A coefficient of discrimination for use with nominal and ordinal regression models

Thomas J. Smith, David A. Walker and Cornelius M. McKenna

Journal of Applied Statistics, 2021, vol. 48, issue 16, 3208-3219

Abstract: This study introduces a coefficient of discrimination for use with nominal and ordinal regression models. Computation of the coefficient is demonstrated with data from the Pew Research Center’s 25th Anniversary of the Web Omnibus Survey pertaining to cell/home phone ownership, where the coefficient of discrimination indicates that respondent age and gender increased the probability of a correct versus incorrect classification by 13.9%. Additionally, the coefficient is compared to existing coefficients.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2020.1796940 (text/html)
Access to full text is restricted to subscribers.

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:taf:japsta:v:48:y:2021:i:16:p:3208-3219

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2020.1796940

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().