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| Analisi Fattoriale Confermativa (AFC)× | Confirmatory factor analysis× | Analisi Fattoriale Esplorativa (AFE)× | |
|---|---|---|---|
| Campo≠ | Statistica | Psicometria | Statistica |
| Famiglia | Latent structure | Latent structure | Latent structure |
| Anno di origine≠ | 1969 | 1969 | — |
| Ideatore≠ | Karl Jöreskog | Karl Gustav Jöreskog | — |
| Tipo≠ | Confirmatory latent variable model | Hypothesis-testing latent variable model | Latent variable / dimension reduction |
| Fonte seminale≠ | Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363 | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ | Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗ |
| Alias≠ | Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model | CFA, confirmatory FA, measurement model, restricted factor analysis | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Correlati | 4 | 4 | 4 |
| Sintesi≠ | Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships. | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. | Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance. |
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