Statistical analysis of the longitudinal fundamental movement skills data in the REACT project using the multilevel ordinal logistic model

Abstract

AbstractObjectivesThe REACT project was designed around two main aims: (1) to assess children's growth and motor development after the COVID‐19 pandemic and (2) to follow their fundamental movement skills' developmental trajectories over 18 months using a novel technological device (Meu Educativo®) in their physical education classes. In this article, our goal is to describe statistical analysis of the longitudinal ordinal motor development data that was obtained from these children using the multilevel ordinal logistic model.MethodsLongitudinal ordinal data are often collected in studies on motor development. For example, children or adolescents might be rated as having poor, good, or excellent performance levels in fundamental movement skills, and such ratings may be obtained yearly over time to assess changes in fundamental movement skills levels of performance. However, such longitudinal ordinal data are often analyzed using either methods for continuous outcomes, or by dichotomizing the ordinal outcome and using methods for binary data. These approaches are not optimal, and so we describe in detail the use of the multilevel ordinal logistic model for analysis of such data from the REACT project. Our intent is to provide an accessible description and application of this model for analysis of ordinal motor development data.DiscussionOur analyses show both the between‐subjects and within‐subjects effects of age on motor development outcomes across three timepoints. The between‐subjects effect of age indicate that children that are older have higher motor development ratings, relative to thoese that are younger, whereas the within‐subject effect of age indicates higher motor development ratings as a child ages. It is the latter effect that is particularly of interest in longitudinal studies of motor development, and an important advantage of using the multilevel ordinal logistic model relative to more traditional methods.