title: Connections Between Graphical Gaussian Models and Factor Analysis.
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.
authors.affiliation: a ISCTE Business School and UNIDE Instituto Universitário de Lisboa.
authors.forename: M Fátima
authors.initials: MF
authors.lastname: Salgueiro
authors.affiliation: b University of Southampton.
authors.forename: Peter W F
authors.initials: PW
authors.lastname: Smith
authors.affiliation: c Institute of Education.
authors.forename: John W
authors.initials: JW
authors.lastname: McDonald