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Salgueiro, M.F., Smith, P.W.F & McDonald, J. W. (2010). Connections between graphical gaussian models and factor analysis. Multivariate Behavioral Research. 45 (1), 135-152
M. D. Salgueiro et al., "Connections between graphical gaussian models and factor analysis", in Multivariate Behavioral Research, vol. 45, no. 1, pp. 135-152, 2010
@article{salgueiro2010_1732201963773, author = "Salgueiro, M.F. and Smith, P.W.F and McDonald, J. W.", title = "Connections between graphical gaussian models and factor analysis", journal = "Multivariate Behavioral Research", year = "2010", volume = "45", number = "1", doi = "10.1080/00273170903504851", pages = "135-152", url = "http://www.tandfonline.com/doi/pdf/10.1080/00273170903504851" }
TY - JOUR TI - Connections between graphical gaussian models and factor analysis T2 - Multivariate Behavioral Research VL - 45 IS - 1 AU - Salgueiro, M.F. AU - Smith, P.W.F AU - McDonald, J. W. PY - 2010 SP - 135-152 SN - 0027-3171 DO - 10.1080/00273170903504851 UR - http://www.tandfonline.com/doi/pdf/10.1080/00273170903504851 AB - 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. ER -