title: [Multivariate repeated measures designs].
abstract: In the social, behavioral, and health researches it is a common strategy to collect data along time on more than one group of participants on multiple dependent variables. To analyse this kind of data is very complicated due to the correlations between the measures taken in different points of the time, and between the answers. Usually to analyse these data the multivariate mixed model, or the doubly multivariate model, are the most frequent approaches. Both of them require combined multivariate normality, equal covariance matrices, independence between the observations of different participants, complete measurements on all subjects, and time-independent covariates. When one ore more of these assumptions are not accomplished these approaches do not control in the correct way the Type I error, and this affects the validity and the accuracy of the inferences. In this paper some solutions that solve the problems with the error Type I will be shown. Several programs for a correct realization of the analyses through the SAS Proc Mixed procedure are presented.
authors.affiliation: Facultad de Psicología, Universidad de Oviedo, Spain. gvallejo@uniovi.es
authors.forename: Guillermo
authors.initials: G
authors.lastname: Vallejo
authors.forename: Luis M
authors.initials: LM
authors.lastname: Lozano
mesh_terms: D000465:Algorithms; D003625:Data Collection; D005189:False Positive Reactions; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D015999:Multivariate Analysis; D012107:Research Design; D012680:Sensitivity and Specificity; D013269:Stochastic Processes