Girls and Boys Typically Have Similar Math Value Beliefs: Replication Evidence Across Historical Time, High School, and Racial/Ethnic Groups

Christine R Starr; Glona Lee‐Poon; Charlott Rubach; Yannan Gao; Nayssan Safavian; Anna‐Lena Dicke; Jacquelynne S Eccles; Sandra D Simpkins

doi:10.1002/jad.12456

. 2025 Jan 9;97(3):808–825. doi: 10.1002/jad.12456

Girls and Boys Typically Have Similar Math Value Beliefs: Replication Evidence Across Historical Time, High School, and Racial/Ethnic Groups

Christine R Starr ^1,^✉, Glona Lee‐Poon ², Charlott Rubach ³, Yannan Gao ⁴, Nayssan Safavian ², Anna‐Lena Dicke ², Jacquelynne S Eccles ², Sandra D Simpkins ²

PMCID: PMC11973846 PMID: 39783780

ABSTRACT

Introduction

Individuals' math value beliefs are theorized to influence who persists in STEM. However, the existing findings on gender differences in adolescents' math value beliefs are inconsistent. The goal of this study was to use three existing datasets to help clarify when gender differences emerge for high school adolescents and for whom (i.e., adolescents across historical time, grade level, and race/ethnicity). Specifically, we examined the extent to which gender differences in adolescents' math value beliefs (i.e., interest, utility, and attainment) replicated (1) across three datasets spanning the 1990s to 2010s, (2) from 9th–12th grade, and (3) within each of the four largest U.S. racial/ethnic groups (i.e., Asian, Black, Latine, and White adolescents).

Methods

We tested these aims with three existing longitudinal U.S. datasets: the California Achievement Motivation Project (CAMP) (n = 8855), the Childhood and Beyond Study (CAB) (n = 582), and the High School Longitudinal Study (HSLS) (n = 21,000). Students were in high school (9th–12th grade) and half were girls (49%–53%). All three datasets included measures with the same or similar math value belief items, making conceptual replication possible.

Results and Conclusions

Overall, we did not find strong evidence for meaningful gender differences in adolescents' math value beliefs overall. We did find meaningful gender differences in the oldest data set (CAB). When examined within each racial/ethnic group, we found no evidence of gender differences in math value beliefs among Black or Latine adolescents, but some differences among Asian and White adolescents. The findings align with the gender similarities hypothesis, suggesting adolescent girls and boys had similar math value beliefs.

Keywords: attainment, importance, interest, STEM, utility

Women ¹ continue to be underrepresented in math‐intensive STEM occupations (Honey et al. 2020; National Science Foundation NSF 2023). Scholars have theorized that math is a gateway to many STEM fields (Schoon and Eccles 2014) and suggest that gender differences in individuals' math value beliefs may be one reason why women are underrepresented in math intensive fields, making them important to investigate (e.g., Chen et al. 2023; Eccles and Wigfield 2020; Umarji et al. 2021). Examining gender differences in adolescents' math value beliefs is critical during high school because it is a time when young people make consequential decisions about their future, such as high school course choices, career planning, and deciding on college majors (e.g., Jiang, Simpkins, and Eccles 2020). However, the existing research on gender differences in adolescents' math value beliefs is mixed (which is reviewed in detail below)—making it unclear whether gender differences in these beliefs lead to gender disparities in individuals' STEM choices. Such inconsistencies are not only a barrier in determining how to ameliorate the current situation for women in STEM, but it is an example of the replication crisis that is a concern for social sciences more broadly.

Scholars argue that when inconsistencies emerge, like in the case of gender differences in adolescents' math value beliefs, testing the extent to which the findings replicate across datasets and identifying when gender differences emerge is critical to move the field forward (Duncan et al. 2014; Plucker and Makel 2021; Youyou, Yang, and Uzzi 2023). One recent study illustrates this point. Rubach et al. (2022) found that gender differences in high school students' math ability self‐concepts generally favored boys over girls in six U.S. datasets from 1988 to 2011. However, the researchers also found across multiple datasets that these gender differences favoring boys replicated among Asian, Latine, and White adolescents, but not Black adolescents, for whom there were no significant gender differences in math ability self‐concept. Finding this pattern consistently across multiple datasets provided strong evidence that gender differences vary by race/ethnicity and that this variability was robust to methodological differences across datasets; moreover, it provides insight into why some of the inconsistencies emerged across prior studies with varying participant characteristics. In this study, we will use the same approach to help settle some of the inconsistent findings in the literature on gender differences in adolescents' math value beliefs (i.e., interest, utility, and attainment values). We tested the robustness of our findings across datasets with varying samples and methods (e.g., Duncan et al. 2014; Youyou, Yang, and Uzzi 2023). Using three U.S. datasets, we examined the extent to which gender differences in adolescents' math value beliefs (i.e., interest, utility, and attainment) emerged (1) historically across the datasets spanning 1994 to 2012, (2) developmentally from 9th‐12th grade, and, when possible, (3) within each of the four largest U.S. racial/ethnic groups (i.e., Asian, Black, Latine², and White adolescents).

1. Gender Differences in Adolescents' Math Value Beliefs

According to situated expectancy‐value theory, students' beliefs about their abilities in and subjective task values for a domain are directly related to their performance and choices in that domain (Eccles, Moses, and Yulish‐Muszynski 1983; Eccles and Wigfield 2020). Three central subjective task value beliefs described in the theory are interest (or intrinsic) value, utility value, and attainment value. Though these three value beliefs are related, they are distinct beliefs based on theory and empirical evidence (Barron and Hulleman 2015; Eccles and Wigfield 2020; Wigfield and Eccles 2020). Interest value (also known as intrinsic value) is one's expected enjoyment of a domain. Utility value is the perceived usefulness of a domain for present or future plans. Attainment value refers to the perceived importance of a domain for one's identity. These three motivational beliefs positively predict students' academic outcomes, including their performance and choices in a variety of domains including math (Eccles and Wigfield 2020; Wigfield and Eccles 2020). Eccles and Wigfield (2020) recently added the word “situated” to expectancy‐value theory to highlight that students' motivational beliefs are situated in individuals' socio‐cultural contexts (Eccles and Wigfield 2020).

Situated expectancy‐value theory and broader theories on social position factors highlight the role of socio‐cultural contexts and argue that gender is a social category that shapes societal expectations, structural barriers, and socializers' behaviors, which, in turn, influence individuals' belief systems including their value beliefs (Eccles, Moses, and Yulish‐Muszynski 1983; Eccles and Wigfield 2020; Garcia‐Coll et al. 1996; Joseph, Hailu, and Boston 2017; Perez‐Felkner et al. 2012). Given the varying gendered experiences among boys and girls in math, it may be that girls display lower value beliefs than boys (e.g., Chen et al. 2023; Eccles and Wigfield 2020; Umarji et al. 2021). Considering the socio‐cultural influences described in situated expectancy‐value and other theories, Hyde (2005, 2014) has argued through the gender similarities hypothesis that historical shifts towards more gender egalitarian beliefs have lessened gender differences over time; such that most gender differences are small or non‐existent and that there are more similarities than differences when considering gender. Thus, we examined gender differences in adolescents' math value beliefs, including potential historical differences. The present work is primarily informed by situated expectancy‐value theory, due to its focus on social contexts as well as its framework for understanding and defining value beliefs. We view this theory as helpful for understanding why gender differences in math value beliefs might emerge, and why they might differ depending on historical time point, grade level, and race/ethnicity.

As mentioned, prior empirical research about gender differences in math value beliefs has found largely mixed results, highlighting the need for more research. Though there have been more studies conducted on interest value than the other two value beliefs, these studies are split between those that have found boys to have higher math interest values than girls and those that found no gender differences (e.g., Parker et al. 2020 [meta‐analysis]; Petersen and Hyde 2017 [majority White U.S. Midwestern sample]; Safavian 2019 [ethnically diverse Southern Californian sample]; Umarji et al. 2021 [ethnically diverse Southern Californian sample]). In contrast, fewer studies have focused on math utility and attainment value, but these studies have mostly found no evidence of gender differences (e.g., Petersen and Hyde 2017; Umarji et al. 2021). Such inconsistencies also emerged in a recent meta‐analysis that documented a substantial degree of heterogeneity across studies (Parker et al. 2020). Though some findings in the meta‐analysis suggested boys and girls were more similar than different in their STEM value beliefs, some gender differences favoring boys emerged for interest value. Yet, even when differences were significant, effect sizes were often negligible (Parker et al. 2020). In sum, although past research about gender differences in value beliefs has been mixed, many studies, including a meta‐analysis, suggest that there are no meaningful gender differences in utility and attainment value beliefs, but that there may be differences in interest value under specific circumstances. Thus, in our datasets, we expected to find gender differences in math interest value beliefs but not necessarily in utility and attainment value beliefs.

2. Using Replication to Identify When and for Whom Gender Differences Emerge

Inconsistent findings across studies are not unique to gender differences in adolescents' math value beliefs. Less than 50% of empirical results in developmental psychology replicate across studies (Youyou, Yang, and Uzzi 2023). This has brought greater attention to the challenges related to research rigor, transparency, and reproducibility issues in scientific inquiry (Callard 2022; Wiggins and Christopherson 2019), including in developmental psychology (e.g., Duncan et al. 2014). Inconsistent findings can occur due to a variety of factors, including demographic and contextual differences. Adolescents' motivational beliefs, including their math value beliefs, are influenced by demographic factors such as gender, race/ethnicity, age, and historical time, as they present different barriers and contextual experiences in life (e.g., Eccles and Wigfield 2020). Given this, we explored potential gender differences in adolescents' math value beliefs based on grade level in high school and historical time, both overall and within each race/ethnic group. We used conceptual replication to test the robustness of these patterns. Conceptual replications test the theoretical soundness of a particular finding across datasets that utilize different methods, such as participant demographics, construct operationalization, or study context (Plucker and Makel 2021). Explicitly testing the robustness of findings makes conceptual replication ideal for rigorously testing tenets of a theory (Plucker and Makel 2021).

2.1. Gender Differences Across Historical Time

Little is known about the potential differences in math value beliefs as a function of historical time. According to the gender similarities hypothesis and situated expectancy‐value theory, gender differences should decrease over historical time as gender equality increases (Eccles and Wigfield 2020; Else‐Quest, Hyde, and Linn 2010; Hyde, 2014). Given the recent push to increase women and girls' participation in STEM, including math (e.g., King and Pringle 2019; Master et al. 2017), it is possible that gender differences in math value beliefs have weakened over the last few decades. However, gender differences in adolescents' math ability self‐concepts have persisted over time. One study among Australian adolescents, for example, found that even though gender differences in math performance had disappeared between the 1980s and 1990s, gender differences in math ability self‐concept favoring boys persisted over time (Parker, Van Zanden, and Parker 2018b). Similarly, a study among six U.S. datasets (four of which had racial/ethnic diverse participants) found that gender differences in math ability self‐concept emerged across a 30‐year period from the 1980s into the 2010s (Rubach et al. 2022). Such findings imply that gender differences in math value beliefs may have also persisted over time. Less historical research has been conducted regarding math value beliefs. One recent meta‐analysis found that although boys were more likely to have higher utility values than girls in studies from the 1980s and older, these differences diminished over time in more recent papers from the 2010s indicating no gender differences in utility value (Parker et al. 2020). The negligible gender differences in math interest and attainment value did not change over time (Parker et al. 2020). Thus, we expected that our oldest data set (CAB) may have larger gender differences in math utility value, but these historical differences may not extend to math interest or attainment value.

2.2. Gender Differences by Grade Level

Multiple U.S. studies largely focusing on White populations have found that gender differences in math interest value (Fredricks and Eccles 2002; Petersen and Hyde 2017) or utility value (Petersen and Hyde 2017) are stable across grade levels. One meta‐analysis also found that age was not a significant moderator for gender differences in math value beliefs (Parker et al. 2020). Thus, we expected there to be stable gender differences in math value beliefs across high school.

2.3. Gender Differences Within Racial/Ethnic Groups

Patterns based on White populations may not accurately reflect the patterns among other racial/ethnic groups (e.g., Hsieh, Simpkins, and Eccles 2021; Rubach et al. 2022). Factors such as racial/ethnic stereotypes and under‐resourced classrooms could alter expected gender differences (Irizarry 2021; Rodriguez and Hernandez‐Hamed 2020). For example, gender differences in math value beliefs may be greater among Black and Latine adolescents compared to Asian and White adolescents as Black and Latina girls and women face greater obstacles in math due to experiences of sexism and racism as well as intersectional race‐gender bias (Delgado et al. 2021; McGee and Bentley 2017; Nix and Perez‐Felkner 2019; Yamaguchi and Burge 2019). Alternatively, some have argued that the opposite pattern may emerge with fewer gender differences in math value beliefs among Black and Latine adolescents compared to Asian and White adolescents. Smaller or nonsignificant gender differences could emerge due to Black and Latine populations' more gender egalitarian or nontraditional beliefs about math (Rowley et al. 2007; Skinner et al. 2021; Starr et al. 2022; Starr et al. 2023; Wood et al. 2010) and that Black and Latine boys (despite being positively stereotyped in math based on gender) are frequently tracked into remedial math courses where they are often ignored or reprimanded (Alfaro et al. 2009; Musto 2019). Though theory and evidence is mixed, the prevailing evidence suggests that gender differences may be small or non‐existent among Black and Latine populations.

Empirical studies from the U.S. with ethnically/racially diverse samples have generally found mixed results for interest value, no or few significant gender differences in utility value, and mixed results for attainment value (e.g., Parker et al. 2020; Safavian 2019; Umarji et al. 2021). Regarding interest value, several U.S. studies with Asian, Latine, and White participants found significant gender differences favoring boys over girls in interest value (Parker et al. 2020; Safavian 2019; Umarji et al. 2021). Regarding utility value beliefs, most research among Asian, Black, Latine, and White participants, including a meta‐analysis, found no significant gender differences (Parker et al. 2020; Safavian 2019, 2013; Umarji et al. 2021). Regarding attainment value, some research among Asian, Black, Latine, and White adolescents found significant differences favoring boys over girls (e.g., Graham and Morales‐Chicas 2015; Safavian 2019; Umarji et al. 2021), whereas one meta‐analysis and another study among Asian adolescents found no significant gender differences (Parker et al. 2020; Safavian, 2013). In sum, past studies have found significant gender differences, at least in math interest and attainment value, favoring boys over girls among Asian, Latine, and White students but not among Black students. Thus, we expected to find greater gender differences among Asian, Latine, and White students, but not among Black students.

3. The Current Study

This study examined the consistency of gender differences in adolescents' math value beliefs across historical time, grade levels, and race/ethnicity, using conceptual replication across three U.S. datasets to test the robustness of the findings (see Figure 1 for included constructs). The first dataset, the Childhood and Beyond Study (CAB), is an older dataset from the 1990s, which provides a historical context for potential gender differences in math value beliefs but does not include racial/ethnic diversity. The next dataset, the California Achievement Motivation Project (CAMP), is a large dataset collected in Southern California in the 2000s, which includes a racial/ethnic diverse population but is not nationally representative. Finally, the last dataset, the High School Longitudinal Study (HSLS), is a nationally representative dataset from the 2010s with the most racial/ethnic diversity of the three datasets and involves randomly selected participants from across the U.S. Taken together, these three datasets provide the means to test for gender differences in adolescents' math value beliefs across historical time (CAB, CAMP, HSLS), grade level in high school (CAB, CAMP, HSLS), and racial/ethnic groups (CAMP, HSLS).

Math motivational beliefs and constructs included in present study.

To test the gender differences in adolescents' math value beliefs, it is important to take into account other central predictors, including family income, parent education, and adolescents' prior achievement in math (Eccles and Wigfield 2020; Simpkins, Price, and Garcia 2015; Schoon and Eccles 2014). For example, prior research has found that working‐class children and adolescents, on average, have higher academic value beliefs than their more affluent counterparts, although this does not translate into higher attainment (Parker et al. 2018a). Furthermore, a meta‐analysis found that gender differences in math interest and attainment value were higher in high SES majority samples compared to working‐class majority samples (Parker et al. 2020). Thus, SES may be related to gender differences in math value beliefs. Similarly, math achievement is frequently positively correlated with math value beliefs (Schoon and Eccles 2014). To provide more comprehensive results, all analyses were conducted with and without these covariates.

Based on prior research, our hypotheses were:

1.
We expected that meaningful gender differences in math interest value beliefs but largely no meaningful gender differences in adolescents' utility and attainment value beliefs (i.e., Hedges' g > 0.20).
2.
Historically, we expected that gender differences in adolescents' utility value beliefs would be smaller over time. We held no expectation for interest or attainment value.
3.
We expected that gender differences would be similar across grade levels.
4.
When examined these differences by race/ethnicity, we expected to find meaningful gender differences in adolescents' interest and attainment value beliefs (but not their utility value beliefs) among Asian, Latine, and White adolescents. We did not expect to find meaningful gender differences in any math value beliefs among Black adolescents.

4. Method

4.1. Data

We used three U.S. datasets: the Childhood and Beyond Study (CAB), the California Achievement Motivation Project (CAMP), and the High School Longitudinal Study (HSLS). The CAB and CAMP datasets included all three math value beliefs (i.e., interest value, utility value, and attainment value) whereas HSLS included measures on interest and utility value. We chose these three datasets because they focus on U.S. high school students and have math value belief items and scales created within the theoretical framework of situated expectancy‐value theory (Eccles and Wigfield 2020). That is, all three datasets included measures with the same or similar items making tests of conceptual replication possible (Plucker and Makel 2021). The three datasets included surveys that took place at different time points and grade levels.

4.2. Participants

For all three datasets, high school students were included if they reported data on their race/ethnicity, gender, and grade level. Data on math value beliefs were imputed when missing at the item level. Furthermore, participants from each dataset were randomly assigned to one grade level to satisfy the assumption of independence in the analysis of variance. Below is a summary paragraph of each dataset; a more in‐depth description of the final sample from each dataset is available in the (Table A1).

4.3. CAB

CAB is a longitudinal, cohort‐sequential study conducted from 1986 to 1999 with students from lower‐ to middle‐income families in Southeastern Michigan. Students from ten schools in four school districts were invited to participate, of which 79% participated. This study included a subsample of 582 students with data collected from 1994 to 1999 (53% girls). Participants included 139 students in 9th grade, 218 students in 10th grade, 78 students in 11th grade, and 147 students in 12th grade. Only White students were included because the sample was primarily White (> 95%). The average family annual income ranged from $39,999 or less (7%) to over 80,000 (18%). Approximately 92% of the students had parents with at least some college or other post‐secondary educational experience.

4.3.1. CAMP

CAMP is a longitudinal, cohort‐sequential study conducted from 2004 to 2008 with middle and high school students from four school districts in Southern California. In this study, a subsample of 8855 students were included (51% girls; 74% Latine, 13% Asian, 13% White). Participants included 2363 students in 9th grade, 2180 students in 10th grade, 2148 students in 11th grade, and 2164 students in 12th grade. Approximately 58% of the students were eligible for free‐ or reduced‐lunch (an indicator for family socioeconomic status) and 16% of the students had parents with at least some college experience.

4.3.2. HSLS

HSLS is a nationally representative longitudinal data set with high school students in 9th grade (2009) who were followed into 11th grade (2012). A two‐stage stratified random sample design was used to select eligible schools and 9th grade students (see NCES survey documentation for more detail; National Center for Education Statistics NCES 2019). In this study, a subsample of 21,000 students across the two waves was included (49% girls; 58% White, 19% Latine, 13% Black, 10% Asian). Participants included 10,550 students in 9th grade and 10,450 students in 11th grade. The average family annual income ranged from $35,000 or less (27%) to over $95,000 (31%). Approximately 59% of the students had parents with at least some college experience.

4.4. Measures

Each data set included indicators of adolescents' math value beliefs based on situated expectancy‐value theory (e.g., Eccles and Wigfield 1995; 2020). These scales have been validated in prior work (e.g., Eccles and Wigfield 1995; Lauermann, Tsai, and Eccles 2017). Math value beliefs were assessed once per academic year for CAB and HSLS, whereas they were assessed twice per academic year for CAMP. We used the average score between the two measures from the same year in CAMP to align with the annual assessments in the other two datasets. HSLS did not include indicators of attainment value; thus, only CAB and CAMP were used to test gender differences in attainment value. In each dataset, measurement invariance was tested across gender and race/ethnicity. The models displayed full configural invariance, full metric invariance, and full or partial scalar invariance for all measures (Tables B1–B8). A full list of items, internal consistency, factor loadings and response scale by dataset for all three math value beliefs are available in Tables C1–C3.

4.4.1. Interest Value

Interest value beliefs were operationalized as students' enjoyment/liking of math across all three datasets. The scales ranged from three to six items, with factor loadings of 0.67 ≤ λ ≤ 0.95 and internal consistency of 0.77 ≤ ω ≤ 0.95.

4.4.2. Utility Value

Utility value beliefs were operationalized as students' perceptions of the usefulness of math in general and for the future (e.g., going to college, getting a job) across all three datasets. The scales ranged from two to seven items, with factor loadings of 0.60 ≤ λ ≤ 0.89 and internal consistency of 0.74 ≤ ω/r ≤ 0.90.

4.4.3. Attainment Value

Attainment value beliefs were operationalized as students' perceptions of the importance of math in shaping their identity in CAB and CAMP. The scales ranged from two to seven items, with factor loadings of 0.70 ≤ λ ≤ 0.92 and internal consistency of 0.79 ≤ ω/r ≤ 0.91.

4.4.4. Background Variables and Covariates

Students' gender and race/ethnicity were obtained through self‐reports in CAB and HSLS, and through the school district data in CAMP. In addition, we included students' prior math achievement, parents' education level, and family socioeconomic status given that they are significantly correlated with math value beliefs (Eccles and Wigfield 2020; Simpkins, Fredricks, and Eccles 2015). The indicators of math achievement were (a) students' general intelligence quotient in CAB, (b) district‐reported math score on California Standards Tests in CAMP, and (c) standardized math score on algebraic reasoning test in HSLS. Across the three datasets, parents' education level was measured using the parents' highest education degree. Across the three datasets, family financial background was measured using information on family income.

4.5. Statistical Analyses

In this study, we examined gender differences across historical time, grade level, and within each racial/ethnic group, and tested the extent to which these differences replicated across multiple datasets, when available. Aligned with prior research, we adopted a two‐step approach and tested overall gender differences within each data set (which varied in historical time) and grade level in Step 1 followed by gender differences within each of the four racial/ethnic groups in Step 2 (see Else‐Quest, Mineo, and Higgins 2013; Rubach et al. 2022).

Given the variability across the three datasets (e.g., sampling methods, sample size, response scales, number of items, study design), all analyses were conducted separately for each dataset in Mplus v8.0 (Muthén and Muthén 1998–2017) except for data preparation, which we used Stata v15. Data from each observation was randomly assigned to one grade level to satisfy the assumption of independence in the analysis of variance. We chose not to examine longitudinal data, given that we were most interested in when and for whom gender differences emerged rather than longitudinal changes.

Subsequently, we estimated general linear models to calculate the means of adolescents' math value beliefs within (a) each data set/historical time and grade level, (b) for the overall sample and within each racial/ethnic group, and (c) with and without controlling for the background variables. To improve estimation procedures, missing values on math value beliefs on the item level and control variables were handled using multiple imputation (Enders 2010). Through this technique, multiple datasets were estimated to provide different possible responses the participant might have had thereby adding potential natural variability in the data set. Following recommendations, 30 datasets were imputed for each of the 3 original datasets using STATA with systematically chosen auxiliary variables, such as math motivational beliefs at other time points, to impute missing values (Enders 2010). These auxiliary variables were chosen based on theory and prior empirical work (e.g., Eccles and Wigfield 2020; Starr et al. 2022). Once the multiple datasets were imputed, we ran our analyses in Mplus, which provided the pooled results across the 30 imputed datasets for each study. By doing so, we were able to obtain a more accurate representation of the findings as compared to relying on other methods of handling missing data (e.g., mean imputation or listwise deletion; Enders 2010). In addition, for HSLS, the models were estimated using sampling weights, strata, and primary sampling units to account for the sampling design. To account for the different Likert scales for the items in each dataset, we calculated the Percent of Maximum Possible (POMP) scores from the mean scores to compare the means across the groups (Common scale from 0 to 100; Cohen et al. 1999). This method is preferable to traditional scale standardization or transformation because it does not change the multivariate distribution and covariance matrix yet still allows group differences to be displayed in the correct proportions when scales differ (Little 2013; Moeller 2015).

Finally, to calculate and compare the effect sizes across datasets, we used a meta‐analytic approach (e.g., Cooper, Hedges, and Valentine 2009; Hedges and Schauer 2019). Although meta‐analysis is traditionally used with published papers, it can also be used to test for replication, by providing combined effect sizes across multiple datasets (Cooper, Hedges, and Valentine 2009; Hussong, Curran, and Bauer 2013). Replication statistics, such as the Q statistic, I ², and combined effect size, allow for rigorous testing of replication across datasets (Hedges and Schauer 2019). We used the Comprehensive Meta‐Analysis v3.3 to calculate and compare the effect sizes across datasets (CMA, Borenstein 2022). First, mean scores were used to calculate the effect size (Hedges' g) for gender differences within each data set and grade level for adolescents overall and within each racial/ethnic group. Hedges' g is an effect size similar to Cohen's d but better at handling smaller sample sizes. In line with prior studies, we interpreted the effect sizes to be meaningful differences at g ≥ 0.20 or close to 0.20 (a small effect size according to Cohen's d rule of thumb) (Else‐Quest, Mineo, and Higgins 2013; Parker et al. 2020). Second, we tested whether effect sizes differed across datasets. Based on the conventional heterogeneity tests using the Q‐statistics (Cochran 1954; Hedges and Schauer 2019), and the percentage of the variance across studies indicated by I ² effects (Higgins and Thompson 2002), Q‐statistics were considered significant at p ≤ 0.10 and the I ² effect was considered large at I ² ≥ 70.

5. Results

Our study examined the extent to which gender differences in adolescents' math value beliefs replicated (1) across three datasets spanning the 1990s to 2010s, (2) from 9th–12th grade, and (3) within each of the four major U.S. racial/ethnic groups (i.e., Asian, Black, Latine, and White adolescents). In the sections below, we report the combined effect sizes across datasets for each grade level, the heterogeneity statistics, and the effect sizes in each individual dataset when relevant. The effect sizes of the difference between girls are boys based on the analyses with covariates are presented in Table 1 for the overall sample and within each racial/ethnic group are presented in Tables 2, 3, 4, 5. The covariates included family income, parent education, and adolescents' math achievement. Descriptive statistics of the gender differences in adolescents' math value beliefs across grade levels and datasets are presented in Tables A2–A4.

Table 1.

Overall gender differences: Effect sizes and heterogeneity statistics in 9th to 12th grade.

	9th grade		10th grade		11th grade		12th grade
	g ^a	[95% CI]	g ^a	[95% CI]	g ^a	[95% CI]	g ^a	[95% CI]
Interest value
Separate effects					−0.01
HSLS	−0.03	[−0.07; 0.01]	—		−0.01	[‐.05; 0.03]	—
CAMP	0.14	[0.06;0.22]	0.12	[0.03; 0.20]	0.11	[0.02; 0.19]	0.11	[0.03; 0.20]
CAB	−0.08	[−0.41; 0.25]	0.05	[−0.21; 0.32]	0.24	[−0.21; 0.69]	−0.05	[−0.38; 0.27]
Combined effect	0.03	[−0.11; 0.17]	0.11	[0.03; 0.19]	0.11	[0.08; 0.15]	0.10	[0.02; 0.18]
Heterogeneity	Q = 13.688, df = 2, p = 0.001; I ² = 85.388		Q = 0.193, df = 1, p = 0.66; I ² = 0.000		Q = 6.685, df = 2, p = 0.035, I ² = 70.085		Q = 0.102, df = 1, p = 0.34; I ² = 0.000
Utility value
Separate effects
HSLS	0.00	[−0.04; 0.04]	—		0.02	[−0.02; 0.06]	—
CAMP	−0.06	[−0.14; 0.02]	−0.12	[−0.20; −0.04]	−0.12	[−0.20; −0.04]	−0.08	[−0.17; 0.00]
CAB	0.07	[−0.26; 0.40]	0.34	[0.07; 0.60]	0.34	[−0.11; 0.79]	0.14	[−0.19; 0.46]
Combined effect	−0.01	[−0.05; 0.02]	0.09	[−0.36; 0.54]	−0.01	[−0.15; 0.13]	−0.07	[−0.15; 0.01]
Heterogeneity	Q = 2.018, df = 2, p = 0.37; I ² = 0.89		Q = 10.272, df = 1, p = 0.001; I ² = 90.26		Q = 11.157, df = 2, p = 0.004; I ² = 82.08		Q = 1.642, df = 1, p = 0.20; I ² = 39.08
Attainment value
Separate effects
CAMP	0.08	[0.00; 0.16]	0.06	[−0.03; 0.14]	0.05	[−0.04; 0.13]	0.08	[0.00; 0.16]
CAB	−0.24	[−0.57; 0.09]	0.00	[−0.27; 0.27]	−0.02	[−0.47; 0.42]	−0.10	[−0.43; 0.22]
Combined effect	0.06	[−0.02; 0.14]	0.05	[−0.03; 0.13]	0.04	[−0.04; 0.13]	0.07	[−0.01; 0.15]
Heterogeneity	Q = 3.405, df = 1, p = 0.07; I ² = 70.63		Q = 0.163, df = 1, p = 0.687; I ² = 0.00		Q = 0.088, df = 1, p = 0.767; I ² = 0.00		Q = 1.164, df = 1, p = 0.28; I ² = 14.06

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Note: Order of datasets according to age from youngest (top) to oldest data set (bottom); reported results included students’ achievement, family financial background, and parent education as covariates. Effect size rule of thumb: Small = 0.20, medium = 0.50, large = 0.80.

Positive effect sizes (g) indicated higher math value beliefs for boys compared to girls.

Table 2.

Asian adolescents gender differences: Effect sizes and heterogeneity statistics in 9th to 12th grade.

	9th grade		10th grade		11th grade		12th grade
	g ^a	[95% CI]	g ^a	[95% CI]	g ^a	[95% CI]	g^g	[95% CI]
Interest value
Separate effects
HSLS	0.03	[−0.09; 0.15]	—		0.07	[−0.06; 0.19]	—
CAMP	0.32	[0.07; 0.57]	0.26	[0.01; 0.51]	0.24	[−0.01; 0.48]	0.13	[−0.06; 0.32]
Combined effect	0.15	[−0.13; 0.44]	—		0.10	[−0.01; 0.21]	—
Heterogeneity	Q = 4.212, df = 1, p = 0.04; I ² = 76.260		—		Q = 1.437, df = 1, p = 0.23; I ² = 30.417		—
Utility value
Separate effects
HSLS	0.18	[0.06; 0.30]	—		0.08	[−0.04; 0.21]	—
CAMP	0.09	[−0.16; 0.33]	−0.14	[−0.39; 0.11]	0.09	[−0.16; 0.33]	0.00	[−0.19; 0.19]
Combined effect	0.16	[0.06; 0.27]	—		0.08	[−0.03; 0.19]	—
Heterogeneity	Q = 0.476, df = 1, p = 0.49; I ² = 0.00		—		Q = 0.002, df = 1, p = 0.97; I ² = 0.00		—
Attainment value
CAMP	0.22	[−0.03; 0.46]	0.13	[−0.12; 0.37]	0.15	[−0.10; 0.40]	0.16	[−0.03; 0.35]

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HSLS Source: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09), Base Year and First Year Follow‐Up

Positive effect sizes (g) indicated higher math value beliefs for boys compared to girls.

Table 3.

Black adolescents: Effect sizes and heterogeneity statistics in 9th to 12th grade.

	9th grade		11th grade
	g ^a	[95% CI]	g ^a	[95% CI]
Interest value
HSLS	−0.07	[−0.18; 0.04]	−0.07	[−0.18; 0.04]
Utility value
HSLS	0.00	[−0.11; 0.11]	−0.05	[−0.16; 0.06]

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Note: Reported results included students’ achievement, family financial background, and parent education as covariates. Effect size rule of thumb: Small = 0.20, medium = 0.50, large = 0.80.

Positive effect sizes (g) indicated higher math value beliefs for boys compared to girls.

Table 4.

Latine adolescents gender differences: Effect sizes and heterogeneity statistics in 9th to 12th grade.

	9th grade		10th grade		11th grade		12th grade
	g ^a	[95% CI]	g ^a	[95% CI]	g ^a	[95% CI]	g ^a	[95% CI]
Interest value
Separate effects
HSLS	0.04	[−0.05; 0.13]	—		0.02	[−0.07; 0.11]	—
CAMP	0.08	[−0.02; 0.17]	0.10	[0.01; 0.20]	0.11	[0.01; 0.20]	0.10	[0.00; 0.20]
Combined effect	0.06	[−0.01; 0.12]	—		0.06	[−0.01; 0.12]	—
Heterogeneity	Q = 0.274, df = 1, p = 0.60; I ² = 0.000		—		Q = 1.519, df = 1, p = 0.22; I ² = 34.148		—
Utility value
Separate effects
HSLS	−0.02	[−0.11; 0.07]	—		0.00	[‐.09; 0.09]	—
CAMP	−0.09	[−0.18; 0.01]	−0.11	[−0.20; −0.01]	−0.17	[−0.27; −0.07]	−0.11	[−0.21; 0.01]
Combined effect	−0.05	[−0.12; 0.01]	—		−0.08	[−0.25; 0.08]	—
Heterogeneity	Q = 0.927, df = 1, p = 0.34; I ² = 0.00		—		Q = 6.308, df = 1, p = 0.01; I ² = 84.15		—
Attainment value
CAMP	0.04	[−0.06; 0.13]	0.03	[−0.06; 0.13]	0.05	[−0.05; 0.15]	0.06	[−0.04; 0.16]

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Positive effect sizes (g) indicated higher math value beliefs for boys compared to girls.

Table 5.

White adolescents gender differences: Effect sizes and heterogeneity statistics in 9th to 12th grade.

	9th grade		10th grade		11th grade		12th grade
	g ^a	[95% CI]	g ^a	[95% CI]	g ^a	[95% CI]	g ^a	[95% CI]
Interest value
Separate effects
HSLS	−0.07	[−0.12; −0.01]	—		0.00	[−0.05; 0.05]	—
CAMP	0.26	[0.03; 0.50]	0.06	[−0.18; 0.29]	0.01	[−0.21; 0.23]	0.12	[−0.14; 0.37]
CAB	−0.08	[−0.41; 0.25]	0.05	[−0.21; 0.32]	0.24	[−0.21; 0.69]	−0.05	[−0.38; 0.27]
Combined effect	0.03	[−0.19; 0.25]	0.06	[−0.12; 0.23]	0.00	[−0.05; 0.05]	0.05	[−0.15; 0.25]
Heterogeneity	Q = 7.047, df = 2, p = 0.03; I ² = 71.617		Q = 0.001, df = 1, p = 0.98; I ² = 0.000		Q = 1.118, df = 2, p = 0.57; I ² = 0.000		Q = 0.633, df = 1, p = 0.43; I ² = 0.000
Utility value
Separate effects
HSLS	0.01	[−0.04; 0.06]	—		0.09	[0.04; 0.14]	—
CAMP	−0.01	[−0.25; 0.22]	−0.19	[−0.43; 0.04]	−0.04	[−0.25; 0.18]	0.02	[−0.23; 0.28]
CAB	0.07	[−0.26; 0.40]	0.34	[0.07; 0.60]	0.34	[−0.11; 0.79]	0.14	[−0.19; 0.46]
Combined effect	0.01	[−0.04; 0.06]	0.07	[−0.45; 0.59]	0.09	[0.04; 0.14]	0.07	[−0.13; 0.27]
Heterogeneity	Q = 0.157, df = 2, p = 0.93; I ² = 0.00		Q = 8.631, df = 1, p = 0.003; I ² = 88.414		Q = 2.414, df = 2, p = 0.30; I ² = 17.15		Q = 0.284, df = 1, p = 0.59; I ² = 0.00
Attainment value
Separate effects
CAMP	0.22	[−0.02; 0.46]	0.06	[−0.18; 0.29]	0.01	[−0.21; 0.23]	0.17	[−0.09; 0.42]
CAB	−0.24	[−0.57; 0.09]	0.00	[−0.27; 0.27]	−0.02	[−0.47; 0.42]	−0.10	[−0.43; 0.22]
Combined effect	0.004	[−0.44; 0.45]	0.03	[−0.14; 0.21]	0.01	[−0.19; 0.20]	0.06	[0.14; 0.26]
Heterogeneity	Q = 4.874, df = 1, p = 0.03; I ² = 79.48		Q = 0.10, df = 1, p = 0.75; I ² = 0.00		Q = 0.02, df = 1, p = 0.89; I ² = 0.00		Q = 1.677, df = 1, p = 0.20; I ² = 40.38

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Note: Order of datasets according to age from youngest (top) to oldest dataset (bottom); reported results included students’ achievement, family financial background, and parent education as covariates. Effect size rule of thumb: Small = 0.20, medium = 0.50, large = 0.80.

Positive effect sizes (g) indicated higher math value beliefs for boys compared to girls.

5.1. Results Without Covariates

We also computed the effect sizes without including the covariates. The effect sizes from the analyses without covariates are presented in Tables A5–A7. The combined effect sizes across the datasets when the covariates were not included were similar to the combined effect sizes from the analyses with covariates (range of change: −0.5 to 0.4) and the effect sizes based on the individual datasets tended to be just slightly larger when covariates were not included (range of change: −0.3 to 0.9), indicating that the covariates had a minimal effect on gender differences in math value beliefs.

5.2. Overall Gender Differences Across Historical Time and Grade Level (RQ1 and RQ2)

Table 1 and Figures 2, 3, 4 displays the size of the gender differences (Hedge's g) when all racial/ethnic groups were combined. We report combined effect sizes (ḡ) when possible and effect sizes separately for each dataset (g). The table includes the effects for interest value, utility value, and attainment value displayed from top to bottom of Table 1 computed at each grade level (i.e., from left to right on Table 1: 9th, 10th, 11th, and 12th grade). For each belief at each grade level, we included three pieces of information: (a) the effect sizes of the differences between boys and girls computed on each of the datasets separately (noted in the columns with g), (b) the combined effect size computed across the available datasets (noted in the Combined rows with ḡ), and (c) heterogeneity statistics that note if the size of the effects significantly varied across the datasets (i.e., Q‐statistic and I²‐statistic, noted in the Heterogeneity rows). We interpreted the combined effect size when the effect sizes were similar across the datasets; we interpreted the effect sizes of each individual dataset when the size of the effects significantly varied across datasets based on the heterogeneity statistics. Positive effect sizes indicate that boys reported higher math value beliefs than girls, whereas negative effect sizes indicate that girls reported higher math value beliefs than boys.

Effect sizes of gender differences in math interest value beliefs by grade level. HSLS Source: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09), Base Year and First Year Follow‐Up.

Effect sizes of gender differences in math utility value beliefs by grade level. HSLS Source: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09), Base Year and First Year Follow‐Up.

Effect sizes of gender differences in math attainment value beliefs by grade level. HSLS Source: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09), Base Year and First Year Follow‐Up.

We largely found no meaningful gender differences in adolescents' math value beliefs. When differences did exist, they tended to be in the oldest dataset (CAB) and favored boys over girls. The combined effects across datasets ranged from 0.03 to 0.11 for interest value, −0.07 to 0.09 for utility value, and 0.04 to 0.07 for attainment value. Regarding historical time, CAB was the oldest data set and had the largest effect sizes favoring boys for interest value (g = 0.24) and utility value (g = 0.34). In contrast, HSLS was the most recent dataset and never had an effect size above 0.02 and CAMP only had effect sizes above 0.10 for interest value (0.11 ≤ g ≤ 0.14). Regarding grade level patterns, no clear pattern emerged, although the largest gender differences tended to exist in 10th and 11th grade (in CAB).

Seven of the 12 heterogeneity statistics shown in Table 1 suggest that the effect sizes were similar across the datasets and these effect sizes showed no meaningful differences emerged between boys and girls. These combined effect sizes across the datasets were less than small (−0.07 ≤ ḡ ≤ 0.11) and did not meet our criteria for a meaningful gender difference. Specifically, boys and girls had similar levels of interest value at 10th and 12th grade (ḡ = 0.11 and 0.11); utility value at 9th and 12th grade (ḡ = −0.01 and −0.07); and attainment value in 10th through 12th grade (04 ≤ ḡ ≤ 0.07). The remaining five effect sizes varied significantly across the datasets (as shown by the bolded heterogeneity statistics in Table 1). In all of these five cases, the Q‐statistic was statistically significant, and the I ²‐statistic was large. In the case of adolescents' 9th grade interest value, although the effect size varied across datasets, none of the effect sizes were meaningful in any of the three datasets. In the remaining four cases, the effect size for CAB was meaningful (i.e., g ≥ 0.20), and significantly larger than the effect sizes in HSLS and CAMP, suggesting historical differences. That is, boys in CAB displayed higher interest value in 11th grade (g = 0.24), higher utility value in 10th and 11th grade (g = 0.34 and 0.34), but lower attainment value in 9th grade (g = −0.24) compared to girls; however, these gender differences were not meaningful in HSLS and CAMP (−0.12 ≤ $g$ ≤ 0.14).

5.3. Gender Differences Across Historical Time and Grade Level Within Each Racial/Ethnic Group (RQ1 and RQ3)

We examined the same gender differences tested on the overall samples in the prior section within each racial/ethnic group. These findings are presented in Tables 2 through 5 for Asian, Black, Latine, and White adolescents respectively. Overall, we found no evidence of meaningful gender differences among Black and Latine adolescents although there were some meaningful gender differences among Asian and White adolescents. Asian, Black, and Latine adolescents were not represented in CAB, and Black adolescents were not represented in CAMP. Thus, for Asian and Latine adolescents, we were only able to calculate the combined effect size and heterogeneity indicators between HSLS and CAMP in 9th and 11th grade for interest and utility value, and we were not able to report replication statistics for Black adolescents. Below, we report the patterns for each racial/ethnic group.

5.3.1. Asian Adolescents

As shown in Table 2, there were largely no gender differences in Asian adolescents' utility value beliefs though some gender differences emerged for interest and attainment value in one of two datasets (CAMP). When looking at the effect sizes, Asian boys and girls sometimes varied in their math interest (0.03 ≤ g ≤ 0.32), had similar levels of utility value (−0.14 ≤ g ≤ 0.18), and sometimes varied in their attainment value (0.13 ≤ g ≤ 0.22). Three of the four comparisons of effect sizes across datasets were nonsignificant suggesting that the effect sizes were similar across HSLS and CAMP. These three combined effect sizes across the two datasets were less than small (0.08 ≤ ḡ ≤ 0.16) and did not meet our criteria for a meaningful gender difference, although in some cases they came close. Specifically, when looking at the combined effect sizes across the two datasets, Asian boys and girls had similar math interest values at 11th grade (ḡ = 0.10), and utility value at 9th and 11th grade (ḡ = 0.08 and 0.16). In one of the four comparisons, there was variation across datasets based on the Q‐ and I²‐statistics. In this comparison, there were meaningful effect sizes in CAMP, where 9th grade boys reported higher interest value than girls (g = 0.32). This comparison was small in HSLS (g = 0.03). Finally, there were eight cases where data were only available in CAMP. In two of these eight cases, the effect sizes were meaningful. Specifically, in CAMP, Asian boys reported higher attainment value in 9th grade (g = 0.22) and higher interest value in 10th grade (g = 0.26) than Asian girls. In the other six cases, effect sizes were less than small (−0.14 ≤ g ≤ 0.16). To summarize, Asian boys had higher math interest in 9th and 10th grade than Asian girls in CAMP; all other gender differences for math interest at 11th and 12th grade as well as utility value and attainment value at any grade level were small.

5.3.2. Black Adolescents

As shown in Table 3, we did not find evidence of meaningful gender differences in Black adolescents' math value beliefs in 9th or 11th in HSLS for interest value (−0.07 ≤ g ≤ −0.07) or utility value (−0.05 ≤ g ≤ 0.00).

5.3.3. Latine Adolescents

As shown in Table 4, there were no meaningful gender differences in Latine adolescents' math value beliefs at any grade level. All of the effect sizes suggest that Latine boys and girls had similar levels of math interest value (0.02 ≤ g ≤ 0.11); utility value (−0.17 ≤ g ≤ 0.00); and attainment value (0.03 ≤ g ≤ 0.06). In three of the four comparisons, the effect sizes were similar across the datasets, as shown with the nonsignificant Q‐statistics and small I ²‐statistics. These combined effect sizes across the datasets were small (−0.08 ≤ ḡ ≤ 0.06) and did not meet our criteria for a meaningful gender difference. In one of the four comparisons, the Q‐ and I ²‐statistics for adolescents' 11th grade math utility value suggested that the effect sizes varied across datasets; however, there were no meaningful effect sizes in either of the two datasets (−0.17 ≤ g ≤ 0.00). There were eight cases where data were only available in CAMP. The effect sizes were not meaningful in any of these cases (−0.11 ≤ g ≤ 0.10); specifically, there were no meaningful gender differences in 10th or 12th grade for interest value beliefs (g = 0.10 and 0.10) or utility value beliefs (g = −0.11 and −0.11). Furthermore, gender differences were not observed across 9th to 12th grade for attainment value beliefs (0.03 ≤ g ≤ 0.06). Similar to the patterns for Black adolescents, there were no meaningful gender differences in Latine adolescents' math interest, utility, or attainment value at any grade level in either of the two datasets.

5.3.4. White Adolescents

As shown in Table 5, there were largely no gender differences in White adolescents' math value beliefs, except sometimes in CAB and CAMP. In nine of the 12 comparisons across datasets, the effect sizes were small (0.00 ≤ ḡ ≤ 0.09) and similar across datasets as shown with the nonsignificant Q‐statistics and small I²‐statistics. For these nine combined effect sizes, White boys and girls had similar math interest values at 10th, 11th, and 12th grade (0.00 ≤ ḡ ≤ 0.06); utility value at 9th, 11th, and 12th grade (0.01 ≤ ḡ ≤ 0.09); and attainment value in 10th, 11th, and 12th grade (0.00 ≤ ḡ ≤ 0.06). In the remaining three of the 12 comparisons, effect sizes varied across datasets based on the Q‐ and I²‐statistics. In all three cases, there were meaningful effect sizes either in CAB or CAMP. Specifically in CAB, which is the oldest data set, White boys reported higher utility value than White girls in 10th grade (g = 0.34). However, in 9th grade, White boys reported lower attainment value than White girls (g = −0.24). In CAMP, White boys reported higher interest value (g = 0.26) and higher attainment value (g = 0.22) in 9th grade compared to White girls. These comparisons were less than small in HSLS (−0.07 ≤ g ≤ 0.09). In sum, meaningful gender differences among White adolescents sometimes occurred in the two older datasets (CAB and CAMP) but never the newer data set (HSLS).

6. Discussion

The present study examined whether gender differences in adolescents' math value beliefs replicated: (1) historically across three datasets spanning the 1990s to 2010s, (2) developmentally from 9 to 12th grade, and (3) within each of the four largest U.S. racial/ethnic groups (i.e., Asian, Black, Latine, and White adolescents). Understanding the extent of gender differences in adolescents' math value beliefs and whether they replicate across historical time, grade level, and racial/ethnic groups can help researchers to better understand gender gaps in STEM as well as when and for whom they may need to get addressed.

Overall, we did not find strong evidence for meaningful gender differences (i.e., effect sizes of 0.20 or larger) in adolescents' math interest, utility, or attainment value. This was true when considering the results with and without the covariates (i.e., family income, parent education, and math grades), indicating that socioeconomic status and math grades may not have much effect on gender differences in math value beliefs. When meaningful gender differences were found, they most often emerged in the oldest data set (CAB, collected in 1994 through 1999). Additionally, grade level differences did not emerge, but there were some differences across racial/ethnic groups. Meaningful gender differences were sometimes found among Asian and White adolescents, but not among Black or Latine adolescents. We also found that the gendered patterns varied by the type of math value belief. Though girls and boys typically had similar math value beliefs, some gender differences were noted for interest value where boys were typically favored over girls. Below, we discuss each of these broader findings.

6.1. Girls' and Boys' Math Value Beliefs Were Similar Overall

Prior research findings regarding gender differences in math value beliefs have generally found mixed results for interest value and no gender differences in utility and attainment value (e.g., Parker et al. 2020; Safavian 2019; Umarji et al. 2021). Our study adds to this literature by offering more evidence that girls and boys have similar math utility and attainment value, and finds they often have similar interest value as well, with some important exceptions (Petersen and Hyde 2017; Umarji et al. 2021; Watt 2004). Our findings may be in contrast with other studies that found significant gender differences, particularly regarding interest value (e.g., Else‐Quest, Hyde, and Linn 2010; Guo et al. 2015; Lazarides, Buchholz, and Rubach 2018; Safavian 2019; Umarji et al. 2021) for several reasons. First, we used effect sizes rather than significance levels to determine meaningful gender differences. Second, we used U.S. datasets. In contrast, some prior research finding gender differences have taken place in Hong Kong (Guo et al. 2015), Germany (Lazarides, Buchholz, and Rubach 2018), or across 69 nations (Else‐Quest, Hyde, and Linn 2010). We found some gender differences among Asian and White U.S. samples, which aligns with findings in Hong Kong and Germany. Finally, data from several of these studies (Else‐Quest, Hyde, and Linn 2010; Guo et al. 2015; Safavian 2019; Umarji et al. 2021) are from the late 1990s and early 2000s, and the gender differences we found were also in our two older datasets within this same time period. Our findings align with situated expectancy‐value theory and the gender similarity hypothesis which suggest that social contexts influence motivational beliefs and that there may be as much or more variability within a gender group than between gender groups currently (Hyde 2005; Hyde et al. 2019).

6.2. Gender Differences Across Historical Time

Though boys and girls always displayed similar math value beliefs in the most recent data set (HSLS, collected in 2009 and 2012), some gender differences were noted in the oldest data set (CAB collected 1993 to 1996) and for interest in CAMP (collected 2004 to 2006). For example, boys displayed higher interest value beliefs compared to girls in both datasets. Thus, we found some evidence of historical differences in which gender differences in adolescents' math interest value beliefs have shifted from being higher among boys than girls in the 1990s to being similar among boys and girls in the late 2000s and early 2010s. Such differences may have emerged in part due to school, national, and community pushes to encourage girls in math or related pursuits (e.g., coding, engineering) (e.g., King and Pringle 2019; Master et al. 2017) as argued in the gender similarities hypothesis (Hyde, 2014).

Our study also indicates that gender differences in math value beliefs are smaller in comparison to math ability self‐concept and may be less likely to be a contributing factor to current gender gaps in STEM participation, particularly. Unlike our findings, consistent gender differences were found in adolescents' math ability self‐concept across 30 years from the 1980s to 2010s, within six different U.S. datasets, three of which are used in the present study (Rubach et al. 2022). In this study, meaningful gender differences were found mostly in the oldest data set (CAB), and sometimes in the second oldest dataset (CAMP). Hence, it is possible that gender differences in math value beliefs may have contributed to STEM gender gaps in the past, but less so over time. That said, gender differences in a construct like math value beliefs is a separate issue than gender differences in the relations among constructs, such as the extent to which math value beliefs predict adolescents' STEM choices. Math value beliefs are still important to study, given ample research that suggests the positive links between math value beliefs and future math outcomes, such as course taking (e.g., Eccles and Wigfield 2020; Wigfield and Eccles 2020). Furthermore, as we discuss more in the future directions section, other aspects of math value beliefs, such as the relative value girls place on reading or other subjects compared to math, may also contribute to gender gaps in math‐intensive fields.

6.3. Gender Differences Across 9th to 12th Grade

Overall, we did not find evidence that the size of gender differences in adolescents' math value beliefs varied from 9th to 12th grade. This aligns with a prior meta‐analysis that did not find age to be a significant moderator of math value beliefs (Parker et al. 2020), as well as prior U.S. studies that found gender differences in math value beliefs to be stable across high school (Fredricks and Eccles 2002; Petersen and Hyde 2017). However, we found that replication occurred most frequently in 12th grade and least frequently in 9th grade and no meaningful gender differences ever emerged in 12th grade, indicating that there might be greater variability in gender differences in math value beliefs in 9th grade compared to 12th grade. It is possible this is due to 9th grade being a transition year between junior high and high school, when students experience new peers, teachers, and school environments (Gniewosz, Eccles, and Noack 2012). During this time comparisons to other classmates may be high and motivational beliefs may be particularly sensitive to peers' and socializers' beliefs (e.g., Gniewosz, Eccles, and Noack 2012).

6.4. Gender Differences Within Racial/Ethnic Groups

We found no evidence of gender differences in adolescents' math value beliefs among Black adolescents (represented in one data set) and Latine adolescents (represented in two datasets) at any grade level. However, among Asian and White adolescents, we sometimes found significant gender differences, always favoring boys over girls (except for attainment value at 9th grade in CAB). Among Asian adolescents, this was found within one of the two datasets (CAMP), and among White adolescents, this was found in two of the three datasets (CAB and CAMP). These racial/ethnic findings are consistent with prior research. For example, Rubach et al. (2022) found gender differences in math ability self‐concepts favoring boys over girls among Asian, Latine, and White adolescents, but not Black adolescents. Socio‐cultural contexts in schools and families may lead to these differences. For example, prior research found that Asian and White parents stereotyped boys as better at math than girls, whereas Black and Latine parents did not (Starr et al. 2022). Additionally, studies have shown that Black parents may endorse higher academic expectations for their daughters compared to their sons (Evans et al. 2011) and teachers may not privilege Black and Latino boys over girls in the classroom (Musto 2019). These different socio‐cultural factors, such as Black and Latine parents' strong support for their daughters in math, may result in fewer gender differences in math value beliefs.

7. Practical Implications

Our study provides additional evidence that girls and boys are likely to be more similar than different in terms of their math value beliefs (Hyde 2005; Hyde et al. 2019). However, the gender disparities in some math‐intensive fields such as engineering have not disappeared (National Science Foundation NSF 2023; Perez‐Felkner, McDonald, and Schneider 2014; Zhao and Perez‐Felkner 2022), and women and girls still have lower math ability self‐concepts compared to boys (Perez‐Felkner, Nix, and Thomas 2017; Rubach et al. 2022). Scholars have pointed out other individual and societal factors as relevant to the persistent underrepresentation of women in some math‐intensive fields, such as the chilly, competitive climate in these fields and the altruistic pursuit of women (Eccles and Wang 2016). Interventions aimed at increasing the representation of women and girls in math‐intensive fields should be holistic in their approach rather than solely aiming to increase girls' math value beliefs, which may not be too different than boys' math value beliefs. One example of holistic interventions is role model interventions, which typically use same‐gender or same‐racial/ethnic role models to motivate girls and Black and Latine adolescents in math‐intensive fields and STEM more broadly (González‐Pérez, Mateos de Cabo, and Sáinz 2020; Shin, Levy, and London 2016; for a review see Gladstone and Cimpian 2021). For example, one recent math intervention study introduced three different women who were leaders in STEM to 304 adolescent girls in small groups (González‐Pérez, Mateos de Cabo, and Sáinz 2020). Inviting three female role models allowed for greater diversity in socio‐demographic factors (such as race/ethnicity, age, and parenthood) as well as personality traits (such as altruistic goals) and professional paths. One month later, adolescent girls in the intervention group had higher math beliefs in several areas, including math ability self‐concepts, value beliefs, and career aspirations (González‐Pérez, Mateos de Cabo, and Sáinz 2020). In addition to increasing math value beliefs, such interventions increased various math motivational beliefs, including areas in which there are gender differences (such as math self‐concepts) (Rubach et al. 2022).

8. Future Directions and Limitations

Though the current study makes notable contributions in understanding gender differences in math value beliefs, there are several limitations to consider. First, our study did not examine adolescents' value beliefs in math compared to other subjects, such as reading or language arts. Situated expectancy‐value theory posits that future outcomes, such as STEM career choice, are partially based on cross‐domain comparisons. Though we found that girls generally hold similar math value beliefs as boys, they might choose to pursue other subjects if they value those subjects more than math. Prior research suggests that relative motivational beliefs might be more predictive of future outcomes than individuals' motivational beliefs in one domain, thus contributing to STEM gender gaps (Eccles and Wigfield 2020).

Our next limitation is related to our sample demographics. Due to the data available, we were only able to examine replication across two out of the three datasets for Asian and Latine adolescents and were not able to examine replication for Black adolescents. Additionally, multiracial/ethnic adolescents were not included in our data analysis due to the small sample within each specific group. Relatedly, the datasets only included gender as a binary category. Given the fluid nature of social identities such as gender and race/ethnicity, future research should consider exploring math motivational beliefs among different gender identities (such as nonbinary) as well as multiracial/ethnic people. Finally, two of the three datasets did not use random selection to choose participants; as a result, the samples in these two datasets may be biased.

Three additional limitations are related to our datasets and measures. The datasets are relatively older; although this allows us to explore math value beliefs among different historical contexts, newer data would be helpful to see if math value beliefs have changed considering the pandemic and other recent events. Additionally, attainment value beliefs were only available in two out of the three datasets. Relatedly, our study focused only on math value beliefs despite greater gender parity in math relative to other STEM subjects (National Science Foundation NSF 2023). Future work might examine value beliefs about other STEM subjects, such as physics, computer science, and mechanical engineering. However, we still believe examining math value beliefs is important, given that it is a gateway to many STEM fields (Schoon and Eccles 2014). Despite these limitations, the present study is the largest replication study to examine math value beliefs.

9. Conclusion

Our study investigated gender differences in adolescents' math value beliefs (i.e., interest, utility, and attainment value) across three different datasets collected between 1994 through 2012. Overall, we found little evidence for meaningful gender differences in math value beliefs. The findings align with the gender similarities hypothesis that suggests more similarities than differences between girls and boys.

Supporting information

Supporting information.

JAD-97-808-s001.docx^{(88.9KB, docx)}

Acknowledgments

Two National Science Foundation grants (DRL‐1760757 and DRL‐2054956) to Sandra Simpkins and Jacquelynne Eccles supported the present analyses and writing of this paper. The original data collection of MADICS was supported by funding from the MacArthur Research Network on Successful Adolescent Development in High‐Risk Settings and funding from NICHD Grant R01 033437. We thank the principals, teachers, students, and parents of the cooperating school districts for their participation in these projects.

Endnotes

For the purposes of this paper, we define gender as socially constructed identities that include norms, behaviors, and roles associated with a gender group (World Health Organization 2024). We categorized girls as anyone who self‐identified as “female”, and boys as anyone who self‐identified as “male.” All participants self‐identified their sex/gender.

Latine refers to people with Latin American heritage. We chose to use the term Latine because it is a gender‐neutral version of Latino/a that was developed by feminist and LGBTQA+ communities in Latin America (Mendez 2023).

Data Availability Statement

The data that support the findings of this study are available from National Center for Educational Statistics. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from https://nces.ed.gov/surveys/hsls09/ with the permission of National Center for Educational Statistics.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting information.

JAD-97-808-s001.docx^{(88.9KB, docx)}

Data Availability Statement

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PERMALINK

Girls and Boys Typically Have Similar Math Value Beliefs: Replication Evidence Across Historical Time, High School, and Racial/Ethnic Groups

Christine R Starr

Glona Lee‐Poon

Charlott Rubach

Yannan Gao

Nayssan Safavian

Anna‐Lena Dicke

Jacquelynne S Eccles

Sandra D Simpkins

ABSTRACT

Introduction

Methods

Results and Conclusions

1. Gender Differences in Adolescents' Math Value Beliefs

2. Using Replication to Identify When and for Whom Gender Differences Emerge

2.1. Gender Differences Across Historical Time

2.2. Gender Differences by Grade Level

2.3. Gender Differences Within Racial/Ethnic Groups

3. The Current Study

Figure 1.

4. Method

4.1. Data

4.2. Participants

4.3. CAB

4.3.1. CAMP

4.3.2. HSLS

4.4. Measures

4.4.1. Interest Value

4.4.2. Utility Value

4.4.3. Attainment Value

4.4.4. Background Variables and Covariates

4.5. Statistical Analyses

5. Results

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

5.1. Results Without Covariates

5.2. Overall Gender Differences Across Historical Time and Grade Level (RQ1 and RQ2)

Figure 2.

Figure 3.

Figure 4.

5.3. Gender Differences Across Historical Time and Grade Level Within Each Racial/Ethnic Group (RQ1 and RQ3)

5.3.1. Asian Adolescents

5.3.2. Black Adolescents

5.3.3. Latine Adolescents

5.3.4. White Adolescents

6. Discussion

6.1. Girls' and Boys' Math Value Beliefs Were Similar Overall

6.2. Gender Differences Across Historical Time

6.3. Gender Differences Across 9th to 12th Grade

6.4. Gender Differences Within Racial/Ethnic Groups

7. Practical Implications

8. Future Directions and Limitations

9. Conclusion

Supporting information

Acknowledgments

Endnotes

Data Availability Statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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