Edit this page
Individual differences researchers very commonly report Pearson correlations between their variables of interest. Cohen (1988) provided guidelines for the purposes of interpreting the magnitude of a correlation, as well as estimating power. Specifically, r = 0.10, r = 0.30, and r = 0.50 were recommended to be considered small, medium, and large in magnitude, respectively. However, Cohen’s effect size guidelines were based principally upon an essentially qualitative impression, rather than a systematic, quantitative analysis of data. Consequently, the purpose of this investigation was to develop a large sample of previously published meta-analytically derived correlations which would allow for an evaluation of Cohen’s guidelines from an empirical perspective. Based on 708 meta-analytically derived correlations, the 25th, 50th, and 75th percentiles corresponded to correlations of 0.11, 0.19, and 0.29, respectively. Based on the results, it is suggested that Cohen’s correlation guidelines are too exigent, as < 3% of correlations in the literature were found to be as large as r = 0.50. Consequently, in the absence of any other information, individual differences researchers are recommended to consider correlations of 0.10, 0.20, and 0.30 as relatively small, typical, and relatively large, in the context of a power analysis, as well as the interpretation of statistical results from a normative perspective.
Link to resource: https://doi.org/10.1016/j.paid.2016.06.069
Type of resources: Primary Source, Reading, Paper
Education level(s): College / Upper Division (Undergraduates)
Primary user(s): Student
Subject area(s): Math & Statistics