A Comparison of Ordinary Least Squares and Logistic Regression

Please use this identifier to cite or link to this item: http://hdl.handle.net/1811/23983

Show simple item record

Files Size Format View
V103N5_118.pdf 1.050Mb PDF View/Open

dc.creator Pohlman, John T. en_US
dc.creator Leitner, Dennis W. en_US
dc.date.accessioned 2006-07-07T18:34:05Z
dc.date.available 2006-07-07T18:34:05Z
dc.date.issued 2003-12 en_US
dc.identifier.citation The Ohio Journal of Science. v103, n5 (December, 2003), 118-125 en_US
dc.identifier.issn 0030-0950 en_US
dc.identifier.uri http://hdl.handle.net/1811/23983
dc.description Author Institution: Department of Educational Psychology, Southern Illinois University en_US
dc.description.abstract This paper compares ordinary least squares (OLS) and logistic regression in terms of their underlying assumptions and results obtained on common data sets. Two data sets were analyzed with both methods. In the respective studies, the dependent variables were binary codes of 1) dropping out of school and 2) attending a private college. Results of both analyses were very similar. Significance tests (alpha = 0.05) produced identical decisions. OLS and logistic predicted values were highly correlated. Predicted classifications on the dependent variable were identical in study 1 and very similar in study 2. Logistic regression yielded more accurate predictions of dependent variable probabilities as measured by the average squared differences between the observed and predicted probabilities. It was concluded that both models can be used to test relationships with a binary criterion. However, logistic regression is superior to OLS at predicting the probability of an attribute, and should be the model of choice for that application. en_US
dc.format.extent 1101906 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.title A Comparison of Ordinary Least Squares and Logistic Regression en_US