A Comparison between Discrimination Indices and Item-Response Theory Using the Rasch Model in a Clinical Course Written Examination of a Medical School.
- Author:
Jong Cook PARK
1
;
Kwang Sig KIM
Author Information
1. Department of Anesthesiology and Pain Medicine, Jeju National University School of Medicine, Jeju, Korea. pjcook@jejunu.ac.kr
- Publication Type:Original Article
- Keywords:
Item response theory;
Discrimination;
Fit statistics;
Rasch model
- MeSH:
Discrimination (Psychology);
Humans;
Joints;
Schools, Medical
- From:Korean Journal of Medical Education
2012;24(1):15-21
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: The reliability of test is determined by each items' characteristics. Item analysis is achieved by classical test theory and item response theory. The purpose of the study was to compare the discrimination indices with item response theory using the Rasch model. METHODS: Thirty-one 4th-year medical school students participated in the clinical course written examination, which included 22 A-type items and 3 R-type items. Point biserial correlation coefficient (C(pbs)) was compared to method of extreme group (D), biserial correlation coefficient (C(bs)), item-total correlation coefficient (C(it)), and corrected item-total correlation coeffcient (C(cit)). Rasch model was applied to estimate item difficulty and examinee's ability and to calculate item fit statistics using joint maximum likelihood. RESULTS: Explanatory power (r2) of Cpbs is decreased in the following order: C(cit) (1.00), C(it) (0.99), C(bs) (0.94), and D (0.45). The ranges of difficulty logit and standard error and ability logit and standard error were -0.82 to 0.80 and 0.37 to 0.76, -3.69 to 3.19 and 0.45 to 1.03, respectively. Item 9 and 23 have outfit > or =1.3. Student 1, 5, 7, 18, 26, 30, and 32 have fit > or =1.3. CONCLUSION: C(pbs), C(cit), and C(it) are good discrimination parameters. Rasch model can estimate item difficulty parameter and examinee's ability parameter with standard error. The fit statistics can identify bad items and unpredictable examinee's responses.
