Effect sizes and power

Content

  • Measures of effect size
  • Power calculations
  • Interplay between sample size, effect and power

Learning objectives

At the end of the session, students should be capable of

  • correctly report effect size for common statistics in analysis of variance models
  • deduce the sample size necessary to replicate a study at a given power
  • explain the interplay between sample size, power and effect size.

Readings

Complementary readings

Slides

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Tip

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Exercise

Code

References

Gelman, A., & Carlin, J. (2014). Beyond power calculations: Assessing type S (sign) and type M (magnitude) errors. Perspectives on Psychological Science, 9(6), 641–651. https://doi.org/10.1177/1745691614551642
Kelley, K., & Preacher, K. (2012). On effect size. Psychological Methods, 17(2), 137–152. https://doi.org/10.1037/a0028086
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. https://doi.org/10.3389/fpsyg.2013.00863
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing experiments and analyzing data: A model comparison perspective (3rd ed.). routledge. https://doi.org/10.4324/9781315642956
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. https://doi.org/10.1037/1082-989X.9.2.164
Zhang, Z., & Yuan, K.-H. (2018). Practical statistical power analysis using Webpower and R. ISDSA Press.