Linear classification methods for multivariate repeated measures data -- a simulation study

TL;DR

This study compares various linear classification methods for multivariate repeated measures data, especially when data are non-normal, using simulations based on psychological questionnaire data to evaluate their effectiveness.

Contribution

It provides a comparative analysis of existing linear classification algorithms for non-normal repeated measures data, highlighting their performance and applicability.

Findings

Robust alternatives may not be necessary for large, unstructured data.

Repeated measures classification techniques are under-discussed in literature.

First comprehensive comparison of multiple classification methods for repeated measures data.

Abstract

Researchers in the behavioral and social sciences use linear discriminant analysis (LDA) for predictions of group membership (classification) and for identifying the variables most relevant to group separation among a set of continuous correlated variables (description). \\ In these and other disciplines, longitudinal data are often collected which provide additional temporal information. Linear classification methods for repeated measures data are more sensitive to actual group differences by taking the complex correlations between time points and variables into account, but are rarely discussed in the literature. Moreover, psychometric data rarely fulfill the multivariate normality assumption.\\ In this paper, we compare existing linear classification algorithms for nonnormally distributed multivariate repeated measures data in a simulation study based on psychological questionnaire data comprising Likert scales. The results show that in data without any specific assumed structure and larger sample sizes, the robust alternatives to standard repeated measures LDA may not be needed. To our knowledge, this is one of the few studies discussing repeated measures classification techniques, and the first one comparing multiple alternatives among each other.