https://cio-wiki.net//index.php?action=history&feed=atom&title=Confirmatory_Factor_Analysis 2026-06-04T10:36:01Z Revision history for this page on the wiki MediaWiki 1.35.1 https://cio-wiki.net//index.php?title=Confirmatory_Factor_Analysis&diff=17500&oldid=prev 2023-09-01T18:10:54Z

<p>Created page with &quot;&#039;&#039;&#039;Confirmatory Factor Analysis (CFA)&#039;&#039;&#039; is a statistical technique used in the field of psychometrics to verify the factor structure of a set of observed variables. Unlike ex...&quot;</p> <p><b>New page</b></p><div>'''Confirmatory Factor Analysis (CFA)''' is a statistical technique used in the field of psychometrics to verify the factor structure of a set of observed variables. Unlike exploratory factor analysis, which allows the data itself to reveal the underlying structure, CFA tests a pre-specified factor model to see how well it fits the observed data.<br /> <br /> <br /> == History ==<br /> The development of Confirmatory Factor Analysis is closely tied to the evolution of Structural Equation Modeling (SEM). Jöreskog was a seminal figure in formalizing the method as a way to validate factor structures suggested by exploratory factor analysis or theoretical constructs.<br /> <br /> <br /> == Key Concepts ==<br /> Factors: Factors are latent variables that explain the correlations or covariances among observed variables.<br /> *Factor Loadings: Factor loadings are the coefficients that indicate the relationship between each observed variable and the underlying factor.<br /> *Error Terms: Error terms account for the variability in observed variables not explained by the factors.<br /> *Model Specification: In CFA, researchers must a priori specify which observed variables are related to which factors. This is typically based on theoretical considerations or previous research.<br /> *Model Estimation: Various methods can be used for estimating the model parameters, such as maximum likelihood, generalized least squares, or weighted least squares.<br /> <br /> <br /> == Model Evaluation ==<br /> *Goodness-of-Fit: Several goodness-of-fit indices exist to evaluate how well the model fits the data, including Chi-square, Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Tucker-Lewis Index (TLI).<br /> *Modification Indices: Modification indices can be inspected to identify paths or constraints that, if changed, would improve the model fit.<br /> <br /> <br /> == Applications ==<br /> *Psychology: CFA is widely used in psychology to confirm the validity of constructs like intelligence, self-esteem, and many others.<br /> *Business: In business, CFA can be used to validate the structure of customer satisfaction surveys, organizational climate scales, and more.<br /> *Healthcare: In healthcare, it is often employed to validate measurement instruments such as health-related quality of life scales.<br /> *Software Tools: Several statistical software packages can perform CFA, including SPSS Amos, Mplus, and R packages like lavaan.<br /> <br /> <br /> == Limitations ==<br /> *Requires large sample sizes for stable and reliable estimates.<br /> *It is confirmatory in nature, meaning it doesn't allow for the discovery of unexpected or novel structures in the data.<br /> *Assumes linear relationships between factors and observed variables.<br /> <br /> <br /> == See Also ==<br /> *[[Exploratory Factor Analysis (EFA)]]<br /> *[[Structural Equation Modeling (SEM)]]<br /> *[[Psychometrics]]<br /> *[[Path Analysis]]<br /> __NOTOC__</div>

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