Mixed-effects models in psychophysiology

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

Research output: Contribution to journalArticle

185 Citations (Scopus)

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
StatePublished - Jan 1 2000

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Psychophysiology
Analysis of Variance
Multivariate Analysis
Mixed Effects Model
Modeling

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)

Cite this

Mixed-effects models in psychophysiology. / Bagiella, Emilia; Sloan, Richard P.; Heitjan, Daniel F.

In: Psychophysiology, Vol. 37, No. 1, 01.01.2000, p. 13-20.

Research output: Contribution to journalArticle

Bagiella, Emilia ; Sloan, Richard P. ; Heitjan, Daniel F. / Mixed-effects models in psychophysiology. In: Psychophysiology. 2000 ; Vol. 37, No. 1. pp. 13-20.
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