Improving Measurement of Trait Competitiveness: A Rasch Analysis of the Revised Competitiveness Index With Samples From New Zealand and US University Students

Author:

Krägeloh Christian U.1ORCID,Medvedev Oleg N.2,Hill Erin M.3,Webster Craig S.2,Booth Roger J.4,Henning Marcus A.2

Affiliation:

1. Faculty of Health & Environmental Studies, Auckland University of Technology, New Zealand

2. Centre for Medical and Health Sciences Education, University of Auckland, New Zealand

3. Department of Psychology, West Chester University, PA, USA

4. Faculty of Medical and Health Sciences, University of Auckland, New Zealand

Abstract

Measuring competitiveness is necessary to fully understand variables affecting student learning. The 14-item Revised Competitiveness Index has become a widely used measure to assess trait competitiveness. The current study reports on a Rasch analysis to investigate the psychometric properties of the Revised Competitiveness Index and to improve its precision for international comparisons. Students were recruited from medical studies at a university in New Zealand, undergraduate health sciences courses at another New Zealand university, and a psychology undergraduate class at a university in the United States. Rasch model estimate parameters were affected by local dependency and item misfit. Best fit to the Rasch model (χ2(20) = 15.86, p = .73, person separation index = .95) was obtained for the Enjoyment of Competition subscale after combining locally dependent items into a subtest and discarding the highly misfitting Item 9. The only modifications required to obtain a suitable fit (χ2(25) = 25.81, p = .42, person separation index = .77) for the Contentiousness subscale were a subtest to combine two locally dependent items and splitting this subtest by country to deal with differential item functioning. The results support reliability and internal construct validity of the modified Revised Competitiveness Index. Precision of the measure may be enhanced using the ordinal-to-interval conversion algorithms presented here, allowing the use of parametric statistics without breaking fundamental statistical assumptions.

Publisher

SAGE Publications

Subject

General Psychology

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