Connections between single-factor analysis and graphical Gaussian models

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Description/Abstract

The classical single-factor model is parametrized as a graphical Gaussian model. The relationship between the classical parametrization of the single-factor model and this alternative parametrization is derived. This relationship provides extra insights into the single-factor model, which facilitates power calculations. The overall power of the first step of a backward elimination model selection procedure to detect an association structure between manifest variables compatible with a single-factor model is investigated. The results are illustrated using a one-factor congeneric measurement model.