ERIC Number: ED397726
Record Type: Non-Journal
Publication Date: 1996-May
Pages: 23
Abstractor: N/A
ISBN: N/A
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EISSN: N/A
Available Date: N/A
Identifying Students at Risk: Utilizing Traditional and Non-Traditional Data Sources. AIR 1996 Annual Forum Paper.
Huesman, Ronald L., Jr.; And Others
A study tracked 3,192 University of Iowa freshmen through their first year and into their second year on campus. Logistic regression analyses using multiple data sources (admissions and registrar files, a standardized entrance test (the American College Testing Program Assessment) student profile section, an entering freshman survey) were conducted to determine models of student persistence at two points: freshman year spring re-enrollment and sophomore year fall re-enrollment. Two relatively successful models for predicting sophomore persistence were derived, although both models predicted correctly rather small percentages of non-persisters. A followup study on the reasons given for withdrawal by false-positives in the model is recommended. For this sample, it was found that modeling persistence was related to college-level academic indicators; the only non-academic factor to enter the prediction equation was students' perceived need for financial aid. (Contains 17 references.) (MSE)
Descriptors: Academic Achievement, Academic Persistence, College Entrance Examinations, College Freshmen, College Sophomores, Dropout Characteristics, Dropout Prevention, High Risk Students, Higher Education, Identification, Information Sources, Institutional Research, Models, Predictor Variables, Student Financial Aid
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A