Ordinal Regression Analysis: Proportional Odds Models for Complex Sample Survey Data Using Stata, SPSS, and SAS

• In Event: 49.061 - Advanced Modeling Strategies for Longitudinal, Multilevel, and/or Survey Data

Abstract

An ordinal logistic regression model with complex sampling designs is different from a conventional proportional odds model since the former needs to take weights and design effects in account. While general-purpose statistical packages, such as Stata, SAS, and SPSS, are all capable of analyzing proportional odds models with complex survey data, they may use different techniques to estimate the models and have different features. The purpose of this paper is to illustrate the use of Stata, SAS and SPSS to fit proportional odds models with complex survey data, and compare the features and results for fitting the models using the Stata svy: ologit, SAS PROC SURVEYLOGISTIC, and SPSS CSORDINAL commands. The linearization method was used to estimate the sampling variance.

Authors

• Xing Liu, Eastern Connecticut State University

• Hari P. Koirala, Eastern Connecticut State University