GLMM Resources

We will be running a series of talks, workshops and discussions about generalised linear mixed models (GLMM). This page will collate links and resources.

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Schedule

All sessions will be 16:00 to 17:00 via Zoom

• 2021-11-03 A Gentle Introduction to Generalized Linear Mixed Models
• 2021-11-10 Rating norms should be calculated from cumulative link mixed effect models (Taylor et al., 2021)
• 2021-11-17 Generalized Linear Mixed Models for RT data (Brysbaert & Stevens, 2018; Lo & Andrews, 2015)
• 2021-11-24 Pushback against GLMMs (Gomila, 2021; Knief & Forstmeier, 2021; Ryan, Evers & Moore, 2021)

Resources

• Learning Statistical Models Through Simulation in R
• Brysbaert, M. and Stevens, M. (2018) Power Analysis and Effect Size in Mixed Effects Models: A Tutorial. Journal of Cognition, 1(1): 9, pp. 1–20, DOI: 10.5334/joc.10
• Probit vs logit
• Logistic regression shiny app
• Simulating binomial data with crossed random factors
• Taylor, J. E., Rousselet, G. A., Scheepers, C., & Sereno, S. C. (2021, August 3). Rating Norms Should be Calculated from Cumulative Link Mixed Effects Models. DOI: 10.31234/osf.io/3vgwk
• Lo, S. and Andrews, S. (2015) To transform or not to transform: using generalized linear mixed models to analyse reaction time data. Frontiers in Psychology 6:1171. DOI: 10.3389/fpsyg.2015.01171
• Gomila, R. (2021). Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. Journal of Experimental Psychology: General, 150(4), 700–709. DOI: 10.1037/xge0000920
• Knief U, Forstmeier W. (2021). Violating the normality assumption may be the lesser of two evils. Behav Res Methods. DOI: 10.3758/s13428-021-01587-5
• William H. Ryan, Ellen R. K. Evers, Don A. Moore; Poisson Regressions: A Little Fishy. Collabra: Psychology 4 January 2021; 7 (1): 27242. DOI: 10.1525/collabra.27242
• Generalized Linear Madness