Contrasts and multiple testing
Content
- Contrasts
- Multiple testing
Learning objectives
At the end of the session, students should be capable of
- specifying and calculating custom contrasts in factorial designs
- determining the number of tests in a family that need to be corrected for
- understanding how to correct p-values to account for multiple testing
- listing multiplicity testing methods suitable depending on context
Readings
- Chapter 4 of the course notes
- Chapter 3 of Meier (2022)
- One-way ANOVA example
Complementary readings
Slides
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Tip
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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
Keppel, G., & Wickens, T. D. (2004). Design and analysis: A researcher’s handbook. Pearson Prentice Hall.
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
Meier, L. (2022). ANOVA and mixed models: A short introduction using R (Chapman & Hall/CRC, Eds.). https://doi.org/10.1201/9781003146216