In applied linguistics research interest has surged in recent years for questions related to individuals’ development and change. The most commonly used methods in the field for analyzing repeated measures data, for studying variation and stability in individual change, and for investigating how individual level outcomes relate to the group may not be the most robust available. In this methods tutorial, we introduce a state-of-the-art longitudinal data analysis method: individual growth curve modeling. We describe the various uses for individual growth curve models (IGCM) in comparison with other cross-sectional or single-subject analytical methods, define important terms related to such models that describe outcome variable Y at time t, and show how IGCM can be used to analyze variables at the individual and group levels simultaneously. In our tutorial, we provide a worked example of IGCM that draws on a dataset of L2 students’ classroom disengagement and use R to exemplify the necessary steps for conducting IGC modeling. Through this example, we demonstrate how IGCM are especially suited to data structured on multiple levels, where repeated measures varying across time are nested within individuals, and where both time-variant and time-invariant measures are of interest. We further demonstrate how, in addition to examining changes over time in the outcome variable for the same individual using repeated measures, IGCM can also capture individual differences in those changes. We conclude with a brief review of how such models have been used recently across a selective sample of research domains in applied linguistics