The Pennsylvania State University

The Graduate School

College of Education

UNDOCUMENTED STUDENTS:

UNDERSTANDING THE CONTEXT FOR POSTSECONDARY ACCESS

A Dissertation in

Higher Education

by

Wilfredo Del Pilar

2013 Wilfredo Del Pilar

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

May 2013

The dissertation of Wilfredo Del Pilar was reviewed and approved* by the following:

Leticia Oseguera

Assistant Professor in Higher Education

Dissertation Advisor

Chair of Committee

Kimberly Griffin

Associate Professor in Higher Education, Student Affairs, and International

Education Policy

Susan R. Rankin

Associate Professor of Education, College of Student Affairs

Jennifer Van Hook

Director, Population Research Institute and Professor of Sociology and

Demography

Roger Geiger

Distinguished Professor of Education (Higher Education

Professor-in-Charge of Higher Education

*Signatures are on file in the Graduate School.

iii

ABSTRACT

Undocumented students’ access to higher education has been an understudied topic, not

due to lack of interest but because of difficulties in obtaining institutional approval for research

and institutional concerns about student disclosure. While the exact number of undocumented

persons in the United States is not known, it is estimated at 11.6 million people. The growth in

this population and their opportunities for upward mobility and access to education has been a

concern in K-12 education and are just emerging in higher education. This study used the

Educational Longitudinal Study of 2002 (ELS: 2002) to examine the effects of students’ social

capital, school context, and state political context on undocumented students’ access to

postsecondary education.

Logistic regression analysis was used to explore the postsecondary enrollment of

undocumented Hispanic and Asian students. Specifically, the analysis used an adapted version of

Perna’s conceptual model on college choice to examine the relationship between social capital,

school context, and state policy context on the decision to enroll or not to enroll in postsecondary

education.

The adapted conceptual model provides insight into the factors that influence the

postsecondary enrollment decisions of undocumented students. Social capital was one of the best

predictors of postsecondary enrollment for both undocumented Hispanic and Asian students, but

the resources that they engaged were quite different. Undocumented Hispanic students were more

likely to consult college publications and websites and immediate family members for

information about postsecondary education. Undocumented Asian students utilized more expert

resources, such as counselors and college publications, websites, and representatives for

postsecondary information. Additionally, states with in-state resident tuition (ISRT) programs

provided undocumented Hispanic and Asian students with a pathway to postsecondary education.

iv

TABLE OF CONTENTS

List of Figures .......................................................................................................................... vii

List of Tables ........................................................................................................................... viii

Acknowledgements .................................................................................................................. x

Chapter 1 Introduction ............................................................................................................. 1

Size and Scope of the Undocumented Population ........................................................... 2 New Diaspora: New Destinations and Challenges for Undocumented

Populations ....................................................................................................... 5 Legal Issues Surrounding Schooling ........................................................................ 8

Theoretical Grounding ..................................................................................................... 13 Research Questions .......................................................................................................... 15

Chapter 2 Literature Review .................................................................................................... 16

Review of Literature on College Choice .......................................................................... 16 Review of Literature on Habitus and Social Capital ................................................ 23 Review of Literature on School Context and Postsecondary Education .................. 39 Review of Literature on State Policy Context and Postsecondary Education .......... 43

Summary .......................................................................................................................... 48

Chapter 3 Methods ................................................................................................................... 50

Data Source and Sample .................................................................................................. 51 Missing Data ............................................................................................................ 52 Undocumented Sample ............................................................................................ 53 Matched Sample ....................................................................................................... 57 Preliminary Data Reduction ..................................................................................... 58

Measures .......................................................................................................................... 62 Dependent Variable .................................................................................................. 63 Habitus Variables in the Analysis ............................................................................ 64 Social Capital Variables in the Analysis .................................................................. 64 School Context Variables in the Analysis ................................................................ 65 Policy Context Variables in the Analysis ................................................................. 66 Academic Preparation Variables in the Descriptive Portrait .................................... 67

Analytic Method .............................................................................................................. 68 Limitations ....................................................................................................................... 71

Chapter 4 Findings ................................................................................................................... 73

Descriptive Portrait of the Hispanic and Asian Undocumented, Matched, and Native

Samples .................................................................................................................... 74

v

Part I: Descriptive Portrait of the Undocumented, Matched, and Native Hispanic

Samples .................................................................................................................... 75 Chi-Square Findings for the Undocumented Hispanic Sample ................................ 76 Descriptive Portrait of the Undocumented, Matched, and Native Asian Samples ... 79 Chi-Square Findings for the Undocumented Asian Sample ..................................... 80

Part II: Logistic Regression Findings for the Undocumented Hispanic and Asian

Samples by Contextual Area .................................................................................... 82 Logistic Regression of Habitus on Postsecondary Enrollment for the

Undocumented Sample ..................................................................................... 82 Logistic Regression of Social Capital on Postsecondary Enrollment for the

Undocumented Samples ................................................................................... 86 Logistic Regression of School Context on Postsecondary Enrollment for the

Undocumented Sample ..................................................................................... 90 Logistic Regression of State Policy Context on Postsecondary Enrollment for

the Undocumented Sample ............................................................................... 95 Individual Contextual Model Summary ........................................................................... 96 Summary of the Logistic Regression Findings for the Undocumented Hispanic and

Asian Samples .......................................................................................................... 98 Part III: Logistic Regression Findings on Adapted Conceptual Model of

Postsecondary Enrollment for Undocumented Students Compared to Matched

Sample ...................................................................................................................... 101 Findings for the Habitus Model for the Undocumented Hispanic Sample

Compared to Matched Hispanic Sample (Model 1) ......................................... 101 Findings for Habitus and Social Capital Model for the Undocumented Hispanic

Sample Compared to Matched Hispanic Sample (Model 2) ............................ 107 Findings for Habitus, Social Capital, and School Context Model for the

Undocumented Hispanic Compared to Matched Hispanic Sample (Model

3) ....................................................................................................................... 109 Findings for the Full Adapted Conceptual Model for the Undocumented

Hispanic Compared to Matched Hispanic Sample (Model 4) .......................... 112 Findings for the Habitus Model for the Undocumented Asian Compared to the

Matched Asian Sample (Model 1) .................................................................... 115 Findings for the Habitus and Social Capital Model for the Undocumented Asian

Compared to the Matched Asian Sample (Model 2) ........................................ 121 Findings for the Habitus, Social Capital, and School Context Model for the

Undocumented Asian Compared to the Matched Asian Sample (Model 3) .... 123 Findings of the Full Adapted Conceptual Model for the Undocumented Asian

Compared to Matched Asian Sample (Model 4) .............................................. 126 Summary of Findings ....................................................................................................... 128

Chapter 5 Discussion, Implications, and Conclusion .............................................................. 131

Summary .......................................................................................................................... 132 Discussion of the Academic Preparation of the Undocumented Sample: Part I .............. 134 Analysis of the Proposed Adapted Conceptual Model ..................................................... 136

Model Assessment Based on Logistic Regression Findings: Part II ........................ 137 Revised Adapted Conceptual Model: Part III .................................................................. 147 Implications ...................................................................................................................... 149

Implications for Research ......................................................................................... 150

vi

Implications for Policy ............................................................................................. 152 Implications for Practice .......................................................................................... 154

Conclusion ....................................................................................................................... 155 References ........................................................................................................................ 157 Appendix A Variables Tested in Adapted Model by Group and Contextual Area ......... 169 Appendix B Variance Inflation Scores for Variables in the Adapted Conceptual

Model ....................................................................................................................... 176 Appendix C ...................................................................................................................... 180 List of Means and Standard Deviations for Variables in the Adapted Conceptual

Model ....................................................................................................................... 180 Appendix D Undocumented Proxy by State for the Hispanic and Asian Samples

(Weighted) ................................................................................................................ 185 Appendix E Chi-Square, Degrees of Freedom and Model Significance Results for

the Adapted Conceptual Model (Hispanic and Asian) ............................................. 186 Appendix F Logistic Regression Results for the Independent Conceptual Model

(Undocumented Hispanic and Undocumented Asian) ............................................. 187 Appendix G ...................................................................................................................... 192 Logistic Regression Results for the Adapted Conceptual Model (Undocumented and

Matched Hispanic and Samples) .............................................................................. 192 Appendix H Logistic Regression Results for the Adapted Conceptual Model

(Undocumented and Matched Asian Samples) ........................................................ 196

vii

LIST OF FIGURES

Figure 2-1. Perna’s (2006) Conceptual Model of College Choice. .......................................... 21

Figure 2-2. Del Pilar Adapted Conceptual Model ................................................................... 23

Figure 3-1. Decision tree to identify undocumented sample. .................................................. 54

Figure 3-2. Variables tested in the adapted conceptual model. ................................................ 62

Figure 5-1. Revised habitus model. ......................................................................................... 138

Figure 5-2. Revised social capital model. ................................................................................ 140

Figure 5-3. Revised school context model. .............................................................................. 145

Figure 5-4. Revised state policy context model. ...................................................................... 146

Figure 5-5. Revised adapted conceptual model. ...................................................................... 149

viii

LIST OF TABLES

Table 1-1. Increases in Immigrant Student Enrollment Pre-K to 5th Grade by State .............. 7

Table 1-2. In-State Resident Tuition Policies by State ............................................................ 10

Table 3-1. Sample Size of the Undocumented Hispanic and Asian Groups (Weighted and

Unweighted) ..................................................................................................................... 56

Table 3-2. Sample Comparison of the Undocumented Population Estimate to the

Undocumented Proxy ....................................................................................................... 57

Table 3-3. Matched and Undocumented Sample Size by Group (Unweighted) ...................... 58

Table 3-4. Undocumented Student Enrollment by Postsecondary Type (Weighted) .............. 63

Table 3-5. Undocumented Student Population by State Policy Context (Weighted) .............. 67

Table 3-6. Sample Size of Undocumented, Matched and Native Groups (Unweighted) ........ 69

Table 4-1. Descriptive Portrait of the Undocumented, Matched, and Native Hispanic

Samples ............................................................................................................................ 76

Table 4-2. Chi-Square on Academic Preparation Variables for Undocumented Hispanic

Sample .............................................................................................................................. 78

Table 4-3. Descriptive Portrait of the Undocumented, Matched and Native Asian Samples .. 80

Table 4-4. Chi-Square on Academic Preparation Variables for the Undocumented Asian

Sample .............................................................................................................................. 81

Table 4-5. Habitus Findings for the Undocumented Hispanic Sample .................................... 83

Table 4-6. Habitus Findings for the Undocumented Asian Sample ......................................... 85

Table 4-7. Social Capital Findings for the Undocumented Hispanic Sample .......................... 87

Table 4-8. Social Capital Findings for the Undocumented Asian Sample .............................. 89

Table 4-9. School Context Findings for the Undocumented Hispanic Sample ....................... 92

Table 4-10. School Context Findings for the Undocumented Asian Sample .......................... 94

Table 4-11. State Policy Context Findings for the Undocumented Hispanic Sample ............. 96

Table 4-12. State Policy Context for the Undocumented Asian Sample ................................. 96

Table 4-13. Nagelkerke R-Squared and Percent Predicted Correct for Contextual Models .... 98

ix

Table 4-14. Logistic Regression Findings for the Adapted Conceptual Model for the

Undocumented and Matched Hispanic Samples .............................................................. 104

Table 4-15. Nagelkerke R-Squared and Percent Predicted Correct for the Undocumented

and Matched Hispanic Samples ....................................................................................... 106

Table 4-16. Logistic Regression Findings for the Adapted Conceptual Model for the

Undocumented and Matched Asian Samples ................................................................... 118

Table 4-17. Nagelkerke R-Squared and Percent Predicted Correct for the Undocumented

and Matched Asian Samples ............................................................................................ 120

x

ACKNOWLEDGMENTS

The journey to a Ph.D in Higher Education started with a household decision to pick up

and move our family from Southern California to State College, Pennsylvania. Without the

support and belief of my family this would not have been possible and I am forever indebted to

them for making the sacrifice. I first want to thank my wife, Sandra Del Pilar, for her support and

encouragement, without her constant questioning of “Are you done with that dissertation yet?” I

am not sure I would be done with this dissertation. She was willing to take on the shared

responsibilities of the household when I had to read an article or write a paper or work on my

dissertation and was and is my better half. I love you.

I also want to thank my two children, Erika and Diego. They are the joy of my life and I

give thanks for them every day. Erika is an amazing young woman and is an inspiration to me,

her work ethic and constant striving for excellence served as a motivating force. When I would

think about taking a break I would feel guilty because she was often engaged in homework at the

kitchen counter after having volleyball practice and a long day at school. I am very proud of you.

To Diego, I don’t know if I would have made it without our bedtime stories. When I was tired

and didn’t feel like thinking or writing, an hour of Percy Jackson provided the break I needed to

work for two more hours. Thank you for sharing that time with me, I enjoyed it as much as you

did. I love you both dearly.

I would also like to thank my parents Wilfredo and Lillian Del Pilar. They have been a

constant source of inspiration and pride to me. This accomplishment is as much mine as it is

yours and credit you both for instilling in me a love of reading (Dad) and a desire to make a

difference in the world (Mom). Your work with the undocumented communities in both Texas

and California made this a very personal project to me and a way for me to make a contribution to

a community I learned so much from. I would also like to thank my family, Lizette, Ruben, Mark

xi

and Maureen for your love and support. I also want to thank my aunts and uncles for their

constant support and pride; I have learned so much from you and love you all dearly. I offer a

special thank you to Uncle Albert and Benny for good conversations on my visits to California.

To my DC family, Ruben, Alba, Frances, and Javi, thank you for your prayers, love and support.

Our trips to DC and your visits to State College made this a much more pleasant journey.

Additionally, I would like to thank my dissertation committee. I feel blessed to have been

advised by such an amazingly talented group of scholars and people. You inspired me and pushed

me to make this work more relevant. Dr. Leticia Oseguera, thank you for the constructive

criticism and the supportive friendship. I don’t know if I would have made it through without

your help. Dr. Kimberly Griffin, your mentorship and guidance has meant the world to me. Dr.

Sue Rankin and Dr. Jennifer Van Hook, thank you for your tireless work and pushing me to

consider things I might have not considered, which made this work stronger and more interesting.

I would also like to thank Dr. Don Heller for his guidance and support and Dr. Dorie Evensen,

Dr. Suet-Ling Pong, Dr. David Post, Dr. Robert Reason, Dr. Lisa Lattuca, Dr. Pat Terenzini, Dr.

Roger Geiger, and Dr. Beverly Lindsay for being such wonderful examples of engaged scholars

and excellent teachers.

Finally, I would like to thank my friends for their wonderful support and encouragement.

Dr. Alex Yin, thanks for visiting me at Starbucks and talking me through methodological issues

and basketball. Dr. David Perez, your friendship means the world to me and yes you were “the

reason” I came to Penn State. Gaby and Immanuel thank you for being our family here in State

College, we love you. I special thank you to LA GRASA, soccer on Saturdays and asados made

State College a more enjoyable place. I would also like to thank my cohort for their support and

friendship. I finally like to thank all of my friends at Chapman and U.C. Santa Cruz for your

support and friendship.

1

Chapter 1

Introduction

The participation of undocumented students in higher education has become a highly

politicized and emotionally charged issue. With nearly 2.5 million undocumented children in U.S.

schools and an estimated 65,000 undocumented high school graduates exiting school each year,

the need to examine postsecondary access for this population of students is of extreme importance

(Horwedel, 2006; Passel, 2003). Persons with higher levels of education pay more in taxes, spend

more, and are more likely to invest in the U.S. economy (Gonzales, 2009). In addition to making

greater economic contributions, populations with higher levels of education are also more likely

to find satisfaction in their work, are more open to new ideas, have a clear sense of self, have

higher voting rates, and participate more frequently in civic and community service (Baum & Ma,

2007; Pascarella & Terenzini, 1991, 2005). Providing a pathway to postsecondary education and

understanding the forces that influence this process benefit individuals, the communities in which

they reside, and the nation. Understanding the educational pathways that students follow, the

resources with which they engage within schools and through their social networks, and the

influence of state policy on postsecondary access will provide a more nuanced understanding for

advocates, educators, and policymakers on how these forces operate in providing postsecondary

access to undocumented students.

This chapter provides information on the size and scope of the undocumented population

in the United States, followed by an overview of the new diaspora/destinations of immigrant

populations and the challenges for undocumented populations within these communities.

2

Additionally, legal issues specific to schooling for undocumented populations are explored. The

chapter concludes with an overview of the theoretical grounding for the study.

Although the U.S. Supreme Court’s ruling in Plyler v. Doe (1982) supported access to

education for undocumented immigrants through high school, it did not extend this right to higher

education. Currently, federal laws do not prohibit undocumented students from enrolling in public

colleges and universities, but there is no consensus at the state level, with states falling along a

continuum in which legislation is being adopted at either end of the spectrum. The absence of

uniform state policies for undocumented students has led institutions to implement policies that

vary greatly by mission and sector (Burkhardt et al., 2011). The lack of state and institutional

consistency regarding postsecondary access adds a layer of complexity to the already confusing

process of enrolling in postsecondary education and is likely to influence negatively the

postsecondary enrollment of undocumented students. Despite a confusing process, an estimated

9,678 undocumented students succeeded in enrolling in postsecondary institutions in California

and Texas (Strayhorn, 2006; Student financial report: Annual report on AB 540 tuition

exemptions, 2006-07 academic year, 2008), the two states that account for over 40% of the

undocumented population (Hoefer, Rytina, & Baker, 2010). In the study described here, the issue

of postsecondary access was framed within the literature on college choice. Specifically, Perna’s

(2006) Conceptual Model on College Choice was adapted to examine how social capital, school

context, and the state political context for undocumented students influence the postsecondary

access of undocumented students.

Size and Scope of the Undocumented Population

According to The Triennial Comprehensive Report on Immigration (1999), five million

undocumented immigrants resided in the United States in 1998. With an estimated 515,000

3

undocumented persons entering the United States each year thereafter, the undocumented

population quickly swelled to an estimated 11.9 million persons by 2008 (Hoefer et al., 2010;

Passel & Cohn, 2009). In a period of 10 years the undocumented immigrant population more than

doubled. It is estimated that 56-59% of the undocumented population (11.9 million) is of Mexican

descent. An additional 22% (2.5 million) of the undocumented population originates from Central

and South America. The second largest racial/ethnic group within the undocumented population

in the United States is from Asian countries, including the Philippines (2%), India (2%), Korea

(2%), and China (1%) (Mexican Immigrants in the United States, 2008, 2009).

Given that immigrants from Mexico, Central and South America, the Philippines, India,

Korea, and China account for 88-90% of the undocumented population in the United States

(Passel, 2006), this study focused on the postsecondary access of students originating from

developing countries in Latin America and Asia, including Mexico, the Philippines, China, and

Korea.

In 2004, the undocumented immigrant population in the United States was composed of

5.4 million adult males (49%) and 3.9 million adult females (35%) (Passel, 2005). Although the

undocumented population is primarily 18 years of age or older, the demographic composition has

changed in recent years. The new growth involved children—13% of undocumented immigrants

are under the age of 18 (Hoefer, Rytina, & Baker, 2009). No longer are undocumented

immigrants the only ones journeying to the United States—families have become part of this

population as the movement between countries has become more costly and treacherous (Massey,

Durand, & Malone, 2002). Given the shift in demographics, educational opportunities have

emerged as a particular concern for this population.

The educational attainment of undocumented immigrants reveals severe disparities.

Forty-seven percent of undocumented immigrants have less than a high school education

compared to 8% of U.S.-born residents (Hoefer et al., 2009). While less educated than the U.S.

4

population, compelling evidence shows that immigrants, undocumented populations included,

have more education than those who choose not leave their country (Feliciano, 2005a). The

educational attainment of undocumented immigrants is significantly lower than that of the native

U.S. population; Feliciano (2005a) argued that stratification systems in U.S. education

disadvantages immigrant youth as they internalize their place within the educational structure.

Within this context, education is not viewed as a means of social mobility but of class

reproduction (Feliciano, 2005a).

One example of class reproduction is the negative effect of high school dropouts.

Research reveals that high school dropouts are 3.5 times more likely to commit a crime (Monrad,

2007). Financially, dropouts contribute $60,000 less in taxes over their lifetime than graduates,

meaning that a minimal increase (5%) in the graduation rate of male students would have an

impact of $7.7 billion annually through reduced crime-related expenditures and increased

individual earnings (Monrad, 2007). The cost to society, the impact on federal and state

governments, and the increased likelihood of negative social outcomes are important factors in

ensuring that this population is afforded educational opportunities that ensure they does not

become a permanent underclass.

Undocumented school-age children are a growing proportion of the students enrolled in

K-12 education. It is estimated that undocumented students make up 1.5% of all children enrolled

in pre-kindergarten (Pre-K) to 5th grade (Capps et al., 2005). The percentage of undocumented

students in the higher grades (6th-12

th), is slightly higher, representing 2.8% of all enrolled

students (Capps et al., 2005). Of those persisting through high school it is estimated that 65,000

undocumented students graduate every year (Passel, 2005). This figure is eclipsed by the 49% of

undocumented students who never graduate (Passel, 2005).

For the population of undocumented students persisting to graduation academic

performance may not be the reason they do not enter postsecondary education. In fact, Mehta and

5

Ali (2003) provided evidence of undocumented students’ high school academic performance

similar to that of legal immigrants. Oropesa and Landale (2009) argued that undocumented

students may disinvest from education to enter the labor market. With motivation to work as the

main force behind migration, undocumented students may view legal status as constricting

educational opportunities and opt to enter the workforce (Oropesa & Landale, 2009). School

provides basic skills needed to enter the workforce, once these skills are acquired undocumented

immigrants seem to be opting out of U.S. education (Oropesa & Landale, 2009). With limited

opportunities for further educational advancement, undocumented immigrants may see limited

advantages in graduating from high school and may be choosing to enter the job market directly

in place of a high school diploma.

onversely, undocumented immigrants’ educational outcomes may not be a result of a

desire to enter the workforce but a reflection of the quality of schools attended by these students.

Research on Hispanic and Asian immigrants has found that this population is more likely to

attend schools that have large student populations, poor academic performance, less experienced

teachers, unsupportive school environments, poor funding, and poor safety records (Crosnoe,

2005b; Han, 2008; Zhou & Bankston, 1994). As such, it could be argued that educational

outcomes are a reflection of school context and not immigrant workforce goals or perceived

future educational benefits.

New Diaspora: New Destinations and Challenges for Undocumented Populations

Destinations of undocumented immigrants have remained fairly predictable over the last

20 years; 51% of undocumented immigrants are concentrated in five states: Arizona, California,

Florida, New York, and Texas (Hoefer et al., 2009). Immigrants have, however, been gravitating

toward new destinations as the state context and opportunities at these traditional destinations

6

have become less receptive. Part of the reason for the change in destinations may be due in part to

changes in established social networks (Massey et al., 2002; Massey & García España, 1987).

These social networks may be directing immigrants to new resources and opportunities that exist

within these new diaspora communities. The questions of how these communities are engaging

social networks and how or if they are using social capital to gain information about school-

related resources and ultimately postsecondary access are less understood and were explored in

this study.

Lee’s (1966) theory of migration provides a framework to understand the new diaspora

of undocumented immigrants. Lee (1966) stated, “Every act of migration involves an origin, a

destination, and an intervening set of obstacles” (p. 49). Origins and destinations each have push

and pull factors, which act to attract and deter migrants. As the pull factors at destinations change

(increased unemployment, negative receptive of immigrants, high cost of living, etc.) immigrants

select alternate destinations that seem more attractive. Massey and colleagues (2002) argued that

the passage of the Immigration Reform and Control Act (ICRA; 1986) and Illegal Immigration

Reform and Immigrant Responsibility Act of 1996 (IIRIRA) had a series of unintended

consequences, including establishing a market for counterfeit documents and causing changes in

the migration patterns of immigrants. The militarization of certain sections of the U.S. – Mexico

border changed points of entry, “pushing” undocumented immigrants to move to low-migration

states. In effect, as the obstacles to enter traditional destinations increased, undocumented

immigrants seemed to be choosing to settle in nontraditional destinations.

The change in destination choice can be clearly seen through Table 1-1, which highlights

increases in immigrant students’ educational enrollments in Pre-K through 5th

grade. These

changes in the migration patterns of immigrants have an enduring effect on the educational

attainment of immigrants. The children of immigrants who progress through the educational

pipeline seeking postsecondary opportunities will be confronted by financial and political realities

7

within their state. Students who are fortunate enough to find themselves in states with in-state

resident tuition policies (ISRT) or state grants to undocumented students will not be faced with

the significant obstacles found in states such as Arizona or Alabama, where the policy around

undocumented immigrants does not provide a pathway to postsecondary education. An

understanding of the factors that affect college access of undocumented immigrants can lead to

policies and practices that encourage postsecondary education for all immigrant populations.

Table 1-1. Increases in Immigrant Student Enrollment Pre-K to 5th Grade by State

State Enrollment Increase

(1990‒2000)

Nevada 206%

North Carolina 153%

Georgia 148%

Nebraska 125%

Arkansas 109%

Arizona 103%

South Dakota 96%

Oregon 96%

Colorado 94%

Iowa 94%

Note. Adapted from Capps et al. (2005).

Despite the growth in immigrant populations in these nontraditional areas, reception for

undocumented immigrants and immigrants in new diaspora states generally has not been positive.

In 2007, 240 state laws relating to immigration were enacted, nearly three times more than in

2006 (84) (Hegen, 2008). There were over 1,500 immigrant/immigration-related bills, on themes

from education to human trafficking, introduced in 2007 (Hegen, 2008). While not all this

legislation was negative, the number of laws and resolutions illustrates the public consciousness

of this issue. In April 2010, Arizona adopted Senate Bill 1070 (SB 1070), and since its passage,

8

Alabama has adopted similar legislation, creating a negative climate for undocumented

immigrants and their children. As undocumented students proceed through K-12 education in

these areas the importance of providing students with a clear pathway through education is

essential. A failure to provide undocumented students with clear pathways may lead to an

increase of dropouts and ultimately a population that is relegated to a permanent underclass.

Legal Issues Surrounding Schooling

As the destinations of undocumented populations change, the legal challenges to

undocumented students’ rights to postsecondary education and scrutiny of K-12 student

enrollment quickly followed. A class-action lawsuit, filed in the United States District Court for

the Eastern District of Texas (1977), became the foremost case concerning undocumented

children’s access to K-12 education. The case was eventually argued before the Supreme Court in

Plyler vs. Doe (1982). In 1975, the Legislature in Texas revised its education laws to withhold

state funds to local school districts that were educating children who were not “legally admitted”

to the United States. The revision also gave local school districts the authority to deny children

not “legally admitted” to the United States enrollment in public schools. The plaintiffs argued

against the exclusion of their children from public schools in the Tyler Independent School

District.

The Supreme Court opinion determined that public schools were prohibited from denying

immigrant students access to a public education. The decision provided undocumented students

the same right to a free public education as U.S. citizens, permanent residents, and other legal

residents. In addition, public schools and their personnel were prohibited from adopting policies

or actions that would deny students access based on immigration status (Brennan, 1982). The

majority argument stated that undocumented students, “Already disadvantaged as a result of

9

poverty, lack of English-speaking ability, and undeniable racial prejudices…, will become

permanently locked into the lowest socio-economic class” (Brennan, 1982, p. 457). Plyler vs. Doe

(1982) provided access to a free K-12 public education, but this right did not extend to higher

education, creating in effect an educational ceiling for undocumented students.

In 1985 the first challenge to how undocumented students were treated in higher

education in California was heard in Leticia A. v. Board of Regents of the University of

California. The suit challenged the practice of the University of California and California State

University systems in charging undocumented students and applicants out-of-state tuition without

consideration of alternate indicators of California residency (Roos, 1997). The ruling, in favor of

the plaintiff, allowed undocumented students to pay in-state tuition at public institutions as well

as gain eligibility for Cal Grants (Abrego, 2008; Perez Huber & Malagon, 2007). Shortly

thereafter, this ruling was challenged in Regents of California v. Superior Court (Bradford),

(1990). The Superior Court ruled that newly enrolled undocumented students be classified as non-

residents for tuition purposes (Perez Huber & Malagon, 2007; Roos, 1997). This ruling, in

essence, priced undocumented students out of higher education, requiring them to pay out-of-state

tuition and eliminating their eligibility for state-based educational grants (Cal Grants) (Perez

Huber, Malagon, & Solorzano, 2009).

These cases have created uncertain postsecondary educational prospects for

undocumented students. Currently, federal laws do not prohibit undocumented students from

enrolling at public colleges and universities, so states have been left with the responsibility of

crafting policy to determine the postsecondary access granted to undocumented immigrants.

States can be categorized into one of five postsecondary education policy contexts for

undocumented students: 1) undocumented students can gain resident tuition status and eligibility

for state aid, 2) undocumented students can be eligible for resident tuition, 3) undocumented

students are denied access to postsecondary education, 4) resident tuition policies have been

10

considered but not formalized as policy, and 5) no policy is in place. Today, 11 states allow

undocumented students to pay in-state tuition (Morse & Birnbach, 2012; Olivas, 2008; Passel &

Cohn, 2009).

Table 1-2 provides some contextual information on the states and the percentage of

population estimated to be undocumented. The vast majority of states had no clear policy on

postsecondary access by undocumented students. In 2006, legislation was passed that barred

undocumented students from paying in-state tuition (Olivas, 2008); by this point, students in the

sample were two years out of high school and any changes in policy were unlikely to affect

postsecondary enrollment decisions. The vagueness in the policy context creates unclear

educational pathways for undocumented students, which can have negative effects on their course

taking, persistence in high school, and access to higher education.

Table 1-2. In-State Resident Tuition Policies by State

State

Legislation

Year

Estimated

undocumented

population in the

State

% of pop.

Texas H.B. 1403 2001 1,450,000 6.0%

California A.B. 549 2001 2,700,000 7.3%

Utah H.S. 144 2002 110,000 4.1%

New York S.B. 7784 2002 925,000 4.8%

Washington H.B. 1079 2003 180,000 2.7%

Oklahoma S.B. 596 2003 (Rescinded 2008) 55,000 1.5%

Illinois H.B. 60 2003 450,000 3.6%

Kansas K.S.A. 76‒731A 2004 70,000 2.5%

New Mexico S.B. 582 2005 80,000 4.0%

Nebraska L.B. 239 2006 45,000 2.7%

Wisconsin A75 2009 (Rescinded 2011) 85,000 1.6%

Maryland S.B. 167 2011 250,000 4.7%

Rhode Island Residency

Policy S-50

2011 30,000 2.8%

11

Note. Adapted from Morse & Birnbach, 2012; Olivas, 2008; Passel & Cohn, 2009.

a. The current study evaluated students who were surveyed as part of the Educational

Longitudinal Survey in 2001-2002 (10th grade). During the time these students would have

been in high school (2001-2004), eight states had ISRT policies in place

For many undocumented students the only hope for legislation that would offer a path

toward legalization is the federal American Dream Act, H. R. 1751, 111th Congress, (2009) or

comprehensive immigration reform. If passed, the federal American Dream Act would allow

undocumented students who were brought to the United States as children and graduated from a

U.S. high school to obtain legal permanent status if they enroll in college or enlist in the military

(Gonzales, 2009). Students who meet these requirements will be eligible to apply for conditional

legal permanent status. Conditional legal status provides an adjustment in status, allowing

undocumented students to gain legal status and making them eligible for limited benefits. This

would be conditional for six years—students who complete at least two years toward a four-year

degree, graduate from a two-year college, or serve at least two years in the U.S. military would be

eligible to apply for a change in status to permanent residency (Gonzales, 2009).

Proponents of the federal American Dream Act argue that undocumented students should

not be punished for the sins of their parents (Olivas, 2009). Many undocumented students came to

the United States as young children, and the limitations of their educational opportunities are

viewed as restrictive and against the “American” ideal of hard work and achievement. In addition,

proponents argue that these students embody an investment and the loss of this resource

represents a greater expense, while students granted citizenship would be able to work legally and

pay taxes (Song, 2003). Lastly, proponents argue that many undocumented students have lived in

the United States for most of their lives and are de facto citizens (Editorial: The dream of

education, 2003).

The federal American Dream Act faces opposition in Congress, however. Opponents are

equally passionate in their resistance to this legislation, arguing that undocumented immigrants

12

are a drain on limited resources (Schwartz & Stiefel, 2004). In addition, some feel that the federal

American Dream Act would create incentives for undocumented immigrants to come to the

United States, effectively rewarding people who entered the country illegally (American

Immigration Lawyers Association Issue Papers, 2003). Others argue that undocumented

immigrants crowd natives out of institutions, taking limited seats in classrooms (Borjas, 2004;

Song, 2003).

The federal American Dream Act and its proponents face rigorous resistance to its

passage. Estimates suggest that passage of the federal American Dream Act would provide

360,000 undocumented students with a legal means to work and an opportunity to attend college

and an additional 715,000 currently enrolled K-12 students with a reason to finish high school

(Gonzales, 2009). The rationale for the decision in Plyler v. Doe (1982) was to prevent the

formation of a permanent underclass. With 49% of undocumented students dropping out of high

school (Passel, 2005) and 40% of undocumented children living below federal poverty standards

(Gonzales, 2009), it seems that access to a K-12 education has not provided undocumented

students with the social mobility necessary to avoid being a part of the U.S. permanent

underclass. Additional structures and opportunities are needed to provide undocumented students

with the possibility to escape from their position of disadvantage.

Our understanding of the educational choices of undocumented students is in its earliest

stages; the bulk of studies on this population of students have been conducted on small samples

and within a very limited context (institution/state specific). The available literature reveals that

undocumented status limits aspirations and views of social mobility (Abrego, 2006, 2008;

Menjivar, 2008), but undocumented populations view higher education as a means to achieve

upward socioeconomic mobility, a route to professional employment, and a legalizing function

(Martinez-Calderon, 2009). Among the factors influencing the postsecondary enrollment

decisions of undocumented students are state of residence, eligibility for state grants, and ISRT

13

policies (Flores, 2010). Findings of this study add significantly to the body of literature on

postsecondary access by using data from a nationally representative high school sample to

examine the undocumented population, the role of social capital, school context, and the effect of

state policy context on postsecondary access for undocumented students.

Theoretical Grounding

The exploration of the contextual factors surrounding postsecondary access by

undocumented students was grounded in the literature on college choice, which provided a means

to examine the context in which undocumented students are deciding whether to enroll at a

postsecondary institution. This study draws heavily from the college choice literature. Although

postsecondary access is conceptually different form college choice, this literature has previously

been used to examine the decision to enroll in postsecondary education (Coy-Ogan, 2009;

Cuellar, Chung, & Lucido, 2012). In fact, Hossler and Gallagher (1987) termed the first stage of

their college choice model “predisposition,” which they defined as the initial stage where students

determine whether or not to pursue postsecondary education. Their research, as well as other

studies, provides evidence of the merits of using college choice literature to examine the factors

operating on the decision to pursue postsecondary education (Coy-Ogan, 2009; Cuellar, Chung,

& Lucido, 2012; Hossler & Gallagher, 1987; Hossler & Stage, 1992).

Two theoretical approaches have largely dominated the college decision-making

process—economic models and status-attainment models (Hossler, Schmit, & Vesper, 1999;

Perna, 2006). Economic models of college decision making view students as rational actors.

These actors conduct a careful cost-benefit analysis on the value, cost, and future benefits of

postsecondary education and act accordingly. Status-attainment models focus largely on the

influence of socioeconomic status (SES) on educational and occupational aspirations. Educational

14

aspirations largely influence academic performance, preparation, and achievement. Students who

demonstrate these characteristics are more likely to receive support from key actors1 (parents,

teachers, community members, etc.); this support increases occupational and educational

aspirations. Status attainment models would predict that increased support increases the

educational attainment of students. While these theories are well established, the failure to

consider factors outside the realm of economic or status attainment that exert influence on the

postsecondary enrollment decisions of students and families limits our understanding of a

complex process.

Perna’s (2006) Conceptual Model of College Choice bridges the gap between economic

and status attainment models by dividing the influential factors on college choice into four

contextual layers: habitus; school and community context; higher education context; and social,

economic, and policy context. Perna’s (2006) model moves beyond the singular consideration of

students as rational actors and places them within families, schools, and economic and political

contexts that influence college-going decisions. In this study, Perna’s theory provided a

theoretical grounding for studying postsecondary access for undocumented students, focusing on

areas prevalent in the college choice literature. The framework was reduced based upon emergent

literature on undocumented immigrants, immigrants, and underrepresented populations. This

literature provides evidence of the importance of social capital, school context, and policy context

as factors in postsecondary decisions. This model reduction allowed for the exploration of the

relationship between habitus (including demographic characteristics, socioeconomic status,

academic achievement, and expected benefits and cost of higher education), social capital

(resources engaged to obtain information about postsecondary education), school context (student

perceptions of school safety, adequate facilities and the student educational environment), and

1 Sewell, Haller, and Portes (1969) investigated the influence of “significant others” on the occupational and

educational aspirations of White males in rural Wisconsin, finding that those with “significant others” had higher

aspirations.

15

state policy context (the presence or absence of an ISRT policy) on enrollment in postsecondary

education.

Research Questions

The purpose of this study was to develop an understanding of the contextual factors that

influence postsecondary enrollment for undocumented students. As such, this study sought to

address the following research questions:

Is the postsecondary academic preparation of undocumented students comparable

to their U.S. citizen peers?

How do social capital, school context and policy context operate independently

on postsecondary enrollment for undocumented students and which social capital

and school level factors are the strongest predictors of postsecondary enrollment

for undocumented students?

Does the adapted conceptual model help explain the likelihood of postsecondary

enrollment for undocumented Hispanic and Asian students? Which of the

contextual areas in the adapted model has the greatest influence on postsecondary

enrollment?

16

Chapter 2

Literature Review

The organization of the literature on the postsecondary access of undocumented students

is divided into the contextual factors that are examined in this study: habitus and social capital,

school context, and policy context. Since the literature on undocumented immigrants is emergent,

literature on immigrants and underrepresented populations serve as a guide for understanding the

factors shaping postsecondary access for undocumented students. The research questions in this

dissertation are focused on the postsecondary enrollment decisions of undocumented students,

and the available literature on college choice provides the theoretical grounding to examine the

context and factors that influence their postsecondary decisions. This discussion is followed by

literature on social capital, school context, and policy context.

Review of Literature on College Choice

Models of the college-going decision process can be divided into two theoretical

approaches: economic models and status-attainment models (Hossler et al., 1999; Perna, 2006).

Economic models assume that students are rational actors seeking to maximize utility and make

decisions based on a cost-benefit analysis (lifetime benefits – expected costs) (Becker & Tomes,

1986). As such, students consider both direct (tuition, fees, books, etc.) and indirect costs

(opportunity costs), using this information to maximize benefits and minimize costs. Economic

models focus on the decision-making process and how students use available information (both

economic and institutional) to select a college (Hossler, Braxton, & Coopersmith, 1989).

17

Status-attainment models focus on the relationship between different variables and how

these variables interact and influence students’ college decision-making process. Socialization

processes become the focus of status-attainment models, examining factors such as family

structure, peer relationships, social networks, school context, etc. and how these influences affect

the college-choice process. The focus of these models is how socioeconomic status affects

educational and occupational decision making and how the decision to pursue postsecondary

education leads to status attainment (Sewell, Haller, & Portes, 1969).

Two college-choice models have largely shaped our understanding of the factors that

influence student college selection process: Hossler and Gallagher’s (1987) college-choice model

and Perna’s (2006) conceptual model of college student choice. Hossler and Gallagher proposed

that college choice consisted of three phases: predisposition, search, and choice. Throughout

these stages various key influences shape students’ decisions (Hossler et al., 1999). Additional

studies on college choice have identified a variety of influences including students’ friends and

peers; school counselors; parents; and institutional features such as financial aid, academic

ability, and school reputation (Heller, 1997; Hossler et al., 1989; Manski & Wise, 1983;

McDonough, 1997; Zemsky & Oedel, 1983). Hossler and Gallagher provided a sequential

understanding of the stages involved in the college-choice process, and Perna (2006) built upon

this understanding, adding the importance and complexity of the contextual factors on college-

choice.

Perna’s (2006) conceptual model proposed that there are four layers that influence

college decisions: individual habitus, school and community context, higher education context,

social economic and policy context. Perna’s (2006) model contextualized the college choice

process by recognizing the differential effect that demographic characteristics, cultural and social

capital, school and community influences, and broader state and local context play in college-

going decisions. As previously stated, this model was used to guide this study because of the

18

comprehensive examination of the inputs that influence postsecondary access. This approach is

very similar to how Stark and Bloom (1985) articulate migration decisions. They stressed that

decisions to migrate are collective household strategies used to minimize risk. Families consider

migration as a broad household strategy that is not exclusive to the individual. Rather, it is viewed

as beneficial or harmful to the family unit (Stark & Bloom, 1985). Similarly, I would expect that

the parents of undocumented students view the decision to enroll in postsecondary education as a

broad household strategy to minimize risk and increase opportunity for the family. As such, the

decision to enroll in postsecondary education would not be a linear but complex and influenced

directly by school and social networks and indirectly by state policy around undocumented

students.

The essence of Perna’s (2006) conceptual model reflects our traditional understanding of

college choice. In the initial phase student’s predisposition, academic preparation, academic

achievement, and availability of financial aid and parental income shape students college options.

The second phase is a traditional cost-benefit model with students acting as rational actors,

weighing benefits and expenses as a part of the college-choice process.

The first layer of Perna’s (2006) conceptual model draws from the sociological approach

to college choice. Perna draws from Bourdieu and Wacquant (1992) and Lin (2001) to develop

her definition of habitus, stressing that it is “an individual’s internalized system of thoughts,

beliefs, and perceptions that are acquired from the immediate environment” (Perna, 2006, p. 113).

This layer consists of seven distinct elements: demographic characteristics, cultural capital, social

capital, demand for higher education, supply of resources, and expected benefits and costs.

Demographic characteristics are strictly defined as gender and race, which is important since

access to institutional resources may be limited based on race/ethnicity and gender (Dika &

Singh, 2002). The next two components (cultural capital and social capital) are conceptualized as

educational credentials and personal and professional knowledge and networks utilized to access

19

resources (Perna, 2006). Within Perna’s framework cultural capital is specifically tied to cultural

knowledge and the value of college attainment. Social capital is defined as social networks and

relationships and how these are mobilized to access information or resources (Perna, 2006). The

use of social capital to access higher education was explored in this study. Specifically, the

sources of information undocumented students access in their search for information on

postsecondary education. Demand for higher education and supply of resources, in Perna’s

model, are a component of habitus but operate more directly on expected benefits and costs and

college choice. Perna defined demand for higher education as students’ academic preparation and

achievement and supply of resources as family income and financial aid. The final component of

the first layer of Perna’s model is expected benefits and expected costs. Expected benefits are the

monetary and non-monetary benefits a student expects to receive from college and expected costs

are the costs associated with college and the earnings relinquished to go to college.

The second layer of Perna’s (2006) model incorporates the effect of schools and

communities on college choice. Perna drew from McDonough’s (1997) conceptualization of

organizational habitus which views schools as social structures that can operate in different

directions for students. School agents can be critical in providing or denying access to academic

opportunities and resources that can be influential in the college choice process (Perna, 2006).

Three constructs constitute this level: availability of resources, types of resources, and structural

supports and barriers (Perna, 2006). For this study, the focus was on school context. As stated

previously, schools function as the primary socializing agent for newly arrived immigrants and

the interaction between school context and postsecondary access were explored for

undocumented immigrants.

The third layer of the model is the higher education context, which Perna (2006) defined

as “the role higher education institutions play in shaping college choice” (p. 118). Specifically,

higher education institutions operate in three dimensions: marketing and recruitment, location,

20

and institutional characteristics. For this study the effect of higher education context was not

examined. As this study is focused not on college choice but on postsecondary enrollment, the

role that higher education institutions play in college choice was less important than the role of

social capital, school context and policy context on postsecondary attainment.

The fourth and final layer of Perna’s (2006) model is the social, economic, and policy

context. This layer acknowledges the effect on college choice of demographic changes, economic

conditions, and public policy (Perna, 2006). This study looked singularly at the effect of the

policy context for undocumented students on postsecondary access and how policy context

interacts with school resources and social capital. Figure 2.1 provides an illustration of Perna’s

full conceptual model of college choice with the interactions between each of the four levels.

21

Figure 2-1. Perna’s (2006) Conceptual Model of College Choice.

Perna’s (2006) Conceptual Model of College Student Choice broadens the factors that

influence the choice process considerably, dividing the influential factors into four contextual

layers: habitus; school and community context; higher education context; and social, economic,

and policy context. These layers have a symbiotic relationship, each inhabiting the context of the

former. That is, higher education context exists within the context of the social, economic, and

policy context of the state. The adapted conceptual model (Figure 2-2) for this study highlights

the effect of habitus, social capital, school context, and policy context to examine the

postsecondary enrollment decisions of undocumented students.

22

The decision to reduce the model was made for several reasons. The emergent literature

on undocumented immigrants, immigrants, and underrepresented populations (Chan, 2010;

Gonzales, 2011; Pérez, 2010; Perez, Espinoza, Ramos, Coronado, & Cortes, 2009) was used to

identify factors that influence postsecondary access. Additionally, the literature on migration

theories(Stark & Bloom, 1985) supports the role of social capital and policy context as factors in

migration decisions, which corroborates the selection of the layers of Perna’s (2006) theory. The

literature on the school experiences of immigrants and underrepresented students (Crosnoe,

2005b, Han, 2008; Hao & Pong, 2008; Peguro, 2009) also brings attention to the effects of

schools on student outcomes as a factor on postsecondary access that has traditionally been

excluded from examinations of postsecondary access. Finally, the sample size (Hispanic: n=196,

Asian: n=208) made model and variable reduction necessary.

In the adapted model (see Figure 2-2), habitus has been reduced to demographic

characteristics, expected costs, and expected benefits. Race and ethnicity were removed since the

study examined Hispanic/Latino and Asian participants separately. Social capital—specifically,

from whom undocumented students are accessing postsecondary information—was examined as

a separate layer. School and community context has been reduced to examine secondary schools

context. Higher education context has been eliminated from the framework, and the final layer

being examined is state policy context. For this study the different policy contexts that

undocumented students face were an important distinction. Additionally, postsecondary access

has been moved outside habitus since for undocumented students each of these contextual layers

may have a direct impact have on the decision of whether to pursue postsecondary education. In

Perna’s (2006) model, the decision to go to college is made, and each of these layers influences

college choice, while the adapted conceptual model proposed that each of these layers influences

whether a student opts to pursue postsecondary education.

23

Figure 2-2. Del Pilar Adapted Conceptual Model .

Review of Literature on Habitus and Social Capital

The first layer of the adapted conceptual model is built upon Bourdieu and Wacquant

(1992) and Lin’s (2001) definition of habitus. Habitus represents how a person views the world

and their place in the world (Bourdieu, 1997; Reay 2004). In Perna’s (2006) model social capital

is incorporated as a component of habitus; in the adapted conceptual model they are examined

separately to isolate how they are associated with postsecondary enrollment. Habitus influences

the choices people make, predisposing them to certain decisions and actions based on behaviors

they perceive as appropriate based on their family history or those who share their same class

standing (Bourdieu, 1997; Reay, 2004). Decisions are not based upon what is viewed as rational

analyses but on what is viewed as a reasonable choice (Griffin, Del Pilar, McIntosh, & Griffin,

2012; McDonough, 1997). Bordieu (1997) viewed actions as tied to personal history, which

24

includes race and class. As these factors interact they shape the perception of available or

appropriate choices (Horvat, 2003). Horvat (2003) argued that the context within which

individuals find themselves will govern not only what is possible but what is appropriate. Thus,

students’ postsecondary attainment would be constrained not only by what postsecondary options

are available but what postsecondary options are or are not possible. The inclusion of habitus in

the model is central because it allows for the consideration of how race, class, and immigration

status can influence undocumented students view of what postsecondary plans are not only

possible but what postsecondary plans are appropriate.

Research on social capital provides evidence supporting the effect of social capital on the

attainment of human capital (Coleman, 1988; Portes & Hao, 2004; Portes & Wilson, 1976;

Sewell et al., 1969). The effect of social capital is not limited to postsecondary access, but the

social capital of institutional agents has been found to influence the type of institutions that

students attend (McDonough, 1997). Recent scholarship has added to our understanding of the

types of social capital and how students are accessing social capital in college choice. This

literature identifies factors very similar to those found in traditional models such as parents, peers,

siblings, external college access/preparatory programs, community-based organizations, and

cultural schools (Ceja, 2004; Gándara, 1995; Gibson, Gándara, & Koyama, 2004; Gonzalez,

Stoner & Jovel, 2003; Kimura-Walsh, Yamamura, Griffin, & Allen, 2009; Pérez & McDonough,

2008; Post, 1990; Zhou, 1997b, 2008; Zhou & Bankston, 1994; Zhou & Li, 2003). Ceja (2004)

conducted a qualitative study with 20 Chicanas at a large urban high school in California and

provided evidence in support of the role of social capital in the postsecondary choice process, but

the origins of postsecondary information are very different than traditional models (Coleman,

1988; McDonough, 1997). Ceja found that his participants had to rely on siblings, schools, and

community agencies as primary sources of information. These findings were supported by the

work of Kimura-Walsh et al. (2009), whose study at a metropolitan high school in Southern

25

California found that while parents are supportive of their students’ educational endeavors, their

lack of experience limits their engagement with the postsecondary choice process. Given the

absence of parental involvement, school counselors, teachers, and external college preparatory

organizations become more important sources of information about postsecondary education. The

qualitative study of 20 Latinas in Southern California by Gonzalez et al. (2003) supported these

findings, highlighting the role siblings, teachers, counselors and external college

access/preparatory programs play as a source of social capital. Interestingly, Gonzalez et al.

(2003) argued that the earlier students begin to accumulate or are exposed to this capital, the

greater the benefit.

In a precursor to these works, Post (1990) found evidence of the inaccurate information

with which Latino students operate during their postsecondary choice process. Post’s quantitative

study, based at a high school in Southern California, found that Chicano students with parents

who were Spanish speakers were more likely to overestimate the cost and underestimate the

benefit of postsecondary education. Students may need to rely on siblings and other sources of

information because of their Spanish-speaking parents’ lower levels of education (10th-grade

average) and less engagement in postsecondary education (Ceja, 2004; Kimura-Walsh et al.,

2009).

The influence of peers as a source of information in the educational attainment of

Hispanic youth is established in the literature (Gándara, 1995; Gibson, Gándara & Koyama,

2004), while the role peers play on postsecondary choice process was less understood. Pérez and

McDonough (2008) began to fill the gap in the literature with a qualitative study on college

choice that included 106 participants (54 Latina and 52 Latino) across three Southern California

high schools. Their study was more comprehensive than other studies on Latino college choice as

it included not only students but parents and counselors in an effort to fully understand the choice

process. Their findings confirmed the findings of previous studies (Ceja, 2004; Post, 1990) on the

26

role of parents in the search process but extended the research finding that Latina/o students rely

on older friends, family members, and peers who have already navigated postsecondary education

(Pérez & McDonough, 2008).

The literature on the use of social capital provides evidence of Asian cultural

infrastructures that encourage and facilitate academic achievement. Institutions (churches,

families, non-profit organizations, and language and culture schools) provide a means of support

and information sharing that encourages and creates structures that facilitate academic

achievement (Portes & Zhou, 1993; Zhou, 1997b, 2008; Zhou & Bankston, 1994; Zhou & Kim,

2006; Zhou & Li, 2003).

In a paper on the role of social capital and the adaptation of second-generation

Vietnamese youth, Zhou and Bankston (1994) used census data, newspaper reports, interviews,

and a questionnaire of the Vietnamese student population to examine how cultural ties serve as a

form of social capital leading to schools and postsecondary success. Zhou and Bankston found

that within the Vietnamese community the preservation of ethnic traditions serves as a form of

social capital, allowing families to receive support and assistance from ethnic organizations, both

religious and social. Zhou and Bankston argued that the group social capital is more important

that human capital for the success of immigrant youth. The group social capital serves as a

protective infrastructure triangulating community, ethnic, and religious organizations to reach

immigrant youth and to provide a consistent message of educational achievement.

Similar adaptation strategies have been employed by Chinese immigrants. Ethnic

organizations within this community form a network that develops shared obligations, social

supports, and social controls that shield members from assimilating values and practices viewed

as negative within the community (Zhou, 1997a). In a qualitative study of the role of community-

based organizations in Chinatown (New York) to provide social capital that eases the adaptation

of Chinese immigrant children, Zhou (1997b) documented the changing role of organizations

27

from primarily social and economic to cultural and educational. These organizations have served

as valuable sources of social capital and networking for parents and a place where traditional

values and norms of immigrant parents can be reinforced. Additionally, these organizations shift

from primarily cultural maintenance to educational reinforcement.

Zhou (2008) furthered our understanding of the role of these institutions on the

maintenance and reinforcement of cultural and educational expectations. Zhou conducted an in-

depth examination on the role of nonprofit and for-profit ethnic institutions that operate as

mechanisms of supplemental instruction for the Chinese immigrant community in Los Angeles.

Zhou argued that informal social settings and cultural heritage serve as ethnic armor for the

Chinese immigrant community that establishes a sense of collective dignity. The Chinese

community in this study utilized economic organizations, sociocultural institutions, and

interpersonal networks to facilitate educational achievement. Many Chinese language schools, in

addition to the maintenance of culture and language, offered academic tutoring, standardized test

preparation, math and science drills, and skill training. Parent involvement was expected, and at

many schools a parallel curriculum was offered to parents on real estate, financial management,

investment, Advanced Placement course selection, financial aid, and standardized test

preparation.

In addition to Chinese schools, for-profit academic “cram” schools, enrichment

programs, and intellectual development programs are popular among Chinese immigrants. Zhou

(2008) argued that the existence of these structures serves not only as a means to improved

academic performance but a social support network and a form of social capital for Chinese

parents that provides resources for navigating U.S. schools. Students also benefit from these

institutions, forming peer networks that can be beneficial to academic success. While these

institutions may explain part of the success of the Chinese community, success does not come

without a cost. Parents exert tremendous pressure on students to achieve parental dreams,

28

providing students with a motivation to escape parental control. The pressure placed on these

youth can lead to depression, running away, and in the most extreme cases, suicide.

The role of Chinese language schools has changed from one of cultural and linguistic

maintenance to structural organizations with the primary goal of increasing educational

performance in U.S. schools and aiding students in gaining admission to prestigious colleges and

universities (Zhou & Li, 2003). Chinese schools developed out of dissatisfaction with U.S. public

schools, with Chinese parents viewing these institutions as supplementing the curricular holes of

U.S. education critical to their children’s ultimate educational success (Zhou & Li, 2003). This

body of research is of relevance to this study as Asian immigrants may be engaging similar

sources of social capital but within a very formalized context. While Hispanic students may be

drawing from the social capital of peers, siblings, and parents, it is not within the context of a

cultural school, cram school, or nonprofit organization that is designed to improve and encourage

educational achievement. As such, we would expect for Asian undocumented students to draw

social capital from these formalized networks to a much greater extent than their Latino peers.

Research on social capital (Coleman, 1988; McDonough, 1998) has established clear

connections between information sources and postsecondary enrollment. Of relevance to this

study are the types of and access to social capital from which undocumented students are drawing

information and the effect this process may have on postsecondary access. This study adds to our

understanding on the role of social capital on the postsecondary access of undocumented students

and how habitus, social capital, school context, and policy context influence postsecondary

access.

Coleman (1988) defined social capital as social structures that facilitate action which,

were they not present, would not be possible. Coleman viewed social capital as dependent on

three elements: trustworthiness of the social environment, the extent of the obligation held, and

information channels. Two of the concepts are relevant to the use and sources of social capital for

29

undocumented students: trustworthiness and information channels. Trustworthiness refers

specifically to the confidence that the provider of the capital has in the recipient that the exchange

will be repaid, and information channels require that the information source be knowledgeable in

the area in which information is being sought. The necessity of trustworthiness in this exchange

may be problematic for undocumented students given the need to disclose undocumented status

to institutional agents (teachers, counselors, etc.). In addition, undocumented students may

question their ability to “repay” the exchange given their uncertain status. This perceived

imbalance may lead to reliance on social capital that is viewed as less threatening (e.g., peers,

siblings, parents).

The reliance on streams of social capital that are viewed as more trustworthy may lead to

information channels that are grounded in unrealistic, incomplete, or misinformed resources.

Coleman (1988) described this as closure. Social structures can be opened or closed. Open social

structures have no connection with actor seeking the social capital (e.g., teacher to student), and

as such their investment may be considerably less than in a closed social structure (e.g., parent to

child) in which the purveyor and receiver of information have some accountability to one another.

Closed structures are more reliant on trust and accordingly are dependent that the information

being received is reliable. Ultimately, Coleman (1988) posited that social capital leads to human

capital, but the level to which this occurs depends on the level of parents’ human capital and the

influence of social capital outside of the family.

The emergent body of research on the postsecondary choice process of undocumented

students reveals factors in their decision making and the sources and utilization of social capital

in postsecondary access/choice process (Chan, 2010; Gonzales, 2011; Pérez, 2010; Perez,

Espinoza, Ramos, Coronado, & Cortes, 2009). Pérez (2010) produced a study that specifically

examined factors that influence the postsecondary choice process of undocumented students,

providing a glimpse of the factors and the use of social capital in the college choice process.

30

Pérez’s study was a mixed-methods study of 14 undocumented Latino students—seven male and

seven female—half of whom had enrolled at a California community college and half of whom

had enrolled at a four-year public institution. Pérez identified three factors that influence the

postsecondary choice process of undocumented students: outreach as opportunity,

cost/affordability, and social networks. Undocumented students in the study were more likely to

attend an institution if they perceived the environment as welcoming for undocumented students.

Undocumented students in Pérez’s study were also sensitive to cost, including tuition, fees,

supplies, and transportation. Finally, the effect of social capital and social networks had a

significant effect on the college choice of her participants. Pérez found that students primarily

relied on information from siblings who were or had been enrolled at an institution. Their siblings

could guide them to advisors, professors, or administrators who had been supportive in the past.

In addition, peer networks played a major role in the process, with students relying on older peers

who had navigated postsecondary education.

In a study on academic resilience, Perez, Espinoza, Ramos, Coronado, and Cortes (2009)

found that undocumented students with high levels of personal and environmental protective

factors (social networks and activities), experienced higher levels of academic success compared

to similar students, despite risk factors (employment, low parent education, etc.). The study used

convenience and snowball sampling to identify 110 undocumented students and employed

regression and cluster analysis for data analysis. While this study focused on college students, the

findings are of relevance for the current study. Resilient and protected students had parents who

highly valued education, were involved in extracurricular activities, and valued volunteerism—

traits and practices that can become social capital resources for students, providing access to

resources and information that can lead to the achievement of human capital.

Finally, in a study of 150 undocumented persons in Southern California, Gonzales (2011)

examined life transitions and identified three stages for undocumented youth: discovery (16‒18),

31

learning to be illegal (18‒24) and coping (25‒29). Gonzales collected life histories from his

participants, gathering data on their school and post-school experiences. Of his 150 participants,

73 dropped out of or completed high school and 77 had attended, were attending, or had

completed college. The variability of their postsecondary experiences provides valuable insight

into the factors on the role of social capital on college access. Gonzales’ findings provide very

interesting insight into the stark differences between college goers and dropouts/high school

graduates regarding access to information. Participants in Gonzales’ study who did not pursue

postsecondary education did not feel that teachers and counselors were supportive of their

academic endeavors. As a result of their undocumented status, his participants were not willing to

confide in teachers and counselors to access information and resources critical for college access.

In contrast, college goers, who were more likely to be in advanced curriculums or in college

tracks, felt that teachers, counselors, and mentors were supportive and were able to disclose their

status to confidants who directed them to opportunities and resources. College goers, due to a

different investment in students perceived to be more talented academically, were able to access

social capital that lead to increased human capital. The same investment, opportunities, and social

capital were not available for dropouts/high school graduates.

Some evidence of this trend was also provided in a study that explored the experiences of

Asian undocumented students. In a qualitative study on undocumented Asian students, Chan

(2010) found that undocumented status is associated with a high level of shame among Asian

participants. As a result of the stigma associated with undocumented status, her participants were

less likely to self-disclose undocumented status to counselors or teachers. Moreover, as being

undocumented is so highly associated with the Latino population, Asian student were able to find

solace in a cloak of invisibility. These students did not have to be concerned with questions about

their documented status, but this invisibility also precluded them from receiving the necessary

and correct advice regarding choosing college (Chan, 2010). Gonzales’ (2011) findings on the

32

necessary resources for undocumented students to make a successful transition to postsecondary

education highlights the need for, “sufficient money to pay for school, family permission to delay

or minimize work, reliable transportation and external guidance and assistance” (p. 613). If

undocumented students are not disclosing their legal status to key sources for postsecondary

education they may not be receiving appropriate postsecondary guidance. For undocumented

students to navigate the confusing and complicated application process successfully, the

availability of social capital is critical for postsecondary access.

This body of research provides some insight into the importance of social capital in the

postsecondary access of undocumented students. Parents of undocumented students are

supportive of the educational advancement of their children, but their lack of experience with

U.S. higher education limits their ability to guide students. As such, undocumented students rely

on the social capital of siblings, family members, and peers who have or are currently enrolled in

postsecondary education. Teachers and counselors can also play an important role in this process,

but the research reveals that counselors make differential investments in students based on their

academic performance (Gonzales, 2011). Additionally, students’ fear of disclosing their

undocumented status decreases their willingness to seek educational assistance. This study will

add to the emergent body of literature on the role of social capital in postsecondary access by

examining different sources of information on undocumented students and the impact these have

on postsecondary access.

The use of sources of social capital to attain human capital is made clear in the research

on the effect of “significant others” (SOI) on the occupational and educational attainment of

populations (Sewell et al., 1969; Portes & Hao, 2004; Portes & Wilson, 1976). In their original

study, Sewell, Haller, and Portes (1969) examined educational and occupational aspirations of

White males from rural Wisconsin. They found that the influence of significant others affects

educational and occupational aspirations, which in turn affect the level of educational attainment.

33

Their analysis found that SOI has direct effects on levels of educational and occupational

aspiration.

In 1976, Portes and Wilson tested the validity of SOI on African American students with

similar results. They found that in the absence of an SOI, African Americans needed some

formalized structure to increase students’ attainment. Finally, and of significance to the current

study, Portes and Hao (2004) tested the applicability of SOI theory to immigrant populations.

Similar to previous studies, Portes and Hao found that given the lack of social and human capital

of immigrant populations, SOI provide access to the necessary resources to increase occupational

and educational attainment. In sum, SOI has direct effects on levels of educational and

occupational aspiration and, ultimately, educational attainment.

The effect of individual social capital on postsecondary attainment is firmly established

in the literature, but the examination of schools as institutions with social capital was unexplored

in the college choice literature. In one of the first comprehensive examinations of college choice,

McDonough (1997) argued that institutions not only have social capital but that this social capital

can and does influence college choice. She examined the college choice process of four White

females and their best friends in California. McDonough found that her participants accessed two

different types of social capital in their college search: individual and institutional. Students from

higher socioeconomic backgrounds accessed social capital and resources not available to students

from lower social classes, leveraging college services, family social capital, and school resources

in their college search. The social capital of families and friends become resources in connecting

students to institutional agents who could provide information and guide students through the

college application process. These findings are important for this study given the limited social

capital of immigrant populations. Immigrants new to communities may be dependent upon

information from family resources that may be incorrect or unreliable (Post, 1990).

Expanding on the concept of social capital (Coleman, 1988) and how distinct populations

34

utilize social capital in their postsecondary search process (McDonough, 1997), the literature

specific to Latina/o and Asian students’ use of social capital was reviewed for this study. The

literature on postsecondary choice confirms that Hispanic students are accessing social capital

and networks in their process (Ceja, 2004; Gándara, 1995; Gibson, Gándara, & Koyama, 2004;

Kim & Gasman, 2011; Pérez & McDonough, 2008; Post, 1990; Teranishi, Ceja, Antonio, Allen,

& McDonough, 2004). Specifically, Hispanic students are more likely to use peers and family as

a resource in the postsecondary choice process (Pérez & McDonough, 2008; Post, 1990). Pérez

and McDonough (2008) established that Hispanic students consult with a wide circle of influence

in making postsecondary choice including parents, school counselors, siblings, other school staff,

and peers. Their extended family also provides an important source of postsecondary information

for Latina/o students who tend to rely more heavily on these resources for postsecondary

information than do other populations of students. The information that students are receiving

may not always be from the most reliable sources; often the information Hispanics students

receive is inaccurate or may be based on a small number of factors (i.e., close to home, brother

went there, cousin recommended, etc.) (Pérez & McDonough, 2004; Post, 1990).

Research on how Asian populations engage social capital in their postsecondary choice

process is largely absent in the literature. Teranishi et al. (2004) argued that Asians have been

excluded from the literature and investigation of postsecondary choice because this group has

long been considered highly successful educationally, that their success has limited the continued

study and investigation on the factors important to this group for postsecondary access and

choice. In one of the few available articles on the postsecondary choice process of Asian

Americans, Kim and Gasman (2011) conducted 14 in-depth interviews with first- and second-

generation Asian Americans college students at an elite, private Northeastern U.S. university to

attempt to understand the factors influential to the choice process. Findings of this study were

consistent with previous studies in that the presence of high parental expectations, distance from

35

home, perceived quality of institution, and employment opportunities post-graduation were all

important factors to participants. Cost of attendance was not a deciding factor for students in Kim

and Gasman’s study. Parents were willing to do whatever it took to assure that their children were

getting a “good” education. Asian students in this study depended on a variety of different

sources of information including parents, older siblings, friends, and school personnel. Students

also independently conducted research using Internet sources and college search engines to secure

information about colleges.

Kim and Gasman’s (2011) findings contradict previous findings by Kim (2004), who

conducted a quantitative analysis of the 1997 Freshman Survey sponsored by the Cooperative

Institutional Research Program (CIRP) at the University of California, Los Angeles. Kim’s study

analyzed the impact of financial aid on students’ college choice with a particular focus on racial

differences. Compared to Latino and African American students, whose college choices were not

influenced by financial aid, Asian American students were strongly influenced by having loans or

a combination of grants and loans when choosing to attend their first-choice colleges. The

distinction between the studies may be the examination of immigrant populations. Specifically,

Kim and Gasman examined the experience of first generation students whose parents may be less

price sensitive if the quality of education is considered superior to alternative institutions.

In the final comprehensive study reviewed on the college choice of Asian students

Teranishi and associates (2004), like Kim (2004), employed data from the 1997 Freshman

Survey. They conducted a quantitative study which disaggregated the Asian Pacific Islander

population to examine the effects that socioeconomic differences, background characteristics, and

group differences play in the college choice process, specifically regarding highly selective

colleges. Their sample included 18,106 students who categorized themselves as Asian Pacific

Islander. Teranishi et al. found that Chinese and Korean American students were more likely to

choose to attend a highly selective college compared to Filipino and Japanese students. Teranishi

36

et al. also found that parental income and parental education are significant predictors of

attendance at a selective college but for students in this study, being a U.S. citizen or permanent

resident is negatively associated with enrolling in a highly selective college. This decrease in

enrollment for U.S. citizens may be due to the price associated with most highly selective

colleges or a result of the social capital these students engage in the process. Teranishi et al.

found that teachers and counselors had no effect on enrollment at a highly selective college, but

distance from home, financial aid concerns, and advice from friends were more likely to lead to

enrollment at a less selective institution. Finally, attendance at an SAT or academic preparation

course was more likely to lead to enrollment at a less selective college.

Immigrant Social Capital Resources

In addition to the sources of capital reviewed above, immigrant students and the children

of immigrants are able to draw upon a number of additional social capital resources that

encourage postsecondary education, including having immigrant parents and their parenting style

(Callahan, 2008; Kao, 1999; Kao & Tienda, 1995; Keller & Harker Tillman, 2008; Portes &

Fernandez-Kelley, 2008; Portes & Rumbaut, 1996, 2001; Portes & Zhou, 1993; Rumbaut, 1997;

Tillman, Guo, & Harris, 2006), extended family (Hagen, MacMillan, & Wheaton, 1996; Sanders

& Nee, 1996; Valenzuela & Dornbusch, 1994; Zhou & Bankston, 1994), and community

organizations (Portes & Hao, 2004; Portes & Zhou, 1993; Zhou & Kim, 2006).

The effect of having immigrant parents has been empirically tested and has been found to

have direct effects on college attendance. Immigrant parents maintain clear and consistently high

educational and occupational expectations (Tillman et al., 2006; Kao, 1999; Kao & Tienda, 1995;

Keller & Harker Tillman, 2008; Portes & Rumbaut, 1996, 2001; Portes & Zhou, 1993; Rumbaut,

1997; Tillman et al., 2006). Immigrant students translate the high educational expectations of

37

their parents into a normative priority. These priorities translate into values, which motivate

students and may even create a fear of failure. Many immigrant students often worry about

disappointing parents who uprooted themselves from their home country, family, and support

systems to provide opportunities for the children. Indeed, this perception serves as a driving force

behind the educational accomplishment of immigrant students (Louie, 2004).

Part of the success of immigrant parents may be their parenting style. Immigrant students

tend to come from homes where stern parental figures exert strict authority and discipline (Portes

& Fernandez-Kelley, 2008). Parents are clear about their position in the household and have clear

rules about expectations, including rules about grades (Callahan, 2008). While parenting style is

not a form of social capital, this structure and clear educational expectations encourage

educational performance and aspirations and may influence the sources of social capital students

are engaging. Coupled with positive selection and the fear of failure, immigrant students’ family

experience creates a climate of achievement.

In addition to the support of parents, immigrants typically maintain strong family ties

(Hagen et al. 1996; Sanders & Nee, 1996; Valenzuela & Dornbusch, 1994). The advantage of

these connections is the provision of social networks for a population who may lack the social

capital necessary to be successful in U.S. education. Besides providing extended social networks,

strong family ties also aid in the maintenance of cultural traditions and pride (Portes &

Fernandez-Kelley, 2008). Extended family may also serve as a source of discipline since often the

family unites to discipline the child, increasing the influence parents have over children and

slowing the adoption of native practices (Zhou & Bankston, 1994).

Limited research on the role of community organizations in facilitating access to

academic and collegiate resources has found that they have largely benefitted Asian and Cuban

immigrants (Portes & Hao, 2004; Portes & Zhou, 1993; Zhou & Kim, 2006). The literature shows

that Asians favor the cultural infrastructures that encourage and facilitate academic achievement.

38

Institutions (e.g., churches, families, language and culture schools) provide a means of support

and information sharing that encourages and creates structures that facilitate academic

achievement (Portes & Zhou, 1993; Zhou & Kim, 2006). Similarly, Cuban immigrants developed

social institutions to support educational achievement—a phenomenon that has not been

witnessed among other Hispanic groups (Portes & Zhou, 2001). While the literature on

immigrants reveals that other Hispanic populations value education, have parents and families

that support education, and in a limited way engage in organizations that support postsecondary

access, they do not appear to achieve the same return on this investment. The absence of sources

of social capital, specifically around postsecondary attainment, and social institutions that act in

support of undocumented students’ educational aspirations may factor into low postsecondary

access. Additionally, low levels of education, limited English skills, and a significant difference

in schooling may also limit the effectiveness of the social capital these groups engage in their

postsecondary decision making (Chan, 2010).

This group of studies adds to our understanding of how Asian students are engaging

social capital in their postsecondary search process. The literature on postsecondary choice

provides evidence of the use of social capital (Kim & Gasman, 2011; Teranishi et al., 2004) in

and during the postsecondary choice process, but the sources and the effect of these sources are

different than those found in the literature reviewed for Hispanic students. Specifically, Asian

students are engaged in more independent research using Internet sources and college search

engines to secure information about colleges (Kim & Gasman, 2011). Additionally, Asian

students have been found to access to and engage cultural schools, cram schools, and external

institutions as a source of social capital (Zhou, 1997b, 2008; Zhou & Bankston, 1994; Zhou &

Kim, 2006; Zhou & Li, 2003).

There were also some similarities in the sources of information used to in the

postsecondary search process, with both groups engaging parents, sibling, friends, and school

39

personnel for information (Kim & Gasman, 2011; Teranishi et al., 2004), but the effect that these

resources have may not always operate similarly. Since Asian students are significantly more

likely to go to college (Portes & Zhou, 2001), the resources they engage in their search process

may provide greater knowledge of entrance and cost requirements that the resources Hispanic

students are engaging. Chan’s (2010) study may confound the use of social capital since these

participants associated undocumented status with shame and were less likely to disclose

undocumented status. Consequently, the information they may receive may be irrelevant for their

postsecondary choice process.

The use of extended family on postsecondary choice provides an important consideration

in the examination of how undocumented students may be using social capital. The specific focus

here was on the impact of family, friends, institutional agents, and external organizations and

institutions on postsecondary choice and how these influences operate within the distinct layers

the adapted conceptual model.

Review of Literature on School Context and Postsecondary Education

Schools are a major socializing agent for immigrant students. Thus, how immigrant

students and their families engage with schools is critical to their ultimate educational outcomes

(Crosnoe, 2005a; Hao & Pong, 2008). Oliverez (2007) makes the case that the biggest obstacle

facing undocumented and immigrant students is the way in which these populations experience

schooling. The schools that low-income Latino and immigrant students attend are

overwhelmingly poorly funded, lack resources and curricular rigor, and provide students with

weak support and direction (Oliverez, 2007). Institutional differences lead to a differential in

resources, which limits choice and constrains mobility (McDonough, 1997).

40

The importance of school context in college choice is made clear by McDonough (1997),

who borrowed from Bourdieu’s (1984) concept of habitus and applied it to schools. McDonough

argued that socioeconomic status is linked to organizational habitus of high schools. High

schools, based on organizational habitus, shape students’ perceptions of appropriate

postsecondary choices and signal to students and parents the appropriate level of occupational

and educational aspiration. Within schools, there is an expectation that parents will be engaged in

the education of their children. This belief may also be dictated by the socioeconomic status and

level of education of parents (Lareau, 1987).

The effect of schools is one of the central components of this study. As such, the context

of the school and how this interacts with postsecondary choice and social capital are vital for this

study. To operationalize school context, Oakes’ (2003) theoretical framework on the critical

conditions for equity and diversity in college access will serve as a framework. Oakes posited that

schools that are successful in preparing its students for college share seven critical conditions: 1)

safe and adequate facilities, 2) college-going school culture, 3) qualified teachers, 4) intensive

academic and social supports, 5) rigorous academic curriculum, 6) opportunities to develop a

multicultural college-going identity, and 7) family-neighborhood-school connections around

college-going. To more closely match Perna’s (2006) conceptualization of school context

(availability of resources, types of resources, and structural supports and barriers) two of Oakes’

(2003) critical conditions are used to conceptualize school context: safe and adequate facilities

and college-going culture.

Safe and Adequate Facilities

Oakes (2003) defined a safe and adequate school as one that is free from “overcrowding,

violence, unsafe and unsanitary conditions and other features of school climate that diminish

41

achievement and access to college” (p. 2). The available literature on the experience of

immigrants in schools paints a very distressing picture of the conditions and challenges

immigrants face as they attempt to engage and navigate schools (Crosnoe, 2005b, Han, 2008; Hao

& Pong, 2008; Peguero, 2009). In an examination of the elementary school context that children

from Mexican immigrant families confronted, Crosnoe (2005b) found that they are more likely to

attend problematic elementary schools. These students contend with larger schools that are poorly

funded and have high risk factors. Han’s (2008) research supported the findings of Crosnoe

(2005b), showing that Hispanic children of immigrants are more likely to attend schools with

poor safety and high concentrations of students in poverty. Moreover, immigrant children from

Hispanic backgrounds seem to be more sensitive to school-level factors than children of Asian

backgrounds (Hans, 2008; Hao & Pong, 2008).

These factors seem to link closely with Peguro’s (2009) research, which found a

relationship between victimization and immigrant generation for Hispanic and Asians. Peguro

found that first- and second-generation Asian immigrants are more likely to be victimized at

school, and that Hispanic and Asian first-generation immigrant students are likely to be afraid

while at school. The available literature on the safety and adequacy of school facilities has

revealed that Hispanic immigrants are more likely to attend schools where the environment is less

than secure and where they are likely to experience fear (Crosnoe, 2008b; Han, 2008; Hao &

Pong, 2008; Peguro, 2009). This literature is of relevance to this study because of the importance

and significance of school context in Perna’s (2006) framework and the link between school

context and safety and postsecondary access.

42

College-Going Culture

The habitus of schools serves as a signal to students and parents on the appropriateness or

even possibility of postsecondary education (McDonough, 1997). Oakes (2003) defined this trend

as college-going culture—specifically the environment that teachers, administrators, parents, and

students establish to foster achievement and postsecondary preparation. When we examine the

available literature on the condition of schools that immigrant students attend, we find that these

schools employ teachers with less experience; have higher percentages of minority student

enrollment, which is traditionally tied to academic risk factors; and have low community support

(Crosnoe, 2008b). Additional research (Crosnoe, 2008b; Orfield, Kucsera, & Siegel-Hawley

(2012) has affirmed these findings. Specifically, children of immigrants are more likely to attend

schools with high concentrations of student in poverty, poor academic performance, and

unsupportive school environments.

The limited literature (Orfield, Kucsera & Siegel-Hawley 2012; Oliverez, 2007) on the

college-going culture of schools reveals severe challenges for immigrants. Academic risks, low

community support, and unsupportive school environments are likely to limit the postsecondary

access of undocumented immigrants. This situation may not hold true for Asian students, as Zhou

and Bankston (1994) found that despite a school context in which a majority of the student

dropped out or had low academic success, students of Vietnamese background were able to

achieve a level of academic success superior to that of their native counterparts. As such, college-

going context may be a bigger factor for populations that do not have external organizations in

support of academic performance.

43

Review of Literature on State Policy Context and Postsecondary Education

The final layer to be examined is the role of policy context on the postsecondary access

of undocumented students. The role of state policies on the postsecondary access of

undocumented students and the effect of this on postsecondary enrollment has been examined

in several state-specific studies (Abrego, 2008; Flores & Chapa, 2009; Vasquez Heilig,

Rodriguez, & Somers, 2011). Much of the current research on the effect of policy has focused on

the impact of state and institutional fiscal policies on postsecondary access. The body of literature

has found that changes in fiscal policies influence students’ decisions to borrow money and attend

public institutions (Heller, 1997; 1999; Kipp, 2002; St. John, 1991; St. John & Noell, 1989).

While this literature highlights students’ sensitivity to fiscal policies, it does little to differentiate

how underrepresented populations perceive and react to state and institutional policies. A related

body of literature highlights the sensitivity that racial/ethnic minorities and low-income students

exhibit toward fiscal policies, but less is known about how these populations react to access

related policies (Perna, Steele, Woda, & Hibbert, 2005; St. John & Noell, 1989; Thompson &

Zumeta, 2001).

While the literature on the effect of fiscal policies is well established, the body of

literature on the effect of affirmative action bans on the enrollment of underrepresented students

provides some context on how distinct populations react to policy shifts and how these responses

affect enrollment behaviors. State-level policy changes that address access reveal

underrepresented students’ sensitivity to policy shifts. Specifically, policy changes that are

perceived as working against a population have had negative effects on the postsecondary

enrollment of underrepresented students. Dickson (2006) found that following the dissolution of

affirmative action in Texas, Hispanic students’ applications decreased by 1.6%, and African

American students’ applications decreased by 2.1%. Brown and Hirschman (2006) examined the

44

effect of the end of affirmative action on Washington State University and found that applications

from Asian/Pacific Islanders, American Indians, Hispanics, and Blacks all decreased the year

following the passage of the law. Underrepresented students perceive these state-level policies as

a negative factor in the postsecondary choice process and apply to affected institutions at lower

rates.

Several studies that examined the national impact of bans on the use of affirmative action

in admissions found that these policy shifts decrease underrepresented student enrollment at

selective universities but has no effect on non-selective institutions (Hicklin, 2007; Hinrichs,

2010; Howell, 2010). While the research on the effect of affirmative action provides evidence

pointing in opposite directions, there is a clear effect on the application patterns of

underrepresented students. These studies demonstrate the way in which policy can influence the

application and enrollment decisions of underrepresented students. Similarly, we would expect

undocumented students to exhibit sensitivity to state and federal policies around education.

The growing body of research on undocumented students provides insight into how

undocumented students interpret the role of federal immigration policy (Menjivar, 2008),

educational advancement opportunities (Abrego, 2008; Martinez-Calderon, 2009), opportunities

for mobility (Abrego, 2008) and the effect of ISRT policies on perceptions of status and access to

college (Abrego, 2008; Flores, 2010).

The effect of federal immigration policy on educational participation and perceptions of

social mobility and educational opportunity is clear. Undocumented students and parents are

hopeful that education will provide a pathway to legitimization (Martinez-Calderon, 2009). As

they begin to make the transition to adulthood, however, the hope for legality begins to fade

(Gonzales, 2011) into a more bleak reality (Abrego, 2008; Menjivar, 2008).

Martinez-Calderon (2009) found that undocumented students from rural Mexico viewed

higher education as a means to achieve upward socioeconomic mobility, a route to professional

45

employment, and a legalizing function. Participants in her study were hopeful that education

would provide a pathway toward legalization and legitimizing their contribution to U.S. society.

Although Martinez-Calderon’s study and others on immigrant populations (Portes & Fernandez-

Kelly, 2008; Portes & Rumbaut, 1996, 2001) placed a very high value on the importance of

education, research on the academic ability of undocumented immigrants remains elusive. Studies

estimate that one-sixth to one-fifth of each undocumented student cohort drops out of high

school. While approximately 65,000 undocumented immigrant high school graduates per year, a

conservative estimate is that 11,000 to 13,000 undocumented immigrant students drop out of high

school and never make it to graduation (Passel, 2003). But these figures may be a reflection of

these students’ legal status and not academic ability.

In a report supporting an in-state tuition policy for undocumented immigrants in Illinois,

Mehta & Ali (2003) found that 64% of undocumented high school students in the state would be

qualified to enter college. This finding indicates that undocumented students are as prepared for

college work as native students. An examination of mean grade point averages (GPA) and ACT

scores among undocumented students revealed that undocumented students’ achievement was not

significantly different from that of legal immigrants, second-generation immigrants, or native-

born students (Mehta & Ali, 2003). Taken together, these findings provide some support for the

positive selection and educational achievement among undocumented immigrants (Feliciano,

2005a). The provision of structural support and enhanced educational opportunity for

undocumented students in Illinois illustrates the potential of undocumented students to be as

successful as native students (Mehta & Ali, 2003).

As undocumented students begin the transition to adulthood and realize the costs and

requirements to continue their education beyond high school, they tend to view their status and

postsecondary education as an uncertain and a distant reality, despite their assimilation toward

American ideals and the belief of an open system of social mobility (Abego, 2008; Menjivar,

46

2008). Undocumented students begin to understand postsecondary education as something

reserved for those with documented status, privilege, and wealth (Abrego, 2008). The limitation

in these studies is the bound nature and small sample size of the undocumented student

population. Without a nationally representative sample which examines the effect of different

policy contexts on postsecondary access, our understanding of the effect on academic

performance and school context will remain limited.

The policy lever that has been shown to have a positive effect on postsecondary access is

states’ adoption of ISRT policies (Abrego, 2008; Flores & Chapa, 2009; Vasquez Heilig,

Rodriguez, & Somers, 2011). In a study on undocumented students in California, Abrego (2008)

found that the ISRT policy provided undocumented students with a level of validation and an

avenue for educational attainment. Undocumented students in California embraced the legal

designation “AB 540” (Assembly Bill 540), which offered a reprieve from the stress of being

associated with illegal status. Vasquez Heilig, Rodriguez, and Somers (2011) provided some

quantitative evidence of English learners’ (EL) reactivity to a change in state policy in Texas.

Their study examined data from the Texas Higher Education Coordinating Board, focusing on

changes enrollments of EL students at over 50 public colleges and universities prior to and after

adoption of the HB 78, 80 (R), (2007) Texas Top Ten Percent Plan (TxTTPP) and the HB 1403,

77 (R) (2001) Texas Dream Act. The TxTTPP was adopted in reaction to Hopwood v Texas

(1996) and mandated that all Texas high school students graduating in the top 10% of their class

be granted admission to any Texas public college or university. The Texas Dream Act created the

mechanism for undocumented students not only to pay in-state tuition but to be eligible for the

Texas grant. Vasquez Heilig and associates were specifically interested in EL students’ college

choice, persistence, and completion following adoption of the legislation. Their findings revealed

student sensitivity to Texas Dream Act legislation, finding that after passage of the legislation, EL

enrollments at the state flagship institutions increased by 1-5% and enrollment at border

47

institutions increased by 11-17%. This literature reveals the sensitivity that undocumented

students have toward both federal and state policies. Federal policy can serve as a deterrent for

educational attainment, although undocumented students remain hopeful that education will

legitimize their presence in the country the difficulty in attaining legal status may outweigh hopes

for upward mobility.

In addition to the effect of ISRT policies on perceptions of educational opportunity and

political involvement, these policies have a positive effect on the postsecondary enrollment of

undocumented students (Flores, 2010). Using the Current Population Survey (CPS) data and

foreign-born non-citizens as a proxy for undocumented students, Flores (2010) examined

differences in postsecondary enrollment between states with ISRT policies and those without.

Flores’ findings demonstrated the importance of ISRT policies in the college-going decisions of

undocumented students. Undocumented students residing in states with ISRT policies were 1.54

times more likely to enroll in college compared to undocumented students who live in states

without ISRT (Flores, 2010). In addition, females maintained an advantage in college enrollment;

undocumented females were 1.53 times more likely to enroll in college than undocumented

males. The positive effect of in-state tuition polices clearly increases postsecondary attendance

among this population.

Research on the effect of state and federal policies on postsecondary access provides

evidence that students are sensitive to changes in policy even if the policy shift would only have a

limited effect on student outcomes. Students’ perceptions of policy can influence their

postsecondary enrollment decisions and may even serve as a deterrent to continued high school

enrollment. This research provides evidence of the importance of policy on postsecondary access,

and the present study adds to the current understanding on the role of differential policy context

on the postsecondary access of undocumented students.

48

Summary

Literature on undocumented students is in its earliest stages, with the bulk of studies on

this population of students having been conducted on small samples and within very limited

contexts (institution/state specific). An analysis of the available literature reveals that

undocumented status limits aspirations and view of social mobility (Abrego, 2006; Abrego, 2008;

Menjivar, 2008). Despite the challenges the undocumented population faces, it appears be

positively selected and have similar academic performance as legal immigrants (Abrego, 2008;

Feliciano, 2005b; Mehta & Ali, 2003). Among the factors influencing the postsecondary

decisions of undocumented students, state of residence, eligibility for state grants, and in-state

resident tuition (ISRT) policies are important to the postsecondary attainment of undocumented

students (Flores, 2010).

In sum, the body of research on undocumented students reveals that they exhibit similar

attitudes and beliefs as legal immigrants regarding the value of education. In spite of their

aspirations, however, the undocumented label negatively affects the educational aspiration and

attainment of students (Abrego, 2008; Menjivar, 2008). Undocumented students living in states

with ISRT policies are advantaged over students in other policy contexts (Flores, 2010; Flores &

Chapa, 2009), but they still face significant obstacles to persistence (Perez Huber et al., 2009).

Research into the aspirations, attainment, and persistence of undocumented students in

higher education is bereft with challenges. Much of the available research includes legal analyses,

policy briefs, congressional research reports, historical reviews, and philosophical analyses

regarding citizenship (Flores, 2010; Vasquez Heilig et al., 2011). Current research provides little

empirical evidence on educational attainment and outcomes of undocumented populations.

Studies that examine undocumented students face severe challenges to obtaining

institutional approval, and federal guidelines prohibit public K-12 education from asking

49

questions regarding legal status (Strayhorn, 2006). This policy radically limits the certainty and

the reliability of studies, bringing findings into question. In addition, there are no governmental

agencies that directly count the undocumented immigrant population (Passel, 2005). Most recent

studies (Flores, 2010; Flores & Chapa, 2009) have used foreign-born non-citizen as a proxy for

undocumented immigrants, but this measure is problematic given that it includes both

undocumented and legal permanent residents, thereby overestimating the undocumented

population.

Research on undocumented students needs to address several gaps in the both methods

and literature. Methodologically, more empirical studies are essential to strengthen the body of

evidence on undocumented student postsecondary access and choice. Longitudinal-level data is

needed to track the outcomes of this population over time. In addition, better measures are needed

to estimate the undocumented population in the United States. The literature on postsecondary

choice and retention of undocumented students is virtually absent in higher education. Additional

research on the role of family, social networks, and the effect of the state level policy context on

postsecondary choice would strengthen the body of research currently available. This study

begins to fill this gap. In the next chapter, I explore the methods employed in this study.

50

Chapter 3

Methods

To assess the role of social capital, school context and policy context on the

postsecondary access of undocumented students, the following research questions guided the

study.

Is the postsecondary academic preparation of undocumented students comparable to their

U.S. citizen peers?

How do social capital, school context and policy context operate independently on

postsecondary enrollment for undocumented students and which social capital and school

level factors are the strongest predictors of postsecondary enrollment for undocumented

students?

Does the adapted conceptual model help explain the likelihood of postsecondary

enrollment for undocumented Hispanic and Asian students? Which of the contextual

areas in the adapted model has the greatest influence on postsecondary enrollment?

This chapter provides information on the data source used to answer the research

questions posed above, followed by a description of how participants are defined in this study.

This chapter also provides a description of how missing data were handled and the variable

selection method used for inclusion in the study. Finally, I provide a summary of the analytic

methods used to answer the research questions and outline the limitations of the study.

51

Data Source and Sample

Data for this study were drawn from the Educational Longitudinal Study (ELS) 2002-

2006 panel, collected for the National Center for Education Statistics (NCES). The first data

collection for this group began in 2002 and included 14,712 10th graders; responses are weighted

and provide a nationally representative sample of 10th graders. Respondents were re-surveyed in

2004 (their senior year of high school) and again in 2006 (two years after high school completion)

to provide a more complete story for this group. Data from the sampled students included

information concerning a variety of influences on the student including parents, teachers, and

school administrators.

The ELS: 2002 was designed to capture students transition from high school to

postsecondary education, if applicable, and into the workforce. The sophomore cohort was

sampled every two years to collect student educational pathways and outcomes. Additionally,

student demographic information, postsecondary and employment goals, and assessments of

students’ academic ability are captured. In addition to students, parents and teachers were

surveyed. Parents were asked about their aspirations for their students, home background,

educational history, and parental involvement and opinions about schools. Teachers were also

sampled, providing information about students and teacher background. The final data element

included in the design was school-level data. School administrators and librarians were asked to

provide information on: school characteristics, student characteristics, teaching staff

characteristics, school policies and programs, technology, and school governance and climate.

This included a facilities checklist which captured the condition of school buildings and facilities.

Panel weights, provided by ELS, are employed in this study. The application of the panel

weights provides an opportunity to better assess the role of social capital, school context, and

policy to the undocumented student population in the United States. The challenge in generalizing

52

results is the changing policy context and the time-bound nature of the dataset. Any changes in

state policies (specifically, ISRT policies) after 2006, when this group would have been out of

high school for two years, would not be representative of the responses participants provided in

the base-year survey, first, or second follow-up, when the postsecondary decision of students in

the dataset is captured. Recognizing this limitation, this work includes students who participated

in data collection at all three time points. This approach decreases the potential cases to be

considered as just 12,554 of the 14,712 cases participated in all three data collections. One of the

strengths of this dataset is the longitudinal nature of the design, which allows us to trace the

influence of students’ and parents’ attitudes and perceptions surrounding college and provides a

more accurate glimpse of their educational outcome at the end of their high school career.

Missing Data

To account for data missing at random due to item non-response, multiple imputation

using information from the sample distributions of the variables was applied to the dataset

(Rubin, 1987). This procedure replaces missing values with randomly generated responses using

contextually appropriate values (Little & Rubin, 1989). The imputation procedure used for this

analysis was done using STATA software, which employs Imputation by Chained Equations

(ICE). ICE uses two methods to replace missing values for binary or ordinal variables. In cases

where the missing value is a continuous variable, ICE draws imputed values from a posterior

distribution using ordinary least squares regression models, whereas when the missing value is an

ordinal variable, logit models are used to replace missing values (Royston, 2004). While multiple

imputation has been criticized for providing variance estimates that are often lower than the

actual population and confidence intervals that are exceedingly narrow (Nielsen, 2003), this

method is preferable to listwise deletion. Listwise deletion often leads to a loss of statistical

53

power and biased estimates or mean substitution, which preserves cases and decreases the

standard error (Howell, 2007). To preserve sample size and to take advantage of a technique that

uses available data to predict missing values, multiple imputation provides the most reliable

technique to handle missing data.

Data from the baseline sample were imputed by the National Center for Education

Statistics (Ingels, Pratt, Rogers, Siegel, & Stutts, 2005). The variables in the analysis imputed by

NCES include gender, family income, and achievement quartile in mathematics. From the first

follow-up survey the following variables were imputed: parents provided information about

college entrance exams, went to counselor for college entrance information, went to other relative

for college entrance information, went to publication and website for college entrance

information, and went to college representative for college entrance information. By contextual

area the percentage of cases imputed included: habitus with 0 to 2.5%, social capital 0.5 to 2.1%,

school context 0 to 5.2% and policy context at 0%.

Undocumented Sample

A major difficulty in studying the undocumented population in the United States is

identifying this population in any dataset. Despite this limitation, this study will focus on

undocumented students originating from Latin American and Asian countries. It is estimated that

56-59% of the undocumented population (11.9 million) are of Mexican descent, and an additional

22% (2.5 million) come from Central and South America, while the second largest racial/ethnic

group is from Asia, including the Philippines (2%), India (2%), Korea (2%), and China (1%)

(Mexican Immigrants in the United States, 2008, 2009). As such, this study will focus on the

college access of students originating from Mexico, Central America, South America, the

Philippines, India, Korea, and China.

54

To attempt to apply filters that would eliminate cases that were unlikely to be

undocumented students, a decision tree was created to assess the likelihood that a participant was

undocumented. See Figure 3-1 for a graphic representation of the selection process. Similar to

other studies on this population (Flores, 2010; Flores & Chapa, 2009) the closest approximation

to undocumented status is foreign-born non-citizen. This filter may overestimate the outcomes of

undocumented students as the final sample may include permanent residents who are eligible for

financial aid and other benefits for which undocumented students do not qualify.

Figure 3-1. Decision tree to identify undocumented sample.

To establish the final sample, students were sorted on a variety of factors that limited the

sample to those most likely to be undocumented. Father’s and mother’s place of birth were the

first filters applied to the full sample. If the parents’ places of birth were the United States, it was

55

likely that their children were also U.S. citizens or were eligible to apply for a pathway to

citizenship, so they were excluded from consideration. Students whose place of birth was the

United States were U.S. citizens and were excluded from consideration for the undocumented

sample. If parents were foreign-born, the next decision point was the number of years that they

had lived in the United States. If parents had been in the country for more than 20 years, the

students were removed from the undocumented category. The rationale behind this decision was

tied to the last major immigration legislation, the Immigration Reform and Control Act of 1986

(IRCA). IRCA provided amnesty for undocumented aliens who had been in the country since

1982. The 20-year restriction period implemented for this study includes parents who arrived in

the United States during and after the cutoff year of 1982. This group was left in the sample

because arrival during the cutoff year (1982) would not have qualified participants for amnesty

under IRCA.

Father’s level of education was also a consideration for inclusion. Parents with a

bachelor’s degree or higher were excluded from the sample since they may have qualified for a

visa or permanent residency based on specialized skills. Students whose parents had some college

but no degree were left in the sample.

The final variable for consideration was participant ethnicity. Given the current literature

on the undocumented population in the United States, students of Hispanic ethnicity were

included in the study. Students who identified as Cuban, Dominican, and Puerto Rican were

excluded from the study since Cubans are eligible for refugee/asylee status2 which provides

access to resources unavailable to undocumented immigrants, Dominicans account for less than

1% of the undocumented immigrant population (Hoefer et al., 2010), and Puerto Ricans are

2 Cubans are granted certain rights through the 1966 Cuban Adjustment Act.

56

United States citizens.3 Participants that selected item “legitimate skip” because they are not

Hispanic were also excluded.

The second largest group within the undocumented population in the United States is

Asian. According to the Asian American Center for Advancing Justice (2011), there were over 1

million undocumented Asians in the United States, with four countries accounting for the

majority: the Philippines, India, Korea, and China. To attempt to capture this group, students who

listed their race as Filipino, South Asian, Chinese, Korean, or Southeast Asian were included in

the sample. Students of Japanese background were excluded from the study because of their lack

of prevalence in the undocumented population in the United States. Table 3-1 provides the

weighted and unweighted sample sizes for the undocumented Hispanic and Asian samples.

Table 3-1. Sample Size of the Undocumented Hispanic and Asian Groups (Weighted and

Unweighted)

Group

Undocumented

Weighted

Undocumented

Un-weighted

Hispanic 57,200 196

Asian 21,160 208

Passel (2003) estimated that between 65,000 and 80,000 undocumented students graduate

from high school each year. The proxy for the undocumented population yields a sample of

78,360, slightly higher than the lower but below the upper estimate. A comparison to Passel and

Cohn’s (2011) estimates for the seven states with the largest undocumented populations provides

support for the undocumented proxy. Table 3-2 provides a comparison between current estimates

and the undocumented proxy. Of the seven states, the percentage in California, New York, and

Illinois are above Passel and Cohn’s (2011) estimate.

3 The Foraker Act of 1900 granted Puerto Ricans U.S. citizenship.

57

Table 3-2. Sample Comparison of the Undocumented Population Estimate to the Undocumented

Proxy

State Population

Percentage

of Total

Undocumented

Proxy

Percentage

of Total

California 2,550,000 22.77%

26,756 34.14%

Texas 1,650,000 14.73%

9,273 11.83%

Florida 825,000 7.37%

3,743 4.78%

New York 625,000 5.58%

5,311 6.78%

New Jersey 550,000 4.91%

2,746 3.50%

Illinois 525,000 4.69%

6,118 7.81%

Georgia 425,000 3.79% 1,213 1.55%

Note. Undocumented estimates obtained from Passel and Cohn (2011).

Matched Sample

The comparison group for this analysis, the matched sample, was established to assess

the appropriateness of the adapted contextual model to examine postsecondary access for the

undocumented sample. It was important that this group have a similar experience or level of

access in terms of parental education and social capital. The same criteria used to determine

undocumented status were used for the matched sample with two exceptions. First, students in the

matched sample were born in the United States (U.S.-born child to foreign-born parents). Similar

to the undocumented sample, the first filter applied was father’s and mother’s place of birth. If

parents’ place of birth was the United States, they were excluded from consideration. Students

whose place of birth was the United States are U.S. citizens and were left in the sample.

If parents were foreign-born, the next decision point was the number of years they had

lived in the United States. Parents of the undocumented sample were restricted to 20 years to

account for the last major immigration reform which granted a pathway to citizenship, but this

restriction was not applied to the matched sample as the number of students left would not allow

for a meaningful comparison. Like with the undocumented sample, parents with a bachelor’s

58

degree or higher were excluded from the matched sample, and parents with some college but no

degree were included.

The final variable for consideration was ethnicity. To maintain consistency, students of

Hispanic ethnicity were included in the study. Students who identified as Cuban, Dominican, and

Puerto Rican, and those who selected item “legitimate skip” because they were not Hispanic were

excluded. Similarly for the Asian population, those who listed Filipino, South Asian, Chinese,

Korean, or South Asian were included in the matched sample. Table 3-3 provides an overview of

the final sample sizes of the undocumented sample and the matched sample, both weighted and

unweighted.

Table 3-3. Matched and Undocumented Sample Size by Group (Unweighted)

Group

Undocumented

Unweighted

Matched Sample

Unweighted

Native Sample

Unweighted

Hispanic 196 238 746

Asian 208 189 397

Preliminary Data Reduction

The review of literature pointed to a number of factors that influence the postsecondary

outcomes of students. Variables were selected that conceptually fit within the areas identified in

the literature as important factors in postsecondary education or within Perna’s (2006) description

of the contextual areas. The factors that influence postsecondary attainment and choice include

gender, racial/ethnic differences, socioeconomic status, and academic preparation (Hossler et al.

1999; Perna, 2006). While not termed habitus in the college choice literature, McDonough (1997)

59

and Perna both established a connection between these attributes as a component of students lived

reality that influence decision making.

Although Perna (2006) considered social capital a component of habitus, I examined

these variables separately in the analysis. The literature on the role of social capital on

postsecondary attainment points to the role of parents, peers, siblings, external college

access/preparatory programs, community-based organizations, and cultural schools (Ceja, 2004;

Gibson, Gándara, & Koyama, 2004; Gándara, 1995; Gonzalez et al., 2003; Kimura-Walsh et al.,

2009; Pérez & McDonough, 2008; Post, 1990; Zhou, 1997b, 2008; Zhou & Bankston, 1994;

Zhou & Li, 2003) as important sources of information for Hispanic and Asian students. As

previously noted, two components of Oakes’ (2003) critical factors on schools are used to

operationalize school context: safe and adequate school facilities and college-going environment.

Safe and adequate schools was selected because the literature on the school experiences of

Hispanic and Asian immigrant students points to a propensity for these populations to attend

schools where the environment is less than secure and where they are likely to experience fear

(Crosnoe, 2005b; Han, 2008; Hao & Pong, 2008; Peguro, 2009). College-going environment was

selected to capture school level factors found in the literature that point to the importance of an

institutional environments that encourage postsecondary attainment (Bankston, 1994;

McDonough, 1997; Oakes, 2003). The final contextual area is state policy. The body of literature

on state policy points to the influence of fiscal policies (Heller, 1997, 1999; Kipp, 2002; St. John,

1991; St. John & Noell, 1989), access-related policies (Perna et al., 2005; St. John & Noell, 1989;

Thompson & Zumeta, 2001), and more recently to ISRT policies (Flores, 2010; Vasquez Helig et

al., 2011). Using this literature as a guide, 46 single-item variables were identified that

conceptually fit within each of the contextual areas.

To reduce the number of variables, a two-step data reduction procedure was applied to

the variables that fit the theoretical constructs represented in Perna’s (2006) model. The first step

60

was to conduct a logistic regression analysis of the variables that conceptually fit within each of

the categories being examined in the analysis. This step allowed for the identification of variables

that were significant in predicting whether participants enrolled in postsecondary education. The

analyses were conducted on both the undocumented and matched samples by group (Hispanic

and Asian) to determine if there were variables that were important predictors for both groups, or

if there were variables that were of more importance for a single group. Analyses were conducted

by blocks to reduce the variables for the five areas being examined in this study:

1. Any postsecondary enrollment = Habitus

2. Any postsecondary enrollment = Social Capital

3. Any postsecondary enrollment = School Context

4. Any postsecondary enrollment = College-Going Culture

5. Any postsecondary enrollment = State Policy Context

Variables fell into one of three categories: variables that were significant in predicting

postsecondary enrollment for both groups, variables significant for one group, or variables not

significant in predicting postsecondary enrollment. Variables considered for final inclusion in the

analysis were found to be significant in the logistic regression analysis for both groups or were

significant for one of the two groups. The complete list of variables tested can be found in

Appendix A.

This initial analysis allowed for the following reduction of variables from 42 to 18 single-

item variables for the model. Habitus was reduced from eight to five variables, social capital was

reduced from 15 to five variables, and school context was reduced from 10 to four variables. As

state policy context is made up of one variable, no reduction was necessary. One variable was

maintained in the model that was not significant in predicting postsecondary enrollment, learning

61

hindered by lack of space, since conceptually it is an important consideration in Oakes’ (2003)

framework on effective schools.

To avoid issues of collinearity, correlation of independent variables, a second analysis

was conducted on the reduced set of variables selected for the models. This test was conducted

independently on both the undocumented and matched Hispanic and Asian samples. Variables

were examined first by contextual area, and finally all variables in the model were examined for

collinearity. The test for collinearity is a linear regression which yields the variance inflation

factor (VIF) of the independent variables in the model. Each independent variable is tested as the

dependent variable, and the linear regression produces a VIF for each of the independent

variables with the variable tested. Below is an example of how habitus variables were tested.

1. Gender = socioeconomic status, math achievement, education is important to get a

job later, would rather work than go to school.

2. Socioeconomic status = math achievement, education is important to get a job later,

would rather work than go to school, gender.

3. Math achievement = education is important to get a job later, would rather work than

go to school, socioeconomic status, gender.

4. Education is important to get a job later = would rather work than go to school,

socioeconomic status, gender, math achievement.

5. Would rather work than go to school = socioeconomic status, gender, math

achievement, education is important to get a job later.

The same analysis was conducted for each contextual area and on each of the

independent variables to test for collinearity. According to Ott and Longnecker (2001) a VIF of 1

indicates no collinearity, if the VIF reaches levels above 10 the independent variables are highly

correlated (O’Brien, 2007; Ott & Longnecker, 2001). The result of the analysis on both samples

62

yielded no VIF scores above 1.3, indicating that the variables are not correlated. See Appendix B

for a table of the VIF scores.

Measures

Measures for this study were grouped by contextual layer, representing the structure of

the adapted conceptual model. Figure 3-1 provides a representation of the adapted conceptual

model tested and variables included in each of the layers.

Figure 3-2. Variables tested in the adapted conceptual model.

63

The four layers in the adapted conceptual model are habitus, social capital, school

context, and policy context. The following section provides greater description of the independent

variables included in each of the contextual layers.

Dependent Variable

The dependent variable used to measure postsecondary access for undocumented students

was the student’s report of postsecondary enrollment within the two-year period following high

school. Frequencies of postsecondary enrollment for the undocumented groups are included in

Table 3-4. Consideration was given to examining postsecondary enrollment by college type, but

as this study focused specifically on the effect of social capital, school context, and policy context

on postsecondary access and not on college choice, the variable was recoded to include

enrollment in any postsecondary education. The original variable was coded by postsecondary

enrollment: for-profit, two-year or less; not-for-profit, two-year or less; for-profit, four-year; and

not-for-profit, four-year. Any postsecondary enrollment was reduced and coded as 0 for no

postsecondary enrollment and 1 for any postsecondary enrollment.

Table 3-4. Undocumented Student Enrollment by Postsecondary Type (Weighted)

Sector

Undocumented

Hispanic n %

Undocumented

Asian n %

No college 29,678 51.9 5,232 24.7

2 year or less 20,627 36.1 7,371 34.8

4 year institution 6,895 12.1 8,586 40.4

Total 57,200 100 21,160 100

64

Habitus Variables in the Analysis

Habitus is operationalized as the factors that shape how students perceive their place in

the world. This layer of the model includes many traditional control variables, including

socioeconomic status (SES), gender, and a measure of academic achievement. Additionally, this

layer of Perna’s (2006) model includes students’ expected costs and benefits of higher education.

SES was taken from the baseline survey and is a composite generated by NCES of parents’

income, occupational prestige, and education. Student’s gender was coded as 0 for male and 1 for

female. To control for academic achievement, a composite score on a standardized test of math

from the baseline study was used. To capture students’ perception of the benefit of postsecondary

education, a measure of the importance of education for employment from the first follow-up

survey was included, and expected cost was measured with inclusion of student’s desire to work

versus entering postsecondary education as indicated in the first follow-up survey. See Appendix

C for a list of the elements, including the means and standard deviations.

Social Capital Variables in the Analysis

Social capital was operationalized for this study as the resources and information from

which students draw to obtain human capital (Coleman, 1988). Specifically, this study focused on

the sources of information (electronic resources, other relatives, counselors, etc.) and the level to

which parents were engaged and the extent to which students view their parents’ engagement. To

capture the role of social capital, five single items were identified from the student and parent

surveys. From the student survey, the source of postsecondary information was captured through

responses to a question concerning whom the student had consulted for postsecondary

information in the 12th grade: counselors, other relative, college publications and websites, and

65

college representatives. Additionally, from the parent surveys a question asking whether the

parent provided advice about postsecondary plans for college entrance exams was used. These

variables provided a measure of the role of social capital in providing college information to

students. See Appendix C for a list of the elements, including the means and standard deviations.

School Context Variables in the Analysis

The school-level variables attempted to capture the extent to which counselors, students

and parents were engaged in creating a condition for postsecondary access. The selection of

variables used to measure safe and adequate facilities was focused on selection of individual level

variables that captured counselors’ perception of facilities and student perceptions of school

safety. College going environment variables selected were chosen to capture peer educational

achievement around course taking, importance of high school graduation and postsecondary

attainment.

Safe and Adequate School Facilities Operationalized

The first condition that Oakes (2003) identified as critical for postsecondary access is the

safe and adequate school facilities. Immigrant students are more likely to attend problematic

schools (Crosnoe, 2005b), be victimized at school (Peguro, 2009), and attend schools where they

are likely to experience fear (Crosnoe, 2005b; Han, 2008; Hao & Pong, 2008). In an attempt to

measure safe and adequate facilities, four single items from the counselor and student

perspectives were drawn to understand the school context. From the counselor survey, opinion of

space was selected. Additionally, three single items that explored student opinions about school

safety were included to measure this condition: student perceptions of gangs in school, if a

66

student felt safe at school, and if the student got into a fight at school. See Appendix C for the

means and standard deviations for the items included in this measure.

College-Going Culture Operationalized

The variables used in this study to explore the college-going culture served to dissect the

school habitus around postsecondary access. Under this concept, three single items were selected

to represent the perspective of the student and school. The student variables included 10th graders’

opinion of the peer context, including peers’ sense of importance of finishing high school; the

number of 12th graders’ friends going to four-year colleges; and the percent of the student body in

Advanced Placement courses in the 12th grade. See Appendix C for a list of the elements,

including the means and standard deviations.

Policy Context Variables in the Analysis

The climate undocumented students face within states is a consideration of this study. As

previously stated, undocumented students face a variety of state contexts related to postsecondary

access, Table 3-5 provides a sense of the disapora’s experience based on state policy context prior

to 2004. As expected, 69% of the undocumented Hispanic sample and 57.9% of the

undocumented Asian sample are located in six states: California, Florida, Illinois, Texas, New

Jersey and New York. The remainder are disbursed throughout the United States. A complete list

of undocumented students by state is available in Appendix D.

Several options were explored to attempt to create a measure that would capture

differences in state context. The first measure examined the possibility of three distinct policy

contexts facing undocumented students: states with an ISRT, states with no policy, and states

67

with limited or no postsecondary access. The small sample size caused significant data

limitations. Another consideration examined was to look at traditional immigrant states, new

diaspora states, and those states without significant immigrant populations. The n’s using these

categories also present significant limitations. To allow for greater statistical power, the variable

was recoded to capture states with ISRT policies and states with no ISRT policies. States with

ISRT policies (California, Illinois, Kansas, Nebraska, New Mexico, New York, Oklahoma,

Texas, Utah, and Washington) in place prior to 2004 were coded as 1 and all other states were

coded 0. See Appendix C for the mean and standard deviation.

Table 3-5. Undocumented Student Population by State Policy Context (Weighted)

Group

In State

Resident

Tuition Policy Percentage No

Policy Percentage Undocumented

Hispanic 39,492 69.0 17,708 31.0 Undocumented

Asian 12,244 57.9 8,916 42.1

Academic Preparation Variables in the Descriptive Portrait

The academic variables in the descriptive portrait were selected to examine the

postsecondary preparation in high school for the undocumented sample compared to their

respective matched and native counterparts. Variables included in the descriptive portrait are

postsecondary plans, GPA, math courses, Scholastic Aptitude Test (SAT) scores, and

postsecondary enrollment.

68

Analytic Method

Three separate analyses were conducted to examine the undocumented population and

the matched sample: a descriptive comparison and a logistic regression analysis on each

contextual layer. The first step provided a descriptive portrait of the undocumented population.

These descriptive statistics provide a sense of the academic preparation of the undocumented

sample compared to the matched and native samples. The native sample was included to provide

a baseline comparison, contextualizing the academic and postsecondary preparation performance

of the undocumented and matched samples. The descriptive portrait examined the academic and

postsecondary preparation of the Hispanic sample followed by the Asian sample. This analysis

addressed this research question:

Is the postsecondary academic preparation of undocumented students comparable

to their U.S. citizen peers?

A chi-square test of independence was included to determine if there was an association

between a group (i.e., undocumented and matched, undocumented and native) and each of the

postsecondary preparation academic variables examined. A chi-square test of independence was

conducted on the undocumented and matched samples and undocumented and native samples to

determine if the groups’ postsecondary preparation were significantly different. As all the

variables examined are categorical, a chi-square test of independence is the most appropriate

analytic method. For the chi-square test, alpha values of .05 or smaller indicate a significant

difference between the groups. In addition to alpha values, phi coefficient for 2-by-2 tables or

Cramer’s V for tables larger than 2-by-2 are reported below. These statistics are an indicator of

effect size, with a phi coefficient value of .10 indicating a small effect, .30 a medium effect, and

.50 a large effect. The effect size for Cramer’s V accounts for the degrees of freedom and requires

that 1 be subtracted from the row variable (R-1) and the same for the column variable (C-1). Once

69

this step is completed, if R-1 or C-1 is equal to 1, a Cramer’s V of .01 indicates a small effect, .30

a medium effect, and .50 a large effect. For values equal to 2, a Cramer’s V of .07 indicates a

small effect, .21 medium and .35 a large effect. Finally for tables with 3 more categories a

Cramer’s V of .06 indicate a small effect, .17 a medium effect, and .29 a large effect (Cohen,

1988). Table 3-6 provides the sample sizes for the unweighted sample. For the descriptive

analysis the chi-square test of independence was left unweighted.

Table 3-6. Sample Size of Undocumented, Matched and Native Groups (Unweighted)

Group Undocumented n Matched Sample n Native Sample n

Hispanic 196 238 746

Asian 208 189 397

The main dependent variable in this study is a dichotomous variable, enrolled in

postsecondary education or did not enroll in postsecondary education. Consequently, the

proposed analytic strategy is a logistic regression. Logistic regression is an appropriate technique

to test the relationship between a categorical outcome variable and categorical or continuous

predictor variables (Cabrera, 1994; Woldbeck, 1998; Ying, Peng, Kuk, & Ingersoll, 2002).

Specifically, two sequential sets of regressions were used in a stepwise procedure to measure the

relationship between each of the layers the adapted conceptual model and postsecondary

enrollment (St. John, 1991). The first analysis examined the effect of individual conceptual area

(habitus, social capital, school context, and policy context) on postsecondary enrollment for the

undocumented student populations only. This analysis was conducted separately on the Hispanic

and Asian samples. These analyses specifically addressed the following research questions: How

do social capital, school context and policy context operate independently on postsecondary

enrollment for undocumented students and which social capital and school level factors are the

strongest predictors of postsecondary enrollment for undocumented students?

70

1. Any postsecondary enrollment = Habitus

2. Any postsecondary enrollment = Social Capital

3. Any postsecondary enrollment = School Context

4. Any postsecondary enrollment = State Policy Context

The findings are presented for each contextual block for the Hispanic and Asian population (i.e.,

habitus findings for Hispanic undocumented sample followed by habitus findings for Asian

undocumented sample).

The third part of the analysis was conducted by contextual blocks (see Teranishi et al.,

2004). This method provides an opportunity to examine the amount of variance explained with

the addition of each conceptual layer. Odds ratios of each model are compared for the

undocumented Hispanic to the matched Hispanic sample and Asian undocumented to the matched

Asian sample, thus providing a clearer sense of how each of the contextual areas is related to

postsecondary enrollment and if the relationships are unique to the undocumented sample when

compared to the matched sample. These analyses will answer the following research questions:

Does the adapted conceptual model help explain the likelihood of postsecondary enrollment for

undocumented Hispanic and Asian students and which of the contextual areas in the adapted

model has the greatest influence on postsecondary enrollment?

Below is a representation of the model building.

1. Any postsecondary enrollment = Habitus

2. Any postsecondary enrollment = Habitus + Social Capital

3. Any postsecondary enrollment = Habitus + Social Capital + School Context

4. Any postsecondary enrollment = Habitus + Social Capital + School Context + State

Policy Context

71

Findings for these models are presented first for the undocumented Hispanic sample followed by

the findings for the undocumented Asian sample.

Limitations

Similar to previous studies on the undocumented population in the United States, this

study has limitations in accounting for undocumented individuals. Every variable available to

exclude persons who are U.S. citizens or permanent residents was used to limit the sample size,

but it is possible that students in the sample were not undocumented. An example of the

limitation of the selection method utilized is the exclusion of the children of highly educated

undocumented immigrants who were not capture in the proxy. Conversely, legal immigrants with

low levels of education and residence in the country of less than 20 years could have been

included in the sample. The undocumented student proxy does not represent the heterogeneity of

the undocumented population but a sample of a group of immigrants with low levels of education

that met the established criteria based the available literature on the undocumented population.

In addition, although the ELS: 2002 was designed as a nationally representative study

with 750 schools selected and 10th grade students randomly selected within each school, the

survey was not designed to capture undocumented students specifically. As a result, the findings

of this study may not be nationally representative. Moreover, the small number of undocumented

cases in the dataset limits what could be tested in the model and what could be said about the

undocumented population.

Finally, because of sample limitations, Hispanic and Asian samples were each placed into

a homogenous group instead of by pan-ethnic group. This approach masks within-group

differences and may lead to underestimation of effects since certain undocumented Hispanic and

undocumented Asian groups may have lower postsecondary enrollment rates than do others.

72

Nevertheless, this study adds to the growing conversation on the undocumented population and

provides an alternative way of studying traditionally understudied populations using secondary

data.

73

Chapter 4

Findings

This chapter is organized around the research questions that guided the study. Part one

presents a descriptive portrait of the undocumented student population compared to the matched

sample and native students, part two presents the logistic regression findings for the

undocumented Hispanic and Asian populations by contextual area, and part three presents the

findings of the full adapted conceptual model by contextual blocks for the undocumented

Hispanic and Asian populations compared to their respective matched sample counterparts.

The descriptive analysis provides a sense of the postsecondary plans, educational

performance, and postsecondary preparation of the undocumented, matched, and native samples

by pan-ethnic group. The analysis for the undocumented, matched, and native Hispanic samples

is presented first, after which the analysis of the undocumented, matched, and native Asian

samples is presented. These analyses address the following research question: Is the

postsecondary academic preparation of undocumented students comparable to their U.S. citizen

peers?

Part two presents the findings from the logistic regression analysis for the undocumented

Hispanic and Asian samples alone and addresses this research question: How do social capital,

school context and policy context operate independently on postsecondary enrollment for

undocumented students and which social capital and school level factors are the strongest

predictors of postsecondary enrollment for undocumented students? This section examines the

relationship by contextual area (i.e., habitus, social capital, etc.) for the undocumented sample.

Findings will be presented by contextual area for each undocumented group (i.e., habitus findings

74

for the undocumented Hispanic sample followed by habitus findings for the undocumented Asian

sample). Finally, a discussion of the relationship between the contextual layers for the

undocumented Hispanic and Asian samples is presented.

Part three presents the results of the logistic regression of the adapted conceptual model

by contextual block for the undocumented Hispanic and Asian sample compared to the respective

matched sample counterparts. This section builds upon habitus through the full model with all the

contextual areas (habitus; habitus and social capital; habitus, social capital, and school context;

etc.). Part three addresses these research questions: Does the adapted conceptual model help

explain the likelihood of postsecondary enrollment for undocumented Hispanic and Asian

students and which of the contextual areas in the adapted model has the greatest influence on

postsecondary enrollment? Findings for part three are presented first for the undocumented

Hispanic sample and then for the undocumented Asian sample.

Descriptive Portrait of the Hispanic and Asian Undocumented, Matched, and Native

Samples

Mehta and Ali (2003) provided a rich portrait of undocumented students in Chicago,

presenting data that supported the argument that undocumented students are just as prepared—

and in some cases better prepared—for postsecondary education as legal immigrant and native

students. In an effort to gain insight into the academic preparation of the undocumented students

in this study, an examination of postsecondary plans, GPA, SAT score, students math course

enrollment, and the actual percentage enrolled in postsecondary education are presented for the

undocumented, matched, and native samples first for the Hispanic group and then for the Asian

group. This analysis provides some context of the academic preparation of the undocumented

Hispanic and Asian samples compared to their respective matched and native counterparts.

75

Part I: Descriptive Portrait of the Undocumented, Matched, and Native Hispanic Samples

The descriptive statistics of the Hispanic sample included in Table 4-1 provide a portrait

of the undocumented, matched, and native Hispanic samples. The undocumented Hispanic

sample holds high postsecondary plans while in high school, with 81% of this sample having

postsecondary plans. For the matched and native Hispanic samples, the percentage is slightly

higher at 85% and 87%, respectively.

The three variables that measure academic preparation and achievement are GPA, SAT

score, and math preparation. Examining the GPAs of the groups shows small differences between

the groups’ educational performance. The undocumented sample has the lowest GPA at 2.55, but

the matched and native groups do not perform at a considerably higher rate, with GPAs of 2.61

and 2.73, respectively. The undocumented Hispanic sample scored an average of 790 on the SAT,

while the matched Hispanic sample scored over 100 points higher (896), and the native Hispanic

sample scored higher than both the undocumented and matched Hispanic samples (930). The

percentage of the undocumented Hispanic sample taking the SAT may be a reflection of lack of

awareness of the requirement, as just over 25% of the undocumented Hispanic sample took the

SAT. Just 36% of the matched Hispanic sample took the SAT, while the native Hispanic group

took the SAT at nearly twice the rate of the undocumented group at 46%. In addition, a strong

indicator of postsecondary preparation is math achievement. Twenty-four percent of the

undocumented Hispanic sample completed Algebra II or higher, which is just slightly lower than

the matched Hispanic sample (27%) and the native Hispanic sample (33%). Finally, 48% of the

undocumented sample enrolled in postsecondary education, compared to 63% for the matched

sample and 65% of the native Hispanic sample.

76

Table 4-1. Descriptive Portrait of the Undocumented, Matched, and Native Hispanic Samples

Group n

Percent with

Postsecondary

Plans

Grade

Point

Average

Average

SAT

Score

Percent

with

Algebra II

or Higher

Percent Enrolled

in Postsecondary

Education

Undocumented

Hispanic

196 81% 2.55 790 24% 48%

Matched

Hispanic Sample

238 85% 2.61 896 27% 63%

Native Hispanic

Sample

745 87% 2.73 930 33% 65%

Chi-Square Findings for the Undocumented Hispanic Sample

The following research question guided the analysis presented in this section: Is the

postsecondary academic preparation of undocumented students comparable to their U.S. citizen

peers? The findings illustrated in Table 4-1 and the chi-square results in Table 4-2 provide a

mixed response to the research question. In terms of GPA, the undocumented Hispanic sample’s

academic performance is similar to both the matched and native Hispanic samples’ performance,

but we see differences in math course enrollment and SAT scores. Undocumented Hispanic

students’ math course enrollment is similar to that of the matched Hispanic sample but

significantly different from the native Hispanic sample. The chi-square on the SAT indicates that

undocumented students’ scores on the SAT are significantly different than those of the matched

and native Hispanic samples. Finally, the proportion of undocumented Hispanics who enrolled in

postsecondary education is significantly different from proportion of matched and native

Hispanic students enrolling in postsecondary education. As the undocumented Hispanic students’

GPA is comparable to that of the matched and native Hispanic students, and math course

enrollment of the undocumented Hispanic sample is comparable to the matched (but not the

77

native) Hispanic sample, the differences in postsecondary enrollment between the undocumented

Hispanic sample and matched and native Hispanic samples may be due in part to differences in

SAT scores.

78

Table 4-2. Chi-Square on Academic Preparation Variables for Undocumented Hispanic Sample

Group Scaling N Chi-

Square df p Phi

Cramer’

s V

Postsecondary Plans

1=No Plans,

3=Four year

college

Undocumented Hispanic/

Matched Hispanic 434 5.067 2 .079 --- .108

Undocumented

Hispanic/Native Hispanic 941 6.601 2 .037 --- .084*

GPA 1= 0.5-1.5, 5=

3.51-4.0

Undocumented Hispanic/

Matched Hispanic 434 2.854 4 .580 --- .081

Undocumented

Hispanic/Native Hispanic 941 7.340 4 .119 --- .088

SAT 1=400-600,

5=1200-1600

Undocumented Hispanic/

Matched Hispanic 434 13.455 4 .009 --- .309***

Undocumented

Hispanic/Native Hispanic 941 29.365 4 .000 --- .272***

Math Course Taking 1=Low, 2=Algebra

II or higher

Undocumented Hispanic/

Matched Hispanic 434 2.065 2 .356 --- .069

Undocumented

Hispanic/Native Hispanic 941 6.202 2 .045 --- .081*

Postsecondary Enrollment

0=No Enrollment,

1=Enrolled in

Postsecondary

Undocumented Hispanic/

Matched Hispanic 434 8.681 1 .003 -.146* ---

Undocumented

Hispanic/Native Hispanic 941 18.187 1 .000 .142* ---

Note. *small effect, **medium effect, ***large effect

79

Descriptive Portrait of the Undocumented, Matched, and Native Asian Samples

The descriptive portrait of the Asian sample provides insight into the postsecondary

aspirations, academic preparation, and postsecondary enrollment of the Asian sample (Table 4-3).

The undocumented Asian sample holds high postsecondary plans while in high school, with 90%

having postsecondary plans. For the matched and native Asian samples, the percentage is

higher—95% and 91%, respectively. As mentioned previously, the three variables that measure

academic preparation are GPA, SAT score, and math preparation. Examining the GPA of the

Asian samples shows interesting differences between the groups’ educational performance. The

matched Asian sample has the lowest GPA at 2.97, followed by the undocumented Asian sample

at 3.06 and the native Asian sample at 3.10. The SAT score for the Asian sample reveals some

differences between the undocumented and matched sample. The difference between the

undocumented Asian and matched Asian sample is only 29 points. The gap between the

undocumented Asian and native Asian samples is much larger at just over 100 points. In terms of

math achievement, over 60% of the undocumented, matched, and native Asian samples

completed Algebra II or higher. Perhaps surprisingly, a higher percentage of the undocumented

Asian group took Algebra II or higher, followed by the matched sample and finally the native

Asian sample. Finally, the percentage of the Asian group that enrolled in postsecondary education

reveals that nearly 80% of all the groups enrolled in postsecondary education. Again, perhaps

surprisingly, the undocumented Asian group enrolled in postsecondary education at a slightly

higher rate of 79% than did the matched Asian and native Asian samples, both of which enrolled

at 78%.

80

Table 4-3. Descriptive Portrait of the Undocumented, Matched and Native Asian Samples

Group n

Percent with

Postsecondary

Plans

Grade

Point

Average

Average

SAT

Score

Percent

with

Algebra II

or Higher

Percent

Enrolled in

Postsecondary

Education

Undocumented

Asian

208 90% 3.06 947 55% 79%

Matched Asian

Sample

189 95% 2.97 976 50% 78%

Native Asian

Sample

397 91% 3.10 1048 55% 78%

Chi-Square Findings for the Undocumented Asian Sample

The chi-square analysis of the high school academic performance of the Asian sample

provides an interesting picture of the academic performance of the undocumented Asian

population compared to the matched and native Asian samples. The findings in Table 4-3 and chi-

square results in Table 4-4 provide a fairly straightforward answer to this research question: Is the

high postsecondary academic preparation of undocumented students comparable to their U.S.

citizen peers? The proportion of undocumented Asian students’ who hold postsecondary

aspirations is no different than the proportion of matched and native Asians who hold

postsecondary aspirations. The GPA of the undocumented Asian sample is not significantly

different than that of their matched and native Asian counterparts. Math course taking presents a

mixed picture, with undocumented Asian students’ enrollment resembling that of the matched

Asian sample but significantly differing from the native Asian sample. The findings for SAT

score tell a similar story, with undocumented Asian students’ scores not being significantly

different from the scores of the matched Asian sample but being significantly different from the

81

native Asians’ scores. Finally, the proportion of undocumented Asians entering postsecondary

education is not different than the matched and native Asian samples.

Table 4-4. Chi-Square on Academic Preparation Variables for the Undocumented Asian Sample

Group Scaling N Chi-

Square df p Phi

Cramer’

s V

Postsecondary Plans

1=No Plans,

3=Four year

college

Undocumented Asian/

Matched 397 1.547 2 .461 --- .062*

Undocumented Asian/Native

Asian 605 4.481 2 .106 --- .089*

GPA 1= 0.5-1.5, 5=

3.51-4.0

Undocumented Asian/

Matched Asian 397 1.244 4 .871 --- .056

Undocumented Asian/Native

Asian 605 1.315 4 .859 --- .048

SAT 1=400-600,

5=1200-1600

Undocumented Asian/

Matched Asian 397 2.805 4 .423 --- .106*

Undocumented Asian/Native

Asian 605 14.479 4 .002 --- .205**

Math Course Taking 1=Low, 2=Algebra

II or higher

Undocumented Asian/

Matched Asian 397 3.983 2 .136 --- .100*

Undocumented Asian/Native

Asian 605 9.566 2 .008 --- .130*

Postsecondary Enrollment

0=No Enrollment,

1=Enrolled in

Postsecondary

Undocumented Asian/

Matched Asian 397 .015 1 .904 .012 ---

Undocumented Asian/Native

Asian 605 .000 1 1.000 .004 ---

Note. *small effect, **medium effect, ***large effect

82

Part II: Logistic Regression Findings for the Undocumented Hispanic and Asian Samples

by Contextual Area

Part II of the analysis examines the association between each of the contextual areas

(habitus, social capital, school context, and policy context) and postsecondary enrollment for

undocumented Hispanic and Asian students. Analysis is presented by contextual area for the

undocumented Hispanic sample followed by the undocumented Asian sample. This approach

allows us to assess the relationship between the conceptual layers and individual variables within

each layer for the postsecondary outcomes of undocumented students. This analysis addresses the

following research questions: How do social capital, school context and policy context operate

independently on postsecondary enrollment for undocumented students, and which social capital

and school level factors are the strongest predictors of postsecondary enrollment for

undocumented students? The final section of Part II provides a summary of the findings for the

undocumented Hispanic and undocumented Asian samples.

Logistic Regression of Habitus on Postsecondary Enrollment for the Undocumented Sample

Habitus Findings for the Undocumented Hispanic Sample

A logistic regression analysis was performed to assess the impact of habitus on the

likelihood of undocumented Hispanic students’ enrollment in postsecondary education. The

model contained five independent variables (gender, socioeconomic status, math achievement,

belief that education is important to get a job, and would rather work than go to school) and was

statistically significant (chi-squares, degrees of freedom, and model significance are presented in

Appendix E), indicating that the habitus model was able to distinguish between undocumented

Hispanic students who did not enroll compared to those who did enroll in postsecondary

83

education. The null model predicted 51.9% of cases correctly, and with habitus added to the

model, the percent predicted correctly improved to 65.5%.

As shown in Table 4-5, four of the five independent habitus variables made a statistically

significant contribution to the model at the p<0.001 level. The strongest predictors for

postsecondary enrollment for undocumented Hispanic students were that education is important

to get a job later, math achievement, and that students would rather work than go to school.

Socioeconomic status was not a significant predictor of postsecondary enrollment.

Undocumented Hispanic students who agreed with the statement that education is

important to get a job later were 2.285 times more likely to have enrolled in college than not.

Math achievement also had a significant impact on postsecondary enrollment. Students in the

highest math quartile were 1.774 times more likely to enroll in college than not. Finally, students

who agreed with the statement they would rather work than go to school increased the odds of

postsecondary enrollment 1.166 times for undocumented Hispanic students. An examination of

gender differences revealed that being female decreased the odds of postsecondary enrollment by

20.7 % compared to males (confidence intervals available in Appendix F).

Table 4-5. Habitus Findings for the Undocumented Hispanic Sample

Variable Variable Scaling Odds Ratio/B

Gender: Female (0=Male; 1=Female) .793*

(-.232)

Socioeconomic Status (-1.53 - 1.39) 1.012

(.012)

10th grade math quartile (1=lowest quartile; 2=second quartile;

3=third quartile; 4=highest quartile)

1.774*

(.573)

Education is important to get a job later (1=strongly disagree; 2=disagree;

3=agree; 4=strongly agree)

2.285*

(.826)

Would rather work than rather go to

school in 12th grade (0=no; 1=yes) 1.166*

(.154)

Note. *p<.001, **p<.05

84

The strongest predictor of postsecondary enrollment for the undocumented Hispanic

sample was the importance of education for obtaining a job later. The value of students’ desiring

to work rather than go to school is a bit perplexing but may be the result of a preference to

contribute to the household income rather than continue their education. For undocumented

Hispanic students, belief in the importance of education for employment may serve as a proxy for

postsecondary aspirations.

Habitus Findings for the Undocumented Asian Sample

The habitus model for the undocumented Asian sample containing all predictors was also

statistically significant, indicating that the model was able to distinguish between undocumented

Asian students who enrolled and did not enroll in postsecondary education. The null model

predicted 75.3% of cases correctly, and the addition of habitus variables improved the percent

predicted correctly to 82.6%.

As illustrated in Table 4-6, all the independent habitus variables made a statistically

significant contribution to the model at the p <0.001 level. The strongest predictors for

postsecondary enrollment for undocumented Asian students were math achievement, importance

of education to get a job, and would rather work than go to school. Math achievement and

expected cost had a similar relationship with postsecondary enrollment. Undocumented Asian

students who agreed with the statement that education is important to get a job later were 2.897

times more likely to have enrolled in postsecondary education than not. There was also a

significant association with math achievement; undocumented Asian students in the highest math

quartile were 2.897 times more likely to enroll in college than not.

Two of the variables in the model had a negative effect on postsecondary enrollment,

socioeconomic status and gender. Perhaps surprisingly, socioeconomic status had a negative

85

effect on postsecondary enrollment, with an increase in socioeconomic status decreasing the odds

of postsecondary enrollment by 54.0%. Additionally, being female decreased the odds of

postsecondary enrollment by 23.1% (confidence intervals available in Appendix F).

Table 4-6. Habitus Findings for the Undocumented Asian Sample

Variable Variable Scaling Odds Ratio/B

Gender: Female (0=Male; 1=Female) .769*

(-.262)

Socioeconomic Status (-1.35 - 1.80) .460*

(-.775)

10th grade math quartile (1=lowest quartile; 2=second quartile;

3=third quartile; 4=highest quartile)

2.897*

(1.064)

Education is important to get a job later (1=strongly disagree; 2=disagree;

3=agree; 4=strongly agree)

2.897*

(1.064)

Would rather work than rather go to

school in 12th grade (0=no; 1=yes) 1.499*

(.405)

Note. *p<.001, **p<.05

The relationship between habitus and postsecondary enrollment revealed several nuances

into how habitus and the variables in the model predict postsecondary enrollment for both the

Hispanic and Asian undocumented populations. The populations have similarities in regard to the

influence and direction of the habitus variables on postsecondary enrollment, but the strength of

the variables in predicting postsecondary enrollment differs. Specifically, habitus and the

variables in the model are better predictors of postsecondary enrollment for the Asian

undocumented sample compared to the Hispanic undocumented sample.

86

Logistic Regression of Social Capital on Postsecondary Enrollment for the Undocumented

Samples

The second analysis conducted examined the relationship between social capital and

enrollment in postsecondary education. The model contained five independent variables (whom

student has consulted for college information: counselors; other relatives; college publications,

websites, and representatives; and parent engagement in college education).

Social Capital Findings for the Undocumented Hispanic Sample

The social capital model containing all predictors was statistically significant, indicating

that the model was able to distinguish between undocumented Hispanic students who enrolled in

postsecondary education and those who did not. The percent predicted correct classified 70.3% of

cases, which is an improvement of 18.4% over the null model.

As shown in Table 4-7 four of the five independent social capital variables made a

statistically significant contribution to the model at the p<0.001 level. The positive predictors for

postsecondary enrollment for undocumented Hispanic students were sources of college

information from college publications and websites, college representatives, and other relatives,

and parents’ providing advice about preparing for college entrance exams. Undocumented

Hispanic students who consulted college publications and websites as a source of college

information were 5.816 times more likely to enroll in postsecondary education than not.

Undocumented Hispanic students who enrolled in postsecondary education were engaging

electronic sources of information, which may be a reflection of a desire to gather information

without revealing their undocumented status. The second most influential source of information

for this sample was college representatives. Students who engaged a college representative for

college entrance information were 1.778 times more likely to enroll in postsecondary education

87

than not. These institutional agents appear to have served as a valuable resource to help students

to navigate the admissions process. Undocumented Hispanic students also relied on other

relatives as important sources of information; students who went to other relatives for college

entrance information were 1.688 times more likely to enroll in postsecondary education. Finally,

parents were providing students with guidance on postsecondary education as well.

Perhaps surprisingly, counselors did not achieve significance as a source of information. The lack

of relationship between counselors as a source of information and postsecondary enrollment

raises some questions as to the role of these institutional agents on the postsecondary advising

process for undocumented Hispanic students (confidence intervals available in Appendix F).

Table 4-7. Social Capital Findings for the Undocumented Hispanic Sample

Variable Variable Scaling Odds Ratio/B

Counselors (0=no; 1=yes) .974

(-.026)

Other relative (0=no; 1=yes) 1.688*

(.523)

College publications and websites (0=no; 1=yes) 5.816*

(1.761)

College representatives (0=no; 1=yes) 1.778*

(.575)

Provide advice about plans for

college entrance exams (10th

grade)

(1=never to 3=often) 1.439*

(.364)

Note. *p<.001, **p<.05

Social Capital Findings for the Undocumented Asian Sample

The social capital model containing all predictors was statistically significant for the

undocumented Asian sample, indicating that the model was able to distinguish between

undocumented Asian students who enrolled and did not enroll in postsecondary education. The

88

null model correctly classified 75.3% of cases, and the addition of social capital variables

increase the percent correctly classified to 80.3%.

As shown in Table 4-8, all five independent social capital variables made a statistically

significant contribution to the model at the p<0.001 level. The positive predictors of

postsecondary enrollment for undocumented Asian students were college publications and

websites, high school counselors, college representatives, and other relatives as sources of college

information. Undocumented Asian students who consulted college publications and websites as a

source of college information were 8.348 times more likely to enroll in postsecondary education.

Counselors also were a significant source of postsecondary information, with the odds of

enrolling in postsecondary education increasing 4.842 times when undocumented Asian students

asked a counselor for college entrance information. College representatives were also a valuable

source of information for this population, with engaging a college representative increasing the

odds of postsecondary enrollment 1.840 times for undocumented Asian students. Other relatives

were a source of information for undocumented Asian students, providing the lowest odds ratio.

Students who sought college entrance information from other relatives increased the odds of

enrolling in postsecondary education 1.769 times. Parents who provided advice about college

entrance exams often decreased the likelihood of postsecondary enrollment by 5.9%. This may be

a reflection of student’s desire to escape parental control and pressure (Zhou, 2008). Similar to

the undocumented Hispanic sample, the strongest postsecondary predictor for the undocumented

Asian sample is the use of college publications and websites for entrance requirements

(confidence intervals available in Appendix F).

89

Table 4-8. Social Capital Findings for the Undocumented Asian Sample

Variable Variable Scaling Odds Ratio/B

Counselors (0=no; 1=yes) 4.842*

(1.577)

Other relative (0=no; 1=yes) 1.769*

(.571)

College publications and websites (0=no; 1=yes) 8.348*

(2.122)

College representatives (0=no; 1=yes) 1.840*

(.610)

Provide advice about plans for

college entrance exams (10th

grade)

(1=never to 3=often) .941**

(-.061)

Note. *p<.001, **p<.05

The relationship between social capital and postsecondary enrollment begins to provide a

sense of how the variables in the model aid in the prediction of postsecondary enrollment for both

the Asian and Hispanic undocumented populations. These populations are drawing postsecondary

entrance information and guidance from different sources. The Internet and college publications

are important sources of information for both populations, but the role of parents and counselors

provides a distressing glimpse into the resources undocumented Hispanic students are engaging in

their postsecondary search process. Prior research (Pérez & McDonough, 2004; Post, 1990)

confirms that parents and relatives may not be the most accurate sources of information, although

they are important for undocumented Hispanic students. Counselors seem to be a source of

discouragement for the Hispanic population, which is troubling since these institutional agents are

influential in course selection and placement. Undocumented Asian students are not using parents

as a source of college information but rely on websites and publications, counselors, and college

representatives.

90

Logistic Regression of School Context on Postsecondary Enrollment for the Undocumented

Sample

The school context model examines the relationship between safe and adequate facilities

and college-going environment on the postsecondary enrollment of undocumented students. The

model for school context contains seven independent variables (learning hindered by lack of

space, gangs in school, does not feel safe at school, got into physical fight at school, important to

friends to finish high school, number of friends going to four-year college and percent of student

body in Advanced Placement courses).

School Context Findings for the Undocumented Hispanic Sample

The school context model containing all predictors was statistically significant, indicating

that the model was able to distinguish between undocumented Hispanic students who enrolled

and did not enroll in postsecondary education. The null model correctly classified 68.7% of cases,

a 12.7% improvement over the null model.

Of the seven school context variables included in the model, six independent variables

were significant predictors of postsecondary enrollment for undocumented Hispanic students (see

Table 4-9) at the p<0.001 level. Two variables stand out in terms of the relationship to

postsecondary enrollment: peer context and student’s perception of school safety. Peer context

(importance of friends to finish high school and number of friends going on to four-year colleges)

was the strongest predictor of postsecondary enrollment. For undocumented Hispanic students in

the sample, as the number of friends who feel it is important to finish high school increased, the

odds of postsecondary enrollment increased 2.757 times. Similarly, the relationship between the

number of friends enrolling in four-year colleges and postsecondary education positively

influenced postsecondary enrollment. As more of undocumented students’ friends go to four-year

91

college, the undocumented students’ odds of enrolling in postsecondary education increased

1.318 times (confidence intervals available in Appendix F).

A puzzling finding is the relationship of postsecondary enrollment with adequate

facilities. Undocumented Hispanic students who attended schools whose counselors felt learning

was hindered by lack of space were 1.089 times more likely to enroll in postsecondary education.

This result may come from immigrant optimism (Suárez-Orozco & Suárez-Orozco, 1995) and be

a reflection of the perceived importance of education as a way of validating their value to U.S.

society (Menjivar, 2008).

The findings around school safety all point to the importance of a secure campus for

increased postsecondary attainment. Undocumented Hispanic students who do not feel safe at

school were 59.4% less likely to enroll in postsecondary education than those who did feel safe.

The presence of gangs in schools had a similar effect on postsecondary enrollment. For

undocumented Hispanic students who strongly agreed with the statement that there were gangs in

school, odds of enrolling in postsecondary education decreased by 54.3%. Broader school

context, as measured by the percent of the student body in Advanced Placement courses, did not

achieve significance.

The analysis of school context points to the importance of peer context for postsecondary

enrollment. The strongest predictor of postsecondary enrollment for undocumented Hispanic

students was the importance of friends to finish high school. A negative peer context also factored

into postsecondary enrollment, as evidenced by the relationship between students’ perception of

school safety, the presence of gangs in school, and the effect of getting into a physical fight.

92

Table 4-9. School Context Findings for the Undocumented Hispanic Sample

Variable Variable Scaling Odds Ratio/B

Learning hindered by lack of space (1=not at all to 4=a lot) 1.089 *

(.085)

There are gangs in school (1=strongly disagree to 4=strongly

agree)

.634 *

(-.455)

Does not feel safe at school (1=strongly disagree to 4=strongly

agree)

.406 *

(-.903)

Got into a physical fight at school (1=never to 3=more than twice) .457 *

(-.783)

Important to friends to finish high

school

(1=not important to 3=very

important)

2.757 *

(1.014)

Number of friends going to four-

year colleges asked in 12th grade (1=none to 3=most or all of them)

1.318 *

(.276)

Percent of student body in

Advanced Placement courses asked

in 12th grade counselor survey

(continuous, 0-100) 1.000

(.000)

Note. *p<.001, **p<.05

School Context Findings for the Undocumented Asian Sample

The school context model containing all predictors was statistically significant, indicating

that the model was able to distinguish between undocumented Asian students who enrolled and

did not enroll in postsecondary education. The null model correctly classified 78.2% of the cases,

and the addition of school context variables increased the percentage correctly classified to 84.2%

of cases.

Of the seven independent school context variables included in the model, five of the

independent variables are significant predictors at the p<.001 level (see Table 4-10) of

postsecondary enrollment for undocumented Asian students. Peer context was the strongest

predictor of postsecondary enrollment. As the number of friends of undocumented Asian students

who were going on to four-year college moved from none to most or all of them, the odds of

93

postsecondary enrollment increased by 6.910 times for the undocumented Asian student sample.

The importance of friends for finishing high school had a positive relationship to postsecondary

enrollment as well, but the association is not as large. As the importance of high school

graduation increased among undocumented Asian students’ peers, the odds of undocumented

Asian students’ enrolling in postsecondary education increased 1.742 times. Similar to the

undocumented Hispanic sample, when counselors felt that learning is hindered by lack of space,

the odds of postsecondary enrollment for undocumented Asians students increased 1.192 times.

As previously discussed, this result may be because of immigrant optimism (Suárez-Orozco &

Suárez-Orozco, 1995), or it could be the result of cultural memory (Model, 2008). Undocumented

Asian and Hispanic immigrants may be comparing U.S. schools to schools in their home country,

and while the counselor may view a school as lacking space, the undocumented Asian students

may be comparing it to school conditions or opportunities in their home country.

Undocumented Asian students, similar to undocumented Hispanic students, displayed

sensitivity to school safety. Undocumented Asian students who strongly agreed with the

statement that they do not feel safe at school were 52.4% less likely to enroll in postsecondary

education compared to those that do feel safe. The effect of individual acts of school violence

also had a relationship to postsecondary enrollment. As the number of physical fights a student

has at school increased, the odds of postsecondary enrollment for undocumented Asian students

decreased by 40.2%. The presence of gangs was no longer significant a predictor of

postsecondary enrollment, however.

Finally, the percentage of the student body in Advanced Placement courses appeared to

be a negative predictor of postsecondary enrollment. As the percentage increased, the odds of

undocumented Asian students’ postsecondary enrollment decreased by less than 1%.

The school context model provided some insight into the factors that influence the

postsecondary enrollment of undocumented Asian Students. It offered evidence that school

94

buildings and safety have a strong association with postsecondary enrollment. Undocumented

Asian students who did not feel safe at school were less likely to enroll in postsecondary

education. Additionally, peer context was an important influence on postsecondary enrollment.

The number of friends going to four-year college was the strongest predictor of postsecondary

enrollment for the undocumented Asian sample (confidence intervals available in Appendix F).

Table 4-10. School Context Findings for the Undocumented Asian Sample

Variable Variable Scaling Odds Ratio/B

Learning hindered by lack of space (1=not at all to 4=a lot) 1.192*

(0.175)

There are gangs in school (1=strongly disagree to 4=strongly

agree)

1.019

(.019)

Does not feel safe at school (1=strongly disagree to 4=strongly

agree)

.476*

(-0.743)

Got into a physical fight at school (1=never to 3=more than twice) .598*

(-0.515)

Important to friends to finish high

school

(1=not important to 3=very

important)

1.742*

(.555)

Number of friends going to four-

year colleges asked in 12th grade (1=none to 3=most or all of them)

6.910*

(1.933)

Percent of student body in

Advanced Placement courses asked

in 12th grade counselor survey

(continuous, 0-100) .993*

(-.007)

Note. *p<.001, **p<.05

An examination of the school context model reveals that school context influences the

undocumented Hispanic and Asian student populations in similar ways, with a few nuances. The

direction of these variables for both undocumented groups is the same; the difference is the

strength of the relationship on postsecondary enrollment. For both groups peer context is very

important in predicting postsecondary enrollment. For undocumented Hispanic students, having

friends who view finishing high school as important is more important to postsecondary

enrollment that it is for the undocumented Asian sample. Alternately, for the undocumented

95

Asian sample, the number of friends going on to four-year college is critical to postsecondary

enrollment.

Logistic Regression of State Policy Context on Postsecondary Enrollment for the

Undocumented Sample

The final layer of adapted conceptual model is state policy context. For this study, state

policy context was defined as the presence or absence of an in ISRT policy. The model for policy

context contained one independent variable (state policy context).

State Policy Context for the Undocumented Hispanic Sample

The state policy context model containing all predictors was statistically significant,

indicating that the model was able to distinguish between undocumented Hispanic students who

enrolled and did not enroll in postsecondary education. The null model correctly classified 51.9%

of cases, and when state policy context was added, the percent predicted correct increased to

59.9% (confidence intervals available in Appendix F).

The odds of enrolling in postsecondary education increased for undocumented Hispanic

students living in states with an ISRT policy 2.391 times, see Table 4-11, compared to students

living in states without an ISRT policy. The importance of creating a pathway to postsecondary

education for undocumented Hispanic students receives support in this analysis, providing

additional evidence of the work of Flores (2010) who found a 1.942 increase in college

enrollment when ISRT and tuition policies were implemented by states.

96

Table 4-11. State Policy Context Findings for the Undocumented Hispanic Sample

Variable Variable Scaling Odds Ratio/B

State Policy Context (0=no/negative policy; 1=In-state

resident tuition policy

2.391*

(.782)

Note. *p<.001, **p<.05

State Policy Context for the Undocumented Asian Sample

The state policy context model containing all predictors was statistically significant,

indicating that the model was able to distinguish between undocumented Asian students who

enrolled and did not enroll in postsecondary education. The null model correctly classified 75.3%

of cases, and when state policy context was added to the model, there was no improvement over

the null model. Undocumented Asian students living in states with an ISRT policy were 1.339

times more likely to enroll in postsecondary education than students living in states with no ISRT

policy, see table 4-12 for results (confidence intervals available in Appendix F).

Table 4-12. State Policy Context for the Undocumented Asian Sample

Variable Variable Scaling Odds Ratio/B

State Policy Context (0=no/negative policy; 1=In-state

resident tuition policy

1.337

(.292)

Note. *p<.001, **p<.05

These findings provide positive evidence of the value of ISRT policies as a lever to create

pathways to postsecondary education for undocumented students. ISRT policies have a greater

impact for undocumented Hispanic students, who seem to have a greater sensitivity them.

Individual Contextual Model Summary

The four contextual models tested provided some measures of the importance of

individual contextual areas in the postsecondary outcomes of undocumented students. Table 4-13

97

provides the Nagelkerke R-squares and the percentages predicted correctly for both the

undocumented Hispanic and Asian samples. The Nagelkerke R-squares and the percentages

predicted correctly for the contextual models point to social capital as the most impactful model

for predicting postsecondary enrollment for the undocumented Hispanic sample. The social

capital model explains 28.1% of the variance in the model, the highest of the four models.

Additionally, the percent predicted correct for the social capital model is 70.3%, an improvement

of 18.4% over the null model.

For the undocumented Asian sample, the social capital model predicted the highest

amount of variance in the model at 37.7%, although it is followed closely by habitus and school

context. The percent predicted correct was highest for the school context model. The null model

predicted 75.3% of cases correctly, and with the addition of school context variables the percent

predicted correct increased to 84.2%, followed closely by habitus and social capital.

The social capital model explained the greatest amount of variance for both the

undocumented Hispanic and Asian samples. This model also correctly predicted the highest

percentage of cases correctly for the undocumented Hispanic sample. For the undocumented

Asian sample, school context variables improve the percentage of cases correctly predicted.

98

Table 4-13. Nagelkerke R-Squared and Percent Predicted Correct for Contextual Models

Measure Undocumented Hispanic Undocumented Asian

Habitus

Nagelkerke R Squared .139 .353

Percent Predicted Null

Model 51.9% 75.3%

Percent Predicted

Correct 65.5% 82.6%

Social Capital

Nagelkerke R-Squared .281 .377

Percent Predicted Null

Model 51.9% 75.3%

Percent Predicted

Correct 70.3% 80.3%

School Context

Nagelkerke R-Squared .216 .333

Percent Predicted Null

Model 51.9% 75.3%

Percent Predicted

Correct 68.7% 84.2%

Policy Context

Nagelkerke R-Squared .052 .006

Percent Predicted Null

Model 51.9% 75.3%

Percent Predicted

Correct 59.9% 75.3%

Summary of the Logistic Regression Findings for the Undocumented Hispanic and Asian

Samples

Habitus, as conceptualized for this study, had mixed results in predicting postsecondary

enrollment for undocumented students. For both samples, math achievement and expected cost

were the strongest predictors of postsecondary enrollment. Surprisingly, socioeconomic status

99

had a negative impact on postsecondary enrollment for undocumented Asian students. The

direction of the variables in the model for habitus were similar for both populations, but the data

showed major differences in the intensity of the effect (i.e., an increase in math quartile increases

the likelihood of postsecondary enrollment by 2.897 times for Asians compared to 1.774 for

Hispanic students). Habitus and the variables in the model were better predictors of

postsecondary enrollment for the Asian undocumented sample than for the undocumented

Hispanic sample.

The sources of social capital with which undocumented students are engaging in their

postsecondary search provides insight into the resources this population of students employs in

their college search process. The main resource that undocumented students are using is the

Internet. It is clear that undocumented students are using the Internet as their primary source for

postsecondary information. Undocumented Hispanic students who used the Internet and college

publications as a source of college information were 5.816 times more likely to enroll in

postsecondary education. For undocumented Asian students the effect is even larger, with Internet

and publication research increasing postsecondary enrollment 8.348 times.

For undocumented Hispanic students, more traditional sources of social capital, including

college representatives (1.778), other relatives (1.688), and parents (1.439), influenced

postsecondary enrollment. All these sources positively influenced postsecondary enrollment for

undocumented Hispanic students. School counselors had no effect on postsecondary enrollment

for undocumented Hispanic students. The Asian sample seem to be accessing a different source

of information in their decision process, engaging counselors (4.842), college representatives

(1.840), other relatives (1.688), and parents (-5.9%). Of the contextual layers examined, social

capital explained the highest amount of variance and correctly predicted the highest percentage of

cases correctly for the undocumented Hispanic sample. Of the models examined for the

100

undocumented Asian sample, school context and habitus explained a greater amount of variance

and correctly predicted a higher percentage of cases.

The school context model seems to influence the undocumented Hispanic and Asian

student populations in similar ways. The direction of these variables for both undocumented

groups is the similar; the difference is the strength of the relationship. A notable difference is the

importance of school safety for both groups, with the effect of not feeling safe decreasing the

odds of postsecondary enrollment 59.4% for undocumented Hispanic students compared to

52.4% for undocumented Asian students. For the college-going culture block of variables, there

was agreement in the direction, but there was a major difference in the strength of the

relationship. For both groups, peer context was very important in predicting postsecondary

enrollment. For Hispanic students, having friends who viewed finishing high school as important

was more important to postsecondary enrollment that it was for the Asian sample, and for

undocumented Asian students the number of friends going on to four-year college was important

to postsecondary enrollment. Finally, the percentage of students in Advanced Placement courses

did not add much value to the postsecondary outcomes of undocumented students.

Policy context appears to have a stronger relationship to the postsecondary enrollment of

undocumented Hispanic students than of undocumented Asian students. Undocumented Hispanic

students living in states with an ISRT policy were 2.391 times more likely to enroll in higher

education than students living in states without an ISRT policy. For the Asian sample, living in an

ISRT state increased the odds of postsecondary enrollment 1.337 times. Hispanic students seemed

to be more sensitive to ISRT policies compared to Asian students, who did not seem to be

deterred by state policies that did not encourage undocumented students’ enrollment in higher

education.

101

Part III: Logistic Regression Findings on Adapted Conceptual Model of Postsecondary

Enrollment for Undocumented Students Compared to Matched Sample

The final part of this analysis examines the relationship of the adapted conceptual model

by building the adapted model by contextual block, adding to habitus until all the contextual areas

are included in the model. This approach allows for a sense of the relationship between contextual

layers and variables for the undocumented population compared to the matched sample. The

findings of the model are first presented for the undocumented Hispanic sample followed by the

findings for the undocumented Asian sample.

Findings for the Habitus Model for the Undocumented Hispanic Sample Compared to

Matched Hispanic Sample (Model 1)

A logistic regression analysis was performed to assess the impact of habitus on the

likelihood of the Hispanic undocumented and matched samples’ enrolling in postsecondary

education. The model contains five independent variables (gender, socioeconomic status, math

achievement, importance of education to get a job, and desire to work rather than go to

school).The habitus model containing all predictors was statistically significant for both groups

(see Appendix E for model fit statistics), indicating that the model was able to distinguish

between undocumented and matched Hispanic students who enrolled and did not enroll in

postsecondary education. The null model predicted 51.9% of cases correctly, and when habitus

variables were added to the model, the percent predicted correct improved to 65.5% for the

undocumented Hispanic sample. For the matched Hispanic sample the null model predicted

63.8% of cases correctly, and with the addition of habitus variables, the percent predicted correct

improved to 71.4%.

102

The strongest predictors of postsecondary enrollment for undocumented Hispanic

students compared to their matched sample counterparts was the importance of education to get a

job, see Table 4-14 for results. For the undocumented Hispanic sample, the importance of

education to get a job odd ratio was 2.285, compared to 1.622 for the matched sample.

Additionally, being an undocumented Hispanic female decreased the odds of postsecondary

enrollment by 20.7%. There was a much higher disadvantage for females in the matched Hispanic

sample, being a Hispanic female in the matched sample decreased the odds of postsecondary

enrollment by 78.7%.

Socioeconomic status, math achievement, and the desire to work rather than go to school

were all stronger predictors of postsecondary enrollment for the matched Hispanic sample. Of

note is the difference in the desire to work rather than go to school. This may be an indication of a

desire contribute to household income but inability to work due to legal status. The comparison to

the matched sample may point to a different reason for the relationship between the variable and

postsecondary enrollment. Specifically, it may be a reflection of the collective decision around

postsecondary education occurring in the homes of immigrants. Stark and Bloom (1985)

theorized that migration decisions were household investments in the person migrating to

improve the household. This finding seems to point to similar thinking around education.

Undocumented and matched sample students would rather work than go to school to help

improve household finances, but postsecondary education is viewed as a means of social mobility

for the entire family. As such, the student would take into account the value added to the

household if they were to forgo work to further their education. Because of the legal status of the

matched Hispanic sample (i.e., U.S. citizens), it is not surprising that it would have higher

predictive value for this population as they would be eligible for federal student aid not available

to undocumented Hispanic students (confidence intervals available in Appendix G).

103

The habitus model provides a sense of the importance of individual habitus variables on

the postsecondary outcomes of undocumented students. Table 4-15 provides the Nagelkerke R-

square and the percent predicted correct for both the undocumented and matched Hispanic

samples. The Nagelkerke R-squares and the percent predicted correct for the contextual models

point to habitus as a better model for predicting postsecondary enrollment for the matched

Hispanic sample. The habitus model explains 13.1% more variance for the matched Hispanic

sample compared to the undocumented Hispanic sample. Additionally, the percent predicted

correct for the habitus model is 71.4% for the matched Hispanic sample compared to 65.5% for

the undocumented Hispanic sample.

104

Table 4-14. Logistic Regression Findings for the Adapted Conceptual Model for the Undocumented and Matched Hispanic Samples

Model 1 Model 2 Model 3 Model 4

Group: Hispanic Undocumented Matched Undocumented Matched Undocumented Matched Undocumented Matched

Habitus Gender: Female .793*

(-.232)

.213*

(-1.546)

1.025

(.025)

.175*

(-1.744)

1.183*

(.168)

.161*

(-1.825)

1.077**

(.074)

.167*

(-1.792)

Socioeconomic Status 1.012

(.012)

1.388*

(.291)

1.024

(.024)

1.191*

(.175)

1.227*

(.205)

.889*

(-.118)

1.148*

(.138)

.893*

(-.113)

10th grade math quartile 1.744*

(.573)

2.102*

(.743)

1.480*

(.392)

1.719*

(.541)

1.108*

(.103)

2.002*

(.694)

1.119*

(.112)

1.975*

(.681)

Education is important to get a job later 2.285*

(.826)

1.622*

(.484)

1.700*

(.531)

1.427*

(.355)

1.467*

(.383)

1.290*

(.254)

1.255*

(.227)

1.303*

(.265)

Would rather work than rather go to

school in 12th grade

1.166*

(.154)

1.590*

(.464)

1.315*

(.274)

1.723*

(.544)

.738*

(-.304)

1.366*

(.312)

.660*

(-.415)

1.355*

(.304)

Social Capital

Counselors .849*

(-.164)

.246*

(-1.403)

1.465*

(.382)

.117*

(-2.143)

1.572*

(.452)

.128*

(-2.057)

Other relative 1.437*

(.362)

1.652*

(.502)

2.061*

(.723)

2.547*

(.935)

1.903*

(.643)

2.629*

(.967)

College publications and websites 4.717*

(1.551)

4.005*

(1.387)

4.031*

(1.394)

3.078*

(1.124)

3.608*

(1.283)

3.084*

(1.126)

College representatives 1.821*

(.599)

2.255*

(.813)

1.915*

(.650)

3.551*

(1.267)

1.946*

(.666)

3.493*

(1.251)

Parents Provide advice about plans for

college entrance exams (10th grade)

1.404*

(.340)

2.589*

(.951)

1.477*

(.390)

2.799*

(1.029)

1.429*

(.357)

2.805*

(1.031)

School Context

Learning hindered by lack of space 1.150*

(.140)

1.003

(.003)

1.228*

(.205)

1.015

(.015)

There are gangs in school .517*

(-.659)

1.982*

(.684)

.569*

(-.563)

2.010*

(.698)

Does not feel safe at school .386*

(-.952)

1.002

(.002)

.350*

(-1.050)

1.001

(.001)

Got into a physical fight at school .535*

(-.625)

.652*

(-.428)

.435*

(-.832)

.631*

(-.460)

Important to friends to finish high 3.332* 1.113* 3.410* 1.111*

105

school (1.204) (.107) (1.227) (.105)

Number of friends going to four-year

colleges asked in 12th grade

1.147*

(.137)

1.797*

(.586)

1.154*

(.143)

1.762*

(.567)

Percent of student body in AP courses

asked in 12th grade counselor survey

.993*

(-.007)

.944*

(-.058)

.990*

(-.010)

.947*

(-.055)

Policy Context

State Policy Context 3.069*

(1.121)

1.314*

(.273)

Constant .020

(-3.907)

.168

(-1.786)

.020

(-3.916)

.098

(-2.318)

.095

(-2.357)

.018

(-4.019)

.138

(-1.980)

.015

(-4.190)

Note. *p<.001, **p<.05

a. See Appendix C for variable scaling.

106

Table 4-15. Nagelkerke R-Squared and Percent Predicted Correct for the Undocumented and

Matched Hispanic Samples

Measure Undocumented Hispanic Matched Hispanic

Habitus

Nagelkerke R-

Square .139 .270

Percent Predicted

Null Model 51.9% 63.8%

Percent Predicted

Correct 65.5% 71.4%

Habitus and Social Capital

Nagelkerke R-

Square .317 .488

Percent Predicted

Null Model 51.9% 63.8%

Percent Predicted

Correct 68.1% 78.3%

Habitus, Social Capital and School Context

Nagelkerke R-

Square .433 .501

Percent Predicted

Null Model 51.9% 63.8%

Percent Predicted

Correct 75.5% 81.9%

Habitus, Social Capital, School Context and Policy Context

Nagelkerke R-

Square .462 .501

Percent Predicted

Null Model 51.9% 63.8%

Percent Predicted

Correct 78.3% 81.9%

107

Findings for Habitus and Social Capital Model for the Undocumented Hispanic Sample

Compared to Matched Hispanic Sample (Model 2)

The habitus and social capital model contains 10 independent variables (five habitus

variables and five social capital variables). Model 2 of the adapted conceptual model containing

all predictors was statistically significant for both the undocumented and matched Hispanic

samples, indicating that the model was able to distinguish between students who enrolled and did

not enroll in postsecondary education (see Appendix E for model fits). The null model predicted

51.9% of cases correctly, and when habitus variables were added to the model, the percent

predicted correct improves to 68.1% for the undocumented Hispanics sample. For the matched

Hispanic sample, the null model predicted 63.8% of cases correctly, and with the addition of

habitus variables, the percent predicted correct improved to 78.3%.

With the addition of social capital to the model, there were several changes in the

relationship between habitus variables and postsecondary enrollment for the undocumented and

matched Hispanic samples, see Table 4-14 for results. Similar to Model 1, the importance of

education to get a job later was a more significant predictor of postsecondary enrollment for the

undocumented Hispanic sample, but gender and socioeconomic status were no longer significant

predictors of postsecondary enrollment for the undocumented Hispanic sample. The non-finding

of socioeconomic status for the undocumented Hispanic sample may be evidence of

undocumented status. For the matched Hispanic sample, an increase in socioeconomic status

increased the odds of postsecondary enrollment, but for the undocumented Hispanic sample this

is not the case. For the matched Hispanic sample, math achievement and desire to work rather

than go to school were better predictors of postsecondary enrollment than for the matched

Hispanic sample. Finally, in the matched samples, being female decreased the odds of enrolling

in postsecondary education 82.5%.

108

The strongest social capital predictors of postsecondary enrollment for the undocumented

Hispanic sample was the use of college publications and websites and counselors as sources of

college entrance information. Undocumented Hispanic students who used college publications

and websites for college entrance information had 4.717 times higher odds of enrolling in

postsecondary education. These anonymous sources of postsecondary information allow

undocumented students to obtain postsecondary information without disclosing their

undocumented status. While the literature on Asian students (Kim & Gasman, 2011) established

the use of the Internet and postsecondary search engines for the Asian sample this finding for

undocumented Hispanic students points to an adaptation to available technologies to gather

postsecondary information for this population. The role of counselors decreased the odds of

postsecondary enrollment for both the undocumented and matched Hispanic samples. While this

finding holds for both the undocumented and matched Hispanic samples, the relationship is much

stronger for the matched Hispanic sample (-15.1% compared to -75.4%).

The role of other relatives and parents were significant predictors of postsecondary

enrollment for both undocumented and matched Hispanic students but were better predictors for

the matched sample. The sizes of the odds ratio for these two sources of information provide

additional evidence of household strategy of both undocumented and matched sample students

regarding postsecondary enrollment. Undocumented and matched Hispanic students who go to

other relatives and parents for postsecondary information were more likely to enroll in

postsecondary education. The lower odds ratio for the undocumented Hispanic sample may be the

result of lack of experience with U.S. education rather than the influence that parents and other

relatives have on the postsecondary decisions of their students. Finally, college representatives as

a source of college entrance information were a stronger predictor of postsecondary enrollment

for the matched Hispanic sample. This finding is not especially surprising given the need for

109

undocumented students to disclose legal status to obtain postsecondary entrance information

relevant to their status (confidence intervals available in Appendix G).

The habitus and social capital model provides a sense of the role of social capital on the

postsecondary outcomes of undocumented students. Table 4-15 provides the Nagelkerke R-

square and the percent predicted correct for both the undocumented Hispanic and matched

Hispanic samples. The habitus and social capital model explains 17.1% more variance for the

matched Hispanic sample than the undocumented Hispanic sample. The percent predicted correct

for the habitus and social capital model is 68.1% for the undocumented Hispanic compared to

78.3% for the matched Hispanic sample.

The addition of social capital to the habitus block of variables had an impact on the role

of gender and socioeconomic status for the undocumented Hispanic sample. The social capital

variables provide evidence that undocumented Hispanic students were engaging a broad group of

resources but that undocumented status may have forced them to focus their search for

postsecondary entrance information on anonymous sources. Counselors also influence the odds of

their enrollment in postsecondary education, but in a negative direction.

Findings for Habitus, Social Capital, and School Context Model for the Undocumented

Hispanic Compared to Matched Hispanic Sample (Model 3)

The third layer of this analysis examines the relationship between school context

variables on postsecondary enrollment when added to habitus and social capital. Model 3 contains

17 independent variables (five habitus variables, five social capital variables, and seven school

context variables). The model containing all predictors was statistically significant for both the

undocumented and matched Hispanic samples, indicating that the model was able to distinguish

between students who enrolled and did not enroll in postsecondary education (see Appendix E for

110

model fits). The null model predicted 51.9% of cases correctly for undocumented Hispanic

students, and when habitus, social capital, and school context variables were added to the model,

the percent predicted correct improved to 75.5%. For the matched Hispanic sample, the null

model predicted 63.8% of cases correctly, and with the addition of habitus, social capital, and

school context variables the percent predicted correct improved to 81.9%. Table 4-14 highlights

the variables that made statistically significant contributions to the adapted conceptual model.

The addition of school context variables to the model begins to point to differences

between the undocumented Hispanic and matched Hispanic samples. For the undocumented and

matched Hispanic samples, gender, socioeconomic status, and the desire to work rather than

continue to postsecondary education operate in opposite directions. Undocumented Hispanic

females were more likely to enroll in postsecondary education than undocumented Hispanic

males with the addition of school context to the model. For the matched Hispanic sample, being

female decreased the odds of postsecondary enrollment. An increase in socioeconomic status

increased the odds of postsecondary enrollment for the undocumented Hispanic sample, and the

opposite was true for the matched Hispanic sample. The strongest predictor of postsecondary

enrollment for the undocumented Hispanic population was the importance of education to get a

job later.

The strongest predictors for postsecondary enrollment for undocumented Hispanic

students continued to be the use of college publications and websites. The addition of school

context variables changed the direction of the role of counselors on postsecondary enrollment for

the undocumented Hispanic students. Undocumented Hispanic students who went to a counselor

for college entrance information were 1.465 times more likely to enroll in postsecondary

education; for the matched Hispanic sample going to a counselor for college entrance information

decreased the odds of postsecondary enrollment by 88.3%. The effect of information provided by

111

college representatives, other relatives, and parents continued to be greater for the matched

Hispanic sample.

The school context variables in the model are all significant predictors of postsecondary

enrollment for the undocumented Hispanic sample. The strongest predictor of postsecondary

enrollment for undocumented Hispanic students was the importance of friends to finish high

school. The difference in the odds ratio is nearly three times higher for the undocumented

Hispanic sample compared to the matched Hispanic sample. This difference is followed by

learning hindered by lack of space, which is a positive predictor of postsecondary enrollment for

undocumented Hispanic students and not significant for the matched sample. This finding

provides some support of the evidence of cultural memory (Model, 2008), with undocumented

Hispanic students recalling the conditions and opportunities available in the home country and

performing regardless of facilities. The presence of gangs in schools and the feeling of school

safety operated in opposite directions for the undocumented and matched Hispanic samples.

Undocumented Hispanic students who attended schools where there were gangs 48.3% less likely

to enroll in postsecondary education. For the matched sample the presence of gangs in schools

was the strongest predictor of postsecondary enrollment of the school context variables. This

finding is confounding and may represent students’ desire to escape violent conditions.

Undocumented Hispanic students who did not feel safe at school were 61.4% less likely to enroll

in postsecondary education, but for the matched Hispanic sample this variable is not significant.

The role of school climate seems to play a role in the postsecondary enrollment of

undocumented Hispanic students. Undocumented Hispanic students who attended schools where

there were gangs, where they did not feel safe, or where they got into a physical fight had

decreased odds of enrolling in postsecondary education. The presence of gangs operated in the

opposite direction for the matched Hispanic sample, and the importance of school safety was not

significant. The importance of peers to finish high school was the strongest predictor of

112

postsecondary enrollment for the undocumented Hispanic sample, and the number of peers going

on to four-year colleges was also a significant predictor, but was a stronger predictor for the

matched Hispanic sample. School facilities that were viewed as less than adequate by school

counselors did not negatively impact the odds of postsecondary enrollment for the undocumented

Hispanic sample (confidence intervals available in Appendix G).

The habitus, social capital, and school context model provides a sense of the role of

school context on the postsecondary outcomes of undocumented students. Table 4-15 provides

the Nagelkerke R-square and the percent predicted correct for both the undocumented Hispanic

and matched Hispanic samples. The habitus, social capital, and school context model explains

6.8% more variance for the matched Hispanic compared to the undocumented Hispanic sample.

The percent predicted correct for the habitus and social capital model is 75.5% for the

undocumented Hispanic compared to 81.9% for the matched Hispanic sample.

The school context block of variables yields similar findings to the model examined in

Part II. Peer context variables are the strongest predictors of postsecondary enrollment for the

undocumented Hispanic sample. School safety continues to be a significant negative predictor for

the undocumented Hispanic sample, which seems more sensitive to issues of safety and the

presence of gangs than the matched sample is.

Findings for the Full Adapted Conceptual Model for the Undocumented Hispanic

Compared to Matched Hispanic Sample (Model 4)

The full adapted conceptual model examines the relationship between habitus, social

capital, school context, and state policy context on the postsecondary enrollment of

undocumented students compared to the matched sample. The model contains 18 independent

variables (five habitus variables, five social capital variables, seven school context variables, and

113

one policy context variable). The full adapted conceptual model containing all predictors was

statistically significant for both the undocumented and matched Hispanic samples (see Appendix

E for model fit), indicating that the model was able to distinguish between students who enrolled

and did not enroll in postsecondary education. The null model correctly classified 51.9% of the

cases, and the addition of policy context improved the percent predicted correct to 78.3% of

cases, an improvement of 26.4%. For the matched sample, the null model correctly classified

63.8% of the cases, and the addition of policy context increases percent predicted correct to

81.9%. As shown in Table 4-14, all of the independent variables make a statistically significant

contribution to the model for the undocumented Hispanic sample.

With the addition of state policy context to the model, there are minimal changes to the

habitus model. For the undocumented and matched Hispanic samples, gender, socioeconomic

status, and the desire to work rather than continue education operated in opposite directions.

Undocumented Hispanic females were more likely to enroll in postsecondary education than

undocumented Hispanic males in the full model. Interestingly, a higher percentage of the

undocumented Hispanic population is female at 60.4% which might explain part of the advantage

for females. Additionally, this might point to a departure from K-12 education for undocumented

Hispanic males who may not be making it to graduation. For the matched Hispanic sample, being

female decreased the odds of postsecondary enrollment. The strongest predictor of postsecondary

enrollment for the undocumented Hispanic population was the belief in the importance of

education to get a job later, but the addition of policy context slightly decreased the association

between the importance of education to get a job later and postsecondary enrollment.

The strongest predictor for postsecondary enrollment for undocumented Hispanic

students continued to be the use of college publications and websites. School counselors had a

positive role on the on postsecondary enrollment of undocumented Hispanic students, but for the

matched Hispanic sample, going to a counselor for college entrance information decreased the

114

odds of postsecondary enrollment by 87.2%. It is important to note that the role of counselors on

the postsecondary enrollment with the addition of school context to the model points to the

importance of school context on postsecondary outcomes. The relationship between the

information provided by college representatives, other relatives, and parents continued to be

greater for the matched Hispanic sample.

The full block of school context variables constitutes significant predictors of

postsecondary enrollment for the undocumented Hispanic sample. Undocumented Hispanic

students’ perceptions of safety and peer context continued to be the strongest predictors of

postsecondary enrollment. Undocumented Hispanic students who attended schools where there

were gangs, where they did not feel safe, or where they got into a physical fight had decreased

odds of enrolling in postsecondary education. The presence of gangs operated in the opposite

direction, and the importance of school safety was not significant, for the matched Hispanic

sample. The importance of peers to finish high school was the strongest predictor of

postsecondary enrollment for the undocumented Hispanic sample. The number of peers going

onto four-year colleges was also a significant predictor for the undocumented sample, but it was a

stronger predictor for the matched Hispanic sample. School facilities that were viewed as less

than adequate by school counselors did not negatively impact the odds of postsecondary

enrollment for the undocumented Hispanic sample and were not a significant predictor for the

matched Hispanic sample.

Finally, state policy context has a statistically significant relationship to postsecondary

enrollment for undocumented Hispanic students. The odds of enrolling in postsecondary

education are 3.069 times greater for undocumented Hispanic students living in states with ISRT

policies than for undocumented Hispanic students living in states without ISRT policies. The

odds of undocumented Hispanic students enrolling in postsecondary education in states with

115

ISRT policies is over two times higher than the matched Hispanic sample (confidence intervals

available in Appendix G).

The full adapted conceptual model provides a sense of the role of policy context on the

postsecondary outcomes of undocumented students. Table 4-15 provides the Nagelkerke R-

square and the percent predicted correct for both the undocumented Hispanic and matched

Hispanic samples. The full adapted conceptual model explains 3.9% more variance for the

matched Hispanic sample than the undocumented Hispanic sample. The percent predicted correct

for the full adapted conceptual model is 78.3% for the undocumented Hispanic sample compared

to 81.9% for the matched Hispanic sample.

The full adapted conceptual model provides evidence of the importance of policy context

for the postsecondary enrollment of undocumented students. Undocumented Hispanic students

living in states with ISRT policies have 3.069 times higher odds of enrolling in postsecondary

education. In the full adapted conceptual model, the only variables that have a stronger

relationship to postsecondary enrollment are the use of college publications and websites (social

capital) and the importance of friends to finish high school (school context).

Findings for the Habitus Model for the Undocumented Asian Compared to the Matched

Asian Sample (Model 1)

The model containing all predictors was statistically significant for both the

undocumented and matched Asian samples, indicating that the model was able to distinguish

between students who enrolled and did not enroll in postsecondary education (see Appendix H for

model fits). The null model for the undocumented Asian sample correctly classified 75.3% of

cases, and the addition of habitus variables improved the percent predicted correct to 82.6%. The

addition of habitus variables only improved the percent predicted correct .5% for the matched

116

Asian sample. The strongest habitus predictors of postsecondary enrollment for undocumented

Asian students compared to the matched sample are math achievement, the importance of

education to get a job, and the desire to work rather than go to school (see Table 4-16 for results).

Undocumented Asian students at the highest math quartile were 2.897 times more likely to enroll

in postsecondary education. For the matched Asian sample, math achievement increased the odds

of postsecondary enrollment 2.094 times. The odds ratio for importance of education to get a job

is nearly two times higher for the undocumented Asian sample. Undocumented Asian students

seem to associate education with occupational attainment at a higher rate than the matched Asian

sample.

Socioeconomic status worked in opposite directions for the undocumented and matched

Asian samples. For undocumented Asians, an increase in socioeconomic status decreased the

odds of postsecondary enrollment 54%. For the matched Asian sample, an increase in

socioeconomic status increased the odds of postsecondary enrollment 1.245 times. Students who

stated that they would rather work than go to school operated in the same way described in the

findings for the undocumented and matched Hispanic samples. Undocumented Asian students

may have a desire to work to contribute to the household but forgo employment to pursue

postsecondary education. Finally, being an undocumented Asian female decreased the odds of

postsecondary enrollment 23.1%. Asian females in the matched sample were also less likely to

enroll in postsecondary education (confidence intervals available in Appendix H).

The habitus model provides a preliminary sense of how the individual habitus variables

operate on the postsecondary outcomes of undocumented Asian students. Table 4-17 provides the

Nagelkerke R-square and the percent predicted correct for both the undocumented Asian and

matched Asian samples. The Nagelkerke R-squares and the percent predicted correct for the

contextual models point to habitus as a better model for predicting postsecondary enrollment for

the undocumented Asian sample. The habitus model explains 25.2% more variance for the

117

undocumented Asian sample compared to the matched Asian sample. Additionally, the percent

predicted correct for the habitus model is 82.6% compared to 76.0% for the matched Asian

sample.

118

Table 4-16. Logistic Regression Findings for the Adapted Conceptual Model for the Undocumented and Matched Asian Samples

Model 1 Model 2 Model 3 Model 4

Group: Asian Undocumented Matched Undocumented Matched Undocumented Matched Undocumented Matched

Habitus Gender: Female .769*

(-.262) .691* (-.370)

1.269* (.238)

.586 (-.024)

4.840* (1.577)

1.649 * (.500)

4.938* (1.597)

1.428* (.356)

Socioeconomic Status .460* (-.775)

1.245* (.219)

.335* (-1.094)

1.350* (.300)

.293* (-1.229)

.741* (-.300)

.283* (-1.264)

.849** (-.164)

10th grade math quartile 2.897* (1.064)

2.094* (.739)

2.812* (1.034)

1.808* (.592)

2.825* (1.039)

2.379* (.867)

2.794* (1.028)

2.139* (.760)

Education is important to get a job later 2.897* (1.064)

1.555* (.441)

1.850* (.615)

1.105** (.100)

1.520* (.418)

.656* (-.421)

1.539* (.431)

.743* (-.297)

Would rather work than rather go to school in 12th grade

1.499* (.405)

1.609* (.476)

1.627* (.487)

1.591* (.464)

2.115* (.749)

1.028 (.028)

2.008* (.697)

.813* (-.208)

Social Capital Counselors 3.188*

(1.159) 4.653* (1.537)

6.378* (1.853)

3.939* (1.371)

5.720* (1.744)

5.236* (1.656)

Other relative 1.034 (.034)

1.785* (.579)

.679* (-.388)

2.018* (.702)

.625* (-.470)

1.906* (.645)

College publications and websites 9.259* (2.226)

2.783* (1.024)

3.560* (1.270)

1.423* (.352)

3.165* (1.152)

1.641* (.495)

College representatives 1.890* (.637)

4.156* (1.424)

1.143** (.134)

9.754* (2.278)

1.338* (.291)

10.645* (2.365)

Parents Provide advice about plans for college entrance exams (10th grade)

1.002 (.002)

1.138* (.129)

.657* (-.420)

1.537* (.430)

.653* (-.427)

1.464* (.381)

School Context Learning hindered by lack of space 1.059

(.057) .836* (-.179)

1.113* (.107)

.785 (-.242)

There are gangs in school 1.070 (.067)

.594* (-.521)

1.121* (.114)

.435* (-.833)

Does not feel safe at school .811* (-210)

1.982* (.684)

.851* (-.161)

1.644* (.497)

Got into a physical fight at school .433* (-.838)

.333* (-1.098)

.337* (-1.089)

.343* (-1.069)

119

Important to friends to finish high school

2.776* (1.021)

.475* (-.745)

3.025* (1.107)

.387* (-.950)

Number of friends going to four-year colleges asked in 12th grade

5.578* (1.719)

2.016* (.701)

6.352* (1.849)

2.086* (.735)

Percent of student body in AP courses asked in 12th grade counselor survey

.990* (-.010)

.965* (-.036)

.992* (-.008)

.962* (-.038)

Policy Context

State Policy Context 2.025* (.706)

.273* (-1.298)

Constant .005 (-5.242)

.109 (-2.214)

.002 (-5.992)

.027 (-3.609)

.095 (-10.321)

.914 (-.090)

.000 (-11.129)

7.048 (1.953)

Note. *p<.001, **p<.05

a. See Appendix C for variable scaling.

120

Table 4-17. Nagelkerke R-Squared and Percent Predicted Correct for the Undocumented and

Matched Asian Samples

Measure Undocumented Asian Matched Asian

Habitus

Nagelkerke R-Square .353 .201

Percent Predicted Null Model

75.3% 75.5%

Percent Predicted Correct

82.6% 76.0%

Habitus and Social Capital

Nagelkerke R-Square .544 .427

Percent Predicted Null Model

75.3% 75.5%

Percent Predicted Correct

87.0% 82.4%

Habitus, Social Capital, and School Context

Nagelkerke R-Square .585 .512

Percent Predicted Null Model

75.3% 75.5%

Percent Predicted Correct

88.8% 87.4%

Habitus, Social Capital, School Context, and Policy Context

Nagelkerke R-Square .594 .538

Percent Predicted Null Model

75.3% 75.5%

Percent Predicted Correct

78.3% 89.1%

121

Findings for the Habitus and Social Capital Model for the Undocumented Asian Compared

to the Matched Asian Sample (Model 2)

The habitus and social capital model containing all predictors was statistically significant

for both the undocumented and matched Asian samples, indicating that the model was able to

distinguish between students who enrolled and did not enroll in postsecondary education. Table

4-16 shows the variables that made a statistically significant contribution to the model.

Math achievement continues to be the best indicator of postsecondary enrollment of the

habitus variables for the undocumented Asian sample—the odds ratio is nearly twice as high as

the matched Asian sample. Undocumented Asian students scoring in the top quartile of math

assessment were 2.812 times more likely to enroll in postsecondary education, compared to 1.808

for the matched sample. Importance of education to get a job later continues to be a stronger

predictor of postsecondary enrollment for the undocumented Asian sample. With the addition of

social capital to the model, the desire to work rather than go to school became a stronger predictor

for the undocumented Asian sample but still operates in the same direction. Socioeconomic status

continues to work in opposite directions for the undocumented and matched Asian samples.

Finally, being an undocumented female increased the odds of postsecondary enrollment 1.269

times, for the matched Asian sample being a female decreased the odds of postsecondary

enrollment 41.4%.

The strongest social capital predictors for postsecondary enrollment for undocumented

Asian students are college publications and websites. Undocumented Asian students who used

publications and websites for college entrance information had 9.259 higher odds of enrolling in

postsecondary education; for the matched Asian sample the odds of postsecondary enrollment

was 2.783. Parents and other relatives as sources of college entrance information did not have a

122

significant relationship to postsecondary information for the undocumented Asian sample. This

finding may be not be an indicator of lack of interest but rather an indication of lack of

knowledge about postsecondary entrance requirements. Undocumented Asian students relied on

college publications and websites, counselors, and college representatives for postsecondary

entrance information. The matched Asian sample seemed to rely on a much broader group of

resources for college entrance information. Counselors, college representatives, other relatives,

and parents were all sources of information for the matched sample in its search for

postsecondary entrance requirements (confidence intervals available in Appendix H).

The habitus and social capital model provides a preliminary sense of how the individual

social capital variables operate on the postsecondary outcomes of undocumented Asian students.

Table 4-17 provides the Nagelkerke R-square and the percent predicted correct for both the

undocumented and matched Asian samples. The Nagelkerke R-squares and the percent predicted

correct for the contextual models point to habitus as a better model for predicting postsecondary

enrollment for the undocumented Asian sample. The habitus model explains 11.7% more

variance for the undocumented Asian compared to the matched Asian sample. Additionally, the

percent predicted correct for the habitus model is 87.0% for the undocumented Asian compared

to 82.4% for the matched Asian sample.

Social capital reveals the importance of college publication and websites, counselors, and

college representatives in the postsecondary decisions of undocumented Asians. Undocumented

Asians seem to be accessing a select group of social capital resources in their postsecondary

search compared to the broader strategy of the matched Asian sample.

123

Findings for the Habitus, Social Capital, and School Context Model for the Undocumented

Asian Compared to the Matched Asian Sample (Model 3)

The habitus, social capital, and school context model containing all predictors was

statistically significant for both the undocumented and matched Asian samples, indicating that

this model was able to distinguish between undocumented students who enrolled and did not

enroll in postsecondary education. With the addition of the school context block of variables,

there are several significant changes to the habitus block of variables (see Table 4-16 for results).

The addition of school context to the model provides several changes to the habitus block

of variables. The advantage for undocumented Asian females was nearly three times higher than

the matched sample. Being an undocumented Asian female increased the odds of postsecondary

enrollment 4.840 times for the matched Asian sample; the odds for the matched Asian sample is

1.649. Gender was the strongest predictor of postsecondary enrollment for the undocumented

Asian sample, followed by math achievement and the desire to work rather than go to school,

which was not significant for the matched Asian sample. Finally, socioeconomic status operated

in a negative direction for both the undocumented and matched Asian samples. This finding may

be a reflection of the importance of education for the undocumented and Asian samples.

Undocumented Asians tie occupational success to education, and socioeconomic status does not

deter this population from enrolling in postsecondary education.

The addition of school context to the model decreases the relationship between college

publications and websites for the undocumented sample, but the difference was still quite large.

Undocumented Asian students who went to college publications and websites for college entrance

information were 3.560 times more likely to enroll in college, while the odds ratio for the

matched sample was 1.423. The strongest social capital predictor of postsecondary enrollment for

the undocumented Asian sample was college counselors.

124

This addition of school context increased the relationship between postsecondary

enrollment and the role of counselors as a source of information. Undocumented Asian students

who went to a counselor for college entrance information were 6.378 times more likely to enroll

in postsecondary education, and the odds ratio for the matched Asian sample was 3.939. Other

relatives and parents became negative predictors of postsecondary enrollment for the

undocumented Asian sample, a marked difference from the matched Asian sample. Additionally,

the role of college representatives for the matched sample was quite striking. Students in the

matched Asian sample who went to college representatives for college entrance information were

9.754 times more likely to enroll in college, while for undocumented Asian students the odds

were 1.143. Undocumented Asian students seemed to leverage counselors in their search process.

Compared to the matched sample, undocumented Asian students’ odds of enrolling in

postsecondary education were higher for those who went to counselors for college entrance

information (confidence intervals available in Appendix H).

The school context block of variables reveals the importance of peers on the

postsecondary enrollment of the undocumented Asian sample. The strongest predictor for

undocumented Asian students was the number of friends going to four-year colleges. As the

number of friends going to four-year colleges increased the odds of postsecondary enrollment for

the undocumented Asian sample was 5.578—more than two times higher than for the matched

Asian sample. Importance of friends to finish high school operated similarly for the

undocumented Asian sample; undocumented Asians whose friends felt it is important to finish

highs school were 2.776 times more likely to enroll in postsecondary education. For the matched

sample, this variable operates in the opposite direction.

The effect of physical space and the presence of gangs were not significant predictors for

undocumented Asian students’ postsecondary enrollment. This non-finding may be further

evidence of the role of cultural memory (Model, 2008) and immigrant optimism (Suárez-Orozco

125

& Suárez-Orozco, 1995). Undocumented Asian students view U.S. schooling as an opportunity

for social mobility and are not deterred by a lack of space or less than adequate schools (Zhou &

Bankston, 1994). Despite this perspective, undocumented Asian students who do not feel safe at

school or get into a physical fight had lower odds of enrolling in postsecondary education. For the

undocumented matched sample, the variable operates in the opposite direction—a quite

unexpected finding.

The habitus and social capital model provides a preliminary sense of how the individual

social capital variables operate on the postsecondary outcomes of undocumented Asian students.

Table 4-17 provides the Nagelkerke R-square and the percent predicted correct for both the

undocumented Asian and matched Asian samples. The Nagelkerke R-squares and the percent

predicted correct for the contextual models point to habitus as a better model for predicting

postsecondary enrollment for the undocumented Asian sample. The habitus model explains 7.3%

more variance for the undocumented Asian compared to the matched Asian sample. Additionally,

the percent predicted correct for the habitus model is 88.8% compared to 87.4% for the matched

Asian sample.

The relationship between the school context and enrollment in postsecondary education

tells a mixed story of the importance of school safety and peer context to the outcomes of

undocumented Asian students. For the undocumented Asian population, the greatest factor in

postsecondary enrollment was peer context—specifically, the importance of peers who were

going on to four-year colleges.

126

Findings of the Full Adapted Conceptual Model for the Undocumented Asian Compared to

Matched Asian Sample (Model 4)

The full adapted conceptual model containing all predictors was statistically significant

for both the undocumented and matched Asian samples, indicating that the model was able to

distinguish between students who enrolled and did not enroll in postsecondary education. Table

4-16 provides a list of the independent variables that make a statistically significant contribution

to the model.

With the addition of the state policy context variable to the model, there are minimal

changes to habitus. The advantage for undocumented Asian females persisted in the full model;

undocumented Asian females had 4.938 times higher odds of enrolling in postsecondary

education than males, and being a female in the matched Asian sample increased the odds of

postsecondary enrollment by 1.428 times. Gender continued to be the strongest predictor of

postsecondary enrollment for the undocumented Asian sample, followed by math achievement

and the desire to work rather than go to school. Socioeconomic status continued to have a

negative relationship with postsecondary enrollment for both the undocumented Asian sample

and the matched Asian sample. Prior studies (Teranishi et al., 2004) have found parental income

to be a significant predictor of enrollment at selective colleges, this study provides evidence that

for the undocumented sample other factors, such as the importance of education to get a job,

academic achievement, and social capital resources are more important for postsecondary

enrollment for the undocumented Asian population than socioeconomic status.

The addition of state policy context to the model made minimal changes to the social

capital variables in the model. Undocumented Asian students who accessed counselors and

publications and websites for information on college entrance requirements had higher odds of

enrolling in postsecondary education. Parents and other relatives as sources of information on

entrance exams continued to operate in a negative direction for the undocumented Asian sample.

127

The strongest predictor for the matched Asian sample was college representatives. Asian students

in the matched sample were 10.645 times more likely to enroll in college, while the

undocumented Asian sample’s odds were 1.338.This may be the result of undocumented Asian

students’ concern of having to disclose undocumented status to college representatives, high

school counselors appear to be viewed as more trusted resources and may be the reason

undocumented students rely on them more heavily for postsecondary entrance information.

With the addition of state policy context to the model, counselors’ perception of space

and the presence of gangs in schools both became positive predictors of postsecondary enrollment

for the undocumented Asian sample. These variables worked in opposite directions for the

matched Asian sample. Similarly, school safety, and importance of friends to finish high school

operated in opposite directions for the undocumented and matched Asian samples. Peers’

educational achievements were important predictors for the undocumented Asian sample. The

higher the number of friends going on to four-year college increased the odds of postsecondary

enrollment for the undocumented Asian sample 6.352 times compared to 2.086 times for the

matched Asian sample. The importance of friends to finish high school also had a positive

association to postsecondary enrollment for the undocumented Asian sample and decreased the

odds of postsecondary enrollment for the matched Asian sample.

The final variable in the model is state policy context. Undocumented Asian students

living in states with ISRT policies were 2.025 times more likely to enroll in postsecondary

education compared to undocumented students who lived in state without ISRT policies. The

variable operates in the opposite direction for the matched Asian sample. Students in the matched

Asian sample who lived in states with ISRT policies were 72.7% less likely to enroll in

postsecondary education (confidence intervals available in Appendix H).

The full adapted conceptual model provides compelling findings regarding the

relationship between state policy context on the postsecondary outcomes of undocumented Asian

128

students. Table 4-17 provides the Nagelkerke R-square and the percent predicted correct for both

the undocumented Asian and matched Asian samples. The Nagelkerke R-squares and the percent

predicted correct for the contextual model provides mixed results. The full adapted conceptual

model explains 5.6% more variance for the undocumented Asian sample compared to the

matched Asian sample, but the percent predicted correct for the full conceptual model is 89.1%

compared to 88.6% for the matched Asian sample.

Summary of Findings

At the outset of this study, I proposed three research questions to examine the likelihood

of postsecondary enrollment by undocumented students. The following summary highlights the

major findings.

Question 1: Is the postsecondary academic preparation of undocumented students comparable to

their U.S. citizen peers?

For both the undocumented Hispanic and undocumented Asian samples, postsecondary

preparation, as measured by GPA, was comparable to their matched and native U.S. citizen

counterparts. Undocumented Hispanic and Asian students’ math course enrollment was

comparable to that of their respective matched sample counterparts but significantly different

from their native sample counterparts. Finally, for the undocumented Hispanic sample, SAT

scores were significantly different than those of the matched and native Hispanic samples. For the

undocumented Asian sample, SAT scores were not significantly different from the matched Asian

sample but were significantly different from that of native Asians.

129

Question 2: How do social capital, school context and policy context operate independently on

postsecondary enrollment for undocumented students and which social capital and school level

factors are the strongest predictors of postsecondary enrollment for undocumented students?

The independent examination of the social capital variables reveals that the main resource

that undocumented students were using for information about postsecondary entrance

requirements were college publication and websites. Undocumented Hispanic students who used

websites and college publications as a source of college information were 5.816 times more likely

to enroll in postsecondary education. For undocumented Asian students, the odds ratio was even

larger, with website and publication research increasing postsecondary enrollment 8.348 times,

which is not surprising since 58.4% of undocumented Asians are using the Internet for

postsecondary research compared to 31.6% of the undocumented Hispanic sample. Of the

individual contextual layers examined, social capital explains the greatest amount of variance for

both the undocumented Asian and Hispanic samples, and the variables in the model predict the

highest number of cases correctly for the undocumented Hispanic sample. In sum, social capital

appears to be the strongest predictor of postsecondary enrollment for both undocumented groups.

The school context model seems to predict the postsecondary enrollment for

undocumented Hispanic and Asian samples in similar ways. The direction of these variables for

both undocumented groups is the similar; the difference is the strength of the relationship. For

both groups, peer context was important in predicting postsecondary enrollment. For

undocumented Hispanic students, having friends who viewed finishing high school as important

was the strongest social capital predictor for postsecondary enrollment. For undocumented Asian

students, the number of friends going on to four-year college was the strongest predictor for

postsecondary enrollment.

Finally, the logistic regression of state policy context provides evidence that the presence

of ISRT policies is important for both the undocumented Hispanic and Asian samples. Policy

130

context appears to have a stronger relationship to the postsecondary enrollment of undocumented

Hispanic students than of undocumented Asian students. Undocumented Hispanic students living

in states with an ISRT policy were 2.391 times more likely to enroll in higher education than

students living in states without an ISRT policy. For the undocumented Asian sample, living in an

ISRT state increased the odds of postsecondary enrollment 1.336 times. Hispanic students seemed

to be more sensitive to ISRT policies compared to Asian students.

Question 3: Does the adapted conceptual model help explain the likelihood of postsecondary

enrollment for undocumented Hispanic and Asian students? Which of the contextual areas in the

adapted model has the greatest influence on postsecondary enrollment?

Analysis of the full adapted conceptual model’s ability to explain the postsecondary

enrollment patterns of undocumented Hispanic and Asian students suggests that the model is a

better predictor of postsecondary enrollment for the undocumented Hispanic sample than the

undocumented Asian sample. Improvement in percent predicted correct with all variables in the

model was better for the undocumented Hispanic compared to the undocumented Asian sample.

The model with habitus, social capital, and school context was the best predictor of postsecondary

enrollment for the undocumented Asian sample.

For the undocumented Hispanic and Asian samples, the contextual factors with the

greatest influence on postsecondary enrollment differ. For the undocumented Hispanic sample,

school context had a greater influence on postsecondary enrollment. For the undocumented Asian

sample, social capital had a greater influence on the prediction of postsecondary enrollment.

131

Chapter 5

Discussion, Implications, and Conclusion

This study was motivated by the scarcity of research on the educational outcomes and

postsecondary pathways of undocumented students. Despite the growth of the undocumented

population enrolled in and progressing through the educational system in the United States little is

known about their experience with schooling, the resources they draw upon in their

postsecondary search process and the factors that shape their decision to enroll in postsecondary

education (Passel & Cohn, 2008). With over 1.5 million undocumented persons in the United

States under the age of 18 an understanding of their postsecondary pathways and factors that

influence these is of growing importance (Hofer et al., 2009). While there is some evidence of

successful navigation through K-12 education with an estimated 65,000 undocumented high

school students graduating from high school every year (Horwedel, 2006; Passel, 2003), there is

conflicting evidence of a serious problem with an estimated 49% of undocumented students

dropping out of school each year (Passel, 2005). The lack of understanding of the factors that

influence educational outcomes merit the investigation of the factors that influence postsecondary

enrollment and policies and practice that will improve the educational opportunities for the

undocumented population.

To address the scarcity of research and to attempt to provide a clearer understanding of

how undocumented students experience schools, utilize social capital in their search process, and

are influenced by state level ISRT policies I adapted Perna’s (2006) conceptual model on college

choice to examine the contextual factors that influence postsecondary enrollment. With 88-90%

of the undocumented population originating from Mexico, Central America, South America,

132

Asia, South East Asia, the Philippines, China and Korea (Passel, 2006) this study used ELS:2002

data to identify a population of Hispanic and Asian students that met a pre-established criteria for

the undocumented sample.(See Figure 3-1 for a complete listing of the data points considered for

inclusion in the undocumented sample). This chapter provides a brief summary of the study as

well as an overview of methods and findings from parts I, II and III. I will first propose

independent contextual models and then a revised adapted conceptual model based on the

findings of the full model. I conclude this by offering implications for research, policy, and

practice.

Summary

The participation of undocumented students in postsecondary education has become a

highly politicized and emotionally charged issue in higher education with proponents arguing that

undocumented students are a resources and have been invested in educationally for whom a

pathway will lead to productive citizenry (Song, 2003) and opponents who counter that

undocumented students are a drain on resources (Schwartz & Stiefel, 2004) and crowd out natives

by filling limited seats in classrooms (Borjas, 2004; Song, 2003). While the Supreme Court

decision in Plyler vs. Doe (1982) provided access to a free K-12 public education this right did

not extend to higher education, creating in effect an educational ceiling for undocumented

students. Although federal laws do not prohibit undocumented students from enrolling in

postsecondary education, with no consensus at the state level, undocumented students are left to

navigate postsecondary pathways that can be complex and confusing (Burkhardt et al., 2011).

Research on undocumented students who successfully navigate the pathway to

postsecondary education reveals that this population of students are utilizing parents, siblings,

family, and peers to access information about postsecondary education (Chan, 2010; Gonzalez,

133

2011; Perez, 2010; Perez, Espinoza, Ramos, Coronado & Cortes, 2009). Additionally, state policy

context—specifically, the presence of ISRT policies—has a positive impact on postsecondary

enrollment (Flores, 2010). However, the bulk of studies on this population of students have been

conducted on small samples and within a very limited context (institution/state specific). While

insightful, much of the available research does not reveal how undocumented students within

different contexts and from different racial/ethnic groups might be influenced by schools, utilize

social capital, or be aware of state policy context. This study contributes to the existing literature

on undocumented students by focusing on Hispanic and Asian undocumented students to examine

the influence of school context, social capital and policy context on postsecondary enrollment.

The research questions guiding this work were:

1. Is the postsecondary academic preparation of undocumented students comparable to their

U.S. citizen peers?

2. How do social capital, school context and policy context operate independently on

postsecondary enrollment for undocumented students and which social capital and school

level factors are the strongest predictors of postsecondary enrollment for undocumented

students?

3. Does the adapted conceptual model help explain the likelihood of postsecondary

enrollment for undocumented Hispanic and Asian students? Which of the contextual

areas in the adapted model has the greatest influence on postsecondary enrollment?

This study adapted Perna’s (2006) Conceptual Model of College Choice to attempt to

construct an understanding of the broad contextual factors that influence postsecondary

enrollment and which individual level factors influence postsecondary enrollment. Perna’s model

divides the influential factors on college choice into four contextual layers: habitus; school and

134

community context; higher education context; and social, economic, and policy context. Perna’s

(2006) model moves beyond the singular consideration of students as rational actors and places

them within families, schools, and economic and political contexts that influence college-going

decisions. In this study, Perna’s theory provided a theoretical grounding for studying

postsecondary access for undocumented students. The framework was reduced based upon

emergent literature on undocumented immigrants, immigrants, and underrepresented populations

which provides evidence of the importance of social capital, school context and policy context as

factors in postsecondary decisions.

This study used a chi-square test of independence and bivariate logistic regression to

answer the research questions posed above. Analysis was divided into three parts. Part I presented

a descriptive portrait of the undocumented student population compared to a matched sample and

native sample. This includes a chi-square test of independence to determine if undocumented

students are significantly different than their respective native counterparts. Part two and three

both employ bivariate logistic regression (Cabrera, 1994; Woldbeck, 1998; Ying, Peng, Kuk, &

Ingersoll, 2002). Part II examined the relationship between individual contextual areas and

postsecondary enrollment for the undocumented Hispanic and Asian populations by contextual

area. Part III examined the findings of the full adapted conceptual model by contextual blocks for

the undocumented Hispanic and Asian populations compared to their respective matched sample

counterparts.

Discussion of the Academic Preparation of the Undocumented Sample: Part I

The descriptive portrait and the chi-square findings for the undocumented Hispanic and

Asian populations provide evidences of similar academic preparation of the undocumented

sample to their respective matched sample counterparts. For the undocumented Hispanic sample

135

their academic preparation as measured by GPA and math course taking is not significantly

different than the matched Hispanic sample. Plainly stated, undocumented Hispanic students earn

similar grades and take a similar pattern of math courses to their U.S. born counterparts. For the

Asian undocumented sample we see a similar pattern, their academic preparation (GPA, math

course taking, and SAT) is no different than their matched sample counterparts.

The fact that the undocumented Hispanic populations enter high school with similar

postsecondary plans as U.S. citizen counterparts represents a lost opportunity for U.S. society

(Song, 2003). Given the similar academic preparation of undocumented Hispanic students to their

matched sample counterparts, the fact that 15% less enroll in college represents a loss of an

economic invest in an already disadvantaged population and a loss in human capital to the U.S.

economy (Baum & Ma, 2007). For the undocumented Hispanic population the lack of a clear

pathway to postsecondary education may impact the habitus of this population. The lack of a

viable pathway may be leading the undocumented population to view college as an incongruous

choice given their legal status. Prior research reveals that undocumented persons associate their

legal status with limited aspirations and social mobility (Abrego, 2006, 2008; Menjivar, 2008).

The fact that the undocumented Hispanic student population enters high school with similar

postsecondary aspiration as their match sample peers reveals that this population does not have

different aspirations than their peers but other factors influence their postsecondary enrollment.

These may be financial or related to family obstacles but what is clear is that academic

preparation is not reason for the lower postsecondary enrollment for the undocumented Hispanic

population.

For the undocumented Asian population the story is very different. While the

undocumented Asian sample enters high school with the lowest percentage of postsecondary

plans, they actually enroll in college at a slightly higher rate than their matched sample

counterparts. The undocumented Asian sample seems to benefit from the solace of invisibility

136

(Chan, 2010) that comes with their undocumented status. While undocumented status may be

associated with shame the population is able to overcome stigma and stereotypes because

undocumented status is associated with the Hispanic population (Chan, 2010). Additionally, the

undocumented Asian population may view education as the only option to improve their

undocumented and financial status, which is strikingly different from what we see for the

undocumented Hispanic population who are forgoing postsecondary education in place of other

pursuits.

Analysis of the Proposed Adapted Conceptual Model

At the beginning of this dissertation, a preliminary model of the factors that influence the

postsecondary access of undocumented students was presented. The model, adapted from Perna

(2006), posited that college choice is a multi-layered process, influenced by habitus (including

demographic characteristics [gender, race/ethnicity]; cultural capital and social capital; school

and community context; higher education context; and social, economic, and policy context). The

contextual layers all influence how the student perceives the expected benefits of college, which

has a direct effect on college choice. While originally proposed as a model for college choice,

Perna’s model contains factors that go beyond college choice and have a relationship with

postsecondary enrollment for the undocumented student population. In the proposed adapted

model, I examine how these contextual layers are related to postsecondary enrollment.

In the adapted model (Figure 2-2), I propose that habitus, social capital, school context,

and state policy have a direct influence on undocumented students’ decision to continue their

education beyond high school, and that this influence is not solely a function of habitus.

Additionally, I propose that Oakes’ (2003) critical factors for access and diversity could be

adapted as a proxy for school context. Specifically, I examine two areas of Oakes’ (2003)

137

framework, safe and adequate schools and college-going environment, to operationalize school

context. Finally, I simplify the model by eliminating higher education (layer 3) context from the

model.

Model Assessment Based on Logistic Regression Findings: Part II

Two sets of logistic regression analysis were conducted to examine the relationship

between each contextual layer and postsecondary enrollment. The first analysis examined the

relationship between contextual layer and postsecondary enrollment for the undocumented

population. The findings from this analysis provided some evidence of how undocumented

Hispanic and Asian students’ likelihood of postsecondary enrollment is associated with each of

the contextual layers and how individual variables may operate in the process. In the following

section I explore both relationships and revise the proposed model based on findings from the

analysis.

Revised Conceptual Model for Habitus

The relationship between habitus and postsecondary education points to similarities

between the undocumented Hispanic and Asian populations. Figure 5-1 shows the habitus model

and the variables used to measure each of the areas in Perna’s (2006) model. The overall habitus

model is a fair predictor of postsecondary enrollment for the undocumented population. Of the

variables examined, three were positive predictors of postsecondary enrollment (math

achievement, importance of education to get a job, and would rather work than go to school),

gender decreased the odds of postsecondary enrollment for females in the habitus model, and the

relationship between socioeconomic status and postsecondary enrollment was not congruent. One

138

of the unexpected findings throughout the study was the relationship between expected costs

(would rather work than go to school) and postsecondary enrollment. One key to thinking about

this finding is that for the undocumented population there is a big difference between a desire and

an ability to work. Undocumented students are traditionally from homes with low socioeconomic

status and are likely to feel a duty or obligation to work and contribute to society, but their legal

status becomes an obstacle to work. The work of Martinez-Calderon (2009) pointed to the finding

that undocumented students from rural Mexico viewed higher education as a route to professional

employment and a legalizing function. If undocumented parents are encouraging postsecondary

education in the hope of improving their legal status and employability, the fact that the desire to

work rather than go to school become a positive predictor of postsecondary enrollment is not

surprising.

Figure 5-1. Revised habitus model.

For undocumented Hispanic students, the strongest habitus predictor of postsecondary

enrollment is the importance of education to get a job, and for undocumented Asian students math

achievement is a slightly better predictor of postsecondary enrollment. Based on the findings, the

revised habitus model excludes socioeconomic status from consideration, and I removed the

directional arrows from demand for higher education and expected costs and benefits to

postsecondary enrollment. These components are aspects of student habitus but do not impact

139

postsecondary enrollment directly. Demand for higher education appears to be universal (81% of

the undocumented Hispanic sample, and 90% of the Asian sample, have postsecondary

aspirations) and is not dictated by math achievement or socioeconomic status (which was not a

significant predictor for Hispanic and was a negative predictor for Asian students). I would

categorize expected benefits and costs as a component of students’ value of education, which

adds to the value of habitus as a predictor of postsecondary enrollment. Additionally, I would

include legal status, as this study and other work (Glick & White, 2003; Keller & Harker Tillman,

2008; Perreira, Mullan Harris, & Lee, 2006) provides evidence that immigrant status influences

postsecondary enrollment. Collectively, these variables provide a framework to examine the

relationship between postsecondary enrollment and habitus.

The habitus model was a better predictor for the undocumented Asian student population

than the Hispanic population. The only model that predicted more cases correctly was the full

school context model. While there were commonalities in the influence of variables, the

association between the population and postsecondary enrollment points to significant

differences.

Revised Conceptual Model for Social Capital

In Perna’s (2006) model, social capital is a component of habitus. While conceptually I

agree with this categorization, for this study I wanted to examine social capital variables

independently from habitus since research has shown that undocumented Hispanic and Asian

students rely on different resources during their postsecondary search processes. Hispanic

students have depended on peers, other relatives, and parents (Ceja, 2004; Kimura-Walsh et al.,

2009; Gándara, 1995; Gibson, Gándara, & Koyama, 2004; Pérez and McDonough, 2008, Post,

1990), conversely, Asian students rely upon institutions (churches, families, language, non-profit

140

organizations, and culture schools) in their search processes. Consequently, I would expect that

these populations would use different resources in their process for deciding whether to continue

onto postsecondary education. Figure 5-2 provides a graphical representation to how the social

capital operates on postsecondary enrollment.

Figure 5-2. Revised social capital model.

The revised social capital model includes areas that were significant for both

undocumented groups. As such, other relatives, publications, the Internet, and college

representatives are all important contributors in the postsecondary search process for the

undocumented populations. It is important to note the difference in odds ratios for publications

and Internet. Not only was it a better predictor but a higher percentage of the Asian sample used

the internet to conduct research on entrance requirements (31.6% for undocumented Hispanic

compared to 58.4% for the undocumented Asian sample). My sense and previous research

supports the use of the Internet, rather than traditional print publications, by Asians (Kim &

Gasman, 2011) in their search process.

The inclusion of the Internet as a form of social capital can be debated, but Coleman

(1988) defined social capital as social structures that facilitate action which, were it not present,

would not be possible. I do not believe that if we were to remove the Internet as a source of

141

information that postsecondary enrollment for this population would “not be possible,” but the

Internet has made the search process much less threatening for this population as its members can

gain information without having to disclose their legal status. Coleman (1988) argued that social

capital is dependent on three elements: trustworthiness, the extent of the obligation held, and

information channels. Two of the concepts are directly relevant to the search process of

undocumented students: trustworthiness and information channels. Trustworthiness, or the

confidence that the provider of the capital has in the recipient that the exchange will be repaid

(Coleman, 1988), becomes null and void in a electronic transfer of information. Internet

information creates an exchange free transfer of information that greatly benefits those with

limited access and less knowledgeable resources. As such, undocumented students would not

have to worry about “paying” back the provider of information. Information channels, which

Coleman specified, require that the information source be knowledgeable in the area in which

information is being sought. Undocumented students are less able to go directly to postsecondary

institution for information, which would be more reliable than using some traditional social

capital resources (such as parents, other relatives, etc,). Based on Coleman’s definition, I would

argue that the Internet is an electronic form of social capital which undocumented students are

accessing in their search process. They subsequently take the information and possibly cross-

reference what they find with other more traditional information sources (counselors, college

representatives, etc). It is important to note that it is not simply having Internet access that

increases the likelihood of postsecondary enrollment but the use of the Internet to do research on

postsecondary entrance requirements. The use of the Internet to conduct this type of research may

be a marker and not necessarily a predictor of postsecondary enrollment.

The use of other relatives as a source of social capital is an established finding in the

literature on college choice (Ceja, 2004; Gándara, 1995; Gibson, Gándara, & Koyama, 2004; Kim

& Gasman, 2011; Pérez & McDonough, 2008; Post, 1990; Teranishi et al., 2004), and this study

142

supports the importance of other relatives in accessing postsecondary education. Surprisingly,

college representatives are more influential in this process than other relatives. This finding is

supported by an analysis of the means for these variables, 53.5% of undocumented Asians went

to a college representative for information compared to 24.0% who went to relatives. The finding

is similar for undocumented Hispanic students with 37.6% going to college representatives and

only 14.3% going to other relatives. For undocumented student populations, college

representatives may be a valuable resource for accessing institutional resources and financial aid

opportunities of which sources external to the organization may not be aware. For undocumented

students, this interaction would require disclosure of their legal status and may be the reason

college representatives are not as strong a predictor as the college publications and websites,

which allow students to remain anonymous.

Two forms of social capital operated in opposite directions for the undocumented student

populations: counselors and parents as a source of information. Gonzales (2003) highlighted the

importance of teachers and counselors for postsecondary attainment, but in a later study (2011) he

noted the fact that counselors and other school-level resources made differential investment in

students based on academic ability—with college goers receiving greater support than students he

qualified as dropouts/graduates. The demographic profile provides some evidence of a difference

in academic achievement between the samples, but the differential investment in Asians is

surprising. The means of the populations show that both Asian and Hispanic undocumented

students go to counselors at similar rates, 83.4% for Asians and 82.0% for Hispanics but the type

of information they received and the impact of that information on postsecondary enrollment is

dramatically different. The role of parents in the postsecondary achievement is also well

established in the literature for both the Hispanic and Asian students (Guo & Harris, 2006; Kao

1999; Kao & Tienda 1995; Keller & Harker Tillman, 2008; Portes & Rumbaut, 1996, 2001;

Portes & Zhou, 1993; Rumbaut, 1997), but the direction of the relationship for the Asian sample

143

is surprising. Undocumented Asian students are going to their parents for postsecondary

information at a higher rate than undocumented Hispanic students (1.91 for Asians compared to

1.79 for Hispanics) but seem to contextualize this information against a selective group of

resources, including “experts” such as the Internet, counselors, college representatives, and other

relatives. Since parents’ familiarity with the higher education system is limited, Asian students

are relying more heavily on other resources.

Of the models examined, the social capital model predicted the highest number of cases

correctly and explained the greatest amount of variance for the Hispanic population. Social

capital seems to be critical for this population.

Revised Conceptual Model for School Context

The role of school context in the postsecondary search process of the undocumented

samples provides an interesting glimpse into how schools and the scholastic environment can

impact students’ postsecondary education decisions. Figure 5-3 shows the school context model

and the variables used to measure each of the areas. Perna (2006) describes layer 2 as school and

community context, which is made up of the availability of resources, types of school-level

factors discussed in the literature, safe and adequate facilities. College-going environment was

adapted from Oakes’ (2003) critical conditions for equity and diversity in college access. Of the

variables examined in this study, two were positive predictors of postsecondary enrollment

(importance of friends to finish high school, and number of friends going on to four-year college).

The absence of gangs at school was not significant for the Asian sample, and the percentage of

the student body in Advanced Placement courses was not significant for the Hispanic sample and

a negative predictor for the Asian sample.

144

Two of the confounding results throughout this study were the results of learning

hindered by lack of space, which was a positive predictor of postsecondary enrollment, and the

percentage of the student body in Advanced Placement courses, which did not achieve

significance for the undocumented Hispanic sample and a negative predictor of postsecondary

enrollment for the undocumented Asian sample. This is partially explained by the low

percentages of students in Advanced Placement courses at the schools undocumented students

attend (15.7% for the undocumented Asian and 12.9% for the undocumented Hispanic students).

This speaks to the quality of the schools that these students attend but also supports the findings

of Zhou and Bankston (1994) who found that despite a school context in which a majority of

students dropped out or had low academic success, Vietnamese students were able to overcome

school context to achieve a level of academic success superior to that of their native counterparts.

In other words, school facilities do not serve as a deterrent for the undocumented immigrant

population. Undocumented students are able to harness what Suárez-Orozco and Suárez-Orozco

(1995) termed immigrant optimism to overcome a school environment that domestic students

might consider less than ideal but which immigrants might view as acceptable because of their

experiences with schooling in their home countries. The literature also points to parents’ high

educational and occupational expectations (Guo & Harris, 2006; Kao 1999; Kao & Tienda 1995;

Keller & Harker Tillman, 2008; Portes & Rumbaut, 1996, 2001; Portes & Zhou, 1993; Rumbaut,

1997), which may quell the negative effect one would expect from attending a school at which

the facilities may be inadequate.

For both undocumented groups it seems that peer context is the most important predictor

of postsecondary enrollment. For undocumented Hispanic students the importance of friends to

finish high school is the strongest predictor of postsecondary enrollment, while for undocumented

Asian students the number of friends going to four-year colleges is the strongest predictor. The

revised model school context considers only those areas that have a positive relationship with

145

postsecondary education and are statistically significant for both groups (Figure 5-3). The revised

model includes detractors for postsecondary enrollment: student does not feel safe, the presence

of gangs in the schools and physical fights, while the percentage of the student body in Advanced

Placement courses was excluded from the model.

Figure 5-3. Revised school context model.

It is worth noting that the school context model predicted the highest number of cases

correctly for the undocumented Asian student population and the second highest, after social

capital, for the undocumented Hispanic population highlighting the importance of school context

for postsecondary enrollment.

Revised Conceptual Model for State Policy Context

State policy is the final variable in the model. The policy context of the state is defined by

the presence or absence of an ISRT policy. The finding from the quantitative analysis points to a

significant advantage for Hispanic students. This work supports the findings of Flores (2010),

who found that undocumented students living in states with ISRT policies were 1.54 times more

146

likely to enroll in college compared to undocumented students living in states without ISRT

policies.

Figure 5-4. Revised state policy context model.

Hispanic students seem to view ISRT policies as a legal justification to pursue

postsecondary education (Abrego, 2008). The relationship between postsecondary enrollment and

ISRT policies is not strong for the undocumented Asian as it is for the undocumented Hispanic

population. This is not surprising given that 69.0% of the undocumented Hispanic sample lived in

states with ISRT policies compared to 57.8% of the undocumented Asian population. As such,

postsecondary enrollment for the undocumented Asian population becomes a household strategy

in which undocumented students are engaging to improve the household situation and because of

the being undocumented is considered a Hispanic issue (Chan, 2010), undocumented are able to

enroll in postsecondary unnoticed. This might also be a strategy of undocumented Asian

households to improve their families’ social and economic mobility. This is very similar to Stark

and Bloom’s (1985) new economics of labor migration, which proposed that migration decisions

are collective household strategies used to minimize risk. Families consider migration as a broad

household strategy not exclusive to the individual but viewed as beneficial or harmful to the

family unit (Stark & Bloom, 1985). Additionally, undocumented populations may view

postsecondary education as a pathway to legalization (Martinez-Calderon, 2009).

147

Revised Adapted Conceptual Model: Part III

While the findings of the individual models provide an important consideration of how

individual contextual layers and variables are associated with the postsecondary outcomes of

undocumented students, the full model provides an assessment of how the postsecondary

enrollment of undocumented students is associated with each of the contextual layers compared

to the matched sample.

Looking at the outcomes of the full adapted conceptual model, it is interesting to observe

that are many similarities between the two populations. Of the habitus variables in the model 10th-

grade math quartile, importance of education for employment and math achievement are

significant predictors of postsecondary enrollment for undocumented Asian and Hispanic

students. Students’ desire to work and socio-economic status are both significant predictors of

postsecondary enrollment but operated in different directions for the undocumented students

samples.

Among the social capital variables there was a similarity in three of the five variables:

counselors, college publications and websites, and college representatives. Both parents and other

relatives worked in opposite directions for the undocumented Hispanic and Asian students.

Of the school context variables, there were several positive predictors of postsecondary

enrollment for the undocumented Hispanic and Asian populations: learning hindered by lack of

space, importance of friends to finish high school and number of friends going on to four-year

colleges. The school safety variables either worked in opposing directions (there are gangs in

school), or were negative predictors of postsecondary enrollment (does not feel safe at school and

got into a physical fight at school). These variables point to the importance of school context for

postsecondary enrollment for undocumented students.

148

Finally, policy context operated in the same directions for the undocumented Hispanic

and Asian population. ISRT policies are a significant positive predictor of postsecondary

enrollment for the undocumented Hispanic and Asian population.

This analysis makes the case for the important differences we must consider within

populations when examining student outcomes. If I were to propose a model which only included

the variables that were significant for both groups, many important variables that were important

predictors of postsecondary education for the population would be excluded. While there are

many similarities on how the variables in the contextual models operate on the undocumented

Hispanic and Asian populations there are important considerations that make a single model

challenging to account for differences (see Figure 5-5).

149

Figure 5-5. Revised adapted conceptual model.

Implications

The body of research on the postsecondary access and college choice of undocumented

students has grown over the past several years (Chan, 2010; Gonzales, 2011; Pérez, 2010; Perez,

Espinoza, Ramos, Coronado, & Cortes, 2009), but to address the continued challenges this

population of students faces in accessing postsecondary education, a more nuanced understanding

of the factors that contribute to or impede postsecondary access is critical. This study provides a

150

framework from which to begin to understand the complexity of factors contributing to

postsecondary access for the undocumented student population. Implications for research, policy,

and practice are proposed in this section.

Implications for Research

Despite the growing body of research on undocumented students, little attention has been

paid to the social capital resources this community is engaging and how they experience schools

(Chan, 2010; Gonzales, 2011; Pérez, 2010; Perez, Espinoza, Ramos, Coronado, & Cortes, 2009).

While several studies have examined the role of state policy on postsecondary access of

undocumented students (Abrego, 2008; Flores & Chapa, 2009; Vasquez Heilig et al., 2011), there

is a need for researchers to examine how the contextual factors operate to facilitate or discourage

school completion and postsecondary attainment. The available studies on undocumented

students have largely focused on a single contextual factor on undocumented student educational

performance or access (Abrego, 2008; Flores, 2010; Martinez-Calderon, 2009; Perez, 2010). This

study contributes to the extant literature on undocumented student access by examining the

complexity of the undocumented population’s experience and attempting to identify the myriad of

factors that influence educational outcomes. Future research that examines the contextual areas

that impact the precollege and college experiences of undocumented populations would add to the

scholarship and understanding of undocumented populations.

Specifically, differences in habitus between populations are an area that deserves further

research and consideration. If habitus shapes one’s world view and the choices the population

views as appropriate or inappropriate (Griffin et al., 2012) legal status and gender seem to shape

how one views the viability of postsecondary education. Horvat (2003) describes habitus as a

lens. Student’s first view their choices through the lens of race/ethnicity, followed by

151

socioeconomic status and for this sample immigration status and gender become additional lenses

that can shape the decision to enroll in postsecondary education. This role of habitus in shaping

postsecondary decisions needs to continue to be scrutinized, students K-12 experiences and

access to resources can be influenced by changes to policy and practice but influencing how and

if a population views themselves in relation to postsecondary education requires additional

inquiry.

Research on undocumented students’ K-12 experiences represent a gap in the current

literature and would add to our understanding of how undocumented students experience school

and how it affects postsecondary access. Gonzales’ (2011) study explored how undocumented

students experience school, but more research is needed on peer context, the role of school safety,

and the influence of school academic culture on postsecondary attainment. Future research should

examine the role of school academic achievement on high school completion and academic

achievement for undocumented populations. In this study I attempted to operationalize this

question by examining the percentage of the student body in Advanced Placement courses, but

more refined definitions of academic and college-going culture are needed that take into account

school resources and the role of teachers on educational outcomes for this population.

Additionally, much of the available research on the undocumented populations has been

conducted on small samples and within a specific institutional or state context (Abrego, 2008;

Gonzales, 2011; Menjivar, 2008; Perez, 2010). Scholars should continue to examine the

undocumented population at this level, but more studies are needed that are nationally

representative of the undocumented population. Flores’ (2010) work using the current population

study or Flores and Chapa (2009) and Vasquez Heilig and associates’ (2011) use of state data to

create a broader understanding of effect of state policy on student access are examples of using

national or statewide data to study the undocumented population. This present study uses

available data and research to identify a proxy for undocumented status in a national data set, and

152

scholars should continue to develop methods to identify undocumented populations in nationally

representative data sets. Furthermore, researchers should consider examining undocumented

populations that are understudied in the literature on undocumented populations. This study

includes an examination of the undocumented Asian population, but research on Asian, African,

Caribbean, and other undocumented groups is largely absent from the literature.

Pérez’s (2010) study examined factors that influenced the college choice process of

undocumented students and found that outreach as opportunity, cost/affordability, and social

networks influenced choice. While studies on college choice add to our understanding of

undocumented students who have successfully navigated K-12 education and college admission

requirements, there is a gap in the literature on those students that never make it to that point. The

adapted conceptual model offers some promise for examining high school completion and

postsecondary enrollment for the undocumented population prior to college choice. Scholarship

on the factors contributing to postsecondary enrollment are needed to address policies and

practice that will contribute to the educational success of undocumented students.

Implications for Policy

This study provides an opportunity to consider the importance of ISRT policies for

providing postsecondary access for undocumented students. While the U.S. Congress has taken

up a broader discussion about creating a pathway for citizenship for undocumented students

through immigration reform, undoubtedly a population of students will be excluded. While there

are not many details on who would benefit if comprehensive immigration reform were passed,

prior versions were tailored to undocumented students who were brought to the United States as

children and who graduated from a U.S. high school (Gonzales, 2009). The grade or age cutoff

being considered by the U.S. Congress is not known or specified, but we can expect that there

153

will continue to be a group of undocumented students in the United States without a pathway to

legitimization and legalization. Undocumented Hispanic students in states with ISRT policies

were 3.025 times more likely to enroll in postsecondary education, and undocumented Asian

students in states with ISRT policies were 2.025 times more likely to enroll in postsecondary

education. Given the possibility that a population of students will be excluded, it is important for

states to maintain policies that provide a pathway to postsecondary education for undocumented

students. This study along with prior studies (Flores, 2010; Vasquez Heilig et al., 2011)

establishes the importance of ISRT policies in creating a pathway to postsecondary education.

Currently 11 states have ISRT policies (see Table 1.2), and with uncertain or negative policy

contexts in the vast majority of states, the opportunity for undocumented populations to escape

the possibility of a permanent underclass (Plyler vs. Doe, 1982).

Additionally, the importance of assuring that students attend schools that are safe and free

of violence is of critical importance for all students. The undocumented Asian and Hispanic

students examined in this study who did not feel safe at school or got into a physical fight at

school had decreased odds of enrolling in postsecondary education. Research on Hispanic and

Asian immigrants (Crosnoe, 2008b; Han, 2008; Hao & Pong, 2008; Peguro, 2009) has revealed

that these populations are more likely to attend schools where the environment is less secure and

where they are likely to experience fear. Policy-makers nationally need to examine school safety

policies to create learning environments that are free from intimidation, the presence of gangs,

and school violence.

One of the most challenging aspects of studying undocumented populations is identifying

them in nationally representative data sets. Federal guidelines prohibit public K-12 education

from asking questions regarding legal status (Strayhorn, 2006). This policy radically limits the

certainty and the reliability of studies, bringing findings into question. In addition, there are no

governmental agencies that directly count the undocumented immigrant population (Passel,

154

2005). While I am not advocating for a shift in federal data collection policy, a more detailed

approach to citizenship and immigrant status should be considered by states and institutions.

Surveys that captured a broader cross-section of citizenship and legal status (i.e., legal permanent

resident, international students, refugee, other) would allow for more accurate identification of

undocumented populations in state and nationally representative data sets.

Implications for Practice

In addition to policy considerations, there are also important considerations for

practitioners. One of the most concerning findings is the role of counselors in the postsecondary

decision-making processes of the Hispanic sample. In his study on undocumented students,

Gonzales (2011) found that counselors made differential investments in undocumented students

based on perceived academic ability. Greater training needs to occur at the graduate-school level

and school level to provide culturally relevant counseling services to students. For undocumented

Hispanic students in this study, it was not until school context was included in the model that

counselors as a source of postsecondary entrance information became a positive predictor for

postsecondary enrollment, and for the matched Hispanic sample, counselors were not positive

predictors of postsecondary enrollment in any of the models.

The types of resources that students are considering also provide an opportunity for a

change in practice. Since undocumented students seem to depend upon other relatives and parents

in their search process (Pérez, 2010, Perez & McDonough, 2008), it is imperative that counselors,

schools, precollege programs, and community organizations create programs and services to

inform these groups of the academic preparation, entrance, and cost requirements to pursue

postsecondary education.

155

Additionally, the use of the Internet as a resource for college information highlights the

change in student fact gathering on postsecondary entrance requirements. While previous

research (Kim & Gasman, 2012) established the use of college search engines in the search

process for Asian students, the use of this resource by undocumented students presents an

opportunity for postsecondary search engines and postsecondary institutions. Postsecondary

institutions and postsecondary education search engines should post information in the languages

of the largest immigrant populations being served. While it may not be possible to provide

website translations into every language, it is important to have publications available online that

will provide entrance information to undocumented students and their parents. Additionally,

creating opportunities for undocumented students to engage with representatives of these

institutions in a format that allows undocumented students to remain anonymous would create

additional resources for undocumented students. Such approaches could include online web chats

or student portals with additional information for undocumented students and their families on

preparation, entrance, and cost information.

Conclusion

Research on the undocumented immigrant population indicates that there are

approximately 11.6 million undocumented immigrants living in the United States (Hoefer et al.,

2009). Of the 11.6 million estimated undocumented persons in the United States, roughly 65,000

undocumented students graduate from high school every year (Passel, 2005). While the

achievement of these 65,000 undocumented students is noteworthy, Passel (2005) has estimated

that 49% of the undocumented student population never graduates. The findings of this study

reveal that undocumented students’ access to postsecondary enrollment is influenced by their

perceptions of the importance of education as pathway to employment, social capital, school

156

context, and ISRT policies. Specifically, school counselors, college publications and websites,

and college representatives were important resources for the undocumented Asian and Hispanic

populations. At the school level, peer context (importance of friends to graduate from high school

and the number of friends going on to four-year colleges) was important for postsecondary

attainments. School safety, not feeling safe at school and engaging in a physical fight, can have a

negative impact on postsecondary enrollment, and lastly, ISRT policies are effective tools in

creating a pathway to postsecondary education for undocumented students. The adapted

conceptual model provides researchers with a mechanism to examine factors that influence the

decision to enroll in postsecondary education and to ensure that educational opportunities are

available to all populations.

157

References

Abrego, L. G. (2006). I can't go to college because I don't have papers: Incorporation patterns of

Latino undocumented youth. Latino Studies, 4, 212-231.

doi:10.1057/palgrave.lst.8600200

Abrego, L. G. (2008). Legitimacy, social identity, and the mobilization of the law: The effects of

Assembly Bill 540 on undocumented students in California. Law and Social Inquiry,

33(3), 709-734. doi:10.1111/j.1747-4469.2008.00119.x

American Immigration Lawyers Association Issue Papers. (2003). Washington DC: American

Immigration Lawyers Association. Document Number)Arizona State Legislature. (2010).

Support our law enforcement and safe neighborhoods act (SB 1070). Retrieved from

http://www.azleg.gov/legtext/49leg/2r/bills/sb1070s.pdf

Asian American Center for Advancing Justice. (2011). A community of contrasts: Asian

Americans in the United States: 2011. Washington, DC. Asian Pacific American Legal

Center, Asian American Justice Center.

Baum, S., & Ma, J. (2007). Education pays. The benefits of higher education for individuals and

society. New York, NY. College Board. Retrieved from College Board website:

http://www.collegeboard.com/prod_downloads/about/news_info/trends/ed_pays_2007.pd

f

Becker, G. S., & Tomes, N. (1986). Human capital and the rise and fall of families. Journal of

Labor Economics, 4(3), S1-S39.

Borjas, G. J. (2004). Do foreign students crowd out native students from graduate programs?

(NBER Working Paper No. w10349). Retrieved from Social Science Research Network

website: http://ssrn.com/abstract=515243

Bourdieu, P. (1984). Distinction: A social critique of the judgment of taste (R. Nice, Trans.).

Cambridge, MA: Harvard University Press.

Bourdieu, P. (1997) The forms of capital, in: A. Halsey, H. Lauder, P. Brown & A. Stuart Wells

(Eds.) Education: Culture, Economy and Society, Oxford: Oxford University Press.

Bourdieu, P., & Wacquant, L. J. D. (1992). An invitation to reflexive sociology. Chicago, IL:

University of Chicago Press.

Brennan, J. Opinion of the Court, Supreme Court of the United States, Plyler v. Doe, 457 U.S.

202. Appeal from the United States Court of Appeals for the Fifth Circuit (1982).Brown,

S. K., Hirschman, C. (2006). "The End of Affirmative Action in Washington State and its

Impact on the Transition from High School to College." Sociology of Education 79:106-

30.

158

Brown, S. K., & Hirschman, C. (2006). The end of affirmative action in Washington state and its

impact on the transition from high school to college. Sociology of Education, 79,106-130.

Burkhardt, J. C., Ortega, N., Vidal Rodriguez, A., Frye, J. R., Nellum, C. J., Reyes, K. A.,

Hussain, O., Kovacheff Badke, L., & Hernandez, J. (2011). Reconciling federal, state and

institutional policies determining educational access for undocumented students:

Implications for professional practice. Ann Arbor, MI: National Forum on Higher

Education for the Public Good.

Cabrera, A. F. (1994). Logistic regression analysis in higher education: An applied perspective. In

John C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 10, pp.

225-256). New York, NY: Agathon.

Callahan, R.M. (2008.). Latino college-going: Adolescent boys’ language use and girls’ social

integration. Bilingual Research Journal, 31(12), 175-200.

Capps, R., Fix, M., Murray, J., Ost, J., Passel, J. S., & Herwantoro, S. (2005). The new

demography of America's schools: Immigration and the No Child Left Behind Act.

Retrieved from the Urban Institute website:

http://www.urban.org/UploadedPDF/311230_new_demography.pdf

Ceja, M. (2004). Chicana college aspirations and the role of parents: Developing educational

resiliency. Journal of Hispanic Higher Education, 3(4), 338-362.

doi:10.1177/1538192704268428

Chan, B. (2010, Winter). Not just a Latino issue: Undocumented students in higher education.

Journal of College Admissions, 206, 29-31.

Cohen, J. W. (1988). Statistical power analysis for the behavioral sciences (2nd

ed.). Hillsdale,

NJ: Lawrence Erlbaum Associates.

Coleman, J. (1988). Social capital in the creation of human capital. The American Journal of

Sociology, 94, S95-S120.

Coy-Ogan, L. (2009). Perceived factors influencing the pursuit of higher education among first-

generation college students (Unpublished doctoral dissertation). Liberty University,

Lynchburg, VA.

Crosnoe, R. (2005a). Double disadvantage or signs of resilience? The elementary school contexts

of children from Mexican immigrant families. American Educational Research Journal,

42(2), 269-303. doi:10.3102/00028312042002269Crosnoe, R. (2005b). The diverse

experiences of Hispanic students in the American educational system. Sociological

Forum, 20(4), 561-588.

Cuban Readjustment Act of 1966, Pub. L. No. 89-732, 80 Stat. 1161 (1966) (codified as amended

at § 1255 (1994 & Supp. II 1996)).

Cuellar, M., Chung, E., & Lucido, J. A. (2012). College access and success assessment: A

conceptual framework for assessing high school college-going cultures. Paper presented

159

at the annual meeting of the American Association for the Study of Higher Education,

Las Vegas, NV.

Dickson, L. M. (2006). “Does Ending Affirmative Action in College Admissions Lower the

Percent of Minority Students Applying to College?,” Economics of Education Review,

25(1), 109-119.

Dika, S. L., & Singh, K. (2002). Applications of social capital in educational literature: A critical

syntheses. Review of Educational Research, 72(1), 31-60.

doi:10.3102/00346543072001031

Feliciano, C. (2005a). Educational selectivity in U.S. immigration: How do immigrants compare

to those left behind? Demography, 42(1), 131-152. doi:10.1353/dem.2005.0001

Feliciano, C. (2005b). Does selective migration matter? Explaining ethnic disparities in

educational attainment among immigrants' children. International Migration Review,

39(4), 841-871. doi:10.1111/j.1747-7379.2005.tb00291.x

Flores, S. M. (2010). State Dream Acts: The effects of in-state resident tuition policies on the

college enrollment of undocumented Latino students in the United States. The Review of

Higher Education, 33(2), 239-283. doi:10.1353/rhe.0.0134

Flores, S. M., & Chapa, J. (2009). Latino immigrant access to higher education in a bipolar

context of reception. Journal of Hispanic Higher Education, 8(90), 90-109.

doi:10.1177/1538192708326996

Foraker Act, Pub. L. No. 56-191, 31 Stat. 77 (1900).

Gándara, P. C. (1995). Over the ivy walls: The educational mobility of low-income Chicanos.

Albany: State University of New York Press.

Gibson, M., Gándara, P., & Koyoama, J. (2004). School connections: U.S. Mexican youth, peers,

and school achievement. New York, NY: Teachers College Press.

Glick, J. E., & White, M. J. (2003). The academic trajectories of immigrant youths: Analysis

within and across cohorts. Demography, 40(4), 759-783.

Gonzales, R. G. (2009). Young lives on hold: The college dreams of undocumented students.

Retrieved from the College Board website:

http://professionals.collegeboard.com/profdownload/young-lives-on-hold-college-

board.pdf

Gonzales, R. G. (2011). Learning to be illegal: Undocumented youth and shifting legal contexts

in the transition to adulthood. American Sociological Review, 76(202), 602-619.

doi:10.1177/0003122411411901

Gonzalez, K. P., Stoner, C., Jovel, J., E. (2003). Examining the role of social capital in access to

college for Latinas: Toward a college opportunity framework. Journal of Hispanic

Higher Education, 2(1), 146-170. doi:10.1177/1538192702250620

160

Griffin, K. A., Del Pilar, W., McIntosh, K., & Griffin, A. (2012). Oh, of course I’m going to go to

college”: Understanding the role of habitus in the college choice process of Black

immigrant college students. Journal of Diversity in Higher Education, 5(2), 96-111.

doi:10.1037/a0028393

Hagen, J., MacMillan, R., & Wheaton, B. (1996). New kid in town: Social capital and the life

course effects on family migration of children. American Sociological Review, 61, 368–

385. doi:10.2307/2096354

Han, W. J. (2008). The academic trajectories of children of immigrants and their school

environments. Developmental Psychology, 44(6), 1572-1590. doi:10.1037/a0013886

Hao, L. & Pong, S. L. (2008). The role of school in upward mobility of disadvantaged

immigrants’ children. The Annals of the American Academy of Political and Social

Science, 620(1), 62-89. doi:10.1177/0002716208322582

Hegen, D. (2008). 2007 enacted state legislation related to immigrants and immigration.

Retrieved from National Center for State Legislatures website:

http://www.ncsl.org/Portals/1/documents/immig/2007Immigrationfinal.pdf

Heller, D. E. (1997). Student price response in higher education: An update to Leslie and

Brinkman. Journal of Higher Education, 68(6), 624-659.

Heller, D. E. (1999). The effects of tuition and state financial aid on public college enrollment.

Review of Higher Education, 23(1), 65-89.

Hicklin, A. (2007). The effect of race-based admissions in public universities: debunking the

myths about Hopwood and Proposition 209. Public Administration Review, 67(2), 331-

340. doi:10.1111/j.1540-6210.2007.00716.x

Hinrichs, P. (2010). The effects of affirmative action bans on the college enrollment, educational

attainment, and the demographic composition of universities. The Review of Economics

and Statistics, 94(3), 712-722. doi:10.1162/REST_a_00170

Hoefer, M., Rytina, M., & Baker, B. C. (2009). Estimates of the unauthorized immigrant

population residing in the United States: January 2008. Retrieved from Department of

Homeland Security website:

http://www.dhs.gov/xlibrary/assets/statistics/publications/ois_ill_pe_2008.pdf

Hoefer, M., Rytina, M., & Baker, B. C. (2010). Estimates of the unauthorized immigrant

population in the United States: January 2010. Retrieved from Department of Homeland

Security website:

http://www.dhs.gov/xlibrary/assets/statistics/publications/ois_ill_pe_2009.pdf

Horvat, E. M., (2003). The interactive effects of race and class in educational research: theoretical

insights from the work of Pierre Bourdieu. Retrieved from Perspectives on Urban

Education website: http://www.urbanedjournal.org/archive/volume-2-issue-1-spring-

2003/interactive-effects-race-and-class-educational-research-theoret

161

Horwedel, D. M. (2006, May 5). For illegal college students, an uncertain future. Diverse Issues

in Higher Education. Retrieved from http://diverseeducation.com/article/5815/

Hossler, D., & Gallagher, K. S. (1987). Studying student college choice: A three-phase model and

the implications for policymakers. College and University, 62(3), 207-222.

Hossler, D., & Stage, F. K. (1992). Family and high-school experience influences on the post-

secondary educational plans of ninth-grade students. American Educational Research

Journal, 29, 425-451. doi:10.3102/00028312029002425

Hossler, D., Braxton, J., & Coopersmith, G. (1989). Understanding student college choice. In J.

Smart (Ed.), Higher education: Handbook of theory and research (Vol. 5, pp. 231-288).

New York, NY: Agathon.

Hossler, D., Schmit, J., & Vesper, N. (1999). Going to college: How social, economic, and

educational factors influence the decisions students make. Baltimore, MD: The Johns

Hopkins University Press.

Howell, D. C. (2007). The analysis of missing data. In W. Outhwaite & S. Turner (Eds.), The

SAGE Handbook of Social Science Methodology (pp. 208-224). London: Sage.

Howell, J. S. (2010). Assessing the impact of eliminating affirmative action in higher education.

Journal of Labor Economics, 28(1), 113-166. doi:10.1086/648415

H.B. 78: Texas Top Ten Percent Plan. 80th Session (R). (2007).

H.B. 1403: Texas DREAM Act. 77th Session (R). (2001)

H.R. 1751--111th Congress: American Dream Act. (2009). Retrieved from

http://www.govtrack.us/congress/bills/111/hr1751

Immigration Reform and Control Act of 1986, 99-603, 99th Cong., (1986).

Illegal Immigration Reform and Immigrant Responsibility Act of 1986, 104-208, 104th Congress,

(1996).

Ingels, S.J., Pratt, D.J., Rogers, J.E., Siegel, P.H., & Stutts, E. S. (2005). Education longitudinal

study of 2002: Base-year to first follow-up data file documentation (NCES 2006–344).

Washington, DC: National Center for Education Statistics.

Kao, G. (1999). Racial identity and academic performance: An examination of biracial Asian and

African American youth. Journal of Asian American Studies, 2(3), 223-249.

doi:10.1353/jaas.1999.0023

Kao, G., & Tienda, M. (1995). Optimism and achievement: The educational performance of

immigrant youth. Social Science Quarterly, 76(1), 1-19.

Keller, U., & Harker Tillman, K. (2008). Post-secondary educational attainment of immigrant and

native youth. Social Forces, 87(1), 121-152. doi:10.1353/sof.0.0104

162

Kim, J. K., & Gasman, M. (2011). In Search of a “Good College”: Decisions and Determinations

Behind Asian American Students’ College Choice. Journal of College Student

Development, 52(6), 706-728. doi: 10.1353/csd.2011.0073

Kimura-Walsh, E., Yamamura, E. K., Griffin, K. A., & Allen W. R. (2009). Achieving the

college dream? Examining disparities in access to college information among high-

achieving and non high-achieving Latinas. Journal of Hispanic Higher Education, 8(3),

298-315. doi:10.1177/1538192708321648

Lareau, A. (1987). Social class differences in family-school relationships. Sociology of

Education, 60(2), 73-85.

Lee, E. S. (1966). A Theory of migration. Demography, 3(1), 47-57.

Leticia A. v. Bd. of Regents of the Univ of Cal., No. 588982-4, slip op. at 2 (May 7, 1985).

Lin, N. (2001). Social capital: A theory of social structure and action. New York, NY:

Cambridge University Press.

Little R. J. A., & Rubin D. B., (1989). The analysis of social science data with missing values.

Social Methods Research, 18, 292-326.

Louie, V. S. (2004). Compelled to excel: immigration, education, and opportunity among Chinese

Americans. Stanford, CA: Stanford University Press.

Manski, C., & Wise, D. (1983). College choice in America. Cambridge, MA: Harvard University

Press.

Martinez-Calderon, C. (2009). Out of the shadows: Undocumented Latino college students (ISSC

Working Paper No. 2007-2008.34). Retrieved from University of California eScholarship

website: http://www.escholarship.org/uc/item/9zj0694b

Massey, D. S., Durand, J., & Malone, N. J. (2002). Beyond smoke and mirrors: Mexican

Immigration in an era of economic integration. New York, NY: Russell Sage

Foundation.

Massey, D. S., & García España, F. (1987). The social process of international migration.

Science, 234(4816), 733-738.

McDonough, P. (1997). Choosing college: How social class and schools structure opportunity.

Albany: State University of New York Press.

Menjivar, C. (2008). Educational hopes, documented dreams: Guatemalan and Salvadoran

immigrants' legality and educational prospects. The Annals of the American Academy of

Political and Social Sciences, 620(1), 177-193. doi:10.1177/0002716208323020

Mehta, C. & Ali, A. (2003). Education for all: Chicago’s undocumented immigrants and their

access to higher education. Retrieved from University of Illinois at Chicago, Center for

Urban Economic Development website: http://www.urbaneconomy.org/node/53

163

Mexican Immigrants in the United States, 2008. (2009). Retrieved from Pew Hispanic Center

website: http://pewhispanic.org/files/factsheets/47.pdf

Model, S. (2008). West Indian Immigrants: A Black Success Story? New York, NY: Russell Sage

Foundation.

Monrad, M. (2007). High school dropout: A quick stats fact sheet. Retrieved from National High

School Center website: www.betterhighschools.org/docs/nhsc_dropoutfactsheet.pdf

Morse, A., & Birnbach, K. (2012). In-state tuition and unauthorized immigrants. Retrieved from

National Conference of State Legislatures website: http://www.ncsl.org/issues-

research/immig/in-state-tuition-and-unauthorized-immigrants.aspx

Nielsen, S. F. (2003). Proper and improper multiple imputation. International Statistical Review,

61, 317-33.

Oakes, J. (2003). Critical Conditions for Equity and Diversity in College Access: Informing

Policy and Monitoring Results. Retrieved from University of California eScholarship

website: http://www.escholarship.org/uc/item/427737xt#page-2

O'Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality

and Quantity, 41, 673-690. doi:10.1007/s11135-006-9018-6

Olivas, M. A. (Ed.). (2008). The DREAM Act and in-state tuition for undocumented students.

Washington, DC: American Association of Collegiate Registrars and Admissions

Officers.

Olivas, M. A. (2009). Undocumented college students, taxation, and financial aid: A technical

note. Review of Higher Education, 32(3), 407-416. doi:10.1353/rhe.0.0068

Oliverez, P. M. (2007). A perilous path: Undocumented immigrant students and the college

pipeline. Metropolitan Universities, 18(4), 87-101.

Orfield, G., Kucsera, J., & Siegel-Hawley, G. (2012). E pluribus … separation: Deepening

double segregation for more students. Retrieved from Civil Rights Project/Proyecto

Derechos Civiles website: http://civilrightsproject.ucla.edu/research/k-12-

education/integration-and-diversity/mlk-national/e-pluribus...separation-deepening-

double-segregation-for-more-students

Oropesa, R. S., & Landale, N. S. (2009). Why do immigrant youths who never enroll in U.S.

schools matter? School enrollment among Mexicans and non-Hispanic Whites. Sociology

of Education, 82(3), 240-266. doi:10.1177/003804070908200303

Ott, L., & Longnecker, M. (2001). An introduction to statistical methods and data analysis (5th

ed.). Pacific Grove, CA: Duxbury.

Pascarella, E. T., & Terenzini, P. T. (1991). How college affects students: Findings and insights

from twenty-years of research (1st ed.). San Francisco, CA: Jossey-Bass.

164

Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of

research (Vol. 2). San Francisco, CA: Jossey-Bass.

Passel, J. S. (2003). Further demographic information: Relating to the Dream Act. Retrieved

from the National Immigration Law Center website: www.nilc.org/document.html?id=20

Passel, J. S. (2005). Estimates of the size and characteristics of the undocumented population.

Retrieved from Pew Hispanic Center website:

http://www.pewhispanic.org/files/reports/44.pdf

Passel, J. S. (2006). The size and characteristics of the unauthorized migrant population in the

U.S. Retrieved from Pew Hispanic Center website:

http://www.pewhispanic.org/files/reports/61.pdf

Passel, J. S., & Cohn, D. (2009). A portrait of unauthorized immigrants in the United States.

Retrieved from Pew Hispanic Center website:

http://www.pewhispanic.org/files/reports/107.pdf

Passel, J. S., & Cohn, D. (2011). A Unauthorized immigrant population: national and state

trends, 2010. Retrieved from Pew Hispanic Center website:

http://www.pewhispanic.org/files/reports/133.pdf

Peguero, A. A. (2009). Victimizing the children of immigrants: Latino and Asian American

student victimization. Youth & Society, 41(2), 186-208. doi:10.1177/0044118X09333646

Pérez, P. A. (2010, Winter). College choice process of Latino undocumented students:

Implications for recruitment and retention. Journal of College Admissions, 206, 21-25.

Pérez, P. A., & McDonough, P. (2008). Understanding Latina and Latino college choice: A social

capital chain migration analysis. Journal of Hispanic Higher Education, 7, 249-265.

doi10.1177/1538192708317620

Perez, W., Espinoza, R., Ramos, K., Coronado, H. M., & Cortes, R. (2009). Academic resilience

among undocumented Latino students. Hispanic Journal of Behavioral Sciences, 31(2),

149-181. doi:10.1177/0739986309333020

Perez Huber, L., & Malagon, M. C. (2007). Silenced struggles: The experience of Latina and

Latino undocumented college students in California. Nevada Law Journal, 7(3), 841-861.

Perez Huber, L., Malagon, M. C., & Solorzano, D. G. (2009). Struggling for opportunity:

Undocumented AB 540 Students in the Latina/o education pipeline (Research Report No.

13). Retrieved from University of California Los Angeles Chicano Studies Research

Center website: http://www.chicano.ucla.edu/publications/report-brief/struggling-

opportunity

Perna, L. W. (2006). Studying college access and college choice: A proposed conceptual model.

In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. XXI, pp.

99-158). Dordrecht, The Netherlands: Springer.

165

Perna, L. W., Steele, P., Woda, S., & Hibbert, T. (2005). State public policies and the

racial/ethnic stratification of college access and choice in the state of Maryland. Review

of Higher Education, 28(2), 245-272.

Plyler v. Doe, 457. 457 U.S. 202. (1982). Retrieved from

http://supreme.justia.com/cases/federal/us/457/202/

Portes, A., & Fernandez-Kelley. (2008). No margin for error: Educational and occupational

achievement among disadvantaged children of immigrants. The Annals of the American

Academy of Political and Social Science, 620, 12-36. doi:10.1177/0002716208322577

Portes, A., & Hao, L. (2004). The schooling of children of immigrants: Contextual effects on the

educational attainment of the second generation. Proceedings of the National Academy of

Sciences of the United States of America, 101(33), 11920-11927.

doi:10.1073/pnas.0403418101

Portes, A., & Rumbaut, R. G. (1996). Immigrant America: A portrait (2nd

ed.). Berkeley:

University of California Press.

Portes, A., & Rumbaut, R. G. (2001). Ethnicities: Children of immigrants in America. Berkeley:

University of California Press.

Portes, A., & Wilson, K. (1976). Black-White differences in educational attainment. American

Sociological Review, 41, 414-431.

Portes, A., & Zhou, M. (1993). The new second-generation: Segmented assimilation and its

variants. The Annals of the American Academy of Political and Social Sciences, 530, 74–

96. doi:10.1177/0002716293530001006

Post, D. (1990). College-going decisions by Chicanos: The politics of misinformation.

Educational Evaluation and Policy Analysis, 12(2), 174-187.

doi:10.3102/01623737012002174

Reay, D. (2004). “‘It’s all becoming a habitus’”: Beyond the habitual use of habitus in

educational research. British Journal of Sociology of Education, 25, 431-444.

doi:10.1080/0142569042000236934

Regents of University of California v. Superior Court (Bradford) (1990). 225 Cal. App. 3d p72

[276 Cal. Rptr.197].

Roos, P. D. (1997). Postsecondary Plyler IHELG (Monograph No. 91-7). University of Houston

Law Center website: http://www.law.uh.edu/ihelg/monograph/91-7.pdf

Royston, P. (2004). Multiple imputation of missing values. Stata Journal, 4(3): 227-241.

Rubin, D. B., (1987). Multiple imputation for nonresponse in surveys. New York, NY: John

Wiley & Sons.

Rumbaut, R. G. (1997). Passages to adulthood: The adaptation of children of immigrants in

Southern California: Russell Sage Foundation.

166

Sanders, J. M. & Nee V. (1996). Immigrant Self-Employment: The Family as Social Capital and

the Value of Human Capital. American Sociological Review, 61, 231-249.

Schwartz, A. E., & Stiefel, L. (2004). Immigrants and the distribution of resources within an

urban school district. Educational Evaluation and Policy Analysis, 26(4), 303-327.

doi:10.3102/01623737026004303

Sewell, W., Haller, A., & Portes, A. (1969). The educational and early occupational attainment

process. American Sociological Review, 34(10), 82-92.

Song, J. (2003, May 13). Raising hope for better life, citizenship. Baltimore Sun. Retrieved from

http://articles.baltimoresun.com/2003-05-13/news/0305130033_1_undocumented-

students-pay-in-state-tuition-immigrant-students

St. John, E. P. (1991). What really influences minority attendance? Sequential analysis of the

high school and beyond sophomore cohort. Research in Higher Education, 32(2), 141-

158.

St. John, E. P., & Noell, J. (1989). The effects of student financial-aid on access to higher-

education – An analysis of progress with special consideration of minority enrollment.

Research in Higher Education, 30(6), 563-581.

Stark, O., & Bloom, D. E. (1985). The new economics of labor migration. The American

Economic Review, 75(2), 173-178.

Strayhorn, C. K. (2006). Undocumented immigrants in Texas: A financial analysis of the impact

to the state budget and economy. Retrieved from Texas Comptroller of Public Accounts

website: http://www.window.state.tx.us/specialrpt/undocumented/undocumented.pdf

Student financial report: Annual report on AB 540 tuition exemptions, 2006-07 academic year.

(2008). Berkeley: University of California Office of the President.

Suárez-Orozco, C., & Suárez-Orozco, M. (1995). Transformations: Migration, family life, and

achievement motivation among Latino adolescents. Palo Alto, CA: Stanford University

Press.

Editorial: The dream of education [Editorial]. (2003, April 28). The Los Angeles Times. Retrieved

from http://articles.latimes.com/2003/apr/28/opinion/ed-dream28

Teranishi, R., Ceja, M., Antonio, A.L., Allen, W., & McDonough, P. (2004). The College-Choice

Process for Asian Pacific Americans: Ethnicity and Socioeconomic Class in Context. The

Review of Higher Education, 27(4), 527-551.

Thompson, F., & Zumeta, F. (2001). Effects of key state policies on private colleges and

universities: Sustaining private-sector capacity in the face of the higher education access

challenge. Economics of Education Review, 20 (6), 517-531. Doi:10.1016/S0272-

7757(00)00031-5

167

Tillman, K. H., Guo, G., & Harris, K. M. (2006). Grade retention among generations of

immigrant students. Social Science Research, 35(1), 129-156.

doi:10.1016/j.ssresearch.2004.07.001

The Triennial Comprehensive Report on Immigration. (1999). Retrieved from U.S. Government

Printing Office website:

http://permanent.access.gpo.gov/lps31411/2nd%20%5B1999%5D/2ndfullTriReport.pdf

U.S. Dept. of Education, National Center for Education Statistics. EDUCATION

LONGITUDINAL STUDY (ELS), 2002: BASE YEAR. ICPSR04275-v1. Washington,

DC: U.S. Dept. of Education, National Center for Education Statistics [producer], 2004.

Ann Arbor, MI: Inter-university Consortium for Political and Social Research

[distributor], 2005-10-11. doi:10.3886/ICPSR04275.v1

Valenzuela, A., & Dornbusch, S. (1994). Familism and social capital in the academic

achievement of Mexican origin and Anglo adolescents. Social Science Quarterly, 75, 18-

36.

Vasquez Heilig, J., Rodriguez, C., & Somers, P. (2011). Immigrant DREAMs" English learners,

the Texas 10% admissions plan and college academic success. Journal of Latinos and

Education, 10(2), 106-126. doi:10.1080/15348431.2011.556521

Woldbeck, T. (1998). A primer on logistic regression. Paper presented at the annual meeting of

the Southwest Educational Research Association, Houston, TX.

Ying, C., Peng, J., Kuk, L. L., & Ingersoll, G. M. (2002). An introduction to logistic regression

analysis and reporting. The Journal of Educational Research, 96(1), 3-14.

doi:10.1080/00220670209598786

Zemsky, R., & Oedel, P. (1983). The structure of college choice. New York, NY: College

Entrance Examination Board.

Zhou, M. (1997a). Growing up American: The challenge confronting immigrant children and

children of immigrants. The Annals of the American Academy of Political and Social

Sciences, 23, 63-95.

Zhou, M. (1997b). Social capital in Chinatown: The role of community-based organizations and

families in the adaptation of the younger generation. In M. Seller & L. Weis (Eds.),

Beyond Black and White: New voices, new faces in U.S. schools (pp. 181-206). Albany:

State University of New York Press.

Zhou, M. (2008). The ethnic system of supplementary education: Nonprofit and for-profit

institutions in Los Angeles’ Chinese immigrant community. In B. Shinn & H. Yoshikawa

(Eds.), Toward positive youth development: Transforming schools and community

programs (pp. 229-251). New York, NY: Oxford University Press.

Zhou, M., & Bankston, C. L., III. (1994). Social capital and the adaptation of the second

generation: The case of Vietnamese youth in New Orleans. International Migration

Review, 18(4), 821-845.

168

Zhou, M. & Li, X. (2003). Ethnic language schools and the development of supplementary

education in the immigrant Chinese community in the United States. In C. Suárez-Orozco

& I. L. G. Todorova (Eds.), New Directions for Youth Development: Understanding the

Social Worlds of Immigrant Youth (pp. 57-73). Retrieved from U.C.L.A. website at;

http://www.sscnet.ucla.edu/soc/faculty/zhou/pubs/Zhou_Li_Chinese_schools.pdf

169

Appendix A

Variables Tested in Adapted Model by Group and Contextual Area

Habitus : Hispanic

Group

Undocumented Hispanic

Matched Sample Hispanic

Variable Scaling Mean S.D. Mean S.D.

Habitus 57,200 70,478 Gender: Female (0=Male; 1=Female) .6049 .48888 .4459 .49707

Socioeconomic Status (-1.53; 1.39) -.0041 .69276 -.0681 .65431

10th grade math quartile (1=lowest quartile; 2=second quartile;

3=third quartile; 4=highest quartile)

1.65 .854 1.97 .995

English Language dominant (0=no; 1=yes) .0640 .24480 .2143 .41031

Family Composition (1=single parent/step-parent, guardian;

2=2 biological parents)

1.6195 .48552 1.6863 .46401

Education is important to get a job later (1=strongly disagree; 2=disagree;

3=agree; 4=agree)

3.50 .667 3.63 .623

No school after high school, cannot afford (0=no; 1=yes) .56 .496 .47 .499

Would rather work than rather go to school (12th grade) (0=no; 1=yes) .47 .499 .37 .484

170

Social Capital: Hispanic

Group

Undocumented Hispanic

Matched Sample Hispanic

Variable Scaling Mean S.D. Mean S.D.

Social Capital

57,200 70,478 Who student has gone to for college entrance information

in 12th grade

Counselors (0=no; 1=yes) .8200 .38417 .8914 .31113

Teacher (0=no; 1=yes) .4494 .49744 .3873 .48714

Coach (0=no; 1=yes) .0315 .17478 .0542 .22640

Parent (0=no; 1=yes) .2046 .40340 .2213 .41510

Friend (0=no; 1=yes) .5732 .49462 .4355 .49582

Sibling (0=no; 1=yes) .1738 .37894 .2786 .44831

Other relative (0=no; 1=yes) .1437 .35081 .1812 .38519

College publications and websites (0=no; 1=yes) .3160 .46492 .3952 .48890

College representatives (0=no; 1=yes) .3764 .48448 .4731 .49928

College college search guides (0=no; 1=yes) .2397 .42688 .2279 .41951

Parents provide advice about plans for college entrance

exams (10th grade) (1=never to 3=often)

1.79 .772 2.04 .816

Provide advice about applying to college (10th grade) (1=never to 3=often) 1.86 .797 2.04 .827

Discussed going to college with parents (12th grade) (1=never to 3=often) 2.54 .648 2.48 .643

Discussed SAT/ACT prep with parents (12th grade) (1=never to 3=often) 1.74 .751 1.69 .689

171

School Context: Hispanic

Group

Undocumented Hispanic

Matched Sample Hispanic

Variable Scaling Mean S.D. Mean S.D.

School Context

57,200 70,478 Parent opinion if school violence is a problem at school

(10th grade)

(0=no; 1=yes) .57 .494 .62 .487

The school is a safe place (0=no; 1=yes) .76 .428 .74 .436

How often are physical conflicts at school a problem (1=happens daily; 5=never happens) 3.05 .958 3.18 .866

How often is gang activity at school a problem (1=happens daily; 5=never happens) 3.99 .877 4.01 .940

Learning hindered by poor conditions of buildings (1=not at all to 4=a lot) 1.82 .987 1.93 .983

Learning hindered by lack of space (1=not at all to 4=a lot) 1.94 .994 1.95 .962

There are gangs in school

(1=strongly disagree to 4=strongly

agree)

2.48 .897 2.33 .865

Does not feel safe at school

(1=strongly disagree to 4=strongly

agree)

2.94 .802 3.12 .749

Got into a physical fight at school (1=never to 3=more than twice) 1.17 .428 1.16 .431

Someone threatened to hurt student (1=never to 3=more than twice) 1.23 .462 1.26 .558

Important to friends to continue education past high school (1=not important to 3=very important) 2.51 .619 2.37 .691

Important to friends to finish high school (1=not important to 3=very important) 2.58 .604 2.65 .597

Number of friends who drop out of school (10th grade) (0=none to 2=most or all .46 .609 .36 .540

Number of friends going to community college (12th grade) (1=none to 3=most or all of them) 2.16 .648 2.17 .544

Number of friends going to four-year college (12th grade) (1=none to 3=most or all of them) 2.10 .645 2.19 .642

Percent of 10th graders in college prep programs (continuous, 0-100) 44.7485 31.11873 48.2240 31.74088

Percent of student body in Advanced Placement courses

(12th grade counselor survey) (continuous, 0-40)

12.91 9.081 13.41 7.298

Percent of 2003 graduates that went to 4-year colleges (1=none to 6=75-100) 3.76 1.024 3.75 1.102

Percent of 2003 graduates what went to 2-year

colleges/vocational school (1=none to 6=75-100)

3.89 .769 3.75 .850

172

Policy Context: Hispanic

Group

Undocumented Hispanic

Matched Sample Hispanic

Variable Scaling Mean S.D. Mean S.D.

Policy Context

57,200 70,478

State Policy Context

(0=no/negative policy; 1=In-state

resident tuition policy .6904 .46233 .6904 .46233

Habitus: Asian

Group

Undocumented Asian

Matched Sample Asian

Variable Scaling Mean S.D. Mean S.D.

N 21,160 20,012

Habitus

Gender: Female (0=Male; 1=Female) .5141 .49981 .4924 .49995

Socioeconomic Status (-1.35; 1.80) .0414 .66049 .0311 .62629

10th grade math quartile (1=lowest quartile; 2=second quartile;

3=third quartile; 4=highest quartile)

2.62 1.106 2.58 1.167

English Language dominant (0=no; 1=yes) .1525 .35950 .2367 .42505

Family Composition (1=single parent/step-parent, guardian;

2=2 biological parents)

1.6566 .47484 1.6227 .48473

Education is important to get a job later (1=strongly disagree; 2=disagree;

3=agree; 4=agree)

3.64 .602 3.61 .649

No school after high school, cannot afford (0=no; 1=yes) .65 .476 .50 .500

Would rather work than rather go to school (12th grade) (0=no; 1=yes) .62 .485 .49 .500

173

Social Capital: Asian

Group

Undocumented Asian

Matched Sample Asian

Variable Scaling Mean S.D. Mean S.D.

Social Capital

21,160 20,012

Who student has gone to for college entrance information in

12th grade

Counselors (0=no; 1=yes) .8347 .37144 .8614 .34550

Teacher (0=no; 1=yes) .5401 .49840 .4491 .49741

Coach (0=no; 1=yes) .0546 .22713 .0194 .13795

Parent (0=no; 1=yes) .3823 .48596 .2395 .42681

Friend (0=no; 1=yes) .7611 .42643 .6812 .46604

Sibling (0=no; 1=yes) .3010 .45872 .3631 .48092

Other relative (0=no; 1=yes) .2401 .42716 .3004 .45846

College publications and websites (0=no; 1=yes) .5842 .49288 .6421 .47940

College representatives (0=no; 1=yes) .5355 .49875 .5687 .49528

College search guides (0=no; 1=yes) .4174 .49315 .4907 .49993

Parents provide advice about plans for college entrance exams

(10th grade) (1=never to 3=often)

1.91 .790 1.88 .770

Provide advice about applying to college (10th grade) (1=never to 3=often) 1.93 .798 2.10 .833

Discussed going to college with parents (12th grade) (1=never to 3=often) 2.64 .501 2.44 .611

Discussed SAT/ACT prep with parents (12th grade) (1=never to 3=often) 1.84 .697 1.68 .762

174

School Context: Asian

Group

Undocumented Asian

Matched Sample Asian

Variable Scaling Mean S.D. Mean S.D.

School Context

21,160 20,012

Parent opinion if school violence is a problem at school (10th

grade)

(0=no; 1=yes) .55 .497 .59 .493

The school is a safe place (0=no; 1=yes) .82 .388 .82 .387

How often are physical conflicts at school a problem (1=happens daily; 5=never happens) 3.16 .919 3.19 .909

How often is gang activity at school a problem (1=happens daily; 5=never happens) 4.12 .785 4.19 .733

Learning hindered by poor conditions of buildings (1=not at all to 4=a lot) 1.69 .783 1.76 .818

Learning hindered by lack of space (1=not at all to 4=a lot) 2.16 1.047 2.06 .999

There are gangs in school

(1=strongly disagree to 4=strongly

agree)

2.57 .988 2.50 .915

Does not feel safe at school

(1=strongly disagree to 4=strongly

agree)

3.02 .855 3.13 .795

Got into a physical fight at school (1=never to 3=more than twice) 1.11 .391 1.15 .470

Someone threatened to hurt student (1=never to 3=more than twice) 1.16 .420 1.18 .474

Important to friends to continue education past high school (1=not important to 3=very important) 2.59 .557 2.45 .630

Important to friends to finish high school (1=not important to 3=very important) 2.71 .526 2.69 .502

Number of friends who drop out of school (10th grade) (0=none to 2=most or all .19 .442 .23 .422

Number of friends going to community college (12th grade) (1=none to 3=most or all of them) 2.09 .555 2.11 .621

Number of friends going to four-year college (12th grade) (1=none to 3=most or all of them) 2.47 .554 2.51 .567

Percent of 10th graders in college prep programs (continuous, 0-100) 56.1444 33.12889 57.2486 29.75563

Percent of student body in Advanced Placement courses (12th

grade counselor survey) (continuous, 0-60)

15.76 13.398 18.14 14.823

Percent of 2003 graduates that went to 4-year colleges (1=none to 6=75-100) 4.17 1.034 4.19 1.047

Percent of 2003 graduates what went to 2-year

colleges/vocational school (1=none to 6=75-100)

3.76 .876 3.72 .841

175

Policy Context: Asian

Group

Undocumented Asian

Matched Sample Asian

Variable Scaling Mean S.D. Mean S.D.

Policy Context

21,160 20,012

State Policy Context

(0=no/negative policy; 1=In-state

resident tuition policy .5786 .49379 .6032 .48925

176

Appendix B

Variance Inflation Scores for Variables in the Adapted Conceptual Model

Variables in Equation Hispanic Undocumented and Matched

Habitus

Generation Status

1.047 1.070 1.049 1.057 1.066

Gender: Female 1.039

1.057 1.057 1.043 1.059

Socioeconomic Status 1.011 1.006

1.011 1.010 1.011

10th grade math quartile 1.019 1.034 1.039

1.040 1.037

Education is important to get a job later 1.017 1.012 1.030 1.031

1.031

Would rather work than rather go to school

in 12th grade 1.012 1.014 1.017 1.014 1.017

177

Variables in Equation Hispanic Undocumented and Matched

Social Capital

Who student has gone to for college

entrance information in 12th grade

Counselors

1.010 1.004 1.010 1.010

Other relative 1.037

1.032 1.025 1.036

College publications and websites 1.341 1.342

1.032 1.346

College representatives 1.369 1.353 1.048

1.350

Provide advice about plans for college

entrance exams (10th grade) 1.017 1.017 1.015 1.003

Variables in Equation Hispanic Undocumented and Matched

Safe and Adequate Facilities

Learning hindered by lack of space

1.032 1.027 1.050

Student perceptions

There are gangs in school 1.055

1.045 1.061

Does not feel safe at school 1.056 1.051

1.065

Got into a physical fight at school 1.032 1.019 1.019

Important to friends to finish high school

1.000 1.007

College-Going Environment

Number of friends going to four-year

colleges asked in 12th grade 1.004

1.007

Percent of student body in Advanced

Placement courses asked in 12th grade

counselor survey 1.004 1.000

178

Variables in Equation Asian Undocumented and Matched

Habitus

Generation Status

1.019 1.019 1.019 1.017 1.001

Gender: Female 1.035

1.026 1.035 1.015 1.027

Socioeconomic Status 1.017 1.008

1.010 1.017 1.017

10th grade math quartile 1.009 1.009 1.002

1.009 1.008

Education is important to get a job later 1.041 1.022 1.042 1.042

1.018

Would rather work than rather go to school

in 12th grade 1.029 1.039 1.047 1.046 1.023

Variables in Equation Asian Undocumented and Matched

Social Capital

Who student has gone to for college

entrance information in 12th grade

Counselors

1.034 1.032 1.024 1.031

Other relative 1.039

1.029 1.035 1.034

College publications and websites 1.294 1.29

1.046 1.295

College representatives 1.283 1.283 1.045

1.297

Provide advice about plans for college

entrance exams (10th grade) 1.01 1.011 1.013 1.015

179

Variables in Equation Asian Undocumented and Matched

Safe and Adequate Facilities

Counselor perceptions

Learning hindered by lack of space

1.005 1.013 1.012

Student perceptions

There are gangs in school 1.072

1.031 1.07

Does not feel safe at school 1.083 1.035

1.063

Got into a physical fight at school 1.04 1.031 1.021

Important to friends to finish high school

1.000 1.000

College-Going Environment

Number of friends going to four-year

colleges asked in 12th grade 1.004

1.000

Percent of student body in Advanced

Placement courses asked in 12th grade

counselor survey 1.004 1.000

180

Appendix C

List of Means and Standard Deviations for Variables in the Adapted Conceptual Model

Habitus: Hispanic

Group

Undocumented Hispanic

Matched Sample Hispanic

Variable Scaling Mean S.D. Mean S.D.

Habitus 57,200 70,478 Gender: Female (0=Male; 1=Female) .6049 .48888 .4459 .49707

Socioeconomic Status (-1.53; 1.39) -.0041 .69276 -.0681 .65431

10th grade math quartile (1=lowest quartile; 2=second quartile;

3=third quartile; 4=highest quartile)

1.65 .854 1.97 .995

Education is important to get a job later (1=strongly disagree; 2=disagree;

3=agree; 4=agree)

3.50 .667 3.63 .623

Would rather work than rather go to school (12th grade) (0=no; 1=yes) .47 .499 .37 .484

181

Social Capital: Hispanic

Group

Undocumented Hispanic

Matched Sample Hispanic

Variable Scaling Mean S.D. Mean S.D.

Social Capital

57,200 70,478 Who student has gone to for college entrance information

in 12th grade

Counselors (0=no; 1=yes) .8200 .38417 .8914 .31113

Other relative (0=no; 1=yes) .1437 .35081 .1812 .38519

College publications and websites (0=no; 1=yes) .3160 .46492 .3952 .48890

College representatives (0=no; 1=yes) .3764 .48448 .4731 .49928

Parents provide advice about plans for college entrance

exams (10th grade) (1=never to 3=often)

1.79 .772 2.04 .816

182

School Context: Hispanic

Group

Undocumented Hispanic

Matched Sample Hispanic

Variable Scaling Mean S.D. Mean S.D.

School Context

57,200 70,478

Learning hindered by lack of space (1=not at all to 4=a lot) 1.94 .994 1.95 .962

There are gangs in school

(1=strongly disagree to 4=strongly

agree)

2.48 .897 2.33 .865

Does not feel safe at school

(1=strongly disagree to 4=strongly

agree)

2.94 .802 3.12 .749

Got into a physical fight at school (1=never to 3=more than twice) 1.17 .428 1.16 .431

Important to friends to finish high school (1=not important to 3=very important) 2.58 .604 2.65 .597

Number of friends going to four-year college (12th grade) (1=none to 3=most or all of them) 2.10 .645 2.19 .642

Percent of student body in Advanced Placement courses

(12th grade counselor survey) (continuous, 0-40)

12.91 9.081 13.41 7.298

Policy Context: Hispanic

Group

Undocumented Hispanic

Matched Sample Hispanic

Variable Scaling Mean S.D. Mean S.D.

Policy Context

57,200 70,478

State Policy Context

(0=no/negative policy; 1=In-state

resident tuition policy .6904 .46233 .6904 .46233

183

Habitus: Asian

Group

Undocumented Asian

Matched Sample Asian

Variable Scaling Mean S.D. Mean S.D.

N 21,160 20,012

Habitus

Gender: Female (0=Male; 1=Female) .5141 .49981 .4924 .49995

Socioeconomic Status (-1.35; 1.80) .0414 .66049 .0311 .62629

10th grade math quartile (1=lowest quartile; 2=second quartile;

3=third quartile; 4=highest quartile)

2.62 1.106 2.58 1.167

Education is important to get a job later (1=strongly disagree; 2=disagree;

3=agree; 4=agree)

3.64 .602 3.61 .649

Would rather work than rather go to school (12th grade) (0=no; 1=yes) .62 .485 .49 .500

Social Capital: Asian

Group

Undocumented Asian

Matched Sample Asian

Variable Scaling Mean S.D. Mean S.D.

Social Capital

21,160 20,012

Who student has gone to for college entrance information in

12th grade

Counselors (0=no; 1=yes) .8347 .37144 .8614 .34550

Other relative (0=no; 1=yes) .2401 .42716 .3004 .45846

College publications and websites (0=no; 1=yes) .5842 .49288 .6421 .47940

College representatives (0=no; 1=yes) .5355 .49875 .5687 .49528

Parents provide advice about plans for college entrance exams

(10th grade) (1=never to 3=often)

1.91 .790 1.88 .770

184

School Context: Asian

Group

Undocumented Asian

Matched Sample Asian

Variable Scaling Mean S.D. Mean S.D.

School Context

21,160 20,012

Learning hindered by lack of space (1=not at all to 4=a lot) 2.16 1.047 2.06 .999

There are gangs in school

(1=strongly disagree to 4=strongly

agree)

2.57 .988 2.50 .915

Does not feel safe at school

(1=strongly disagree to 4=strongly

agree)

3.02 .855 3.13 .795

Got into a physical fight at school (1=never to 3=more than twice) 1.11 .391 1.15 .470

Important to friends to finish high school (1=not important to 3=very important) 2.71 .526 2.69 .502

Number of friends going to four-year college (12th grade) (1=none to 3=most or all of them) 2.47 .554 2.51 .567

Percent of student body in Advanced Placement courses (12th

grade counselor survey) (continuous, 0-60)

15.76 13.398 18.14 14.823

Policy Context: Asian

Group

Undocumented Asian

Matched Sample Asian

Variable Scaling Mean S.D. Mean S.D.

Policy Context

21,160 20,012

State Policy Context

(0=no/negative policy; 1=In-state

resident tuition policy .5786 .49379 .6032 .48925

185

Appendix D

Undocumented Proxy by State for the Hispanic and Asian Samples (Weighted)

Undocumented Population Residency by State

State Hispanic

Frequency Percent

Asian

Frequency Percent

Alabama 60 .1 0 0

Arizona 4546 7.9 0 0

Arkansas 135 .2 0 0

California 20322 35.5 6434 30.4

Colorado 1437 2.5 76 .4

Connecticut 299 .5 78 .4

District of

Columbia 42 .1 0 0

Florida 3314 5.8 429 2.0

Georgia 477 .8 736 3.5

Hawaii 15 .0 456 2.2

Illinois 5348 9.3 770 3.6

Indiana 248 .4 92 .4

Iowa 840 1.5 218 1.0

Kansas 839 1.5 0 0

Kentucky 226 .4 19 .1

Louisiana 0 0 448 2.1

Maine 54 .1 88 .4

Maryland 196 .3 248 1.2

Michigan 0 0 512 2.4

Massachusetts 415 .7 0 0

Minnesota 198 .3 1623 7.7

Montana 0 0 87 .4

Mississippi 38 .1 0 0

Missouri 19 .0 0 0

Nebraska 0 0 155 .7

Nevada 1240 2.2 0 0

New Jersey 1644 2.9 1102 5.2

New Mexico 225 .4 0 0

New York 2720 4.8 3041 14.4

North Carolina 825 1.4 468 2.2

Ohio 21 .0 225 1.1

Oklahoma 93 .2 134 .6

Oregon 580 1.0 188 .9

Pennsylvania 0 0 382 1.8

Rhode Island 82 .1 0 0

South Carolina 257 .4 81 .4

Tennessee 30 .1 0 0

Texas 8794 15.4 479 2.3

Virginia 357 .6 757 3.6

Washington 1151 2.0 1231 5.8

Wisconsin 112 .2 602 2.8

Total 57,200 100% 21,160 100%

186

Appendix E

Chi-Square, Degrees of Freedom and Model Significance Results for the Adapted

Conceptual Model (Hispanic and Asian)

Undocumented and Matched Hispanic

Model & Group n Df

Chi-

square P

Habitus

Undocumented Hispanic 57,200 5 6312.547 .001

Matched Hispanic Sample 70,478 5 15488.184 .001

Habitus and Social Capital

Undocumented Hispanic 57,200 10 15498.557 .001

Matched Hispanic Sample 70,478 10 31004.265 .001

Habitus, Social Capital and School Context

Undocumented Hispanic 57,200 17 15072.539 .001

Matched Hispanic Sample 70,478 17 21782.195 .001

Habitus, Social Capital, School Context and Policy Context

Undocumented Hispanic 57,200 18 16326.453 .001

Matched Hispanic Sample 70,478 18 21830.005 .001

Undocumented and Matched Asian

Model & Group n df

Chi-

square P

Habitus

Undocumented Asian 21,160 5 5744.251 .001

Matched Asian Sample 20,012 5 2899.915 .001

Habitus and Social Capital

Undocumented Asian 21,160 10 9642.813 .001

Matched Asian Sample 20,012 10 6761.239 .001

Habitus, Social Capital and School Context

Undocumented Asian 21,160 17 7361.599 .001

Matched Asian Sample 20,012 17 5643.614 .001

Habitus, Social Capital, School Context and Policy Context

Undocumented Asian 21,160 18 7495.45 .001

Matched Asian Sample 20,012 18 5998.665 .001

187

Appendix F

Logistic Regression Results for the Independent Conceptual Model (Undocumented Hispanic and Undocumented Asian)

Logistic Regression Results of Habitus

Undocumented Hispanic 95% CI

Variable

B S.E. P

Odds

Ratio Lower Upper

Habitus

Gender: Female -

0.232 0.018 0.000 0.793 0.765 0.822

Socioeconomic Status 0.012 0.013 0.362 1.012 0.987 1.037

10th grade math quartile 0.573 0.011 0.000 1.774 1.735 1.813

Education is important to get a job later 0.826 0.015 0.000 2.285 2.218 2.353

Would rather work than rather go to school (12th grade) 0.154 0.018 0.000 1.166 1.126 1.208

-

3.907 0.061 0.000 0.020

188

Logistic Regression Results of Social Capital

Undocumented Hispanic 95% CI

Variable

B S.E. P

Odds

Ratio Lower Upper

Social Capital

Who student has gone to for college entrance information in 12th grade -0.026 0.025 0.289 0.974 0.927 1.023

Counselors 0.523 0.028 0.000 1.688 1.597 1.784

Other relative 1.761 0.024 0.000 5.816 5.545 6.099

College publications and websites 0.575 0.022 0.000 1.778 1.702 1.857

College representatives

Parents provide advice about plans for college entrance exams (10th

grade) 0.364 0.012 0.000 1.439 1.405 1.474

-1.516 0.032 0.000 0.220

189

Logistic Regression Results of School Context

Undocumented Hispanic 95% CI

Variable

B S.E. P

Odds

Ratio Lower Upper

School Context

Learning hindered by lack of space 0.085 0.011 0.000 1.089 1.066 1.113

There are gangs in school 0.455 0.014 0.000 1.576 1.535 1.619

Does not feel safe at school 0.903 0.017 0.000 2.466 2.386 2.549

Got into a physical fight at school -0.783 0.028 0.000 0.457 0.432 0.483

Important to friends to finish high school 1.014 0.020 0.000 2.757 2.648 2.870

Number of friends going to four-year college (12th grade) 0.276 0.017 0.000 1.318 1.275 1.363

Percent of student body in Advanced Placement courses (12th grade

counselor survey) 0.000 0.001 0.950 1.000 0.997 1.003

-6.036 0.110 0.000 0.002

Logistic Regression Results of Policy Context

Undocumented Hispanic 95% CI

Variable

B S.E. P

Odds

Ratio Lower Upper

Policy Context

State Policy Context 0.872 0.019 0.000 2.391 2.306 2.480

-0.344 0.010 0.000 0.709

190

Logistic Regression Results of Habitus

Undocumented Asian 95% CI

Variable

B S.E. P

Odds

Ratio Lower Upper

Habitus

Gender: Female -0.262 0.038 0.000 0.769 0.715 0.828

Socioeconomic Status -0.775 0.029 0.000 0.460 0.435 0.487

10th grade math quartile 1.064 0.019 0.000 2.897 2.790 3.008

Education is important to get a job later 1.064 0.029 0.000 2.897 2.740 3.064

Would rather work than rather go to school (12th grade) 0.405 0.039 0.000 1.499 1.390 1.617

-5.242 0.123 0 0.005

Logistic Regression Results of Social Capital

Undocumented Asian 95% CI

Variable

B S.E. P

Odds

Ratio Lower Upper

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors 1.577 0.045 0.000 4.842 4.429 5.293

Other relative 0.571 0.051 0.000 1.769 1.602 1.955

College publications and websites 2.122 0.044 0.000 8.348 7.662 9.096

College representatives 0.610 0.042 0.000 1.840 1.695 1.999

Parents provide advice about plans for college entrance exams (10th grade) -0.061 0.025 0.015 0.941 0.895 0.988

-1.300 0.058 0.000 0.273

191

Logistic Regression Results of School Context

Undocumented Asian 95% CI

Variable

B S.E. P

Odds

Ratio Lower Upper

School Context

Learning hindered by lack of space 0.175 0.023 0.000 1.192 1.140 1.246

There are gangs in school 0.019 0.025 0.448 1.019 0.971 1.070

Does not feel safe at school 0.743 0.025 0.000 2.103 2.002 2.209

Got into a physical fight at school -0.515 0.054 0.000 0.598 0.538 0.664

Important to friends to finish high school 0.555 0.040 0.000 1.742 1.612 1.883

Number of friends going to four-year college (12th grade) 1.933 0.050 0.000 6.910 6.269 7.617

Percent of student body in Advanced Placement courses (12th grade

counselor survey) -0.007 0.002 0.000 0.993 0.990 0.996

-6.614 0.216 0.000 0.001

Logistic Regression Results of Policy Context

Undocumented Asian 95% CI

Variable

B S.E. P

Odds

Ratio Lower Upper

Policy Context

State Policy Context 0.292 0.033 0.000 1.339 1.256 1.428

0.995 0.020 0.000 2.706

192

Appendix G

Logistic Regression Results for the Adapted Conceptual Model (Undocumented and Matched Hispanic and Samples)

Logistic Regression Results of Habitus

Undocumented Hispanic 95% CI

B S.E. P

Odds

Ratio Lower Upper

Variable

Gender: Female -.232 .018 .000 .793 .765 .822

Socioeconomic Status .012 .013 .362 1.012 .987 1.037

10th grade math quartile .573 .011 .000 1.774 1.735 1.813

Education is important to get a job later .826 .015 .000 2.285 2.218 2.353

Would rather work than rather go to school in 12th grade .154 .018 .000 1.166 1.126 1.208

Constant -3.907 .061 .000 .020

193

Logistic Regression Results of Habitus and Social Capital

Undocumented Hispanic 95% CI

B S.E. P

Odds

Ratio Lower Upper

Variable

Gender: Female .025 .020 .222 1.025 .985 1.066

Socioeconomic Status .024 .014 .088 1.024 .996 1.052

10th grade math quartile .392 .013 .000 1.480 1.444 1.518

Education is important to get a job later .531 .016 .000 1.700 1.647 1.756

Would rather work than rather go to school in 12th grade .274 .020 .000 1.315 1.264 1.367

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors -.164 .026 .000 .849 .806 .893

Other relative .362 .030 .000 1.437 1.354 1.524

College publications and websites 1.551 .025 .000 4.717 4.491 4.953

College representatives .599 .023 .000 1.821 1.740 1.905

Provide advice about plans for college entrance exams (10th grade) .340 .013 .000 1.404 1.369 1.440

Constant -3.916 .069 .000 .020

194

Logistic Regression Results of Habitus, Social Capital and School Context

Undocumented Hispanic 95% CI

B S.E. P

Odds

Ratio Lower Upper

Variable

Gender: Female .168 .029 .000 1.183 1.118 1.253

Socioeconomic Status .205 .019 .000 1.227 1.181 1.274

10th grade math quartile .103 .018 .000 1.108 1.071 1.148

Education is important to get a job later .383 .022 .000 1.467 1.405 1.531

Would rather work than rather go to school in 12th grade -.304 .027 .000 .738 .700 .778

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors .382 .032 .000 1.465 1.375 1.561

Other relative .723 .046 .000 2.061 1.885 2.253

College publications and websites 1.394 .034 .000 4.031 3.775 4.306

College representatives .650 .031 .000 1.915 1.801 2.035

Provide advice about plans for college entrance exams (10th grade) .390 .018 .000 1.477 1.426 1.529

School context

Learning hindered by lack of space .140 .014 .000 1.150 1.119 1.181

There are gangs in school -.659 .017 .000 .517 .500 .534

Does not feel safe at school -.952 .020 .000 .386 .371 .402

Got into a physical fight at school -.625 .037 .000 .535 .498 .576

Important to friends to finish high school 1.204 .025 .000 3.332 3.174 3.498

Number of friends going to four-year colleges asked in 12th grade .137 .020 .000 1.147 1.103 1.193

Percent of student body in Advanced Placement courses asked in 12th

grade counselor survey

-.007 .002 .000 .993 .990 .996

Constant -2.357 .138 .000 .095

195

Logistic Regression Results of Habitus, Social Capital, School Context and Policy Context

Undocumented Hispanic 95% CI

B S.E. P Odds Ratio Lower Upper

Variable

Gender: Female .074 .030 .013 1.077 1.016 1.142

Socioeconomic Status .138 .020 .000 1.148 1.104 1.195

10th grade math quartile .112 .018 .000 1.119 1.080 1.159

Education is important to get a job later .227 .023 .000 1.255 1.200 1.312

Would rather work than rather go to school in 12th grade -.415 .028 .000 .660 .625 .697

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors .452 .033 .000 1.572 1.474 1.677

Other relative .643 .046 .000 1.903 1.739 2.081

College publications and websites 1.283 .035 .000 3.608 3.368 3.865

College representatives .666 .033 .000 1.946 1.826 2.074

Provide advice about plans for college entrance exams (10th grade) .357 .018 .000 1.429 1.379 1.481

School Context

Learning hindered by lack of space .205 .014 .000 1.228 1.194 1.262

There are gangs in school -.563 .018 .000 .569 .550 .589

Does not feel safe at school -1.050 .022 .000 .350 .335 .365

Got into a physical fight at school -.832 .040 .000 .435 .403 .471

Important to friends to finish high school 1.227 .025 .000 3.410 3.246 3.582

Number of friends going to four-year colleges asked in 12th grade .143 .020 .000 1.154 1.109 1.201

Percent of student body in Advanced Placement courses asked in 12th grade

counselor survey

-.010 .002 .000 .990 .987 .993

Policy Context

State Policy Context Hispanic 1.121 .033 .000 3.069 2.879 3.272

Constant -1.980 .144 .000 .138

196

Appendix H

Logistic Regression Results for the Adapted Conceptual Model (Undocumented and Matched Asian Samples)

Logistic Regression Results of Habitus

Undocumented Asian 95% CI

B S.E. P Odds Ratio Lower Upper

Variable

Gender: Female -.262 .038 .000 .769 .715 .828

Socioeconomic Status -.775 .029 .000 .460 .435 .487

10th grade math quartile 1.064 .019 .000 2.897 2.790 3.008

Education is important to get a job later 1.064 .029 .000 2.897 2.740 3.064

Would rather work than rather go to school in 12th grade .405 .039 .000 1.499 1.390 1.617

Constant -5.242 .123 .000 .005

197

Logistic Regression Results of Habitus and Social Capital

Undocumented Asian 95% CI

B S.E. P Odds Ratio Lower Upper

Variable

Gender: Female .238 .044 .000 1.269 1.165 1.382

Socioeconomic Status -1.094 .036 .000 .335 .312 .359

10th grade math quartile 1.034 .022 .000 2.812 2.692 2.938

Education is important to get a job later .615 .033 .000 1.850 1.734 1.975

Would rather work than rather go to school in 12th grade .487 .045 .000 1.627 1.489 1.777

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors 1.159 .054 .000 3.188 2.868 3.543

Other relative .034 .056 .552 1.034 .926 1.155

College publications and websites 2.226 .055 .000 9.259 8.316 10.309

College representatives .637 .050 .000 1.890 1.713 2.085

Provide advice about plans for college entrance exams (10th grade)

.002 .028 .939 1.002 .948 1.059

Constant -5.992 .159 .000 .002

198

Logistic Regression Results of Habitus, Social Capital and School Context

Undocumented Asian 95% CI

B S.E. P

Odds

Ratio Lower Upper

Variable

Gender: Female 1.577 .068 .000 4.840 4.238 5.527

Socioeconomic Status -1.229 .046 .000 .293 .267 .320

10th grade math quartile 1.039 .030 .000 2.825 2.663 2.998

Education is important to get a job later .418 .056 .000 1.520 1.363 1.694

Would rather work than rather go to school in 12th grade .749 .063 .000 2.115 1.870 2.393

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors 1.853 .068 .000 6.378 5.579 7.291

Other relative -.388 .071 .000 .679 .591 .780

College publications and websites 1.270 .072 .000 3.560 3.094 4.096

College representatives .134 .066 .042 1.143 1.005 1.301

Provide advice about plans for college entrance exams (10th grade)

-.420 .036 .000 .657 .612 .705

School context

Learning hindered by lack of space .057 .030 .059 1.059 .998 1.124

There are gangs in school .067 .032 .038 1.070 1.004 1.140

Does not feel safe at school -.210 .038 .000 .811 .752 .873

Got into a physical fight at school -.838 .090 .000 .433 .363 .516

Important to friends to finish high school 1.021 .054 .000 2.776 2.499 3.084

Number of friends going to four-year colleges asked in 12th grade

1.719 .066 .000 5.578 4.897 6.354

Percent of student body in Advanced Placement courses asked in 12th grade

counselor survey

-.010 .002 .000 .990 .986 .994

Constant

-

10.321

.412 .000 .000

199

Logistic Regression Results of Habitus, Social Capital, School Context and Policy Context

Undocumented Asian 95% CI

B S.E. P

Odds

Ratio Lower Upper

Variable

Gender: Female 1.597 .068 .000 4.938 4.324 5.640

Socioeconomic Status -1.264 .046 .000 .283 .258 .309

10th grade math quartile 1.028 .030 .000 2.794 2.634 2.964

Education is important to get a job later .431 .055 .000 1.539 1.381 1.715

Would rather work than rather go to school in 12th grade .697 .063 .000 2.008 1.776 2.271

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors 1.744 .070 .000 5.720 4.987 6.562

Other relative -.470 .071 .000 .625 .544 .719

College publications and websites 1.152 .072 .000 3.165 2.751 3.642

College representatives .291 .067 .000 1.338 1.173 1.526

Provide advice about plans for college entrance exams (10th grade)

-.427 .037 .000 .653 .607 .702

School Context

Learning hindered by lack of space .107 .031 .000 1.113 1.048 1.182

There are gangs in school .114 .032 .000 1.121 1.052 1.194

Does not feel safe at school -.161 .038 .000 .851 .791 .917

Got into a physical fight at school -1.089 .097 .000 .337 .278 .407

Important to friends to finish high school 1.107 .056 .000 3.025 2.709 3.378

Number of friends going to four-year colleges asked in 12th grade

1.849 .069 .000 6.352 5.554 7.265

Percent of student body in Advanced Placement courses asked in 12th

grade counselor survey

-.008 .002 .000 .992 .987 .996

Policy Context

State Policy Context Hispanic .706 .062 .000 2.025 1.794 2.286

Constant

-

11.129

.419 .000 .000

200

Logistic Regression Results of Habitus

Matched Asian Sample 95% CI

B S.E. P Odds Ratio Lower Upper

Variable

Gender: Female -.370 .037 .000 .691 .642 .743

Socioeconomic Status .219 .029 .000 1.245 1.176 1.319

10th grade math quartile .739 .017 .000 2.094 2.028 2.163

Education is important to get a job later .441 .028 .000 1.555 1.471 1.643

Would rather work than rather go to school in 12th grade .476 .037 .000 1.609 1.496 1.731

Constant -2.214 .122 .000 .109

201

Logistic Regression Results of Habitus and Social Capital

Matched Asian Sample 95% CI

B S.E. P Odds Ratio Lower Upper

Variable

Gender: Female -.024 .044 .586 .976 .895 1.065

Socioeconomic Status .300 .033 .000 1.350 1.265 1.441

10th grade math quartile .592 .020 .000 1.808 1.738 1.882

Education is important to get a job later .100 .039 .011 1.105 1.023 1.193

Would rather work than rather go to school in 12th grade .464 .043 .000 1.591 1.463 1.731

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors 1.537 .054 .000 4.653 4.189 5.168

Other relative .579 .054 .000 1.785 1.605 1.985

College publications and websites 1.024 .045 .000 2.783 2.548 3.039

College representatives 1.424 .046 .000 4.156 3.796 4.550

Provide advice about plans for college entrance exams (10th grade)

.129 .028 .000 1.138 1.078 1.201

Constant -3.609 .166 .000 .027

202

Logistic Regression Results of Habitus, Social Capital and School Context

Matched Asian Sample 95% CI

B S.E. P Odds Ratio Lower Upper

Variable

Gender: Female .500 .068 .000 1.649 1.444 1.884

Socioeconomic Status -.300 .051 .000 .741 .670 .820

10th grade math quartile .867 .030 .000 2.379 2.245 2.520

Education is important to get a job later -.421 .059 .000 .656 .585 .737

Would rather work than rather go to school in 12th grade .028 .061 .644 1.028 .913 1.159

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors 1.371 .064 .000 3.939 3.474 4.466

Other relative .702 .069 .000 2.018 1.762 2.313

College publications and websites .352 .061 .000 1.423 1.262 1.604

College representatives 2.278 .070 .000 9.754 8.501 11.192

Provide advice about plans for college entrance exams (10th grade)

.430 .040 .000 1.537 1.422 1.661

School context

Learning hindered by lack of space -.179 .032 .000 .836 .785 .890

There are gangs in school -.521 .035 .000 .594 .554 .636

Does not feel safe at school .684 .046 .000 1.982 1.812 2.168

Got into a physical fight at school

-

1.098

.063 .000 .333 .295 .377

Important to friends to finish high school -.745 .056 .000 .475 .425 .530

Number of friends going to four-year colleges asked in 12th grade

.701 .056 .000 2.016 1.808 2.247

Percent of student body in Advanced Placement courses asked in 12th grade

counselor survey

-.036 .002 .000 .965 .961 .969

Constant -.090 .320 .779 .914

203

Logistic Regression Results of Habitus, Social Capital, School Context and Policy Context

Matched Asian Sample 95% CI

B S.E. P

Odds

Ratio Lower Upper

Variable

Gender: Female .356 .071 .000 1.428 1.242 1.642

Socioeconomic Status -.164 .053 .002 .849 .766 .942

10th grade math quartile .760 .029 .000 2.139 2.019 2.266

Education is important to get a job later -.297 .057 .000 .743 .664 .831

Would rather work than rather go to school in 12th grade -.208 .063 .001 .813 .718 .919

Social Capital

Who student has gone to for college entrance information in 12th grade

Counselors 1.656 .068 .000 5.236 4.580 5.985

Other relative .645 .071 .000 1.906 1.657 2.192

College publications and websites .495 .063 .000 1.641 1.450 1.856

College representatives 2.365 .074 .000 10.645 9.208 12.307

Provide advice about plans for college entrance exams (10th grade) .381 .041 .000 1.464 1.350 1.588

School Context

Learning hindered by lack of space -.242 .033 .000 .785 .736 .838

There are gangs in school -.833 .041 .000 .435 .401 .471

Does not feel safe at school .497 .045 .000 1.644 1.506 1.795

Got into a physical fight at school -1.069 .060 .000 .343 .305 .386

Important to friends to finish high school -.950 .062 .000 .387 .343 .437

Number of friends going to four-year colleges asked in 12th grade .735 .058 .000 2.086 1.863 2.335

Percent of student body in Advanced Placement 12th grade counselor

survey

-.038 .002 .000 .962 .959 .966

Policy Context

State Policy Context Hispanic -1.298 .071 .000 .273 .237 .314

Constant 1.953 .355 .000 7.048

204

VITA

Wilfredo Del Pilar

199 Constitution Avenue • State College, PA 16801

Home (310) 850-6834 • wzd107@psu.edu

EDUCATION

The Pennsylvania State University – State College, PA

Doctor of Philosophy, Higher Education, Anticipated May 2013

California State University, Dominguez Hills – Carson, CA

Master of Education, Counseling, May 2007

Chapman University – Orange, CA

Bachelor of Arts, Communications, May 1995

Concentration: Public Relations

Publications

Perez II, D., & Del Pilar, W. (July 2008). California Assembly Bill 540 [Electronic Version]. Poli

Memos, 5, from http://community.livejournal.com/polimemo/?skip=2

Griffin, K., Del Pilar, W., McIntosh, K. & Griffin, A. (In Press). “Oh of Course I’m Going to

College”: Understanding How Habitus Shapes the College Choice Process of Black

Immigrant Students. Journal of Diversity in Higher Education.

SELECTED PRESENTATIONS

“Pathways to Postsecondary Education for Immigrant Students”

Association for the Study of Higher Education, Indianapolis, IN, November 2010

“Oh, of course I’m going to go to college: Understanding the role of habitus in the college

choice process of Black immigrant college students”

Association for the Study of Higher Education, Indianapolis, IN, November 2010

“An International Spectrum: LGBT International Students in American Higher

Education”

American College Personnel Association, Boston, MA, March 2010

“Exploring the Emerging Presence of Black Immigrants in College”

NASPA, Student Affairs Administrators in Higher Education, Chicago, IL, March 2010

“Latino Males: Where does the Disparity Begin”

American Association of Hispanics in Higher Education, Costa Mesa, CA, March 2010

“The End of the Pipeline at the Millennium: Using Stories from New African American

Attorneys to Inform Theories of Intervention”

Association for the Study of Higher Education, Vancouver, BC, Canada November 2009

“Using Segmented Assimilation Theory to Conceptualize Student Retention”

First Triennial Conference on Latino Education and Immigration, Athens, GA, October 2009