Mixed Effects Models
The very basics of how to run linear models with random effects
This page assumes that you already understand the basics of how to run a linear model and how to interpret linear model output.
Linear mixed models can be built in R using the lme4 package. Other packages to work with mixed-effects models are GLMMadaptive, glmmML, glmmTMB, and MCMCglmm.
Linear mixed models have the following structure: Outcome variable ~ fixed effects + random effects
Outcome variable = the variable that is hypothesized to be modified by the predictors. For example in a looking while listening study looking time would be a likely outcome variable.
Fixed effects are the predictors that are hypothesized not to have individual random variance. For example, in a looking while listening task where children heard some mixed language phrases, language mixing is a fixed predictor of looking time because mixing was always the same in each experiment for each participant.
Random effects are the predictors that are hypothesized to have individual random variance. For example, in a looking while listening task we expect each child to perform differently based on their individual traits, so participant is a random predictor of looking time.
To learn more about linear mixed models:
Book: Statistics for linguistics: An introduction using R. Bodo Winter (2019). Find it in the lab's library.
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