Purpose The Korea Medical Licensing Exam (KMLE) typically contains a large number of items. The purpose of this study was to investigate whether there is a difference in the cut score between evaluating all items of the exam and evaluating only some items when conducting standard-setting.
Methods We divided the item sets that appeared on 3 recent KMLEs for the past 3 years into 4 subsets of each year of 25% each based on their item content categories, discrimination index, and difficulty index. The entire panel of 15 members assessed all the items (360 items, 100%) of the year 2017. In split-half set 1, each item set contained 184 (51%) items of year 2018 and each set from split-half set 2 contained 182 (51%) items of the year 2019 using the same method. We used the modified Angoff, modified Ebel, and Hofstee methods in the standard-setting process.
Results Less than a 1% cut score difference was observed when the same method was used to stratify item subsets containing 25%, 51%, or 100% of the entire set. When rating fewer items, higher rater reliability was observed.
Conclusion When the entire item set was divided into equivalent subsets, assessing the exam using a portion of the item set (90 out of 360 items) yielded similar cut scores to those derived using the entire item set. There was a higher correlation between panelists’ individual assessments and the overall assessments.
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Purpose This study aimed to identify the best way of developing equivalent item sets and to propose a stable and effective management plan for periodical licensing examinations.
Methods Five pre-equated item sets were developed based on the predicted correct answer rate of each item using linear programming. These pre-equated item sets were compared to the ones that were developed with a random item selection method based on the actual correct answer rate (ACAR) and difficulty from item response theory (IRT). The results with and without common items were also compared in the same way. ACAR and the IRT difficulty were used to determine whether there was a significant difference between the pre-equating conditions.
Results There was a statistically significant difference in IRT difficulty among the results from different pre-equated conditions. The predicted correct answer rate was divided using 2 or 3 difficulty categories, and the ACAR and IRT difficulty parameters of the 5 item sets were equally constructed. Comparing the item set conditions with and without common items, including common items did not make a significant contribution to the equating of the 5 item sets.
Conclusion This study suggested that the linear programming method is applicable to construct equated-item sets that reflect each content area. The suggested best method to construct equated item sets is to divide the predicted correct answer rate using 2 or 3 difficulty categories, regardless of common items. If pre-equated item sets are required to construct a test based on the actual data, several methods should be considered by simulation studies to determine which is optimal before administering a real test.
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