Models for Stereotype–Achievement Analyses For reasons discussed in Models,” our analyses of cross-national “Meta-Regression 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
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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