विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बहुस्तरीय राश मॉडल× | विभेदक आइटम कार्यप्रणाली (DIF)× | |
|---|---|---|
| क्षेत्र | मनोमिति | मनोमिति |
| परिवार | Latent structure | Latent structure |
| उद्भव वर्ष≠ | 1997 | 1970s–1993 |
| प्रवर्तक≠ | Adams, Wilson & Wu | William H. Angoff and colleagues (ETS); systematized by Holland & Wainer |
| प्रकार≠ | Hierarchical item response model | Item-level bias detection |
| मौलिक स्रोत≠ | Adams, R. J., Wilson, M. & Wu, M. (1997). Multilevel item response models: An approach to errors in variables regression. Journal of Educational and Behavioral Statistics, 22(1), 47–76. DOI ↗ | Holland, P. W. & Wainer, H. (Eds.) (1993). Differential Item Functioning. Lawrence Erlbaum Associates. ISBN: 978-0805809589 |
| उपनाम | hierarchical Rasch model, random-effects Rasch model, multilevel IRT Rasch, MRCML model | DIF, item bias analysis, measurement non-equivalence, item-level measurement bias |
| संबंधित | 5 | 5 |
| सारांश≠ | The multilevel Rasch model extends the standard Rasch model to data with a nested structure — for example, students within classrooms within schools — by embedding person ability parameters inside a hierarchical linear model. It yields item difficulty estimates on a logit scale while simultaneously partitioning person-ability variance across cluster levels and correcting standard errors for non-independence. | Differential item functioning identifies test or survey items that behave differently for examinees from different groups — such as gender, ethnicity, or language background — after controlling for the underlying ability or trait being measured. DIF analysis is essential for fairness evaluation in educational testing and psychological scale development. |
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