Running head: ADOLESCENT GIRLS’ IMPLICIT MATH ATTITUDES
The Effect of Dating Activity and School Environment on Adolescent Girls’ Explicit and
Implicit Math Attitude
Sarah E. Puckett
Distinguished Majors Thesis
University of Virginia
April 2009
Advisor: Fred Smyth
Second Reader: Brian Nosek
Adolescent Girls’ Implicit Math Attitudes 2
Abstract
This study investigated the effects of dating and school environment on implicit and explicit
math attitudes among adolescent girls who applied to the Young Women’s Leadership Charter
School (YWLCS), which emphasizes math and science competence. Ninety-three female
adolescents, (66 YWLCS and 27 control participants), completed two web-based study sessions.
The first session was available beginning the first month of the academic year, and the second
beginning in January. Each session included a math attitude Implicit Association Test (IAT) and
self-report items gauging explicit academic attitudes and dating behaviors. Contrary to
hypotheses, neither attending YWLCS nor dating had a significant effect on students’ implicit
math attitudes. These findings support the need for more research on adolescent dating and
implicit academic attitudes.
Adolescent Girls’ Implicit Math Attitudes 3
The Effect of Dating Activity and School Environment on Adolescent Girls’ Explicit and
Implicit Math Attitude
A little more than fifteen years after Mattel’s Teen Talk Barbie declared, “Math class is
tough!” the stereotype that math and science are masculine domains persists in American culture.
However, studies show that there are few significant differences in actual cognitive abilities in
math and science between men and women (Hyde, Fennema, & Lamon, 1990; Hyde, Lindberg,
Linn, Ellis, & Williams, 2008). Although there has been significant progress in terms of the
numbers of women receiving doctoral degrees in science and mathematics, there has not been a
comparable improvement in the numbers of women faculty members in these fields (Bleeker &
Jacobs, 2004). Given that gender differences in mathematical ability alone cannot account for
why women should be underrepresented in math and science careers, it is imperative to
investigate what other factors may be involved. Research has shown that women, unlike men, are
frequently faced with negative stereotypes regarding their mathematical abilities and that these
stereotypes can impair performance (Spencer, Steele, & Quinn, 1999). Despite the similarity in
mathematical abilities between males and females, research consistently indicates a pervasive
implicit perception that math and science are masculine domains (Nosek, Banaji, & Greenwald,
2002; Nosek, Greenwald, & Banaji, 2006). These attitudes and stereotypes are fostered in girls
(and boys) throughout their lives, passed on, perhaps unconsciously, by parents (Parsons, Adler,
& Kaczala, 1982), teachers (Garrahy, 2001; Jussim & Eccles, 1992), and the media (Wilgosh,
2001).
There is evidence that parental perceptions of children’s capabilities are stronger
influences than past academic performance on the child’s own view of his or her abilities
(Parsons et al., 1982). Parsons et al. found that mothers and fathers have sex-differentiated views
Adolescent Girls’ Implicit Math Attitudes 4
of their children’s math abilities such that daughters were perceived as having to work harder to
succeed in math than sons were. Additionally, parents of sons viewed math as more important
for their child than did parents of daughters. Such beliefs influence children throughout their
development, and have long-term implications for children’s achievement choices and their selfperception (Bleeker & Jacobs, 2004). Aside from parental influences at home, teachers’
expectations of student performance are predictive of how students will actually perform
(Rosenthal & Jacobson, 1968, 1992; Jussim & Eccles, 1992). Rosenthal and Jacobson found that
teachers’ expectations caused change in students’ academic achievement even when accounting
for the students’ past achievement and motivation. Jussim and Eccles conducted a partial
replication of Rosenthal and Jacobson’s research in which they investigated teachers’ perceptual
biases with regard to math and gender. As predicted, they found that teachers view girls as
performing more highly than boys, and as trying harder than boys. However, teachers view boys
as having more math talent than girls. According to this view, girls who succeed in math do so
because they compensate for their lack of math talent by working hard.
Such biased views can unintentionally influence the way teachers treat students. Teachers
may treat students differently based on the student’s sex, even when they believe they are acting
in a gender-blind manner towards their students (Garrahy, 2001); however, this is not always the
case (Helwig, Anderson, & Tindal, 2001). If teachers treat boys and girls differently when
teaching math, and parents do not view math as being as important for their daughters as for their
sons, young women may be less likely to pursue challenging math classes at the college level,
thereby decreasing the likelihood of electing for a career in math or science. Consistent with this,
girls are also less likely to report a desire to pursue a mathematics-related career and are more
likely to have a lower self-efficacy in math and science than boys, regardless of their actual
Adolescent Girls’ Implicit Math Attitudes 5
performance and abilities (Bleeker & Jacobs, 2004). Indeed, boys consistently rate their selfefficacy for male-typed domains (like math and sports) higher than girls, while girls rate their
self-efficacy for female-typed domains (like language arts) higher than boys. Such gender
differences in self-beliefs may have a role in observed gender differences in certain achievement
behaviors (Jacobs, Lanza, Osgood, Eccles, & Wigfield, 2002).
Additionally, girls are susceptible to stereotypical media images of women, including
portrayals of men disapproving of female success (Wilgosh, 2001). Wilgosh concludes that such
images of women in media can lead girls to downplay their intelligence and academics. This in
turn can affect their choice of academic pursuits and careers, specifically in math and science
(Jacobs & Eccles, 1985).
Societal stereotypes are prevalent and difficult to avoid, and research indicates that girls
are influenced by the stereotypes about their gender and math at a fairly early point in their
education (Steele, 2003). Steele’s experiments show support for a theory of stereotype
stratification, where members of a negatively stereotyped group, despite viewing themselves as
belonging to the group, view themselves as an exception to the stereotype. Steele found that
elementary-school girls rated men as liking math more and being better at it than women;
however they viewed girls and boys as liking math equally and being equally talented in math.
In another study, elementary-school girls were told either a story about an adult mathematician
or about a child mathematician. When told the adult version, the girls were more likely to draw
an adult man, but when told the child version, they were more likely to draw a picture of a young
girl. This suggests that young girls believe at some level, perhaps an implicit level, that
something happens between childhood and adulthood that leads men to be more mathematically
inclined than women, even if, in reality, men are not.
Adolescent Girls’ Implicit Math Attitudes 6
Awareness of these stereotypes leaves girls vulnerable to stereotype threat, meaning that
simply knowing that one’s group is expected to perform less well in a domain will increase the
likelihood of poorer performance (Spencer et al., 1999; Steele & Aronson, 1995; Steele, 1997).
Research on stereotype threat has shown that it is likely to occur when participants care about
doing well on the given task, when the test items are of a high level of difficulty, when
participants believe they will be evaluated, and when negative stereotypes about their group are
directly applicable to the situation (Steele, 1997). These effects have been specifically
demonstrated for women in math and science (Keller, 2002; Shih, Pittinsky, & Ambady, 1999;
Spencer et al., 1999). Spencer et al. conducted several studies replicating the method used by
Steele and Aronson (1995) to investigate whether stereotype threat is implicated in women’s
math performance. The study demonstrated that women underperform on math tests when
stereotype threat is high. Additionally, when stereotype threat is lowered by telling participants
that the test produces no gender differences in performance, women perform better.
Despite the unsettling reality of the negative effects of stereotype threat, recent studies
have suggested that it can be reduced through interventions (Cohen, Garcia, Apfel, & Master,
2007; Dar-Nimrod & Heine, 2006). Dar-Nimrod and Heine investigated how women’s
mathematical performance is affected by the source to which gender differences in math
performance are attributed. Dar-Nimrod and Heine concluded that stereotype threat in this
domain can be reduced by presenting experiential causes, instead of genetic causes, of gender
differences in math performance. Thus, by exposing women to scientific theories that suggest
that math performance is the result of experience and not genetics, women’s performance in
math can be improved.
Adolescent Girls’ Implicit Math Attitudes 7
Furthermore, stereotype threat can be reduced by protecting women’s math performance
through the presence of female role models in a mathematics domain (Marx & Roman, 2002).
Marx and Roman found that women perform better when a math test is administered by a female,
and that this effect is heightened when the female administrator is highly math-competent,
compared to when she is not. Additionally, studies have shown that being in the presence of a
female role model can lead to women having higher career aspirations, by demonstrating
multiple-role self-efficacy, thereby communicating that it is possible to be successful in both
career and home life (Nauta, Epperson, Kahn, 1998). Having such a role model within the
classroom can change academic attitudes (Evans, Whigham, & Wang, 1995), and Nosek and
Banaji (2001) demonstrated in the laboratory a similar effect on implicit math attitudes. Building
on this literature, studies have also indicated that at-risk girls who have consistent contact with a
female mentor in a technology-rich, all-girls mathematics environment can dramatically improve
their confidence and performance in mathematics (Reid & Roberts, 2006). These benefits may
occur in large part because of operations at an implicit level, where girls are not aware that their
performance and confidence are improving, yet it appears in measures. Thus, the current research
aims to more directly measure implicit associations within the context of an all-girls learning
environment.
There is evidence that simply being in a single-sex learning environment can enhance
girls’ academic achievement (Inzlicht & Ben-Zeev, 2000) and reduce adolescent delinquency
(Caspi, Lynam, Moffitt, & Silva, 1993). Inzlicht and Ben-Zeev (2000) studied the effects of
being a numerical minority or majority during difficult math and verbal tests on women’s test
performance. Female participants in the minority condition displayed impaired performance for
the math test only. The researchers also found that female participants’ performance was slightly
Adolescent Girls’ Implicit Math Attitudes 8
impaired when they were in a mixed-sex group with one other female and one male where
females were still the majority, suggesting that performance is proportional to the number of
males present in the group. The researchers conclude that girls benefit from being placed in
single-sex classrooms. Riordan (1998) cites a reduction in gender differences in opportunities, in
gender differences in curriculum, and in gender bias in student-teacher and peer interactions as
potential benefits of a same-sex learning environment. Additionally, the culture of same-sex
schools emphasizes academics over youth-culture values, creating an environment that fosters
feelings of empowerment within the student (Riordan, 1998). However, contrary to Inzlicht and
Ben-Zeev (2000) and Caspi et al. (1993), Riordan asserts that single-sex schools work only for
those from a low socio-economic status or who are otherwise disadvantaged in society by racial
or gender factors. Despite encouraging evidence for the benefits of same-sex education for girls,
the data is mixed, with some studies showing no difference in academic performance for girls
from same-sex versus coeducational schools (Haag, 1998). There is even evidence that all-girls
schools perpetuate sexism through academic dependence and less rigorous instruction more than
co-educational schools that promote gender equality in the student and faculty bodies (Lee,
Marks, & Byrd, 1994).
A potential drawback to most of the research on girls’ attitudes about academics is that it
relies on self-report measures that, while capturing explicit attitudes, are not sensitive to the
implicit attitudes that affect behavior. Humans have the ability to introspect; however,
introspection is limited when it comes to accessing information that people are either unwilling
or unable to report (Nisbett & Wilson, 1977). Individuals may be unwilling to report information
that is in opposition to other values or to acceptable societal norms, and, even if an individual is
willing to report such information, it may simply be inaccessible to them (Nosek, Greenwald, &
Adolescent Girls’ Implicit Math Attitudes 9
Banaji, 2006). Implicit constructs, according to Greenwald and Banaji (1995), are
“introspectively unidentified (or inaccurately identified) trace[s] of past experience” that mediate
a response (p. 5). They proposed that studies of implicit cognition could provide researchers with
access to information that, via self-report, has formerly been inaccessible. In an effort to access
such unconscious attitudes and beliefs, researchers have developed implicit measures and applied
them to various areas of research, including racial attitudes, political attitudes, and gender
attitudes (Nosek et al., 2006). Despite the unconscious nature of these attitudes, they have been
shown to predict certain kinds of behavior (Greenwald, Poehlman, Uhlmann, & Banaji, in press).
The Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) has proved an
effective measure the strength of associations among concepts (Nosek et al., 2006). The IAT is a
within-participants experimental measure that compares the time a participant takes to sort words
or images into categories under two counterbalanced conditions, which, theoretically, indicates
the relative strength of the cognitive associations between concepts (Lane, Banaji, Nosek, &
Greenwald, 2007). Nosek et al. (2006) reviewed the Implicit Association Test in terms of its
internal validity, addressing the characteristics of the stimuli used, the procedure, and possible
extraneous influences. They also examined its construct validity, convergent and discriminant
validity, predictive validity, as well as issues of interpretation of IAT effects. They concluded
that there is strong evidence for the IAT’s internal, construct, and predictive validity.
The IAT has been applied to the study of men and women’s implicit attitudes about math
and science (Nosek et al., 2002). They found that implicit math-gender stereotypes favoring men
were equally strong for among men and women, and these stereotypes are related to one’s
performance in, liking of, and identification with math. Individuals with a strong implicit female
gender identity tended to have greater implicit negativity towards math and weaker self-
Adolescent Girls’ Implicit Math Attitudes 10
identification with math. The researchers concluded that groups (such as male and female)
communicate certain expectations (in this case, regarding math) of its members that can
influence preferences and performance.
Research by Kiefer and Sekaquaptewa (2007) has shown that implicit, but not explicit,
stereotyping interacts with gender identification to influence not only women’s will to pursue a
career in mathematics, but also women’s performance on math exams, indicating that implicit
attitudes influence behavioral outcomes even outside of an individual’s awareness. Their
research also indicated that a math=male stereotype impairs math performance for women.
Kiefer and Sekaquaptewa measured implicit gender-stereotyping of math among female college
students enrolled in a calculus class. Participants also completed explicit surveys on gender
identification, explicit gender stereotyping, and career goals. Findings indicate that implicit
gender-math stereotyping interacted with gender identification to predict women’s calculus
performance as well as their desire to pursue a career in math. Women who did not identify as
strongly with their gender showed less implicit gender stereotyping regarding math, performed
better in the class, and were more likely to report a desire to pursue a career in math. The authors
conclude that the underrepresentation of women in mathematics may be in part due to implicit
beliefs about women’s mathematical abilities.
Dasgupta and Asgari’s (2004) research applied implicit methodology to the study of
same-sex learning environments. They found that an all-girls educational setting reduces implicit
gender-stereotypic attitudes. When exposed to female leaders, the female participants were less
likely to express automatic stereotypic beliefs about women. Additionally, Dasgupta and Asgari
found that male-dominated university classes, such as courses in math and science, produced an
increase in automatic stereotypic beliefs, but only among students attending coed college, and
Adolescent Girls’ Implicit Math Attitudes 11
not for those attending a women’s college. This research indicates that local environments
influence women’s implicit beliefs about their ingroup.
The present research was conducted through the Full Potential Initiative (FPI), a program
of research the University of Virginia, which focuses on girls in a single-sex charter school, the
Young Women’s Leadership Charter School (YWLCS) where math and science competence is
emphasized. The YWLCS is the only all-girls public school in the city of Chicago. The goal of
the school is to offer urban girls in grades 7-12 a college preparatory curriculum emphasizing
math, science and technology, in hopes of equipping students with the skills necessary to pursue
degrees and careers in these fields where women have been historically underrepresented.
Applicants are admitted to the YWLCS by an annual lottery, and the student population mirrors
that of Chicago Public Schools as a whole, with 78 percent African American, 15 percent Latina,
6 percent Caucasian, 1 percent Mixed Race and 1 percent Asian. Additionally, 80 percent of the
students are eligible for either free or reduced price lunch. FPI applies implicit methodology to
the study of adolescent girls’ attitudes and stereotypes concerning math and science and gender
identities and compares their attitude and stereotype development with that of girls who were not
selected in YWLCS’s annual random lottery for admission. Much research on the effects of
same-sex environments is flawed by a selection bias in terms of who attends these schools. Girls
who apply and are accepted on basis of academic merit to all-girls schools are unique from those
in public schools to begin with. In such instances it would be difficult to determine with
confidence that differences exhibited in the two groups over time are attributable to the affects of
the school environment and not to preexisting differences. For example, some research indicates
that differences in outcomes between co-educational and same-sex schools disappear when
socio-economic status is taken into account (Haag, 1998). FPI’s quasi-experimental research
Adolescent Girls’ Implicit Math Attitudes 12
design capitalizes on YWLCS’s random admission lottery by recruiting enrolled and nonaccepted students into treatment and control groups, respectively. To the extent that
representative samples of each group are recruited, one can assume that any individual
differences among the girls are randomly distributed between the two groups; thus, the
composition of the two groups should be similar on all aspects aside from YWLCS attendance.
Participating girls complete IATs on math attitudes, stereotypes, and gender-role identification
every few months. Preliminary findings indicate that girls attending YWLCS show more implicit
and explicit preference for mathematics, show a weaker math=male stereotype, and identify less
with the stereotypically female identity.
Girls’ academic performance is by no means solely based on the students’ educational
environments. Individual factors, such as whether the student matures earlier or later relative to
her peers, are also related to academic outcomes. Early maturing girls are at greater risk for
delinquency, however this association is only found for girls in a mixed-sex school setting (Caspi
et al., 1993). Caspi et al. propose that the onset of puberty triggers a stimulus to others in the
environment, creating a press for more adult-like behavior among adolescent girls. They note
that in our age-graded society, girls become biologically mature long before they are expected to
be socially and financially independent. In an effort to acquire adult privileges, girls may
participate in norm-defying behavior. Caspi et al. cite their finding that early-maturing girls in
mixed-sex settings have more contact with deviant peers and have participated in more deviant
behaviors themselves than their peers in same-sex schools.
Early maturing girls also tend to start dating at a younger age than late maturing girls
(Phinney, Jensen, Olsen, & Cundick, 1990; Lam, Shi, Ho, Stewert, & Fan, 2002). This is
particularly problematic because early dating is correlated with lower academic achievement for
Adolescent Girls’ Implicit Math Attitudes 13
girls (Brendgen, Vitaro, Doyle, Markiewicz, & Bukowski, 2002; Neeman, Hubbard, and Masten,
1995; Scott, Stewart, & Wolfe, 2005). Brendgen et al. (2002) administered surveys to early
adolescents which asked about their social acceptance among same-sex peers, whether or not
they had a boyfriend or girlfriend, and their self-esteem. Additionally, teachers completed
measures on the students’ antisocial behavior and academic performance. Brendgen et al.
hypothesized that only those adolescents who had healthy relationships with same-sex peers
would benefit from a romantic relationship during early adolescence. Those in unhealthy
relationships with same-sex peers were predicted to be negatively affected by the presence of a
romantic relationship, because they would not have yet developed the skills necessary to handle
relationships in a same-sex context. Researchers found that the level of same-sex peer acceptance
moderated the relationship between having a romantic boyfriend or girlfriend and the students’
self-esteem and problem behavior. However, among these students there was no indication that
being in a relationship is related to specifically positive emotional and behavioral adjustment.
For early adolescents who were unpopular with same-sex peers, having a romantic relationship
was correlated with lower levels of self-esteem and higher levels of antisocial behavior.
Additionally, regardless of whether or not the student was popular with same-sex peers, being in
a relationship during early adolescence was negatively related to academic performance,
however only for girls. The researchers concluded that being in a romantic relationship is related
to self-esteem and antisocial behavior, but only for those students who are rejected by their
same-sex peers. By contrast, same-sex peer acceptance did not moderate a negative relationship
between being in a romantic relationship and academic performance for girls.
The current research focuses on the effects of dating activity in the context of quasiexperimental groups in the FPI study. Consistent with FPI’s preliminary findings, I predict that
Adolescent Girls’ Implicit Math Attitudes 14
girls attending YWLCS will show an increase in positive attitudes towards math from the fall to
the spring, but less so for the girls who have dated. I also predict that girls in the non-YWLCS
schools will show a decrease in positive attitudes towards math from the fall to the spring,
consistent with previous findings. Additionally, this decrease will be greatest in girls who have
dated. See Figure 1 for a graphical representation of hypotheses.
Method
Participants
Female adolescents between the ages of 12 and 19 attending schools in the Chicago,
Illinois area who had applied to attend the Young Women’s Leadership Charter School (YWLCS)
were eligible to participate in this study. Of these students, 208 completed a web-based study
session in September 2008. 143 were students enrolled in the YWLCS, and the remaining 65
participants were students enrolled in other schools. In January 2009, 139 participants completed
a study session, 104 of whom were enrolled in the YWLCS and 35 of whom were enrolled in
other schools. At the completion of the study, 93 participants had completed both sessions, 66
from YWLCS and 27 from the comparison group. YWLCS students were, on average, about a
year older (M=15.5, SD= 1.7) than control students (M= 14.4, SD= 1.1). All participants were
recruited from yearly lists of applicants to the YWLCS. Those enrolled in YWLCS are recruited
through their teachers to participate in studies through the Full Potential Initiative. Non-YWLCS
students are recruited through presentations at open houses prior to the admission lottery or by
direct mail from lists provided by YWLCS. As an encouragement to seriously considering
registering with FPI, students were offered a $5.00 gift card to their choice among Subway
restaurants, Target stores, or Barnes & Noble booksellers for returning signed consent materials,
Adolescent Girls’ Implicit Math Attitudes 15
even if they decided not to register. An additional $5.00 gift card is awarded each time the
student completes a study session.
The Implicit Association Test
The Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) was used
to measure participants’ implicit math attitude. By measuring the time participants take to sort
stimuli from four categories (e.g., a person smiling or scowling; math words or language arts
words) into two response options, the IAT provides a relative measure of the strength of
automatic associations among concepts. For this study, I used a math attitude IAT developed by
the Full Potential Initiative to measure participants’ implicit attitudes towards language arts and
math. The IAT was modeled after the recommendations set forth by Greenwald, Nosek, and
Banaji (2003), which suggests seven blocks. See Table 1 for the sequence of trial blocks for the
math attitude IAT. For the purposes of this study, we reduced the number of trials in each block
by one-third.
To measure positive and negative associations with math and language arts, we used
pictures of an individual either smiling or scowling. Thus, positive, or good, associations were
operationalized by a smiling face, and negative, or bad, with a scowling face. The IAT measured
the degree to which participants associate good with language arts and bad with math compared
to the opposite combination. In two counter-balanced conditions, participants sort stimuli from
these four concepts (good, bad, math, and language arts) into two combination categories (either
good/math and bad/language arts or bad/math and good/language arts). Response options are
represented by the “E” key or “I” key on the computer keyboard. Participants complete a practice
phase to learn how to take the IAT before proceeding the critical phases. In the first critical phase
of the IAT, words or images representing two concepts (e.g., scowling faces and words such as
Adolescent Girls’ Implicit Math Attitudes 16
count, divide) are classified with one key. Items representing the remaining two concepts
(smiling faces and words such as letter, sentence) are classified with the other key. In the second
critical phase, the concepts are sorted in the reverse manner, such that good expressions are
associated with math words, and bad expressions are associated with language arts words. The
face images were counterbalanced by race and gender. Researchers assume that if the participant
implicitly associates positive feelings (represented by a smiling face) with language arts, then
associating these concepts together should be easier than the reverse. Ease of association is
determined by the speed at which the participant responds (with slower responses indicating
weaker associations) and by the frequency of errors (with more errors indicating weaker
associations).
An IAT effect for an individual is determined by taking the difference between the mean
response latencies of the two critical conditions (i.e., math-bad/language arts-good and mathgood/language arts-bad). Response latencies were measured from the beginning of a trial until
the time of a correct response and were calculated using the D algorithm to standardize the IAT
effect for each individual. The effect was calculated such that positive and higher D scores
indicate a stronger implicit positive attitude towards math (Greenwald et al., 2003). For a
complete list of math/language arts words and good/bad expression images used in the IAT, see
Appendix A.
Procedure
Participants in this study completed two 15-minute experimental sessions online.
Students were informed that new sessions were available via email messages, and YWLCS
science teachers made announcements in their classes. Participants completed the sessions at
their leisure during the time span during which the session was available, in locations and with
Adolescent Girls’ Implicit Math Attitudes 17
computers of their choosing. We began administering the first session in September, about one
month into the academic school year for YWLCS students; the second session administration
began in January, at the mid-point of the academic year. The first session was open to nonYWLCS students from September 19, 2008 to January 7, 2009 and to YWLCS students from
September 19 to October 15, 2008. The second session was available from January 13, 2009 to
February 8, 2009. Using web-based accounts on the Full Potential Initiative website, participants
logged into their account using a user name, thereby completing the sessions confidentially.
Participants did not need to have completed the first session to participate in the second.
The first session included three distinct IATs: math attitude, math-gender stereotype, and
gender identity. The second session included only the math attitude and the math-gender
stereotype IATs. Both sessions included several self-report survey items. Explicit and implicit
measures were counterbalanced such that participants either completed all IATs first, followed
by the survey items, or the reverse. Survey questions concerned participants’ self-efficacy and
attitudes towards language arts and math, beliefs about gender differences in academic abilities
in language arts and math, their career/academic aspirations. All participants in the second
session were presented with the dating items as the last section of the session. This study focused
on the information gathered from the questions regarding dating and romantic relationships,
specifically, whether the participant reported ever having had a romantic or dating relationship.
Appendix B contains all explicit and self-report items included in the sessions.
Results
Group Comparison at First Measurement Occasion
We first tested whether baseline (Time 1) explicit and implicit attitudes differed by
groups (YWLCS and control). Descriptive statistics and correlations for these and other variables
Adolescent Girls’ Implicit Math Attitudes 18
are listed in Table 2 for YWLCS and Table 3 for control students. Since we found that age
differed for the two groups, in these and all other analyses we included age and the age X group
interaction term as covariates to account for possible effects of age. By regressing each
dependent variable on the grouping variable “YWLCS student” (where controls were coded “0”
and YWLCS coded “1”), we found that groups did not differ significantly on any of the attitude
measures at Time 1. With age held constant, the estimated mean implicit math attitude for
YWLCS and control participants were -.04 and -.05, respectively, p=.95. Because neither mean
is significantly different from zero, these data indicate that neither YWLCS nor control
participants had a preferential bias towards math or language arts at Time 1; that is, both groups
seemed to have equally positive or negative implicit attitudes towards math and language arts.
Likewise, explicit math attitudes (a 7-point scale, coded -3, hate it, to 3, love it) between
YWLCS and control participants did not differ significantly, with estimated means of .79 and
.86, respectively, p= .82. Nor did explicit language arts attitudes differ between YWLCS and
control participants (a 7-point scale, coded -3, hate it, to 3, love it), with estimated means of .80
and 1.18, respectively, p=.11. For math and language arts explicit attitudes, both groups
evidenced attitudes slightly (between 0= “it’s okay” and 1= “like it”) in the positive direction.
Observed means for math attitudes differed significantly from zero for both YWLCS and control
students (t= 4.95 and 4.56, respectively, p< .0001), as did observed means for language arts
attitudes (t= 7.19 and 6.85, respectively, p< .001).
Group Comparison at Second Measurement Occasion
As with Time 1, group means on implicit attitudes measured by the Math Attitude IAT
did not significantly differ from one another at Time 2. Means for YWLCS and control
participants, respectively, were M=-.09 (SD= .42 and .47), p= .94. Both groups exhibited a
Adolescent Girls’ Implicit Math Attitudes 19
slightly negative attitude towards math, although only significantly for YWLCS participants (t=
-2.17, p=.03). Explicit math attitudes between YWLCS (M=1.1, SD= 1.6) and control (M= 1.4,
SD= 1.5) did not significantly differ, p= .32, with both groups’ observed means indicating a
statistically significant slightly positive explicit math attitude (t= 6.4 and 5.6, respectively,
p<.0001). Explicit language arts attitudes also did not significantly differ between YWLCS (M=
1.8, SD=1.3) and control participants (M= 1.2, SD= 1.2), p= .90. As with Time 1, both YWLCS
and control groups evidenced slightly positive attitudes (between 1=“like it” and 2= “love it”)
towards both math and language arts, and observed means significantly differed from zero (t=
8.9 and 5.9, respectively, p< .0001).
In addition to explicit and implicit attitude measures, dating behavior measures were
administered at Time 2. Among all participants, both YWLCS and control, 70 percent reported
having had at least one romantic or dating relationship (n=89). Of those, most (61 percent)
reported being in a relationship at the time they participated in the session, and most (79 percent)
reported having had their first dating relationship at or before age 14, M= 13.0, SD= 1.8. Since
the primary dating variable is dichotomous, a yes or no response, we used logistic regression to
test whether YWLCS and control participants differed in their likelihood of having ever dated
and found that they did not (p= .16). Controlling for age, among those who had dated, and again
using standard multiple regression, we found no group differences for YWLCS and control for
the age at which they had their first dating relationship (M=13.1, SD= 1.9; M= 12.9, SD=1.1,
respectively, p=.23), or the length of their longest relationships, p=.16, (see Figure 1). Based on
the manner in which we coded the length of longest relationship variable, these data indicate that
the mean length of longest relationship for YWLCS students was between six months and one
and a half years. For control students, the mean length of longest relationship was between five
Adolescent Girls’ Implicit Math Attitudes 20
months and one year. There were, however, significant group differences in whether participants
were currently in a dating relationship and the number of dating relationships participants have
had (see Figure 2). YWLCS participants were more likely to report currently being in a
relationship than control participants (p=.01) and to report having had more relationships than
control participants, p=.0098. Based on the coding scale used for the number of relationships
variable, YWLCS participants had a mean of between three and four dating relationships, while
control participants had a mean of between two and three. These group differences should be
interpreted cautiously, however, due to the significant attrition that occurred between Time 1 and
Time 2, which resulted in a low number of participants used in this study. Of the 208 participants
at Time 1, only 93 also participated in Time 2. Due to the low number of participants who
completed both Time 1 and Time 2, we were unable to further break down participants who
reported having had a dating relationship into groups of those who had began dating before age
14 and those who began at or after. Thus, we decided to investigate the effect of having had at
least one dating relationship in order to maximize our potential statistical power.
Change in Attitudes
Implicit Attitude Change
Our primary research interest concerned whether attendance of YWLCS would increase
positive implicit math attitudes and whether dating activity would decrease them. We
hypothesized a main positive effect of attending YWLCS on implicit attitude change and a main
negative effect of dating on attitude change. We also predicted an interaction of school and
dating such that attending YWLCS would dampen the negative effects of dating. Change in
implicit attitude was indexed by the difference between the attitudes at Time 1 and 2 (IAT D
score at Time 2 minus D score at Time 1). For a graphical representation of group change, see
Adolescent Girls’ Implicit Math Attitudes 21
Figure 4. Although none of the groups evidenced significant implicit attitude change from Time
1 to Time 2, control students who reported not having had a dating relationship showed the
greatest change in implicit attitudes, becoming more negative over time (M= -.39). Thus,
although not significant, this change was nearly a full standard deviation different from the Time
1 attitude (M=-.04, SD= .41). Control students who have dated (M= -.02, SD= .36) and YWLCS
students who had not dated (M=-.05, SD=.58) became more negative in implicit attitudes over
time, but given that the change was not significant, this small difference over time is negligible.
YWLCS students who reported dating showed no change from Time 1 to Time 2 (M=.00,
SD=.50).
We tested the effects of school and dating on attitude change by estimating a series of
multiple regression models. With age and the age X group interaction term included as covariates,
we found the estimated average change score for YWLCS students was about zero (b=.00) and .14 for control students, a trend partly in line with our prediction, though non-significant (p=.29).
That is, we expected negative change for the control participants’ implicit math attitudes over
time, but we did not expect to find no change for YWLCS participants. Next, we tested the effect
of dating and its possible interaction with school by adding the dichotomous dating variable and
the dating X school interaction term to the model. Neither the main effect of dating (p=.13) nor
the interaction of dating with school was significant (p=.31). We report the following estimates
anyway, because they are sizeable in some cases and would be meaningful if they held for a
larger sample. For a control participant at the mean age who has not had a dating or romantic
relationship, the predicted IAT attitude change was -.40, compared to -.01 for a control
participant who had dated. For an YWLCS participant at the mean age who has not had a dating
relationship, the predicted IAT attitude change was -.07, compared to .01 for an YWLCS
Adolescent Girls’ Implicit Math Attitudes 22
participant who has dated. Thus, a hypothetical control participant who has dated evidenced a
trend contrary to our hypotheses; estimated implicit math attitude change was less negative than
for a control participant who has dated. Likewise, for a hypothetical YWLCS participant who has
not dated, predicted implicit math attitude change was more negative than for an YWLCS
participant who has dated. Figure 5 shows estimated mean change in implicit math attitude by
group.
Explicit Attitude Change
We conducted a similar progression of multiple regression analyses to examine explicit
attitude change. For explicit math attitude change, controlling for age, there was no significant
effect of school. The estimated average change score for YWLCS participants was .3, compared
to .2 for control participants, p=.60. Next, we tested the effect of dating and its possible
interaction with school, and, as with the implicit attitudes, there was neither a significant main
effect for dating (p= .47) nor a significant interaction of dating with school (p=.72). For both
control, nondating and for control, dating students, mean explicit math attitude change was .1
(SD=1.1). For YWLCS, nondating students, mean change was .2 (SD=.9), while for YWLCS,
dating students, mean change was .3 (SD= .9). Accounting for age and whether or not
participants had ever been in a dating relationship, the predicted explicit math attitude change
was b= -.9 for a control participant who has not had a dating relationship, compared to -.6 for a
control girl who has. For an YWLCS participant who has not had a dating relationship, predicted
explicit math attitude change was -.7, compared to -.5 for an YWLCS participant who has. Thus,
for all groups, predicted change in explicit math attitudes is in the direction of increasingly
negative attitudes from Time 1 to Time 2; however this is less so for YWLCS and control
participants who report having dated.
Adolescent Girls’ Implicit Math Attitudes 23
For explicit language arts attitude change, analyses indicate no significant effect of
school for YWLCS and control students, controlling for age. The estimated average change score
for YWLCS participants was zero, and .4 for control participants, p= .24. Tests for the effect of
dating revealed no significant differences in language arts attitude change (p=.29). Analyses did
reveal, however, a significant interaction of dating and attending YWLCS, such that those
students who attend YWLCS and have dated have greater decrease in explicit language arts
positivity from Time 1 to Time 2 (p= .02). For control students who had not dated, mean change
was -.4 (SD= .8), compared to .1 (SD= 1.1) for control girls who had dated. For YWLCS
students who had not dated, mean change was 1.1 (SD= 1.7), compared to .1 (SD= 1.0).
Accounting for age and whether participants had ever been in a dating relationship, the predicted
explicit language arts attitude change was -.4 for a control participant who has not had a dating
relationship, compared to .2 for one who has. For an YWLCS participant who has not had a
dating relationship, predicted explicit language arts attitude change was 1.2, compared to .1 for
one who has. Thus, predicted explicit language arts attitude increases more for a control
participant who has dated than for an YWLCS participant who has dated. Additionally, predicted
explicit language arts attitude increases less for an YWLCS participant who has dated compared
to one who has not, while having dated for a control girl is associated with increased explicit
language arts attitude compared to a decreased explicit language arts attitude for one who has not.
Discussion
Because of the YWLCS’s strong emphasis on math and science education for adolescent
girls, we hypothesized that attending this school would result in an increase in positivity in
implicit and explicit math attitudes over time. Furthermore, we predicted that girls who do not
attend YWLCS would evidence a decrease in positivity in implicit and explicit attitudes. We
Adolescent Girls’ Implicit Math Attitudes 24
were specifically interested in the potential effect of dating activity on attitude change. We
hypothesized that YWLCS participants who reported having had a dating relationship would
show less improvement in math attitudes, while control participants who reported having had a
dating relationship would show even more worsening math attitudes.
None of our hypotheses were supported by the study results. We found that school type
(YWLCS vs. control) did not predict attitude change, either implicitly or explicitly. Furthermore,
dating activity was not related to attitude change for either group. Results also failed to support
the predicted interaction of school and dating,i.e., that attending YWLCS would dampen the
negative effects of dating. Overall, there was no significant implicit or explicit attitude change as
a function of school or dating, except for explicit language arts attitude change. YWLCS students
who have dated were more likely to show decrease in explicit language arts positivity. This is
partly in line with our hypothesis that dating would negatively affect academic attitudes;
however, the other results are not consistent with our hypotheses. Thus, the study found no
evidence that attendance of YWLCS or that dating activity affected the implicit and explicit math
attitudes of the participants.
These results are seemingly inconsistent with FPI’s preliminary findings that girls
attending YWLCS evidence more implicit and explicit preference for mathematics. Our results
also do not correspond with prior research indicating that a same-sex learning environment can
reduce implicit gender-stereotypic attitudes (Dasgupta & Asgari, 2004) and that dating can have
a detrimental effect on girls’ academic achievement (Brendgen et al., 2002; Neeman et al., 1995;
Scott et al., 2005). A failure to find change in implicit math attitudes over time could be related
to the finding that neither the YWLCS nor the control group evidenced a differential preference
for math or language arts; in other words, the participants evidenced about equal implicit
Adolescent Girls’ Implicit Math Attitudes 25
attitudes towards math and language arts and did not significantly prefer one domain over the
other. One possibility for why participants did not indicate the stereotypically negative attitude
towards math is that all participants in the study liked math at least enough to encourage them to
apply to YWLCS, where math is emphasized. Since all potential participants were randomly
assigned to YWLCS and control groups by the school’s random admissions lottery, it is logical
to expect that groups would not differ in attitude at the beginning of the school year.
Additionally, we could still expect relatively neutral preferences for math versus language arts in
both groups, because it is reasonable to assume that the kind of student who would apply to
attend a math/science charter school is a more motivated student generally. Therefore, the
student could likely have no preference for one academic domain over another. This might be
especially true for participants in the control group, who self-select to participate far more than
YWLCS participants, who have teacher encouragement to participate (about half of all enrolled
YWLCS students participate, but only about 10% of potential control students). Another factor
contributing to our finding that attitudes did not appear to differ at Time 1, Time 2, or over time
is the possibility that YWLCS math courses may be more challenging than those in the control
schools; thus, the YWLCS students may not show an increase in positive math attitude because
they perceive their courses to be difficult.
It is initially intuitive to propose that the reason we did not find any group or individual
change in implicit attitudes from Time 1 to Time 2 is that we were measuring attitude change
over a relatively short time. After all, since implicit attitudes are related to stereotypes that are
pervasive in an individual’s life from an early age, it would indeed be very impressive that a
lifetime of implicit bias could be reversed over a period of a few months. However, Dasgupta
and Asgari (2004) observed implicit attitude change over a year.
Adolescent Girls’ Implicit Math Attitudes 26
A more plausible explanation for why there was no difference between YWLCS and
control at Time 1 and Time 2, and between the two times, is the self-selection of control
participants. Because significantly fewer control students than YWLCS students participate, it is
logical to suspect that the control students who self-select to participate are more motivated or
have more supportive-than-average parents. Unlike YWLCS students, control students do not
have encouragement from their teachers to participate. Thus, control students make the decision
to continue completing sessions through FPI far more independently than do YWLCS students. It
is likely that these control students have an intrinsic interest in the research FPI conducts and
enjoy thinking and answering questions about their academic preferences and attitudes. Thus, our
groups may have differed in a fundamental way that could have affected our results. Our data on
dating behaviors support the hypothesis that the two groups are actually different. Controlling for
age, YWLCS students were significantly more likely to report currently being in a relationship
than control students; additionally, YWLCS students report having had significantly more dating
relationships than control students. Thus, YWLCS students seem to be more active in their
dating behaviors than control students. This may be indicative of other important differences that
could potentially explain why control students in this sample do not exhibit the predicted
decrease in positive math attitudes over time in high school.
Given the pervasiveness and consistency of the research evidencing that early dating (age
14 or younger) is associated with lower academic achievement, lower self-esteem, and higher
rates of delinquency, among other negative outcomes (Brendgen et al., 2002; Neeman et al.,
1995; Scott et al., 2005), it is surprising that this study found no evidence for this. Furthermore,
in most other studies (Brendgen et al., 2002), approximately 30 percent of adolescent girls begin
dating at age 14 or younger. Because 70 percent of the participants in our sample who have had a
Adolescent Girls’ Implicit Math Attitudes 27
dating relationship began dating at age 14 or younger, it would be logical to think that negative
academic outcomes, and negative academic attitudes, could be particularly pronounced in our
sample. The present study did not find any evidence for this, however. Because “early” dating is
the norm in this particular sample, perhaps the normalcy of this dating behavior in this cohort
mitigates the negative effects typically associated with early dating (Dornbusch, Carlsmith,
Gross, Martin, Jennings, Rosenberg, & Duke, 1981). Thus, beginning to date at age 14 may not
actually be early relative to their peers in this particular sample. Furthermore, all participants in
this study live in the Chicago, Illinois area, and many come from, or at least are exposed to,
lower socioeconomic status backgrounds, which is a demographic associated with higher rates of
teen pregnancy (Miller & Moore, 1990). Presuming that sexually active teens are more likely to
be active daters, early dating may be more normative among the participants of the present study.
Most research indicates negative outcomes for girls who date early relative to their peers, but
most of the girls in this study were already dating by age 14. This may be why dating was not
associated with negative academic attitudes in this study.
A significant limitation that likely accounts for the dearth of statistically significant
differences by group in attitude change is the overall lack of statistical power resulting from a
low number of participants, especially in the control group. Given that our groups had a very low
number of participants who had completed both sessions, particularly within the nondating
groups, low statistical power is an important drawback to this study. Thus, any trends reported in
this study must be interpreted cautiously. Additionally, a methodological limitation to this study
is that, although we controlled for age, we did not control for year in school. Because
participants had been in either YWLCS or a control school for varying amounts of time, it is
possible that our first measurement in September was not truly a baseline measurement.
Adolescent Girls’ Implicit Math Attitudes 28
However, the impacts of this on our results are likely fairly small, considering that controlling
for age should approximate controlling for year in school.
Although this study did not find significant results, it is important that future research
continue to investigate possible obstructions and aids to young women’s math attitudes. A future
project should investigate the effect of dating on implicit and explicit math attitudes among
young women in a representative sample in order to gain a better understanding of the affects of
dating in the general population. It would also be interesting to investigate adolescent boys’
implicit academic attitudes in order to draw comparisons to those of girls. This will allow
researchers to determine if implicit attitudes are gendered, and, if they are, how to improve the
attitudes of the disadvantaged group. Additionally, future work should address the question of
how various aspects of dating affect academic attitudes. For instance, does the length of the
romantic relationship have a mediating effect on negative outcomes in academic attitudes, as it
does in other domains (Furman, Ho, & Low, 2007)? Is there a difference in attitudes between
girls who have had several relationships as opposed to a few? Relationship quality and
seriousness should also be taken into account. These investigations could also address affects on
other academic subjects in addition to math. It would be particularly interesting to see if dating
affects math and science attitudes similarly.
Continuing research in this field is vital to addressing the disparity between the numbers
of men and of women in math and science career fields. Understanding the environmental and
societal influences on achievement and attitudes in this domain will have implications for current
teaching practices and educational environments, as well as for current gender norms and
stereotypes. Despite the significant progress made over the past century for women’s equality,
women remain disadvantaged in their careers in terms of salaries and prestige. In order to change
Adolescent Girls’ Implicit Math Attitudes 29
this, it is imperative that women be given every equal opportunity to pursue their interests,
regardless of what society’s stereotypes deem as appropriate feminine behavior. By investigating
how to improve girls’ math attitudes we can come closer to eradicating the math=male stereotype,
finally giving young women the equal playing field they deserve in math and science. And
perhaps, someday, Mattel will release a Mathematician Barbie.
Adolescent Girls’ Implicit Math Attitudes 30
References
Bleeker, M. M. & Jacobs, J. E. (2004). Achievement in math and science: Do mothers’ beliefs
matter 12 years later? Journal of Educational Psychology, 96(1), 97-109.
Brendgen, M., Vitaro, F., Doyle, A. B., Markiewicz, D., & Bukowski, W. M. (2002). Same-sex
peer relations and romantic relationships during early adolescence: Interactive links to
emotional, behavioral, and academic adjustment. Merrill-Palmer Quarterly, 48(1), 77
103.
Caspi, A., Lynam, D., Moffitt, T. E., & Silva, P. (1993). Unraveling girls’ delinquency:
Biological, dispositional, and contextual contributions to adolescent misbehavior.
DevelopmentalPsychology, 29(1), 19-30.
Cohen, G. L., Garcia, J., Apfel, N., Master, A. (2007). Reducing the Racial Achievement Gap: A
Social-Psychological Intervention. Science, 313, 1307-1310.
Dar-Nimrod, I. & Heine, S. J. (2006). Exposure to scientific theories affects women’s math
performance. Science, 314, 435.
Dasgupta, N. & Asgari, S. (2004). Seeing is believing: Exposure to counterstereotypic women
leaders and its effect on the malleability of automatic gender stereotyping. Journal of
Experimental Social Psychology, 40, 642-658.
Dornbusch, S., Carlsmith, J., Gross, R., Martin, J., Jennings, D., Rosenberg, A., & Duke, P.
(1981). Sexual development, age, and dating: A comparison of biological and social
influences upon one set of behaviors. Child Development, 52, 179-185.
Evans, M. A., Whigham, M., & Wang, M. C. (1995). The effect of a role model project upon the
attitudes of ninth-grade science students. Journal of Research in Science Teaching, 32(2),
195-204.
Adolescent Girls’ Implicit Math Attitudes 31
Furman, W., Ho, M. J., & Low, S. M. (2007). The rocky road of adolescent romantic experience:
Dating and adjustment. In Engles, R. C. M. E., Kerr, M., & Stattin, H. (Eds). Friends,
Lovers, and Groups: Key Relationships in Adolescence (pp. 61-80). West Sussex,
England: John Wiley & Sons Ltd.
Garrahy, .D.A. (2001). Three third grade teachers' gender-related beliefs and behavior. The
Elementary School Journal, 102(1), 81-94.
Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and
stereotypes. Psychological Review, 102(1), 3-25.
Greenwald, A. G., McGhee, D. E., & Shwartz, J. L. K. (1998). Measuring individual differences
in implicit cognition: The Implicit Association Test. Journal of Personality and Social
Psychology, 16, 270-280.
Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit
Association Test I: An improved scoring algorithm. Journal of Personality and Social
Psychology, 85(2), 197-216.
Greenwald, A. G., Poehlman, T. A., Uhlmann, E., & Banaji, M. R. (in press). Understanding and
using the Implicit Association Test: III. Meta-analysis of predictive validity. Journal of
Personality and Social Psychology.
Haag, P. (1998). Single-sex education in grades K-12: What does the research tell us? In S.
Morse (Ed.), Separated by sex: A critical look at single-sex education for girls (pp. 13
38). Washington, DC: American Association of University Women Educational
Foundation.
Adolescent Girls’ Implicit Math Attitudes 32
Helwig, R., Anderson, L., & Tindal, G. (2001). Influence of elementary student gender on
teachers’ perception of mathematics achievement. The Journal of Educational Research,
95(2), 93-102.
Hyde, J. S., Fennema, E., & Lamon, S. J. (1990). Gender differences in mathematics
performance: A meta-analysis. Psychological Bulletin, 107(2), 139-155.
Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender
similarities characterize math performance. Science, 321, 494-495.
Inzlicht, M. & Ben-Zeev, T. (2000). A threatening intellectual environment: Why females are
susceptible to experiencing problem-solving deficits in the presence of males.
Psychological Science, 11(5), 365-371.
Jacobs J. E. & Eccles, J. S. (1985). Gender differences in math ability: The impact of media
reports on parents. Educational Researcher 14, 20–25.
Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in
children’s self-competence and values: Gender and domain differences across grades one
through twelve. Child Development, 73(2), 509-527.
Jussim, L. & Eccles, J. S. (1992). Teacher expectations II: Construction and reflection of student
achievement. Journal of Personality and Social Psychology, 63(6), 947-961.
Keller, J. (2002). Blatant stereotype threat and women’s math performance: Self-handicapping as
a strategic means to cope with obtrusive negative performance expectations. Sex Roles,
47(3/4), 193-198.
Kiefer, A. K. & Sekaquaptewa, D. (2007). Implicit stereotypes, gender identification, and math
related outcomes: A prospective study of female college students. Psychological Science,
18(1), 13-18.
Adolescent Girls’ Implicit Math Attitudes 33
Lam, T. H., Shi, H. J., Ho, L. M., Stewart, S. M., & Fan, S. (2002). Timing in pubertal
maturation and heterosexual behavior among Hong Kong Chinese adolescents. Archives
of Sexual Behavior, 31, 359–366
Lane, K. A., Banaji, M. R., Nosek, B. A., & Greenwald, A. G. (2007). Understanding and using
the Implicit Association Test: IV. What we know (so far) about the method. In B.
Wittenbrink & N. Schwarz (Eds.) Implicit Measures of Attitudes (pp. 59-102). The
Guilford Press, New York.
Lee V. E., Marks H. M., & Byrd, T. (1994). Sexism in single-sex and coeducational independent
secondary school classrooms. Sociology of Education, 67(2), 92-120.
Marx, D. M. & Roman, J. S. (2002). Female role models: Protecting women’s math test
performance. Personality and Social Psychology Bulletin, 28, 1183-1193.
Miller, B. & Moore, K. (1990). Adolescent sexual behavior, pregnancy, and parenting: Research
through the 1980s. Journal of Marriage and the Family, 52, 1025-1044.
Nauta, M. M., Epperson, D. L., & Kahn, J. H. (1998). A multiple-groups analysis of predictors of
higher level career aspirations among women in mathematics, science, and engineering
majors. Journal of Counseling Psychology, 45(4), 483-496.
Neeman, J., Hubbard, J., & Masten, A. S. (1995). The changing importance of romantic
relationship involvement to competence from late childhood to late adolescence.
Development and Psychopathology, 7(4), 727-750.
Nisbett, R. E. & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental
processes. Psychological Review, 84(3), 231-259.
Nosek, B. A., & Banaji, M. R. (2001). The go/no-go association task. Social Cognition. 19(6),
625-666.
Adolescent Girls’ Implicit Math Attitudes 34
Nosek, B. A., Banaji, M. R., Greenwald, A. G. (2002) Math= male, me= female, therefore math
≠ me. Journal of Personality and Social Psychology, 83, 44-59.
Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2006). The Implicit Association Test at age
7: A methodological and conceptual review. In J. A. Bargh (Ed.), Automatic Processes
in Social Thinking and Behavior (pp. 265-293). New York: Psychology Press.
Parsons, J. E., Adler, T. F., & Kaczala, C. M. (1982). Socialization of achievement attitudes and
beliefs: Parental influences. Child Development, 53(2), 310-321.
Phinney, V. G., Jensen, L. C., Olsen, J. A. & Cundick, B. (1990). The relationship between early
development and psychosexual behaviors in adolescent females. Adolescence, 25(98),
321-332.
Quatman, T., Sampson, K., Robinson, C., & Watson, C. M. (2001). Academic, motivational, and
emotional correlates of adolescent dating. Genetic, Social, and General Psychology
Monographs, 127(2), 211-234.
Rosenthal, R., & Jacobson, L. (1968). Pygmalion in the classroom: teacher expectation and
pupils' intellectual development. New York, Holt, Rinehart and Winston.
Rosenthal, R. & Jacobson, L. (1992). Pygmalion in the classroom. Expanded edition. New
York: Irvington
Reid, P. T. & Roberts, S. K. (2006). Gaining options: A mathematics program for potentially at
risk adolescent girls. Merrill-Palmer Quarterly, 52(2), 288-304.
Riordan, C. (1998). The future of single-sex schools. In S. Morse (Ed.), Separated by sex: A
critical look at single-sex education for girls (pp. 53-62). Washington, DC: American
Association of University Women Educational Foundation.
Adolescent Girls’ Implicit Math Attitudes 35
Scott, K., Stewart, L. L., & Wolfe, D. (2005). Dating relationships among at-risk adolescents: An
opportunity for resilience?. In R. DeV. Peters, B. Leadbeater, and R. J. McMahon (Eds).
Resilience in Children, Families and Communities: Linking Context to Practice and
Policy (pp. 83-100). New York: Kluwar Academic/Plenum Publishers.
Shih, M., Pittinsky, T., & Ambady, N. (1999). Stereotype susceptibility: Identity salience and
shifts in quantitative performance. Psychological Science, 10(1), 80-83.
Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threat and women’s math
performance. Journal of Experimental Social Psychology, 35, 4-28.
Steele, C. M. (1997). A threat in the air: How stereotypes shape intellectual identity and
performance. American Psychologist, 52, 613-629.
Steele, C. M. & Aronson, J. (1995). Stereotype threat and the intellectual test performance of
African Americans. Journal of Personality and Social Psychology, 69(5), 797-811.
Steele, J. (2003). Children’s gender stereotypes about math: The role of stereotype stratification.
Journal of Applied Social Psychology, 33(12), 2587-2606.
Wilgosh, L. (2003). Examining gender images, expectations, and competence as perceived
impediments to personal, academic and career development. International Journal for the
Advancement of Counseling, 24, 239-260.
Adolescent Girls’ Implicit Math Attitudes 36
Appendix A
Math Attitude IAT Exemplars
Adolescent Girls’ Implicit Math Attitudes 37
Appendix B
Explicit Questionnaire Items
Time 1 and Time 2
When you think about who is really good at Language Arts (English), is there a girl-boy
difference? Pick the choice that is closest to what you think:
…at Math,…?
Response options:
I think girls are usually much better at
than boys
I think girls are usually somewhat better at
than boys
I think girls are usually just a little better at
than boys
I think girls and boys are usually about the same at
I think boys are usually just a little better at
than girls
I think boys are usually somewhat better at
than girls
I think boys are usually much better at
than girls
Compared to other students in your grade, how good are you at Language Arts (English)?
…at Math?
Response Options:
The best
One of the best
Above average
Average
Below Average
One of the worst
The worst
How do you feel about Language Arts (English)?
…about Math?
Response Options:
Love it
Like it a lot
Like it
It’s okay
Dislike it
Dislike it a lot
Hate it
Adolescent Girls’ Implicit Math Attitudes 38
Time 2 only
Have you ever been in a dating relationship or been going out with someone?
Response Options:
No
Yes
Out of all your dating relationships, how long did your longest relationship last?
Response Options:
Less than 1 month
1-2 months
3-4 months
5-6 months
6 months to a year
1 year to 1 year and 6 months
1 year and 6 months to 2 years
2 or more years
How old were you when you started dating?
Response Options:
10 or younger
11
12
13
14
15
16
17
18 or older
Are you dating anyone now?
Response Options:
No
Yes
Adolescent Girls’ Implicit Math Attitudes 39
How many people have you dated
Response Options:
1
2
3-4
5-6
7+
Were you in a dating relationship in September 2008?
Response Options:
No
Yes
Adolescent Girls’ Implicit Math Attitudes 40
Table 1
Sequence of Trial Blocks in the Math Attitude IAT
Function
Block
1
No. of
trials
14
Practice
Items assigned to left-key
response
Math words
Items assigned to right-key
response
Language Arts words
2
14
Practice
Good expression images
Bad expression images
3
20
Practice
4
20
Test
5
26
Practice
Math words + Good
expression
Math words + Good
expression
Language Arts words
Language Arts words + Bad
expression
Language Arts words + Bad
expression
Math words
6
20
Practice
Language Arts words + Good Math words + Bad expression
expression
7
20
Test
Language Arts words + Good Math words + Bad expression
expression
Note. The positions of Blocks 1, 3, and 4 are counterbalanced with those of Blocks 5, 6, and 7 in
order to reduce potential sequence effects.
Adolescent Girls’ Implicit Math Attitudes 41
Table 2
Summary Statistics for YWLCS Students at Time 1 and Time 2 Change
Age
N
Mean
SD
Range
Age (years)
IAT Math Attitude
145
15.53
1.72
12, 19
IAT
EM
121
-.05
.44
-1.16, 1.07
ELA
119
.75
1.65
-3, 3
120
.80
1.23
-3, 3
Rel.
91
.76
.43
0, 1
IATDS
66
.00
.53
-1.20, 1.00
EMC
60
.27
.94
-2, 2
1.00
-.10
.19*
.1.00
.09
-.11
.01
1.00
Relationship
.31*
.06
-.07
.05
1.00
IAT Difference Score
-.03
-.59*
.07
.04
.04
1.00
Explicit Math Change
-.05
.08
-.28*
-.10
.04
-.04
1.00
Explicit Language Arts Change
-.12
-.12
.10
-.47*
-.36*
.08
.23
Explicit Language Arts
63
.37
1.21
-2, 6
1.00
.00
Explicit Math
ELAC
1.00
Adolescent Girls’ Implicit Math Attitudes 42
Table 3
Summary Statistics for Control Students at Time 1 and Time 2 Change
Age
N
Mean
SD
Range
Age (years)
IAT Math Attitude
70
14.37
1.14
12, 18
IAT
EM
62
-.04
.41
-.78, .77
ELA
62
1.03
1.78
-3, 3
62
1.15
1.32
-3, 3
Rel.
35
.51
.51
0, 1
IATDS
27
-.17
.44
-1.37, .98
EMC
27
.11
1.09
-3, 2
27
-.11
1.01
-2, 2
1.00
.04
1.00
-.15
.43*
.1.00
Explicit Language Arts
.03
-.12
-.15
1.00
Relationship
.23
-.16
.05
.35
1.00
IAT Difference Score
.09
-.51*
-.29
.32
.41*
1.00
Explicit Math Change
.06
-.26
-.52*
.09
.02
.15
1.00
Explicit Language Arts Change
.20
.20
-.27
-.39*
.21
.02
.29
Explicit Math
ELAC
1.00
Adolescent Girls’ Implicit Math Attitudes 43
Figure 1. Hypothetical change in IAT from Time 1 to Time 2 by school group and dating activity.
Adolescent Girls’ Implicit Math Attitudes 44
Figure 2. Distribution of responses on dating behavior items with no group differences.
Adolescent Girls’ Implicit Math Attitudes 45
Figure 3. Distribution of responses on dating behavior items with group differences.
Adolescent Girls’ Implicit Math Attitudes 46
Figure 4. Box and whisker plots of Math Attitude IAT D scores by participant group and
measurement time.
Adolescent Girls’ Implicit Math Attitudes 47
Figure 5. Estimated mean change in implicit math attitudes by group.