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Cognitive and non-cognitive factors as predictors of retention among academically at-risk college students: A structural equation modelling approach

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Cognitive and non-cognitive factors as predictors of retention among academically at-risk college students: A structural equation modelling approach

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COGNITIVE AND NON-COGNITIVE FACTORS
AS PREDICTORS OF RETENTION
AMONG ACADEMICALLY AT-RISK COLLEGE STUDENTS:
A STRUCTURAL EQUATION MODELING APPROACH
By
Patricia Elaine Tobey
A DISSERTATION
Presented to the
Faculty of the Graduate School
University of Southern California
In Partial Fulfillment
Of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
(Education - Counseling Psychology)
May 1996
Copyright 1996 Patricia E. Tobey
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UMI Number: 9636757
Copyright 1996 by
Tobey, Patricia Elaine
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UNIVERSITY OF SOUTHERN CALIFORNIA
THE GRADUATE SCHOOL
UNIVERSITY PARK
LOS ANGELES, CALIFORNIA 90007
This dissertation, written by
...........Patricia Elaine Tobey
under the direction of h.ex. Dissertation
Committee, and approved by all its members,
has been presented to and accepted by The
Graduate School, in partial fulfillment of re
quirements for the degree of
DOCTOR OF PHILOSOPHY
Dean of Graduate Studies
Date .A pril i# 19
DISSERTATION COMMITTEE
Chairperson
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ABSTRACT
ii
Purpose. The purpose of this study was to develop and examine a
theoretical model of early intervention for retention of academically at-risk
first-year college students at a private four-year urban university.
Furthermore, this study examined the construct validity and reliability of
academic and non-academic self-concept measures of the Dimensions of
Self-Concept Form-H (DOSC-H), the Self-Description Questionnaire-Ill
(SDQ-m), and the Intellectual Achievement Responsibility Questionnaire
(IAR).
Method. Structural equation modeling was utilized in the
development of the early intervention prediction model. The measurement
portion of the model was comprised of DOSC-H and the SDQ-m self-
concept scales as well as academic achievement variables such as the
Scholastic Aptitude Test Mathematics and Verbal portions (SAT-V, SAT-M),
high school grade point average (GPA), college Fall and Spring semester
GPAs and number of college units completed each semester.
Academically at-risk students (N=129) who were admitted to an
academic support program during their first year of enrollment were
required to complete successfully the first year of studies in order to enroll
for the second year. Successful completion was defined by the number of
units completed and the cumulative GPA after the first year. Each student
that participated in the support program completed the non-cognitive
instruments during the Fall orientation program prior to the beginning of
classes.
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Results. Results indicated that self-concept, specifically affective
components relating to academic issues in college such as anxiety, and non-
academic components of self-concept such as support of family and friends,
are important predictors of retention. Their influences can be utilized for
counseling purposes for academically at-risk college students. Interestingly,
for the population of students in this study, high school GPA was a more
valid predictor of success than were the SAT-V and SAT-M measures.
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ACKNOWLEDGMENTS
iv
This project, after many years of study, marks the ending and the
beginning of my journey in psychology and higher education. I was
fortunate to have met many people along the way who provided sources of
inspiration and encouragement Specifically, I would like to thank my
daughter, Christina, for the joy and patience she has given me throughout
the years as I completed my undergraduate and graduate studies. On a
more formal note, I would like to thank the Division of Student Affairs at
the University of Southern California for the support and encouragement I
received during my doctoral studies-spedfically, Drs. Alonzo Anderson,
James Dennis, Kristine Dillon, Janet Eddy, Robert B. Jones and Bradford
King. Also, important to my success are my dear friends I met at USC, Dr.
Mary Maresh for being my 'study-buddy' and confidant during our entire
doctoral program, Dr. Barry Gribbons for his analytical acumen and wry
sense of humor, Dr. Scott Whiteley for his steadfast encouragement and
constant reminders to "finish, finish, finish...!", Edward Trickey for his
unabashed ideas for graduate student survival, Christine Fredericks for her
moral and professional support, and Althea Myrie for her endless kind
words and prayers.
Lastly, I would like to thank my dissertation committee members,
Dr. Michael Newcomb for his patience with my "whats, whys, and huhs,"
especially during the final analysis of this project; Dr. William B. Michael
for his continued support in my professional presentations and publishing
ideas with this work as well as other endeavors; and finally, Dr. Dallas
Willard for his philosophical wisdom.
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V
DEDICATION
To all students, especially those who made this study possible,
who continue to persist in their own studies and endeavors.
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vi
TABLE OF CONTENTS
PAGE
ABSTRACT.......................................................................................................ii
ACKNOWLEDGEMENTS............................................................................iv
DEDICATION.................................................................................................. v
LIST OF TABLES...........................................................................................viii
LIST OF FIGURES........................................................................................... ix
CHAPTER
I. Background and Conceptual Frameworks:
A Review of the Literature...................................................... 1
Objectives of the Study.................................................. 1
Overview of Chapter....................................................... 2
Historical Antecedents.................................................. 2
Undergraduate Student Support Programs............... 3
Freshman Year Experience.............................................. 7
Learning Support Programs........................................ 9
Issues of Retention and Attrition..................................10
Early Intervention Programs......................................... 11
Theoretical Models/College Student Development.. .12
Cognitive Development Theories..................................13
Sociological Theorists.....................................................14
Social Learning Theory.................................................. 15
Locus of Control.............................................................17
Social Cognitive Theory/Attribution Theory..............18
Self-Concept.........................................................19
Academic Self-Concept as a Construct 21
Research on Academic Self-Concept.............................21
II. The Research Problem............................................................... 28
Overview.........................................................................28
The Problem Situation....................................................28
Research on Retention and Attrition............................29
Statement of the Research Problem...............................31
The Research Questions.................................................33
III. Methods and Procedures...........................................................35
Research Sample............................................................ 35
Collection of Data.......................................................... 37
Variables and Their Measures...................................... 38
Non-Cognitive Variables and Their Measures............38
Dimensions of Self-Concept (Form H)..............38
Self-Description Questionnaire IE.................... 42
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vii
Intellectual Achievement Responsibility
Questionnaire.....................................................44
Cognitive Variables and Their Measures 47
Psychometric Analyses................................................. 47
Construct Validity and Reliability................... 48
Confirmatory Factor Analysis of
Hypothesized Measurement Models............... 48
Structural Equation Modeling......................... 53
Methodological Assumptions...................................... 54
Delimitations................................................................ 54
Limitations.................................................................. 55
IV. Analyses and Interpretation of Results.................................. 56
Statistical Outcomes................................................................ 56
Descriptive Statistics and Reliability Estimates 56
Item-Analyses.............................................................. 58
Exploratory Factor Analysis...................................... 58
Confirmatory Factor Analysis and Hierarchical
Confirmatory Factor Analysis of the DOSC-H
and the SDQIII......................................................... 59
Correlations.................................................................. 61
Confirmatory Factor Analysis of Hypothesized
Measurement Models............................................. 61
Alternative Structural Models......................................83
The Research Questions........................................................... 87
Other Findings of the Study.....................................................90
Conclusion................................................................................ 91
V. Discussion of Results.............................................................. 92
Introduction.............................................................................. 92
Discussion of the Data Analysis............................................. 93
Validity and Reliability................................................. 93
Exploratory Factor Analysis........................................ 94
Construct Validity......................................................... 95
CFA of Initial Measurement Model............................ 96
Structural Equation Model or Path Model...................98
Results of the Conceptual/Theoretical Model 99
The Research Questions............................................... 100
Other Findings of this Study........................................ 103
Clinical Implications..................................................... 108
Further Clinical Implications and Research............... 109
Conclusion................................................................................ I l l
REFERENCES................................................................................................. 113
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LIST OF TABLES
Table Page
1. Description of Sample Characteristics of
Academically At-Risk First-Year College
Students.............................................................................. 36
2. Summary of Academic Achievement Variables
of Academically At-Risk First-Year College
Students.............................................................................. 49
3. Description of Academic Achievement Variables
of University Comparison Sample of First-Year
College Students................................................................. 50
4. Correlation of Academic Achievement Variables
of University Sample of First-Year Students
(Lower Triangle) and First-Year Academically
At-Risk Students (Upper Triangle)................................... 51
5. Summary of Descriptive Statistics and
Estimates of Intemal-Consistency (Coefficient Alpha)
for the Scales on the DOSC-H, SDQ HI, and
the IAR................................................................................ 57
6. Summary of First-Order and Second-Order
Confirmatory Factor Analysis (CFA) and Related
Fit Indices fo the DOSC-H and the SDQ III...................... 60
7. Self-Concept Factor Intercorrelations of the
DOSC-H, IAR, and the SDQ III......................................... 62
8. Intercorrelations of Academic Variables...........................63
9. Academic and Non-Academic Factor
Intercorrelations of the DOSC-H, IAR, and
the SDQ HI......................................................................... 64
10. Summary of Confirmatory Factor Analysis (CFA)
of Hypothetical Measurement Models,
Structural Models, and Related Fit Indices of the
DOSC-H and the SDQ ffl..................................................65
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LIST OF TABLES (Continued)
Table Page
11. Intercorrelation Matrix of Final CFA -
Measurement Model.........................................................85
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X
LIST OF FIGURES
Figure Page
1. Shavelson’ s Structure of Self-Concept.................................22
2. Theoretical Intervention Model of Academically
At-Risk First-Year College Students...................................32
3. DOSC-H One-Factor General Academic Self-Concept
Model.................................................................................... 67
4. Model 1: DOSC-H One-Factor General Academic Self-
Concept Comprised of Four Variables with Anxiety
as a Correlated Variable.......................................................68
5. SDQ IE One-Factor General Academic Self-Concept
Model.................................................................................... 69
6. SDQ HI Two-Factor Model Comprised of Academic
and Non-Academic Indicators............................................ 71
7. Model 2: SDQ HI Two-Factor Model Comprised of
Academic and Non-Academic Indicators-Both
Factors Utilizing General Self-Concept as an
Indicator................................................................................72
8. DOSC-H and. SDQ m One-Factor General Self-
Concept Model.....................................................................74
9. DOSC-H and SDQ m Two-Factor General Self-
Concept Model.....................................................................75
10. Academic Self-Concept and General Self-Concept
Two-Factor Model Utilizing Factor Indicators of the
DOSC-H and the SDQ HI.....................................................77
11. Academic Self-Concept, Interpersonal Self-Concep
and General Self-Concept Three-Factor Model
Utilizing Factor Indicators of the DOSC-H
and the SDQ III..................................................................... 78
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LIST OF FIGURES (Continued)
xi
Figure Page
12. Academic Self-Concept, Interpersonal Self-Concept,
Physical Self-Concept and General Self-Concept
Four-Factor Model Utilizing Factor Indicators of the
DOSC-H and the SDQ m ................................................... 79
13. DOSC-H General Self-Concept and SDQ III General
Self-Concept Two-Factor Model with Anxiety as a
Separate Correlated Variable.............................................81
14. Initial Measurement Model: DOSC-H General Self-
Concept, SDQ HI Academic Self-Concept and SDQ in
Non-Academic Self-Concept Three-Factor Model
with Anxiety as a Separate Correlated Variable.............. 82
15. Final Measurement Model: DOSC-H General Self-
Concept, SDQ in Academic Self-Concept, and SDQ IH
Non-Academic Self-Concept Three-Factor Model with
Anxiety as a Separate Correlated Variable....................... 84
16. Diagram of Structural Model..............................................86
17. Final Structural Model with Standardized
Parameter Estimates........................................................... 88
\
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1
CHAPTER I
BACKGROUND AND CONCEPTUAL FRAMEWORKS:
A REVIEW OF THE LITERATURE
William James' oft-quoted phrase, "a big, blooming buzzing
confusion," aptly describes modem educational theory and
practice....This confusion in modem education grows out of
two conflicts: one, a conflict between two philosophies and the
other a conflict between two methodologies. In die former the
intellectualists contend that mental discipline is the sole raison
d'etre of education, while the personalists demand equally
vigorously that the school facilitate the growth of the whole
individual. The second conflict springs from the mass method
of instruction as contrasted with individualized techniques
(Williamson, 1939, p. 1).
Objectives of the Study
Two central objectives of this investigation were (a) to develop a
theoretical model of early intervention based on a sample of one hundred
and twenty-nine academically at-risk first-year college students at a private
urban university and (b) to ascertain through the use of multivariate
analytic approaches employing structural equation modeling the direct and
indirect contributions of selected cognitive and affective constructs and their
measures to the prediction of retention of students in the sample.
Secondary objectives were concerned with obtaining evidence of the
reliability, construct validity, and predictive validity of scores on two
widely used measures of self-concept.
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2
Overview of Chapter
Subsequent to a consideration of the college freshman learning
experience and the availability of support and intervention programs, this
chapter is concerned primarily with a review of the literature that provides
a number of alternative conceptual frameworks that directly or indirectly
contribute to an understanding of developmental cognitive and affective
factors associated with the success of students in higher education. This
review affords the reader background information providing a perspective
within which both the problem area of the study and the research questions
set forth in Chapter II can be more readily comprehended and interpreted.
Historical Antecedents
Since the 1939 publication of E. G. Williamson’ s text How to Counsel
Students: A Manual of Techniques for Clinical Counselors, not much has
changed in the debate of educational and psychological theorists, although
there has been, a compromise by the formation of such disciplines as
educational psychology, counseling psychology, social psychology, and
cognitive psychology. Many researchers are still stymied by what resembles
the old adage of the mind/body dichotomy. However, the 1990s and the
twenty-first century bring with them the demand that student academic
support programs in higher education assist in the retention of students in
both public and private institutions. Concurrent with this type of demand,
is the necessity of institutional research and evaluation of such programs to
determine their effectiveness in supporting students. Notwithstanding
either is cost effectiveness regarding retention outcome for the institution as
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3
a whole. In the area of academic assessment, counseling, and intervention,
strategies are developed to provide early intervention warning signs for
academically at risk students. A typical strategy used in program models is
to obtain cognitive variable information such as admission records
containing demographic information and achievement scores (e.g., the
Scholastic Aptitude Test of the College Entrance Board [Educational
Testing Service, 1948-1996], high school grade point average and dass
rank). Academic support program models also collect non-cognitive
variables such as interest and opinion surveys that provide
psychoeducational information about the student's view of self and others.
Academic support programs for spedally admitted students also utilize this
information in planning tailored intervention strategies during group
orientation sessions, as well as individual meetings with students during
their first year of studies.
In sum, one is brought full circle into the arena of the "big blooming
buzzing confusion" as is so well stated by William James (1890) and
reiterated by E. G. Williamson (1939). This concept has been addressed by
this theoretical research study in cognitive (mental, intellectualists) and non-
cognitive (individualized, personalists) factors pertaining to the success or
non-success of first year college students.
Undergraduate Student Support Programs
Historically, undergraduate student support programs have usually
been administered in Student Affairs Divisions at colleges and universities.
Such programs typically have addressed student development issues as
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4
well as the burgeoning interest in the field of teaching in higher education,
the concept of the 'whole student'. Student development traditionally has
been philosophically as well as theory-based and has been the mainstay of
the purpose of Student Affairs Divisions and the application of human
development theory to college students (Miller, Winston, & Associates,
1991). Student Affairs Divisions have addressed in their mission statements
student support services that require professionals to be well versed in the
disciplines of education, psychology, sociology and management skills. The
student affairs specialist, therefore, has been required to have a vast array of
knowledge which, at best, has been multi-disciplined, but could sometimes
cause discordant approaches when each approach is based on a single
discipline. Traditionally, however, student development theory has been
utilized by professionals as a means of conceptualizing the many faceted
issues that students encounter during their college experience. Therefore,
student development theory, as it is presented today, is an array of many
developmental theories rather than one sole theory of student development.
One of the areas with which student affairs specialists have been
charged has been the mission of academic intervention programs for
academically at-risk or underprepared first-year college students. The
challenge to such professionals has been the clearly defined goals and
purposes of an intervention program. Part of the program mission has been
for its administrators to be knowledgeable in student development as well
as in educational measurement. Part of the intervention process has been
diagnostic, which has been used to help a student learn and take advantage
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5
of his/her own educational experiences. A comphrehensive diagnostic
assessment can give the student and the student affairs specialist
information concerning the college student's learning strategies (Nelson et
al., 1993; Pokay & Blumenfeld, 1990), level of academic preparation and
developmental status at the beginning of his or her first year of studies (Barr
& Upcraft, 1990). Typically, college and university student support
programs have utilized student affairs specialists in Learning Skills Centers.
Intervention approaches that utilize learning strategies and styles are
becoming more often referred to in the discipline of cognitive psychology.
Researchers have found that theory and application of cognitive and
developmental psychology has produced distinctive methods in teaching
students how to learn (Pintrich & DeGroot, 1990; Pintrich, McKeachie & Lin,
1989; Pintrich & Schrauben, 1992; Weinstein & Meyer, 1991). Assessment of
study skills and learning strategies has, therefore, provided information
that can clearly be used as an early intervention tool in academic counseling
and as part of the classroom experience (Astin, 1991). In addition, colleges
and universities have been developing coursework that addresses
educational and cognitive psychology at the undergraduate level as an
elective or a requirement for students to study about learning and,
specifically, about their own learning.
In many cases, students may not have been successful in preparing
for college during their secondary educational experience. Such
underpreparation of students graduating from secondary education has
presented a challenge for colleges and universities to involve student affairs
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6
specialists in teaching developmental education courses, psychoeducational
counseling and assessment, and developmental skills. The specialist who is
involved in advising and counseling students in intervention programs
must be aware of the student's level of preparation and cognitive skills.
Another essential ingredient in the overall assessment of a student is
the non-cognitive or developmental status of that student as he or she enters
the college or university. First-year students present a vast array of facets of
student growth that is becoming more diverse as the population of students
entering post-secondary education is multi-cultural and shares, as well as
contributes, many differing views. As a result, student development theory
is continually evolving to be more cognizant of differing cultures, gender,
age, and sexual orientations (Moore, 1990). Therefore, student development
is multi-dimensional as well as life-long.
In sum, today's student affairs specialist is presented with a
challenging mission, not only to contribute to the development of retention
and first-year programs, but to deliver the best possible quality of education
for students who continue their education beyond the first year. Rapidly
changing issues in student developmental theory must be addressed as
must the assessment and diagnostic intervention strategies for first-year
students. In this sense, attention needs to be focused on the theoretical
foundations of program planning and assessment and on its application.
College impact studies and theories of student development have been the
sine qua non of scholars conducting research in their own discipline.
However, very little transfer of research findings to practical application in
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7
higher education, especially program planning, assessment, evaluation, and
intervention strategy guidelines has been utilized (Erwin, 1991).
For the purposes of this research study, assessment is defined as a
systematic method for making inferences and for prescribing intervention
strategies for a student's learning and development. This study assesses
both cognitive and non-cognitive measures that are entry characteristics of
first-year students who participate in an academic support program at a
private four-year university.
On the basis of the multi-dimensional nature of both academic and
non-academic meaures, a structural equation model is presented that is
used as a framework to aid in assessing the best possible early intervention
strategy for a diverse population of potential academically at-risk college
students. This study utilizes existing theory and develops a research model
of assessment and its application for use in a psychoeducationally based
early intervention program in higher education at a four year private
university. Finally, the researcher specifically investigates different aspects
of self-concept, both academic and non-academic, as mediating factors in
academic success at the completion of the first year of studies. The use of
self-concept factors as an assessment for early intervention may be valid as a
future guideline for further program development.
Freshman Year Experience
The main drive on the problem was led, however, first by
Kathleen M. Darley, J. G. Darley, Cornelia Williams, and
assisted by Harold Pepsky, and others. They interviewed
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8
students by the hundreds...ad vised by a technical committee
chairmanned by D. G. Paterson...the homes of the students
were visited. The parents took the same tests, answered the
same questions....The results of the study...were a considerable
shock to most of us who read them carefully...we found
strikingly few differences between graduates and non
graduates....By and large they had the same anxieties and
problems, the same inconsistencies in attitude and behavior
and philosophy of life and living. Far too small a proportion of
things learned had remained in their memories after the
registrar had recorded course grades....In general, they read
the same newspapers, preferred the same magazines, and
thought pretty much the same thoughts...whether they had
been A, B, or C students...or had been dropped from the
university as being "unfit for college" (Williamson, 1949, p. 34).
E. G. Williamson’ s text, Trends in Student Personnel Work, published
in 1949, was originally a collection of papers read at a conference sponsored
by the University of Minnesota to celebrate a quarter century of student
personnel work and to honor Donald G. Paterson. Paterson's research on
methods and techniques in student personnel work hallmarked the
University of Minnesota as the forerunner of student counseling and
counseling psychology, in general. Donald Paterson brought to the
attention of educators the need for research in the field of counseling
college students. Although the anecdote just presented gives one pause to
be certain of the type and methodology of our endeavors, nonetheless, it is
the province of trying to understand the captured sample of college
students that has given counseling psychologists a glimpse into the
developmental issues of first-year students.
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9
The freshman year or first-year experience at an institution of higher
education is a stressful time of transition for students. It is during this time
that many developmental and essential identity issues are encountered. The
student is on his or her own for perhaps the first time in his/her life (Astin,
1973; Baird, 1990). Most first-year students prefer to live on-campus or near
to campus rather than commute, although there are students who do prefer
to live at home and to commute for personal or economic reasons. The
student's life on campus can be the first experience of an unstructured,
decision-making enterprise that can be confusing and yet be a liberating
experience from home. In many instances this experience can have negative
consequences when inappropriate dedsions are made that are reflected in
the student's lifestyle and academic pursuits. Colleges and universities try
to be proactive by providing freshman seminars and college dormitory
learning experiences that are guidance oriented. That is, students are given
informative educational tools for survival in health, personal, and academic
skill development. The aim of the college community is to provide a safe,
rewarding, and significant learning experience for the student during the
course of his or her studies.
Learning Support Programs
For many students who are admitted to the university with
underprepared academic skills, learning support programs provide a secure
base of information that can aid the student in addressing any of his or her
weaker academic skills. Skill development is usually the foundation of such
programs, whether it is through tutoring or academic counseling. Colleges
and universities may also offer admission for some students based on their
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10
participation in a learning support program during their first year of
studies. Other learning support programs may be an option for students
during part or the entire term of their undergraduate years. For example,
programs for first-generation college students, minority populations, and
older adults, stress the importance of access rather than exclusion from the
academic and social milieu of the institution (Council for the Advancement
of Standards for Student Services/Development Programs, 1986).
Issues of Retention and Attrition
Institutions of higher education recognize the need to investigate as
well as to evaluate issues of retention and attrition in order to improve the
educational experience of underprepared or "high risk" students (Billson &
Terry, 1987; Cone, 1991; Feldman, 1993; Francis, Kelley & Bell, 1993; Kinnick
& Ricks, 1993; Kobrak, 1992; Levin & Levin, 1993; Levin & Levin, 1991;
Miller, 1988; Rowser, 1990; Youn, 1992). The term "retention" has been
defined as whether a student remains at an institution of higher education
until the student graduates (Youn, 1992). Attrition or "dropping-out" is a
term researchers (Astin, 1975; Terenzini & Pascarella, 1977; 1978; 1980;
Terenzini, et al., 1994; Tinto, 1975,1987, 1988) refer to as an institution's
failure to retain students. However, academic or student conduct dismissal,
financial concerns, or other personal reasons that are unintentional may also
be the cause of attrition. Fundamentally, institutions of higher education
have admitted students on the basis of selective admission procedures
(Ancrum, 1992; Blackburn, 1990; Engelgau, 1991) that predict whether or not
a student may be successful. In so doing, the institution defines the student
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11
body as those students who will most likely do well and complete their
studies. However, there are many intervening variables such as social
climate or institutional environment, family support, support from friends,
self-concept, and other developmental issues that contribute to a student's
success both personally and academically.
Early Intervention Programs
Retention programs in higher education not only are proactive in the
personal and academic life of a student but also provide early intervention
strategies so that a student can have the best possible chance to be
successful. It is the institution's commitment to provide advisement,
counseling, and academic support as a means to enhance student success.
In many cases, early intervention during a student's first semester can
provide an opportunity for strategic planning, from dropping a course to
tutoring in a specific subject area. For instance, a program’ s counseling
component can provide a student with information that he or she may need
to make a timely decision, provided that the student seeks the advisement
or academic help. Mid-term grade interventions, with the help of faculty,
can provide information to the counselor, faculty and student that are early
warning signs of academic problems. Peer counselors, small group
meetings, career counseling and faculty mentoring (Cams, Cams & Wright,
1993; Dowaliby, Garrison & Dagel, 1993; Lamport, 1993; Polansky, Horan &
Hanish, 1993; Robbins & Smith, 1993; Toder & Hartsough, 1993) have also
provided institutional intervention and support for students that facilitates
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12
the process of bonding with the institution, especially during the student's
first year of studies.
Theoretical Models - Undergraduate Student Development
Historically, student development theory was based on the theory of
in loco parentis, that is, the institution of higher education functioned as the
student's "parents." During the seventeenth century, the average age of
most freshmen students was fourteen and the school did, in fact, assume the
role of parents. In early colonial colleges, it was believed that the
developmental rationale for their students was to facilitate the development
of Christian moral character (Moore, 1990). This view of student
development predominated until the mid-twentieth century.
College student development then branched to include theories of
career development begun by Frank Parsons in 1909, John Holland and
Donald Super in the 1960s. Psychological development also was
emphasized by Sigmund Freud, Carl Jung, B. F. Skinner, and most notably,
Carl Rogers. Carl Rogers (1951; 1961) provided a strong foundation for a
"client-centered" theory of counseling with college students. He introduced
the concept of "unconditional positive regard," which was postulated as the
central principle of interpersonal relations adopted by student affairs
practitioners (Moore, 1990). Students were then encouraged to explore
issues of self, while a nonjudgemental attitude was adopted by the
counselor. This approach faired well with the concept of identity
development of youth as presented by Erik Erikson during the 1950s and
1960s. Erickson (1963; 1968) viewed personality development within a
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13
social context. He postulated that, especially during the college years,
which was considered a time of turmoil during the "identity crises,"
students redefined themselves. Jean Piaget (1964; 1977) was also influential
during the 1960s and 1970s with his theory of mental development which he
defined as leading to the ability for complex analysis. In turn, this analysis
facilitated an individual's development of more complex mental processes
during a period of confusion and disquilibrium. Once the student passed
through the stage of disequilibrium and a new solution(s) to a problem was
discovered, then the mental processes restored to an equilibrium stage.
In sum, this formation by students of their self-concept personally,
socially, academically and morally begin to crystallize during the
developmental stages of adolescence and young adulthood. This formation
provided not only the base for theorists to focus on developing theories
about how students grow and change, but also redefined the relationship of
the student and the institution.
Cognitive Development Theories
Theories of cognitive development and reasoning began to emerge
during the 1970s. William Perry's (1970) theory of ethical and intellectual
development postulated that students integrate into their identities nine
stages of development on views of knowledge, multiple points of view, and
personal commitments. Lawrence Kohlberg (1971) developed a theory of
moral development that included the decision-making process, problem
solving, the social perspective, the personal perspective and the logic of
making a moral choice (Moore, 1990).David Kolb (1984) introduced a
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14
cognitive development model of the cycle of learning. Kolb indicated that
the student brings with him or her learning preferences that are redefined
during the college years through reflective observation, abstract
conceptualization, active experimentation, and concrete experience.
Sociological Theorists
Sociological theorists contributed to the understanding of student
development through theories of environment and interaction. Their focus
was on the campus environment and on the influence of the peer group,
especially with the support of the institution. Researchers such as
Chickering (1969; 1974), Schlossberg, Lynch & Chickering (1989), and Astin
(1973; 1985) reinforced such theories. They found that the more a student
was involved with the campus, the more he or she had invested physical
and psychological energy in the academic experience (Moore, 1990).
Students must also feel that they "matter" and that people care about them.
In this sense, the student develops a bond with the institution. On the other
hand, if a student does not feel a part of the institution, then the student
experiences "marginality." Consequently, he or she will be less likely to
succeed in college.
Vincent Tinto (1987) introduced a theory of freshman development
that aided student affairs in conceptualizing the process of student
departure. According to Tinto, when a student separates from his or her
family, community, and prior schools, he or she rejects prior values in order
to adopt values that are needed to adapt to college life. The student then
goes through a transition stage of bridging new bonds with the college. If
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15
the differences between the old and new values vary widely, then the
student may experience more difficulty in establishing new bonds. This is
especially significant for minority students entering a majority culture
dominated college, as well as for older adults, and first generation college
students (London, 1989; Maroufi, 1988). During the 1980's, developmental
issues of gender (Belenky, Clinchy, Goldberger, & Tarule, 1986; Gilligan,
1982; Redding & Dowling, 1992)), students of color (Asante, 1988; Cross,
1978; Helms, 1984; 1993; Mentzer, 1993), and sexual orientation (Cass, 1984)
that created different outcomes than had been previously postulated by
theorists were being addressed. Previously, developmental theories were
based on the majority culture of white middle dass heterosexual males and
ignored the gender and cultural differences of the "marginalized"
populations (Ashar & Skenes, 1993). Freshmen students and the institution
have an important task in fadlitating the transition to college via support
groups, activities, and knowledge of the different resources of the
university. Tinto also stressed the importance of the student's being able to
have interaction with a least one caring faculty or staff member.
Sorial Learning Theory
In general terms, the freshman in college is a novice in an
unfamiliar social organization, and is therefore confronted
with the values, norms, and role structures of a new social
system and various new subsystems...Therefore, regardless of
the degree to which the new college environment matches
what the entering freshmen expected, he (sic) faces a variety of
expected and unexpected academic, intellectual and social
challenges (Feldman & Newcomb, 1969, p.89).
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16
In 1969, Kenneth A. Feldman and Theodore Newcomb published The
Impact of College on Students as a compendium of research on higher
education and college students which was commissioned by the Carnegie
Foundation for the Advancement for Teaching. Their findings during the
late 1960s, stressed the fact that although research on college students was
intricately multivariate, it was presented as unidimensional because of the
obstacles faced by research methodology at that time. Thirty years later,
researchers, then able to apply methods of multivariate analysis, were just
beginning to be able to address the multidimensionality of academic,
intellectual, and social factors which play such an important role in the
adjustment of first-year college students, specifically for academically at-
risk populations.
Social learning theory has been an important contribution to student
development theory. The roots of social learning theory began with Kurt
Lewin and Edward Tolman (Corsini, 1987). Lewin's theory of behavior as a
function of life space led to later research in social climates and aggression.
Tolman introduced the concept of the intervening variable, otherwise
known as unobservable factors. Tolman induced that learning could happen
in the absence of a goal. However, what was learned would only manifest
itself at a later date when a goal was introduced. He coined the term "latent
learning." John Dollard and Neal Miller were the first to use the term "social
learning" to researchers, whereas, Julian Rotter introduced the first
comprehensive social learning theory (Corsini, 1987).
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17
Locus of Control
Rotter's theory germinated the construct called locus of control
(Rotter, Seeman & Livrant, 1962). Locus of control refers to the relationship
between behavior and the occurrence of rewards and punishments, of
actions and outcomes. Social learning theorists have utilized the term
internal and external locus of control when referring to this construct
(Lefcourt, 1991). An external locus of control refers to the reinforcement of
behavior and learning that is based on luck, fate, chance, or the actions of
other people. Internal locus of control refers to reinforcements that are the
result of a perception held by an individual of his or herself's own behavior,
efforts, and hard work. Seligman (1975) described passivity as learned
helplessness. That is, individuals could learn that outcomes were
independent of their actions. For the present study, locus of control is
defined as the belief held by an individual that his or her actions concerning
academic responsibility are the result of effort (internal locus of control) or
chance (external locus of control) (Bandura, 1977; Dweck, 1986; Dweck &
Leggett, 1988).
Not long after Rotter’ s I-E Scale (Corsini, 1987; Lefcourt, 1991) was
introduced to researchers, Crandall, Katkovsky & Crandall (1965) designed
the Intellectual Achievement Responsibility Questionnaire (LAR). This scale
was used to measure children's achievement behavior as well as
achievement success and failure experiences. Specifically, the IAR
differentiated causal factors relevant to success from those that involve
failure (Crandall & Lacey, 1972; Crandall & Crandall, 1983; Katkovsky,
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18
Crandall & Good, 1967; McGhee & Crandall, 1968). Later, a shorter form of
the IAR was used for high school age students (Boss & Taylor, 1989).
Limited information is available from the short form. In view of the
multidimensional nature of the IAR, factor analytic information is still
desirable, as none has been evident in the literature (Lefcourt, 1991).
Current studies investigating the possible relationship between student
study behaviors, locus of control (Rigby et al., 1992; Sansone & Morgan,
1992), self-efficacy, and academic achievement (Boss & Taylor, 1989; Klein
& Keller, 1990; Magnusson & Perry, 1989; 1993; Van Overwalle, 1989;
Wilhite, 1990) have found that students, who perceived that their own effort
and skill development contributed positively to academic achievement,
were successful in their studies.
Social Cognitive Theory / Attribution Theory
As mentioned earlier in this chapter, specific disciplines such as
personality psychology and social psychology to study the central
theoretical issues of personality and social situations were created. Student
development taps into both disciplines, especially when one considers the
plethora of research on self-concept, self-expectancies, perceived self-
efficacy, and such self-beliefs as having been mediating roles in various
models of motivation and self-regulation in learning (Higgins, 1990).
Specifically, attribution theory (Heider, 1958; Weiner, 1990) is concerned
with causal inferences, or perceived reasons, why an event has occurred.
Social cognitive theory and attribution theory cross the same borders as well
in student development. That is, attribution theorists have tended to focus
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19
on the role of causal reasons for academic/achievement related behavior
such as success or failure.
Personality and motivational theorists such as Bernard Weiner (1985;
1986), Martin Covington (1985; 1992), and Deborah Stipek (1993) have also
contributed to the literature on self-worth theory. They have conceptualize
that a student's self-worth is one way of protecting a sense of ability and is,
in fact, the student's highest priority. Students may purposefully handicap
themselves by not studying, because to try hard and to fail anyway reflects
poorly on their own ability (Covington, 1992; Covington, Omelich &
Schwartzer, 1986). Further studies in the area of achievement motivation
and performance in college students (Graham & Weiner, 1991; Magnusson
& Perry, 1992; Mone & Baker, 1992; Perry, Hechter, Menec & Weinberg,
1993), and specifically developmental psychology (Graham & Weiner, 1986),
have revealed that self-beliefs, self-esteem, self-efficacy, emotions, and
action have provided the ground work for further research into the causal
factors of achievement (Zimmerman & Martinez-Pons, 1990).
Self-Concept
There has been a considerable amount of literature on self-concept;
most authors have stressed the importance of early childhood experiences
(Rosenberg, 1979), social interaction (see previous discussion in this
chapter), and stage development (Erickson, 1963) in analyzing self-concept.
Adolescents, in particular, are more inclined to express general thoughts
and feelings as well as to experience the increased awareness of the
dimensions of physical self presentation (Hattie, 1992; Rosenberg, 1979). The
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20
adolescent tends more to introspection as he/she begins to develop a
greater ability to conceptualize (Piaget, 1977). During late teens to
adulthood, the development of self-concept has been little researched
(Hattie, 1992). However, some research (Protinslcy & Farrier, 1980;
Rosenberg, 1965; Wylie, 1979,1989), especially in the college population
(Bachman, O'Malley & Johnston, 1978; Mortimer, Finch & Kumka, 1982) has
revealed that other dimensions of self-concept become influential such as
social self-concepts, relationships with significant others, career, sexuality,
physical self-concepts, and aging. In this respect, self-concept can be
culturally bound (Gerardi, 1990; Gosman, Dandridge, Nettles & Thoeny,
1983; Lay & Wakstein, 1985; Osborne & LeGette, 1984; Quevedo-Garda,
1987; Tracey & Sedlacek, 1985; Wright, 1987). Influenced by significant
others, self-concept may differ across generations. It may be sodally
defined in reference to gender development roles (Caffarella & Olson, 1993;
Bachman & O'Malley, 1977). It also can mediate and guide behavior in
various situations. Self-concept has been found to be generally more stable
in adult life (Hattie, 1992). Ironically, this stability confirms William James’
(1890) hundred year-old comment that the self was ’ set like plaster’ by age
30.
Basically, the study of self-concept has progressed from a
unidimensional specific construct, to a global construct of self-concept
which is conceptualized as a multidimensional hierarchical model
(Rosenberg, 1979; Shavelson & Bolus, 1982; Shavelson & Marsh, 1986). The
terminology of self-concept has also been refined in meaning. Whereas, the
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21
term self-esteem connotes the evaluative aspects of self, self-concept refers to
the knowledge of one's self. In this sense, self-esteem is generally referred to
as a component of the structure of self-concept. For example, people with
low self-esteem may have poorly defined self-concepts (Baumeister, 1993).
Academic Self-Concept as a Construct
Academic self-concept has been of special interest to researchers in
regard to its possible link to various measures of performance or
achievement and motivation (Covington, 1985,1991; Covington, Omelich &
Schwarzer, 1986; Hansford & Hattie, 1982), especially as it relates to college
attrition (Arbona & Novy, 1990; Gerardi, 1990; House, 1992). Wylie (1979)
found that educators assume that achievement and ability are strongly
related to overall general self-concept and to students' self-assessments of
ability and achievement. However, this relationship is not precise and clear
because of theoretical and methodological difficulties in defining the
complexity of relationships between such variables and factors (Hattie,
1992).
Research of Academic Self-Concept
Herbert Marsh and colleagues have researched and published
studies assessing the adequacy of the Shavelson model (Shavelson, Hubner
& Stanton, 1976) (permission by the authors to reproduce the model has
been granted and is described in Figure 1) as it relates to academic, social,
emotional and physical self-concept (Marsh, 1990c, 1990e, 1992; Marsh &
Hocevar, 1985; Shavelson & Marsh, 1986). Marsh’ s findings suggest that
there is much evidence to support a multifaceted and hierarchical model of
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Genera]
Self -Concept General
Non-Academic Self-Concept
Academic
Self-Concept
Social
Self-Concept
Emotional
Self-Concept
Physical
Self-Concept
Academic
& Non-Academic
Self-Concept
Subareas
of Self-
Concept
Physical Physical
Appearance
Particular
Emotional
States
Significant
Others
English History Peers Math Science
Evaluation
of Behavior
in Specific I” I"
Situations U L Du OD
Figure 1. Shavelson’ s Structure of Self-Concept (Shavelson, Hubner, and Stanton, 1976)
23
self-concept that also includes academic self-concept as divided into two
subfacets: reading and mathematics (Hattie, 1992). According to this model,
general self-concept can be divided into two facets that are higher-order, or,
second-order factors: non-academic and academic self-concepts. Academic
self-concept can be subdivided into first-order factors that are subject areas,
such as mathematics, science, and English. Non-academic self-concept, a
factor, can also be subdivided into first-order factors called emotional, social
and physical self-concepts, and perhaps into other facets yet to be specified.
To test the model empirically, (Marsh 1990d, 1990e, 1992; Marsh &
Bryne, 1993; Marsh, Relich & Smith, 1983) Herbert Marsh developed the Self
Description Questionnaire (SDQ). There are three versions of the SDQ; for
use with elementary and middle-school (SDQ, SDQ II), and for use with
high school and college age students (SDQ HI). Restricted factor analysis of
the SDQ (Marsh & Hocevar, 1985; Shavelson & Marsh, 1986) revealed a
satisfactory fit of a seven-factor model from a sample of 600 subjects,
comprising of second to fifth grade students. Further analysis of the factors
yielded a solution of a model that included two correlated second-order
factors defined by the four non-academic and three academic factors
(Hattie, 1992).
A college level version of the SDQ was administered to 296 senior
high school females (Marsh & O'Neill, 1984) and to a second sample of 151
college students. The expanded version of the SDQ (SDQ-III) had 136 items
divided into 13 scales. The scales used included: Physical Abilities, Physical
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24
Appearance, Relationship with Parents, Relationship with Peers/Same Sex,
Relationship with Peers/Opposite Sex, Honest/Reliability,
Religion/Spirituality, General Self, Emotional Stability, Problem
Solving/Creative Thinking, Mathematics, Reading, and Academic Self-
Concept. Again, there was evidence that there were two second-order
factors; academic and non-academic. In general, however, it appeared that
the studies done by Marsh and colleagues provided support for a
multifaceted model especially for adolescent and college age samples,
whereas, for younger children a more unitary model seemed to afford a
better fit. These findings have implications in developmental theory,
especially with college students.
Construct development for academic self-concept or self-concept
measures emphasizing school-related activities has been utilized as a tool
for individualized psychoeducational assessment, intervention counseling
and planning activities to enhance self-concept (Michael, Smith & Michael,
1989). Michael and his colleagues developed the Dimensions of Self-
Concept (DOSC) as an assessment measure of five dimensions of self-
concept (Michael & Smith, 1976) and specifically a form (DOSC-H) for
college and university students (Michael, Kim & Michael, 1984; Michael,
Denny, Knapp-Lee & Michael, 1984; Michael, Michael & Denny, 1985). The
five dimensions of activity that the authors chose as central to school-related
self-concept included: Level of Aspiration, Anxiety, Academic Interest and
Satisfaction, Leadership and Initiative, and Identification versus Alienation.
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25
The authors of the DOSC selected the five main dimensions based on the
following rationale of affectivity in school learning:
An unrealistic level of aspiration-either too high or too low-
was hypothesized to be related to the probable subsequent
occurrence of anxiety. Students who set unrealistic high levels
of expectation could well become discouraged, depressed and
fearful (anxious) of loss of status and of possible criticism
(symbolic punishment) from parents, peers or significant
others. Students who set unrealistically low levels of
expectation might already be anxious and fearful of possible
failure. Setting low level goals could generate a certain degree
of immediate security (preservation of self-esteem) but at the
expense of later development of positive attitudes toward
learning and toward opportunities for positive recognition
and leadership roles. Highly anxious students are likely to
lose academic interest, fail to acquire a sense of satisfaction
with their schoolwork, to forgo opportunities for leadership
roles in the school setting, and eventually to develop feelings
of alienation and rejection accompanied by a manifestation of
hostility toward the school as an institution. On the other
hand, students relatively free of anxiety who are successful in
light of realistic levels of aspiration attain success that
engenders academic interest and feelings of satisfaction with
the school experience. Such satisfaction can be anticipated to
lead to greater self-confidence and to numerous opportunities
to exercise initiative and to assume leadership responsibilities,
which in turn are reinforcing mechanisms for attaining even
greater academic satisfaction and interest, for assuming new
leadership roles and for evolving positive identification with
the school establishment. In other words, frequent success
leads to further success; repeated failure, to a greater sense of
failure, frustration and alienation (Michael, Smith & Michael,
1976 pp. 522-523; 1989 p. 1)
This rationale lends support to developmental theory, for as a child
grows, cognitive development becomes more complex; the older child and
adolescent become more reflective of self. However, as the adolescent
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26
launches into adulthood, self-concept becomes more stable. For attribution
theorists, this finding may complement internal and external locus of
control theory. As the child develops, depending on environmental and
situational influences, he or she may learn that the locus of causation is
within the self, whereas others may learn that the locus of causation is
outside the self. People with high internal locus of control, or as postulated
by Bernard Weiner by the term "locus of causation," are more likely to have
higher self-concepts and to attribute their successes to themselves (effort
and ability), whereas, those with high external locus of causation attribute
their success more to factors external to themselves (luck and level of task
difficulty) (de Charms, 1986; Ded, 1975; Hattie, 1992; Weiner, 1986). As for
success and failure in academics, Weiner (1986) further added two other
dimensions of locus of causation called causal stability, which is related to
the relative endurance of the individual and to a facet called controllability.
Bernard Weiner included (a) ability as an internal facet that is stable and
uncontrollable, and (b) effort as internal, unstable and controllable facet
within the dimensions of his model. His theory would then postulate that a
student with a high internal locus of causation would be more able to
tolerate failure because effort perceived as unstable can be controlled by
one's activities such as studying more and working harder. Students with
an external locus of causation are more affected by failure than are students
with an internal locus of control because they may believe that ability is a
strong causation which cannot be controlled by more effort and study.
Thus, depending on the presence of a high or low self-concept, the
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multidimensional nature of self-concept, the age of the individual and his or
her tendency towards internal or external locus of control may shed some
light how the self-belief systems of a student affects his or her academic
success and school-related activities.
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28
CHAPTER II
THE RESEARCH PROBLEM
Overview
In the latter portion of the previous chapter an extensive review of
the educational, psychological, and sociologically oriented literature was
presented emphasizing a developmental approach to understanding the
cognitive and affective constructs related to learning and personal growth of
students. In this chapter, the research problem and research questions are
set forth within the context of a model of retention that incorporates many
of the cognitive and affective constructs treated in the previous chapter.
The Problem Situation
Various models of student attrition, integration, retention,
persistence, as well as prediction models of success have utilized non-
cognitive and cognitive measures. In this sense, the plethora of studies has
been interested with the basic question: What makes a student successful in
college?
One of the initial issues in retention is predicting the type of student
that is the best match for the institution. Once the student has been
admitted and has matriculated, the second issue is: What will keep the
student at the institution? If the student is experiencing difficulties (whether
academic or non-academic), what are the early warning signs? Finally,
what are the most effective means of assistancing academically at-risk
students?
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29
Research on Retention and Attrition
Two models that have been repeatedly cited in the literature
pertaining to retention and attrition of college students have been Bean's
Student Attrition Model (Bean, 1985,1990) and Tinto's Student Integration
Model (Pascarella & Terenzini, 1980; Tinto, 1975, 1987,1988). Tinto's model
hypothesizes that a student's persistence is a function of the match between
the institution's academic and social characteristics and the student’ s
motivation and academic ability. The match between the characteristics of
the individual and the characteristics of the institution provides an
indication of the level of commitment to completing college. However, a
major gap in Tinto's theory and research findings has been the role of
external factors such as the influence of significant others, financial
difficulties, and an intervention helping the student to identify persistence
strategies (Cabrera, Casteneda, Nora & Hengstler, 1992).
Bean's model, on the other hand, postulates that behavioral
intentions are shaped by a process that forms attitudes which, in turn, shape
behavioral intents. The beliefs are affected by the student's experiences of
the institution such as assistance with courses, friends, and quality of the
institution. In this manner, Bean's model addresses non-cognitive factors
that play a major role in dropout decisions such as family approval and
environment. Both models postulate persistence as a complexity of
interactions over time (Cabrera, Casteneda, Nora & Hengstler, 1992).
Similarly, Brower (1992) developed a model that addressed a
component of student integration that he called the 'second half' of Tinto’ s
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30
model. That is, Brower postulated that the effects of the life task of the
student, as he or she shapes the environment, is the result of his or her
college life task priorities. Therefore, identifying individual motivations as
related to persistence is pertinent to persistence in college. Intervention
strategies should make use of students’ experiences, goals, motivations,
values as well as program/ institutional policies to facilitate retention.
A psychological model of student persistence developed by Corinna
Ethington (1990), draws upon causally stated relationships between various
cognitive factors, social factors (self-concepts, behaviors, attitudes), success
expectancies (Gordon, 1989), task values, and students goals. These
constructs are hypothesized to influence directly outcomes and/or serve to
act as mediators of influence on other constructs. Ethington found that
prior achievement had the strongest total effect on the variables in the
model and that level of aspirations exhibited as strong a direct influence on
persistence as did value. Interestingly, Ethington also found that level of
aspiration was the dominant mediator of the indirect influence of self-
concept on persistence.
Further studies utilizing non-cognitive factors (Arbona & Novy, 1990;
Boyer & Sedlacek, 1988; Chartrand, 1990; Himelstein, 1992; Holmes, 1992;
Krotseng, 1992; Rowe & Smith, 1990; Sedlacek & Adams-Gaston, 1992;
White & Sedlacek, 1986) have also indicated that predictive and causal
models need to include multi-facet designs. The results of these studies
have aided the development of conceptual models of intervention as well as
planning strategies in retaining students for non-traditional, minority,
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31
international, academically at-risk students and athletes. Very few
conceptual frameworks, however, have applied structural equation
modeling techniques to the analysis of empirical data (Cabrera, Nora &
Casteneda, 1993). That some studies have utilized constructs based on very
few items has made the confirmatory factor analytic techniques exploratory
in nature or suspect to interpretation. For those studies that use normative
data, comparison to non-traditional populations may be biased. Local
sampling for institution-specific populations may render a better fit for the
instruments used in the model as well as the factors relevant to the specific
population.
Statement of the Research Problem
In view of the complexity of previously cited factors and their
contribution to retention of students, this researcher has made use of a
multidimensional approach to examine self-concept and prior indicators of
school performance as they contribute to the survival of first-year
undergraduate academically at-risk students. A multifactor latent-variable
model of early intervention of academically at-risk undergraduate college
students utilizing multidimensional self-concept variables as mediating
factors as predictors of individual differences affecting academic
achievement will be developed and analyzed (see Figure 2). The theoretical
intervention model comprises three main components: (a)prior achievement
indicators (exogenous cognitive variables) such as high school grade point
average (HSGPA), and SAT-Verbal and Math scores;(b) mediating factors
(endogenous non-cognitive variables) such as academic and non-academic
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Prior
Achievement
High School
Grade Point
Average
Scholastic
Aptitude Test
Verbal
Scholastic
Aptitude Test
Math
Cognitive
Variables
Figure 2.
Mediating
Factors
Intervention
Semester
College
Achievement
Academic
Self-Concept
Internal and
External Locus
of Academic
Responsibility
Non-Academic
Self-Concept
Non-Cognitive_
Fall Units
Completed
Spring Units
Completed
Retention
Grade Point
Average
Grade Point
Average
Variables
Outcome
Variables
Theoretical Intervention Model of Academically At-Risk First-Year College Students
w
to
33
self-concept scales; and (c) college achievement (outcome variables) such as
Fall and Spring completed units and grade point average (GPA), and
retention. For the purposes of this study, achievement will be defined as (a)
successfully completing the required units of coursework and (b)
maintaining a grade point avenage of 2.0 or higher; and retention is defined
as continuing enrollment in coursework during the Fall semester of the
second year. The aforementioned model was designed to demonstrate the
effects of prior achievement and self-concept mediating factors on student
academic success and retention. The analysis of the structural model
utilizes structural equation approaches to assess the measurement model,
the latent variables, and the paths within the structural model.
The assessment or measurement portion of the model employs
existing instruments for mediating factors of self-concept (DOSC-H; SDQ-
m) and intellectual achievement responsibility (IAR). Performance data
such as HSGPA and scores on the SAT are used to define prior achievement.
Performance data for satisfactory academic progress such as college grade
point average and number of units completed will be used to define
academic success.
The Research Questions
On the basis of the preceding literature review affording theoretical
rationale, the following research questions were considered:
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34
1. To what extent, if any, did prior achievement influence the
development of self-concept? - - Specifically, academic self-
concept?
2. To what degree, if any, did self-concept act as a mediating
factor between academic achievement and retention (academic
success in college)?
3. To what extent, if any, did academic responsibility as a
measure of internal and external locus of control act as a
mediating factor between academic achievement and retention
(academic success in college)?
4. Were academic self-concept factors more influential than
social self-concept factors as mediators for academic success?
In relation to the preceding review of the empirical literature, the
following expectations arose:
1. Anxiety would be a significant indicator of academic success.
2. Level of aspiration would be a predictor of academic success.
3. Social support (friends and family) would be a predictor of
academic success.
4. Prior academic achievement would not be a strong predictor
of academic success.
The following Chapter Three sets forth the methodology and
psychometric procedures that were used to answer the previously stated
research questions.
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CHAPTER III
METHODS AND PROCEDURES
35
In this chapter, information is presented concerning, (a)
characteristics of the research sample, (b) procedures followed in data
collection, (c) variables and their measures, (d) methological assumptions,
(e) deliminatations, and (f) limitations. Psychometric procedures are
considered in both this chapter and the next chapter in locations that are
deemed appropriate.
Research Sample
The subjects in this investigation were 129 first-year undergraduate
students between the ages of 18 and 21 years old at a four-year private
university (Table 1). The data set included a population of academically at-
risk students that participated in a one-year academic support program
during their first year of studies. The students have been selected by the
University’ s Admissions Office according to standards that do not meet the
criteria for regular admissions to the University. The students in the
population typically had either a lower high school grade point average
(HSGPA) and/or lower scores on the Scholastic Aptitude Test (SAT)
Mathematics (SAT-M) or Verbal portion (SAT-V). However, the University
viewed some of these students as gifted and talented in specific disciplines
such as music, architecture, engineering, cinema, theatre, athletics, and
business. In other words, these students were not academically "well-
rounded" to meet the regular admission requirements. However, they had
something to offer the University Community and showed promise to be
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36
Table 1
Description of Sample Characteristics of
Academically At-Risk First-Year College Students
(N=129)
Gender
N
% L
Female 62 48.1
Male 67 51.9
Ethnidtv
African-American 27 20.9
Caucasian 70 54.3
Chicano/Latino 16 12.4
Asian-American 13 10.1
Native-American 2 1.6
Other 1 .8
Mai or
Architecture 6 4.7
Engineering 5 3.9
Music 10 7.8
Theatre 4 3.1
Business 21 16.3
Undeclared and/or
Undecided 83 64.3
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37
successful if given the opportunity to participate in an academic support
program during their first year of enrollment.
As part of the first-year academic contract agreement with the
University, these students must maintain a 2.0 or higher grade point
average (GPA) and complete a required amount of general education
coursework while remaining on a full-time student status (i.e., complete a
minimum of 24 academic units for satisfactory academic progress and
maintain a letter grade of "C" or higher). The students were also monitored
throughout the year for grades, academic counseling, and personal
concerns. Stipulated in the contract agreement was the requirement that
each student take a battery of assessment tests which included an
assessment of reading and writing skills. In addition, this investigator
administered three instruments that measured non-cognitive dimensions of
self — the Dimensions of Self-Concept - Form H (DOSC-H) (Michael,
Michael & Denny, 1985), the Self-Description Questionnaire IE (SDQIII)
(Marsh, 1991), and the Intellectual Achievement Responsibility
Questionnaire, Form B (IAR) (Crandall, 1978).
Collection of Data
The DOSC-H, SDQIII, and the IAR measures were administered
during the Orientation Program for the sample during the week prior to the
beginning of the Fall 1993 semester of classes. The students were requested
to respond as truthfully as possible to the statements to which there were no
right or wrong responses. The students were also told that the surveys
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38
contained statements on feelings, attitudes, and opinions about school-
related matters. The answer sheets were computer scanned by the
University’ s testing office and a dataset was identified that later matched
student identification number with undergraduate semester grades, units
completed, SAT scores, and HSGPA from the University’ s registration and
admissions records. The student identification number was then recoded by
a case number so that the identity of the student would remain confidential
in the data set according to the requirements of the Buckley Amendment
and the University's Institutional Review Board requirements.
Variables and Their Measures
Non-Cognitive Variables and Their Measures
Three measures of academic and social self-concept were used to
define the factors in this study. They were as follows:
The Dimensions of Self-Concept - Form H-College (DOSC-H) The
DOSC-H assesses academic self-concept utilizing five subscales in the
inventory entitled Level of Aspiration, Anxiety, Academic Interest and
Satisfaction, Leadership and Initiative, and Identification versus Alienation.
The construct validity of the DOSC-H has been established by at least three
studies (Carcosta & Michael, 1986; Michael, Denny, Ireland-Galman &
Michael, 1986; Michael, Smith & Michael, 1989). The instrument has 80
items with 16 items per subscale. Each item provides five response
alternatives: never, seldom, about half the time, very often, and always.
Differential weights of 1,2,3,4, or 5 were assigned, respectively, to the
response alternatives. Each of the five factor dimensions had a potential
score between 16 and 80. A higher score on each scale purportedly
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39
represents a greater degree of the represented construct. The DOSCH is a
self-administered instrument that takes about 15 to 35 minutes to complete.
Directions are printed on the front cover of the test booklet. There is no
time limit.
As stated in Chapter I the rationale for the development of the
instrument was based on a theory of academic self-concept (Michael &
Smith, 1976). The five factor dimensions measured by the DOSC-H are
described as follows accompanied by two illustrative items as reported in
the technical manual (Michael, Smith & Michael, 1989, pp. 1-3):
Level of Aspiration - This factor is a manifestation of
patterns of behaviors that portray the degree to which
achievement levels and academic activities of students are
consistent with their perceptions of their potentialities in
terms of scholastic aptitude or of past and current
attainments.
46. Given the opportunity, I do additional assignments for
extra credit.
61. It is important for me to receive and A in every class I
take.
Anxiety - This second factor reflects behavior patterns
and perceptions associated with emotional instability, a lack
of objectivity, and a heightened or exaggerated concern about
tests and the preservation of self-esteem in relation to
academic performance. Underlying this dimension is often a
failure syndrome that indicates a marked discrepancy
between the stabilized perception of what a student believes
that he can achieve satisfactorily and his idealized perception
of his expectations concerning what his teachers or parents
maintain that he can do. This attitudinal pattern can become
generalized to a self-concept indicative of feeling oneself to be
an unworthy individual ridden with guilt with a possible
need for self-punishment or even self-destruction.
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2. I become quite worried about how well I am doing in
my classes.
72. As test deadline approaches, I become more and more
tense.
Academic Interest and Satisfaction. - This third dimension
portrays the sheer love of learning and pleasure gained by
students doing academic work and in studying new subject
matter, an affective state much like that realized by the
dedicated scholar who gains tremendous satisfaction in
working in the library, reading great books, in writing
research papers, and in conceptualizing new theories or
explanations for observed phenomena-an intrinsic motivation
involving learning for its own sake.
3. I enjoy doing classroom assignments.
78. I like to browse around in the library to find the latest
copies of journals or new books.
Leadership and Initiative. - This fourth factor appears to
represent those behavior patterns and perceptions that are
associated with star-like qualities, in which a student has an
opportunity to demonstrate his mastery of knowledge, to help
others, to give direction to group activities, to become the
respected expert whom others consult, to put forth (hopefully
diplomatically) sound suggestions for classroom activities
reflecting the consensus of other students in a group, to
exhibit a willingness to take the initiative in starting a project
or assignment-either an individual or group endeavor-and to
follow it through to successful completion, and to take pride
without display of conceit of one’ s capabilities to do a job
quickly and well.
9. I enjoy answering questions in class.
14. Other students ask for my help or seek my advice in
completing their assignments.
Identification vs. Alienation. - This fifth dimension is intended
to represent the extent to which a student feels that he has
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41
been accepted as part of the academic community and has
been regarded by his teachers and peers as a significant
person who is respected for his own personal worth and
integrity as a human being, in contrast to a feeling of being
isolated or rejected in the academic environment-a feeling
manifested by hostility toward the academic institution and
its m em bers-fellow students, teachers, counselors,
administrators, and significant others; alienation embodies
considerable resentment if not even defiance of regulations
and rules of the school campus.
5. Professors care about their students.
25. Professors make their courses enjoyable.
The items corresponding to each of the five factors of the DOSC-H
are arranged in an order to minimize the creation of a response set that
could be associated with a particular factor. Every item is arranged in a
cyclical order in Form H and is related to the same factor as follows:
Factor
Aspiration 1, 6,11,16, 21, 26, 31,36,
41,46, 51, 61,66, 71, 76
Related Items
Anxiety 2, 7,12,17,22,27, 32,37,
42, 47,52, 57, 62, 67, 72, 78
Academic Interest
and Satisfaction
3,8,13,18,23,28,33,38,
43,48, 53, 58,63, 68, 73,78
Leadership and
Initiative
4, 9,14,19, 24,29,34, 39,
44,49, 54, 59, 64, 69, 74,79
Identification vs.
Alienation
5, 10, 15,20,25,30,35,40,
45, 50, 55, 60,65, 70, 75, 80
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42
The Self Descriptive Questionnaire - III (SDO-III) TheSDQ-III
purportedly measures general self-concept, social self-concept and
academic self-concept. The SDQIII contains thirteen subscales hypothesized
to measure each of the following facets of self-concept: Mathematics,
Verbal, General Academic, Problem-Solving, Physical Ability, Appearance,
Relations with the Same Sex, Relations with the Opposite Sex, Relations
with Parents, Religion/Spirituality, Honesty/Reliability, Emotional
Stability, and General Self. The construct validity of SDQm was based on
two studies by Marsh & O'Neill (1984) with university-aged respondents.
The reliability estimates of scores on the 13 factors were high (median alpha
being 0.89) and intercorrelations of scores among the factor scales were low
(median correlation being 0.09). Two studies for convergent and
discriminant validity of the responses to the SDQIII were supported
(Marsh, Barnes, & Hocevar, 1985; Marsh, H. H. & Bryne, 1993).
Further, a hierarchical structure of self-concept hypothesized from
the Shavelson Model supported first- and second-order factors derived
from utilizing hierarchical confirmatory factor analysis. Marsh separated
the thirteen facets into second-order academic and non-academic
components. The academic subscale facets were defined by
Mathematics/ Academic and Verbal/ Academic first-order components
(Marsh, 1987). The norms were based on a total of 2,436 Australian subjects.
Marsh (1991) furthers stated in his SDQIII technical manual:
The construct, self-concept, has been widely evoked to explain
overt behaviors across a wide spectrum of situations, and the
attainment of a positive self-concept has been posited as a
desirable goal in education, in child and personality
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43
development, in clinical treatments, and a wide variety of
other settings. Its importance notwithstanding, reviews of
research and evaluations using self-concept continue to point
out important short-comings such as the lack of a theoretical
basis for defining and interpreting the construct and
particularly the poor quality of measurement instruments
used to assess it. In an attempt to remedy this situation,
Shavelson, Hubner and Stanton (1976) posited a multifaceted,
hierarchical model of self-concept, and reviewed well known
criteria for evaluating the measurement of self-concept. This
model served as the basis for all three of the SDQIII
instruments (p. 5).
The instrument when self-administered for respondents 16 years of
age and older, takes about 20 minutes to complete, although there is no time
limit. The instrument has 136 items representing the 13 facets. However,
for the purpose of this study, the two scales -- Honesty and Reliability; and
Religiosity and Spirituality — were omitted from the questionnaire because
they were not relevant to this investigation. Therefore, the questionnaire
retained 112 items. Each scale comprised 10 items, except for the scale
"General Self-Concept" which was composed of 12 items. Each item
provides for eight response alternatives: definitely false, false, mostly false,
more false than true, more true than false, mostly true, true, definitely true.
Differential weights of 1,2,3,4,5, 6,7, or 8 were assigned, respectively, to
the eight response alternatives. Each scale contained five items that were
negatively worded and that were reversed scored. A computerized scoring
program was provided by Herbert Marsh, with permission, to use for the
purposes of this study which was incorporated into the EQS (Bentler, 1993)
statistical analysis control program. Changes were made to the scoring
program to coincide with the 112 items representing the 11 scales ultimately
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44
used for this study. Approximately every twelfth item corresponded to each
of the eleven factors to facilitate scoring and to minimize the creation of a
response set that could be associated with a particular factor. High scores
on each scale of the SDQ - HI can be interpreted that the respondent has a
postive self-perception for each factor. The following is a sample of an item
from each scale (Marsh, 1991, p.12):
1. Mathematics - 1 have good mathematical skills/reasoning ability.
2. Verbal - 1 have good verbal skills/reasoning ability.
3. General Academic - 1 am a good student in most school subjects.
4. Problem Solving - 1 am good at problem solving/creative thinking.
5. Physical Ability - 1 am good at sports and physical activities.
6. Appearance - 1 am physically attractive/good looking.
7. Relations with the Same Sex - 1 have good
interactions /relationships with members of the same sex.
8. Relations with the Opposite Sex - 1 have good
interactions/relationships with members of the opposite sex.
9. Relations with Parents - 1 have good interactions/relationships
with my parents.
10. General Self-Concept - 1 have self-respect, self-confidence, self
acceptance, positive self-feelings and a good self-concept.
11. Emotional Stability - 1 am an emotionally stable person.
The Intellectual Achievement Responsibility Questionnaire (IAR) - B.
The IAR-B assesses beliefs of students about their control and responsibility
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45
for success and failure experiences in the intellectual achievement area. The
IAR is a scale that has demonstrated questionable test-retest reliability of .65
for scores of a sample of 70 ninth grade students (Crandall, Katkovsky &
Crandall, 1965) as well as evidence of divergent and convergent validity
(Lefcourt, 1991). Numerous samples ranging from third to twelfth grade
students have been studied (Crandall, Katkovsky & Preston, 1962; McGhee
& Crandall, 1968). An IAR bibliography of 450 studies dated from 1961-1989
was obtained and persmission to use the instrument for this study was
realized from personal communication with Virginia Crandall, (personal,
March, 19,1994). The IAR was originally developed within the context of a
large research program concerning children's achievement development.
The theory related to the development of the instrument as stated by
Crandall et al, (1965) is as follows:
The IAR also differs from the other assessment methods in the
external environmental forces described....The IAR limits the
source of external control to those persons who most often
come in face-to-face contact with a child, his (sic) parents,
teachers and peers. This restriction was based on two
considerations. The first had to do with the possibility that a
child may attribute different amounts of power or control to
various external agents (Rotter, Seeman & Liverant, 1962).
Consequently, at this early state of investigation, it was
thought advisable to restrict the scale to one type of external
control. A second reason was that it seemed important from a
developmental point of view to focus particularly on
children’ s beliefs in the instrumentality of their own actions
compared with that of other people in their immediate
environment (pp. 93-94).
The questionnaire originally consisted of 32 items with two response
alternatives. V.C. Crandall (1978) reported the development of two short
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46
forms; one for third-fifth graders and one for sixth-twelfth graders.
Correlations between sets of scores on the long and short form subscales
were reported to be quite high: Internal Responsibility for Academic
Success (1+) = .90 and .89; Internal Responsibility for Academic Failures (I-)
= .91 and .88, for younger and older children respectively. For the purposes
of this study, the 20-item short form B was used. Each item stem describes a
positive or negative achievement experience. Each stem is followed by one
alternative stating that the event was caused by the student and another
stating that the event occurred because of the behavior of someone else.
One half of the items measures the student’ s acceptance of responsibility for
positive events; the remaining items measure negative events. Thus, in
addition to a total score for I (internal or self-responsibility) scores, separate
subscores can be obtained for beliefs in internal responsibility for success
(1+ score) and for failures (I- scores). Scores range from 0 (external) to 20
(internal).
The following two items illustrate, respectively, a success experience
and a failure experience:
7. When you leam something quickly in school, is it usually
A. because you paid close attention, or
B. because the teacher explained it clearly?
10. When you don't do well on a test at school, is it
A. because the test is especially hard, or
B. because you didn't study for it?
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47
Cognitive Variables and Their Measures
Both the Scholastic Aptitude Test - Mathematics (SAT-M) and the
Verbal (SAT-V) portion (Educational Testing Service, 1948-96) as well as
students' high school grade point average (HSGPA) were obtained from
university records for the purpose of this study. Students' identification
numbers and case numbers were matched by computer from the
University's Student Information System. The SAT-M, SAT-V and HSGPA
were used as components of the admission procedure for all applicants of
the University. Most University admissions personnel have utilized this
information in selection and prediction of retention of qualified applicants
(Ancrum, 1992; Hossler, 1984,1990,1991). Course grades, number of units
completed, and grade point average (GPA) were also obtained at the end of
the Fall and Spring semesters by utilizing the same procedure as previously
described.
Psychometric Analysis
Utilizing the Statistical Package for the Social Sciences (SPSS, 1990)
on the University's UNIX computer system, the researcher obtained
descriptive statistics, (means and standard deviations) for each semester
GPA, overall GPA, number of units completed, SAT-V and SAT-M scores
(Table 2). Additionally, a comparison sample of regularly admitted students
was selected from the University data base that met the descriptive criteria
of gender and ethnicity for the same year as that for the admitted at-risk
students (Table 3). Correlations between scores on corresponding pairs of
variables were computed to compare the University regularly admitted
sample and the at-risk sample on the just cited cognitive variables (Table 4).
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48
Means and standard deviations were computed for scores on each of the
subscales of the DOSC-H, SDQ HI and the IAR. Intercorrelations on scores
of corresponding pairs of variables for the two samples were determined.
Construct Validity and Reliability
Intemal-consistency estimates of reliability, (alpha coefficients)
(Cronbach, 1951) of scores were computed on each non-cognitive subscale
of the DOSC-H, the SDQ HI, and the IAR. In addition, an item-analysis
was performed by correlating item scores with the unweighted composite
scores of each subscale of the DOSC-H and the SDQ ID (SPSS, 1990). In
instances of items being correlated with subscales of which it was an
intended member, a corrected item total correlation was used to correct for
spurious overlap (SPSS, 1990). A hit rate was calculated by counting the
number of times item scores were correlated higher with the subscale scores
with which it was hypothesized to be a member than with other subscale
scores. Further, principal axis factoring (exploratory factor analysis)
utilizing oblique (oblimin) and orthogonal (varimax) rotation of factor axes
was performed on the DOSC-H and the SDQ HI.
Intercorrelation matrices of all cognitive and non-cognitive variables
were generated as part of the inspection and validation of the measurement
component for this study.
Confirmatory Factor Analysis of Hypothesized Measurement Models
Utilizing procedures similar to first-order factor analysis, this
researcher analyzed each selected hypothesized measurement model for
the goodness of fit indices such as the following: The Non-Normed Fit
Index (NNFI), the Confirmatory Factor Index (CFI), Chi-Square (X2); and
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49
Table 2
Summary of Academic Achievement Variables of
Academically At-Risk First-Year College Students
(N=129)
Variable Mean Standard
Deviation
Range
SAT-Verbal 404.23 76.71 200-610
SAT-Math 484.07 81.03 300-730
High School GPA 2.87 .33 2.01-3.69
Fall Units 14.50 2.89 4-18
Fall GPA 2.53 .65 .43-3.97
Spring Units 13.35 4.43 0-19
Spring GPA 2.30 .81 .00-3.78
Overall GPA 2.45 .57 .60-3.72
Overall Units 27.77 6.19 10-36
Note: Retention rate = 88% after first year of enrollment for this sample
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50
Table 3
Summary of Academic Achievement Variables of
University Comparison Sample of
First-Year College Students
(N=125)
Variable Mean Standard
Deviation
SAT-Verbal 508.51 94.09
SAT-Math 579.09 91.48
High School GPA 3.31 .44
Fall GPA 2.52 1.05
Spring GPA 2.39 1.16
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51
Table 4
Correlations of Academic Achievement Variables of
University Sample of First-Year College Students (Lower Triangle) and
First-Year Academically At-Risk College Students (Upper Triangle)
Variable I II III IV V
I Fall GPA 1.00 .27** -.10 -.05 .21*
II Spring GPA .40** 1.00 -.10 -.04 .15
III SAT-Math .25** .08 1.00 .36** -.05
IV SAT-Verbal .22** .09 .49** 1.00 -.13
V High School GPA .33** .36** .41** .33** 1.00
*p<.05; **p<.01
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52
Chi-Square/degrees of freedom (X^/df). Inferential tests of statistically
significant loadings were also performed.
Construct validity was assessed on the DOSC-H and the SDQ IE for
both first-order and higher-order factors by utilizing confirmatory factor
analysis (CFA) with EQS (Bentler, 1990). A two-step procedure was used
where the first step was to assess first-order factors; the second-step
assessed plausible empirical relationships based on first-order factors, for
the higher-order factor structure (Benson & Bandalos, 1992; Bryne, 1994;
Loehlin, 1987; Marsh, 1990c, 1990e; Marsh & Hocevar, 1985). Therefore,
confirmatory maximum likelihood factor analysis (CFA) was performed
which produced solutions for several first-order factor models. The first-
order factor models were compared by using the aforementioned indexes of
fit. For higher-order confirmatory factor analysis (HCFA), partially
aggregated models used HCFA which is similar to procedures used in first-
order CFA (Bagozzi & Heatherton, 1994). The null HCFA model
represented uncorrelated first-order factors instead of the uncorrelated
subtests. The Tucker-Lewis Goodness of Fit Index (which is identical to the
Non-Normed Fit Index) is equivalent to the goodness of fit indicator (the
Hierarchical Tucker-Lewis index) in the HCFA approach.
A final latent factor model was then selected, based on significant
psychometric properties and theortical rationale that served as part of the
measurement component in the structural equation model. Finally, for each
model, the same sequence of analysis was performed until a final model
was selected on the degree of fit of the model, the adequacy of the
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53
hypothesized factor loadings, and the intercorrelations between latent
constructs and observed variables.
Structural Equation Modeling
For the purposes of this study, a structural equations modeling
(SEM) approach was employed that incorporated the measurement
component, which assessed the relationship between the factors and the
measures used to define them, and the structural component which
assessed the relationship among the independent and dependent variables.
Finally, the structural model with the latent variables was estimated and
evaluated simultaneously. In this manner, the structural equation modeling
approach incorporated the strengths of multiple regression analysis, factor
analysis, and multivariate analysis in a single model that could be evaluated
statistically (Bentler, 1980; 1990; Bollen, 1989; Fassinger, 1987; Hoyle &
Smith, 1994; Newcomb, 1990; Pedhazur & Schmelkin, 1991). The strength in
this methodology is the process of including the measurement model in the
path analysis to correct for attenuation due to measurement error.
Theoretically, the use of multiple indicator latent factors helps to separate
random measurement error from true scores which, in turn, can assess
residuals as predictors or outcomes that can utilize nonstandard or specific
paths (Newcomb, 1994).
The EQS structual equations program (Bentler, 1990) was used to
perform the CFA for the structural model. The Lagrange Multiplier test
was applied to examine the possibility of freeing previously fixed
parameters based on sound theoretical rationale (Bentler, 1990; Bryne, 1994).
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54
The Wald test was also utilized to evaluate the effect of removing
nonsignificant paths in the initial structural model (Bentler & Chou, 1990).
Mediating factor hypotheses were also tested using structural
equation modeling which was advocated by Baron and Kenny (1986). The
SEM approach, therefore, avoids problems of over and underestimation of
mediated effects by controlling for measurement error and permits
estimation of models that include multiple mediators (Bollen, 1989; Hoyle &
Smith, 1994; Shadish & Sweeney, 1991).
In this manner, the hypothesized theoretical model in this study was
tested for reliability and validity and as a measurement model involving an
early predictive intervention for academically at-risk first-year college
students was evaluated.
Methodological Assumptions
The following methodological assumptions were made in this study:
1. The subjects responded honestly and accurately.
2. The data were accurately recorded, analyzed and interpreted.
3. The design, sample collection, and data analysis technique were
appropriate to this investigation.
Delimitations
1. This study is limited to academically at-risk first-year college
students at a private four-year urban university.
2. This study was limited to subjects that were part of a one-year
academic support program that were required to participate in the
orientation program assessment.
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55
Limitations
1. The internal and external validity of the study would be limited to
the extent that any of the methodological assumptions cited
previously were not met.
2. This study would need to be cross-validated because of the
exploratory nature of the analyses.
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CHAPTER IV
56
ANALYSES AND INTERPRETATION OF RESULTS
In this chapter the statistical outcomes of the investigation are
reported to furnish the information base for answering the research
questions.
Statistical Outcomes
Descriptive Statistics and Reliability Estimates
Descriptive statistics and estimate of intemal-consistency reliability
using coefficient alpha which were analyzed by SPSS (SPSS, 1990) for each
of the subscales of the DOSC-H, SDQ-III and the IAR are presented in Table
5. For the DOSC-H intemal-consistency reliability estimates (coefficient-
alpha) for scores on Level of Aspiration (ASP), Anxiety(ANX), Academic
Interest and Satisfaction (ALAS), Leadership and Initiative (LAI), and
Identification vs. Alienation (IA) were .90, .90, .84, .86, and .84, respectively.
Coefficient alpha for the SDQ HI subscales scores on Mathematics (MATH),
Verbal (VERB), Problem-Solving (PROB), Academic Self-Concept (ACAD),
Relations with the Same Sex (SSEX), Relations with the Opposite Sex
(OPSEX), Relations with Parents (PAR), Emotional Stability (EMOT),
Physical Ability (PHYAB), Physical Appearance (PHYAP), and General
Self-Concept (GEN) were .94, .84, .80, .88, .78, .89, .84, .84, .92, .88, and .93,
respectively. All subscales of the DOSC-H and the SDQ HI reached a
satisfactory level of internal reliability as indicated by coefficient alpha.
Internal reliability of the IAR scales 1+ and I- were .46 and .61 respectively.
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57
Table 5
Summary of Descriptive Statistics and Estimates of
Intemal-Consistency Reliability (Coefficient Alpha) for
the Scales on the DOSC-H, SDQ HI, and IAR
Variable Mean Standard
Deviation
Reliability
(alpha)
DOSC-H
Level of Aspirations 58.20 10.57 .90
Anxiety 42.97 11.36 .90
Academic Interest & Satisfaction 47.37 8.40 .84
Leadership and Initiative 46.08 9.12 .86
Identification vs. Alienation 53.32 6.90 .84
SDQ III
Academic Scales
Math 45.80 17.38 .94
Verbal 50.57 12.27 .84
Problem-Solving 54.21 10.78 .80
Academic 53.24 11.69 .88
Non-Academic Scales
Same Sex 60.34 9.54 .78
Opposite Sex 60.29 12.92 .89
Parents 58.92 11.72 .84
Emotional Stability 52.32 12.34 .84
Physical Ability 60.36 13.97 .92
Physical Appearance 54.24 11.86 .88
General Self 77.21 14.81 .93
IAR
Intemality + 11.28 1.56 .46
Intemality - 13.28 2.10 .61
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58
Item-Analvses
Item-analyses data for the DOSC-H and the SDQ III were obtained
for scores on each of the subscales. A hit rate was obtained by counting the
number of times items were correlated higher with the subscales of which
they were members than they were with other subscales. For instances of
items being correlated with the subscale of which they were members, a
corrected item/ total correlation was used to adjust for spurious overlap
(SPSS, 1990). Of the 80 items on the DOSC-H, 78 were correlated more
highly with the subscale for which they were intended to be members than
with any other subscales. The hit rates for ASP, ANX, AIAS, LAI, and IA
were 16/16,16/16,16/16,16/16, and 14/16 respectively. The overall hit
rate was 97.5%. For the 112 items on the SDQ HI, 111 were correlated more
highly with the subscale for which they were intended to be members. The
hit rates for MATH, GENR, OSEX, VERBAL, EMOT, PRNT, ACAD, PROB,
PHYSAP, SSEX, and PHYSAB were 10/10,12/12,9/10,10/10,10/10,
10/10,10/10,10/10,10/10,10/10 and 10/10, respectively. The overall hit
rate for the SDQ HI was 99%.
Exploratory Factor Analyses
Exploratory factor analyses were carried out for the DOSC-H and the
SDQ HI. Factor loadings and factor pattern matrices were analyzed from
orthogonal (varimax) and oblique (oblimin) solutions for each self-concept
measurement instrument (SPSS,1990). Subtests that had high loadings, .40
or higher, on the hypothesized factor had lower loadings on the other
factors. For the SDQ HI, principal axis factoring was performed using a
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59
covariance matrix of 33 subtests. A high degree of simple structure was
obtained for ten of the eleven scales on the SDQ El on both oblique and
orthogonal matrices. Principal axis factoring obtained on the DOSC-H
utilized a covariance matrix of 20 subtests. Simple structure was obtained
on four of the five scales as indicated in both the oblique and orthogonal
matices. hi other words, both the SDQ El and the DOSC-H principal axis
factoring indicated that one of the subscales did not have evidence of higher
loadings on the factor that it had been hypothesized to represent. However,
the remaining subscales, ten for the SDQ El and four for the DOSC-H,
indicated evidence of high loadings on the factor for which they were
hypothesized to represent.
Confirmatory Factor Analysis (CFA) and Hierarchical Confirmatory Factor
Analysis (HCFA) of the DOSC-H and the SDQ IE.
Further evidence of construct validity was sought by utilizing EQS
(Bentler, 1990) confirmatory maximum likelihood factor analysis (CFA)
procedure which was employed to assess first-order factor models for both
the DOSC-H and the SDQ IE. The first-order and higher-order factor
models were assessed by using four types of statistical evidence: X^/df, p-
value, and indices of goodness-of-fit. Results of these analysis are
presented on Table 6. CFA findings for the DOSC-H best supported the
five-factor oblique model with the Non-Normed Fit Index (NNFI) (Bentler,
1993) and Comparative Fit Index (CFI) (Bentler, 1990) values of .93 and .94.
respectively. The CFA findings for the SDQ IE best supported the eleven-
factor oblique model with the NNFI and CFI values of .93 and .94
respectively.
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60
Table 6
Summary of First-Order and Second-Order Confirmatory Factor Analysis (CFA) and
Related Fit Indices of the DOSC-H and the SDQ III
Model X2
Degrees of
Freedom
x2/df P-Value NNFI CFI
DOSC-H
• Null 1680.07 190 8.84 <.001 .00 .00
• G Factor 870.17 170 5.12 <.001 .48 .53
• 2 Factor Oblique 557.30 169 3.30 <.001 .71 .74
• 4 Factor Oblique 331.58 164 2.02 <.001 .87 .89
• 5 Factor Orthog. 486.40 170 2.86 <.001 .76 .79
• 5 Factor Oblique 245.17 160 1.53 <.001 .93 .94
SDQ III
• Null Model 3509.71 528 6.65 <.001 .00 .00
• 11 1st Order & G 852.75 484 1.76 <.001 .87 .88
• 11 1st Order &
2 Correlated
Second Order 829.75 482 1.72 <.001 .87 .87
• 11 1st Order &
2 Second Order
& General 836.01 483 1.73 <.001 .87 .88
• 11 1st Order &
3 Second Order
& General 812.25 481 1.69 <.001 .89 .89
•111st Order &
4 Second Order
& General 804.72 481 1.67 <.001 .88 .89
• 11 Factors
Orthogonal 1276.59 495 2.58 <.001 .72 .74
•11 Factor
Oblique 728.78 440 1.66 <.001 .88 .90
NNFI = non-normed fit index (Bentler, 1993); this is identical to the Tucker-Lewis fit index
(Tucker & Lewis, 1973).
CFI = comparative fit index (Bentler, 1990).
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61
Correlations
Additionally, zero-order correlations matrices were computed
among all cognitive and non-cognitive variables. Non-cognitive variable
intercorrelations of the subscales of the DOSC-H, SDQ HI, and the LAR
measures are presented in Table 7. Cognitive variable intercorrelations of
the SAT-Verbal, SAT-Mathematics, HSGPA, Fall GPA, Spring units
(number of), Spring GPA, cumulative GPA, cumulative units, composition
and general education courses combined, composition courses, and
retention status, are presented in Table 8. Lastly, combined non-cognitive
and cognitive variable intercorrelations are presented in Table 9.
Confirmatory Factor Analysis (CFA) of Hypothesized
Measurement Models
Measurement models were hypothesized and analyzed for the
measurement component of the structural model to ascertain that the
measured variables and the mediating factors were supported as well as to
examine the intercorrelations among the latent constructs and exogenous
variables. In each model, all factors and variables which were indicators of
factors were allowed to correlate freely, all factor loadings were freed, and
factor variances were constrained at 1.00 in order to identify constructs.
Presented in Table 10 is a summary of all fit indices for the intial and
final CFAs for the DOSC-H, SDQ III, and the combined variables of the
DOSC-H and the SDQ HI. Results of the CFAs of all the hypothesized
models are presented along with the corresponding figures.
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Table 7
Self-Concept Factor Intercorrelations of the DOSC-H, IAR, and the SDQ-I11
Factor 1 1 1 1 1 1 IV V VI VII VIII IX X XI XII XIII XIV XV XVI XVII XVIII
I
Level of Aspiration
1.00
II
Artsiety
.18* 1.00
III
Acad, Interest A Satisfaction
.66" .15 1.00
IV
Leadership ft Inbabve
.35" -.32“ .62” 1.00
V
Identification vs. Alienation
.53" -.03 .60” .44” .1.00
VI
Responsibility for N'eg. Events
.19" .17 .11 .14 -.02 1.00
VII
Responsibility for I'm Events
.02 .38" -.04 -.14 -.22* .28” 1.00
VIII
Math Self-Concept
.08 -.15 .13 .11 .14 -.05 -.29" 1.00
IX
General Self-Concept
.16 -.47** .22" .38" .42" .05 -.34“ .22* 1.00
X
Relationship sstth O pposite Set
.02 -.43“ .01 .28" .24” .16 -.23" -.03 .59" 1.00
X I
Verbal Self-Concept
.10 -.44“ .22* .43" .19* -.04 -.18* -.16 .46" .34" 1.00
XII
Emotional Stability
-.22* -.57" -.15 .11 .07 -.13 -.23" .16 .60" .43" .33" 1.00
XIII
Relationship with Parents
.08 -.25" .17 .09 33" .03 -.24** .07 .52" .37" .25** .33" 1.00
XIV
Academic Self-Concept
.46* -.24" .57" .49** .55" .02 -.32** .29" .50" .19* .44" .20* .31” 1.00
XV
Problem-Solving SeKConcept
.26" -.35" .36” .50" .31“ .06 -.14 .30" .52" .17* .40” .32” .26” .52” 1.00
XVI
Physical Appearance SelfConcept
.00 -.32" .13 .27** .13 .16 -.36" .18* .54" .43" .36** .35” .32** .34** .37** 1.00
XVII
Relationship w ith Same Sen
.07 -20* .02 .12 .19* -.07 -.23" .03 .44" .46" .40" .34" .21* .24" .18* .25” 1.00
XVIII
Physical Ability SelfConcept
.12 -.20* .13 .13 03 -.04 -.26" .09 .42** .30" .24** .36" .26“ .23" .24" .50" .26" 1.00
■ p<.05; "p<.01
Note: Factors l-V = DOSC-H; Factors VI VII = IAR; Factors VII1-XVI1I = SDQ-lll.
O S
N J
63
Table 8
Intcrcorrelations of Academic Variables
Factor 1 II III IV V VI Vll VIII IX X XI XII XIII
1 SAT-Verbal 1.00
II SAT-Math .36" 1 .1 X 1
III Fall Units -.14 -.20* 1.00
IV High School GPA -.13 -.05 .01 1.00
V Fall GPA -.05 -.10 .43** .21* 1.00
VI Spring Units -.09 -.19* .41** .16 .25“ 1.00
Vll Spring GPA -.04 -.10 .29** .15 .27" .69" 1.00
VIII Cumulative GPA -.01 -.08 .45** .23* .81“ .41** .63“ 1.00
IX Cumulative Units -.10 -.23* .74** .09 .37“ .90“ .63" .50" 1.00
X Comp. & GF Courses .07 -.02 .34** .11 .82" .13 .14 .62” .24" 1.00
XI Composition Courses -.13 -.14 .43“ -.01 .65" .16 .26" .64" .31" .74" 1.00
XII General Ed. (GE) Courses .00 .00 .29" .08 .79“ .10 .05 .57" .19 .88* .3 8 " 1.00
XIII Retention -.19* -.05 .31“ .10 .24" .38” .43" .40" .42" .09 .11 .07 1.00
*p<.05; **p<.01
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Table 9
64
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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
65
Table 10
Summary of Confirmatory Factor Analysis (CFA) of Hypothesized Measurement Models and
Related Fit Indices of the DOSC-H and the SDQ III
MODEL X 2
Degrees of
Freedom
X2 /d f P-Value NNFI CFI Figure ft
DOSC-H
• 1 Factor C 53.28 5 10.66 <.001 .59 .80 3
• 1 Factor (4 variables)
& Anxiety 1234 4 3.08 .02 .91 .97 4
(Model 1)
SDQ III
• 1 Factor 117.72 44 2.68 <.001 .79 .83 5
• 2 Factor
(Non-Academic &
Academic) 93.85 43 2.18 <.001 .85 .89 6
* 2 Factor & General
(Model 2) 88.78 42 2.11 <.001 .86 .90 7
DOSC-H & SDQ III
• 1 Factor G 504.99 104 4.86 <.001 .47 54 8
• 2 Factor
DOSC-H &
SDQ III 366.19 481 356 <.001 .65 .70 8
• 2 Factor
Acad. & General 442.74 103 4.30 <.001 .55 .61 10
• 3 Factor
Acad., Interpersonal,
& General 434.73 101 4.30 <.001 55 .62 11
• 4 Factor
Acad., Interpersonal,
Phys., & General 418.62 99 433 <.001 .56 .63 12
• 2 Factor &
Anxiety 351.87 102 3.45 <.001 .66 .71 13
• 3 Factor & Anxiety
D O SC -H *
SDQ III Acad. &
SDQ III Non-Acad. 237.07 112 2.12 <.001 .72 .83 14
(Model 3 - Initial M easurem ent Model)
• 3 Factor & Anxiety
DO SC-H*
SDQ III Acad. &
SDQ HI Non-Acad. 218.26 110 1.98 <.001 .75 .88 15
(Model 4 - Final M easurem ent Model)
Structural Model
• Initial STR Model 215.81 110 1.96 <.001 .76 .86
• Final STR Model 191.56 142 1.35 .1 X 1 4 ,U 1 «4 17
NNFI = non-normed fit index (Bentler, 1993); this is identical to theTuckcr-Lewis fit index (T ucker* Lewis, 1973).
CFI = comparative fit index (Bentler, 1990)
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66
Diquensions of Self-Concept - Form H (DOSC-H)
One-Factor General. The One-Factor General DOSC-H CFA model
had a poor fit according to the goodness of fit indicators, X2 (5, N=129) =
53.28, x 2 /d f = 10.66, p<.001, NNFI = .59, and CFI = .80. All hypothesized
factor loadings, however, were statistically significant in the expected
directions. These loadings are presented in Figure 3. Further, it was
hypothesized that the variable, Anxiety which had a weaker loading, would
provide a better fit if it was correlated with the DOSC-H General Factor as
follows:
One-Factor with four variables and Anxiety (Model 1). The One-
Factor (Four Variable Indicator) and Correlated Anxiety CFA model fit well
according to the goodness of fit indicators, X 2 (4, N=129) = 12.34, X2/d f =
3.08, p<.05, NNFI = .91 and CFI = .97. All hypothesized factor loadings
were statistically significant in the expected direction and are presented in
Figure 4. The hypothesized correlation of the variable, Anxiety, proved to
have a non-significant correlation with the DOSC-H General Factor. It was
decided to retain Model 1 as a component of the measurement model and to
continue with CFA procedures with the SDQ HI as follows:
Self-Description Questionnaire HI (SDQ HI)
One-Factor: General. The One-Factor SDQ HI General model had a
poor fit according to the goodness of fit indicators, X2 (44, N=129) = 117.72,
X2/d f = 2.68, gc.001, NNFI = .79 and CFI = .83. All hypothesized factor
loadings were statistically significant in the expected direction. They are
presented in Figure 5. However, as theoretically hypothesized, and as a
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67
Level of
A spiration © - ►
© " ►
.69’
Academic
Interest &
Satisfaction
.96
.64'
Identification
vs. A lienation
DOSC-H
G - Academic
.63
.12’
© - ►
Anxiety
Figure 3. One-Factor Model: DOSC-H General
Academic Self-Concept
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68
. 68 ’
..97'
.63'
DOSC-H
G - Academic
.63
.15 (n.s.)
Identification
vs. A lienation
A nxiety
Level of
A spiration
Academic
Interest &
Satisfaction
Figure 4. One-Factor Model: DOSC-H General
Academic Self-Concept with Anxiety
as a Correlated Variable
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69
© “ ►
® - +
M ath
Self-Concept
Verbal
Self-Concept
Problem-
Solving
Self-Concept
Academic
Self-Concept
/T7N R elationship
W ‘Y' * ■ "
W ith Parents
@ -
0 - ^
Relationship
With
Same Sex
R elationship
With
O pposite Sex
Physical
Ability
Self-Concept
Physical
A ppearance
Self-Concept
Emotional
Stability
General
Self-Concept
Figure 5. One-Factor Model: SDQ III General
Academic Self-Concept
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70
result of the weak goodness of fit indicators, it was decided to perform
further CFA’ s on a two-factor model comprising Non-Academic and
Academic Self-Concept factors. The results of the CFAs are in the
paragraph to follow:
Two-Factor: Non-Academic and Academic. The Two-Factor SDQ ID
Academic and Non-Academic model had a relatively poor fit according to
the goodness of fit indicators, (43, N=129) = 93.85, X^/ df = 2.18, pc.001,
NNFI = .85 and CFI = .89. All hypothesized factor loadings presented in
Figure 6 were statistically significant in the expected direction. In order to
improve the goodness of fit indicator in conjunction with inspection of the
Lagrange Multiplier Test, it was further hypothesized from the research
literature of Herbert Marsh that both the Academic and Non-Academic
factors would be loaded on the variable General Self-Concept. Therefore,
the results obtained are presented in the next paragraph:
Two-Factor: Non-Academic and Academic and General Self-Concept
(Model 2). The Two -Factor SDQ El Non-Academic and Academic with
General Self-Concept model was a good model fit according to the
goodness of fit indicators, X^ (42, N=129) = 88.78, X^/df = 2.11, pc.001,
NNFI = .86 and CFI = .90. All hypothesized factor loadings were
statistically significant in the expected direction. They are set forth in
Figure 7. An improved goodness of fit as indicated by the CFI was
obtained. Therefore, Model 2 was retained as a component of the
measurement model.
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71
M ath
Self-Concept
.28'
Verbal
Self-Concept .58'
Problem-
Solving
Self-Concept
.71
SDQ III
Academic
.73
Academic
SelfC oncept
Relationship
W ith Parents
.55'
.74’
R elationship
With
Same Sex
.50
R elationship
With
O pposite Sex
.65
•76.
SDQ III
Non-Academic
Physical
Ability
SelfC oncept
.50
.62
Physical
A ppearance
SelfC oncept
.63
Emotional
Stability
.93
General
SelfC oncept
Figure 6. Two-Factor Model: SDQ III Academic and
Non-Academic
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© - ►
M ath
Self-Concept
V erbal
Self-Concept
Problem-
Solving
Self-Concept
/ 0 \ ^ Academic
Self-Concept
S —^ G eneral
\ 1 £ / ^ ’ Self-Concept
(& -+ ■
R elationship
W ith Parents
R elationship
W ith
Sam e Sex
R elationship
W ith
O pposite Sex
Physical
Ability
Self-Concept
Physical
A ppearance
Self-Concept
Emotional
Stability
SDQ III
Academic
SDQ 1 1 1
Non-Academic
Figure 7. Two-Factor Model: SDQ III
Academic and Non-Academic Utilizing
General Self-Concept (Model 2)
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73
DOSC-H and SDQ in Combined
Further hypothesized models were analyzed as combinations of both
the DOSC-H and the SDQ-in. The following is an analysis of the goodness
of fit indicators:
One-Factor: General. The One-Factor DOSC-H and SDQ in General
Self-Concept model comprising 16 variables was not a good model fit
according to the goodness of fit indicators, X2 (104, N=129) = 504.99, X2 /df
= 4.86, pc.001, NNFI = .47 and CFI = .54. All hypothesized factor loadings
were statistically significant in the expected direction. They are cited in
Figure 8. However, the NNFI and the CFI provided poor fit results.
Therefore, it was hypothesized that a two factor model of DOSC-H General
and SDQ HI General factors be analyzed for goodness of fit. The results
were as follows:
Two-Factor: DOSC-H General and SDQ III General. The Two-Factor
DOSC-H General and SDQ HI General Self-Concept model was not a good
fit according to the goodness of fit indicators, X2 (481, N=129) = 366.19, X2
/d f = 3.56, gc.001, NNFI = .65 and CFI = .70. All hypothesized factor
loadings were statistically significant in the expected direction. They are
reported in Figure 9. It was hypothesized that the variables comprising
solely Academic Self-Concepts and the remaining variables as General Self-
Concepts be analyzed for goodness of fit indicators. The results of the
analyses was as follows:
Two-Factor: Academic Self-Concept and General. The Two-Factor
Academic Self-Concept and General Self-Concept model was not a good fit
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74
Level of
Aspiration
Academic
Interest &
Satisfaction
Verbal
Self-Concept
_ I’roblem
1 7 9 ) - # ■ g iv in g
^ Self-Concept
Academic
SelfConcept
Identification
vs. Alienation
Leadership &
Initiative
DOSC-H &
SEX} III
General
Relationship
With Parents
Relationship
With
Same Sex
Relationship
With
Opposite Sex
Physical
Anility
Physical
Appearance
Self-Conceot
Anxiety
Emotional
Stability
General
SelfConcept
Figure 8. One-Factor Model: DOSC-H and SDQ III
General Self-Concept
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75
0- > -
©"►
Level of
Aspiration
Academic
Interest &
Satisfaction
.91*
.69*
Identification
vs. Alienation
DOSC-H
General
Math
Self-Concept
.22-
Verbal
Self-Concept
.55'
.82.
.57'
Academic
Self-Concept .56-
Relationship
With Parents
.54'
Relationship
With
Same Sex
.49*
SDQ III
General
Relationship
With
Opposite Sex
Physical
Ability
Self-Concept
Physical
Appearance
Self-Concept
.63*
.90" Emotional
Stability
.44
Figure 9. Two-Factor Model: DOSC-H and SDQ III
General Self-Concept
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76
according to the goodness of fit indicators, X2 (103, N=129) = 442.74, X2/df
= 4.30, £<.001, NNFI = .55 and CFI = .61. All hypothesized factor loadings
were statistically significant in the expected direction. They are presented
in Figure 10. On the basis of the research findings by Herbert Marsh and
his colleagues it was hypothesized that a three factor model comprising of
Academic Self-Concept, Interpersonal Self-Concept and General Self-
Concept be assessed for goodness of fit indicators. The results were as
follows:
Three-Factor: Academic Self-Concept, Interpersonal and General.
The Three-Factor Academic Self-Concept, Interpersonal and General Self-
Concept model was not a good fit according to the the goodness of fit
indicators, X2 (101, N=129) = 434.73, x2/df = 4.30, pc.001, NNFI = .55 and
CFI = .62. All hypothesized factor loadings were statistically significant in
the expected direction. They are reported in Figure 11. Further
hypothesized factors for a Four Factor Model included Academic Self-
Concept, Interpersonal Self-Concept, Physical Self-Concept, and General
Self-Concept. The results of the Four Factor Model was as follows:
Four-Factor: Academic Self-Concept. Interpersonal. Physical, and
General. The Four-Factor Academic Self-Concept, Interpersonal, Physical,
and General Self-Concept model was not a good fit according to the
goodness of fit indicators, X2 (99, N=129) = 418.62, X2/d f = 4.23, £<.001,
NNFI = .56 and CFI = .63. All hypothesized factor loadings were
statistically significant in th expected direction and are presented in Figure
12. As a result of the previous results of the One Factor DOSC-H General
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77
Level of
Aspiration
Academic
Interest &
Satisfaction
.64*
Math
Self-Concept
.29*
.49*
.63*
.87*
,—v . Academic
(4 ‘ P ► Self-Concept
Identification
vs. Alienation
.43*
Leadership &
Initiative
[43*
Relationship
With Parents
* 5 5 *
Relationship
With
Same Sex
49*
Opposite Sex
Physical
Ability
Self-Concept
General
.61*
Physical
Appearance
Self-Concept
Emotional
Stability
Figure 10. Two-Factor Model: DOSC-H and SDQ III
General and Academic Self-Concept
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78
Level of
Aspiration
Academic
Interest &
Satisfaction
Academic
5c) f-Concept
® -*> & ,V crb al
Sclf-Concept
iTobiem
Solving
Sd f-Concept
Academic
Self-Con ccpt
Identification
vs. Alienation
Leadership &
Initiative
Relationship
With Parents
Interpersonal
Relationship
With
Same Sex
Relationship
With
Opposite Sex
Physical
Physical
( hoV —► ) Appearance
Anxiety
Genera
Emotional
Stability
General
Sdf-Concept
Figure 11. Three-Factor Model: DOSC-H and SDQ III
Academic, Interpersonal, and General Self-Concept
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79
Level of
Aspiration
Academic
Interest &
Satisfaction
Math
Self-Concept
Academic
Self-Concept
Verbal
Self-Concept
Problem-
C 7 9 > -> • Solving
• Self^oncegt
Academic
Self-Concept
Identification
vs. Alienation
Leadership &
initiative
Kelationshi
interpersonal
With Paren
Relationship
With
Same Sex
Relationship
With
Opposite Sex
Physical
Ability
Self-Concept
Physical
Appearance
Self-Concept
Anxiety
Genera
Emotional
Stability
General
Self-Concept
Figure 12. Four-Factor Model: DOSC-H and SDQ III
Academic, Interpersonal, Physical, and General Self-Concept
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80
and correlated variable Anxiety (refer to Figure 4) it was decided to analyze
a two factor model comprising of DOSC-H General, SDQ HI General and
correlate the variable Anxiety with both Factors. The findings were as
follows:
Two-Factor: DOSC-H General. SDQ HI General and Anxiety. The
Two-Factor DOSC-H General Self-Concept, SDQ HI General Self-Concept
and Anxiety model was not a good fit according to the goodness of fit
indicators, X2 (102, N=129) = 351.87, X 2/d f = 3.45, pc.001, NNFI = .66 and
CFI = .71. All hypothezsized factor loadings were statistically significant in
the expected direction and are presented in Figure 13. The final
hypothesized model was the following Three Factor Model comprising of
DOSC-H General factor correlated with the variable Anxiety, SDQ HI
Academic Self-Concept factor, and SDQ HI Non-Academic factor. The
results of the hypothesized initial measurement model were as follows:
Three Factor: DOSC-H General. SDQ IP Academic. SDQ III Non-
Academic and Anxiety (Model 3 - Initial Measurement Model). The Three-
Factor DOSC-H General, SDQ IP Academic, SDQ PI Non-Academic and
Anxiety model was not a good fit according to the goodness of fit indicator,
X2 (112, N=129) = 237.07, X2/d f = 2.12, p<.001, NNFI = .72 and CFI = .83.
All hypothesized factor loadings were statistically significant in the
expected direction. They are dted in Figure 14. Lagrange and Wald tests
were inspected to correlate errors and loadings on Mathematics and Verbal
Self-Concept variables. The result improved the goodness of fit indicators
for the final measurement model as follows:
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81
© “►
Level of
Aspiration
.71*
Academic
Interest &
Satisfaction
.93*
.68* Identification
vs. Alienation
DOSC-H
General
.64*
.10
Math
V 2 2 T -P ■ Self-Concept
.22“
Verbal
Self-Concept
.57*
Anxiety
Problem-
Solving
Self-Concept
.58*
Relationship
With Parents
Relationship
With
Same Sex
.49*
SDQ III
General
Relationship
With
Opposite Sex
.49*
.62*
Physical
Appearance
Selt-Conceot
.89*
General
Self-Concept 46
Figure 13. Two-Factor Model with Anxiety as a Correlated
Variable to the DOSC-H and SDQ III General Self-Concepts
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82
Anxiety
Level of
Aspiration
DOSC-H
General
Identification
vs. Alienation
verbal
Self-Concept
SDQ 1 1 1
Academic
Problem
Solving
Self-Concept
Academic
Self-Concept
Relationship
With Parents
Relationship
With
Same Sex
SDQ III
Non-Academic
Relationship
With
Opposite Sex
Physical
Appearance
H f C Sdf-Concept
Emotional
Stability
Figure 14. Three-Factor Model with Anxiety as a Correlated Variable
to the DOSC-H General, and the SDQ III Academic and
Non-Academic Self-Concepts (Initial Measurement Model)
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83
Three Factor: DOSC-H General. SDQ in Academic. SDQ III Non-Academic
and Anxiety (Model 4 - Final Measurement Model). Further refinement of
the Three-Factor DOSC-H General, SDQ ID Academic, SDQ HI Non-
Academic and Anxiety model resulted in a better fit according to the
goodess of fit indicators, X2(112, N=129) = 237.07, X^/df = 2.12, p<.001,
NNFI = .72 and CFI = .83. All hypothesized factor loadings were
statistically significant in the expected direction. They are reported in
Figure 15 as the final factor measurement model to be utilized in the
structural model. An intercorrelation matrix of the final factor
measurement model is presented in Table 11. Intercorrelations of the
relevant factors and variables were computed indicating significance levels.
Alternative Structural Models
Overview. The final factor model (Figure 15) as confirmed by the
CFA procedure was used as the foundation for the initial and final
structural or path model. All constructs and variables revelant to the
structural model are presented in Figure 16. Newcomb (1994) has
recommended that all paths be initially tested and utilizes the procedure of
a saturated path model which estimates all paths, whether hypothesized or
not, all correlations among the independent variables as well as among
disturbance terms of dependent constructs. All nonstatistically significant
paths and correlations were then removed from the model. The Lagrange
Multiplier modification procedure (Bentler & Chou, 1990) and the Wald test
were utilized to evaluate the effect of freeing previously fixed parameters
and the effect of removing nonstatistically significant paths in the initial
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84
Anxiety
Level of
Aspiration
DOSC-H
General
Identification
vs. Alienation
Verbal
Self-Concept
SDQ III
Academic
Problem
Solving
Self-Concept
Academic
Self-Concept
Relationship
With Parents
Relationship
With
Same Sex
SDQ 1 1 1
Non-Acadcmic
Relationship
With
Opposite Sex
Physical
STf’ c o n rc p t
Emotional
Stability
Figure 15. Three-Factor Model with Anxiety as a Correlated Variable
to the DOSC-H General, and the SDQ III Academic and
Non-Academic Self-Concepts (Final Measurement Model)
.66* * *
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T able 11
Intercorrelation M atrix of Final CFA - M easurem ent M odel
Factors & V ariables VI V2 V3 FI F2 F3 V4 V5 V6 V7 V8 V9
VI SAT-Math 1.00
V2 SAT-Verbal .47*** 1.00
V3 High School GPA -.10 -.04 1.00
FI General-DOSC-H -.06 .08 .35*** 1.00
F2 General-SDQ Ill-Academic .08 -.02 21* .71*** 1.00
F3 Cenerat -SDQ III-Non-Acad. .26** .17 -.10 .15 .65*** 1.00
V4 Anxiety -.18* .00 .18 .10 -.43*** -.66*** 1.00
V5 Fall Units -.08 -.07 .01 .35*** .26** .01 .05 1.00
V6 Fall GPA -.10 -.01 20* .18 .14 -.05 .09 .43*** 1.00
V7 Spring Units -.06 -.04 .17 -.03* -.03 .02 .12 .42*** .28** 1.00
V8 Spring GPA -.00 -.03 .16 .29** .09 .14 -.01 .27** .28** .63*** 1.00
V9 Retention -.02 -.17* .10 .30** .11 .03 .06 .31*** .24** .37*** .40*** 1.00
*p<.05; **p<.01; ***p<.001 (tw o-tailed)
Anxiety
86
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Figure 1 6 . D iagram o f Structural Model
87
structural model. The final model was evaluated for the presence or absence
of hypotheszed paths as well as any paths not anticipated.
Initial Structural Model. The Initial Academically At-Risk Student
Intervention Structural model did not have too good a fit according to the
goodness of fit indicators, (110, N=129) = 218.26, X^/df = 1.96, pc.001,
NNFI = .76 and CFI = .86. All parameter estimates except LOA, ACAD, IA,
and EMOT, were statistically significant in the expected direction.
Therefore, nonstandard or specific paths were added to the initial structural
model and then all nonstatistically significant paths and correlations were
deleted.
Final Structural Model. The Final Academically At-Risk College
Student Intervention Structural model did have a good fit according to two
of the following goodness of fit indicators, X^ (142, N=129) = 191.56, X^/df
= 1.35, pc.Ol, NNFI = .76 and CFI = .91. That is, the X^/df and the CFI were
the two goodness of fit indicators that reported a good fit for this model.
However, all parameter estimates were statistically significant in the
expected direction except LOA, ACAD, IA, and EMOT and are presented in
Figure 17.
The Research Questions
At this point the statistical outcomes that have been extensively
reported are interpreted within the framework of the research questions
posed in Chapter II.
To what extent, if any, did prior achievement influence the
development of self-concept? Specifically, academic self-concept? Based on
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Anxiety
L eveIof
Aspiration
D05C-H
General
Identification
vs. Alienation
Math
Self-Concept Q2£>
High bchooi
Grade Point
Average
Verbal
Self-Concept
Fall Units L 4 0
IroRem
Solving
Self-Concept
SDQ III
Academic
•22*\ | Spring Units
SAT - Verba
Academic
Self-Concept
.36* ^ A h Retention
Relationship
With Parents
Fall
Grade Point
Average
SAT - Math
Relationship
With
Same Sex
Relationship
With
Non-Ac
Opposite Sex
Physical
Appearance —
o n c e P > ■ , 1 9
Emotional f 7 C i \
Stability
Figure 17. Final Structural Equation Model with Standardized Parameter Estimates
o o
o o
89
the findings of this study, the structural model indicates that high school
grade point average had a statistically significant influence on the
development of the mediating latent construct SDQ HI which in turn had a
significant influence on academic self-concept.
To what extent, if any, did self-concept act as a mediating factor
between academic achievement and retention (success in college)? The
findings of the study suggested that the latent factors of self-concept, both
general feelings towards academic endeavors and non-academic, that is
social, emotional and physical self-concepts, acted as mediating factors
between academic achievement and success in college.
To what extent, if any, did academic responsibility as a measure of
internal and external locus of control act as a mediating factor between
academic achievement and retention (success in college)? In the final
structural model, this study did not utilize the Intellectual Achievement
Responsibility (IAR) scales (I+, I-) because the scale 1+ did not reach an
acceptable alpha level. The I- scale was found not to have a significant
positive correlation with other variables in the study.
Was academic self-concept factors more influential than social self-
concept factors as mediators for academic success? This study revealed that
feelings towards academic endeavors had a direct and significant influence
on academic success and that social support from family and friends did
have a signficant influence on academic success. Both self-concept latent
factors did act as mediators for retention.
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90
Other Findings of this Study
Anxiety as a significant mediating variable. This study utilized the
anxiety scale of the DOSC-H as a separate variable in the final CFA and
Structural models. Anxiety acted as a mediating variable with significant
positive parameter estimates between HSGPA and the variable number of
spring units as well as between SAT-Mathematics and number of spring
units. This finding has interesting clinical implications that are discussed
in Chapter V.
Emotional Stability as a significant direct and mediating variable. In
this study, the Emotional Stability scale from the SDQ HI was utilized as an
indicator for the latent factor SDQ IE Non-Academic. Emotional Stability as
a variable indicated a significant negative direct influence on retention.
Emotional Stability also was a mediating variable between the variable
HSGPA and retention. This finding also had interesting clinical implications
that are discussed in Chapter V.
Level of aspiration as a predictor of academic success. This study did
not find any indication of the Level of Aspiration variable as a predictor of
academic success.
Social support (family and friends) as a predictor of academic
success. Findings from this study indicated that the relationship with the
Same Sex variable had a significant direct influence on retention. The two
variables - Relations with parents, and Relations with Opposite Sex - had
significant influence on Spring units and Fall Grade Point Average,
respectively.
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91
Prior academic achievement not a strong predictor of academic
success. This study indicated that SAT-Verbal had a negatively significant
direct influence on retention whereas HSGPA had a significant indirect
influence on retention.
Conclusion
This study has revealed that for academically at-risk first-year
college students, social, academic, and affective self-concepts were
mediating factors that influenced academic success. Direct influences of
college academic success were, (a) academic achievement as indicated by
the SAT-Verbal measure, (b) General Affective self-concept measures as
represented by the DOSC-H General mediating latent factor, and (c) the
variables of Emotional Stability and Relations with Same Sex, which were
two of the five indicators of the SDQ III Non-Academic mediating latent
factor. Chapter V presents a discussion of the clinical and practical
implications of the findings for intervention strategies for counseling
academically at-risk first-year college students.
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CHAPTER V
DISCUSSION OF RESULTS
92
Introduction
The purpose of this study was to investigate and to develop a
theoretical model for early intervention for academically at-risk first year
college students. Also, this study examined the basic question: What makes
a student successful in college?
Further, this study examined a multidimensional approach to study
self-concept and achievement as it relates to academic success. A
multifactor latent variable model of early intervention of academically at-
risk college students was developed and analyzed as a reliable method of
utilizing mediating latent factors of self-concept that were related to the
affective, academic and non-academic dimensions of self. In this manner,
the individual differences in the multidimensional aspects of self-concept
were addressed rather than those in the traditional uni dimensional
approach. Also, advanced statistical analysis of structural equation
modeling has provided a multivariate, simultaneous analysis of mediating
latent factors and constructs within a theoretical model of achievement
indicators and criterion outcome variables. Further, this study has
inspected and validated the use of multidimensional academic and social
self-concept measurement instruments as to their sound application as
psychological constructs for use in counseling college students for early
intervention purposes.
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93
Discussion of the Data Analysis
The following is a discussion of the findings regarding first-year
students who have been participants of an academically at-risk support
program at a private four-year urban university.
Validity and Reliability
This study investigated the validity and reliability of three
measurement instruments pertaining to self-concept and academic
responsibility--the Dimensions of Self-Concept-Form H (DOSC-H), the Self-
Description Questionnaire in (SDQ m) and the Intellectual Achievement
Responsibility Questionnaire (IAR). Evidence supported the intemal-
consistency reliability scores on the DOSC-H and the SDQ HI. The IAR
scores did not meet acceptable coefficient alpha levels and, therefore, was
not used in the final structural model. Nor was further analysis performed
on the instrument other than calculation of correlations. The lowest alpha
coefficient for the DOSC-H was .84 and the highest was .90 with an overall
alpha of .87. For the SDQ HI, alpha coefficients for the eleven subscales
ranged from .78 to .94 with the overall alpha also of .87. The IAR on the
other hand, evidenced lower alpha coefficients of the two subscales of .46
and .61. The item-analysis of the DOSC-H and the SDQ El indicated a hit
rate of 104 out of 112 items (97.5%) with the highest correlations of the
hypothesized scales, and 79 out of 80 items (99%) to the hypothesized
scales, respectively.
In sum, both the DOSC-H and SDQ HI showed evidence of intemal-
consistency and reliability as measurement instruments, whereas the IAR
did not for this specific population. The IAR is an instrument with
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94
dichotomously scored items the scores on which would tend to have lower
internal consistency. Also, the instrument might not have been tapping into
what the specific issues are with which this population of first-year at-risk
college students admitted to the university as participants in an academic
support program were concerned.
Exploratory Factor Analysis.
Principal components axis factoring was performed on the DOSC-H
and the SDQ HI utilizing orthogonal (varimax) and oblique (oblimin)
rotation of the hypothesized factors. For the DOSC-H, a covariance matrix
of 20 subtests was generated from the data to analyze the principal
components. For the SDQ IE, a covariance matrix of 33 subtests was
generated from the data. A simple structure of four factors of the five
hypothesized was found for the DOSC-H, and a ten factor simple structure
solution of the hypothesized eleven factors for the SDQ HI was realized.
Both measurement instruments provided information that was
specific to the population of this study. However, because of the
complexities of the self-concept instruments as being composed of scales of
a multidimensional nature, there would naturally be some 'overlap' of the
items that tap into corresponding factors. Evidence of the hit rate of the
item analysis was very high for the homogeneity of each scale as was the
evidence provided by the low correlations of terns in a given scale with the
total scores on the remaining scales. This finding was not surprising
knowing the mutidimensionality of the self-concept constructs into which
the measurement instruments were tapping. Therefore, further analysis of
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95
first and second-order factors was pursued to find evidence and
confirmation of the structure and validity of the factors.
Construct validity.
First and second-order confirmatory maximum likelihood factor
analysis (CFA) was performed to assess both the construct validity as well
as the nature of the order of the factors within each instrument. For the
SDQ HI and the DOSC-H, the partially aggregated eleven-factor oblique
and the five factor oblique solutions, respectively, evidenced the highest
goodness of fit. All parameters were within reasonable ranges with
loadings that were statistically significant from zero (p<.05).
Higher-order factor representation was minimal. However, it is
worthy to note that the four-factor oblique solution of the DOSC-H
instrument had a Non-Normed Fit Index (NNFI) of .87 and a Comparative
Fit Index (CFI) of .89 (with .90 or higher being the level of evidence of an
acceptable goodness of fit). Perhaps this outcome was a reflection of the
findings of the exploratory factor analysis for this population. Further, the
SDQ III had an NNFI index of .89 and a CFI index of .89 for both eleven
first-order and three second-order factors as well as eleven first-order and
four second-order factors. Statistical evidence from the CFA indicated that
the DOSC-H and the SDQ IH are acceptable measurement instruments for
the hypothesized first-order factors that they were intended to measure
with regard to the data from this population.
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96
Confirmatory Factor Analysis of the Initial Measurement Model as a
Component of the Final Structural Model
In order to assess the best fitting measurement model for the final
structural model that utilized both self-concept instruments, the researcher
hypothesized mediating latent factors as well as the structure of the
combined self-concept scales would show promise. A decision was made at
this point in the study to maintain the simple structure of the hypothesized
measurement constructs to avoid issues of complex loadings and theoretical
misidentification of parameters relating to the hypothesized constructs.
Hypothesized models were constructed and examined for goodness of fit
by utilizing a standardized data set that aided the convergence of the
statistical solutions for the complex factor structures and loadings. In each
model, all factors and variables were allowed to correlate freely; factor
variances were constrained at 1.00 in order to identify constructs and all
other factor loadings were non-constrained. Further analysis of the final
measurement model was pursued by employing the Lagrange Multiplier
and the Wald test to examine nonsignificant paths and correlation of
residual errors based on theoretical rationale concerning this study.
The analysis initially revealed that the DOSC-H evidenced a
mediating latent general factor comprising of the scales Level of Aspiration,
Academic Interest and Satisfaction, Identification vs. Alienation, and
Leadership and Initiative with a separate scale that measured Anxiety
which correlated with the General latent factor (refer to Figure 4). Thus, for
Model 1, the NNFI indicated a fit index of .91 and the CFI indicated .97.
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97
The statistical evidence indicated that the DOSC-H had exhibited a good fit
for the structural model.
The SDQ HI initially indicated an acceptable goodness of fit for a
two-factor model with an Academic and Non-Academic latent factors with
a shared scale, General Self-Concept (refer to Figure 7). This model
comprised of Mathematics, Verbal, Problem-Solving, and Academic Self-
Concept scales as well as the General Self-Concept scale, all comprising the
hypothesized mediating latent factor SDQ HI— Academic Self-Concept. The
mediating latent factor SDQ IE - Non -Academic Self-Concept consisted of
the scales Relationship with Parents, Relationship with Same Sex peers,
Relationship with Opposite Sex peers, Physical Ability, Physical
Appearance, Emotional Stability as well as General Self-Concept. Both
latent mediating factors (Academic and Non-Academic) were significantly
correlated (pc.001). The goodness of fit indicators were .86 and .90 for the
NNFI and CFI, respectively. The finding for this model further supported
its use in the measurement component.
Finally, a combined analysis of the final measurement model (Model
4) comprising of the DOSC-H and the SDQ III was performed (refer to
Figure 15). A three-factor model and the separate scale Anxiety evidenced
an NNFI of .75 and a CFI of .88. The three factors consisting of the DOSC-H
general factor comprising of Level of Aspiration, and Identification vs.
Alienation, with the correlated Anxiety scale; along with the two factor
SDQ III components, General Academic, which is comprised of Verbal,
Math, Problem-Solving, and Academic Self-Concept scales; and the Non-
Academic factor comprising of Relationship with Parents, Relationship with
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98
Same Sex Peers, Relationship with Opposite Sex Peers, Physical Appearance
and Emotionality as the base hypothesized combined measurement model.
The two scales from the DOSC-H that were not utilized were Academic
Interest and Satisfaction, and Leadership and Initiative, whereas the SDQ ID
dropped the two scales General Self-Concept and Physical Ability Self-
Concept because of non-significant parameter estimates and non-significant
intercorrelations with other variables in the study. Although the goodness
of fit indicators did not meet the .90 or higher criterion, it was decided to
continue with Model 4 as the final measurement component of the
structural model with further analysis of the full model of exogenous
variables and hypothesized mediating factors.
The Structural Equation Model (SEM) or Path Model
The final path model (refer to Figure 16) represents the path model
without the parameter estimates. Further confirmatory factor analysis of
the initial structural model which included all variables pertinent to the
study with the measurement component indicated a NNFI of .76 and CFI of
.86. Upon inspection of the Lagrange Multiplier and Wald tests, correlated
errors were found for the SDQ III Verbal and Mathematics Scales which
proved theoretically sound for identification purposes and from the Wald
Test, increments of the statistic for adding parameters Verbal, Relations
with Samesex Peers and Anxiety with Retention. In Figure 17, the final
structural model goodness of fit indicators increased to .91 for the NNFI
and .94 for the CFI. All parameter estimates were significant except the two
indicators, Level of Aspiration, and Identification vs. Alienation, for the
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99
DOSC-H General factor, and Emotional Stability on the SDQ El Non-
Academic factor.
Results of the Conceptual/Theoretical Model
The final structural model (refer to figure 17) indicates that two
factors do act as mediating latent factors in the model. Those factors are the
DOSC-H General and the SDQ HI Non-Academic factors. The variable,
Anxiety, also did act as a mediating variable between SAT-Math and High
School Grade Point Average to the outcome variables. The paths that
directly influenced Retention were SAT-Verbal, the DOSC-H General
mediating latent factor, Emotional Stability, Spring Grade Point Average,
and Relationship with Same Sex Peers. Therefore, the model did support
the theory that self-concept, specifically affective components relating to
academic issues in college, and non-academic components of self-concept
such as support of family and friends, and emotional stability are promising
indicators to utilize for counseling purposes for academically at-risk college
students. Furthermore, for the population of this study, two of the
exogenous variables that have been used for admission criteria, HSGPA and
SAT-Verbal were significant indicators of retention. The HSGPA had a
positive indirect influence on Retention (direct positive influence on Fall
Units), but SAT-Verbal had a negative direct influence on Retention. This
outcome was not surprising as previous literature had indicated that
HSGPA was a more valid predictor of academic success through the first
semester and year of college studies, (Astin, 1982, Crouse, 1988; Duran,
1986) especially for academically at-risk populations. Also, HSGPA was a
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100
more valid indicator of academic preparedness such as study habits and
follow through of academic tasks.
The Research Questions
To what extent, if any, did prior achievement influence the
development of self-concept: specifically, academic self-concept? Based on
the findings of this study, the structural model indicates that HSGPA had a
significant influence on the development of the mediating latent construct
SDQ HI Academic which in turn had a significant path indicator, academic
self-concept.
Other findings in this model also revealed that HSGPA had a direct
positive influence on anxiety which, in turn, had a direct positive influence
on Spring units. Anxiety could be a motivating feeling for many students
who could be positively mobilized to attend to and complete academic
tasks. Students who have had a 'failure' syndrome and have experienced
heightened levels of anxiety may have begun to feel unworthy, guilt ridden,
and may even have experienced panic that disables the student from
attending to academic tasks. Learned helplessness may also have been a
contributing component to the pattern that allows the student to experience
negative self-perceptions and beliefs. This study revealed that individually
acceptable levels of anxiety could have a positive influence on completing
the needed Spring units. Also, the student's prior experience of HSGPA
could have, (when anxiety was a mediating variable) a positive influence on
self-confidence as well as accommodate for acceptable, however, not
disabling, levels of anxiety as productive components that motivate the
individual to succeed without 'burning oneself out7 in the process.
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101
To what extent.if any, did self-concept act as a mediating factor
between academic achievement and retention (success in college)? The
findings of this study suggest that the latent factors of self-concept, both
general feelings towards academic endeavors and non-academic, that is
social, emotional, and physical self-concepts, act as mediating factors
between academic achievement and success in college.
The self-concept measures indicated that students with a significant
social support system, that is the support of family and friends, have a
higher sense of self-concept with social systems. A student who feels
supported by significant people in his/her life will increase his/her own
level of self confidence as he or she encounters new learning and living
situations. However, the study also revealed that the relationship with
peers of the opposite sex had a negative direct influence on Fall grade point
average in comparison to the finding of a direct positive influence of peers
of the same sex and retention. Students who may 'over-socialize'--that is,
those who do too much socializing (e.g., dating, partying, over involvement
in social organizations)— may suffer the consequences of not meeting a
satisfactory GPA during their first semester in college. Students who are
not adept at balancing their time and efforts with academic commitments,
as well as their need for pursuing social activities, may not realize the short-
and long-term effects on their academic goals. Poor study habits and
patterns (e.g. missing classes, submitting coursework late, and spending
less time for studying) may begin to develop that have an impact on a
student's academic success.
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102
On the one hand, students are encouraged to participate in campus
activities to become mostly self-directed to seek friendships and to 'bond'
with the academic institution by taking part in student run activities and
organizations. On the other hand, students who are academically at-risk
need to be aware of the pitfalls of 'over-involvement' in social activities.
Student support groups, study groups, mentoring and peer counseling are
alternative social groups that may be helpful to supplement and to enhance
the student's support system.
To what extent, if any, did academic responsibility as a measure of
internal and external locus of control act as a mediating factor between
academic achievement and retention (success in college)? The results
would indicate a negative answer to this question. In the final structural
model, this study did not utilize the Intellectual Achievement
Responsibility (IAR) scales (I+, I-) because the scale 1+ did not reach an
acceptable alpha level. The I- scale was found not to have a significant
positive correlation with other variables in the study.
Was academic self-concept factors more influential than social self-
concept factors as mediators for academic success? This study revealed that
feelings towards academic endeavors had a direct and significant influence
on academic success and that social support from family and friends also
had a significant influence on academic success. Both self-concept latent
factors did act as mediators for retention.
However, this study indicated that the SDQ III Non-Academic latent
factor and the DOSC-H General affective components of self-concept were
significant mediating factors relating to academic pursuits and success in
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103
college (retention). Both non-academic and affective self-concepts in
learning situations were important contributors and mediating factors
towards a student's success in college. The clinical implications for those
students who were admitted to an academic support program during their
first-year of studies, would be to ascertain that the application of
intervention strategies enhance the student's self-concept in academic
pursuits during the first two semesters of studies. In this manner, the
intervention would support a view held by a student of him or herself as an
independent learner. Furthermore, Tinto (1987) stressed, in his theory of
student development, that one caring and supportive faculty or staff person
would make a difference in facilitating the success of a student during his or
her first year (and subsequent years) of studies.
Other Findings of this Study
Anxiety as a significant mediating variable. This study utilized the
Anxiety scale of the DOSC-H as a separate variable in the final CFA and
structural models. Anxiety acted as a mediating variable with significant
positive parameter estimates between HSGPA and Spring units as well as
SAT-Math and Spring units.
As indicated in the prior discussion of the role of anxiety as a
mediating factor, Michael, Smith & Michael, (1989) have developed the
DOSC-H based on a rudimentary theory of affectivity in school learning.
Anxiety is manifested as an unrealistic level or aspiration that can be either
too low or too high. Unrealistic levels of expectations could induce a
student to become depressed and discouraged. Clinically, students who
have manifested too high levels of anxiety may exhibit panic-type
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104
symptomology concurrent with waves of depression. Such students are
'paralyzed' in what appears to be an inablility to attend to academic tasks.
As their self-expectations for success are jeopardized, students are likely to
lose academic interest, develop feelings of alienation and rejection, and
even begin to have difficulty 'bonding' with the institution. Students with
relatively lower levels of anxiety, who set realistic goals, are more likely to
gain a greater sense of self-confidence and satisfaction with academic
pursuits. This study supported Michael and Smith's (1976) theory that
underlies the development and factor validity of the DOSC-H.
Emotional Stability as a significant direct and mediating variable. In
this study, the Emotional Stability scale from the SDQ HI was utilized as an
indicator for the latent factor SDQ III Non-Academic. Emotional Stability as
a variable indicated a significant direct negative influence on retention. In
this study, Emotional Stability also was a mediating variable between
HSGPA and Retention.
The clinical implications of the emotional state or trait that an
individual experiences, whether it is a generalized and life-long experience
(trait) or a contextual one that is exhibited by the individual in certain
situations (state), can have implications for a student's academic success.
The SDQ III Non-Academic latent factor in this study utilized the scale
Emotional Stability as one of its indicators. It is interesting to note that
Emotional Stability scores demonstrated a significant negative correlation to
Anxiety in this study. Emotional Stability was described by Marsh (1991) as
the self-concept of an individual's emotional stability. Statements such as "I
worry a lot," or "I am usually pretty calm and relaxed," are the types of
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105
items that an individual would respond to as an indicator of his or her
degree of self-concept regarding emotional stability. This study does not
suggest that the scale be utilized as a clinical indicator for 'emotional
stability/ However, for the students who participated in this research, it
appears that the higher the score on the Emotional Stability self-concept
scale, the more likely one would be successful in college during the first
year of studies. Intuitively, lower scores on the Emotionaly Stability self-
concept scales would indicate (along with anxiety as a mediating variable)
that students who bring with them the tendency to be emotionally reactive
(e.g. tend to worry and/or be highly strung and restless) may have a
difficult time attending to and focusing on their academic tasks. The
findings of this study suggest that the academically at-risk students of this
population who have higher self-concepts of emotional stability are
retained after their first year of studies. Furthermore, the first-year college
students of this study were admiteed to a one-year academic support
program as a provision of admission were more likely inclined to have
experienced some counseling intervention as a component of the academic
support services of the program. The clinical implications indicate that
academic and personal counseling for these students in the form of stress
reduction and time management skills may be helpful intervention
strategies. Further, mentoring and peer support counseling may address
the need for social support systems for these individuals. A student who
finds him or herself depressed and in isolation from others is in contrast to
the student who is more relaxed, enjoys learning new ideas and developing
more social skills in interacting with others. Learning and personal self-
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106
development is enhanced when the individual's affective state (or trait) is
one of a self-confident and calming nature.
Level of Aspiration as a predictor of academic success. This study
did not find any indication that the Level of Aspiration variable is a
predictor of academic success. However, Level of Aspiration was utilized
as an indicator for the latent mediating factor DOSC-H General Self-
Concept in the structural equation model. The parameter estimate for the
indicator was not found to be statistically significant, although the
indicator contributed to the general DOSC-H factor construct.
Social Support (family and friends) as a predictor of academic
success. Findings from this study indicate that the relationship with the
Same Sex variable had a significant direct influence on Retention. The two
variables, Relations with Parents, and Relations with Opposite Sex Peers,
had significant influence on the number of Spring units and Fall grade point
average, respectively.
As previously discussed in this chapter, Social Support, whether
from Relations with parents and/or Same Sex Peers, had a significant
influence on Retention, both directly and indirectly. Students who are able
to grow and individuate from their family of origin as they experience their
first year of studies and are supported for their efforts, are more likely to be
able to succeed. Relationships with peers of the same sex, as indicated by
this r-tudy, are more supportive of success (retention) than opposite sex
peers during the student's first year of studies. As mentioned in Chapter I
of this study, the student's life on campus can be the first experience of an
unstructured, decision-making enterprise that can be confusing and yet
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107
liberating. Developmental theorists view this time in a student's life as that
which is informative and additive to the individual's life-long learning. For
many first generation and minority students encountering the college
experience, the cultural and familial support of family and friends is an
essential ingredient to academic success. Intervention strategies that
provide information to parents via newsletters and orientation programs
can afford a sense of a shared commitment to facilitate and to support the
success of a student's first year. Also, student-run organizations that
provide a sense of belonging rather than marginality of a student's
participation within the campus community can actualize a student's social
self-concept. Students need to know that they matter and that admission
was offered to the student because the institution believes the student will
succeed.
Prior academic achievement is not a strong predictor of academic
success. This study indicated that SAT-Verbal scores had a negatively
significant direct influence on retention, whereas HSGPA had a significant
indirect influence on Retention. SAT-Mathematic measures also had a
negatively significant indirect influence on Anxiety, Level of Aspiration,
and Fall units.
For academically at-risk students in this study, SAT-V scores were
not usually viewed as predictive indicators of success by the admissions
criteria of the university. Therefore, this study validated the descriptive
findings that the mean SAT-Verbal scores for the academically at-risk
students in this study was 404 compared to the University comparison
sample mean of 509 and that SAT-Verbal is not a direct positive indicator
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108
for retention in the structural model. SAT-Mathematic scores was also a
negative predictor for Fall units. This observation may be problematic for
engineering, science, business and architecture students who are required to
take mathematics courses during their first year of studies. SAT-
Mathematics did have a direct path to SDQ HI Non-Academic latent factor.
However, that path was not significant.
Further, SAT-Verbal did have a significant direct path to SDQ IE
Non-Academic latent factor but a negative significant path to Mathematics
self-concept. Students who have a need to be social, most likely would have
a higher self-concept of their verbal skills. This study also indicated that
SAT-Verbal scores 'indicated a positively significant path to Problem-
Solving self-concept. Again, the model indicated that SAT-Verbal skills are
congruent with those skills needed in problem-solving.
Qinical Implications
The clinical implications for intervention strategies would be to
utilize the student's prior successes as indicators for future success rather
than failure. Students may have to be flexible to acquire new learning
strategies as they encounter more sophisticated knowledge acquisition.
However, to draw upon the student's tried and true strategies (and upon
the knowledge of students about themselves), in relation to attitudes and
feelings about their own academic self-concepts, can provide information
for effective early intervention procedures. Such measures can build upon
the students self-confidence, and facilitate the growth of an independent
learner.
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109
Ginical implications relevant to this study are that for academically
at-risk students, other indicators such as HSGPA, social support from
family and peers, lower levels of anxiety and higher levels of emotional
stability are important indicators for academic success as defined by
retention after the first year of studies. Academically at-risk student first-
year college students admitted to a university one-year academic support
program are under pressure to succeed by the end of the first year.
Universities that admit students as participants of academic support
programs have identified such students as potentially successful, provided
that the student receives academic and social support from the institution
whether through only the first year of studies or beyond until graduation.
Universities and colleges that acknowledge that the usual admission
criteria, such as SAT scores, are not so strong an indicator for success for
these students as are other indicators, need to have an institutional
commitment to provide such students with a support program.
Specifically, a support program that is based on clinical as well as
psychometrically sound applications of intervention strategies. Further,
those professional staff who are trained in academic and personal
counseling, as well as in psychoeducational theory, can best serve students
with complex needs such as indicated by this study's theoretical and
empirical approach in studying academically at-risk first-year students.
Further Clinical Implications and Research
Support programs for transfer students, commuting students, non-
traditional students, and other identified academically at-risk students
beyond the first year may be able to benefit from the use of this model.
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110
However, because of the specificity of the populations identified, a separate
study of the model with use for application with different populations is
essential. This study's limitations are based on the sample utilized for this
study.
Another area for research would be to investigate the specific context
that may change with reference to self-concept. That is, students may have
different social self-concepts depending on the frame of reference of the
individual's contextual experience. A student may have a higher social self-
concept based on his or her experience in high school or residential
community. However, the external frame of reference may alter social self-
concept when a student encounters the college campus. The academic self-
concept of an individual may change from the high school or community
college experience when the student experiences large lecture halls, a larger
student population, or a different environment whether geographically or
culturally. A student who was the 'A' student at a previous institution may
become the 'D' student at the larger institution. Does this event mean that
the student cannot succeed, or does it mean that the altering self-concept
has mediated the outcome of academic success?
Further research in the area of self-concept in academic disciplines
may provide researchers with further multidimensional natures of an
individual's flexibility in self-concept. For example, a student's self-concept
of computer applications in information science may be quite different from
that in a course in computer engineering, or a student's self-concept in
Organic Chemistry may be quite different that in Biology. Both examples
are comparisons within the same fields of study that may be representative
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I l l
of a student's chosen major and associated academic struggles within the
major. Does this circumstance mean that a student may not succeed in the
major? Or, does it mean that the altering self-concept is a mediating factor?
Finally, this study is limited to the research findings of identified
potentially academically at-risk students to whom the self-concept
instruments were administered during the first week classes of the Fall
semester. Further research using multi-wave studies (i.e. post testing at
specific time intervals) to assess change in self-concept, especially with the
advent of time and experience of the first and last semester of studies as
well as second year, would provide additional information for research
purposes. Does the self-concept remain stable? Does self-concept change
with experience as a natural maturational issue? Are intervention strategies
effective in altering self-concept for academically at-risk students?
Conclusion
This study has revealed that for academically at-risk college
students, social, academic, and affective self-concepts are mediating factors
that have influenced academic success. Direct influences of academic
success have been academic achievement as indicated by the SAT-Verbal
(although negatively), General Affective Self-Concept as indicated by the
DOSC-H General mediating latent factor, and the variables Emotional
Stability, and Relationship with Same Sex, which have been two of the five
indicators of the SDQ m Non-Academic mediating latent factor.
As part of a validity study, the DOSC-H and the SDQ III instruments
were reliable and useful as part of the overall structural model for early
intervention of academically at risk students. Self-concept has been
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112
portrayed as a mediating latent factor for both academic and non-academic
self-concepts in this study. Structural equation modeling provided a means
for a psychometric approach to the multidimensional and complex topic of
self-concept, to capture residual and measurement error, while estimating
parameter loadings and paths. Furthermore, what may be specific or
endemic to one population can be quite different for another, such as the
academically at-risk population of this study. Such evidence was found in
the negatively significant influence of SAT-Verbal scores to retention for
this group of students when compared to the retention of the University's
comparison regularly admitted sample.
In conclusion, the study of self-concept is a many faceted and
extremely complex issue, both psychometrically and psychologically. The
field of counseling psychology has been especially involved and actually
evolved in research, theory and application pertaining to self-concept and
developmental issues of college students. E. G. Williamson (1939) and
Donald Paterson (1938), founders of the field of counseling psychology with
college students (student personnel work), recognized the importance of
guidance (counseling) which is "beyond the competency of teachers and
untrained advisors." However, today one recognizes that multidisciplinary
approaches in the social/ psychological/biological model have influenced
the training of counseling psychologists. Today's world is becoming ever
more complex as are the inhabitants of our societies. There is no doubt that
the complexities of self-concept may serve or may hinder individuals,
especially young adults who are striving to complete their own education.
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113
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Creator Tobey, Patricia Elaine (author)

Core Title Cognitive and non-cognitive factors as predictors of retention among academically at-risk college students: A structural equation modelling approach

Degree Doctor of Philosophy

Degree Program Education - Counseling Psychology

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