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
Fun fact: If you type ? (or shift + /) while going through the slides, you can see a list of slide-specific commands.
Exercise
- Compute means and contrasts listed in the abstract of Baumann et al. (1992).
References
Baumann, J. F., Seifert-Kessell, N., & Jones, L. A. (1992). Effect of think-aloud instruction on elementary students’ comprehension monitoring abilities. Journal of Reading Behavior, 24(2), 143–172. https://doi.org/10.1080/10862969209547770
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