When Kappa = 1, perfect agreement exists.The higher the Kappa, the stronger the agreement and more reliable your measurement system. The interpretation of the Kappa value is pretty simple. If repeatability for raters is poor, then reproducibility is meaningless. Step 2 is to create a contingency table of probabilities.Ī similar process would be followed for calculating the within Kappas for raters B and C, and the between Kappa for all the raters. Step 1 is to create a summary table of the results. We would use the same method for calculating Kappa for raters B and C. This calculation will be looking at repeatability, or the ability of rater A to be consistent in their rating.
#Kappa sigma how to#
Using the following sample set of data for our three raters listening to 20 calls twice, let’s see how to calculate Kappa for rater A. P chance is the proportion of agreements expected by chance = (proportion rater A says good x the proportion rater B says good) + (proportion rater A says bad x the proportion B says bad).
#Kappa sigma plus#
P observed is the sum of the proportions when both raters agree something is good plus when both raters agree something is bad. Kappa is the ratio of the proportion of times the raters agree (adjusted for agreement by chance) to the maximum proportion of times the raters could have agreed (adjusted for agreement by chance). If agreement is poor, you might question the usefulness of your measurement system. If there is significant agreement, the ratings are probably accurate. The Kappa statistic tells you whether your measurement system is better than random chance. Plus, if the call is recorded and listened to again, each rater should agree with him/herself the second time around ( repeatability ). To have any confidence in the rating results, all three raters should agree with each other on the value assigned to each call ( reproducibility ). Each rater can assign a good or bad value to each call. Kappa measures the degree of agreement between multiple people making qualitative judgements about an attribute measure.Īs an example, let’s say you have three people making a judgement on the quality of a customer phone call. This article will describe the calculations and interpretation of Kappa along with its benefits and best practices. And to RSVP to The 125th + 1 Founding Anniversary events as details emerge.The measurement system for attribute data (type of defect, categories, survey rankings, etc.) requires a different analysis than continuous data (time, length, weight, etc.).įor continuous data, you would use Measurement System Analysis or Gage R&R to judge the capability of your measurement system to give you reliable and believable data.Īn Attribute Agreement Analysis relying on Kappa is used for the same purpose but for attribute data.
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