Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Kasvusekamalli (GMM)× | Latenttilaluokka-analyysi (LCA)× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Latent structure | Latent structure |
| Syntyvuosi≠ | 1999 | 1950 |
| Kehittäjä≠ | Bengt O. Muthén & Kerby Shedden | Paul F. Lazarsfeld |
| Tyyppi≠ | Latent class / longitudinal growth model | Latent variable / probabilistic clustering |
| Alkuperäislähde≠ | Muthén, B. O. & Shedden, K. (1999). Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55(2), 463–469. DOI ↗ | Hagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press. ISBN: 978-0521594516 |
| Rinnakkaisnimet≠ | Büyüme Karışım Modeli (Growth Mixture Model — GMM), GMM, latent class growth analysis extension, mixture latent growth curve model | Gizil Sınıf Analizi (LCA), latent class model, latent structure analysis |
| Liittyvät≠ | 5 | 3 |
| 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. | Latent class analysis is a probabilistic model-based clustering technique that identifies unobserved subgroups — latent classes — within a population on the basis of patterns of categorical, binary, or ordinal indicator responses. Originating in sociological measurement theory with Lazarsfeld's latent structure work around 1950 and formalised computationally by Goodman in the 1970s, it is widely used in the social, health, and behavioural sciences to reveal hidden population heterogeneity. |
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