Handling Missing Data in the Modeling of Intensive Longitudinal Data

Abstract

Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms. They include a full MI approach, in which all dependent variables and covariates are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with MI, whereas missingness in the dependent variables is handled via full information maximum likelihood estimation. We found that under correctly specified models, partial MI produces the best overall estimation results. We discuss the strengths and limitations of the two MI approaches, and demonstrate their use with an empirical data set in which children's influences on parental conflicts are modeled as covariates over the course of 15 days (Schermerhorn, Chow, & Cummings, 2010).

Keywords: missing data; multiple imputation; multivariate time-series model.

Figures

FIGURE 1

A comparison of the accuracies of the point estimates: (a) RMSEs for the time-series parameters; (b) biases for the time-series parameters; (c) RMSEs for the covariate-related parameters; and (d) biases for the covariate-related parameters.

FIGURE 2

A comparison of the quality of SE estimates: (a) DSEs averaged across all parameter estimates; (b) DSEfulls averaged across all parameter estimates.

FIGURE 3

A comparison of the coverages: (a) for the time-series parameter estimates; (b) for the covariate-related parameter estimates.