Vertaile menetelmiä
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| Kasvusekamalli (GMM)× | Eksploratiivinen faktorianalyysi (EFA)× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Latent structure | Latent structure |
| Syntyvuosi≠ | 1999 | — |
| Kehittäjä≠ | Bengt O. Muthén & Kerby Shedden | — |
| Tyyppi≠ | Latent class / longitudinal growth model | Latent variable / dimension reduction |
| Alkuperäislähde≠ | Muthén, B. O. & Shedden, K. (1999). Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55(2), 463–469. DOI ↗ | 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 ↗ |
| Rinnakkaisnimet≠ | Büyüme Karışım Modeli (Growth Mixture Model — GMM), GMM, latent class growth analysis extension, mixture latent growth curve model | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | The Growth Mixture Model, introduced by Muthén and Shedden in 1999, is a longitudinal latent variable method that identifies distinct subpopulations — latent trajectory classes — each following its own growth curve over time. It extends the standard Latent Growth Curve (LGC) model by allowing the sample to be composed of an unknown mixture of classes with different intercepts, slopes, and variance structures. | 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. |
| ScholarGateAineisto ↗ |
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