The author is jointly appointed in Psychology and the Center for Research Methods and Data Analysis at the University of Kansas.
Evaluation of dominance-based ordinal multiple regression for variables with few categories
Article first published online: 1 MAY 2012
© 2012 The British Psychological Society
British Journal of Mathematical and Statistical Psychology
Volume 66, Issue 1, pages 169–188, February 2013
How to Cite
Woods, C. M. (2013), Evaluation of dominance-based ordinal multiple regression for variables with few categories. British Journal of Mathematical and Statistical Psychology, 66: 169–188. doi: 10.1111/j.2044-8317.2012.02046.x
- Issue published online: 17 JAN 2013
- Article first published online: 1 MAY 2012
- Received 7 November 2011; revised version received 27 January 2012
Dominance-based ordinal multiple regression (DOR) is designed to answer ordinal questions about relationships among ordinal variables. Only one parameter per predictor is estimated, and the number of parameters is constant for any number of outcome levels. The majority of existing simulation evaluations of DOR use predictors that are continuous or ordinal with many categories, so the performance of the method is not well understood for ordinal variables with few categories. This research evaluates DOR in simulations using three-category ordinal variables for the outcome and predictors, with a comparison to the cumulative logits proportional odds model (POC). Although ordinary least squares (OLS) regression is inapplicable for theoretical reasons, it was also included in the simulations because of its popularity in the social sciences. Most simulation outcomes indicated that DOR performs well for variables with few categories, and is preferable to the POC for smaller samples and when the proportional odds assumption is violated. Nevertheless, confidence interval coverage for DOR was not flawless and possibilities for improvement are suggested.