دورية أكاديمية
Two IRT Characteristic Curve Linking Methods Weighted by Information
| العنوان: | Two IRT Characteristic Curve Linking Methods Weighted by Information |
|---|---|
| اللغة: | English |
| المؤلفون: | Wang, Shaojie (ORCID |
| المصدر: | Journal of Educational Measurement. Win 2022 59(4):423-441. |
| الإتاحة: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 19 |
| تاريخ النشر: | 2022 |
| نوع الوثيقة: | Journal Articles Reports - Research |
| Descriptors: | Item Response Theory Error of Measurement Accuracy Monte Carlo Methods Sample Size Test Length Guidelines Comparative Analysis Test Items Item Analysis |
| DOI: | 10.1111/jedm.12315 |
| تدمد: | 0022-0655 1745-3984 |
| مستخلص: | Traditional IRT characteristic curve linking methods ignore parameter estimation errors, which may undermine the accuracy of estimated linking constants. Two new linking methods are proposed that take into account parameter estimation errors. The item- (IWCC) and test-information-weighted characteristic curve (TWCC) methods employ weighting components in the loss function from traditional methods by their corresponding item and test information, respectively. Monte Carlo simulation was conducted to evaluate the performances of the new linking methods and compare them with traditional ones. Ability difference between linking groups, sample size, and test length were manipulated under the common-item nonequivalent groups design. Results showed that the two information-weighted characteristic curve methods outperformed traditional methods, in general. TWCC was found to be more accurate and stable than IWCC. A pseudo-form pseudo-group analysis was also performed, and similar results were observed. Finally, guidelines for practice and future directions are discussed. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| رقم الأكسشن: | EJ1361263 |
| قاعدة البيانات: | ERIC |
Full Text Finder كن أول من يترك تعليقا!