STEM development: Predictors for 6th-12th grade girls' interest and confidence in science and math

Abstract

In order to increase the representation of women in the science, technology, engineering, and math (STEM) fields, it is important to understand the developmental factors that impact girls' interest and confidence in STEM academics and extracurricular programs. Research indicates that greater confi-dence leads to greater interest and vice versa. This study identifies factors that impact girls' interest and confidence in math and science, defined as girls' STEM development. Using Bronfenbrenner's bioecological model of human development, several factors were hypothesized as having an impact on girls' STEM development; specifically, the macrosystems of region of residence and race/ethnicity, and the microsystems of extracurricular STEM involvement, family STEM influence, and math/sci-ence teacher influence. Hierarchical regression analysis results indicated that extracurricular STEM involvement and math teacher influence were statistically significant predictors for 6th–12th grade girls' interest and confidence in math. Furthermore, hierarchical regression analysis results indicated that the only significant predictor for 6th–12th grade girls' interest and confidence in science was sci-ence teacher influence. This study provides new knowledge about the factors that impact girls' STEM development. Results can be used to inform and guide educators, administrators, and policymakers in developing programs and policy that support and encourage the STEM development of 6th–12th grade girls.

Full text

Journal of Women and Minorities in Science and Engineering 19(2), 121–142 (2013)
ISSN 1072-8325/13/$35.00 Copyright © 2013 by Begell House, Inc. 121
STEM DEVELOPMENT: PREDICTORS
FOR 6TH-12TH GRADE GIRLS’
INTEREST AND CONFIDENCE IN
SCIENCE AND MATH
Carol A. Heaverlo,1,* Robyn Cooper,2 & Frankie Santos Lannan3
1Department of Program for Women in Science & Engineering, Iowa State
University, Ames, Iowa 50011, USA
2School of Education, Drake University, Des Moines, Iowa 50311, USA
3School of Education, Iowa State University of Science and Technology, Ames,
Iowa 50011, USAl
* Address all correspondence to: Carol Heaverlo, E-mail: heaverlo@iastate.edu
In order to increase the representation of women in the science, technology, engineering, and math
(STEM) elds, it is important to understand the developmental factors that impact girls’ interest and
condence in STEM academics and extracurricular programs. Research indicates that greater con-
dence leads to greater interest and vice versa. This study identies factors that impact girls’ interest
and condence in math and science, dened as girls’ STEM development. Using Bronfenbrenner’s
bioecological model of human development, several factors were hypothesized as having an impact on
girls’ STEM development; specically, the macrosystems of region of residence and race/ethnicity,
and the microsystems of extracurricular STEM involvement, family STEM inuence, and math/sci-
ence teacher inuence. Hierarchical regression analysis results indicated that extracurricular STEM
involvement and math teacher inuence were statistically signicant predictors for 6th–12th grade
girls’ interest and condence in math. Furthermore, hierarchical regression analysis results indicated
that the only signicant predictor for 6th–12th grade girls’ interest and condence in science was sci-
ence teacher inuence. This study provides new knowledge about the factors that impact girls’ STEM
development. Results can be used to inform and guide educators, administrators, and policymakers
in developing programs and policy that support and encourage the STEM development of 6th–12th
grade girls.
KEY WORDS: STEM, math, science, interest, condence, girls, development, 6th–12th
grade
1. INTRODUCTION
“By the age of 12, children have already formed rm beliefs about the
subjects at which they excel and those at which they fail.” (Burke and Mattis, 2007)
A lack of female representation in science, technology, engineering, and mathematics (STEM)
elds is not a new dilemma in the United States. According to the American Association of

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Heaverlo, Cooper, & Lannan
122
University Women (AAUW) (AAUW, 2010), in the 1960s women made up a mere 1% of the
engineers and 27% of the biologists. Forty years later, in 2000, 11% of the engineers were female
and 44% of biologists. Although the percent of females employed in social science careers has
almost reached parity, women still represent a very small percentage of those employed in the
physical science careers, including engineering, physics, and chemistry elds (AAUW, 2010). In
2000, the Commission on the Advancement of Women and Minorities in Science, Engineering,
and Technology released their ndings on the US Science, Engineering, and Technology (SET)
labor force. Recognizing the omission of underrepresented populations, the commission called
for a drastic change so the SET workforce more accurately represented the US population and
was inclusive of women, ethnic minorities, and persons with disabilities. As jobs requiring skills
in science, technology, engineering, and math continued to increase, the commission urged a
nationwide call to action to increase the number of students in the STEM pipeline beginning at
the elementary and middle school levels (AAUW, 2010).
Despite this call to action, young girls and women are still confronted with obstacles on
their pathway to an education and career in STEM. From a lack of female role models and men-
tors; engrained societal gender stereotypes reinforced by friends, family, and community; lack of
condence due to internal feelings of inadequacy (Imposter Syndrome); to differential teaching
practices in the classroom (Besecke and Reilly, 2006; Buck et al., 2008; 2002; Cleaves, 2005),
there is no single or simple solution to this complex challenge. Whether it is a look into the past
or contemplating the future, scientic exploration and technological innovation are deeply con-
nected to the economic sustainability of the United States. Advancement in STEM is essential for
national security, economic growth, health, and stability of the nation and this country’s citizens
(Burke and Mattis, 2007). The education system in the United States must begin to produce a
larger and more diverse group of exceptional scientists and engineers in order to remain globally
competitive (Clewell et al., 1992). Margolis and Fisher (2002) emphasized that the way to ensure
competitiveness and maximize creativity and innovation in the STEM workforce is to attract and
retain women.
Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Eco-
nomic Future was drafted in response to a congressional request to create a list of the top 10
priority actions that federal policymakers could initiate to increase economic vitality, ensure
prosperity, and improve the global competitiveness of the United States. Many of the recom-
mendations in the original report were directly related to science and engineering (e.g., 10,000
Teachers, 10 Million Minds: Increase America’s talent pool by vastly improving K–12 science
and math education) (National Research Council, 2007). A few years later, Rising Above the
Gathering Storm Revisited: Rapidly Approaching Category 5 (National Research Council, 2010)
suggested that despite some valiant educational efforts during the previous ve years, the public
school system (14,000 systems) has improved very little, particularly in the areas of math and
science.
“Scientists are made not born” (Burke and Mattis, 2007, p. 4) and the literature reveals nu-
merous obstacles girls encounter that inuence the process while impacting their interest in sci-
ence and math education. Sadker et al. (2009) suggested that the barriers girls encounter in their
pursuit of STEM education and careers often begin early on in their academic experiences. Girls
receive less encouragement at home and in the classroom than do boys who indicate an interest
in STEM, there is a lack of female STEM role models, fewer STEM extracurricular activities,
societal gender role stereotypes, and a culture that supports male competence (AAUW, 2010;
Andre et al., 1999; Herbert and Stipek, 2005; Jacobs et al., 2002; Simpkins and Davis-Kean,

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STEM Development: Predictors
123
2005). As a result, girls are beginning to opt out of science and math courses in 6th–8th grades
(Burke and Mattis, 2007).
Research has shown that the gender gaps in middle and high school math and science test
scores and achievement are no longer statistically signicant (AAUW, 2010; Corbett et al., 2008;
Planty et al., 2009; Lee, Grigg, and Donahue, 2007), and while girls are performing as well as
boys in math and science, there is a distinct loss in interest and lack of condence in STEM
areas that begins early on in their academic experience (AAUW, 1998; James and Smith, 1985;
Brottman & Moore, 2008). Girls and boys begin to form opinions about their abilities as early
as elementary school, and as they progress from sixth through twelfth grade and math becomes
more challenging, students report receiving less support from their parents, teachers, and peers
(Eccles et al., 1993; Jones et al., 2000). Both boys and girls appear to be equally motivated to
do well academically; girls, however, seem less condent that their endeavors will be successful
(Huang and Brainard, 2001; Lantz and Smith, 1981). The gender gap in self-condence begins
to widen during high school when boys indicate higher levels of self-condence and girls report
higher levels of anxiety and lower levels of condence about their abilities in science and math
courses. As students’ beliefs that math courses increase in difculty, so too does their level of
anxiety about their ability to do well (Beilock et al., 2010; Fredericks and Eccles, 2002; Hyde
et al., 1990; McGraw et al., 2006; Pajares and Miller, 1994). Researchers have shown that lack
of self-condence in one’s ability to do math is detrimental to the continuation of math studies
(AAUW, 1998; Burke and Mattis, 2007; Fennema, 2000; Hannula and Malmivouri, 1997; Linn
and Hyde, 1989; Sherman, 1982). Some researchers have shown that the level of self-condence
a student has in high school is the strongest predictor for girls choosing to pursue a STEM degree
program in college (Ethington and Wole, 1988; Huang and Brainard, 2001).
During the past 20 years, a great deal of research has focused on gender differences in sci-
ence and math achievement. However, there is little research that takes a holistic approach by
simultaneously examining the impact of various environments in which girls develop that may
inuence their interest and condence in science and math.
The purpose of this study was to examine the extent to which the various environments (e.g.,
home, school, peer groups) in which girls develop inuence their interest and condence in sci-
ence and math. Using Bronfenbrenner’s (2005) bioecological model of human development as a
conceptual framework, several key environments were identied and hypothesized as impacting
6th–12th grade girls’ interest and condence in science and math. Understanding the factors that
inuence girls’ interest and condence in science and math will inform strategies that may poten-
tially increase participation and retention in STEM elds for girls and women.
2. CONCEPTUAL FRAMEWORK
According to Clewell and Campbell (2002), the theories used to trace the trajectory and progress
of women in STEM fall into four main categories: “testing-based theories, biologically-based
theories, social-psychological theories, and cognitive theories” (p. 255). Role-model theory has
also emerged as a signicant framework in which a girl’s STEM development has been discussed
(Gilmartin et al., 2007; MacDonald, 2000; Wallace and Haines, 2004; Zirkel, 2002). However,
little is known about how aspects of these life segments, combined in the day-to-day lives of mid-
dle and high school girls, affect their interest and condence in science and math in particular.
This study takes a unique holistic approach using Bronfenbrenner’s (2005) bioecological theory

Journal of Women and Minorities in Science and Engineering
Heaverlo, Cooper, & Lannan
124
of human development, which allows for simultaneously examining the impact of several envi-
ronments in which middle and high school (grades 6–12) girls interact on a daily basis that may
impact their STEM development, specically, their interest and condence in science and math.
Bronfenbrenner’s (2005) model is comprised of ve evolving “systems” classied as the mi-
crosystem, mesosystem, exosystem, macrosystem, and chronosystem. The classication of the ve
“nested” systems progresses from the layer or level closest to the individual (microsystem) to the
outermost layer (macrosystem). Microsystems are distinguished as “patterns of activities, roles, and
interpersonal relations experienced by the developing person in a given face-to-face setting with
particular physical and material features” (Bronfenbrenner, 2005, p. 148). Bronfenbrenner suggest-
ed that systems at the micro level may include, for example, the developing person’s home, school,
or playground. The mesosystem includes the associations and processes that are occurring between
two or more microsystems containing the developing individual. The third layer is the exosystem,
in which the developing individual does not actively participate, but is inuenced by the events
and processes that occur between settings in the system. The macrosystem, according to Bronfen-
brenner (2005), “may be thought of as a societal blueprint for a particular culture, subculture, or
other broader social context” (p. 150). Bronfenbrenner further explained macrosystems as belief
systems, social conduct, and economic resources that are passed on from generation to generation.
Examples of macrosystems include social class, ethnicity, and region of residence (Bronfenbrenner,
2005). The chronosystem was included by Bronfenbrenner to measure the temporal changes within
an individual’s environment. The design in this study is cross-sectional; therefore, the inuences
of the chronosystem are not measured. Bronfenbrenner noted that changes that occur in any one
system will reverberate throughout each of the other layers.
In this study, three microsystems, teacher inuence, family STEM inuence, and extracur-
ricular STEM involvement, and two macrosystems, race/ethnicity and region of the state, were
hypothesized as impacting girls’ STEM development, specically, interest and condence in
math. Figure 1 provides an illustration of the adaptation of Bronfenbrenner’s (2005) model and
the identication of the study variables in each of the systems.
2.1 STEM Microsystems
2.1.1 Teacher Inuence
The K–12 classroom sets the educational foundation for the pathway to a STEM career. Gains
in the last three decades in terms of girls’ achievement in math and science education demon-
strate how critical learning environments are in encouraging abilities and interests AAUW, 2010;
NCES, 2000). According to Huang and Brainard (2001), the attention of researchers has increas-
ingly turned to the effects that institutional and classroom climate has on a student’s interest and
condence in science and math abilities. They suggested that in addition to the effect that per-
formance and experience has on self-condence, the quality of teaching also plays a signicant
role in condence levels.
It is also well documented in the literature that girls have received less instructional time
in the classroom, less help, and fewer challenges resulting in a lack of engagement, lower self-
condence, performance, and persistence in STEM courses (Burke and Mattis, 2007; Colbeck et
al., 2001; Klein , 2004; Morozov et al., 2008; Sadker et al., 2009). During one particular profes-
sional development opportunity described by Sadker et al. (2009), teachers were stunned to look
back at classroom videotapes and see themselves teaching subtle gender lessons. Sadker et al.
(2009) reported observing hundreds of classrooms in which male students regularly monopo-

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STEM Development: Predictors
125
FIG 1: Hypothesized predictor variables – adaptation of Bronfenbrenner’s (2005) Bioecological
Model

Journal of Women and Minorities in Science and Engineering
Heaverlo, Cooper, & Lannan
126
lized classroom conversations, asked and answered more questions, received more praise, and
received help when perplexed. While usually unintentional, the microinequities that occur in the
classroom and that are not addressed continue to reinforce girls as spectators in the classroom
rather than engaged participants (Sadker et al., 2009).
2.1.2 Family STEM Inuence
Family is one of the most signicant contexts of socialization in early childhood and adolescent
development. Parental inuence has been found to impact career preferences especially when
it comes to nontraditional careers (Dryler, 1998). Clewell and Anderson (1991) note that a lack
of parental expectation and encouragement discourages girls’ interest in science. Furthermore,
family background and parental inuence have been linked to math achievement and attitudes
toward math coursework as well (Clewell and Anderson, 1991). Hoffman et al. (2010) found that
engineering parents shaped their daughters perceptions of the engineering eld. A review of the
literature also indicates that girls are more likely to pursue a degree in STEM if a parent is em-
ployed in a career involving STEM (AAUW, 2010, 2004, 1998; Burke and Mattis, 2007; Clewell
et al., 1992; Corbett et al., 2008; Jeffers et al., 2004).
2.1.3 Extracurricular STEM Involvement
Extracurricular activities are an essential component of gender equity intercession according to
the AAUW report Under the Microscope (2004). Many out-of-school activities (e.g., science
and math clubs, 4-H, Mindstorm) provide girls with experiential learning and investigative op-
portunities in academic areas that are not part of the regular school day, but play an integral role
in shaping interest and condence in STEM courses and careers (Bruyere et al., 2009; Darke et
al., 2002). Wood (2002) studied the impact of extracurricular science activities and found that
involvement affects interest in future science participation.
2.2 STEM Macrosystems
2.2.1 Region of Residence
Access to resources remains one of the primary educational challenges to date for schools in
rural districts (Alliance for Education, 2010; Mathis, 2003; Monk, 2007). Many teachers within
smaller school districts are being asked to teach outside their area of expertise. Clewell et al.
(1992) noted that if an educator conveys a lack of condence in teaching the content of a specic
subject, that lack of self-condence can affect girls’ interest and condence in the subject. Hiring
teachers certied to teach more than one higher-level class in math and science is an economic
issue for smaller schools as well as a hiring and retention issue based on the salaries smaller dis-
tricts are able to offer in comparison to larger districts (Wellenstein et al., 2006).
Hands-on, experiential STEM activities have been highlighted as a strategy to increase girls’
interest in STEM elds (AAUW, 2010; AAUW, 1998, 2004; Bottoms and Uhn, 2001; Burke and
Mattis, 2007; Clewell, 2002; Corbett et al., 2008). If a district has the money to purchase equip-
ment and stock their labs, teachers are more prepared to provide interactive experiences than
those districts without adequate funding for STEM resources, usually rural school districts. Fur-
thermore, because urban areas are more populated as a result of the diverse business and industry
sector, there may be fewer opportunities for girls from a rural school district to connect with fe-
male role models and professionals from STEM business and industry (Wellenstein et al., 2006).

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127
2.2.2 Race/Ethnicity
Women of color are often faced with racism in addition to sexism in science (Fancsali, 2000).
In terms of girls and STEM, the research investigating the relationship of gender and ethnicity
is very limited (Fancsali, 2000). The barriers are complex and involve both psychological and
structural factors that are generally present in high school and make it more difcult for under-
represented minority groups and women to succeed in STEM elds (Gilmartin et al., 2006). The
National Action Council for Minorities in Engineering (Markow and Moore, 2001) found that
interest in taking advanced math courses among minority girls (74%) was greater than that of
nonminority girls (67%); however, the availability of such courses was less at the minority stu-
dent’s schools (45%) than the availability at nonminority schools (52%).
There is a tremendous amount of literature (both current and historical) on the lack of fe-
males in STEM elds, potential reasons why, and strategies for inclusiveness. Although the
achievement gap between girls and boys in early science and math has closed, there is still much
to ascertain about what may impact and/or inuence 6th–12th grade girls’ interest in math and
science.
3. METHODS
The following research questions guided this study.
1. Is there a statistically signicant difference between middle school girls’ (6th–8th grade)
and high school girls’ (9th–12th grade) (a) interest in math, (b) interest in science, (c)
condence in math, and (d) condence in science?
2. To what extent do race/ethnicity, region of residence, family STEM inuence, extracur-
ricular STEM involvement, and math teacher inuence predict (a) math interest and
(b) math condence for middle school (6th–8th grade) and high school girls (9th–12th
grade)?
3. To what extent do race/ethnicity, region of residence, family STEM inuence, STEM
extracurricular STEM involvement, and science teacher inuence predict (a) science in-
terest and (b) science condence for middle school (6th–8th grade) and high school girls
(9th–12th grade)?
3.1 Sample
Participants were middle and high school girls who attended one of three Taking the Road
Less Traveled Career Conferences for Girls (TRLT) conducted by the Program for Women
in Science and Engineering (PWSE) at Iowa State University. As a signature K–12 outreach
program, the TRLT conferences occur three times per semester and were developed for middle
and high school girls to expose them to nontraditional career opportunities in STEM. The
conference is advertised to middle school and high school girls through their math and sci-
ence teachers and in some cases talented and gifted teachers. Girls who are interested in or
encouraged to attend the one-day conference register through the teacher(s) who accompanies
the girls to the conference. In an effort to identify interest and condence in math and science
prior to the start of conference activities and minimize the inuence of conference activities on
participant responses, surveys were distributed and collected prior to the start of each confer-

Journal of Women and Minorities in Science and Engineering
Heaverlo, Cooper, & Lannan
128
ence. Nevertheless, participants initially indicated an interest or were encouraged to attend the
STEM-focused career conference; thus, participants may not represent the general population
of middle school and high school girls.
A total of 885 middle school girls and 398 high school girls attended one of the three TRLT
conferences with 591 (66.8%) middle school girls and 280 (70.3%) high school girls completing
and returning surveys for an overall sample size of n = 871. Participants’ ages ranged from 11 to
18 (M = 13.86, SD = 1.55) with approximately 16% (140) identifying in a race/ethnic minority
group and 37% (323) indicating they attended school in a rural district.
Data were collected using a 47-item survey instrument developed for this study. The sur-
vey instrument included questions about math and science classes, experiences in their math
and science classes relative to teaching factors (e.g., my teacher encourages me to ask ques-
tions), afterschool activities in which they are currently involved, the extent of involvement,
and those activities in which they would like to be involved, parents’ employment, and partici-
pant demographic questions. Response options were both Likert-type scales and open ended.
3.2 Variables
Four outcome variables were analyzed in this study. Students self-assessed both their interest and
condence levels in math and science. All four outcome variables were measured on a four-point
Likert-type scale with response options ranging from 1 = no interest to 4 = very interested, and 1
= no condence to 4 = very condent (e.g., I always do well and am comfortable in this activity
area).
Five predictor variables were identied based upon the Bronfenbrenner’s micro- and macro-
systems discussed early in this paper. Macrosystem variables included the dichotomous variables
of race/ethnicity (0 = minority group) and region of residence (0 = rural). Microsystem variables
included the dichotomous variable of family STEM inuence (1 = at least one parent was em-
ployed in a STEM occupation), extracurricular STEM involvement (0 to 4 activities), and the
constructs of math teacher inuence and science teacher inuence. Both the math and science
teacher inuence constructs were created from separate exploratory factor analyses using a prin-
cipal component with a varimax rotation approach. Students responded to a series of statements
about their math and science classes using a 5-point Likert-type scale, with 1 = strongly disagree
to 5 = strongly agree. For the math teacher inuence construct, 9 of the original 14 items aligned
to represent one factor with an eigenvalue = 6.30 and variance explained = 45.0% (see Table
1 for factor structure and loadings). For the science teacher inuence construct, all 14 of the
original items aligned to represent one factor with an eigenvalue = 6.54 and variance explained
= 46.7% (see Table 1 for factor structure and loadings).
3.3 Data Analysis
Descriptive statistics were run on all variables as well as analyses to assure assumptions of nor-
mality were met, an important consideration when conducting independent samples t-tests and
multiple regression analyses (Green and Salkind, 2011).
To address the rst research question, independent samples t-tests were conducted on each
of the four outcome variables. Signicant difference between groups (middle vs. high school)
on each of the outcome variables would suggest a need for an additional predictor variable to
account for age or school level.

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STEM Development: Predictors
129
To address the second and third research questions, regression analysis with a sequential
hierarchical approach was used. In this approach, predictor variables are entered in the regression
equation in an order determined by the researcher (Tabachnik and Fidell, 2007). A sequential hi-
erarchical regression approach was the best analysis method for this study because it accounted
for the specic inuences of each of the systems identied in Bronfenbrenner’s model. Predictor
variables were entered in two blocks for each of the four different regression models. The rst
block included the macrosystem predictor variables of race/ethnicity and region of residence,
and the second block added the microsystem predictor variables of family STEM inuence, ex-
tracurricular STEM involvement, and math or science teacher inuence that corresponded with
the math or science outcome variable. Figure 2 depicts a visual model of the sequential hierarchi-
cal regression approach.
TABLE 1 : Factor analysis for the math and science teacher inuence construct
Factor
Item loadings
Math teacher inuence (α = .870)
The assignments given help me learn the subject being taught. .760
My teacher encourages my responsibility and effort. .668
I am comfortable asking questions in class. .661
My teacher encourages us to ask questions. .643
My teacher communicates high expectations. .629
I get helpful feedback from my teacher. .629
My teacher creates a classroom environment that allows me to learn. .624
My teacher asks questions that challenge me to think. .516
I enjoy learning the material in this class. .498
Science Teacher Inuence (α = .909)
I get helpful feedback from my teacher. .789
My teacher creates a classroom environment that allows me to learn. .747
My teacher encourages my responsibility and effort. .738
My teacher tells the class about resources that will help us learn about
the subject we are studying, when appropriate. .737
The assignments given help me learn the subject being taught. .723
My teacher encourages us to ask questions. .699
My teacher asks questions that challenge me to think. .696
In class, we use a variety of classroom activities and resources that help me learn. .693
My teacher encourages us to apply what we’ve learned to situations outside of class. .673
My teacher communicates high expectations. .646
My teacher talks about possible careers in science, technology, engineering,
and/or math. .618
I enjoy learning the material in this class. .616
We use technologies in class that help me learn. .576
I am comfortable asking questions in class. .573

Journal of Women and Minorities in Science and Engineering
Heaverlo, Cooper, & Lannan
130
FIG 2: Visual model of blocks in the sequential hierarchical regression analyses

Volume 19, Issue 2, 2013
STEM Development: Predictors
131
4. RESULTS
Prior to conducting descriptive and inferential statistical analyses, data were screened for outli-
ers and missing values. Further screening was then conducted to assess whether the variables
met assumptions of normality. Screening variables to ensure that data are distributed normally
is a precursor to conducting most inferential statistical analyses (Green and Salkind, 2011). This
study used independent samples t-tests and multiple regression (MR) analyses, both requiring
that assumptions of data normality are not violated. Table 2 displays the descriptive statistics for
all predictor and outcome variables.
4.1 Correlations
Examining bivariate correlations can assess the degree that variables are linearly related as well
as detect the existence of multicollinearity between two variables. Tabachnick and Fidell (2007)
state that “when variables are multicollinear or singular, they contain redundant information and
they are not all needed in the same analysis” (p. 83). Any bivariate correlation above .90 is con-
sidered to be multicollinear (Tabachnick and Fidell, 2007).
Pearson correlation coefcients were computed among all predictor and outcome variables,
resulting in 45 correlation coefcients that are represented in Table 3. Results showed no instanc-
es of multicollinearity between variables. However, when several correlations are computed, a
Bonferroni approach to control for a Type 1 error should be used in determining statistically
signicant correlations (Green and Salkind, 2011). Using the Bonferroni approach the new sig-
nicance level was .0011 (.05/45). With .0011 as the revised and conservative signicance level,
17 of the 45 correlations were deemed statistically signicant. These 17 signicant correlations
are noted with an asterisk (*) in Table 3.
TABLE 2: Descriptive statistics for predictor and outcome variables (n = 871)
Variables Min Max Mean SD
Region of residence (0 = rural) 0 1 .63 .48
Race/Ethnicity (0 = minority) 0 1 .84 .37
Family STEM Inuence (1 = STEM) 0 1 .32 .47
Extracurricular STEM involvementa0 4 .48 .73
Math teacher inuence 11 45 36.81 6.09
Science teacher inuence 19 70 56.83 9.22
Math interestb1 4 2.87 .90
Science interestb1 4 3.08 .91
Math condencec1 4 3.10 .84
Science condencec1 4 3.17 .80
aScale: 0 = 0 activities, 1 = 1activity, 2 = 2 activities, 3 = 3 activities, and 4 = 4 activities. bScale: 1 =
not interested, 2 = slightly interested, 3 = interested, 4 = very interested. cScale: 1 = no condence, 2 =
slightly condent, 3 = condent, 4 = very condent.

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132
4.2 Independent Samples t-Tests
Analysis of the four independent samples t-tests indicated that none of the four independent samples
t-tests produced statistically signicant results. Specically, results revealed there was no difference
between middle school girls and high school girls and their interest in math, t(869) = -1.61, p = .11.
A second independent samples t-test revealed there was no difference between middle school girls
and high school girls and their condence in math, t(869) = -1.50, p = .13. Third and fourth indepen-
dent samples t-tests revealed that there were no statistically signicant differences between these
two groups in their interest in science, t(869) = 1.72, p = .09, or their condence in science, t(869)
= -.176, p = .86. Table 4 provides a summary review of results for the independent samples t-tests.
4.3 Hierarchical Regression Analyses
Four sequential hierarchal regression analyses were conducted with two blocks for each regression
analysis. The rst block included the macrosystem variables of regions of residence and race/eth-
nicity. The second block included the microsystem variables of math or science teacher inuence,
family STEM inuence, and extracurricular STEM involvement. Because results of the indepen-
TABLE 3: Correlation matrix – all predictor and outcome variables (n = 871)
123456789
1
Region of
residence (0 =
rural)
––
2Race/ethnicity
(0 = minority) -.19* ––
3
Family STEM
involvement (1
= STEM)
.04 .01 ––
4
Extracurricular
STEM
involvement
-.21 .08 -.02 ––
5Math teacher
inuence .02 .05 -.05 .01 ––
6Science teacher
inuence .04 -.05 .01 -.02 .39* ––
7 Math interest .05 .04 .01 .13* .34* .16* ––
8Math condence .04 .03 .02 .07 .30* .14* .59* ––
9Science interest .02 -.02 .05 .06 .14* .39* .26* .18* ––
10 Science
condence .05 -.03 .01 .06 .14* .37* .20* .33* .60*
Note: * p < .0011 Bonferroni adjustment for multiple correlations to minimize chances of a Type 1 error.

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133
dent samples t-tests showed no statistically signicant differences between middle school and high
school girls’ interest and condence in math and science, there was no division of these groups in
the regression analyses. The following sections report results for the regression analyses on each of
the outcome variables.
4.3.1 Math Interest
For the outcome variable math interest, Table 5 shows results for block 1, F(2, 868) = 2.05, p = .13,
and block 2, (full model), F(5,865) = 28.03, p < .001, with only math teacher inuence (β = .340,
p < .001) and extracurricular STEM involvement (β = .128, p < .001) identied as statistically sig-
nicant predictors of math interest, accounting for 14% (R2 = .139) of the variance in math interest.
TABLE 5: Hierarchical regression coefcients for math interest (n = 871), R2 = .139
Variable blocks b SE b β
Macrosystems (block 1)
Constant 2.703 .093
Region of residence .108 .064 .058
Race/ethnicity .120 .084 .049
Macrosystems and microsystems (block 2 – full model)
Constant .790 .191
Region of residence .140 .061 .075
Race/ethnicity .061 .079 .025
Math teacher inuence .050 .005 .340***
Family STEM inuence .034 .061 .018
Extracurricular STEM involvement .170 .040 .138***
R2 = .005 for block 1; .139 for block 2 – full model. Note: * p < .05 , ** p < .01, *** p < .001.
TABLE 4: Independent samples t-tests – summary of results (n = 871)
Middle
school girls
High
school girls
Condence
intervals
M SD M SD tdf pLower Upper
Math interest 2.91 .90 2.80 .90 -1.61 869 .11 -.23 .02
Science interest 3.05 .91 3.16 .89 1.72 869 .09 -.02 .24
Math
condence 3.13 .83 3.04 .86 -1.50 869 .13 -.21 .03
Science
condence 3.17 .81 3.16 .05 -0.18 869 .86 -.12 .10
Note: Levene’s test for equal variances was not signicant, indicating that variances were assumed equal.

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4.3.2 Science Interest
For the outcome variable science interest, Table 6 shows results for block 1, F(2, 868) = .265, p
= .78, and block 2 (full model), F(5, 865) = 33.70, p < .01. In the full model, only science teacher
inuence (β = .396, p < .001) was a statistically signicant predictor of science interest, account-
ing for 16% (R2 = .163) of the variance in science interest.
4.3.3 Math Condence
For the outcome variable math condence, Table 7 shows results for block 1, F(2, 868) = 1.290, p =
.28, and block 2 (full model), F(5, 865) = 19.463, p < .01, with math teacher inuence (β = .303, p <
.001) and extracurricular STEM involvement (β = .078, p < .05) identied as statistically signicant
predictors of math condence, accounting for 10% (R2 = .10) of the variance in math condence.
4.3.4 Science Condence
For the outcome variable science condence, Table 8 shows results for block 1, F(2, 868) =
1.526, p = .22, and block 2 (full model), F(5, 85) = 27.698, p < .01, with only science teacher
inuence (β = .361, p < .001) identied as a signicant predictor of science condence, account-
ing for 14% (R2 = .138) of the variance in science condence.
5. DISCUSSION AND IMPLICATIONS
Increasing the number of underrepresented populations in the STEM elds is one strategy that
has been suggested in responding to the decreasing numbers of scientists and engineers in the US
(Starobin et al., 2010; Starobin and Laanan, 2008). For one to show interest in a discipline there
TABLE 6: Hierarchical regression coefcients for science interest (n = 871), R2 = .163
Variable blocks b SE b β
Macrosystems (block 1)
Constant 3.109 .094
Region of residence .023 .065 .012
Race/ethnicity -.048 .085 .085
Macrosystems and microsystems (block 2 – full model)
Constant .803 .191
Region of residence .013 .061 .007
Race/ethnicity -.009 .078 -.004
Science teacher inuence .039 .003 .396***
Family STEM inuence .107 .060 .055
Extracurricular STEM involvement .053 .040 .043
R2 = .001 for block 1; .163 for block 2 – full model. Note: * p < .05, ** p < .01, *** p < .001.

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STEM Development: Predictors
135
must be some level of afrmation to that discipline. Thus, to increase the number of girls pursuing
STEM elds, it is essential to develop strategies that encourage their interest and afrm their con-
dence in the areas of science and math. Note that for the girls in this study, results indicated there
was no signicant loss of interest or condence in math and science from middle school to high
school. This is encouraging since earlier research has noted a loss in interest and declining con-
dence that increases with age for girls (AAUW, 1998; Fennema, 2000; Herbert and Stipek, 2005).
TABLE 8: Hierarchical regression coefcients for science condence (n = 871), R2 = .138
Variable blocks b SE b β
Macrosystems (block 1)
Constant 3.160 .082
Region of residence .082 .057 .050
Race/ethnicity -.052 .075 -.024
Macrosystems and microsystems (block 2 – full model)
Constant 1.329 .176
Region of residence .080 .054 .048
Race/ethnicity -.022 .070 -.010
Science teacher inuence .031 .003 .361***
Family STEM inuence .029 .054 .017
Extracurricular STEM involvement .054 .035 .050
R2 = .004 for block 1; .138 for block 2 – full model. Note: * p < .05, ** p < .01, *** p < .001.
TABLE 7: Hierarchical regression coefcients for math condence (n = 871), R2 = .101
Variable blocks b SE b β
Macrosystems (block 1)
Constant 2.980 .087
Region of residence .084 .060 .048
Race/ethnicity .081 .079 .036
Macrosystems and microsystems (block 2 – full model)
Constant 1.418 .182
Region of residence .093 .058 .054
Race/ethnicity .036 .075 .016
Math teacher inuence .042 .004 .303***
Family STEM inuence .064 .058 .036
Extracurricular STEM involvement .089 .038 .078*
R2 = .031 for block 1; .101 for block 2 – full model. Note: * p < .05, ** p < .01, *** p < .001 .

Journal of Women and Minorities in Science and Engineering
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The goal of this study was to identify environments that contribute positively to girls’ STEM
development. Consequently, results of this study will aid in the development of strategies aimed
at encouraging girls’ interest and afrming their condence in the areas of math and science.
Results revealed that of the ve hypothesized predictor variables within the macro- and micro-
systems, family STEM inuence, extracurricular STEM involvement, teacher inuence, race/
ethnicity, and region of residence, only teacher inuence was a signicant predictor for interest
and condence in both math and science.
Because the construct of teacher inuence was a signicant predictor for girls’ interest and
condence in math and science, it is valuable to review those items that led to the development
of the teacher inuence factors (see Table 8). A synthesis of the common items that loaded for
both constructs revealed that teachers who encouraged girls’ responsibility and challenged them
within a supportive environment that inspired active engagement in their learning contributed
positively to girls’ interest in math and science. By focusing on encouraging and developing each
of these items in a teacher’s pedagogy and classroom environment, it is possible to advance girls’
interest and condence in math and science.
Progress during the past 30 years in terms of girls’ academic achievement in science and
math has demonstrated how critical positive learning environments are in generating interest
and growing abilities (AAUW, 2010). In addition to students’ performance, Huang and Brainard
(2001) found that the pedagogical skills of the classroom teacher are instrumental in developing
students’ condence.
A synthesis of the additional ve items that loaded for the science teacher inuence construct
demonstrate the effect that hands-on activities have in promoting girls’ interest and condence in
science. Specically, science teachers who used a variety of classroom activities, encouraged ap-
plication of concepts learned in class to outside activities, and provided additional resources that
helped students learn the activities were signicant in advancing girls’ interest and condence
in science. When hands-on activities are used in the science classroom, it is important to nd
those activities that are engaging for both male and female students. Weber and Custer (2005)
suggest that activities that are inherently appealing to boys should be reviewed and, if necessary,
revised to ensure they are gender-balanced in order to overcome the disparity in topical interest.
Introducing girls to hands-on science, technology, engineering, and math activities early on in
their educational experience is critical for cultivating interest in STEM (Baine, 2008 ). Results of
this study provide detailed information on teaching skills that positively impact girls’ interest and
condence in math and science. The identication of these skills can be used to improve science
and math teaching.
Additionally, as demonstrated through the results of this study, when opportunities for extra-
curricular activities relating to math are offered, their impact is signicant in developing girls’ in-
terest in math, suggesting the need to further fund and provide math-type extracurricular activities.
5.1.1 Recommendations
The results of this study identied the specic math and science classroom experiences that sup-
ported girls’ interest and condence in math and science. Therefore, based on the results of this
study, the following strategies are recommended toward encouraging, developing, facilitating,
and afrming girls’ interest and condence in math and science:
Teacher Related
1. Communicate high expectations while providing support for meeting those expectations.

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STEM Development: Predictors
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2. Encourage a positive learning environment, where questions are encouraged and girls
feel comfortable asking questions.
3. Discuss career opportunities in STEM.
4. Provide additional resources to encourage further exploration.
Example resources:
• Engineer Your Life – www.engineeryourlife.org
• Girl Start – www.girlstart.org
• Scitable by nature Education – www.nature.com/scitable
• Figure This! – www.gurethis.org
• WEPAN Knowledge Center – www.wepanknowledgecenter.org
5. Create a connection between classroom content and real world applications.
6. Use a variety of classroom activities and resources.
7. Integrate the use of various technologies that support learning.
8. Provide feedback to students that will help them be successful in class.
Extracurricular Related
9. Share what extracurricular STEM opportunities are available for girls in STEM.
Example Activities:
• Math and Science clubs
• Environmental clubs
• Career Conference for Girls
• 4-H, Future Farmers of America (FFA)
• State Science Fair
• Project Lead the Way
• First Lego League
10. Determine what kinds of STEM extracurricular opportunities currently exist in the
school district and community. If there are gaps in availability of programs or diversity
in programs, then develop programming to meet the needs based on interests.
6. FUTURE RESEARCH AND LIMITATIONS
The use of Bronfenbrenner’s bioecological model of human development to investigate the fac-
tors that inuence the STEM development of 6th–12th grade girls is unique to this study. While
examining relationships within and between these nested systems, future research might con-
sider investigating the impact of STEM professional development on student engagement in the
classroom. Also of interest would be exploring whether there are differences between public,
parochial, and magnet schools in the STEM development of students. Examining the impact of
factors that are situated within the exosystem (No Child Left Behind, State STEM initiatives, for

Journal of Women and Minorities in Science and Engineering
Heaverlo, Cooper, & Lannan
138
example) and how they inuence the classroom environment and STEM development may be
appropriate for future examination as well.
Additional research should be conducted that incorporates the same research design used
in this study, but with a longitudinal approach thus addressing Bronfenbrenner’s (2005) chrono-
system not accounted for in this study. The results of this study were achieved through a cross-
sectional design and determining the “developmental” factors that contribute to girls’ interest and
condence in math and science might be better served through a longitudinal study that measures
these factors throughout their adolescent developmental trajectory.
Since this study focused only on 6th–12+ grade girls and the factors that inuence their
interest and condence in math and science, there were no gender comparisons made with 6th–
12th grade boys or control groups used in the research design. However, since US academic
achievement scores for both girls and boys are dismal when compared internationally (OECD,
2009), it would be benecial to conduct a similar study to determine if the same factors identi-
ed in this study were also signicant predictors for boys’ interest and condence in math and
science.
Similar studies should also be conducted in different regions of the country. Results of
this study revealed that region of residence was not a signicant predictor of interest and con-
dence in math and science. However, it is possible that these results were not signicant because
Iowa is a fairly homogeneous state with minimal to at most medium variations between rural and
nonrural state demographics and resources. Similar studies in other regions of the country would
help to determine whether the results of this study are unique to Iowa and its demographics or if
the macrosystem, region of residence, is not a universal signicant predictor for girls’ interest and
condence in math and science.
Although the inuence of peers was outside the scope of this study, previous research has
suggested the important role that peers have on whether girls’ pursue and persist in STEM elds
(Clewell, 2002; Sadker et al., 2009; Stake and Nickens, 2005). Future research might include the
inuence of peers at the microsystem level of STEM development.
Finally, the middle school and high school girls in this study self-selected or were encour-
aged to attend a STEM career conference and may not be representative of the general middle
school and high school population. Future research could replicate this study by using a sample
of participants that are not a part of a STEM-related program or event.
7. CONCLUSION
The lack of girls and women pursuing and persisting in STEM elds is a complex issue that be-
gins with experiences early on in a girls’ academic journey. This study illuminates the important
role that math and science teachers, along with extracurricular STEM involvement, have on girls’
STEM development. What is particularly encouraging within the results of this study is (1) that
positive outcomes regarding girls’ interest and condence in math and science can be impacted
through professional development opportunities for educators, parental awareness of STEM re-
sources and STEM careers, and evaluating and adding extracurricular STEM activities based on
girls’ interest in participating, and (2) that girls’ interest and condence in math and science is
being maintained from middle school to high school.
It is not enough for change to take place at the microsystem levels alone. State and federal
agencies have indicated that increasing the number of girls and minorities in STEM is a priority

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in order to address new and emerging issues that require a growing and diverse pool of scientists
and engineers. As a result, reports and initiatives have been generated to illustrate the pathway to
STEM; however, to implement the recommendations made by these reports it is imperative that
monetary resources are consistently available.
Not every girl will choose a STEM career, but she should have the opportunity to make that
decision by knowing what is available to her. If she desires to pursue a STEM eld, her STEM
developmental path should be cultivated, reinforced, and supported.
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