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

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Title: Impact of Preadmission Variables on USMLE Step 1 and Step 2 Performance
Language: English
Authors: Kleshinski, James, Khuder, Sadik A., Shapiro, Joseph I., Gold, Jeffrey P.
Source: Advances in Health Sciences Education. Mar 2009 14(1):69-78.
Availability: Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Peer Reviewed: Y
Page Count: 10
Publication Date: 2009
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Descriptors: Predictor Variables, College Admission, Medical Schools, Licensing Examinations (Professions), Regression (Statistics), Grade Point Average, Academic Achievement, College Choice, Race, Age, Biological Sciences
Geographic Terms: United States
Assessment and Survey Identifiers: Medical College Admission Test, United States Medical Licensing Examination
DOI: 10.1007/s10459-007-9087-x
ISSN: 1382-4996
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.
Abstractor: As Provided
Entry Date: 2009
Accession Number: EJ828779
Database: ERIC
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– Name: Abstract
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  Data: 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.
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      Pagination:
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    Subjects:
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: College Admission
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      – SubjectFull: Medical Schools
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      – TitleFull: Impact of Preadmission Variables on USMLE Step 1 and Step 2 Performance
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