Temporal Misalignment in Intensive Longitudinal Data: Consequences and Solutions Based on Dynamic Structural Equation Models

Abstract

Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact of temporal misalignment on parameter estimation were investigated in a simulation study, which showed that temporal misalignment led to incomparable cross-lagged effects between variables. Then, two solutions, model adjustment and data interpolation, were proposed, and their performance was compared with those of the naive estimation which blindly treating temporally misaligned data as aligned. The simulation results supported the effectiveness of the model adjustment method over the other two methods. Finally, all three methods were applied to two empirical data collected by daily diaries and empirical sampling method, and recommendations were made for collecting and analyzing intensive longitudinal data.