Considering Multiple Criteria for Social Categorization Can Reduce Intergroup Bias

Abstract

Two experiments tested the notion that considering multiple criteria for social categorization can reduce intergroup bias. In both experiments, participants were required to consider alternative ways in which people could be classified, other than an initially salient intergroup dichotomy. In Experiment 1, the authors found that generating alternative social classifications that were unrelated to an initial target dichotomy reduced intergroup bias compared to a control condition. In Experiment 2, this effect was replicated and the authors found that unrelated, but not related, categorizations were necessary to reduce bias. This article adds support to the view that increasing categorical complexity is a useful tool in bias reduction. These findings are discussed in the context of a developing model of multiple categorization effects.