Adjusted Residuals for Evaluating Conditional Independence in IRT Models for Multistage Adaptive Testing
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| Foilsithe in: | Psychometrika vol. 89, no. 1 (Mar 2024), p. 317 |
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| Príomhchruthaitheoir: | |
| Rannpháirtithe: | , , |
| Foilsithe / Cruthaithe: |
Springer Nature B.V.
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| Ábhair: | |
| Rochtain ar líne: | Citation/Abstract Full Text - PDF |
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Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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MARC
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| 024 | 7 | |a 10.1007/s11336-023-09935-4 |2 doi | |
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| 045 | 2 | |b d20240301 |b d20240331 | |
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| 100 | 1 | |a van Rijn, Peter W. |u ETS Global, Amsterdam, The Netherlands | |
| 245 | 1 | |a Adjusted Residuals for Evaluating Conditional Independence in IRT Models for Multistage Adaptive Testing | |
| 260 | |b Springer Nature B.V. |c Mar 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The key assumption of conditional independence of item responses given latent ability in item response theory (IRT) models is addressed for multistage adaptive testing (MST) designs. Routing decisions in MST designs can cause patterns in the data that are not accounted for by the IRT model. This phenomenon relates to quasi-independence in log-linear models for incomplete contingency tables and impacts certain types of statistical inference based on assumptions on observed and missing data. We demonstrate that generalized residuals for item pair frequencies under IRT models as discussed by Haberman and Sinharay (J Am Stat Assoc 108:1435–1444, 2013. <ext-link xlink:href="https://doi.org/10.1080/01621459.2013.835660" ext-link-type="doi">https://doi.org/10.1080/01621459.2013.835660</ext-link>) are inappropriate for MST data without adjustments. The adjustments are dependent on the MST design, and can quickly become nontrivial as the complexity of the routing increases. However, the adjusted residuals are found to have satisfactory Type I errors in a simulation and illustrated by an application to real MST data from the Programme for International Student Assessment (PISA). Implications and suggestions for statistical inference with MST designs are discussed. | |
| 653 | |a Statistics | ||
| 653 | |a Statistical inference | ||
| 653 | |a Models | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Item Response Theory | ||
| 653 | |a Adaptive Testing | ||
| 700 | 1 | |a Ali, Usama S. |u Educational Testing Service, Sacramento, USA (GRID:grid.286674.9) (ISNI:0000 0004 1936 9051); South Valley University, Qena, Egypt (GRID:grid.412707.7) (ISNI:0000 0004 0621 7833) | |
| 700 | 1 | |a Shin, Hyo Jeong |u Sogang University, Seoul, Republic of Korea (GRID:grid.263736.5) (ISNI:0000 0001 0286 5954) | |
| 700 | 1 | |a Joo, Sean-Hwane |u University of Kansas, Lawrence, USA (GRID:grid.266515.3) (ISNI:0000 0001 2106 0692) | |
| 773 | 0 | |t Psychometrika |g vol. 89, no. 1 (Mar 2024), p. 317 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3049518970/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3049518970/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |