinearity, will allow the researcher to determine the
level of independence of all exogenous and endogenous variables, and normality of the
sample.
Sample Size. Large sample size is important for any SEM analysis (Kline, 2005;
Schumacker & Lomax, 2004). However, it is even more critical when using a WLS
estimation method. Sample sizes that are too small are likely to result in extremely high
χ2 values during SEM analysis causing the researcher to falsely asses a model as having
poor fit. The population for this study includes all respondents who began postsecondary
study during the 1995-96 academic year at public, two-year colleges (n ≈ 1500). Kline
(2005) has suggested a 20:1 ratio of test subjects per free parameter, with a 10:1
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minimum ratio. Free parameters represent proposed direct effects of one variable on
another. Model complexity suggests 87 free parameters, and as such, a sample of 870 –
1740 would be adequate.
Missing Data. To meet the assumption of no systematic missing data, a variable
originally intended to be part of the model has been excluded. Academic ability as
demonstrated through high school grade point average was postulated by both Bean
(1980) and Tinto (1975) as an important background characteristic in any predictive
model of student retention. However, NCES did not gather high school GPA as part of
the CATI or CAPI interview process. Instead, these data are available only for those
students who self-reported high school GPA when taking the ACT or SAT examination.
Therefore not only does this variable have a high rate of missing data for all respondents
(49%), but it is disproportionately high for students attending two-year institutions
(85%). Imputation of such a large number of scores would undoubtedly create a bias
within the data, and since the effect of academic ability as measured by high school GPA
has not been validated in studies of two-year college persistence, or in studies of nontraditional students attending four-year colleges, this observation has been omitted from
the analysis.
The remaining missing data will be imputed. There are three reasons why data
imputation is appropriate for this study. First, pairwise and listwise deletion methods
assume that data are at a minimum missing at random. However, Kline (2005) points out
that missing data patterns in the social science are rarely, if ever completely randomized.
Second, pairwise deletion is not compatible with WLS. Weighted least squares
estimation requires the use of an asymptotic covariance matrix, and computation of this
77
matrix using a pairwise deletion method increases the likelihood of unacceptable
correlations and as such increases the likelihood of a fatal error. Finally, the use of
listwise deletion both decreases the effective sample size and will create an inherent bias
within the data should the missing data not be at random. Since the use of the WLS
estimation method requires a large sample size, data imputation not only avoids these
pitfalls, but also maximizes the number of cases available for analysis.
Complex Sample Design. As previously noted, BPS: 96/98 utilizes a complex
sampling design methodology. This design methodology can create bias within the
sample as groups of individuals may share characteristics that are being tested in the
model. In fact, the majority of behavioral and social science research can be
characterized in different levels of aggregation (Julian, 2001). It is these levels of
aggregation that can potentially blur the within-groups and between-group effects.
Researchers are thus presented with two choices: using more advanced statistical
procedures that account for the hierarchical clustered nature of the data, or using standard
analytical procedures while at the same time realizing the limitations and potential
modeling complications. There is considerable debate among users of SEM as to the best
treatment method for multi-level data. Though Thomas and Heck (2001) suggest that
researcher should always use sample weights, a study by Haus-Vaughn (2006) found that
among the most recent work using secondary data in premier journals of higher education
research, fully 27% of these studies failed even to discuss weights. Continuing, HausVaughn recommends that if an author chooses not to use variable weights, rationale
should be presented along with a discussion of the possible implications and bias.
78
Data in this analysis are used in their raw, un-weighted form. Julian (2001)
studied the effects of testing multilevel data with traditional methodologies. He found
that even though this biased the chi-square estimate, it did so by inflating the chi-square.
Since SEM researchers testing goodness-of-fit desire a non-significant chi-square, this
inflation increases the likelihood of Type II error, potentially labeling a model as having
poor fit with the data when that may not be the case. Julian cautions that when testing
multi-level data using traditional analysis methods that researchers must be careful
relying upon alternative fit indices when chi-square is significant as these may also be
inflated ultimately increasing the likelihood of Type I error.
Despite this, Julian (2001) found that when group size is small that the
consequences of ignoring data dependence within multilevel structures are negligible.
Julian tested different distributions of five hundred respondents using standard SEM, and
then compared the results to those obtained using multi-level SEM. When group size
represented no more than 1% of the overall sample, results for the standard SEM analysis
approximated the multi-level results. Respondents in this study (n ≈ 1500) are clustered
within 109 groups (two-year institutions). Though this represents a mean score of
approximately 13 which is less than 1% of the total sample, respondents are not equally
distributed among institutions. Instead they range from 1 to 25. Using t-tests it was
determined that given the sample size, groups of 24 or less were not statistically greater
than 1%. Only one group (n = 25) is significantly different (t = 2.01) than the small
group size (≈ 1%) suggested by Julian.
A second issue relating to complex sample design is the oversampling of selected
characteristics. Oversampling helps to ensure that underrepresented populations are
79
sampled in sufficiently large enough numbers to allow for statistical analysis. In
addition, by increasing the sample size the proportion of variance for select variables may
be decreased. Despite these benefits, oversampling makes generalizing the results to the
entire population more difficult. However, the current version of LISREL only allows
for design sampling weights for continuous variables. Since it is not possible to adjust
for sampling weights for ordinal and nominal variables using the selected software, it is
important that both the researcher and the readers understand the distribution of key
characteristics within the sample.
Nora, Cabrera, and Castaneda (1992, 1993) used SEM to study models of student
persistence on convenience samples. They noted the importance of understanding and
reporting the differences in distribution between the sample and the overall population
being studied. Using SPSS the variables gender and race were dummy coded for males
and females, Whites, Blacks, Hispanics, Asian/Pacific Islanders, and American
Indians/Alaskan Natives. Using a t-test the mean distribution for each variable in the
final sample analysis was then compared with national data. All sample demographics
are reported in the next chapter. Overall, Caucasions, Hispanics, Asians/Pacific
Islanders, and American Indians/Alaskan Natives were representative of the weighted
distribution of first-time beginners as obtained from the Data Analysis System and
reported previously in this chapter. However, the oversampling of both females and
Blacks was noted. Females were overrepresented, 54.1% as compared with 50.8% (t =
2.612), while Blacks were overrepresented at 13.6% compared with the weighted
estimates of 11.3% (t = 2.572).
80
Model Evaluation
As consistent with other statistical methods such as multiple or logistic regression,
a researcher using SEM must consider the degree of variance explained by the model
being advanced. However, the degree of “fit” that exists between the model and the
available data must also be considered (Pascarella & Terenzini, 1991; Smart &
Pascarella, 1987). The most basic fit statistic is the Model Chi-square (χ2m). It should be
noted that chi-square is particularly sensitive to sample size, with small samples
increasing the likelihood of Type-I error and large samples increasing the likelihood of
Type-II error. For this reason, Kaplan (2000) and Kline (2005) both advise against the
sole use of a single goodness-of-fit measure. Instead, the use of multiple measures is
recommended (Cabrera, Nora, & Castaneda, 1993; Napoli & Wortman, 1998). Model fit
will be examined using a variety of additional fit indexes, including the Goodness of Fit
index (GFI), the Adjusted Goodness of Fit Index (AGFI), the Root Mean Square Error of
Approximation (RMSEA), and the Akaike Information Criterion (AIC).
Model Chi-Square. As stated before, the model chi-square is the most basic fit
index (Kaplan, 2000; Kline, 2005). All recursive models (where effects are in one
direction with no multiple measures, and no feedback loops) are considered identified.
Models that are just-identified offer a perfect fit between the data and the model; in short,
there is only one answer. Just-identified models occur when the number of model
parameters is equal to the number of observations. When a path model has fewer
parameters than observations, it is considered overidentified. Most real-world problems
offer multiple solutions, and as such are overidentified. In short, the chi-square compares
the overidentified model, with a hypothesized just-identified model.
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Model parameters are estimated using Weighted Least Squares (WLS). Though
Maximum Likelihood estimation is the default method for parameter estimation in most
SEM software programs, it is not appropriate for estimating parameters for ordinal or
mixed data (Joreskog & Sorbom, 1996; Schumacker & Lomax, 2004). Using both the
raw data file and the asymptotic covariance matrix SEM software programs compare
covariance matrices based on the specified models with those from the actual sample.
Discrepancies between observed data and the specified model are represented as FWLS
and calculated using the following formula (Kaplan, 2000):
FWLS = (s – σ)W-1 (s – σ)
where s = vech(S) and σ = vech [Σ(Ω)] are vectorized elements of S the sample
covariance matrix and Ω the collective parameter vector. The model chi-square is
expressed as:
n x FWLS
where n = (N – 1). Chi-square should always be reported with the model degrees of
freedom (dfM).
As stated previously the chi-square is susceptible to over-inflation with large
sample size (Kline, 2005) and as such increases the likelihood of failing to reject the null
hypothesis. Conversely, a sample size that is too small is likely to result in rejection of a
correct null hypothesis. Therefore, researchers using SEM must not rely solely on the chisquare statistic to determine appropriate model fit. This study will examine and report
four additional indicators of fit.
Goodness of Fit. The Goodness of Fit (GFI) indicator was originally associated
with LISREL, but is now calculated by multiple SEM programs (Kline, 2005). This
82
matrix proportion of explained variance usually produces results between 0.0 and 1.0,
with 1.0 representing a perfect fit between the data and the model and numbers
approaching zero suggesting a very poor fit. A GFI > .90 usually indicates good fit.
Negative measurements are often associated with a sample size that is too small, while
numbers greater than 1.0 may also suggest a near perfect fit. The GFI can be represented
as follows:
GFI = 1- Vres/Vtot
where Vres represents the residual variance and Vtot the total variance suggested by the
model (Kline, 2005)
Adjusted Goodness of Fit Index. The Adjusted Goodness of Fit Index (AGFI) is
adjusted for model degrees of freedom relative to the number of variables in the model
(Schumacker & Lomax, 2004). Kline (2005) describes AGFI as analogous to R2. Like
GFI, it can range between 0 and 1 with numbers approaching 1.0 being desirable.
GFI = 1 –
( p + q)( p + q + 1)
(1 − GFI )
2d
where p + q is the number of observed variables analyzed (Joreskog & Sorbom, 1996).
Root Mean Square Error of Approximation. The Root Mean Square Error of
Approximation (RMSEA) was designed to account for varying sample size. As such it is
considered a parsimony-adjusted index. A value of zero is considered the best fit, with
higher numbers suggesting an increasingly worse fit. Results < 0.05 are considered a
good fit (Kaplan, 2000).
RMSEA =
δˆ
df ( N − 1)
83
where df = model degrees of freedom, and δ represent a calculated non-centrality
parameter (Kline, 2005).
Akaike Information Criterion. The Akaike Information Criterion (AIC) is a
predictive fit index that “assesses model fit in hypothetical replication samples of the
same size and randomly drawn from the same population” (Kline, 2005). Unlike other fit
indices the AIC may not have a specified range. According to Shumacker and Lomax
(2004), different SEM programs calculate the AIC in different ways with some
calculations being based upon the number of free parameters while others are based upon
the model degrees of freedom. Kline (2005) recommends that the AIC be used to
compare alternative models tested on the same data. When other fit factors are equal the
model with the lowest AIC may have better fit. AIC in LISREL is calculated as follows:
AIC = χ2M + 2q
where q = the number of free parameters in the model (Schumacker & Lomax, 2004).
Model Interpretation
Kline (2005) cautions that good fit does not mean high predictability. Large
disturbances (unexplained variance) could mean that a model with perfect fit may not
have good predictive ability. Researchers must also consider the direct, indirect and total
effects of one variable on another. Direct effects, or path coefficients (ω), act much like a
standard regression coefficient (β), in that a single unit change in the predictor variable
represents a specific change in the subsequent variable. In this study Weighted Least
Square (WLS) is the procedure used to estimate path coefficients for each of the free
parameters. When reported along with the standard error (SE) a z-score (t-test in
LISREL) can be used to test for significance of the path coefficient:
84
z = ωi/seωi
Indirect effects are interpreted in the same way as direct effects and are figured as the
product of a series of direct effects where one variable acts upon another through a third.
Total effects represent the sum of direct and indirect effects of one variable on another.
Model Modification
An understanding of both the model fit and the path coefficients allows the
researcher to make modifications as necessary. The most common way to improve
model fit is to relax restrictions. A restriction occurs when a model assumes that no
direct effect exists between two variables (ω = 0). When a recursive specified model has
zero restrictions, it is assumed to be just-identified, and the data and model will have
perfect fit. However most models are created to help the researcher better explain how
predictor variables affect the criterion variable, and as such, models that are just as
complicated as the data themselves do not allow for increased understanding. When
comparing two models with approximately equal fit, Kline (2005) suggests that the
simplest model, or the one with the greatest number of restrictions, should be selected.
Structural equation modeling (SEM) has significant advantages over its traditional
statistical components, factor analysis and path analysis. As parameters are
simultaneously estimated for both the measurement (confirmatory factor analysis) and the
structural (path analysis) models, all parameter estimates are relative to the other
observed variables and the model as a whole. It also allows the researcher to test not only
the fit between the data and the model, but to examine the summative effects of variables
as they relate to one another.
85
There are a number of commercially available statistical programs for SEM.
LISREL, is a statistical software package that allows for analysis of data and performs
SEM functions, as well as a variety of other advanced statistical methods such as
generalized linear modeling and hierarchical linear and non-linear modeling (Joreskog &
Sorbum, 1996). Despite the complexity of analysis allowed by LISREL, it is userfriendly for relatively simple, straightforward modeling in that it allows for model
specification in four different ways; 1) use of matrix algebra, 2) use of LISREL command
syntaxes, 3) use of SIMPLIS command syntaxes, or 4) by drawing the model using a path
diagram function (Kline, 2005). In addition, many of the published resources for
exploring SEM assume the use of LISREL, thus a number of references and examples for
use and application are available. LISREL 8.80 is bundled with PRELIS 2.0. This
software allows for data analysis, data cleaning, and data imputation. Despite recent
advances in statistical modeling that allow for the use of sample weights to account for
both nested data and oversampling, LISREL is limited in that these weights may only be
applied to continuous variables.
Procedures
Data analysis using Structural Equation Modeling is a multi-step process. The
following is a step-by-step outline that details the process used by the researcher to
complete the analysis.
1.
Using the Electronic Code Book (ECB), variables are selected for
extraction. The ECB program will provide SPSS syntax to allow for
extraction of the data.
86
2.
Using SPSS 14.0 data are extracted from the restricted data files and
recoded.
3.
SPSS 14.0 is used to compute and report demographic data.
4.
The data file created in SPSS is then imported into PRELIS for data
imputation.
5.
PRELIS uses the Expectation-Maximization algorithm (EM): logLi = Ci –
½ log│Σ│ - ½ (xi – μi)΄Σi-1 (xi – μi) to impute missing data. The complete
data set is then stored as a new data file.
6.
PRELIS is then used to create the asymptotic covariance matrix
7.
Using SIMPLIS command syntax the model is then specified in LISREL.
8.
Using PRELIS data and matrix files created in steps 5 & 6, LISREL will
estimate the parameters and provide model fit statistics.
9.
If model modification is necessary, steps 7 & 8 will be repeated.
10.
If the model fit or parameter estimates remain undesirable, alternative
models may need to be considered. This will also require steps 7 & 8 to
be repeated.
11.
Finally, descriptive statistics for each model must be computed and
reported in the results chapter.
Summary
This chapter has presented to the reader an overview of the research questions, a
review of the methods used by NCES to gather data for NPSAS: 96 and BPS: 96/98, the
conceptual model as operationalized using BPS: 96/98 data, a summary of the statistical
methods used in structural equation modeling, and a step-by-step outline of the analysis
87
process. This study is intended to contribute to the small but growing literature on
student persistence at two-year institutions. By examining the nontraditional
undergraduate student attrition model using data that represents a national cross-section,
this study explores a gap in the current literature. In addition, Bean and Metzner (1985)
suggested that the overall understanding of nontraditional student persistence would
continue to be advanced by increasing understanding of the indirect effects of student
background and environmental pull variables on student persistence. As such, the use of
SEM, achieves this end and adds to the already existing literature.
88
CHAPTER FOUR
RESULTS
This study’s intention was to test the theoretical underpinnings of the Bean and
Metzner (1985) model using national level data drawn from the Beginning Postsecondary
Students Longitudinal Study 1996-98 (BPS: 96/98). The results of the study are presented in
this chapter in three parts. The first part includes descriptive statistics of the sample and/or
population of interest. The second part presents the basic findings of the measurement and
structural models including information on model goodness of fit. The final portion is a
presentation of the main findings organized around the six research questions previously
presented.
Descriptive Statistics
Descriptive statistical analyses of the weighted population estimates were completed
using the Data Analysis System (DAS) available through the National Centers for Education
Statistics website, while descriptive data for only the respondents included in the sample
were estimated using SPSS 14.0. As described in the previous chapter, all data are secondary
data that were gathered originally in a variety of ways including phone interviews, personal
interviews, and abbreviated interviews. This differing methodology combined with the
ability to refuse specific questions or simply answer “don’t know” lends to wide variation of
missing data among the predictor variables. The percent missing for each are presented in
table 4.1. Six variables – gender, race, socioeconomic diversity index, diploma, declaring a
major, and dependent children – had less than 2% missing values. The two work related
variables, working full-time and working off campus, had the highest percent of missing
data: 38.1% and 23.6% respectively. Missing data for the remaining nine variables ranged
from 6.2% - 19.6%.
89
Table 4.1
Missing Data for Predictor Variables
Constructs
Variables
Background
Age at First Enrollment
Gender
Race/Ethnicity
Socioeconomic Diversity Index
Received Diploma or Passed GED
Educational Goal
Enrollment Status
Student Experiences
Academic Integration
Social Integration
Declared a Major
Environmental Pull
Dependent Children
Work Off Campus
Full-Time Employment
Unmet Financial Need Index
Academic Performance
Cumulative GPA – First Year
Student Satisfaction
Overall Satisfaction – 1st Institution
Respondents (n ≈ 1500)
Percentage Missing
17.0%
0.0%
0.0%
1.1%
0.0%
9.6%
19.6%
13.3%
13.3%
0.1%
1.7%
23.6%
38.1%
6.2%
17.6%
13.7%
The sample is comprised of approximately 1500 First-Time Beginners (FTB’s)
who began their college careers during the 1995-96 academic year. Complete data were
available for only three variables prior to imputation; gender, race, and having received a
diploma. The distribution based on the valid responses for all variables are reported in
Table 4.2. The gender distribution includes approximately 700 men (45.8%) and 800
women (54.8%). White, non-Hispanics accounted for 67.8% (n ≈ 1000) of the sample,
90
Black non-Hispanic 13.6% (n ≈ 200), Hispanic 14.0% (n ≈ 200), Asian/Pacific
Islander 3.4% (n ≈ 50), American Indian/Alaskan Native 0.8% (< = 20), and other 0.4%
(< = 10). The vast majority of the sample, 89.2% (n ≈ 1300), reported receiving a
traditional high school diploma, while only 7.3% (n ≈ 100) reported passing the General
Education Development Test (GED). The remaining 3.5% (n ≈ 50) had no diploma, had
not passed the GED, or did not answer the question.
Table 4.2
Demographic Characteristics of Respondents
Demographic Characteristics
Age at First Enrollment (age)
Traditional
Slightly Nontraditional
Moderately Nontraditional
Highly Nontraditional
Gender (gender)
Male
Female
Race/Ethnicity (race)
White, non-Hispanic
Black, non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaskan Native
Other
Socioeconomic Diversity Index (ses)
Not Disadvantaged
Minimally Disadvantaged
Moderately or Highly Disadvantaged
Received Diploma or Passed GED (diploma)
Received Diploma
Passed GED
Other
Respondents
Approximate
Number of Valid
Percent
Responses
1200
67.8%
12.9%
7.0%
12.3%
1500
45.8%
54.2%
1500
67.8%
13.6%
14.0%
3.4%
0.8%
0.4%
1500
38.7%
42.7%
18.6%
1500
89.2%
7.3%
3.5%
91
Table 4.2 (continued)
Demographic Characteristics of Respondents
Demographic Characteristics
Educational Goals (edgoals)
No Degree/No Certificate
Certificate Only
Associate’s Degree
Bachelor’s Degree
Master’s Degree
Doctorate/First Professional Degree
Enrollment Status (enroll)
Full-Time
Part-Time
Academic Integration (academic)
Little/No Integration
Moderately Integrated
Highly Integrated
Social Integration (social)
Little/No Integration
Moderately Integration
Highly Integrated
Declared a Major (decmajor)
Dependent Children (family)
Work Off-Campus (offcamp)
Employed Full-Time (fulltime)
Unmet Financial Need Index (needcat)
Low Need
Moderate Need
Moderately High Need
Very High Need
Cumulative GPA – First Year (gpa)
“F”
“D”
“C”
“B”
“A”
Respondents
Approximate
Number of Valid
Percent
Responses
1400
16.5%
4.2%
10.7%
35.6%
24.8%
8.2%
1200
55.6%
44.4%
1300
57.0%
35.0%
8.0%
1300
84.0%
13.7%
2.2%
1500
74.6%
1500
19.6%
1100
96.4%
900
35.1%
1400
52.1%
23.0%
17.4%
7.4%
1400
20.9%
12.7%
24.1%
22.2%
20.1%
92
Table 4.2 (continued)
Demographic Characteristics of Respondents
Demographic Characteristics
Overall Satisfaction – First Institution
(satisfac)
Very Unsatisfied
Somewhat Unsatisfied
Neutral
Somewhat Satisfied
Very Satisfied
Persisted (persist)
Respondents
Approximate
Number of Valid
Percent
Responses
1300
1200
0.8%
1.9%
6.9%
27.8%
62.8%
57.1%
Demographic statistics for all imputed data are reported in Table 4.3. Due to
complete data, the distribution of the background characteristics age, gender, and
receiving a diploma remained unchanged. Data were imputed for the following student
background characteristics; age, socioeconomic status, educational goals, and enrollment
status. Most respondents in the sample, 66.3% (n ≈ 1000), were of traditional college age
upon entry in fall of 1995. Those defined as slightly nontraditional accounted for 14.2%
of the sample, while moderately nontraditional and highly nontraditional students
accounted for 9.2% and 10.3% of respondents respectively. Only 18.4% of the
respondents (n ≈ 300) were highly/moderately economically disadvantaged, while 43.2%
were minimally economically disadvantaged, and 38.5% were not disadvantaged. More
than half (57.5%) of respondents were enrolled full-time, and 69.5% of respondents (n ≈
1000) aspired to completion of a bachelor’s degree or higher.
93
Table 4.3
Demographic Characteristics of Respondents (Imputed Data)
Demographic Characteristics
Age at First Enrollment (age)
Traditional
Slightly Nontraditional
Moderately Nontraditional
Highly Nontraditional
Gender (gender)
Male
Female
Race/Ethnicity (race)
White, non-Hispanic
Black, non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaskan Native
Other
Socioeconomic Diversity Index (ses)
Not Disadvantaged
Minimally Disadvantaged
Moderately or Highly Disadvantaged
Received Diploma or Passed GED (diploma)
Received Diploma
Passed GED
Other
Educational Goals (edgoals)
No Degree/No Certificate
Certificate Only
Associate’s Degree
Bachelor’s Degree
Master’s Degree
Doctorate/First Professional Degree
Enrollment Status (enroll)
Full-Time
Part-Time
Academic Integration (academic)
Little/No Integration
Moderately Integrated
Highly Integrated
Respondents (n ≈ 1500)
Percent
66.3%
14.2%
9.2%
10.3%
45.8%
54.2%
67.8%
13.6%
14.0%
3.5%
0.8%
0.4%
38.5%
43.2%
18.4%
89.2%
7.3%
3.5%
14.9%
3.8%
11.8%
39.7%
22.4%
7.4%
57.5%
42.5%
56.2%
36.9%
6.9%
94
Table 4.3 (continued)
Demographic Characteristics of Respondents (Imputed Data)
Demographic Characteristics
Social Integration (social)
Little/No Integration
Moderately Integration
Highly Integrated
Declared a Major (decmajor)
Dependent Children (family)
Work Off-Campus (offcamp)
Employed Full-Time (fulltime)
Unmet Financial Need Index (needcat)
Low Need
Moderate Need
Moderately High Need
Very High Need
Cumulative GPA – First Year (gpa)
“F”
“D”
“C”
“B”
“A”
Overall Satisfaction – First Institution
(satisfac)
Very Unsatisfied
Somewhat Unsatisfied
Neutral
Somewhat Satisfied
Very Satisfied
Persisted (persist)
Respondents (n ≈ 1500)
Percent
86.2%
11.9%
1.9%
74.6%
19.9%
97.3%
39.1%
50.5%
26.0%
16.5%
7.0%
19.3%
13.0%
27.3%
21.7%
18.7%
0.7%
1.6%
5.9%
31.2%
60.6%
58.6%
Three observed variables are used to measure the construct Student Experiences;
having declared a major, level of academic integration, and level of social integration.
Though 74.6% of respondents had indicated declaring a major, only 43.8% reported high
or moderate levels of academic integration. The majority of respondents, 56.2% (n ≈
800) reported little or no academic integration. An even higher number of respondents,
95
86.2% (n ≈ 1300), reported little or no social integration. Despite low index scores
for both academic and social integration, 60.6% of respondents (n ≈ 900) reported being
very satisfied with the first college attended, a single indicator for the separate construct
Satisfaction, while only 2.3% (n < 50) expressed any level of dissatisfaction.
The construct Environmental Pull is shaped by four variables; having dependent
children, working off-campus, working full-time, and the level of unmet financial need.
Nearly one in five (19.9%) respondents had dependent children. Almost all respondents
(97.3%) reporting being employed off-campus, while 39.1% (n ≈ 600) reported being
employed full-time. Slightly more than half (50.5%) of all respondents had low levels of
unmet financial need. These students owed less than $500 per year in out of pocket
expenses. Students having between $500 and $2500 of out of pocket expenses per
academic year (moderate need) accounted for 26.0% of respondents (n ≈ 400) while those
with moderately high and very high levels of unmet need accounted for 16.5% and 7.0%
of respondents respectively.
As previously discussed, the construct, Satisfaction, had only a single indicator.
Two additional constructs also had single indicators, Academic Performance and
Persistence. Academic performance was measured only by college reported GPA which
was then scaled and labeled A, B, C, D, or F. These values accounted for 18.7%, 21.7%,
27.3%, 13.0%, and 19.3% of respondents respectively. Means, medians and standard
deviations are reported along with the correlations in table 4.4. The indicator for
academic performance was normally distributed with a mean score of 3.07 and standard
deviation of 1.36. Finally, persisters, or students who had received a certificate, an
96
associate’s degree or continued to be enrolled, accounted for 58.6% (n ≈ 900) of all
respondents.
The correlation matrix identifies only one relationship as having a large effect size
(r < .5); age and having dependent children (r = -.613). Recalling that increasing age
equates to a higher score, while having children was coded 1 and not having children 2,
the large, negative correlation suggests that as age increases, respondents are far more
likely to indicate having dependent children. Three relationships have a medium effect
size (r > .30). The first, age at first enrollment and enrollment status (r = .308), suggests
that age increases the likelihood of part-time enrollment. The second, academic
integration and social integration (r = .306), suggests that these two indices have a
positive relationship. The third, full-time employment and persistence (r = -.360),
indicates a negative relationship suggesting that students who also work full-time were
less likely to have received a degree or certificate or to be enrolled three years after
beginning postsecondary education.
Within the construct Background, age was significantly correlated with all other
construct measures with the exception of race, while socioeconomic status (SES) was
significantly correlated with all variables except enrollment status. Additionally, five of
the observed variables from this construct were significantly related to other predictor
variables from different constructs at a level greater than r = .20; age and full-time
enrollment (r = -.227), receiving a diploma and having dependent children (r = -.204),
SES and dependent children ( r = -.239), SES and unmet financial need (r = .273),
educational goals and academic integration (r = .207), enrollment status and academic
integration (r = -.247), enrollment status and having declared a major (r = .212),
97
Table 4.4
Intercorrelations of Observed Variables
Observed Variables
1
2
3
1. Age at First Enrollment
2. Gender
3. Race/Ethnicity
4. Socioeconomic Diversity
Index
5. Received Diploma or
Passed GED
6. Educational Goal
7. Enrollment Status
8. Academic Integration
9. Social Integration
10. Declared a Major
11. Dependent Children
12. Work Off Campus
13. Full-Time Employment
14. Unmet Financial Need
Index
15. Cumulative GPA – First
Year
16. Overall Satisfaction –1st
Institution
17. Persisted
M
Mdn
SD
--
.113**
--
-.043
.007
--
* p <.05. ** p < .01. *** p <.001.
4
5
6
Respondents (n = 1503)
.208**
.237**
-.255**
.102**
.019
-.010
.239**
-.030
.000
--
1.63
1
1.3
1.54
2
.50
1.57
1
.95
1.80
2
.73
7
8
9
.308**
.040
.084**
-.143**
-.020
.027
-.175**
-.072**
.035
.086**
-.106**
-.005
-.016
-.067**
--
-.088**
.123**
.006
-.047
--
-.147**
--
.207**
-.247**
--
.136**
-.123**
.306**
--
3.73
4
1.44
1.43
1
.50
1.51
1
.62
1.16
1
.41
1.14
1
.44
98
Table 4.4 (continued)
Intercorrelations of Observed Variables
1. Age at First Enrollment
2. Gender
3. Race/Ethnicity
4. Socioeconomic
Diversity Index
5. Received Diploma or
Passed GED
6. Educational Goal
7. Enrollment Status
8. Academic Integration
9. Social Integration
10. Declared a Major
11. Dependent Children
12. Work Off Campus
13. Full-Time Employment
14. Unmet Financial Need
Index
15. Cumulative GPA –
First Year
16. Overall Satisfaction –
1st Institution
17. Persisted
M
Mdn
SD
* p <.05. ** p < .01. *** p <.001.
10
11
12
-.038
-.061*
.005
-.613**
-.154**
-.013
-.048
.039
.041
-.112**
-.239**
.103**
-.094**
.273**
-.040
-.204**
.002
-.139**
.003
.212**
-.118**
.043
--
.142**
-.209**
.096**
.125**
.080**
--
.077**
-.028
.100**
.153**
.005
-.019
--
.172**
-.247**
.210**
.127**
.007
.254**
.075**
--
1.25
1
.44
1.80
2
.40
1.03
1
.16
13
14
15
Respondents (n = 1503)
-.227**
.000
.197**
.120**
.112**
.079**
.078**
.179**
-.143**
1.61
2
.49
16
17
.082**
-.009
-.096**
.209**
.057*
.013
-.004
.006
.115**
.048
-.020
-.008
.119**
.014
-.184**
.083**
-.016
-.103**
-.150**
-.021
.039
.057*
-.045
.110**
-.003
-.092**
-.116**
.021
.178**
-.083**
-.015
-.015
.008
.002
-.045
-.013
.118**
-.205**
.182**
-.221**
-.121**
.000
-.150**
-.033
-.360**
--
.052*
-.020
.001
--
.180**
-.268**
--
-.099**
4.50
5
.74
-1.41
1
.49
1.80
1
.95
3.07
3
1.36
99
enrollment status and having dependent children (r = -.209), and finally enrollment status
and full-time employment (r = -.247).
Within the construct Experiences, academic and social integration have a
statistically significant correlation (r = .306), as does academic integration and having
declared a major (r = -.118). This does not hold true for social integration and having
declared a major. Only academic integration was significantly correlated with other
variables outside the construct; educational goals (r = .207), enrollment status (r = -.247),
and full-time employment (r = -.210).
The construct Environmental Pull, has two notable statistically significant
relationships. First, a direct relationship appears to exist between having dependent
children and working full-time (r = .254). Secondly the negative relationship between
having dependent children and unmet financial need (r = -.150) suggests that those with
dependent children were less likely to have high levels of unmet need. All significant
correlations with observed variables from outside the construct have been noted in the
previous paragraphs.
Cumulative GPA and overall satisfaction with first institution both had a number
of statistically significant correlations with other variables, but all had a small effect size
(r < .20). The single criterion variable, persistence, also demonstrated a number of
statistically significant correlations with the observed variables, but only five at a level
greater than r = .20: age (r = .209), educational goals (r = -.205), academic integration (r
= -.221), full-time employment (r = -.360), and GPA (r = -.268)
Overall, all correlations were within the expected range and demonstrated
anticipated directionality. The correlation matrix was also used to examine the observed
100
variables for possible multicollinearity. Kline (2005) suggests that researchers might
reasonably screen for multicollinearity by looking for unusually high correlations (r >
.85). As the highest correlation was -.613, one can reasonably assume that redundancy
due to multicollinearity does not exist for any single pair of observed variables.
Measurement Model
Using a two-step structural equation modeling (SEM) process the Bean and
Metzner (1985) nontraditional undergraduate student attrition model was
operationalized as described in the previous chapter. Results for the first step, the
measurement model, are presented in Table 4.5. In its most basic form, the testing of the
measurement model can be seen as analogous to a confirmatory factor analysis. Using an
asymptotic covariance matrix and weighted least squares estimation method, LISREL
estimates the chi-square statistic, other goodness-of-fit statistics, factor loadings, standard
errors, and the amount of variance explained by each variable (uniqueness).
In evaluating any portion of the SEM model, including both the measurement and
structural models, it is important to examine first the goodness-of-fit statistics. Standing
in sharp contrast to the typical use of χ2, good model fit is confirmed when χ2 is not
significant for the given degrees of freedom. Even when significance does exist, a model
may also be considered a good fit when χ2 < 2df (Stahl & Pavel, 1992). For the above
measurement model χ2 = 41.60 (p = .999) when df = 74 suggests that the sample data fit
well with the hypothesized model.
Model fit should not be evaluated by the χ2 statistic alone. A number of
alternative model fit indices have been developed. The root mean square error of
approximation (RMSEA) suggests increasingly poor fit with higher values. Values < .05
101
indicate close approximate fit. The root mean square residual or RMR is like RMSEA in
that higher values represent increasingly poor model to data fit.
Table 4.5
Measurement Model for Latent Constructs
Construct
Observed Variable
Background
Age
Gender
Race
Socioeconomic Diversity Index
Received Diploma or Passed GED
Educational Goals
Enrollment Status
Experiences
Academic Integration
Social Integration
Declared a Major
Environmental Pull
Dependent Children
Work Off-Campus
Employed Full-Time
Unmet Financial Need Index
Unstandardized
Factor Loading
SE
Uniqueness
.67**
.14*
.35**
.42***
.19
-.46
.09
.08
.06
.12
.05
.12
.32
.06
.43
.07
.14
.33
.20
.11
.03
.16*
.32***
.04
.09
.16
.11
.08
.81
.01
.35***
.01
.15*
-.50***
.04
.13
.06
.09
.76
.00
.10
.28
Goodness of Fits Statistics for Measurement Model
74
df
2
41.60+
χ
RMSEA
.00
RMR
.066
GFI
.98
AGFI
.98
* p <.05. ** p < .01. *** p <.001. + p>.05.
The final two measures, the Goodness-of-Fit index (GFI) and the Adjusted Goodness-ofFit index (AGFI) should approach 1.0 with values >.95 suggesting a close fit.
102
Once a model has been deemed to be a good fit to the data, the factor loadings and
unique contribution can be considered for each observed variable. The construct
Background is measured using seven observed variables. Four of the seven have
significant factor loadings: age = .67, gender = .14, race = .35, and ses = .42. Of the
three observed variables for Experiences, only two, academic integration (academic =
.16) and social integration (social = .32), had significant factor loadings. Finally three
observed variables for the construct Environmental Pull had significant factor loadings:
family = .35, fulltime = .15 and needcat = -.50.
Structural Model
In two-stage SEM analysis, the fit of the observed variables as measurements of
the construct are first tested. Once this is completed, the structural model can then be
evaluated. Results of the SEM analysis of the structural model are presented here in
Table 4.6 and include factor loadings, standard errors, unique variances, and model fit
statistics. Analysis of the structural model is presented in this section within the context
of the six research questions identified in the previous chapters.
Model Analysis
Question One: Does the hypothesized model fit the observed data?
The structural model, like the measurement model appears to have good fit with
the data. For a model with 112 degrees of freedom a χ2 = 59.20 (p = 1.00) is not
significant. RMSEA of 0.0 is < .05 and both the GFI (.98) and the AGFI (.97) are greater
than .95. As stated before, SEM in its most simple form is a simultaneous factor analysis
and path analysis. This means that all model parameters are estimated with respect to all
other specified parameters in the model. For this reason, factor loadings, standard errors,
103
and measures of uniqueness related to the constructs, Background, Experiences, and
Environmental Pull may differ slightly from those expressed in the measurement model.
Most notable of these changes involves both a decrease in the value of the factor loading
of social integration on Experiences as well as the loss of significance. Conversely,
student enrollment status (enroll) now has a significant factor loading of .11 (p < .05).
Table 4.6
Structural Model Analysis
Construct
Observed Variable
Background
age
gender
race
ses
diploma
edgoals
enroll
Experiences
academic
social
decmajor
Environmental Pull
family
offcamp
fulltime
needcat
Student Satisfaction
satisfac
Academic Performance
gpa
Unstandardized
Factor Loading
SE
Uniqueness
.72***
.12*
.35**
.40***
.22
-.47
.11*
.07
.06
.18
.04
.18
.31
.06
.50
.06
.14
.40
.25
.11
.05
.53
.13
-.04
.31
.10
.73
.10
.01
.33
-.02
.15**
-.49***
.10
.06
.09
.69
.02
.10
.26
.74
-
-
1.37
-
-
Goodness of Fits Statistics for Measurement Model
112
df
59.20+
χ2
RMSEA
0.0
GFI
.98
AGFI
.97
AIC
141.20
* p <.05. ** p < .01. *** p <.001. + p>.05.
104
It is also important to note that the variables academic and family, both previously
noted as significant measurements for their respective construct are absent a value for
standard error. In SEM, endogenous or y-variables, often have one observed variable that
is used to scale the measurement of the construct. This variable, known as the reference
variable, is often assumed significant and is reported without the standard error (Kline,
2005). In addition, the single variable predictors for Student Satisfaction (satisfac) and
Academic Performance (gpa) are reported without reference both to standard error and
unique variance explained. Schumacher and Lomax (2004) recommend when using a
single indicator for a construct that the error variance for the observed variable be set to
zero. Though it is unlikely within the social sciences that any variable is measured
without error, failure to do this will likely result in convergence problems for the SEM
software.
Question Two: Do student background characteristics directly or indirectly affect
persistence?
Once the model has been found to have good fit with the data, it is then necessary
to examine the significance of the relationships between the latent constructs. These data
are represented here in two ways. First, Figure 4.1 presents the graphical representation
of the relationships among constructs. Significant relationships are represented by a bold
arrow. The absence of the bold arrow between Background and Persistence suggests that
Background does not have direct effects upon the outcome of student persistence. Table
4.7 presents the total effect decomposition. This includes the factor loading and standard
error of the direct, indirect and total effects of the constructs. Though the direct and
105
indirect effects of the background variables on persistence are not significant, the total
effects appear to be (.32, SE = .12, p < .01). This is important as many of these variables
by themselves have been shown to be significant predictors of persistence. This may
indicate that the path(s) between Background and other latent constructs may not be
correctly specified.
Question Three: Does academic performance as measured by college GPA directly
affect student persistence?
Consistent with previous research (Chen & Thomas, 2001; Boughan & Clagett,
1995; Cabrera, Nora & Castaneda, 1993; Goel, 2002; Milem & Berger, 1997; Pascarella
& Chapman, 1983; Sandler, 2000; Titus, 2004), Academic Performance as measured by
college GPA does indeed have a significant, positive, and direct effect (-0.22, SE = .10, p
<.05) on student persistence. In this study, academic performance was coded so that
those having the highest GPA had a correspondingly high categorical ranking, while
persisters and non-persisters were coded 1and 2 respectively. Thus here, the negative,
direct effect actually suggests a positive relationship.
Question Four: Do student experiences have an indirect effect on student persistence?
The construct, Student Experiences, was measured here using three observed
variables; academic integration, social integration, and having declared a major. The
construct Student Experiences was hypothesized to have indirect effects on student
persistence through two other constructs; Student Satisfaction and Academic
Performance. However, student experiences do not appear to have direct effects on either
of these two constructs, nor do they have indirect effects on overall student persistence.
106
Note. Bold lines are significant. BNKGRND to ENVPULL p <.01. ACAD to PER p <.05
Figure 4.1: Analysis of Structural Model
.
107
This stands in stark contrast to much of the previous research. Both measures of student
integration, academic and social, are often cited in the literature as significant predictors
of persistence among students attending four-year colleges (Berger & Braxton, 1998;
Braxton, Milem & Sullivan, 2000; Donavan,1984; Eaton & Bean, 1985; Mallette &
Cabrera; Metzner, 1989; Metzner & Bean, 1987; Pascarella & Terenzini, 1991).
Additionally, a number of authors (Goel, 2002; Halpin, 1990; Napoli & Wortman, 1998;
Williamson and Creamer, 1988) have found academic integration to be of particular
importance in predicting two-year college persistence.
Question Five: Do environmental pull factors have a direct and/or indirect effect on
student persistence?
Environmental Pull as a separate and distinct construct is one characteristic about
the Bean and Metzner (1985) nontraditional undergraduate student attrition model that
makes it unique when compared with previous models. Variables such as having a
family, working full-time, or simply not being able to meet ones out of pocket expenses
can create barriers to attendance. Believing that these variables are not necessarily
related to one’s educational commitment or goals, Bean and Metzner posited a direct and
negative effect on student persistence. However, the results of this analysis do not
confirm these direct effects. Additionally, indirect effects (the product of two factor
loadings) acting through Student Satisfaction (0.15) or Academic Performance (0.10)
were also found not to be significant. These results only add to the confusion from
previous studies. Metzner and Bean (1987) testing this model using regression analysis
found that environmental factors did not directly affect student persistence,
recommending however that future work focus on indirect effects. More recently, Horn
108
Table 4.7
Effect Decomposition for Structural Model
Constructs
Background
Factor
Loading
SE
Experiences
Direct Effect
-1.67
2.86
Indirect Effects
1.56
2.87
Total Effects
-0.12
.08
Environmental Pull
Direct Effects
-0.97*** .10
Indirect Effects
--Total Effects
-0.97*** .10
Academic Performance
Direct Effects
0.10
1.09
Indirect Effects
-0.01
.03
Total Effects
.08
.09
Student Satisfaction
Direct Effects
--Indirect Effects
.01
.03
Total Effects
.01
.03
Persistence
Direct Effects
1.43
1.37
Indirect Effects
-1.11
1.36
Total Effects
.32**
.12
* p <.05. ** p < .01. *** p <.001. + p>.05.
Experiences
Factor
SE
Loading
Environmental Pull
Factor
Loading
SE
Academic
Student
Performance Satisfaction
Factor
Factor
SE
SE
Loading
Loading
----
----
-1.61
--1.61
2.91
-2.91
----
----
----
----
----
----
----
----
----
----
----
----
0.10
-0.10
.24
-.24
--0.15
-0.15
-.47
.47
----
----
----
----
-0.07
--0.07
.21
-.21
-0.10
0.10
-.38
.38
----
----
----
----
--0.02
-0.02
-.05
.05
1.13
0.04
1.16
1.39
.10
1.40
-0.22*
--0.22*
.10
-.10
0.01
-0.01
.19
-.19
109
(1996) and Zhai, Monzon, and Grimes (2005) found that several of the variables used to
operationalize this construct were indeed predictors of student persistence. Conversely,
Stahl and Pavel (1992), while testing a variation of this model, found that their construct
Academic Interference had direct and positive effects on student persistence.
Question Six: Does student satisfaction directly affect student persistence?
Titus (2004), using BPS: 96/98 to test and operationalize a portion of the Bean
(1990) student attrition model on students enrolled at four-year institutions, found
satisfaction to be highly correlated with student persistence. However, student
satisfaction as measured in this analysis does not have direct effects on student
persistence among those enrolled at two-year colleges. The low standard error (SE = .10)
along with the small factor loading (0.01) suggests that a student’s decision to continue in
postsecondary education is independent from one’s level of overall satisfaction with
one’s first institution as measured in this study. One should note however, that less than
3% of respondents (Table 4.3) reported being dissatisfied with the first institution, while
more than 92% were somewhat or highly satisfied. Considering such a high level of
satisfaction and quite different results as compared with previous work regarding the
effect of satisfaction on student persistence, researchers must consider the possibility that
constructs have different levels of impact and may need to be measured differently for
students attending two-year colleges as compared with their four-year peers.
Analysis of structural equation models can be quite difficult. As an a priori
process, it is important that researchers choosing to use SEM be sure that models are
correctly specified and well grounded in previous research (Kline, 2005). The
simultaneous estimation of all parameters means that poor model specification can lead to
110
improperly estimated parameters (Schumacker & Lomax, 2004). For this reason, models
that fit the data may still have very poorly specified relationships. In this study, the
model fits the data well, however very few significant relationships are evident. Only the
direct effect of college GPA on student persistence was demonstrated. All other
hypothesized direct and indirect effects were found not to be significant. Additionally,
total effects of Background on Persistence (0.32, SE = .12, p < .01) were significant while
neither the direct nor indirect effects were found to be so. This suggests that alternative
models should be explored, paying particular attention to the relationships between the
construct Background and other latent constructs. The following section details the
model modification process.
Model Modification
A number of authors (Kaplan, 2000; Kline, 2005; Schumacker & Lomax ,2004)
have cautioned that good fit does not indicate that all parameters and relationships are
specified correctly. In fact, it is quite possible that a model may have good fit while a
number of parameters fail to demonstrate significance. When this occurs, it is often
necessary to either make modifications to the hypothesized model or to test alternative
models.
According to Bollen (1989), Kaplan (2000), and Kline (2005) model modification
usually involves freeing restrictions placed upon the model to improve fit. Restrictions or
fixed parameters are simply hypothesized relationships among constructs believed to be
zero. In model diagrams a fixed parameter is most often represented by the lack of an
arrow connecting two constructs. An example in this study (Figure 4.1) involves the
111
construct Background which is believed not to have direct effects on student satisfaction;
as a result, it is fixed to zero.
LISREL output includes an index that suggests modifications that may improve
overall model fit. Suggested modifications include adding additional paths or
covariances between parameters. This process was alluded to previously and is known as
freeing parameters or freeing restrictions. Researchers using SEM should free only one
parameter at a time (Kline, 2005). Bollen (1989) suggests freeing those parameters that
create the largest potential for change in chi-square, while not violating the major
theoretical constructs of the model. However, due to the good overall fit of this model
with the data, no modifications indices were suggested by LISREL. As the model
modification process is designed to improve overall fit, and not the parameter estimates
or the total variance explained, testing of alternative models must be considered instead.
In most cases, when alternative models are being tested, they are often
conceptualized simultaneously with the hypothetical model prior to the data analysis.
However in this study, the resulting good fit of the hypothetical model along with
relatively non-significant parameter estimates makes such a retrospective model
development quite prudent. Three alternative models were developed and tested (Figure
4.2). Each is a further simplified version of the tested hypothetical model. The
development of these three models represents single-step changes informed both by
theory and analysis of the previous model.
The analysis of the nontraditional undergraduate student attrition model as
conceptualized and operationalized in this study demonstrated that the model had good fit
with the data. However it also demonstrated that even though the total effects of
112
Academic
Performance
Background
Characteristics
Student
Experiences
Satisfaction
Persistence
Environmental
Pull
AL2
Academic
Performance
Background
Characteristics
Student
Experiences
Satisfaction
Persistence
Environmental
Pull
AL3
Academic
Performance
Background
Characteristics
Student
Experiences
Environmental
Pull
Figure 4.2: AL1
Satisfaction
Persistence
113
background variables on persistence were significant, no significant direct or indirect
effects were found. Metzner and Bean (1987) recommended that future research should
pay particular attention to the indirect effects of the background characteristics as they act
through other constructs. The construct Background was originally believed to have
significant direct effects on student experiences, environmental influences, academic
performance, and student persistence. Working from the previously tested model
Alternate Model one (AL1) represents a fixed relationship of zero – no direct relationship
– between Background and Academic Performance. Alternate Model two (AL2)
represents an additional fixed relationship between Background and Experiences, and
Alternate Model three (AL3) an additional fixed relationship between Background and
Persistence. Table 4.8 compares a number of fit indices for the originally hypothesized
model and the three alternative models.
Table 4.8
Fit Indices for Model Comparison
df
χ2
RMSEA
GFI
AGFI
AIC
Hypothesized Model
112
59.20
(p = 1.00)
0.0
.98
.97
141.20
AL1
113
60.11
(p = 1.00)
0.0
.98
.97
140.11
AL2
114
61.98
(p = 1.00)
0.0
.98
.97
139.98
AL3
115
62.08
(p = 1.00)
0.0
.98
.97
138.08
Model
114
All three models seem to fit the data well. Each has a non-significant chi-square,
an RMSEA of < .05 (0.0), and GFI and AGFI values > .95 (.98 & .97 respectively). This
supports Schumacker and Lomax’s (2004) supposition that a number of models may offer
equally good fit to the same data. Kline (2005) suggests that when choosing between
multiple models of equal fit, the Akaike Information Criterion (AIC) can be used to
compare models. Most often the lowest score represents the best overall fit. Following
this logic, the AL3 model has the best overall fit.
In addition to overall fit, the AL3 model is also the least complex model tested.
Kline (2005) calls this the parsimony principle stating, “given two different models with
similar explanatory power for the same data, the simpler model is to be preferred” (p.
136). Factor loadings, standard errors and uniqueness for the AL3 structural model are
presented in Table 4.9. The restricted model values represent only slight changes from
the original structural model, however, gender ceases to be a significant background
variable, while having a high school diploma becomes significant.
Effect decompositions for the AL3 model are presented in Table 4.10.
Examination of the direct, indirect, and total effects of the constructs on one another
reveals some distinct differences from the first model tested. First, the abnormally large
standard errors for the direct and total effects of Environmental Pull on Experiences and
the direct and indirect effects of Background on Persistence have been replaced by more
acceptable numbers. Second, the construct, Background, in addition to significant total
effects, has significant indirect effects on Persistence through Environmental Pull.
Finally, the construct Environmental Pull has direct, negative effects on student
persistence.
115
The final structural model for AL3 can be expressed:
PER = - 0.37(ENVPULL) - 0.23(ACAD) + 0.046(SAT), R² = 0.19
where R2 is the percent of variance in the construct Persistence explained by this model.
These results compare favorably to work by Zhai, Monzon and Grimes (2005). Testing
the nontraditional undergraduate student attrition model using path analysis (latent
constructs were not used) they found the model to explain 16% of the overall variance in
Table 4.9
Structural Model Analysis – AL3
Construct
Observed Variable
Background
age
gender
race
ses
diploma
edgoals
enroll
Experiences
academic
social
decmajor
Environmental Pull
family
offcamp
fulltime
needcat
Student Satisfaction
satisfac
Academic Performance
gpa
* p <.05. ** p < .01. *** p <.001. + p>.05.
Unstandardized
Factor Loading
SE
Uniqueness
.71***
.11
.38**
.42***
.25*
-.38
.13*
.07
.06
.12
.05
.11
.30
.05
.48
.05
.16
.33
.33
.11
.06
.53
.11
-.04
.42
.15
.89
.08
.01
.33
-.02
.20**
-.49***
.10
.06
.10
.75
.01
.16
.26
.74
-
-
1.36
-
-
116
Table 4.10
Effect Decomposition for AL3
Constructs
Background
Factor
Loading
SE
Experiences
Direct Effect
--Indirect Effects
--Total Effects
--Environmental Pull
Direct Effects
-0.97*** .10
Indirect Effects
--Total Effects
-0.97*** .10
Academic Performance
Direct Effects
--Indirect Effects
-0.007 .03
Total Effects
-0.007 -03
Student Satisfaction
Direct Effects
--Indirect Effects
.01
.02
Total Effects
.01
.02
Persistence
Direct Effects
--Indirect Effects
.33*** .10
Total Effects
.33*** .10
* p <.05. ** p < .01. *** p <.001. + p>.05.
Experiences
Factor
SE
Loading
Environmental Pull
Factor
Loading
SE
Academic
Student
Performance Satisfaction
Factor
Factor
SE
SE
Loading
Loading
----
----
0.10
-0.10
.07
-.07
----
----
----
----
----
----
----
----
----
----
----
----
0.08
-0.08
.29
-.29
-0.01
0.01
-.03
.03
----
----
----
----
-0.06
--0.06
.27
-.27
--0.01
-0.01
-.03
.03
----
----
----
----
--0.02
-0.02
-.08
.08
-0.37***
0.00
-0.37***
.11
.01
.11
-0.23*
--0.23*
.10
-.10
0.05
-0.05
.19
-.19
117
student persistence. This is far less than the 29% reported by Metzner and Bean (1987)
when first testing the model on older, commuter students attending a four-year residential
campus. However, Zhai, Monzon and Grimes (2005), considering a number of
differences between two-year college students and their four-year peers, found that
adding additional observed variables such as success in developmental education
increased the variance explained to as high as 39%. As it was not the intent of this study
to expand or refine the nontraditional undergraduate student attrition model, but instead
to test its constructs on a national sample of students who were first-time beginners at
two-year colleges, additional variables were not added to the model.
Summary
Bean and Metzner (1985) developed a recursive conceptual model of student
persistence that was ideal for testing using structural equation modeling. They believed
that by capturing variables unique to the nontraditional student that this model would
better serve researchers seeking to understand persistence in an often forgotten yet
significant population. Bean and Metzner defined nontraditional students as those who
were older, attended part-time, and commuted to school. As most two-year colleges are
commuter campuses and serve a diverse student body that includes both older and parttime students, the nontraditional undergraduate student attrition model appears ideal to
study persistence at two-year colleges.
Unique to the Bean and Metzner model is the construct Environmental Pull. This
study demonstrates that when background characteristics such as age, gender, race, SES,
enrollment status, educational goals, and having a high school diploma are believed to
have only indirect effects on student persistence, that environmental pull characteristics
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such as having a family, working full-time, working off-campus, and having high levels
of unmet financial need do directly and negatively impact student persistence.
This study also supports previous research that suggests academic performance as
measured by college GPA is a significant predictor of student persistence (Chen &
Thomas, 2001; Boughan & Clagett, 1995; Cabrera, Nora & Castaneda, 1993; Goel, 2002;
Milem & Berger, 1997; Pascarella & Chapman, 1983; Sandler, 2000; Titus, 2004).
Despite establishing strong relationships between Background, Environmental Pull, and
Academic Performance with student persistence, this study failed to accurately explain a
significant amount of the variance for the constructs Experiences, Academic
Performance, and Satisfaction.
Overall, this model explains well how background characteristics affect
persistence acting through direct barriers to attending college, such as having dependent
children, working full-time, and not being able to meet the financial obligations of going
to school. However, the Bean and Metzner (1985) nontraditional undergraduate student
attrition model considers only student level data. Specifically it does not theorize how or
at what point institutional characteristics, practices and policies may affect individual
student persistence. The implications of this are considered more fully in the final
chapter.
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CHAPTER FIVE
DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE
RESEARCH
Introduction
Each year, more than six million students attend classes at two-year colleges
across the U.S. Like all other students, they bring with them their background, their
financial strains, their outside influences, and their preconceived notions. Unlike their
four-year counterparts, these students are more likely to be older, have dependent
children, be financially independent, work full-time, and be attending part-time. Horn
(1996) described each of these as risk factors that negatively influences student
persistence. In addition, Horn’s work demonstrated that the presence of more than one
risk factor had multiplicative effects. Berkner, Horn, and Clune (2000) reported that
students attending two-year colleges were six times more likely to have four or more risk
factors than their four-year peers.
More than a decade before these reports, Bean and Metzner (1985) recognized the
unique effects of these characteristics. As a result, they theorized the nontraditional
undergraduate student attrition model. Unlike the works of Horn (1996) and Berkner,
Horn, and Clune (2000), which are based on research using national data, Bean and
Metzner (1985) developed this model from an extensive review of persistence literature
and a sound understanding of what makes nontraditional students different from their
more traditional counterparts. However to this date, the nontraditional undergraduate
student attrition model remains relatively untested and unvalidated in the literature.
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Therefore, it was the intent of this study to utilize national level data to operationalize and
test the major constructs of this model.
Discussion
The Bean and Metzner (1985) nontraditional undergraduate attrition model
represents a synthesis of work by Bean (1980, 1982, 1983), Tinto (1975), Donavon
(1984), Munro (1981), Pascarella and Chapman (1983), and Terenzini and Pascarella
(1977). Unique to this model was the construct of Environmental Pull characteristics.
These characteristics, such as having children, number of hours worked, and the ability to
meet financial obligations, were believed by Bean and Metzner (1985) to have direct,
negative influences on student persistence. Using structural equation modeling (SEM)
the direct and indirect effects of one construct upon another could be studied in this
recursive model.
Previous studies testing the Bean and Metzner (1985) model have done so in a
manner that falls short of the original intent of the model. Two studies (Metzner and
Bean, 1987; Zhai, Monzon & Grimes, 2005) used path analysis, which only allows for
the estimation of direct effects, thus ignoring the indirect effects that result from
interaction amongst the variables. Stahl and Pavel (1992) tested the model using SEM,
but did not report factor loadings, variances, or other pertinent information as they found
that the model did not fit the data. Additionally, all three of these analyses used a
convenience sample gathered either from a single institution, or in the case of Zhai,
Monzon and Grimes (2005), a single community college district.
Using structural equation modeling to test a national sample of students who
began postsecondary enrollment in fall of 1995, the intent of this study was to examine
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the ability of the nontraditional undergraduate student attrition model to predict
individual rates of student persistence among those attending two-year colleges. A
discussion of the results follows and is presented around the six research questions
postulated in the first chapter of this work.
Question One: Does the hypothesized model fit the observed data?
In this study, the model as conceptualized and presented in Chapter Three
appeared to fit well with the data. However, despite having a Goodness of Fit (GIF =
.98) and Adjusted Goodness of Fit (AGFI = .97) that approached 1.0, an RMSEA < .05,
and a non-significant chi square, the tested model, explained less than 25% of the
variance among persisters, and demonstrated that only one of the constructs, Academic
Performance, was a significant predictor of persistence. Also, the total effects of
Background on Persistence were significant, while the direct and indirect effects were
not. In addition, unusually large standard errors (> 1.0) were noted in several of the
relationships. Kline (2005) suggests that when using SEM, multicollinearity across
combinations of constructs can exist even when observed variables fail to have high
correlations, creating in effect a “hidden” multicollinearity.
Metzner and Bean (1987) suggested that future work on persistence should pay
particular attention to the indirect effects of background and environmental pull
characteristics as they act through other constructs to affect student persistence. As
model modification is intended only to improve model fit, it was necessary to develop
and test alternative models. Beginning with the original model, three additional models
were developed and tested. Labeled AL1 to AL3, they represented an increasingly
restrictive approach to the impact of the construct Background on other constructs by
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fixing the direct relationships to zero. Model AL3 was the most restrictive and
demonstrated the best overall fit (lowest AIC).
Question Two: Do student background characteristics directly or indirectly affect
persistence?
The final model restriction involved fixing the relationship between Background
and Persistence to zero. In actuality, this means that background characteristics do not
directly impact persistence, but instead, do so indirectly through the construct
Environmental Pull. Though the model fit was only slightly improved as compared to
AL2, the construct Environmental Pull now becomes a significant predictor of
persistence along with Academic Performance. This then means that the construct
Background has significant indirect effects that align with the significant total effects
measure.
Question Three: Does academic performance as measured by college GPA directly
affect student persistence?
Both in the original conceptual model as well as each increasingly restrictive
iteration, the construct Academic Performance as measured by college GPA had a
significant, direct, and positive effect on student persistence. Despite the overall good fit
with the data, and the significance of Academic Performance, Environmental Pull, and
Background, the model explains only 18% of the total model variance. As reported
previously, this aligns closely with the work of Zhai, Monzon, and Grimes (2005).
Question Four: Do student experiences have an indirect effect on student persistence?
In this analysis, the construct, Experiences, is represented by three variables;
academic integration, social integration, and having declared a major. Bean and Metzner
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(1985) believed academic integration to be of key importance in predicting student
persistence among two-year college students, and since then, a number of empirical
works have supported this supposition (Goel, 2002; Halpin, 1990; Napoli & Wortman,
1998; Williamson and Creamer, 1988). However, social integration was believed by
Bean and Metzner to be of less importance when considering student persistence and that
is why they included social integration in a tangential way in the original model.
However, in a later model of student persistence, Bean (1990) included the social
integration measure, and made no mention of the level of importance as it pertains to
two-year college students.
More recent work about two-year persistence has been equally inconclusive.
Chen and Thomas (2001), Napoli and Wortman (1998), and Williamson and Creamer
(1988) all found social integration to be important while Goel (2002) did not. This
debate is further confounded by the number of different ways social and academic
integration can be defined. In this study academic and social integration are believed to
not be mutually exclusive, but instead to represent a summative construct, the student
experience. Despite this, not only does the construct fail to demonstrate indirect effects
on student persistence, but the social integration index does not appear to explain a
significant portion of the variance within the construct as a whole.
Question Five: Do environmental pull factors have a direct effect on student
persistence?
As previously stated, when the relationship between Background and Persistence
is fixed to zero, the construct, Environmental Pull, has direct, negative effects on student
persistence. Three of the four observed variables – having dependent children, having
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high levels of unmet financial need, and working full time – had significant factor
loadings for the construct Environmental Pull and explained 69%, 26% and 10% of the
variance within the construct respectively. Only working off-campus was non-significant
for this construct. These results support work by Berkner, Horn, and Clune (2000) who
found that working full time and having dependent children were both negatively related
to two-year student persistence. Also studying two-year college students, Hippensteel,
St. John, and Starkey (1996) found that adult students were particularly sensitive to
tuition increases and as a result this was a strong predictor of within-year student
persistence. However, these results stands in stark contrast to work by Titus (2004) that
tested this same model on four-year college students finding that working on-campus and
working additional hours were both related to increased student persistence, while having
dependent children and unmet financial need were not significant predictors. This may
offer additional support for the need for different models for studying persistence among
two-year college students. As shown here, the same variables, operationalized in the
same manner, from the same dataset, and among the same cohort of students have very
different effects for two-year students as compared with their four-year peers.
Question Six: Does student satisfaction directly affect student persistence?
The results show that student satisfaction is not a predictor of student persistence.
Work by both Stahl and Pavel (1992) and Zhai, Monzon, and Grimes (2005) also failed
to demonstrate overall student satisfaction to be a significant predictor of student
persistence. Perhaps more striking is the fact that the construct, Experiences, explains
less than 1% of the variance for student satisfaction. This is far less than even previous
research by Napoli and Wortman (1998) who found that five different variables used to
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measure satisfaction accounted for only 27% of the total variance in overall student
satisfaction. Again, these results contrast those of Titus (2004), who found satisfaction to
be a significant predictor of student persistence among four-year college students using
BPS: 96/98 data.
It is also important to note that student satisfaction scores were unusually high.
Less than three percent of respondents reported being unsatisfied with the first college
attended and nearly 92% reported higher than average levels of satisfaction. This gives
cause for several theories, not the least of which is that students attending two-year
colleges have more realistic expectations for their “college experience.” An alternative
theory might be that two-year college students may also have less need to be engaged
“socially” due to a variety of environmental pull issues. Whatever the reason, when
considered along with the very different results in a similar study by Titus (2004) on
four-year students, it brings to question the validity of measuring factors such as
satisfaction and social integration using the same indicators for both groups.
Conclusions
The conceptual model presented and tested in this study (Figure 5.1) can be
simplified by viewing it in two parts. The first part includes background information, the
student experiences, measures of student satisfaction, and academic performance, or
more simply, the top half of the model. Each construct is composed of a number of
variables that have been examined numerous times among the collective research, nearly
all of which have been found at one time or another to be related to student persistence.
The second part of the model is represented by the relationships between background
characteristics, environmental pull characteristics and student persistence. It is this
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portion of the model that is distinct from previous models, and it is within this context
that the five major conclusions drawn from this study are here presented.
Academic
Performance
Background
Characteristics
Student
Experiences
Satisfaction
Persistence
Environmental
Pull
Figure 5.1: Final Tested Model (AL3)
First, researchers wishing to study persistence among students attending two-year
colleges must consider carefully the continued application of more traditional models
whose validity has arisen from repeated testing among traditional aged, four-year college
students. This conclusion is consistent with positions by Pascarella and Terenzini (1991,
2005) as well as Tierney (1992). This study suggest that student persistence among those
attending two-year colleges may be both more complex, and at the same time more
simple, than their four-year peers. Variables and constructs like student satisfaction and
social integration known to directly affect student persistence do not have the “same”
effects as previously observed among four-year students, suggesting that the longitudinal
nature of the model is different. Additionally, it appears that some variables, such as fulltime employment, having dependent children, and having high levels of unmet need have
127
the ability to directly interrupt this longitudinal process despite the presence of more
positive factors.
Second, the significance of the construct Environmental Pull suggests that the
Bean and Metzner (1985) nontraditional undergraduate student attrition model could
serve as a solid foundation for future research on two-year persistence. Environmental
Pull is fundamental to the argument of Bean and Metzner (1985) that nontraditional
students are unique. This is important as relatively few studies have attempted to test the
model. One such study using SEM (Stahl & Pavel, 1992) found the model to have poor
fit with the data, and as a result did not report the effects. Other studies (Metzner and
Bean, 1987; Zhai, Monzon, & Grimes, 2005) tested this model using methodologies that
ignored the treatment of the constructs. Despite this, their work demonstrated the ability
of a number of the variables from this construct to be significant predictors of student
persistence. This is also true for works by others such as Horn (1996) and Berkner,
Horn, and Clune (2000) both finding that having dependent children and working fulltime were each negatively associated with student persistence. This is not to say that the
model should be adopted without question. All four iterations of this model failed to
explain a significant portion of the variance for student experiences, student satisfaction,
and cumulative GPA. In addition, only three of the expected effects were validated by
this analysis.
Third, this model was developed and tested under the assumptions that student
satisfaction and social integration are generally the same for two-year and four-year
college students. These assumptions extend beyond simply the potential impact of the
variable on student persistence to the more basic level of measurement for each variable.
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Student satisfaction as measured in BPS: 96/98 is based on student responses about their
satisfaction with the campus climate regarding students of different racial or ethnic
backgrounds, class sizes, cost of attendance, any counseling services they had used,
course availability, any cultural activities they had participated in, instructors’ teaching
ability, their intellectual growth, any job placement services they had used, the prestige of
the school, social life, and sports and recreational facilities. In this analysis, overall
student satisfaction with their first institution was quite high (> 92%), despite feelings of
both social and academic isolation from the college system. Previous work on four-year
college persistence suggests that satisfaction is positively influenced by increased levels
of both social and academic integration (Metzner, 1989; Titus, 2004). Also, student
satisfaction has been shown to be an important predictor of student persistence (Bean,
1980. 1982, 1983, Metzner, 1989, Metzner & Bean, 1987; Titus, 2004), yet here the
overall effect of student satisfaction on student persistence is not significant.
A similar situation seems to exist with the variable social integration. Though
social integration was a significant predictor of the construct Experiences during the
testing of the measurement model, it failed to continue to be during the evaluation of the
structural model. This variable is an index of student scores on participation in several
areas: fine arts activities, intramural or non-varsity sports, varsity or intercollegiate
sports, school clubs, and going places with friends from school. For decades, researchers
of student persistence have argued about whether or not social integration was in fact
important. Bean and Metzner (1985) and Stahl and Pavel (1992) even argued that social
integration was less important among nontraditional and two-year students.
129
Unfortunately, the majority of debate has centered on the relative importance of the
construct and not the measurement of it.
Few could argue that students must have some type of social system of which
they are a part, but why do researchers continue to believe that social systems are the
same for all individuals? As discussed previously, two-year college students are less
homogenous than their four-year peers. They not only bring with them more risk factors,
but a number of different educational goals. Few live on campus and as such relatively
few attend colleges that are a considerable distance from home. This means simply that
most may already have an established social system that they go home to at the end of
each day. Previous research has demonstrated that both social integration and student
satisfaction are likely important predictors of student persistence, however, this analysis
fails to demonstrate that importance. As such, researchers seeking to examine these
constructs must seek first to better define them fully considering the unique nature and
characteristics of two-year colleges and the students that elect to attend them.
Fourth, the construct Experiences is not only poorly explained by this model, but
fails to demonstrate significant indirect effects on student persistence. In this study, the
construct Experiences was measured using three variables, an index of academic
integration, an index of social integration, and having declared a major. Though
academic and social integration are often measured as constructs separate from one
another, previous research as reported in the literature does not offer consistent
definitions for each. For example, Tinto (1975, 1987, 1993) viewed having conversations
with faculty outside of class as a measure of social integration, while Bean (1980, 1982,
1990) views this as a measure of academic integration. It quickly becomes clear that
130
these two measures are not mutually exclusive, but at the most basic level may be part of
a more broadly defined construct. A correlation of .306 (p = .01) supports the idea of a
strong direct relationship between these two variables.
Bean and Metzner (1985) and Bean (1990) hypothesized that student background
characteristics shaped the level of both academic and social integration. However, this
study failed to demonstrate a significant relationship among these constructs. Previous
discussion has suggested that measures of social integration should be further improved
for students attending two-year colleges, but it is quite likely that improved measurement
of the construct will not lead to increased significance in future iterations, as there has
been less debate regarding the measurement and validity of academic integration. A
number of authors have demonstrated that student background characteristics play an
important role in shaping student experiences for those attending four-year colleges
(Berger & Milem, 1999; Pascarella & Chapman, 1983; Stoecker, Pascarella, & Wolfle,
1988). As this study suggests that background characteristics play little role in shaping
student experiences at two year colleges, researchers wishing to further explore issues of
persistence, must consider how the policies and practices of individual institutions might
serve as the primary vehicle for shaping student experiences.
Finally, the construct Academic Performance was not well explained by this
model. Despite demonstrating that college GPA has significant, direct, and positive
effects on student persistence, the relationships specified in this model do not explain a
significant portion of the variance within the construct. Two possible explanations
should be considered; the effects of institutional practices and polices or the improper
specification of relationships. It may well be the case that the practices and policies of
131
individual institutions go a long way in shaping student academic success. For example,
institutions that have highly integrated developmental educational programs with
mandatory placements are probably more likely to foster academic success than those
institutions that have a grab bag approach that allows student self-selection. This theory
is supported by the work of Zhai, Monzon, and Grimes (2005). Studying nearly 800
students at a single community college district using the Bean and Metzner (1985) model,
they found that adding developmental education and tutoring to the model helped to
increase the level of variance explained from 16% to 39%. Additionally, they found
student success in English remediation to be a strong predictor of cumulative GPA.
A second possible reason for high unexplained variance for the variable college
GPA might involve model specification. In the original iteration of the model tested,
both Background and Experiences were believed to directly affect Academic
Performance. Additionally, Environmental Pull was believed to have indirect affects
acting through Experiences. However, Background was shown not to have a significant
relationship to Academic Performance as measured by college GPA, and as a result this
relationship was fixed to zero in the final model. Therefore, in the final model tested,
only the direct affects of Experiences and indirect affects of Environmental Pull
continued to be advanced. Possible direct effects might exist between Environmental
Pull and Academic Performance, but as this was not advanced by Bean and Metzner
(1985), this relationship was not tested. If such a relationship does exist, this would mean
that Background would indirectly affect Academic Performance acting through
Environmental Pull.
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The Bean and Metzner (1985) nontraditional undergraduate student attrition
model was intended to be a synthesis of previous research on persistence that provided
for special consideration of the nontraditional students. Here, the model is advanced as a
possible platform for studying persistence among those attending two-year colleges.
Using more recent work by Horn (1996) and Berkner, Horn, and Clune (2000) this model
was operationalized using a number of variables (risk-factors) known to be significantly
related to student persistence. The synthesis of the Bean and Metzner model with Horn’s
student risk-factors demonstrates the utility of the nontraditional undergraduate student
attrition model in describing how Background characteristics acting through
Environmental Pull might negatively affect student persistence. This model fails
however, to explain how Experiences and Satisfaction relate to student persistence. The
final section of this analysis explores first the limitations of this study, followed by
recommendations for future research using the nontraditional undergraduate student
attrition model as well as for gaining a better understanding of how constructs believed to
be important might be better measured.
Limitations
In the first chapter, five limitations for this study were advanced. Three
limitations arose directly from the use of secondary data. First, the ability to fully
operationalize the constructs is limited by both the survey questions asked and the data
that are reported. A glaring example of this is the inability to include measures of high
school academic performance and rigor as a student background characteristic due to
large amounts of missing data for the desired population. Second, the sample size of the
population limits the ability of the researcher to disaggregate the data further. As SEM
133
using the weighted least squares estimation method requires a large sample size, the
researcher was unable to test and compare the model fit and variance explained for key
demographic characteristics such as gender and race. Third, as the population
demographics are ever changing, so too is the universe for first-time beginners. As such
data regarding students who first matriculated into higher education more than a decade
ago may not mirror the same population today. Despite this, the longitudinal nature of
this data makes it possible to examine how student characteristics impact persistence over
time.
A fourth limitation arises out of the lack of a critical mass of research on both
two-year college persistence and the Bean and Metzner (1985) nontraditional
undergraduate student attrition model. Research findings are best validated through
replication, and though a number of studies have examined factors affecting student
persistence using the Bean and Metzner framework, each reported analysis has used only
a single institution or single college district. In addition, only the Stahl and Pavel (1992)
work has tested the Bean and Metzner constructs using structural equation modeling,
ultimately finding that the model did not fit well with the data. Finally, the LISREL
software used for this analysis does not allow for use of sample weights for ordinal
variables. The use of unweighted data in SEM analysis may over-inflate chi-square
estimates ultimately increasing the likelihood of type two error. Each of these issues
leads to a decrease in the generalizability of these results to the entire population as well
as limiting the level and types of statistical analyses that can be performed. The
following section details recommendations for future research that seek to increase the
134
depth of analysis, improve the generalizability, and expand understanding of important
constructs as they pertain to students at two-year colleges.
Recommendations for Future Research
First, efforts aimed at gathering data for secondary analysis as part of large scale
postsecondary databases should seek to increase the overall number of students sampled,
particularly among two-year colleges. Sophisticated statistical analyses using large
numbers of variables require large sample sizes. These large sample sizes are important
for examining within-group characteristics in hierarchical data, as well as accounting for
non-normality in the data that result from using ordinal or mixed data. There were
approximately 1500 respondents for BPS: 96/98 representing two-year students as
compared to more than 5000 four-year respondents. Berkner, Horn, and Clune (2000)
reported that nearly 50% of students beginning their postsecondary career elect to attend
two-year colleges. Future iterations of NPSAS and BPS should seek to include a
representative mix of students from different institution types, thus increasing the number
of two-year college respondents ultimately making more advanced analysis possible.
Second, researchers wishing to better understand how social factors affect
persistence among those attending two-year colleges should consider critically the utility
of using current measures of social integration. For decades the relevance of a student’s
integration into the social structure of an institution has been debated. Could it be that
these arguments are misplaced? Instead of debating and testing the importance, should
researchers instead be debating and testing the relative merits of the assessments? The
measurements of social integration utilized in this study are based on decades of research
on four-year college students. These students are most often of traditional college age
135
and are transitioning from the social structures of high school to the social structures of a
college. Those who live on campus and those who move a considerable distance from
home often give up established support structures in the way of friends and family, while
many who are attending two-year colleges continue to live with parents, their spouses and
children, and within a short commuting distance of home. Traditional measures
presuppose the elimination of a student’s primary social support structures, and as such
do not measure the relative social support that a student may actually posses.
Third, understanding the high levels of satisfaction with the first college attended
as reported by students attending two-year institutions is important. Titus (2004),
studying students attending four-year institutions, found satisfaction to be a strong
predictor of persistence, but this analysis not only found satisfaction to be unrelated to
persistence, but measures of satisfaction to be highly skewed with only 1 in 20 students
reporting any level of dissatisfaction. Two potential explanations might exist. First,
students at two-year colleges may have more realistic expectations of what their college
experience will be like, and second, having more risk factors may decrease the utility of
some satisfaction measures. Whichever proves to be the case, it is clear that the measures
of both student satisfaction and social integration do not have the same utility in
predicting postsecondary persistence for two-year college students as they do for their
four-year peers.
Fourth, future analyses of this model should consider utilizing both weighted data
and hierarchical SEM. Structural Equation modeling is intended to test the model to data
and as such, the use of sample weights is not necessary to judge that fit. However, the
use of weight variables allows for greater generalizability, by adjusting the sampling
136
distribution to better represent the intended population. Though this study found the
model to have good fit with the data, any generalizations to the entire population must be
made with some reservation due, as LISREL does not allow for the weighting of ordinal
or dichotomous variables. However, other SEM software does. Researchers wishing to
utilize these alternative software applications should first have a strong working
knowledge of SEM and its application, as many of the how-to resources are predicated on
the use of LISREL.
Hierarchical modeling considers the nested nature of data. In BPS and other large
scale databases, simple random sampling is not possible. Instead data are often gathered
through multistage random sampling. Using hierarchical modeling allows the researcher
to ascertain any within-group effects. Failure to consider the hierarchical nature of the
data increases the likelihood of rejecting a model due to poor fit when in actuality the chisquare may have been artificially inflated. Julian (2001) argues that within-group effects
are likely negligible when small groups exist within very large samples. However, the
use of hierarchical analysis is a topic of much debate in both the SEM (Julian, 2001;
Stapleton, 2006) and education research (Haus-Vaugh, 2006; Thomas & Heck, 2001)
communities. One potential problem in considering within-group analysis is the
requirement of at least two respondents from the most immediate, higher-level structure.
Such treatment effectively eliminates all groups with only single respondents, thereby
reducing the effective sample size and potentially biasing the data.
Fifth, future tests of the nontraditional undergraduate student attrition model
should control for key background variables such as race and gender. Berkner, Horn, and
Clune (2000) demonstrated that being female, Black, or Hispanic increases the likelihood
137
of risk-factors for first-time beginners. In this analysis, gender was no longer a
significant background variable in the final model. By separately testing and comparing
males to females and ethnic minorities to Caucasian’s the utility of this model for
different segments of the population can be better understood.
As weighted least squares (WLS) estimation procedures require large sample
sizes, this type of analysis is not possible with the number of available respondents.
Other estimation procedures such as Maximum Likelihood estimation (ML) require much
smaller sample sizes. Though Joreskog and Sorbom (1996) believe ML to be
inappropriate for analysis of ordinal data, a number or authors believe otherwise (Fan &
Sivo, 2005; Gold, Bentler, & Kim, 2003; Lomax & Schumacker, 2004; Savalei &
Bentler, 2005; Satora & Bentler, 1994). Utilizing adjusted chi-square measurements such
as the Satorra-Bentler chi-square, ML estimation procedures can be used to estimate nonnormal data for small sample sizes. Without significantly larger datasets, such an
analysis would allow researchers an opportunity to examine model performance across
smaller subpopulations of the sample.
Summary
Bean and Metzner (1985) first drew attention to the non-traditional student more than two
decades ago. Since that time Horn (1996) expanded the definition of the nontraditional
student by identifying seven student risk factors highly related to student persistence, and
work by Berkner, Horn, and Clune (2000) demonstrated that students attending two-year
colleges are significantly more likely than their four-year peers to be nontraditional. As
such, the Bean and Metzner nontraditional undergraduate student attrition model was
used in this study to examine persistence among two-year college students. This study
138
demonstrates the potential utility of the Bean and Metzner (1985) model for future study
of two-year college persistence. However, this work also demonstrates that the model
does not explain well at least two constructs considered important to student persistence;
social integration and student satisfaction. This likely arises from the fact that the
nontraditional undergraduate student attrition model was conceptualized not for use at
two-year colleges, but for nontraditional students attending four-year institutions. As
such, it may reflect some of the same biases as other models. In addition, large scale data
collection efforts have also been shaped significantly by past research on four-year
colleges.
Future efforts must move the research community towards a better and more
thorough understanding of the unique nature of two-year college students. By
considering how social needs and diverse educational goals interact to change college
expectations, federal, state, and college-level policy makers might better consider how
scarce resources can best be used in fostering student success and persistence among the
nation’s fastest growing college sector.
139
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