title: Modeling Change in the Presence of Non-Randomly Missing Data: Evaluating A Shared Parameter Mixture Model.
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, termedrandom 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.
authors.affiliation: Duke University.
authors.forename: Nisha C
authors.initials: NC
authors.lastname: Gottfredson
authors.affiliation: The University of North Carolina at Chapel Hill.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Bauer
authors.affiliation: Brigham Young University.
authors.forename: Scott A
authors.initials: SA
authors.lastname: Baldwin
keywords: Growth Mixture Models; Growth Models; Longitudinal Data; Missing Data; Shared Parameter Mixture Models