This chapter describes a class of statistical model that is able to account for most of the cases of nonindependence that are typically encountered in psychological experiments, linear mixed-effects models, or mixed models for short. It introduces the concepts underlying mixed models and how they allow accounting for different types of nonindependence that can occur in psychological data. The chapter discusses how to set up a mixed model and how to perform statistical inference with a mixed model. The most important concept for understanding how to estimate and how to interpret mixed models is the distinction between fixed and random effects. One important characteristic of mixed models is that they allow random effects for multiple, possibly independent, random effects grouping factors. Mixed models are a modern class of statistical models that extend regular regression models by including random-effects parameters to account for dependencies among related data points.
|Original language||English (US)|
|Title of host publication||New Methods in Cognitive Psychology|
|Publisher||Taylor and Francis|
|Number of pages||28|
|State||Published - Oct 28 2019|
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