An introduction to mixed models for experimental psychology

Henrik Singmann, David Kellen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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 languageEnglish (US)
Title of host publicationNew Methods in Cognitive Psychology
PublisherTaylor and Francis
Pages4-31
Number of pages28
ISBN (Electronic)9781000617467
ISBN (Print)9781848726307
DOIs
StatePublished - Oct 28 2019

ASJC Scopus subject areas

  • Psychology(all)

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