Statistical Properties of the Single Mediator Model with Latent Variables in the Bayesian Framework
abstract: Statistical mediation analysis has been widely used in the social sciences in order to examine the indirect effects of an independent variable on a dependent variable. The statistical properties of the single mediator model with manifest and latent variables have been studied using simulat...
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ndltd-asu.edu-item-444232018-06-22T03:08:37Z Statistical Properties of the Single Mediator Model with Latent Variables in the Bayesian Framework abstract: Statistical mediation analysis has been widely used in the social sciences in order to examine the indirect effects of an independent variable on a dependent variable. The statistical properties of the single mediator model with manifest and latent variables have been studied using simulation studies. However, the single mediator model with latent variables in the Bayesian framework with various accurate and inaccurate priors for structural and measurement model parameters has yet to be evaluated in a statistical simulation. This dissertation outlines the steps in the estimation of a single mediator model with latent variables as a Bayesian structural equation model (SEM). A Monte Carlo study is carried out in order to examine the statistical properties of point and interval summaries for the mediated effect in the Bayesian latent variable single mediator model with prior distributions with varying degrees of accuracy and informativeness. Bayesian methods with diffuse priors have equally good statistical properties as Maximum Likelihood (ML) and the distribution of the product. With accurate informative priors Bayesian methods can increase power up to 25% and decrease interval width up to 24%. With inaccurate informative priors the point summaries of the mediated effect are more biased than ML estimates, and the bias is higher if the inaccuracy occurs in priors for structural parameters than in priors for measurement model parameters. Findings from the Monte Carlo study are generalizable to Bayesian analyses with priors of the same distributional forms that have comparable amounts of (in)accuracy and informativeness to priors evaluated in the Monte Carlo study. Dissertation/Thesis Miocevic, Milica (Author) MacKinnon, David P. (Advisor) Levy, Roy (Advisor) Grimm, Kevin (Committee member) West, Stephen G. (Committee member) Arizona State University (Publisher) Quantitative psychology Bayesian latent variables mediation analysis eng 124 pages Doctoral Dissertation Psychology 2017 Doctoral Dissertation http://hdl.handle.net/2286/R.I.44423 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2017 |
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English |
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Doctoral Thesis |
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Quantitative psychology Bayesian latent variables mediation analysis |
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Quantitative psychology Bayesian latent variables mediation analysis Statistical Properties of the Single Mediator Model with Latent Variables in the Bayesian Framework |
description |
abstract: Statistical mediation analysis has been widely used in the social sciences in order to examine the indirect effects of an independent variable on a dependent variable. The statistical properties of the single mediator model with manifest and latent variables have been studied using simulation studies. However, the single mediator model with latent variables in the Bayesian framework with various accurate and inaccurate priors for structural and measurement model parameters has yet to be evaluated in a statistical simulation. This dissertation outlines the steps in the estimation of a single mediator model with latent variables as a Bayesian structural equation model (SEM). A Monte Carlo study is carried out in order to examine the statistical properties of point and interval summaries for the mediated effect in the Bayesian latent variable single mediator model with prior distributions with varying degrees of accuracy and informativeness. Bayesian methods with diffuse priors have equally good statistical properties as Maximum Likelihood (ML) and the distribution of the product. With accurate informative priors Bayesian methods can increase power up to 25% and decrease interval width up to 24%. With inaccurate informative priors the point summaries of the mediated effect are more biased than ML estimates, and the bias is higher if the inaccuracy occurs in priors for structural parameters than in priors for measurement model parameters. Findings from the Monte Carlo study are generalizable to Bayesian analyses with priors of the same distributional forms that have comparable amounts of (in)accuracy and informativeness to priors evaluated in the Monte Carlo study. === Dissertation/Thesis === Doctoral Dissertation Psychology 2017 |
author2 |
Miocevic, Milica (Author) |
author_facet |
Miocevic, Milica (Author) |
title |
Statistical Properties of the Single Mediator Model with Latent Variables in the Bayesian Framework |
title_short |
Statistical Properties of the Single Mediator Model with Latent Variables in the Bayesian Framework |
title_full |
Statistical Properties of the Single Mediator Model with Latent Variables in the Bayesian Framework |
title_fullStr |
Statistical Properties of the Single Mediator Model with Latent Variables in the Bayesian Framework |
title_full_unstemmed |
Statistical Properties of the Single Mediator Model with Latent Variables in the Bayesian Framework |
title_sort |
statistical properties of the single mediator model with latent variables in the bayesian framework |
publishDate |
2017 |
url |
http://hdl.handle.net/2286/R.I.44423 |
_version_ |
1718701514528129024 |