Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Multidimensional Item Response Theory× | Testlet Response Theory× | |
|---|---|---|
| Nyanja | Education | Education |
| Familia | Latent structure | Latent structure |
| Mwaka wa asili≠ | 2009 | 2007 |
| Mwanzilishi≠ | Mark Reckase; foundations in factor analysis of items (Bock, McDonald) | Howard Wainer, Eric Bradlow & Xiaohui Wang |
| Aina≠ | Item response model with multiple latent ability dimensions | Item response model accommodating local dependence within item bundles (testlets) |
| Chanzo asilia≠ | Reckase, M. D. (2009). Multidimensional Item Response Theory. Springer. DOI ↗ | Wainer, H., Bradlow, E. T., & Wang, X. (2007). Testlet Response Theory and Its Applications. Cambridge University Press. ISBN: 9780521681261 |
| Majina mbadala | MIRT, Multidimensional IRT, Compensatory MIRT, Bifactor IRT | TRT, Testlet Models, Random-Effects Testlet Model, Item-Bundle IRT |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | Multidimensional item response theory (MIRT) generalizes IRT to tests that measure more than one latent ability at once. Instead of a single ability θ, each examinee is characterized by a vector of abilities, and each item by a vector of discriminations indicating how strongly it taps each dimension. MIRT unites the logic of item response theory with the structure of factor analysis, letting analysts model, for example, that a word-problem item draws on both reading and mathematics. Synthesized in Reckase's authoritative treatment, it underlies the analysis of complex, multi-skill assessments. | Testlet response theory (TRT) extends item response theory to tests built from testlets — bundles of items sharing a common stimulus, such as several questions about one reading passage. Standard IRT assumes items are conditionally independent given ability, but items within a testlet violate this because they draw on the same passage. TRT adds a testlet-specific random effect that absorbs this local dependence, preventing the overstated precision and biased parameters that result from ignoring it. Developed by Wainer, Bradlow, and Wang, it is widely used wherever passage-based or scenario-based items appear. |
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