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ERIC Number: EJ1454679
Record Type: Journal
Publication Date: 2024
Pages: 14
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0895-7347
EISSN: EISSN-1532-4818
Available Date: N/A
IRT Characteristic Curve Linking Methods Weighted by Information for Mixed-Format Tests
Shaojie Wang; Won-Chan Lee; Minqiang Zhang; Lixin Yuan
Applied Measurement in Education, v37 n4 p377-390 2024
To reduce the impact of parameter estimation errors on IRT linking results, recent work introduced two information-weighted characteristic curve methods for dichotomous items. These two methods showed outstanding performance in both simulation and pseudo-form pseudo-group analysis. The current study expands upon the concept of information weighting in the context of linking mixed-format tests. Three new linking methods were proposed, including category-information-weighted characteristic curve (CWCC), item-information-weighted characteristic curve (IWCC), and test-information-weighted characteristic curve (TWCC) methods. Both a simulation study and a pseudo-form pseudo-group analysis were conducted to evaluate their relative performances under the non-equivalent groups with anchor test design. In general, IWCC and TWCC outperformed their respective counterparts, whereas the advantage of CWCC was not readily apparent. Among the three new methods, IWCC and TWCC showed better performance. Practical recommendations and future directions are discussed.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A