Mixed-effects models in psychophysiology.

E Bagiella; R P Sloan; D F Heitjan

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PubMed · 10705763

Mixed-effects models in psychophysiology.

E Bagiella·R P Sloan·D F Heitjan

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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.

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Algorithms Analysis of Variance Models, Psychological Psychophysiology Journal Article Research Support, U.S. Gov't, P.H.S.

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E Bagiella, R P Sloan, D F Heitjan. 2000. Mixed-effects models in psychophysiology.. https://pubmed.ncbi.nlm.nih.gov/10705763/

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