Please use this identifier to cite or link to this item:
|Title:||Connections between graphical gaussian models and factor analysis|
McDonald, J. W.
|Publisher:||Psychology Press Ltd/Taylor & Francis|
|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.|
|Description:||WOS:000276098300005 (Nº de Acesso Web of Science)|
|Publisher version:||The definitive version is available at: http://dx.doi.org/10.1080/00273170903504851|
|Appears in Collections:||BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica|
Files in This Item:
|publisher_version_Multivariate_Behavioral_Research2010.pdf||328.23 kB||Adobe PDF||View/Open Request a copy|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.