Data Analysis Plan
We intend to use general linear mixed models (GLMM) using the package lme4 in R to examine the effects of language forms and kinds of properties (i.e., content) on different aspects of essentialist beliefs (i.e., causal explanation and heritability). To analyze children’s data, we plan is to run stepwise mixed-effect logistic regression models for each of the measure (Explanation and Heritability). We will start with a full model on each measure coded as a binomial variable (essentialist/non-essentialist) with language forms (generic, specific), kinds of properties (biological, cultural), age (continuous), and all two way and the three-way interactions as fixed effects, and ID and scenario as random effects. The non-significant interaction terms will then be dropped in subsequent models and the model will be reduced step-wise until we have the best fitting model. When applicable, we will transform coefficients to odd ratios as measures of effect size. Significant interactions will be examined using simple slope tests.
For the Explanation task, we also will compare children’s responses to test properties (mentioned in the storybook) and their responses to the control property (cross-condition properties) for each of the experimental conditions. This will be a logistic regression model with property (test, control), age, and the interaction terms as fixed effects and ID and scenario as random effects. In addition, responses to switched-at-birth questions will be compared with the chance rate using t tests.
A similar procedure will be used for the adult sample, except that these models would not include age. We also plan to analyze a full sample with a similar plan and include age group as a categorical factor. This enables us to explore any developmental shifts from childhood to adulthood in how generics lead to essentialism.