- Subject index
‘The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.’
- John Fox, Professor, Department of Sociology, McMaster University
‘The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.’
- Ben Jann, Executive Director, Institute of Sociology, University of Bern
‘Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for ...
Chapter 9: Regression Models for Nominal and Ordinal Outcomes
Regression Models for Nominal and Ordinal Outcomes
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 ...