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Contents
- Abstract
- Models of Intersectional Intergroup Bias
- Compounding Biases: Additive and Interactive Models
- Category Dominance
- Existing Evidence Regarding Intersectional Implicit Bias
- The Present Research
- Study 1
- Stimuli Creation and Pilot Studies
- Participants and Procedure
- Single-Target IATs
- Demographics
- Results
- Evaluative ST-IATs
- Wealth ST-IAT
- Discussion
- Study 2
- Toward a Target-Level Analysis: The Target D Score
- A Data-Driven Approach to Person Perception
- Target Photographs
- Participants and Procedure
- ST-IATs
- Race IAT
- Difference Ratings
- Demographics
- Results
- MDS
- Calculating and Validating Target D Scores
- Predicting Target D Scores From MDS Dimensions
- Predicting Target D Scores From Explicit Target Ratings
- Race IAT Results
- Discussion
- Study 3
- Stimuli Development
- Faces
- Bodies
- Attaching Faces to Bodies
- Participants and Procedure
- ST-IATs
- Difference Ratings
- Explicit Ratings of Targets
- Demographics
- Results
- Manipulation Checks
- Predicting Target D Scores
- Study 3a
- Study 3b
- Discussion
- Study 4
- Participants and Procedure
- ST-IATs
- EPT
- AMP
- Explicit Ratings of Targets
- Demographics
- Results
- Calculating Target D Scores
- Predicting Target D Scores
- ST-IAT Target D Scores
- EPT Target D Scores
- AMP Target D Scores
- Discussion
- Study 5
- Study 5a: Exploring Moderators
- Results
- Discussion
- Study 5b: Testing for Category Dominant Subgroups
- Results
- Discussion
- General Discussion
Figures and Tables
Abstract
Little is known about implicit evaluations of complex, multiply categorizable social targets. Across five studies (N = 5,204), we investigated implicit evaluations of targets varying in race, gender, social class, and age. Overall, the largest and most consistent evaluative bias was pro-women/anti-men bias, followed by smaller but nonetheless consistent pro-upper-class/anti-lower-class biases. By contrast, we observed less consistent effects of targets’ race, no effects of targets’ age, and no consistent interactions between target-level categories. An integrative data analysis highlighted a number of moderating factors, but a stable pro-women/anti-men and pro-upper-class/anti-lower-class bias across demographic groups. Overall, these results suggest that implicit biases compound across multiple categories asymmetrically, with a dominant category (here, gender) largely driving evaluations, and ancillary categories (here, social class and race) exerting relatively smaller additional effects. We discuss potential implications of this work for understanding how implicit biases operate in real-world social settings.
People display implicit evaluative biases––differences in...