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Kappa Coefficients for Missing Data.

Alexandra De Raadt1, Matthijs J Warrens1, Roel J Bosker1

  • 1University of Groningen, Groningen, the Netherlands.

Educational and Psychological Measurement
|May 21, 2019
PubMed
Summary

This study evaluates methods for calculating Cohen's kappa (κ) with missing data. Listwise deletion and Gwet's kappa are recommended for accurate agreement assessment when data is missing.

Keywords:
Cohen’s kappaGwet’s kappainter-rater reliabilitylistwise deletionmissing datanominal ratings

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Area of Science:

  • Statistics
  • Psychometrics
  • Biostatistics

Background:

  • Cohen's kappa (κ) is a standard metric for inter-rater reliability on nominal scales.
  • Handling missing data in kappa calculations is crucial for robust agreement analysis.
  • Existing kappa variants may produce biased results when data is incomplete.

Purpose of the Study:

  • To evaluate three variants of Cohen's kappa for their ability to handle missing data.
  • To compare the performance of these variants under different missing data mechanisms.
  • To provide recommendations for choosing the most appropriate kappa method with missing data.

Main Methods:

  • Simulation study comparing three Cohen's kappa variants.
  • Analysis under two missing data mechanisms: Missingness Completely At Random (MCAR) and Missingness Not At Random (MNAR).
  • Assessment of bias and mean squared error (MSE) for each variant.

Main Results:

  • Gwet's kappa and listwise deletion kappa showed minimal bias and MSE under MCAR.
  • These two methods also demonstrated small bias and MSE under MNAR.
  • The variant treating missing ratings as a category exhibited significant bias and high MSE.

Conclusions:

  • Listwise deletion of units with missing ratings is a reliable and computationally simple method for estimating Cohen's kappa.
  • This method is recommended when missingness can be assumed to be MCAR or MNAR.
  • Avoid using kappa variants that treat missing data as a separate category due to potential bias.