Repeated Measures ANOVA Repeated Measures ANOVA Between Subjects
Repeated Measures ANOVA
Repeated Measures ANOVA • Between Subjects Design – ANOVA in which each participant participated in one IV Level • Within Subjects or Repeated Measures Design – Participants participate in more than one or even all IV levels
Advantages of Longitudinal Studies • • Economizes participants Subjects serve as own control Between subject variation removed from error Can separate aging effects (changes over time within individuals) from cohort effects (differences in between individuals at baseline) • Can provide information about individual change
Challenges of longitudinal studies • Susceptibility to practice effects or differential carry-over effects • Observations are not independent, so we must account for dependency – more computationally intensive • Attrition • Missing data, unbalanced designs, different time intervals • Time-varying covariates
Approaches to Longitudinal Data Analysis • Longitudinal Analysis – ANOVA for repeated measures • Focuses on mean levels for each level of the within subjects variables – MANOVA for repeated measures • Focuses on whether a multivariate effect exists between the differences (e. g. , time 2 - 1; time 3 2, etc. ) – Mixed-effects regression models / Multilevel Models
Repeated Measures ANOVA • All participants participate in all treatment conditions • Participant emerges as an independent source of variance (though in RM ANOVA we are not interested in it) • The other sources of variance include the repeated measures IV and the Participant x IV interaction
Statistical Assumptions of RM ANOVA • • Independence Normality Homogeneity of within-treatment variances Sphericity
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