Background: Radiologic technology programs train students to be entry level radiologic technologists. These programs have historically had a high rate of attrition. Students selected for the program usually fill a finite number of seats, and every student who fails to complete the radiologic technology program creates a vacancy. Every vacancy results in lost resources (time, educational materials, and lost revenue) by the radiologic technology program, and also results in another qualified applicant being denied the opportunity to attend the program. Therefore, it is important for the right applicants to be selected. Objective: The purpose of this study was to determine which admissions criteria correlate with student acceptance and graduation from a radiologic technology program and then build and validate statistical models to predict student success within the program. Design: A nonexperimental correlative study was selected using historical student data from the Midwestern State University (MSU) radiologic technology program. Bivariate analysis was run to identify admissions criteria, which correlated with both student acceptance and graduation. Predictive statistical models were built using logistic regression. The models were validated by constructing receiver operator characteristic (ROC) curves to determine the sensitivity and specificity of each model. The area under the curves (UAC) was then calculated to determine the C-statistic to validate the models. Results: Chi-square analysis determined that the data from the associate degree program and the bachelor program had a statistically significant degree of difference and separate models were constructed for each program. For the associate degree program (n = 317), the key predictive variables for acceptance were: MSU science GPA, Exp. [beta] = 0.81, p < 0.01; MSU anatomy and physiology grades, Exp. [beta] = 0.17, p < 0.01; MSU medical terminology course grade, Exp. [beta] = -2.02, p < 0.01; and MSU GPA, Exp. [beta] = 1.07, p < 0.01. The results for the associate degree program, the key predictive variables for graduation were: MSU science GPA, Exp. [beta] = 0.88, p < 0.01; RADS 1001 MSU medical terminology course grade, Exp. [beta] = -2.44, p <0.01; MSU Introduction to Radiologic Sciences course grade, [beta] = 1.47, p = 0.04; and MSU GPA, [beta] = 1.36, p < 0.01. The bachelor's degree program (n=266) key predictive variables for acceptance were high school GPA, [beta] = 1.61, p < 0.01; MSU medical terminology course grade, [beta] = -2.89, p < 0.01; MSU Introduction to Radiologic Sciences course grade, [beta] = 3.04, p < 0.01; and MSU GPA [beta] = 1.12, p < 0.01. The bachelor's degree program (n = 266) key predictive variables for graduation were high school GPA, [beta] = 1.41, p < 0.01; MSU medical terminology course grade, [beta] = -3.55, p < 0.01; MSU Introduction to Radiologic Sciences grade, [beta] = 3.70, p < 0.01; and MSU GPA [beta] = 1.34, p < 0.01. Conclusion: The study led to the conclusion that there were significant differences in the admission criteria, which would predict acceptance and graduation between the associate and baccalaureate programs. MSU medical terminology course grade and MSU GPA were the best predictors of acceptance for both programs. MSU medical terminology course grade, MSU Introduction to Radiologic Sciences course grade and MSU GPA were the best predictor of graduation in both programs. High school GPA was also a significant predictor for both acceptance and graduation from the baccalaureate program. The associate degree model had a C-statistic of 0.73 indicating the model has good predictive ability for student acceptance and success. The baccalaureate model had a C-statistic of 0.79 indicating this model had a stronger outcome in predicting student acceptance and success. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]