Incorporating Covariates in Bayesian Piecewise Growth Mixture Models

Lamm, Rik

doi:10.3102/1884335

Cited by 2 publications

(3 citation statements)

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“…As noted previously, there are several newer extensions of the presented model, which we encourage readers to explore. These extensions include piecewise models that directly estimate knot placements (Kohlixet al, 2015;Lockxet al, 2018), employ covariates (Lamm, 2022), or capture bivariate piecewise trajectories (Peraltaxet al, 2022). Additionally, PGCMs with higher-order polynomials (e.g., cubic) or inherently nonlinear functions (e.g., exponential) are also possible.…”

Section: Potential Extensions Of the Current Workmentioning

confidence: 99%

“…Bayesian PGCMs extend conventionally-taught linear growth models by altering both the functional form of growth and the estimation framework. This is an active area of methodological development, with recent extensions that enable the direct estimation of knot placement (Kohli, Hughes, Wang, Zopluoglu,x& Davison, 2015;Lock, Kohli,x& Bose, 2018), incorporation of covariates (Lamm, 2022), and capturing the interdependent nature of bivariate piecewise trajectories (Peralta, Kohli, Lock,x& Davison, 2022). Our intended scope for the current paper is to provide an introductory, hands-on walkthrough to the novice data scientist or graduate student.…”

Section: Introductionmentioning

confidence: 99%

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Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories

Marvin

Liu

Depaoli

2023

JBDS

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Bayesian growth curve modeling is a popular method for studying longitudinal data. In this study, we discuss a flexible extension, the Bayesian piecewise growth curve model (BPGCM), which allows the researcher to break up a trajectory into phases joined at change points called knots. By fitting BPGCMs, the researcher can specify three or more phases of growth without concern for model identification. Our goal is to provide substantive researchers with a guide for implementing this important class of models. We present a simple application of Bayesian linear BPGCMs to childrens' math achievement. Our tutorial includes Mplus code, strategies for specifying knots, and how to interpret model selection and fit indices. Extensions of the model are discussed.

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Section: Potential Extensions Of the Current Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories

Marvin

Liu

Depaoli

2023

JBDS

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“…Allows the inclusion of outcome-and/or class-predictive covariates. SeeLock et al (2018) andLamm (2022) for more details.Arguments dataData frame in long format, where each row describes a measurement occasion for a given individual. It is assumed that each individual has the same number of assigned timepoints (a.k.a., rows).…”

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confidence: 99%