Research in Higher Education

, Volume 43, Issue 3, pp 259–293 | Cite as

The Use and Interpretation of Logistic Regression in Higher Education Journals: 1988–1999

  • Chao-Ying Joanne Peng
  • Tak-Shing Harry So
  • Frances K. Stage
  • Edward P. St. John
Article

Abstract

This article examines the use and interpretation of logistic regression in three leading higher education research journals from 1988 to 1999. The journals were selected because of their emphasis on research, relevance to higher education issues, broad coverage of research topics, and reputable editorial policies. The term “logistic regression” encompasses logit modeling, probit modeling, and tobit modeling and the significance tests of their estimates. A total of 52 articles were identified as using logistic regression. Our review uncovered an increasingly sophisticated use of logistic regression for a wide range of topics. At the same time, there continues to be confusion over terminology. The sample sizes used did not always achieve a desired level of stability in the parameters estimated. Discussion of results in terms of delta-Ps and marginal probabilities was not always cautionary, according to definitions. The review is concluded with recommendations for journal editors and researchers in formulating appropriate editorial policies and practice for applying the versatile logistic regression technique and in communicating its results with readers of higher education research.

logistic regression logit probit tobit delta-P marginal probability odds ratio higher education research multivariate statistics 

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Copyright information

© Human Sciences Press, Inc. 2002

Authors and Affiliations

  • Chao-Ying Joanne Peng
    • 1
  • Tak-Shing Harry So
    • 2
  • Frances K. Stage
    • 3
  • Edward P. St. John
    • 2
  1. 1.Department of Counseling and Educational Psychology, school of EducationIndiana UniversityBloomington
  2. 2.Indiana University–BloomingtonUSA
  3. 3.New York UniversityUSA

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