Vertaile menetelmiä
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| Osittaisen luottamusmallin (PCM / GPCM) malli× | Eksploratiivinen faktorianalyysi (EFA)× | |
|---|---|---|
| Tieteenala≠ | Psykometriikka | Tilastotiede |
| Menetelmäperhe | Latent structure | Latent structure |
| Syntyvuosi≠ | 1982 | — |
| Kehittäjä≠ | Geoff N. Masters (PCM, 1982); Eiji Muraki (GPCM, 1992) | — |
| Tyyppi≠ | Item Response Theory / Polytomous IRT | Latent variable / dimension reduction |
| Alkuperäislähde≠ | Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149–174. 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≠ | Kısmi Kredi Modeli (PCM / GPCM), Generalized Partial Credit Model, GPCM, PCM | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | The Partial Credit Model is an extension of the Rasch measurement framework designed for ordered polytomous items — items whose responses fall into more than two ordered categories, such as partial-credit tasks in performance assessment or open-ended scoring rubrics. Proposed by Geoff Masters in 1982 and later generalised by Eiji Muraki in 1992, the model estimates a separate threshold (step) parameter for each adjacent-category transition within every item, allowing fine-grained calibration of how much each additional credit level contributes to locating a person on the latent trait. | 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. |
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