Ordinal Regression Analysis: Fitting the Proportional Odds Model Using Stata, SAS and SPSS

Researchers have a variety of options when choosing statistical software packages that can perform ordinal logistic regression analyses. However, statistical software, such as Stata, SAS, and SPSS, may use different techniques to estimate the parameters. The purpose of this article is to (1) illustrate the use of Stata, SAS and SPSS to fit proportional odds models using educational data; and (2) compare the features and results for fitting the proportional odds model using Stata OLOGIT, SAS PROC LOGISTIC (ascending and descending), and SPSS PLUM. The assumption of the proportional odds was tested, and the results of the fitted models were interpreted. Introduction The proportional odds (PO) model, also called, is a commonly used model for the analysis of ordinal categorical data and comes from the class of generalized linear models. It is a generalization of a binary logistic regression model when the response variable has more than two ordinal categories. The proportional odds model is used to estimate the odds of being at or below a particular level of the response variable. For example, if there are j levels of ordinal outcomes, the model makes J-1 predictions, each estimating the cumulative probabilities at or below the j th level of the outcome variable. This model can estimate the odds of being at or beyond a particular level of the response variable as well, because below and beyond a