More recently, Hemphill (2003) provided quantitatively-based guidelines for the purposes of interpreting correlation coefficients on the basis of a review of two meta-meta-analyses. However, Cohen, 1988, Cohen, 1992 impression of an average effect was not based on a systematic, quantitative analysis of data. Additionally, Cohen, 1988, Cohen, 1992 suggested that a medium effect is about the average effect observed in the literature across various disciplines. 1 Cohen's effect size guidelines were based upon the notion that a medium effect should be noticeable to the naked eye of a careful observer (Cohen, 1988). Cohen, 1988, Cohen, 1992 recommended Pearson r values of 0.10, 0.30, and 0.50 to demarcate small, medium, and large effects, respectively. Individual differences researchers very commonly report correlation coefficients to represent the magnitude of the association between two continuously scored variables.
Cohen, 1988, Cohen, 1992 provided guidelines for the purposes of interpreting the magnitude of effect sizes across a number of statistical analyses. Researchers in the behavioural and cognitive sciences have been recommended to report and interpret effect sizes in their research papers (Wilkinson & the APA Task Force on Statistical Inference, 1999, p. 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. 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. 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. 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. However, Cohen's effect size guidelines were based principally upon an essentially qualitative impression, rather than a systematic, quantitative analysis of data. Specifically, r = 0.10, r = 0.30, and r = 0.50 were recommended to be considered small, medium, and large in magnitude, respectively. Cohen (1988) provided guidelines for the purposes of interpreting the magnitude of a correlation, as well as estimating power. Individual differences researchers very commonly report Pearson correlations between their variables of interest.
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