Persons as Contexts: Evaluating Between-Person and Within-Person Effects in Longitudinal Analysis

Relationships among multiple variables over time are of interest in many developmental areas and are frequently examined using time-varying predictors in multilevel models. Yet an incomplete specification of time-varying predictors will usually result in biased model effects. Specifically, the impact of constant, between-person sources of variation must be differentiated from the impact of time-specific, within-person sources of variation - that is, persons should be modeled as contexts. The current didactic article expands upon previous work to address why and how to model persons as contexts in longitudinal analysis. An electronic appendix of syntax for estimating these models is also provided.