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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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Beckstead, Jason W. – Multivariate Behavioral Research, 2012
The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…
Descriptors: Multiple Regression Analysis, Predictor Variables, Factor Analysis, Structural Equation Models
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Pek, Jolynn; Sterba, Sonya K.; Kok, Bethany E.; Bauer, Daniel J. – Multivariate Behavioral Research, 2009
The graphical presentation of any scientific finding enhances its description, interpretation, and evaluation. Research involving latent variables is no exception, especially when potential nonlinear effects are suspect. This article has multiple aims. First, it provides a nontechnical overview of a semiparametric approach to modeling nonlinear…
Descriptors: Structural Equation Models, Cognitive Processes, Social Sciences, Evaluation
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Reichardt, Charles S. – Multivariate Behavioral Research, 2011
Maxwell, Cole, and Mitchell (2011) demonstrated that simple structural equation models, when used with cross-sectional data, generally produce biased estimates of meditated effects. I extend those results by showing how simple structural equation models can produce biased estimates of meditated effects when used even with longitudinal data. Even…
Descriptors: Structural Equation Models, Statistical Data, Longitudinal Studies, Error of Measurement
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Coffman, Donna L.; MacCallum, Robert C. – Multivariate Behavioral Research, 2005
The biasing effects of measurement error in path analysis models can be overcome by the use of latent variable models. In cases where path analysis is used in practice, it is often possible to use parcels as indicators of a latent variable. The purpose of the current study was to compare latent variable models in which parcels were used as…
Descriptors: Measurement, Error of Measurement, Path Analysis, Structural Equation Models
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Whittaker, Tiffany A.; Stapleton, Laura M. – Multivariate Behavioral Research, 2006
Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each…
Descriptors: Multivariate Analysis, Selection, Structural Equation Models, Evaluation Methods