Women’s Representation in STEM
Running Head: WOMEN’S REPRESENTATION IN STEM
Women’s Representation in
Science and Technology (STEM) Fields of Study,
1976-2006
Linda J. Sax, University of California Los Angeles
Jerry A. Jacobs, University of Pennsylvania
Tiffani A. Riggers, University of California Los Angeles
Paper presented at the annual meeting of the Association for the Study of Higher Education
(ASHE), November 2010, Indianapolis, Indiana
Women’s Representation in STEM, 1
Women’s Representation in
Science and Technology (STEM) Fields of Study,
1976-2006
Abstract
This paper examines women’s under-representation in science and technology (STEM)
fields at the point of college entrance. Specifically, we document and explain trends over time in
the expected majors of entering first-year college students. Data from 1976, 1986, 1996 and 2006
are drawn from the CIRP (Cooperative Institution Research Program) conducted by UCLA’s
Higher Education Research Institute. The results indicate a modest growth in women’s
representation in STEM fields, fueled as much by men’s declines as by women’s advances.
There are strong continuities in the factors predicting major choice despite the many changes in
gender roles during this period of time. Women’s higher grades in high school, relative to men,
facilitate women’s entry into STEM fields, but their lower academic self assessments, especially
in math, continue to impede women’s pursuit of STEM majors and careers. These and other
notable results are reviewed in the findings and discussion sections of the paper.
Women’s Representation in STEM, 2
Introduction
The gender segregation of majors is an enduring feature of the undergraduate landscape.
Research in this area has focused mostly on trends, indicating a general decline in segregation
over time, both among entering college students and graduating seniors (Jacobs, 1995; 1996;
Sax, 2008). Despite a narrowing of the gender gap in specific academic and professional fields,
such as law, medicine and education, historical patterns of sex-segregation have persisted,
especially in the most male-dominated disciplines within STEM (science, technology,
engineering, mathematics, and physical sciences) (e.g. Spelke, 2005; Turner & Bowen, 1999).
Given the need for a scientifically literate, technologically skilled, and diverse workforce,
women’s continued underrepresentation in scientific disciplines represents a persistent societal
challenge.
Over the past 30 years, researchers have identified a number of reasons why women are
less likely to choose a STEM major than men (see, for example, Margolis & Fisher, 2002; Sax,
2001; Seymour & Hewitt, 1994; Sonnert, 1995; Xie and Shauman, 2003). These reasons include
a lack of exposure to STEM in elementary and high school (Blickenstaff, 2005; Kinzie, 2007;
Huang & Brainard, 2001), cultural norms and societal expectations (Baker & Leary, 1995;
Blickenstaff, 2005; Singh, Allen, Scheckler, and Darlington, 2007; Sonnert; 1995; Thorne, 1993;
Walford, 1981), demographics (Cole & Espinosa, 2008; Espinosa, 2009; Huang, Taddese &
Walter, 2000; Sax, 1994, 2001), academic preparation (Huang & Brainard, 2001; Kinzie, 2007;
Margolis, Fisher, & Miller, 2000; Seymour & Hewitt, 1994), and affective characteristics, such
as self-confidence, individual goals and personality type (Cohoon, 2001; Cole, 1997; Huang, et
al., 2000; Margolis & Fisher, 2002; Sax, 1994, 2008; Yasuhara, 2005). Among these, scholars
Women’s Representation in STEM, 3
have identified academic preparation and affective characteristics as the factors which most
strongly influence student major choice (Seymour & Hewitt, 1994; Turner & Bowen, 1999).
While such predictors of STEM aspirations may have been identified in numerous
studies, research has generally not examined the extent to which explanations for women’s
underrepresentation in STEM have shifted over the years (with Turner and Bowen (1999) as a
notable exception). Given women’s advancements in education and the workforce over the past
few decades, we seek to ascertain whether some of the factors that have deterred women from
participating in STEM fields in the past may be less salient forces today.
Thus, while entry into STEM fields has been studied extensively, there are nonetheless
important gaps in the literature which we see to fill. In this paper, we deploy data from a unique
longitudinal study to examine two major questions:
1. How has women’s share of prospective STEM field majors changed over the period
1976-2006?
2. Have there been any noteworthy changes over time in the factors associated with the
gender disparity in STEM fields?
Trends in Women’s Representation in STEM Fields
The period of history we examine is an important one for understanding the
transformation of women’s roles in society. Women’s labor force participation has increased,
especially on the part of mothers with young children (Lee and Mather, 2008). The age at first
marriage has increased, thus expanding the period of economic self-reliance for women (U. S.
Bureau of the Census, 2010). Divorce rates have declined somewhat but remain quite high in the
U. S. (U. S. National Center for Health Statistics, 2008). Additionally, women’s representation
among college students has increased to the point where they now represent a sizable majority of
Women’s Representation in STEM, 4
college students and new bachelor’s degree recipients, reversing their historical minority position
in higher education (National Center for Education Statistics, 2009).
On the other hand, a number of prominent analysts have noted that much gender
inequality remains and that the gender revolution sought by second-wave feminists has been
incomplete. Susan Faludi (1991) noted the rise of a backlash against feminism during the 1980s.
Prominent observers, including Arlie Hochschild (1989) and Kathleen Gerson (2009), have
pointed to a stalled or incomplete transformation of roles. For example, recent data suggest that
women’s advances into formerly male-dominated occupations have slowed and even begun to
reverse for young women (Institute for Women’s Policy Research, 2010).
Gender segregation has historically been most evident in the STEM fields. Indeed, in
2008, 57.3 percent of bachelor’s degree recipients were women, while women comprised 34.9
percent of STEM graduates.1 A full 29.3 percent of women bachelor’s degree recipients would
have had to change majors in order to be distributed in the same manner as men.2 After declining
markedly through the 1980s and the first half of the 1990s, the index of dissimilarity (or level of
segregation) has remained roughly flat through the first decade of the 21st century (Jacobs, 1995;
England and Li, 2006). Gender segregation encompasses the full spectrum of majors, from
English and education to chemistry and physics. It is possible that women could have made
advances in STEM fields that were offset by shifts in other undergraduate domains. Our first
task, then, will be to determine whether women’s share of STEM fields has increased over time.
Author’s calculation. Data drawn from Digest of Education Statistics, Table 276.
Author’s calculation using data from the 2009 Digest of Education Statististics, Table 275. Calculation based on 32
fields of study using the index of dissimilarity, which refers to the proportion of women (or men) who would have to
change majors in order to achieve an equal gender distribution.
1
2
Women’s Representation in STEM, 5
Determinants of Gendered Choices
Our second objective involves determining whether the factors associated with gender
segregation have shifted over time. To frame that investigation, it is first important to review
what the literature suggests about gender and decisions about STEM. As discussed by Turner
and Bowen (1999) in their longitudinal study of the gender gap in major selection, there are a
number of theoretical explanations for women’s major choices. They suggest that theories about
major selections fall into one of two camps: differences in environments (structural barriers) and
differences in skills and preferences (individual barriers). Though early research emphasized the
importance of structural barriers in the under-participation of women in STEM, more current
research acknowledges that individual barriers are more influential in the decision of women to
pursue STEM degrees and careers (Seymour & Hewitt, 1994; Sonnert, 1995; Yasuhara, 2005). In
light of studies which consistently suggest that students who choose to major in STEM differ
most from those who do not in terms of pre-college reasons for choosing the major (Seymour &
Hewitt, 1994; Turner & Bowen, 1999; Yasuhara, 2005), this study will focus on the individual
characteristics that predict women’s choice of a STEM major. However, the literature on both
structural and individual characteristics informs the nature of this study.
Structural Characteristics
Gender-role socialization. While societal stereotypes may be less of a factor in the
twenty-first century than in the past, it appears that stereotypical images of women in STEM
continue to play a role in the absence of women from these fields. Research from the 1980s
reported that science textbooks often portrayed scientists as boys, with girls as passive observers
(Walford, 1981). Girls also reported being treated differently than boys in class, commenting that
they were less likely to be called upon to answer questions and given less positive feedback
Women’s Representation in STEM, 6
when answering questions (Eccles, 1992; Kahle, 1996). Biases in textbooks and by teachers
continued to be identified as a deterrent to young women in the 1990s (Baker & Leary, 1995;
Thorne, 1993). These biases contributed to a lack of opportunity for girls to participate in science
and math classes (Kahle & Lakes, 1983; American Association of University Women, 1992).
Research from the late 1990s and early 21st century continues to find that women encounter
gendered stereotypes in their classrooms (Singh et al, 2007; Sonnert, 1995).
Further explaining the effect of bias, historical gender roles and a lack of female role
models may play a strong part in major choice. In the 1990s, Sonnert (1995) emphasized that “in
spite of emerging egalitarian patterns” (p.4), women still held primary responsibility for duties
related to the home. These expectations may serve as deterrents toward pursuing a career in
STEM, which young women consistently view as less conducive to their feminine roles and
responsibilities (Thorne, 1993; Blickenstaff, 2005). This dearth of role models combined with
the reinforcement of gender roles and stereotypes at home and in class contributes to lower
enrollment in STEM fields.
Classroom experience. As noted, gender-role socialization often plays out in classroom
biases. The lack of classroom experience may account for some of women’s attrition from
science and math courses throughout middle and high school, leaving women under-prepared for
the rigors of college-level math and science (Kinzie, 2007; Huang & Brainard, 2001). Research
from the 1980s suggests that a lack of encouragement in the classroom discouraged women from
continuing in STEM courses, which then also lowered their scores on standardized and
placement exams (Kahle, 1996). This scenario was most visible in science, where the gender gap
actually increased as students progressed through elementary and middle school (Vetter, 1996).
The same differential pattern in course-taking is evident today; Blickenstaff (2005) notes that
Women’s Representation in STEM, 7
women are less likely to take science and math classes in high school than their male
counterparts, which partly accounts for their opting out of STEM majors in college. For example,
students who complete less-advanced STEM courses in high school are also less likely to
become STEM majors in college, often because they feel under-prepared (Kinzie, 2007; Huang
& Brainard, 2001).
When women are exposed to STEM in high school, their experience in the classroom
begins to play a strong role in their college major choice (Wyer, 2003). When women perceive
the classroom climate as positive, they are more likely to aspire to a STEM degree; women who
persist in STEM majors in college indicate that they had more positive classroom experiences
which encouraged them to take more STEM-related courses (Wyer, 2003). However,
Blickenstaff (2005) notes that when researchers controlled for number of courses taken and a
positive classroom climate, women were still less likely than men to choose a STEM major and
persist in the major if they did choose it. This finding suggests that there are more factors at play
in women’s decision to major in STEM than just a lack of experience in the classroom or genderrole socialization and stereotypes (Blickenstaff, 2005; Margolis & Fisher, 2001; Sonnert, 1995;
Yasuhara, 2005). Indeed, these studies seem to emphasize the importance of women’s individual
characteristics on their choice to pursue a major in STEM.
Individual Characteristics
Demographics. The nature of who enters STEM disciplines is highly dependent on
demographics (Huang, et al., 2000; Sax, 2008). In addition to women, students of color and
students from lower SES households have been persistently underrepresented in STEM majors
(Ascher, 1985; Huang et al, 2000; Mohrman, 1987; Porter, 1990; Rotberg, 1990; Wilson, 1990).
Some data show Latino, African American, and American Indian students to be the most
Women’s Representation in STEM, 8
underrepresented in STEM, a factor that is often correlated to poor preparation in elementary and
high school (Bonous-Hammarth, 2000; Cole & Espinosa, 2008; Huang, et al., 2000). However,
recent studies have shown that Latino and African American students are now enrolling in
STEM at similar rates to Asian and White students, though they are withdrawing from these
majors at a higher rate (Anderson & Kim, 2006; Hanson, 2004; Smyth & McArdle, 2000).
Although the enrollment gap may be narrowing for students of all races and ethnicities,
socio-economic status and family income continue to be a persistent explanation for nonenrollment in the STEM fields. Research has consistently found that family income is a strong
predictor of choosing STEM (Morhman, 1987; Porter, 1990; Rotberg, 1990; Seymour & Hewitt,
1994; Wilson, 1990). Some research suggests that students from lower SES backgrounds are
more likely to select fields which promise more lucrative financial possibilities (such as
business) or which allow them to contribute to their community (such as education) (Green,
1989).
Race and class are especially important to our understanding of the changing gender gap
in STEM because women in college are more likely than men to come from underrepresented
racial/ethnic groups, and are increasingly more likely to hail from lower-income families (Sax,
2008). We question whether changes in the demographic backgrounds of college women and
men have contributed to shifts in the gender gap in STEM.
Academic performance and self-confidence. While women choose to major in STEM
fields for a variety of reasons, research has shown that high school grades and academic selfconfidence are consistent predictors of STEM enrollment (Ellis & Eng, 1991; Adelman, 1991;
Sax (1994, 2001). STEM majors generally have higher grades in high school than other students
(Green, 1989; White, 1992), and women generally have higher grades in high school than men
Women’s Representation in STEM, 9
(Sax, 1994; 2001; 2008). Additionally, women who enroll in STEM courses and majors in
college are as academically successful as men (Huang, Taddese & Walter, 2000). Despite this,
women continue to opt out of STEM-related majors and those who enroll in them leave these
majors at a higher rate than men (Margolis, Fisher, & Miller, 2000; Seymour & Hewitt, 1994),
leaving researchers to suspect that self-confidence may be a more important factor than GPA in
the decision to attempt a STEM degree (Adelman, 1991; Ascher, 1985; Astin & Sax, 1996;
AAUW, 1992; Margolis & Fisher, 2002; Cohoon, 2001; Sax, 2008?, Yasuhara, 2005).
In the 1990s, The American Association of University Women suggested that girls’
confidence erodes in adolescence as they experience classroom biases and stereotypes. Though
women who choose STEM usually do so because they feel they are good at math and science
(Ellis & Eng, 1991), girls are less likely to believe that they are good at these subjects than boys
are (Adelman, 1991). This is also true for women of color, who consistently express lower selfconfidence in math and science than their male counterparts. Some of the loss in self-confidence
may be evident in more recent findings such as those of Cole (1997), Cohoon (2001), and
Margolis and Fisher (2002), who found that girls perform increasingly worse than boys on
standardized math and science tests as they matriculate through high school.
In college, women tend to rate their academic abilities, and especially their math and
science abilities, lower than men’s (Sax, 2008). Gender differences in self-confidence hold true
even for women who have similar academic achievement as their male counterparts (Sax, 2008;
Zhao, Carini, & Kuh, 2005). A lack of confidence in their academic abilities generally, and math
and science abilities, specifically, may further discourage women from pursuing majors in
STEM. Based on these findings, we would expect that high school GPA would be an important
Women’s Representation in STEM, 10
predictor of STEM enrollment across time, but that once GPA is controlled, self-confidence
would remain a predictor and would contribute to explaining the persistent gender gap in STEM.
Perceptions of science careers. Over time, young women have often held a perception
that STEM fields do not have a real-life or practical application (Baker & Leary, 1995; Sax,
1994; 2001; Thompson & Windschitl, 2005). Research conducted on college students in the
1980s found that women tended not to perceive scientific careers as a vehicle for improving the
human condition; thus many of the most scientifically promising women—especially those with
a social change orientation—opted for other fields of study (Sax, 1994, 2001). Baker and Leary
(1995) found that young girls distinguished between life-scientists (such as those in biologyrelated fields) and “scientist-scientists” (p.18) (such as those in chemistry- or physics-related
fields). By and large, girls identified the latter category as particularly masculine or appropriate
for boys. Similar to findings by Sax (2001) and Thompson and Windschitl (2002), the girls in the
Baker and Leary (1995) study suggested that their interest in the life sciences had to do with
helping others and caring for people and animals. More recently, researchers have found that
women are less likely to pursue STEM fields, and computer science specifically, because their
classes do not emphasize how these fields might have an impact on society (Weinberger, 2004;
Wilson, 2003). In fact, the narrowing of the gender gap in the sciences is primarily due to an
increase in women’s enrollment in biology, a field where women see more application to societal
problems (Blickentsaff, 2005; NSF, 2008). Women tend to report that biology applies more
directly to human problems and societal improvements (Miller, Rosser, Benigno, & Zieseniss,
2000; Sax, 1995). If women today have become more likely to see the link between scientific
study and societal improvements (as might be fostered by the recent expansion of fields such as
Women’s Representation in STEM, 11
genomics and biotechnology), then we might expect that activist orientations would not deter
women from STEM, and might even recruit women into these fields.
Shifting Explanations for the Gender Gap in STEM?
In this study, we question whether there have been changes in the factors contributing to
the gender gap in STEM (including many that are described above). In other words, are shifts in
the gender composition of STEM majors due to compositional shifts in the populations of
women and men in college (with relatively constant determinants of STEM major), or have
changes in the gender composition of STEM been due to a reconfiguration of the determinants of
STEM major selection?
One reason to expect change over time is based on the contrasting experiences of
different generations. A number of observers have noted that the pioneering generation of
women entering science or other formerly male-dominated fields is distinctive (Charlton, 1997;
Kolstedt, 1978). Pioneering women have very strong academic records, high self confidence, and
considerable determination to prove that they (and other women) can succeed. Subsequent
generations of women may feel that they have less to prove, and have the luxury of making
personal choices in a less contentious environment (Bickel and Brown, 2005).
A second reason to expect change is that number of important attributes of college
students have changed since the 1970s. Moreover, as women have become a majority of
undergraduates, the relative position of men and women has shifted on a number of key
indicators. For example, the gender gap in the priority placed on financial success has narrowed
considerably. During the 1970s, female college students expressed considerably less interest in
being very well off financially than did their male classmates. By 2006, this gap had almost
disappeared (Sax, 2008).
Women’s Representation in STEM, 12
Another shift relates to students’ socioeconomic backgrounds. When women were a
minority of college students, they were disproportionately drawn from affluent families. Now
that women are the majority of college students, they are on average less well off than their male
counterparts (Sax, 2008).
Despite some shifts in gender disparities, several other gender differences have endured
in recent decades. For instance, women continue to earn higher grades in high school but
nonetheless rate their own academic performance lower than do their male counterparts.
Women’s high grades in high school may enable them to pursue the academically challenging
STEM fields, while their own low academic self-assessment may point in the opposite direction.
We examine whether these factors have changed the impetus behind gender segregation of
college majors during this period.
Methods
Data and Sample
Data are drawn from a national study of first-year college students conducted by UCLA’s
Higher Education Research Institute. All subjects completed the Cooperative Institutional
Research Program (CIRP) Freshman Survey, which asks entering college students about their
background characteristics, attitudes, values, educational achievements, and future goals. The
CIRP is administered annually by hundreds of colleges and universities nationwide. Given the
wide range of questions included on the survey, the CIRP data are uniquely suited for examining
the role of attitudes, values and social background on the shifting gender gap in STEM major
choice.
To gain a longitudinal perspective of gender and STEM major choice, this study focuses
exclusively on CIRP respondents in the following years: 1976, 1986, 1996 and 2006. These
Women’s Representation in STEM, 13
years were selected because they contained the most consistent core of survey items at evenlyspaced decade intervals. (Raw data from the earliest survey years (1966-1970) no longer exist.)
Table 1 shows the sample counts for men and women in each year, with a total of 883,498
students across the four selected years. For the purpose of analysis, the data are weighted to
compensate for bias associated with institutional participation in the CIRP Surveys. This
weighting scheme, detailed in Pryor, et al. (2007), utilizes institutional control, type, selectivity
and enrollment to make the sample representative of the population of first-time full-time college
students at all four-year institutions in the United States at the time of the survey.
Variables
The study is focused primarily on students’ choice of STEM major, which includes
biological sciences, computer science, physical sciences, mathematics/statistics, and engineering.
For descriptive analyses, these STEM subfields are considered separately, but for regression
analyses, STEM major is a dichotomous dependent measure indicating intended major in any
STEM field vs. all other fields.
Independent variables used the regression analyses include many of the factors that have
been identified as predictors of STEM major in prior studies: student demographics (including
gender, race/ethnicity, family income, parental education); measures of academic ability and
confidence (including GPA and self-rated abilities); academic goals (including degree
aspirations, reasons for attending college, and longer-term interest in science); and affective
orientation (including personality and value orientations such as the Status Striver, Artist, and
Social Activist factors that have been used in prior studies of college students (Astin, 1993; Sax,
2008)). Means and standard deviations for all independent variables are shown in Appendix A.
Women’s Representation in STEM, 14
Analyses
To assess the gender gap in STEM major aspirations over time, Chi-square analysis
compared the proportion of women and men selecting particular STEM majors, as well as STEM
majors as a whole, within each year (1976, 1986, 1996, and 2006). A cutoff of p < .01 was used
to determine statistical significance.
Next, logistic regression analyses were conducted for each year on combined samples of
women and men using STEM major as the dependent variable. Within each regression, gender
(1=male, 2=female) was force-entered at the first step so as to indicate the simple correlation
between gender and STEM major. The regression coefficient for gender was then monitored at
each step in the regression in order to see which independent variables reduced (or sometimes
magnified) the relationship between gender and interest in STEM. Independent variables that
served to significantly alter (t.01 > 2.326) the strength of the regression coefficient for gender can
be viewed as possible explanations for the existing gender difference. A comparison across
survey years reveals whether the forces that contribute to gender differences in STEM have
shifted over the years.
In most cases, the entry of independent variables caused the coefficient for gender to
become smaller, a dynamic referred to as a “normal” effect (Astin, 1991). Normal effects occur
when two independent variables share variance in predicting the dependent variable. In this case,
some portion of the gender difference in choice of STEM major can be explained by gender
differences in the newly-entered independent variable. Identifying such explanatory variables
yields information on which student characteristics account for the observed gender difference in
STEM.
Women’s Representation in STEM, 15
In some cases, the standardized regression coefficient for gender becomes significantly
larger when another independent variable enters the equation. This condition is known as a
“suppressor effect” (Astin, 1991) and occurs under one of two conditions: (1) the two
independent variables each have a positive relationship with the dependent variable, but a
negative relationship with each other; or (2) the two independent variables have opposite
relationships with the dependent variable (one positive, one negative), and a positive relationship
with each other. In the context of this study, a suppressor effect suggests that we would expect
gender differences in STEM major selection to have been even larger if not for the relationship
between gender and the newly-entered independent variable. This generally means that students
of the gender more likely to pursue STEM (i.e., men) have scores that are lower on an
independent variable that positively predicts STEM selection or have scores that are higher on an
independent variable that negatively predicts STEM selection. As discussed by Astin and Dey
(1996), this approach to monitoring step-by-step changes in regression coefficients enables an
understanding of direct and indirect effects that might otherwise be assessed via path analytic
techniques, but with the added benefit of being able to address a large number of variables
simultaneously. Use of this approach to reveal direct and indirect effects is demonstrated in Astin
and Denson (2005) and Sax and Harper (2007).
Results
Descriptive results
Table 2 displays the proportions of women and men aspiring to specific STEM majors in
1976, 1986, 1996, and 2006. When looking at the aggregate percentages across all STEM
majors, we find that overall interest in STEM fields has changed relatively little between 1976
and 2006. Among first-year students across all years, fewer than one in three men and fewer than
Women’s Representation in STEM, 16
one in six women express interest in STEM fields. The overall paucity of interest in STEM fields
is the first empirical finding from our study that is cause for concern.
Despite the overall stagnation in interest in STEM fields, there has been a modest degree
of narrowing of the gender gap. Between 1976 and 2006, men’s interest in STEM fields declined
from 31.9 percent in 1976 to 27.9 percent in 2006. In contrast, women’s interest inched up, from
12.6 percent in 1976 to 14.3 percent in 2006. Expressed in terms differences in percentages,
there was a 19.3 percentage point difference in favor of men in 1976, compared with a 13.6
percentage point different in favor of men in 2006. In odds ratio terms, men’s odds of pursuing
STEM fields fell from 3.25 times that of women in 1976 to 2.32 times in 2006.
The decade-by-decade pattern of change, while generally modest in size, is a bit more
complex: in two periods, 1976-86 and 1996-2006, the interest in STEM fields declined for both
men and women; while between 1986 and 1996, interest grew for men and especially for
women.
Thus, the nature of change has varied across the decades we examined. Moreover,
the pattern of change varies across specific STEM subfields. In the biological sciences, for
example, a small gender differential favoring men in has been replaced by an equivalent
differential now favoring women. Gender gaps favoring men in aspirations for other fields have
either remained largely the same (e.g., engineering and math/statistics), have narrowed
somewhat (e.g., physical sciences) or have widened over time, even if inconsistently (e.g.,
computer science). Thus, additional research beyond what we are conducting here will be
needed to differentiate trends within the STEM fields.
Women’s Representation in STEM, 17
Regression Results
To what extent are the gender differences in STEM aspirations explained by men’s and
women’s differing characteristics? Table 3 displays the logistic regression coefficient for gender
at two steps: (1) when only gender is in the model, and (2) after all other student characteristics
have been controlled. The table shows that the predictive power of gender has generally
diminished over time, with the unstandardized regression coefficient for gender falling from a
high of -1.20 in 1986 to a lower of -.85 in 2006. In other words, the gender gap in STEM has
narrowed over time, but remains significant even when accounting for differences in men’s and
women’s demographic characteristics, academic backgrounds and affective orientations.
The table also reveals that the role of student characteristics in explaining the gender gap
has diminished over time. For example, in 1976 the initial predictive power of gender (b = -1.16)
is reduced by 17.4% (to b = -1.01) after controlling for all predictors in the model. The extent to
which student predictors account for the gender gap fell from 17.4% in 1976 to 10.7% in 1996,
suggesting that the reasons for women’s underrepresentation in STEM included in this model
have become less salient forces over time. Interestingly, between 1996 and 2006 the extent to
which these student characteristics subsumed the effect of gender grew slightly (from 10.7% to
12.9%). Because we are limited to only four time points, it is difficult to know from these data
whether this reflects a reversal of the trend. However, we can generally conclude that the student
characteristics predicting selection of STEM major account for a fairly small and generally
declining share of the gender gap in STEM over the past three decades.
Table 4 presents key findings related to the question of whether there have been shifts in
the relative role of specific student characteristics in explaining the gender gap. The table lists
each independent variable whose entry into the regression caused the predictive power of gender
Women’s Representation in STEM, 18
to change significantly (p<.01), shows the direction of effect for that independent variable, and
indicates whether that variable’s entry caused the predictive power of gender to become smaller
(“normal” effect) or larger (“suppressor” effect). Independent variables from three of the four
categories (all except demographics) were shown to influence the predictive power of gender in a
statistically significant way. While discussion focuses primarily on these variables, occasional
reference is made to the direct effects of independent variables, regardless of whether they
influenced the predictive power of gender. Logistic regression coefficients for all variables
within each year are provided in Appendix A.
Student demographics. In each cohort, race/ethnicity, family income and parents’
education contributed to the selection of STEM major. Though the inclusion of these
demographic characteristics did not weaken the predictive power of gender in any of the cohorts
examined, their unique predictive power is worth discussing. First, consistent with prior
research, socioeconomic indicators of parental education and family income tended to positively
predict STEM major choice. However, the direction of effect for income in 1986 was negative,
suggesting that, for that year, lower income students were more likely to pursue STEM. This
particular finding is at odds with prior research documenting the underrepresentation of lowerincome students in STEM fields (Morhman, 1987; Porter, 1990; Rotberg, 1990; Seymour &
Hewitt, 1994; Wilson, 1990), and thus highlights an area for further investigation.
When it comes to race/ethnicity, the predictive power of Asian-American is consistently
positive, which is in line with historical trends. Interestingly, being African American or Latino
also predicted STEM major choice; this is at odds with some research (especially early research)
documenting the underrepresentation of these groups in STEM. Further exploration of
regression results shows that in 1976 and 1986, the positive effect of being Latino or African
Women’s Representation in STEM, 19
American emerges after controlling for parental education and high school grades (i.e., a
suppressor effect). In other words, though students from these groups were underrepresented
among STEM aspirants, they would have been more likely to select STEM if it were not for their
lower grades and parental education levels, factors which themselves assist in the selection of
STEM. In 1996 and 2006, however, the positive coefficients do reflect a greater propensity of
African American and Latino students to select STEM, even before other variables are
controlled. Thus, findings from the past decade support research showing a more recent
narrowing of the racial/ethnic gap in STEM (Anderson & Kim, 2006; Hanson, 2004).
Academic ability. In each of the four cohorts, high school grades positively predicts the
choice of STEM major and, upon entering the equation, significantly alters the predictive power
of gender. In each case, the negative predictive power of gender becomes larger when high
school GPA is included in the model, indicating a suppressor effect. An interpretation of this
finding is that women would be even less likely to major in STEM if it were not for the fact that
they consistently earn higher grades in high school.
Students’ self-rated abilities also help to account for the gender gap in selection of STEM
major. For all four years, the predictive power of gender is reduced by the inclusion of self-rated
academic ability and self-rated mathematical ability. Normal effects are present in each of these
cases, and indicate that women’s lower likelihood of selecting a STEM major is partially
attributable to their lower self-ratings in academic and mathematical ability. Similar results are
found for self-rated intellectual self-confidence, though only in the past two time points (1996
and 2006). Intellectual self-confidence operates in a similar fashion as self-rated academic and
writing ability, such that women’s lower levels of intellectual self-confidence contribute to their
decision not to major in STEM.
Women’s Representation in STEM, 20
An emerging force contributing to the gender gap in STEM major selection is selfassessed writing ability. However, in the case of writing ability, the coefficient for gender
becomes smaller because women have more confidence in their writing skills than do men. In
other words, women’s greater confidence in their writing skills is a trait which now contributes
to their decision to select fields other than STEM.
Academic goals. Among academic-related goals, only two alter the predictive power of
gender. The first is attending college to learn about one’s interests, which, upon entry into the
regression, contributes to a small increase in the negative predictive power of gender (i.e., a
suppressor effect). This finding suggests that in 1976 only, women would have been even less
likely to major in STEM if it were not for the fact that they are more likely than men to attend
college in order to learn about what interests them. In the years since 1976, it remains true that
women place more value than men do on the intellectual value of college, however this fact no
longer differentiates men from women in the selection of STEM major. This is likely due to the
fact that the gender difference on this reason for college has narrowed over time.
The second academic goal to influence the predictive power of gender is the student’s
commitment to making a theoretical contribution to science. The entry of this variable weakens
the predictive power of gender in each of the four study years. This can be explained by the fact
that women consistently rank scientific goals lower than men, so when the commitment to
science is entered into the model, it accounts for a portion of the gender gap in STEM major
selection.
Affective orientation. Across several of the study years, gender differences in students’
personality type contribute to the gender gap in STEM major selection. The only personality
indicator that influences the predictive power of gender at all four time points is the Social
Women’s Representation in STEM, 21
Activist (i.e., the importance students assign to helping others in difficulty, influencing social
and political values, and participating in community action). Women consistently score higher
than men on the Social Activist type, but an activist orientation negatively predicts interest in
STEM majors. Because women have stronger activist orientations than men, the entry of the
Social Activist factor significantly reduces the predictive power of gender. In other words,
women’s generally lower STEM aspirations can be explained in part by their stronger
commitment to social activism.
A similar dynamic occurs when the Artist personality enters the model. In two study
years (1976 and 2006), the predictive power of gender is reduced when accounting for students’
artistic orientations. In each case, women’s stronger artistic orientations relative to men
contribute their tendency to choose non-STEM fields of study. Because this effect is seen only
at the beginning and end of the thirty-year time frame, it is difficult to know whether this is an
emerging (or re-emerging) trend.
In three of the four years (all except 1996), the predictive power of gender becomes
larger with the entry of the Status Striver type (i.e., students with greater concern for making
money and achieving professional status and authority). This occurs because students with status
orientation are less likely to pursue STEM majors, and women score lower than men on statusoriented goals. Put another way, we would expect the gender gap in STEM aspirations to be
even larger if women placed as much emphasis as men do on money and status.
Finally, another suppressor effect is found with the entry of self-rated leadership ability in
2006 only. Here, the dynamic is such that women score lower than men on leadership selfconfidence, a variable which itself negatively predicts selection of STEM major. The
interpretation for this is similar as for the Status Striver orientation, such that the
Women’s Representation in STEM, 22
underrepresentation of women in STEM would be even more severe if women’s leadership
confidence matched that of men’s.
Limitations
While this study contributes new knowledge about whether and how the determinants of
the STEM gender gap have shifted over time, it is important to acknowledge several key
limitations. First, the study could not include all possible determinants of STEM major
selection. Due to available data, we focused on the role of demographics, academic ability,
academic self-confidence, academic goals, and various indicators of personality and values.
However, we did not have data on factors shown to be important in other research on STEM
selection, especially structural factors such as gender-role socialization and experiences in K-12.
Further, the explanatory power of these variables declined over the years in our study, suggesting
that other more salient forces have emerged, but remain unaccounted for in our study.
Second, because these data only reflect students from baccalaureate institutions, the
experiences of community college students are unaccounted for. This is especially important for
a study on gender disparities because women are over-represented in community colleges, where
the gender distribution in STEM may be quite different than in four-year institutions. This is an
important consideration for future research, which would ideally consider both two- and fouryear student major aspirations.
Third, the study is limited to intended major among students at the point of college entry.
Considering that major choice can and does change over time, longitudinal data tracking major
selection over the course of college would be ideal. Such data may exist given the follow-up
surveys administered in conjunction with the Freshman Survey, but the longitudinal samples are
smaller and typically far less representative than those yielded by the Freshman Survey. Further,
Women’s Representation in STEM, 23
compatibility between different follow-up survey instruments is much more difficult to achieve
than with the Freshman Survey.
Finally, the selection of STEM major is considered as dichotomous in this study, with
students either selecting STEM or selecting another field. However, the fact is that not all STEM
fields attract students for the same reasons, and there may be important gender differences in
those reasons. Thus, the present study points the way for future research to examine this very
question for specific STEM subfields. In particular, the influx of women into biological science
fields raises the question of whether we might uncover unique shifts in the student characteristics
that differentiate women’s and men’s selection of the biological sciences as opposed to the more
persistently male-dominated STEM fields.
Summary
At a time of continued underrepresentation of women in STEM, this paper raises two
primary questions. First, to what extent has the gender gap in selection of STEM majors shifted
over the years 1976-2006 for STEM as a whole and for specific subfields? And second, have the
determinants of the gender gap in STEM shifted over this time period?
Results for the first question suggest that overall interest in STEM has slowed, and that
there has been only modest narrowing of the gender gap. When considering specific STEM
subfields, gender disparities in historically male-dominated majors like engineering and
math/statistics have remained fairly constant over time, with engineering exposing the most
severe underrepresentation of women. The gender gap in aspirations for other STEM fields has
shown either a widening (computer science) or a narrowing (physical science), with a disparity
that has remained significant over time. Thus, despite tremendous educational and professional
progress for women during this time period, and significant efforts to recruit women into
Women’s Representation in STEM, 24
engineering, and physical and computer sciences (e.g., AWIS (Association for Women in
Science) and WEPAN (Women in Engineering Proactive Network)), women have not made
significant inroads in the most historically male-dominated STEM fields.
The most notable shift in STEM aspirations has occurred in the biological sciences,
where a small gender gap formerly favoring men now favors women. As discussed above, the
unique trajectory of biology relative to the other STEM fields may reflect that field’s more
obvious connection to careers (such as medicine) that help to improve lives, a feature that may
weigh heavily in attracting women. As suggested earlier, future research will need to consider
the gender gap in biology as distinct from other STEM fields.
As for the second question—whether the reasons for the gender gap may have changed
over time—results suggest a modest degree of stability among student characteristics
contributing to the gender differential in STEM. Chief among these is self-confidence. Over the
three-decade span of this study, despite the fact that women earn higher grades than men, they
continue to report lower self-assessments of their academic mathematical abilities, a fact which
contributes to women’s underrepresentation in STEM. Other consistent explanations for the
gender gap in STEM include women’s relatively weaker scientific orientations and stronger
activist orientations.
Over time, we have also witnessed the emergence of new factors that influence the
gender gap in STEM. These include intellectual self-confidence (a trait which is lower among
women but predicts interest in STEM) and self-rated writing ability (a trait which is stronger
among women but weakens interest in STEM). However, each of these variables explains only a
very small portion of the gender gap in STEM relative to that explained by women’s lower math
confidence and scientific orientations.
Women’s Representation in STEM, 25
Discussion
This study has identified several traits that consistently predict students’ interest in STEM
majors and also contribute to the longstanding gender gap in STEM due to gender difference on
these same characteristics. Most salient are the role of students’ self-confidence and orientations
toward science and social change.
Self-confidence
Gender differences in self-confidence have been documented for many years and have
consistently contributed to women’s decision not to pursue STEM majors in college (Sax, 1994,
2001, 2008). At a time when women have made such progress in college representation and
achievement, it is surprising that women continue to be plagued by lower self-confidence in
academic and mathematical, even when their grades and performance on standardized tests
would suggest otherwise. Indeed, an analysis of Freshman Survey trends over the past four
decades shows a substantial and unwavering gender gap in mathematical self-confidence, and a
gap in academic self-confidence that has actually widened over the years.3 In other words, while
self-confidence is a critical determinant of students’ selection of STEM, women’s academic and
mathematical self-confidence has not improved relative to men’s in recent decades.
Future research ought to tackle the confidence issue head on by identifying programs and
strategies that encourage young women to believe more strongly in their skills and abilities, and
to identify experiences that dampen women’s mathematical and overall academic selfconfidence. Some context is offered by Sax (2008), who identified experiences that promote the
development of various forms of self-confidence for women during college. Specifically,
women’s development of mathematical self-confidence benefits from activities such as studying
collaboratively with other students, tutoring other students, and feeling that faculty took them
3
Author’s calculation based on Freshman Survey data 1971-2009.
Women’s Representation in STEM, 26
seriously in the classroom. Improvements in women’s broader academic and intellectual selfconfidence also resulted from experiences such as tutoring other students and having positive
and supportive interactions with faculty. Women also experience unique academic/intellectual
self-confidence benefits from taking honors courses and doing independent study with
professors. Taken together, these examples from the college years suggest that women’s math
and academic confidence is enhanced by activities that engage them intellectually with students
and with faculty, especially in ways that are challenging and supportive. Maximizing such
experiences for women prior to and during college may be one effective strategy for reducing the
gender gap in STEM.
Scientific orientations
As this study has shown, women’s underrepresentation among STEM majors is explained
in part by their lower ratings on the goal of “making a theoretical contribution to science.”
Similar to the unwavering gender disparities in academic self-ratings, the significant gender gap
on this survey question has held steady for decades.4 We wonder whether this persistent gap in
scientific orientation is attributable—at least in part—to the way in which the question is
worded, with emphasis on “theoretical” rather than “practical” contributions to science.
It is also important to consider whether women’s lower stated scientific goals reflect the
enduring image of science as a masculine domain. Challenging this conception of science
remains an important educational and societal challenge. Research ought to address why, despite
decades of efforts to recruit young women into science, the masculine image remains.
Similarly, research needs to examine what practices in K-12 and beyond are effective at
dispelling the impact of gender socialization and encouraging women’s development of science
4
Author’s calculation based on Freshman Survey data 1971-2009.
Women’s Representation in STEM, 27
identities. During the college years, Sax (2008) has identified several practices that strengthen
women’s scientific identities, including collaboration with other students and spending more
time engaged with faculty in challenging academic work (research opportunities, independent
study, honors courses). These reflect many of the same practices that encourage the
development of women’s academic and mathematical confidence, discussed earlier.
Social activist orientations
Students, and especially women, who care more about helping others and improving
society tend to opt for non-STEM fields. As discussed by many scholars (e.g., Baker & Leary,
1995; Sax, 1994; 2001; Thompson & Windschitl, 2005), often women do not view STEM fields
as having practical or societal relevance. Indeed, the persistent gender gap in STEM majors is
due in part to the fact that women continue to express stronger commitments to helping others
and influencing social change than men do, a fact which has held steady over time. 5
Thus, we are left with a persistent question of what can be done to alter the perception of
science as disconnected from societal contexts. More research is needed to uncover how such
perceptions are formed, and specifically the role that science curriculum, pedagogy, and even the
media can play in highlighting the innumerable ways in which scientific work benefits society
and the human condition. If more women understood the direct and indirect applications of
science to society, it is likely that the longstanding gender gap in STEM could be ameliorated.
In fact, the biological sciences are an interesting case in point. A central motivation for
pursuing medicine is to contribute positively to society by improving the human condition.
Perhaps it is women’s recognition of the societal relevance of the biological sciences that has
helped to reverse the gender gap in biology majors. This is an important question for research,
and one that we are undertaking in a separate study.
5
Author’s calculation based on Freshman Survey data 1971-2009.
Women’s Representation in STEM, 28
Conclusion
This study has shown that the gender gap in STEM remains strong, and that gender
differences in college students’ self-confidence, interests, and orientations continue to remain
salient explanations for at least part of this gap. Thus, efforts to reduce the gender gap in STEM
ought to focus on encouraging the development of self-confidence among young women
throughout K-12 and into college, challenging the persistent image of science as a masculine
domain, and encouraging all students (men and women) to more clearly recognize the
connections between science and society. However, these suggestions are not new, and the fact
that these forces continue to play a role suggests that efforts to recruit women into STEM fields
have not been successful at addressing the reasons for women’s underrepresentation in STEM.
Clearly, more work is needed to address students’ internal motivations and sense of self as root
causes of continued gender disparities in the sciences.
Women’s Representation in STEM, 29
References
Adelman, C. (1991) Women at thirtysomething: Paradoxes of attainment. Washington, DC: U.S.
Department of Education, Office of Educational Research and Development.
Agresti, A. & Finlay, B. (1997). Statistical methods for the social sciences (3rd ed.). New Jersey:
Prentice Hall, Inc.
American Association of University Women (AAUW). (1992). How schools shortchange girls.
Washington, D. C.: Author.
Anderson, E. L. & Kim, D. (2006). Increasing the success of minority students in science and
technology. Washington, D.C.: American Council on Education.
Ascher, C. (1985). Increasing science achievement for disadvantaged students. Washington,
D.C.: National Institute o Education. (ERIC: Document Reproduction Service No. ED
253 623).
Astin, A. W. (1991). Assessment for excellence: The philosophy and practice of assessment and
evaluation in higher education. Phoenix, AZ: The Oryx Press.
Astin, A. W. (1993). What matters in college? Four critical years revisited. San Francisco:
Jossey-Bass.
Astin, A. W. & Denson, N. (2005) Long-term effects of college on students' political
orientation. Los Angeles: Higher Education Research Institute, UCLA.
Astin, A. W., & Dey, E. (1996). Causal analytical modeling via blocked regression analysis
(CAMBRA): An introduction with examples. Los Angeles: Higher Education Research
Institute, UCLA.
Astin, H. S., & Sax, L. J. (1996). Developing scientific talent in undergraduate women. In C.
Davis, A. B. Ginorio, C. S. Hollenshead, B. B. Lazarus, P. M. Rayman, et al. (Eds) The
Women’s Representation in STEM, 30
equity equation: Fostering the advancement of women in the sciences, mathematics, and
engineering (pp. 96-121). San Francisco: Jossey-Bass.
Baker, D. & Leary, R. (1995) Letting girls speak out about science, Journal of Research in
Science Teaching, 32(1), 3–27.
Bickenstaff, J. C. (2005). Women and science careers: leaky pipeline or gender filter? Gender
and Education, 17(4), 369-386.
Bonous-Hammarth, M. (2000). Pathways to success: Affirming opportunities for science,
mathematics, and engineering majors. Journal of Negro Education, 69(1-2), 92-111.
Charlton, J. (1997). Clergywomen of the pioneer generation: A longitudinal study. Journal for
the Scientific Study of Religion 36(4): 599-613.
Cohoon, J. M. (2001). Toward improving female retention in the computer science major.
Communications of the ACM, 44(5), 108-114.
Cole, D. & Espinoza, A. (2008). Examining the academic success of latino students in science
technology engineering and mathematics (STEM) majors. Journal of College Student
Development, 49(4), 285-300.
Cole, N. S. (1997) The ETS gender study: How females and males perform in educational
settings (Princeton, NJ, Educational Testing Service).
Eccles, J. S. (1992, January). Girls and mathematics: A decade of research. Report presented to
the Committee on Equal Opportunities in Science and Engineering. Arlington, VA:
National Science Foundation.
Ellis, R., & Eng, P. (1991). Women and men in engineering. (Engineering Manpower Bulletin
107). Washington, D.C.: Engineering Workforce Commission of the American
Association of Engineering Societies.
Women’s Representation in STEM, 31
England, P. and Li, S. (2006). Desegregation stalled: The changing gender composition of
college majors, 1971-2002. Gender and Society 20(5): 657-677.
Espinosa, L. L. (2009). Pipelines and pathways: Women of color in STEM majors and the
experiences that shape their persistence. Unpublished Doctoral Dissertation, University
of California, Los Angeles.
Faludi, S. (1991). Backlash: the undeclared war against American women. New York: Crown.
Gerson, K. (2009). The unfinished revolution: How a new generation is reshaping family, work
and gender in America. New York: Oxford University Press.
Green, K. C. (1989). A profile of undergraduates in the sciences. The American Scientist, 78,
475-480.
Hanson, S. L. (2004). African American women in science: Experiences from high school
through the post-secondary years and beyond. NWSA Journal, 16(1), 96-115.
Hochschild, A. (1989). The second shift. New York: Avon Books.
Huang, G., Taddese, N., and Walter, E. (2000). Entry and persistence of women and minorities
in college science and engineering education (NCES 2000-601).
Huang P. M. & Brainard, S. G. (2001). Identifying determinants of academic self-confidence
among science, math, engineering, and technology students. Journal of Women and
Minorities in Science and Engineering, 7, 315-337.
Jacobs, J. A. (1995). Gender and academic specialties: Trends among recipients of college
degrees during the 1980s. Sociology of Education, 68(2), 81-98.
Jacobs, J. A. (1996). Gender inequality and higher education. Annual Review of Sociology, 22,
153-85.
Women’s Representation in STEM, 32
Kahle, J. B. (1996). Opportunities and obstacles of science education in the schools. In C. Davis,
A. B. Ginorio, C. S. Hollenshead, B. B. Lazarus, P. M. Rayman, et al. (Eds) The equity
equation: Fostering the advancement of women in the sciences, mathematics, and
engineering (pp. 57-95). San Francisco: Jossey-Bass.
Kahle, J. B., & Lakes, M. K. (1983). The myth of equality in science classrooms. Journal of
Research in Science Teaching, 20(2), 131-140.
Kinzie, J. (2007). Women’s paths in science: A critical feminist analysis. New Directions for
Institutional Research, 133, 81-93.
Kohlstedt, S. G. (1978). In from the periphery: American women in science, 1830-1880. Signs
4(1): 81-96.
Lee, M. A. and Mather, M. (2008). U. S. labor force trends. Population reference
Bureau, Population Bulletin, 63:2.
Margolis, J., & Fisher, A. (2002). Unlocking the clubhouse: Women in computing. Cambridge,
MA: MIT Press.
Margolis, J., Fisher, A., & Miller, F. (2000). The anatomy of interest: Women in undergraduate
computer science. Women’s Studies Quarterly, 28(1/2), 104-127.
Miller, P. H., Rosser, S. V., Benigno, J. P. & Zieseniss, M. L. (2000). A desire to help others:
Goals of high-achieving female science undergraduates. Women’s Studies Quarterly,
28(1/2), 128-142.
Mohrman, K. (1987). Unintended consequences of federal student aid policies. The Brookings
Review, Fall, 24-30.
National Center for Education Statistics. (2009). Digest of education statistics, Table 268.
Retrieved August 10, 2010 from http://nces.ed.gov/programs/digest/d09/tables.
Women’s Representation in STEM, 33
National Science Foundation, Division of Science Resources Statistics (2008). Science and
engineering degrees: 1966–2006 (Detailed Statistical Tables NSF 08-321). Arlington,
VA. Retrieved March 24, 2009 from
http://www.nsf.gov/statistics/nsf08321/pdf/nsf08321.pdf.
Porter, O. (1990). Undergraduate completion and persistence at four-year colleges and
universities: Completers, persisters, stop-outs, and drop-outs. Washington, D. C.:
Naitonal Institute of Independent Colleges and Universities.
Pryor, J. H., Hurtado, S. Saenz, V., Santos, J., & Korn, W. S. (2007). Forty year trends. Los
Angeles: Higher Education Research Institute, UCLA.
Rotberg, I. C. (1990). Sources and reality: The participation of minorities in science and
engineering education. Phi Delta Kappan, 71, 672-679.
Sax, L. J. (1994). Retaining tomorrow’s scientists: Exploring the factors that keep male and
female college students interested in science careers. Journal of Women and Minorities in
Science and Engineering, 1, 45-61.
Sax, L. J. (1995). Predicting gender and major-field differences in mathematical self-concept
during college. Journal of Women and Minorities in Science and Engineering, 1(4), 291307.
Sax, L. J. (2001). Undergraduate science majors: Gender differences in who goes to graduate
school. The Review of Higher Education, 24(2), 153-172.
Sax, L. J. (2008). The gender gap in college: Maximizing the developmental potential of women
and men. San Francisco: Jossey-Bass.
Women’s Representation in STEM, 34
Sax, L. J. & Harper, C. E. (2007). Origins of the gender gap: Pre-college and college influences
on differences between men and women. Research in Higher Education, 48(6), 669694.
Seymour, E. & Hewitt, N. M. (1994). Talking about leaving: Why undergraduates leave the
sciences. Boulder, CO: Westview Press.
Singh, K., Allen, K.R., Scheckler, R., & Darlington, L. (2007). Women in computer-related
majors: A critical synthesis of research and theory from 1994 to 2005. Review of
Educational Research, 77(4), 500-533.
Smyth, F. L. & McArdle, J. J. (2004). Ethnic and gender differences in science graduation at
selective colleges with implications for admission policy and college choice. Research in
Higher Education, 45(4), 353-381.
Sonnert, G. (with Holton, G). (1995). Who succeeds in science? The gender dimension. New
Brunswick, NJ: Rutgers University Press.
Spelke, E. S. (2005). Sex differences in intrinsic aptitude for math and science? A critical review.
American Psychologist 60(9), 950-958.
Thompson, J. J., & Windschitl, M. (2005). "Failing girls": Understanding connections among
identity negotiation, personal relevance, and engagement in science learning from
underachieving girls. Journal of Women and Minorities in Science and Engineering,
11(1).
Thorne, B. (1993) Gender play. Girls and boys in school. New Brunswick, NJ, Rutgers
University Press.
Turner, S. E. and Bowen, W. G. (1999). Choice of major: The Changing (unchanging) gender
gap. Industrial and Labor Relations Review, 52(2), 289-313.
Women’s Representation in STEM, 35
U. S. Bureau of the Census (2010). Retrieved on August 10, 2010 from
http://www.infoplease.com/ipa/A005062.html.
U. S. Department of Education (1972). National longitudinal survey of high school seniors of
1972 (NLS-72). Washington, D. C.: Author.
U. S. National Center for Health Statistics (2008). Births, Marriages, Divorces and Deaths:
Provision Data for 2007. National Vital Statistics Reports, 56(21).
Vetter, B. M. (1996). Myths and realities of women’s progress in the sciences, mathematics, and
engineering. In C. Davis, A. B. Ginorio, C. S. Hollenshead, B. B. Lazarus, P. M.
Rayman, et al. (Eds) The equity equation: Fostering the advancement of women in the
sciences, mathematics, and engineering (pp. 29-56). San Francisco: Jossey-Bass.
Walford, G. (1981) Tracking down sexism in physics textbooks, Physics Education, 16, 261–
265.
Weinberger, C. J. (2004). Just ask! Why surveyed women did not pursue IT courses or careers.
IEEE Technology and Society Magazine, 23(2), 28-35.
White, P. E. (1992). Women and minorities in science and engineering: An update. Washington,
D.C.: National Science Foundation.
Wilson, F. (2003). Can compute, won’t compute: Women’s participation in the culture of
computing. New Technology, Work, and Employment, 18(2), 127-142.
Wilson, R. (1990). Only fifteen percent of students graduate, a new study finds. Chronicle of
Higher Education, February 21, 1990; A-1, 1-42.
Wyer, M. (2003). Intending to stay: Images of scientists, attitudes toward women, and gender
influence on persistence among science and engineering majors. Journal of Women and
Minorities in Science and Engineering, 9, 1-16.
Women’s Representation in STEM, 36
Yasuhara, K. (2005). Choosing computer science: Women at the start of the undergraduate
pipeline. Paper presented at the 2005 American Society for Engineering Education
Annual Conference & Exposition.
Xie, Y. and Shauman, K. (2003). Women in Science: Career Processes and Outcomes.
Cambridge, MA: Harvard University Press.
Zhao, C.M., Carini, R.M., & Kuh, G.D. (2005). Searching for the peach blossom Shangri-La:
Student engagement of Men and Women SMET majors. The Review of Higher
Education, 28(4), 503-525.
Women’s Representation in STEM, 37
Table 1. Student and Institutional Counts, by Year and Gender
Sample Counts for
Survey Year
Women
Men
# of Institutions
1976
91,355
92,854
305
1986
99,743
87,540
316
1996
133,471
107,094
445
2006
153,579
117,862
393
Women’s Representation in STEM, 38
Table 2. Proportion of Each Gender Choosing STEM Major by Year +
1976
1986
1996
M
Intended Major
+
W
M
W-M
v. W
Biological Science
7.2
8.9
-1.8
Engineering
2.0
15.0
Mathematics or Statistics
1.2
Physical Sciences
2006
M
W
M
W-M
**
4.5
4.6
-0.1
-13.0
**
3.2
17.9
-14.7
1.6
-0.5
**
0.9
1.1
1.7
5.2
-3.5
**
1.2
Computer Science
0.6
1.2
-0.5
**
STEM Total
12.6
31.9
-19.3
M
v. W
W
M
8.9
**
-0.2
2.9
1.2
11.0
M
W-M
v. W
W
M
7.3
1.7
**
9.2
3.4
17.3
-13.9
**
**
0.6
0.7
-0.1
-1.7
**
1.7
2.6
2.6
-1.4
**
1.2
29.2
-18.2
15.9
Chi-square tests used to compared differences between men/women within each year: *p<.01, **p<.001
W-M
v. W
7.3
1.9
**
2.4
14.6
-12.2
**
*
0.7
1.0
-0.3
**
-0.8
**
1.9
3.0
-1.1
**
4.6
-3.5
**
0.3
2.1
-1.8
**
32.5
-16.6
14.3
27.9
-13.6
Women’s Representation in STEM, 39
Table 3. Changes in the Predictive Power of Gender When Controlling for Student Characteristics, by Year
Unstandardized logistic regression
coefficient for gender
1976
1986
1996
2006
Step 1: When gender is sole predictor
-1.16
-1.20
-.93
-.85
Step 25: After controlling for student characteristics
-1.01
-1.01
-.83
-.74
% reduction in predictive power of gender
17.4%
15.8%
10.7%
12.9%
Women’s Representation in STEM, 40
Table 4. Variables Influencing the Predictive Power of Gender on STEM Major, by Year
Direction of effect for
How the variable affects the predictive power of gender b
Entering Variables a
Entering Variable
1976
1986
1996
2006
Academic Ability
High school GPA
+
Suppressor
Suppressor
Suppressor
Suppressor
Self-rated academic ability
+
Normal
Normal
Normal
Normal
Self-rated mathematical ability
+
Normal
Normal
Normal
Normal
Self-rated writing ability
Normal
Normal
Academic Goals
Reason for college: To learn about
+
Suppressor
interests
Goal: Make a theoretical contribution to
science
+
Normal
Normal
Normal
Affective Orientation
Artist Personality
Normal
Normal
Status Striver Personality
Suppressor
Suppressor
Suppressor
Social Activist Personality
Normal
Normal
Normal
Normal
Intellectual self-confidence
+
Normal
Normal
Self-rated leadership ability
Suppressor
a
This table only lists variables that significantly (t.01 > 2.326) altered the predictive power of gender in at least one year.
b
Cells are blank if the variable did not alter the predictive power of gender in that particular year.
Women’s Representation in STEM, 41
Appendix A: Descriptive Statistics and Regression Results
Table A1. Independent Variables by Year by Gender+
1976
Men
Variables
1986
Women
Mean
SD
Mean
Men
SD
Mean
1996
Women
SD
Mean
Men
SD
Mean
2006
Women
SD
Mean
Men
SD
Mean
Women
SD
Mean
SD
Demographics
Race: African American
1.09
.28
1.11
.35
**
1.08
.27
1.11
.31
**
1.09
.29
1.12
.33
**
1.10
.29
1.11
.32
**
Race: Asian American
1.01
.11
1.01
.11
*
1.03
.18
1.03
.17
**
1.06
.24
1.05
.22
**
1.08
.27
1.07
.26
**
Race: Latino/Chicano
1.01
.11
1.01
.10
*
1.02
.13
1.01
.13
1.05
.21
1.05
.21
1.06
.24
1.08
.27
**
Family Income
6.07
3.58
5.91
3.65
**
11.92
5.42
11.44
5.53
**
14.30
5.73
13.48
5.94
**
16.76
5.77
15.60
6.18
**
Concern about Finances
Highest Parental Education
Level
1.78
0.69
1.89
0.69
**
1.68
0.67
1.83
0.68
**
1.76
0.67
1.95
0.67
**
1.66
0.63
1.83
0.64
**
5.10
1.99
5.11
1.97
5.63
1.91
5.55
1.93
**
5.81
1.86
5.66
1.86
**
5.96
1.82
5.76
1.88
**
High School GPA
5.28
1.65
5.75
1.50
**
5.25
1.75
5.65
1.60
**
5.65
1.66
6.10
1.50
**
5.95
1.51
6.37
1.36
**
Self-Rated Academic Ability
3.82
0.75
3.75
0.72
**
3.89
0.74
3.77
0.71
**
3.91
0.73
3.78
0.71
**
3.92
0.72
3.80
0.70
**
Self- Rated Mathematical Ability
3.44
1.02
3.04
0.98
**
3.58
0.98
3.19
0.98
**
3.54
1.00
3.17
0.99
**
3.53
1.00
3.15
0.98
**
Self- Rated Writing Ability
3.26
0.88
3.36
0.83
**
3.42
0.87
3.48
0.82
**
3.40
0.91
3.48
0.85
**
3.42
0.91
3.51
0.85
**
Academic Ability
Academic Goals
Degree Aspirations
Reason to attend college:
Gain a general education.
Reason to attend college:
Make me a more cultured
person
Reason to attend college:
To learn more about interests
Goal: Make a theoretical
contribution to Science
**
5.35
1.58
5.01
1.47
**
5.10
1.38
5.06
1.34
**
5.28
1.31
5.39
1.28
**
5.26
1.34
5.35
1.32
**
2.57
0.57
2.73
0.48
**
2.54
0.56
2.68
0.50
**
2.53
0.60
2.68
0.52
**
2.51
0.61
2.67
0.53
**
2.10
0.69
2.29
0.67
**
2.14
0.65
2.33
0.63
**
2.16
0.71
2.36
0.66
**
2.14
0.73
2.35
0.68
**
2.66
0.52
2.80
0.43
**
2.68
0.50
2.79
0.42
**
2.70
0.52
2.78
0.45
**
2.69
0.54
2.79
0.45
**
1.77
0.85
1.55
0.76
**
1.73
0.84
1.51
0.75
**
1.82
0.90
1.68
0.86
**
1.85
0.92
1.71
0.88
**
+
Variable coding is located in Table A3.
41
Women’s Representation in STEM, 42
Table A1. (Con’t)
1976
Men
Variables
Mean
1986
Women
SD
Mean
Men
SD
Mean
1996
Women
SD
Mean
Men
SD
Mean
2006
Women
SD
Mean
Men
SD
Mean
Women
SD
Mean
SD
Affective Orientation
Artist Personality
7.19
2.44
7.88
2.56
**
7.27
2.51
7.47
2.49
**
7.45
2.69
7.47
2.59
7.55
2.83
7.82
2.73
**
Status Striver Personality
12.97
2.87
11.89
2.82
**
13.73
2.87
13.29
2.88
**
13.32
3.16
12.79
3.09
**
13.23
3.28
12.81
3.18
**
Social Activist Personality
8.90
2.33
9.18
2.13
**
8.54
2.28
8.92
2.13
**
8.69
2.58
9.31
Self- Rated Leadership Ability
3.62
0.82
3.46
0.83
**
3.77
0.83
3.60
0.86
**
3.75
0.90
3.62
2.38
**
8.96
2.71
9.44
2.52
**
0.91
**
3.82
0.89
3.69
0.88
**
Self- Rated Drive to Achieve
Self- Rated Intellectual
Self-Confidence
Self- Rated Social
Self-Confidence
3.85
0.77
3.86
0.75
3.86
0.80
3.85
0.76
**
3.94
0.85
3.95
0.80
**
3.93
0.86
4.05
0.79
**
3.64
0.76
3.42
0.76
**
3.88
0.78
3.59
0.78
**
3.86
0.82
3.59
0.83
**
3.88
0.81
3.58
0.83
**
3.40
0.81
3.28
0.79
**
3.62
0.88
3.47
0.85
**
3.61
0.92
3.46
0.88
**
3.66
0.93
3.50
0.90
**
Political Orientation
3.11
0.78
3.15
0.67
**
2.99
0.81
3.09
0.69
**
2.95
0.82
3.09
0.74
**
3.00
0.86
3.12
0.81
**
*p<.01, **p<.001
42
Women’s Representation in STEM, 43
Table A2. Regression Results by Year
1976
Entering Variable
Demographics
Race: African American
Final
b
1986
Chg in
Gdr
Final
b
1996
Chg in
Gdr
Final
b
Chg in
Gdr
2006
Final
b
.23**
.62**
.63**
.46**
Race: Asian American
.36**
.56**
.53**
.55**
Race: Latino/Chicano
.26**
.48**
.36**
.29**
Family Income
.00*
-.01**
.00**
.00**
Concern about Finances
.00
-.04**
.01
.01
Highest Parental Education Level
.06**
.04**
.05**
Academic Ability
High School GPA
Self-Rated Academic Ability
Self-Rated Writing Ability
Self-Rated Mathematical Ability
Academic Goals
Degree Aspirations
Reason to attend college: Gain
General Education
Reason to attend: Become more
Cultured
Reason to attend: Learn about
Interests
Goal: Make a Theoretical
Contribution to Science
Affective Orientation
Artist Personality
.03**
Chg in
Gdr
.14**
0.20
.17**
0.19
.13**
0.16
.15**
0.15
.12**
- 0.07
.14**
-0.08
.08**
-0.07
.15**
-0.08
-.09**
-0.03
-.14**
-0.03
.48**
-0.12
.47**
-0.11
-.11**
.58**
-.11**
-0.18
.01**
.64**
-0.14
-.09**
-.04**
-.02
-.06**
-.05**
-.02*
-.09
-.07**
-.01*
-.05**
.17**
0.03
.14**
1.32**
-0.04
1.29**
-0.13
.06**
.26**
.17**
1.13**
1.16**
-0.04
-.08**
-0.05
-.08**
-.08**
-.09**
-0.02
Status Striver Personality
-.11**
0.04
-.11**
0.04
-.07**
-.08**
0.02
Social Activist Personality
-.18**
-0.04
-.19**
-0.04
-.18**
-.17**
-0.04
Self-Rated Leadership Ability
-.03**
-.01*
-.04**
-.09**
0.02
Self-Rated Social Self-Confidence
Self-Rated Intellectual SelfConfidence
-.04**
-.08**
-.09**
-.11**
.01
.01*
.03**
.01*
.04**
.02*
Self-Rated Drive to Achieve
Political Views
*p<.05, **p<.001
.06**
-.10**
-.04**
-0.07
-0.02
.05**
-0.03
.07**
.00
43
Women’s Representation in STEM, 44
Table A3. Variable List and Coding
Demographics
Race: African American
Race: Asian American
Race: Latino/Chicano
Family Income
Concern about Finances
Highest Parental Education Level
Academic Ability
High School GPA (Average grade in High
School)
Self-Rated Academic Ability+
Self-Rated Writing Ability+
2-point scale: 1 = “No”, 2 = “Yes”
2-point scale: 1 = “No”, 2 = “Yes”
2-point scale: 1 = “No”, 2 = “Yes”
25-point scale: 1 = “less than $6,000” to 25 = “$250,000 or more”
3-point scale: 1 = “None”, 2=”Some”, 3 =”Major”
8-point scale: 1 = “Grammar School” to 8 = “Graduate Degree”
8-point scale: 1= “D” to 8= “A or A+”
5-point scale: 1= “Lowest 10%” to 5= “Highest 10%”
5-point scale: 1= “Lowest 10%” to 5= “Highest 10%”
Self-Rated Mathematical Ability+
5-point scale: 1= “Lowest 10%” to 5= “Highest 10%”
Academic Goals
Degree Aspirations
10-point scale: 1=”None”, 6= “PhD”, 10= “Other”
Reason to attend college: Gain General
Education
3-point scale: 1=”Not Important”, 2=”Somewhat…”, 3= “Very…”
Reason to attend: Become more Cultured
3-point scale: 1=”Not Important”, 2=”Somewhat…”, 3= “Very…”
Reason to attend: Learn about Interests
3-point scale: 1=”Not Important”, 2=”Somewhat…”, 3= “Very…”
Goal: Make a Theoretical Contribution to
Science
4-point scale: 1 = “Not Important” to 4= “Essential”
Affective Orientation
Artist Personality a
Self-Rated Artistic Ability+
5-point scale: 1= “Lowest 10%” to 5= “Highest 10%”
Goal: Become Accomplished in the
Performing Arts
4-point scale: 1 = “Not Important” to 4= “Essential”
Goal: Write Original Works
4-point scale: 1 = “Not Important” to 4= “Essential”
Goal: Create Artistic Works
4-point scale: 1 = “Not Important” to 4= “Essential”
Status Striver Personality b
Goal: Become an Authority in my Field
4-point scale: 1 = “Not Important” to 4= “Essential”
Goal: Obtain Recognition from my Colleagues 4-point scale: 1 = “Not Important” to 4= “Essential”
Goal: Have Administrative Responsibilities
4-point scale: 1 = “Not Important” to 4= “Essential”
Goal: Be Well Off Financially
4-point scale: 1 = “Not Important” to 4= “Essential”
Goal: Become Successful in a Business of My
Own
4-point scale: 1 = “Not Important” to 4= “Essential”
Social Activist Personality c
Goal: Influence the Political Structure
4-point scale: 1 = “Not Important” to 4= “Essential”
Goal: Influence Social Values
4-point scale: 1 = “Not Important” to 4= “Essential”
Goal: Participate in a Community Action
Program
4-point scale: 1 = “Not Important” to 4= “Essential”
Self-Rated Leadership Ability
5-point scale: 1= “Lowest 10%” to 5= “Highest 10%”
Self-Rated Social Self-Confidence
5-point scale: 1= “Lowest 10%” to 5= “Highest 10%”
Self-Rated Intellectual Self-Confidence
5-point scale: 1= “Lowest 10%” to 5= “Highest 10%”
Self-Rated Drive to Achieve
5-point scale: 1= “Lowest 10%” to 5= “Highest 10%”
+
Self-Rating items asked respondents to compare themselves to their peers.
a
b
Alpha reliability: 1976=.69; 1986=.69; 1996=.71; 2006=.71.
Alpha reliability: 1976=.67; 1986=.69; 1996=.72; 2006=.74.
c
Alpha reliability: 1976=.68; 1986=.67; 1996=.72; 2006=.73.
44