6.2.1. Introduction to mixed-effects modeling¶
Dejan Draschkow May 19, 2019
A hands-on crash course in reproducible mixed-effects modeling¶
This is a very preliminary resource, established for a satellite event at VSS 2019. Its aim is to establish the basis for a comprehensive book chapter. By gathering feedback as early as possible it will hopefully appeal to a broader audience.
Mixed-effects models are a powerful alternative to traditional F1/F2-mixed model/repeated-measure ANOVAs and multiple regressions. Mixed models allow simultaneous estimation of between-subject and between-stimulus variance, deal well with missing data, allow for easy inclusion of covariates and modeling of higher order polynomials. This workshop provides a focused, hands-on treatment of applying this analysis technique in an open and reproducible way. More introductory or extensive resources are currently available and I can highly recommend them.