This chapter provides a non-technical introduction to mixture modeling for sport and exercise sciences researchers. Although this method has been around for quite some time, it is still underutilized in sport and exercise research. The data set used for this illustration consists of a sample of 10,000 students who annually completed physical fitness tests for 7 years in Singapore. First, we illustrate latent profile analyses (LPA). Next, we illustrate how to include covariates in LPA and how to test the invariance of LPA solutions across groups, as well as over time using latent transition analyses. Following that, we illustrate the estimation of mixture regression models to identify subgroups of participants differing from one another at the levels of the relations among constructs. Finally, a growth mixture modeling example is shown to identify subgroups of participants following distinct longitudinal trajectories
