Increasing Academic Achievement: Student Success in a ...

Increasing Academic Achievement: Student Success in a College

and Career Readiness Program

Krista Anitra Coleman

A Dissertation Submitted to the

College of Education in Partial Fulfillment of the Requirements

for the Degree of Doctor of Education

University of Mary Hardin-Baylor

2014

ii

APPROVAL PAGE

This dissertation was submitted by Krista A. Coleman under the direction of the persons

listed below. It was submitted to the College of Education and the Dean of the Graduate

School and approved in partial fulfillment of the requirements for the degree of Doctor of

Education at the University of Mary Hardin-Baylor.

________________________________________ _______________________

Robert Rose, Ph.D. Date

Committee Chair

________________________________________ _______________________

Joan Berry, Ed.D. Date

Committee Member

________________________________________ _______________________

Christie Bledsoe, Ed.D. Date

Committee Member

________________________________________ _______________________

Marlene Zipperlen, Ed.D. Date

Director of Graduate Studies

Dean of the College of Education

________________________________________ _______________________

Colin Wilborn, Ph.D. Date

Dean of the Graduate School

iii

ABSTRACT

Increasing Academic Achievement: Student Success in a College and Career Readiness

Program. Coleman, K. A. 2014: Dissertation, University of Mary Hardin-Baylor,

College of Education. Keywords: achievement gap, average daily attendance (ADA),

annual dropout rate, at-risk students, adequately yearly progress (AYP), cohort, drop out,

early intervention programs.

In this quantitative comparative study, the researcher examined the effects of college and

career readiness initiatives on student achievement and attendance of at-risk youth. The

principal investigator also examined the academic achievement and attendance rates of a

cohort group (*control) in a college bound track compared to a cohort group (*variable)

not on a college bound track, but on a career tech track instead. The sample size for this

study included a convenience sample of approximately 350 students from a traditional

high school located in Texas. The educational philosophy of the high school in this study

was teamwork, college and career readiness, and instructional remediation for TAKS. In

an effort to maintain fidelity to the program, philosophy, and participants, the researcher

obtained informed consent from district personnel, local high school administrators, and a

statistician to gain access to archived data. An independent samples t-test was used to

examine academic achievement and attendance amongst experimental and comparison

groups. Similarly, TAKS scale scores in ELA and mathematics were compared after

intensive study program treatments using a one-way ANOVA to determine variability in

scores between both cohort groups.

iv

ACKNOWLEDGEMENTS

First, I would like to thank Almighty God who is the head of my life. To my

beautiful daughter, Reese, this is our legacy and mommy wanted to show you the

importance of finishing what you start — follow thru; thank you for giving me smiles and

encouragement the entire way. You are my best friend and I love you more than words

can express. Next, I would like to thank Dr. Rose, my esteemed dissertation chair, and

committee members, Dr. Christie Bledsoe and Dr. Joan Berry. Dr. Rose, I really

appreciate the fact that you believed in me when I did not believe in myself and wanted

to quit the program after sitting out for almost a year. Your faith in me and allowing me

to resume my doctoral study, along with God’s help, got me through one of the darkest

hours in my soul. Based on my research and experiences in this leadership program, I

truly believe the most important secret to developing excellent leaders is to take people

out of their safe environment and away from the people they know and throw them into a

new arena they know little about. Thank you, sir, for challenging me and pushing me

outside of my comfort zone into higher education. To the sisters in my inner circle-

Laurie (sister from another mother), Arlene and Roslyn (prayer partners and mentor), and

Dr. Rosa Cabrera (iron sharpener), I could not have made it through the last year of this

doctoral program without your prayers, prodding, love, and support. I thank God for

allowing me to cross paths with these DIVAS (Divine, Intelligent, Virtuous, and

Anointed Sisters). Lastly, I would like to thank my family-Calvin Coleman (father),

Amy Coleman (mother/she roe), Calvin Chad Coleman (brother), Waylon Davis (uncle),

and Annie Bell Davis (grandmother/she roe) for your undying love and support. You are

the wind beneath my wings.

v

TABLE OF CONTENTS

Page

CHAPTER 1: INTRODUCTION 1

Nature of the Problem 2

Statement of the Problem 2

Research Method 2

Purpose of the Study 3

Research Method and Design 3

Background of the Problem 4

Evidence of the Problem 4

Setting/Organizational Profile 5

Research Questions and Related Hypotheses 6

Theoretical Framework 7

Significance of the Study 8

Definition of Terms 9

Scope of Study 11

Limitations 11

Delimitations 11

Assumptions 11

Summary 12

CHAPTER 2: REVIEW OF LITERATURE 13

Documentation 13

Historical Overview 13

vi

Theoretical Framework 15

Pierre Bourdieu: Social and Cultural Capital 16

Social Capital in At-Risk Youth 17

Current Findings 17

Achievement Gap for Minority Students 17

High School Dropouts 21

Historical Definition of a Dropout in Texas 22

College Readiness for At-Risk Youth 24

Graduation Rates 26

Longitudinal Graduation Rates in Texas 27

Best Practices in Increased Graduate Rates (Independent

Schools) 30

Building a Nation of Graduates 33

Conclusion 36

Summary 39

CHAPTER 3: METHOD 41

Research Method, Purpose, and Design 41

Setting or Organization Profile 43

Participant Selection Method 44

Research Instruments 44

Procedures for Data Collection 45

Procedures for Data Analysis 46

Summary 48

vii

CHAPTER 4: ANALYSIS OF DATA 50

Demographic Characteristics of Cohort Groups 51

Research Questions and Related Hypotheses 51

Findings 53

Summary 60

CHAPTER 5: RECOMMENDATIONS AND IMPLICATIONS 61

Summary of the Study 61

Recommendations and Implications 63

Future Research 66

Summary and Conclusions 67

REFERENCES 69

Appendix A 79

Appendix B 82

viii

LIST OF TABLES

Table Page

1 Are States on Track for 2020? 37

2 Descriptive Statistics for t-test 52

3 Independent Samples Test – Inferential Statistics 54

4 Descriptive Statistics – Attendance Results 56

5 Independent Samples Test – Inferential Statistics – Attendance 57

6 One-Way – ANOVA Descriptive Statistics – TAKS MAT Scale Scores 58

7 Test of Homogeneity of Variances – Scale Scores 59

8 ANOVA – Scale Scores 59

1

CHAPTER 1: INTRODUCTION

In a global economy where the most valuable skill you can sell is your

knowledge, a good education is no longer just a pathway to opportunity – it is a

prerequisite . . . and yet . . . we have one of the highest high school dropout rates

of any industrialized nation. . .. And half of the students who begin college never

finish. This is a prescription for economic decline. (Obama, 2009, para. 61-63)

As President Obama (2009) spoke those ominous words to the American people,

he issued a strategic plan challenging every American to commit to at least one year or

more of higher education or technical career training. The President cited attending a

community college, a four-year school, obtaining vocational training, or completing an

apprenticeship as potential solutions to what he identified as the national educational

crisis (Obama, 2009). Dropping out of high school is an option young Americans cannot

afford, and America cannot afford for so many of them to drop out. Tying this issue to

national needs, he stated, “It’s not just quitting on yourself, it’s quitting on your country .

. .” (Obama, 2009, para. 66). President Obama closed the address by promising to

provide the necessary financial support for all young Americans to complete college. A

new goal for 2020 is for America to have the highest proportion of college graduates in

the world.

This speech laid out the framework for the American Recovery and Reinvestment

Act of 2009, and it is within this context this research is posited. The researcher

introduces the nature of this educational crisis by tracing historical data to the present

time. In Chapter 1, the following topics are a) the problem and purpose of the study, b)

the theoretical framework, and c) the research questions and hypotheses.

2

Nature of the Problem

Historical data for at-risk youth indicate significant increases in dropout rates for

cohort groups at urban high schools across the state of Texas (Austin ISD, Dallas ISD,

Houston ISD, Manor ISD) (Texas Education Agency, 2011). District personnel in urban

districts have attempted to solve this complex problem for more than a decade but have

intensified their efforts in the past three years. Addressing the growing student academic

achievement gap between Hispanic students, African American students, and White

students is a key area of concern for non-urban school districts as well (“Manor ISD

dropout rates,” 2008).

Statement of Problem

After having been identified as not making adequate yearly progress (AYP) by

federal standards more than 10 years ago, school officials in several urban districts in

Texas have been working to solve the complex dropout problem. Moreover, the general

problem is districts’ dropout rates across the nation are not meeting federal standards.

The problem is the low academic achievement of at-risk high school students.

As a result, many school districts’ have initiated college and career readiness

programs targeted to increase graduation rates using interventions in both tracks. One

might assume more research may help to determine the impact of these interventions on

academic achievement.

Research Method

The method for this research was a quantitative comparative design study, which

assessed the impact of academic achievement and attendance because of targeted college

and career readiness initiatives. The study compared the academic achievement rates

3

among cohorts in a traditional high school program using college and career readiness

interventions. Consequently, the high school is located in an urban setting with similar

student demographics in cohorts. The principal investigator reviewed existing and

archival data to determine if college and career program initiatives that targeted at-risk

students influenced student achievement. A whole grade-level approach, involving all

students, was used in program treatments.

Purpose of the Study

The purpose of this study was to examine the effects of college and career

readiness initiatives on student achievement and attendance of at-risk youth. The

academic achievement and attendance rates of a cohort group (*control) on a college

bound track were compared to a cohort group (*variable) that was not a college bound

track, but a career tech track instead.

Research Method and Design

The principal investigator used a quantitative comparative design approach to

determine if college and career readiness initiatives implemented in an intensive study

program from August 2009 through June 2013 affected student achievement and

attendance rates for at-risk youth in an urban district. A series of college and career

initiatives grew from a set of goals developed through collaboration among district

personnel, local community colleges, universities, and community partnerships with local

companies like Tenneco Oil Company.

Freshmen who are at-risk of not graduating in 2013 were identified and targeted

in 2009 using their benchmark scores. Instructional coaches disaggregated data to

identify grade level deficits in TEKS and/or college and career readiness strands

4

embedded in TAKS. The prescribed intensive program study and/or treatments in the

high school consisted of (a) placement in pre-AP courses, (b) a 90-minute block of

intense TAKS prep in ELA/Math, (c) Advancement via Individual Determination

(AVID) tutors, and (d) engaging students in summer leadership institutes and college

fairs.

Background of the Problem

Data from the National Center for Educational Statistics (2010) indicated 32.8%

of U.S. Hispanics ages 16 through 24 years old born outside the United States dropped

out of high school. Hispanics born in the United States had lower academic achievement

rates than immigrant Hispanics (10.5 % and 10.8% for first generation and second

generation or higher, respectively). NCES statisticians pointed out college and career

readiness programs in high schools across the state that target this demographic must

acknowledge this dynamic.

Evidence of the Problem

One of the major problems historically limiting the number of African Americans

and Hispanics attending college was the low number completing high school (Office of

Academic Affairs at University of Texas Austin, 2005). Among 18 to 24 years old in

1980, only 69.7 % of African Americans and 54% of Hispanics completed high school,

compared to 82.6 % of Whites (Maddi et al., 2002). By 2002, African Americans had

improved their high school completion rate to 77.5%, while Hispanics and Whites had

increased to 62.6% and 87% respectively (Maddi et al., 2002). Eighty-five percent of

Americans aged 25 years and older had at least a high school diploma in 2003 (Stoops,

2004). This represents the highest recorded high school graduation rate in the nation’s

5

history. This rate has been roughly the same (87%) for the last 20 years (Stoops, 2004).

However, of this age group, only 57% of Hispanics and 79% of African Americans,

compared to 88.7% Whites, had graduated from high school. In addition, the percentage

of all Whites with at least a bachelor’s degree was higher (29%) than that of African

Americans (17%) (Christen & McKinnon, 1977; Ramirez, 2002; Stoops, 2004). In

addition, more than 25% of Hispanics had less than a ninth-grade education, compared

with only 4% for Whites (Ramirez, 2002). High school graduation numbers between

African Americans and Hispanics and those of Whites is still a wide gap and does not

meet the nation’s labor needs. In addition, this lingering gap makes it more difficult for

African Americans and Hispanics to increase their rates of college attendance and

graduation.

According to Maddi et al. (2002), in 2001 the total enrollment in American

colleges and universities was 16 million, composed in part of 1.7 million African

Americans, 1.5 million Hispanics, and 10.1 million Whites. This represented an increase

of 36.9% since 1991 for African Americans and 75% for Hispanics, while White student

enrollment declined by 4.6% (Maddi et al., 2002). In 1980, the college-going rates of

African American, Hispanic, and White high school graduates aged 18-24 were roughly

equal — 27.6%, 29.8%, and 31.8% respectively. By 2000, the rate for White students

had increased to 44.2%, while African American and Hispanic students were 39.4% and

36.3% respectively (Maddi et al., 2002).

Setting/Organizational Profile

The principal investigator compared the academic achievement and attendance of

two cohort groups in a traditional high school: one participating in an intensive college

6

bound study program and the other in a career readiness program located in an urban

school district with a population of approximately 50% Hispanic and 50% African

American students, most of who are considered at-risk of not graduating from high

school let alone attending college. The term cohort is used to describe the approach used

by college and career readiness participants. A whole grade-level approach involving

cohort participants will be used in program treatments since approximately 90% of cohort

groups in the high school are considered low SES.

Research Questions and Related Null Hypotheses

The researcher in this quantitative comparative study posed two central questions:

R1: What is the impact on 11th grade ELA/Math posttest TAKS results of at-risk

high school seniors (C1) who participated in an intensive college bound study program as

compared to the at-risk high school seniors who did not participate in the intensive study

program (C2)?

H01: There will be no statistically significant difference in academic achievement

gains of at-risk high school seniors who participate in an intensive college bound study

program as compared to the at-risk high school seniors who did not participate in the

intensive study program.

Null Hypothesis: The principal investigator accepts the null hypothesis.

R2: What is the impact of an intensive study program on attendance participation

for students who participated in a college bound program compared to the comparison

group who participated in the career tech track?

H02: There will be no statistically significant difference in attendance of at-risk

high school seniors who participate in an intensive college bound study program as

7

opposed to at-risk high school seniors who did not participate in the intensive study

program.

Null Hypothesis: The principal investigator accepts the null hypothesis.

Theoretical Framework

Cabrera and James (2007) involved in studies of at-risk youth cited Bourdieu’s

cultural and social capital development theory as providing a theoretical framework for

understanding why intervention programs have a positive effect on college readiness, by

dealing with underlying factors known to foster the development of social and cultural

capital. Bourdieu believes that cultural capital has its origins in one’s habitus or system

of lasting (as cited in Portes, 1998). However, in a school setting, a student’s cultural

capital lends itself to cultural signals, attitudes, skills, preferences, knowledge, goals, and

expected behaviors that are reinforced or rewarded when certain outcomes are achieved,

such as high achievement or if a student sets a goal to aspire to go to college.

According to Bourdieu, social capital is a set of durable, deliberate,

institutionalized relationships and the benefits that accrue for individuals as the result of

the existence of social bonds (as cited in Portes, 1998). Therefore, the social class one

belongs to determines that habitus and shapes one’s approach to school and aspirations

for the future. Based on this theory, students from lower socioeconomic backgrounds

lack the social and cultural capital necessary to compete for academic rewards because

the home and school environment (habitus) is insufficient to produce academic success.

Therefore, Bourdieu and Passeron (1990) emphasized that schools with large populations

of at-risk youth perpetuate inequities by failing to teach students the valued cultural

capital necessary to succeed in school. Incorporating specific academic interventions and

8

helping at-risk youth develop social and cultural capital strategies that foster graduating

from high school and encouraging college-going behaviors is a more pragmatic way of

dealing with this group.

Significance of the Study

This research was initiated due to national trends, or confirmed by historical data

within local, urban school districts, of problems with at-risk academic achievement. The

results of this study contribute to the body of knowledge by adding an evaluation of an

innovative, college and career preparatory program in two traditional high schools in

Texas; one that used targeted initiatives and program treatments with a predominantly at-

risk population and the other that opted out of program treatments.

Cabrera and James (2007) highlighted the significance of student participation in

a comprehensive intervention program that used a holistic approach. They examined the

effect on student achievement regarding graduation and college entrance rates. A

statistical significance was evident when incorporating multiple strategies and

interventions that resulted in significant gains of academic achievement for at-risk youth

cohorts. This study expanded on Cabrera and James’ research (2007) to examine the

effects of student participation in a comprehensive college and career preparatory

program as a freshman to determine if those students increased academic achievement

gains and they did in fact graduate from high school.

Researchers compare the effectiveness of pre-college interventions and activities

for multiple years when working with at-risk youth in high schools in urban districts in

few studies. . The implications of this study are two-fold. First, recent legislative cuts in

school finance have forced funders and policymakers to streamline budgets by

9

prioritizing which outreach programs to fund. Funding programs that are research-based

and produce expected outcomes should take precedence over programs that do not

support best practices.

Secondly, outcomes of this study, at the school district and campus level, will arm

and empower district decision-makers, principals, program directors, and teachers.

Ultimately, this will transform and realign urban districts’ program goals, curricula, and

objectives.

Definition of Terms

Achievement gap is the difference in performance between low-income and

minority students compared to that of their peers on standardized tests. Traditionally,

low-income and minority children have not performed as well as their peers on the tests

(Chen & Kaufman, 2009).

Adequately yearly progress (AYP) is a term used to define the federal graduation

rate standard in Texas.

Annual dropout rate was first calculated by the Texas Education Agency (1988)

for the 1987-88 school year as the number of dropouts from Grades 7-12 divided by the

total number of students enrolled in Grades 7-12 the fall semester of that same year.

At-risk students are defined as a group of students who possess characteristics that

prevent them from being successful in school. At-risk students are characterized in this

study as having one or more of the risk factors associated with dropping out of high

school (Kozel & Mostofsky, 1991).

10

Average daily attendance (ADA) is a calculated by determining the average

number of days each cohort group participant attended their last academic year as a

senior.

Cohort is the term used to describe the group dynamics used by college and career

readiness initiatives. A whole grade-level approach is used in program treatments (Chen

& Kaufman, 2009).

Dropout is a student who was enrolled in 2007-08 in a Texas public school in

Grades 7- 12 but did not return to a Texas public school the following fall within the

school-start window, was not expelled, did not graduate, receive a GED, continue high

school outside the Texas public school system, begin college, or die (Cabrera & James,

2007).

Early intervention programs refer to programs designed to provide at-risk youth

with opportunities to develop the necessary skills, knowledge, confidence, and

aspirations needed to graduate from high school and prepare for college (Cabrera &

James, 2007).

TAKS ELA refers to the English language arts posttest each of the cohort

participants took in their junior year as part of the exit requirement for graduation.

TAKS MAT refers to the mathematics posttest each of the cohort group

participants took in their junior year as part of the exit requirement for graduation.

Scope of the Study

This quantitative comparative research study was subject to a number of

limitations, delimitations, and assumptions. In this study, the researcher focused on

examining the effects of college and career readiness initiatives on student achievement.

11

The researcher examined the academic achievement and attendance rates of a cohort

group at a traditional high school in an urban district who participated in an intensive

study program compared to a cohort group that did not participate in an intensive study

program.

Limitations

Creswell (2008) defined a limitation as potential problems or threats with the

study. A limitation in this study could potentially be inconsistencies in historical data

available at the district level. Likewise maintaining fidelity to the treatment of college

and career readiness initiatives in both districts over a four-year period could also be a

weakness. There are several other factors like sample size and mobility rate of cohort

group participants that could influence results.

Delimitations

According to Creswell (2008), delimitations are characteristics selected by the

principal investigator, which define the boundaries of the study. Therefore, delimitations

in this quantitative comparison study were that comprehensive college and career

initiatives described are indicative of many initiatives taking place across the county.

Consequently, the researcher was cautious before drawing broad generalizations in this

study.

Assumptions

It was assumed that implementation of program treatments will vary significantly

from cohort to cohort. In addition, another assumption was that cohorts would remain

stable over the course of four academic years. This is true due to the reality that

campuses where at-risk students matriculate typically have a higher mobility rate.

12

Summary

Chapter 1 explored the nature of the decline in graduation rates and emphasized

historical data to date. As part of this study, the author presented the problem and

purpose statements that guided the study, established a theoretical framework, and posed

two research questions, as well as formed a general hypothesis or some assumptions

about how to solve this complex problem. This research was initiated due to the

significance of at-risk students’ dropout rates.

In addition, the principal investigator defined several key terms about the study

and discussed limitations that may occur. This research contributes to the body of

knowledge by adding an evaluation of a specific college and career preparatory program

in local, urban high schools in Texas that used targeted college and career initiatives with

an at-risk population. The review of literature is presented in Chapter 2.

13

CHAPTER 2: REVIEW OF LITERATURE

The general problem is districts’ low graduation rates are not meeting federal

standards or making adequately yearly progress. Consequently, many urban school

districts in Texas have established college and career readiness programs. These

programs are designed to increase student achievement and attendance. The problem is

college and career readiness programs targeted to increase graduation rates have not yet

been evaluated. There are a disproportionate number of at-risk students not graduating

from high schools in Texas. The purpose of this study was to determine if newly

implemented college and career readiness initiatives influences student achievement and

attendance. This chapter provided a comprehensive review of the literature pertaining to

this subject.

Documentation

More than 75 articles and publications from Education Research Complete and

ERIC databases were reviewed. Chapter 2 contains historical data from the Texas

Education Agency as well as district data for a local high school in an urban district in

Texas. Ninety percent of the articles and historical data were archived from 1987-2011

and consisted of current and relevant trends with respect to graduation rates.

Historical Overview

According to Losen, Orfield, and Balfanz (2006), Texas historically has had an

extremely influential role in the making of educational policy, in part because Texas

educates one-fifth of the nation’s Latino children. Likewise, it has a plethora of tools,

resources, and leaders who have made tremendous headway with respect to high school

completion and graduation rates (Losen et al., 2006).

14

According to the Federal Reserve Bank of Dallas (2005), if Texas is to be

competitive in the next generation; it can solve the complex problem of decreased

dropout rates or fall behind the national average. Texas has also experienced a rapidly

changing workforce over the past 10 years. Furthermore, Losen et al. (2006)

emphatically cited the dropout crisis amongst Black and Latino youth, in civil rights

terms, is at an all-time high; completing high school is paramount for young people. If

more than half of the Black and Latino young men in Texas are employed in the

workforce without a high school diploma, these communities will seriously face their

demise with no prospects of a future. Additionally, Losen, Orfield, and Balfanz

suggested Texas increase counseling support for children trying to navigate through

adolescence while also confronted with coming from an impoverished background.

Losen, Orfield, and Balfanz proposed that instruction in the classrooms be effective,

challenging, and interesting to bring students up to speed and re-engage them in school.

Lastly, the system in place must make certain that all of our high schools have real paths

to college and decent jobs (Losen, Orfield, & Balfanz, 2006).

Texas has been under the microscope and intense scrutiny for more than a decade

since claiming an educational miracle tied to the state’s education policy. Much of the

empirical work focused on the efficacy of test scores (Dworkin & Toenjes, 2002; Kester

& Linton, 2003; Stecher et al., 2000), while other research focused on measures of

educational progress such as grade retention, dropout rates, and graduation rates (Carnoy,

Loeb, & Smith, 2001; Darling-Hammond & Vasquez-Heilig, 2008; Hoff, D. J., 2008;

Miao & Haney, 2004). Those high schools that had notable increases in their students’

pass rates on the 10th grade TAAS also saw a decline in dropout rates (Miao & Haney,

15

2004). The most statistically significant relationship between test scores and dropouts

was found in urban high schools serving low SES demographics. One major urban

independent school district (IDS) in Texas is the subject of intense scrutiny. The U.S.

Department of Education listed Houston, Texas, as having one of the highest dropout

rates in the United States (NCES, 2003). This is in contrast to dropout rates reported by

the Houston Independent School District (HISD) to the Texas Education Agency (TEA).

HISD reported a dropout rate that hovered below 2% (Texas Education Agency, 2001).

Peabody (2011) reported that nearly 15 traditional high schools in HISD had

falsified Public Education Information Management Systems (PEIMS) leaver codes,

which resulted in nearly 3,000 students who were not coded as dropouts. Furthermore,

Darling-Hammond and Vasquez-Heilig (2008) discovered that Texas high schools that

pushed out students reported the largest increase in test scores and accountability ratings.

Theoretical Framework

Most of the literature involved in studies of at-risk youth’s graduation rates and

college attendance utilized Bourdieu’s (1977) cultural and social capital development

theory to provide a theoretical framework for understanding why intervention programs

will have a positive effect on college readiness. Bourdieu’s theory deals with underlying

factors known to foster the development of social and cultural capital. The fundamental

concept of cultural and social capital development has been drawing more attention and

research in the past few decades (Bourdieu, 1977; Coleman, 1988; Foley, McCarthy, &

Chaves, 2001; Gittell & Thompson, 2001; James, Schulz, & Van Olphen, 2001; Putnam

et al., 2004; Warren, Thompson, & Saegert, 2001). Yet in this body of work disconnects

exist because of different interpretations in the cultural and social development theory.

16

This theoretical framework, therefore, offers a critical synthesis of the term social capital

as defined by its theoretical fathers (Bourdieu, 1977; Coleman, 1988; Putnam et al.,

2004). This study expanded on Bourdieu’s theory based on the evidence that supports

research and theory of social capital among young people, which has not gained as much

attention as research exploring social capital among adults. The author of this study

examined the importance and benefits of investigating social capital among at-risk

children and youth.

Pierre Bourdieu: Social and Cultural Capital

Pierre Bourdieu’s early definition of social capital is often overlooked in the

historical context (Morrow, 2001; Portes, 1998). According to Bourdieu (1984), social

capital consists of two dimensions: (a) social networks as well as (b) the ability to adapt

socially in one’s environment. Bourdieu believed that one must not only have support

systems but must also understand the importance of how to make these networks operate

and how one can maintain and utilize these relationships over time. Bourdieu also

emphasized that social networks must be created and cultivated in order for the person to

utilize their resources. Bourdieu (1977) developed and described the concept of cultural

capital. Cultural capital is transmitted through family heritage to children by spending

economic resources on culturally valued and specific items such as books, tickets to the

theater or museums, and other culturally specific artifacts. This concept specifically

incorporates an understanding and familiarity of a dominant culture and language in

society (Bourdieu, 1977). When Bourdieu proposed that cultural capital is most

beneficial for upper-class students, he was primarily interested in understanding how

17

people utilized these two forms of capital, as well as how they worked together to

reproduce social inequalities.

Social Capital in At-Risk Youth

Most studies involving social capital with high-risk youth focus on the adult

population. Consequently, there are only a few researchers investigating social capital

among at-risk youth, and those studies have generally focused on adolescents. Studies

involving at-risk youth appear to have conflicting problems with respect to the role

assessment plays. Initially investigators failed to integrate at-risk youth’s perceptions of

their relationships and their environment, and instead focused on information from

parents and/or teachers (Goddard, 2003; Marjoribanks, 1992; Parcel & Menaghan, 1993).

Continuing to explore social capital among at-risk youth is critical, since previous

research with adults has tied the concept to beneficial individual and societal outcomes.

If this theory is true, similar results will apply to at-risk youth. Researchers can

focus on interventions that support the notion of social capital (e.g., creating effective

interventions to increase student achievement, graduation, and completion rates).

Likewise, Morrow (1999) argued future research involving social capital with

impoverished youth should also consider gender, age, and background as well as

socioeconomic status, income, and environmental differences.

Current Findings

Achievement Gap for Minority Students

One cannot examine and analyze the complex problem of low graduation rates

among at-risk youth in high schools across Texas without first exploring the huge

achievement gap that exists among demographic groups. Therefore, the review of

18

literature begins with an in-depth analysis of the term achievement gap. Achievement

gaps can be defined as the difference in performance between low-income and minority

students compared to that of their peers on standardized tests. Traditionally low-income

and minority children have not performed as well on achievement tests as their white

peers.

Kozel and Mostofsky (1991) discussed the pervasive racial and socioeconomic

disparities in student achievement by using such terms as “savage inequalities” to define

the achievement gap found in our nation’s schools (p. 213). Although students of color

made significant gains in closing this gap during the early 1980s, it widened once again

during the 1990s (Carnoy, 1994; Lee, 2002). Thus, in an era of No Child Left Behind

(NCLB) and higher accountability, educational leaders now find themselves in another

educational crisis in the United States in regard to the academic achievement gap.

Slaby, Loucks, and Stelwagon (2005) believed strongly these pervasive

achievement gaps exist based on race and class divisions. This is mostly due to factors

attributed to family resources, school quality, social capital, and educational opportunity.

While some researchers have blamed schools for deficiencies and inequities in

educational outcomes, others have turned their attention toward the failure of families to

prepare youth adequately for the educational challenges that lay ahead (Roscigno, 1999).

Rumberger and Williams (1992) cited faulty policymakers for ignoring the pervasive

nature of school inequality, institutionalized racism, and segregation, especially in large

urban centers found in Minnesota and Texas. In addition, researchers have proven that

schools and families can make a difference in closing the gap (Kober, 2001; McCombs et

al., 2012).

19

According to the Education Commission of the States (2010), “. . . the average

black or Hispanic high school student achieved at about the same level as the average

white student in the lowest quartile of white achievement” (p. 282). Across a number of

measures of educational success, African Americans and Hispanics consistently perform

at a lower level than non-Hispanic Whites do. These disparities often result from a

student’s socioeconomic status (SES), with those below the poverty line falling behind

those above the poverty line. The most recent National Assessment of Educational

Progress (NAEP) (2010) data for mathematics and reading assessments indicated marked

achievement gaps at both the fourth and eighth grade levels across racial and ethnic

groups.

Levine and Eubanks (1990) argued that minority groups in Texas scored lower on

standardized assessments than their White and Asian peers. The minority achievement

gap is present not only in underfunded urban schools; minorities in suburban schools,

which are presumably well funded, perform at lower levels than non-minorities on

reading proficiency exams taken by 13-year-olds (Levine & Eubanks, 1990).

Furthermore, this gap was present even when parents’ college attendance was held

constant, which suggested a more multifaceted relationship.

A case study in Minneapolis, based on higher concentrations of at-risk

populations and a higher density of urban districts than anywhere else in the country,

suggested the gap for African American children starts even before they get to

kindergarten and persists throughout their educational experience (Minneapolis

Kindergarten Readiness Assessment, 2007). According to the study, African American

children entered kindergarten 40 percentage points behind White students. The 2010

20

Minnesota Comprehensive Assessment (MCA) testing data showed that, at every critical

benchmark, from third grade through 11th grade, African American students were

consistently between 20-30 percentage points behind the overall statewide standard in

reading, math, and science (Minneapolis Measurement of the African American

Academic Achievement Gap Best Practices Committee, 2010).

Hepburn and White (1990) cited one of the major consequences of the academic

achievement gap is the disproportionally significant increase in the number of high

school dropouts; minority and low-income students are more likely to drop out of

traditional high schools. In turn, dropping out of high school relates to a number of

negative social outcomes, including a higher likelihood of unemployment, low wages,

and diminished earning power (U.S. Census Bureau, 2004). Across the nation, research

has proven that individuals who do not possess a high school diploma earn an average of

$18,734 annually, compared with $27,915 for those with a high school diploma, and

$51,206 for those with a baccalaureate degree. High school dropouts are also more likely

to be welfare dependent and utilize other social services (Rumberger & Williams, 1992).

The Coleman (1966) report described the huge discrepancies that exist in

educational outcomes between White and minority students, but more than 45 years later,

these achievement gaps still exist. Researchers have demonstrated an achievement gap in

both high poverty schools and those that serve predominantly minority students;

however, some schools in disadvantaged neighborhoods perform above expectations

(Education Trust, 1999). Isolating what distinguishes high performing schools that are

operating in the midst of severely distressed communities and replicating these programs

and attributes is at the heart of bridging the gap in achievement and educational outcomes

21

thereby inevitably impacting drop out and graduation rates in states like Texas

(Kannapel, Clements, Taylor, & Hibpshman, 2005).

Finally, Hoerandner and Lemke (2006) also argued that bridging the achievement

gap is more complex than simply throwing money at the problem or developing

intervention programs. As such, researchers have questioned the degree to which NCLB

can remedy the achievement gaps exposed by standardized testing (Chubb & Loveless,

2002; McCombs et al., 2012). Rather, the ultimate solution to the achievement gap must

include a more holistic and comprehensive approach that takes individual students into

account, as well as the social context in which they live (McCombs et al., 2012).

High School Dropouts

Up to now, many researchers cite the failure to bridge the achievement gap

amongst at-risk youth in traditional high school settings as a national crisis. America’s

youth are dropping out of high school at or before completing graduation requirements.

This is true according to Kaufman, Kwon, Klein, and Chapman (2000), who noted

African Americans and Hispanics abandon high school at an even more alarming rate

than other groups. In fact, in the past three decades, the distribution of high school

dropouts suggests that the dropout rate for African Americans is 1.5 to 2 times the

dropout rate for Whites (Lee, 2002), with slightly over half of Black and Hispanic

students graduating from high school (Greene & Forrester, 2003).

Historical Definition of a Dropout in Texas

The dropout rate for students with a family income below $20,000 is more than

three times that of students with a family income over $50,000 (Shin, 2005). Shin (2005)

22

stated that of those who do graduate, fewer than 20% of Black students and 16% of

Hispanic students are not prepared for college. Therefore, the author will set the

historical context for the drop out crisis as it correlates to bridging the achievement gap

by defining the term dropout in Texas.

During the late 1980s, a high school dropout was considered a student in seventh

through 12th grade who did not earn a high school diploma or General Equivalency

Diploma (GED) and who did not attend school for 30 or more consecutive days without

being enrolled in another educational entity, either public or private (TEC §11.205,

1988). Consequently, the State Board of Education (SBOE) required school

administrators to excvlude students with excused absences from the dropout criteria.

Added were students who returned to school following an extended absence (Title 19 of

the Texas Administrative Code [TAC] §61.64, 1988).

Beginning in 1994-95, the definition of a dropout was removed from state law and

State Board of Educators rule. The state legislature decided to delete the dropout

definition from the Texas Education Code altogether. It an assumption that students who

met all coursework requirements for a diploma but dropped out of high school without

passing standardized tests were not considered in the dropout calculation (Texas

Education Agency, 1994, 1995). In comparison, that same year students who left school

to return home to their native countries were also not factored in the dropout count, even

if the districts failed to prove students reenrolled in school. Consequently, in 1997 the

legislature raised the compulsory attendance age for students to attend school

(mandatory) until they reached their 18th birthday (TEC §25.085, 1997).

23

It was not until the late 1990s that Texas saw a major shift in the historical

definition, initial dropout records were submitted for students who received this label

during the 1987-88 academic year. The original dropout definition in the 1988-1989

PEIMS Data Standards (Texas Education Agency, 1989) excluded the following students

in the calculation: (a) students who received GED certificates; (b) students who dropped

out; (c) students who withdraw, and (d) incarcerated students . However, when the

mandatory attendance rates increased from 16 years of age to 17 years old in 1989, the

dropout definition changed to include an exemption for students who were at least 17

years of age and enrolled in General Equivalency Diploma (GED) programs (TEC §

21.032, 1990; TEC § 21.033, 1990).

The state legislature added two groups of students who to exempt from the

dropout number in 1999. Senate Bill (SB) 1472 excused students who were at least 16

years old and attending the Job Corps program (TEC §25.086, 1999). Similarly, Senate

Bill 103 released students expelled from districts entirely from the dropout count. In

2003, the Texas Legislature passed Senate Bill 186, which changed the dropout indicator.

Specifically, the legislators designing Senate Bill 186 required districts to report dropout

data, and TEA was obligated to calculate dropout rates, graduation rates, and completion

rates consistent with the standards and definitions of the U.S. Department of Education’s

National Center for Education Statistics (NCES) (TEC §39.051, 2004). Districts in

Texas began collecting data in the 2005-06 school year. According to NCES’ definition,

a dropout is as

a student who is enrolled in public school in Grades 7-12 but does not return to

public school the following fall, is not involved in an expulsion sentence, and

24

does not: graduate, receive a GED, continue school outside the public school

system, begin college, or die. (Texas Education Agency, 2011, p. 10)

In 2009, the Texas legislature passed House Bill 3 (TEC §39.053, 2010), which

federally mandated TEA to release six groups of students from dropout and completion

rates when evaluating dropout and completion data for accreditation and performance

ratings. The groups of students included: (a) previous dropouts; (b) students for whom

school districts were not receiving compensatory; (c) students who have been court

ordered to attend GED programs, but have not earned GED certificates; (d) students who

were incarcerated in state jails and federal penitentiaries as adults as well as persons

certified to stand trial as adults; (e) unschooled refugees or political asylees in Grades 7

through 12; and (f) students detained in county detention facilities that are located outside

the students’ home districts.

The latest exclusions took effect beginning with school attendance in 2010-11,

which affected rates calculated in the 2011-12 school year. Exclusions required by state

statute apply only to rates used for state accountability. Graduation rates calculated for

federal accountability did not include the exclusions (TEA, 2009).

College Readiness for At-Risk Youth

College preparation programs for at-risk youth have been established for the

country to address the disproportionate number of at-risk students who are not graduating

by developing intervention programs in high schools that emphasize college readiness.

These reformers realize for many minority students from impoverished backgrounds

college is not accessible without a high school diploma. Census data from 2004 projects

30.6 % of non-Hispanic Whites have at least a baccalaureate degree; on the other hand,

25

only 12.1 % of Hispanics and 17.6 % of African Americans have a baccalaureate degree

(U.S. Census Bureau, 2004).

Carnoy (1994) argued Blacks do not attend college due to drastic cuts in student

financial aid—coupled with negative feelings about adapting to college environments and

the payoff of post-secondary education. Yet, African Americans and Hispanics have

experienced incidences of increased college enrollment; a recent analysis of college

composition revealed that in 1983, 4% of students were Hispanic and 10% were Black,

but more than 20 years later in 2003, 10% of students were Hispanic and 13% were Black

(Shin, 2005). In comparison, as high school graduation rates are decreasing, college

graduation rates are not increasing at the same level as enrollment, suggesting that

college retention for minorities remains a significant concern.

In the Complete College America (CCA) blog, the editor, Sugar (2012)

considered the college dropout problem in the nation. The editor discussed the fact that

over 37 million of the nation’s college students have some degree of college education,

but no formal degree. He stressed the importance of obtaining a college education for the

economic opportunity it provided. If there are not more college graduates, this will be of

grave significance for the 21st century economy.

Sugar (2012) stated the main reason students do not finish college was they are in

desperate need of remedial education based on their lack of readiness when leaving high

school. “The biggest hindrance to completing college isn’t really financial; it’s academic

fitness” (Sugar, 2012, p. 2). Current data supports upwards of 50% of all students

entering postsecondary education require at least one remedial education course (Sugar,

2012). Finally, Sugar challenged President Obama and Secretary of Education Arne

26

Duncan to continue to provide leadership on this higher education issue of college

students finishing their degree by improving federal higher education data collection to

allow more effective tracking of college completion numbers and getting students on a

clearer path of student achievement measures for administering financial aid.

Graduation Rates

Achieving Graduation for all by the National Governors Association (2009) cites

Texas as implementing many of the necessary steps required to correct the dropout

problem. Consequently, state policy makers and the SBOE were tasked with ensuring the

following four actions for increasing graduation rates in Texas: (a) increasing the

maximum compulsory and allowable school attendance ages, including graduation rates

in accountability systems, and delegating responsibility for dropout prevention; (b)

targeting at-risk youth demographics by creating early identification systems; (c) creating

incentives for dropout recovery, employing outreach strategies to re-engage dropouts, and

establishing re-entry programs; and (d) providing rigorous, relevant options for earning a

high school diploma by turning around low-performing schools and awarding credit for

performance, not seat time. Governor Rick Perry said,

One student who drops out is one dropout too many . . . Working with legislators,

educators and community leaders, Texas has taken deliberate steps to decrease the

dropout rate to ensure students have a chance to succeed, and we will continue

working together to apply innovative approaches to keep our students in the

classroom and on the path to success. (Office of the Governor, 2009, para. 2)

In an effort to increase graduation rates, Texas passed legislation allowing

individuals up to the age of 26 to attend high school in 1997. By designing this

27

legislation, Texas became one of 20 states with a maximum compulsory attendance age

of 18 and by doing this, made a strong statement that dropping out of school is not an

option. Texas also requires schools and districts to be accountable by including dropout

rates and graduation rates into the state’s accountability system. The National Governors

Association (2009) also recommended that states not only calculate their dropout rate, but

also consider including the cohort dropout rate, which measures the proportion of

students in a certain cohort who left school in a defined period. Further, out of 22 states,

Texas is the only one that calculates and publicly reports dropout rates based on cohort

data and one of only 15 states that factors the four-year cohort graduation rates (called

completion rates) into the state accountability system (National Governors Association,

2009). For the purpose of the current research study, a cohort is whole grade-level

approach used in program intervention treatments. The findings of the accountability

report also note that the cohort rate is the most decisive means of depicting the dropout

problem because it is based on longitudinal data. Consequently, Texas had a graduation

rate of 79.1 % for the class of 2008.

Longitudinal Graduation Rates in Texas

According to TEA (2009), more than 84% of the ninth-grade cohort graduated in

2010; or total enrollment of 314,079; approximately 7% continued in high school

following their expected graduation date; approximately 1% received General

Educational Development (GED) certificates, and approximately 7% quit altogether.

Additionally, the four-year, on-time graduation rate (80.6%) showed a significant

increase from the class of 2009 by 3.7% points while the longitudinal dropout rate (9.4%)

decreased.

28

“Texas believes that it’s important to hold schools accountable for students who

drop out and that accountability has produced gains in the fight for our children’s future,”

said Commissioner of Education Robert Scott (Office of the Governor, 2009, para. 5).

Texas assumes a laser like focus with its high-risk population (Chen & Kaufman, 2009).

In addition, Texas has developed several key initiatives that prevent this pervasive

problem by providing social emotional services to at-risk students. Dropout prevention

programs and services are to meet the needs of students from pre-kindergarten to ninth

grade transition programs. At-risk students are as a group of students who possess

characteristics that prevent them from being successful in school. The at-risk students in

Chen and Kaufman’s (2009) study had one or more of the risk factors associated with

dropping out of high school.

Dropout prevention and recovery programs, grants, and initiatives provided in

Texas include, but are not limited to the following: (a) 9th Grade Transition, (b)

Collaborative Dropout Reduction Pilot Program, (c) Communities in Schools (CIS), (d)

Dropout Recovery, (e) Intensive Summer Programs, (f) Intensive Technology-Based

Academic Intervention Pilot, and (g) Texas GEAR UP (Texas Education Agency, 2009).

Early intervention programs are programs designed to provide at-risk youth with

opportunities to develop the necessary skills, knowledge, confidence, and aspirations

needed to graduate from high school and prepare for college.

In an effort to increase graduations rates, the Texas Education Agency (2009)

agreed to a third-party research study on best practices in dropout prevention. The study

highlighted programs with a proven record of accomplishment. It featured highly

29

effective dropout prevention programs and strategies, and provided recommendations for

key legislative action with respect to dropout prevention efforts (TEA, 2009).

According to the report, the most effective programs should encompass

prevention efforts because “no matter how effective a state’s dropout prevention efforts,

students will invariably fall through the cracks” (TEA, 2009, para. 11). In addition, the

Texas Education Agency also went a step further by creating a Dropout Recovery Pilot

Program that provides grants to school districts, non-profit education organizations, and

education service centers to identify and recruit students who have dropped out of school.

Grantees receive awards if $1,000 for each student who receives a high school diploma,

GED plus college credit, or earns advanced technical credit (TEA, 2009).

In contrast, many school districts have increased dropout recovery efforts by

implementing a program called Reach Out to Dropouts. District personnel work

collaboratively with a team of volunteers to recover students who do not return to school

in the fall. One urban district in particular, Houston Independent School District (2009)

has recovered more than 5,500 students since 2004.

Greismer (2011) cited one of the reasons the number of high school dropout rates

have decreased in Texas is that views on intervention programs have evolved over the

last three decades to better address the learning needs of a wider range of students. These

schools range from being public education programs, either within an established school

or on its own, to non-profit organizations meeting the needs of at-risk youth. Despite

evidence that demonstrates Texas is making significant gains in increasing graduation

rates, there is still much work to complete to make sure every student receives a high

school diploma and is college- and career-ready. Texas is definitely on track by the year

30

2020 to decrease the state’s dropout rate and increase the number of public-school

students headed towards a successful path with the help of business and industry,

educators, volunteers, legislators, and policymakers (TEA, 2011).

Best Practices in Increased Graduation Rates (Independent Schools)

Hefferren (2009) cited non-profits like Prep for Prep, which has existed since

1978, as a best practice model for increasing high school graduation rates. Since its

inception, Prep for Prep has made it a top priority to identify gifted minority students in

New York’s public schools. Then Prep for Prep goes on to equip and empower talented

students of color with the academic prowess necessary to enter the country’s top boarding

and private schools. In addition, other non-profit organizations, like Boys Club of New

York, established the nation’s first Independent School Placement program in 1957 and

A Better Chance (ABC) developed a targeted minority program shortly thereafter. It was

not until the early 1980s that the number of nonprofit organizations reached their peak

and made it their mission to ensure that minority students of color had a level playing

field with respect to creating diversity in education and leadership (Hefferren, 2009).

Other non-profit organizations like the Oliver Program and Early Steps also grew

rapidly during the 1980s. In contrast, The TEAK Fellowship began in 1998 and has

stood on the shoulders of its predecessors, but all in all these non-profit organizations

serve as a model for best practices by giving access to primarily African American and

Hispanic students from economically disadvantaged households. The researcher recently

informally interviewed a former Prep for Prep alumni, Ms. Safiyah Greene (personal

communication, March 20, 2013). Ms. Greene credits non-profit organizations, like the

ones mentioned, with her graduating from a top boarding school in New York City and

31

then going on to graduate from Princeton University on a full academic scholarship, as

the key to her success.

Additionally, since the 1980s tens of thousands of minority students have

graduated from these programs in record numbers. These students received the

opportunity of a lifetime that they might not have otherwise had. Which ultimately

catapulted them to go on to attend ivy league colleges and universities as well as after

graduation attaining prominent positions in education, medicine, law, business, and

government (Hefferren, 2009).

According to New York Mayor Michael Bloomberg and Schools Chancellor Joel

Klein, the educational landscape outside of non-profits has shifted towards charter

schools like KIPP (Knowledge is Power Program) and Achievement First (Hefferren,

2009). These non-profits did not exist 30 years ago yet have recently won national

attention and praise for boosting test scores in the City’s underprivileged boroughs.

According to Chantel Stevens, Director of the College Preparatory Schools Program at A

Better Chance, “Under No Child Left Behind, schools are mandated to meet certain

expectations, but too often they’re teaching to the test, or just getting to the next

benchmark. We serve those kids whose academic needs are not being met” (Hefferren,

2009, p. 112). A 2007 study conducted by personnel at the Jack Kent Cooke Foundation

concluded:

Public schools may be getting better, but that improvement is relative - high

achieving lower-income students enter what we have come to call the

“achievement trap” - educators, policymakers, and the public assume they can

fend for themselves when the data shows otherwise. (Hefferren, 2009, p. 113)

32

Project Grad (2010) is another example of a research-based reform best practice

model striving to increase high school graduation rates, and presumably, college

attendance rates, of at-risk students. This program focuses on delivering high quality,

rigorous instruction and skill building in academic content courses such as reading, math,

and writing (Bruner, 2011). Historical data has shown significant progress across all

groups of students, especially at-risk or low socioeconomic status (SES), on standardized

TAKS tests in the area of reading and mathematics. At the high school level, the number

of graduating seniors has doubled over the last decade. As a result, four times as many

students go on to college than did before the reform strategy began at the beginning of the

21st century (Project Grad, 2010).

Similarly, a traditional high school located in the fourth largest urban school

district in the country, has had an 87.4% graduation rate for more than a decade (Project

Grad, 2010). The principal who initiated Project Grad experienced significant gains in

minority graduation rates from 1988-2001. This high school has a population of

approximately 50% Hispanic and 50% African American students, most of whom low

SES.

Similarly, a traditional high school located in central Texas has had an average

graduation rate of 83.9% the past five years (Project Grad, 2010). The principal at one

high STEM high school in the district has experienced significant gains in minority

graduation rates from 2008-2013 which produced a 100% graduation rates in 2012. In

contrast, the traditional high school has a population of approximately 50% Hispanic and

50% African American students, most of who are low SES.

Building a Nation of Graduates

33

In a recent update of Building a Grad Nation: Progress and Challenge in Ending

the High School Dropout Epidemic (Balfanz, Bridgeland, Bruce, & Fox, 2013) released

by the Alliance for Excellent Education, America’s Promise Alliance, Civic Enterprises,

and the Everyone Graduates Center at John Hopkins University, data highlighted that the

nation is definitely on track to meet the national graduation rate goal of 90% by the class

of 2020. Subsequently, more than 200,000 additional students received high school

diplomas in 2010 than 2006. In retrospect President George H.W. Bush set a 90%

graduation goal by 2000 in his 1990 State of the Union address; this goal did not come to

fruition, but Presidents Clinton, Bush, and Obama reaffirmed the goal. Researchers cite

gains in graduation rates due to increased graduation rates for Hispanic and African

American demographics, Hispanic rates skyrocketed 10% points from 61% in 2006 to

71.4% in 2010 (Balfanz et al., 2013).

In comparison, African American graduation rates jumped from 59.2% in 2006 to

66.1% in 2010 (Balfanz et al., 2013). Further, southern states account for the accelerated

pace, citing five of the top 10 states with the highest gains in graduation rates since 2006

as well as seven states with the most significant decrease in dropout factory high schools.

In this context a dropout factory is a high school where 12th grade enrollment is 60% or

less than ninth grade enrollment for 3 consecutive years. According to Charlene Lake

(2013), Chief Sustainability Officer and Senior Vice President for Public Affairs at

AT&T:

This groundbreaking series of Grad Nation reports has identified problems,

proposed solutions, documented progress, measured success, and encouraged us

all to persevere; we’re proud to be a sponsor of such quality and critical work.

34

The incremental progress demonstrated in this year’s report is good, and we

should be encouraged by it, but we can’t let ourselves be distracted by success.

We must be energized by it. And we must agree together to double down on our

efforts. (p. 49)

The report also highlights while there is no one magic bullet, the acceleration of

graduation rate increases coincided with the onset of targeted education reform efforts in

the mid-2000s. This is true because during that time, active and sustained dropout

prevention efforts in a few big cities began to spread more broadly while researchers,

foundations, governors, policymakers, school districts, communities, businesses, and

others came together in an unprecedented mobilization around the dropout crisis (Balfanz

et al., 2013).

In addition, better data and its usage, combined with heightened awareness of the

problem, its impact and solutions along with a renewed focus on high quality instruction,

accountability, and alternative pathways to graduation became more mainstream (Balfanz

et al., 2013). A combination of all this resulted in increases in student and school

achievement.

This is no longer the Silent Epidemic that we wrote about six years ago, said John

Bridgeland, CEO of Civic Enterprises and co-author of the report. For the first

time, the nation is on pace to meet the 90 percent high school graduation rate

goal. While this is a historic milestone, graduation gaps affecting our most

disadvantaged students threaten our progress in reaching this goal and fulfilling

the American dream for all. (Balfanz et al., 2013, p. 18)

35

Other findings in the report include: (a) the national graduation rate increased 5

percentage points since 2006 and 2.7 percentage points between 2009 and 2010; (b) the

number of dropout factories decreased by 583 between 2002 and 2011; and (c) even

though increases in African American and Hispanic graduation rates drove the national

average, significant gaps still exist (Balfanz et al., 2013). The nation’s ability to close

these gaps will determine whether it meets and maintains a 90% graduation rate by 2020.

For the first time in our nation’s history a comprehensive drop out report set a precedence

by consisting of relevant data available from three indicators: the Averaged Freshman

Graduation Rate (AFGR) in 2010, the Adjusted Cohort Graduation Rate (ACGR) from

2009-2011 and Promoting Power in 2011.

Balfanz et al. (2013) noted the nation now has a holistic comparison of all state

graduation rates by AFGR and ACGR (see Table 1) which resulted in consistent trends

amongst most states between the two methodologies. Nine states: Texas, Indiana,

Connecticut, Arkansas, Minnesota, Colorado, Mississippi, Oregon, and Georgia showed

inconsistencies (at least +/- five-point difference) between the two rates. Nearly all states

reported ACGR; 47 states and the District of Columbia, which is the newer and more

accurate graduation rate calculation. ACGR data were not available for Idaho,

Oklahoma, and Kentucky (Balfanz et al., 2013). This is the first time in the literature

review the principal investigator notes a single report that features innovative

intervention programs and efforts across the nation that involve schools, nonprofits,

businesses, media, educational and governmental institutions, including case studies in

the following states: Texas; Orlando, FL; Shelbyville, IN, and Portland, OR. Per John

Gomperts (2013), President and CEO, America’s Promise Alliance:

36

This report tells us that the nation is making real and important progress in high

school graduation. Today, one million fewer students are trapped in failing

schools than was the case a decade ago. The task now is to learn from what has

worked and redouble the efforts to stay on track toward the goal of 90%

graduation by 2020. (p. 20)

Table 1 shows the AFGR as of 2010. There are only two states, Vermont and Wisconsin,

which have already reached the 90% graduation rate.

Conclusion

In conclusion, a comprehensive review and analysis of the literature, to support

the disproportionate number of at-risk students not graduating from high schools in

Texas, resulted in conclusive evidence that a correlation does in fact exist between the

achievement gap with respect to at-risk youth and their declining graduation rates. In

addition, there is a significant gap in the literature for holistic college and career

readiness programs at high schools across the state. Much of the literature reviewed

discussed program treatments at traditional high schools. The current research will add to

the existing body of knowledge or the lack there of.

37

Table 1

Are States on Pace to Reach 90% Graduation Rate Goal by 2020?

State Average Annual Growth in AFGR

2006-2010 (% Point)

2010 AFGR (%)

Nation 1.25 78.2

Tennessee 2.45 80.4

Louisiana 2.33 68.8

Vermont 2.28 91.4

Alaska 2.25 75.5

California 2.25 78.2

New York 2.15 76.0

South Carolina * 2.03 68.2

Georgia 1.88 69.9

Florida 1.80 70.8

Kansas 1.73 84.5

Virginia 1.68 81.2

(continued)

38

State Average Annual Growth in AFGR

2006-2010 (% Point)

2010 AFGR (%)

Maine 1.63 82.8

Texas 1.61 78.9

North Dakota 1.58 88.4

Alabama 1.41 71.8

New Hampshire 1.30 86.3

North Carolina 1.28 76.9

Colorado 1.08 79.8

Washington 1.08 77.2

Arizona 1.05 74.7

Indiana 0.97 77.2

Michigan 0.93 75.9

Wisconsin 0.91 91.1

Wyoming 0.90 80.3

Idaho 0.88 84.0

Oregon 0.82 76.3

Massachusetts 0.78 82.6

Kentucky 0.68 79.9

Missouri 0.68 83.7

New Jersey 0.60 87.2

Maryland 0.57 82.2

Illinois 0.55 81.9

Ohio 0.55 81.4

(continued)

39

State Average Annual Growth in AFGR

2006-2010 (% Point)

2010 AFGR (%)

Minnesota 0.50 88.2

Nevada 0.50 57.8

Pennsylvania* 0.40 84.1

West Virginia 0.35 78.3

Iowa 0.25 87.9

Oklahoma 0.18 78.5

Mississippi 0.07 63.8

New Mexico 0.01 67.3

Montana 0.00 81.9

Utah 0.00 78.6

Hawaii -0.02 75.4

Delaware -0.20 75.5

Rhode Island -0.35 76.4

South Dakota -0.68 81.8

Nebraska -0.80 83.8

Arkansas -1.35 75.0

Connecticut -1.68 75.1

Note. AFGR is the Averaged Freshman Graduation Rate.

* No 2006 AFGR, used 2005 AFGR

Source: (Stillwell & Sable, 2013)

40

Summary

The main point of analysis of historical and current literature reviewed was to

substantiate the researcher’s claim that the general problem is there is a disproportionate

number of at-risk students who are dropping out of high school and not graduating in

Texas. The general problem is that districts’ dropout rates across the nation are not

meeting federal standards. As a result, many school districts’ have initiated college and

career readiness programs targeted to increase graduation rates using interventions.

Research based evidence, which support best practices will assist in program evaluation.

Chapter 3 details the methodology, purpose, and design instruments.

41

CHAPTER 3: METHOD

The purpose of this study was to examine the effects of college and career

readiness initiatives on academic achievement and attendance rates. Chapter 3 contains

information outlining the research method, purpose, and participant selection. Chapter 3

also covers the research instruments, procedures for collecting data, and methodology

used for data analysis.

Research Method, Purpose, and Design

In 2010, there were over two million students (45.8%) in Texas who were at-risk

(TEA, 2010). A comprehensive list of high schools in Texas matching the criterion of

the study came from Texas Education Agency (TEA, 2010). The method for this

research was a quantitative experimental design study, which assessed the impact on

academic achievement of targeted cohort college and career readiness initiatives. The

researcher compared cohorts at a traditional high school program using college and career

readiness interventions. Consequently, the cohorts at the traditional high school located

in an urban setting consist of similar student demographics. The principal investigator

reviewed the two cohorts’ existing and archival data to determine if college and career

program initiatives that targeted at-risk students influenced student achievement. A

whole grade-level approach, involving all students, was used in program treatments.

Targeted college and career initiatives grew out of a set of goals developed in

collaboration with district personnel, local community colleges, universities, and

community partnerships with local companies like Tenneco Oil Company.

The college bound freshmen cohort (CORE) participating in an intensive study

program, identified in 2009, as not making adequately yearly progress in academic

42

achievement and attendance. As part of the campus improvement plan (CIPs),

instructional coaches disaggregated data to identify grade level deficits in TEKS/and or

college and career readiness strands embedded in 11th grade TAKS ELA, mathematics,

science, and social studies. As a result, the prescribed intensive study program and/or

program treatments consisted of the following:

1. Participants placed into Pre-AP classes

2. Intense TAKS prep in ELA/Math taught by instructional coaches.

Specifically, during the 2009-2013 school years, the 90-minute block consisted of a

complete master schedule change based on middle of the year scores for the college

bound cohort to place them in TAKS prep with an instructional coach. Generally,

speaking, the 90 minute block consisted of a 10 minute warm up activity which addressed

the cohorts’ in ELA/Math; 20 minutes of a guided lesson based on content specific TEKS

by the instructional coaches; 15 minutes of independent practice, and 45 minutes of

intense small group instruction. More specifically, students received interventions on a

rotating A/B schedule; during A days the 90-minute block consisted of a 10-minute warm

up activity in content specific area (ELA/mathematics); a 20-minute guided content

specific lesson by the instructional coach; 15 minutes of independent practice, and 45

minutes of intense small group instruction. Subsequently, during B days the 90-minute

block consists of a 10-minute warm up activity in content specific area (science/social

studies); a 20-minute guided content specific lesson by the instructional coach; 15

minutes of independent practice, and 45 minutes of intense small group instruction.

3. AVID tutors as well as college and career awareness via college fairs for

targeted cohorts

43

4. Participating in three summer leadership institutes to get a sense of college

life.

5. A college bound contract signed by each parent ensuring participants would

maintain a GPA of at least a 3.5 in order to receive a partial scholarship

funded by Tenneco Oil Company to the school of their students’ choice.

In contrast, the career tech freshmen cohort at the same high school was not given

any of the prescribed intensive program study treatments. Similarly, parents of the career

tech cohort did not agree to a contract or to a partial scholarship funded by Tenneco Oil

Company. Moreover, career tech cohort participants were not expected to take pre-AP

courses and attend any summer leadership institutes at the University of Houston

Downtown. Therefore, the purpose of this study was to examine the effects of college

and career readiness initiatives on student achievement and attendance of at-risk youth.

The academic achievement and attendance rates of a cohort group at a traditional high

school in an urban school district were compared to a cohort group that did not

participate in an intensive study program.

Setting or Organizational Profile

The principal investigator compared the academic achievement of two cohort

groups in a traditional high school participating in an intensive study program (college

bound vs. career tech) located in an urban school district that has a population of

approximately 50% Hispanic and 50% African American students, most of who are

considered low SES. The term cohort to describe the approach used by college and

career readiness participants. A whole grade-level approach involving all students was

44

used in program treatments since approximately 90% of student cohort groups in the high

school are considered low SES.

Cohort One (C1-College bound): the control group is comprised of students that

participated in the intensive study program for four years at a traditional high school in an

urban school district. This cohort of students began their freshman year in 2009 and

graduated from high school in 2013.

Cohort Two (C2-Career Tech): the comparison group is comprised of students

who did not participate in the intensive study program, but in a career tech program at the

same traditional high school. This cohort of students also began their freshman year in

2009 and graduated from high school in 2013.

Participant Selection Method

The sample size included a convenience sample size of approximately 350

students from a traditional high school located in Texas. The educational philosophy of

the high school in this study was teamwork, college and career readiness, and

instructional remediation for TAKS. In an effort to maintain fidelity to the program,

philosophy, and participants, the researcher obtained informed consent from district

personnel and local high school administrators and statistician to gain access to archived

data.

Research Instruments

The investigator utilized 11th grade TAKS posttest results from a traditional high

school in an urban district during the 2009-2013 school years. The database included

student data for cohorts sorted by name, grade level, attendance rates, and exit level

achievement scores, as well as economic status. The researcher compared 11th grade

45

TAKS scores in ELA and mathematics for both cohort groups; one cohort (C1-College

bound) participated in a college and career readiness intensive study program which

included TAKS prep, and the other cohort (C2-Career Tech) participated in career-tech

program for at-risk youth. The district’s database stores historical data and is a relatively

reliable data mine, which the local school district in this study is required to implement as

a means of storing educational information about their students. Administrative

personnel and data clerks are required to input and update student records daily. The data

are then uploaded to the Public Education Information Management System (PEIMS),

which is regulated by the Texas Education Agency (TEA, 2010).

Reliability of this data was of concern because data were collected and inputted

by different personnel sources. Different people are responsible for inputting data into

the SASI database; thus, the potential for error is possible. However, in this urban school

district, data input is monitored by the Information and Technology Department for

accuracy before submitting items to Texas Education Agency’s PEIMS. To ensure

accuracy of this data from school districts, TEA implemented a set of standards,

definitions, codes, formats, editing procedures, a required database design, a procedure

for uploading data to TEA’s database, and documentation describing numeric values for

data.

Procedures for Data Collection

An independent samples t-test was used to examine academic achievement,

attendance between the experimental, and comparison groups. Similarly, TAKS scale

scores in ELA and mathematics were compared for both cohorts after intensive study

program treatments using a one-way ANOVA to determine variability in scores between

46

both cohort groups. Therefore, the cohort group with the lowest mean rank is the group

with the greatest number of low scores. Equally, an independent samples t-test was used

and recorded in the data analysis to find a z score, which indicated how far and in what

direction each item deviated from its distribution’s mean, expressed in units of its

distribution’s standard deviation to determine the cohort group with highest number of

participants meeting minimum state standards.

The results of this study can be generalized to the high school participating in the

study. In a true comparative experimental design, some confounding variables may

threaten internal validity. These threats include history, statistical regression, selection,

high mobility rate of participants, testing, instrumentation, and design contamination.

However, this design fares quite well when evaluated on its ability to control threats to

internal validity. Except for history, control of the other threats occurs by the presence

of the series of pre-measures.

Maturation, testing, instrumentation, regression, and attrition produce gradual

changes that would be operating between all measures. Because the control group was

the previous year’s cohort, the issue of selection was a factor. Thus, history, selection,

and mobility rate of participants were potential threats to internal validity (Monette,

Sullivan, & DeJong, 2002).

Procedures for Data Analysis

The principal investigator was the only person with knowledge of data stored on a

flash drive, maintained the confidentiality of student names and id numbers from both

cohorts. The researcher obtained permission from the Research and Evaluation

Department of the participating district (Appendix A). The author submitted an external

47

research application to the Research and Evaluation Department. Before approval was

granted by the school district, the Research and Evaluation Department requested the

approval from the University of Mary Hardin Baylor Institutional Review Board (IRB)

(Appendix B). Once permission came from the IRB, the Research and Evaluation

Committee released the existing archived data. The dependent variables were as follows:

(a) academic achievement scale scores on 11th grade TAKS exams in ELA and

mathematics, and (b) attendance rates of both cohort group participants. Academic

achievement rates were measured by the percentage of students meeting the minimum

passing standard after receiving intensive program treatments.

Statistical analyses occurred by entering data in SPSS. Inferential statistics and an

independent samples t-test were utilized in evaluating the two research questions:

R1: What is the impact on 11th grade ELA/Math posttest TAKS results of at-risk

high school seniors (C1) who participated in an intensive college bound study program as

compared to the at-risk high school seniors who did not participate in the intensive study

program (C2)?

H01: There will be no statistically significant difference in academic achievement

gains of at-risk high school seniors who participate in an intensive college bound study

program as compared to the at-risk high school seniors who did not participate in the

intensive study program.

Null Hypothesis: The principal investigator accepts the null hypothesis.

R2: What is the impact of an intensive study program on attendance participation

for students who participated in a college bound program compared to the comparison

group who participated in the career tech track?

48

H02: There will be no statistically significant difference in attendance of at-risk

high school seniors who participate in an intensive college bound study program as

opposed to at-risk high school seniors who did not participate in the intensive study

program.

Null Hypothesis: The principal investigator accepts the null hypothesis.

The researcher used inferential statistics such as the Levene’s test of equal variance to

compare individual academic achievement rates on TAKS ELA/MAT posttest and

analyze attendance rates between target cohort and the comparison group.

Summary

The purpose of this study was to examine the effects of college and career

readiness initiatives on student achievement. The academic achievement and attendance

rates of an at-risk cohort group that participated in targeted interventions were compared

to an at-risk cohort group that did not receive the interventions. This chapter contained

information regarding the research method, purpose, design, participants, research

instruments, procedures for collecting data, and procedures for data analysis.

This study included an independent samples t-test. In Chapter 4, the principal

investigator discusses the results of the data collected from archived data at the district

level and determined if there was an impact on academic achievement of at-risk students

who participated in targeted interventions versus the comparison group that did not

participate in targeted interventions. The research questions results are in Chapter 4.

49

CHAPTER 4: ANALYSIS OF DATA

Historical data for at-risk youth have shown an increasing number of high school

dropouts particularly in large urban school districts (Houston ISD, Dallas ISD, Austin,

ISD, Manor ISD) (TEA, 2009). These data revealed that school districts’ dropout rates

across the nation are not meeting federal standards. The specific problem is the low

academic achievement and attendance of at-risk high school students. As a result, many

school districts in Texas have established college and career readiness programs. The

purpose of this study was to examine the effects of college and career readiness initiatives

on student achievement and attendance of at-risk youth. Because these college and career

intensive programs are to improve graduation rates, the academic achievement and

attendance rates of two cohort groups were compared in this quantitative study. Students

in one group participated in an intensive college bound study program, and the others

participated in a career tech intensive program.

Chapter 4 includes the findings generated from the statistical data. An

independent samples t-test was used to compare means. This was followed by running an

analysis of variance (ANOVA) to determine the strength and/or validate findings from

the t-test results. According to George and Mallery (2011), an analysis of variance

(ANOVA) is a procedure to compare means in an effort to see if there is enough evidence

to infer greater variability of means between large groups or population distributions.

One-way analysis of variance offers a better explanation when contrasted with t-tests.

The data includes both descriptive and inferential statistics. First, the descriptive

statistics table will show the statistics for each of the two cohort groups as defined by the

50

grouping variable. (See Table 2) The second part of the output will display the

inferential statistics (see Table 3).

Demographic Characteristics of Cohort Groups

The two cohort groups of students participating in this quantitative comparative

study consisted of students from a public school located in an urban school district in

Texas that has a population demographic of approximately 50% Hispanic and 50%

African American students. Further, 90% of the students are at-risk of not graduating

from high school and advancing on to college. Approximately 90% of the students in

both cohort groups qualified as low SES.

Cohort One (C1-College Bound track) was the experimental group consisting of

students who attended a regular high school in an intensive AP (advanced placement)

course of study for three years. This cohort of students began their freshman year in the

fall of 2009 and was expected to graduate from high school in the spring of 2013.

Cohort Two (C2-Career Tech track) was the comparison group consisting of

students who attended a regular high school in a career tech program track. This cohort

of students began their freshman year in the fall of 2009 and was expected to graduate

from high school in the spring of 2013.

Research Questions and Related Hypotheses

The purpose of this quantitative comparative study was to determine the impact of

college bound initiatives on student achievement specifically targeted for the college

bound cohort student track (C1). The achievement of the college bound track cohort was

then compared to the achievement of the career tech cohort (C2).

51

The first research question and null hypothesis are:

R1: What is the impact on 11th grade ELA/Math posttest TAKS results of at-risk

high school seniors (C1) who participated in an intensive college bound study program as

compared to the at-risk high school seniors who did not participate in the intensive study

program (C2)?

H01: There will be no statistically significant difference in academic achievement

gains of at-risk high school seniors who participate in an intensive college bound study

program as compared to the at-risk high school seniors who did not participate in the

intensive study program.

The second research question and null hypothesis were posed to explore the

impact of attendance for each of the participants in the college bound cohort as compared

to the career tech cohort; the second research question, and null hypothesis are as

follows:

R2: What is the impact of an intensive study program on attendance for students

who participated in college bound program compared to the comparison group who

participated in the career tech track?

H02: There will be no statistically significant difference in attendance of at-risk

high school seniors who participate in an intensive college bound study program as

opposed to at-risk high school seniors who did not participate in the intensive study

program.

The principal investigator applied inferential statistics using an independent

samples t-test to compare individual academic achievement on TAKS posttests and

52

attendance rates between the target cohort (C1) and the comparison group (C2). To

analyze the two research questions, the researcher used SPS for statistical analysis.

Findings

Descriptive statistics regarding achievement scores for each of the two cohorts are

presented in (Table 2). The researcher utilized an independent samples t-test as an

appropriate measure for comparing the two cohort groups where each sample is not

related. The two groups share some variable of common interest: however, there is no

connection in membership of both cohort groups (George & Mallery, 2011). One cohort

participated in the college bound program (C1) and the other, career tech (C2).

Table 2

Descriptive Statistics

Variable (Posttest) Cohorts N Mean Std. Deviation Std. Error

Mean

ELA TAKS College Bound 105 2183.838 208.0664 13.6607

Career Tech 238 2169.025 211.1887 20.3052

Math TAKS College Bound 105 2215.569 233.9691 15.1342

Career Tech 238 2160.848 216.3870 21.1172

As shown in Table 2, the average scale scores of the college bound cohort

participants (C1) on the TAKS posttest ELA (M = 2183.838, SD = 208.0664, N = 105)

and career tech cohort participants (C2) on the TAKS posttest ELA (M = 2169.025, SD =

211.1887, N = 238) differed slightly. The college bound cohort TAKS posttest ELA

scale scores were slightly higher than their counterparts in the career tech cohort.

However, the average scale scores of college bound cohort participants (C1) on the

53

TAKS posttest MAT (M = 2215.569, SD = 233.9691, N = 105) and the average scale

scores of career tech cohort participants (C2) on the TAKS MAT posttest (M = 2160.848,

SD = 216.3870, N = 238) differed. There were more participants in the college bound

cohort with considerably higher scale scores on the TAKS MAT posttest than their

counterparts in the career tech cohort. Table 3 shows the inferential statistics using the t-

test.

Table 3

Independent Samples Test – Inferential Statistics

Levene’s test for equality of variances indicates whether an assumption of the t-

test has been met (George & Mallery, 2011). The variability of each group being

Variable

Levene’s Test

for Equality of

Variances t-test for Equality of Means

F Sig. T df

Sig. (2-

tailed)

Mean

Diff.

Std.

Error

Diff.

95%

Confidence

Interval of the

Difference

Lower Upper

TAKS ELA Equal

variances

assumed

.147 .702 -.602 342 .548 -14.8 24.61 -63.23 33.60

Equal

variances

not

assumed

-.602 201.42 .546 -14.8 24.47 -63.07 33.44

TAKS MAT Equal

variances

assumed

.094 .759 2.043 342 .042 54.72 26.78 2.04 107.4

Equal

variances

not

assumed

2.106 213.64 .036 54.72 25.98 3.51 105.9

54

approximately equal is an assumption of the t-test. If that assumption is not met, then

another form of the t-test is used. However, if this value is less than or equal to α level

which is usually set at .05 level of significance, the principal investigator can reject the

null hypothesis that the variability of the two groups is equal, implying the variances are

unequal. If the p value is less than or equal to α level, then the researcher uses the

bottom row of the output (the row labeled equal variances not assumed). Lastly, if the p

value (results produced from t-test) is greater than α level (.05), then the top row of the

output (the row labeled equal variances assumed) is utilized.

As displayed in Table 3, the Levene’s test of variance was not significant at the

.05 level for the TAKS posttest results in ELA; therefore, it is appropriate for the

principal investigator to surmise equal variances are assumed. Since t (342) = -.602,

p=0.55. Since p >.05, there is not a significant difference between the average scale

scores of the college bound cohort participants and average scale scores for the career

tech cohort participants on the TAKS ELA posttest. The average scale scores for college

bound cohort students on the TAKS ELA posttest were slightly higher than the average

scale scores for the career tech cohort. Since there is not a significant enough difference

based on the p value, the researcher fails to reject the null hypothesis.

According to the Levene’s test of variance of distribution among cohort groups on

the TAKS MAT posttest, there is a significant difference at the .05 level; therefore, it is

appropriate to surmise equal variances are not assumed. Since t (342) = 2.106, p=0.042.

Since p<.05, there is a significant enough difference between the average scale scores of

college bound cohort participants and the average scale scores for the career tech cohort

participants on the TAKS MAT posttest. Therefore, the researcher rejects the null

55

hypothesis. There is a difference in math scores for students in the two cohorts. Students

in the college bound group scored higher.

The second research question and null hypothesis were posed to explore the

impact of attendance for each of the participants in the college bound cohort

as compared to the career tech cohort. As shown in Table 4, descriptive statistics

represent the two cohort groups average daily attendance rates.

Table 4

Descriptive Statistics – Attendance Results

Variable Cohorts N Mean Std. Deviation Std. Error Mean

[Attendance-

Participation]

College Bound 105 87.5 .498 .052

Career Training 238 82.3 .459 .029

The average number of college bound cohorts’ attendance-participation (M =

87.5, SD = .498, n = 105) and the average number of career tech cohorts’ attendance-

participation (M = 82.3, SD = .459, n = 238) differed. Slightly more students in the

college bound cohort (C1) met the average daily attendance rate (ADA) as opposed to

their counterparts in the career tech cohort (C2). The college bound cohort slightly

outperformed their peers in the college bound cohort in attendance-participation.

Inferential statistics for attendance by each cohort group is displayed in Table 5.

56

Table 5

Independent Samples Test – Inferential Statistics - Attendance

The Levene’s test of variance was significant at the .05 level; therefore, it is

appropriate to surmise equal variances are not assumed. Since t (150.69) =-2.260, p=.025

which is less than .05, there is a significant difference between the average daily

attendance rates of the college bound compared to the career tech cohort participants.

Since there was a significant difference based on the p value, the researcher rejects the

null hypothesis. Students in the college bound cohort had higher attendance rates.

Since the first research question dealt with determining the impact of increased

student achievement for the college bound cohort participants on the TAKS ELA/MAT

posttest, a second parametric test, an ANOVA, was also run-in contrast to the t-test as a

means of comparing the average scale scores for both groups on the TAKS MAT

posttest. Furthermore, this also helps validate study findings (statistical significance)

resulting from the t-test in TAKS MAT posttest results for the college bound group in an

Variable

Levene’s Test

for Equality

of Variances t-test for Equality of Means

F Sig. T df

Sig. (2-

tailed)

Mean

Diff.

Std.

Error

Diff.

95%

Confidence

Interval of the

Difference

Lower Upper

Attendance

Equal

variances

assumed

13.235 .000 -2.348 343 .019 -.134 .057 -.247 -.022

Equal

variances

not

assumed

-2.260 150.69 .025 -.134 .059 -.252 -.017

57

effort to further contrast variability among scale scores between both groups (Denton,

2013).

As shown in Table 6, the researcher displayed descriptive statistics by comparing

the average scale score means using an ANOVA. Dependent variables included a

comparison of cohort groups (college bound and career tech) and their respective scale

scores on the TAKS MAT posttest. It is important to note that 2100 is considered a

passing standard for the 11th grade TAKS MAT posttest with 2400 resulting in

commended performance.

Table 6

One-Way ANOVA – Descriptive Statistics – TAKS MAT Scale Scores

Cohort N Mean

Std.

Deviation

Std.

Error

95% Confidence Interval for

Mean

Min. Max. Lower Bound Upper Bound

1 105 2211.45 174.130 16.242 2162.36 2226.35 1120 2852

2 238 2194.35 250.575 16.993 2177.75 2245.15 1864 2710

Total 343 2199.59 229.769 12.406 2175.18 2223.99 1120 2852

As shown in Table 6, the average scale scores of college bound cohort

participants (C1) on the TAKS MAT posttest (M = 2211.45, SD = 174.130) and career

tech cohort participants (C2) in MAT (M = 2194.35, SD = 250.575) differed. In

comparison to the t-test findings for college bound cohort group, students in the college

bound cohort also achieved higher scale scores on the TAKS MAT posttest as opposed to

their counterparts in the career tech cohort. Overall, the college bound cohort

outperformed their peers in the career tech cohort.

58

As shown in Table 7, the Test of Homogeneity of Variances resulted in statistical

significance at the .05 level; therefore, it is appropriate to assume equal variances. Since

p = .194 and is greater than .05.

Table 7

Test of Homogeneity of Variances – Scale Scores

Levene’s Statistic df1 df2 Sig.

1.697 1 341 .194

Finally, Table 8 shows the one-way ANOVA (SS = 1.81 between groups, MS =

21290.898, df = 1) as compared to (SS = 1.80 within groups, MS = 52886.060, df = 341).

Table 8

ANOVA – Scale Scores

Variables Sum of Squares Df Mean Square F Sig.

Between Groups 21290.898 1 21290.898 .403 .526

Within Groups 1.803 341 52886.060

Total 1.806 342

Consequently, there was not a significant difference in the variability among TAKS MAT

scale scores within groups. The p value at the .05 level = .403. Since the calculated f

exceeds the critical f value, p is greater than .05. As a result, there is not a significant

enough difference between the average number of college bound cohort participants’

scale scores compared to their career tech cohort participants’ scale scores on the TAKS

59

MAT posttest. The evidence provides sufficient reason to fail to reject the null

hypothesis. The difference is not statistically significant.

Summary

The principal investigator presented the data in Chapter 4 using an independent

samples t-test comparing the average scale scores between both cohort groups. In

contrast to the t-test, a second test, a one-way ANOVA, was also presented to strengthen

and validate the findings resulting from the t-test in TAKS MAT posttest for the college

bound group. As a result, evidence provided sufficient reason to fail to reject the null

hypothesis. In response to the first research question: increased student achievement was

not attained for the college bound cohort on the TAKS ELA posttest. However, the

college bound cohort group (C1) scale scores were slightly higher on the TAKS MAT

posttest after participating in an intensive college bound course of study over a three-year

period compared to their counterparts in the career tech cohort (C2) based on t-test

findings, but not statistically significant on ANOVA.

In response to the second research question: the college bound cohort group had

higher attendance participation rates than their career tech counterparts did based on t-test

findings. In Chapter 5, the researcher gives recommendations for further study and

discusses implications for increasing academic achievement using interventions while

working with at-risk youth.

60

CHAPTER 5: RECOMMENDATIONS AND IMPLICATIONS

The purpose of this study was to examine the effects of college and career

readiness initiatives on student achievement and attendance of at-risk youth. Because

these college and career intensive programs are designed to improve graduation rates, the

academic achievement and attendance participation of two cohort groups were compared.

The researcher compared two cohort groups; one group participated in an intensive

college bound study program and the other cohort group participated in a career tech

intensive program. Historical data for at-risk youth has indicated an increasing

percentage of high school dropouts particularly in large urban school districts is a

problem (Austin ISD, Dallas ISD, Houston ISD, Manor ISD-Texas Education Agency,

2011). The specific problem was the low academic achievement of at-risk high school

students. To address this problem many school districts in Texas have established

college and career readiness programs. Chapter 5 contains the researcher’s

recommendations (best practices) and implications for future research affecting student

achievement with at-risk youth.

Summary of the Study

The general problem is the low academic achievement of at-risk high school

students. As a result, over the past five years many school districts-initiated college and

career readiness programs targeted to increase graduation rates using interventions. The

author wanted to determine the impact of these interventions on academic achievement

and attendance participation.

61

This quantitative comparative study posed two questions:

R1: What is the impact on 11th grade ELA/Math posttest TAKS results of at-risk

high school seniors (C1) who participated in an intensive college bound study program as

compared to the at-risk high school seniors who did not participate in the intensive study

program (C2)?

R2: What is the impact of an intensive study program on attendance participation

for students who participated in a college bound program compared to the comparison

group who participated in the career tech track?

This study compared cohorts at a traditional high school that implemented a

college and career readiness intervention program. The cohorts at the traditional high

school located in an urban setting consisted of students with similar demographics. In

addition, the principal investigator reviewed existing and archived data to determine if

college and career program initiatives that targeted at-risk students influenced student

achievement and attendance. A whole grade-level approach, involving all students, was

used in program treatments. Targeted college and career initiatives grew out of a set of

goals developed in collaboration with district personnel, local community colleges,

universities, and community partnerships with local companies like Tenneco Oil

Company.

The t-value resulting from the independent t-test between the average scale scores

of the college bound group participants (C1) and the career tech cohort group participants

(C2) yielded higher scores for the college bound cohort on the TAKS MAT posttest, but

not on the TAKS ELA posttest compared to their counterparts in the career tech group.

However, the ANOVA confirmed the difference in math scores was not statistically

62

significant. Furthermore, slightly more students in the college bound cohort (C1) met the

average daily attendance rate (ADA) as opposed to their counterparts in the career tech

cohort (C2). The college bound cohort slightly outperformed their peers in the college

bound cohort in attendance-participation. It appears, therefore, that the interventions

were slightly more successful with college bound students than with their peers. While

the data did not reveal what types of interventions might have been more successful, the

results of the study indicate that more or different interventions need to be provided if all

students are to succeed academically. Many researchers involved in studies of at-risk

youth cited Bourdieu’s cultural and social capital development theory as providing a

theoretical framework for understanding why intervention programs like the one in this

study have a positive effect on college readiness, by also dealing with underlying factors

known to foster the development of social and cultural capital (Coleman, 1988; Foley,

McCarthy, & Chaves, 2001; Gittell & Thompson, 2001; James, Schulz, & Van Olphen,

2001; Portes, 1998; Putnam et al., 2004; Warren, Thompson, & Saegert, 2001). This

study also attempted to address some of those underlying factors over a three-year period.

Recommendations and Implications

The purpose of this research was to determine the impact of college bound

initiatives on student achievement and attendance specifically targeted for the college

bound cohort student track. Compared to the achievement and attendance of the career

tech cohort findings (C2) were the college bound cohort findings (C1). Based on the

TAKS MAT posttest findings resulting from the t-test, the researcher in this study has

three recommendations, which could contribute to increased academic achievement and

attendance participation in other urban districts with similar demographics. The first

63

recommendation is for additional studies involving larger and more comparable sample

sizes, specifically targeted for a college bound track. Such studies might address the fact

that college bound track participants (C1) were less attracted to the rigorous, advanced

placement (AP) curriculum as opposed to their counterparts in the career tech track (C2).

In addition, the current data were drawn from a short time period. Local high school

administrators in this study admitted the three-year time period in which interventions

specifically targeted the college bound cohort track was not adequate for those initiatives

to have a significant impact; needed to determine the impact of academic interventions is

more time. The administration went on to say that parental understanding and

involvement was important. Further grounding Bourdieu’s (1977) cultural and social

capital development theory. In a school setting, a student’s cultural capital lends itself to

cultural signals, attitudes, skills, preferences, knowledge, goals, and expected behaviors

that are reinforced or rewarded when certain outcomes are achieved, such as high

achievement or if a student sets a goal to aspire to go to college. Because students from

lower socioeconomic backgrounds lack the social and cultural capital necessary to

compete for academic rewards because of their home and school environment (habitus),

the interventions in this study may have been insufficient to produce academic success.

It is possible the college bound track participants (C1) in this study lacked sufficient

parental involvement as well as access to academic resources to supplement the rigorous,

advanced placement curriculum at home. Likewise, many of the college bound cohort

participants (C1) were also faced with the challenge of being the first in their families to

attend college and were not as adept at understanding college entrance requirements as

well as the financial aid application process. Bourdieu and Passeron (1990) emphasized

64

that schools like those that were mentioned in this study with large populations of at-risk

youth perpetuate inequities by failing to teach students the valued cultural capital

necessary to succeed in school. A second recommendation would be for additional

studies to explore how to help at-risk youth bridge this social and cultural gap.

Finally, the principal investigator recommends that Career Technical Educational

(CTE) curriculum writers in the U.S. closely examine the model used in the Netherlands,

Germany, France, and Finland where the high school curriculum’ design is in part to

meet local workforce needs (Freeman, Hersch, & Mishel, 2005). These countries have

developed a high school curriculum that provides multiple curriculum pathways to

graduation. Their non-college curriculum is designed to teach students the various skills

needed by the local workforce. European students consistently outperform American

students in terms of overall student achievement based on their tracking system. Perhaps

providing multiple pathways like the ones in use in Europe would increase student

achievement and attendance. Stromquist (2007) suggested part of the problem with the

existing American high school curriculum is the design , which promotes most high

school students attending college. However, projections are 40 to 50% of the jobs in the

new economy (globalized economy) will not require a college degree but will require

technical skills and/or trade credentials. Consequently, the local and globalized economy

will not only need individuals with highly advanced knowledge (experts), but it will also

need persons with skills appropriate for labor-intensive jobs.

65

Future Research

This study expanded on the research of James and Cabrera (2007) study, which

examined the effects of student participation in a comprehensive college and career

preparatory program beginning the freshman year of high school. Although the

interventions examined in this study did not have as much of an impact, the

aforementioned studies did explore the impact of increased student achievement and

attendance in a college and career preparatory program. There are a limited number of

multiyear studies in which researchers compare the effectiveness of pre-college

interventions and activities with at-risk youth in high schools in urban districts.

Further studies are needed that specifically examine the impact of college prep

and career tech programs on high school graduation rates. Additionally, intervention

programs designed to close the achievement gap should start sooner than high school

(for example in middle school) as this could possibly have a greater impact on

graduation rates. It is also important curriculum changes resulting in intervention

programs for at-risk students be introduced sooner than freshman year in high school.

The interventions outlined in this study are more than simply placing students in a

college bound or career tech track. They are about working with students and their

families directly implementing interventions as well as addressing the underlying

factors that foster cultural and social capital development. This research can be a

catalyst for change in other urban districts. Finally, it is not enough to focus only on

college bound students versus career and technical students. Studies are needed which

compare student achievement and attendance of all the other students attending a given

high school.

66

Summary and Conclusions

In conclusion, Chapter 5 outlined the principal investigator’s recommendations

(best practices) and implications for future research designed to impact student

achievement and attendance with at-risk high school youth. This dissertation will add to

the limited body of knowledge of intervention programs that are successful with respect

to increasing academic achievement and attendance of at-risk youth. The results of this

study should also increase awareness regarding the need for curriculum change designed

to impact, transform, and inform the decision-making process as it relates to changing

curriculum better suited for our at-risk high school youth attending urban districts. By

resolving this complex issue of designing a curriculum that better meets high school

students’ skill set, students should increase their academic achievement, which will in

turn positively impact graduation rates (AYP). The interventions conducted in this

research study are promising and can be a catalyst for change in other urban districts.

Policymakers, district decision-makers, principals, program directors, and

teachers must be willing to reallocate finances to address the challenge of improving the

academic achievement and attendance of our at-risk school population and correct the

21st century injustice to our students. It is imperative the curriculum taught in middle

and high school be better constructed to create new educational opportunities designed to

better meet the needs of at-risk youth attending public education institutions.

Policymakers, district decision-makers, principals, program directors, and teachers can do

a better job of educating all our students and better preparing them to enter the workforce.

67

As President Obama noted in his State of the Union address in 2009:

In a global economy where the most valuable skill you can sell is your

knowledge, a good education is no longer just a pathway to opportunity--it is a

prerequisite, and yet, we have one of the highest high school dropout rates of any

industrialized nation, and half of the students who begin college never finish; this

is a prescription for economic decline. (Obama, 2009, para. 61-63)

68

REFERENCES

Ahmnad, S.C. (2005). Optimizing historical black colleges and universities entrance

rates. The Network Journal, 8(1), 73-82.

American Recovery and Reinvestment Act of 2009, S. 17, 111-5 Cong. (2009).

Balfanz, V., Bridgeland, G., Bruce, E., & Fox, H. (2013). Building a grad nation:

progress and challenge in ending the high school dropout epidemic. Alliance for

Excellent Education, 45(12), 89.

Bourdieu, P. (1977). The peculiar history of scientific reason. Sociological Forum, 6(1),

3.

Bourdieu, P. (1984). The peculiar history of scientific reason. Sociological Forum, 48(9),

18.

Bourdieu, P., & Passeron, J.-C. (1990). Reproduction in education, society and culture

(2nd ed.). London, England: Sage.

Bruner, A. (2011). Jefferson Davis historical data. Houston ISD, AEIS Report. Retrieved

from http://www.houstonisd/aeis.edu

Cabrera, A., & James, N. (2007). Part II: Revising Tinto’s theory: Economic influences

on persistence reconsidered. Reworking the Student Departure Puzzle (pp. 102-

120). Nashville, TN: Vanderbilt University Press.

Carnoy, M. (1994). Making the grade. Dissent, 51(4), 93.

Carnoy, M., Loeb, S., & Smith, T. (2001). Do higher test scores in Texas make for better

high school outcomes? Educational Measurement: Issues and Practice, 24(4), 19-

31.

69

Chen, A., & Kaufman, P. (2009). Dropouts who complete high school with a diploma or

GED. Paper presented at annual meeting of the American Educational Research

Association, San Francisco, CA.

Chubb, A., & Loveless, T. (2002). Comparing new school effects in charter and

traditional public schools. The American Journal of Education, 118(4), 427-453.

Coleman, J. S. (1966). Equality of educational opportunity study. Ann Arbor, MI: Inter-

university Consortium for Political and Social Research.

doi:10.3886/ICPSR06389.v3

Coleman, J. S. (1988). Social capital and the creation of human capital. The American

Journal of Sociology, 94(1), 95-120.

Creswell, J. W. (2008). Educational research: planning, conducting, and evaluating

quantitative and qualitative research. Saddle River, NJ: Pearson Prentice Hall.

Darling-Hammond, L., & Vasquez-Heilig, C. (2008). Restoring our schools: The quest

for equity in the United States. Education Canada, 51(5), 14-18.

Denton, C. (2013). University of Texas reading intervention study (RT3). University of

Texas in Austin, PACT study report. Austin, TX: University of Texas.

Dworkin, T., & Toenjes, R. (2002). Efficacy of test scores. Applied Measurement in

Education, 13(2), 139-160. doi:10.1207/S15324818AME1302_2

Education Commission of the States. (2010). The condition of education. 2010 (NCES

2010-045). Retrieved from http://www.nces.ed.gov/pubs2010/2010006.pdf

Education Trust. (1999). The American achievement gap 1999 (NCES 1999-07).

Retrieved from http://www.educationtrust.gov

70

Federal Reserve Bank of Dallas. (2005, October 25). Report. [Clipping from an

unidentified Dallas, TX newspaper]. Copy in possession of author.

Foley, M. W., McCarthy, B. W., & Chaves, T. (2001). Much ado about social capital.

Contemporary Sociology, 30(3), 227-230.

Freeman, R. B., Hersch, J., & Mishel, L. (2005). Emerging labor market institutions for

the twenty-first century. Chicago, IL: University of Chicago Press.

George, D., & Mallery, P. (2011). SPSS for Windows step by step: A simple guide and

reference 18.0 update (11th ed.). Boston, MA: Pearson Education.

Gittell, M., & Thompson, V. (2001). Social capital and social change. Urban Affairs

Review, 36(2), 123-147.

Goddard, R. (2003). A social capital’s perspective on student’s chance for success.

Educational Evaluation and Policy Analysis, 25(1), 59-74.

Gomperts, J. (2013). National grad report. Alliance Schools Report. Retrieved from

http://www.allianceschoolsreport.com

Greene, J., & Forrester, H. (2003). Graduation statistics: Caveat emptor. Education Week,

21(18), 52-53.

Greismer, M. (2011). Dropout statistics in Texas. Education Week, 78(9), 101-109.

Hefferren, R. (2009). Independent schools, diversity, and the age of Obama. Retrieved

from https://www.prepforprep.org/ftpimages/455/misc/misc_78009.pdf

Hepburn, L., & White, B. (1990). Let’s get over the slump. Education Week, 28(4), 28-

32.

Hoerandner, R., & Lemke, B. (2006). The increasing achievement gap. Education Week,

46(2), 187.

71

Houston Independent School District. (2009) Academic excellence indicator system

report. Retrieved from

http://www.ritter.tea.state.tx.us/perfreport/aeis/district.tx.us

James, F., Schulz, R., & Van Olphen, M. (2001). Social capital then and now. Political

Theory, 33(1), 6-33.

Kannapel, P. J., Clements, S. K., Taylor, D., & Hibpshman, T. (2005). Inside the black

box of high-performing high-poverty schools. Lexington, KY: Prichard

Committee for Academic Excellence.

Kaufman, P., Kwon, J. Y., Klein, S., & Chapman, C. D. (2000). Dropout rates in the

United States: 1999 NCES 2001-022. Washington, DC: U.S. Department of

Education, National Center for Education Statistics.

Kester, M., & Linton, D (2003). Efficacy of test scores. Applied Measurement in

Education, 13(2), 139-160. doi:10.1207/S15324818AME1302_2

Kober, N. (2001). Is achievement improving and are gaps narrowing? Center on

Educational Policy, 23(4), 65-87.

Kozel, N. J., & Mostofsky, D. I. (1991). Perception of emotion: The role of race, sex, and

presentation mode. The Journal of Social Psychology, 88(2), 213-222.

doi:10.1080/00224545.1972.9918677

Lake, C. (2013). National grad report. Alliance Schools Report. Retrieved from

http://www.allianceschoolsreport.com

Lee, D. L. (2002). The disconnect of poor-urban education: Equal access and a pedagogy

of risk taking. Psychology in the Schools, 42(8), 855-863. doi:10.1002/pits.20116

72

Levine, T. H., & Eubanks, A. S. (1990). Closing the achievement gap through teacher

collaboration: Facilitating multiple trajectories of teacher learning. Journal of

Advanced Academics, 19(1), 116-138.

Slaby, R., Loucks, S., & Stelwagon, P. (2006). Why is preschool essential in closing the

achievement gap? Educational Leadership & Administration: Teaching &

Program Development, 17, 47-57.

Maddi, S. R., Khoshaba, D. M., Jensen, K., Carter, E., Lu, J. L., & Harvey, R. H. (2002)

Hardiness training for high-risk undergraduates. NACADA Journal, 22(1), 45-55.

Manor ISD dropout rates. (2008, September 18) [Clipping from an unidentified Manor,

TX newspaper]. Copy in possession of author.

Marjoribanks, K. (1992). Family capital, children’s individual attributes, and academic

achievement. Journal of Psychology: Interdisciplinary and Applied, 126(5), 529-

538.

McCombs, J. S., Augustine, C., Schwartz, H., Bodilly, S., McInnis, B., Lichter, D., &

Cross, A. B. (2012). Making summer count: How summer programs can boost

children’s learning. Education Digest, 77(6), 47-52.

Christen, W., & McKinnon, B. E. (1977). Opportunity hall: A place for dropouts–

truants–kickouts. Personnel & Guidance Journal, 55(10), 605-607.

doi:10.1002/j.2164-4918. 1977.tb04313.x

Hoff, D. J. (2008). States to face uniform rules on grad data. Spellings to propose

formula, extent of mandates unclear. Education Week, 27(32), 1-10.

73

Miao, J., & Haney, W. (2004). High school graduation rates: Alternative methods and

implications. Retrieved from

http://www.bc.edu/research/nbetpp/statements/nbr5.pdf

Minneapolis Kindergarten Readiness Assessment. (2007). Kindergarten readiness

assessments. Retrieved from

http://www.ci.minneapolis.mn.us/results/edu/kindergarten

Minneapolis Measurement of the African American Academic Achievement Gap Best

Practices Committee. (2010). Kindergarten reading assessments. Retrieved from

http://education.state.mn.us/MDE/EdExc/ReadWell/ BestPractReadK3/index.html

Monette, H., Sullivan, R., & DeJong, T. (2002). Test-retest reliability, internal item

consistency, and concurrent validity of the mobility rates assessment tool. Test

Reliability, 17(2), 98-107.

Morrow, V. (1999). Conceptualising social capital in relation to the well-being of

children and young people: A critical review. Sociological Review, 47(4), 744-

766.

Morrow, V. (2001). Retrieving Bourdieu’s concept of social concept. International

Journal of Sociology and Social Policy, 21(4), 37-63.

National Assessment of Educational Progress. (2010). Reading and mathematics

assessments (NAEP 2010-152). Retrieved from http://www.naep.edu

National Center for Education Statistics. (2003). The condition of education 2004 (NCES

2004-098). Retrieved from http://www.nces.ed.gov/pubs2004/2010006.pdf

National Center for Education Statistics. (2010). The condition of education 2010 (NCES

2010-045). Retrieved from http://www.nces.ed.gov/pubs2010/2010006.pdf

74

National Center for Educational Statistics, NCES Data. (2009). Secondary school

completion and dropouts, Grades 7-12 (NCES 2009-35). Retrieved from

http://www.nces.gov/edu

National Center for Educational Statistics, NCES Data. (2010). Secondary school

completion and dropouts, Grades 7-12 (NCES 2010-45). Retrieved from

http://www.nces.gov/edu

Obama, B. (2009). Remarks of President Barack Obama – As prepared for delivery

address to joint session of Congress, Tuesday, February 24th, 2009.

http://www.whitehouse.gov/the_press_office/Remarks-of-President-Barack-

Obama-Address-to-Joint-Session-of-Congress

Office of Academic Affairs at University of Texas Austin. (2005, October). College

dropout statistics. [Clipping from an unidentified UT publication]. Copy in

possession of staff members.

Office of the Governor. (2009). Texas recognized for best dropout prevention practices in

NGA report (press release). Retrieved from http://governor.state.tx.us/news/press-

release/13863/

Losen, D., Orfield, G., & Balfanz, R. (2006). Confronting the graduation rate crisis in

Texas. Cambridge, MA: Harvard Civil Rights Project.

Parcel, T. L., & Menaghan, E. G. (1993). Family social capital and children’s behavior

problems. Social Psychology Quarterly, 56(2), 120-135. doi:10.2307/2787001

Peabody, D. (2011). Beliefs and instructional practices among secondary teachers within

selected high- and low-performing high schools. Florida Journal of Educational

Administration & Policy, 4(2), 181-192.

75

Portes, A. (1998). Social capital: Its origins and applications in modern sociology. Annual

Review of Sociology, 24(1), 1-24. doi: 10.1146/annurev.soc.24.1.1

Project Grad. (2010). Historical data. PG statistics. Secondary School Completion and

Dropouts Grades 7-12(PGD 2010-90). Retrieved from

http://www.projectgrad.tx.edu

Putnam, R. D., Light, I., de Souza Briggs, X., Rohe, W. M., Vidal, A. C., Hutchison, J.,

. . . Woolcock, M. (1993). Using social capital to help integrate planning theory,

research, and practice. Journal of the American Planning Association, 70(2), 142-

192.

Ramirez, A. (2002). More than one gap: Dropout rate gaps between and among black,

Hispanic, and white Students. Journal of Advanced Academics, 19(1), 32-64.

Roscigno, L. (1999). Charter schools and the public good. Contexts, 6(1), 31-36.

Rumberger, R., & Williams, T. (1992). The impact of racial and ethnic segregation on the

achievement gap. Educational Evaluation and Policy Analysis, 14(4), 377-396.

Schmidt, P. (2003). Ways & means. Chronicle of Higher Education, 53(21), A18.

Shin, D.G. (2005). Battling for human rights and social justice: A Latina/o critical race

media analysis of Latina/o student youth activism in the wake of 2006 anti-

immigrant sentiment. Social Justice, 35(1), 7-27.

Stecher, B. M., Klein, S. P., Solano-Flores, G., McCaffrey, D., Robyn, A., Shavelson, R.

J., & Haertel, E. (2000). The effects of content, format, and inquiry level on

science performance assessment scores. Applied Measurement in Education,

13(2), 139-160. doi:10.1207/S15324818AME1302_2

76

Stillwell, R., & Sable, J. (2013). Public school graduates and dropouts from the common

core of data: School year 2009-10: First look (provisional data) (NCES 2013-

309). Washington, DC: U.S. Department of Education.

Stoops, L. (2004). High school completion rates for limited English proficient students

and blacks. Journal of Education, 182(3), 19-35.

Sugar, T. (2012, December). College dropouts. Retrieved from

http://completecollegeamerica.com

Tarleton State University. (2004). What do I do about Chapter 4? Social Psychology

Quarterly, 45(7), 48-58.

TEC § 11.205 (1988). Historical definition of a drop-out in Texas.

TEC § 21.032 (1990). Historical definition of a drop-out in Texas.

TEC § 21.033 (1990). Historical definition of a drop-out in Texas.

TEC § 21.086 (1999). Historical definition of a drop-out in Texas.

TEC § 25.085 (1997). Historical definition of a drop-out in Texas.

TEC § 39.051 (1999). Historical definition of a drop-out in Texas.

TEC § 39.051 (2004). Historical definition of a drop-out in Texas.

TEC § 39.053 (2010). Historical definition of a drop-out in Texas.

Texas Education Agency, Education Statistics. (1988). Texas dropout rates 1988.

Retrieved from http://www.tea.tx.edu

Texas Education Agency, Education Statistics. (1989). Texas dropout rates 1989.

Retrieved from http://www.tea.tx.edu

Texas Education Agency, Education Statistics. (1994). Texas dropout rates 1994.

Retrieved from http://www.tea.tx.edu

77

Texas Education Agency, Education Statistics. (1995). Texas dropout rates 1995.

Retrieved from http://www.tea.tx.edu

Texas Education Agency, Education Statistics. (2001). Texas dropout rates 2002.

Retrieved from http://www.tea.tx.edu

Texas Education Agency, Education Statistics. (2008). Texas dropout rates 2008.

Retrieved from http://www.tea.tx.edu

Texas Education Agency. (2011). Secondary school completion and dropouts in Texas

public schools, 2009-10 (Document No. GE11 601 08). Austin, TX: Author.

Title 19 of the Texas Administrative Code, TAC § 61.64 (1988).

U.S. Census Bureau, Census Data Statistics. (2004). Unemployment and wage earnings,

2004 (CBS 2004-008). Retrieved http://www.uscensusbureau.ed.gov

Warren, B., Thompson, D., & Saegert, C. (2001). What does ‘social capital’ mean?

Australian Journal of Social Issues, 44(2), 145-161.

Yates, J. R. (2008). Demographic imperatives for educational reform for culturally and

linguistically diverse students. Multiple Voices for Ethnically Diverse Exceptional

Learners, 11(1), 4-12.

78

APPENDIX A

District Approval Letter

79

80

81

APPENDIX B

IRB Approval

82