Usporedite metode
Pregledajte odabrane metode jednu uz drugu; retci koji se razlikuju su istaknuti.
| Eksploratorna faktorska analiza (EFA)× | Hierarchijsko linearno modeliranje (HLM / Multilevel Modeling)× | |
|---|---|---|
| Područje | Statistika | Statistika |
| Obitelj≠ | Latent structure | Hypothesis test |
| Godina nastanka≠ | — | 1986 |
| Tvorac≠ | — | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| Vrsta≠ | Latent variable / dimension reduction | Parametric nested-data regression |
| Temeljni izvor≠ | 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 ↗ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 |
| Drugi nazivi≠ | common factor analysis, açımlayıcı faktör analizi, factor analysis | HLM, MLM, multilevel modeling, multilevel analysis |
| Srodne | 4 | 4 |
| Sažetak≠ | 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. | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. |
| ScholarGateSkup podataka ↗ |
|
|