Salīdzināt metodes

	Daudzlīmeņu eksploratīvā faktoru analīze (ML-EFA)	Bifaktora modelis (vispārējie un specifiskie faktori)
Nozare	Psihometrija	Psihometrija
Saime	Latent structure	Latent structure
Izcelsmes gads	1994	1937
Autors	Bengt O. Muthén	Holzinger & Swineford (1937); modern revival by Reise (2012)
Tips	Latent variable / multilevel dimension reduction	Confirmatory latent variable model
Pirmavots	Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398.	Reise, S. P. (2012). The Rediscovery of Bifactor Measurement Models. Multivariate Behavioral Research, 47(5), 667–696.
Citi nosaukumi	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
Saistītās	3	6
Kopsavilkums	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.