Journal article
Convergence Between Cluster Analysis and the Angoff Method for Setting Minimum Passing Scores on Credentialing Examinations
Evaluation & the health professions, v 30(4), pp 362-375
Dec 2007
PMID: 17986670
Abstract
Cluster analysis can be a useful statistical technique for setting minimum passing scores on high-stakes examinations by grouping examinees into homogenous clusters based on their responses to test items. It has been most useful for supplementing data or validating minimum passing scores determined from expert judgment approaches, such as the Ebel and Nedelsky methods. However, there is no evidence supporting how well cluster analysis converges with the modified Angoff method, which is frequently used in medical credentialing. Therefore, the purpose of this study is to investigate the efficacy of cluster analysis for validating Angoff-derived minimum passing scores. Data are from 652 examinees who took a national credentialing examination based on a content-by-process test blueprint. Results indicate a high degree of consistency in minimum passing score estimates derived from the modified Angoff and cluster analysis methods. However, the stability of the estimates from cluster analysis across different samples was modest.
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Details
- Title
- Convergence Between Cluster Analysis and the Angoff Method for Setting Minimum Passing Scores on Credentialing Examinations
- Creators
- Brian Hess - American Board of Internal MedicineRaja G. Subhiyah - National Board of Medical ExaminersCarolyn Giordano - National Board of Medical Examiners
- Publication Details
- Evaluation & the health professions, v 30(4), pp 362-375
- Publisher
- Sage
- Number of pages
- 14
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- MD (Doctor of Medicine) Program
- Web of Science ID
- WOS:000250807600004
- Scopus ID
- 2-s2.0-35648957777
- Other Identifier
- 991021930846604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Health Care Sciences & Services
- Health Policy & Services