Comparing Neural Network and Ordinal Logistic Regression to Analyze Attitude Responses

Aisyah Larasati (), Camille DeYong () and Lisa Slevitch ()
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Aisyah Larasati: School of Industrial Engineering and Management, Oklahoma State University, Stillwater, USA
Camille DeYong: School of Industrial Engineering and Management, Oklahoma State University, Stillwater, USA
Lisa Slevitch: School of Hotel and Restaurant Administration, Oklahoma State University, Stillwater, USA

Service Science, 2011, vol. 3, issue 4, 304-312

Abstract: Many social studies analyze attitude responses using the linear regression model. This model typically treats questionnaire data as continuous scales, although the data is merely ordinal. One type of regression model that is more appropriate to analyze rank-order responses is the Ordinal Logistic Regression (OLR) model. In addition to the use of regression, the Artificial Neural Network (ANN) model has recently been applied in various studies. This paper delivered comparative descriptions of both the ANN and OLR models. The theoretical features and properties, which include parameters, variable selection, and model evaluation, followed by comparisons of the disadvantages and advantages of both models were analytically reviewed. [ Service Science , ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3.]

Keywords: artificial neural networks; ordinal logistic regression; ordinal data (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:3:y:2011:i:4:p:304-312

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