Impact of Preadmission Variables on USMLE Step 1 and Step 2 Performance

Autor/inn/en	Kleshinski, James; Khuder, Sadik A.; Shapiro, Joseph I.; Gold, Jeffrey P.
Titel	Impact of Preadmission Variables on USMLE Step 1 and Step 2 Performance
Quelle	In: Advances in Health Sciences Education, 14 (2009) 1, S.69-78 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1382-4996
DOI	10.1007/s10459-007-9087-x
Schlagwörter	Predictor Variables; College Admission; Medical Schools; Licensing Examinations (Professions); Regression (Statistics); Grade Point Average; Academic Achievement; College Choice; Race; Biological Sciences; United States; Medical College Admission Test; United States Medical Licensing Examination + Suchen Sie Ihr Suchwort? Prädiktor; Hochschulzugang; Hochschulzulassung; Zulassung; Medizinische Ausbildung; Regression; Regressionsanalyse; Schulleistung; Studienortwahl; Rasse; Abstammung; Abwasserbiologie; USA
Abstract	Purpose: To examine the predictive ability of preadmission variables on United States Medical Licensing Examinations (USMLE) step 1 and step 2 performance, incorporating the use of a neural network model. Method: Preadmission data were collected on matriculants from 1998 to 2004. Linear regression analysis was first used to identify predictors of performance on step 1 and step 2. A generalized regression neural network (GRNN) as well as a feed forward neural network (FFNN) was then developed in an effort to more accurately predict step 1 and step 2 scores from these preadmission data. Results: Statistically significant predictors for step 1 and step 2 included science grade point average (SGPA), the biologic science (BS) section of the Medical College Admissions Test (MCAT), college selectivity, race, and age of the applicant. Neural networks were found to predict a significant portion of the variance, and the FFNN demonstrated some superiority over that obtained with linear regression models as well as the GRNN. Conclusions: The results have implications that could impact the selection of applicants to medical school and the neural networks that we developed could be used in a prospective manner. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10