Flexible Multiple Imputation of Missing Data in Time-Structured Longitudinal Designs

Missing data are common in longitudinal designs and are often addressed with multiple imputation (MI), either as single-level MI, which treats repeated measures as separate variables, or multilevel MI, which treats repeated measures as nested within participants. Previous research has shown that both approaches can perform well in time-structured designs but has largely focused on growth modeling applications, where the assumptions underlying multilevel MI were met. In the present article, we argue that single-level MI is a more flexible method for handling missing data in time-structured designs that requires fewer assumptions and can accommodate a wider range of analyses than multilevel MI. In this context, we also consider applications of single-level MI to longitudinal multiple-indicator designs, in which single-level MI can be extended with composite scores or dimension reduction techniques such as partial least squares (PLS) to accommodate the potentially large number of variables in these types of designs. Our results from two simulation studies suggest that single-level MI provides a flexible treatment of missing data and that PLS in particular can facilitate single-level MI in applications with many variables. We conclude by discussing implications for applied research and by illustrating the application of single-level MI in an empirical example.