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How college affects Latinas' STEM career decision-making process: a psychosociocultural approach

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How college affects Latinas' STEM career decision-making process: a psychosociocultural approach

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Content Copyright 2015 Michelle Castellanos
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HOW COLLEGE AFFECTS LATINAS’ STEM CAREER DECISION-MAKING PROCESS:
A PSYCHOSOCIOCULTURAL APPROACH

by

Michelle Castellanos

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
Doctor of Philosophy
(URBAN EDUCATION POLICY)

August 2015

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DEDICATION
I dedicate this dissertation to my nieces and nephews—my inspiration. May you always keep
God close to your heart, follow your dreams, and inspire others to do the same.

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ACKNOWLEDGEMENTS
I feel immensely fortunate to have had the love and support of many throughout my
educational journey. First and foremost, I would like to thank God for all of the individuals and
blessings he has bestowed upon me.
I thank my family for serving as my major source of inspiration and support. To my parents
(Mary and Jorge Castellanos), thank you for your teaching me the value of hard work, humility,
and faith. To my twin sister (Georgina Castellanos), thank you for your consistent and constant
encouragement and company. To my significant other (Jonathan Diaz), thank you for being there
with and for me every step of the way. To my grandmother (Mama Tila), gracias por tu amor
incondicional y por tus rezos. Se que Dios te escucha.
To my Rossier friends and colleagues, especially Ji, Sable, Raquel, Jenna, Diane, and Rudy, I
am grateful to have shared my time at Rossier with such wonderful individuals. Ji and Sable
thank you for being my academic family. I have learned so much from you. To my childhood
and college friends who have supported and cheered me on along the way: thank you. I am
grateful for your friendship, understanding, and flexibility.
To my committee chair and advisor, Dr. Darnell Cole, thank you for always believing in me
and helping me become an independent scholar. I would not have had the opportunity to explore
my intellectual creativity without your confidence and flexibility. To my mentor and role model,
Dr. Jeanett Castellanos, thank you for your guidance, inspiration, and generosity. Words cannot
express how grateful I am to have had your support.
To my dissertation committee members, Dr. Darnell Cole, Dr. Jeanett Castellanos, Dr. Ruth
Chung, and Dr. John Jack McArdle, thank you for your guidance, enthusiasm, and support of my
work. To the UC Berkeley and USC faculty and staff who have shaped my journey and
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development: thank you for the role each of you has played in my development and success (Dr.
Kurt Organista, Sherman Boyson, Dr. Darnell Cole, Dr. Jeanett Castellanos, Dr. Ruth Chung, Dr.
John J. McArdle, Dr. Richard Andalon, Dr. Tatina Melguizo, Dr. William Tierney, Dr. Adriana
Kezar, and Dr. Morgan Polikoff).

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Abstract
As the United States strives to maintain its global economic competitiveness, there exists a need
to support and encourage racial and ethnic minorities in pursuing science, technology,
engineering, and math (STEM) fields. While the career decision-making process may be stressful
for many students, high achieving Latina college students face the unique challenge of, often,
having to reconcile their career aspirations with competing sociocultural concerns. Despite the
critical need to support Latinas in STEM, few researchers have examined their career decision-
making process. In the current dissertation, I draw insight from the counseling, vocational,
psychology, and higher education literature in order to examine Latina college students’ STEM
career decision-making process and goals. Social cognitive career theory and the
psychosociocultural framework provide the theoretical perspectives guiding the study. Findings
from the current study have important implications for student affairs administrators, academic
departments, institutional policies, and counseling services on how to best support the career
development of one of the nation's fastest growing populations. Implications for research and
theory are discussed.
Keywords: career development, higher education, Latina college students, STEM
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Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
Abstract ........................................................................................................................................... v
Chapter One: Introduction .......................................................................................................... 1
Background and Purpose of the Current Study ....................................................................... 3
Career Development ............................................................................................................... 6
Career Decision-Making Process ............................................................................................ 7
Theoretical Framework ........................................................................................................... 8
Psychosociocultural Framework ............................................................................................. 2
Chapter Two: Review of the Literature .................................................................................... 14
Psychological Factors ........................................................................................................... 14
Social Factors ........................................................................................................................ 16
Cultural Factors ..................................................................................................................... 19
Psychosocial and Sociocultural Factors ................................................................................ 22
Research Gaps and Limitations ............................................................................................ 24
Conceptual Model ................................................................................................................. 26
Chapter Three: Methods ............................................................................................................ 29
Participant Characteristics and Study Sites ........................................................................... 29
Data Collection Procedures ................................................................................................... 34
Measures and Instruments ..................................................................................................... 35
Mediating and Outcome Variables ................................................................................... 41
Analytic Approach ................................................................................................................ 46
Chapter Four: Findings .............................................................................................................. 50
Measurement Phase .............................................................................................................. 50
Structural Model ................................................................................................................... 61
Direct Effects ........................................................................................................................ 61
Indirect Effects ...................................................................................................................... 63
Test of Alternative Models ................................................................................................... 64
Limitations ............................................................................................................................ 68
Chapter Five: Discussion and Implications .............................................................................. 69
Distal Contextual Affordances .............................................................................................. 70
Role of Proximal Contextual Affordances ............................................................................ 76
Implications ........................................................................................................................... 78
Research and Theory .......................................................................................................... 78
Policy and Practice ............................................................................................................. 80
Conclusion ............................................................................................................................ 81
References .................................................................................................................................... 83
Appendix A ................................................................................................................................ 110
Appendix B ................................................................................................................................ 112
Appendix C ................................................................................................................................ 113
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LIST OF FIGURES

Figure 1. SCCT model of person, contextual, and experiential factors affecting career-related
choice behavior ............................................................................................................................. 11
Figure 2. Hypothesized model for Latina STEM career goals ..................................................... 28
Figure 3. Final Measurement model: Standardized paths presented ............................................ 58
Figure 4. Summary of statistically significant standardized covariances and path coefficients
for Latina STEM career goal model ............................................................................................. 65

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LIST OF TABLES AND APPENDICES

Table 1. Undergraduate Student Enrollment for Participating Universities ................................. 31
Table 2. Variable Description, Value Codes, and Descriptive Statistics of Study Participants ... 32
Table 3. Individual Confirmatory Factor Analyses and Internal Consistency (ρ) for Latent
Constructs ..................................................................................................................................... 54
Table 4. Summary of Data Model Fit Statistics ............................................................................ 64
Table 5. Direct, Indirect, and Total Effects for Final Model ........................................................ 66
Appendix A. ................................................................................................................................ 109
Recruitment Materials ................................................................................................................. 109
Information Sheet ........................................................................................................................ 110
Appendix B. ................................................................................................................................ 112
Table 6. Characteristics of Holland’s RIASEC Personality and Environment Types ................ 112
Appendix C. ................................................................................................................................ 112
Table 7. Covariance Matrix ........................................................................................................ 113
Table 8. Residual Matrix ............................................................................................................. 123

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Chapter One: Introduction

According to the U.S. Census Bureau, Latina/os represent the youngest U.S. population
and are projected to comprise 30% of the U.S. population by the year 2050 (Ennis, Rios-Vargas,
& Albert, 2011; Passel, Cohn, & Lopez, 2011; Pew Hispanic Center, 2009). While Latina/os
represent one of the nation’s fastest growing populations, they continue to be underrepresented in
highly selective institutions of higher education, fields that offer the greatest opportunities for
financial rewards, and overrepresented in unskilled, service-oriented, and midlevel technical
occupations (Guzman, 2001; Miller & Brown, 2005). The demands of an increasing knowledge
economy and the need for a highly educated work force further positions Latina/os as a
population of critical importance (Taningco, Mathew, & Pachon, 2008). In 2011, Latina/os
represented 15% of the total U.S. workforce but only 7% of the science, technology, engineering,
and math (STEM) workforce (Landivar, 2013). Their presence in the U.S. workforce and impact
on the nation’s global standing will continue to increase as the young population reaches
adulthood. Currently, 18% of the U.S.’s young adult population and 31% of youth under age 16
are of Latina/o descent (Pew Hispanic Center, 2009).
As more Latina/os enroll in institutions of higher education there is a critical need to
examine how such institutions influence their career development and outcomes (Fry, 2002).
Latina/os and other historically marginalized populations have been know to have high career
aspirations; yet, perceive several barriers to reaching their goals (Arbona, 2001b; Flask &
Thomas, 2007; Flores & O’Brien, 2002; Fouad & Byars-Winson, 2005; Hurtado, Saenz, Santos,
& Nolan, 2008; Swanson & Fouad, 2010). Latina/os who attend highly selective and
predominately white institutions (PWI) of higher education, in particular, have been known to
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experience several instances of racism and discrimination on campus (Gloria & Robinson
Kurpius, 1996; Hurtado & Carter, 1997; Hurtado, Griffin, Arellano, & Cuellar, 2008). Such
students may internalize negative stereotypes about members of their racial/ethnic group and fail
to realize their potential (Fouad & Bryars-Winston, 2005; Luzzo & McWhirter, 2001).
Although similar college experiences have been noted for Latinas (females) and Latinos
(males), unique differences have also been observed. For example, Latina students often report
greater stressors associated with leaving home to attend college and balancing familial
expectations (Rodriguez, Guido-DiBrito, Torres, & Talbot, 2000). Latinas’ triple minority status,
as defined by their race/ethnicity, gender, and, often, economic disadvantage, further positions
them at greater risk for psychological distress, as they are often made to cope with instances of
racism and discrimination while navigating multiple social identities in college (Vasquez, 1994).
When compared to Asian and African American women, Latina women experience the largest
gender gaps in terms of their representation in STEM fields (Taningco, Mathew, & Pachon,
2008). In 2010, Latinas obtained 61% of baccalaureate degrees awarded to Latina/os, but only
37% of degrees awarded in STEM (Aud et al., 2012; Excelencia in Education, 2007). Thus,
scholars have called for greater attention to the specific challenges faced by Latinas in higher
education (Rodriguez et al., 2000; Gloria, Castellanos, & Orozco, 2005).
In addition to the stressors associated with Latinas’ minority status on PWIs, such
students must also contend with the challenges associated with their phase in development. The
age students typically enter college—late adolescence and early adulthood—represents an
important period in a young person’s psychosocial and career development (Erikson, 1968;
Super, 1996; Swanson & D’Achiardi, 2005). While pre-college experiences influence Latinas
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career pursuits, students’ college experiences also play a critical role in their career development;
this is especially true for students with limited exposure to various career options (Luzzo, 2000).
Unfortunately, few researchers have examined how aspects of the college environment influence
Latinas’ career decision-making process (Flores et al., 2006). Beyond the practical significance
of addressing the career decision-making process of the nation’s fastest growing population,
scholars have noted the importance of career choice on an individual’s identity and personal
fulfillment (Brown & Lent, 2012). Addressing the career decision-making process of Latina
college students has important implications for the life outcomes and overall well-being of
Latinas (Swanson & Fouad, 2010).
As the U.S. seeks to maintain global economic competitiveness, there is a need to better
understand how aspects of the college environment influence Latinas decisions to pursue, or not
pursue, careers in STEM fields. Although matriculation and persistence in college are critical to
realizing students’ career aspirations and success, a college degree does not guarantee that
student will meet their goals. The extent to which students gain access and exposure to career-
related information and networks also plays a critical role in their career development and
success (Degenee & Forse, 1999; Rios-Aguilar & Deil-Amen, 2012).
Background and Purpose of the Current Study

In the current study, I draw from Lent, Brown, and Hackett’s (1994) social cognitive
career theory and the psychosociocultural framework (Gloria & Rodriguez, 2000; Castellanos &
Gloria, 2007) in order to examine Latinas’ STEM career decision-making process and goals.
Given the scarce attention to Latina college student career development in the current literature,
as well as its implications for Latinas’ academic and life satisfaction, psychological well-being,
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and ultimate career choice, a study that holistically examines Latina college students’ career
decision-making process is long overdue (Caldera, Robitschek, Frame & Pannell, 2003; Flores et
al., 2006a; Swanson & Fouad, 2010). The research questions that guide the study are as follows:
a) What psychosociocultural factors influence Latinas’ STEM career decision-making process
(i.e., STEM career self-efficacy, outcome expectations, interest, and goals)? b) Does SCCT
produce an adequate structural model for Latina STEM career decision-making? In what follows,
I briefly describe the Latina U.S. population and define pertinent career-related terms. I then
outline the theoretical framework that will guide the study: social cognitive career theory (Lent,
Brown, & Hackett, 1994).
U.S. Latina Population. The term “Latina” is a gendered and pan-ethnic word used to
describe females from Latin American origins including Mexico, Puerto Rico, Cuba, Central
America, and South America (Arbona, 1990; Gloria & Castellanos, 2012). While I utilize the
term “Latina” throughout the current manuscript, I acknowledge the controversies associated
with the labeling of a diverse group of individuals. The terms “Hispanic” and “Latino”, in
particular, are commonly used in the literature and popular discourse. “Hispanic” is used in most
government documents (e.g., U.S. Census) but has received considerable criticism given its
governmentally imposed roots and emphasis on people from Spanish decent (Comas-Diaz, 2001;
Haynes-Bautista & Chapa, 1987). The term “Latino”, on the other hand, acknowledges the
diverse indigenous roots of people from Latin American decent (Haynes-Bautista & Chapa,
1987) but has been criticized for its subjugation of women under a male referent term (i.e. its
masculine ending “o”) and exclusion of people from Spanish decent (Gloria & Castellanos,
2012; Haynes-Bautista & Chapa, 1987). While acknowledging that the labeling of this diverse
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population continues to be a topic of scholarly debate, I utilize the terms Latina, Latino, and
Latina/o to refer to females, males, and both genders from Latin American origins, respectively.
Demographics. In the U.S., Latina/os from Mexican descent represent the majority of the
Latina/o population (63%), followed by Puerto Ricans (9.2%), and Cubans (3.5%; Ennis et al.,
2011). The combined population of Latina/os from Central and South American origins is 16%.
The majority of Latina/os are concentrated in seven states (California, Texas, Arizona, Colorado,
New Mexico, Illinois, and New Jersey), with the majority residing in California. Latina/os make
up 38% California’s population (Passel et al., 2011). While Latina/os have been known to share
similar experiences, differences by national origin, generation status, and geographical location
should also be noted (Hernandez & Lopez, 2004). The timing and context (e.g., voluntary or
involuntary migration) of when Latina/os enter the U.S., in particular, plays a major role in
shaping their lived experiences and outcomes (Gloria & Castellanos, 2012). For example, the
higher social economic and occupational outcomes of Latina/os from Cuban descent, compared
to Mexican and Puerto Rican descent, has been attributed the presence of a prominent middle
class among early Cuban refugees (Arbona & Novy, 1991b).
Landscape of Latinas in Higher Education and STEM Fields. While the percent of
Latinas pursuing higher education is rapidly increasing, Latinas are still widely underrepresented
in terms of baccalaureate attainment, especially in STEM fields (NCES, 2012). When compared
to their While counterparts, Latina and other underrepresent minority students are just as likely
to aspire to major and enroll in a STEM field; however, they are more likely to switch to non-
science field and less likely to complete a STEM degree (Chubin & Babco, 2003). Substandard
STEM attainment rates are especially problematic given the important role that Latinas play in
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meeting the increasing demand for STEM occupations. Over the past two decades, the growth of
STEM occupations has increased more rapidly than that of non-STEM occupations. For
instance, the number of STEM occupations has reached a 17% increase, compared to 9.8% for
non-STEM occupations in just the last decade (Langdon, McKittrick, Beede, Khan, & Doms,
2011).
Furthermore, achieving a racially diverse workforce is critical to both maintaining the
nation’s global economic competitiveness and domestic welfare. Previous research indicates that
racially and culturally diverse individuals play a critical role in increasing the much-needed
access to healthcare, educational resources, and mentorship for underserved minority
communities. Culturally competent racially and ethnically diverse individuals, for instance, have
been known to provide underserved minority populations with greater quality of healthcare and
satisfaction with care (Cohen, Gabriel, & Terrell, 2002; Smedley, Butler, &Bristow, 2004;
Sullivan, 2004). Similarly, faculty of color play an important role in mentoring and sustaining
underrepresented racial and ethnic minorities in STEM (Hernandez & Lopez, 2004; Turner,
Gonzalez, & Wood, 2008). While the Latina/o population currently makes up 17% of the U.S.
population, their representations in health professions such as medicine, dentistry, and nursing is
less than 3% (Ennis, Rios-Vargas, & Albert, 2011; Sullivan, 2004; Waver et al, 2005).
Career Development

Career development can be defined as a dynamic process that encompasses much of an
individual’s life span (Lent & Brown, 2012). An individual’s career development begins during
childhood and is influenced by both formal and informal experiences that provide knowledge of
particular occupations, interests, talents, and values (Brown & Lent, 2012; Super, Savickas, &
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Super, 1996). Most college students are concentrated in the career exploration phase of their
career development. It is during this phase that students begin to crystalize, specify, and make
initial decisions about their career directions (Super et al., 1996; Swanson & D’Achiardi, 2005).
Career choice may be defined as the narrowing of options during the career exploration
phase and the implementation of a particular career choice (Brown & Lent, 2012). While not all
college students reach tangible career outcomes (e.g., career satisfaction), most leave college
with a particular goal or plan in mind. As such, examining aspects of the career decision-making
process, rather than career choice, per say, is more appropriate when studying college students.
Career Decision-Making Process

Students’ career decision-making process occurs throughout their career exploration and
has been defined as the process by which students make choices regarding their educational and
career directions (Brown & Lent, 2012; Hartung & Niles, 2000; Super, 1990). While the career
decision-making process occurs during the career exploration phase of an individuals’ career
development, career development encompasses a broader timeframe, which includes the career
decision-making process. Given that the current study pertains to college students, any reference
made to career development refers to the career exploration phase of career development.
It is important to note that the terms career, vocation, and occupation are often used
interchangeably in the literature. Although the three of these terms describe work behavior, each
has been praised and/or criticized based on its historical roots. In the current study, I utilize the
terms “occupation” or “career” given their more frequent use in contemporary settings (Brown &
Lent, 2012).
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Theoretical Framework

Social cognitive career theory (SCCT) represents the primary theoretical perspective that
informs the current study. Building from early career theorists (e.g., Super, Holland, and
Vondracek), Bandura’s (1986) social cognitive theory, and Astin’s (1984) perceived opportunity
structure, Lent, Brown, and Hackett (1994) describe SCCT as a unifying approach for
understanding educational and occupational behavior (Lent, 2013). Two major assumptions
guide SCCT: a) people have the capacity to exercise some degree of agency and b) individuals
contend with several factors that strengthen, weaken, or override personal agency (Bandura,
1986; Lent, 2013). Central to these assumptions is the interplay among three key social cognitive
variables: self-efficacy beliefs, outcome expectations, and personal goals. The interplay among
these three variables is highlighted given their role in enabling individuals to exercise agency
throughout the career development process. SCCT addresses four specific career-related
processes including how individuals a) develop vocational interests, b) make occupational
choices, c) achieve varying levels of career success and stability, and d) experience satisfaction
or well-being in the educational or work place (Lent, 2013). The career choice model is assessed
in the current study.
The career choice model holds that the experiences and feedback that young people
receive from others gradually influence their skills, personal performance standards, self-
efficacy, and outcome expectations for various tasks and domains. Self-efficacy and outcome
expectations pertaining to specific activities help mold career interests. Together, self-efficacy,
outcome expectations, and interest encourage students’ goals and intentions to engage in certain
activities. When individuals feel competent (self-efficacious) and believe that participating in a
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particular activity will result in desired outcomes (positive outcome expectations), their interests
are likely to be strengthened. The reverse is true when students do no feel competent and expect
negative outcomes as a result of participating in a particular activity. Whether interests are
solidified or weakened is determined by students’ participation, exposure, and performance in
such domains. These experiences prompt individuals to rethink and/or expand their career related
self-efficacy and outcome expectations. As self-efficacy and outcome expectations change so do
interests and goals.
Two additional clauses are included in the career choice model. First, the model notes
that self-efficacy and outcome expectations do not only influence career choice goals, actions,
and performance directly, but also indirectly through their effect on students’ career interest.
Second, the career choice model acknowledges that occupational choices are often, but not
always, linked to career interests. In doing so, the model further addresses the influence of
contextual and socio-contextual factors (please see Figure 1). That is, career interests are subject
to further revisions given that individuals and their environments are dynamic entities (Lent,
2013; Lent, Brown, & Hackett, 1994). As individuals operate in their environments various
events and circumstances may arise that influence their career aims (e.g., barriers, new paths,
opportunities).
Two types of contextual factors are discussed in this model: background/distal and
proximal contextual factors. Background influencers include those that help shape an
individual’s early self-efficacy, outcome expectations, and interests (e.g., culture, race, gender
socialization, role models, and opportunities). These factors are said to influence students’
performance accomplishments and subsequent support and barriers (proximal affordances).
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Proximal contributors include environmental factors that are present during the career choice
process (e.g., social support, financial support, discrimination in college). Proximal contextual
factors are hypothesized to influence career choice in two ways. First, these factors are thought
to directly influence choice goals and actions. Second, proximal contextual factors are believed
to moderate the effect of interest on career goals and career goals on actions, with more
favorable outcomes resulting from supportive environments. This process is seen as continuously
repeating itself prior to career entry (please see Figure 1 below).
While SCCT has received empirical support with samples of Latina/o high school and
middle school students (Risco & Duffy, 2011; Navarro, Flores, & Worthington, 2007), such
studies are limited in number. Furthermore, few studies have examined the applicability of
SCCT among Latina college students (Rivera, Blumberg, Chen, Ponterotto, & Flores, 2007). In
the current study, I utilize SCCT and previous literature in order to examine indicators of Latina
college students’ desires to pursue STEM careers fields. While SCCT provides the foundation
for the model, previous research findings provide insight into additional paths pertinent to Latina
college students.
For instance, in their work with engineering college students, Lent and colleagues (2005)
considered additional paths not posed by SCCT but highlighted in social cognitive theory
(Bandura, 1999). Such paths pertain to the indirect effect of proximal contextual affordances to
career choice goals through self-efficacy. While SCCT posits that proximal contextual
affordances influence career choice goals directly, social cognitive theory also considers their
indirect effect on goals, through self-efficacy. In other words, such factors are thought to inform
students’ self-efficacy, which, in turn, influences their career goals. Previous research findings
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concur, suggesting the need to include additional pathways from proximal contextual affordances
to self-efficacy (Lent et al., 2001; Lent et al., 2005; Lent, Brown, Schmidt et al., 2003). In Figure
1, I provide a visual representation of the model.

Figure 1. SCCT model of person, contextual, and experiential factors affecting career-related choice
behavior (Lent, Brown, & Hackett, 1994). Note. Paths in gray are not examined in the current study.
Person Inputs
-Predispositions
-Gender
-Race/Ethnicity
-Disability/
Health Status

Background
Environmental
Influences
Interest
Proximal Environmental Influences
Experienced During Choice-Making
(e.g., Support and Barriers).
Choice
Goals
Choice
Actions
Performance
Domains and
Attainment
Self-
Efficacy
Expectations
Outcome
Expectations
Learning
Experiences
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Psychosociocultural Framework

The psychosociocultural (PSC) model represents a comprehensive framework that
accounts for psychological (e.g., individual self-beliefs and perceptions), social (e.g.,
environment, networks, and mentors), and cultural (e.g., value validation) factors that influence
Latina/os educational experiences and outcomes within the university context (Castellanos &
Gloria, 2007). Gloria and Rodriguez (2000) first introduced the framework as an approach for
counseling Latina/o students. Castellanos and Gloria (2007) later applied the PSC framework as
research model for studying Latina/o students’ college experiences. The scholars note that in
order to adequately understand Latina/o students’ educational experiences, researchers must
examine each of these dimensions simultaneously.
Both SCCT and the PSC framework have received empirical support with samples of
Latina students; however, SCCT has been largely applied to Latina high school and middle
school students (Risco & Duffy, 2011), while the PSC model is typically used to examine Latina
college students’ persistence outcomes (Gloria, Castellanos, & Orozco, 2005). When applied to
Latina career decision-making processes and outcomes, the PSC model aligns with SCCT, in that
both outline psychological (e.g., self-efficacy and outcome expectations), social (e.g., proximal
contextual affordances), and cultural (e.g., distal contextual affordances) factors that influence
students’ career decision-making process and goals. In doing so, both frameworks offer a unique
opportunity to examine both mediating processes and outcomes.
Although the final research model, discussed in chapter two, is conceptualized within the
SCCT framework, the PSC framework provides guidance for the model. In chapter two, I review
the literature on Latina career decision-making and highlight limitations in the current literature.
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I organize previous research findings according to the PSC framework. In chapter three, I
present the methods utilized for the study and structural equation modeling as the analytic
technique utilized to address the research questions. I provide the research findings and
implications resulting from the study in chapters four and five, respectively.

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Chapter Two: Review of the Literature

In the present chapter, I outline the literature on Latina career decision-making and
highlight gaps in the current literature. I organize the chapter according to the PSC framework
and discuss the distal and proximal contextual affordances included in the research model for the
current study. I begin by reviewing the existing literature on psychological (individual), social
(environmental and contextual), and cultural factors that influence Latina college students’ career
development. I then note the gaps in the existing literature and provide the theoretically and
empirically informed conceptual model that will be examined in the study.
While the following review of the literature is organized according to the PSC
dimensions, it is important to note that much overlap exist among the dimensions (Castellanos &
Gloria, 2007). As such, I include an additional, psychosocial and sociocultural category to
highlight variables that represent multiple dimensions. By acknowledging overlap among such
constructs researchers are better able to understand how constructs work individually and
collectively.
Psychological Factors

Like all college students, Latinas enter college with pre-existing values, abilities,
experiences, and beliefs. These inputs influence Latinas’ career decision-making process directly
and indirectly through their interactions with the college environment (Brown, 2004). For
instance, the extent to which Latinas possess insight into their career-related interest (i.e., self-
clarity) and knowledge about the type of careers that fit those interests has been know to play an
important role in their career decidedness and career choice comfort (Risco & Duffy, 2011).
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The primary variables of interest in the current study represent of psychological
variables, which collectively embody students’ STEM career decision-making processes
(CDMP) and ultimate career goals. Such variables include STEM career self-efficacy, outcome
expectations, interest, and goals. As suggested by their names, STEM career interest and goals
represent students’ STEM related career interest and goals, respectively. STEM career self-
efficacy refers to students’ beliefs about their ability to successfully organize and execute a
course of action to achieve their STEM career goal (Bandura, 1986), while STEM career
outcome expectations encompass students’ beliefs about the consequences of pursuing those
goals (Lent & Brown, 2006). These CDMP (self-efficacy, outcome expectations, interest, and
goals) may be described as intermediary outcomes (Swanson & D’Achiardi, 2005), meaning that
while they are of unique interest they are also interrelated. Thus, such constructs are often
examined as both dependent and independent variables. For example, while several researchers
have examined factors that predict career decision-making self-efficacy (e.g., Gloria & Hird,
1999), others have utilized the career decision-making self-efficacy construct to predict career
indecision (e.g., Choi et al., 2012). The influence of intermediate outcomes on more tangible, or
target outcomes such as career choice, certainty of choice, and satisfaction with choice has been
well established in the literature (Swanson & D’Achiardi, 2005).
In what follows, I describe previous research findings regarding the effect of
social/contextual and cultural factors on Latinas’ career choice goals and CDMP variables of
interest. While I attempt to distinguish social and cultural factors, it is important to remember
that much overlap exists. In order to account for such overlap, I include an additional section on
psychosocial and sociocultural factors.
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Social Factors

Career development is an interactive process, whereby personal characteristics interact
with social/contextual and cultural factors to influence an individual’s career interests and
outcomes (Whiston & Keller, 2004). Research regarding the role of social/contextual factors on
Latina career decision-making is centered on parental support and familial relationships.
Unfortunately, few studies examine the influence of contextual factors such as aspects of the
college environment. In what follows, I discuss previous research on the role of familial support
on Latina career decision-making. I then discuss the role of aspects of the college environment
on distinct, but related student outcomes—e.g., college persistence and satisfaction.
In regard to the role of familial relationships, parental support, in particular, has been
known to have a salient influence on Latina career decision-making. Latinas who perceive
greater parental support tend to demonstrate higher career aspirations, career-planning maturity,
career self-efficacy, vocational exploration and commitment, and are less likely to foreclose on
career options prematurely (Leal-Muniz & Constantine, 2005; Fisher & Griggs, 1995; Fisher &
Padmawidjaja, 1999; Flores & O’Brien, 2002; Gomez et al., 2001; Kenny, 1990; Torres &
Solberg, 2001). Such findings are not surprising given Latinas’ cultural values and high esteem
for the family.
While close familial relationships often serve as a major source of support and
encouragement for Latinas, qualitative research findings indicate that Latinas also report
difficulties balancing familial pressures and career goals (Flores & O’Brien, 2002; Gomez et al.,
2001; Niemann, 2001; Whiston & Keller, 2004). For example, Latinas describe feelings of guilt
associated with leaving home to attend college, not contributing to the family’s finances, and
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17
pressure to be a positive role model for younger family members (Arredondo, 1991; Gonzalez,
Jovel, & Stoner, 2004; Rodriguez, Guido-DiBrito, Torres, & Talbot, 2000; Sy & Romero, 2008).
Gomez et al. (2001) found that in some cases, Latina/o parents even discouraged their daughters
from pursuing nontraditional career goals for women; however, such discouragement was
derived from parental fear that societal barriers would limit their daughters from reaching male
dominated fields. These findings highlight the importance of considering the social reality in
which Latinas operate when interpreting research findings.
While several researchers have examined how aspects of the college environment (e.g.,
campus climate, student-faculty interactions) influence Latina/o students sense of belonging,
college satisfaction, academic success, psychological well-being, and persistence in college, few
have examined how Latinas’ college experiences, influence their career decision-making process
(e.g., Castillo et al., 2006; Cole, 2008; Cole & Espinoza, 2008; Gloria, Castellanos, & Orozco,
2005; Gloria & Robinson-Kurpius, 1996; Hurtado & Carter, 1997; Hurtado & Ponjuan,2005).
Give previous research on Latinas in higher education, four college experiences seem
especially pertinent to their career decision-making. Each of the experiences described in what
follows has been known to play an important role on Latinas’ educational outcomes. First,
faculty and staff are likely to influence Latina students’ career decision-making process through
their role as a source of information and support (Figueroa, 2003; Nora & Crisp, 2007). Latina
college students are often the first in their families to attend college, and subsequently have
limited access to career related information and knowledge about existing career services on
campus (Castellanos, 2015). As such, faculty and staff play an important role in providing
Latinas with a source of information (Berrios-Allison, 2011; Luzzo, 2000; Rendon, 1994; Rios-
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18
Aguilar & Deil-Amen, 2012). In addition to providing Latina students with information, faculty
mentorship has been known to play an important role on Latinas’ college persistence, self-image,
comfort in the university environment, and social integration on campus, all of which are likely
to influence their career decision-making process (Bordes & Arredondo, 2005; Bordes-Edgar,
Arredondo, Kurpius, & Rund, 2011; Gloria, Castellanos, Lopez, & Rosales, 2005; Hernandez,
2000; Mayo, Murguia, & Padilla, 1995; Torres & Hernandez, 2007).
Latina/o faculty and staff, in particular, may serve as role models for Latinas (Gloria &
Rodriguez, 2000); however, opportunities for Latinas to interact and be mentored by Latina/o
faculty is restricted by their limited representation on campus (Yosso & Solorzano, 2006).
Although faculty and staff may play an important role in Latinas’ career development, Latinas
who attend PWIs report few interpersonal interactions with faculty and staff, including career
counselors (Anaya & Cole, 2001; Berrios-Allison, 2011; Rios-Aguilar & Deil-Amen, 2011).
Second, peers serve as additional mentors and role models who may provide Latina
students with access to career related information and support. Latina/o peers with similar
backgrounds, values, and experiences, provide younger students with psychosocial support
including cultural validation, coping skills, and motivation to pursue their goals (Castellanos,
2015; Figueroa, 2003; Gloria & Castellanos, 2003; Hernandez, 2000; Villalpando, 2003). Social
support from ethnic minority peers, in particular, has been known to reduce Latina/o students’
perceptions of barriers (Castellanos, 2015; Gloria et al., 2005b), and increase their motivation to
succeed (Hernandez, 2000), academic success (Cerezo & Chang 2013), and academic persistence
decisions (Hernandez, 2000). Latina students who lack familiarity with successful students from
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19
their cultural backgrounds are at greater risk for internalizing negative messages about members
of their racial/ethnic group (Berrios-Allison, 2011; Castellanos, 2015).
Third, the importance of student involvement on campus has long been established in the
higher education literature (Astin, 1984; Pascarella & Terenzini, 1991). Latina/o students who
are more involved in student organizations tend to have a greater sense of belonging and are
more likely to persist in college (Hernandez, 2000; Hurtado & Carter, 1997). Internships,
research, tutoring, and volunteer opportunities are also likely to influence Latinas’ career
decision-making process by providing students with opportunities to explore potential interests,
build greater self-efficacy, access career-related information, and social networks (Brown, 2004;
Villarejo, Barlow, Kogan, & Veazey, 2008). Latina/o student organizations, in particular, may
serve as an academic family (Castellanos & Gloria, 2007) and provide Latinas with valuable
opportunities to discuss and negotiate contextual and cultural challenges in their career pursuits
(Berrios-Allision, 2011; Castellanos, 2015).
Cultural Factors

Culture shapes the values, assumptions, and worldviews that informs an individual’s
perceptions of appropriate career related actions (Carter & Cook, 1992; Gomez et al., 2001).
Traditional Latina/o cultural values emphasize loyalty, solidarity, reciprocity, and a strong
commitment towards the family (i.e., familismo; Castellanos & Gloria, 2007; Santiago-Rivera et
al., 2002). The cultural value of marianismo pertains specifically to Latinas and describes the
ideal Latina woman in relation to her adherence to traditional customs regarding sexuality,
gender roles, family, motherhood, relationships, and behaviors. According to the norms
subscribed by marianismo, Latinas must sacrifice their needs for those of their family.
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While limited to a few studies, previous research indicates that cultural values such as
familsmo (familism) and marianismo (marianism) influence several Latina career decision-
making process outcomes and career interests that are closely tied to family, community, and
traditional gender roles (Arbona & Novy, 1991a; Arbona & Novy, 2001a; Duffy & Sedlacek,
2007a, 2007b; Miller & Brown, 2005; Gloria & Castellanos, 2012; Marano-Rivera, 2000; Risco
& Duffy, 2011). Although social and intrinsic work values are common among all women
(Duffy & Sedlacek, 2007a), there is some evidence that suggests that this may be especially true
for incoming Latina college students (Arbona & Novy, 2001a; Risco & Duffy, 2011).
Intrinsic values emphasize autonomy and personal interests. Social values refer to an
importance place on making a contribution to society and working with people (Duffy &
Sadlacek, 2007b). In their quantitative single-institution study, Duffy and Sadlacek (2007b)
found that Latina/o students were more likely to express intrinsic work values (i.e., autonomy
and interest) and less likely to express extrinsic values (i.e., monetary rewards and job security)
compared to their African American and Asian American peers. Gomez et al. (2001) conducted a
qualitative study with 20 post-baccalaureate high-achieving Latinas and found that the desire to
make a difference was the most prominent motivator in Latinas’ career trajectories. Similarly, in
their review of Latina/o college freshmen, Hurtado and colleagues (2008) found that incoming
Latina/o college students were significantly more likely to report strong community-oriented
values and desires to promote racial understanding, compared to their non-Hispanic White peers.
Those who leave their Latina/o communities and enter a predominately white institution
(PWI) of higher education have been known to experience difficulty negotiating distinct Anglo
and Latina/o value systems (Davenport & Yurich, 1991; Gloria & Rodriguez, 2000; Marano-
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21
Rivera, 2000). Challenges associated with a lack of university fit (e.g., cultural incongruence,
non-supportive university environments, inhospitable campus climates), are likely to influence
Latinas career decision-making process (Gloria & Robinson Kurpius, 1996; Ponterotto, 1990).
An institution’s campus climate has been found to influence Latina/o students’ sense of
belonging, institutional attachment, and social and academic involvement on campus (Hurtado,
Carter, & Spuler, 1996; Hurtado & Ponjuan, 2005; Museus, Nichols, & Lambert, 2008). Latina
college students who attend PWIs and perceive their university climate as inhospitable are less
likely to be engaged on campus (Jones, Castellanos, & Cole, 2002; Hurtado & Carter, 1997;
Nora & Cabrera, 1996; Rankin & Reason, 2005). Reduced levels of student engagement may, in
turn, limit Latina students’ opportunities for career exploration and pursuits (Oseguera, Hurtado,
Denson, Cerna, & Saenz, 2006). Additionally, stereotypes and instances of racism and
discrimination have been known to influence Latinas’ self-concept, perceptions of barriers,
psychological distress, and career aspirations (Arbona, 1990; Gomez et al., 2001; McWhirter,
1997; Leal-Muniz & Constantine, 2005; Ojefa & Flores, 2008; Torres & Hernandez, 2007).
Constantine and Flores (2006) found that psychological distress positively predicted
career indecision, which negatively predicted career certainty for Latina/o students. Although no
study has empirically examined the influence of campus climate on Latina college students’
career goals outcomes directly, Tomlinson and Fassinger’s (2003) found that perceptions of a
more positive campus climate had a direct and positive effect on lesbian college students’
psychological vocational development and vocational purpose. The vocational purpose measure
comprised of three subscales (i.e., vocational competence, commitment, and organization), while
the psychological vocational development measure reflected a 15-item composite score with
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22
higher scores indicating less career indecision, greater career decision-making self-efficacy, and
a clearer vocational identity.
Psychosocial and Sociocultural Factors

The extent to which Latinas adhere to traditional Latina/o cultural values and such factors
influence their college experiences and outcomes has been known to vary depending on several
psychosocial and sociocultural factors, most notably generational status, socioeconomic status,
acculturation, enculturation, and ethnic identity (Berrios-Allison, 2011; Castellanos & Gloria,
2007; Gomez et al., 2001; Hernandez & Lopez, 2004; Torres & Hernandez, 2007; Ojeda &
Flores, 2008). Generational status refers to the number of generations a family has been in the
United States. The higher the generational and socioeconomic status (SES), the longer a family
has been in the U.S., and the more likely they are to have acculturated to the dominant culture
(Lucero-Miller & Newman, 1999; Ortiz, 2004).
Acculturation refers to individual changes resulting from socialization to dominant
cultural norms (Berry, 2007; Berry, 2003; Graves, 1967). On the other hand, enculturation refers
the process of (re)learning and/or maintaining the norms of an individual’s indigenous culture
(Alamilla et al., 2010; Kim & Abreau, 2001). Ethnic identity is distinct from these concepts in
that, it specifically addresses the extent to which an individual integrates a sense of ethnicity into
his or her sense of self (Quintana & Scull, 2009). Mixed findings exist regarding the significance
of acculturation and ethnic identity on Latina students’ educational and career outcomes. For
example, while some research indicates that acculturation and ethnic identity influence Latinas’
college self-efficacy, college outcome expectations, traditionality of career choice, educational
goals, career decision-making self-efficacy, and occupational goals, others have failed to find
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statistically significant effects (e.g., Caldera et al., 2003; Flores & O’ Brien, 2002; Flores et al.,
2008; Gomez et al., 2001; Lucero-Miller & Newman, 1999; Ojeda, 2009; Ojeda, Flores, &
Navarro, 2011).
Differential findings regarding the influence of these sociocultural factors on Latina
career outcomes may be attributed to the different manner in which researchers operationalize
such constructs as well as numerical, regional, and ethnic differences in the Latina research
samples. For example, researchers often conceptualize acculturation using one of two models:
unilinear or bilinear conceptual models (Miller & Kerlow-Myers, 2009). Unilinear models are
concerned with how individuals internalize and adapt to a second culture (Gordon, 1964; 1978).
A notable limitation of such models is their assumption that changes in cultural adherence occur
on a single continuum (Miller & Kerlow-Myers, 2009). As individuals become more
acculturated to the second (dominant) culture, they are thought to move away from their native
culture.
Bilinear models, on the other hand, consider an individual’s competence in both the
second and native culture through two distinct but intersecting continua (Berry, 1979). In the
bilinear model, individuals are thought to have the capacity to develop competence in a second
culture while retaining competence in the primary culture (i.e., enculturation; Kim & Abreau,
2001). Greater support has been established for bilinear models over unilinear models (Chun,
Organista, & Marin, 2003; Kim & Abreu, 2001). While more work is needed in this area, there is
also a dire need to understand psychosocial and sociocultural factors interact with aspects of the
college environment to influence Latinas’ CDMP.

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Research Gaps and Limitations

While the current literature provides some insight into Latina college students’ career
decision-making process, several gaps and limitations remain (Flores et al., 2006a). First, more
research is needed to substantiate previous findings on the influence of cultural and contextual
factors that influence Latina college students’ career development. The counseling and
vocational literature provides some indication of the influence of cultural factors on Latina
college students’ career development; however, empirical studies that investigate such
relationships are limited in number (Fouad & Kantamneni, 2013). Although various aspects of
the college environment have been known to influence Latinas’ college persistence and
adjustment in the higher education literature, no study to date has examined how such factors
influence Latina college students’ career decision-making process and decisions to purse STEM
fields. To my knowledge, the current study is the first to apply SCCT to Latinas within a
predominately white university context.
Second, few researchers have holistically examined the complexity of Latina career
development. While some studies address psychological (i.e., personal beliefs) or cultural
factors, few do so simultaneously (Brown, 2004; Fouad & Kantamneni, 2013). Researchers’
ability to investigate the influence of several psychological, socio, and cultural factors is often
restricted by their choice of methodology and sample size. For instance, few quantitative studies
on Latina/o career development utilize advanced multivariate analyses such as structural
equation modeling (SEM). Unlike unilinear analyses, SEM allows for the testing of multiple
hypotheses on several outcomes. Furthermore, existing studies on Latina/o career development
often reflect single-institutions studies, which are limited in two ways. First, research findings
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25
based on single-institution studies possess limited generalizability to students in different
university context. Second, such studies often contain small sample sizes, which limit a
researcher’s ability to simultaneously test multiple hypotheses. For example, the complexity of
structural equation models are limited by the number of observations included in the study
(Kline, 2011).
Third, established measures that address career decision-making outcomes may contain
gender and culturally biased items that require further psychometric validation for Latina college
students (Swanson & Fouad, 2010). Additionally, there exists a need for greater consistency in
the manner in which researchers operationalize career constructs. For example, although several
measures have received empirical support to assess work values (e.g., Minnesota Importance
Questionnaire, Basic Value Survey), some researchers employ single-items or author-developed
instruments, which have less reliability (E.g., Risco & Duffy, 2011; Duffy & Sadlacek, 2007b).
Given the challenges associated with the financial cost and accessibility of established measures,
as well as item-survey fatigue, such limitations are not uncommon. There remains, however, a
need to for greater consistency in the use of such measures. The same may be said for
sociocultural factors such as acculturation and ethnic identity. While some researchers utilize
unilinear models to measure acculturation, others employ bilinear measures (Miller & Kerlow-
Myers, 2009). The latter has been found to better represent acculturation given that it addresses
both Anglo and Latina/o values (Miller & Kerlow-Myers, 2009).
Mixed findings regarding sociocultural factors may also be attributed to geographical and
ethnic backgrounds of the study sample populations. As previously noted, most studies on Latina
career development have been conducted in single institutions of higher education where the
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26
representation of various Latina/o subgroups may vary drastically (e.g., Risco & Duffy, 2011).
For instance, Latina/o college students from Mexican decent are likely to represent the largest
Latina/o subgroup in California and Texas universities, but Cuban and Puerto Ricans Latina/os
are likely to represent the majority in Florida and New York (Brown & Lopez, 2013). Given that
the sociopolitical context in which Latina/os enter the U.S. influences the way they see and
interpret the world it is necessary to consider the ethnic subgroups that represent such study
populations (Gloria & Castellanos, 2012).
Fifth, while much can be gained from the higher education, counseling, and vocational
literature, few researchers integrate these bodies of literature (Tomlinson & Fassinger, 2003). By
incorporating the higher education literature, the vocation and career literature may gain valuable
insight into the cognitive and psychosocial factors that students from underrepresent minority
groups encounter in college. The majority of studies on Latinas’ career development within the
vocational literature represent quantitative studies with limited information regarding the
contextual factors specific to the college environment which may influence Latinas’ career
decision-making process (Flores et al., 2006). Likewise, the higher education literature may
benefit from the well-researched career constructs in the career and vocational literature
(Tomlinson & Fassinger, 2003).
Conceptual Model

In the current study, I survey Latina students from four PWIs and utilize structural
equation modeling to better address the complexity of Latina career decision-making process
outcomes and decisions to pursue a STEM career fields. Previous single-institutions studies are
limited in their ability to provide a holistic account of Latina career development given their
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27
limited sample sizes. By sampling Latinas from multiple institutions and utilizing SEM, I
address various psychosociocultural factors pertinent to Latina career development.
SCCT asserts that a) student background characteristics, including cultural values,
influence their career interest and goals through their effect on self-efficacy and outcome
expectations and b) proximal contextual factors also work to influence students’ career interest
and goals. While I expect these relationships to hold true for Latina college students, I also draw
from the previous research findings to include pertinent pathways that are not accounted for in
SCCT: notably, paths between proximal contextual affordances and self-efficacy. For instance,
given what is known from previous literature, it is likely that proximal contextual factors such as
stereotype threat and inhospitable campus climate influence Latinas’ career goals directly and
indirectly, through their influence of self-efficacy. Furthermore, I emphasize both distal and
proximal contextual factors. I consider a bilinear acculturation measure (ARMAS-II), and
gender-specific cultural values for Latinas including marianismo (e.g., Latina Value Scale;
Marano Rivera, 2000) and Latina cultural strengths (Nogales, 2003) to account for the role of
Latina cultural backgrounds. The conceptual model utilized to examine Latinas’ STEM career
decision-making process is provided below.

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Figure 2. Hypothesized model for Latina STEM career goals. Note. Dashed arrows represent
paths not accounted for in SCCT but grounded in social cognitive theory.

Faculty
Support

Social
Class
Latina/o ethnic
subgroup!
Generational
Status!
Math &
Science
Learning
Experiences

Latina
Strengths

Peer
Support
STEM
Career
Goal
STEM
Career
Outcome
Expectations
STEM
Career Self-
Efficacy
STEM
Career
Interest

Classroom
Climate

Academic
Involvement

Familial
Support

Self-
Sacrifice
Marianismo

Acculturation
AOS

Enculturation
MOS
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Chapter Three: Methods

In what follows, I outline the research design utilized for the current study. The research
questions are as follows: 1) What psychosociocultural factors influence Latinas’ STEM career
decision-making process? 2) Does SCCT produce an adequate structural model to represent the
relationships between the psychosociocultural factors that influence Latinas’ STEM career
decision-making process outcomes and goals? I begin by describing my procedures for data
collection and choice of instruments. I then outline the statistical analyses utilized to address the
research questions.
Participant Characteristics and Study Sites

The data utilized for the current study consists of 458 Latinas undergraduate who were in
their second year or more in college: second year (23%), third year (31%), fourth year (30%),
fifth and sixth year (16%). Participants attended one of four highly selective and historically
white institutions of higher education located in California institutions. I performed an analysis
of variance to assess whether students differed in their desires to pursue a STEM career by
institution. Findings indicated no statistically significant institutional differences, F (1, 424) =
1.26, p. 262. As such, I performed all analyses utilizing the entire sample. The student
demographic information for each of the four institutions is detailed in Table 1.
California PWIs were targeted for two reasons. First, the purpose of the current study was
to better understand the career decision-making process and STEM careers goals of Latinas at
PWIs. Historically white institutions tend to espouse Anglo values which may complicated
Latinas’ career development, given conflicting cultural value systems (Hurtado et al., 1998).
Research is needed to better understand what aspects of the college environment influence
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30
Latinas’ career decision-making process at PWIs. Second, California has the highest
concentration of Latina/o students—38% of the state’s population is of Latina/o heritage (Ennis
et al., 2011). As such, California institutions are more likely to enroll a critical mass of Latina/os,
compared to states with more limited Latina/o populations.
The majority of participants were of Mexican heritage (69%) and from family household
incomes of lower than 40,000 (43%) or 40,000 to 80,000 (35%). Most participants were second
generation Latinas (65%), meaning that they, but not their parents or grandparents, were born in
the U.S. Parental educational levels ranged from high school or lower (76.81%) to baccalaureate
attainment (13.25%), and masters (6.35%) or advance graduate training (3.54%). The means and
standard deviations for all of the measures included the study are presented in Table 2. In order
to facilitate interpretation of the latent variable scores, I provide the mean scale score for each
person, rather than total scores.

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Table 1

Undergraduate Student Enrollment for Participating Universities
Institution Percentage (Count)
Institution 1 (public) 100% (27,126)
Female 52% (14,132)
Underrepresented Minority 17.5% (4,745)
Latina/o 3.7% (1,009)
Institution 2 (public) 100% (23,530)
Female 54% (12,778)
Underrepresented Minority 28.5% (6,706)
Latina/o 24% (5, 543)
Institution 3 (public) 100% (29,633)
Female 55.7% (16,500)
Underrepresented Minority 23.7 % (7,023)
Latina/o 19.1% (5,663)
Institution 4 (private) 100% (19,000)
Female 50.4% (9,500)
Underrepresented Minority 17.3 % (3, 205)
Latina/o 12 % (2,280)
Note. Data obtained from institutional profiles during the 2014-2015 academic term

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Table 2
Variable Description, Value Codes, and Descriptive Statistics of Study Participants
Variable Description and Codes M SD
Year in College 1= second year; 2= third year;
3= fourth year; 4= fifth year; 5= sixth year
-- --

Ethnicity
1= Mexican/Mexican American;
2= Central American;
3= Mexican + Central American;
4= Cuban American; 5= Dominican;
6= South American; 7= Spanish American;
8= Puerto Rican; 9= Other

--

--
Generational Status 1= first-generation in the U.S.;
2= second-generation in the U.S.;
3= third-generation in the U.S.;
4= fourth-generation in the U.S.
-- --
Socioeconomic Status
Family Income
1=$0-40,000; 2=$40,000-80,000;
3=$80,000-120,000; 4=$120,000-150,000;
5= $150,000-200,000
2.01 1.3
Father’s Education
Mother’s Education
1= completed grade school; 2= some college;
3=completed college;
4= master’s degree; 5= doctorate
2.05
2.13
.91
1.06
Anglo Acculturation
Latina/o Acculturation
1= not at all; 2= very little;
3= moderately;
4= very often; 5= almost always
3.92
3.34
.48
.55
Latina Strengths 1= strongly disagree; 2= disagree;
3= neither; 4= agree; 5= strongly agree
3.84 .43
Self-sacrifice
(Marianismo)
1= strongly disagree; 2= disagree;
3= neither; 4= agree; 5= strongly agree
3.47 .75
Previous Learning
Experiences
Science SAT score= 200-800
Math SAT score= 200-800
607.2
596.1
89.9
94.58

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Table 2 (cont.)
Variable Description, Value Codes, and Descriptive Statistics of Study Participants
Variable Description and Codes M SD
Parental
Encouragement
1= not at all; 2= a little; 3= somewhat;
4= very; 5=extremely; 6= not applicable
4.38 .86
Peer Support 1=strongly disagree; 2=disagree; 3=neither;
4=agree; 5=strongly agree
3.69 .84
Faculty Support and
Encouragement
1= strongly disagree; 2= disagree;
3=neither; 4=agree; 5= strongly agree
3.40 .87
Academic
Involvement
1=0 hours; 2=1-2 hours;
3=3-5 hours; 4=6-10 hours; 5=11-15 hours;
6=16-20hours; 7= 21+ hours

3.12 .74
Classroom Climate
(negative)
1= strongly disagree; 2= disagree;
3= agree; 4= strongly agree
2.36 .49
STEM Career Self-
Efficacy
1= not at all confident; 2= not confident;
3= somewhat not confident;
4=somewhat confident; 5= confident;
6=completely confident

3.32 1.31
STEM Outcome
Expectations
(negative)
1=not at all likely; 2=not likely;
3=somewhat likely; 4=likely;
5=extremely likely; 6=don’t know
3.34 1.21
STEM Career Interest 1= strongly dislike; 2= dislike;
3= indifferent; 4= like; 5= strongly like

2.31 .78
STEM Career Goal
1= strongly disagree; 2= disagree;
3= neither; 4= agree; 5= strongly agree

2.39 1.46

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Data Collection Procedures

Social cognitive career theory addresses how dynamic and situation-specific personal
characteristics (e.g., self-efficacy, outcome expectations) and environments influence an
individual’s performance in specific domains (Lent & Brown, 2006). In the current study, I focus
on the STEM domain. A major advantage of the SCCT approach is the opportunity to assess
people’s ability to change, develop, and regulate their own behavior. The specificity needed to
assess domain-specific social cognitive indicators, however, often requires the design of new
measures (Lent & Brown, 2006). While the counseling and vocational literature provides well-
researched career constructs, more research is needed to assess their validity on Latina college
students. As such, I conducted a pilot study utilizing existing measures prior to distributing the
final survey instrument.
I began by obtaining Institutional Review Board approval (exempt review) for each site
and conducted seven cognitive interviews with Latina undergraduate students. Findings from the
cognitive interviews were taken into account in the development of the pilot survey. A total of
thirty participants from various academic departments and grade levels at a single institution
were recruited to complete the pilot survey in the summer of 2014. This procedure ensured that
the survey items were appropriate for the study population. Previous scholars have noted the
importance of creating, piloting, and validating measures to appropriately address cultural
dimensions and concerns pertinent to Latinas (Gloria & Castellanos, 2012).
Following my analysis of the pilot study and refinement of survey items, I began data
collection for the study. I recruited students to participate in the study via e-mail solicitations
during the fall of 2014. Participation in the study consisted of students’ voluntary completion of
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one 30-minute online survey. All submissions were anonymous and non-identifiable. Students
who agreed to participate in the study were offered an opportunity to win one of twenty-five
Apple or Starbucks gift cards. E-mail invitations included a description of the study aims, survey
link, and detailed information sheet (Appendix A). Gift cards ranged in value from two hundred
fifty dollars to ten dollars. Participants who wished to be entered in the raffle were asked to click
on a separate link and enter their e-mail addresses at the conclusion of the survey. This procedure
ensured that student responses remained anonymous. The study was advertised through academic
departments, multicultural centers, campus flyers, and student activity centers. E-mail invitations
were distributed from various academic departments, multicultural centers, and student
organizations within each of the participating institutions. For Institution 4 (pseudonym), the
office of undergraduate programs also agreed to distribute the e-mail to a random sample of
Latina/o students, which resulted greater participation rates for students at that particular
institution. Student participation by institution is as follows: Institution 1 (n=94), Institution 2
(n=30), Institution 3 (n=44), and Institution 4 (n=289). In table one, I provide an overview of the
student demographics characteristics of each institution, including the percentage of female
students and underrepresent racial and ethnic minority students (i.e., African American/Black,
Hispanic/Latina/o, Native American).
Measures and Instruments

I utilized previously established measures in the higher education, vocational, and
counseling literature for the current study. Two observed variables and 15 latent constructs were
examined. In what follows, I provide a description of each measure. I organize the measures
according to their place within SCCT framework. I begin by describing distal contextual
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36
affordances, followed by proximal contextual affordances and the mediating dependent
variables.
Distal Contextual Affordances. I included six distal contextual affordances in the
hypothesized model: ethnicity, generational level, social class, acculturation, enculturation, self-
sacrifice (marianismo), and Latina strengths. First, I assessed ethnicity by utilizing a single item
regarding which ethnic subgroup students belonged. Second, I assessed generational level by
utilizing the sum of three items. Students indicated the country in which they, their parents, and
grandparents were born. Third, I determined students’ social class utilizing three indicators:
family household income, father’s educational background, and mother’s educational
background. Four additional latent variables included in the model were also considered as distal
contextual affordances and are described below.
Acculturation. In order to assess acculturation, I utilized the revised Acculturation Rating
Scale for Mexican Americans-II (ARSMA-II; Cuellar, Arnold, & Maldonado, 1995). The
ARSMA-II assesses individual beliefs, attitudes, behavior and values for Mexican Americans.
The 30-item scale consists of two subscales: the Mexican Orientation Subscale (MOS; 17-items)
and the Anglo Orientation Subscale (AOS; 13-items). The subscales are assessed separately and
measured on a five-point scale ranging from 1(not at all) to 5 (extremely or almost always).
Higher scores on each of the sub-scales indicate a stronger orientation towards Mexican culture
(MOS) and Anglo culture (AOS).
The ARSMA-II represents the most widely used acculturation scale (Zane & Mak, 2003).
Satisfactory internal consistency measures of .70 and to .87 have been reported for the AOS and
MOS subscales, respectively, with a sample of Latina college students (Gloria, Castellanos,
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37
Segura-Herrera, & Mayorga, 2010); however, researchers have called for more confirmatory
approaches utilizing robust statistical analyses to further validate the scale (Gutierrez et al.,
2009). Sample items from the AOS include “My thinking is done in the English language” and
“My friends, while I was growing up were of Anglo origin.” Sample items from the MOS
include “I associate with Mexicans and/or Mexican Americans.” Given the diverse Latina
population in the current study, I modified the MOS subscale to be more inclusive of other
Latina/o ethnic subgroups. Following previous researchers, I altered the format of the items to
depict participants’ own classification of their ethnic identity (Cabassa, 2003; Gutierrez et al.,
2009).
Marianismo-Self Sacrifice. Rivera Marano (2000) proposed a 40-item Latina Values
Scale (LVS) based on the concept of marianismo. The measure consists of seven sub-constructs
including self-sacrifice, conflict, responsibility, putting other’s needs first, assertion, guilt, and
self-blame. While the LVS necessitates further validation, its subdomains provide a good starting
point to examine Latina cultural values. The self-sacrifice subconstruct consists of 5-items and
was utilized in the current study. This particular construct has been found to have a reliability of
.745 with a sample of Latina women (Melendez, 2004). Sample items include “I try making
others happy at all cost” and “ I believe sacrificing yourself for others makes you a better
person.”
Latina Strengths. The Latina Strength Scale (LSS) is rooted in the work by Nogales
(2003) and is designed to measure seven strengths that women inherit from the Latino culture. In
her work with Latinas, Nogales noted how several creative, courageous, and determined Latinas
overcame various obstacles in order to accomplish their dreams. After analyzing Latina success
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stories, Nogales identified seven core strengths that Latinas possess as a result of being born into
and growing up in Latino communities: creative spirit, passionate determination, comadres’
(girlfriend) networking ability, diplomáticas’s (diplomat’s) discretion, atrevida’s (risk taker’s)
courage, malabrista’s (multitasker’s) balance, la reina’s (diva’s) confidence and strength.
Castellanos and Gloria adopted Nogales’ dimensions and proposed a working scale to measure
these strengths for Latinas within the higher education context.
Castellanos and Castellanos (in progress) further developed, piloted, and validated the
scale for Latina undergraduates. The LSS represent a 43-item multidimensional construct and
consists of seven subscales: creative spirit (8-items), passionate determination (5-items),
networking ability (7-items), diplomat’s discretion (6-items), risk taker’s courage (6-items),
multitasker’s balance (6-items), and Diva’s confidence (5-items). Students were asked to rate the
extent to which they agree or disagree with forty-three statements on a scale from 1 (strongly
disagree) to 5 (strongly agree). Two items are reverse coded in each subscale. Example items
include “I explore several ideas to create an educational plan that I can put into action”, “I
integrate several ideas to create a vision for my future career” and “When confronted with an
obstacle, I am unable to find creative ways to negotiate the challenge” (Creative Spirit). The use
of Nogales dimensions allows for a strength-based approach when examining the college
experiences and subsequent outcomes of Latinas in higher education.

Proximal Contextual Affordances. Following previous research findings, I considered
five major sources of support and barriers for Latina undergraduates. The following sources of
supports and barriers represent the proximal contextual affordances examined in the current
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study: familial encouragement, faculty mentors, peer mentors, academic involvement, and
campus climate. In what follows, I describe each of these measures.
Familial Encouragement. Latinas have long been known to hold family in high esteem
(Castellanos & Gloria, 2007). I utilize an adapted version of Gloria’s (1993) familial
encouragement and support measure in order to assess Latinas perceived familial encouragement
in the current study. The original measure assessed students’ perceived familial encouragement
in seeking a bachelor’s degree. In the current study, students indicated the extent to which their
immediate (i.e., mother, father, siblings) and extended family members encouraged their career
aspirations on a five-point scale ranging from 1(not at all) to 5 (extremely). Sample items include
“Mother encourages your career goals” and “Father encourages your career goals.”
Faculty Degree and Career Support. I utilize Crisp’s (2009) College Student Mentoring
Scale (CSMS) in order to assess the effect of faculty career support on Latinas’ STEM career
goals. The CSMS represents a 25-item scale and is informed by previous research (Cohen, 1995;
Levinson et al., 1987; Roberts, 2000). Four subdomains comprise the multidimensional
construct: psychological and emotional support (8-items), degree and career support (6-items),
academic subject knowledge support (5-items), existence of role models (6-items). The degree
and career support subscale was of particular interest in the current study. Students indicated the
extent to which they agreed or disagreed with five statements on a scale from 1(strongly
disagree) to 5 (strongly agree). Sample items include “Faculty/staff at my university have helped
me examine my degree or certificate options”, “…encouraged me to consider educational and
career opportunities beyond my current plans”, and “… encouraged me to question my appraisal
of my personal knowledge.”
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Peer Degree and Career Support. The peer career mentoring scale utilized in the current
study was informed by the degree and career subdomain of the CSMS and is identical to the
faculty mentoring scale (Crisp, 2009). Sample items include “Friends/peers/students at my
university have encouraged my educational efforts” and “…helped me examine my degree and
certificate options.”
Academic Involvement. According to Astin (1999), the amount of time and effort that
students devote to particular activities determine the extent to which they can achieve
developmental goals in such domains. Students’ academic involvement in college has been
measured through the number of hours they spend engaging in academic activities (Astin, 1999).
Consistent with previous research, I measure students’ academic involvement in college by
utilizing a 5–item construct (Museus, Nichols, & Lambert, 2008). Students indicated the number
of hours they spent engaging in formal and informal academic activities on a scale from 1 (0
hours) to 7 (21+ hours). Sample items include the numbers of hours spent attending class
(formal) and participating in study groups (informal).
Classroom Climate. Previous research indicates that students of color are less likely to
pursue and persist in STEM fields when faced with hostile racial climates (Crisp & Nora, 2012;
Garcia & Hurtado, 2011; Hurtado et al., 2007). Following previous research, I measure racial
classroom climate utilizing a latent variable comprised of five indicators. Students were asked to
indicate the extent to which they agree or disagree with five statements regarding their
perception of their racial classroom climate and experiences in the classroom. Responses ranged
from 1 (strongly disagree) to 4 (strongly agree). Example items include “I feel I have to work
harder than other students to be perceived as a good student" and "I have heard faculty express
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41
stereotypes based on race/ethnicity, gender, sex or religious affiliation." This measure captures
both behavior and psychological dimensions of classroom racial climate (Hurtado & Guillermo-
Wann, 2013).
Mediating and Outcome Variables

STEM Career Self-Efficacy. According to Bandura (1986), self-efficacy beliefs refers
to an individual’s judgment about his or her capacity to organize and execute courses of action
required to attain a specific aim. I assessed students’ STEM career self-efficacy by utilizing an
adapted version of Betz and Hackett (1981) occupational self-efficacy measure. The measure
account for students’ self-efficacy in completing both the educational and occupational
requirements needed to gain success in a range of fields.
Hackett, Esposito, and O’Hallaron (1989) and Byars (1997) have made revisions to Betz
and Hackett’s (1981) occupational self-efficacy measure. I carefully considered the original and
revised measures before making revisions. In each version of the scale, students are asked to rate
their confidence in their ability to successfully complete the a) educational requirements and b)
occupational requirements of twenty-eight occupational fields. Three primary considerations
were utilized to ensure a wide representation of occupational fields: Holland’s occupational
themes, the percent of occupational fields employed by females (U.S. Labor Statistics, 2001),
and a balanced representation of science and non-science fields.
First, Holland’s (1985) six occupational themes were utilized as the overarching
framework in order to represent a range of occupational fields. According to Holland (1985)
individuals and work environments can be categorized into six dominant personality types:
realistic, investigative, artistic, social, enterprising, or conventional (RIASEC). Each personality
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type is characterized by a unique array of preferred activities, abilities, self-beliefs, and values
(please see Table 6 in Appendix C for further details regarding the RIASEC themes).
Second, occupational fields were selected to represent a balance of fields traditionally
employed by women and men (Betz & Hackett, 1981). Occupations that employed less than 30%
of women, according U.S. Bureau of Labor Statistics (1991), were considered male-dominated
fields (12-items), those that employed 31% to 63% were considered gender neutral (7-items),
and those that employed more than 68% of women were considered female-dominated fields (9
items; Byars, 1997). In reviewing more recent figures (U.S. Bureau of Labor Statistics, 2011), I
found that five of the original occupations were no longer listed (i.e., school super intendant, art
teacher, mathematician, editorial writer, home economist). Additionally, when holding the
original percentage ranges true for the male dominated field classification (less than 30%
female), only six (versus 12) fields were present in the scale. In order to update the measure to
represent more recent labor statistic and achieve greater parity among gender-dominated fields, I
revised the measure and classification scale so that those occupations, which employed less than
35% of women, were considered male dominated fields, 36% to 64% gender neutral, and 65%
and greater was considered female dominated fields.
Third, occupational fields represent a range of STEM (12-items) and non-STEM fields
(16-items; Byars, 1997). When reviewing the two subscales, I noted that, unlike the gender
conceptualization, the STEM and non-STEM subscales included in Byars (1997) did not
represent parity among Holland’s RIASEC themes as intended by the original authors, Betz and
Hackett (1981). In order to hold true to the original conceptualization, I made minor modification
to ensure that the occupational fields included in the science and non-science subscales varied
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according to Holland’s occupational themes. This process was facilitated by the previously
established need to select new occupational fields to replace those that were no longer present in
the U.S. Bureau of Labor Statistics (2011) report. I selected the new fields while keeping in mind
the needs of the science and non-science subscales. The revised measure consists of 17 STEM
fields and 13 non-STEM fields (please see Table 3 for the revised scale items).
While several revisions were made to the scale, in the current study, I utilize only the
STEM educational and occupational career subscales. Each subscale consists of 17-items
representing a range of STEM occupations and Holland’s RIASEC themes. Students rated their
confidence in their ability to successfully complete the a) educational requirements and b) job
duties for each of the occupations on a scale from 1 (not at all confident) to 6 (completely
confident). For example, students rated their confidence in their ability to successfully complete
the a) educational requirements and b) occupational duties for a career as a mechanical engineer,
clinical lab technician, and artifact pilot. The dominant RIASEC theme represented in these three
pair of items is the realistic (R) theme but secondary and tertiary themes are also present (e.g.,
mechanical engineer-RIS; clinical lab technician-RIE; artifact pilot-RIE). The career self-
efficacy scale has been found to have internal consistency coefficients of .91 and .86 for the
educational and job duties subscales, respectively, in a sample of Latina community college
students (Rivera, Blumberg, Chen, Pontero, & Flores, 2007).
STEM Career Outcome Expectations. Outcome expectations concern an individual’s
beliefs about the consequences of performing particular behaviors (Lent & Brown, 2006). In the
current study, STEM career outcome expectations refer to students’ outcome expectations from
pursuing a STEM career field. Previous researchers have assessed career outcome expectations
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by utilizing positive or negative indicators. Given previous literature on Latinas and the fact that
this group represents a historically marginalized group, I assess students’ negative STEM career
outcome expectations. I focused specifically on negative outcome expectations regarding gender
discrimination, racial discrimination, and family-career conflict.
This multi-dimensional latent variable consists of a total of 12 items: four for each sub-
construct. Students were asked to indicate the extent to which they believe they are likely to
experience twelve (negative) outcomes if they purse a STEM career, on a scale from 1 (not likely
at all) to 5 (extremely likely). An additional response category 6 (do not know) was also
provided. Sample items include “discrimination from faculty or peers because of my
race/ethnicity”, “discrimination from co-workers/supervisors because of my gender”, and
“conflict between my marriage/family and my career plans.” This measure represents an adapted
version of McWhirter’s (1997) revised perceptions of barriers scale (POB) scale. The revised-
POB measures students’ perceptions of potential educational and career barriers and consists of
24-item scale regarding students’ anticipated racial/ethnic discrimination, gender discrimination,
as well as their expected family conflict in college. The revised POB scale has been validated for
Latinas in higher education (e.g., Flores & O’Brien, 2002; Gloria, Castellanos, Lopez, &
Rosales, 2005).
Science and Math Learning Experiences. Bandura (1997) note four important sources
of self-efficacy: personal performance accomplishments, vicarious learning, social persuasion,
and physiological and affective states. Of these four sources, personal accomplishments have
been known to have the greatest effect on self-efficacy (Lent & Brown, 2006). Successful
experiences accomplishing tasks in a certain domain tend to increase an individual’s self-efficacy
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in that domain. Unsuccessful experiences are likely to reduce self-efficacy in that domain. In the
current study, I assessed students’ science and math performance accomplishments utilizing a
latent variable comprised of two-items. One items corresponds to students’ self-reported science
scholastic aptitude test (SAT) score. The second item consist of students’ self reported math SAT
scores. This measure of science and math previous performance accomplishments resembles
those used in previous studies (e.g., Navarro, Flores, & Worthington, 2007).
STEM Career Interest. The STEM career interest scale consists of the same 17-items
and conceptualizations as those in the STEM career self-efficacy scale. However, students were
asked to indicate the extent to which they are interested in the various STEM occupations, rather
than their confidence in their ability to succeed in the educational and job requirements needed
for each STEM occupation.
STEM Career Goals. The STEM career goal measure represents the dependent variable
in the current study. I utilized an adaptation of Lent et al.’s (2005) major choice goal measure.
While Lent and colleagues ask students to indicate their level of agreement with four statements
regarding their intentions to pursue an academic major in engineering, I ask students about their
intentions to pursue a career in a STEM field. The four items pertain to students’ intentions to
pursue a STEM career. Sample items include “I am fully committed to entering a career in a
STEM field” and “I intend to pursue a STEM career after my educational training.” Students
indicated their agreement with each statement on a scale from 1 (strongly disagree) to 5 (strongly
agree). In Table 2 (chapter 4), I provide student descriptive statistics and the numerical codes for
each measure included in the study.

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Analytic Approach

In order to address the research questions for the current study, I utilized structural
equation modeling (SEM). SEM is a theory-driven analytical approach for testing specified
hypotheses regarding causal relations among measured and/or latent variables (Kline, 2011).
SEM is appropriate in the current study given its emphasis on theory verification and ability to
simultaneously test multiple hypotheses and account for measurement error (residuals). Sample
size guidelines for studies utilizing SEM range from 5 to 20 cases per model parameter (Jackson,
2003; Muller & Hancock, 2010). While the target sample size for the current study was 1,000,
the final sample achieved was only 460 , resulting in a reduction of power to detect a significant
effects.
Model Building. In order to test the hypothesized model, I utilized R (3.1.2) statistical
software program and lavaan package. I employed a two-phase modeling process: the
measurement phase and the structural phase. The relationships between the observed and
unobserved variables were assessed in the measurement phase (Byrne, 2006). It is necessary to
establish the measurement model and ensure that each latent variable is psychometrically sound
before including the measures in the structural model (Byrne, 2006; Mueller & Hancock, 2010).
The measurement phase included preliminary analyses such as descriptive statistics, correlations,
reliability analyses, and confirmatory factor analysis (CFA) for each of the constructs included in
the study. Modifications made during the first phase of modeling (the measurement phase) were
included in the second phase (structural phase).
Measurement Phase (Phase 1). Prior to performing confirmatory factor analyses (CFA)
for the latent variable of interest, I assessed for normality of the data. Univariate skewness and
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kurtosis values of more than two, and Mardia’s normalized multivariate kurtosis coefficients of
more than three indicated that the data did not meet the assumptions for normality (Bandalos &
Finney, 2010). When not adequately addressed, violations of normality may result in
underestimation of standard errors, inflation of chi-squared values, and biased fit indices
(Bandalos & Finney, 2010). In order to account for deviations from normality, I utilized the
MLR (Robust Maximum Likelihood) estimator, which is a maximum likelihood estimation
method with standard errors and a chi-squared test statistic that are robust to non-normality. In
MLR the standard errors are computed using the Huber-White sandwich estimator and the chi-
squared test statistic is scaled. The chi-squared statistic utilized is asymptotically equivalent to
the Yuan-Bentler (2000) test statistic (Beaujean, 2014). Such corrections are appropriate for non-
normal data with complete or incomplete data (Rosseel, 2013). I utilized full-information
maximum likelihood (FIML) given that missing data values were assumed to be missing at
random (MAR; Carter, 2006). FIML has been known to produce unbiased parameter estimates
and standard errors when data is MAR or missing completely at random (MCAR; Carter,2006).
Utilizing these approaches, I conducted CFAs in order to assess the validity of each latent
variable included in the hypothesized model. I evaluated the factorial structure of each latent
construct by examining global goodness-of-fit estimates (χ
2
, CFI, RMSEA, SRMR), factor
loadings, and internal consistencies. I utilized the rho coefficient to examine the internal
consistency of the measures. The rho coefficient is conceptually similar to Cronbach’s alpha in
that it represents the ratio of a scale's estimated true score variance relative to its total variance;
however, unlike Cronbach’s alpha, the rho coefficient acknowledges the possibility of
heterogeneous item-construct relations and estimates (Acock, 2013; Raykov, 2004). When no
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correlated errors are present, Cronbach’s alpha represent the lower limit of the rho coefficient
and may be greater when correlated terms are present (Acock, 2013). The fit indices and internal
consistency estimates (ρ) for each latent variable are provided in the following findings chapter
(Table 3).
Structural Phase (Phase 2). In the structural phase, I examined the complete
hypothesized model including the relationships among the unobserved variables (Bryne, 2008). I
consulted the Lagrange Multiplier (LM) test in order to examine whether the model fit would be
significantly improved by estimating additional parameters. The LM test identifies the amount
by which the model χ
2
statistic will decrease as a result of estimating a previously fixed-to-zero
parameter; however, it is the researcher’s responsibility to ensure changes based on the LM test
are theoretically sound (Kline, 2011). After achieving a satisfactory model fit, I considered two
alternative models. Kline (2011) recommends that researchers explain why their model should be
preferred over other models with statistical equivalence. Such explanations require theoretical
grounding (Kline, 2011).
Fit Indices. As recommended by Kline (2011), I utilized the chi-squared (χ
2
) test,
comparative fit index (CFI), root-mean-square error of approximation (RMSEA), and standard
root mean squared residual (SRMR) in order to determine whether the model is a good fit to the
data. While a nonsignificant χ
2
(i.e. p > .05) is indicative of a good model-to-data fit, quantitative
methodologists consider the χ
2
test to be overly strict (Muller & Hancock, 2011). As such,
methodologists recommend that researchers report multiple fit indices (Muller & Hancock,
2010). CFI represents an incremental index, which assumes that all latent variables are
uncorrelated and compares the sample covariance matrix with the baseline (null) model (Bentler,
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49
1990; Hooper, Coughlan, & Mullen, 2008; Muller & Hancock, 2010). A notable advantage of
CFI is its ability to perform well with small sample sizes (Tabachnick & Fidell, 2007). CFI
values above .90 or .95* are viewed as representing a good model fit (Hooper et al., 2008).
RMSEA represents a parsimonious index that evaluates the discrepancy between
observed and implied covariance matrices while accounting for the model’s complexity (Hooper
et al., 2008; Muller & Hancock, 2010). RMSEA is highly regarded for its sensitivity to the
number of parameters included in a model (Hooper et al., 2008). Values below .07 or .05* are
considered a good fit under the RMSEA fit statistic (Hooper et al., 2008). SRMR values range
from zero to one and represent the average discrepancy between the observed sample and
hypothesized correlation matrices (Bryne, 2008). Recommended values for SRMR are below .08
or .05* (Bryne, 2008; Mueller & Hancock, 2010) In chapter four, I discuss the model fit indices
and estimates for the measurement and structural phase.

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Chapter Four: Findings

In the present chapter, I detail the findings for the current study. I begin by describing the
findings from the measurement phase of the analysis. In doing so, I provide the scale reliabilities
(ρ) and my rationale for selecting the measures included in the final model. I then present the
results from the structural phase of the analysis, including the overall model fit, direct, indirect,
and total effects.
Measurement Phase

A total of 176 manifest variables comprise the 15 latent variables initial included in
measurement phase. Prior to performing the confirmatory factor analyses (CFA), I created
parcels for the latent constructs with 12 or more manifest variables: Latina strengths (43); AOS
(13); MOS (17); STEM career self-efficacy (34), STEM career outcome expectations (12),
STEM career interest (17). When numerous indicators are specified for one model, difficulties
with estimation are likely to occur (Acock, 2013). The parceling of indicators provides an
excellent way of reducing limitations due to model complexity (Little, Rhemtulla, Gibson, &
Schoemann, 2013). When properly constructed, parcels may help clarify the representation of
both unidimensional and multidimensional constructs (Graham, Tatterson, & Widaman, 2000).
In order to ensure that parcels created for the current study reflected well-balanced
representations of their respective latent constructs, I balanced and anchored each parcel with the
construct’s highest factor loadings. This approach has been known to balance error sources,
thereby reducing the likelihood of encountering convergence problems (Acock, 2013). I assigned
two to five items per parcel, depending on the construct of interest. I considered both the item
factor loadings and the underlying sub-dimensions when assigning items to parcels.
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The resulting number of indicators for each of the constructs is as follows: Latina
strengths (7), AOS (4), MOS (6), STEM career self-efficacy (5), STEM career outcome
expectations (4), and STEM career interest (5). The Latina Strength Scale and the STEM career
outcome expectations measures represent multidimensional constructs, while the AOS, MOS,
STEM career self-efficacy and STEM career interest represent unidimensional constructs;
however, subgroup fields for the STEM constructs may be identified based on Holland’s
REASIC themes.
After creating parcels for constructs with more than 12 indicators, I conducted a
confirmatory factor analysis (CFA) for each of the latent constructs. All of the hypothesized
residual covariances were included in the initial CFAs. With the exception of the campus
climate, academic involvement, AOS, and MOS scales, all of the latent constructs achieved a
good model fit (please see Table 3). I examined the modification indices for these measures and
added the recommended residual covariances only if the relationship between the residuals were
theoretically substantiated and resulted in substantial model improvement. The campus climate
and academic involvement measures reached adequate model fit; however, the AOS and MOS
subscales did not.
Given my inability to confirm the presumed structure for the AOS and MOS subscales, I
searched for a reasonable multifactor solution utilizing the original indicators—rather than
parcels—and exploratory factor analyses (EFA). I utilized varimax rotation and considered
factors with eigenvalues above 1 and factor loadings above the .40. This procedure yielded a
two-factor structure for each of the subscales. After completing the EFAs, I tested the factors
utilizing a confirmatory approach. Once again, the models did not fit the data well; this was true
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52
even after I included appropriate residual covariances (AOS: χ
2
= 360.27(df=62), p<.000, CFI
=.551, RMSEA=.126, SRMR=.132; MOS: χ
2
= 622.01(df=116), p<.000 , CFI=.731,
RMSEA=.120, SRMR= .085). Given the unsatisfactory model fit of both of these subscales, I did
not include them in the final structural model. In Table 3, I provide the scale reliabilities (ρ),
standardized coefficients, variance explained, and model fit for each of the latent construct
included in the model.
After assessing the model fit of each construct, I examined the full measurement model
with all of the latent constructs included. The revised measurement model involved a total of 60
manifest variables, which served as indicators of 13 latent constructs: Latina strengths,
socioeconomic status, self-sacrifice (marianismo), parental encouragement, faculty support, peer
support, campus climate, academic involvement, math and science learning experiences, STEM
career self-efficacy, STEM career outcome expectations, STEM career interest, and STEM
career goals.
All of the modifications made in the measurement phase were carried forward in the
structural phase. The final measurement model met the recommended RMSEA (.049) and SRMR
(.069) values, indicating that the model fit the data well. The chi-squared test and CFI did not
follow conventional guidelines (χ
2
(1618, N=458)= 3355, p<.000, CFI= .858). A significant chi-
squared test was not unexpected given its sensitivity to sample size and the high number of
degree of freedom included in the model. When small sample sizes are used, the chi-squared
statistic lacks power and is unable to discriminate between a good and poor fitting model
(Kenny & McCoach, 2003). Unlike the chi-squared test, the CFI is not susceptible to sample
size. This statistic compares the sample covariance matrix with the null model and assumes that
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53
all latent variables are uncorrelated (Hooper, Coughlan, & Mullen, 2008). I provide findings
from the final measurement model, including the standardized coefficients and residual loadings
in Figure 3. I provide the covariance matrix (Table 7) and residual matrix (Table 8) for the final
measures in Appendix C. A summary of the model fit indices is provided in Table 4.

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Table 3
Individual Confirmatory Factor Analyses and Internal Consistency (ρ) for Latent Constructs

Latent and Manifest Variables
Standardized Parameter Estimates
and Variance Explained

Socioeconomic Status (ρ=.73)

Std.

R
2

Ses1: Family household income

.590

.349
Ses2: Mother’s highest education achieved .761 .588
Ses3: Father’s highest education achieved .771 .595

Model Fit Indices: χ
2
= 0 (df=0), p<.000, CFI= 1.00, RMSEA=.00, SRMR=.00

Self-Sacrifice (Marianismo; ρ=.72)

Std.

R
2

SelfS1: I believe sacrificing yourself for others makes
you a better person

.473

.224
SelfS2: I find myself putting the needs of others in front
of my own
.605 .366
SelfS3: I find myself doing things I prefer not to do to
make others happy
.727 .529
SelfS4: I try to make my family happy at all cost .568 .322
SelfS5: I feel guilty when I prioritize my goals at
school over the needs of my family
.503 .253
SelfS6: I feel guilty when I prioritize my goals at
school over the needs of others
.509 .259
E.SelfS1~E. SelfS2 .296
E.SelfS5~E. SelfS6 .489

Model Fit Indices: χ
2
= 15.32 (df=7), p<.032, CFI= .98, RMSEA=.06, SRMR=.03

Latina Strengths (ρ=.83)

Std.

R
2

LSS 1: Creative Spirit

.329

.514
LSS 2: Passionate Determination .494 .544
LSS 3: Network Ability .275 .134
LSS 4: Risk Taker .507 .614
LSS 5: Multitasker .311 .296
LSS 6: Confidence .373 .397
LSS 7: Diplomacy .352 .353

Model Fit Indices χ
2
= 31.06 (df=14), p<.005, CFI= .97, RMSEA=.06, SRMR=.04
!
55
Table 3 (cont.)
Individual Confirmatory Factor Analyses and Internal Consistency (ρ) for Latent Constructs

Familial Encouragement (ρ=.84)

Std.

R
2

FamE1: Mother encourages you to achieve your career goals

.764

.583
FamE2: Father encourages you to achieve your career goals .748 .559
FamE3: Siblings support you to achieve your career goals .739 .546
FamE4: Extended family encourages you to achieve your career
goals
.695 .483

Model Fit Indices χ
2
= 8.39 (df=2), p<.015, CFI= .97, RMSEA=.09, SRMR=.03

Faculty Support (ρ=.82)

Std.

R
2

FacS1: Encouraged my educational efforts

.530

.281
FacS2: Helped me examine my degree or certificate
Options
.645 .416
FacS3: Discussed the implications of my degree choice with me .818 .669
FacS4: Encouraged me to consider educational and
career opportunities beyond my current plans
.827 .685
FacS5: Encouraged me to question my appraisal of my personal
knowledge and skills
.809 .655
E.FacS2~E. FacS3 .448
E.FacS1~E. FacS2 .276

Model Fit Indices χ
2
= 1.362 (df=3), p<.714, CFI= 1.00, RMSEA=.00, SRMR=.01

Peer Support (ρ=.90)

Std.

R
2

PeerS1: Encouraged my educational efforts

.555

.308
PeerS2: Helped me examine my degree or certificate options .794 .630
PeerS3: Discussed the implications of my degree
choice with me
.816 .665
PeerS4: Encouraged me to consider educational and
career opportunities beyond my current plans
.894 .800
PeerS5: Encouraged me to question my appraisal of my personal
knowledge and skills
.887 .786
E.PeerS2~E. PeerS3 .442

Model Fit Indices χ
2
= 4.497 (df=4), p<.343, CFI= 1.00, RMSEA=.02, SRMR=.01
!
56
Table 3 (cont.)
Individual Confirmatory Factor Analyses and Internal Consistency (ρ) for Latent Constructs

Academic Involvement (ρ=.54)

Std.

R
2

AcIn1: Studying/ doing homework

.690

.475
AcIn2: Attending class/lab .557* .311
AcIn3: Participating in study groups .272 .074
AcIn4: Using the internet for research or homework .458 .210
AcIn5: Working on a research project .300* .090
E.AcIn4~E. AcIn5 .538

Model Fit Indices χ
2
= 1.701 (df=4), p<.791, CFI= 1.00, RMSEA=.000, SRMR=.015

Campus Climate (ρ=.50)
Std. R
2

ClCli1: I feel uncomfortable sharing my own perspective and
experiences in class

.269

.072
ClCli2: I have been singled out in class because of my race
ethnicity, gender, or sexual orientation
-.434* .188
ClCli3: I feel I have to work harder than other students to be
perceived as a good student
-.846* .715
ClCli4: I have heard faculty express stereotypes based on race,
ethnicity, gender, sex, or religious affiliation
-.348 .121
ClCli5: I don’t feel comfortable contributing to class discussions -.392 .153
E.ClCli2~E. ClCli4 .475
E.ClCli1~E. ClCli5 -.516

Model Fit Indices χ
2
= 3.837 (df=3), p<.280, CFI= 1.00, RMSEA=.03, SRMR=.01

Science and Math Learning Experiences (ρ=.77)

Std.

R
2

MSL1: Science SAT scores

N/A

N/A
MSL2: Math SAT Scores N/A N/A
Model Fit Indices N/A

STEM Career Outcome Expectations (ρ=.91)

Std.

R
2

NoutE1: Gender Discrimination Parcel

.756

.572
NoutE2: Family Conflict, Racial, Gender, Discrimination Parcel
.990 .979
NoutE3: Racial Discrimination Parcel
.798 .636
NoutE4: Familial Conflict Parcel
.635 .403
E.NoutE1* E.NoutE1
.575
Model Fit Indices χ
2
= 4.425 (df=1), p<.515, CFI= 1.00, RMSEA=.000, SRMR=.004
!
57
Table 3 (cont.)
Individual Confirmatory Factor Analyses and Internal Consistency (ρ) for Latent Constructs

STEM Career Self-Efficacy (ρ=.92)

Std.

R
2

SelfEff1: Conventional (C) Parcel
Certified Public Accountant (CSE); Financial Analyst
(CSI); Budget Analyst (CER)

.716

.512
SelfEff 2: Social (S) Parcel
Registered Nurse (SIA); Research Dietitian (SIE)
Physical Therapist (SIE)
.758 .574
SelfEff 3: Realistic (R) Parcel
Mechanical Engineer (RIS); Clinical Lab Technician
(RIE); Aircraft Pilot (RIE)
.957 .915
SelfEff 4: Investigative 1 (I-R) Parcel
Civil Engineer (IRE); Electrical Engineer (IRE)
Architect (AIR); Chemist (IRE)
.944 .890
SelfEff 5: Investigative 2 (I-S) Parcel
Dentist (ISR); Physician (ISE; IRA)
Computer Programmer (IER); Psychologist (IES)
.850 .723
E.SelfEff 2* E.SelfEff 4 -.402
E.SelfEff 2* E.SelfEff 5 .414

Model Fit Indices χ
2
= 3.401 (df=3), p<.343, CFI= 1.00, RMSEA=.018, SRMR=.006

STEM Career Interests (ρ=.81)

Std.

R
2

CarInt1: Conventional (C) Parcel
Certified Public Accountant (CSE); Financial Analyst
(CSI); Budget Analyst (CER)

.300

.090
CarInt 2: Social (S) Parcel
Registered Nurse (SIA); Research Dietitian (SIE);
Physical Therapist (SIE)
.528 .279
CarInt3: Realistic (R) Parcel
Mechanical Engineer (RIS); Clinical Lab Technician
(RIE); Aircraft Pilot (RIE)
.955 .912
CarInt4: Investigative 1 (I-R) Parcel
Civil Engineer (IRE); Electrical Engineer (IRE)
Architect (AIR); Chemist (IRE)
.875 .765
CarInt5: Investigative 2 (I-S) Parcel
Dentist (ISR); Physician (ISE; IRA)
Computer Programmer (IER); Psychologist (IES)
.669 .448
E.CarInt2*E. CarInt5 .502
E.CarInt1*E. CarInt4 .333
Model Fit Indices χ
2
= 13.642 (df=3), p<.003, CFI= .985, RMSEA=.094, SRMR=.013
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58

Table 3 (cont.)
Individual Confirmatory Factor Analyses and Internal Consistency (ρ) for Latent Constructs

STEM Career Goals (ρ=.97)

Std.

R
2

Goal1: I intend to pursue a STEM career after my educational
training

.968

.937
Goal2: Entering a STEM career is a realistic goal for me .938 .880
Goal3: I am fully committed to entering a STEM career .978 .957
Goal4: I intent to pursue graduate school in a STEM field .884 .781

Model Fit Indices χ
2
= 11.719 (df=2), p<.003, CFI= .984, RMSEA=.107, SRMR=.006

*Note. Estimates marked * are significant at the .01 level. All other estimates are significant
at the .001 level
.65
SelfS1
SelfS3
SelfS4
SelfS6

.470
.700
.579
.537
.30
.51
.66
.47

Self Sacrifice
(Marianismo)
SelfS2
.589
SelfS5
.538
LSS1
LSS3
LSS5
LSS7

.720
.393
.550
.607
.48
.85
.70
.63

Latina
Strengths
LSS2 .728
.47
LSS6
.633
.60
LSS4
.769
.41
Figure 3. Final Measurement Model: Standardized paths presented (all paths significant at
the p <.001 level).
.71
.71
.78
!
59

Ses1
Ses2
Ses3
.593
.779
.758
.65
.39
.46

Socioeconomic
Status
PeerS3
.822

.32
PeerS1

PeerS2

PeerS4
PeerS5

.566
.801
.888
.885
.68
.21
.22

Peer
Support
Figure 3. Final Measurement Model: Standardized paths presented (all paths significant at
the p <.001 level) cont.
.56
.59
.85
.80
.62
…
.36
.91
.93
.50
.43
ClCli3
-.596
.87
ClCli1

ClCli2

ClCli4
ClCli 5

.452
-.356
-.298
-.616
.64 -.41

Classroom
Climate
AcIn3
.262
.93
AcIn1

AcIn2

AcIn4
AcIn5

.801
.495
.389
.262
.36
.74
.41

Academic
Involvement
FacS3
.811
.45
FacS1

FacS2

FacS4
FacS5

.550
.646
.815
.821
.27
.34
.33

Faculty
Support
.34
.58
.69
FamE1
FamE2
FamE3
FamE4

.666
.638
.789
.767
.35
.38
.41

Familial
Encouragement
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60
MSL1
MSL2
.623
.820
.61
.33

Math and
Science
Learning
Experiences
CarInt3
.900
.20
CarInt1

CarInt2

CarInt4
CarInt5

.387
.567
.925
.669
.85
.14
.47

STEM
Career Interest
SelfEff3
.955

.41
SelfEff1

SelfEff2

SelfEff4
SelfEff5

.712
.756

.947
.849
.49
.43

STEM Career
Self-Efficacy
Goal1
Goal2
Goal3
Goal4

.967
.940
.979
.883
.06
.12
.04
.22

STEM
Career Goals
Figure 3. Final Measurement Model: Standardized paths presented (all paths significant at the
p <.001 level) cont.
.09
NoutE1
NoutE2
NoutE3
NoutE4

.776
.964
.820

.649
.07
.54
.58

STEM Career
Outcome
Expectations
(negative)
.40
.38
.68
.55
.28
.10
-.42
-.32
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61
Structural Model

Given the result of the measurement model, I reassessed my hypothesized model and
removed the AOS and MOS subscales. I examined the hypothesized model, which was based on
SCCT and previous research findings. That is, I considered additional relationships between the
proximal contextual affordances and the central mediating cognitive variables that are not
hypothesized by SCCT. The model fit indices indicated that the model was a good fit to the data
(Model 1: χ
2
= 3585.79(df=1752, N=460), p<.000, CFI= .859, RMSEA=.047, SRMR=.71). The
statistically significant standardized path coefficients for each of the paths estimated in the final
model are provided in Figure 4. Each path coefficient represent a standard deviation change in
the exogenous variable and corresponds to a one unit change in the endogenous variable when all
other variables in the model are held constant.
Direct Effects

Independent and Mediating Variables. The hypothesized relationships between
exogenous and endogenous variables included in a SEM studies are called direct effects. Such
relationships are represented by single headed arrows from exogenous to endogenous variables.
Several direct effects reached statistical significance in the hypothesized model. First, as
hypothesized, the path between socioeconomic status (std.β= .548, p<.001), generational status
(std.β= .196, p<.05), self sacrifice (std.β= -.243, p<.05) and Latina students’ math and science
(MS) learning experiences were statistically significant. While the paths between socioeconomic
status and generational status to MS learning experiences were positive, the path between self-
sacrifice and MS learning experiences was negative. These findings suggest that Latina students
who are from higher SES backgrounds and generational status are likely to have better MS
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62
performance and learning experiences, as measured by math and science SAT scores. Those who
possess greater self-sacrifice values, however, tend to have lower MS performance experiences.
The paths between Latina strengths and ethnicity on MS learning experiences were not
statistically significant.
Second, the path between MS performance and learning experiences and STEM career
self-efficacy was significant and positive (std.β=.183, p<.05). Contrary to the hypotheses set
forth by SCCT, however, neither MS learning experiences nor STEM career self-efficacy were
statistically significant in predicting Latina students’ (negative) STEM career outcome
expectations. Third, STEM career self-efficacy, but not STEM career outcome expectations,
predicted STEM career interest (std.β=.669, p<.000).
Fifth, in examining the paths between the proximal contextual affordances and the central
mediating model, three paths were significant: academic involvement and self efficacy
(std.β=.198, p<.05) , faculty mentorship and STEM interest (std.β=.111, p<.05), classroom
climate and STEM interest (std.β= -.193, P<.01). The first finding suggests that Latina
undergraduate students that are more academically involved on campus are more likely to have
greater self-efficacy regarding their ability to successfully complete the educational and
occupational requirements necessary for success in a STEM field. The next two findings suggest
that Latinas with greater faculty support through career related mentorship are more likely to be
interested in a STEM field, while those who experience more hostile classroom environments are
less likely to be interested in a STEM field.
Primary Dependent Variable. Regarding the direct paths between the central mediating
constructs and STEM career goals, only STEM career self-efficacy (std.β= .302, p<.000) and
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63
STEM career interest (std.β=.239, p<.01) were statistically significant. Such findings indicate
that as hypothesized, Latina students’ STEM career self-efficacy and interest influence their
desires to pursue a STEM field. When examining the five proximal contextual affordances
included in the model, only classroom climate, (std.β= -.193, p< .01) was statistically significant
and negative in predicting STEM career goals. That is, students who perceive their institution as
having hostile classroom climates are less likely to pursue a STEM field. No other hypothesized
direct effect was statistically significant in predicting Latina undergraduates’ STEM career goals.
Indirect Effects

In SEM, it is possible for a variable to be exogenous in relation to one variable (direct
effect) but endogenous in relation to another. This dual role is referred to as an indirect
(mediator) effect. I examined six indirect effects in the current study, three of which were
statistically significant. First, the results indicated that STEM career self-efficacy (std.β=.162,
p<.01) and campus climate (std.β = -.047, p<.01), had a statistically significant indirect effect on
STEM career goals, through their effect on STEM career interest. While STEM career self-
efficacy had a positive effect, Latina students’ perceptions of hostile classroom climates had a
negative effect on their STEM career goals. Additionally, academic involvement had a
statistically significant indirect effect on STEM career goals (std.β =.060, p<.05), through self-
efficacy. The indirect effect of academic involvement on STEM career goals, however, was
small. I provide both the standardized and unstandardized indirect, direct, and total effects in
Table 5.

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64
Test of Alternative Models

In order to establish the appropriateness of the hypothesized model, I examined two
alternative models. First, I examined a one-factor model, which consisted all of the domain-
specific constructs, including the central mediating outcomes and dependent variable. As
expected the model did not fit the data well (Model 2: χ
2
= 6646.48 (df=1778), p<.000, CFI=.613,
RMSEA=.077, SRMR=.090). Next, I examined a model identical to the hypothesized model, but
based solely on SCCT: no additional parameters from the proximal contextual affordances
constructs to the central mediating constructs of interest were included (Model 3: χ
2
= 3610
(df=1770), p<.000, CFI= .855, RMSEA=.048, SRMR .075). While two of the four model fit
indices met the recommended values, the hypothesized model with additional parameters
retained a better fit.

Table 4
Summary of Data Model Fit Statistics (n=458)
Model

χ
2

df

CFI

SRMR

RMSEA
90% CI for
RMSEA

Measurement Model
(with covarying residuals pairs)
3356 1618 .861 .069 .048 .046, .051

Model 1: Final Structural Model
(SCCT model with additional
parameters)
3618.37 1752 .859 .071 .047 .045, .049

Model 2: Alternative Model
(One factor model)
6646.48 1778 .613 .090 .077 .075, .079

Model 3: Alternative Model
(SCCT model)
3610.13

1770

.854

.075 .048 .045, .050
!
65

-.210*
.174*
.276***
.329***
.189*
Figure 4. Summary of statistically significant standardized covariances and path coefficients for Latina
STEM career goal model (p<.000***, p<.01**, p<.05*). Path coefficients for non-statistically path
coefficients are not provided in figure (see Table 5).

Faculty
Support

Social
Class
Latina/o
ethnic
subgroup!
Generational
Status!

Pre-college
Math &
Science
Performance

Latina
Strengths

Peer
Support

STEM
Career
Goal

STEM
Career
Outcome
Expectations

STEM
Career Self-
Efficacy

STEM
Career
Interest

Classroom
Climate

Academic
Involvement

Familial
Support

Self-
Sacrifice
Marianismo
χ
2
= 3531 (df=1752, n=460), p<.000,
CFI=.859, RMSEA=.047, SRMR=.071
.554***
.164***
-.244*
.198*
.670***
.242**
-.193**
-.193**
.108*
.302***
.483***
.319*
.237**

.384***
-.098
-.137
-.132
.193*
.517***
.199*
.175**
.301***
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66
Table 5

Direct, Indirect, and Total Effects for Final Model
Variable Direct
Effects
Indirect
Effects
Total
Effects
β B β B β B
M & S Learning Experiences
-- -- -- --
Latina Strengths -3.52 -.02 -- -- -- --
SES 42.68*** .55 -- -- -- --
Generational Status 7.26** .09 -- -- -- --
Ethnicity .78 .03 -- -- -- --
Self-Sacrifice (Marianista
Value)
-31.94* -.24 -- -- -- --
STEM Self-Efficacy
M & S Learning Experiences .004** .189 -- -- -- --
Academic Involvement .235* .198 -- -- -- --
STEM Outcome Expectations

M & S Learning Experiences -.002 -.111 -- -- -- --
STEM Self-Efficacy -.062 -.072 -- -- -- --
STEM Career Interest

STEM Self-Efficacy .203*** .67 -- -- -- .--
STEM Outcome Expectations .008 .02 -- -- -- --
Classroom Climate -.146** -.19 -- -- -- --
Faculty Mentorship
.082* .11 -- -- -- --
STEM Career Goals

STEM Self-Efficacy .375*** .302 .201** .162 .577*** .464
STEM Outcome Expectations -.118 -.082 .008 .005 -.111 -.077
STEM Career Interest .989** .239 -- -- -- --
Campus Climate -.600** -.193 -.145** -.047 -.745** -.240
Family Support .205 .081 -- -- -- --
Peer Mentorship -.023 -.007 -- -- -- --
Faculty Mentorship -.044 -.014 .081 .026 .038* .012
Academic Involvement .070 .047 .088* .060 .158 .107
Note: *p<.05, **p<.01, ***p<.001
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67
Limitations

Several limitations may be noted from the current study. First, the data utilized represents
cross-sectional data, which are collected at one single point in time. While longitudinal data is
typically preferred, cross-sectional data offers an opportunity to explore the hypotheses of
interest without the time and monetary constraint of collecting longitudinal data. Findings from
the current study may help inform and encourage further research utilizing longitudinal designs.
Second, the study sample size did not meet the recommended number of cases per
parameter estimate. While previous guidelines for SEM studies suggest the inclusion of five
cases per parameter estimate (Bentler & Chou, 1987), a total of 263 parameters were estimated
with a sample size of 460. Failure to meet the recommended sample size for SEM studies is not
uncommon (Kenny, 2014). Given the limited study sample size, however, several relationships
hypothesized by SCCT were excluded from the model. For instance, SCCT hypothesizes that
students’ distal contextual affordances (i.e., cultural values, strengths, ses) influences their
proximal support and barriers (i.e., faculty mentors, campus climate, academic involvement) and
that the effect of students’ career interest on goals is moderated by their proximal contextual
affordances; however, such hypotheses were not examined in the current study. Additionally,
despite their relevance, several control variables (e.g., financial aid, transfer student status) were
not included in the model.
Third, given the inadequate model fit of the AOS and MOS subscales, I was unable to
include a measure of acculturation in the current study. Additionally, it is important to note that I
utilized a mathematically based approach, rooted in the eigenvalue greater than 1 rule, when
determining how many factors to retain from EFAs. While this approach is widely used,
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68
limitations have also been noted in the literature. Future researchers should consider multiple
methods when determining the number of factors that should be retained, such as Parallel
Analysis and the Minimum Average Partial procedures (Bandalos & Finney, 2010).
Fourth, the findings from the current study have limited generalizability to Latinas at
PWIs in California. While limited generalizability presents a limitation in the current study, vast
geographical diversity exists with regard to the representation of the Latina/o population. I
selected Latina/o subgroups across California PWIs given that such institutions are more likely
to possess a critical mass Latina/o population. Latina/os’ pre-college environment and lived
experiences has been known to influence their perceptions of the university context (Gloria &
Castellanos, 2012). For example, those who grown up in a predominately Latina/o community
may experience greater difficulty adjusting to a PWI, compared to those who grow up in more
diverse and/or Anglo community.
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69
Chapter Five: Discussion and Implications

My primary aim in the current study was to examine the propositions set forth by SCCT
and their applicability to Latina undergraduates within California PWIs in the domain of science,
technology, engineering, and math. I examined Latinas’ STEM career decision-making process
through four key process outcomes (i.e., STEM career self-efficacy, negative STEM career
outcome expectations, STEM career interest, and STEM career goals). The research findings
support the use of SCCT with Latina college students. Such findings are consistent with the
limited, yet important body of literature on Latina career development and social cognitive
indicators of educational success (Lent et al., 2005). While a few studies have examined aspects
of the career decision-making process for Latina/o middle school (Navarro et al., 2007), high
school (Risco & Duffy, 2011; Ojeda & Flores, 2008), and community college students (Rivera et
al, 2007), the current study is the first to apply SCCT with a sample of Latinas at predominately
white institutions of higher education.
While neither the predicted paths to and from (negative) STEM career outcome
expectations were statistically significant, the remaining central mediating paths followed their
predicted relationships. Latina college students’ previous STEM performance accomplishments
predicted their STEM career self-efficacy, which in turn predicted both STEM career interest and
STEM career goals, as expected. STEM career interest also predicted STEM career goals in the
expected direction. Such findings highlight the importance of considering cognitive-person
variables for Latina college students. Latina students with greater pre-college STEM
performance experiences possessed greater STEM career self-efficacy and were more likely to
be interested in a STEM career field as a result. Those with greater STEM career self-efficacy
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70
and STEM career interest were more likely to have STEM career goals. With the exception of
the nonsignificant finding for negative STEM career outcome expectations, such findings are
consistent with previous research on general and diverse student populations (Hackett et
al.,1992; Lent et al., 2005; Navarro et al., 2007; O’Brien et al., 1999). Fewer studies, however,
have examined the effect of outcome expectations (Fouad & Guillen, 2006). Further discussion
regarding the nonsignificant effect of negative STEM career outcome expectations in the current
study will be discussed in the next section.
In an effort to enhance the model’s predictive validity for Latinas at PWIs, I examined
the role of several distal (demographic and cultural) and proximal (support and barriers) factors
in influencing Latinas’ STEM career decision-making process outcomes and goals. While
personal factors play a role in students’ initial interest and goals (Holland, 1985), such aims are
also influenced by students’ cultural values and college experiences. Among the five distal
factors examined, two measures were included as indicators of Latinas’ demographic
characteristics (SES and ethnicity) and three were included as indicators of cultural values
(Latina strength, self-sacrifice, and generational status).
Distal Contextual Affordances

Two distal factors were statistically significant in predicting Latinas’ performance
accomplishments: SES and self-sacrifice. These findings support the assertion that distal
contextual affordances influence Latinas’ math and science performance experiences. Social
class standings, as measured by family household income and parental education, were
associated with pre-college math and science performance accomplishments in the expected
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71
direction. A greater alignment with traditional Latina values rooted in the marianista principle of
self-sacrifice, however, was associated with lower math and science performance experiences.
Findings regarding the role of social class on Latinas’ pre-college math and science
performance accomplishments parallel societal educational inequities across social class statuses.
That is, individuals who come from families with higher social class standings are likely to have
higher math and science performance accomplishments. On the other hand, individuals from
lower social class standings are likely to possess limited access to information and resources that
may augment their training, and subsequently, limited self-efficacy in STEM fields. On average,
the study sample family household incomes were between 40,000 to 80,000 and the highest level
of parental education achieved was a high school diploma.
The negative effect of the Latina self-sacrifice cultural value on pre-college math and
science accomplishments also warrants much attention, in that, such findings shed light on the
essence of what it means to be a Latina female and the conditions under which Latinas operate.
Latinas’ emphasis on prioritizing the needs of others reflects collectivistic values inherent in the
Latina/o culture, and cultural norms rooted in marianismo for Latina women, specifically (Gloria
& Castellanos, 2012). An emphasis on prioritizing the needs of others, however, may pose
challenges as Latinas also strive to accomplish their educational pursuits, especially in
demanding fields such as STEM. STEM majors and career fields have been known to require
additional hours of studying, which allows less time for family and community activities.
Dissonance regarding the balancing or prioritizing of the self and one’s own career goals, over
the needs of the family and community may represent a challenge for Latinas (Gomez et al.,
2001; Sy & Romero, 2008). Similar self-sacrifice patterns have been noted for Latinas across the
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72
higher education literature. For instance, Latinas have been known to experience feelings of guilt
for leaving home to attend college (Gonzalez, Jovel, & Stoner, 2004).
The Latina strength variable, generational status, and ethnic subgroup were not
statistically significant in predicting pre-college math and science accomplishments. In addition
to their role in influencing students’ pre-college math and science performance accomplishments,
SCCT holds that students’ background characteristics and distal contextual affordances also
predict their college experiences (i.e., proximal affordances); however, I was unable to examine
such paths given the limited sample size in the current study. The addition of such paths would
result in a reduction of power and ability to detect statistically significant effects. Nonetheless,
the statistically significant correlational relationship among several of the sociocultural (distal)
and college experience (proximal) variables warrant attention. Two patterns, in particular,
provide further insight into the likely effect of distal factors on future supports and barriers while
in college.
First, Latina strengths, social class, and self-sacrifice values were all correlated with
classroom climate. While Latina strengths and social class were positively correlated with
negative perceptions of classroom climate, self-sacrifice values were negatively correlated with
classroom climate. In other words, Latinas who possessed lower self-sacrifice values were more
likely to perceive their classroom climate as hostile, while those from higher social class
backgrounds and with greater Latina strengths were more likely to perceive their university
classroom climate as hostile. Such relationships suggest that as Latinas gain greater sociocultural
resources (i.e., Latina strengths, social class standings, prioritizing self versus others), they
become more aware and attentive to their social environments. Second, Latina strengths were
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also correlated with the remaining four proximal contextual affordance variables: familial
support, peer mentors, faculty mentor, and academic involvement. Such findings are indicative
of how Latina cultural resources and strengths may augment students’ academic involvement and
the seeking out of faculty and peer mentorship relationships.
Not surprisingly, social class was correlated with both generational status and ethnic
subgroup. Interestingly, the correlations between Latina strengths, self-sacrifice, and social class
were not statistically significant, indicating the unique role of each of these sociocultural
variables. Latinas who are from lower social class backgrounds, first-generation to attend
college, and culturally centered (self-sacrifice) are more likely to experience challenges in their
STEM educational pursuit. While not included as predictive paths in the current study, the
statistically significant correlational pattern among distal and proximal factors call for further
examination. These relationships are postulated by SCCT.
Latina cultural values and strengths may prove useful in helping to predict Latinas’
college experiences and educational success. Furthermore, Latina cultural resources, and the
Latina strength scale, in particular, may be useful in explaining the nonpredictive findings for
negative STEM career outcome expectations. As previously noted, negative STEM career
outcome expectations did not predict STEM career interest or goals as hypothesized by SCCT.
On average, Latinas’ in the current study indicated that they were “somewhat likely” to “likely”
to experience negative outcomes (i.e., racial discrimination, gender discrimination, and familial
conflict) as a result of pursuing a STEM career (µ= 3.34); yet, such beliefs do not appear to
significantly influence their STEM career interest or goals. While few in number, similar
findings have been noted in previous studies (Flores & O’Brien, 2002; Luzzo & Hutcheson,
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1996; McWhirter et al., 1998). In their work with Latina community college students, Rivera and
colleagues (2007) did not find a predictive association between perceived barriers and Latinas’
self-efficacy in male-dominated career fields or career considerations (Rivera et al., 2007).
According to Lent, Brown, & Hackett (2000), the role of outcome expectations may be
influenced by students coping self-efficacy, or beliefs about their ability to negotiate domain-
specific obstacles. Gloria, Castellanos, and Orozco, (2005) found that Latina college students
perceived several barriers to their educational success, yet believed that they could overcome any
obstacle that stood in their way. In further examining Latina coping responses, the authors found
that Latinas took an active and positive approach towards coping. Latina positive coping
mechanisms, according to the authors, may help explain their willingness to stay in a system
despite perceiving barriers to success and experiencing cultural incongruity (Gloria et al., 2005).
Such coping responses are encompassed within the Latina strength dimension of the current
study. For instance, the Latina strength sub-dimensions of creative sprit, passionate
determination, and risk taking describe cultural resources, which allow Latinas to persist despite
challenges (Nogales, 2003). The potential moderating effect of Latina strengths on STEM
outcome expectations, however, is not estimated in the current study.
Anzaldua’s notion of the mestiza consciousness provides further insight into the origin
and role of Latina cultural resources. Anzaldua (1987) notes that as a result of their mestiza
(mixed) ancestry, Latinas possess an ability to tolerate contradictions and ambiguity: to operate
within varying worldviews and practices. The new mestiza is characterized by a dual identity,
which is born from both oppression and the conscious struggle against it. That is, Latinas’
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cultural strengths may act as a buffer, allowing them to persist despite their perceptions of
negative outcome expectations.
In addition to the aforementioned cultural variables, and in accordance with previous
research, I considered the role of acculturation utilizing the ARMAS-II. Findings from the
measurement phase of the analysis indicated that AOS and MOS subscales did not fit the data
well. The ARMAS-II represents the most widely used acculturation scale. In fact, over 117
empirical articles have utilized the ARMAS-II scale in the last decade (Jones & Mortimer, 2014).
Despite its wide use, the findings from the current study indicate the need to further examine the
psychometric properties of the AOS and MOS subscales. The current study was among the first
to adopt a confirmatory approach in examining the factor structure of the AOS and MOS
subscales.
Furthermore, it is important to acknowledge that while SCCT highlights the role of
cultural factors, such factors are considered to influence Latinas’ career decision-making process
outcomes through pre-college math and science performance experiences and proximal
contextual affordances. The PSC model suggests that cultural factors may also influence Latinas’
educational outcomes directly and in conjunction with psychological and social factors. For
instance, cultural factors may play a proximal role in influencing Latina’s career self-efficacy
and goals. While the PSC framework has received much support with samples of Latina college
students (Castellanos & Gloria, 2007; Gloria, Castellanos, & Orozco, 2005b), few researchers
have adopted the PSC framework when considering students’ career decision-making.

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Role of Proximal Contextual Affordances

Previous research findings generally indicate that Latina students who attend PWIs face
several challenges that may complicate their career decision-making process. While previous
studies have examined general supports and barriers (proximal contextual affordances), the
current study is among the first to examine critical aspects of the college environment and Latina
students’ college experiences. Five proximal contextual affordances were examined in the
current study: faculty mentorship, peer mentorship, campus climate, academic involvement, and
familial encouragement.
Three of these college experience variables were statistically significant in predicting the
STEM career decision-making process outcomes and career goals of Latina undergraduates.
First, classroom climate was statistically significant and had direct effect on Latina students’
STEM career goals. This was the only proximal factor that had direct effect on STEM goals.
Latina students who perceived their university classroom climate as more hostile were less
inclined to pursue a STEM career goal. The college environment has been known to play an
important role in conveying certain messages about students’ likelihood to succeed. While
students may be interested and efficacious about their ability to complete the tasks required in a
STEM field, perceived barriers as a result of a hostile campus climate may dissuade Latinas from
pursuing such fields (Flores & O’Brien, 2002; Ojeda & Flores, 2008). Such findings are
consistent with previous research regarding the negative effect of inhospitable racial campus
climates on Latina/o students’ educational outcomes (Gloria et al., 2005; Jones, Castellanos, &
Cole, 2002; Museus et al., 2008; Rankin & Reason, 2005).
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Second, Latinas who perceived greater faculty career support were more likely to be
interested in a STEM career fields. Access to career-related information and social support is
especially important for Latina students, who are often the first in their family to attend college,
come from lower socioeconomic backgrounds, and have difficulty navigating the college
environment (Hurtado et al., 2008). Interactions with institutional personnel are likely to provide
Latinas with access to information and social support to pursue their goals (Berrios-Allison,
2011; Luzzo, 2000; Rendon, 1994; Rios-Aguilar & Deil-Amen, 2012). The importance of faculty
support and encouragement on the educational outcomes on Latina/o and other underrepresented
students of color have been documented in the literature (Cole & Espinoza, 2008; Figueroa,
2003; Maton & Hrabowski, 2004; Nora & Crisp, 2007; Packer, 2004).
Third, Latinas students who were more academically involved on campus were more
likely to have greater STEM career self-efficacy. Such findings are consistent with previous
research indicating the positive effect of academic programs such as undergraduate research
programs and tutoring (Astin, 1984; Perna et al., 2009; Pascarella & Terenzini, 1991). Such
programs are beneficial in that they promote the academic confidence of ethnic minority students
(Brown, 2004; Villarejo, Barlow, Kogan, & Veazey, 2008; Perna et al., 2009).
Contrary to previous research, familial support and peer career mentorship were not
statistically significant in predicting STEM career goals in the current study (Cerezo & Chang
2013; Flores & O’Brien, 2002). While the mean score for perceived familial encouragement
(4.38), was higher than any other support variable, familial encouragement did not predict STEM
career goals over and above the other variable in the model. The nonsignificant findings for
familial encouragement were surprising given previous literature indicating the importance of
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78
family for Latina/o students. However, as previously noted, the average parental educational
level for the study participants was a high school diploma. It is possible that for STEM domains,
Latina students require more direct and academic STEM specific support. That is not to say that
familial encouragement is not important, but rather familial encouragement may offer additional
psychosocial benefits not accounted for in the current model. For instance, the positive and
statistically significant correlational relationships between familial encouragement and Latina
strengths, faculty career mentorship, and peer mentoring offer areas for further exploration.
Similarly, peer career mentoring had a positive and statistically significant correlational
relationship with academic involvement and faculty support. It is possible that rather than
influencing Latina STEM career goals directly, peer mentorship relationships play an indirect
role in Latinas’ career decision-making process by encouraging or bridging opportunities for
Latinas to interact with faculty and engage in academic activities on campus. More research is
needed to examine such hypotheses; however, SCCT, does not consider the predictive role
between proximal contextual affordances. Such relationships are well documented in the higher
education literature and warrant further investigation (e.g., Jones, Castellanos, & Cole, 2002;
Hurtado & Carter, 1997; Museus et al., 2008; Nora & Cabrera, 1996; Rankin & Reason, 2005).
Implications
Research and Theory. Findings from the current study have several implications for
future researchers, theory, institutional policies, student affairs administrators, academic
departments, and counseling services on how to best support the career development of Latinas
at predominately white institutions of higher education. Regarding implications for research and
theory, SCCT represents an evolving framework and requires more research in order to assess its
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validity on diverse students. The findings from the current study provide support for the use of
SCCT with Latina college students. The PSC framework provides an additional lens by which to
consider additional paths not accounted for by SCCT; namely, the direct effect of cultural factors
on Latinas’ career decision-making outcomes and direct effect of contextual factors on Latinas’
self-efficacy. The direct effect of proximal contextual factors is also supported by Bandura’s
social cognitive theory and previous research findings. Additionally, future researchers are
encouraged to examine the direct and indirect effect of Latina cultural resources (e.g., Latina
cultural values, strength, and bicultural identity) on negative STEM career outcome expectations.
Such propositions may help explain previous findings regarding Latinas’ drive to achieve amidst
challenges and barriers (Hurtado et al. 2008).
In alignment with previous research, findings from the current study suggest that multiple
points of considerations and stressors may complicate Latinas’ career decision-making process.
Fortunately, Latina cultural resources, namely Latina strengths and active coping responses
(Gloria et al., 2005) may help alleviate such stressors; yet, to leave such instances to chance is to
provide such students with a disservice, ignore the realities of Latinas’ college experiences, and
forgo their role as key players in the nation’s future economic well-being. As Latinas enroll at
institutions of higher education and enter the U.S. workforce at faster rates than ever before, their
role in helping to fill the STEM workforce is of critical importance.
Furthermore, while not assessed in the current study, previous literature indicates the
importance of culturally similar and competent mentoring relationships for students of color
(Berrios-Allison, 2011; Gloria & Rodriguez, 2000). Future researchers are encouraged to
consider the role of faculty and peer racial and ethnic backgrounds. While the findings for the
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current study provide support for SCCT and highlight the need to examine the influence of
cultural and college environment variables in influencing Latinas’ STEM career decision-making
process outcomes more research is needed. Longitudinal research designs are needed as such
designs better capture temporal relationships among the SCCT variables.
Policy and Practice. Overall, the findings from the study support the use and need for
sociocognitive-based STEM related career interventions for Latinas at PWIs. University career
and counseling services are well advised to consider Latina college students’ intersecting
identities as well as cultural resources and challenges when navigating the career decision-
making process (Berrios-Allison, 2011). A need exists to assist Latinas in dealing with potential
dissonance resulting from inhospitable classroom climates, non-dominant collectivistic cultural
values (e.g., self-sacrifice), and their personal career goals. A thorough understanding of Latinas’
paradigmatic beliefs, one that does not cast judgment, but rather adopts a strength-based
approach is needed for effective practice.
For instance, university-counseling personnel may highlight the long-term benefits that
Latinas may contribute to their communities by temporarily prioritizing the self and their
personal academic goals. In this way, Latina cultural values are validated while offering an
additional lens by which Latinas may prioritize the self as a way of contributing to the collective.
University-counseling personnel are also reminded that Latinas represent a heterogeneous
population and vary widely in their cultural values and beliefs. Further research is needed to
better understand Latinas’ intersection of identities and how such identities influence their career
decision-making process.
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Additionally, the findings from the current study indicate the need to create and support
university mentoring and academic support programs aimed at encouraging Latinas in STEM.
Such services would necessitate adequate university reward systems that acknowledge and
encourage faculty and staff to engage in such services. Student leaders may also play a role in
supporting Latinas in STEM, yet adequate systems that support such services are needed.
Conclusion

Examining the career decision-making process of Latinas who attend PWIs is of critical
importance for at least three reasons. First, Latinas represent one of the nation’s fastest growing
populations yet are disproportionately concentrated in low paying occupations and are severely
underrepresented in science, math, and engineering fields (Guzman, 2001; NCES, 2010).
Second, while several studies address the career decision-making process of students in general,
few examine critical cultural and contextual factors pertinent to Latinas in higher education, and
PWIs, specifically (Flores, 2006). Given previous research indicating that Latinas experience
various instances of racism and discrimination on PWIs and the implications of such
experiences, it is critical that researchers examine how aspects of the college environment
influence the career development of Latinas. Third, given its tie to personal identity and
fulfillment, the career decision-making process is likely to influence the overall psychological
well-being, life outcomes, and academic satisfaction of Latinas college students (Swanson &
Fouad, 2010). Insights regarding the career decision-making of Latinas who attend PWIs may
help inform institutional policies and programs, student affairs administrators, academic
departments, and counseling services on how to best support the career development and goals of
Latina in STEM.
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The STEM higher education literature currently emphasizes student persistence and
degree completion; yet, the findings from the current study indicate a need to examine the
processes and paths leading up to such outcomes (Wang, 2012). The current findings align with
the assertions made by previous scholars, who have stressed the importance of taking a process,
rather than outcome-based approach when assessing the educational success of Latina/o college
students (Castellanos & Gloria, 2007). A need exists to examine the intermediary processes that
influence Latina college students’ path to and away from STEM fields. Latinas represent a
rapidly increasing population and are entering higher education institutions at higher rates than
ever before; yet, Latinas remain highly underrepresented in STEM fields. Their unique status as
triple minorities positions them as a population of critical importance. Without examining Latina
students’ college experiences and cultural backgrounds, institution policy makers, researchers,
and student affairs administrators may fail to adequately understand and support Latinas’
pathway to STEM careers. Research, programs, and services that address the STEM career
decision-making process and career goals of Latina college students are necessary if institutions
of higher education are to successfully promote and guide Latinas in STEM fields. By utilizing a
process, versus, outcome approach and by highlighting the role of Latina cultural values and
college experiences, the current study contributes to the career and higher education literature
and calls for the need to continue such work.

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83
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ethnic minority populations: A content analysis and an alternative empirical strategy.
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108
In K. M. Chun, P. B. Organista, & G. Marin (Eds.), Acculturation: Advances in theory,
measurement, and applied research (pp. 39-60). Washington, DC: American
Psychological Association.

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109
Appendix A

Recruitment Materials

*Recruitment e-mail sent on behalf of university personnel

Subject line: Calling all Latina >>>Identifier<<< Students

Greeting >>>Identifier<<<,

A Ph.D. Candidate, Michelle Castellanos, is in need of your support. As part of her dissertation,
Michelle is investigating how Latina/o students experience college and navigate the career
decision-making process. Her aim in conducting such research is to help inform university
policies and program on how to best support Latina/os achieve their post- baccalaureate aims;
however, such work would not be possible without the help and support of students like yourself.

Please support Michelle and research pertinent to the Latina/o community by following the link
below to complete a 30-minute survey regarding where you are in your career decision-making
process. *As a thank you for your participation you will be entered in a drawing to win one of
twenty-five Apple and Starbucks gift cards valued up to $250.

Please click on the following link to begin the questionnaire:
>>>>>>>>>>Survey Link<<<<<<<<<<<<<<
Attached you will find an information sheet with further details regarding the study.
Thank you,
>>>University Personnel Name<<<

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110
Information Sheet:

How College Affects Latinas Career Decision-Making Process

Greetings >>> Student Identifier <<< !

My name is Michelle Castellanos and a Ph.D. Candidate in the Urban Education Policy program
at the University of Southern California. As part of my dissertation study, I am examining the
college experiences and career decision-making process of Latina undergraduate students. My
aim in conducting this study is to help inform institutional policies and programs on how to best
support Latina/o undergraduate students in achieving their career goals.

I would like to invite you to participate in this effort by completing a 30-minute online
questionnaire. While I strongly encourage you to finish the survey in one sitting, you may also
opt to complete the survey in multiple sessions by simply clicking the link (below). Your
responses will automatically be saved when you exit the survey. I do ask that you please make
sure to complete the survey within the following day if you chose to complete the survey at a
later time.

Please click here to begin:

>>>>>>>>>>>>>>>>>>>>Survey Link<<<<<<<<<<<<<<<<<<<<<<<<<

*In appreciation for you participation in this study, you will be eligible to win one out of twenty-
five Apple and Starbucks gift cards valued up to $250.

Below I have outlined details regarding what your participation in the study would entail if you
chose to participate. I hope that you will consider participating in the study and thereby assisting
me in advancing programs and policies to better serve Latina/o college students.

INFORMATION ABOUT PARTICIPANTS' INVOLVEMENT IN THE STUDY

Participation

Your participation in the study consists of completing one 30 minute online questionnaire
regarding your college experience and career plans. Your participation is completely voluntary.
You may decline to participate or withdrawal your participation at any point during the study.
If you chose to participate, I ask that you please respond to all questions with outmost honesty.
Genuine responses are essential in order to adequately represent the experiences of Latina/os on
your campus.
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111
Potential Risks and Benefits

Your participation in the study does not involve any direct risks or benefits. Findings from this
study may, however, pose indirect benefits given potential programs and policies to support
Latina/o students which may result from the findings .

In appreciation for you participation, you will be eligible to win one out of twenty-five prizes
valued up to $250 (i.e., $250, $100, $50, $25, $10, $5 Apple/Starbucks gift cards).

Confidentiality

Participants’ names will not be identified at any point in the study. All study records will be kept
confidential and stored on an encrypted computer.

Please note that the University of Southern California’s Human Subjects Protection
Program (HSPP) may access the non-identifiable data. The HSPP reviews and monitors
research studies to protect the rights and welfare of research participants.

Investigator’s Contact Information

If you have questions at any time about the study or the procedures, you may contact the
principle investigator, Michelle Castellanos, at Waite Phillips Hall—WPH 500 3470 Trousdale
Parkway Los Angeles, California 90089, e-mail castellm@usc.edu

IRB CONTACT INFORMATION

University Park IRB, Office of the Vice Provost for Research Advancement, Stonier Hall, Room
224a, Los Angeles, CA 90089-1146, (213) 821-5272 or upirb@usc.edu

Thank you in advance for your participation and support in this research project!

Sincerely,
Michelle Castellanos
PhD Candidate, Urban Education Policy
Gates Millennium Scholar
Rossier School of Education
University of Southern California

!
112

Appendix B

Table 6
Characteristics of Holland’s RIASEC Personality and Environment Types
Realistic Investigative Artistic Social Enterprising Conventional
Enjoys
working with
things
things and
ideas
ideas and
people
people
data and
people
data and
things

Personality
characteristics
frank
practical
focused
mechanical
determined
rugged
analytical
intellectual
reserved
independent
ambitious
complicated
original
impulsive
independent
expressive
creative
cooperative
helpful
empathic
kind tactful
warm
sociable
generous
persuasive
energetic
sociable
adventurous
ambitious
assertive
careful
conforming
conservative
responsible
controlled
Preferred
activities and
skills
mechanical
manual
physical
athletic tasks
working with
abstract ideas
solving
intellectual
problems
collecting
data
using
imagination
creative
expression
interacting
with and
helping
people
teaching
guiding
leading
managing
persuading
and
organizing
people
ordering
attending to
details

Sample
careers
fitness trainer
firefighter
mechanic
builder
farmer
landscaper
biologist
researcher
physician
computer
systems
analyst
mathematicia
n
artist
musician
actor
creative
writer
photographer
teacher
clergy
counselor
nurse
school bus
monitor
managers
lawyers
business
administrator
s politician
accountant
banker actuary
editor
office
manager
librarian

Sample major
criminal
justice
athletic
training
construction
management
botany
engineering
mathematics
premed food
technology
art, theater
graphic
designer
music
nursing
education
counseling
social work
prelaw
business
management
political
science
business
accounting

Values

tradition
freedom
independence

independence
logic
scholarly
achievement
aesthetic
experience
self-
expression
imagination
nonconformit
y

altruism
ethics
equality

tradition
economic
achievement
ambition
tradition
ambition
obedience
economic
achievement
comfort
Note: Adapted from Nauta (2013), Grottfredson and Holland (1996), Holland (1997), Prediger (1982)
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113
Appendix C

Table 7

Covariance Matrix
1 2 3 4 5 6 7 8
1 careff1par 2.836
2 careff2par 1.332 2.175
3 careff3par 1.708 1.585 2.219
4 careff4par 1.716 1.404 2.043 2.297
5 careff5par 1.295 1.477 1.542 1.55 1.623
6 noute1par -0.117 -0.109 -0.14 -0.14 -0.106 1.843
7 noute2par -0.14 -0.13 -0.167 -0.168 -0.127 1.293 1.621
8 noute3par -0.134 -0.124 -0.159 -0.16 -0.121 1.652 1.475 2.151
9 noute4par -0.109 -0.101 -0.129 -0.13 -0.098 1.002 1.198 1.143
10 int1par 0.287 0.267 0.342 0.344 0.26 -0.013 -0.016 -0.015
11 int2par 0.421 0.39 0.501 0.503 0.38 -0.02 -0.024 -0.022
12 int3par 0.737 0.684 0.877 0.882 0.665 -0.035 -0.041 -0.039
13 int4par 0.706 0.655 0.84 0.845 0.638 -0.033 -0.04 -0.038
14 int5par 0.455 0.423 0.542 0.545 0.411 -0.021 -0.026 -0.024
15 g_stem_1 0.845 0.784 1.006 1.011 0.763 -0.182 -0.217 -0.207
16 g_stem_2 0.816 0.758 0.971 0.976 0.737 -0.176 -0.21 -0.2
17 g_stem_3 0.838 0.778 0.998 1.003 0.757 -0.18 -0.216 -0.206
18 g_stem_4 0.683 0.634 0.813 0.817 0.617 -0.147 -0.176 -0.168
19 sat_math 14.321 13.292 17.045 17.131 12.931 -7.747 -9.264 -8.836
20 sat_s 17.351 16.104 20.651 20.756 15.667 -9.386 -11.224 -10.706
21 ses4_1 0.138 0.128 0.165 0.165 0.125 -0.064 -0.076 -0.073
22 parented1_1 0.127 0.118 0.151 0.152 0.115 -0.059 -0.07 -0.067
23 parented1_2 0.143 0.133 0.171 0.172 0.13 -0.066 -0.079 -0.076
24 lss1cparR 0.015 0.014 0.018 0.018 0.014 0.001 0.001 0.001
25 lss2cparR 0.022 0.021 0.027 0.027 0.02 0.001 0.001 0.001
26 lss3cparR 0.014 0.013 0.016 0.016 0.012 0.001 0.001 0.001
27 lss4cparR 0.023 0.022 0.028 0.028 0.021 0.001 0.001 0.001
28 lss5cparR 0.015 0.014 0.017 0.017 0.013 0.001 0.001 0.001
29 lss6cparR 0.018 0.016 0.021 0.021 0.016 0.001 0.001 0.001
30 lss7cparR 0.017 0.016 0.02 0.02 0.015 0.001 0.001 0.001
31 fams6__1 0.018 0.016 0.021 0.021 0.016 -0.007 -0.008 -0.008
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32 fams6__2 0.022 0.021 0.026 0.026 0.02 -0.008 -0.01 -0.009
33 fams6__3 0.023 0.021 0.027 0.027 0.02 -0.008 -0.01 -0.01
34 fams6__4 0.028 0.026 0.033 0.033 0.025 -0.01 -0.012 -0.012
35 peers9__1 0.014 0.013 0.016 0.016 0.012 0.002 0.002 0.002
36 peers9__2 0.028 0.026 0.033 0.033 0.025 0.003 0.004 0.004
37 peers9__3 0.028 0.026 0.034 0.034 0.026 0.004 0.004 0.004
38 peers9__4 0.031 0.028 0.037 0.037 0.028 0.004 0.005 0.004
39 peers9__5 0.03 0.028 0.036 0.036 0.028 0.004 0.005 0.004
40 facs9__1 -0.005 -0.005 -0.006 -0.006 -0.005 0.004 0.005 0.005
41 facs9__2 -0.007 -0.007 -0.009 -0.009 -0.007 0.007 0.008 0.007
42 facs9__3 -0.01 -0.009 -0.012 -0.012 -0.009 0.008 0.01 0.01
43 facs9__4 -0.01 -0.009 -0.012 -0.012 -0.009 0.008 0.01 0.01
44 facs9__5 -0.01 -0.009 -0.011 -0.011 -0.009 0.008 0.01 0.01
45 involve__1 0.254 0.236 0.302 0.304 0.229 -0.024 -0.028 -0.027
46 involve__2 0.137 0.127 0.163 0.164 0.124 -0.013 -0.015 -0.015
47 involve__3 0.057 0.053 0.068 0.068 0.051 -0.005 -0.006 -0.006
48 involve__4 0.129 0.119 0.153 0.154 0.116 -0.012 -0.014 -0.014
49 involve__5 0.083 0.077 0.099 0.099 0.075 -0.008 -0.009 -0.009
50 clclass6__1 0.021 0.02 0.025 0.025 0.019 -0.01 -0.012 -0.011
51 clclass6__2 -0.01 -0.009 -0.012 -0.012 -0.009 0.005 0.006 0.005
52 clclass6__3 -0.021 -0.019 -0.025 -0.025 -0.019 0.01 0.012 0.011
53 clclass6__4 -0.01 -0.009 -0.012 -0.012 -0.009 0.005 0.006 0.005
54 clclass6__5 -0.029 -0.027 -0.034 -0.034 -0.026 0.013 0.016 0.015
55 selfsac6__1 -0.027 -0.025 -0.032 -0.032 -0.024 0.019 0.023 0.022
56 selfsac6__2 -0.035 -0.032 -0.041 -0.041 -0.031 0.025 0.029 0.028
57 selfsac6__3 -0.046 -0.043 -0.055 -0.055 -0.041 0.032 0.039 0.037
58 selfsac6__4 -0.038 -0.035 -0.045 -0.045 -0.034 0.027 0.032 0.03
59 selfsac6__5 -0.04 -0.037 -0.047 -0.047 -0.036 0.028 0.033 0.032
60 selfsac6__6 -0.038 -0.035 -0.046 -0.046 -0.035 0.027 0.032 0.031
61 born_gen 0.034 0.032 0.04 0.041 0.031 -0.026 -0.031 -0.03
62 ethnicity 0.108 0.1 0.128 0.129 0.097 -0.041 -0.049 -0.047
63 stemse 1.435 1.332 1.708 1.716 1.295 -0.117 -0.14 -0.134
64 noexp -0.117 -0.109 -0.14 -0.14 -0.106 1.081 1.293 1.233
65 stemint 0.287 0.267 0.342 0.344 0.26 -0.013 -0.016 -0.015
66 stemgoal 0.845 0.784 1.006 1.011 0.763 -0.182 -0.217 -0.207
67 learnexp 14.321 13.292 17.045 17.131 12.931 -7.747 -9.264 -8.836
68 ses_f 0.138 0.128 0.165 0.165 0.125 -0.064 -0.076 -0.073
69 lss 0.015 0.014 0.018 0.018 0.014 0.001 0.001 0.001
70 famsup 0.018 0.016 0.021 0.021 0.016 -0.007 -0.008 -0.008
71 peerment 0.014 0.013 0.016 0.016 0.012 0.002 0.002 0.002
72 factment -0.005 -0.005 -0.006 -0.006 -0.005 0.004 0.005 0.005
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73 acainv 0.254 0.236 0.302 0.304 0.229 -0.024 -0.028 -0.027
74 clclimate 0.021 0.02 0.025 0.025 0.019 -0.01 -0.012 -0.011
75 selfsac -0.027 -0.025 -0.032 -0.032 -0.024 0.019 0.023 0.022
9 10 11 12 13 14 15 16
9 noute4par 2.252
10 int1par -0.012 1.332
11 int2par -0.018 0.194 1.036
12 int3par -0.032 0.339 0.497 1.019
13 int4par -0.031 0.476 0.476 0.834 0.988
14 int5par -0.02 0.21 0.58 0.538 0.515 0.724
15 g_stem_1 -0.169 0.259 0.378 0.663 0.635 0.41 2.37
16 g_stem_2 -0.163 0.25 0.365 0.64 0.613 0.396 2.141 2.34
17 g_stem_3 -0.167 0.256 0.375 0.658 0.63 0.406 2.199 2.124
18 g_stem_4 -0.136 0.209 0.306 0.536 0.514 0.331 1.793 1.731
19 sat_math -7.181 2.032 2.974 5.21 4.992 3.22 6.617 6.391
20 sat_s -8.701 2.462 3.603 6.313 6.049 3.901 8.017 7.743
21 ses4_1 -0.059 0.014 0.021 0.037 0.035 0.023 0.055 0.053
22 parented1_1 -0.055 0.013 0.019 0.034 0.032 0.021 0.051 0.049
23 parented1_2 -0.061 0.015 0.022 0.038 0.036 0.024 0.057 0.055
24 lss1cparR 0.001 -0.004 -0.006 -0.01 -0.01 -0.006 -0.039 -0.037
25 lss2cparR 0.001 -0.006 -0.008 -0.015 -0.014 -0.009 -0.056 -0.054
26 lss3cparR 0.001 -0.003 -0.005 -0.009 -0.008 -0.005 -0.034 -0.033
27 lss4cparR 0.001 -0.006 -0.009 -0.015 -0.014 -0.009 -0.059 -0.057
28 lss5cparR 0.001 -0.004 -0.005 -0.009 -0.009 -0.006 -0.037 -0.035
29 lss6cparR 0.001 -0.004 -0.006 -0.011 -0.011 -0.007 -0.044 -0.042
30 lss7cparR 0.001 -0.004 -0.006 -0.011 -0.011 -0.007 -0.043 -0.041
31 fams6__1 -0.006 0.005 0.008 0.014 0.013 0.009 0.07 0.068
32 fams6__2 -0.008 0.007 0.01 0.017 0.017 0.011 0.087 0.084
33 fams6__3 -0.008 0.007 0.01 0.018 0.017 0.011 0.089 0.086
34 fams6__4 -0.01 0.009 0.012 0.022 0.021 0.014 0.11 0.107
35 peers9__1 0.002 0.008 0.011 0.02 0.019 0.012 0.012 0.012
36 peers9__2 0.003 0.016 0.023 0.04 0.039 0.025 0.024 0.024
37 peers9__3 0.003 0.016 0.024 0.041 0.04 0.026 0.025 0.024
38 peers9__4 0.004 0.017 0.025 0.045 0.043 0.028 0.027 0.026
39 peers9__5 0.004 0.017 0.025 0.044 0.042 0.027 0.027 0.026
40 facs9__1 0.004 0.007 0.01 0.018 0.018 0.011 -0.039 -0.038
41 facs9__2 0.006 0.011 0.016 0.027 0.026 0.017 -0.059 -0.057
42 facs9__3 0.008 0.014 0.02 0.035 0.034 0.022 -0.076 -0.074
43 facs9__4 0.008 0.014 0.02 0.035 0.034 0.022 -0.076 -0.074
44 facs9__5 0.008 0.014 0.02 0.035 0.034 0.022 -0.075 -0.073
45 involve__1 -0.022 0.05 0.073 0.128 0.122 0.079 0.211 0.204
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116
46 involve__2 -0.012 0.027 0.039 0.069 0.066 0.042 0.113 0.11
47 involve__3 -0.005 0.011 0.016 0.029 0.027 0.018 0.047 0.046
48 involve__4 -0.011 0.025 0.037 0.065 0.062 0.04 0.107 0.103
49 involve__5 -0.007 0.016 0.024 0.042 0.04 0.026 0.069 0.066
50 clclass6__1 -0.009 -0.023 -0.034 -0.06 -0.057 -0.037 -0.151 -0.145
51 clclass6__2 0.004 0.011 0.016 0.028 0.027 0.017 0.07 0.068
52 clclass6__3 0.009 0.023 0.034 0.059 0.057 0.036 0.148 0.143
53 clclass6__4 0.004 0.011 0.016 0.028 0.027 0.017 0.07 0.067
54 clclass6__5 0.012 0.032 0.046 0.081 0.077 0.05 0.203 0.196
55 selfsac6__1 0.018 0.002 0.003 0.006 0.005 0.003 0.018 0.017
56 selfsac6__2 0.023 0.003 0.004 0.007 0.007 0.004 0.023 0.022
57 selfsac6__3 0.03 0.004 0.005 0.01 0.009 0.006 0.031 0.029
58 selfsac6__4 0.025 0.003 0.004 0.008 0.008 0.005 0.025 0.024
59 selfsac6__5 0.026 0.003 0.005 0.008 0.008 0.005 0.026 0.025
60 selfsac6__6 0.025 0.003 0.005 0.008 0.008 0.005 0.025 0.025
61 born_gen -0.024 0.001 0.002 0.003 0.003 0.002 -0.009 -0.009
62 ethnicity -0.038 0.025 0.037 0.064 0.061 0.04 0.134 0.13
63 stemse -0.109 0.287 0.421 0.737 0.706 0.455 0.845 0.816
64 noexp 1.002 -0.013 -0.02 -0.035 -0.033 -0.021 -0.182 -0.176
65 stemint -0.012 0.132 0.194 0.339 0.325 0.21 0.259 0.25
66 stemgoal -0.169 0.259 0.378 0.663 0.635 0.41 2.217 2.141
67 learnexp -7.181 2.032 2.974 5.21 4.992 3.22 6.617 6.391
68 ses_f -0.059 0.014 0.021 0.037 0.035 0.023 0.055 0.053
69 lss 0.001 -0.004 -0.006 -0.01 -0.01 -0.006 -0.039 -0.037
70 famsup -0.006 0.005 0.008 0.014 0.013 0.009 0.07 0.068
71 peerment 0.002 0.008 0.011 0.02 0.019 0.012 0.012 0.012
72 factment 0.004 0.007 0.01 0.018 0.018 0.011 -0.039 -0.038
73 acainv -0.022 0.05 0.073 0.128 0.122 0.079 0.211 0.204
74 clclimate -0.009 -0.023 -0.034 -0.06 -0.057 -0.037 -0.151 -0.145
75 selfsac 0.018 0.002 0.003 0.006 0.005 0.003 0.018 0.017
17 18 19 20 21 22 23 24
17 g_stem_3 2.275
18 g_stem_4 1.778 1.856
19 sat_math 6.564 5.35 8832.049
20 sat_s 7.953 6.483 4244.749 7926.891
21 ses4_1 0.055 0.045 28.263 34.243 1.717
22 parented1_1 0.05 0.041 26 31.502 0.543 0.823
23 parented1_2 0.057 0.046 29.323 35.527 0.613 0.564 1.109
24 lss1cparR -0.038 -0.031 -0.837 -1.014 -0.025 -0.023 -0.025 0.211
25 lss2cparR -0.056 -0.045 -1.219 -1.477 -0.036 -0.033 -0.037 0.16
26 lss3cparR -0.034 -0.028 -0.743 -0.9 -0.022 -0.02 -0.023 0.097
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117
27 lss4cparR -0.058 -0.047 -1.268 -1.536 -0.037 -0.034 -0.039 0.166
28 lss5cparR -0.036 -0.03 -0.796 -0.964 -0.023 -0.021 -0.024 0.104
29 lss6cparR -0.044 -0.036 -0.953 -1.155 -0.028 -0.026 -0.029 0.125
30 lss7cparR -0.042 -0.034 -0.921 -1.116 -0.027 -0.025 -0.028 0.121
31 fams6__1 0.07 0.057 2.817 3.413 0.065 0.06 0.067 0.034
32 fams6__2 0.087 0.071 3.514 4.257 0.081 0.074 0.084 0.042
33 fams6__3 0.089 0.072 3.588 4.347 0.082 0.076 0.086 0.043
34 fams6__4 0.109 0.089 4.438 5.377 0.102 0.094 0.106 0.053
35 peers9__1 0.012 0.01 -1.302 -1.578 -0.013 -0.012 -0.013 0.041
36 peers9__2 0.024 0.02 -2.648 -3.208 -0.026 -0.024 -0.027 0.084
37 peers9__3 0.025 0.02 -2.708 -3.281 -0.027 -0.025 -0.028 0.086
38 peers9__4 0.027 0.022 -2.919 -3.536 -0.029 -0.027 -0.03 0.092
39 peers9__5 0.027 0.022 -2.898 -3.511 -0.029 -0.026 -0.03 0.092
40 facs9__1 -0.039 -0.032 -2.077 -2.516 -0.036 -0.033 -0.037 0.061
41 facs9__2 -0.059 -0.048 -3.114 -3.772 -0.053 -0.049 -0.055 0.091
42 facs9__3 -0.076 -0.062 -4.033 -4.886 -0.069 -0.064 -0.072 0.118
43 facs9__4 -0.076 -0.062 -4.026 -4.878 -0.069 -0.064 -0.072 0.118
44 facs9__5 -0.075 -0.061 -3.982 -4.825 -0.068 -0.063 -0.071 0.117
45 involve__1 0.209 0.17 4.048 4.905 0.129 0.119 0.134 0.079
46 involve__2 0.113 0.092 2.179 2.641 0.07 0.064 0.072 0.043
47 involve__3 0.047 0.038 0.905 1.096 0.029 0.027 0.03 0.018
48 involve__4 0.106 0.086 2.05 2.484 0.066 0.06 0.068 0.04
49 involve__5 0.068 0.056 1.32 1.6 0.042 0.039 0.044 0.026
50 clclass6__1 -0.149 -0.122 4.431 5.369 0.071 0.065 0.074 0.082
51 clclass6__2 0.069 0.057 -2.061 -2.497 -0.033 -0.03 -0.034 -0.038
52 clclass6__3 0.147 0.12 -4.363 -5.286 -0.07 -0.064 -0.072 -0.081
53 clclass6__4 0.069 0.056 -2.047 -2.48 -0.033 -0.03 -0.034 -0.038
54 clclass6__5 0.201 0.164 -5.968 -7.231 -0.096 -0.088 -0.099 -0.111
55 selfsac6__1 0.018 0.014 -8.839 -10.709 -0.048 -0.044 -0.05 -0.019
56 selfsac6__2 0.023 0.019 -11.422 -13.839 -0.062 -0.057 -0.065 -0.025
57 selfsac6__3 0.03 0.025 -15.086 -18.279 -0.082 -0.076 -0.085 -0.033
58 selfsac6__4 0.025 0.02 -12.36 -14.976 -0.068 -0.062 -0.07 -0.027
59 selfsac6__5 0.026 0.021 -13.037 -15.795 -0.071 -0.066 -0.074 -0.028
60 selfsac6__6 0.025 0.021 -12.59 -15.254 -0.069 -0.063 -0.071 -0.027
61 born_gen -0.009 -0.008 12.281 14.88 0.164 0.151 0.17 -0.001
62 ethnicity 0.133 0.109 17.59 21.312 0.301 0.277 0.312 -0.008
63 stemse 0.838 0.683 14.321 17.351 0.138 0.127 0.143 0.015
64 noexp -0.18 -0.147 -7.747 -9.386 -0.064 -0.059 -0.066 0.001
65 stemint 0.256 0.209 2.032 2.462 0.014 0.013 0.015 -0.004
66 stemgoal 2.199 1.793 6.617 8.017 0.055 0.051 0.057 -0.039
67 learnexp 6.564 5.35 3503.424 4244.749 28.263 26 29.323 -0.837
!
118
68 ses_f 0.055 0.045 28.263 34.243 0.591 0.543 0.613 -0.025
69 lss -0.038 -0.031 -0.837 -1.014 -0.025 -0.023 -0.025 0.11
70 famsup 0.07 0.057 2.817 3.413 0.065 0.06 0.067 0.034
71 peerment 0.012 0.01 -1.302 -1.578 -0.013 -0.012 -0.013 0.041
72 factment -0.039 -0.032 -2.077 -2.516 -0.036 -0.033 -0.037 0.061
73 acainv 0.209 0.17 4.048 4.905 0.129 0.119 0.134 0.079
74 clclimate -0.149 -0.122 4.431 5.369 0.071 0.065 0.074 0.082
75 selfsac 0.018 0.014 -8.839 -10.709 -0.048 -0.044 -0.05 -0.019
25 26 27 28 29 30 31 32
25 lss2cparR 0.449
26 lss3cparR 0.142 0.562
27 lss4cparR 0.242 0.147 0.422
28 lss5cparR 0.152 0.093 0.158 0.328
29 lss6cparR 0.182 0.111 0.189 0.119 0.352
30 lss7cparR 0.176 0.107 0.183 0.115 0.137 0.353
31 fams6__1 0.049 0.03 0.051 0.032 0.039 0.037 0.768
32 fams6__2 0.062 0.038 0.064 0.04 0.048 0.047 0.63 1.325
33 fams6__3 0.063 0.038 0.065 0.041 0.049 0.048 0.441 0.55
34 fams6__4 0.078 0.047 0.081 0.051 0.061 0.059 0.545 0.68
35 peers9__1 0.06 0.037 0.062 0.039 0.047 0.045 0.087 0.109
36 peers9__2 0.122 0.074 0.127 0.08 0.095 0.092 0.177 0.221
37 peers9__3 0.125 0.076 0.13 0.081 0.097 0.094 0.181 0.226
38 peers9__4 0.134 0.082 0.14 0.088 0.105 0.101 0.195 0.244
39 peers9__5 0.133 0.081 0.139 0.087 0.104 0.101 0.194 0.242
40 facs9__1 0.089 0.054 0.092 0.058 0.069 0.067 0.056 0.07
41 facs9__2 0.133 0.081 0.138 0.087 0.104 0.1 0.084 0.105
42 facs9__3 0.172 0.105 0.179 0.112 0.135 0.13 0.109 0.135
43 facs9__4 0.172 0.105 0.179 0.112 0.134 0.13 0.108 0.135
44 facs9__5 0.17 0.104 0.177 0.111 0.133 0.128 0.107 0.134
45 involve__1 0.115 0.07 0.12 0.075 0.09 0.087 0.03 0.037
46 involve__2 0.062 0.038 0.065 0.041 0.049 0.047 0.016 0.02
47 involve__3 0.026 0.016 0.027 0.017 0.02 0.019 0.007 0.008
48 involve__4 0.058 0.036 0.061 0.038 0.046 0.044 0.015 0.019
49 involve__5 0.038 0.023 0.039 0.025 0.029 0.028 0.01 0.012
50 clclass6__1 0.12 0.073 0.124 0.078 0.094 0.09 0.019 0.023
51 clclass6__2 -0.056 -0.034 -0.058 -0.036 -0.044 -0.042 -0.009 -0.011
52 clclass6__3 -0.118 -0.072 -0.123 -0.077 -0.092 -0.089 -0.018 -0.023
53 clclass6__4 -0.055 -0.034 -0.057 -0.036 -0.043 -0.042 -0.009 -0.011
54 clclass6__5 -0.161 -0.098 -0.168 -0.105 -0.126 -0.122 -0.025 -0.031
55 selfsac6__1 -0.028 -0.017 -0.029 -0.018 -0.022 -0.021 -0.003 -0.003
56 selfsac6__2 -0.036 -0.022 -0.038 -0.024 -0.028 -0.027 -0.004 -0.004
!
119
57 selfsac6__3 -0.048 -0.029 -0.05 -0.031 -0.037 -0.036 -0.005 -0.006
58 selfsac6__4 -0.039 -0.024 -0.041 -0.026 -0.031 -0.03 -0.004 -0.005
59 selfsac6__5 -0.041 -0.025 -0.043 -0.027 -0.032 -0.031 -0.004 -0.005
60 selfsac6__6 -0.04 -0.024 -0.041 -0.026 -0.031 -0.03 -0.004 -0.005
61 born_gen -0.002 -0.001 -0.002 -0.001 -0.001 -0.001 0.007 0.009
62 ethnicity -0.011 -0.007 -0.012 -0.007 -0.009 -0.008 0.04 0.05
63 stemse 0.022 0.014 0.023 0.015 0.018 0.017 0.018 0.022
64 noexp 0.001 0.001 0.001 0.001 0.001 0.001 -0.007 -0.008
65 stemint -0.006 -0.003 -0.006 -0.004 -0.004 -0.004 0.005 0.007
66 stemgoal -0.056 -0.034 -0.059 -0.037 -0.044 -0.043 0.07 0.087
67 learnexp -1.219 -0.743 -1.268 -0.796 -0.953 -0.921 2.817 3.514
68 ses_f -0.036 -0.022 -0.037 -0.023 -0.028 -0.027 0.065 0.081
69 lss 0.16 0.097 0.166 0.104 0.125 0.121 0.034 0.042
70 famsup 0.049 0.03 0.051 0.032 0.039 0.037 0.346 0.431
71 peerment 0.06 0.037 0.062 0.039 0.047 0.045 0.087 0.109
72 factment 0.089 0.054 0.092 0.058 0.069 0.067 0.056 0.07
73 acainv 0.115 0.07 0.12 0.075 0.09 0.087 0.03 0.037
74 clclimate 0.12 0.073 0.124 0.078 0.094 0.09 0.019 0.023
75 selfsac -0.028 -0.017 -0.029 -0.018 -0.022 -0.021 -0.003 -0.003
33 34 35 36 37 38 39 40
33 fams6__3 0.901
34 fams6__4 0.694 1.472
35 peers9__1 0.111 0.137 0.636
36 peers9__2 0.226 0.279 0.413 1.312
37 peers9__3 0.231 0.285 0.422 1.051 1.304
38 peers9__4 0.249 0.308 0.455 0.926 0.947 1.291
39 peers9__5 0.247 0.305 0.452 0.919 0.94 1.013 1.287
40 facs9__1 0.071 0.088 0.104 0.212 0.216 0.233 0.232 0.758
41 facs9__2 0.107 0.132 0.156 0.317 0.325 0.35 0.347 0.509
42 facs9__3 0.138 0.171 0.202 0.411 0.42 0.453 0.45 0.444
43 facs9__4 0.138 0.171 0.202 0.41 0.42 0.452 0.449 0.443
44 facs9__5 0.137 0.169 0.2 0.406 0.415 0.447 0.444 0.438
45 involve__1 0.038 0.047 0.079 0.162 0.165 0.178 0.177 0.013
46 involve__2 0.02 0.025 0.043 0.087 0.089 0.096 0.095 0.007
47 involve__3 0.008 0.01 0.018 0.036 0.037 0.04 0.04 0.003
48 involve__4 0.019 0.024 0.04 0.082 0.084 0.09 0.09 0.006
49 involve__5 0.012 0.015 0.026 0.053 0.054 0.058 0.058 0.004
50 clclass6__1 0.024 0.029 0.025 0.05 0.051 0.055 0.055 0.073
51 clclass6__2 -0.011 -0.014 -0.011 -0.023 -0.024 -0.026 -0.026 -0.034
52 clclass6__3 -0.023 -0.029 -0.024 -0.049 -0.05 -0.054 -0.054 -0.072
53 clclass6__4 -0.011 -0.014 -0.011 -0.023 -0.024 -0.026 -0.025 -0.034
!
120
54 clclass6__5 -0.032 -0.04 -0.033 -0.067 -0.069 -0.074 -0.074 -0.099
55 selfsac6__1 -0.003 -0.004 0.015 0.03 0.031 0.033 0.033 0.01
56 selfsac6__2 -0.005 -0.006 0.019 0.039 0.04 0.043 0.043 0.013
57 selfsac6__3 -0.006 -0.007 0.025 0.052 0.053 0.057 0.057 0.017
58 selfsac6__4 -0.005 -0.006 0.021 0.042 0.043 0.047 0.046 0.014
59 selfsac6__5 -0.005 -0.006 0.022 0.045 0.046 0.049 0.049 0.015
60 selfsac6__6 -0.005 -0.006 0.021 0.043 0.044 0.048 0.047 0.014
61 born_gen 0.01 0.012 -0.01 -0.021 -0.021 -0.023 -0.022 0.006
62 ethnicity 0.051 0.063 -0.071 -0.144 -0.147 -0.159 -0.158 -0.075
63 stemse 0.023 0.028 0.014 0.028 0.028 0.031 0.03 -0.005
64 noexp -0.008 -0.01 0.002 0.003 0.004 0.004 0.004 0.004
65 stemint 0.007 0.009 0.008 0.016 0.016 0.017 0.017 0.007
66 stemgoal 0.089 0.11 0.012 0.024 0.025 0.027 0.027 -0.039
67 learnexp 3.588 4.438 -1.302 -2.648 -2.708 -2.919 -2.898 -2.077
68 ses_f 0.082 0.102 -0.013 -0.026 -0.027 -0.029 -0.029 -0.036
69 lss 0.043 0.053 0.041 0.084 0.086 0.092 0.092 0.061
70 famsup 0.441 0.545 0.087 0.177 0.181 0.195 0.194 0.056
71 peerment 0.111 0.137 0.203 0.413 0.422 0.455 0.452 0.104
72 factment 0.071 0.088 0.104 0.212 0.216 0.233 0.232 0.229
73 acainv 0.038 0.047 0.079 0.162 0.165 0.178 0.177 0.013
74 clclimate 0.024 0.029 0.025 0.05 0.051 0.055 0.055 0.073
75 selfsac -0.003 -0.004 0.015 0.03 0.031 0.033 0.033 0.01
41 42 43 44 45 46 47 48
41 facs9__2 1.23
42 facs9__3 0.921 1.308
43 facs9__4 0.664 0.861 1.293
44 facs9__5 0.657 0.851 0.85 1.248
45 involve__1 0.019 0.024 0.024 0.024 1.613
46 involve__2 0.01 0.013 0.013 0.013 0.547 1.18
47 involve__3 0.004 0.005 0.005 0.005 0.227 0.122 0.723
48 involve__4 0.009 0.012 0.012 0.012 0.515 0.277 0.115 1.738
49 involve__5 0.006 0.008 0.008 0.008 0.332 0.179 0.074 0.762
50 clclass6__1 0.11 0.142 0.142 0.14 0.019 0.01 0.004 0.009
51 clclass6__2 -0.051 -0.066 -0.066 -0.065 -0.009 -0.005 -0.002 -0.004
52 clclass6__3 -0.108 -0.14 -0.14 -0.138 -0.018 -0.01 -0.004 -0.009
53 clclass6__4 -0.051 -0.066 -0.065 -0.065 -0.009 -0.005 -0.002 -0.004
54 clclass6__5 -0.148 -0.191 -0.191 -0.189 -0.025 -0.013 -0.006 -0.013
55 selfsac6__1 0.015 0.02 0.02 0.02 0.029 0.016 0.007 0.015
56 selfsac6__2 0.02 0.026 0.026 0.025 0.038 0.02 0.008 0.019
57 selfsac6__3 0.026 0.034 0.034 0.033 0.05 0.027 0.011 0.025
58 selfsac6__4 0.021 0.028 0.028 0.027 0.041 0.022 0.009 0.021
!
121
59 selfsac6__5 0.023 0.029 0.029 0.029 0.043 0.023 0.01 0.022
60 selfsac6__6 0.022 0.028 0.028 0.028 0.042 0.023 0.009 0.021
61 born_gen 0.009 0.011 0.011 0.011 -0.055 -0.03 -0.012 -0.028
62 ethnicity -0.113 -0.146 -0.146 -0.144 0.173 0.093 0.039 0.088
63 stemse -0.007 -0.01 -0.01 -0.01 0.254 0.137 0.057 0.129
64 noexp 0.007 0.008 0.008 0.008 -0.024 -0.013 -0.005 -0.012
65 stemint 0.011 0.014 0.014 0.014 0.05 0.027 0.011 0.025
66 stemgoal -0.059 -0.076 -0.076 -0.075 0.211 0.113 0.047 0.107
67 learnexp -3.114 -4.033 -4.026 -3.982 4.048 2.179 0.905 2.05
68 ses_f -0.053 -0.069 -0.069 -0.068 0.129 0.07 0.029 0.066
69 lss 0.091 0.118 0.118 0.117 0.079 0.043 0.018 0.04
70 famsup 0.084 0.109 0.108 0.107 0.03 0.016 0.007 0.015
71 peerment 0.156 0.202 0.202 0.2 0.079 0.043 0.018 0.04
72 factment 0.343 0.444 0.443 0.438 0.013 0.007 0.003 0.006
73 acainv 0.019 0.024 0.024 0.024 1.017 0.547 0.227 0.515
74 clclimate 0.11 0.142 0.142 0.14 0.019 0.01 0.004 0.009
75 selfsac 0.015 0.02 0.02 0.02 0.029 0.016 0.007 0.015
49 50 51 52 53 54 55 56
49 involve__5 1.53
50 clclass6__1 0.006 0.672
51 clclass6__2 -0.003 -0.107 0.725
52 clclass6__3 -0.006 -0.226 0.105 1.058
53 clclass6__4 -0.003 -0.106 0.45 0.104 0.913
54 clclass6__5 -0.008 -0.411 0.144 0.305 0.143 0.792
55 selfsac6__1 0.01 -0.046 0.021 0.045 0.021 0.061 0.916
56 selfsac6__2 0.012 -0.059 0.027 0.058 0.027 0.079 0.468 0.98
57 selfsac6__3 0.016 -0.078 0.036 0.076 0.036 0.105 0.348 0.45
58 selfsac6__4 0.013 -0.064 0.03 0.063 0.029 0.086 0.285 0.369
59 selfsac6__5 0.014 -0.067 0.031 0.066 0.031 0.09 0.301 0.389
60 selfsac6__6 0.014 -0.065 0.03 0.064 0.03 0.087 0.291 0.376
61 born_gen -0.018 0.04 -0.019 -0.04 -0.019 -0.054 -0.041 -0.053
62 ethnicity 0.056 -0.065 0.03 0.064 0.03 0.088 -0.042 -0.054
63 stemse 0.083 0.021 -0.01 -0.021 -0.01 -0.029 -0.027 -0.035
64 noexp -0.008 -0.01 0.005 0.01 0.005 0.013 0.019 0.025
65 stemint 0.016 -0.023 0.011 0.023 0.011 0.032 0.002 0.003
66 stemgoal 0.069 -0.151 0.07 0.148 0.07 0.203 0.018 0.023
67 learnexp 1.32 4.431 -2.061 -4.363 -2.047 -5.968 -8.839 -11.422
68 ses_f 0.042 0.071 -0.033 -0.07 -0.033 -0.096 -0.048 -0.062
69 lss 0.026 0.082 -0.038 -0.081 -0.038 -0.111 -0.019 -0.025
70 famsup 0.01 0.019 -0.009 -0.018 -0.009 -0.025 -0.003 -0.004
71 peerment 0.026 0.025 -0.011 -0.024 -0.011 -0.033 0.015 0.019
!
122
72 factment 0.004 0.073 -0.034 -0.072 -0.034 -0.099 0.01 0.013
73 acainv 0.332 0.019 -0.009 -0.018 -0.009 -0.025 0.029 0.038
74 clclimate 0.006 0.23 -0.107 -0.226 -0.106 -0.309 -0.046 -0.059
75 selfsac 0.01 -0.046 0.021 0.045 0.021 0.061 0.204 0.264
57 58 59 60 61 62 63 64
57 selfsac6__3 1.234
58 selfsac6__4 0.487 1.18
59 selfsac6__5 0.514 0.421 1.523
60 selfsac6__6 0.496 0.406 0.915 1.431
61 born_gen -0.07 -0.057 -0.06 -0.058 0.547
62 ethnicity -0.071 -0.058 -0.062 -0.059 0 4.242
63 stemse -0.046 -0.038 -0.04 -0.038 0.034 0.108 1.435
64 noexp 0.032 0.027 0.028 0.027 -0.026 -0.041 -0.117 1.081
65 stemint 0.004 0.003 0.003 0.003 0.001 0.025 0.287 -0.013
66 stemgoal 0.031 0.025 0.026 0.025 -0.009 0.134 0.845 -0.182
67 learnexp -15.086 -12.36 -13.037 -12.59 12.281 17.59 14.321 -7.747
68 ses_f -0.082 -0.068 -0.071 -0.069 0.164 0.301 0.138 -0.064
69 lss -0.033 -0.027 -0.028 -0.027 -0.001 -0.008 0.015 0.001
70 famsup -0.005 -0.004 -0.004 -0.004 0.007 0.04 0.018 -0.007
71 peerment 0.025 0.021 0.022 0.021 -0.01 -0.071 0.014 0.002
72 factment 0.017 0.014 0.015 0.014 0.006 -0.075 -0.005 0.004
73 acainv 0.05 0.041 0.043 0.042 -0.055 0.173 0.254 -0.024
74 clclimate -0.078 -0.064 -0.067 -0.065 0.04 -0.065 0.021 -0.01
75 selfsac 0.348 0.285 0.301 0.291 -0.041 -0.042 -0.027 0.019
65 66 67 68 69 70 71 72
65 stemint 0.132
66 stemgoal 0.259 2.217
67 learnexp 2.032 6.617 3503.424
68 ses_f 0.014 0.055 28.263 0.591
69 lss -0.004 -0.039 -0.837 -0.025 0.11
70 famsup 0.005 0.07 2.817 0.065 0.034 0.346
71 peerment 0.008 0.012 -1.302 -0.013 0.041 0.087 0.203
72 factment 0.007 -0.039 -2.077 -0.036 0.061 0.056 0.104 0.229
73 acainv 0.05 0.211 4.048 0.129 0.079 0.03 0.079 0.013
74 clclimate -0.023 -0.151 4.431 0.071 0.082 0.019 0.025 0.073
75 selfsac 0.002 0.018 -8.839 -0.048 -0.019 -0.003 0.015 0.01
73 74 75
73 acainv 1.017
74 clclimate 0.019 0.23
75 selfsac 0.029 -0.046 0.204
!
123

Table 8

Residual Matrix
1 2 3 4 5 6 7 8 9
1 careff1par -0.007
2 careff2par -0.004 0.013
3 careff3par -0.029 0.008 -0.01
4 careff4par 0.032 -0.014 -0.014 -0.012
5 careff5par 0.021 0.011 0.005 -0.022
-
0.002
6 noute1par -0.02 -0.038 -0.038 0.033
-
0.018 0.014
7 noute2par -0.019 0.015 -0.028 -0.002 0.004 0.011 0.009
8 noute3par -0.121 -0.07 -0.095 -0.08
-
0.059 0.016 0.014 0.02
9 noute4par -0.054 -0.065 -0.087 -0.154
-
0.041 -0.036 0.014 0.002 0.012
1
0 int1par 0.941 -0.026 0.158 0.206 0.061 -0.008
-
0.072 -0.044
-
0.028
1
1 int2par -0.116 0.503 0.05 -0.088 0.197 -0.1
-
0.033 -0.063 -0.04
1
2 int3par -0.239 -0.101 0.009 -0.045
-
0.109 -0.003 0.019 -0.015
-
0.027
1
3 int4par -0.062 -0.138 0.025 0.155
-
0.045 0.017 0.015 -0.001
-
0.049
1
4 int5par -0.11 0.194 0.023 -0.037 0.225 -0.02 0.003 0.009 -0.04
1
5 g_stem_1 -0.295 -0.004 -0.07 -0.106 -0.01 -0.108 0.084 -0.025 0.09
1
6 g_stem_2 -0.064 0.126 0.162 0.151 0.151 -0.177 0.031 -0.113 0.008
1
7 g_stem_3 -0.247 0.011 -0.002 -0.026 0.017 -0.092 0.065 -0.022 0.121
1
8 g_stem_4 -0.235 0.025 -0.051 -0.084 0.041 -0.076 0.112 0.008 0.078
1
9 sat_math 28.87 9.373 20.366 27.175 7.054
-
10.276
-
1.659
-
13.489
-
5.875
2
0 sat_s 3.272 -5.752
-
14.942
-
12.755
-
7.953 9.339 9.126 2.403
-
0.274
2
1 ses4_1 0.218 0.252 0.19 0.187 0.204 -0.192
-
0.121 -0.272
-
0.241
2 parented1_1 0.083 0.024 0.033 0.04 0.057 -0.147 - -0.215 -
!
124
2 0.108 0.127
2
3 parented1_2 0.054 -0.01 -0.015 0.035 0.03 -0.082
-
0.072 -0.156
-
0.134
2
4 lss1cparR 0.009 0.032 0.007 0.028 0.062 0.019 0.002 -0.025
-
0.047
2
5 lss2cparR 0.032 0.024 -0.002 -0.004 0.053 -0.009
-
0.042 -0.029
-
0.089
2
6 lss3cparR -0.083 -0.049 -0.026 -0.007
-
0.005 0.078
-
0.005 0.009
-
0.015
2
7 lss4cparR -0.018 0.009 -0.022 0.024 0.08 0.029
-
0.042 -0.007
-
0.067
2
8 lss5cparR 0.073 0.052 0.002 -0.018 0.059 -0.085
-
0.103 -0.144
-
0.144
2
9 lss6cparR 0.108 -0.008 -0.01 -0.002 0.037 0.048
-
0.002 0.036
-
0.028
3
0 lss7cparR 0.029 0.02 -0.019 0.007 0.052 -0.065
-
0.117 -0.126 -0.21
3
1 fams6__1 0.052 -0.011 0.09 0.095 0.062 -0.025
-
0.059 -0.04
-
0.065
3
2 fams6__2 0.175 0.129 0.219 0.181 0.182 -0.218
-
0.171 -0.193
-
0.166
3
3 fams6__3 0.095 -0.06 0.041 0.081 0.08 -0.043 -0.05 -0.026
-
0.031
3
4 fams6__4 -0.102 0.014 -0.029 -0.013 0.033 -0.143
-
0.132 -0.129
-
0.156
3
5 peers9__1 -0.03 -0.011 -0.017 -0.05 0.01 -0.021
-
0.068 -0.066
-
0.073
3
6 peers9__2 -0.053 0.104 0.141 0.141 0.057 -0.116
-
0.108 -0.209
-
0.095
3
7 peers9__3 -0.17 0.038 0.042 0.051
-
0.006 -0.155
-
0.146 -0.185
-
0.064
3
8 peers9__4 -0.035 0.029 0.103 0.115 0.057 -0.04
-
0.076 -0.057
-
0.115
3
9 peers9__5 0.012 0.057 0.119 0.113 0.017 -0.048
-
0.085 -0.114 0.002
4
0 facs9__1 0.146 0.137 0.159 0.169 0.155 -0.056
-
0.143 -0.126
-
0.152
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0.126 -0.201
-
0.115
4
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0.222
4
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0.012 -0.008
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0.037
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!
125
4 0.143 0.111
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0.038 0.132 0.084 0.062 0.104
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0.118 -0.187 -0.06
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0.029 -0.041
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0.018
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0.246 0.071 0.119 0.091 0.112
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0.128 -0.109
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0.079
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0.031 0.293 0.258 0.315 0.259
5
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0.035 0.194 0.172 0.194 0.239
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0.131 0.053 0.15 0.18 0.162
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0.007 0.016 0.12 0.074 0.128
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0.157 -0.179
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0.078
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0.028 -0.138 0
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9 selfsac6__5 -0.098 0.053 0.116 0.058
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0.069 0.053 0.074 0.068 0.301
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0.038
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2 ethnicity -0.115 -0.025 -0.038 0.003
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0.046 -0.172
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0.113 -0.073
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0.104
10 11 12 13 14 15 16 17 18
1
0 int1par -0.014
1
1 int2par -0.032 -0.008
1
2 int3par -0.009 0.036 -0.003
!
126
1
3 int4par -0.001 -0.067 0.001 0.001
1
4 int5par -0.007 -0.005 0.003 -0.014
-
0.002
1
5 g_stem_1 -0.264 0.109 -0.07 -0.128 0.124 -0.012
1
6 g_stem_2 -0.14 0.207 0.017 0.015 0.164 -0.012 0.004
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-
0.001 -0.015
1
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0.039 -0.016
-
0.006
1
9 sat_math 12.143 4.188 9.967 16.77
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0.617 30.573
43.16
2 31.087
28.08
9
2
0 sat_s 1.233 -6.464
-
13.606 -6.881
-
9.858 3.441 8.296 -0.468 7.044
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1 ses4_1 -0.02 0.109 0.009 0.031 0.101 0.18 0.22 0.212 0.178
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2 parented1_1 0.022 0.019 0.015 0.051 0.052 0.074 0.064 0.04 0.074
2
3 parented1_2 -0.001 -0.026 -0.01 0.006
-
0.007 0.056 0.046 0.049 0.04
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0.021 -0.007
-
0.005
2
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0.091 -0.093 -0.05
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7 lss4cparR -0.051 0.008 -0.033 0.001 0.048 -0.013
-
0.006 0.016 0.044
2
8 lss5cparR -0.036 0.021 -0.05 -0.049 0 -0.024 0.004 -0.014
-
0.005
2
9 lss6cparR 0.1 0.006 0 0.001 0.021 -0.09
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0.071 -0.072
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0.053
3
0 lss7cparR -0.008 0.017 -0.034 0
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0.003 -0.052
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0.033 -0.056
-
0.042
3
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3
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3
3 fams6__3 0.019 0.001 -0.062 -0.019 0.004 -0.001 0.031 0.017 0.002
3
4 fams6__4 -0.158 0.114 -0.076 -0.077 0.013 -0.029 0.006 0.001 0.002
!
127
3
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-
0.023 0.004 0.026 -0.012
-
0.045
3
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3
7 peers9__3 -0.101 0.09 -0.026 -0.019
-
0.034 0.095 0.193 0.134 0.035
3
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0.017 -0.04 0.053 -0.018
-
0.119
3
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0.045 -0.001 0.051 0.03
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0.096
4
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0.021 -0.047
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0.069
4
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-
0.055
4
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4
3 facs9__4 0.015 0.113 0.089 0.11 0.105 0.083 0.056 0.07 0.035
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4 facs9__5 -0.065 0.111 0.051 0.047 0.048 0.033 0.041 0.076 0.023
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5 involve__1 0.075 -0.033 -0.01 0.08 0.003 -0.026
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0.043 0.008
-
0.067
4
6 involve__2 0.103 0.027 0.015 0.069 0.018 0.063 0.002 0.095 0.102
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7 involve__3 0.1 0.091 0.072 0.077 0.086 0.201 0.213 0.231 0.185
4
8 involve__4 0.08 -0.132 0.025 0.065
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0.003 -0.071
-
0.145 -0.109
-
0.086
4
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0.253 -0.183
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0.076
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0.035 -0.002
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0.001
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0.029 -0.044 -0.09 -0.045
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0.072
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0.067 0.006 -0.01
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0.078 -0.096 -0.12 -0.083
-
0.087
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0.039 -0.057
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0.096 -0.077
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0.049
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0.023
5
6 selfsac6__2 -0.007 -0.032 -0.093 -0.051 -0.01 -0.078 0 -0.055
-
0.094
!
128
5
7 selfsac6__3 -0.01 0.028 0.022 -0.009 0.048 0.054 0.054 -0.008
-
0.026
5
8 selfsac6__4 0.11 0.034 0.051 0.079 0.078 0.208 0.182 0.187 0.191
5
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0.038
6
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6
2 ethnicity -0.132 -0.151 -0.17 -0.116
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0.061 0.117 0.066 0.075 0.064
19 20 21 22 23 24 25 26 27
1
9 sat_math 93.546
2
0 sat_s 107.53 10.644
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1 ses4_1 -0.745 -6.215 0.004
2
2 parented1_1 1.93 2.266 -0.004 0.001
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3 parented1_2 -1.14 0.92 0.018 0.002 0.001
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-
0.015 -0.008 0.001
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-
0.072 0.012 0.003 -0.003
2
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-
0.003
2
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-
0.023
2
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0.033 -0.005 0.003 -0.001 -0.02
3
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0.019 -0.063
-
0.004
3
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0.009 -0.037
-
0.042 0.07
-
0.063
3
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-
0.045 0.054
-
0.089
3
3 fams6__3 1.092 -5.491 -0.004 -0.072
-
0.087 -0.034
-
0.013 0.112 0
3 fams6__4 -6.217 -5.139 0.112 -0.021 - 0.006 - 0.144 0
!
129
4 0.002 0.021
3
5 peers9__1 4.003 1.562 -0.095 -0.022
-
0.061 0.042 0.028 0.204 0.05
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-
0.027 0.262
-
0.035
3
7 peers9__3 4.73 10.673 0.016 -0.02 0.036 0.015
-
0.017 0.215 0.011
3
8 peers9__4 -5.31 2.574 0.049 -0.002 0.005 0.021
-
0.038 0.247
-
0.027
3
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-
0.007 -0.019
-
0.071 0.221
-
0.049
4
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4
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-
0.045 0.049 0.012 0.188 0.041
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0.054 0.198
-
0.017
4
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0.075 0.01 -0.06 0.179
-
0.033
4
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-
0.072 0.015
-
0.049 0.184 0.021
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5 involve__1 1.561 1.895 0.008 -0.029 0.144 0.02 0.032 0.057
-
0.044
4
6 involve__2 5.492 5.224 0.014 -0.003 0.095 0.008 -0.03 0.023
-
0.034
4
7 involve__3 -1.896 -4.908 -0.023 -0.07
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0.114 -0.011 0.032 0.13
-
0.042
4
8 involve__4
-
35.842
-
10.476 -0.303 -0.199 -0.12 -0.004
-
0.051 0.006
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0.066
4
9 involve__5
-
27.109 -3.356 -0.223 -0.161
-
0.107 0.051 0.068 0.09 0.018
5
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0.024 0.015
-
0.031 0.037
-
0.001
5
1 clclass6__2 -2.081 -3.85 0.102 0.052 0.017 0.011
-
0.017 0.109 0.034
5
2 clclass6__3
-
12.813
-
19.057 -0.097 -0.119
-
0.096 0.068 0.099 0.116 0.098
5
3 clclass6__4 2.98 3.076 0.13 0.094
-
0.002 0.001 0.008 0.12 0.062
5
4 clclass6__5 -0.558 -5.855 -0.011 0.026 0.061 -0.002 0.003 0.037
-
0.007
5
5 selfsac6__1 6.564 -7.65 -0.046 -0.104
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0.022 0.036 0.048 0.092 0.018
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!
130
6
5
7 selfsac6__3 9.55 7.02 0.036 0.021 0.092 -0.002
-
0.028 0.072 -0.07
5
8 selfsac6__4 8.658 -8.186 0.018 0 0.045 0.039 0.05 0.184
-
0.003
5
9 selfsac6__5 -2.682 -7.839 -0.171 -0.114
-
0.017 0.014
-
0.019 0.16
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0.051
6
0 selfsac6__6 -9.548 -4.627 -0.13 -0.096 0.039 -0.003
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0.034 0.1
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0.116
6
1 born_gen -3.924 1.472 -0.006 0.008 0.003 0.007
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0.017 -0.032 0.007
6
2 ethnicity 0.981 4.545 -0.005 0.053
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0.012 -0.042 0.04 -0.09 0.117
28 29 30 31 32 33 34 35 36
2
8 lss5cparR -0.003
2
9 lss6cparR 0.013 0
3
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3
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3
2 fams6__2 0.092 -0.042 -0.025 -0.009
-
0.018
3
3 fams6__3 0.05 0.034 0.041 0.004
-
0.003 -0.004
3
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0.043 -0.057 0.096 0.039 0
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0.125 -0.071 0.047 -0.012
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0.011
3
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0.161 -0.057 0.009 -0.026 0.003
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0.017 -0.083 0.149 0.001 0.06
!
131
4
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-
0.187 -0.019 0.089 0.012
-
0.021
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0.126 -0.032 0.106 0.06 0.005
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0.004
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0.109 0.012 0.06
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0.038 0.034 0.077
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0.115 0.018
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0.002 -0.04
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0.011 -0.082 0.088
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2 ethnicity -0.044 -0.129 0.016 0.085
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0.105 0 0.006
37 38 39 40 41 42 43 44 45
3
7 peers9__3 0
3 peers9__4 -0.003 0.002
!
132
8
3
9 peers9__5 -0.016 0.015 -0.003
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-
0.007
4
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-
0.008 0.002
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-
0.031 -0.002 0.003 0.001
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-
0.072 -0.031
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0.014 -0.017
-
0.015
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0.021 0.068 0.031 0.009
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0.014
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0.016 -0.032
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0.005
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0.002 0.119 0.11 0.134
-
0.016
5
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-
0.084
5
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5
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-
0.082
5
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0.025 -0.032
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0.014
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!
133
0 0.167 0.003
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0.003 -0.04 0.017
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46 47 48 49 50 51 52 53 54
4
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0.002
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0.002
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0.004 0.013
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0.001
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0.054
5
6 selfsac6__2 -0.033 -0.011 0.099 0.104 0.097 0.001 0.024 -0.101
-
0.083
5
7 selfsac6__3 -0.037 -0.001 0.197 0.124
-
0.022 -0.071
-
0.024 -0.139
-
0.052
5
8 selfsac6__4 0.001 0.077 0.209 0.177 0.06 -0.025 0.048 -0.063
-
0.016
5
9 selfsac6__5 -0.146 0.112 0.166 0.149
-
0.035 0.184 0.285 0.039 0.079
6
0 selfsac6__6 -0.144 0.094 0.066 0.138
-
0.048 0.113 0.114 0.006 0.077
6
1 born_gen 0.042 -0.016 -0.088 -0.134
-
0.042 0.014
-
0.069 0.029 0.001
6
2 ethnicity 0.026 -0.055 -0.16 -0.123 0.013 0.221
-
0.166 0.191 0.02
55 56 57 58 59 60 61 62
5
5 selfsac6__1 0.021
5 selfsac6__2 0.01 0.008
!
134
6
5
7 selfsac6__3 0.019 0.095 0.016
5
8 selfsac6__4 -0.019 -0.036 -0.005 -0.002
5
9 selfsac6__5 0.031 -0.063 -0.087 0.12
-
0.001
6
0 selfsac6__6 -0.026 -0.059 0.015 0.011
-
0.001 0.012
6
1 born_gen -0.005 -0.009 0.027 -0.051
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0.008 -0.001 0
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2 ethnicity -0.282 -0.132 0.024 0.262 0.043 0.055 0.075 0

Abstract (if available)

Abstract As the United States strives to maintain its global economic competitiveness, there exists a need to support and encourage racial and ethnic minorities in pursuing science, technology, engineering, and math (STEM) fields. While the career decision-making process may be stressful for many students, high achieving Latina college students face the unique challenge of, often, having to reconcile their career aspirations with competing sociocultural concerns. Despite the critical need to support Latinas in STEM, few researchers have examined their career decision-making process. In the current dissertation, I draw insight from the counseling, vocational, psychology, and higher education literature in order to examine Latina college students’ STEM career decision-making process and goals. Social cognitive career theory and the psychosociocultural framework provide the theoretical perspectives guiding the study. Findings from the current study have important implications for student affairs administrators, academic departments, institutional policies, and counseling services on how to best support the career development of one of the nation's fastest growing populations. Implications for research and theory are discussed.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Castellano, Michelle (author)

Core Title How college affects Latinas' STEM career decision-making process: a psychosociocultural approach

School Rossier School of Education

Degree Doctor of Philosophy

Degree Program Urban Education Policy

Publication Date 07/31/2015

Defense Date 04/22/2015

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag career development,Higher education,Latina college students,OAI-PMH Harvest,self-efficacy,STEM

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Cole, Darnell G. (committee chair), Castellanos, Jeanett (committee member), Chung, Ruth G. (committee member), McArdle, John J. (committee member)

Creator Email castellm@usc.edu,castellm2014@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-620423

Unique identifier UC11304148

Identifier etd-Castellano-3769.pdf (filename),usctheses-c3-620423 (legacy record id)

Legacy Identifier etd-Castellano-3769.pdf

Dmrecord 620423

Document Type Dissertation

Format application/pdf (imt)

Rights Castellano, Michelle

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA