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
BYU ScholarsArchive Citation
Baldwin, Scott A.; Gottfredson, Nisha C.; and Bauer, Daniel J., "Modeling Change in the Presence of Non-Randomly Missing Data: Evaluating A Shared Parameter Mixture Model" (2014). Faculty Publications. 6067.
https://scholarsarchive.byu.edu/facpub/6067
Document Type
Peer-Reviewed Article
Publication Date
2014-1
Permanent URL
http://hdl.lib.byu.edu/1877/8796
Language
English
College
Family, Home, and Social Sciences
Department
Psychology