Psyc 6680

Syllabus

Lecture Notes

Review-Textbook

• • R Programming (learning module)

R Scripts

Anderson's Statistics Resource Page

Articles and Other Resources

• • Demo: Main Effects and Interactions

  • Demo: Bayesian Inference

  • Demo: Linear Mixed-Effects Model

• • Some Computational Examples of Analysis of Variance​

    • One-Factor ANOVA​

    • Two-Factor ( 3 by 3) ANOVA​

    • Three-Factor (2 by 2 by 2) ANOVA​

    • One-Factor Within-Subject (one-factor repeated-measures) ANOVA​

    • Multi-Factor Within-Subject (multi-factor repeated-measures) ANOVA

Linear Mixed-Effects Analysis

• Example 1a, Repeated Measures

• Example 1b, Linear Mixed-Effects

• Example 2a, Repeated Measures

• Example 2b, Linear Mixed-Effects

• Example 3, jamovi file comparing repeated-measures ANOVA to a Linear Mixed-Effects Model

• References

https://gamlj.github.io/mixed_example2.html

https://gamlj.github.io/mixed_example3.html

Your complex, linear mixed-effect model might be wrong:

• • https://www.researchgate.net/publication/322600137_Why_You_Should_Always_Include_a_Random_Slope_for_the_Lower-Level_Variable_Involved_in_a_Cross-Level_Interaction

  • Oberauer, K. (2022). The importance of random slopes in mixed models for Bayesian hypothesis testing. Psychological Science, 33(4), 648–665. https://doi.org/10.1177/09567976211046884

  • https://www.middleprofessor.com/files/applied-biostatistics_bookdown/_book/lmm.html

  • Meteyard, L., & Davies, R. A. I. (2020). Best practice guidance for linear mixed-effects models in psychological science. Journal of Memory and Language, 112. https://doi-org.ezproxy.bgsu.edu/10.1016/j.jml.2020.104092