Complex Stereotypes: Going Beyond Warmth and Competence in Spontaneous Associations

Abstract

This project investigates whether the well-established Stereotype Content Model, which relies on the dimensions of warmth and competence to understand social categorization, captures the full range of stereotypes applied across different groups. We analysed approximately 11,000 spontaneously generated stereotypes for 32 target groups, varying by gender, ethnicity and religion. The data were collected from a quota-representative sample drawn from an opt-in panel in Germany (n = 1,832), using a between-subjects design. First, we created a German version of the original English SADCAT dictionary and explored to what extent the spontaneously generated stereotype content maps onto the two core dimensions of warmth and competence. Second, we identified social groups that are not well represented by the existing model. Following an intersectional approach, we expected lower dictionary coverage for target groups with multiple subordinate identities. Third, we explored non-overlapping stereotype content and classified it based on semantic similarity to identify emergent stereotype dimensions beyond warmth and competence. Our preliminary findings confirm that stereotypes cannot be fully understood through competence and warmth alone: non-coverage rates range from 47% to 79% across the target groups, with Muslim target groups being particularly less well-captured by the Stereotype Content Model. Our work contributes methodologically to a recent line of research on spontaneously generated stereotypes and theoretically to the study of intersectionality and the invisibility of groups with multiple subordinate identities.
Joint work with Valentina Di Stasio and Susanne Veit