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