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Mixed-effects models in psychophysiology

Published online by Cambridge University Press:  01 January 2000

EMILIA BAGIELLA ,
RICHARD P. SLOAN  and
DANIEL F. HEITJAN
EMILIA BAGIELLA
Affiliation:
Division of Biostatistics, School of Public Health, Columbia University, New York, New York, USA Behavioral Medicine Program, Columbia-Presbyterian Medical Center, New York, New York, USA The Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York, USA
RICHARD P. SLOAN
Affiliation:
Behavioral Medicine Program, Columbia-Presbyterian Medical Center, New York, New York, USA Division of Clinical Psychobiology, Department of Psychiatry, Columbia University, New York, New York, USA New York State Psychiatric Institute, New York, New York, USA
DANIEL F. HEITJAN
Affiliation:
Division of Biostatistics, School of Public Health, Columbia University, New York, New York, USA The Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York, USA
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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.

Keywords

Mixed effects modelsRepeated measures designsVariance-covariance matrix
Type
Research Article
Information
Psychophysiology , Volume 37 , Issue 1 , January 2000 , pp. 13 - 20
Copyright
© 2000 Society for Psychophysiological Research

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