Model Selection Criteria for Latent Growth Models Using Bayesian Methods

Research in applied areas, such as statistical, psychological, behavioral, and educational areas, often involves the selection of the best available model from among a large set of candidate models. Considering that there is no well-defined model selection criterion in a Bayesian context and that latent growth mixture models are becoming popular in many areas, the goal of this study is to investigate the performance of a series of model selection criteria in the framework of latent growth mixture models with missing data and outliers in a Bayesian context. This study conducted five simulation studies to cover different cases, including latent growth curve models with missing data, latent growth curve models with missing data and outliers, growth mixture models with missing data and outliers, extended growth mixture models with missing data and outliers, and latent growth models with different classes. Simulation results show that almost all the proposed criteria can effectively identify the true models. This study also illustrated the application of these model selection criteria in real data analysis. The results will help inform the selection of growth models by researchers seeking to provide states with accurate estimates of the growth of their students.

[1]  H. Akaike A new look at the statistical model identification , 1974 .

[2]  Ke-Hai Yuan,et al.  SEM with Missing Data and Unknown Population Distributions Using Two-Stage ML: Theory and Its Application , 2008, Multivariate behavioral research.

[3]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[4]  D. Dunson,et al.  Bayesian latent variable models for clustered mixed outcomes , 2000 .

[5]  Brenda White,et al.  DEPARTMENT OF LABOR , 2006 .

[6]  Allan S. Cohen,et al.  Bayesian Methods and Model Selection for Latent Growth Curve Models with Missing Data , 2013 .

[7]  T. W. Anderson,et al.  Classification into two Multivariate Normal Distributions with Different Covariance Matrices , 1962 .

[8]  G. Casella,et al.  Explaining the Gibbs Sampler , 1992 .

[9]  Zhiyong Zhang,et al.  Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data , 2011, Multivariate behavioral research.

[10]  Zhiyong Zhang,et al.  Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application , 2014, Comput. Stat. Data Anal..

[11]  C. Robert,et al.  Deviance information criteria for missing data models , 2006 .

[12]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[13]  Roderick J. A. Little,et al.  Modeling the Drop-Out Mechanism in Repeated-Measures Studies , 1995 .

[14]  Jonathan M. Keith,et al.  Model selection in Bayesian segmentation of multiple DNA alignments , 2011, Bioinform..

[15]  B. Muthén,et al.  Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm , 1999, Biometrics.

[16]  Zhiyong Zhang,et al.  Bayesian Inference and Application of Robust Growth Curve Models Using Student's t Distribution , 2013 .

[17]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[18]  Donald B. Rubin,et al.  Selection Modeling Versus Mixture Modeling with Nonignorable Nonresponse , 1986 .

[19]  H. Bozdogan Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .

[20]  V. Yohai,et al.  Robust Statistics: Theory and Methods , 2006 .

[21]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[22]  R. Little Pattern-Mixture Models for Multivariate Incomplete Data , 1993 .

[23]  S. Sclove Application of model-selection criteria to some problems in multivariate analysis , 1987 .