Date of Award

4-2012

Degree Name

Doctor of Philosophy

Department

Educational Leadership, Research and Technology

First Advisor

Dr. Louann Bierlein Palmer

Keywords

optometry; predictors; academic; success; achievement; students

Abstract

Optometry school admissions are very competitive. With more applicants than available slots, admission committees must choose those students whom they feel will be successful graduates. Previous studies in the health profession schools have demonstrated that the predictors of academic achievement are preadmission science grade point average (GPA), preadmission cumulative GPA, and standardized entrance tests. However, with the advent of the Optometry Centralized Applications Service (OptomCAS), no research could be found on the predictors of success as it relates to those variables. This study, therefore, evaluates the ability of these variables to predict the GPA and graduation of students in the Michigan College of Optometry (MCO). The study employs a non-experimental, ex post facto research design which covers students who entered the MCO from 1995 through 2004. The sample size includes 322 subjects who took 13,203 courses. All courses taken by students are categorized into the OptomCAS variables. Using linear regression and logistic regression, these variables are evaluated for the predictability of academic success. Using linear regression analysis on the interval data, the study finds that the Optometry Admission Test (OAT) Academic Average and, based upon the year of the student, the OAT Reading Comprehension and pre-optometry GPA in math, biology, and non-science, are predictors for first, second, third, and fourth year optometry GPAs. In addition, the study reveals that both OAT scores and undergraduate course GPAs are better predictors of first, second, third, and fourth year optometry GPAs than undergraduate course GPAs alone. Thus, the standardized OAT does add value to the selection process. In reference to predicting the nominal variable, graduation, logistic regression revealed different findings. Using a 50% cut off for evaluating graduation, the logistic regression equation sensitivity for the study was 96.3% and the specificity was 0%. Therefore, the logistic regression equation did not reveal any variables which were predictive of identifying individuals who will not graduate from the MCO. Overall, the results of the study increase the current knowledge on optometry school selection criteria variables and the importance of the OptomCAS variables. It also provides optometry admission committees additional tools to improve their selection process.

Access Setting

Dissertation-Open Access

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