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| Multilevel Exploratory Factor Analysis (ML-EFA)× | Bifaktormodell (allgemeine und spezifische Faktoren)× | |
|---|---|---|
| Fachgebiet | Psychometrie | Psychometrie |
| Familie | Latent structure | Latent structure |
| Entstehungsjahr≠ | 1994 | 1937 |
| Urheber≠ | Bengt O. Muthén | Holzinger & Swineford (1937); modern revival by Reise (2012) |
| Typ≠ | Latent variable / multilevel dimension reduction | Confirmatory latent variable model |
| Wegweisende Quelle≠ | Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. DOI ↗ | Reise, S. P. (2012). The Rediscovery of Bifactor Measurement Models. Multivariate Behavioral Research, 47(5), 667–696. DOI ↗ |
| Aliasnamen | ML-EFA, multilevel factor analysis, two-level exploratory factor analysis, hierarchical exploratory factor analysis | Bifaktör Modeli — Genel ve Spesifik Faktörler, hierarchical factor model, general-specific factor model, Schmid-Leiman model |
| Verwandt≠ | 3 | 6 |
| Zusammenfassung≠ | Multilevel exploratory factor analysis uncovers latent factor structures simultaneously at two or more levels of a data hierarchy — for example, both within individuals and between groups — without imposing a fixed structure in advance. It is essential whenever survey or test items are collected from respondents nested inside classrooms, organisations, or clinics. | The bifactor measurement model specifies that every indicator loads simultaneously on a single general factor and on one of several specific (group) factors. Formally introduced by Holzinger and Swineford in 1937 and brought into mainstream psychometrics by Reise (2012), it is now the standard tool for evaluating whether a multidimensional scale can legitimately yield a single composite score. |
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