Publication type
Journal Article
Authors
Publication date
June 1, 2010
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.
Published in
Multivariate Behavioral Research
Volume
Volume: 45 (1):135-152
DOI
http://dx.doi.org/10.1080/00273170903504851
Subject
Notes
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