An introduction to latent growth models: analysis of repeated measures physical performance data

The purpose of this paper is to introduce the Latent Growth Model (LGM) to researchers in exercise and sport science. Although the LGM has several merits over traditional analysis techniques in analyzing change and was first introduced almost 20 years ago, it is still underused in exercise and sport science research. This statistical model can be applied to any repeated measures data, but it is most useful when one has an a priori hypothesis about the patterns of change. The strengths of latent growth modeling include: (a) both individual and group levels of change are estimated, (b) either a linear or a curvilinear trajectory can represent individual change, (c) occasions of measurement need not be equally spaced, (d) the statistical model can account for measurement errors, (e) the model can easily include multiple predictors or correlates of change, and (f) as in general structural equation models, statistical models are flexible and allow one to extend the basic idea in several ways, such as comparing changes between groups and examining the change in multivariate latentfactors. In this paper, the basics and an extension of latent growth modeling are explained, and examples with longitudinal physical performance data are presented, along with detailed analysis procedures and considerations.