• Please log in or register to access this feature.

Research Article

Testing the equality of two dependent kappa statistics

  1. Allan Donner1,*,
  2. Mohamed M. Shoukri2,
  3. Neil Klar3,
  4. Emma Bartfay1

Article first published online: 25 JAN 2000

DOI: 10.1002/(SICI)1097-0258(20000215)19:3<373::AID-SIM337>3.0.CO;2-Y

Issue

Statistics in Medicine

Statistics in Medicine

Volume 19, Issue 3, pages 373–387, 15 February 2000

Additional Information

How to Cite

Donner, A., Shoukri, M. M., Klar, N. and Bartfay, E. (2000), Testing the equality of two dependent kappa statistics. Statist. Med., 19: 373–387. doi: 10.1002/(SICI)1097-0258(20000215)19:3<373::AID-SIM337>3.0.CO;2-Y

Author Information

  1. 1

    Department of Epidemiology and Biostatistics, The University of Western Ontario, London, Ontario, N6A 5C1, Canada

  2. 2

    Department of Population Medicine, University of Guelph, Guelph, Ontario, N1G 2W1, Canada

  3. 3

    Dana-Farber Cancer Institute, Department of Biostatistical Science, 44 Binney Street, Boston, MA 02115, U.S.A.

*Department of Epidemiology and Biostatistics, Kiesge Building, The University of Western Ontario, London, Ontario, Canada N6A 5C1

Publication History

  1. Issue published online: 25 JAN 2000
  2. Article first published online: 25 JAN 2000
  3. Manuscript Accepted: APR 1999
  4. Manuscript Received: AUG 1998

Funded by

  • Natural Sciences and Engineering Research Council of Canada
  • Harvard School of Public Health
  • Schering–Plough Corporation

SEARCH

SEARCH BY CITATION

ARTICLE TOOLS

Get PDF (116K)

Abstract

Procedures are developed and compared for testing the equality of two dependent kappa statistics in the case of two raters and a dichotomous outcome variable. Such problems may arise when each of a sample of subjects are rated under two distinct settings, and it is of interest to compare the observed levels of inter-observer and intra-observer agreement. The procedures compared are extensions of previously developed procedures for comparing kappa statistics computed from independent samples. The results of a Monte Carlo simulation show that adjusting for the dependency between samples tends to be worthwhile only if the between-setting correlation is comparable in magnitude to the within-setting correlations. In this case, a goodness-of-fit procedure that takes into account the dependency between samples is recommended. Copyright © 2000 John Wiley & Sons, Ltd.

Get PDF (116K)