Keywords

Missing Data; Shared Parameter Mixture Models; Growth Models; Growth Mixture Models; Longitudinal Data

Abstract

In longitudinal research, interest often centers on individual trajectories of change over time. When there is missing data, a concern is whether data are systematically missing as a function of the individual trajectories. Such a missing data process, termed random coefficient-dependent missingness, is statistically non-ignorable and can bias parameter estimates obtained from conventional growth models that assume missing data are missing at random. This paper describes a shared-parameter mixture model (SPMM) for testing the sensitivity of growth model parameter estimates to a random coefficient-dependent missingness mechanism. Simulations show that the SPMM recovers trajectory estimates as well as or better than a standard growth model across a range of missing data conditions. The paper concludes with practical advice for longitudinal data analysts

Document Type

Peer-Reviewed Article

Publication Date

2014-1

Language

English

College

Family, Home, and Social Sciences

Department

Psychology

Included in

Psychology Commons

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