Different types of Kappa values

Different types of kappa coefficients are used to assess inter-rater reliability, each tailored to specific contexts and data types. Here’s a summary of the key differences among various kappa values:

Cohen's Kappa

• Purpose: Measures agreement between two raters for categorical items.
• Use Case: Ideal for dichotomous (two-category) classifications.
• Calculation: It accounts for the proportion of observed agreement minus the proportion of agreement expected by chance.
• Interpretation: Values range from -1 (complete disagreement) to 1 (perfect agreement), with 0 indicating no agreement beyond chance. Guidelines suggest:
  • < 0.00: Poor agreement
  • 0.00 - 0.20: Slight agreement
  • 0.21 - 0.40: Fair agreement
  • 0.41 - 0.60: Moderate agreement
  • 0.61 - 0.80: Substantial agreement

  • 0.80: Almost perfect agreement [2].

Fleiss' Kappa

• Purpose: Extends Cohen's kappa for multiple raters.
• Use Case: Suitable for assessing agreement among three or more raters classifying items into categories.
• Calculation: Similar to Cohen's but adjusts for the number of raters and categories, providing a more comprehensive view of reliability across multiple observers.
• Interpretation: The same scale as Cohen's kappa applies, but it can handle more complex scenarios involving multiple raters.

Watson's Kappa

• Purpose: A variation designed to address specific issues in the calculation of kappa.
• Use Case: Often used in psychological and educational testing where the data may not fit traditional assumptions of independence.
• Calculation: It modifies the standard kappa formula to better reflect situations where rater independence is questionable.

PABAK (Prevalence-Adjusted and Bias-Adjusted Kappa，PABAκ)

• Purpose: Addresses limitations of Cohen's kappa, particularly in cases with imbalanced categories.
• Use Case: Useful when the prevalence of categories is unequal or when there is a significant bias in rater classifications.
• Calculation: Adjusts the expected agreement based on the prevalence of categories, providing a more accurate reflection of rater agreement under these conditions.
• Interpretation: Similar interpretation guidelines as Cohen's kappa, but offers a clearer picture in skewed distributions.

Altman's Kappa

• Purpose: A variant that also considers the weighting of disagreements based on their severity.
• Use Case: Particularly useful in clinical settings where some disagreements may be more critical than others.
• Calculation: It incorporates weights into the kappa calculation, allowing for differentiation between types of disagreement.
• Interpretation: Like other kappas, it ranges from -1 to +1, but provides nuanced insights into the nature of disagreements.

Summary Table

These variations provide researchers with tools suited to different types of data and research questions, enhancing the assessment of reliability across diverse fields.

Citations:
[1] https://link.springer.com/article/10.1007/s11634-020-00394-8
[2] https://en.wikipedia.org/wiki/Cohen's_kappa
[3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9517543/
[4] https://www.scribbr.com/methodology/types-of-validity/
[5] https://onlinelibrary.wiley.com/doi/full/10.1002/sim.7679
[6] https://online.stat.psu.edu/stat504/lesson/11/11.2/11.2.4
[7] https://www.ncbi.nlm.nih.gov/books/NBK531518/table/ch3.tab1/
[8] https://academic.oup.com/ptj/article/85/3/257/2805022