Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfumo wa Mikopo Sehemu (PCM / GPCM)× | Uchambuzi wa Utendaji Tofauti wa Kipengee (DIF)× | |
|---|---|---|
| Nyanja | Saikometriki | Saikometriki |
| Familia | Latent structure | Latent structure |
| Mwaka wa asili≠ | 1982 | 1988 |
| Mwanzilishi≠ | Geoff N. Masters (PCM, 1982); Eiji Muraki (GPCM, 1992) | Paul W. Holland & Dorothy T. Thayer (Mantel-Haenszel approach, 1988) |
| Aina≠ | Item Response Theory / Polytomous IRT | Item-level fairness / measurement equivalence analysis |
| Chanzo asilia≠ | Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149–174. DOI ↗ | Holland, P. W. & Thayer, D. T. (1988). Differential Item Performance and the Mantel-Haenszel Procedure. ETS Research Report Series. link ↗ |
| Majina mbadala≠ | Kısmi Kredi Modeli (PCM / GPCM), Generalized Partial Credit Model, GPCM, PCM | Madde Yanlılık Analizi (DIF — Differential Item Functioning), item bias analysis, Mantel-Haenszel DIF, Lord chi-square DIF |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | 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. | Differential Item Functioning analysis examines whether examinees from different groups — such as gender, ethnicity, or language background — who have the same underlying ability respond differently to a test item. First formalised by Holland and Thayer in 1988 via the Mantel-Haenszel procedure, it is the principal tool in modern test development for detecting and removing item bias. |
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