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Author: Lamm, Rik Z.
Resulting in 1 citation.
1. Lamm, Rik Z.
Incorporation of Covariates in Bayesian Piecewise Growth Mixture Models
Ph.D. Dissertation, Department of Educational Psychology, University of Minnesota, 2022
Cohort(s): NLSY97
Publisher: ProQuest Dissertations & Theses (PQDT)
Keyword(s): Bayesian; High School Completion/Graduates; High School Dropouts; Income; Modeling, MIxture Models/Finite Mixture Models

Permission to reprint the abstract has not been received from the publisher.

The Bayesian Covariate Influenced Piecewise Growth Mixture Model (CI-PGMM) is an extension of the Piecewise Growth Mixture Model (PGMM, Lock et al., 2018) with the incorporation of covariates. This was done by using a piecewise nonlinear trajectory over time, meaning that the slope has a point where the trajectory changes, called a knot. Additionally, the outcome data belong to two or more latent classes with their own mean trajectories, referred to as a mixture model. Covariates were incorporated into the model in two ways. The first was influencing the outcome variable directly, explaining additional random error variance. The second is the influence of the covariates on the class membership directly with the use of multinomial logistic regression. Both uses of covariates can potentially influence the class memberships and along with that, the trajectories and locations of the knot(s). This additional explanation of class memberships and trajectories can provide information on how individuals change, who is likely to belong in certain unknown classes, and how these class memberships can affect when the rapid change of a knot will happen.

The model is shown to be appropriate and effective using two steps. First, a real data application using the National Longitudinal Survey of Youth is used to show the motivation for the model. This dataset measures income over time each year for individuals following high school. Covariates of sex and dropout status were used in the class predictive logistic regression model. This resulted in a two-class solution showing effective use of the covariates with the logistic regression coefficients drastically affecting the class memberships.

Bibliography Citation
Lamm, Rik Z. Incorporation of Covariates in Bayesian Piecewise Growth Mixture Models. Ph.D. Dissertation, Department of Educational Psychology, University of Minnesota, 2022.