Repeated-measure analyses: Which one? A survey of statistical models and recommendations for reporting.

Repeated-measure analysis of variance is a general term that can imply a number of different statistical models used to analyze data from studies in which measurements are taken from each subject on more than one occasion. Repeated-measure analyses encompass univariate models (with or without sphericity adjustment), multivariate models, mixed models, analysis of covariance, multilevel models, latent growth models, and hybrids of these models. These models are based on different assumptions, especially regarding correlations (sphericity) between within-subject factors, which comprise the variance-covariance matrix. Violation of this assumption may lead to misleading and erroneous conclusions. Because many papers do not provide enough information about what analysis was really conducted, and about why it was done, the reader is unable to evaluate the validity of the analysis. Here a brief overview of several of the most commonly used models for analyzing data from repeated-measure designs is provided, and guidance is suggested for describing the statistical approach employed. The goals of this paper are (1) to give authors an overview of the diversity of commonly used models and associated assumptions, and (2) to facilitate reporting sufficient information about the tests to allow the reader to evaluate the validity of the tests and the credibility of the inferences made by the authors. Among the available approaches to repeated-measure analyses, the mixed model is recommended for its flexibility in handling different covariance structures and its insensitivity to missing data. Whether or not it is used, the overall guiding principles in reporting should always be Accuracy, Completeness, and Transparency (ACT principles): tell the reader precisely all what you did and why.