Signaling Student Retention with Prematriculation Data

Signaling Student Retention with Prematriculation Data

Logistic regression is employed to develop a model that enhances early identification of freshmen at risk of attrition. Independent variables employed to predict attrition include demographics; high school experiences; and attitudes, opinions, and values as reported on a survey administered during f...

Full description

Saved in:
Bibliographic Details
Published in:NASPA journal Vol. 41; no. 1
Main Authors: Glynn, Joseph G, Sauer, Paul L, Miller, Thomas E
Format: Journal Article
Language:English
Published: 02-12-2003
Subjects:
Academic Persistence
College Freshmen
Dropout Research
Early Identification
High Risk Students
Predictor Variables
Prevention
Student Attrition
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Logistic regression is employed to develop a model that enhances early identification of freshmen at risk of attrition. Independent variables employed to predict attrition include demographics; high school experiences; and attitudes, opinions, and values as reported on a survey administered during freshman orientation. Model and results are presented along with a brief description of program designed to enhance student persistence. (Contains 22 references, 5 tables, 1 figure, and 1 appendix.) (Author/ADT)
ISSN:1559-5455
DOI:10.2202/0027-6014.1304