Mixed-effects models in psychophysiology

Emilia Bagiella, Richard P. Sloan, Daniel F. Heitjan

Research output: Contribution to journalArticle

186 Scopus citations

Abstract

The current methodological policy in Psychophysiology stipulates that repeated-measures designs be analyzed using either multivariate analysis of variance (ANOVA) or repeated-measures ANOVA with the Greenhouse-Geisser or Huynh-Feldt correction. Both techniques lead to appropriate type I error probabilities under general assumptions about the variance-covariance matrix of the data. This report introduces mixed-effects models as an alternative procedure for the analysis of repeated-measures data in Psychophysiology. Mixed-effects models have many advantages over the traditional methods: They handle missing data more effectively and are more efficient, parsimonious, and flexible. We described mixed-effects modeling and illustrated its applicability with a simple example.

Original languageEnglish (US)
Pages (from-to)13-20
Number of pages8
JournalPsychophysiology
Volume37
Issue number1
DOIs
Publication statusPublished - Jan 1 2000

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Keywords

  • Mixed effects models
  • Repeated measures designs
  • Variance-covariance matrix

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Physiology
  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Physiology (medical)

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