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.
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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.
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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)
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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.
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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%
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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
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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.
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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.
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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
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& 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.
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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.
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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
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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.
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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.
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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,
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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,
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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
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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
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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
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(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)
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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).
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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.
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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).
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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
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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
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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
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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
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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 • [email protected]
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