title: Mixed-effects models in psychophysiology.
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.
authors.affiliation: Division of Biostatistics, School of Public Health, Columbia University, New York, New York, USA. bagiella@biostat.columbia.edu
authors.forename: E
authors.initials: E
authors.lastname: Bagiella
authors.forename: R P
authors.initials: RP
authors.lastname: Sloan
authors.forename: D F
authors.initials: DF
authors.lastname: Heitjan
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D008960:Models, Psychological; D011603:Psychophysiology