Guide-linked quiz · Medium Level

Cohen's Kappa Quiz

Explore observed and expected agreement, weighted kappa and annotation reliability through worked examples of categorical labels.

Related guide pathsCohens Kappa
Question 1 of 8

Two annotators have observed agreement of 0.8 and expected agreement of 0.5. Which kappa value follows?

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  1. Two annotators have observed agreement of 0.8 and expected agreement of 0.5. Which kappa value follows?
    • 0.3
    • 0.4
    • 0.8
    • 0.6
  2. One annotator uses a category 60% of the time and the other uses it 50% of the time. How much does that category contribute to expected agreement?
    • 0.10
    • 0.30
    • 0.55
    • 1.10
  3. Why does the expected-agreement term in kappa not establish that annotators were guessing randomly?
    • It is calculated from the number of disagreements alone
    • It is identical to the observed agreement for every dataset
    • It is a reference derived from label frequencies, not an observation of their reasoning
    • It assumes every category must have equal frequency
  4. For low, medium and high incident severity, why might a team choose weighted kappa?
    • To give different penalties to one-level and two-level disagreements
    • To remove all disagreements from the calculation
    • To guarantee a higher reliability score
    • To avoid specifying a category order
  5. Both annotators assign every case to the same single category. Why should ordinary kappa not be reported as a straightforward reliability success?
    • Perfect raw agreement automatically makes kappa one
    • The score ignores the common category and evaluates only disagreements
    • Subtracting expected agreement alone gives a complete kappa calculation
    • Expected agreement is one, leaving a zero denominator
  6. A second reviewer copies the first reviewer's labels and achieves perfect agreement. Which conclusion is justified?
    • The labels have been independently validated
    • The agreement does not establish independent reliability
    • Every label is necessarily correct
    • The dataset can be used without checking instructions
  7. Which accompanying information helps interpret a kappa score when category frequencies are uneven?
    • Only the raw agreement percentage, without category counts
    • Only whether kappa exceeds a universal cutoff
    • Sample size, raw agreement and the agreement table
    • Only the weighted score, without specifying category order
  8. Two annotators both follow a mistaken rule and agree strongly. What does their high kappa fail to establish?
    • That their labels are factually correct
    • That their observed agreement exceeds its frequency-based expectation
    • That they used the same labels on many cases
    • That their assignments show consistency under the shared rule

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