Child Development, January/February 2014, Volume 85, Number 1, Pages 250–263
The Roots of Stereotype Threat: When Automatic Associations Disrupt Girls’
Math Performance
Silvia Galdi and Mara Cadinu
Carlo Tomasetto
University of Padova
University of Bologna
Although stereotype awareness is a prerequisite for stereotype threat effects (Steele & Aronson, 1995), research
showed girls’ deficit under stereotype threat before the emergence of math–gender stereotype awareness, and
in the absence of stereotype endorsement. In a study including 240 six-year-old children, this paradox was
addressed by testing whether automatic associations trigger stereotype threat in young girls. Whereas no indicators were found that children endorsed the math–gender stereotype, girls, but not boys, showed automatic
associations consistent with the stereotype. Moreover, results showed that girls’ automatic associations varied
as a function of a manipulation regarding the stereotype content. Importantly, girls’ math performance
decreased in a stereotype-consistent, relative to a stereotype-inconsistent, condition and automatic associations
mediated the relation between stereotype threat and performance.
Stereotype threat research (Steele & Aronson, 1995)
has shown that both adults and children underperform in difficult tests when a negative domainrelevant in-group stereotype is made salient (see
Inzlicht & Schmader, 2012, for a review). Despite
this overwhelming evidence, the specific requirements for children’s underperformance under stereotype threat are still unclear. With the aim of
providing new insights into research on stereotype
threat, this study investigates automatic associations
as a potential requirement for the emergence of stereotype threat underperformance in children.
Automatic associations are those associations
between concepts (e.g., me–woman), or between
concepts and attributes (e.g., flower–positive) that
come to mind unintentionally when an individual
encounters a relevant object, that are difficult to control once they have been activated, and that are not
necessarily explicitly endorsed (e.g., Gawronski &
Bodenhausen, 2006). Such automatic associations are
often contrasted with conscious beliefs, which are
those mental contents that an individual explicitly
endorses as accurate (e.g., Gawronski & Bodenhausen, 2006). Most measures of automatic associations
are based on participants’ performance on computer-based, speeded categorization tasks (see Gawronski & Payne, 2010, for a review). These measures,
commonly referred to as implicit measures, differ
from self-reports, described as explicit measures,
which assess conscious beliefs.
This study employed implicit and explicit
measures jointly to show that automatic associations consistent with a negative in-group stereotype
may lead to stereotype-induced underperformance
at early stages of development, even though
children do not possess the cognitive competencies
for stereotype awareness yet, and in the absence of
evidence that they endorse the stereotype content.
Correspondence concerning this article should be addressed to
Silvia Galdi, Dipartimento di Psicologia dello Sviluppo e della
Socializzazione, Universita di Padova, Via Venezia 15, Padova
35131, Italy. Electronic mail may be sent to [email protected].
© 2013 The Authors
Child Development © 2013 Society for Research in Child Development, Inc.
All rights reserved. 0009-3920/2014/8501-0017
DOI: 10.1111/cdev.12128
Prerequisites for Stereotype Threat Susceptibility
According to the stereotype threat model, for stereotypes to affect performance, children need to (a)
have developed a concept of social categories (category awareness), (b) be able to identify themselves as
members of a social category (self-categorization), and
(c) know that the in-group category is negatively
related to specific domains or attributes (stereotype
awareness; Aronson & Good, 2003). When children
enter elementary school, they possess the cognitive
competencies of category awareness and self-categorization (e.g., Martin & Ruble, 2010) but not stereotype
awareness (e.g., McKown & Strambler, 2009).
Research has shown that, between the ages of 3
and 4 years, children become aware of social
categories, such as gender and race (e.g., Aboud,
Stereotype Threat Before Stereotype Awareness
1988; Katz & Kofkin, 1997), and are able to identify
themselves as members of these categories (e.g.,
Martin & Ruble, 2010; Quintana, 1998). Moreover,
by this age children develop personal stereotypic
beliefs about abilities and characteristics of ethnic
(Aboud, 1988) and gender groups. For example,
they hold personal stereotypic beliefs about gender
differences in toy preferences, dressing, and aggressive versus prosocial behaviors (Martin & Ruble,
2010). However, personal stereotypic beliefs,
referred to as stereotype endorsement, differ from the
knowledge of stereotypes held by others (not necessarily personally endorsed), which is defined as
stereotype awareness (e.g., Martinot & Desert, 2007;
McKown & Strambler, 2009). Thus, although in
many cases personal beliefs overlap with socially
shared stereotypes, this does not imply necessarily
that young children are aware of stereotypes.
Indeed, the literature on children’s social perspective taking (e.g., Selman, 1980), theory of mind
(e.g., Perner & Wimmer, 1985), and person perception (e.g., Rholes & Ruble, 1984) suggests that children develop the cognitive competencies for
stereotype awareness only by middle childhood,
and that up to then they do not distinguish their
personal stereotypic beliefs from others’ beliefs.
Consistently, children have been shown to be aware
of common ethnic and gender stereotypes about
academic abilities by the ages of 8 and 9 years (e.g.,
McKown & Weinstein, 2003; Quintana, 1998).
The evidence that children do not possess stereotype awareness when they enter elementary school
has important consequences. As noted earlier,
stereotype awareness is a specific requirement for
stereotype threat. Specifically, the stereotype threat
model posits that people’s negative stereotypes
hamper performance only in situations that induce
targets to become concerned about being judged by
others on the basis of relevant stereotypes (Steele,
1997). Consistently, research on performance decrements induced by ethnic stereotypes confirms that
only children who are aware of broadly held stereotypes are vulnerable to stereotype threat effects
(McKown & Strambler, 2009; McKown & Weinstein,
2003). Therefore, in principle we should not expect
declines in performance under stereotype threat
prior to 8–9 years of age, or even later (Aronson &
Good, 2003). Nevertheless, Ambady, Shih, Kim, and
Pittinsky (2001) showed that 5- to 7-year-old Asian
American girls underperformed on a math task
when their gender identity was made salient, as
compared to children in a control condition.
Similarly, Tomasetto, Alparone, and Cadinu (2011)
found that gender identity activation hampered
251
math performance among 5- to 7-year-old Italian
girls, and Neuville and Croizet (2007) obtained
similar findings among 7-year-old French girls.
The last three studies raise a theoretical paradox:
How can stereotype-induced performance decrements be found in girls who do not possess stereotype awareness yet? One could assume that for a
negative stereotype to affect performance in children who have not developed stereotype awareness
yet, it is sufficient that children hold the personal
stereotypic belief that their in-group is less skillful
in the relevant domain. Indeed, given that young
children do not distinguish their own stereotypic
beliefs from others’, and rather assume that their
beliefs are shared by others as well (e.g., Augoustinos & Rosewarne, 2001), in principle children’s
personal negative beliefs about abilities of their
in-group could be sufficient to trigger stereotype
threat. If this were the case, stereotype endorsement
would be the key to identify the sources of stereotype threat in young children.
To date, the potential role of children’s endorsement of the math–gender stereotype as a prerequisite for stereotype threat effects does not fit with
the available evidence. For example, Ambady et al.
(2001) found that 5- to 7-year-old American girls
believe that boys and girls are equal at math (for
similar results on Italian children, see Tomasetto
et al., 2011); on the contrary, 5- to 7-year-old American boys state that boys are better at math, thus
exhibiting in-group favoritism (e.g., Powlishta,
1995; Yee & Brown, 1994). Similarly, other research
showed no endorsement of the math–gender stereotype until 8–9 years of age among Italian and
French children (Martinot & Desert, 2007; Muzzatti
& Agnoli, 2007).
To our knowledge, only one study found stereotype endorsement as early as 6–7 years of age in a
Western country (i.e., United States; Cvencek,
Meltzoff, & Greenwald, 2011). However, this discrepancy from the other findings might be due to
the type of measures used. Whereas all the studies
discussed elsewhere in the article assessed the
endorsement of the gender stereotype regarding
math ability, Cvencek et al. (2011) assessed the
endorsement of the stereotype that boys like math
and girls like language. Therefore, it is possible that
6- to 7-year-olds already hold the personal stereotype that boys like math, but still do not endorse the
stereotype about girls’ and boys’ math ability.
Consistent with this reasoning, Steele (2003) found
that 6- to 10-year-old children do not endorse the
stereotype regarding boys’ and girls’ abilities in math
(see also Martinot, Bages, & Desert, 2012), even
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Galdi, Cadinu, and Tomasetto
though they endorse the same stereotype about men
and women (stereotype stratification). Thus, to date,
there is no evidence that young children endorse
the math–gender ability stereotype, at least in
Western countries. Nevertheless, research shows
that it is specifically the ability component of the
math–gender stereotype that affects women’s performance, whereas other stereotypical beliefs (e.g.,
women are less interested in math) do not trigger
stereotype threat effects (Thoman, White, Yamawaki, & Koishi, 2008).
To summarize, young girls show math underperformance before being aware of the math–gender
stereotype, and in the absence of evidence that they
endorse such belief as a personal stereotype. One
possibility to explain this paradox is that previous
findings on children’s math–gender stereotype have
been based only on self-reports, which may not
capture all relevant aspects of the stereotype. Thus,
in this research, we used implicit and explicit measures jointly to investigate automatic associations as
an alternative route for the emergence of stereotype-induced underperformance in young children.
Insights From Social-Cognitive Research
Implicit measures, such as the well-known Implicit Association Test (IAT; Greenwald, McGhee, &
Schwartz, 1998), have been developed to assess the
strength of automatic associations between concepts
or concepts and attributes. These procedures are
based on the rationale that individuals should be
faster at pairing concepts, or concepts and attributes
that are strongly associated in their cognitive map,
as compared to those that are weakly or not at all
associated. Research has shown that men and
women differ in the strength of their automatic
associations between gender and academic domains
(Nosek, Banaji, & Greenwald, 2002) and that automatic associations consistent with the math–gender
stereotype predict attitudes toward math, domain
identification, and math performance (e.g., Kiefer &
Sekaquaptewa, 2007; Nosek & Smyth, 2011).
Together, these results show that automatic associations play an important role in people’s academic
achievement, interest, and performance, and that
the combined use of implicit and explicit measures
may allow researchers to provide useful insights
that neither implicit measures nor explicit measures
alone would offer.
Theorizing on the meaning of implicit and
explicit measures posits that explicit measures
reflect more recent as well as newly formed
representations that are not strong enough yet to
be automatically activated (e.g., Rudman, 2004;
Wilson, Lindsey, & Schooler, 2000), whereas implicit measures detect only those highly overlearned
associations between concepts whose activation has
become automatic over the course of long-term
experiences. According to this widespread assumption, in terms of emergence, conscious beliefs reflect
more recently formed representations, whereas
automatic associations reflect only the older representations. However, an emerging body of research
suggests that the opposite may be true as well.
Recent findings showing experimentally induced
changes in implicit but not explicit measures (e.g.,
Gawronski & LeBel, 2008; Olson & Fazio, 2006)
suggest that automatic associations may reflect
more recently formed representations as compared
to the corresponding conscious beliefs. These results
are also consistent with studies using implicit and
explicit measures in predicting future choices of
decided and undecided individuals (for a review,
see Gawronski & Galdi, 2011). These studies demonstrate that well-structured automatic associations
about a specific object are present also in the cognitive maps of those individuals who, on explicit
measures, report being undecided and do
not endorse well-defined conscious beliefs yet.
These findings come from research on attitudes
(i.e., dealing with the affective evaluation of an
object; Eagly & Chaiken, 1993), whereas stereotypes
are cognitive beliefs about the link between a category and an attribute (Hamilton & Trolier, 1986);
nonetheless they suggest that in terms of emergence, automatic associations may precede conscious
beliefs. Thus, there is reason to believe that implicit
measures can also detect recently formed automatic
associations between concepts and stereotypical
attributes (e.g., boys–math) that are not reflected on
explicit measures yet.
Insights From Research on Malleability of Automatic
Associations
Other research has demonstrated that external
cues (e.g., situational stimuli) may increase or
decrease the activation of automatic associations in
adults. For example, Dasgupta and Asgari (2004)
found that participants exposed to counterstereotypical women showed lower activation of automatic associations consistent with gender–role
stereotypes, as compared to control participants.
Similarly, women exposed to gender-stereotypical
women in TV commercials showed increased activation of automatic associations consistent with
the traditional female stereotype, and this higher
Stereotype Threat Before Stereotype Awareness
activation of automatic associations led to worse
math performance (Davies, Spencer, Quinn, & Gerhardstein, 2002). The last results are consistent with
research demonstrating that stereotypical automatic
associations affect working memory resources
needed to perform complex cognitive tasks. Specifically, Forbes and Schmader (2010) found that
women trained via an IAT to associate their gender
with being good at math showed higher working
memory and increased math performance. Taken
together, these findings suggest that it may be specifically in automatic associations between group
membership and stereotypical attributes, and in the
malleability of such automatic associations, where
we should look not only to investigate stereotypeinduced underperformance but also to reduce
stereotype threat effects. Unfortunately, to date no
study has tested whether automatic associations are
malleable in children as well, or whether changes
in activation of stereotypical automatic associations
may reflect changes in performance. Therefore, we
hypothesized that situational cues making a negative stereotype salient may automatically activate
corresponding automatic associations. The activation of such stereotypical automatic associations
should in turn affect the performance of children
who are stereotype targets, even in the absence of
evidence for stereotype endorsement. Conversely,
cues challenging the negative stereotype should
reduce the activation of corresponding automatic
associations, and such a reduction should lead to
improved performance.
Aims of This Study
To test the hypotheses mentioned above, we
employed explicit and implicit measures jointly to
assess two aspects of the math–gender stereotype,
stereotype endorsement and automatic associations,
and their differential relation with young children’s
stereotype threat vulnerability.
Research with children has already used implicit
measures to assess automatic associations (e.g.,
Baron & Banaji, 2006). Regarding math–gender stereotypes, Steffens, Jelenec, and Noack (2010) found
stereotypical automatic associations in 9- to 14-yearold girls and no automatic associations for boys at
any age. Conversely, Cvencek et al. (2011) found
stereotype-consistent automatic associations in both
girls and boys as young as 6–7 years of age. However, neither Steffens et al. nor Cvencek et al.
assessed either performance or automatic associations under stereotype threat. Therefore, these studies are not directly relevant to our main hypothesis.
253
To our knowledge, only Ambady and collaborators used an implicit stereotype awareness task
(Ambady et al., 2001, p. 387) to assess the math–
gender stereotype in a study on stereotype threat in
young children. The measure was a memory recall
task, in which the experimenter told a story about a
student especially good at math, and recorded
whether the participant, when repeating the story,
used the pronoun he or she to identify the protagonist. However, rather than a measure of stereotypical automatic associations, this task should be
considered as an indirect measure of children’s
stereotypes. Therefore, it is still unknown whether
automatic associations may be responsible for stereotype threat performance deficits in young children.
In this study, we focused on first-grade children
for two reasons. So far, the only study showing the
presence of automatic associations consistent with
gender stereotypes about math and language in
young children (Cvencek et al., 2011) considered
children from 6 to 7 years altogether (Grades 1 and
2). Second, studies showing stereotype-induced
math performance decrements in young girls have
addressed 5- to 7-year-old children (Ambady et al.,
2001; Tomasetto et al., 2011) or 7-year-olds (Neuville
& Croizet, 2007). Thus, the focus on 6-year-olds
(Grade 1) allowed us to investigate stereotypical
automatic associations as well as stereotype threat
effects specifically when children enter elementary
school, thus becoming acquainted for the first time
with formal teaching of math (at least in the Italian
school system).
The study had four goals. First, using a childfriendly version of the IAT (Child–IAT; Baron &
Banaji, 2006) we tested whether gender stereotypes
about math and language are present as automatic
associations in 6-year-olds. Consistent with Cvencek
et al. (2011), we expected stereotype-consistent automatic associations for both girls and boys.
Second, we investigated the malleability of children’s automatic associations. It was hypothesized
that children’s stereotypical automatic associations
would increase or decrease in activation depending
on the stereotype content of a prior manipulation
task. We predicted an increased activation of stereotypical automatic associations in a stereotype-consistent
condition than in a stereotype-inconsistent condition,
respectively, confirming or contradicting the traditional gender stereotype about math, with results in
the middle in a control condition, in which the math–
gender stereotype was not salient.
Third, using an explicit measure, we investigated
whether 6-year-olds endorse the belief that boys are
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Galdi, Cadinu, and Tomasetto
better than girls at math. Three reasons led us to
assess stereotype endorsement: (a) children first
develop stereotype endorsement and then stereotype awareness (e.g., Aboud, 1988; Martin & Ruble,
2010), (b) 6-year-olds do not possess stereotype
awareness yet (e.g., Rholes & Ruble, 1984; Selman,
1980), and (c) children’s personal stereotypic beliefs
could be sufficient to trigger stereotype threat, specifically because at this age children assume that
their beliefs are shared by others as well (e.g.,
Augoustinos & Rosewarne, 2001). Consistent with
previous research (e.g., Ambady et al., 2001; Muzzatti & Agnoli, 2007), we expected no evidence of
endorsement of the math–gender stereotype.
Finally, like in past research on stereotype threat
including 6-year-olds (e.g., Tomasetto et al., 2011),
we expected girls’ math performance to decrease
under stereotype threat, that is, in the stereotypeconsistent as compared to the control condition and
the stereotype-inconsistent condition, which should
lead to the highest performance. Importantly, we
predicted the increased activation of stereotypeconsistent automatic associations to mediate the
decrease in girls’ performance under stereotype
threat.
Method
Participants
Two hundred and seventy-six first-grade children (143 girls) participated in the study. All of
them were born in Italy and were of typical age for
their grade (M = 77.60 months, SD = 0.28). Children attended one of six elementary schools in one
of three small towns in the northeast of Italy. Permissions to conduct the study were granted by
school principals and parents. Six female experimenters were involved in the data collection, conducted during school hours. All children were
tested in the same school period to avoid potential
effects of variability in mathematics curricula across
classes at the moment of data collection.
Procedure
Children were tested individually in a quiet room
of their school, while sitting at a desk facing the
experimenter. Each experimental session started by
asking participants to color one of three pictures,
depending on the experimental condition (stereotype consistent, control, and stereotype inconsistent)
to which they were randomly assigned. When children finished coloring, the experimenter asked them
to describe the picture, and then left the picture on
one side of the desk. After the manipulation task,
children were invited to play a computer game (i.e.,
the Child–IAT) using a laptop computer. Participants were told that they would see pictures during
the game, and that they would press the red (A key)
or the green (L key) button of the computer board to
indicate which picture they saw. After the Child–
IAT, children performed eight math calculations
(i.e., math test). The experimenter asked one calculation at a time (e.g., “How much is 5 plus 5?”) and
children had to respond within 5 s. For each question, the experimenter noted whether the response
was correct or not (i.e., incorrect or no response by
5 s), without providing any feedback. Finally, children performed the explicit stereotype-endorsement
task. They were shown a picture of a boy and a girl
side by side and were told: “These are a boy and a
girl. They are 6-year-olds and they are good at
school. Is the boy better at math, the girl better at
math, or are they the same at math?” Next, the
experimenter recorded the response, and participants were thanked, given a candy bar, and dismissed.
Thus, the administration of the implicit measure
always followed the experimental manipulation and
always preceded the math test followed by the explicit measure. This order of the tasks was chosen
because we aimed at testing the effects of the experimental manipulation on automatic associations
ruling out the possibility that counterbalancing
implicit and explicit measures might weaken the
effects of the experimental manipulation on automatic associations. Moreover, there is no evidence
that performing the IAT before a self-report induces
reactance or assimilation tendencies in the subsequent self-report (Nosek, Greenwald, & Banaji, 2005).
Materials
Experimental manipulation. According to the
stereotype threat model, it is the salience of a negative domain-relevant in-group stereotype that
impairs targets’ performance. A negative domainrelevant in-group stereotype can be made salient in
three ways: (a) describing the task that targets will
subsequently perform as diagnostic of the ability
related to the negative in-group stereotype, (b) making targets’ in-group salient, or (c) making the content of the negative stereotype salient (Inzlicht &
Schmader, 2012). In this study, the manipulation
choice was to make salient the stereotype content
(math–gender stereotype). Thus, participants were
randomly assigned to one of three experimental
Stereotype Threat Before Stereotype Awareness
conditions by inviting them to color a picture, in
which: (a) a boy correctly resolves a math calculation
on a blackboard, whereas a girl fails to respond
(stereotype-consistent condition); or (b) a girl
correctly resolves the calculation, whereas a boy fails
to respond (stereotype-inconsistent condition); or
(c) a landscape was depicted (control condition).
Implicit measure: The math(language)-gender Child–
IAT. A Child–IAT (Baron & Banaji, 2006) was used
to assess the relative strength of automatic associations between the target category boy and the
attribute category mathematics, and the target category girl and the attribute category language, as
compared to the opposite pairings (i.e., boy–language and girl–mathematics).
Categories and stimuli for the Child–IAT. Four pictures of math-related objects (i.e., numbers) and four
pictures of reading- and writing-related objects (i.e.,
letters), pretested with first-grade children for familiarity and comprehension, were used as stimuli for
the attribute categories mathematics and language.
Four face-pictures of boys and four face-pictures of
girls represented the target categories boy and girl.
Procedure of the Child–IAT. Children were instructed to respond fast and accurately to three simplecategorization (practice) blocks and two (third and
fifth) critical double-categorization blocks. Each
practice block included 16 trials; each critical block
included 32 trials. On each trial, a target or attribute
picture appeared on the screen until participants
gave their response. Children categorized each picture by pressing a key on the computer board, the
left-hand response A or the right-hand response L
key. To simplify the task, the left-hand key (A) was
colored in red, and the right-hand key (L) was colored in green. The intertrial interval was 200 ms. A
red cross, which remained on the screen for 200 ms,
followed incorrect responses.
In the first block of the task, children were presented with pictures for the target categories girl
and boy, with each picture randomly presented
twice. Participants had to indicate whether the picture on the screen was a boy or a girl by pressing
the red key for girl and the green key for boy. In
the second block, children were presented with pictures for the attribute categories language and
mathematics, with each picture randomly presented
twice. Participants had to press the red key when
the picture was a reading- or writing-related object
and the green key when the picture was a mathrelated object. In the third double-categorization
block, both pictures of boys and girls, and pictures
of math-related and reading- or writing-related
objects (with each picture representing each cate-
255
gory randomly presented twice) appeared on the
screen. In this case, children pressed the red key
when they saw either a girl or a reading- or writingrelated object, and the green key when they saw
either a boy or a math-related object. In the fourth
block, participants categorized again the pictures
representing the categories mathematics and language. However, different from the key assignment
in the second block, participants had to press the
red key when they saw a math-related object and
the green key when they saw a reading- or writingrelated object. Finally, in the fifth double-categorization block, children categorized the same pictures of
boys and girls and the same pictures of math-related
and reading- or writing-related objects. However,
participants now pressed the red key when they saw
either a girl or a math-related object, and the green
key when they saw either a boy or a reading- or
writing-related object. Following the procedure
employed in the third block, each picture representing each of the four categories was randomly presented twice. The order of the two critical blocks,
third and fifth, was counterbalanced across participants to avoid order effects.
Math test. Three days before the data collection,
a math test was developed in collaboration with all
teachers of the classes participating in the study, to
create a set of difficult math calculations taking into
account the mathematics achievement of all classes.
The math test was a retrieval of numerical facts
including five additions and three subtractions (i.e.,
5 + 5, 8
4, 6 + 4, 10
5, 2 + 3, 2 + 2, 6
3, and
4 + 5). Children had to resolve each math calculation in 5 s.
Explicit stereotype endorsement. As in Ambady
et al.’s (2001) study, children were presented with a
photograph of a boy and a girl described as good
at school, and had to choose whether the boy or the
girl is especially good at math or whether they are
equal at math. The two photographs as well as the
questions’ wording for the boy or the girl were
presented in counterbalanced order.
Results
Preliminary Analyses
Twenty-three girls and thirteen boys were
excluded only because of 30% or higher error rates in
at least one critical (double-categorization) block of
the Child–IAT. Preliminary analyses confirmed that
excluded participants were evenly distributed across
conditions (i.e., 13 in the stereotype-consistent, 10 in
the control, and 13 in the stereotype-inconsistent
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Galdi, Cadinu, and Tomasetto
conditions), and that their responses did not differ
from those of retained children on both the math test
and the stereotype endorsement. The final sample
included 240 children (120 girls).
Individual scores of automatic associations were
calculated by means of the D-algorithm, designed for
analyzing data with the IAT (Greenwald, Nosek, &
Banaji, 2003). The D-algorithm compares the extent
to which performance on the incompatible critical
block (i.e., girl–mathematics and boy–language sharing the same response key) is impaired relative to
performance on the compatible critical block (i.e.,
girl–language and boy–mathematics sharing the
same response key), taking into account participants’
individual response latencies, standard deviations of
latencies, and error rates in each of the two blocks.
Scores were calculated so that positive scores reflect
stronger boy–mathematics and girl–language automatic
associations. To estimate reliability of the Child–IAT,
two Child–IAT scores were calculated: one using the
first half and the other using the second half of the
two critical blocks. Internal consistency was satisfactory (Cronbach’s a = .84).
A one-way analysis of variance (ANOVA) was
then conducted on scores of automatic associations
using conditions (stereotype consistent, control, stereotype inconsistent), gender (male, female), and
the order of administration of the critical blocks
(1 = third block: girl–language and boy–math, fifth block:
girl–math and boy–language; 2 = third block: girl–math
and boy–language, fifth block: girl–language and boy–
math) as the independent variables. A main effect for
order of administration was found, F(1, 234) = 6.64,
p = .01, gp2 = .03. However, because order of
administration produced no significant interactions
(all ps > .09), it is not further discussed.
For each participant, correct responses to the
math test (i.e., correct math calculation in 5 s) were
coded +1, and incorrect responses (i.e., no response
or incorrect math calculation in 5 s) were coded 0.
For each child, responses were then added up in a
single math score. To correct for potential variability in mathematics proficiency across classes, math
scores were standardized within each class of the
six schools. Internal consistency of the math test
based on all eight calculations was satisfactory
(Cronbach’s a = .89).
Separate one-way ANOVAs were conducted on
scores of automatic associations and math scores,
using condition (stereotype consistent, control, stereotype inconsistent), gender (male, female), and
the schools as independent variables. Schools did
not lead to any significant result (all ps > .70), and
thus this variable is not further discussed.
Table 1
Zero-Order Correlations Between Automatic Associations, Math Performance, and Explicit Stereotype Endorsement for Girls and Boys
1
Girls (n = 120)
Automatic associations
Math performance
Explicit stereotype endorsement
Boys (n = 120)
Automatic associations
Math performance
Explicit stereotype endorsement
2
3
—
.267**
.115
—
.142
—
—
.045
.000
—
.034
—
Note. Pearson’s r coefficients are reported for automatic associations
and math performance. Spearman’s rs coefficients are reported
for the categorical variable explicit stereotype endorsement.
**p < .01.
Zero-order correlations between all dependent
variables (automatic associations, math performance,
and explicit math–gender stereotype endorsement),
separately for girls and boys across experimental
conditions, are presented in Table 1.
Automatic Associations and Their Malleability
We predicted stereotype-consistent automatic
associations for both girls and boys in the control
condition, in which the math–gender stereotype
was not made salient. In addition, we aimed at testing for the malleability of children’s automatic associations. Higher scores of automatic associations
were expected in the stereotype-consistent than in
the stereotype-inconsistent condition, with results in
the middle for the control condition.
A two-way ANOVA was conducted on scores of
automatic associations, with condition (stereotype
consistent, control, stereotype inconsistent) and
gender (male, female) as the between-participants
variables. Results showed a significant Condition 9
Gender interaction, F(2, 234) = 6.35, p = .002,
gp2 = .05. To assess the effect of condition within
gender, simple-effect analyses were conducted separately for girls and boys. Table 2 presents the average scores of automatic associations in each
experimental condition for both boys and girls,
together with information about the planned contrasts between the means.
No effect of condition emerged for boys’
automatic associations (p > .30). Moreover, one-sample t tests against 0 showed no effect in any condition (all ps > .20). Conversely, simple-effect analysis
on girls’ automatic associations revealed a significant effect of condition, F(2, 117) = 7.75, p < .01,
Stereotype Threat Before Stereotype Awareness
Table 2
Average Scores of Automatic Associations and Math Scores as a Function of Condition (Stereotype Consistent, Control, Stereotype Inconsistent) for Girls and Boys
Stereotype
consistent
Girls (n = 120)
Automatic
associations
Math test
Boys (n = 120)
Automatic
associations
Math test
Control
Stereotype
inconsistent
**0.28a
(SD = 0.45)
0.36a
(SD = 0.73)
*0.19a
(SD = 0.78)
0.01ab
(SD = 0.49)
0.10b
(SD = 0.43)
0.16b
(SD = 0.92)
0.03a
(SD = 0.44)
0.28a
(SD = 1.03)
0.05a
(SD = 0.30)
0.05a
(SD = 1.02)
0.10a
(SD = 0.50)
0.03a
(SD = .75)
Note. Means within rows not sharing the same subscript are significantly different from each other at the p < .05 level (Bonferroni test).
*p < .02. **p < .001.
gp2 = .06, linear trend, p < .001. Post hoc tests with
Bonferroni correction of the alpha level for multiple
comparisons showed that girls’ scores of automatic
associations were lower in the stereotype-inconsistent as compared to the control (p < .02) and the
stereotype-consistent (p < .01) conditions. As shown
in Table 2, although girls’ automatic associations
were the highest in the stereotype-consistent
condition, scores in the control and the stereotypeconsistent conditions did not differ from each other
(p > .90). Moreover, one-sample t tests against 0
showed that girls’ scores of automatic associations
were different from 0 in the stereotype-consistent
and in the control (both ps < .02) but not in the
stereotype-inconsistent condition (p = .20).
Importantly, simple-effect analyses on the main
effect of gender within conditions revealed that
boys’ scores were lower than girls’ scores of
automatic associations in the stereotype-consistent,
F(1, 234) = 5.90, p < .03, gp2 = .02, and control condition, F(1, 234) = 5.84, p < .03, gp2 = .02. Although
this difference fell short of significance, boys’ scores
of automatic associations tended to be higher than
those of girls in the stereotype-inconsistent condition, F(1, 234) = 3.79, p = .053, gp2 = .01.
In sum, although automatic associations consistent with math(language)–gender stereotypes were
not found for boys, girls revealed stereotype-consistent automatic associations in both the control and
the stereotype-consistent conditions. Importantly,
girls’ automatic associations showed the expected
malleability, thus differing in activation as a function
of the experimental condition: They were higher in
257
the stereotype-consistent and the control conditions
than the stereotype-inconsistent conditions. No effect
of the experimental manipulation was found for
boys’ automatic associations.
Math Performance
For girls, we expected performance decrements
in the stereotype-consistent condition as compared
to the control and the stereotype-inconsistent conditions. Conversely, no effect of condition on boys’
math performance was hypothesized.
An ANOVA on math scores, with condition
(stereotype consistent, control, stereotype inconsistent) and gender (male, female) as the betweenparticipants variables, was conducted. A Condition 9 Gender interaction emerged, F(2, 234) = 4.69,
p = .010, gp2 = .04. Therefore, the effect of condition
was tested separately for girls and boys. Simpleeffect analyses on boys’ math scores (Table 2)
showed that boys performed equally well across
conditions (p > .30). To the opposite, an effect of
condition was found for girls, F(2, 117) = 3.66,
p < .03, gp2 = .03, linear trend, p < .01: Girls underperformed in the stereotype-consistent as compared
to the stereotype-inconsistent condition (p < .03). On
the contrary, math scores in the control and both in
the stereotype-consistent and in the stereotypeinconsistent conditions were not different from each
other (both ps > .20). Thus, whereas the salience of
the negative in-group stereotype led girls to perform
worse on the math test, the removal of the same
stereotype led girls to perform better in the
stereotype-inconsistent condition as compared to
the stereotype-consistent condition.
Explicit Stereotype Endorsement
To assess stereotype endorsement, participants
were shown a picture of a boy and a girl and were
asked to say whether the boy or the girl is especially good at math, or whether they are equal. For
each child, stereotype-inconsistent response (i.e., the
girl is better at math) was coded 1, neutral choice
(i.e., the boy and the girl are equal) was coded 0, and
stereotype-consistent choice (i.e., the boy is better at
math) was coded +1. We expected no evidence that
children endorsed the math–gender stereotype.
A logistic regression for ordinal dependent measures was performed on children’s choices at the task
( 1 = stereotype-inconsistent, 0 = neutral, +1 = stereotype-consistent choice), with gender (0 = male,
1 = female), condition ( 1 = stereotype inconsistent,
0 = control, +1 = stereotype consistent), and the product
258
Galdi, Cadinu, and Tomasetto
term as predictors. A main effect of gender emerged,
Wald v2 = 11.49, p < .001, indicating that the probability of a counterstereotypical versus neutral versus
stereotypical response was not equal for boys and
girls. Regardless of condition, 57% of boys and 57%
of girls favored their own gender and indicated the
boy or the girl, respectively, as the most talented for
math. Only 12% of boys and 21% of girls responded
that the boy and the girl were the same. Of the
remaining children, 22% of girls identified the boy
as being better at math, whereas 31% of boys
believed that the outstanding math student was the
girl. Thus, consistent with predictions, 6-year-olds
did not endorse the math–gender stereotype, but
simply manifested gender in-group favoritism.
Mediation Analysis
Because of null findings for boys on both automatic associations and math scores, a mediation
analysis was conducted only for girls to test
whether automatic associations mediated the relation between condition and math performance.
Given that the predictor (i.e., condition) was a categorical variable with three levels, we created two
dummy-coded variables to conduct the mediation
analysis (Hayes & Preacher, 2012) with the stereotype-consistent condition as the reference group.
Specifically, Contrast 1 tested the effect of the stereotype-consistent (coded 0) versus stereotypeinconsistent (coded 1) condition, with the control
condition coded 0. Contrast 2 tested for the residual
difference between the stereotype-consistent (coded
0) and the control (coded 1) conditions, with the
stereotype-inconsistent condition coded 0.
Consistent with the univariate analyses reported
above, the effect of Contrast 1 on performance was
significant, b = .55, t(111) = 3.38, p < .01, whereas
the effect of Contrast 2 fell short of significance,
b = .34, t(111) = 1.91, p = .06. Similarly, the effect of
Contrast 1 on automatic associations was significant,
b = .37, t(111) = 3.56, p < .001, whereas the effect
of Contrast 2 was not, p > .40. Moreover, when
automatic associations and the two contrasts were
entered simultaneously in the model predicting
performance, the effect of automatic associations
was significant, b = .36, t(111) = 2.32, p < .03, indicating that automatic associations negatively affect
performance. Importantly, the effect of Contrast 1
(i.e., stereotype-consistent vs. stereotype-inconsistent
condition) was reduced, b = .38, t(110) = 2.06,
p < .05, whereas the effect of Contrast 2 (i.e., stereotype-consistent vs. control condition) remained not
significant, b = .31, t(111) = 1.77, p = .07. Figure 1
Automatic Associations
-.36**
-.37***
Experimental
Condition
.38* (.55**)
Math Performance
Figure 1. Results of mediation analyses testing indirect effects of
experimental condition (stereotype consistent = 0, stereotype inconsistent = 1) on math performance via automatic associations in
girls (n = 120).
*p < .05. **p < .01. ***p < .001.
summarizes results for Contrast 1. To test for mediation, we calculated a bias-corrected 95% confidence
interval for the indirect effect (.13, SE = .06) using a
bootstrapping technique (Preacher & Hayes, 2008).
As the null hypothesis of no mediation states that
the indirect effect is 0, the null hypothesis is rejected
when the confidence interval does not include 0. In
the present analysis, the confidence interval (with
5,000 resamples) for the estimate of the indirect
effect of Contrast 1 on performance did not include
0, 95% CI [0.02, 0.28], thus confirming that automatic associations mediated the relation between
condition (stereotype consistent vs. stereotype
inconsistent) and girls’ performance.
Supplementary Analyses
Although main analyses showed that stereotype
endorsement did not vary as a function of
condition, supplementary analyses were conducted
to test whether the relations among condition,
girls’ automatic associations, and girls’ math performance were further moderated by stereotype
endorsement. Two-way analyses of variance were
conducted on both scores of automatic associations
and math performance, with condition (stereotype
consistent, control, stereotype inconsistent) and
explicit stereotype endorsement (stereotype-consistent, neutral, stereotype-inconsistent response) as
the between-participants variables. In both
ANOVAs, explicit stereotype endorsement yielded
no significant effects: The effect of condition was
significant on both scores of automatic associations, F(2, 111) = 7.76.12, p < .001, and math performance, F(2, 111) = 4.12, p < .02, and was not
further qualified by explicit stereotype endorsement (all ps > .15). However, given that the relatively low number of girls who provided a neutral
response or indicated the other gender group as
Stereotype Threat Before Stereotype Awareness
better at math resulted in unbalanced cell frequencies, this finding could be a consequence of low
statistical power. To rule out this possibility, we
repeated the same analyses by recoding explicit
stereotype endorsement into a dichotomous variable, comparing girls indicating their gender group
as better at math with those who either indicated
boys as better or provided a neutral response.
Results were very similar: The effect of condition
remained significant on both automatic associations, F(2, 114) = 6.83, p < .01, and math performance, F(2, 114) = 4.59, p < .01, and was not
further qualified by explicit stereotype endorsement (all ps > .09).
We also tested whether automatic associations may
act as a moderator, rather than a mediator, of the
effect of stereotype activation on girls’ math performance. An analysis of covariance (ANCOVA) was
performed on math performance with condition
(stereotype consistent, control, and stereotype inconsistent), automatic associations, and the interaction
between condition and automatic associations as
predictors. The main effect of automatic associations
was significant, F(1, 114) = 5.57, p < .02, whereas the
interaction between condition and automatic associations was not (p > .20), thus confirming that automatic associations acted as a mediator of the relation
between condition and performance.
Finally, we tested whether automatic associations
and stereotype endorsement may interact with each
other in determining girl’s vulnerability to stereotype threat. We carried out a three-way ANCOVA
on math performance with condition (stereotype
consistent, control, stereotype inconsistent), automatic associations, and explicit stereotype endorsement response (stereotype consistent, neutral,
stereotype inconsistent) as predictors. Again, only
the main effect of automatic associations was significant, F(1, 114) = 4.01, p < .05, whereas no other
main effect or higher order interaction attained significance (all ps > .10).
Although boys showed null findings on all
dependent variables, the same supplementary analyses were conducted for them as well. In all analyses, neither main effects nor interactions reached
significance (all ps > .08).
Discussion
Although the stereotype threat model identifies stereotype awareness as a requirement for stereotype
threat effects, research has shown that gender identity activation affects girls’ math performance before
259
the emergence of stereotype awareness, and in the
absence of endorsement of the math–gender stereotype (Ambady et al., 2001; Neuville & Croizet,
2007; Tomasetto et al., 2011). The present research
disentangles this paradox and extends the stereotype threat model by demonstrating that automatic
associations consistent with a negative in-group stereotype represent a key factor that may trigger
stereotype threat in young children.
Consistent with research showing that automatic
associations can precede conscious beliefs (e.g., Gawronski & Galdi, 2011), we demonstrated for the first
time that girls acquire the math–gender stereotype as
automatic associations before its emergence at the
explicit level: Despite the absence of evidence that
children endorsed the math–gender stereotype,
6-year-old girls, but not boys, even in the control
condition (i.e., in the absence of any experimental
manipulation aimed at increasing or decreasing the
salience of the stereotype) showed stereotype-consistent automatic associations between boy and mathematics, and girl and language. Moreover, consistent
with research showing malle-ability of automatic
associations in adults (e.g., Dasgupta & Asgari,
2004), for the first time, malleability of girls’ automatic associations was found. After coloring a drawing of a boy correctly solving a math problem, girls’
stereotypical automatic associations were activated,
as compared to the control condition. At the same
time, after coloring a picture of a girl succeeding in
math, stereotypical automatic associations were
reduced.
Importantly, the activation of automatic associations in the stereotype-consistent condition hampered girls’ math performance as compared to the
stereotype-inconsistent condition, in which, conversely, the reduced activation of automatic associations led girls to the highest performance.
Furthermore, highlighting the role of counterstereotypical information in counteracting stereotype
threat effects, these findings are parallel to research
showing that providing information about equal
gender abilities in math (i.e., stereotype-inconsistent
information) is an effective strategy to prevent
women’s performance deficits (e.g., Cadinu, Maass,
Frigerio, Impagliazzo, & Latinotti, 2003).
An open issue of this study concerns the potential processes underlying girls’ underperformance in
the stereotype-consistent versus -inconsistent condition. Consistent with Forbes and Schmader (2010),
one may speculate that activated stereotypical automatic associations in the stereotype consistent condition may have burdened working memory capacity,
thus disrupting subsequent math performance.
260
Galdi, Cadinu, and Tomasetto
Conversely, the reduced activation in the stereotype-inconsistent condition may have freed up
working memory resources, thus enhancing subsequent performance. However, no such conclusions
can be drawn as this study did not test how girls’
automatic associations affect performance. Therefore, a direct test of the working memory hypothesis should be the goal of future studies.
Another potential process underlying girls’ worse
performance in the stereotype-consistent than the
stereotype-inconsistent condition could be a mere
priming effect, which would lead individuals to
behave automatically consistent with a cognitively
activated image (e.g., Wheeler & Petty, 2001). However, if a priming effect were at work, one should
expect underperformance even for boys in response
to stereotype-related stimuli. To the contrary, neither boys’ automatic associations nor performance
were affected by the activation of the math–gender
(counter)stereotype. Thus, differently from a priming effect, we argue that in this study it was the
membership in a negatively stereotyped group
(whose stereotype has been acquired via automatic
associations) that was responsible for girls’ performance deficit under stereotype threat.
Although girls’ automatic associations increased
and decreased in activation depending on the stereotype content of the experimental condition, no
changes emerged at the level of endorsement of stereotypical beliefs. Consistent with research showing
gender in-group bias in young children, the majority of children across conditions indicated their gender as superior in math. We argue that this lack of
endorsement of the math–gender stereotype further
strengthens the role of automatic associations in the
process of stereotype acquisition in children.
Relative to the latest point, the present pattern of
findings raises the tricky issue concerning the
sources and mechanisms underlying the formation
of automatic associations. Up to date, highly influential theorizing has posited that automatic associations stem from attitudes and conscious beliefs that
have become overlearned and automatized over time
(e.g., Rudman, 2004; Wilson et al., 2000). However,
recent research (e.g., Gawronski & Galdi, 2011), as
well as the present results, suggests that also other
underlying mechanisms may be responsible for the
formation of automatic associations. Drawing on the
distinction between associative and propositional
learning (Gawronski & Bodenhausen, 2006), we
argue that the automatic formation of stereotype-consistent associative links in children’s cognitive map
could stem from repeated co-occurrences of objects
or stimuli in children’s social environment. For
example, ample evidence shows that children are
sensitive to adults’ nonverbal behavior and that such
behaviors, often occurring in automatic and unconscious ways, may represent an important channel
through which attitudes are transmitted (e.g., Rudman, 2004; Walden & Ogan, 1988). Thus, even
though adults may explicitly encourage nonstereotypical interests in children, they may exhibit different patterns of nonverbal behaviors: Parents may
purchase more games or manipulative materials
related to math and science for their sons than for
their daughters (Jacobs & Bleeker, 2004), or intrude
more often in their daughters’ than in their sons’
math homework to offer unsolicited help (e.g.,
Bahnot & Jovanovic, 2005). As a result, such repeated
co-occurrences of objects and events might promote
the automatic formation of stereotype-consistent
associative links in children’s cognitive maps.
In contrast to girls, boys did not reveal stereotypical automatic associations in any conditions.
Consistent with research demonstrating that elementary school boys tend to manifest in-group favoritism
regarding both math and language (Steele, 2003),
these results could simply reflect the fact that boys
showed in-group–serving automatic associations
about both math and language domains (Steffens
et al., 2010), regardless of the stereotype content conveyed by the experimental condition. Indeed, the
presence of in-group-serving boy–mathematics and
boy–language automatic associations would result in
fast response latencies in both critical blocks of the
Child–IAT. Thus, given that the D-algorithm compares the extent to which performance on the incompatible (i.e., girl–math and boy–language sharing the
same response key) is impaired relative to the
compatible critical block (i.e., girl–language and
boy–math sharing the same response key), in-group–
serving automatic associations could have led to the
small or null score of automatic associations that was
found for boys across conditions. However, because
both gender stereotypes regarding math and
language contribute to the Child–IAT score, and cannot be separated (Nosek et al., 2005), this possibility
could not be tested in this study. To our knowledge,
only one study used an implicit measure to assess
math–gender and language–gender stereotypes
separately (i.e., Go-No-Go association task [GNAT];
Nosek & Banaji, 2001) in a sample of 14-year-olds
and university students. Consistent with the ingroup–serving explanation, Steffens and Jelenec’s
(2011) male participants revealed both boys–math
stereotypical automatic associations and boys–language counterstereotypical automatic associations.
However, as Steffens and Jelenec noted, “the internal
Stereotype Threat Before Stereotype Awareness
consistency of the GNAT was low . . . and the IAT
clearly appears more sensitive” (Steffens & Jelenec,
2011, p. 332). Thus, a goal of future studies should be
to test whether other implicit measures that combine
the measurement quality of the IAT with the separate
measurement of math and language gender stereotypes, such as the GNAT, can be implemented and
adapted for use with young children.
Studies on the development of cognitive competencies offer alternative explanations for boys’ lack
of automatic associations. For example, previous
research has shown earlier knowledge of gender
categories (Zosuls et al., 2009) and earlier achievement of gender constancy (Ruble et al., 2007) by
girls than boys. At the same time, differences in
gender categorization abilities between girls and
boys could also be the result of socialization processes. For example, women show stronger automatic associations between the self, their gender,
and the in-group gender stereotype as compared to
men (Cadinu & Galdi, 2012), and children from
low-status groups are more likely to be aware of
self-relevant stereotypes than children from highstatus groups (McKown & Weinstein, 2003).
Although consistent with Steffens et al. (2010),
the result that boys did not manifest stereotypical
automatic associations is inconsistent with Cvencek
et al.’s (2011) findings, showing stereotypical automatic associations between gender and academic
subjects in both 6- to 7-year-old boys and girls.
However, this discrepancy might reflect crosscultural variations in the strength of gender stereotypes at the societal level, which have been found
to be associated with either smaller or bigger gender differences in the strength of stereotypical automatic associations (Nosek et al., 2009), as well as in
the endorsement of gender stereotypes about
academic abilities (e.g., Evans, Schweingruber, &
Stevenson, 2002). Moreover, in a study involving
Chinese, Japanese, and American children, Lummis
and Stevenson (1990) found that whereas American
and Chinese 6-year-olds do not believe that there
are gender differences in math, 6-year-old Japanese
children tend to believe that boys are better than
girls at math (see also Del Rio & Strasser, 2013).
These results show that cultural differences in the
strength of gender stereotypes about academic abilities, in schooling, and in out-of-school experiences,
may lead to different acquisition ages of the
endorsement of these stereotypes, and suggest that
the same could be true regarding the acquisition of
stereotypical automatic associations.
In conclusion, this study has the potential to contribute to the stereotype threat model by suggesting
261
automatic associations as a novel prerequisite for
stereotype-induced underperformance in young
children. Importantly, by showing also that children’s automatic associations are malleable, these
findings are promising in terms of interventions to
promote gender equality in math and science
because they suggest that girls can be protected
from the deleterious impact of math–gender stereotypes. Relevant to this claim is a study (Dasgupta &
Greenwald, 2001) demonstrating that Caucasian
participants exposed to images of admired Black
and disliked White exemplars showed lower proWhite automatic associations than participants
exposed to images of admired Whites and disliked
Blacks. Interestingly, such a decrease in pro-White
automatic associations lasted for 24 hr, suggesting
that relatively enduring changes in automatic associations can be obtained. If so, repeatedly presenting
girls with exemplars of successful women in math
and science could promote the reduction in stereotypical automatic associations, long before girls have
acquired any awareness or endorsement of the stereotype favoring males in math. This study suggests
that this strategy could protect girls’ performance in
stereotype-threatening situations and potentially
help them to expand their interests toward traditionally male domains.
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