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 |
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| 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 |
| 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. |
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| ISSN: | 1382-4996 |
| DOI: | 10.1007/s10459-007-9087-x |
