השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל ראָש רב-שכבתי× | תיאוריית תגובת פריט (IRT)× | |
|---|---|---|
| תחום | פסיכומטריה | פסיכומטריה |
| משפחה | Latent structure | Latent structure |
| שנת המקור≠ | 1997 | 1952–1968 |
| הוגה השיטה≠ | Adams, Wilson & Wu | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| סוג≠ | Hierarchical item response model | Probabilistic measurement model |
| מקור מכונן≠ | 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 ↗ | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| כינויים | hierarchical Rasch model, random-effects Rasch model, multilevel IRT Rasch, MRCML model | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| קשורות | 5 | 5 |
| תקציר≠ | 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. | Item response theory models the probability that a respondent answers an item correctly (or endorses it) as a function of the respondent's latent trait level and the item's own statistical properties — difficulty, discrimination, and guessing. Unlike classical test theory, IRT places persons and items on the same scale, yielding measurement that is sample-independent for items and test-independent for persons. |
| ScholarGateמערך נתונים ↗ |
|
|