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Stochastic search item selection for factor analytic models.
Br J Math Stat Psychol 2014; 67(2):284-303BJ

Abstract

In this paper we implement a Markov chain Monte Carlo algorithm based on the stochastic search variable selection method of George and McCulloch (1993) for identifying promising subsets of manifest variables (items) for factor analysis models. The suggested algorithm is constructed by embedding in the usual factor analysis model a normal mixture prior for the model loadings with latent indicators used to identify not only which manifest variables should be included in the model but also how each manifest variable is associated with each factor. We further extend the suggested algorithm to allow for factor selection. We also develop a detailed procedure for the specification of the prior parameters values based on the practical significance of factor loadings using ideas from the original work of George and McCulloch (1993). A straightforward Gibbs sampler is used to simulate from the joint posterior distribution of all unknown parameters and the subset of variables with the highest posterior probability is selected. The proposed method is illustrated using real and simulated data sets.

Authors+Show Affiliations

Department of Primary Education, University of Ioannina, Greece; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Greece.No affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23837882

Citation

Mavridis, Dimitris, and Ioannis Ntzoufras. "Stochastic Search Item Selection for Factor Analytic Models." The British Journal of Mathematical and Statistical Psychology, vol. 67, no. 2, 2014, pp. 284-303.
Mavridis D, Ntzoufras I. Stochastic search item selection for factor analytic models. Br J Math Stat Psychol. 2014;67(2):284-303.
Mavridis, D., & Ntzoufras, I. (2014). Stochastic search item selection for factor analytic models. The British Journal of Mathematical and Statistical Psychology, 67(2), pp. 284-303. doi:10.1111/bmsp.12019.
Mavridis D, Ntzoufras I. Stochastic Search Item Selection for Factor Analytic Models. Br J Math Stat Psychol. 2014;67(2):284-303. PubMed PMID: 23837882.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Stochastic search item selection for factor analytic models. AU - Mavridis,Dimitris, AU - Ntzoufras,Ioannis, Y1 - 2013/07/10/ PY - 2012/03/26/received PY - 2013/05/17/revised PY - 2013/7/11/entrez PY - 2013/7/11/pubmed PY - 2015/7/7/medline KW - Gibbs sampling KW - Markov Chain Monte Carlo KW - Prior Specification KW - Stochastic Search Variable Selection KW - factor selection SP - 284 EP - 303 JF - The British journal of mathematical and statistical psychology JO - Br J Math Stat Psychol VL - 67 IS - 2 N2 - In this paper we implement a Markov chain Monte Carlo algorithm based on the stochastic search variable selection method of George and McCulloch (1993) for identifying promising subsets of manifest variables (items) for factor analysis models. The suggested algorithm is constructed by embedding in the usual factor analysis model a normal mixture prior for the model loadings with latent indicators used to identify not only which manifest variables should be included in the model but also how each manifest variable is associated with each factor. We further extend the suggested algorithm to allow for factor selection. We also develop a detailed procedure for the specification of the prior parameters values based on the practical significance of factor loadings using ideas from the original work of George and McCulloch (1993). A straightforward Gibbs sampler is used to simulate from the joint posterior distribution of all unknown parameters and the subset of variables with the highest posterior probability is selected. The proposed method is illustrated using real and simulated data sets. SN - 2044-8317 UR - https://www.unboundmedicine.com/medline/citation/23837882/Stochastic_search_item_selection_for_factor_analytic_models_ L2 - https://doi.org/10.1111/bmsp.12019 DB - PRIME DP - Unbound Medicine ER -