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- W2045109291 abstract "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 predictor variables are nonnormally distributed. The nonnormal predictor distribution is approximated by a finite mixture distribution. We conduct a simulation study that demonstrates the advantages of the proposed Bayesian model over contemporary approaches (Latent Moderated Structural Equations [LMS], Quasi-Maximum-Likelihood [QML], and the extended unconstrained approach) when the latent predictor variables follow a nonnormal distribution. The conventional approaches show biased estimates of the nonlinear effects; the proposed Bayesian model provides unbiased estimates. We present an empirical example from work and stress research and provide syntax for substantive researchers. Advantages and limitations of the new model are discussed." @default.
- W2045109291 created "2016-06-24" @default.
- W2045109291 creator A5068482322 @default.
- W2045109291 creator A5086840674 @default.
- W2045109291 date "2012-09-01" @default.
- W2045109291 modified "2023-10-06" @default.
- W2045109291 title "A Bayesian Model For The Estimation Of Latent Interaction And Quadratic Effects When Latent Variables Are Non-Normally Distributed" @default.
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- W2045109291 doi "https://doi.org/10.1080/00273171.2012.715560" @default.
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