ERIC Number: EJ745905
Record Type: Journal
Publication Date: 2006
Reference Count: 32
Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression
Anderson, Joan L.
College and University, v81 n4 p19-29 2006
Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models yielded similar results, indicating that the combination of the following factors could predict 10-12 percent of the variance in graduate grade point average: (1) college to which the student was applying; (2) marital status; (3) gender; (4) GRE verbal and analytical scores; and (5) residency region of students. (Contains 8 tables.)
Descriptors: Graduate Students, Grade Point Average, Predictor Variables, Success, Multiple Regression Analysis, Statistical Analysis, Graduate Study, Admission (School), Models, Academic Achievement, College Applicants, College Choice, Marital Status, Scores, Place of Residence, College Entrance Examinations, Student Characteristics, Gender Differences, Artificial Intelligence
American Association of Collegiate Registrars and Admissions Officers (AACRAO). One Dupont Circle NW, Suite 520, Washington, DC 20036. Tel: 202-293-9161; Fax: 202-872-8857; e-mail: email@example.com; Web site: http://www.aacrao.org/publications/.
Publication Type: Journal Articles; Reports - Evaluative
Education Level: Higher Education
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Graduate Record Examinations