השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| Multilevel Differential Item Functioning× | תיאוריית תגובת פריט (IRT)× | |
|---|---|---|
| תחום | פסיכומטריה | פסיכומטריה |
| משפחה | Latent structure | Latent structure |
| שנת המקור≠ | 2001 | 1952–1968 |
| הוגה השיטה≠ | Kamata (2001) and subsequent multilevel IRT/DIF literature | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| סוג≠ | Bias detection / multilevel measurement model | Probabilistic measurement model |
| מקור מכונן≠ | French, B. F., & Finch, W. H. (2008). Multigroup confirmatory factor analysis: Locating the invariant referent sets. Structural Equation Modeling: A Multidisciplinary Journal, 15(1), 96–113. DOI ↗ | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| כינויים | multilevel DIF, hierarchical DIF analysis, cross-level DIF, ML-DIF | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| קשורות | 5 | 5 |
| תקציר≠ | Multilevel DIF analysis detects whether individual test or survey items function differently across groups when respondents are clustered within higher-level units — such as students nested in schools, employees in organizations, or patients in clinics. By accounting for hierarchical data structure, it separates genuine item bias from artificial DIF signals caused by ignoring clustering. | 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 ↗ |
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