Intersectionality, Institutions, & Inequality: STEM Majors and Status Competition Processes in the U.S. Higher Education System. Público

Steidl, Christina R. (2012)

Permanent URL: https://etd.library.emory.edu/concern/etds/mk61rg95s?locale=pt-BR
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Abstract

This dissertation studies the effects of the intersectionality of gender, race and class on
majoring in science, technology, engineering and math (STEM) fields within a status
competition framework. It expands research on inequality in STEM in three ways. First, I
focus representation of intersectional status groups, allowing for comparisons among men
and among women, as well as between men and women. Second, I analyze disparities
among STEM majors - comparing life science and mathematical science (physics, math,
engineering, computer science) majors. Third, I explore interactions between institutional
context and status effects. I conduct weighted logistic regression analyses on recent data
from the Beginning Postsecondary Students Longitudinal Survey (BPS:04/09) and the
Integrated Postsecondary Educational Data System (IPEDS). STEM majors are students
who have completed a bachelor's degree in or are working toward a bachelor's degree in
a STEM field in what would be their sixth year. Results confirm previous findings that
Asians and men are overrepresented in STEM, but also show significant intersectional
effects. Parental income operates differently by race; income is negatively associated
with STEM rates for whites, blacks and Asians, but positively associated with STEM
rates for Hispanics. Among women, parental education levels are positively associated
with STEM rates (no effect for me). I also find variation among STEM majors. Men are
more likely to major in math sciences than life sciences, as are black students (especially
from lower income families), lower income Asian men, and women of color (vs. white
women). Asian men are more likely to major in the life sciences than the math sciences.
Finally, I explore five institutional contexts (public, Carnegie Doctorates, land-grants,
HBCUs and Hispanic-serving institutions) and find that the salience of intersectionality
varies by context. I argue that the activation of particular intersectional statuses in certain
institutional contexts (and not in others) results from variation in status competition
processes for STEM majors. Thus, my results demonstrate the importance of considering
intersectional status in the creation of policies that seek to broaden participation in STEM
and reinforce the existence of multiple pathways into STEM instead of a single "leaky"
pipeline.

Table of Contents


TABLE OF CONTENTS

Chapter 1: Inequality, Intersections, and Institutions .................................................. 1
Different or Unequal? ................................................................................................. 3
Two Axes of Inequality ................................................................................................ 4
Structure of the Dissertation ........................................................................................ 5
Intersectionality ............................................................................................................. 7
Status Competition Processes in Higher Education .................................................. 9
Institutional Contexts: Status Competition Within and Between Institutions ........... 15
Theoretical Contributions .......................................................................................... 17
Chapter 2: Studying Students who Study STEM ........................................................ 20
Gender Inequality in STEM ...................................................................................... 22
Racial Inequality in STEM ........................................................................................ 24
Class Inequality in STEM .......................................................................................... 25
Intersections of Race, Class, and Gender ................................................................. 27
Astin's I-E-O Model .................................................................................................... 30
Institutional Effects ..................................................................................................... 33
Carnegie Classifications ........................................................................................... 35
Institutional Control: Public vs. Private ................................................................... 36
Land-Grant Institutions ............................................................................................ 37
Historically Black Colleges and Universities ........................................................... 38
Hispanic Serving Institutions .................................................................................... 41
Chapter 3: Data and Methods ....................................................................................... 43
Data .............................................................................................................................. 43
Construction of the Dataset ...................................................................................... 43
Subsampling .............................................................................................................. 46
Variables ...................................................................................................................... 46
Dependent Variables ................................................................................................. 46
Independent Variables: Student Status Characteristics ........................................... 48
Independent Variables: Institutional Context ........................................................... 51
Control Variables ..................................................................................................... 52
Missing Values .......................................................................................................... 54
Methods of Analysis .................................................................................................... 55
Weighted Logistic Regression Models ...................................................................... 55
Survey Weights, Sampling Weights and Subsampling Procedures ........................... 60
Chapter 4: Intersectional Status Effects on STEM Majoring .................................... 62
Data and Methods ....................................................................................................... 67
Hypotheses ................................................................................................................... 70
Results .......................................................................................................................... 71
Two-Way Intersectional Effects ................................................................................ 73
Three-Way Intersectional Effects .............................................................................. 79
Significance of Student Development Controls ......................................................... 86
Conclusions .................................................................................................................. 89

Chapter 5: Difference in Equity between STEM Majors ............................................ 94
Closing the Gender Gap in the Life Sciences? ......................................................... 94
Data and Methods ....................................................................................................... 96
Hypotheses ................................................................................................................... 99
Results ........................................................................................................................ 101
Mathematical Science Majors vs. Non-STEM Majors ............................................ 101
Life Science Majors vs. Non-STEM Majors ............................................................ 109
Mathematical Science Majors vs. Life Science Majors .......................................... 116
Effects of Student Development Controls ............................................................... 124
Conclusions ................................................................................................................ 127
Chapter 6: How Institutional Context Moderates Intersectional Status Effects .... 133
STEM Majors and Institutional Type ...................................................................... 134
Data and Methods ..................................................................................................... 140
Construction of the Dataset .................................................................................... 140
Effects of Institutional Type .................................................................................... 143
Method of Analysis .................................................................................................. 144
Results ........................................................................................................................ 144
Carnegie Doctorate Institutions ............................................................................. 144
Public Institutions ................................................................................................... 152
Land-Grant Institutions .......................................................................................... 162
Historically Black Colleges and Universities (HBCU) .......................................... 168
Hispanic-Serving Institutions (HSI) ........................................................................ 176
Conclusions ................................................................................................................ 179
Chapter 7: Conclusions: Pipelines, Pathways and Policies ....................................... 184
Methodological Issues ............................................................................................... 184
Key Findings and Contributions ............................................................................. 187
How does Intersectional Status Influence STEM Majoring? .................................. 187
How Do Intersectional Status Effects Differ by STEM Major? .............................. 192
How does Institutional Context Influence Intersectional Status Effects on STEM
Majoring? ............................................................................................................... 194
Policy Implications and Recommendations ............................................................ 197
Postsecondary Institutions ...................................................................................... 199
State and National Policy-Makers .......................................................................... 205
Future Directions ...................................................................................................... 207
REFERENCES .............................................................................................................. 209

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