JOURNAL OF RESEARCH IN SCIENCE TEACHING
VOL. 44, NO. 7, PP. 980–1009 (2007)
Gender Ratios in High School Science Departments:
The Effect of Percent Female Faculty on Multiple
Dimensions of Students’ Science Identities
Shannon Gilmartin, Nida Denson, Erika Li, Alyssa Bryant, Pamela Aschbacher
1200 East California Boulevard, Mail Code 1-98, Pasadena, CA 91125
Received 24 August 2005; Accepted 1 September 2006
Abstract: To examine how school characteristics are tied to science and engineering views and
aspirations of students who are underrepresented in science and engineering fields, this mixed-methods
study explores relationships between aspects of students’ science identities, and the representation of
women among high school science teachers. Quantitative analyses tested the hypothesis that percent female
faculty would have a positive effect on girls’ science interests, and perceptions in particular, given the
potentially greater availability of women role models. Findings indicate that percent female science faculty
does not have an effect on a range of science measures for both male and female students, including the
ways in which they understand scientific practice, their science self-concept, and their interest in sciencerelated college majors. As qualitative data demonstrate, this could reflect practical constraints at schools
where female faculty are concentrated and narrow perceptions of science teachers and ‘‘real’’ science.
ß 2007 Wiley Periodicals, Inc. J Res Sci Teach 44: 980–1009, 2007
Keywords: general science; gender; equity; attitudes; achievement
The gender gap in science participation in the United States is well documented. Although
girls and boys earn similar grades in math and science classes, girls have less positive attitudes
towards science, tend to score lower on standardized science tests, engage in fewer science-related
activities, and enroll in fewer science and math courses during middle and high school (Catsambis,
1995; Hanson, 1996). Significantly more men than women enter science and engineering fields
during college (U.S. Department of Education, National Center for Education Statistics, 2000). In
the workforce, women have made gains in the biological sciences, but comprise just 23% of
physical scientists and 10% of engineers (National Science Board, 2004; National Science
Foundation, Division of Science Resource Statistics, 2004).
Analyses of national data indicate that psychological factors such as motivation and personal
interest help to explain the gender gap in science (U.S. Department of Education, National Center
Contract grant sponsor: National Science Foundation; Contract grant number: REC 0231878
Correspondence to: Shannon Gilmartin; E-mail: [email protected]
DOI 10.1002/tea.20179
Published online 26 January 2007 in Wiley InterScience (www.interscience.wiley.com).
ß 2007 Wiley Periodicals, Inc.
GENDER RATIOS IN HIGH SCHOOL SCIENCE DEPARTMENTS
981
for Education Statistics, 2000). The extent to which school context and culture contribute to the
gender gap is not so clear, in part because important cultural and structural measures are missing
from the literature, or simply not discussed. One of these measures is the percent of teachers in
high school science departments who are women. Little is known about the representation of
women in high school science contexts, and how this might affect girls’ and boys’ persistence
through the science pipeline over time. Relatedly, very few studies consider how female science
teachers in high school act as role models for their students, and/or introduce new and different
forms of science that deviate from dominant images.
As such, this study is designed to examine the relationship between percent female science
faculty and students’ science experiences in high school. Drawing from critical feminist and
practice theories, as well as literature on role models and teacher gender effects, the research
questions that guide this study are as follows:
1. How does the percentage of female science faculty affect high school students’ science
perceptions, achievement, views, self-concept, and college major aspirations, which
collectively define and reinforce their science identities?
2. Are the effects of percent female science faculty different for girls and boys?
These questions are addressed using hierarchical linear modeling techniques and longitudinal
survey data collected from over 1,000 students across five high schools in Southern California, in
addition to in-depth interviews with a stratified subset of 59 survey respondents at two of these five
schools. The objective of this study is to explore if and how a greater proportion of women among
high school science teachers could mitigate the disproportionate number of girls who opt out of the
science and engineering pipeline before they enter college. Even more broadly, this study
considers how a greater percentage of female science teachers on campus might expand all
students’ sense of connectedness to the scientific enterprise and create an inclusive scientific
environment that promotes more equitable pipeline outcomes.
Background of the Study
Critical feminist and practice theories frame our perspective on both the construct of
science identity and the relationship between female science teachers and their students.
Per critical feminist theory, we conceptualize science as a predominantly androcentric and
Eurocentric domain that systematically excludes voices from socioeconomic and political
margins (Harding, 1991; Schiebinger, 1989). Historically, women have been relegated to the
margins, as have non-White and non-Asian ethnic minority groups. When women and minority
groups enter science, they ‘‘cross the line’’ and challenge prevailing scientific images. Critical
feminist efforts to transform science involve deep analyses of these images, lines, and margins that
set the sociocultural parameters of ‘‘objective’’ scientific knowledge and practices. Critical
feminist analyses of science consider how power, resources, and rewards are distributed in
dominant science contexts such that some groups are better positioned to pursue scientific careers,
or more likely to see themselves as scientists, than are others. These types of analyses are
conducted to demystify scientific methods, invite debate about scientific norms, and ultimately
encourage a greater plurality of experiences in a more transparent scientific world.
Per practice theory, we conceptualize students’ science identities as being realized through
local science practice in reference to local science meanings. Put differently, students’ social
positions inform their science identities but do not determine them—local context, symbols,
actors, and choices are just as relevant (Eisenhart & Finkle, 1998; Holland, Lachicotte, Skinner,
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& Cain, 1998). There is self-authorship and improvisation at work; identities are dynamic rather
than static or structurally fixed. Also instructive is Holland et al.’s idea of ‘‘figured worlds,’’ or
those ‘‘socially produced, culturally constructed activities’’ (pp. 40–41) that order the very
individuals who bring them to life. We contend that school science is one such figured world to
which students acclimate as they develop a sense of themselves as scientific participants. Our
understanding of science identity borrows from Tajfel’s (1981) notion of social identity as well:
science identities refer to awareness of being a member of a group (a figured scientific community)
and the subjective value ascribed to this membership. Although social identity theory is a rich
framework that goes on to explain how group membership is linked to intergroup discrimination
and bias, we are, in this article, mainly concerned with the central point that identity has both
affective and social dimensions.
As for science identity per se—components of science identity, mediators of science
identity, definitions of science identity—we propose that science identity involves and reflects a
combination of students’ (self-) perceptions and interest in science and science-related work.
Students’ perceptions include their views of science as a field of study and their science selfconcept, or how they assess themselves in a given science context. Students’ interests include
the activities that they enjoy doing, the topics that they like to read or think about, and the jobs
that they aspire to. Together, these interests and perceptions both reveal and give shape to
students’ science identity, or their sense of (present and future) self in and around a locally
interpreted scientific enterprise and community (for the moment, we are not addressing
the temporal or causal order of each of these components and/or mediators, at the very least
because we imagine that it can vary widely for different students, but also because it is beyond
the scope of this article). For example, students who have a strong and positive science identity
may be those who: see science as compatible with their own values and personalities; see science
practices and domains as accessible to them and relevant to their lives; want to participate—
and have participated—in out-of-school science activities; enjoy ‘‘doing’’ science on an
affective level; feel confident about their science abilities; and aspire to conduct—or express
strong interest in—science-related work. These are students who see themselves as the
‘‘science type,’’ as belonging in a scientific world, a world that they value and respect and feel
engaged in.
Science identity is an evolving construct. Rather than absolute categories of identity, we
suggest that science identity falls along a continuum—or multiple continuums, different for
different groups of students with different sets of experiences and contexts. We are open to the
possibility that strong identities can develop in the absence of one or more ‘‘parts’’ or dimensions
described above, especially given unequal social and educational distribution of science resources
and information. This possibility does not preclude measurement of identity, it only nuances the
construct. Our definition is an expansive and unfolding one, and very much informed by scholars
such as Brickhouse, who suggests that students who have strong science identities are those who
want to understand the world scientifically (e.g., Brickhouse, Lowery & Schultz, 2000); and
Barton (1998), who argues that science identity denotes who we think we must be in order to
engage in science. Implicit in both perspectives are students’ perceptions of science itself—what it
means to think scientifically, to take part in scientific endeavors, to be a scientist in contexts outside
of traditional scientific environments. However, we think that the construct and indicators of
science identity are both broader and finer than what previous literature suggests, and requires
more research and conversation.
Two additional lines of research situate our study: (1) science teacher characteristics and
gender effects, and (2) role models in school and in science. Each topic is discussed below,
followed by a series of hypotheses that we generated based on these perspectives.
Journal of Research in Science Teaching. DOI 10.1002/tea
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Science Teacher Characteristics and Gender Effects
According to recent data, the percent of high school science teachers who are women in the
U.S. has increased since 1990, although change varies by science subject (Blank & Langesen,
2003). For example, women now comprise 52% of high school Biology faculty and 47% of high
school Chemistry faculty, up from 37 and 34%, respectively, in 1990. The proportional increase is
smaller in Physics: in 1990, 22% of high school Physics faculty members were women, compared
to 28% in 2002. These numbers mirror degree attainment patterns in science and engineering
fields, where women account for half of all undergraduate science and engineering degrees with
most clustered in the biological sciences (National Science Foundation, Division of Science
Resource Statistics, 2004).
Research on the effects of faculty gender ratios or the differences between male and female
faculty typically aggregates subject areas and does not look at science specifically. In a major study
of nearly 25,000 college students in the United Staes, Astin (1993) found that percent female
faculty across all departments was a positive predictor of student satisfaction, trust in the
administration, and knowledge of a particular discipline or field. Other postsecondary and
secondary studies indicate that having female teachers is positively related to students’ selfconfidence and feelings of comfort in the classroom, but is unrelated to students’ achievement and
aspirations (Canes & Rosen, 1995; Ehrenberg, Goldhaber, & Brewer, 1995; Huffman, Lawrenz, &
Minger, 1997; Sax & Bryant, 2006). Additional evidence suggests that both male and female
students often prefer female teachers over male teachers, feeling that their female teachers are less
strict and more approachable than are their male teachers (Crombie, Pyke, Silverthorn, Jones, &
Piccinin, 2003; Galguera, 1998; Le Mare & Sohbat, 2002). In one study of postsecondary faculty,
female professors were most likely to employ feminist pedagogical techniques in the classroom
(Wakai, 1994), hallmarks of which include validation of personal experience, encouragement of
social activism, and cultivation of critical thinking and open-mindedness (Hoffman & Stake,
1998). However, findings such as these may be partly conflated with gender roles and
expectations; for example, Swaffield (1996) speculates that students judge male teachers who take
on feminist classroom characteristics as less competent in these areas than are female teachers
who have these characteristics, meaning that women may employ feminist strategies more easily
than do men.
In the few studies that examine faculty gender effects in high school and postsecondary
science classrooms, results are similar to those of classrooms generally. For example, Bender
(1994) observed that male and female students favored the science teaching strategies of their
female teachers. These students also talked more often about and responded more positively to
their female science teachers. At the postsecondary level, Robst, Keil, and Russo (1998) noted a
positive relationship between first-year retention rates of female science students and the
percentage of math and science classes taught by women faculty. This finding is in line with that of
Tidball (1985), who linked increased numbers of advanced degrees in medicine and science
among women to institutions with high female faculty to female student ratios.
Role Models in the Classroom and in Science
Role model theory underlies much of the research on teacher gender effects. The traditional
definition of a role model is that of a person in an influential position who provides an example for
individuals to imitate (Erikson, 1985). Role models have long been thought to shape adolescent
development, aspirations, and achievement by providing examples of possible selves (King &
Multon, 1996; Nauta, Epperson, & Kahn, 1998). Family members are often the most significant
Journal of Research in Science Teaching. DOI 10.1002/tea
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GILMARTIN
role models in students’ lives, but teachers also act as role models who directly and indirectly
affect students’ school experiences, self-esteem, and educational and occupational outcomes
(Evans, 1992; Galbo, 1989; Gates, 2002; Rothstein, 1995; Solnick, 1995; Willcoxson & Phelps,
2004).
Many studies cite the mere presence of a role model as critical to student achievement, though
Evans (1992), Karunanayake and Nauta (2004), and Zirkel (2002) emphasize the importance of a
gender- and/or ethnic-matched role model. These are exemplars with whom students most
strongly identify; when students have access to a role model who shares their background or
‘‘looks’’ like they do, they are more likely to perform well in school, invest in achievement, and
give careful thought to future goals. Matched role models send students information about their
own possibilities and opportunities in society. Lockwood (2006) found that women more often
than men choose to have gender-matched career role models, with some women explicitly stating
the value of having a gender-matched role model who has achieved success in a traditionally maledominated profession.
With respect to science, middle and high school students who have a science role model or
mentor have more positive attitudes toward science and scientists, increased persistence in
advanced science courses, and greater interest in science careers (Evans, Whigham, & Wang,
1995; Lee, 2002; MacDonald, 2000). Some studies find that students who have male science role
models or mentors enjoy more advantages in the science pipeline (Gates, 2002; Wallace & Haines,
2004). However, other studies find that girls who have a matched role model in male-dominated
domains such as science might benefit most from role modeling. For example, Evans et al. (1995)
observed that female role models had a positive impact on the science attitudes of both high school
girls and boys who participated in an in-school science intervention project, but the positive effect
of female role models was stronger among girls. Relatedly, Wallace and Haines (2004) reported
that female engineering students with matched role models were more likely to commit to a career
in engineering immediately after college, and were more likely to believe that they could have both
a family and an engineering career. Similarly, Seymour and Hewitt (1997) noted that college
women who majored in math and science were more confident and felt more support when they
had female instructors. Several young women in Seymour and Hewitt’s study rejected the
traditional (and masculine) image of science as all-consuming and incompatible with other
relationships and activities, and instead looked to successful female faculty for information about
science careers. Conversely, young women in Carlone’s (2003) study did not identify with the
stereotypical images of science that their male science teacher promoted, and were less personally
invested in the class even as they earned good grades and enjoyed his teaching style. Given these
findings, it is not surprising that research has identified the absence of female role models as a key
barrier to girls’ entry and persistence in advanced science classes and, consequently, science and
engineering fields (Blin-Stoyle, 1983; Ehrenberg, 1995; Rosser, 1997, 2004; Taylor, Erwin,
Ghose, & Perry-Thornton, 2001).
Hypotheses
Our study expands on previous literature that addresses female science teachers and girls’
interest in science. Given our critical feminist understanding of science, our evolving definition of
science identity, the emphasis that practice theory places on local context and self-authored
identities, and prior work on role models, we consider the possibility that female science teachers
challenge the dominant masculine image of science and act as role models for students (especially
girls); in doing so, they can provide a space within which students develop science identities that
also challenge conventional notions of what science means or looks like. That is, the visibility and
Journal of Research in Science Teaching. DOI 10.1002/tea
GENDER RATIOS IN HIGH SCHOOL SCIENCE DEPARTMENTS
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availability of female science teachers in schools might have a positive impact on students’
sense of self in science and, ultimately, their science persistence. Because they demonstrate that
science is not so narrowly practiced, female science teachers might be particularly important to the
development of girls’ science identities in that they directly counter the perception of science as
being masculine or male-dominated.
We propose the following hypotheses for this study, which we test using quantitative methods
and investigate more fully using qualitative interview data. The first hypothesis suggests that a
greater percentage of science teachers who are women might help to construct a ‘‘figured world’’
of school science within which more students see science as inclusive and open, can envision
themselves as scientific actors or agents, enjoy learning science, and want to pursue college majors
in the science pipeline. In other words, we link percent female science faculty to multiple
dimensions (components and mediators) of science identity as outlined earlier (again, we are not
establishing the temporal or causal order of these measures in this study, although we suspect that
some might precede others and plan to test this in future analyses of longitudinal data):
(a) Percent female science faculty will have a positive effect on students’ science class
perceptions, science self-concept, and science college major aspirations; percent
female science faculty will have a negative effect on stereotypical science views.
The next three hypotheses derive from ‘‘matched’’ role model literature, which points to the
benefits of female role models among female students, and from practice theory, or the idea that girls’
science identities are sensitive to local contexts within which women are (or are not) represented.
These hypotheses essentially suggest that girls will benefit from having a greater proportion of
women in science departments more so than will boys, on a range of science-related measures:
(b) The effect of percent female science faculty will reduce or ‘‘neutralize’’ significant
gender effects on dimensions of science identity that favor boys.
(c) The positive effect of percent female science faculty on class perceptions, selfconcept, and aspirations will be stronger for girls than for boys.
(d) The negative effect of percent female science faculty on stereotypical science views
will be stronger for girls than for boys.
In addition to these variables, we also explore the effect of science faculty gender ratios on
students’ science grades, a measure for which we posit no directional effect (mainly because
previous literature and our theoretical perspectives do not suggest a relationship one way or
another, that is, the relationship between representation of women, role models, identity, and grade
point average is unclear—we do not consider grades to be a necessary component of science
identity, but more likely a behavioral correlate or mediator).
It is important to recognize that many of these hypothesized effects may depend on not only
gender, but race/ethnicity, socioeconomic background, and generation status of students and
teachers (i.e., percent female science faculty might have a stronger impact on some female
students more than others, depending on students’ racial/ethnic backgrounds and that of the
women who teach at their schools—this takes the matched role model argument even further).
Given the quantitative data available to us, however (small samples for statistical analyses by
racial/ethnic group and school, and inability to match individual students to individual teachers
with gender and racial/ethnic information), we focus on gender differences and similarities in this
article, although we are able to control for students’ racial/ethnic and socioeconomic background
in the multivariate work (as described below). We look forward to future analyses to break down
these relationships in greater detail.
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Methodology
Sample for the Quantitative Analyses
The sample for the quantitative component of this study is comprised of 1,138 10th-grade
students at five Southern California high schools that agreed to participate in a larger study of
science identity development among secondary student populations.1 Funded by the National
Science Foundation, the larger research project is titled ‘‘Is Science Me?’’ (ISME), and explores
how family, peer, classroom, and school contexts interface with students’ perceptions of, attitudes
toward, and achievement in science as they prepare for college and position themselves in or
outside of the science and engineering pipeline. ISME particularly focuses on the science attitudes
and identities of students who are underrepresented in later stages of the pipeline (e.g., female
students, African American students, Latino/a students), with the ‘‘pipeline’’ loosely defined as
the constellation of curricular and extracurricular behaviors, attitudes, and choices that prepare
students for undergraduate, graduate, and professional work in science and engineering fields.
The students in this sample represent approximately 39% of the total 10th-grade cohort across
all five schools (N ¼ 2,934). To participate in the survey, students received permission slips in their
classes and various return incentives (e.g., entry into a lottery for a $50.00 gift certificate at a local
Best Buy). Permission slips were distributed to 85% of 10th-grade students at these schools, with a
60% return rate;2 the difference between the permission return rate and the survey participation
rate is due to absences on the days of survey administration and a small number of ‘‘no’’ responses
on the permission slip. Odds of returning the permission slips were generally higher among female
students and among Asian/Asian American students.
Students completed a multipage survey about their science experiences and attitudes in Fall
2003. Female students comprised 57% (N ¼ 653) of the sample. Based on self-reported survey
data, Asian/Asian American students comprised 24.3% of the survey respondents, Latino/a
students, 21.4%; White/Caucasian students, 19.9%; Black/African American students, 6.5%;
with an additional 12.7% of students classified as ‘‘Dual Ethnicity’’ (meaning they marked two
racial/ethnic groups) and 15.1% classified as ‘‘Other’’ (meaning they marked three or more racial/
ethnic groups, marked ‘‘Other,’’ marked a racial/ethnic category that comprised less than 5% of
the full sample, e.g., American Indian/Alaska Native, or skipped the question). Drawing from
students’ responses to other survey items, approximately 36% of the sample was categorized as
‘‘high socioeconomic status (SES),’’ 39% as ‘‘mid SES,’’ and 25% as ‘‘low SES.’’ (Complete
definitions of these variables are located in Appendix A.) The sample is slightly smaller
(N ¼ 1,028) for the subanalyses of science class grades and perceptions because some survey
respondents were not enrolled in a science class at the time of the survey.
The participating schools were diverse in terms of student race/ethnicity, student
socioeconomic background, and, per the variable of central interest to this study, percent female
science faculty. Table 1 provides details about these five schools. Note that the schoolwide racial/
ethnic statistics are derived from a statewide database and are not directly comparable to 10thgrade students’ self-reported racial/ethnic backgrounds on the ISME survey (e.g., statewide data
draw from parents’ reports to districts; district racial/ethnic subcategories are slightly different
from ISME categories, and the state database reports multiple ethnicities as a uniform ‘‘Other’’).
Still, these statistics evidence the considerable differences between each participating high school,
and show that Black/African American students are particularly underrepresented in the survey
respondent sample for this study, at least compared to the demographic profiles of the schools. The
statistics also indicate that schools with proportionately more female science teachers are larger
schools with a greater proportion of students who receive federally subsidized lunches; these
Journal of Research in Science Teaching. DOI 10.1002/tea
GENDER RATIOS IN HIGH SCHOOL SCIENCE DEPARTMENTS
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Table 1
School Characteristics
School Population: Ed-Data dataa
School
Sycamore
Treadwell
Century
Valencia
Paso Flores
Asian
41.1
54.4
6.5
6.4
4.8
c
Black Latino White
FRL
3.8
1.0
23.7
21.2
24.9
5.7
31.9
32.7
38.0
45.7
15.0
14.4
34.5
38.8
46.9
31.4
29.5
34.4
32.7
23.3
ELL
d
California
California
Language
e
Math Exam Arts Exame
5.4
12.2
8.7
13.7
16.4
88.0
83.0
38.0
20.0
33.0
70.0
55.0
35.0
34.0
28.0
Female
Science
Faculty
17.0
20.0
38.0
58.0
65.0
ISME survey respondents: Self-reported survey datab
School
Asian
Black Latino White
Dual
Other
Girls
Survey N
Total 10th
Grade N
Sycamore
Treadwell
Century
Valencia
Paso Flores
45.6
57.5
5.4
4.1
5.3
0.0
0.0
9.9
13.0
10.6
12.3
5.4
14.5
15.4
17.0
11.8
15.1
21.5
10.7
14.8
59.3
54.1
60.7
60.4
54.2
204
259
242
169
264
400
485
732
621
696
8.8
8.9
22.3
30.2
37.1
21.6
13.1
26.4
26.6
15.2
a
These racial/ethnic and free/reduced lunch data derive from Ed-Data, a statewide database on public schools, and reflect
the entire school population (all grades), not just the 10th grade ISME survey respondents at each school for the 2003–2004
academic.
b
These racial/ethnic and gender data derive from students’ self-reported race/ethnicity and sex on the ISME survey
instrument. ISME racial/ethnic categories are described in the Methodology section.
c
Free/Reduced Lunch.
d
English Language Learner.
e
Percent who scored ‘‘Proficient’’ or above.
schools enroll higher percentages of Latino/a and African American students, report lower scores
on statewide Math and Language Arts exams, and have more students designated as ‘‘English
Language Learners’’ as well.
Instrumentation and Variables in the Quantitative Analyses
Items on the ISME 10th-grade survey instrument drew from existing scales and questions on
related surveys (e.g., Phinney’s Multigroup Ethnic Identity Measure, 1992), science education and
pipeline literature (e.g., Hanson, 1996; Seymour & Hewitt, 1997), ongoing discussions with a
national advisory team, and pilot testing in participating schools. The 10-page survey covered a
broad range of topics, including: perceptions of science and scientists; interest in science, sciencerelated, and nonscience-related college majors and careers; family and peer relationships;
perceptions of science class and teachers; science-related activities and behaviors in and outside of
science class; views about ethnicity, gender, and self; and demographic characteristics. Most
survey questions were measured on a two-, three-, or four-point scale.
The eight dependent variables in this study derive from the ISME 10th-grade survey data;
these variables measure students’ perceptions of science in and beyond the classroom, their
interest in studying science or engineering during college, their self-concept as a future scientist,
and their performance in science class. Some of these dependent variables (and independent
variables) are multi-item factors that were calculated using principal components methods and
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varimax rotation techniques. Items with factor loadings less than .40 were automatically dropped
from the factor to maximize reliability. Individual responses to each item in a factor were summed
to compute factor scores. When items were measured on different scales, responses were first
standardized (i.e., converted to z-scores) and then summed. See Appendix A for a full definition of
all variables tested in the analyses.
The dependent variables include three views of science, each coded on a four-point scale from
‘‘Disagree strongly’’ to ‘‘Agree strongly’’: ‘‘Scientists spend most of their time working by
themselves,’’ ‘‘Scientists’ own opinions do not matter in their work,’’ and ‘‘People who are the
same gender as I am have trouble getting jobs in science in this country.’’ These views reflect
students’ understanding of science in stereotypical terms and as a gendered domain, arguably
grounding students’ sense of belonging and self in science, or sense of compatibility with a
stereotypically or nonstereotypically perceived scientific community. To explore students’
science-related aspirations beyond high school, two measures of students’ interest in science and
engineering college majors were examined: interest in a Physical Science/Engineering major, and
interest in a Life Science major. One measure of students’ sense of self as a future scientist was
tested (‘‘I could be a good scientist one day,’’ coded on a four-point scale from ‘‘Disagree
strongly’’ to ‘‘Agree strongly’’), along with one measure of students’ perceptions of their current
science class (a five-item factor comprised of items such as ‘‘My teacher thinks I could be a good
scientist one day’’), which captures students’ assessments of their science teacher and, given high
factor loadings and a strong Cronbach’s alpha value, related views of their own aptitude in and
enjoyment of the class. Finally, one measure of students’ grades in their current science class was
included (coded on a four-point scale, from ‘‘Below C’’ to ‘‘A’’).
In previous analyses of ISME data using several of these variables (although the variables
were modeled differently in response to a different set of research questions), the measure of
students’ self-concept as a future scientist was strongly and significantly related to students’
interest in science careers net of several control variables and regardless of gender and race/
ethnicity, whereas the relationship between other variables, such as science class perceptions and
interest in science careers, was less clear and/or conditional (Gilmartin, Li, & Aschbacher, in
press). This underscores the point that science identity may involve both perceptions and interests,
some of which are perhaps more important or ‘‘closer to the core’’ of identity than are others (and
with some variation between groups of students). Nonetheless, we treat all dependent variables in
this study as potential components or mediators of identity in order to fully explore the links
between the representation of women and multiple dimensions of students’ social, cognitive, and
affective experience of ‘‘self-in-science’’; grades, as discussed, may also be a behavioral correlate
of identity.
All student-level independent variables also derive from students’ responses to items on the
10th-grade survey instrument.3 These independent variables include students’ gender; socioeconomic and racial/ethnic background; grade point average; and ‘‘Family Science Orientation,’’
a three-item factor that measures the extent to which students’ family members are interested in
science and prioritize science education. (This last variable was found to be a strong and
significant predictor of key student outcomes in other multivariate analyses of the ISME data, e.g.
Gilmartin et al., in press.) The school-level independent variable, ‘‘Percent female science
faculty,’’ is a continuous variable as listed in Table 1.
Quantitative Analytic Methods
As noted in our hypotheses, our primary aim was to examine the effects of ‘‘Percent female
science faculty’’ on a variety of measures. Because of the multilevel nature of our data and
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research questions (e.g., the effect of percent female science faculty—a school-level variable—on
students’ science class perceptions—a student-level variable), this type of analysis could only be
accomplished using a multilevel methodology such as Hierarchical Linear Modeling (HLM)
(Raudenbush & Bryk, 2002). HLM accounts for hierarchical differences in units of analysis such
that student- and school-level variables can be examined simultaneously. This technique
overcomes the limitations to single-level models that neglect the nested or hierarchical nature of
multilevel data (e.g., Burstein, 1980; Raudenbush & Bryk, 2002). We conducted four stages of
modeling to observe the effects of different independent variables on our dependent variables.
The One-Way Analysis of Variance (ANOVA): Model 1. The first model was a fully
unconditional model because no predictors were specified at either Level-1 (student level) or
Level-2 (school level). Equivalent to a one-way ANOVAwith random effects, this model provides
useful preliminary information about how much variation in each dependent variable lies within
and between schools.
In modeling stages 2 through 4, we developed conditional models where predictors were
specified at either Level-1 (student level) and/or Level-2 (school level). In Model 2, we included
all student-level variables to predict each dependent variable, thus allowing us to assess the withinschool variance explained by these student-level measures. In Model 3, we added ‘‘Percent female
science faculty’’ to predict each dependent variable, in order to assess the between-school
variance explained by the percent female science faculty measure.
The Final Model: Model 4. The final (intercepts and slopes-as-outcomes) model was also a
conditional model because it contained both Level-1 (student level) and Level-2 (school level)
predictors. In the final model, ‘‘Percent female science faculty’’ was used to predict each
dependent variable as well as the nonrandomly varying gender slope for each dependent variable.
The following equations (1 and 2) describe the model estimated in the final stage of these HLM
analyses.
Level-1 for Model 4:
Yij ¼ b0j þ b1j (Female) þ b2j (White) þ b3j (Black) þ b4j (Latino/a) þ b5j (Asian) þ b6j (Dual
ethnicity) þ b7j (SES) þ b8j (GPA) þ b9j (Family science orientation) þ rij rij N (0, s2)
(1)
Level-2 for Model 4:
b0j ¼ g00 þ g01 (% female science faculty) þ u0j u0j N (0, t00)
(2)
b1j ¼ g10 þ g01 (% female science faculty) u1j u1j N (0, t11) where i ¼ 1, 2,. . ., nj students in
school j, and j ¼ 1, 2,. . ., 5 schools. All Level-1 predictors were grand-mean centered and all
Level-2 predictors were grand-mean centered so that the intercept term (b0j) represented the
adjusted mean for school j. Including the aggregates in combination with this centering allows
the compositional (or contextual) effects to be estimated directly. For example, g01 represents the
contextual effect of ‘‘Percent female science faculty’’ (see equation 2). In other words, the
contextual effect of ‘‘Percent female science faculty’’ is the increment of an outcome, or
dependent variable, that accrues to a student by virtue of being in his/her school context versus
another. Through contextual effects, we can examine how certain educational contexts (in this
case, the percentage of female science faculty in a school) influence students.
In the Level-2 model, the intercept (b0j) was specified as random, whereas b1j was specified as
fixed in the final model. The term b1j represents the school average of the gender slope for school j.
The gender slope, or gender effect, describes the student-level effect of being female (compared to
being male) on the dependent variable; it describes, in other words, the gender difference on the
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eight dimensions of science identity. Initially, the gender effect was not assumed to be constant
across schools. The variance of this coefficient was calculated, separating parameter variance
from error variance, and then tested to determine whether this effect varied across schools. Based
on the results of the chi-square tests, the b1j coefficient was then specified to be fixed in the final
Level-2 model for all dependent measures (see equation 2).
Sample and Instrumentation for the Qualitative Analyses
Nearly 70% of survey respondents also received permission to be interviewed as part of the
ISME research project. Among these students, approximately 10% were selected for in-depth
interviews in Spring 2003 using a stratified sampling design where a greater proportion of mid- to
low-income female students, non-White students, and non-Asian students were selected for
interviews. The purpose of this sampling design was to focus attention on students who were
underrepresented in the survey respondent sample (compared to schoolwide characteristics) and
in the science and engineering pipeline. Because of this sampling design, the final interview
sample was more closely aligned to the overall racial/ethnic composition of students at
participating schools.
For the qualitative part of this study, we analyzed the transcripts of all 23 students whom we
interviewed at the high school with one of the lowest percentages of female science teachers
(Treadwell High School; see Table 1), and all 36 students at the high school whom we interviewed
with the highest percentage of female science teachers (Paso Flores High School). Female students
comprised approximately 60% of the interview sample at each school. At Treadwell, the interview
sample was 52.2% Asian/Asian American, 17.4% White, and 17.4% Latino/a (no Black/African
American students were able to be interviewed at this campus), while at Paso Flores, the interview
sample was 2.8% Asian American, 16.7% White, 27.8% Latino, and 36.1% African American
(with the balance at both campuses in the Dual Ethnicity category). At Treadwell, 34.8% of the
students whom we interviewed were earning ‘‘As’’ in their science class at the time of the 10thgrade survey, compared to 40.5% of all survey respondents at this campus; at Paso Flores, 27.8%
of the students whom we interviewed were earning ‘‘As,’’ compared to 28.6% of all survey
respondents on campus. By the time all of these students were in the 11th grade, 32%, or seven
students, at Treadwell were enrolled in an AP science class, versus 6.9%, or two students, at Paso
Flores. However, Paso Flores offered only three AP science sections for the entire school, versus
eight AP science sections at Treadwell.
During the interviews, all of which were tape-recorded and most of which required
approximately one high school class period (55 minutes), students were asked about their science
experiences in past years as well as at present, their science attitudes and views, their role models
and social networks, their family context, and their plans for the future, according to a
semistructured interview protocol that was developed and piloted at participating schools over a
period of 3 months. A group of 16 scholars and practitioners conducted the interviews; each
member of the interview team, screened by the Principal Investigator of the project, participated in
an intensive orientation session to the protocol. Examples of questions on the protocol include:
‘‘Which science class are you taking this year, and what kinds of things are you learning?’’; ‘‘How
are your science class experiences in high school different from your science experiences in
middle school?’’; ‘‘What do you think your family members would say if you told them you were
interested in science?’’; ‘‘Could you ever imagine having a job in science one day?’’; and ‘‘Whom
do you feel comfortable talking to about your future?’’ The protocol had 29 questions in total, with
anywhere between 5 and 10 probes for each question.
In Fall 2005, follow-up interviews were conducted with 13 of the students at Treadwell and 13
of the students at Paso Flores, who were at this point in the 12th grade (this was a slightly smaller
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interview sample because some students did not receive permission to participate in the second
and third years of the study). The follow-up interviews asked students about their views of their
gender and ethnicity, especially as these broader markers of identity related to their science and
nonscience interests and goals. As with the baseline interview protocol, the follow-up interview
protocol was pilot tested with a small sample of students at participating schools. The data for this
study draw primarily from the baseline interviews, but secondarily from the follow-up interviews
to learn more about role models in and outside of science class.
Qualitative Analytic Methods
Interviews were transcribed and coded according to an initial coding scheme that was derived
from the interview protocol. Seven coders tested and refined this coding scheme using a sample of
eight interview transcripts. Once consensus had been reached for all codes across the eight
transcripts, the team coded the remaining transcripts with 30% marked for double coding (i.e., two
team members would code one transcript and mark points of agreement or disagreement).
Interrater reliability was .80 or above. Coding of the follow-up interviews proceeded along lines
similar to that of the baseline interviews.
For the present study, we searched for information about student–teacher relationships and
students’ perceptions of their science experiences in high school using the general codes in the
coding scheme described above (and the qualitative software package Atlas). Once we identified
relevant data in each interview, we looked into students’ testimonies more deeply—how they
spoke about their science experiences (positive or negative), if their observations of female
teachers differed from those of male teachers,4 whom they identified as role models (teachers or
nonteachers), the features that they attributed to the science context at their schools (high levels of
teacher turnover, adequate or inadequate instructional materials, etc.). We counted the comments
in emergent subcodes (within which there were several levels, e.g., sub, subsub, and subsubsub),
compared and contrasted comments within each subcode, and constantly checked and countered
our interpretations (Huberman & Miles, 1998). We were able to discern distinct patterns in
students’ stories by school, which we discuss after presenting the findings of our quantitative
survey analyses. To protect the confidentiality of the students whom we interviewed, all names of
students and their teachers as well as other identifying information have been changed.
Quantitative Results
The One-Way ANOVA: Model 1
Table 2 presents results from the unconditional model (i.e., the one-way random-effects
ANOVA base model) for the eight dependent variables (Appendix B lists means and standard
deviations for all variables in these analyses). The table shows the maximum likelihood point
estimate for the grand mean and the estimated values of the within-school variance (s2) and
between-school variance (t00) for each measure. For example, the maximum likelihood point
estimate for the grand mean of students’ science class grade is 2.67, with a standard error of 0.13,
indicating a 95% confidence interval of 2.67 1.96 (0.13) ¼ (2.42, 2.92). To take another
example, the maximum likelihood point estimate for the grand mean of ‘‘Interest in a Physical
Science/Engineering College Major’’ (a composite variable) is 6.48 with a standard error of 0.16,
indicating a 95% confidence interval of 6.48 1.96 (0.16) ¼ (6.17, 6.79).
Because the unconditional model had no Level-1 or Level-2 predictors, we first explored
student-level variance as a function of within-school and between-school differences per
Raudenbush and Bryk (2002). In other words, we ‘‘decomposed’’ the total variance in each
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Table 2
HLM unconditional model
Fixed Effects
Science class perceptions
Intercept (g00)
Science self-concept
Intercept (g00)
Science college major aspirations
Science aspirations: Physical science/Engineering
major Intercept (g00)
Science aspirations: Life science major
Coefficient
SE
t-Ratio
15.69
.42
37.66**
2.18
.05
48.02**
6.48
.16
39.32**
3.24
.08
38.21**
2.67
.13
20.53**
2.44
.02
101.80**
1.90
.04
43.07**
1.69
.02
69.92**
Variance Component
df
Chi-square
.78
17.37
4
35.93**
.01
.94
4
9.93*
.11
4
26.80**
4.83
.03
4
22.49**
.08
1.03
4
71.49**
.00
4
3.23
.64
.00
4
11.61*
.74
.00
4
.87
Intercept (g00)
Science class grade
Intercept (g00)
Stereotypical science views
Scientists spend most of their time working by
themselves Intercept (g00)
Scientists’ own opinions do not matter in their work
Intercept (g00)
People who are the same gender as I am have trouble
getting jobs in science in this country Intercept (g00)
Random Effects
Science class perceptions
Between school (t00)
Within school (s2)
Science self-concept
Between school (t00)
Within school (s2)
Science college major aspirations
Science aspirations: Physical science/Engineering
major Between school (t00)
Within school (s2)
Science aspirations: Life science major Between
school (t00)
Within school (s2)
Science class grade
Between school (t00)
Within school (s2)
Stereotypical science views
Scientists spend most of their time working by
themselves Between school (t00)
Within school (s2)
Scientists’ own opinions do not matter in their work
Between school (t00)
Within school (s2)
People who are the same gender have trouble getting
jobs in science in this country Between school (t00)
Within school (s 2)
*
p <.05;
p <.001.
**
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GENDER RATIOS IN HIGH SCHOOL SCIENCE DEPARTMENTS
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dependent variable to determine the proportion that was due to individual differences between
students, and the proportion that was due to differences between schools. For instance, the
estimated value of the variance at the student level (i.e., within schools) of science class grade,
represented by sigma squared (s2), is 1.03. The estimated value of the variance at the school level
(i.e., between schools) of science class grade, represented by tau (t00), is 0.08. To establish a
better sense of the variation across schools, Raudenbush and Bryk recommend examining the
intraclass correlation, which represents the proportion of variance in each outcome that is due to
between-school differences. The intraclass correlation is computed by the following formula:
r ¼ t00 /(t00 þ s2)
Applying this formula, we found that differences in science class grade were to a greater extent
the result of individual differences than differences in the types of schools the students attended.
The results of this calculation show that less than one-tenth (7.2%) of the variance in science
class grade was due to between-school differences, whereas the majority (92.8%) of the total
variance was explained by differences among students. Thus, most of the variation in science
class grade is at the student level, although a statistically significant (p < .001) portion of the
variance remains between individual schools.
As a second example, the estimated value of the variance at the student-level (s2) of ‘‘Interest
in a Physical Science/Engineering College Major’’ is 4.83, and the estimated value of the variance
at the school-level (t00) of ‘‘Interest in a Physical Science/Engineering College Major’’ is 0.11.
Using the intraclass correlation formula, we found that 2.2% of the variance in ‘‘Interest in a
Physical Science/Engineering College Major’’ was due to between-school differences, while
97.8% of the total variance was explained by differences between students. That is, the vast
majority of the variation in students’ interest in a physical science or engineering college major is
at the student level, but a small and statistically significant (p < .001) portion of the variance is
attributable to the differences between the schools in the ISME sample.
The Final Model: Model 4
Tables 3 through 5 present the findings of the final models for the eight dependent variables
(Model 4). We discuss our findings in order of our hypotheses:
Hypothesis (a): Percent female science faculty will have a positive effect on students’
science class perceptions, science self-concept, and science college major aspirations;
percent female science faculty will have a negative effect on stereotypical science views.
(In addition to these variables, we also explored the effect of percent female science faculty
on students’ science grades, a relationship for which we posit no directional effect.)
Hypothesis (a) examines the direct effects of ‘‘Percent female science faculty’’ on eight
proposed components and mediators of science identity. Contrary to our expectations, the
percentage of female high school science faculty had no direct effect on any of these measures
after controlling for all student-level variables (gender, socioeconomic background, etc.). The
strongest predictors of science class perceptions and science self-concepts were students’ grade
point averages and their family members’ orientation to science, meaning that students who earn
high grades overall and who perceive their family members to be very interested in science are
more likely to have stronger science self-concepts, more positive science class perceptions, and
higher science grades. Family science orientation also had a positive effect on both measures of
students’ interest in a science-related college major. Very few racial/ethnic and socioeconomic
differences between students were statistically significant. Notably, however, there were
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Table 3
HLM final model for science class perceptions and science self-concept
Science Class Perceptions
Fixed Effects
School mean
Base (g00)
% Female science faculty (g01)
Gender slope
Base (g10)
% Female science faculty (g11)
White (g20)
African American/Black (g30)
Latino/a (g40)
Asian/American (g50)
Dual ethnicity (g60)
Socioeconomic status (g70)
GPA (g80)
Family science orientation (g90)
Random Effects
Between school (t00)
Within school (s2)
*
Science Self-Concept
Coefficient (SE)
t-Ratio
Coefficient (SE)
t-Ratio
15.68 (.43)
4.26 (2.26)
36.41*
1.88
2.18 (.03)
.00 (.16)
85.50*
.01
.35 (.24)
1.51 (1.23)
.25 (.41)
.66 (.57)
.15 (.42)
.40 (.42)
.16 (.46)
.03 (.18)
.53 (.08)
.60 (.06)
Variance
Component (df)
.85 (3)
14.43
1.49
1.23
.60
1.15
.36
.95
.36
.15
6.74*
10.56*
Chi-square
38.71***
.33 (.05)
.07 (.26)
.02 (.09)
.09 (.12)
.08 (.09)
.08 (.09)
.10 (.10)
.05 (.04)
.08 (.02)
.19 (.01)
Variance
Component (df)
.00 (3)
.71
6.40*
.26
.23
.72
.95
.93
1.05
1.32
5.02*
16.12*
Chi-square
2.44
p <.001.
significant gender differences on four of the eight dependent measures. Specifically, girls had
lower science self-concepts, lower science college major aspirations in physical science and
engineering fields, and lower science class grades than did boys in this sample. Girls were also
more likely than were boys to agree with the statement ‘‘People who are the same gender as I am
are less likely to become scientists than are people of the opposite gender.’’
Hypothesis (b): The effect of percent female science faculty will reduce or ‘‘neutralize’’
significant gender effects on dimensions of science identity that favor boys.
Hypothesis (b) examines the crosslevel effects of ‘‘Percent female science faculty’’ on the
gender slope for our eight dependent variables. In other words, this hypothesis explores whether or
not percent female science faculty affects the gender difference between boys and girls on each
dimension of science identity. For example, do higher percentages of female science teachers
reduce the difference between boys and girls in terms of their science self-concept? Are girls and
boys equally as optimistic about gender ratios in science and becoming a scientist when women
are better represented among faculty? Again contrary to our expectations, the percent of
female science faculty had no discernible effect on the gender slope (i.e., gender differences) for
science class perceptions, science self-concept, science aspirations, or stereotypical science views
(see Tables 3 through 5).
Hypothesis (c): The positive effect of percent female science faculty on class perceptions,
self-concept, and aspirations will be stronger for girls than for boys.
Hypothesis (d): The negative effect of percent female science faculty on stereotypical
science views will be stronger for girls than for boys.
Journal of Research in Science Teaching. DOI 10.1002/tea
**
p <.05;
p <.01;
***
p <.001.
*
School mean
Base (g00)
% Female science faculty (g01)
Gender slope
Base (g10)
% Female science faculty (g11)
White (g20)
African American/Black (g30)
Latino/a (g40)
Asian/American (g50)
Dual ethnicity (g60)
Socioeconomic status (g70)
GPA (g80)
Family science orientation (g90)
Random Effects
Between school (t00)
Within school (s2)
Fixed Effects
3.23 (.07)
.24 (.39)
.05 (.07)
.50 (.35)
.09 (.12)
.26 (.16)
.11 (.12)
.06 (.12)
.07 (.13)
.01 (.05)
.01 (.02)
.18 (.02)
Variance Component (df)
.02 (3)
1.29
12.85***
1.64
.27
1.46
1.82
2.16*
1.48
1.36
.18
11.95***
Chi-square
19.12***
1.50 (.12)
.98 (.59)
.05 (.20)
.40 (.27)
.37 (.20)
.43 (.20)
.32 (.22)
.12 (.09)
.01 (.04)
.32 (.03)
Variance Component (df)
.09 (3)
3.68
Coefficient (SE)
45.44***
.09
t-Ratio
.68
1.44
.72
1.62
.90
.50
.52
.16
.24
11.26***
Chi-square
13.32**
45.42***
.62
t-Ratio
Science College Major Aspirations: Life
Science
6.48 (.14)
.07 (.76)
Coefficient (SE)
Science College Major Aspirations:
Physical Science/ Engineering
Table 4
HLM final model for science class grade and science college major aspirations
.11 (.05)
.09 (.26)
.04 (.09)
.18 (.12)
.09 (.09)
.01 (.09)
.01 (.10)
.03 (.04)
.37 (.02)
.04 (.01)
Variance Component (df)
.02 (3)
.62
2.66 (.07)
.58 (.39)
Coefficient (SE)
t-Ratio
2.12*
.36
.48
1.55
1.08
.17
.09
.91
22.50***
3.35**
Chi-square
29.03***
36.01***
1.48
Science Class Grade
GENDER RATIOS IN HIGH SCHOOL SCIENCE DEPARTMENTS
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Journal of Research in Science Teaching. DOI 10.1002/tea
Coefficient (SE)
Journal of Research in Science Teaching. DOI 10.1002/tea
**
p <.05;
p <.01;
***
p <.001.
*
School mean
Base (g 00)
2.44 (.02)
% Female science faculty (g01)
.06 (.15)
Gender slope
Base (g10)
.04 (.05)
% Female science faculty (g11)
.28 (.25)
.05 (.08)
White (g20)
African American/Black (g30)
.05 (.11)
Latino/a (g40)
.20 (.08)
Asian/American (g50)
.09 (.08)
.01 (.09)
Dual ethnicity (g60)
Socioeconomic status (g70)
.01 (.03)
GPA (g80)
.02 (.02)
Family science orientation (g90)
.02 (.01)
Random Effects
Variance Component (df)
Between school (t00)
.00 (3)
Within school (s2)
.63
Fixed Effects
39.70***
1.03
.59
.12
.18
.84
.24
.27
.48
1.10
1.36
1.12
Chi-square
10.36*
.03 (.05)
.03 (.27)
.02 (.09)
.10 (.12)
.02 (.09)
.02 (.09)
.05 (.10)
.04 (.04)
.02 (.02)
.01 (.01)
Variance Component (df)
.01 (3)
.74
.77
1.16
.61
.41
2.45*
1.16
.08
.37
1.39
1.90
Chi-square
2.24
t-Ratio
1.90 (.05)
.27 (.26)
Coefficient (SE)
Science View: Scientists’ Own Opinions
Do Not Matter in Their Work
102.14***
.41
t-Ratio
Science View: Scientists Spend Most of
Their Time Working by Themselves
Table 5
HLM final model for science views
.16 (.05)
.17 (.25)
.15 (.08)
.19 (.12)
.15 (.08)
.09 (.08)
.24 (.09)
.04 (.04)
.00 (.02)
.01 (.01)
Variance Component (df)
.00 (3)
.66
1.69 (.02)
.02 (.15)
Coefficient (SE)
3.29**
.68
1.87
1.66
1.80
1.07
2.55*
1.05
.07
.74
Chi-square
.79
70.13***
.16
t-Ratio
Science View: People Who Are the
Same Gender as I Am Have Trouble
Getting Jobs in Science in This Country
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GENDER RATIOS IN HIGH SCHOOL SCIENCE DEPARTMENTS
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Hypotheses (c) and (d) were relevant only if the findings regarding hypothesis (b) were
significant. Because ‘‘Percent female science faculty’’ had no discernible effect on the gender
slope for any of the dependent variables, further analyses (i.e., separate analyses for boys and girls)
were unwarranted.
Qualitative Results
In our qualitative work, we hoped to learn more about why the representation of women
among high school science faculty did not have a significant effect on several aspects of students’
science identities. What could we learn by listening to the voices of students themselves? What
could we learn about female science teachers and their relationship to students and science
generally? Again, for this part of the study, interview transcripts were analyzed for 23 students at
Treadwell High School (where 20% of science faculty were women) and 36 students at Paso Flores
High School (where 65% of science faculty were women).
‘‘Good’’ Science Teachers were Caring and Engaged, and Had ‘‘Real-Life’’ Science
Experience and Credentials
When students at both schools spoke about their science teachers, girls and boys agreed on the
characteristics of a ‘‘good’’ teacher: energetic, caring, passionate, and patient, with high
expectations for student success. They did not have an explicit preference for male or female
teachers, attributing positive and negative characteristics to each in ostensibly ‘‘nongendered’’
ways:
He was a good teacher because like if we didn’t do the work he would give us chances to do
the work, because he really wanted us to understand and get better grades in his class. He
cared about us. (Keisha, Paso Flores)
Ms. Nelson just has a way of teaching that we, we get a lot from the whole lesson
plan. . .She’s real specific on things, but somehow she, even though we’re covering a lot,
she just goes smoothly through whatever we’re learning. (Robert, Paso Flores)
Mr. Gregory is extremely hands-on. So it’s like finally I have someone that is as hands-on
as I am. So that’s probably the number one thing [that explains why I am more interested in
science this year]: if you have a passion for what you’re teaching, it shows. (Katrina,
Treadwell)
Students were very critical of mundane approaches to teaching science (or teachers who
‘‘didn’t really teach,’’ a common refrain among students at Treadwell), and they appreciated
instructors who were willing to challenge them and make sure they stayed engaged. They also
spoke highly of teachers who had real-life science experience and/or concrete science credentials
and interests:
[My teacher] has a nice sense of humor [and] he has a degree, a major degree. So, he has a
lot of experience by telling us more in detail [about] a disease. Like, you know how some
teachers you ask ‘‘What happened and what else and why did they do that’’ And they’re
like, ‘‘I don’t know. That’s only what the book says.’’ Well, he just goes on. He doesn’t
have like a ‘‘no’’ for an answer. He just keeps going in more detail [to] like answer your
deepest questions. He just goes on and on. (Christina, Paso Flores)
[My teacher] is really funny. And so he has like, he makes it fun. And we don’t just sort of
read out of the textbook. He like, he explains it. But he doesn’t like read it and then explain
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it or something. He’ll like have everything prepared and he has like a lot of jokes to go
along with it, so that’s, I like that. . . He lectures a lot. And we watch videos, and he has a
lot of slides, like because he was in, he was an assistant surgeon in the Vietnam War, so he
has a lot of like the old slides from the war and stuff. And what was going on. And he
showed us like some pictures, like the slides of like people, like the surgeons operating and
he was like helping, too. (Kelly, Treadwell)
Mr. Garcia is my teacher. And he’s, he’s leaving next year. . .Cause he’s going to study to
be a pediatrician. So he’s going to [a local research university] to study. So, yeah. He’s a
great teacher. This is his first year here. . . But he, he knows what he’s doing. So I like the
class. (Michael, Paso Flores)
Yet it was in the domain of real-life science experiences and credentials that gender
differences in teaching emerged, despite standards for ‘‘good’’ instruction that seemed genderneutral. Students consistently named male science teachers as those with science backgrounds and
interests that were ‘‘real’’—they had been surgeons and worked in labs and planned to attend
medical school (i.e., science teaching was a ‘‘temporary’’ career). Female science teachers met
many other standards of good teaching, but they were generally not associated with real-life
science experience (either because they did not talk about their background or because they did not
have this background—our interview data left open both possibilities). On the other hand, female
science teachers were described as ‘‘hard’’ more often than were male teachers at Treadwell and
Paso Flores, which was perceived as a positive by some (e.g., setting high standards) and as a
negative by others (e.g., having expectations that were unreasonably high).
There were very few differences in the ways that male students and female students spoke
about their teachers, with the exception of girls at Paso Flores: they were the only group that did not
make negative comments about their male science teachers (all other students—boys at Paso
Flores and girls and boys at Treadwell—had both positive and negative things to say about their
female and male teachers). In other words, girls at the school with the highest percentage of female
teachers were not having necessarily better experiences with these teachers than they were with
male teachers.
Science Teachers Were Not Role Models
Although we asked students to name and describe their role models or mentors during the
10th-grade interview, no student at Treadwell or Paso Flores named a high school science teacher
(nor did they name a person with another type of science job), even among those students who
aspired to a career in science. One male student with strong science interests at Paso Flores
described his science teacher as someone whom he respected very much, but it was unclear
whether he considered this person a role model, or someone who exemplified a ‘‘possible self’’
(King & Multon, 1996; Nauta, Epperson, & Kahn, 1998) in science and encouraged him—by
example and/or by words—to pursue science-related work:
He made me a better person. My experience with him was probably like the best I’ve had
with a teacher. We didn’t really get along, we didn’t get along so much in class, but as a
person he was a really cool person. He made me understand like, most of my teachers let
me have work, but he made me work for what I had to get. (Allen)
By the 12th grade, one student at Treadwell spoke of his female AP Chemistry teacher as the
person who had inspired him to become a science teacher:
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I wanna be a Chem teacher now. . . My teacher got me really into Chem and now I really
wanna major in Chem, like be a Chem teacher. . . I got along really well with my teacher,
like she brought her baby to class. It was really friendly and we always talked to her, and I
got talking to her outside of school. . . The way she taught was very simple. She always
went step by step and if anyone had questions, she’d always ask and no one was
embarrassed to ask. . .A lot of high school teachers these days, like they’re really bad and
they’re just there to like, they just teach the material and like that’s it. They don’t wanna
talk to students. But the few teachers that are really good, they inspire me because I wanna
be a good teacher when I grow up. (Charles)
However, Charles was an exception—as in the 10th grade, most 12th grade students did not
name science teachers as role models (or any other person with a science-related job). One of
Charles’ classmates named another male Chemistry teacher on campus as someone who
‘‘motivated’’ her, although this was because he was a successful person from an educationally
disadvantaged background who shared her ethnicity, meaning the ethnicity and socioeconomic
match was more important to her than was the gender and science match.
Shedding light on possible reasons why science teachers were typically not viewed as role
models, Keisha, a student at Paso Flores who hoped to become a psychologist, explained that her
science teachers were not scientists and did not know ‘‘what it took’’ to become a scientist.
Therefore, they were not people to whom she would turn for more information about science
careers:
They don’t seem like people I would talk to about being a scientist, because they’re not
scientists. They’re teachers, but they kind of, to me they’re not. . . I don’t know. I just don’t
think that they’re the person I could go up to and say, ‘‘What does it take to be a scientist?
Do you know?’’ They’re probably like, well, they’d be like, ‘‘I really don’t know. I know
about science and stuff, but I don’t know what it would take to be one because I’m not
really one.’’ I don’t know if they’d have the answers.
So for most students in the interview sample at each school, science teachers were not
exemplars to whom students looked as they prepared for careers in science and science-related
fields, perhaps because, as Keisha emphasized, most teachers were not seen as ‘‘real scientists.’’
In fact, students did not have many science role models at all, in or outside of the science
department.
Female Faculty Were Concentrated at Schools with Weaker Support and
Resources for Science
Woven throughout students’ comments about their teachers were references to the science
context at each school. As Table 1 indicates, Treadwell was smaller than Paso Flores and enrolled a
different student population. Interview data suggest that the differences between schools extended
into the science curriculum and culture on campus. For example, students at Treadwell and Paso
Flores reported having teacher substitutes, but this was a more common observation among
students at Paso Flores; both groups of students also talked about science equipment being
unavailable, although for students at Treadwell, it was science equipment to take home, to
continue doing lab work outside of the classroom, whereas for students at Paso Flores, it was
science equipment in the classroom itself. Moreover, science (and nonscience) classes at Paso
Flores often involved disciplinary action and ‘‘class control’’ according to the students whom we
interviewed, a characteristic that was completely absent from the interviews with students at
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Treadwell. Mark’s comments were representative of other students with whom we spoke at Paso
Flores:
I: So this year you are taking Biology?
Mark: Yeah.
I: And how is that so far?
Mark: Well, not too good actually. It’s like the teacher’s completely, like really weird. He’s
messed up, like, I don’t think he knows how to control his kids. Um, when I first got into
that class, the kids are crazy. One of the, actually, one of the most crazy classes is that one.
‘Cause the kids are out of control.
A far greater number and percentage of students at Paso Flores also indicated that their school
did not ‘‘support’’ science, in response to an interview question about ‘‘how much your school
supports science interests and activities.’’ Allen, who was very interested in science, commented
that Paso Flores did not offer enough extracurricular science opportunities, while Keisha was
frustrated by the fact that most staff and teachers at Paso Flores did not encourage students to take
science seriously:
I: If you’re asked how supportive this school is of students’ interests in science, what would
you say?
Allen: I never really like get into it, but I can’t really count on it, but like, I’d probably say
like they don’t seem very supportive. Doesn’t seem like there’s too many programs for
science, for the kid who likes science here, and that’s really sad for me.
I: Do you know if there are [science] clubs?
Allen: I think there’s one. I heard from a teacher. . . she teaches Oceanography. Besides
that one, I don’t think there’s anything else.
I: How about, you don’t feel like, there’s no—
Allen: I think they just give you a class to give you a class.
Nobody [at this school] ever tells me, ‘‘Oh, I think you should do science, that’s good. You
should.’’ (Allen) Nobody tells me nothing about science. It’s like what I have to do. I have
to do science no matter what. . . They don’t talk to me about science. (Keisha)
Among the smaller number and percentage of students at Treadwell who felt that their school
was not supportive of students’ science interests, their comments were brief, vague, and
indifferent, that is, ‘‘I guess we could have more [science] activities’’ (Jennifer). All told, these
interviews suggested that Treadwell, with one of the lowest percentages of female science
teachers, enjoyed fewer constraints to its science program than did Paso Flores, with the highest
percent of female science teachers; these constraints included lack of appropriate science
equipment, frequent use of science teacher substitutes, discipline issues that superseded
instruction, and lack of school-wide support for science.
Discussion
This study was designed to examine a structural characteristic of secondary schools that has
yet to be fully explored in the science education and pipeline literature: percent female high school
science faculty. Counter to our hypotheses, the findings from this study indicate that the percent of
female science faculty did not have an effect on multiple components and mediators of students’
evolving science identities, nor did it affect gender differences in these measures. We suspect that
these findings speak to criteria for and constraints to role models in school environments as much
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as they do to some of the methodological limitations of our sample. Several points to this end are
discussed below.
Science Teachers and ‘‘Real Science’’
The qualitative data demonstrate that students respond positively to male and female science
teachers who are caring, challenging, engaged, passionate, fair, and/or linked to the ‘‘actual’’
practice of science in some concrete way. For the students whom we interviewed at two of the five
schools in this sample, teacher gender was an issue only to the extent that female science teachers
may not have had the concrete science experience that students respected and enjoyed. Of course,
female science teachers might have been different from their male colleagues in other significant
(and positive) ways, but this finding helps to explain why a greater proportion of female teachers
might not make a net difference to students’ science (self-) perceptions and interests overall. The
question then turns to why female science teachers perhaps do not have the same background as do
their male peers, which could reflect gender differences in teaching career paths and reasons for
teaching generally (e.g., men may enter teaching before or after an extended career elsewhere,
while women enter teaching as a lifetime profession—see Feistritzer, 2005; McCray, Sindelar,
Kilgore, & Neal, 2002). One practical lesson from this finding is the importance of recruiting a
greater number of former or soon-to-be female scientists into the teaching profession in order to
‘‘level the field,’’ and encouraging these teachers to talk about their science histories and expertise.
Per the hypotheses for this study, it seems difficult for women to introduce a more expansive and
inclusive view of science (and therefore build students’ sense of connectedness to and interest in
science) when they are not associated with ‘‘real science’’ per se.
Applying a critical feminist lens, however, we also must ask the question: to what extent are
women not associated with ‘‘real science’’ precisely because they are women (above and beyond
their background and training)? More generally, why do students separate science teaching from
the ‘‘real’’ practice of science? In doing so, they limit the types of relationships that they can
develop with their teachers; specifically, they limit the number of science role models available to
them, science role models who, as the qualitative data show, are already in short supply. Girls may
be particularly disadvantaged by placing a premium on real-life science experience and by
drawing a line between ‘‘scientist’’ and ‘‘science teacher’’—it assures that they will have few, if
any, matched science role models in high school, and that science departments with higher
percentages of women faculty will have little effect on girls’ science outcomes, which is exactly
what the quantitative results suggest.
Of course, students probably do not close themselves off to role models advertently; rather, it
is something that surrounding contexts let happen, given an historically narrow definition of the
science and engineering pipeline. In this narrow pipeline, only a few traditional science careers are
considered ‘‘real’’ science (and only men are considered ‘‘real’’ scientists), meaning that many
students (even—or especially—students with strong interest in ‘‘real’’ science jobs) are robbed of
the opportunity to have role models or examples of ‘‘possible scientific selves’’ in formative stages
of their academic experiences. Practically, it is imperative to think deeply about the stereotypes
and assumptions surrounding both science and science teaching, that is, how the educational
community—male and female science teachers included—might have a stronger impact on
students’ science identities by making explicit the relationships between high school science
teaching and scientific work. This would necessarily involve professional development for
teachers to enrich their knowledge and classroom applications of scientific research.
Importantly, emphasizing connections between teaching and ‘‘real’’ science may do more
than strengthen girls’ science identities, or their sense of self in a perceived scientific world—it
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may strengthen science identities for students from other underrepresented demographic groups
as well, much as was proposed in the first hypothesis for this study. Female science teachers in the
ISME sample were concentrated at public schools with a greater percentage of Latino/a and
Black/African American students. This means that those students were more likely to have
science teachers who did not seem like ‘‘real’’ scientists, and this means that they had fewer role
models, or fewer sources of ‘‘credible’’ information available to them. This is a subtle way in
which inequities in the science pipeline may be reproduced, underscoring the need to bridge the
gap between science teaching and ‘‘real-life’’ practice.
Science Identity in Context
There is a second and related interpretation of the findings from this study: female science
teachers could not make a positive impact because they were constrained by the school contexts in
which they taught. First, the quantitative data show that for several dimensions of students’ science
identities (including college major interests, science grades, and science class perceptions) there
was a small but statistically significant difference that was attributable to school context. In this
sample, moreover, higher percentages of female science teachers were found in school contexts
with a greater proportion of lower performing and lower income students, and the qualitative data
indicate that for at least one of these schools, discipline was heavy, equipment could be scarce, and
students perceived a lack of schoolwide support for science. Put differently, more women were
teaching in an environment where strong science identities were potentially mitigated by fewer
contextual resources and different pedagogical demands. Perhaps these women simply had less
time to actively mentor students, and less freedom or latitude to challenge dominant scientific
images and assumptions, their science pedagogy as efficient (and traditional) as was that of their
male colleagues. Their focus was on the basics. Role modeling and radical inversions of scientific
epistemology were luxuries.
This leads us to the question of why female science teachers are concentrated at resourceconstrained, lower income schools in our sample. Perhaps women are attracted to this type of
environment because it speaks to their interest in social change (Gordon, 1993; McCray et al.,
2002). Alternately, perhaps they find more opportunities to teach in these environments, as the rate
of turnover among teachers is high. From a critical feminist perspective, however, we view this
finding as additional evidence of the reproductive nature of schooling and science; in this case, the
distribution of high school science teachers by gender and across schools reproduces a broader
sociopolitical system within which men occupy more elite science positions and have access to
more science resources than do women (just as Eisenhart & Finkel (1998) found among female
scientists). Women are ‘‘doing’’ science, but at a distance. The troubling corollary is that women
(and their male colleagues in these environments) cannot be effective catalysts for change in the
lives of the students for whom science identity development is most critical given these
parameters.
Looking at this argument from the perspective of practice theory, female science teachers
introduce students into ‘‘figured worlds’’ of school science within which they play largely
supporting roles, in service of practical exigencies of campuses that must meet the academic and
social needs of lower achieving and lower income student populations. Students may be
responsive to the bounded nature of science as their female (and male) teachers at these campuses
experience it, and develop a sense of possibility in the sciences that recognize and incorporate
these boundaries. Their teachers do not make a negative impact per se (they do not dissuade
students from developing confidence as scientific knowers and participants, or from pursuing
scientific careers), but they do not make a net positive effect either, so circumscribed are their
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efforts in educational contexts that must prioritize the nuts and bolts of education, such as
mathematics literacy and reading comprehension. Students at higher income schools, on the other
hand, might be more free to develop a ‘‘boundless’’ orientation to science, at least among those for
whom science as usual (androcentric, White, etc.) is familiar.
Conclusion
To be sure, our quantitative analyses were limited by the small number of school-level cases;
with only five units at Level 2, strong contextual effects are more difficult to identify (much as it is
difficult to identify strong student-level effects with a small student sample). Future research
should test the results of this study using a larger sample of institutions than that in the ISME study.
Future research would benefit as well from including variables that not only measure the aggregate
percent of female science faculty (as well as faculty from underrepresented racial/ethnic groups),
but also the types and character of teachers’ relationships with their students, to build on our
qualitative data and quantify which dimensions of these relationships are most important to
positive science outcomes for boys and girls. It will be imperative, moreover, to explore these
questions longitudinally and among older adolescents in postsecondary institutions, in order to
learn about the long-term impact of gender ratios among high school faculty and to hear students’
voices and attributions at a developmentally different stage.
Findings from this study send a clear message: far from reversing inequitable trends in the
science pipeline, teacher distribution patterns might reinforce them, as do narrow definitions of
what constitutes ‘‘real’’ science. The long-term answer we see is concerted analyses of the
processes by which teachers are ‘‘sorted’’ by gender (i.e., which career attitudes and teacher
training and hiring practices help to explain why women are located in more challenging
educational contexts?) as well as the processes by which school science resources can be more
evenly distributed, so that all students have access to an optimal combination of matched role
models and well-funded science programs. The goal is to instill in all students the sense that being
a scientist, knowing science, and feeling capable in science is possible, and to provide all students
with the information and inspiration they need to pursue their science interests. Questions about
role models also need to be addressed—specifically, how can educators strengthen the link
between science teaching and scientific work so that more science teachers are perceived as role
models, especially for students who think they may want to pursue science or science-related
careers? Programs and curricula that discuss science in broader terms—that is, where and how
science is practiced in a broader sense—would seem to be an important place to start. This would
have the additional effect of refining the construct of science identity, which we envision as being a
formidable cornerstone of future research on how to expand and bolster the pipeline.
Acknowledgments
The content of this article reflects the ideas and opinions of the authors and not necessarily
those of the funding agency. We also acknowledge the students, teachers, and administrators for
their cooperation and the entire CAPSI research staff for their collaboration on the ISME project,
especially Jerome Pine, Ellen Roth, and Claire Haagenson.
Notes
1
Two additional high schools participated in the research, but the survey respondent sample at each of
these schools was too small to include in the quantitative analyses given the methods selected.
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2
Because of the literacy level required by the survey, permission slips were not distributed to some
students in ELL and RSP classes.
3
That the dependent variables in this study derive from the same instrument as do the student-level
control variables underscores why we do not conduct an analysis of causal or temporal order among the
various parts of science identity in this article; again, analysis of longitudinal ISME data (to be conducted as
a follow-up to this study) will be able to tease apart the sequence of some of these variables.
4
During the interview, we did not ask students to compare male teachers and female teachers, that is, to
talk about the gender of their teacher(s) specifically. However, students indicated the gender of their
teachers in their descriptions of their science classes, and we sorted their comments accordingly, looking for
more subtle differences in the way they spoke about male and female faculty.
Appendix A: Variables Used in Multivariate Modeling
Dependent Variables
(1) Science class perceptions (six-item factor, a ¼ .79 for students in full 10th-grade
sample) (factor loadings for each item in parentheses):
—
—
—
—
Science class perception: my teacher thinks I could be a good scientist one day (.73)
Science class perception: I enjoy learning science this year (.73)
Science class perception: my teacher has high expectations of me (.65)
Science class behavior: I know as much about the content of this class as other
students do (.64)
— Science class perception: my teacher cares if I think science is interesting (.62)
— Science class perception: my teacher cares if I learn science (.61) (All items were
measured on a four-point scale, from 4 ¼ Agree strongly to 1 ¼ Disagree strongly)
(2) Science class grade (measured on a four-point scale, from 4 ¼ A to 1 ¼ Below C)
(3) Science view: scientists spend most of their time working by themselves (measured
on a four-point scale, from 4 ¼ Agree strongly to 1 ¼ Disagree strongly)
(4) Science view: scientists’ own opinions do not matter in their work (measured on a
four-point scale, from 4 ¼ Agree strongly to 1 ¼ Disagree strongly)
(5) Science view: people who are the same gender as I am have trouble getting jobs in
science in this country (measured on a four-point scale, from 4 ¼ Agree strongly to
1 ¼ Disagree strongly)
(6) Science self-concept: I could be a good scientist one day (measured on a four-point
scale, from 4 ¼ Agree strongly to 1 ¼ Disagree strongly)
(7) Science college major aspirations: interest in a Physical Science/Engineering college
major (continuous additive scale computed by summing students’ self-reported
interest in a Chemistry major, an Earth and Space Sciences major, a Physics major,
and an Engineering major, each measured on a four-point scale, from 4 ¼ Very
interested to 1 ¼ Not interested)
(8) Science college major aspirations: interest in a Life Science college major
(continuous additive scale computed by summing students’ interest in a Biology
major and an Environmental Studies major, each measured on a four-point scale,
from 4 ¼ Very interested to 1 ¼ Not interested)
Independent Variables—Student Level
(1) Gender (one dummy variable, 1 ¼ Male and 2 ¼ Female)
(2) Socioeconomic status (measured on a three-point scale, from 3 ¼ High to 1 ¼ Low;
items that comprise the SES scale include mother’s college attendance, father’s
college attendance, number of computers at home, and single-mother household; for
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the majority of students in this sample, SES data were corroborated by district free/
reduced lunch data)
(3) Racial/ethnic background (a series of five dummy variables: White, African
American/Black, Latino/a, Asian/Asian American, and Dual Ethnicity; reference
group was Other)
[Note: Students were classified as ‘‘Latino/a’’ if they marked one or more of the
following subgroups: Mexican/Mexican American/Chicano; South American/
Central American; Other Latino/Hispanic (Puerto Rican, etc.). Students were
classified as ‘‘Asian/Asian American’’ if they marked one or more of the following
subgroups: Chinese/Chinese American; Filipino/Filipino American; Japanese/
Japanese American; Korean/Korean American; Thai/Thai American; Southeast
Asian (Vietnamese, Cambodian, Laotian)/Southeast Asian American; Pacific
Islander/Samoan, Hawaiian, or Guamanian/Other Pacific Islander/Pacific Islander
American; South Asian (Indian subcontinent)/South Asian American. ‘‘Dual
Ethnicity’’ students marked two of the racial/ethnic categories: White, African
American/Black, Latino/a, Asian/Asian American, American Indian, and Middle
Eastern/Middle Eastern American. A sixth racial/ethnic category, ‘‘Other,’’ marked
three or more categories, marked the category Other, or skipped the question.]
(4) GPA (measured on a seven-point scale, from 7 ¼ Mostly As to 1 ¼ Mostly below Cs)
(5) Family orientation to science (three-item factor, a ¼ .79 for students in full 10th-grade
sample) (factor loadings for each item in parentheses):
— Perception of parents: they think science is interesting (.85)
— Perception of parents: they think it is important for me to learn science (.82)
— Perception of parents: they would be happy if I decided to pursue a science career
(.79) (All items measured on a four-point scale, from 4 ¼ Agree strongly to
1 ¼ Disagree strongly)
Independent Variable—School Level
(1) Percent female science faculty (continuous variable)
Appendix B: Statistical Description of Variables
Variable
Minimum
Maximum
Student-Level Variables (N ¼ 1138 students except where noted)
Dependent Variables
Science class perceptions (N ¼ 1028)
0.00
24.00
Science class grade (N ¼ 1028)
0.00
4.00
Science view #1: Scientists spend most of their
0.00
4.00
time working by themselves
Science view #2: Scientists’ own opinions do
0.00
4.00
not matter in their work
Science view #3: People who are the same
0.00
4.00
gender as I am have trouble getting science
jobs in this country
Science self-concept
0.00
4.00
Science college major aspirations: Physical
0.00
12.00
Science/Engineering
Science college major aspirations: Life Science
0.00
6.00
Mean
Std. Deviation
15.71
2.68
2.44
4.23
1.05
0.80
1.91
0.86
1.69
0.82
2.18
6.50
0.97
2.22
3.23
1.21
(Contiued )
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GILMARTIN
Appendix B: (Contiued )
Independent Variables
Gender: Female
Socioeconomic status
White
African American/Black
Latino/a
Asian/Asian American
Dual Ethnicity
GPA
Family science orientation
Institution-Level Variables (N ¼ 5 schools)
Independent Variable
Percent female science faculty
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
3.00
2.00
3.00
2.00
2.00
2.00
2.00
2.00
7.00
12.00
1.57
1.90
1.20
1.07
1.21
1.24
1.13
4.54
8.62
0.49
0.77
0.40
0.25
0.41
0.43
0.33
1.76
2.19
0.17
0.65
0.40
0.22
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