Mixed-Effects Regression

Below are materials to introduce students to the fundamentals of multi-level modelling or longitudinal data using Linear Mixed-Effect Regression (LMER). I try to use open-source software from the R Project for Statistical Computing and R Studio. In the spirit of those open access programs, all of these materials are freely available here. I am happy for anyone to use and adapt these materials with attribution. 

If you are totally new to R and R Studio, please see my installation instructions here and I have also created a brief video on opening files and installing packages in R Studio. 

As an outside reference, I highly recommend Longitudinal Data Analysis for the Behavioral Sciences Using R by Jeff Long. As part of a reading group at Auburn, I commented and updated a lot of the code that accompanies Dr. Long's textbook. I highly recommend that you buy this book to learn more about longitudinal data analysis. 

Materials:

1. My work in progress website Applied Mixed-Effect Regression for the Clinical Sciences: https://keithlohse.github.io/mixed_effects_models/ 

2. ACRM Workshop on Longitudinal Data Analysis that I developed with Al Kozlowksi: https://github.com/keithlohse/LMER_Clinical_Science/tree/master/ACRM_2018 

3. A paper I wrote with Jincheng Shen and Al Kozlowski contrasting mixed-effect regression against RM ANOVA for longitudinal data analysis. Pre-print: https://osf.io/preprints/sportrxiv/b4uev/ and final paper: https://journals.humankinetics.com/view/journals/jmld/aop/article-10.1123-jmld.2019-0007/article-10.1123-jmld.2019-0007.xml 

4. A paper I wrote with Al Kozlowski and Mike Strube on a general approach to random-effects for different types of study designs: https://storkjournals.org/index.php/cik/article/view/52 

[my website is permanently a work in progress; last updated 2024-05-02]