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Connections between graphical Gaussian models and factor analysis

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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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Citation

Salgueiro, M. Fátima, Smith, Peter and McDonald, John (2010) Connections between graphical Gaussian models and factor analysis Multivariate Behavioral Research, 45, (1), Spring Issue, pp. 135-152. (doi:10.1080/00273170903504851).

More information

Published date: 25 February 2010

Identifiers

Local EPrints ID: 154865
URI: http://eprints.soton.ac.uk/id/eprint/154865
PURE UUID: 24b77fa5-a5cd-4292-b34b-78851346d537
ORCID for Peter Smith: ORCID iD orcid.org/0000-0003-4423-5410

Catalogue record

Date deposited: 26 May 2010 10:57
Last modified: 18 Jul 2017 12:46

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Contributors

Author: M. Fátima Salgueiro
Author: Peter Smith ORCID iD
Author: John McDonald

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