Abstract
This paper described the versatility of the multiple-indicator multilevel (MIML) model in helping to resolve four common challenges in studying growth using longitudinal data. These challenges are (1) how to deal with changes in measurement over time and investigate temporal measurement invariance, (2) how to model residual dependence due to the nested nature of longitudinal data, (3) how to model observed trajectories that do not follow well-known functions commonly discussed in the methodology literature (e.g., a linear or quadratic curve), and (4) how to decide which predictors are relatively more important in explaining individuals’ change over time. With an example of psychological well-being from the Wisconsin Longitudinal Study, we illustrated how the four methodological challenges can be resolved using the 3-phase MIML procedures and the Pratt’s importance measures.
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