As applications of multilevel modelling in educational research increase, researchers realize that multilevel data collected in many educational settings are often not purely nested. The most common multilevel non-nested data structure is one that involves student mobility in longitudinal studies. This article provides a methodological review of three statistical methods for handling student mobility in longitudinal studies: a multilevel approach, a cross-classified approach, and a cross-classified multiple membership approach. The strengths and weaknesses of each approach and the essential differences between the three approaches are discussed. The Early Childhood Longitudinal Study Kindergarten Cohort data are analysed to demonstrate the differences in parameter estimates and statistical inference between the three approaches. Potential applications of the three approaches in educational research and beyond and directions for further methodological investigations are discussed.