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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
Sterba, Sonya K. – Multivariate Behavioral Research, 2009
A model-based framework, due originally to R. A. Fisher, and a design-based framework, due originally to J. Neyman, offer alternative mechanisms for inference from samples to populations. We show how these frameworks can utilize different types of samples (nonrandom or random vs. only random) and allow different kinds of inference (descriptive vs.…
Descriptors: Statistical Inference, Models, Sampling, Psychology
Huo, Yan; Budescu, David V. – Multivariate Behavioral Research, 2009
Dominance analysis (Budescu, 1993) offers a general framework for determination of relative importance of predictors in univariate and multivariate multiple regression models. This approach relies on pairwise comparisons of the contribution of predictors in all relevant subset models. In this article we extend dominance analysis to canonical…
Descriptors: Multivariate Analysis, Correlation, Regression (Statistics), Models
Hayes, Andrew F.; Preacher, Kristopher J. – Multivariate Behavioral Research, 2010
Most treatments of indirect effects and mediation in the statistical methods literature and the corresponding methods used by behavioral scientists have assumed linear relationships between variables in the causal system. Here we describe and extend a method first introduced by Stolzenberg (1980) for estimating indirect effects in models of…
Descriptors: Computation, Methods, Models, Statistical Analysis