Table of Interpretation for Different Effect Sizes

Here, you can see the suggestions of Cohen (1988) and Hattie (2009 S. 97) for interpreting the magnitude of effect sizes. Hattie refers to real educational contexts and therefore uses a more benignant classification, compared to Cohen. We slightly adjusted the intervals, in case, the interpretation did not exactly match the categories of the original authors.

 Cohen (1988) reports the following intervals for r: .1 to .3: small effect; .3 to .5: intermediate effect; .5 and higher: strong effect.


Borenstein (2009). Effect sizes for continuous data. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and (pp. 221-237). New York: Russell Sage Foundation.

Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis, Chapter 7: Converting Among Effect Chichester, West Sussex, UK: Wiley.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2. Auflage). Hillsdale, NJ: Erlbaum.

Cohen, B. (2008). Explaining psychological statistics (3rd ed.). New York: John Wiley & Sons.

Dunlap, W. P., Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996). Meta-analysis of experiments with matched groups or repeated measures designs. Psychological Methods, 1, 170-177.

Elis, P. (2010). The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results. Cambridge: Cambridge University Press.

Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2-18.

Furukawa, T. A., & Leucht, S. (2011). How to obtain NNT from Cohen's d: comparison of two methods. PloS one, 6, e19070.

Hattie, J. (2009). Visible Learning. London: Routledge.

Hedges, L. & Olkin, I. (1985). Statistical Methods for Meta-Analysis. New York: Academic Press.

Klauer, K. J. (2001). Handbuch kognitives Training. Göttingen: Hogrefe.

Morris, S. B., & DeShon, R. P. (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychological Methods, 7(1), 105-125.

Morris, S. B. (2008). Estimating Effect Sizes From Pretest-Posttest-Control Group Designs. Organizational Research Methods, 11(2), 364-386.

Rosenthal, R. (1994). Parametric measures of effect size. In H. Cooper & L. V. Hedges (Eds.), The Handbook of Research Synthesis (231-244). New York, NY: Sage.

Rosenthal, R. & DiMatteo, M. R. (2001). Meta-Analysis: Recent Developments in Quantitative Methods for Literature Reviews. Annual Review of Psychology, 52(1), 59-82. :10.1146/annurev.psych.52.1.59

Thalheimer, W., & Cook, S. (2002, August). How to calculate effect sizes from published research articles: A simplified methodology. Retrieved March 9, from

In case you need a reference to this page in a scientific paper, please the following citation:

Lenhard, W. & Lenhard, A. (2016). Calculation of Effect Sizes. available: Dettelbach (Germany) DOI: 10.13140/RG.2.1.3478.4245

G*POWER Information

Behavior Research Methods

May 2007, Volume 39, Issue 2, pp 175–191

G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

· Authors · Authors and affiliations

· Franz Faul Email author

· Edgar Erdfelder Email author

· Albert-Georg Lang

· Axel Buchner


If you use G*Power for your research, then we would appreciate your including one or both of the following references (depending on what is appropriate) to the program in the papers in which you publish your results:

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191. Download PDF

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160. Download PDF


G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (Windows 10.0 and MAC) and covers many different statistical tests F, and χ test families. In addition, exact tests. G*Power 3 provides improved effect size calculators and options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.

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