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KMID : 1038320180150010026
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보건의료교육평가
2018 Volume.15 No. 1 p.26 ~ p.26
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Liner programming method to construct equated item sets for implementation of periodical computer-based testing of Korean medical licensing examination
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Seo Dong-Gi
Kim Myeong-Gi
Kim Na-Hui
Shin Hye-Sook
Kim Hyun-Jung
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Abstract
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Purpose: This study aimed to find the best way of developing equivalent item sets and to propose a stable and effective management plan for the periodical licensing examinations.
Method: Five pre-equated item sets were developed based on the predicted correct answer rate of each item by using linear programming. These pre-equated item sets were compared to the ones that were developed with random item selection method based on the actual answer rate and difficulty from item response theory (IRT). Also, the results with and without common items were compared in the same way. ACAR and the IRT difficulty was used to determine whether there is a significant difference between pre-equating conditions.
Results: There was a statistically significant difference in IRT difficulty among the results from different pre-equated conditions. As predicted correct answer rate was divided into 2 or 3 difficulty boundaries, the actual answer rate and IRT difficulty parameters of the 5 item sets were equally constructed. Comparing item sets conditions with common items and without common items, including common items did not contribute much for the equating of 5 item sets.
Conclusion: This study suggested the linear programming method is applicable to construct equated-item sets that reflect each content area. The best method to construct equated item sets suggested is to divide the predicted correct answer rate into 2 or 3 difficulty boundaries regardless of common items. If pre-equated item sets are required to construct a test based on the actual data, several optimal methods should be considered by simulation studies before administrating a real test.
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KEYWORD
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Korea Medical Licensing Examination (KMLE), periodical examination, linear programming (LP), predicted correct answer rate (PCAR), actual answer rate (ACAR)
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