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| Bestätigende Faktorenanalyse (CFA)× | Hauptkomponentenanalyse× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Maschinelles Lernen |
| Familie≠ | Latent structure | Machine learning |
| Entstehungsjahr≠ | 1969 | 2002 |
| Urheber≠ | Karl Jöreskog | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Typ≠ | Confirmatory latent variable model | Unsupervised dimensionality reduction |
| Wegweisende Quelle≠ | Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363 | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Aliasnamen≠ | Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | 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. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
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