11.6: Chapter 11 References and Suggested Readings

Browner, W. S., Newman, T. B. (1987). Are all significant P values created equal? The analogy between diagnostic tests and clinical research. JAMA 257:2459-2463.

Cohen, J. (1992). Statistical power analysis. Current directions in Psychological Science 1:98-101.

Colegrave, N., and Ruxton, Graeme D. (2003) Confidence intervals are a more useful complement to nonsignificant tests than are power calculations. Behavioral Ecology 14(3):446-447

Eng, J. (2003). Sample Size Estimation: How Many Individuals Should Be Studied? Radiology 227:309-313.

Everitt, B. S., Hothorn, T. (2007) A handbook of statistical analyses using R, 2nd edition. Chapman & Hall/CRC Press.

Freeman, E., Robson, E., Bates, B., & Sierra, K. (2008). Head first design patterns. ” O’Reilly Media, Inc.”.

Hansen, W. B., Collins, L. M. (1994). Seven ways to increase power without increasing N, pp 184-195 in: Advances in Data Analysis for Prevention Intervention Research, Collins LM, Seitz LA (eds). NIDA Research Monograph 142.

Hoenig, J. M., Heisey, D. M. (2001). The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis. American Statistician 55:19-24.

Kanda, Y. (2013). Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone marrow transplantation 48:452-458.

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in psychology, 4, 863.

Yuan, K.-H., Maxwell, S. (2005). On the Post Hoc Power in Testing Mean Differences. Journal of Educational and Behavioral Statistics 30(2):141-167.

Zhang, Y., Hedo, R., Rivera, A., Rull, R., Richardson, S., & Tu, X. M. (2019). Post hoc power analysis: is it an informative and meaningful analysis?. General psychiatry, 32(4).

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