Statistical Criteria for Setting Thresholds in Medical School Admissions

Autor/inn/en	Albanese, Mark A.; Farrell, Philip; Dottl, Susan
Titel	Statistical Criteria for Setting Thresholds in Medical School Admissions
Quelle	In: Advances in Health Sciences Education, 10 (2005) 2, S.89-103 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1382-4996
DOI	10.1007/s10459-004-1122-6
Schlagwörter	Grade Point Average; Medical Schools; Qualifications; College Admission; Admission Criteria; Access to Education; Statistical Analysis; Court Litigation; Predictor Variables; Selection; College Entrance Examinations; Affirmative Action; Cutting Scores; Medical College Admission Test; United States Medical Licensing Examination + Suchen Sie Ihr Suchwort? Medizinische Ausbildung; Qualifikation; Qualifikationsstufe; Hochschulzugang; Hochschulzulassung; Zulassung; Admission; Admission procedures; Zulassungsbedingung; Zulassungsverfahren; Education; Access; Bildung; Zugang; Bildungszugang; Statistische Analyse; Rechtsstreit; Prädiktor; Auslese; Aufnahmeprüfung
Abstract	In 2001, Dr. Jordan Cohen, President of the AAMC, called for medical schools to consider using an Medical College Admission Test (MCAT) threshold to eliminate high-risk applicants from consideration and then to use non-academic qualifications for further consideration. This approach would seem to be consistent with the recent Supreme Court ruling on the University of Michigan admissions cases. Research to support a threshold approach has been reported in many different ways, making comparability problematic. This study examines an assortment of statistical indices that have been used to determine thresholds in undergraduate science grade point average (USGPA) and MCAT sub-scores and total sum and compares them in terms of their sensitivity and specificity for determining Medical Licensing Exam (USMLE Step 1) first time failure. Data for medical school entering classes of 1992-1998 (N=752) from a large Midwestern medical school are used to determine the set of statistics that provide the most useful information for this purpose. The results support plots of risk differences, odds-ratios, sensitivity and specificity for setting cut-scores. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10