The Impact of Moderate Priors For Bayesian Estimation and Testing of Item Factor Analysis Models When Maximum Likelihood is Unsuitable-Reference-Cited by-同舟云学术

The Impact of Moderate Priors For Bayesian Estimation and Testing of Item Factor Analysis Models When Maximum Likelihood is Unsuitable

Published:2018-08-13 Issue:1 Volume:26 Page:80-93
ISSN:1070-5511
Container-title:Structural Equation Modeling: A Multidisciplinary Journal
language:en
Short-container-title:Structural Equation Modeling: A Multidisciplinary Journal

Author:

Bainter Sierra A.¹^ORCID,Forster Daniel E.¹

Affiliation:

1. University of Miami

Publisher

Informa UK Limited

Subject

General Economics, Econometrics and Finance,Sociology and Political Science,Modeling and Simulation,General Decision Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/10705511.2018.1505521

Reference53 articles.

1. Asparouhov, T. & Muthén, B. (2010). Bayesian analysis of latent variable models using Mplus. Retrieved from https://www.statmodel.com/techappen.shtml

2. Bayesian Estimation for Item Factor Analysis Models with Sparse Categorical Indicators

3. PValues for Composite Null Models

4. MCMC estimation and some model-fit analysis of multidimensional IRT models

5. Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm

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