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Journal Article

Connections between graphical Gaussian models and factor analysis

Authors

Publication date

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

45 (1):135-152

DOI

http://dx.doi.org/10.1080/00273170903504851

Notes

Web of Knowledge alert; Online in A/S except current year; not held in Res Lib - bibliographic reference only


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