The statistical recommendations of the American Psychological Association Publication Manual: Effect sizes, confidence intervals, and meta-analysis

Australian Journal of Psychology

Volumn 64, Issue 3, 2012, Pages 138-146

  Cumming, Geoff ^a   Fidler, Fiona ^a   Kalinowski, Pav ^a   Lai, Jerry ^a  

^a LA TROBE UNIVERSITY   (Australia)

Author keywords

APA Publication Manual; Confidence intervals; Effect sizes; Meta analysis; Null hypothesis significance testing; Statistical reform

Indexed keywords

EID: 84865150766     PISSN: 00049530     EISSN: 17429536     Source Type: Journal    
DOI: 10.1111/j.1742-9536.2011.00037.x     Document Type: Article

References (47)

1. Altman, D. G., Machin, D., Bryant, T. N., & Gardner, M. J. (2000). Statistics with confidence: Confidence intervals and statistical guidelines (2nd ed.). London: BMJ Books.
2. American Psychological Association. (2010). Publication manual of the APA (6th ed.). Washington, DC: Author.
3. American Psychological Association, Council of Editors. (1952). Publication manual of the American Psychological Association. Psychological Bulletin, 49(Suppl., Pt. 2), 389-450.
4. Beck, A. T., Steer, R. A., Ball, R., & Ranieri, W. F. (1996). Comparison of Beck Depression Inventories-IA and -II in psychiatric outpatients. Journal of Personality Assessment, 67, 588-597. doi:10.1207/s15327752jpa6703_13
5. Bird, K. D. (2004). Analysis of variance via confidence intervals. London: Sage.
6. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester: Wiley.
7. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
8. Cooper, H. M. (2009). Research synthesis and meta-analysis: A step-by-step approach (4th ed.). Thousand Oaks, CA: Sage.
9. Cooper, H. M. (2011). Reporting research in psychology: How to meet Journal Article Reporting Standards. Washington, DC: APA Books.
10. Coulson, M., Healey, M., Fidler, F., & Cumming, G. (2010). Confidence intervals permit, but don't guarantee, better inference than statistical significance testing. Frontiers in Quantitative Psychology and Measurement, 1(26), 1-9. doi:10.3389/fpsyg.2010.00026
11. Cumming, G. (2006). Meta-analysis: Pictures that explain how experimental findings can be integrated. Paper presented at The 7th International Conference on Teaching Statistics (ICOTS-7). Salvador, Brazil. In A. Rossman, & B. Chance (Eds.), ICOTS-7 Proceedings. Retrieved from http://www.stat.auckland.ac.nz/~iase/publications/17/C105.pdf. Also available from http://tinyurl.com/teachma
12. Cumming, G. (2007). Inference by eye: Pictures of confidence intervals and thinking about levels of confidence. Teaching Statistics, 29, 89-93. doi:10.1111/j.1467-9639.2007.00267.x
13. Cumming, G. (2008). Replication and p intervals: p values predict the future only vaguely, but confidence intervals do much better. Perspectives on Psychological Science, 3, 286-300. doi:10.1111/j.1745-6924.2008.00079.x
14. Cumming, G. (2009). Inference by eye: Reading the overlap of independent confidence intervals. Statistics in Medicine, 28, 205-220. doi:10.1002/sim.3471
15. Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. New York: Routledge.
16. Cumming, G., & Fidler, F. (2009). Confidence intervals: Better answers to better questions. Zeitschrift für Psychologie / Journal of Psychology, 217, 15-26. doi:10.1027/0044-3409.217.1.15
17. Cumming, G., Fidler, F., Leonard, M., Kalinowski, P., Christiansen, A., Kleinig, A., ...Wilson, S. (2007). Statistical reform in psychology: Is anything changing? Psychological Science, 18, 230-232.
18. Cumming, G., Fidler, F., & Vaux, D. L. (2007). Error bars in experimental biology. Journal of Cell Biology, 177, 7-11. doi:10.1083/jcb.200611141
19. Cumming, G., & Finch, S. (2001). A primer on the understanding, use and calculation of confidence intervals that are based on central and noncentral distributions. Educational and Psychological Measurement, 61, 532-574. doi:10.1177/0013164401614002
20. Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals, and how to read pictures of data. American Psychologist, 60, 170-180. doi:10.1037/0003-066X.60.2.170. Retrieved from http://www.apastyle.org/manual/related/cumming-and-finch.pdf. Also available from http://tinyurl.com/inferencebyeye
21. Cumming, G., & Maillardet, R. (2006). Confidence intervals and replication: Where will the next mean fall? Psychological Methods, 11, 217-227. doi:10.1037/1082-989X.11.3.217
22. Cumming, G., Williams, J., & Fidler, F. (2004). Replication, and researchers' understanding of confidence intervals and standard error bars. Understanding Statistics, 3, 299-311. doi:10.1207/s15328031us0304_5
23. Faulkner, C., Fidler, F., & Cumming, G. (2008). The value of RCT evidence depends on the quality of statistical analysis. Behaviour Research and Therapy, 46, 270-281.
24. Fidler, F., Faulkner, S., & Cumming, G. (2008). Analyzing and presenting outcomes: Focus on effect size estimates and confidence intervals. In A. M. Nezu, & C. M. Nezu (Eds.), Evidence-based outcome research: A practical guide to conducting randomized controlled trials for psychosocial interventions (pp. 315-334). New York: OUP.
25. Fidler, F., & Loftus, G. (2009). Why figures with error bars should replace p values: Some conceptual arguments and empirical demonstrations. Zeitschrift für Psychologie/Journal of Psychology, 217, 27-37. doi:10.1027/0044-3409.217.1.27
26. Finch, S., & Cumming, G. (2009). Putting research in context: Understanding confidence intervals from one or more studies. Journal of Pediatric Psychology, 34, 903-916. doi:10.1093/jpepsy/jsn118
27. Good, P. I. (1999). Resampling methods: A practical guide to data analysis. Berlin: Birkhauser.
28. Grayson, D., Pattison, P., & Robins, G. (1997). Evidence, inference, and the "rejection" of the significance test. Australian Journal of Psychology, 49, 64-70. doi:10.1080/00049539708259855
29. Gregson, R. A. M. (1997). Signs of obsolescence in psychological statistics: Significance versus contemporary theory. Australian Journal of Psychology, 49, 50-63. doi:10.1080/00049539708259854
30. Grissom, R. J., & Kim, J. J. (2005). Effect sizes for research: A broad practical approach. Mahwah, NJ: Erlbaum.
31. Hammond, G. (1996). The objections to null hypothesis testing as a means of analysing psychological data. Australian Journal of Psychology, 48, 104-106. doi:10.1080/00049539608259513
32. Kazdin, A. E. (1999). The meanings and measurement of clinical significance. Journal of Consulting and Clinical Psychology, 67, 332-339. doi:10.1037/0022-006X.67.3.332
33. Kelley, K., & Maxwell, S. E. (2008). Sample size planning with applications to multiple regression: Power and accuracy for omnibus and targeted effects. In P. Alasuutari, L. Bickman, & J. Brannen (Eds.), The Sage handbook of social research methods (pp. 166-192). London: Sage.
34. Kirk, R. E. (1996). Practical significance: A concept whose time has come. Educational and Psychological Measurement, 56, 746-759. doi:10.1177/0013164496056005002
35. Kirk, R. E. (2003). The importance of effect magnitude. In S. F. Davis (Ed.), Handbook of research methods in experimental psychology (pp. 83-105). Malden, MA: Blackwell.
36. Kline, R. B. (2004). Beyond significance testing. Reforming data analysis methods in behavioral research. Washington, DC: APA Books. Chapter 3. Retrieved from http://www.apastyle.org/manual/related/kline-2004.pdf Also available from http://tinyurl.com/klinechap3
37. La Greca, A. M. (2005). Editorial. Journal of Consulting and Clinical Psychology, 73, 3-5.
38. Maxwell, S. E., Kelley, K., & Rausch, J. R. (2008). Sample-size planning for statistical power and accuracy in parameter estimation. Annual Review of Psychology, 59, 537-563. doi:10.1146/annurev.psych.59.103006.093735
39. Rao, S. (2008). Editorial. Neuropsychology, 22, 1-2.
40. Rodgers, J. L. (2010). The epistemology of mathematical and statistical modeling: A quiet methodological revolution. American Psychologist, 65, 1-12. doi: 10.1037/a0018326
41. Smithson, M. (2003). Confidence intervals. Thousand Oaks, CA: Sage.
42. Steiger, J. H. (2004). Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis. Psychological Methods, 9, 164-182.
43. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Pearson.
44. Velicer, W. F., Cumming, G., Fava, J. L., Rossi, J. S., Prochaska, J. O., & Johnson, J. (2008). Theory testing using quantitative predictions of effect size. Applied Psychology: An International Review, 57, 589-608. doi:10.1111/j.1464-0597.2008.00348.x
45. Wilcox, R. R. (2005). Introduction to robust estimation and hypothesis testing (2nd ed.). San Diego, CA: Academic Press.
46. Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604. doi:10.1037/0003-066X.54.8.594. Retrieved from http://www.apastyle.org/manual/related/wilkinson-1999.pdf. Also available from http://tinyurl.com/tfsi1999
47. Young, C., & Horton, R. (2005). Putting clinical trials into context. The Lancet, 366, 107-108. doi:10.1016/S0140-6736(05)66846-8