Connections between graphical Gaussian models and factor analysis
Connections between graphical Gaussian models and factor analysis
Connections between graphical Gaussian models and classical single-factor models are obtained by parameterizing the single-factor model as a graphical Gaussian model. Models are represented by independence graphs, and associations between each manifest variable and the latent factor are measured by factor partial correlations. Power calculations for the single-factor graphical Gaussian model are facilitated by expressing the manifest partial correlations as functions of the factor partial correlations. The power of selecting a graphical Gaussian model with an association structure between manifest variables compatible with a single-factor model is investigated. The results are illustrated using 2 examples: the 1st is a hypothetical factor model with parallel measures. The 2nd uses data from the British Household Panel Survey on job satisfaction.
135-152
Salgueiro, M. Fátima
79450d95-f7a3-4695-9aa0-b2b18c4dc3ac
Smith, Peter
961a01a3-bf4c-43ca-9599-5be4fd5d3940
McDonald, John
ec27b570-0e59-4a8c-ac64-512bed4d7341
25 February 2010
Salgueiro, M. Fátima
79450d95-f7a3-4695-9aa0-b2b18c4dc3ac
Smith, Peter
961a01a3-bf4c-43ca-9599-5be4fd5d3940
McDonald, John
ec27b570-0e59-4a8c-ac64-512bed4d7341
Salgueiro, M. Fátima, Smith, Peter and McDonald, John
(2010)
Connections between graphical Gaussian models and factor analysis.
Multivariate Behavioral Research, 45 (1), Spring Issue, .
(doi:10.1080/00273170903504851).
Abstract
Connections between graphical Gaussian models and classical single-factor models are obtained by parameterizing the single-factor model as a graphical Gaussian model. Models are represented by independence graphs, and associations between each manifest variable and the latent factor are measured by factor partial correlations. Power calculations for the single-factor graphical Gaussian model are facilitated by expressing the manifest partial correlations as functions of the factor partial correlations. The power of selecting a graphical Gaussian model with an association structure between manifest variables compatible with a single-factor model is investigated. The results are illustrated using 2 examples: the 1st is a hypothetical factor model with parallel measures. The 2nd uses data from the British Household Panel Survey on job satisfaction.
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Published date: 25 February 2010
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Local EPrints ID: 154865
URI: http://eprints.soton.ac.uk/id/eprint/154865
PURE UUID: 24b77fa5-a5cd-4292-b34b-78851346d537
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Date deposited: 26 May 2010 10:57
Last modified: 14 Mar 2024 02:35
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Author:
M. Fátima Salgueiro
Author:
John McDonald
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