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| Bestätigende Faktorenanalyse (CFA)× | Cronbachs Alpha (Reliabilitätsanalyse)× | Hauptkomponentenanalyse× | |
|---|---|---|---|
| Fachgebiet≠ | Statistik | Statistik | Maschinelles Lernen |
| Familie≠ | Latent structure | Latent structure | Machine learning |
| Entstehungsjahr≠ | 1969 | 1951 | 2002 |
| Urheber≠ | Karl Jöreskog | Lee J. Cronbach | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Typ≠ | Confirmatory latent variable model | Reliability / internal consistency coefficient | Unsupervised dimensionality reduction |
| Wegweisende Quelle≠ | Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363 | Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Aliasnamen≠ | Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model | coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha) | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Verwandt≠ | 4 | 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. | Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research. | 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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