![]() i.Sound GoSync Portable Bluetooth Receiver. It allows you to control your playlists via A2DP stereo Bluetooth v2.0 through any standard 3.5mm or RCA input speaker, right from the comfort of your couch. 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. This article will describe the calculations and interpretation of Kappa along with its benefits and best practices. 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. Each rater can assign a good or bad value to each call. To have any confidence in the rating results, all three raters should agree with each other on the value assigned to each call ( reproducibility ). Plus, if the call is recorded and listened to again, each rater should agree with him/herself the second time around ( repeatability ). The Kappa statistic tells you whether your measurement system is better than random chance. If there is significant agreement, the ratings are probably accurate. If agreement is poor, you might question the usefulness of your measurement system. 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). P observed is the sum of the proportions when both raters agree something is good plus when both raters agree something is bad. 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). 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. This calculation will be looking at repeatability, or the ability of rater A to be consistent in their rating. We would use the same method for calculating Kappa for raters B and C. Step 1 is to create a summary table of the results. ![]() 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. If repeatability for raters is poor, then reproducibility is meaningless. The interpretation of the Kappa value is pretty simple.
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