Longitudinal measurement enables the examination of behavioral or psychological change. One approach to examining longitudinal measurements is the use of latent growth curve modeling (LGCM). This approach affords the assessment of inter- and intraindividual change. Yet, this approach likely is underused in exercise science. The purpose of the current study was to describe and demonstrate the use of LGCM to examine change using multiple measurements in the field of exercise science. We first provide a substantive review of LGCM. We highlight the use of unconditional models to find an appropriate model of change, how and why to utilize autoregressions, and how to examine predictors of change in conditional models. We then provide an illustration of the approach using data from the Michigan State Motor Performance Study. In the conclusion, we discuss the advantages and limitations of the approach and suggest future directions when assessing longitudinal data in exercise science.