THE IMPACT OF UC DAVIS’ EARLY ACADEMIC · PDF fileTHE IMPACT OF UC DAVIS’ EARLY...

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

ii

[Optional]

Copyright © 2012 Timoteo Rico

All rights reserved

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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

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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. Date

v

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 all while raising our two daughters,

Elizabeth Josefina Rico and Victoria Sofia Rico, during our educational journey.

Brilliantly and diligently, she would attend to me during the discouraging lashes brought

forth from the doctoral enlightenment process by encouraging and giving me faith in my

abilities. No doctorate degree could affirm her beauty from within or outside her persona.

Too few men experience the beauty of such a woman who not only has given me 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.

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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.

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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 P-

16 Education. The College Board.

viii

Abstract

of


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.

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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 non-

weighted 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 four-

year 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,

x

Pacific Islanders and Other were also impacted positively toward college persistence.

Yet, low-income participants where statistically impacted by EAOP to attaining a degree.

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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

xii

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

xiii

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

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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

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,

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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,

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[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 first-

generation and low-income students (Johonson, 2011; Shulock, 2010).

As an effort to address the shortfall of approximately one-million college-

educated 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

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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

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(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


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

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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.

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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.

1 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.

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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

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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 “[student-

centered 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 student-

centered 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 self-

expectations 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 co-

curricular 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 (Bonous-

Hammarth & 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, student-

centered 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 college-

going 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 first-

generation 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

2 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.

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 student-

centered 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).

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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 thirty-

five years, there is an unclear understanding of which elements contribute to the college-

going rate and retention of underserved students, primarily that of economically and

educationally disadvantaged participants. Research suggests that the activities of student-

centered 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, student-

centered 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.



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):





35


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 first-

generation 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 time-

stamped 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 six-

year 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 4-

year 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 non-

weighted 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

Variable Type

Output: Dependent variable

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

Retention Enrolled to the same postsecondary institution one year following initial enrollment. Binary variable. 0 if no, 1 if yes.

Persistence Count of semesters of continuous enrollment from high school graduation up to 12 semesters. Continuous variable.

Environment: Independent variable

API Average API of school during the participant’s secondary school enrollment. Continuous variable.

Hours of Academic Advising

Hours of attendance in Academic Advising activity standard. Continuous variable.

Hours of College Information

Hours of attendance in College Information activity standard. Continuous variable.

Hours of Personal Motivation

Hours of attendance in Personal Motivation activity standard. Continuous variable.

Enrollment 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.

Input: Independent variable

African-American

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).

non-weighted 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 ( plus the sum of the coefficients

( ) times the independent variables ( ). 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.



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):






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

Variable 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

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 four-

year 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 low-

income 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 socio-

economic 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

Economically Disadvantaged

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)

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 four-

year 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

African

American American

Indian Asian

Chicano Latino

Pacific Islander

White Other

Educationally Disadvantaged Level

Low 128

(12.7%) 7 (7.1%)

403 (27.8%)

114 (7.1%)

66 (17.6%)

116 (15.3%)

76 (13.5%)

Mod 383

(38.2%) 24

(24.5%) 325

(22.4%) 319

(19.7%) 119

(31.8%) 199

(26.3%) 121

(21.4%)

High 494

(49.1%) 67

(68.4%) 723

(49.8%) 1184

(73.2%) 189

(50.5%) 441

(58.3%) 367

(65.1%)

Total 1005

(100%) 98

(100%) 1451

(100%) 1617

(100%) 374

(100%) 756

(100%) 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 Enrollment Percentage Among

African

American American

Indian Asian

Chicano Latino

Pacific Islander

White Other

Overall Postsecondary Enrollment

Not Enrolled

25.5% (256)

28.6 % (28)

21.0% (304)

31.5% (510)

19.0% (71)

23.0% (174)

23.8% (134)

Enrolled 74.5% (749)

71.4% (70)

79.0% (1147)

68.5% (1107)

81.0% (303)

77.0% (582)

76.2% (430)

Percent of Institutional Type of Enrolled Participants

Two-Year Institution 85.4% (640)

82.9% (58)

91.3% (1047)

88.2% (976)

84.5% (256)

82.1% (478)

89.1% (383)

Four-Year Postsecondary 14.6% (109)

17.1% (12)

8.7% (100)

11.8% (131)

15.5% (47)

17.9% (104)

10.9% (47)

Note. Non-weighted N = 5865, active participants.

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 non-

weighted 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.

71

Table 8

Correlation Matrix of Continuous Independent Variables

Hours of College

Information

Hours of Personal

Motivation

Semesters of Laboratory

Science

Semesters of Visual &

Performing Art

Semesters of College Prep

Electives

Non-weighted HS

GPA API

Educational Disadvantage

d Level

Postsec Institution Type of

Enrollment

Hours of College

Information 1 .154** .263** .096** .076** -.167** .232** -.017 -.014

Hours of Personal

Motivation .154** 1 .085** .052** .019 -.038** .090** .014 -.029*

Semesters of Laboratory

Science .263** .085** 1 .290** .184** -.261** .610** .169** -.038*

Semesters of Visual &

Performing Art .096** .052** .290** 1 .106** -.455** .292** .157** .014

Semesters of College Prep

Electives .076** .019 .184** .106** 1 -.012 .322** -.057** -.023

Non-weighted GPA

-.167** -.038** -.261** -.455** -.012 1 -.197** -.059** .029*

API .232** .090** .610** .292** .322** -.197** 1 .028 -.127**

Educational Disadvantaged

Level -0.009 .050** .034* 0.014 0.001 -0.022 .034* 1 0.002

Postsec Institution Type of

Enrollment

-.044** 0.003 -.047** -.064** -.080** .028* -.119** 0.002 1

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

Retention Percentage Among

African

American American

Indian Asian

Chicano Latino

Pacific Islander

White Other

Two-Year Institution

Not Retain

35 (5.5%)

6 (10.3%)

39 (3.7%)

76 (7.8%)

13 (5.1%)

26 (5.4%)

16 (4.2%)

Retain 605

(94.5%) 52

(89.7%) 1008

(96.3%) 900

(92.2%) 243

(94.9%) 452

(94.6%) 367

(95.8%)

Four-Year Institution

Not Retain

4 (3.7%)

2 (16.7%)

4 (4.0%)

5 (36.4%)

1 (2.1%)

4 (3.8%)

2 (4.3%)

Retain 105

(96.3%) 10

(83.3%) 96

(96.0%) 126

(96.2%) 46

(97.9%) 100

(96.2%) 45

(95.7%)

Percent of Institutional Type of Enrolled Participants

Overall

Not Retain

39 (5.2%)

8 (11.4%)

43 (3.7%)

81 (7.3%)

14 (22.5%)

30 (5.2%)

18 (4.2%)

Retain 710

(94.8%) 62

(88.6%) 1104

(96.3%) 1026

(92.7%) 289

(95.4%) 552

(94.8%) 412

(95.8%)

Note. Non-weighted N = 4388.

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 non-

weighted 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

Non-weighted HS GPA 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

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 R-

squared 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

Predictors B Standard

Error Stand Beta

T Significance Tolerance VIF

Hours of Academic Advising

-.135 .149 -.019 -.907 .365 .834 1.199

Hours of College Information

-.512* .191 -.060 -2.687 .007 .776 1.289

Hours of Personal Motivation

-.098 .194 -.011 -.502 .616 .841 1.189

Semesters of College Prep Elective

.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

79

Semesters of Laboratory Science*

.400 .105 .110 3.805 .000 .461 2.168

Semesters of Mathematics

.142 .092 .046 1.543 .123 .439 2.276

Semesters of Visual/Performing Arts

-.032 .071 -.010 -.455 .649 .815 1.226

Non-weighted HS GPA

-.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

Some College Attained

2.631 1.141 .047 2.306 .021 .939 1.064

Certificate Degree Attained

2.213 1.751 .025 1.264 .207 .966 1.035

Associate Degree Attained*

4.509 .581 .164 7.761 .000 .860 1.163

Bachelor’s Degree Attained*

3.456 .390 .238 8.852 .000 .533 1.876

Master’s Degree Attained*

6.024 1.065 .118 5.655 .000 .881 1.135

Doctorate Degree Attained

3.577 5.235 .014 .683 .495 .967 1.034

Low Educationally Disadvantaged Level

.201 .401 .011 .503 .615 .802 1.247

Moderately Educationally Disadvantaged Level

-.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 4-Year Institution*

4.403 .396 .315 11.125 .000 .478 2.091

Constant .639 1.429 .447 .655

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”)

507 8.6 8.6 8.6

Bachelor’s award or greater award (Level = “2”)

1589 27.1 27.1 35.7

No award (Level = “0”)

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

Likelihood Ratio Test

-2 Log Likelihood Chi-Square df Significance

Al Intercept 3294.515

Final 1701.624 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 sub-

baccalaureate increases proportionately. Furthermore, the positive or negative coefficient

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(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

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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 sub-

baccalaureate 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.

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Table 14 Parameter estimates for Independent Variables, Sub-bachelors

Sub-bac award

Independent Variable B Standard

Error Wald Significance

Exp (B)

Interval -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 -.147 .570 .066 .797 .864

Low Educationally Disadvantaged

-.108 .323 .112 .738 .898

Moderately Educationally Disadvantaged

-.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


.839 .240 12.239 .000 2.314

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.

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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.

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Table 15 Parameter estimates for Independent Variables, Bachelors Bachelors Award or

greater Independent Variable B

Standard Error

Wald Significance Exp (B)

Interval -18.876 4.047 21.760 .000

Hours of Academic Advising -.035 .084 .176 .675 .965

Hours of College Information .017 .107 .024 .877 1.017

Hours of Personal Motivation -.136 .107 1.613 .204 .873

Semesters of College Prep Electives

.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

Low Educationally Disadvantaged

.164 .231 .505 .477 1.178

Moderately Educationally Disadvantaged

.183 .192 .908 .341 1.201

Retention* 2.038 .412 24.528 .000 7.677

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


7.762 1.217 40.710 .000 2349.9

0

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


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 &

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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



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:






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.

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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

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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 first-

year 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.

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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 sub-

baccalaureate 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

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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.

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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

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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, K-

12 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.

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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 student-

centered 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

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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 student-

centered 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 first-

generation 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

101

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

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community college system into higher education without the need of completing a four-

year degree. The restructuring of the course would assist at promoting transfer to a four-

year 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.

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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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