Models for Stereotype–Achievement Analyses For reasons discussed in “Meta-Regression Models,” our analyses of cross-national relationships between gender-science stereotypes and gender differences used mixed-effects meta-regression models, whereas Nosek et al. used weighted OLS regression models. In our meta-regression models, national stereotypes were the dependent variables and achievement differences were the predictor variables. One limitation of these models was that they modeled only sampling variance of the dependent variables (stereotypes) and assumed that predictor variables (achievement differences) were measured without error. Hence, these models incorporated no sampling statistics about the achievement data. We choose to model the sampling variance of the stereotype data, rather than achievement data, because sampling variance was far more variable for the stereotype data (e.g., sample sizes ranged from one person to hundreds of thousands). We reanalyzed all achievement–stereotype relationships using Nosek et al.’s weighted OLS strategy (averaging log-transformed inverse variance weights for the stereotype sample and untransformed inverse variance weights for the TIMSS samples). Results were largely consistent with those using meta-regression models. Sample Size for Stereotype–Achievement Analyses We included nine nations with 2003 TIMSS data that Nosek et al. had excluded: Armenia, Bahrain, Botswana, Egypt, Estonia, Ghana, Lebanon, Morroco, and Saudi Arabia. These nations were previously excluded because they had no comparable TIMSS data point in 1995 or 1999. The data table that Nosek et al. used (Gonzales et al., 2004, Table C10) excluded any nations that did not have at least one other comparable TIMSS data point in 1995 or 1999. We did not consider lack of data in previous years to be a compelling reason for exclusion, unlike other criteria such as a minimum sample size or percentage of Internet users. If the
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Figure S1. Moderation by participant’s gender. Statistics presented for “Diff” are the ratio in estimated slopes for females and males and the p value for the difference. Error bars represent standard errors. Table S1 Multiple Regression Analyses Relationship in Fig. 2 Panel a b Panel c Model Variable N b p N b p N b p a. Base model Women's repa,b 60 −6.58 <.001 58 −7.08 <.001 54 −7.65 <.001 b. Broad gender equity repa,b 53 −7.99 <.001 55 −7.91 .001 50 −7.91 .001 GEM −0.05 .845 −0.27 .385 −0.07 .778 GGI −0.46 .521 0.49 .556 0.40 .568 All covariatesc .247 .600 .771 c. gender equity Women's repa,b 50 −5.50 .012 49 −7.84 .010 47 −8.67 <.001 GGI_eco 0.12 .675 0.33 .269 −0.01 .969 GGI_edu_logb −0.76 .954 −6.25 .622 −4.10 .727 TertArtsFb −0.31 .909 −1.44 .593 4.14 .088 TertTeachFb −5.09 .081 −0.09 .982 5.35 .042