Improving Measurement Precision of Test Batteries Using Multidimensional Item Response Models
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| Title: | Improving Measurement Precision of Test Batteries Using Multidimensional Item Response Models |
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| Language: | English |
| Authors: | Wang, Wen-Chung, Chen, Po-Hsi, Cheng, Ying-Yao |
| Source: | Psychological Methods. Mar 2004 9(1):116-136. |
| Availability: | American Psychological Association, 750 First Street, NE, Washington, DC 20002-4242. Tel: 800-374-2721 (Toll Free); Tel: 202-336-5510; TDD/TTY: 202-336-6123; Fax: 202-336-5502; e-mail: journals@apa.org |
| Peer Reviewed: | Y |
| Page Count: | 10 |
| Publication Date: | 2004 |
| Document Type: | Journal Articles Reports - Descriptive |
| Descriptors: | Item Response Theory, Test Items, Testing, Test Validity, Correlation |
| ISSN: | 1082-989X |
| Abstract: | A conventional way to analyze item responses in multiple tests is to apply unidimensional item response models separately, one test at a time. This unidimensional approach, which ignores the correlations between latent traits, yields imprecise measures when tests are short. To resolve this problem, one can use multidimensional item response models that use correlations between latent traits to improve measurement precision of individual latent traits. The improvements are demonstrated using 2 empirical examples. It appears that the multidimensional approach improves measurement precision substantially, especially when tests are short and the number of tests is large. To achieve the same measurement precision, the multidimensional approach needs less than half of the comparable items required for the unidimensional approach. |
| Abstractor: | Author |
| Entry Date: | 2005 |
| Access URL: | https://www.apa.org/journals |
| Accession Number: | EJ685052 |
| Database: | ERIC |
| Abstract: | A conventional way to analyze item responses in multiple tests is to apply unidimensional item response models separately, one test at a time. This unidimensional approach, which ignores the correlations between latent traits, yields imprecise measures when tests are short. To resolve this problem, one can use multidimensional item response models that use correlations between latent traits to improve measurement precision of individual latent traits. The improvements are demonstrated using 2 empirical examples. It appears that the multidimensional approach improves measurement precision substantially, especially when tests are short and the number of tests is large. To achieve the same measurement precision, the multidimensional approach needs less than half of the comparable items required for the unidimensional approach. |
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| ISSN: | 1082-989X |
