Data analysis with ordinal and interval dependent variables: examples from a study of real estate salespeople

G. Martin Izzo () and Barry E. Langford
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G. Martin Izzo: Mike Cottrell School of Business North Georgia College & State University Dahlonega, Georgia USA
Barry E. Langford: Florida Gulf Coast University Fort Myers, Florida USA

Review of Economic and Business Studies, 2008, vol. 1, 103-116

Abstract: This paper re-examines the problems of estimating the parameters of an underlying linear model using survey response data in which the dependent variables are in discrete categories of ascending order (ordinal, as distinct from numerical) or, where they are observed to fall into certain groups on a continuous scale (interval), where the actual values remain unobserved. An ordered probit model is discussed as an appropriate framework for statistical analysis for ordinal dependent variables. Next, a maximum likelihood estimator (MLE) derived from grouped data regression for interval dependent variable is discussed. Using LIMDEP, a packaged statistical program, survey data from an earlier manuscript are analyzed and the findings presented.

Date: 2008
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