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de Leeuw, Christiaan; Klugkist, Irene – Multivariate Behavioral Research, 2012
In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this…
Descriptors: Data, Multiple Regression Analysis, Bayesian Statistics, Models
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Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel – Multivariate Behavioral Research, 2012
In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…
Descriptors: Bayesian Statistics, Factor Analysis, Models, Simulation
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Song, Hairong; Ferrer, Emilio – Multivariate Behavioral Research, 2012
Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…
Descriptors: Bayesian Statistics, Computation, Factor Analysis, Models
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Rast, Philippe; Hofer, Scott M.; Sparks, Catharine – Multivariate Behavioral Research, 2012
A mixed effects location scale model was used to model and explain individual differences in within-person variability of negative and positive affect across 7 days (N=178) within a measurement burst design. The data come from undergraduate university students and are pooled from a study that was repeated at two consecutive years. Individual…
Descriptors: Individual Differences, Undergraduate Students, Psychological Patterns, Stress Variables
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Kelava, Augustin; Nagengast, Benjamin – Multivariate Behavioral Research, 2012
Structural equation models with interaction and quadratic effects have become a standard tool for testing nonlinear hypotheses in the social sciences. Most of the current approaches assume normally distributed latent predictor variables. In this article, we present a Bayesian model for the estimation of latent nonlinear effects when the latent…
Descriptors: Bayesian Statistics, Computation, Structural Equation Models, Predictor Variables
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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
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Biesanz, Jeremy C.; Falk, Carl F.; Savalei, Victoria – Multivariate Behavioral Research, 2010
Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an independent variable's influence on the dependent variable is mediated through an intervening variable. Classic approaches to assessing such mediational hypotheses (Baron & Kenny, 1986; Sobel, 1982) have in recent years…
Descriptors: Computation, Intervals, Models, Monte Carlo Methods
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Hsieh, Chueh-An; von Eye, Alexander A.; Maier, Kimberly S. – Multivariate Behavioral Research, 2010
The application of multidimensional item response theory models to repeated observations has demonstrated great promise in developmental research. It allows researchers to take into consideration both the characteristics of item response and measurement error in longitudinal trajectory analysis, which improves the reliability and validity of the…
Descriptors: Item Response Theory, Change, Adolescents, Social Isolation
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Hoijtink, Herbert – Multivariate Behavioral Research, 2001
Discusses , in the context of confirmatory latent class analysis, model selection using Bayes factors and (pseudo) likelihood ratio statistics. Uses a small simulation study to show that in this context, Bayes factors and the pseudo likelihood ratio statistics have the best properties. (SLD)
Descriptors: Bayesian Statistics, Mathematical Models
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Poon, Wai-Yin; Tang, Fung-Chu – Multivariate Behavioral Research, 2002
Studied a multiple group model with ordinal categorical observed variables that are manifestations of underlying normal variables. Proposed to apply across-group stochastic constraints on thresholds to identify the model and used a Bayesian approach to analyze the model. Simulation findings and the analysis of a real data set show the usefulness…
Descriptors: Bayesian Statistics, Models, Multivariate Analysis, Simulation
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Loken, Eric – Multivariate Behavioral Research, 2004
Mixture models are appropriate for data that arise from a set of qualitatively different subpopulations. In this study, latent class analysis was applied to observational data from a laboratory assessment of infant temperament at four months of age. The EM algorithm was used to fit the models, and the Bayesian method of posterior predictive checks…
Descriptors: Probability, Personality, Infants, Bayesian Statistics
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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