THE IMPACT OF UC DAVIS’ EARLY ACADEMIC OUTREACH PROGRAM ON
DEGREE ATTAINMENT
[Single Space the Title]
Timoteo Rico
B.S., University of California, Davis, 2000
M.A., California State University, Sacramento, 2007
DISSERTATION
Submitted in partial fulfillment of
the requirements for the degree of
DOCTOR OF EDUCATION
in
EDUCATIONAL LEADERSHIP
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2012
[Optional]
Copyright © 2012
Timoteo Rico
All rights reserved
ii
THE IMPACT OF UC DAVIS’ EARLY ACADEMIC OUTREACH PROGRAM ON
DEGREE ATTAINMENT
A Dissertation
by
Timoteo Rico
Approved by Dissertation Committee:
_________________________________
Su Jin Jez, Ph.D., Chair
_________________________________
Caroline Turner, Ph.D.
_________________________________
Robert William Wassmer, Ph.D.
_________________________________
[Optional Reader]
SPRING 2012
iii
THE IMPACT OF UC DAVIS’ EARLY ACADEMIC OUTREACH PROGRAM ON
DEGREE ATTAINMENT
Student: Timoteo Rico
I certify that this student has met the requirements for format contained in the University
format manual, and that this dissertation is suitable for shelving in the library and credit is
to be awarded for the dissertation.
___________________________, Graduate Coordinator
Caroline Turner, Ph.D.
iv
_________________
Date
DEDICATION
For over fifteen year, my wife Leah Marisa Rico has continued to be the
motivation and inspiration toward achieving excellence. Her selflessness has inspired our
family to persevere against the greatest challenges while raising our two daughters,
Elizabeth Josefina Rico and Victoria Sofia Rico, in our educational journey. Brilliantly
and diligently she would attend to me during the discouraging lashes brought forth from
the process of enlightenment by encouragement and giving me faith in my abilities. No
doctorate degree could affirm her beauty from within or without. Too few men
experience the beauty of such a woman who not only has given you the world but the
universe, too.
To my children, who have sacrificed time away from their father and understood
the importance of education as a familial value. The simple gestures of innocence,
gratitude and hope inspired me to believe tomorrow is always a better day. The uncanny
snuggles and kisses that continued to fill my heart with your blessed love secures the
sense of hope in my life.
v
ACKNOWLEDGEMENTS
[
With great gratitude, thank you Leah for the patience and tireless support during
my doctoral process. This milestone in our families would have not been possible without
your support, help, and love.
Thank you, Dr. Su Jin Jez’ and Dr. Caroline Turner’s, for your genuine interest in
my persona, research interest and self-being. To the brilliant Dr. Robert Wassmer who in
the moments of discourse engaged me into greater thought and possibilities in applied
and theoretical research. Your wealth of knowledge and wisdom add a new dimension to
the world of research and evaluation in outreach.
To COHORT III, who I am very grateful for your continued support, camaraderie,
companionship and your willingness to embrace me into your families. Thank you for
sharing your personal lives, trusting in me and opening my eyes to a new world beyond
my own. Your spirits have been present during the final week of this journey.
vi
CURRICULUM VITAE
Education
Bachelors of Science, University of California, Davis
Master of Arts, California State University, Sacramento
Doctorate of Education, California State University, Sacramento
Professional Employment
University of California, Davis, Associate Director of Recruitment and
Admissions, Undergraduate Admissions
University of California, Davis, EAOP Director
Woodland Community College, Upward Bound Director
University of California, Davis, EAOP Regional Outreach Coordinator
Publications
Latino Education: A Synthesis of Recurring Recommendations and Solutions in P16 Education. The College Board.
vii
Abstract
of
THE IMPACT OF UC DAVIS’ EARLY ACADEMIC OUTREACH PROGRAM ON
DEGREE ATTAINMENT
by
Timoteo Rico
Statement of the Problem
Too many high school graduates who enroll in California’s public postsecondary
institutions do not persist to degree completion. The low persistence and graduation rate
of undergraduates from the secondary schooling system is threatening the state’s
economy and California is facing a work force deficit of approximately one-million
college-educated graduates by 2025. Improving the graduation rate of the State’s most
disadvantaged populations who are enrolled in higher education could help drastically to
mitigate the future economic gloom. Although student-centered outreach programs have
increased the postsecondary enrollment of secondary school historically and
underrepresented student, little is known as to whether student-centered outreach
intervention strategies influence a student’s propensity towards retention, persistence and
degree completion.
viii
Sources of Data
Longitudinal empirical data from former high school participants from the Early
Academic Outreach Program at the University of California Davis is used to assess the
impact toward degree attainment of the high school graduating cohorts in the Class of
2000 through 2006. The data includes the participation of specific activities, high school
course transcript, and the postsecondary institution of enrollment and graduation.
Conclusions Reached
The hours of academic advising, college information and personal motivation
provided by EAOP has no impact on first-year retention or degree attainment of its
participants when analyzed in a bivariate linear regression and nominal logit regression,
respectively. EAOP participant’s first-year retention is impacted by the number of
laboratory sciences successfully passed in secondary education and a strong nonweighted high school GPA. In addition, an ordinary lest squares (OLS) method in a
regression analysis, the hours of college information, successful completion of English
courses provided to participants, and being a male had a negative impact toward a fouryear persistence. In other words, EAOP participants who benefit from the college
information activities are more likely to attain a degree sooner than non-participants.
Additionally, participants who attained a higher degree had an increasing positive impact
on persistence and participants who identified as African-American, Asian, Chicano,
ix
Pacific Islanders and Other were also impacted positively toward college persistence.
Yet, low-income participants where statistically impacted by EAOP to attaining a degree.
x
TABLE OF CONTENTS
Page
Dedication ......................................................................................................................v
Acknowledgements ...................................................................................................... vi
Curriculum Vitae ....................................................................................................... vii
List of Tables ............................................................................................................. xiii
List of Figures ............................................................................................................ xiv
Chapter
1. INTRODUCTION ..................................................................................................1
Problem Statement .............................................................................................5
Nature of the Study ............................................................................................6
Theoretical Base and Conceptual Framework ...................................................7
Organization of Study ........................................................................................7
2. REVIEW OF RELATED LITERATURE ...............................................................9
History................................................................................................................9
Theoretical Underpinning of Student-centered Outreach Programs ................12
Student Attributes and Environment. ...............................................................21
Summary ..........................................................................................................31
3. METHODOLOGY ................................................................................................34
Introduction ......................................................................................................34
Population ........................................................................................................35
xi
Design of the Study..........................................................................................37
Data Collection ................................................................................................41
Instrumentation ................................................................................................43
Data Analysis Procedure ..................................................................................43
4. DATA ANALYSIS AND FINDINGS ..................................................................57
Introduction ......................................................................................................57
Descriptive Statistics ........................................................................................58
Correlation Analysis ........................................................................................68
Impact of EAOP on Retention .........................................................................72
Impact of EAOP on Persistence .......................................................................77
Impact of EAOP on Award ..............................................................................80
Summary ..........................................................................................................86
5. CONCLUSIONS AND RECOMMENDATIONS ................................................88
Overview ..........................................................................................................88
Purpose of the Study ........................................................................................89
Summary of Findings .......................................................................................90
Discussion ........................................................................................................92
Policy Implications ..........................................................................................96
Recommendations ............................................................................................99
Future Research .............................................................................................103
REFERENCES ..........................................................................................................105
xii
LIST OF TABLES
Page
1. Definition of Variables for IEO Model
48
2. Descriptive Statistics of Independent Variable
58
3. Ethnic Distribution of EAOP Participants
60
4. Percentage of Economically Disadvantaged by Ethnicity
61
5. Percentage of Educationally Disadvantaged by Ethnicity
62
6. Participant’s Postsecondary Enrollment by Ethnicity
63
7. High Correlation (r ≥ 0.500) between Independent Variables
69
8. Correlation Matrix of Continuous Independent Variables
71
9. Freshmen Undergraduate Retention 1-yr HS Graduation
73
10. Bivariate Logistic Regression Results, Dependent Variable Retention
76
11. Linear Regression Results, Dependent Variable Persistence
78
12. Frequency of Dependent Variable Levels, Award
80
13. Model Fitting Information on Award where Referent Level is No Award
81
14. Parameter estimates for Independent Variables, Sub-Bachelors
85
15. Parameter estimates for Independent Variables, Bachelors
87
xiii
LIST OF FIGURES
1. The IEO Model (Astin, 1991)
38
2. Triple E Theory
40
3. Average Non-weighted GPA Distribution
65
4. Average Non-weighted GPA Excluding Outliers
66
5. Semester Courses Attempted
67
6. Semester Courses Attempted Excluding 0.00 Non-weighted GPA
68
7. Phase Sequence of Degree Attainment
90
8. Phase Model on Enrollment, Retention, Persistence, and Degree Attainment
97
9. Triple E Theory
98
xiv
1
Chapter 1
INTRODUCTION
Access to California’s public and independent higher educational system is a
choice guaranteed to residents through the Master Plan of Higher Education (1960). The
intent of the legislation in the Master Plan of Education is that “public higher education
in California strive to provide educationally equitable environments that give Californian,
regardless of age, economic circumstances, [disability, gender, nationality, or race], a
reasonable opportunity to develop fully his or his potential” (California Education Code,
2011, Section 66030). Simply, the State “must support an educational system that
prepares all Californians for responsible citizenship and meaningful careers in a
multicultural society” (California Education Code, 2011, Section 66200). Although
California’s higher educational system provides an opportunity to its residents degree
completion rates are too low.
After years of investment in preparing the general populous of secondary school
students towards postsecondary opportunities, too many high school graduates who enroll
into the public system of higher education do not persist towards degree completion
(Dadashova, Hossler, Shapiro, Chen, Martin, Torres, Zerquera, & Ziskin, 2011; Institute
for Higher Education Policy [IHEP], 2011; Stoutland, 2011; Turner, 1992; Turner, 1990;
Turner & Fryer, 1990).The educational attainment of the workforce positively impacts
the State’s economic prosperity, and the continued low persistence rate of undergraduates
at completing a degree is worsening (Carnevale & Rose, 2011; Jones, 2011; Center,
2
2011; Storper & Scott, 2009). According to IHEP President Michelle Asha Cooper
(2011):
Near-completers may have jobs and earning that at least partially reflect their
investment in higher education, but these individuals continue to lose out on the
significant labor market advantages associated with college credentials. Thus,
transforming near-completers into college graduates would translate into win for
students, who realize long-term opportunities for economic and social benefit; it is
also a win for institutions, policymakers, employers, and other stakeholders, all
which have a vital interest in increasing the number of graduates.
The shortage of college graduates in California is threatening the State’s economy
(Johnson, 2009; Gershwin, M, Coxen, T. Kelley, B, & Yakimov, G, 2007).
A person who completed an advanced degree earned a mean income of $70,856
whereas a high school graduate earned a mean income of $31,283 in one year (U. S.
Census Bureau, 2011; 2005). Other studies confirm the greater the educational degree
attainment, the greater the return to the beneficiary (Grubb, 2002; Marcotte, Bailey,
Borkoski, & Kienzl, 2005). According to Baum & Payea (2005, p. 9), “over their
working lives, typical college graduates earn about 73 percent more than typical high
school graduates, and those with advanced degrees earn two to three times as much as
high school students.” Estimates by the U.S. Census Bureau (2001) calculate an
additional one-million dollars college graduates will earn during their working life
compared to high school graduate with no bachelor’s degree. There is a life-long benefit
to a person who advances their educational attainment in health, employment and greater
financial stability, and society too benefits too from the financial return at investing into
low-income and first-generation students with low college-going rates (Wheelan, 2008;
Nevarez & Rico, 2007; Humphreys, 2002). The Miller Center (2011, p. 54) reports that,
3
[H]ighly educated citizens have a higher quality of life and contribute to society
rather than take from [society]. They have fewer health problems…able to cope
with the complicated choices being forced down to the individual (retirement,
health care, etc.), they do not end up in the corrections systems or become
dependent on social services, and they engage much more actively in the civic life
of the community.
Further investment in educating the populus will also help stimulate the economic
workforce needs demanded of the state by the 2025 (Johnson, 2011; Johnson, 2009).
California will have a deficit in the college educated workforce needed by the year 2025
if current enrollment trends towards degree completion are not addressed for firstgeneration and low-income students (Johonson, 2011; Shulock, 2010).
As an effort to address the shortfall of approximately one-million collegeeducated workers needed with a bachelor’s degree by 2025 (Center on Education and the
Workforce, 2011; Johnson, 2011; Miller Center, 2011), California is improving the
educational opportunities available by improving the educational attainment of students
between K-12 and higher education (Cannon & Lipscomb, 2011; Larsen, Lipscomb &
Jaquet, 2011; Larson & Weston, 2011). The urgency to prepare high school students for a
college education has recently been magnified further by California stakeholders to
meeting the workforce demands projected by 2025 (Bedsworth, Gordon, Hanak, Johnson,
Kolko, Larsen & Weston, 2011; Reed, 2008). In the last decade, the low postsecondary
enrollment rate of high school graduates has led outreach practitioners to design projects
that counteract the barriers students face in the public K-12 educational setting (LAO,
2007; Corlett, Gulatt, & Heisel, 2003; University of California Office of the President,
2003; Reagents of the University of California, 1999; Saenger, 1998). Also, researchers
and practitioners are exploring for indicators that may function has catalyst in the
4
expedient preparation and enrollment of high school graduates into higher education
(University of California Office of the Vice President, 2010). According to Shulock,
Moore, Offenstein and Kirlin (2008), for every two degree attain graduates produced in
higher education the State will need three to remain a competitive economy in 2025. The
dilemma at producing needed college graduates with degrees for every two produced
continues to be the enigma plaguing society.
A potential solution to the workforce needs of the State is to concentrate
educational reform efforts specifically at increasing the preparation of first-generation
and low-income students (Domina, 2009; Hill, 2007; Rueda, 2005; Perna & Swail, 2001).
In many instances, the most disadvantaged students are from an underrepresented ethnic
group such as the African-American, Latino, or the Native American community (Pitre &
Pitre, 2009; Timar, Ogawa, & Orillion, 2004; Perna, 2002; Gandara & Bial, 2001). In
comparison to other traditionally underrepresented groups, the Latino community is
growing exponentially in California’s K-12 system and is projected to be the major group
in the state by 2050 (Johnson, 2008). As the largest growing group with historically lower
levels of educational attainment, a modest increase of postsecondary enrollment, a twenty
percent improvement in transfer rates, and improvement in degree completion at the CSU
system will reduce the workforce needs by one-half by 2025 (Johnson, 2011).
By investing in statewide student-centered outreach programs and researching
strategies that increase the college-going of traditionally disadvantaged groups, California
will be able to meet the workforce deficits predicted by economists (Johnson & Sengupta,
2011). Student-centered outreach programs like the Early Academic Outreach Program
5
(EAOP) have demonstrated evidence at increasing the college-going rate of its
participants, efficiently and effectively (Sanchez, 2008; Villalobos, 2008, Bookman,
2005; Quigley, 2002). Other evidence suggests that strong relationships with high schools
and four-year institutions further increases transfer rate among students in two-year
institutions (Serban, 2008).
To date, research has concentrated on the supplemental services provided to
disadvantaged students outside the classroom of instruction, such as academic advising,
mentoring, and counseling. These out of classroom services that focus on academic
opportunities have helped to minimize the negative educational conditions disadvantaged
students face in public education and increase the college-going rate of disadvantaged
secondary students (CPEC, 1989; CPEC, 1996; CPEC 2004; Gandara, 2001; Gandara &
Mejorado, 2004; Hayward, Brandes, Kirst, & Mazzeo, 1997; Outreach Task Force, 1997;
Quigley, 2002; Sanchez, 2008; Yeung, 2010). Yet, research has not determined the
impact of student-centered outreach programs towards degree attainment.
Problem Statement
Too many high school graduates who enroll in California’s public postsecondary
institutions do not persist to degree completion (Dadashova, Hossler, Shapiro, Chen,
Martin, Torres, Zerquera, & Ziskin, 2011; Institute for Higher Education Policy [IHEP],
2011; Stoutland, 2011; Turner, 1992; Turner, 1990; Turner & Fryer, 1990). The low
persistence and graduation rate of undergraduates from the secondary schooling system is
threatening the state’s economy. California is facing a work force deficit of
6
approximately one-million college-educated graduates by 2025 (Johnson, 2011).
Improving the graduation rate of the State’s most disadvantaged populations who are
enrolled in higher education could help drastically to mitigate the future economic gloom.
Although student-centered outreach programs have increased the postsecondary
enrollment of secondary school historically and underrepresented student, little is known
as to whether student-centered outreach intervention strategies influence a student’s
propensity towards degree completion.
Nature of the Study
The purpose of the study is to determine whether student-centered outreach
programs help participants persist towards degree completion at two-year and four-year
institutions. The study will explore the three categorical standard activities that are
believed to contribute to a participant’s likelihood to complete a degree six years after
high school graduation. In an effort to understand the program’s operations, the following
questions are investigated in the study through a positivistic paradigmatic approach using
the General Systems Theory (GST):
1. Do EAOP activities significantly contribute to a participant’s retention during the
first year of undergraduate education?
2. Do EAOP activities significantly contribute to a participant’s persistence toward a
degree completion in higher education?
3. Do EAOP activities significantly contribute to a participant’s degree attainment?
7
The practices from secondary school student-centered outreach programs will provide a
greater understanding as to whether serviced rendered to participants (inputs) through
student-centered outreach mediums contributes towards a participant’s likelihood of
attaining a degree (output) in a college environment.
Theoretical Base and Conceptual Framework
The General Systems Theory (GST), although an abstract conceptualization of a
system, uses general principals to explain both natural and social phenomena that can
reveal connections between components of the system and the environment (Bess & Dee,
2008). The unit of measurement in the system is the participant in the student-centered
outreach program where hours of services are delivered. GST will provide a greater
understanding of the input (student-centered outreach services and participant’s
attributes) in the environment and an understanding of the relationship to the output
(degree attainment at two and four-year institutions). The system responsible for the
participant’s transformational process is the UC Davis’ Early Academic Outreach
Program (EAOP). To understand the system, the generalizations of the following design
model will provide the needed understanding of the unit of study, the participant.
Organization of Study
The next chapter begins to explore the evolutionary stages of outreach services
from recruitment efforts by providing a historical perspective of how prospective college
undergraduates were sought out by practitioners. Commonly, recruiters searched for key
8
environmental factors in society that were most probable at producing academically
prepared students. The chapter will show how a shift in educational policy following the
development of the Master Plan of Education which caused postsecondary institutions to
cultivate prime undergraduate candidates rather than depend on status quo. Changes in
historical educational policy resulted to the pressures to address the needed growing
workforce and the gap disadvantaged individuals could fill in the employment deficit.
The chronological development of student-centered outreach served as a key vehicle at
creating and increasing access to higher education for secondary school students in
disadvantaged environment. With the lingering understanding of which elements
contribute to college and university enrollment, the chapter concludes by exploring the
different types of activities and theories linked to the postsecondary retention of students
in their undergraduate education at attaining a college degree. In Chapter three, the
methodological approach is explored further by using the General Systems Theory (GST)
and Astin’s Input-Environment-Outreach (IEO Model) model to understand. The chapter
further provides an understanding of the environment variables referred to as efficiency,
effectiveness, and efficacy in a proposed postulate theory known as the Triple E Theory.
Following chapter 3, data and statistical analysis of the historical data in the study will be
used to draw conclusions of the research questions. Lastly, R will provide
recommendations following the conclusion of the statistical analysis in the realms of
policy, leadership and decision-making related to education.
9
Chapter 2
REVIEW OF RELATED LITERATURE
The chapter begins by describing the historical elements that have contributed to
the shift of recruitment efforts from recruitment to student-centered initiatives, and
presents a condensed theoretical underpinning of student-centered outreach programs
related to enrollment in higher education. A summary of the categorical services, as
recommended by leading educational practitioners, are then aligned with the research
with traits of efficacy and effectiveness. The section follows with a summary of the
effectiveness of student-centered outreach programs as it relates to college retention and
degree attainment. Following the section on effectiveness, the chapter addresses which
factors in a college environment impact a student towards degree attainment in higher
education.
History
At the infancy of the nation’s educational system, geographic recruitment was
important among the private colleges, universities, and charter schools (Karabel, 2005).
Recruitment during the time was referred to as the act of intentionally seeking individuals
with the skill set to benefit the institution in its short and long-term objectives. Typically,
geographic recruitment identified key environmental attributes where academic readiness
was a fundamental component among prospective college students. Many low-income
candidates were overlooked because affluent families were more likely to have students
10
who were academically prepared to graduate from the national educational system
(Karabel, 2005). Educational institutions in California did not begin intentional
recruitment of academically talented students until the State enacted legislation under the
Donahue Act known as the Master Plan of Education (1960).
For nearly a century, California’s public educational system lacked standards of
accountability for high schools. The introduction of the 1960 California Master Plan of
Education of 1960 established the common goal, objectives, and responsibilities of the
higher education system to seek students beyond geographic location and to begin
exploring candidates reflecting the demographics of California. The Master Plan of
Education states that “the mission of the public segment of higher education shall also
include a broad responsibility to the public interest …to support programs of public
service” (California Digital Library, n.d.). Therefore, postsecondary institutions struggled
with assessing which applicants were eligible for an undergraduate education and persist
toward a degree attainment because of social inequities related to student demographic
characteristics.
The California legislature established the Educational Opportunity Program
(EOP) to support and retain historically underrepresented1 students in the public higher
educational systems. With a stagnant entry rate of historically underrepresented students,
poor retention and graduation rates from postsecondary institutions, educational
practitioners recognized the importance to prepare students as early as middle school.
In California’s history, students who are economically and educationally disadvantaged are categorized
under such terms as used in current research. In Federal policy, such students are known as disadvantaged
or underserved.
1
11
Appealing to the state legislature for the inequity of preparation for college admissions,
student-centered programs were developed to support public middle and high schools to
create opportunities for students to prepare for undergraduate study. Student-centered
programs such as the EAOP, Mathematics Engineering Science Achievement (MESA),
and the Puente Project promoted services to deter social inequities impacting historically
disadvantaged and underrepresented students in secondary schools. As Betts et al. (2000)
states,
Given these inequalities – especially in teacher preparation and high school
curriculum – and the variations among rural, urban, and suburban schools, a
natural question is whether disadvantaged children get less of the school resource
pie. The answer is a resounding ‘yes’…inequalities represent a systematic bias
against disadvantaged students, and minority students in particular, in their quest
to attend a university after graduation. Within a given district, schools with
particularly disadvantaged students are likely to have less highly educated and
less highly experienced teachers and to offer fewer advanced classes at the high
school level. (p. xvii - xviii)
Yet, Timar et al. (2004) states, “[E]arly intervention programs – designed to improve
academic preparation and college readiness of underrepresented groups – are not
intended to address systemic problems in schools” (p. 189).
The system of higher education began to realize that its responsibility to the State
were not being met, and student-centered services more than ever looked at as the
fundamental contributors to help address issues of diversity in colleges and universities.
Race-based challenges through Affirmative Action soon came into play in the political
arena because student-centered efforts targeted set populations. Student-centered
programs became extremely important in addressing techniques to increase enrollment
and support the preparation of unprivileged high school students following statewide
12
legislation of Proposition 209 prohibited against discrimination or preferential treatment
by public entities (California Secretary of State, 1996).
Student-centered outreach programs began to function as the main mechanism
assessing the individual educational inequities of students in elementary and secondary
schools throughout the state by providing supplemental learning services. It is not
intended to serve as systemic changes rather function as a temporary solution until issues
of inequities are resolved in public education. These programs were to function as a
temporary solution until a systemic resolution had been developed.
With the growing disparity in the public K-12 educational system, the Outreach
Task Force (OTF) report (1997) generated by the UC system stated that “[studentcentered programs] were employed to assure that [higher education] remains accessible
to students of diverse backgrounds.” By utilizing surrounding resources to level the
playing field, student-centered outreach programs continue to increase the number of
participants who are prepared for higher education (OTF, 1997).
Theoretical Underpinning of Student-centered Outreach Programs
This section will provide a condensed set of theories which student-centered
outreach programs are based. For the purpose of this study, student-centered outreach
services are defined as those programs operated by private or public postsecondary
institutions that target individual secondary school-students providing supplemental
services towards preparation for the first-year of undergraduate education. These
programs are commonly referred to in the political arena as academic preparation
13
programs, not intervention programs. Specifically, the Early Academic Outreach
Program (EAOP) is one of many subsets of student-centered outreach programs in the
State and EAOP is the focus of this study. The mission of EAOP is to contribute to
educational equity and access by motivating and preparing students to pursue and
succeed in postsecondary opportunities.
Ending the section, categorical services are summarized based on conceptual
theories known to affect a participant’s decision to enroll into higher education where the
participant is more likely to attain a degree. From the services rendered by studentcentered outreach programs prior to enrolling in higher education, participant’s
knowledge of the institution impacts their decision to enroll in an institution where they
are more likely to attain a degree. A classification topology which will be referred to as
the program standards is developed from the services rendered to participants.
A student-centered outreach program is not an intervention program and the term
cannot be used synonymously. As Gandara (2001) states, intervention programs attempt
to correct disciplinary or behavioral problems, teach civility, and are not directly linked
to academic success, rather are linked to social conditions that are affecting student
demeanor (p. xi). Student-centered outreach services intend to improve a participant’s
educational attainment but do not aim to alter the school’s disciplinary or behavior
problems in the academic structure. Educational attainment refers to the highest level of
education a person attains. Also, student-centered outreach services do not attempt to
alter the environmental conditions of the school setting of those who are responsible for
curriculum and instruction. The structure and the curriculum that is taught are decisions
14
made by administrators at the school site and the Department of Education (Zarate &
Pachon, 2004; Betts, Rueben, & Danenber, 2000; ). Alterations of school curriculum
through outside agencies are traditionally referred as school-centered outreach programs,
not student-centered outreach.
A student-centered outreach program supplements and extends a participant’s
curricular and extracurricular experiences by relaying appropriate and timely information
about the importance of educational attainment beyond high school. Activities known as a
type of service is a deliverable made available to individual students in middle and high
school prior to postsecondary enrollment. Activities are designed by EAOP for a school
setting in which participants are scheduled out of the class instructional curriculum.
Minimally, delivered on a monthly basis during the academic year, activities are
supplemental sessions stressing the importance of a participant’s need to prepare
academically for California’s system of higher education and viable resources to support
their success toward a postsecondary degree. Activities are designed to be delivered
minimally in fifty minute sessions. Each activity is measured in dosages, or hours of
participation, and each activity is delegated into the three categorical standards
developed: Academic Advising, College Knowledge, and Personal Motivation.
Services provided by student-centered outreach programs are supported by a
combination of theories which influence a participant’s likelihood to matriculate into
postsecondary institutions. An applicable theory used by student-centered outreach
programs is developing activities in which highlight the econometric theory. “The
econometric theory of human capital highlights the importance in which individuals
15
calculate the net long-term benefit based on all short-term alternatives made available at
the time of the decision” (Perna, 2005, p. 118). Yet, in working with participants, the
proper environmental setting is essential to the success of the econometric theory.
According to econometric theory, participants who enroll in their preferred institution of
higher education calculate the net benefit of success and take into consideration the
environmental factors that would make the participant more successful. EAOP services
help participants to understand what elements in an institution will produce the greatest
benefit during their undergraduate education.
In many cases, student-centered outreach programs exist in school settings in
which exposure or development of postsecondary aspirations are not implemented.
Predisposition, as defined by Swail (2001), are the environmental aspects that impact the
decisions of a student to aspire, prepare, and go to college at the time in which decisions
need to be made. Services rendered by EAOP create a facilitative environment in which
participants continue to assess their decisions related towards postsecondary enrollment.
Student-centered outreach programs utilize tactics that address separate and independent
needs of program participants that promote an attitude of perseverance and aspiration
toward a degree attainment. As further supported by Tierney, Coyar, and Corwin (2003),
developing a setting that promotes college awareness and exposure are associated with
postsecondary aspirations as predictors of college enrollment and degree attainment.
As the fundaments of student-centered outreach programs, different techniques
are used to deliver informational resources to participants by infusing the econometric
theory and predisposition concept. These techniques are advising, counseling, and
16
mentoring. Although used interchangeably generally in education, the terms cannot be
used synonymously. The following sections describe the uniqueness of each term and it
will also provide insight as to the how the tactic relates to the economic theory and the
concept of predisposition.
According to McDonough (2004), counseling is a relationship between a
counselor and a student that helps to resolve or cope constructively with problems and
developmental concerns as defined by the American School Counselor Association.
Counselors are those individuals “licensed/trained educators [who] advocate in
cooperation with other organizations to promote academic, career, and personal/social
development” (McDonough, 2004, p. 70). It is important to stress that a student-centered
outreach practitioner partners with school counselors in order to promote academic and
career development of its participants in conjunction to other personal/social
development needs. The distinguished roles in a school setting are clearly made by the
partnering parties and such relationship is critical in predisposition.
Advising is defined as the student-to-practitioner relationship that ensures students
calculate the impact of today’s decisions based on academic resources made available at
a school that may affect the student’s future outcome – the emphasis is around the
econometric theory. It is important to stress that in this technique social and personal
developmental needs are not incorporated in the decision-making process. The
development of social and personal skills is infused in tactics such as mentoring.
Mentoring is the “informal relationship of guidance that may take the form of a
caring role model or informal advisor … that requires the mentor and the protégé are in
17
agreement about what they are seeking in the relationship” (Gandara & Mejorado, p. 70 92). In the structuring of the informal relationship, the mentor and participant assess
social and personal developmental needs in which improvement may be needed. As
described by Lin (1999), “the extensity of strong ties that represent commitment, trust,
obligation, and motivation can help mobilize and make resources readily available for
college eligibility attainment” (p. 467). With the econometric theory and predisposition
used commonly by student-centered outreach programs, the three tactics stimulate social
capital growth in the participant’s life.
Without a setting of predisposition, underserved students are isolated from
opportunities and do not have access to resources necessary towards the college
admissions process. The interwoven social network that entail advising, counseling, and
mentoring infuses social capital gain that further propagates a setting of predisposition.
Social capital, as defined by Stanton-Salazar (1997), is “the social support networks and
institutional connections that are required in order for individuals to acquire the
opportunities and resources that are controlled by the dominant group but that facilitate
college enrollment” (p. 119). EAOP brings services to schools which lack an external
social support network for its students. Cook and Boyle (2011) note that a student’s
enrollment decision is relative to the diverse option of postsecondary institutions within
range of the high school location. Without a setting of predisposition, underserved
students are isolated from opportunities and do not have access to resources necessary
towards the college admissions process. Astin (1977) further claims that students’
expectations or self-prediction can reasonably estimate what is to happen to them in
18
higher education. Furthermore Perna (2005) states, sociological attainment models
predict that individuals with greater levels of academic excellence receive greater
encouragement from others which in turn positively impacts higher aspirations such as
greater educational and professional career attainments. Development of selfexpectations and social support contribute to a higher probability of postsecondary
enrollment and degree attainment.
Yet, the concept of the feasibility of resources under Bourdieu’s Concept of
Habitus, as described by Perna (2005), states that “individuals function on a habitual
decision-making processes based on internalized thoughts, beliefs, and perceptions from
one’s surroundings developed through cultural anchoring to community practices” (p.
118). Under the habitual decision-making process, student-centered outreach programs
are familiar with incidents in which eligible college bound participants choose not to
enroll into a four-year institution because of lack of information (i.e., financial aid
process, college application process, test registration deadline). Unless the participant is
informed, he/she will decide on the next best advice made available in the community.
Theoretically, not all practices in a community may influence a participant’s decision to
pursue or complete a degree in higher education.
Supported by student development theories, student-centered outreach programs
also utilize extracurricular activities in its service model. According to Hearn and
Holdsworth (2005), there are indirect links between student’s attainment and cocurricular activities which entails a level of interdependency that promotes the shaping of
self-concepts and venues of personal accomplishment, a suggested positive effect to
19
affect college prospects. Specifically, group settings that function as formal forums for
participants to interact socially with similar interests, like in sports, contribute to
postsecondary aspiration of students.
However, Hearn and Holdsworth (2005) stress evidence “that not all racial or
ethnic groups reap the same benefits from sports participation and that benefits may be
different for different kinds of students and different kinds of sports” (p. 141). Further
extended studies conclude that additional sociological factors within differing schools
and communities affect the degree of student benefit in extra-curricular activities (Guest
& Schneider, 2003). In establishing a setting of predisposition, student-centered outreach
programs take into account elements of the student’s environmental setting in order to
strengthen the college choice process toward degree completion success.
From the framework suggested above, student-centered outreach programs utilize
a model to support a participant’s decision to enroll and persist in the higher educational
system towards a degree. As described by Bonous-Hammarth and Allen (2005), “a
college choice process contributes to the student’s academic competencies and
knowledge of college enrollment process” (p. 158). From contributions of the college
choice process, students ascribe to a series of actions of learning; (1) the predisposition
stage in which the college attendance decision is made by the student; (2) the search
stage, or the phase in which information is gathered about possible colleges and
universities of enrollment; (3) the selection stage, the process of submitting applications
and matriculating into a college (Bonous-Hammarth & Allen, 2005). The fundamental
element in this process is ensuring the participant makes the commitment towards an
20
educational aspiration while assessing all the negative factors that may derail their degree
attainment. The tactics used by student-centered outreach programs ensure that the
actions of learning are delivered to the participant in a timely manner without disrupting
their timeline toward degree attainment.
Yet, research shows that educational aspiration alone is not a very good predictor
of who will go on to and complete college (Gandara & Mejorado, 2004). A better
predictor is the consistent response over time of what a student is planning after high
school; the development of educational aspiration arises from the reflection of
expectations develop from important adults in the student’s environment (BonousHammarth & Allen, 2005). Therefore, many student-centered outreach programs do not
restrict students from enrolling into the program if the participant does not demonstrate
interest toward a college education initially.
Based on the commonalities shared amongst student-centered outreach programs,
Tierney et al. (2003) “developed nine hypotheses pertaining to the central aspects of
college preparation programs … [and found] what the research literature said about the
influences … on college preparation and enrollment.” Generally, the common and most
effective elements hypothesized and utilized in student-centered programs are placed in
the five categories: (i) academic preparation & support services, (ii) academic, college,
and career counseling services, (iii) academic enrichment services, (iv) family services
and involvement, and (v) academic, college, and career mentoring services. As an
addendum to the research by Tierney et al., Ruedas (2005) provides a summary of nine
propositions and their prioritized importance with the intended effects:
21
1. It is helpful but not critical to emphasize the culture of the student.
2. Family engagement is critical.
3. Peer groups are helpful but not critical.
4. Programs need to begin no later than the ninth grade and have structured activities
throughout the year.
5. Having knowledgeable counselors available at the core of the program is critical.
6. Access to a college prep curriculum is the most critical variable.
7. Co-curricular activities are irrelevant.
8. Mentoring is helpful but not critical.
9. There is a positive relationship between the cost of program delivery and
achieving college readiness.
With an understanding of applicable theories and the type of impact, studentcentered outreach programs can improve the retention and persistence effect of
participants through certain activities prior to their postsecondary enrollment.
Student Attributes and Environment
The economic success of the state is dependent on the future number of graduates
from postsecondary institutions and has raised questions as to the potential causes to the
shortage of postsecondary graduates. Neglect of the educational pipeline will affect
California’s economic prosperity as a world power (Shulock, Moore, Offenstein, &
Kirlin, 2008; Business-Higher Education Forum, 2003). The inadequate training of a
diverse population affects everyone in the state (Shulock et al., 2008; Johnson, 2011;
22
Johnson, 2007). A student’s attributes and environment impacts degree attainment. But
the inadequate training begins as early as primary and secondary schools as to what
attribute are refined in the student. Large portions of disadvantaged students are enrolled
in schools with the greatest educational disparities when comparing schools with the
lowest and highest API (Betts, 2000). Such environments do not flourish a student’s
attributes.
The API, a scale-utilized by the Department of Education in California, assesses
the quality of academic preparation and growth in a variety of academic subject areas in a
school (California Department of Education, 1999; Bookman, 2005). A high API reflects
the ideal public educational institution capable of preparing its students for high school
graduation, college admissions, and a higher probability of college degree attainment
(Betts et al, 2003).
Although the overall eligibility of underserved students continues to increase, the
students who are enrolled in low API schools are less likely to fulfill the eligible
requirements for admissions to four-year institutions and require greater attention in
undergraduate remediation (Adelman, 2006; Bowen, Chingos, & McPherson, 2009). An
abundance of research shows that teachers and counselors in schools with low API and
high concentrations of low income students tend to have lower expectations for their
students than more affluent schools (Zarate & Pachon, 2004; Betts et al., 2000, Haycock,
1998; McDonough, 1997). According to Goldsmith (2011), “[researchers] finds that
secondary school students from minority-concentrated schools (less than 41% Black and
Latino) achieve and attain less education than similar students in White-concentrated
23
schools” (p. 508). (Bankston & Caldas, 1996, 1998a, 1998b; Dawkins & Braddock, 1994;
Pong, 1998). Goldsmith further stresses that “…minority composition of schools is
particularly related to long-term outcomes (LaFree & Arum, 2006; Wells & Crain, 1994).
A recent study of National Education Longitudinal Study (NELS) data finds that students
from high schools with proportionately more black and Latino students attain less
education in the long run, net of controls for many individual characteristics (Goldsmith,
2009). At these schools, student-centered outreach program contribute to the collegegoing rate by helping students become college eligible (Alexander & Ekland, 1974;
Alexander et al, 1978, 1987; Alwin & Otto, 1977; Thomas, 1980; Bourus & Carpenter,
1984; Hossler, Braxton & Coopersmith, 1989; St. John, 1991; Altonji, 1992; Lucas,
1999; Perna, 2000a).Therefore, high aspirations are a product of high expectations and
encouragement from adults in the student’s home and schooling environment in
mentoring, counseling and advising tactics (Astin, 1977; Perna, 2005; McDonough, 1997;
Bourdieu & Passeron, 1977; Stanton-Salazar, 1997).
The preparation of disadvantage students in institutions with limited resources is a
great challenge (Adelman, 1999, 2009; Bowen, Kurzweil, & Tobin, 2005; Horn, Kojaku,
& Caroll, 2001; Reed, 2005). From an initial cohort of undergraduates who enrolled at a
four-year institution and two-year institution, respectively, 39% and 68% of the
undergraduates did not attain a degree in a six-year period (Attewel, Heil, & Reisel,
2011). Among a cohort of 9th grade students in California, 37 enter college the fall after
graduating from high school, 7 graduated with a bachelor’s degree in four years, and 5
graduated with an associate’s degree in three years (Complete College American, 2011).
24
More specific, data report from the 2009 Survey of Entering Student Engagement
[SENSE] states that undergraduates entering the community college system have
different college experiences – one-fifth of incoming freshmen completed college credit
while in high school, and a little less than half of the same entering freshmen are firstgeneration undergraduates. The Center for Community College Student Engagement
(2010), state, “Most community college students have one attribute: limited time. Most
are attending classes and studying while working; caring for dependents; and juggling
personal, academic, and financial challenges” (p. 5).
Yet, the continued success of student-centered outreach programs is constantly
compared to schools with ideal resources. On a national sample study conducted by
Attewel, Heil, and Reisel (2011), the research indicates that factors such as integration,
academic preparation in high school, and paid work or financial aid play an independent
role and is not mediated by the other. Contrary to belief of how inexpensive community
college education is, the study further demonstrates a financial aid is statistical
significance and positive correlation towards a degree attainment at a two-year
institution. On the other hand, the study also demonstrates that academic preparation is a
strong statistically significant determinant of graduation at four-year institutions than in
two-year institutions (Attewel, Heil, & Riesel, 2011). Yet, Attewell et al. (2011) stress
that “[a]cademic preparation does not explain the current high rates of non-completion
among two-year college entrants” (p. 553). Regardless of level of resources utilized to
prepare disadvantaged students for higher education, student-centered outreach programs
are held accountable to the same statewide accountability standards as low, moderate, and
25
highly affluent schools (Bookman, 2005; California Postsecondary Education
Commission [CPEC], 2006; University of California Student Academic Preparation and
Educational Partnerships Report [UC SAPEP], 2006, 2005, 2006, 2007, 2008, 2009).
Therefore, greater attention as to what preparatory factors student-centered outreach
programs are held accountable to requires further scrutiny.
The minimum eligibility requirements for admissions into public 4-year
institutions requires applicants to successfully complete2 the 15 unit Subject
Requirement3 (also known as the ‘a-g’ requirements), obtain a minimal non-weighted
grade point average4 (GPA) of 2.00 in the Subject Requirements, and obtain a minimal
admission test score correlating to the overall ‘a–g’ GPA. The correlating table is also
known as the eligibility index at the California State University (CSU) system and UC
system. In schools with the least resources, the difficulty for students to fulfill the
minimum admissions requirements into four-year institutions becomes less likely to
occur (Finkelstein & Fong, 2008; Bookman, 2005; Quigley, 2002; Zarate & Pachon,
2006; Betts et al., 2000). Unlike the public four-year institutions, the California
Community College requires a prospective enrollee to be eighteen years of age or
complete a high school diploma (or equivalent). According to Center for Community
College Student Engagement (2010), approximately 75% of secondary school students
enroll in a postsecondary institution within two years of graduation; yet, only 28% of the
Courses completed with a “C-” or greater in 7th -12th grade for freshman admission as stipulated under the
Subject Requirements of the public four-year postsecondary institutions.
3
The implementation of the minimal one-year advance Visual & Performing Art (VPA) requirement for
the Class of 2003 or greater was design to align the subject requirements between the CSU system and UC
system.
4
The CSU minimum GPA for eligible freshman applicants is 2.00. Beginning with the Class of 2007, the
UC minimum GPA for eligible freshman applicants will shift from 2.80 to 3.00.
2
26
enrollees at two-year institutions graduate with a degree within three years, and 45% earn
a degree in a six-year period. The same study identifies that only 52% of the entering
cohorts of students at the public community college system return for the second year.
Students who do not meet the requirements to the four-year institutions have the only
option to enroll in the public two-year institutions upon high school graduation and less
of a probability of returning the second year of the college experience.
Critically important, and regardless of the type of institution of enrollment upon
high school graduation, when controlling for curricular track, aptitude, and academic
family background, the number of years of postsecondary education completed increased
with each additional year of high school science, math, and foreign language (Altonji,
1992; Adelman 1999; Perna & Titus, 2001). Therefore, preparation in the ‘a – g’
requirements is critical in the retention of student regardless of the postsecondary
institutional type of enrollment.
The delayed start of college entry, beginning college as a part-time student or
having dependents is also associated with lower graduation probability and these students
are commonly known as Nontraditional status students (Attewell et al., 2011).
Nontraditional status also has a strong correlation of non-completion in community
colleges as well as less selective institutions where many nontraditional students are
typically enrolled. According to Attewell et al. (2011), other variables that affect degree
attainment are the family socio-economic status, race/ethnicity, gender, and mediation
upon entry are predictors of degree attainment. Furthermore, Tinto’s interactionlist theory
(1975) states that undergraduates who enroll and enter higher education with a variety of
27
personal, familial and academic skills impacts their willingness to withdraw from the
institution. The degree of integration into the social and academic realms of the
institution based on such skills, in turn, influences a student’s commitment to their
personal goal towards degree attainment. Atteweil (2011) states, “Personal connections
are an important factor in student success…Focus group participants report that
relationships with other students, faculty, and staff members strengthened their resolve to
return to class the next day, the next month, and the next year (Center for Community
College Student Engagement, p. 3). Therefore, the development of networks is critical for
student success in higher education.
Yet, networks are not sufficient rather coupling institutional support that promote
academic excellence is also critical. The Center for Community College Student
Engagement (2010) state, “In school, work, and play – in life generally – people perform
better when they are expected to do so…Unfortunately, there are many people that think
some students cannot or will not succeed” (p. 2). The study further stresses the
importance of students receiving “support they need to rise to those expectations” (Center
for Community College Student Engagement, p. 4). Yet, in many instances as the report
states, undergraduates are unaware of such services that foster excellence toward higher
standards, and as a result of non-established networks, the students do not take advantage
of services, do not know how to access services, are inconvenienced by the services, or
are stigmatized for using such services. Among the respondents of the Community
College Survey of Student Engagement (Center for Community College Student
28
Engagement , 2009), 34% rarely or never use academic advising and planning services,
and 37% of the respondents rarely or never use the skills lab in their campus.
Although the standards of admissions have become stringent and services for
success are not readily available, no supplemental resources have been introduced to low
API schools with economically and educationally disadvantaged students (Weston, 2010;
Brunner & Rueben, 2001; Sonstelie, Brunner, & Ardon, 2000). Statewide budget cuts
between 2000 through 2010 in student-centered outreach programs and public education
have constrained preparation efforts attempting to address equitable changes in
admissions process and retention of disadvantaged student (UC SAPEP, 2004, 2005,
2006, 2007, 2008, 2009). Since 2000, programs have continued to face a reduction each
year. Without student-centered services, economically and educationally disadvantaged
students will continue to be less likely to enroll and persist in postsecondary institutions
(Rose, Sonstelie, & Reinhard, 2006; Zarate & Pachon, 2004; Betts et al., 2000).
Yet, strong evidence suggests that student-centered outreach programs positively
influence the preparation of disadvantaged students in schools with limited resources
(Villalobos, 2008, Sanchez, 2008; Rico, 2007; Bookman, 2005; Quigley, 2002). As the
Quigley (2002) report states, “Participants of outreach and academic preparation
programs who applied to a four-year institution, originating from low and mid-range API
schools, were twice more likely to be eligible for admission than other non-program
participants” (p. 20). Valentine et al. (2011) and Cook and Boyle (2011) further stresses
the importance to investigate programs services and student characteristics as a method to
29
determine effectiveness towards a type of student who pursue postsecondary
opportunities.
For example, in Ishitani’s research demonstrates that in schools without university
outreach programs, “students who took ACT/SAT preparation courses in high school
were 33% less likely to drop out than those who did not. Students whose parents were
contacted by teachers for selecting colleges were also 14% less likely to depart than
students whose parents were not consulted by teachers. Students who often talked to their
parents about attending college were 22% less likely to depart” (p. 8). From the study,
participation in ACT/SAT preparation courses reduced the likelihood of departure by
42% or 55% in the second or third year in college, while receiving assistance in financial
aid application increased the odds of departure by 89% in the second year” (Ishitani,
2004). These reports support the notion that a social network may influence the success
of its students in a school if designs are not established properly. Therefore, effective
student-centered outreach programs are essential in schools lacking educational and
information resources.
A similar outcome was also documented with the Indiana Career and
Postsecondary Advancement Center (ICPAC) when a “massive guidance information and
awareness campaign” was made available to students and families as early as 7th grade
(Gandara, 2001, p. XX). Through its program assessment, it had been determined that
ICPAC’s high college matriculation was due to informational resources made as part of
the mainstream of the educational system (Gandara, 2001).
30
Moreover, when the College Reach-Out Program (CROP) was evaluated by
Florida’s Postsecondary Education Planning Commission (PEPC), evidence showed that
when leveling the playing field, outcomes were impacted by strong parental involvement,
a close faculty/administrative relationship with participants, consistent contact with
students, and monitoring of students to the program relationship (Proctor, 1994). To the
contrary. results from a telephone survey conducted to over one-thousand Latino parents
in Chicago, New York and Los Angeles area found that 65.7% of the parents are NOT
knowledgeable about the crucial steps that lead to college, especially to selective
institutions and four-year institutions (The Tomas Rivera Policy Institute, 2002). Students
who initially enroll at a more selective college or university are more likely to complete a
bachelor’s degree than those who choose less selective pathways (Fry, 2004) but without
the family involvement students may choose a school doomed for failure. Evidence
demonstrated that proper regulation and monitoring of resources is capable of
maximizing the effect of community members of any such setting.
The two-year postsecondary institutions are no different. Through an inclusion
criteria in a study of entering freshmen who were identified as needing remedial
curriculum at two-year institution upon freshmen enrollment, Alderman (1998) finds one
credit semester college orientation classes, tutoring and remedial coursework positively
impact the retention of students following the semester ending the intervention, but the
study’s effect size for GPA was heterogeneous and not statically significant. In addition,
Cox (2002) shows in a study of incoming freshmen who placed below the math
placement test cutoff that study skills curriculum included in the math instruction did
31
better than their counterparts but not at a statistical significant level. Stovall (1999)
further identifies that entering undergraduates at two-year institutions who scored below
level in the college level placement in reading and English were positively impacted in
their retention by student success courses that focused on the college transition, career
development and life management at a statistical significant level. Likewise, Scrivener
(2008) further identifies that learning communities of approximately 25 undergraduates at
a two-year institution with three linked curricula courses coupled with tutoring services is
more likely to impact the participants passing grade in the English course than their
counterparts who were randomly assigned to the institution’s general curricula.
Summary
Changes in techniques and methodologies from recruitment efforts to studentcentered outreach services have further increased the college-going rate of the
economically and educationally disadvantaged students in California. With the success of
services, institutions have transitioned its priorities from recruitment to student-centered
outreach efforts. With an acknowledgement of the educational barriers existing in the
disadvantaged communities and the establishment of student-centered outreach programs,
there continues to be a greater increase of prepared participants who enroll into higher
education from low performing schools (CPEC, 1989; CPEC, 1996; CPEC 2004;
Gandara, 2001; Gandara & Mejorado, 2004; Hayward, Brandes, Kirst, & Mazzeo, 1997;
Outreach Task Force, 1997; Quigley, 2002; Sanchez, 2008; Yeung, 2010).
32
The outcome-only assessment models utilized by many student-centered outreach
programs have continued to demonstrate an improvement in the college-going rates of
participants in disadvantaged communities and low performing schools. Yet, the degree
attainment continues to be too low. With changes in accountability standards implicated
by the state Legislature, student-centered outreach programs are now required to assess
the type of activities that directly demonstrate an efficacy toward the college-going rate
and degree attainment of the underserved students in California’s educational system.
While many tactics have been used by the outreach services for the past thirtyfive years, there is an unclear understanding of which elements contribute to the collegegoing rate and retention of underserved students, primarily that of economically and
educationally disadvantaged participants. Research suggests that the activities of studentcentered outreach programs have a positive effect on student outcomes, including
enrolling and persistence in higher education. Yet, the new structures of accountability
require documentation of activity participation in order to assess the efficacy and
effectiveness of such longitudinal studies.
The section concludes with indicators which positively influences a student’s
likelihood to attain a degree from a two-year and four-year postsecondary institution.
With student-centered outreach programs becoming a key vehicle at implementing
services to California’s most disadvantaged students, evidence suggest that such service
may potentially impact the retention and degree attainment of students.
In the next chapter, the research defines the setting and parameters of the study.
The research is interested on exploring whether the categorical activities of EAOP have a
33
positive influence towards a participant’s likelihood to attain a degree and persist in a
postsecondary institution.
34
Chapter 3
METHODOLOGY
Introduction
Traditionally, student-centered outreach programs have utilized outcome-only
assessment models to document success of the college-going rate of its disadvantaged
participants. With recent pressure to demonstrate efficacy and effectiveness, studentcentered outreach programs are concerned with which elements are essential in program
designs at promoting the persistence toward degree attainment. Moreover, the incorrect
selection of activities affects longitudinal studies of programs and jeopardizes the
college-graduation rate of economically disadvantaged and educationally disadvantaged
undergraduates.
The purpose of the study is to determine whether student-centered outreach
programs help participants persist towards degree completion at two-year and four-year
institutions. The purpose of the study is to determine whether the categorical activities
delivered by the EAOP are effective and demonstrate an impact towards a degree
attainment in higher education. In an effort to understand the program’s operations, the
following questions are investigated in the study through a positivistic paradigmatic
approach using the General Systems Theory (GST):
1. Do EAOP activities significantly contribute to a participant’s retention during the
first year of undergraduate education?
2. Do EAOP activities significantly contribute to a participant’s persistence toward a
degree completion in higher education?
35
3. Do EAOP activities significantly contribute to a participant’s degree attainment?
Population
The sample is comprised of 17,836 EAOP participants who are identified as
economically and educationally disadvantaged in the Sacramento region. Participants
who are enrolled in EAOP are students who are academically underserved in California’s
educational system. The term underserved refers to the social and educational
disadvantages in an environment that affects a participant’s likelihood to pursue a
postsecondary education. Such disadvantages include living in a community with a low
college-going rate, attending a school whose Scholastic Aptitude Test (SAT) are below
the national average, attending a school with a limited college preparatory curriculum
intended to fulfill the ‘a-g’ subject requirements, or attending a public school with an
Academic Performance Index (API) lower than six-hundreds. The API is the number
scale used by the California Department of Education to measure academic performance
in public schools as a result of the California Public School Accountability Act of 1999.
Scores are measured on a scale of 200 – 1000, with 200 being the lowest and 1000 being
the highest possible score. These schools are known as EAOP schools.
Historically underrepresented refers to the low disproportionate representation of
students in higher education and the historic term primarily includes American-Indian,
African-American, Latino students. However, the emergence of new underserved ethnic
groups residing in the country has posed a challenge to the definition of
underrepresented. Cambodian, Hmong, Vietnamese, Iu-Mien, or Laotian is referred to as
underrepresented students in higher education. Note the intentional neglect to include
36
historically when referring to the merging groups. Regardless of the classification of
underrepresentation by race and ethnicity, students are affected by economic
disadvantages or educational disadvantages attain in the household within their respective
community. This group is known as traditionally underrepresented, or students who are
disadvantaged by class-based categorizations that is independent of cultural, ethnic, or
racial identity. No emphasis is made between the two classifications of
underrepresentation.
Educationally disadvantaged is defined by the level of education attained by the
participant’s parents at the point of enrolling into EAOP. Three classifications of
educationally disadvantages are made to distinguish the parental educational level.
Highly educationally disadvantaged students are students in which neither parent has
attained a four-year degree or greater. In historical terms, the participant is a firstgeneration college-bound student of the household into higher education. Moderately
educationally disadvantaged refers to participants in which only one parent in the
household has attained a four-year degree or greater. The least educationally
disadvantaged participant is defined as a participant who has both parents in the
household who have both attained a four-year degree but the participant is enrolled in a
low performing school or community with a low-college going rate. The definition of
educationally disadvantaged does not necessarily incorporate the economic prosperity of
the household but does create classification in the system under study.
Economically disadvantaged participants are those participants who fulfill the
low-income criteria utilized by the United States Department of Education, Office of
37
Postsecondary Education for their federal TRiO programs. The comparative table
published by the agency utilizes the annual adjusted gross income of the household and
number of household dependents in order to determine if the household exceeds the
poverty levels establish by the U.S. Census Bureau.
The Sacramento region encompasses Sacramento, San Joaquin, Solano, and Yolo
counties. The sample size in the regression analysis is comprised of alumni EAOP
participants who were enrolled in a school whose API is less than the state average, have
a mean SAT score within in the first quartile of the national average, or offers the
minimum fifteen unit subject requirements for necessary for admissions to California’s
public four-year institutions.
The student must have attended an EAOP school as early as fall semester of the
freshman year and graduate by spring semester of the senior year – four fall semesters,
and four Spring semesters throughout high school – specifically, students who are a part
of the graduating class of 2000 through 2006. Students who depart from the school prior
to graduation are excluded from the study. The total number of participants in the study
following the criteria is 5,865 participants.
Design of the Study
The quantitative approach in the experimental longitudinal study is designed with
the Input-Environment-Output Model (IEO) created by Alexander W. Astin (1991). The
model explores the environmental variables in relations to the output variables. In
addition, the model incorporates initial input variables that may affect the outputs of the
38
design by including the participant’s personal qualities the participant brings initially to
the educational program. According to Astin (2011), “The basic purpose of the IEO
design is to allow us to correct or adjust for such input differences in order to get a less
biased estimate of the comparative effects of different environments on outputs” (p. 19).
Figure 1
The IEO Model (Astin, 1991)
According to Astin’s (1991) rose analogy, suppose at a county fair a judge
examines the different entries to a rose contest. Although some roses have stronger
features (i.e., size, stronger fragrant, beauty), the observation from the output does not
provide much insight about how to cultivate such flowers. Information about the initial
attributes about the flower (i.e., type of seeds, cut) and the conditions of the environment
(i.e., soil, planting method, light, fertilizer, water scheduling) contribute to the outcome
(i.e., beauty of flower). Astin (2011, p. 20) states,
These environmental factors are important considerations in how effectively the
grower can develop the rose’s ‘talents’. In other words, simply having input and
outcome data of a group of students over a period of time is of limit value if you
do not know what forces were acting on these students during the same period of
time.
39
Therefore, the output’s effectiveness is dependent on the environmental
conditions and strategies introduced based on the initial inputs. Efficiency, effectiveness
and efficacy are variables that impact the environmental setting in the IEO model. Figure
2 summarizes what I have shown as the Triple E Theory, a visual explanation as to how
program effectiveness is impacted in an environment. The model exemplifies the
potential impact of a participant’s degree completion through the General System Theory.
For example, suppose you and a colleague are outreach officers at a low
performing high school where each of you provides services to 20 participants in a two
hour session. Your task is to ensure the delivery of college resources that would help the
majority of the 20 participants in each group towards attaining a college degree upon high
school graduation. During a fiscal challenging year, you are required to provide support
to an additional 20 participants as a result of reduced staffing (i.e., colleague is laid off).
Although there is no increase in funding to the cohort (constant), the efficiency to provide
the services to the participants improved (i.e., cost per participant decreases), the
effectiveness of services is reduced (i.e., outreach officer time spent per student
decreases), and the probability of a participant’s efficacy decreases (i.e., ability for
participant to acquire the knowledge to act on is less likely).
To the contrary, on a great fiscal year, you and your colleague’s cohort of
participants are split from 20 to 10 participants with the hiring of two additional outreach
officers. In this scenario, the services to the initial 40 participants become inefficiency
(i.e., cost per participant increases), the effectiveness of services rendered is improved
(i.e., outreach officer time spent per student increases), and the probability of a
40
participant’s efficacy increases (i.e., ability for participant to acquire the knowledge to act
on is more likely). It is critical to point out that in the environmental unit of the IEO
model, the model’s outputs is affected by three intermediate subunit variables referred to
as efficiency, effectiveness and efficacy. These intermediate subunits variables I refer to
the Triple E Theory in the IEO model.
Figure 2
Triple E Theory
As a quantitative methodological study, the design is set to investigate if the
EAOP activities have an association with the dependent measure: persistence and degree
attainment. Logistic regression analysis will be used to estimate the association between
participation of activities and four-year enrollment because of the dichotomous nature of
the dependent variable. Linear regression analysis will be used to determine if an
association exists between activity participation and degree attainment.
As part of the input variables of the IEO model, the program collected
demographic values such as the participant’s gender, ethnicity, parent’s educational level,
41
household income at the point of entry into the program, and the ‘a-g’ GPA ending the
ninth grade year. As environmental inputs, the program documented the attendance of
activity, the type of activity, and date of the activity. However, for the purpose of the
study, the hours of participation and the classification of activities delivered will function
as environmental variable. As the output variables, the program collected the type of
institution the participant enrolled following six months from high school graduation, and
the overall ‘a–g’ GPA.
Data Collection
Longitudinal data collected by EAOP is utilized for the study. The following
section describes as to how the information was collected and stored for participants in
the study.
Upon enrollment into EAOP, participant’s demographical information was
collected from an enrollment application submitted by student and parent. Primarily, the
data elements collected from the application are the student’s gender, ethnicity,
household’s income, free-reduced lunch eligibility, and parent’s highest educational level
attained. Applications were available to the students through school administrators, the
program’s online web site, and distributed throughout the school by EAOP staff. The
screening of applications were based on the program’s definitions of economically and
educationally disadvantaged.
Ending the semester in which the participant enrolled into the program, a high
school transcript request is made to school officials. Upon receipt of the transcript for
42
each participant, the program evaluated the number of course attempted by the participant
and the grades received in ‘a-g’ subject requirements.
Furthermore, each semester in which the participant was enrolled in the program,
high school transcripts were again requested. Evaluation of transcripts generated the
number of courses attempted, the average ‘a-g’ GPA for that semester, and to monitor
whether the participants were fulfilling the admissions requirements to four-year
institutions. Transcript information fulfilling ‘a-g’ requirements were entered and timestamped into the program’s database by EAOP coordinators trained and knowledgeable
of admissions policies.
In addition to transcript evaluation, program coordinators administered activities
on a monthly basis. Attendees were required to provide a signature to a pre-generated
roster of registered participants. A participation log for each activity is recorded, filed,
and entered into the database system for respective participant in one of three categorical
activity standards: Academic Advising, College Information, and Personal Motivation.
Participants received service of activities with peers of the same grade level.
Program coordinators were expected to submit the original pre-generated signature page
of registered participants within two weeks of administering the activity. Details of the
activity were included with an activity report form (ARF) as part of the data entry.
Within nine months of graduation, participants were searched through the
National Student Loan Center (NSLC) in order to determine the type of institution of
enrollment. Participants with social security numbers were match with the NSLC search
query feature. For participants for which no social security number was available, key
43
identifiers were used such as last name, first name, and date of birth. All participants
were assured confidentiality and anonymity through parental consent upon program
enrollment.
Instrumentation
The statistical software IBM SPSS 19.0 is used for the analysis of the data. No
additional instruments or tools were used in the analysis of the EAOP historical data.
Data Analysis Procedures
The variables utilized in the study were collected by the initial student-centered
outreach applications submitted by program participants with a signed consent form from
the participant’s parent. In addition, high school transcript information was collected on a
semester basis for participants. The method as to how variables were defined is discussed
below.
Variables
Enrollment
As an independent variable of the study, the enrollment variable is a measure of
whether the participant enrolled in a public or private postsecondary institution within six
months of high school graduation. The entry date of enrollment, the name of the
educational institution, and the type of postsecondary institution were collected from the
NSLC web site. The type of institution was cross referenced with the National Center of
Educational Statistics (NCES) in order to determine whether it is a public four-year
44
postsecondary institution or public two-year institution, or any combination thereof.
Based on the information retrieved from NSLC and NCES, values representing outcomes
were placed into categories. A nominal dummy variable was develop to represent
whether the participant enrolled within six months of high school graduation by
institution type (0 – did not enroll, 1 - enrolled into two-year institution of higher
education, 2 – enrolled into a four-year institution). Unlike retention and persistence,
analysis for degree attainment analysis is compared relative to participants who did not
enroll in higher education from the same environmental setting. It is critically important
to note that the enrollment variable determines if the participant enrolled in a six-month
interval following high school graduation versus if the participant enrolled in higher
educational at all.
Retention, Persistence & Degree variables.
The retention variable is dummy variable reflecting whether the participant
returned to the same institution one year later (0 – did not return to the initial institution,
1 – returned to the initial institution). In addition, a persistence continuous variable is
developed to reflect the consecutive semester of postsecondary enrollment following high
school graduation. The persistence variable includes intersession enrollment as a part of
the persistence variable, but participants who selected to not enroll in intersession (i.e.,
summer) are still included as participants without a disruption in the persistence measure.
The persistence measure is not concerned with whether the participant remains at the
same institution initially enrolled following high school graduation. The persistence
variable is limited to six-year equivalent semesters as a method to reference whether
45
participants graduate sooner or later. The average bachelor’s degree attain is within a sixyear period, the threshold for the study is confined to twelve consecutive semesters of
continuous enrollment.
Based on the same process to determine the enrollment variable, a ordinal
variable degree is created based on the highest degree attained within 6-years from
enrollment into a postsecondary institution, regardless of the initial institution of
enrollment (0 – did not attain a degree, 1 –attained a certificate, 2 – attained an
Associate’s degree, 3 – attained a Bachelor’s degree, 4 –attained an advanced degree).
Subject Requirement.
Recall, the minimal subject requirements for admissions into California public 4year institutions requires applicants to successfully complete the 15 unit Subject
Requirement (also known as the ‘a-g’ requirements). From review of the transcript
information during the participant’s enrollment in their secondary education, an ordinal
variable is defined for each subject requirement (i.e., History, English, Mathematics,
Laboratory Science, Foreign Language, Visual/Performing Arts, College Prep Electives)
since each semester of advancement in the subject content is built off the previous
semester in that subject matter. Each ordinal variable will reflect the total number of
semester completed with a passing grade of “C-” or greater in each of the subject
categories. For example, for a participant who completed two years of science in the
laboratory science of the subject requirements, the notation of the variable is “laboratory
science = 4 semesters”.
46
GPA variables.
From the transcript information collected, the program coordinators entered the
coursework attempted and the grades received by the participant into an MS Access
database with a Visual Basic interface. Program coordinators compared courses taken by
the participant to the ‘a-g’ course list published at the UCs course articulation web site at
http://doorways.ucop.edu by the corresponding year in which the course was taken.
Courses not approved by the articulation process are voided and not entered into EAOP’s
database system.
From the database system, a non-weighted and weighted ‘a-g’ GPA is calculated
for each semester of the participant’s education as well as the mean ‘a-g’ GPA of the
participant’s educational history. Course with grades of “A” received a value of 4, “B”
received a value of 3, “C” received a value of 2, “D” received a value of 1, and “F”
received a value of 0. Courses in which grades of “incomplete” or “withdrawn” were not
included in the study since admissions processes void such courses. Courses with a
“Pass” notation are also excluded from the GPA calculation. The average AGGPA is
calculated by the sum of grade points accumulated divided by the number of courses
attempted by the participant. A grade level AGGPA is distinguished by the following
nomenclature: ninth AGGPA, tenth grade ‘a-g’ GPA, eleventh grade ‘a-g’ GPA, and
twelve grade ‘a-g’ GPA. Regardless of grade level, all AG GPAs are calculated with nonweighted value for honors, advanced placement, or international baccalaureate.
47
The AG GPA variable entails the calculated non-weighted AG GPA of all course
work attempted from ninth grade through the end of the senior year of high school.
Below in Table 1 is a summary of the variables in the study.
Activity type variables.
Activity rendered to participants were placed into three categorical areas and
stored in the database system. Each record of attendance was stored for each participant
during their duration of program enrollment. For each participant, the hours of attendance
was calculated for each standard: academic advising (hours of academic advising time
variable), college information (hours of college information time variable), and personal
motivation (hours of personal motivation time variable). Each participant in the study
reflects values for each of the categories. Participants who did not attend activities in one
of the standards received a value of zero hours in the respective standard. Among the
services offered, the total number of hours in attendance was recorded for each of the
categorical activities.
Ethnicity variable.
Twenty-one categorical ethnic choices were made available to participants on the
enrollment application. As one of the choices, applicants had the option of stating
“Other.” The intention was to increase the likelihood of a participant to select an ethnic
identity, if applicable, or opt out of indicating. Ethnicity categories were then compiled
into six categories and each received a categorical value (as noted in parenthesis);
African American (AF = 1), American Indian (AI = 2), Asian (AS = 3), Latino (LA = 4),
White (WH = 5), and Other (OT = 6). Next dummy variables were created for all groups,
48
including White, which serves as the reference group in all the regression analysis. This
recoding procedure resulted in 5 dichotomous ethnicity variables (see Table 1).
Participants received a value of one for the identified ethnic group and received a value
of zero for the remaining ethnic groups. The matrix utilized in the IEO model is
summarized below in Table 1.
Table 1
Definition of Variables for IEO Model
Descriptors
Model
Section
Output:
Dependent
variable
Variable
Type
Degree
Attain a degree within six-years of initial enrollment in higher
educational institution. Ordinal variable of the type of degree completed.
0 if no degree, 1 if technical degree, 2 if certificate, 3 if Associate degree,
4 if bachelor’s degree, 5 if Master degree, 6 if Doctorate
Enrolled to the same postsecondary institution one year following initial
enrollment. Binary variable. 0 if no, 1 if yes.
Retention
Environment:
Independent
variable
Input:
Independent
variable
Persistence
Count of semesters of continuous enrollment from high school graduation
up to 12 semesters. Continuous variable.
API
Average API of school during the participant’s secondary school
enrollment. Continuous variable.
Hours of
Academic
Advising
Hours of
College
Information
Hours of
Personal
Motivation
Enrollment
Hours of attendance in Academic Advising activity standard. Continuous
variable.
Hours of attendance in College Information activity standard. Continuous
variable.
Hours of attendance in Personal Motivation activity standard. Continuous
variable.
AfricanAmerican
If in enrolled in higher education, whether the participant enrolled into
postsecondary institution six months after high school graduation. 0 if no,
1 if yes.
Dichotomous African American identifier. 0 if not African-American, 1 if
African-American.
American
Indian
Dichotomous American Indian identifier. 0 if not American Indian, 1 if
American Indian.
Asian
Dichotomous Asian identifier. 0 if not Asian,
1 if Asian.
49
Chicano
Dichotomous Chicano/Latino identifier. 0 if not Chicano/Latino, 1 if
Chicano/Latino.
Other
Dichotomous Other identifier. 0 if not Other, 1 if Other.
Pacific
Islander
Dichotomous Pacific Islander identifier. 0 if not Pacific Islander, 1 if
Pacific Islander.
White
Dichotomous White identifier. 0 if not White, 1 if White.
Ninth AG
GPA
Non-weighted GPA calculated based on ‘a-g’ courses attempted in ninth
grade. Continuous variable.
OT Group
Dichotomous Other ethnic identifier. 0 if not Other, 1 if Other (not AF,
AI, AS, CH, or WH).
nonweighted
HS GPA
Non-weighted GPA calculated based on ‘a-g’ courses attempted between
ninth through twelfth grade. Continuous variable
In order to determine whether the program’s activities contribute to a participant’s
enrollment into higher educational institution upon high school graduation or attained a
degree six years from initial enrollment, the following section will describe the process in
which the longitudinal data will be analyzed. Two levels of analysis will be utilized in the
study. The first level of analysis entails descriptive analysis which would also include
correlation of the independent variable with the dependent variables. The second level of
analysis incorporates dichotomous logit loglinear regression for the nominal dependent
variable retention and an ordinal regression analysis for the ordinal dependent variable
degree in each of the logit models. The ordinary least squares (OLS) method will be
applied to the interval dependent variable persistence The study will concentrate on the
logit model that explores the variable degree through an multinomial logit regression.
Activities delivered to participants are the environmental independent variable of the
IEO model. In order to assess the importance of the activities, the categorical activities
function as the choice to attend the activity as a participant. The demographic variables
50
function as the input independent variables of the IEO model. The ninth AG GPA and
AG GPA variable also function as the input variables in the study.
The category inactive includes those participants who left the schools served by
EAOP to a non-served school, for whatever reason. The active category entails those
participants in which the opportunity to attend activities was available and did not leave
the EAOP school site. Active participants are the center of the data analysis protocol
outlined.
Utilizing a SPSS statistical software package, descriptive statistics will provide the
mean, minimum, maximum and standard deviations values for the independent and
dependent variables. Then, frequency distributions will examine the distribution of the
independent variables in order to determine the level of skewness and kurtosis necessary
for correlation analysis.
Normal distribution increases the statistical significance in correlation analysis when
the statistical values are between ± 1.0 and ±5.0, respectively, for skewness and kurtosis.
These factors would strengthen the correlation coefficients in comparative analysis.
Crosstabulation will further describe the type of institutions participants enrolled into by
ethnic category.
Then, correlation analysis will provide an additional insight as to the possibility of
multicollinearity between the input and output variables. A Pearson correlation will be
used for a normal distribution, and a Spearman’s correlation will be used for non-normal
distributions in the correlation analysis. The significance from the statistical procedure
will provide a representation as to the degree of rarity of the results (p ≤ 0.05).
51
Furthermore, the correlation will establish the directional relationship between the
independent and dependent variables. In order to ensure a plausible strength and direction
of association between variables, the correlation coefficients will provide insight as to the
variable correspondence.
The next tier of the regression analysis is based on the type of measurement of the
dependent variable. The logistic regression for a nominal dependent variable is best to
assist in understanding outcome prediction of the dependent and help establish the best-fit
line of the dependent variable. The analysis will create a stronger model for
predictability. On an ordinal variable, a similar multinomial logistic will create the best
fit line to the outcome dependent variable. The goal of the analysis is to understand and
control the inputs that may affect the outcome variable through a backward step process.
Although regressions provide accurate measures of probability of the dependent variable,
the study is interested at the odds of an outcome of the dependent variable: a logit model.
A logit construction is the natural logarithm of the odds of an event occurring
where the ratio of the intended event occurs. Logits fit linear models by taking into
account the odds of such predictions of the dependent variable. The construct will help to
understand whether the activity standards delivered were helpful instead of understanding
how much the activity standards helped in the linear regression analysis. Below is the
relationship between the probability and odds that interconnect the linear and logistic
regression analysis.
The logistic regression equation for degree attainment is,
52
Where,
is the odd of the predicted value,
is the regression coefficients,
is each independent variable in the model.
The output variable ( ) is a function of a constant (
( ) times the independent variables (
plus the sum of the coefficients
). The output variable
for each model
represents the predictive dependent value of degree and retention where the input
variable,
represents the independent variables that influence the outcome of the
dependent variable. Below, the equation reflects the natural log odds (not probability) of
the dependent variable of the study as a function of a constant, and the weighted averages
of the independent variables. Alternatively, since
represents the predicted value of the
linear model, then an alternative representation of
value is the probability of the event
to occur, or the odd of such occurrence.
Log odds =
The probability calculation for an outcome is from the equation above is,
Where, e = 2.71828
Z=
for multiple predictors.
Again, the use of the logit construct will help us to understand whether the independent
variables were helpful instead of understanding how helpful the independent variables
were in the linear regression analysis.
53
The regressions include an appropriate logit model for each dependent variable.
For the dependent variable persistence, a bivariate linear regression will be used to
determine the predictability of the independent variables. Whether the dependent variable
is nominal or ordinal in nature, the logit models are affected. At this level, the analysis
incorporates a dichotomous logit model for the nominal dependent variable retention and
an ordinal regression analysis for the ordinal dependent variable degree. For example, in
an multinomial logistic regression analysis, the ordinal categorical dependent variable
degree is of ranking order with no clear interval between each category whereas in a
multinomial logistic regression ranking order is loss in the statistical analysis. For this
variable, it will be converted to award. Multinomial logistic regression determines if a
relationship exists between the independent variable and degree attainment. Although
there are over six levels for the dependent variable degree, the polytomous dependent
variable is converted to three levels in order for its use in the multinomial logistic
regression. Some of the levels contain too few records which limit the analysis and
clustering resolves the condition.
The dependent variable clustering referred into this section as award does not
impact prior analysis. The distinguishing factor between variable the two variables is that
award is clustering of degrees whereas degree is the highest actual degree awarded to the
participant. For the levels of the dependent variable award, “0” represents no degree, “1”
represents sub-baccalaureate degree, and “2” represents Bachelor’s degree or greater.
Vocational, certificate and technical degrees are consolidated with sub-baccalaureate
degree (level 1), and Master or doctoral degrees are clustered with Bachelor’s degree or
54
greater (level 2). In the multinomial logistic regression, the category no degree (level 0)
is the referent point in which estimates are made on. Table 12 reflects the frequency for
each level of the dependent variable degree and reaffirms the selection of “no degree” as
the referent point since the level has the greatest frequency count. These factors are
critical in assessing the degree of impact based on a stratified order.
In building a logit model, for a multinomial regression, a parameter estimates will
be further evaluated using Wald statistics to determine if a relationship exists between the
independent and dependent variable. When fitting an multinomial regression, there is an
assumption that the relationship between the independent variables and the logits are the
same for all the logits, and a testing of parallel lines is required to test the assumption by
reviewing the Chi-square value and comparing to the maximum likelihood parameter and
its standard error. To determine if he model fits, a comparison of observed and expected
probability values will further solidify the appropriateness of the model for each
classification type of the dependent nominal variable, award. The Pearson residual is
measured between the predicted and observed probability, and a computation of the
Pearson and Deviance goodness-of-fit statistic will result from the variables of Pearson
residual. If the results of the Pearson and Deviance goodness-of-fit results have a large
significant level, the model fits and examination of the coefficients will determine if
parallelism assumption exists by evaluating the observation significance after testing for
parallelism. The examination of the coefficients will determine if the independent
variables have an impact on award variable. Lastly, strength of association between
dependent and independent variables will be determined either by the Cox and Snell,
55
Nagelkerke’s, or McFadden’s R2 statistic. Following these data analysis findings,
recommendations will be discussed in an effort to enhance program effectiveness and
degree attainment of its participants.
Critically important, discriminant function analysis is an alternative to predicting
group membership but since the independent variables is a combination of categorical
and continuous measure (i.e., nominal, ordinal, and scale). Although discriminant
function analysis focuses on correlational weights that reflect the percentage of correct
classifications, the logistic regression focuses on the likelihood of a specific outcome
such that observations may not necessarily be independent from one another and the
assumptions of normality do not need to be met. In addition, in logistic regression assume
there is no linear relationship between the dependent and independent variable that is
restrictively found in standard linear regressions. Lastly, the logistic regression does not
require homoscedasticity assumptions to be met and, unlike the linear regressions, the
probabilities results in a range of 0 and 1. In linear regressions, values less than 0 and
greater than 1 do not have meaning.
Hypothesized Signs
In exploring the questions of the study, Ho represents the null hypothesis,
suggesting no relationship between the independent variable and the dependent variable,
or there is no change in the odd ratio of the independent variable. Ha represents the
alternative hypothesis, indicating an increase in the odd ratio as the independent variable
56
changes. Specifically, below is the detailed hypothetical expression as they pertain to the
research.
1. Do EAOP activities significantly contribute to a participant’s retention during the
first year of undergraduate education?
Ho Hours of Academic Advising = 0, Ha Hours of Academic Advising > 0;
Ho Hours of College Information = 0, Ha Hours of College Information > 0;
Ho Hours of Personal Time = 0, Ha Hours of Personal Time > 0.
2. Do EAOP activities significantly contribute to a participant’s persistence toward a
degree completion in higher education?
Ho Hours of Academic Advising = 0, Ha Hours of Academic Advising > 0;
Ho Hours of College Information = 0, Ha Hours of College Information > 0;
Ho Hours of Personal Time = 0, Ha Hours of Personal Time > 0.
3. Do EAOP activities significantly contribute to a participant’s degree attainment?
Ho Hours of Academic Advising = 0, Ha Hours of Academic Advising > 0;
Ho Hours of College Information = 0, Ha Hours of College Information > 0;
Ho Hours of Personal Time = 0, Ha Hours of Personal Time > 0.
57
Chapter 4
DATA ANALYSIS AND FINDINGS
Introduction
The purpose of the study is to determine whether student-centered outreach programs
help participants persist towards degree completion at two-year and four-year institutions;
specifically, whether the categorical activities delivered by EAOP are effective and
demonstrate an impact towards a degree attainment in higher education. In an effort to
understand the program’s operations, the following questions are investigated through a
positivistic paradigmatic approach using the General Systems Theory (GST):
1. Do EAOP activities significantly contribute to a participant’s retention during the
first year of undergraduate education?
2. Do EAOP activities significantly contribute to a participant’s persistence toward a
degree completion in higher education?
3. Do EAOP activities significantly contribute to a participant’s degree attainment?
In order to determine whether the program’s activities contribute to a participant’s
likelihood to persist, first-year retention, and degree attainment, regression and logits will
be conducted. Two levels of analytical results will be presented in the study. The first
level of results presented includes descriptive analysis and the correlational coefficients
between the independent and the dependent variables. The second level of analytic results
presented focuses on inferential statistics. The presentation of the results displays the
58
sequence of events toward degree attainment. Analysis will be presented by first
exploring program predictability on first-year retention, followed an ordinary least
squares (OLS) regression on the dependent variable persistence, and conclude with a
multinomial logit on the dependent variable degree. The analytic process will provide a
road map of what phenomenon is experienced by participants of EAOP. .
Descriptive Statistics
Table 2 represents the size of each independent and dependent variable. The table
includes the sample size (n), minimum value, maximum value, mean, and standard
deviation.
Table 2
Descriptive Statistics of Independent Variables
n
Min
Max
Mean
Standard
Deviation
African-American
5865
0.00
1.00
0.1714
0.3769
American Indian
5865
0.00
1.00
0.0167
0.1282
Asian
5865
0.00
1.00
0.2474
0.4315
Chicano/Latino
5865
0.00
1.00
0.2757
0.4469
Other Ethnicity
5865
0.00
1.00
0.0962
0.2948
Pacific Islander
5865
0.00
1.00
0.0638
0.2444
White
5865
0.00
1.00
0.1289
0.3351
Hours of Academic Advising
5865
0.00
21.50
1.3689
2.7778
Hours of College Information
5825
0.00
16.00
0.7041
1.9493
Hours of Personal Motivation
4818
0.00
4.00
0.0944
0.6017
Semesters of College Prep Elective
4008
0.00
11
2.2300
1.436
Semesters of English Subject
4607
0.00
12
5.9400
2.3070
Variable
59
Semesters of Foreign Language Subject
4417
0.00
12
4.3200
2.142
Semesters of History Subject
4535
0.00
10
4.2400
1.7990
Semesters of Lab Science Subject
4357
0.00
12
4.0300
2.2080
Semesters of Math Subject
4531
0.00
15
5.2700
2.5540
Semesters of Visual & Perform Arts Subject
3513
0.00
20
2.5800
2.205
Non-weighted HS GPA
4667
0.00
4.00
2.7373
0.8762
School Academic Performance Index
(API by 1000 point scale)
5865
552
839
644
59.3463
No Degree Attained
5865
0.00
1.00
0.6426
0.4793
Some College Attained
5865
0.00
1.00
0.0186
0.1351
Certificate Attained
5865
0.00
1.00
0.0066
0.0813
Associates Degree Attained
5865
0.00
1.00
0.0612
0.2397
Bachelor’s Degree Attained
5865
0.00
1.00
0.2529
0.4347
Master’s Degree Attained
5865
0.00
1.00
0.0160
0.1256
Doctorate Degree Attained
5865
0.00
1.00
0.0020
0.0452
Low Educationally Disadvantaged Level
5865
0.00
1.00
0.1552
0.3621
Moderately Educationally Disadvantaged
Level
5865
0.00
1.00
0.2542
0.4355
Highly Educationally Disadvantaged Level
5865
0.00
1.00
0.5906
0.4918
Enrolled in college 6 months after HS grad
5110
0.00
1.00
0.8600
0.3490
Retained during Freshmen Yr Retained at
Initial Institution
5110
0.00
1.00
0.710
0.453
Enrolled into 2-Year Institution
5110
0.00
1.00
0.8789
0.3263
Enrolled into 4-Year Institution
5110
0.00
1.00
0.1211
0.3263
Low Income
5865
0.00
1.00
0.4300
0.4960
Male
5865
0.00
1.00
0.3333
0.4714
Semesters of Continuous Persistence
5865
0.00
76
10.680
7.985
Transferred to 4-yr Institution
4491
0.00
1.00
0.5800
0.4940
The sample size is 5,865 participants. Among the participants who were offered
60
high school services from ninth grade through the end of their senior year, 67.7% are
females and 33.3% male in the sample. Approximately 59.1% of the participants’ parents
do not have a four-year degree, 25.4% of participants have only one parent with a fouryear degree, and 15.5% of the participants have both parents with a four-year degree. The
distribution described by combination of parental educational level is referred to as
highly educationally disadvantaged, moderately educationally disadvantaged, and least
educationally disadvantaged, respectively. Among the participants, 43.5% are from lowincome households. Table 3 summarizes the ethnic distribution of the participants.
Table 3
Ethnic Distribution of EAOP Participants
Ethnicity
Chicano
Latino
Asian
African
American
White
Other
Pacific
Islander
American
Indian
Total
27.6%
(1617)
24.7%
(1451)
17.1%
(1005)
12.9%
(756)
9.6%
(564)
6.3%
(374)
1.7% (98)
5865
(100%)
The cross-tabulation in Table 3 and Table 4 provides a summary of the socioeconomic and educational level statistics based on ethnicity. The greater educational level
of the participant’s parents impacts the level of disposable income most likely available
toward education attainment. The information provides insight to determine if household
fiscal resources are readily available for one group versus another and, if so, the results in
the analysis may impact the analysis if the raw number of one group dominates another.
The evidence below in Table 2 shows a disproportionate distribution of participants who
are economically disadvantaged related to parental educational level classification.
61
Although it would be expected that enrollment into the program based on low-income
criteria would be similar for all ethnic groups, the analysis demonstrate that the
proportion of Asian participants who are economically disadvantaged is bigger than the
any of other ethnic groups. Further investigate shows this the result of merging ethnic
minorities from the Southeast Asia such as Hmong, Iu-Mien, and Cambodian.
Table 4
Percentage of Economically Disadvantaged by Ethnicity
Ethnicity
African
American
American
Indian
Asian
Chicano
Latino
Pacific
Islander
White
Other
No
65.0%
(653)
74.5%
(73)
33.2%
(482)
61.7%
(997)
68.7%
(257)
64.9%
(491)
64.2%
(362)
Yes
35.0%
(352)
25.5%
(25)
66.7%
(969)
38.3%
(620)
31.3%
(117)
35.1%
(265)
35.8%
(202)
Total
100%
(1005)
100%
(98)
100%
(1451)
100%
(1617)
100%
(374)
100%
(756)
100%
(564)
Economically
Disadvantaged
Also, the cross-tabulation (Table 5) provides the percent distribution of the
participants based on their parental education disadvantaged classification. Traditionally,
educational disadvantaged has historically been defined by whether a parent has a fouryear degree or greater. Since the level of educational of each parent impacts a
participant’s social capital, the study makes distinguishing factors based on the number of
parental degrees in the household. Table 5 shows that among each of the ethnic groups,
with the exception of Asian, there is a similar proportionate percent distribution of
participants based on the three level educational classifications. Each ethnic category
shows approximately three-quarters of its population is moderately or highly
62
educationally disadvantaged. It is critical to assess the distribution of both of these
attributes to triangulate that no group skews the distribution favorably on any of the
independent variables. Studies related to education continuously compare ethnicity as a
basis for comparison. Therefore, the results of the analysis provide a base for comparison
among proportions. More importantly, most of the participants must meet one of the two
selection criteria: low-income or educationally disadvantaged, not ethnic background.
Table 5
Percentage of Educationally Disadvantaged by Ethnicity
Ethnicity
Low
Educationally
Disadvantaged
Level
Mod
High
Total
African
American
128
(12.7%)
383
(38.2%)
494
(49.1%)
1005
(100%)
American
Indian
7 (7.1%)
24
(24.5%)
67
(68.4%)
98
(100%)
Asian
403
(27.8%)
325
(22.4%)
723
(49.8%)
1451
(100%)
Chicano
Latino
114
(7.1%)
319
(19.7%)
1184
(73.2%)
1617
(100%)
Pacific
Islander
66
(17.6%)
119
(31.8%)
189
(50.5%)
374
(100%)
White
Other
116
(15.3%)
199
(26.3%)
441
(58.3%)
756
(100%)
76
(13.5%)
121
(21.4%)
367
(65.1%)
564
(100%)
EAOP primarily provides services to participants who are economically
disadvantaged or educationally disadvantaged participants. Approximately, three-quarters
(75.1%) of the participants enroll in a postsecondary institution six months after high
school graduation. Among the 5,865 participants who enrolled into higher education,
87.4% enrolled at a two-year postsecondary institution and 12.6% enrolled at four-year
institution. Below in Table 4 are the enrollment descriptive statistics by the type of
institution for ethnic group following six-months of high school graduation.
63
Table 6
Participants’ Postsecondary Enrollment by Ethnicity
Not
Overall
Enrolled
Postsecondary
Enrollment
Enrolled
Enrollment Percentage Among
Chicano Pacific
Asian
Latino
Islander
African
American
American
Indian
25.5%
(256)
28.6 %
(28)
21.0%
(304)
31.5%
(510)
74.5%
(749)
71.4%
(70)
79.0%
(1147)
68.5%
(1107)
White
Other
19.0%
(71)
23.0%
(174)
23.8%
(134)
81.0%
(303)
77.0%
(582)
76.2%
(430)
Percent of Institutional Type of Enrolled Participants
85.4%
82.9%
(640)
(58)
14.6%
17.1%
Four-Year Postsecondary
(109)
(12)
Note. Non-weighted N = 5865, active participants.
Two-Year Institution
91.3%
(1047)
8.7%
(100)
88.2%
(976)
11.8%
(131)
84.5%
(256)
15.5%
(47)
82.1%
(478)
17.9%
(104)
89.1%
(383)
10.9%
(47)
Table 6 indicates that participants in the African-American, Native American and Pacific
Islander ethnic group percentages are similar to the White ethnic group. The enrollment
rate of Asian and Chicano/Latino is greater at two-year institutions than four-year
institutions when comparing to other ethnic groups. The data results show different
enrollment types by ethnicity.
For example, participants in the Chicano/Latino group have a 68.5%
postsecondary enrollment rate, whereas 77.0% of White EAOP participants attended a
postsecondary institution. Furthermore, when comparing participants who did enrolled in
higher education six months after high school graduation in the Chicano/Latino and
White groups, the enrollment rate to four-year institutions is 11.8% and 17.9%,
respectively. Unlike the other ethnic groups, Chicano/Latino group is the only group that
experiences a disproportionate enrollment by institution type. Critically important,
Attewell (2011) determines that the type of institution impacts degree attainment.
64
The histogram below in Figure 3 displays the average non-weighted GPA for
participants whose final senior year transcripts were evaluated. The non-weighted GPA
reflects the average GPA for all courses attempted from the start of the ninth grade
through high school graduation. To test for normal distribution necessary for correlation
and regression, the histogram reflects a negative skew with a value of -1.176 and a
kurtosis value of 1.653, measures within acceptable parameters of ±1.0 and ±5.0,
respectively. Likewise, the mean non-weighted GPA of 2.74 is approximately equal to
the median non-weighted GPA of 2.88 for the participants. When the mean and median
are approximately equal with acceptable kurtosis and skewness parameters, the
distribution is normally distributed and the criteria are met to do relational analysis
(Sheskin, 2011). Figure 4 displays the histogram distribution excluding outlier records
where the non-weighted GPA is equal to 0.00. The exclusion of the outliers resembles a
fit bell-curve distribution by non-weighted GPA.
65
Figure 3
Average Non-weighted GPA Distribution
Note. Sample of n = 4,667 active participants with a mean of 2.74, a standard deviation of
0.876, skewness of -1.176 and kurtosis of 1.653.
66
Figure 4
Average Non-weighted GPA Excluding Outliers
Note. Sample of n = 4,448 active participants with a mean of 2.85, a standard deviation of
0.698, skewness of -0.484 and kurtosis of -0.278.
Similar analytic histograms are generated for the independent variable courses
attempted by the participants as a method to test for normal distribution. Since nonweighted GPA is used as an indicator of a participant’s likelihood for undergraduate
success during the freshmen year, normal distribution would ensure we do not favor one
side of the spectrum toward the study’s outcomes. Figure 3 summarizes the semester
courses attempted for all active participants from Figure 1. Courses attempted and
successfully completed by the participant are correlated to first-year retention and
67
persistence. Similar analysis is completed for each variable to test for the best-fit line in
the regression analysis.
Too much or too low courses attempted may skew the results, not providing the
best-fit line in the regressions or logit models if the normal distribution condition is not
met.. Figure 5 displays the courses attempted by the participants. The results of the
analysis for Figure 5 show a negative skewness of 0.549 and a negative kurtosis of 0.210.
When excluding the participants with a non-weighted GPA of 0.00, Figure 6 reflects a
negative skewness of 0.362 and a negative kurtosis of 0.430. Both figures reflect similar
statistics in mean and median that further strengthens the criteria of normal distribution.
Figure 5
Semester Courses Attempted
Note. Sample of N = 4,662 active participants with a mean of 26.85, a standard
deviation of 11.217, skewness of -0.549 and kurtosis of -0.210.
68
Figure 6
Semester Courses Attempted Excluding 0.00 Non-weighted GPA.
Note. Sample of n = 4,487 active participants with a mean of 27.90, a standard deviation
of 10.075, skewness of -0.362 and kurtosis of -0.430.
Correlation Analysis
Multicollinearity occurs when Pearson coefficient (r ≥ 0.50) is high between two
independent variables. Removal of one of the two independent variables who have a
collinear relationship will increase the outcome predictability in regression analysis
(Green & Salkind, 2011). The following table describes only those independent variables
where the Pearson coefficient is at a significant level (p ≤ 0.05) present in
multicollinearity between variables. Although the association of independent variables is
69
outline below in Table 7, the variance inflation factor (VIF) is presented as an alternative
option to test for multicollinearity among independent variables (Table 8). The tolerance
is the amount of variability on a dependent variable which cannot be explained by the
other predictor variables. The tolerance value of less than 0.1 is an indicator of serious
multicollinearity, and a value between 0.1 and 0.2 is suggestive of a problem (Bowerman
and O’Connell, 1990).
Table 7
High Correlation (r >=0.500) between Independent Variables
Independent Variable 1
Independent Variable 2
Pearson Correlation
Coefficient
Hours of Academic Advising
Hours of College Information
0.510
Semesters of History
Semesters of English
0.732
Semesters of History
Semesters of Math
0.594
Semesters of History
Semesters of Laboratory Science
0.607
Semesters of History
Semesters of Foreign Language
0.507
Semesters of History
Non-weighted GPA
0.522
Semesters of English
Semesters of Math
0.678
Semesters of English
Semesters of Laboratory Science
0.659
Semesters of English
Semesters of Foreign Language
0.611
Semesters of English
Non-weighted GPA
0.636
Semesters of Math
Semesters of Laboratory Science
0.692
Semesters of Math
Semesters of Foreign Language
0.583
Semesters of Math
Non-weighted GPA
0.687
Semesters of Laboratory Science
Semesters of Foreign Language
0.581
Semesters of Laboratory Science
Non-weighted GPA
0.610
Semesters of Foreign Language
Non-weighted GPA
0.585
70
Since academic advising is designed by EAOP to address the academic readiness
that contributes to a participant’s retention, persistence, and degree attainment, the
independent variable hours in college information time measured is selected over hours in
academic advising time since there was multicollinearity greater than 0.500. In addition,
recent research by (Attewell, 2011) indicates a correlation exists between degree
completion and courses attempted in mathematics, science and language other than
English during secondary schooling. As a result of these research findings, the semester
of laboratory science is prioritized in the multicollinearity over the other subject courses:
semesters of history, English, mathematics, and Foreign Language. The semesters of
laboratory science requirement is selected since many pre-requisites need to be met by
the participant that overlaps with the other subjects. Note there is no potential
multicollinearity between the semesters of laboratory science and semesters of visual &
performing arts, or semesters of college prep elective. Although collinearity also exists
between the semesters of laboratory science and the non-weighted HS GPA variables, the
non-weighted HS GPA variable will remain in the correlation model since the variable is
a factor at determining admission to four-year institutions. In the next page, Table 8
shows the correlation matrix for the continuous independent variables where
multicollinearity may have occurred and further analysis through variance influence
factors will further assess collinearity.
Table 8
Correlation Matrix of Continuous Independent Variables
Hours of
College
Informatio
n
Hours of
Personal
Motivation
Semesters of
Laboratory
Science
Semesters of
Visual &
Performing
Art
Semesters of
College Prep
Electives
Nonweighted HS
GPA
1
.154**
.263**
.096**
.076**
.154**
1
.085**
.052**
.263**
.085**
1
.096**
.052**
.076**
Non-weighted
GPA
API
Hours of
College
Information
Hours of
Personal
Motivation
Semesters of
Laboratory
Science
Semesters of
Visual &
Performing Art
Semesters of
College Prep
Electives
Educational
Disadvantaged
Level
Postsec
Institution
Type of
Enrollment
API
Educational
Disadvantage
d Level
Postsec
Institution
Type of
Enrollment
-.167**
.232**
-.017
-.014
.019
-.038**
.090**
.014
-.029*
.290**
.184**
-.261**
.610**
.169**
-.038*
.290**
1
.106**
-.455**
.292**
.157**
.014
.019
.184**
.106**
1
-.012
.322**
-.057**
-.023
-.167**
-.038**
-.261**
-.455**
-.012
1
-.197**
-.059**
.029*
.232**
.090**
.610**
.292**
.322**
-.197**
1
.028
-.127**
-0.009
.050**
.034*
0.014
0.001
-0.022
.034*
1
0.002
-.044**
0.003
-.047**
-.064**
-.080**
.028*
-.119**
0.002
1
71
72
Impact of EAOP on Retention
Binary Logistic Regression, Dependent Variable Retention
In the following section, the statistical analysis explores if EAOP impacts
retention from the first year to the second year of college for those participants who
enrolled in higher education, controlling for API, gender, ethnicity, and type of institution
enrolled. Among the 5865 participants, 4388 (74.8%) enrolled in higher education within
six-months of high school graduation but 5110 (87.1%) enrolled eventually into higher
education. Since the first year of undergraduate education is dependent on the college
preparatory curriculum in which EAOP provides advising, counseling and motivational
services, this section explores which independent variable had a greater impact on a
participant’s odds of being retained in the first year of an undergraduate education.
Specifically, the primary hypothesis evaluated in the logistic regression is whether or not
hours of academic advising, college information, and personal motivation related to
program services significantly predict a participant’s retention.
In Table 9, the preliminary analysis summarizes the retention rate by institution
type for participants who enrolled in higher education. Attewell & Reisel (2011)
determines that the type of institution a participant enrolls impacts the retention of
positively or negatively a specific ethnic group and since the research question explores
retention regardless of institution type, it is critical to determine that no one ethnic group
in the sample skews the results with outliers. Although the retention rates are high for
most groups from the EAOP participants, the American Indian group has a lower
percentage rate at both two-year and four-year institutions. Emphasis about where each
73
ethnic group enrolls after high school is noted in Table 9 because research has noted that
certain groups have a greater likelihood to enroll into one type of system versus another
(Yueng, 2010; Villalobos, 2008; UC SAPEP, 2010, 2009, 2008, 2007, 2006; Timar et al.,
2004; Tierney et al., 2003; Tomas Rivera Policy Institute, 2002).
Table 9
Freshmen Undergraduate Retention 1-yr HS Graduation
Two-Year
Institution
Four-Year
Institution
Retention Percentage Among
Chicano Pacific
Asian
Latino
Islander
African
American
American
Indian
Not
Retain
35
(5.5%)
6
(10.3%)
39
(3.7%)
76
(7.8%)
Retain
605
(94.5%)
52
(89.7%)
1008
(96.3%)
Not
Retain
4
(3.7%)
2
(16.7%)
Retain
105
(96.3%)
10
(83.3%)
White
Other
13
(5.1%)
26
(5.4%)
16
(4.2%)
900
(92.2%)
243
(94.9%)
452
(94.6%)
367
(95.8%)
4
(4.0%)
5
(36.4%)
1
(2.1%)
4
(3.8%)
2
(4.3%)
96
(96.0%)
126
(96.2%)
46
(97.9%)
100
(96.2%)
45
(95.7%)
Percent of Institutional Type of Enrolled Participants
39
(5.2%)
8
(11.4%)
43
(3.7%)
81
(7.3%)
14
(22.5%)
30
(5.2%)
18
(4.2%)
710
(94.8%)
Note. Non-weighted N = 4388.
62
(88.6%)
1104
(96.3%)
1026
(92.7%)
289
(95.4%)
552
(94.8%)
412
(95.8%)
Overall
Not
Retain
Retain
The simple model is the model associated with the null hypothesis stating the predictor
variable does not contribute to group classification, retention. The null hypothesis is that
the combination of independent variables, including hours of academic advising, hours of
college information, and hours of personal motivation do impact first-year retention. If
latter analysis yields a significant result, it is an indication that the simple model should
be rejected and the independent variables in fact contribute significantly toward
74
predicting categorization on the dependent variable, retention. The logistic regression
model used to test the hypotheses includes the independent variables from Table 10 such
that LR decreased from 1177.438 to 998.129 with a significance at p <0.001. Since the
LR for the full model which includes all the dependent variables is smaller in value than
the LR value of the simple model, then the full model provides a better fit that explains
the use of independent variables. Further analysis of the model of the Omnibus Test of
Model Coefficients, which includes the independent variables, evaluates the null
hypothesis: it determines whether adding the independent variables does increase the
statistical significance of predictability. Since the values for the Omnibus Test is
statistically significant (χ2 = 152.051, df = 51, p = 0.000), this is evidence that including
the independent variables increases the predictability when contrasted with the simple
model. It is possible that some independent variables when included in the model produce
a less fit line. Therefore, the following will tests will strengthen the best-fit-line.
In addition, the Cox & Snell R2 and Nagelkerke R2 values of 0.131 and 0.183,
respectively, are above Cohen’s criterion of R2 ≥ 0.13 for medium effect size. These two
measures of effect size indicate a high proportion of the variance in the dependent
variable could be explained by the logistic regression model’s independent variables. In
other words, 13.1% to 18.3% of the variability in retention is explained by the variation
in the independent variables. Lastly, the Hosmer and Lemeshow test probability of 0.807
is greater than 0.05 indicates that χ2 = 4.527 are not significant. The χ2 results align with
the previous results where the independent variables contribute significantly to prediction
of retention. Table 11 reflects the independent variables in the full model with the
75
coefficient computed for each independent variable, the constant to the equation, the
Wald test, the statistical significance to each independent variable and odds [Exp(B)]
with its corresponding 95% confidence interval. All tests are successful to indicate the
full model which included the independent variable is the best approach to predict
classification of dependent variable, retention.
Hours of academic advising, college information and personal motivation do not
have an impact on the odds of retention of EAOP participants. Therefore, the null
hypothesis is accepted that EAOP activities do not contribute to a participant’s retention.
Yet, the logit model reflects that the significant predictors of the dependent variable are
semesters of laboratory science, the non-weighted high school GPA for participants who
enrolled in higher education.
The odds of being retained during the first-year of undergraduate education is
1.19 times larger than a participant who did not take a semester of laboratory science
course when controlling for all the independent variables for participants who enrolled in
higher education. The semester of laboratory science traditionally has prerequisites for
participants to enroll in additional courses such as fluency in English, minimal Algebra
course knowledge, and fluency to learning a new jargon in the science realm. When
controlling for the impact of courses completed in laboratory science variable and
whether a participant’s enrolled into higher education after high school graduation, the
odds of retention increases by 1.975 than a participant with one point less in the nonweighted high school GPA when controlling for the school and API.
76
Table 10
Bivariate Logistic Regression Results, Dependent Variable Retention
Independent Variable
Exp (B)
Significance
Hours of Academic Advising
.984
.857
Hours of College Information
.878
.272
Hours of Personal Motivation
1.021
.865
Semesters of College Prep Elective
1.038
.581
Semesters of English
1.072
.288
Semesters of Foreign Language
1.056
.325
Semesters of History
.934
.415
Semesters of Laboratory Science
1.190*
.006
Semesters of Mathematics
1.047
.411
Semesters of Visual/Performing Arts
.937
.102
1.975*
.001
API
.998
.798
African-American
.813
.518
American Indian
.526
.259
Asian
1.110
.759
Chicano
.678
.196
Other
.916
.813
Pacific Islander
.727
.427
Low Educationally Disadvantaged Level
.890
.640
Moderately Educationally Disadvantaged Level
1.312
.188
Enrolled to 2-Year Institution
1.421
.158
Low Income
1.142
.457
Male
1.255
.224
Constant
0.000
-.403
Non-weighted HS GPA
Note. N=1430. Reference category for ethnicity is White.
School dummy variable for each of the 33 high school is included in the regression model: Antioch HS, Bear
Creek HS, McClatchy HS, Center HS, Cordova HS, Davis HS, Dixon HS, Edison HS, Elk Grove HS,
Encina HS, Esparto HS, Florin HS, Foothill HS, Franklin HS, Galt HS, Grant Union HS, Highlands HS,
Johnson HS, Johnson West Campus HS, Kennedy HS, Laguna Creek HS, Burbank HS, Mt. Diablo HS,
Natomas HS, Pioneer HS, Pittsburg HS, River City HS, Sacramento HS, Sheldon HS, Stagg HS, Tokay HS,
Valley HS, and Woodland HS. Nagelkerke R2 = 0.183 and Cox & Snell R2 = 0.131 and df = 51, p ≤ 0.05.
77
Impact of EAOP on Persistence
To determine whether the independent variables statistically significantly impact
the persistence, I used linear regression analysis. By using the ordinary least squares
(OLS) methods, the best fit line between the dependent and independent variables are
used at determined the linear predictability in persistence. Persistence is the number of
continuous semesters capped at twelve.
The composition of the linear regression model, as noted in Table 11, has an Rsquared value of 0.474, indicating that 47.4% of the variance in persistence can be
explained by the variance of the independent variables in the model. As noted below in
Table 11, the hours of college information is statistically significant with p ≤ 0.01 and
hours of personal motivation and academic advising are not statistically significant (p >
0.05). For every one-hour increase in college information, we expect a decrease in
persistence of 0.512 semesters. In addition, for each semester of high school history,
laboratory science and college prep electives increase relates to an increase in persistence
of 0.274, 0.400, and 0.313 semesters, respectively. On the other hand, a semester of high
school English increase negatively impacts persistence by 0.396. The evidence suggests
participants should be encouraged to succeed beyond the minimum requirements
established by schools.
Moreover, participants who identified as African-American, Asian, Chicano,
Pacific Islander, and Other, their persistence increased by approximately by 1.304, 2.219,
0.940, 1.824 and 1.669, respectively, relative to the reference ethnic control group White.
When controlling for other independent variables aside from ethnicity, EAOP does
78
positively impact the persistence of these groups toward persisting to up to twelve
semesters of postsecondary education For instance, participants who aimed to attain the
highest ordinal degree increased their persistence longer by 4.509, 3.456, and 6.024,
respectively, based on Associate degree, Bachelor’s degree or Master’s degree attainment
goals when compared to reference group of White participants. Further evidence in the
analysis indicates that the greatest impact toward the increase in persistence was if a
participant was retained during the first-year of undergraduate education by 4.309
semesters, and persistence increased by 4.403 semesters if the participant transferred to a
four-year institution. Critically more important, participants who were retained during the
first year of undergraduate education persisted longer. Although participants may have
not partaken on services rendered through the hours of college information, the retention
is accounted for in the scenario participants did not engage in such activities. When
controlling for other independent variables, EAOP does positively impact the persistence
of these groups toward persisting to up to twelve semesters of postsecondary education.
Table 11
Linear Regression Results, Dependent Variable Persistence
B
Standard
Error
Stand
Beta
T
Significance
Tolerance
VIF
-.135
.149
-.019
-.907
.365
.834
1.199
-.512*
.191
-.060
-2.687
.007
.776
1.289
-.098
.194
-.011
-.502
.616
.841
1.189
.313*
.111
.062
2.822
.005
.788
1.269
Semesters of English
-.396*
.123
-.089
-3.229
.001
.507
1.974
Semesters of Foreign
Language
.117
.094
.031
1.246
.213
.618
1.618
Semesters of History
.274*
.136
.055
2.007
.045
.513
1.948
Predictors
Hours of Academic
Advising
Hours of College
Information
Hours of Personal
Motivation
Semesters of College
Prep Elective
79
Semesters of
Laboratory Science*
Semesters of
Mathematics
Semesters of
Visual/Performing
Arts
Non-weighted HS
GPA
.400
.105
.110
3.805
.000
.461
2.168
.142
.092
.046
1.543
.123
.439
2.276
-.032
.071
-.010
-.455
.649
.815
1.226
-.211
.372
-.018
-.567
.571
.396
2.527
African-American
1.304
.547
.072
2.383
.017
.421
2.374
American Indian
-.933
1.111
-.018
-.839
.401
.874
1.145
Asian
2.219*
.529
.136
4.193
.000
.363
2.757
Chicano
.940*
.505
.058
1.861
.063
.393
2.543
Other
1.824*
.589
.083
3.097
.002
.531
1.882
Pacific Islander
1.669*
.658
.063
2.536
.011
.618
1.618
2.631
1.141
.047
2.306
.021
.939
1.064
2.213
1.751
.025
1.264
.207
.966
1.035
4.509
.581
.164
7.761
.000
.860
1.163
3.456
.390
.238
8.852
.000
.533
1.876
6.024
1.065
.118
5.655
.000
.881
1.135
3.577
5.235
.014
.683
.495
.967
1.034
.201
.401
.011
.503
.615
.802
1.247
-.013
.331
-.001
-.039
.969
.862
1.160
Retention*
4.309
.436
.214
9.873
.000
.815
1.226
Enrolled to 4-Year
Institution
.672
.525
.033
1.281
.200
.579
1.728
Low Income
-.382
.308
-.027
-1.240
.215
.798
1.253
Male*
-.827
.310
-.055
-2.665
.008
.894
1.119
Transferred to 4Year Institution*
4.403
.396
.315
11.125
.000
.478
2.091
Constant
.639
1.429
.447
.655
Some College
Attained
Certificate Degree
Attained
Associate Degree
Attained*
Bachelor’s Degree
Attained*
Master’s Degree
Attained*
Doctorate Degree
Attained
Low Educationally
Disadvantaged Level
Moderately
Educationally
Disadvantaged Level
Note. N = 1430. Reference category for ethnicity is White.
Dependent variables with tolerance ≤ 0.10, API, No Degree Attained, Highly Educationally Disadvantaged,
and Enroll to 2-Year Institution. School dummy variable for each of the 33 high school: Table 10 Notes.
Dependent variable, Persistence, with twelve semesters of consecutive enrollment. R2= 0.474. *p ≤ 0.10,
two-tailed.
80
Impact of EAOP on Award
Multinomial logistic regression is used to determine if a relationship exists
between the independent variable and award attainment. Although there are over six
levels for the dependent variable degree, the polytomous dependent variable is converted
to three levels in order for its use in the multinomial logistic regression. Some of the
levels contain too few records which limit the analysis and clustering resolves the
condition.
For the levels of the dependent variable award, “0” represents no award, “1”
represents sub-baccalaureate award, and “3” represents Bachelor’s award or greater. In
the multinomial logistic regression, the category no award (level 0) is the referent point
on which estimates are made. Table 12 reflects the frequency for each level of the
dependent variable award.
Table 12
Frequency of Dependent Variable Levels, Award
Frequency
Percent
Valid Percent
Cumulative
Percent
Sub-baccalaureate
award
(Level = “1”)
Bachelor’s award or
greater award
(Level = “2”)
No award
(Level = “0”)
507
8.6
8.6
8.6
1589
27.1
27.1
35.7
3769
64.3
64.3
100.0
Total
5865
100
100
Also, multinomial analysis requires independent variables to be nominal in
measurement therefore conversion of continuous data is necessary. The independent
81
variable, hours of academic advising, hours of college information and hours of personal
motivation in which no hours were attended by the participant is represented by the
coding “0”. The following interval levels reflect four-hour intervals where the code value
of “1” represents 0.01 – 4.00 hour intervals, “2” represents 4.01 – 8.00 hour intervals, “3”
represents 8.01 – 12.00 hour intervals, and “4” represents 12.01 – 16.00 hours. Among all
the independent and dependent variables that are included in the model, there is 1627
combination of predictor variables that consist of records that have the same value in the
outcome variable, no award.
Multinomial logistic regression assesses whether any of the independent variables
create a better model that could explain the relationship of an outcome. Typically, the
null hypothesis in multinomial logistic regression is that no independent variables could
produce the best fit to the outcome model. The intercept only reflects a model where no
independent variables are include and a final takes into account all the independent
variables that have a statistical significance at decreasing the -2 log likelihood. Table 13
summaries the model fitting information for the multinomial regression between the
intercept and final model.
Table 13
Model Fitting Information on Award where Referent Level is No Award.
Model Fitting
Criteria
-2 Log Likelihood
Al Intercept
3294.515
Final
1701.624
Likelihood Ratio Test
Chi-Square
df
Significance
1592.890
114
0.000
82
The information summarizes that the final model when including the independent
variable is a better fit. Since the -2 log likelihood decreases in the final model with a
statistical significance (p = 0.000), minimally one of the independent variable’s
regression coefficients is not equal to zero and impacts the relationship of the outcome.
With minimally one independent variable’s coefficient that is not equal to zero, the
following information describes the parameter estimates of the multinomial logistic
regression that may have a relationship to the outcome.
The results are discussed by sub-bachelor’s award followed by bachelor’s award
or greater category where the point of reference is participants who enrolled in higher
education but did not attain an award. The hours of academic advising, hours of college
information, and hours of personal motivation are not highly statistically significant (p ≥
0.10) when comparing those who earned a sub-bachelor’s award to participants who
attained no award.
African-American and Chicano/Latino students when referenced to
White participants who were retained during the first-year of undergraduate education,
participants who transferred to a four-year institution, and low-income participants were
statistically significantly (p ≤ 0.05) more likely to attain the sub-bachelor’s award than
attain no award. Male participants were less likely to attain a sub-bachelor’s award by
67% than females when compared to no award recipients.
When evaluation the odds ratio in Table 16 for those independent variables where
there is a statistical significance (p ≤ 0.05), we find that a relationship of a change of each
independent variable by one unit of measurement, the odds of attaining a subbaccalaureate increases proportionately. Furthermore, the positive or negative coefficient
83
(B) indicates which independent variable has the greatest magnitude impact relative to
each independent variable. The greater the coefficient (B) on the independent variable,
the higher the magnitude of impact toward the odds ration toward a sub-bachelor’s award
than an outcome of no award outcome. This allows a practitioner to assess which
independent variable, when compared among each other, has the greatest magnitude
impact the intended outcome.
For example, a participant’s retention during the first-year of undergraduate
education has 9.457 times more likely to impact the attainment of sub-bachelor’s award
than no award. Although the odd ratio is 9.457, the coefficient states that the second most
impactful independent variable following retention is an individual’s ethnic identify such
as African-American and Chicano/Latino where the odd ratio that contribute to the
outcome is 2.408 time, and 2.538 times, respectively. Although the odd ratio is
approximately close for both African-American and Chicano/Latino, respectively, the
magnitude in the coefficient indicates that Latinos benefit more toward the outcome than
African American .These monumental findings indicate participants in these programs
benefit individuals identified as African-American and Chicano/Latino.
In Table 14, it shows that the hours of academic advising, college information,
and hours of personal motivation does not have a statistically significant impact on
receiving a sub-baccalaureate award versus no award. To the contrary, program
participants identified as African American and Chicano/Latino were 2.408 and 2.538
times more likely to attain a sub-baccalaureate award, respectively, than no an award for
each ethnic group where a statistical significance exists. In addition, participants who
84
were retained during the first-year of undergraduate education regardless of the type of
public postsecondary institution were 9.457 times more likely to attain a subbaccalaureate award than non-awardees. Participants who transferred to a four-year
institution were 2.314 times more likely to attain a sub-baccalaureate award when
controlling for all the independent variables. Males are more likely to attain no award
than a sub-baccalaureate award by 0.672.
85
Table 14
Parameter estimates for Independent Variables, Sub-bachelors
Sub-bac
Standard
Independent Variable
B
award
Error
Interval
Wald
Significance
Exp
(B)
-3.995
3.653
1.196
.274
Hours of Academic Advising
.102
.091
1.271
.260
1.107
Hours of College Information
.108
.148
.536
.464
1.114
Hours of Personal Motivation
-.020
.134
.022
.882
.980
Semesters of College Prep
Electives
.068
.080
.716
.397
1.070
Semesters of English
-.007
.083
.007
.935
.993
Semesters of Foreign Language
.005
.068
.004
.947
1.005
Semesters of History
.083
.094
.769
.380
1.086
Semesters of Lab Science
.014
.076
.035
.851
1.014
Semesters of Math
-.099
.068
2.144
.143
.905
Semesters of Visual &
Performing Arts
-.033
.057
.326
.568
.968
Non-weighted HS GPA
.216
.246
.771
.380
1.241
API
-.003
.006
.238
.625
.997
African-American*
.879
.413
4.534
.033
2.408
American-Indian
.274
.876
.098
.755
1.315
Asian
.298
.438
.465
.495
1.348
Chicano/Latino*
.931
.382
5.934
.015
2.538
Other
.471
.472
.996
.318
1.602
Pacific Islander
Low Educationally
Disadvantaged
Moderately Educationally
Disadvantaged
-.147
.570
.066
.797
.864
-.108
.323
.112
.738
.898
-.056
.242
.054
.817
.945
Retention*
2.247
.534
17.670
.000
9.457
Enrolled to 4-Year Institution
1.271
.800
2.527
.112
3.565
Low Income*
.500
.224
4.989
.026
1.649
Male*
-.398
.229
3.006
.083
.672
Transferred to 4-Year
.839
.240
12.239
.000
2.314
Institution*
Note. N=1430. Reference category, no award. Include dummy variables for each of the 33 schools: See
Table 11 notes. *p ≤ 0.10, two-tailed.
86
Similarly, in Table 15, the hours of academic advising, hours of college
information and personal motivation do not have an impact on bachelor’s award relative
to no award. However, for each semester of mathematics taken by a participant in high
school increases their likelihood to attain a four-year degree by 1.184 times when
compared to participants with no award. In addition, for each one unit increase in the
non-weighted HS GPA positively impacts a participant’s likelihood to attain a bachelor’s
degree or greater by 3.938 times when compared to no award. Participants who were
retained during the first-year of undergraduate education were 7.677 times more likely to
attain a bachelor’s degree than participants with no award. Although participants who
enrolled at four-year institutions were 464.549 times more likely to attend a bachelor’s
degree or greater than participants with no degree, participants who enrolled in two-year
institution were 0.159 times less likely to attain a bachelor’s degree than non-awarded
participants. Evermore, participants who were male were less 0.583 times less likely to
attain a bachelor’s degree than females. Nonetheless, for participants who attain a
bachelor’s award or higher, the non-weighted HS GPA shows it is 3.838 times more
likely to improve a participant bachelor’s degree award.
Summary
Services rendered by EAOP as hours of academic advising, hours of college
information, and hours of personal motivation are not statistically significant predictors
of retention or degree attainment. Yet, hours of college information is a statistically
significant (p ≤ 0.01) predictor of a participant’s persistence when controlling for other
variables.
87
Table 15
Parameter estimates for Independent Variables, Bachelors
Bachelors
Standard
Award or
Independent Variable
B
Error
greater
Interval
-18.876
4.047
Wald
Significance
21.760
.000
Exp
(B)
Hours of Academic Advising
-.035
.084
.176
.675
.965
Hours of College Information
.017
.107
.024
.877
1.017
Hours of Personal Motivation
Semesters of College Prep
Electives
-.136
.107
1.613
.204
.873
.022
.065
.118
.731
1.022
Semesters of English
-.027
.080
.116
.733
.973
Semesters of Foreign Language
.084
.054
2.381
.123
1.088
Semesters of History
.118
.079
2.211
.137
1.125
Semesters of Lab Science
.070
.062
1.287
.257
1.073
Semesters of Math*
.169
.055
9.348
.002
1.184
Semesters of Visual &
Performing Arts
-.013
.041
.102
.749
.987
Non-weighted HS GPA*
1.345
.209
41.545
.000
3.838
API
.009
.006
2.356
.125
1.009
African-American
-.232
.314
.547
.460
.793
American-Indian
-.416
.723
.331
.565
.660
Asian
.131
.298
.195
.659
1.140
Chicano/Latino
-.211
.293
.521
.470
.809
Other
-.001
.333
.000
.998
.999
Pacific Islander
-.168
.369
.208
.648
.845
.164
.231
.505
.477
1.178
.183
.192
.908
.341
1.201
2.038
.412
24.528
.000
7.677
Low Educationally
Disadvantaged
Moderately Educationally
Disadvantaged
Retention*
Enrolled to 4-Year Institution*
6.141
1.367
20.188
.000
464.54
9
Low Income*
.431
.182
5.582
.018
1.539
Male*
-.540
.177
9.277
.002
.583
Transferred to 4-Year
2349.9
7.762
1.217
40.710
.000
0
Institution*
Note. N=1430. Reference category, no award. Include dummy variables for each of the 33 schools: See
Table 11 notes. *p ≤ 0.10, two-tailed.
88
Chapter 5
CONCLUSIONS AND RECOMMENDATIONS
Overview
Too many high school graduates who enroll in California’s public postsecondary
institutions do not persist to degree completion (Dadashova, Hossler, Shapiro, Chen,
Martin, Torres, Zerquera, & Ziskin, 2011; Institute for Higher Education Policy [IHEP],
2011; Stoutland, 2011; Turner, 1992; Turner, 1990; Turner & Fryer, 1990). The low
persistence and graduation rate of undergraduates from the secondary schooling system is
threatening the state’s economy. California is facing a work force deficit of
approximately one-million college-educated graduates by 2025 (Johnson, 2011).
Improving the graduation rate of the State’s most disadvantaged populations who are
enrolled in higher education could help drastically to mitigate the future economic gloom.
Although student-centered outreach programs have increased the postsecondary
enrollment of secondary school historically and underrepresented student, little is known
as to whether student-centered outreach intervention strategies influence a student’s
propensity towards degree completion.
To date, research has concentrated on the supplemental services provided to
disadvantaged students outside the classroom of instruction, such as academic advising,
mentoring, and counseling. These out-of-classroom services that focus on academic
opportunities have helped to minimize the negative educational conditions disadvantaged
students face in public education and increase the college-going rate of disadvantaged
secondary students (CPEC, 1989; CPEC, 1996; CPEC 2004; Gandara, 2001; Gandara &
89
Mejorado, 2004; Hayward, Brandes, Kirst, & Mazzeo, 1997; Outreach Task Force, 1997;
Quigley, 2002; Sanchez, 2008; Yeung, 2010). Yet, research has not determined the
impact of student-centered outreach programs towards degree attainment.
Purpose of the Study
The purpose of the study is to determine whether student-centered outreach
programs help participants persist towards degree completion at two-year and four-year
institutions; specifically, whether the categorical activities delivered by EAOP are
effective and demonstrate an impact towards a degree attainment in higher education.
The staple categories of EAOP are known as Academic Advising, College Information
and Personal Motivation. In an effort to understand the program’s operations, the
following questions were investigated through a positivistic paradigmatic approach using
the General Systems Theory (GST) and Astin’s Input-Environment-Outreach (IEO
Model) model:
1. Do EAOP activities significantly contribute to a participant’s retention during the
first year of undergraduate education?
2. Do EAOP activities significantly contribute to a participant’s persistence toward a
degree completion in higher education?
3. Do EAOP activities significantly contribute to a participant’s degree attainment?
Based on the results of the study, the following section will provide recommendations
including a perspective of environment variables referred to as efficiency, effectiveness,
and efficacy in a proposed postulate theory known as the Triple E Theory.
90
To help outline the phenomenon experienced by EAOP, the conclusion and the
recommendation are outline in phases as noted in Figure 7.
Figure 7
Phase Sequence of Degree Attainment
Immediately following high school graduation, the analysis results provides insight as to
how far into the participant’s educational journey using hours of academic advising,
college information, and personal motivation has impacted retention, persistence and
award attainment.
Summary of Findings
Academic advising, college information, and personal motivation services
provided by EAOP has no statistically significant impact on first-year retention of its
participants. Alternative efforts through EAOP capture the success of its participants
when analyzing the impact of retention, persistence, and award attainment. For example,
EAOP impact is captured by participants who have been encouraged to take many
laboratory science courses and sustain a strong non-weighted GPA in order to favor a
participant’s likelihood for freshmen retention in. A similar trend is evident with
persistence where the participants who completed a greater number of laboratory courses
were more likely to complete a greater number of consecutive semesters of undergraduate
education. Although a similar trend also exist with the number of mathematics taken in
91
high school and the non-weighted HS GPA impact the odds of bachelor’s award or
greater, no impact on high school curriculum is detected toward a sub-bachelors award.
In chapter two of the literature review it indicates that student-centered outreach
programs have a history of preparing and enrolling disadvantaged participants into higher
education when controlling for ethnicity and gender (Sanchez, 2008; Villalobos, 2008,
Bookman, 2005; Quigley, 2002), the evidence in this study suggests that program service
in academic advising, college information, and personal motivation do not influence firstyear college retention after controlling for environmental elements of how EAOP works.
Completing curricular courses and increasing the non-weighted HS GPA by such
programs have the greatest impact on participant’s retention, persistence and award
attainment.
Counter intuitively, I find that college information participation has a negative
impact on college persistence when controlling for ethnicity, secondary schooling, and
other environmental variables such as parent’s education level and household income.
The services rendered by EAOP are design to enroll the participant in higher education,
but the services do seem to highlight which elements in an institution are essential for
academic success to such groups.
Historically, EAOP has continued to promote postsecondary opportunities where
low-income and educationally disadvantaged students toward award attainment. The
multinomial nominal logistic regression demonstrate that African-American and
Chicano/Latino participants in the program are approximately 2.5 times more likely to
attain a sub-baccalaureate award than no award when referenced to White participants.
92
The finding in the study further strengthens the benefit to its low-income participants
versus non low-income such that they are 1.6 times more likely to attain a subbaccalaureate award than no award. Knowing that EAOP primarily provides services to
low-income (40% of the participants) and educationally disadvantaged students, where
over 44% of the participants in the program who are of African-American and Chicano
Latino descent. In addition, low-income participants in the program were statistically
more likely to attain a bachelor’s award or higher than no award by 1.539. Although the
hours of academic advising, hours of college information, and hours of personal
motivation do not contribute to sub-baccalaureate or bachelor’s award attainment, other
factor in the programs positively impacting these disadvantaged populations..
Discussion
In review of the services rendered by EAOP, the study outcomes note that student
success is dependent on the completion of curricular instruction in English, laboratory
science, and mathematics in high school impact one of the three dependent variables
know as retention persistence, and award attainment. Interestingly, for each analytic
model used for retention, persistence, and degree attainment, API did not demonstrate
significance towards influencing the outcome on the dependent variable. Although
contradictory to the findings of other research that claim API impacts a student’s
postsecondary success (Betts, Rueben, & Danenber, 2006; Bowen, Chingos, &
McPherson, 2009), the results of this study support the claim other from similar research
that student-centered outreach programs help participants persist toward award
93
attainment (Alexander & Ekland, 1974; Alexander et al, 1978, 1987; Alwin & Otto,
1977; Thomas, 1980; Bourus & Carpenter, 1984; Hossler, Braxton & Coopersmith, 1989;
St. John, 1991; Altonji, 1992; Lucas, 1999; Perna, 2000a).
Factors that positively impact the persistence of a participant are the number of
English and laboratory science courses that are successfully passed by the participant in
secondary education.
Although there is evidence that college information services impact the
likelihood of a participant to enroll in a four-year institution (Rico, 2007), this study
further solidifies that the pathway a participant chooses does not impact positively or
negatively a participant’s persistence in higher education regardless of the initial type of
institution of enrollment. The idea of transferring may have a phenomenological impact
towards persistence, but it is unknown from the since transfer could range anytime in the
twelve week persistence. The key factor that results of the study is that transfer pathway
must be an essential piece in the two-year college system regardless of a participant’s
intended educational goal since this variable impacts persistence, sub-baccalaureate,
bachelor’s award or greater award attainment. This evidence clearly indicates that
participants who enroll in a two-year institution must be immediately be placed in a
transfer pathway rather than allowing certificate or technical options to deter. More
importantly, however, placement towards the transfer pathway does not necessarily mean
jeopardizing requirements in a certification and associate degree program rather it raises
the immediate concern to sync course requirement within an institution towards a culture
of transferring.
94
Degree attainment, on the other hand, is not directly impacted by the hours of
academic advising, hours of college information, or hours of personal motivation.
Therefore, an intermediate variable exist between hours of service and retention,
persistence, or award attainment that requires further research.
The results of this study raise a very important and critical question. How could
college information services impact persistence but not retention or degree attainment?
To answer the inquiry, recall from chapter two that the intent of the services in academic
advising, college information and personal motivation is to increase the likelihood of
participants to enroll into higher education. Academic advising is designed by EAOP to
address the academic readiness that contributes toward a participant’s retention,
persistence, and award attainment in the first-year of undergraduate education. During the
first-year of undergraduate education, participants may attain a short-term certificate
without persisting beyond two semesters of college education. Although academic
advising has been statistically show to contribute to the likelihood of participants to
enroll into a four-year institution by 7% (Rico, 2007), the study demonstrates that the
hours of academic advising does not impact postsecondary retention, persistence, or
award attainment. The academic advising services rendered to an EAOP participant do
influence their course completion patterns. Therefore, it is hypothesized that although
hours of academic advising impact course selection and completion, which contribute to
postsecondary retention, persistence, and degree attainment outcomes.
Although many admitted students in higher education fulfill courses that impact
retention, persistence and degree attainment, students at lower API schools are less
95
probable to complete these requirements. These findings align to Attewell (2011) in
which he notes that a correlation exists between degrees and courses attempted in
mathematics, science and language other than English during secondary schooling.
Therefore, academic advising is a precursor to increasing the probable odds favorable
toward a participant in a disadvantaged environment. Courses successfully completed are
intermediate independent variables that impact the preferred results, degree attainment.
A similar phenomenon results with college information. College information
services assist participants to understand the importance of college education and what
resources at each institution are readily available to assist towards a degree. From this
study, we find that college information service do not impact retention and does impact
persistence, even though other research shows that college information services do
impact a participant’s likelihood to enroll into higher education (Rico, 2007). Therefore,
there is an existing intermediate independent variables present in the model between
enrollment and degree attainment that influence retention and persistence. The findings
from this study created an additional hypothesis as to whether participants engaging in
these services really benefit directly or indirectly through other measureable means. It is
possible for a student who experienced college information services to feel that they
gained toward long-term planning while not address the immediate or short term needs of
the institution at the institution during the freshmen year.
Lastly, personal motivation has no impact on retention, persistence and degree
attainment. Rico (2007) also found that personal motivation does not impact
postsecondary enrollment. Traditionally, these services have been utilized to entice
96
participants to engage in program services by using advertising and marketing
techniques. For example, I surmise that the option to participate in personal motivation
programs such as a residential academy based on attendance in academic advising and
college information services is more an incentive strategy to influence a participant’s
involvement.
Policy Implications
A new phase model that could help explain the impact on the degree attainment
pathway is presented based on the conclusion outlined above. The model has immediate
implications on policy and future assessment of student-centered outreach programs. To
respond to the statewide deficit of an educated workforce, leaders in policy, business, K12 and higher education must make informed decisions on models that have begun to
monitor the impact of student-centered outreach programs than redeveloping new
longitudinal studies.
Below in Figure 8, the services rendered to participants are outline as to which
dependent variable, respectively, are positively or negatively impacted. Although most of
the variables may have a positive impact on retention, persistence, and degree attainment
respectively, it is critical to point that college information services has a negative impact
on persistence and a positive impact on degree attainment.
97
Figure 8
Phase Model on Enrollment, Retention, Persistence, and Degree Attainment
This phenomenon is the basis for the discussion on the Triple E theory (Figure 3)
proposed earlier in chapter 3. According to the propose theory I presented in this
research, maximizing on both efficiency and effectiveness is not possible. When one of
the parameters in the Triple E Theory is maximized, the other parameter is impacted in a
way that efficacy is affected positively or negatively, respectively. Figure 8 is a visual
explanation as to how program effectiveness is impacted in an environment by the
parameters of efficiency and effectiveness.
98
Figure 8
Triple E Theory
Policy-makers and administrators recently have proposed to extend the
longitudinal measures of the student-centered outreach program to include degree
attainment as the basis for continued funding. New pressure to determine if studentcentered outreach programs impact degree attainment is mandated without an
understanding of the services rendered. In the scenario of this study, student-centered
outreach programs such as EAOP develop services that promote its participants to enroll
into higher education. This new pressure will cause programs to modify their current
services which would negatively impact postsecondary effectiveness toward
postsecondary enrollment of their already disadvantaged participants. To the contrary, the
shift would help create a positive impact toward retention, persistence and degree
attainment. The Triple E Theory clearly states programs could not be mandated to do
99
both without any maximum success to either objective. The phenomenon is clearly
present in the service college information.
Recommendations
The following sections are recommendations made to practitioners, elected
officials, policymakers, and all other constituents who directly affect the degree
attainment of disadvantaged students who enroll into higher education.
Set clear measureable objectives toward a core goal for each type of studentcentered outreach programs based on the parameters of efficiency OR effectiveness.
During fiscal abundance or fiscal scarcity in the economy, each type of student-centered
outreach program could be called upon to meet the economic workforce demands. When
fiscal times are challenging, the parameter of efficiency needs to be implemented with an
understanding of how the effectiveness of program services would impact other variables
in education. To the contrary, when funds are more readily available, efficiency could be
abandoned to increase the quality of graduates. This approach allows a “facet-like”
approach where more or less college bound participants will choose to graduate from
higher education. The clear-cut models will allow policy administrators to accurate
predictability based on the economic climate.
Mandate the “a – g” subject course requirements as the State’s staple toward
high school graduation. Regardless of whether high school student intends to enter the
immediate workforce or immediately enroll into higher education within six months of
high school graduation, the foundation is critical for all students assuming a quality
100
instruction is provided to each pupil. Greater emphasis should be placed by high schools
to promote especially English, laboratory science, and mathematics. These courses,
regardless of the higher educational pathway a participant chooses, have great positive
impact toward the retention, persistence and award attainment of participants.
Expand the academic advising and college information service model to other
student-centered outreach programs or homerooms classrooms that target firstgeneration and low-income participants in low API schools. Although the provision of
academic advising and college information is not statistically significant in promoting
retention, persistence or award attainment, it is known that selection of the proper high
school courses is a result of the academic advising services rendered to EAOP
participants (Villalobos, 2008; Rico, 2007). A majority of the state’s K-12 schools whose
API is under 600 do not have a student-centered outreach program (Betts, Rueben &
Danenber, 2006). Alternatively, many high schools have homeroom courses where
participants are required to check in on a daily basis. The instructor of the homeroom
sessions could implement these activities as a method to encourage a greater number of
students to enroll in higher education and gain a greater probability toward a degree.
All high school graduates must minimally submit an application to one four-year
postsecondary institution during the fall semester of the senior year. The majority of the
State’s four-year postsecondary application submission occurs during the month of
October and November of the student’s senior year in order for a prospect to enroll the
following fall term. Although the California Community College (CCC) system accepts
applications for all participants throughout the year, the mandate that all students to
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submit an application would begin to influence students to think about life after high
school and whether a four-year institution is the right option toward a degree. Since the
hours of college information does impact persistence, the early college awareness that
develops at thinking about exit secondary education would help students transition into
higher education and succeed. In the scenario a four-year postsecondary institution is not
an option students in the CCC transfer pathway are now required to begin the
matriculation process early February in the spring semester of the senior year. Many
secondary school students neglect the deadlines immediately starting the senior year. The
study indicates that transfer pathway impacts award attainment positively impacts a
participant’s retention, persistence and degree attainment. Therefore, by requiring schools
to enforce this option all students are more probable to have the option available.
Implement three-unit instructional or online course required for first-time
enrolled community college participants to gain knowledge about the transfer pathway.
Many community colleges already implement college success course that support the
assistance of students during the lower division curriculum in certificate, technical and
Associate’s degrees. However, many of the current models do not implement transfer
information such as the inter-segmental education transfer curriculum (IGETC), general
education requirements, and transfer agreements between four-year institutions. The
process would engage a greater number of faculty in the articulation process while
comprehending the requirement development in specific disciplines. In addition, students,
administrators and faculty become more acquainted with tools such as assist.org, the
transfer agreement guarantee (TAG), and graduate level opportunities from the
102
community college system into higher education without the need of completing a fouryear degree. The restructuring of the course would assist at promoting transfer to a fouryear institution, an indicator favorable toward degree attainment. As a strong predictor,
participants who transfer are more likely to persist and attain an award in twelve
continuous semesters of enrollment in higher education.
Align K-12 centered outreach programs with legislated California Community
College matriculation laws related to retention. The matriculation process is aligned to
course placement and course placement is impacted by a participant’s likelihood to
succeed in such courses. These courses further impact retention, persistence, and award
attainment. The California Community Colleges (CCC) have student-centered outreach
programs that specifically concentrate on retention such as the Cal-works, Education
Opportunity Program and Services (EOP&S), Disabled Support Programs and Services
(DSPS), Puente, and Mathematic Engineering and Science Achievement (MESA). Yet,
many of the programs begin recruitment during the freshmen year and do not complete
identification of candidates until the end of the first year. By syncing efforts with K-12
student-centered outreach programs, CCC retention programs could identify participants
as early as the start of the spring semester of the senior year for graduating high cohorts.
This, in turn, would allow programs to facilitate enrollment, introduce summer
transitional services and continue to transition the cohort model among its regional
participants.
103
Future Research
Future studies should analyze the impact of activity participation of EAOP
participants in two and four-year institutions through different retention programs.
Participants who transition into higher education begin to experience a change in
philosophy, development, and awareness of the world around them. The introduction into
higher education is fluid and dynamic such that many social factors could negatively
impact a student’s success from the environment. Likewise, there are services introduced
at the institution that help mitigate the obstacles that may influence the success of
participants. The results of future study will help executive administrators and program
directors to prioritize services that have greatest impact towards retention, persistence and
degree attainment.
Research the impact of EAOP services toward degree attainment based on type of
degree discipline. The results of the analysis indicate laboratory science, language other
than English and college preparatory electives. Since many academic disciplines are
highly affiliate with these courses especially in the sciences, cultural studies and liberal
arts, participants who deviate from these majors may experience a higher degree of
difficulty. Or to the contrary, participants may experience a path of least resistance since
many of the degrees will begin at the same level for all its students. In others works,
unlike the sciences where each person enters higher education with different degrees of
knowledge, the inputs of each participant in non-science related majors may be the same
and the basis of instruction is rudimentary for each student. It is unknown how
knowledge of a participant may impact degree attainment. This factor is very important
104
especially if disadvantaged students from low API schools select a major where prior
academic knowledge is fundamental.
105
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