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Can we really reduce ethnic prejudice outside the lab? A meta‐analysis of direct and indirect contact interventions
Abstract
The present meta‐analysis tested the effectiveness of contact‐based interventions for the reduction of ethnic prejudice. Up to now, a meta‐analysis summarizing the results of real‐world interventions that rest on the intergroup contact theory has been missing. We included evaluations of programs realizing direct (i.e., face‐to‐face) and/or indirect (i.e., extended or virtual) contact in real‐world settings outside the lab. The interventions' effectiveness was tested shortly after their end (k = 123 comparisons, N = 11 371 participants) and with a delay of at least 1 month (k = 25, N = 1650). Our data show that contact interventions improve ethnic attitudes. Importantly, changes persist over time. Furthermore, not only direct but also indirect contact interventions are successful. In addition, contact programs are effective even in the context of a serious societal conflict (e.g., in the Middle East). Although changes are typically larger for ethnic majorities, there is an impact on minorities, too. Finally, contact interventions not only improve attitudes toward individuals involved in the program, their effects also generalize to outgroups as a whole. In sum, social psychology provides an intervention for prejudice reduction that can be successfully implemented in the practical field. Copyright © 2015 John Wiley & Sons, Ltd.
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Interventions to reduce partisan animosity
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Metadata
- Authors
- Lemmer, G., & Wagner, U.
- Venue
- European Journal of Social Psychology
- Publisher
- Wiley
- Published
- 2015-02-13
- Volume
- 45
- Issue
- 2
- Pages
- 152-168
- ISSN
- 0046-2772 (ISSN-L)
- 1099-0992
- OpenAlex
- W1584259989
- Cited by
- 522
- References
- 106
- FWCI
- 98.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Funders
- Deutsche Forschungsgemeinschaft (WA 1150/9-1/2)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.