A Comparison of the General Linear Mixed Model and Repeated Measures ANOVA Using a Dataset with Multiple Missing Data Points

TLDR

Longitudinal analyses typically rely on linear models, but the general linear mixed model offers advantages for dynamic phenomena by handling missing data and modeling nonlinear individual trajectories. This study demonstrates the benefits of the mixed model over repeated measures ANOVA for nonlinear longitudinal data with multiple missing points using an experimental dataset. The authors outline the decision‑making steps for applying both the mixed model and repeated measures ANOVA to the data.

Abstract

Longitudinal methods are the methods of choice for researchers who view their phenomena of interest as dynamic. Although statistical methods have remained largely fixed in a linear view of biology and behavior, more recent methods, such as the general linear mixed model (mixed model), can be used to analyze dynamic phenomena that are often of interest to nurses. Two strengths of the mixed model are (1) the ability to accommodate missing data points often encountered in longitudinal datasets and (2) the ability to model nonlinear, individual characteristics. The purpose of this article is to demonstrate the advantages of using the mixed model for analyzing nonlinear, longitudinal datasets with multiple missing data points by comparing the mixed model to the widely used repeated measures ANOVA using an experimental set of data. The decision-making steps in analyzing the data using both the mixed model and the repeated measures ANOVA are described.