Registered report: Stress testing predictive models of ideological prejudice

In this registered report, we stress-tested existing models for predicting the ideology-prejudice association, which varies in size and direction across target groups. Previous models of this relationship use perceived ideology, status, and choice in group membership of target groups to predict the ideology-prejudice association across target groups. These analyses show that models using only the perceived ideology of the target group are more accurate and parsimonious in predicting the ideology-prejudice association than models using perceived status, choice, and all three characteristics in one model. Here, we stress-tested the original models by testing the models' predictive utility with new measures of explicit prejudice, a comparative operationalization of prejudice, the Implicit Association Test (IAT), and additional target groups. In Study 1, we directly tested the previous models using absolute measures of prejudice that closely resemble the measures used in the original study. Our results indicated that the models replicate with distinct, yet conceptually similar measures of prejudice. As in previous work, our ideology-only and ideology, status, and choice models were the best predictors of the ideology-prejudice association. In Study 2, we developed new ideology-prejudice models for a comparative operationalization of prejudice using both explicit measures and the Implicit Association Test. We tested these new models using data from the Ideology 2.0 project collected by Project Implicit. Our results indicate that this model-building strategy was not effective for relative or IAT prejudice measures. We found no significant differences in predictive ability between the models. These results indicate that the ideology-only and ideology, status, and choice models are effective in predicting the ideology-prejudice association in a variety of absolute prejudice measures, but our results suggest this may not generalize to relative or IAT measures.