Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| SIBTEST× | DINA mudel× | Necessary Condition Analysis× | |
|---|---|---|---|
| Valdkond | Psühhomeetria | Psühhomeetria | Psühhomeetria |
| Perekond | Latent structure | Latent structure | Latent structure |
| Tekkeaasta≠ | 1993 | 2001 | 2016 |
| Looja≠ | Richard Shealy, William F. Stout | Brian Junker, Klaas Sijtsma | Jan Dul |
| Tüüp≠ | Differential item functioning (DIF) assessment | Discrete latent class model | Set-theoretic configurational analysis |
| Algallikas≠ | Shealy, R., & Stout, W. F. (1993). A model-based standardization approach that separates true bias/DIF from group differences and detects test bias/DTF. Psychometrika, 58(2), 159-194. DOI ↗ | Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25(3), 258-272. DOI ↗ | Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of "necessary but not sufficient" causality. Organizational Research Methods, 19(1), 10-52. DOI ↗ |
| Rööpnimetused≠ | — | DINA | NCA |
| Seotud≠ | 5 | 4 | 5 |
| Kokkuvõte≠ | SIBTEST (Simultaneous Item Bias Test) is a non-parametric method for detecting differential item functioning (DIF) and differential test functioning (DTF) developed by Shealy and Stout (1993). Unlike parametric approaches, SIBTEST does not assume a particular item response model and directly tests whether groups differ in their probability of correct responses at equal levels of overall ability. | The DINA Model (Deterministic Inputs, Noisy Outputs) is a cognitive diagnostic model developed by Junker and Sijtsma (2001) that classifies examinees into latent skill classes based on their item response patterns. DINA assumes a deterministic relationship between skill mastery and correct responses, with probabilistic error accounting for guessing and slips. | Necessary Condition Analysis (NCA) is a set-theoretic method developed by Dul (2016) that identifies conditions necessary (but not necessarily sufficient) for an outcome to occur. Unlike regression, which estimates average effects, NCA identifies absolute thresholds: conditions that must be present at a certain level for the outcome to be possible, regardless of other factors. |
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