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The proportional odds with partial proportionality constraints model for ordinal response variables
1Department of Sociology, Oklahoma State University, 431 Murray, Stillwater, OK 74078, United States.
Social Science Research
|September 29, 2012
Summary
The proportional odds assumption in ordered logit models can be relaxed using the novel proportional odds with partial proportionality constraints (POPPC) model. This method offers an alternative to partial and generalized models for analyzing cumulative odds.
Area of Science:
- Statistics
- Econometrics
- Social Sciences
Background:
- The proportional odds assumption in ordered logit models is frequently violated in practice.
- Violations indicate that independent variable effects differ across model cutpoint equations.
- Existing alternatives include partial and generalized cumulative odds models.
Purpose of the Study:
- To introduce and justify the proportional odds with partial proportionality constraints (POPPC) model.
- To present an improved estimation method for the POPPC model.
- To demonstrate the POPPC model's utility with real-world data.
Main Methods:
- Proposed the proportional odds with partial proportionality constraints (POPPC) model.
- Developed an estimation method not requiring person-threshold data.
- Applied the POPPC model to two datasets from the 2008 General Social Survey.
Main Results:
- The POPPC model offers a flexible alternative for relaxing the proportional odds assumption.
- The proposed estimation method is practical and does not rely on person-threshold data.
- Empirical examples demonstrate the model's applicability and interpretability.
Conclusions:
- The POPPC model provides a valuable, under-utilized tool for cumulative odds modeling.
- The improved methodology enhances the practical application of this statistical approach.
- This research contributes to more nuanced analysis in social science research.
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