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ERIC Number: ED501264
Record Type: Non-Journal
Publication Date: 2008-May-12
Pages: 29
Abstractor: Author
Fitting Proportional Odds Models to Educational Data in Ordinal Logistic Regression Using Stata, SAS and SPSS
Liu, Xing
Online Submission, Paper presented at the Annual Meeting of the American Educational Research Association (AERA) (Chicago, IL, 2007)
The proportional odds (PO) model, which is also called cumulative odds model (Agresti, 1996, 2002 ; Armstrong & Sloan, 1989; Long, 1997, Long & Freese, 2006; McCullagh, 1980; McCullagh & Nelder, 1989; Powers & Xie, 2000; O'Connell, 2006), is one of the most commonly used models for the analysis of ordinal categorical data and comes from the class of generalized linear models. Researchers currently 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. The data of a survey instrument Teachers' Perceptions of Grading Practices (Liu, 2004; Liu, O'Connell & McCoach, 2006) is used to demonstrate the PO analysis. This demonstration clarifies some of the issues that researchers must consider in using different statistical packages for analysis of ordinal data. (Contains 3 tables and 8 figures.)
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A