A new way for handling mobility in longitudinal data.

In the social sciences, applied researchers often face a statistical dilemma when multilevel data is structured such that lower-level units are not purely clustered within higher-level units. To aid applied researchers in appropriately analyzing such data structures, this study proposes a multiple membership growth curve model (MM-GCM). The MM-GCM offers some advantages to other similar modeling approaches, including greater flexibility in modeling the intercept at the time-point most desired for interpretation. A real longitudinal dataset from the field of education with a multiple membership structure, where some students changed schools over time, was used to demonstrate the application of the MM-GCM. Baseline and conditional MM-GCMs are presented, and parameter estimates were compared with two other common approaches to handling such data structures – the final school-GCM that ignores mobile students by only modeling the final school attended and the delete-GCM that deletes mobile students. Additionally, a simulation study was conducted to further assess the impact of ignoring mobility on parameter estimates. The results indicate that ignoring mobility results in substantial bias in model estimates, especially for cluster-level coefficients and variance components. [ABSTRACT FROM AUTHOR]