.png){kind=icon}
.png){kind=icon}
Return
Connections Between Graphical Gaussian Models and Factor Analysis
.png){kind=icon}
PRE
AI
DOI：10.1080/00273170903504851.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
SINGLE-FACTOR MODEL
TESTS
IDENTIFICATION
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
M
IF:
3.5
Papers:
1.8K
Citations:
9.4K