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ERIC Number: ED451210
Record Type: Non-Journal
Publication Date: 2001-Feb-2
Pages: 19
Abstractor: N/A
Reference Count: N/A
ISBN: N/A
ISSN: N/A
How To Handle Discrete Dependent Variables in the Univariate Case: A Primer on Logistic Regression.
Brooks, B. Meade
This paper presents an overview of logistic regression and illustrates the method with the data transformations that are conducted. It also discusses the interpretation of logistic regression results. To make the discussion more concrete, an analysis of a data set is presented in which logistic regression is used to predict the likelihood of a college student's withdrawing or failing a course. Logistic regression is a well-suited analysis technique when a dichotomous dependent variable is involved because the logit transformation allows for direct linear comparisons of the effect different indicators have on the outcome. Logistic regression also aids in defining quantitatively the combination of predictors that leads to varying degrees or probabilities of the outcome variable. Applying the approach to identifying high-risk students in educational settings is especially practical. (SLD)
Publication Type: Reports - Descriptive; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A