Premium
Mixed‐effects models in psychophysiology
Author(s) -
Bagiella Emilia,
Sloan Richard P.,
Heitjan Daniel F.
Publication year - 2000
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/1469-8986.3710013
Subject(s) - psychophysiology , repeated measures design , analysis of variance , variance (accounting) , psychology , multivariate statistics , mixed design analysis of variance , analysis of covariance , multivariate analysis of variance , statistics , covariance , missing data , econometrics , mathematics , accounting , neuroscience , business
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.