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Tong, Xin; Zhang, Zhiyong – Grantee Submission, 2020
Despite broad applications of growth curve models, few studies have dealt with a practical issue -- nonnormality of data. Previous studies have used Student's "t" distributions to remedy the nonnormal problems. In this study, robust distributional growth curve models are proposed from a semiparametric Bayesian perspective, in which…
Descriptors: Robustness (Statistics), Bayesian Statistics, Models, Error of Measurement
Zhang, Zhiyong – Grantee Submission, 2016
Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is…
Descriptors: Bayesian Statistics, Models, Statistical Distributions, Computation
Wang, Lijuan; Zhang, Zhiyong – Structural Equation Modeling: A Multidisciplinary Journal, 2011
This study investigated influences of censored data on mediation analysis. Mediation effect estimates can be biased and inefficient with censoring on any one of the input, mediation, and output variables. A Bayesian Tobit approach was introduced to estimate and test mediation effects with censored data. Simulation results showed that the Bayesian…
Descriptors: Statistical Analysis, Mediation Theory, Censorship, Bayesian Statistics
Lu, Zhenqiu Laura; Zhang, Zhiyong; Lubke, Gitta – Multivariate Behavioral Research, 2011
"Growth mixture models" (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class…
Descriptors: Bayesian Statistics, Statistical Inference, Computation, Models
Zhang, Zhiyong; Hamagami, Fumiaki; Wang, Lijuan Lijuan; Nesselroade, John R.; Grimm, Kevin J. – International Journal of Behavioral Development, 2007
Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data…
Descriptors: Computation, Bayesian Statistics, Statistical Analysis, Longitudinal Studies
Zhang, Zhiyong; Nesselroade, John R. – Multivariate Behavioral Research, 2007
Dynamic factor models have been used to analyze continuous time series behavioral data. We extend 2 main dynamic factor model variations--the direct autoregressive factor score (DAFS) model and the white noise factor score (WNFS) model--to categorical DAFS and WNFS models in the framework of the underlying variable method and illustrate them with…
Descriptors: Bayesian Statistics, Computation, Simulation, Behavioral Science Research