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Using Logistic Regression to Identify New “At-Risk” Freshmen

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  • Jerry L. Nichols
  • Paul M. Orehovec
  • Scott Ingold

Abstract

Retention and graduation rates at institutions of higher education receive a great deal of attention from a wide range of constituents. The search to identify students who are attrition risks early in their collegiate careers often is elusive. More often, practitioners rely on the random initiation of program strategies aimed at improving retention and graduation rates without any viable research base. To address this issue, a logit model was developed using historical data to identify characteristics inherent in a student's decision to withdraw from college. Once the model was derived, predicted probabilities of retention for an incoming class were computed.

Suggested Citation

  • Jerry L. Nichols & Paul M. Orehovec & Scott Ingold, 1998. "Using Logistic Regression to Identify New “At-Risk” Freshmen," Journal of Marketing for Higher Education, Taylor & Francis Journals, vol. 9(1), pages 25-37, March.
    • Handle: RePEc:taf:jmkthe:v:9:y:1998:i:1:p:25-37
    DOI: 10.1300/J050v09n01_03
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