ERIC Number: EJ828779
Record Type: Journal
Publication Date: 2009-Mar
Abstractor: As Provided
Reference Count: N/A
Impact of Preadmission Variables on USMLE Step 1 and Step 2 Performance
Kleshinski, James; Khuder, Sadik A.; Shapiro, Joseph I.; Gold, Jeffrey P.
Advances in Health Sciences Education, v14 n1 p69-78 Mar 2009
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.
Descriptors: Predictor Variables, College Admission, Medical Schools, Licensing Examinations (Professions), Regression (Statistics), Grade Point Average, Academic Achievement, College Choice, Race, Age, Biological Sciences
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education
Authoring Institution: N/A
Identifiers - Location: United States
Identifiers - Assessments and Surveys: Medical College Admission Test; United States Medical Licensing Examination