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J.scott Long

In: The SAGE Handbook of Regression Analysis and Causal Inference

Chapter 9: Regression Models for Nominal and Ordinal Outcomes

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Regression Models for Nominal and Ordinal Outcomes
Regression models for nominal and ordinal outcomes
J.scottLong*
Introduction to the Method

Ordinal and nominal outcomes are common in the social sciences, with examples ranging from Likert items in surveys to assessments of physical health to how armed conflicts are resolved. Since the 1980s numerous regression models for nominal and ordinal outcomes have been developed. These models are essentially sets of binary regressions that are estimated simultaneously with constraints on the parameters. While advances in software have made estimation simple, the effective interpretation of these non-linear models is a vexingly difficult art that requires time, practice, and a firm grounding in the goals of your analysis and the characteristics of your model. Too often interpretation is limited to a table of ...

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