Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs

Frontiers in Psychology

Volumn 4, Issue NOV, 2013, Pages

  Lakens, Daniël ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Cohen's d, eta squared; Effect sizes; Power analysis; Sample size planning

Indexed keywords

EID: 84889688852     PISSN: None     EISSN: 16641078     Source Type: Journal    
DOI: 10.3389/fpsyg.2013.00863     Document Type: Article

References (45)

1. Aberson, C. L. (2010). Applied Power Analysis for the Behavioral Sciences. New York, NY: Routledge.
2. Bakeman, R. (2005). Recommended effect size statistics for repeated measures designs. Behav. Res. Methods 37, 379-384. doi: 10.3758/BF03192707
3. Bakker, M., van Dijk, A., and Wicherts, J. M. (2012). The rules of the game called psychological science. Perspect. Psychol. Sci. 7, 543-554. doi: 10.1177/1745691612459060
4. Borenstein, M., Hedges, L. V., Higgins, J. P., and Rothstein, H. R. (2011). Introduction to Meta-Analysis. Hoboken, NJ: Wiley.
5. Brand, A., Bradley, M. T., Best, L. A., and Stoica, G. (2008). Accuracy of effect size estimates from published psychological research. Percept. Mot. Skills 106, 645-649. doi: 10.2466/pms.106.2.645-649
6. Cohen, J. (1965). Some statistical issues in psychological research," in Handbook of Clinical Psychology, ed B. B. Wolman (New York, NY: McGraw-Hill), 95-121.
7. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. New York, NY: Routledge Academic.
8. Cohen, J. (1995). The earth is round (p<. 05): Rejoinder. Am. Psychol. 50, 1103. doi: 10.1037/0003-066X.50.12.1103
9. Cumming, G. (2012). Understanding the New Statistics: Effect sizes, Confidence Intervals, and Meta-Analysis. New York, NY: Routledge.
10. Cumming, G., and Finch, S. (2005). Inference by eye: confidence intervals and how to read pictures of data. Am. Psychol. 60, 170-180. doi: 10.1037/0003-066X.60.2.170
11. Dunlap, W. P., Cortina, J. M., Vaslow, J. B., and Burke, M. J. (1996). Meta-analysis of experiments with matched groups or repeated measures designs. Psychol. Methods 1, 170-177. doi: 10.1037/1082-989X.1.2.170
12. Dutilh, G., van Ravenzwaaij, D., Nieuwenhuis, S., van der Maas, H. L., Forstmann, B. U., and Wagenmakers, E. J. (2012). How to measure post-error slowing: a confound and a simple solution. J. Math. Psychol. 56, 208-216. doi: 10.1016/j.jmp.2012.04.001
13. Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge: Cambridge University Press. doi: 10.1017/CBO9780511761676
14. Faul, F., Erdfelder, E., Buchner, A., and Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods 41, 1149-1160. doi: 10.3758/BRM.41.4.1149
15. Fidler, F. (2002). The fifth edition of the APA Publication Manual: Why its statistics recommendations are so controversial. Educ. Psychol. Meas. 62, 749-770. doi: 10.1177/001316402236876
16. Fiedler, K., Kutzner, F., and Krueger, J. I. (2012). The long way from α-error control to validity proper problems with a short-sighted false-positive debate. Perspect. Psychol. Sci. 7, 661-669. doi: 10.1177/1745691612462587
17. Glass, G. V., McGaw, B., and Smith, M. L. (1981). Meta-Analysis in Social Research. Beverly Hills, CA: Sage.
18. Grissom, R. J., and Kim, J. J. (2005). Effect Sizes for Research: A Broad Practical Approach. Mahwah, NJ: Lawrence Erlbaum Associates.
19. Hayes, W. L. (1963). Statistics for Psychologists. New York, NY: Holt, Rinehart and Winston.
20. Hedges, L. V., and Olkin, I. (1985). Statistical methods for meta-analysis. San Diego, CA: Academic Press.
21. Kelley, K. (2005). The effects of nonnormal distributions on confidence intervals around the standardized mean difference: bootstrap and parametric confidence intervals. Educ. Psychol. Meas. 65, 51-69. doi: 10.1177/0013164404264850
22. Keppel, G. (1991). Design and Analysis: A researcher's handbook. Englewood Cliffs, NJ: Prentice Hall.
23. Kline, R. B. (2004). Beyond Significance Testing: Reforming Data Analysis Methods in Behavioral Research. Washington DC: American Psychological Association. doi: 10.1037/10693-000
24. Lane, D. M., and Dunlap, W. P. (1978). Estimating effect size: bias resulting from the significance criterion in editorial decisions. Br. J. Math. Stat. Psychol. 31, 107-112. doi: 10.1111/j.2044-8317.1978.tb00578.x
25. Loftus, G. R., and Masson, M. E. (1994). Using confidence intervals in within-subjects designs. Psychon. Bull. Rev. 1, 476-490. doi: 10.3758/BF03210951
26. Maxwell, S. E., and Delaney, H. D. (2004). Designing experiments and analyzing data: A model comparison perspective, 2nd Edn. Mahwah, NJ: Erlbaum.
27. Maxwell, S. E., Kelley, K., and Rausch, J. R. (2008). Sample size planning for statistical power and accuracy in parameter estimation. Annu. Rev. Psychol. 59, 537-563. doi: 10.1146/annurev.psych.59.103006.093735
28. McGrath, R. E., and Meyer, G. J. (2006). When effect sizes disagree: the case of r and d. Psychol. Methods 11, 386-401. doi: 10.1037/1082-989X.11.4.386
29. McGraw, K. O., and Wong, S. P. (1992). A common language effect size statistic. Psychol. Bull. 111, 361-365. doi: 10.1037/0033-2909.111.2.361
30. Morris, S. B., and DeShon, R. P. (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychol. Methods 7, 105-125. doi: 10.1037/1082-989X.7.1.105
31. Murphy, K., Myors, B., and Wolach, A. (2012). Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests. New York, NY: Routledge Academic.
32. Murphy, K. R., and Myors, B. (1999). Testing the hypothesis that treatments have negligible effects: minimum-effect tests in the general linear model. J. Appl. Psychol. 84, 234-248. doi: 10.1037/0021-9010.84.2.234
33. Olejnik, S., and Algina, J. (2000). Measures of effect size for comparative studies: applications, interpretations, and limitations. Contemp. Educ. Psychol. 25, 241-286. doi: 10.1006/ceps.2000.1040
34. Olejnik, S., and Algina, J. (2003). Generalized eta and omega squared statistics: measures of effect size for some common research designs. Psychol. Methods 8, 434-447. doi: 10.1037/1082-989X.8.4.434
35. Poincaré, H. (1952). Science and Method. New York, NY: Dover Publications.
36. Preacher, K. J., and Kelley, K. (2011). Effect size measures for mediation models: quantitative strategies for communicating indirect effects. Psychol. Methods 16, 93-115. doi: 10.1037/a0022658
37. Rabbit, P. M. A. (1966). Errors and error correction in choice reaction tasks. J. Exp. Psychol. 71, 264-272. doi: 10.1037/h0022853
38. Rosenthal, R. (1991). Meta-analytic procedures for social research. Newbury Park, CA: SAGE Publications, Incorporated.
39. Rosenthal, R. (1994). "Parametric measures of effect size," in The hand-book of research synthesis, eds H. Cooper and L. V. Hedges (New York, NY: Sage), 231-244.
40. Schmidt, F. L. (1992). What do data really mean. Am. Psychol. 47, 1173-1181. doi: 10.1037/0003-066X.47.10.1173
41. Smithson, M. (2001). Correct confidence intervals for various regression effect sizes and parameters: the importance of noncentral distributions in computing intervals. Educ. Psychol. Meas. 61, 605-632. doi: 10.1177/00131640121971392
42. Tabachnick, B. G., and Fidell, L. S. (2001). Using Multivariate Statistics, 4th Edn. Boston: Allyn and Bacon.
43. Thompson, B. (2006). Foundations of Behavioral Statistics: An Insight-Based Approach. New York, NY: Guilford.
44. Thompson, B. (2007). Effect sizes, confidence intervals, and confidence intervals for effect sizes. Psychol. Sch. 44, 423-432. doi: 10.1002/pits.20234
45. Winkler, R. L., and Hays, W. L. (1975). Statistics: Probability, Inference, and Decision, 2nd Edn. New York, NY: Holt.