Growth curve modeling provides a more informative alternative to traditional ANOVA and MANOVA by characterizing both group-level and individual-level effects in repeated measures designs.
Psychophysiologists often use repeated measures analysis of variance (RMANOVA) and multivariate analysis of variance (MANOVA) to analyze data collected in repeated measures research designs. ANOVA and MANOVA are nomothetic approaches that focus on group means. Newer multilevel modeling techniques are more informative than ANOVA because they characterize both group-level (nomothetic) and individual-level (idiographic) effects, yielding a more complete understanding of the phenomena under study. This article was written as an introduction to growth curve modeling for applied researchers. A growth model is defined that can be used in place of RMANOVAs and MANOVAs for single-group and mixed repeated measures designs. The model is expanded to test and control for the effects of baseline levels of physiological activity on stimulus-specific responses. Practical, conceptual, and statistical advantages of growth curve modeling are discussed.
Kristjansson et al. (Tue,) reported a review. Growth curve modeling vs. Repeated measures ANOVA and MANOVA was evaluated. Growth curve modeling provides a more informative alternative to traditional ANOVA and MANOVA by characterizing both group-level and individual-level effects in repeated measures designs.