Adjusting the consensus measure to target ordinal scale arguments

William J. Tastle, Mark J. Wierman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Much research data is collected and analyzed as ordinal scale data. The Likert scale is an example of such a data collection tool. Comparing different Likert scale data sets can be difficult, for no specific tool has been identified that is conceptually sound, though researchers typically apply ratio scale mathe-matics to the data. In this paper a modification of the consensus measure is examined and shown to be a valuable tool in the comparison and ranking of ordinal data. By identifying a specific target category, i.e., the "strongly agree" category of a Likert scale, the target consensus measure calculates a value between 0 and 1, the 0 identifying a Likert scale frequency distribution as being the furthest distance possible from the targeted value, and the 1 representing all values being assigned to the targeted category.

Original languageEnglish (US)
Title of host publicationAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS
Pages403-407
Number of pages5
DOIs
StatePublished - Dec 1 2006
EventNAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society - Montreal, QC, Canada
Duration: Jun 3 2006Jun 6 2006

Publication series

NameAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS

Other

OtherNAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society
CountryCanada
CityMontreal, QC
Period6/3/066/6/06

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

Cite this

Tastle, W. J., & Wierman, M. J. (2006). Adjusting the consensus measure to target ordinal scale arguments. In Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS (pp. 403-407). [4216836] (Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS). https://doi.org/10.1109/NAFIPS.2006.365443