A Conceptual Framework for Ordered Logistic Regression Models

Andrew S. Fullerton
Additional contact information
Andrew S. Fullerton: Oklahoma State University, Stillwater, andrew.fullerton@okstate.edu

Sociological Methods & Research, 2009, vol. 38, issue 2, 306-347

Abstract: Ordinal-level measures are very common in social science research. Researchers often analyze ordinal dependent variables using the proportional odds logistic regression model. However, this ‘‘traditional’’ method is one of many different types of logistic regression models available for the analysis of ordered response variables. In this article, the author identifies 12 distinct models that rely on logistic regression and fit within a framework of three major approaches with variations within each approach based on the application of the proportional odds assumption. This typology provides a degree of conceptual clarity that is missing in the extant literature on logistic regression models for ordinal outcomes. The author illustrates the similarities and differences among the different models with examples from the General Social Survey and the American National Election Study.

Keywords: ordered logit; parallel regression assumption (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0049124109346162 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:38:y:2009:i:2:p:306-347

DOI: 10.1177/0049124109346162

Access Statistics for this article

More articles in Sociological Methods & Research
Bibliographic data for series maintained by SAGE Publications ().