During the selection and retention process in a medical record technology program, it is advantageous to identify those students who will fulfill the program's course requirements, as well as subsequently pass the accreditation examination. Graduates of such a program at Wbstern Kentucky University were studied to identify these demoaraphic and academic indicators. Using a stepwise multiple regression analysis, the variables of high school grade point average (GPA), college GPA, and composite ACT score were found to be significantly related to medical record GPA. Using the maximum Rsquare improvement technique, the variables of high school GPA, college GPA, age entering the program, English ACT score and number of credits at graduation were found to be significantly related to overall college GPA at graduation. Multiple regression analysis indicated that high school GPA and composite ACT score were found to be the best predictors of overall performance on the accreditation exam, though the relationship was not significant. Using the discriminant analysis technique, several variables were found to be significant in determining which students will pass the exam and which students will fail. Based on information age when entering the program, and number of medical record core courses repeated contributed significantly to the model. The model was applied to the actual data with a pass/fail accuracy rate of 82% for all students and a rate of 84% for those students passing the exam. Based on additional information after graduation, the variables of medical record GPA, composite ACT score, age when entering the program, and number of credits received at graduation contributed significantly to the model. The model was applied to the actual data with a pass:fail accuracy rate of 78% for all students and a rate of 89% for those students failing the exam.
| Identifer | oai:union.ndltd.org:WKU/oai:digitalcommons.wku.edu:theses-3816 |
| Date | 01 May 1989 |
| Creators | Sansom, Karen |
| Publisher | TopSCHOLAR® |
| Source Sets | Western Kentucky University Theses |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses & Specialist Projects |
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