Mixed-effects models in psychophysiology

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

Research output: Contribution to journalArticlepeer-review

234 Scopus citations


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
Issue number1
StatePublished - Jan 2000


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

ASJC Scopus subject areas

  • Neuroscience(all)
  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Neurology
  • Endocrine and Autonomic Systems
  • Developmental Neuroscience
  • Cognitive Neuroscience
  • Biological Psychiatry


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