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Empirically derived guidelines for effect size interpretation in social psychology

Lovakov, Andrey, Agadullina, Elena R.·2021·European Journal of Social Psychology

Abstract

This study estimates empirically derived guidelines for effect size interpretation for research in social psychology overall and sub‐disciplines within social psychology, based on analysis of the true distributions of the two types of effect size measures widely used in social psychology (correlation coefficient and standardized mean differences). Analysis of empirically derived distributions of 12,170 correlation coefficients and 6,447 Cohen's d statistics extracted from studies included in 134 published meta‐analyses revealed that the 25th, 50th, and 75th percentiles corresponded to correlation coefficient values of 0.12, 0.24, and 0.41 and to Cohen's d values of 0.15, 0.36, and 0.65 respectively. The analysis suggests that the widely used Cohen's guidelines tend to overestimate medium and large effect sizes. Empirically derived effect size distributions in social psychology overall and its sub‐disciplines can be used both for effect size interpretation and for sample size planning when other information about effect size is not available.

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Metadata

Authors
Lovakov, A., & Agadullina, E. R.
Venue
European Journal of Social Psychology
Publisher
Wiley
Published
2021-04
Volume
51
Issue
3
Pages
485-504
ISSN
  • 0046-2772 (ISSN-L)
  • 1099-0992
OpenAlex
W3128899026
Cited by
632
References
33
FWCI
46.34
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Empirically derived guidelines for effect size interpretation in social psychology. https://app.cochat.ai/papers/10.1002/ejsp.2752
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Lovakov, A., & Agadullina, E. R. (2021). Empirically derived guidelines for effect size interpretation in social psychology. European Journal of Social Psychology. https://doi.org/10.1002/ejsp.2752

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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.