Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

Autor/in	Anderson, Joan L.
Titel	Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression
Quelle	In: College and University, 81 (2006) 4, S.19-29 (11 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0010-0889
Schlagwörter	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; Graduate Record Examinations + Suchen Sie Ihr Suchwort? Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Prädiktor; Erfolg; Statistische Analyse; Analogiemodell; Schulleistung; College applications; Studienbewerber; Studienortwahl; Familienstand; Wohnort; Aufnahmeprüfung; Geschlechterkonflikt; Künstliche Intelligenz
Abstract	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.) (Author).
Anmerkungen	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: pubs@aacrao.org; Web site: http://www.aacrao.org/publications/.
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10