Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial
Bibliographic record
Abstract
Many research designs require the assessment of inter-rater reliability (IRR) to demonstrate consistency among observational ratings provided by multiple coders.However, many studies use incorrect statistical procedures, fail to fully report the information necessary to interpret their results, or do not address how IRR affects the power of their subsequent analyses for hypothesis testing.This paper provides an overview of methodological issues related to the assessment of IRR with a focus on study design, selection of appropriate statistics, and the computation, interpretation, and reporting of some commonly-used IRR statistics.Computational examples include SPSS and R syntax for computing Cohen's kappa and intra-class correlations to assess IRR.The assessment of inter-rater reliability (IRR, also called inter-rater agreement) is often necessary for research designs where data are collected through ratings provided by trained or untrained coders.However, many studies use incorrect statistical analyses to compute IRR, misinterpret the results from IRR analyses, or fail to consider the implications that IRR estimates have on statistical power for subsequent analyses.This paper will provide an overview of methodological issues related to the assessment of IRR, including aspects of study design, selection and computation of appropriate IRR statistics, and interpreting and reporting results.Computational examples include SPSS and R syntax for computing Cohen's kappa for nominal variables and intraclass correlations (ICCs) for ordinal, interval, and ratio variables.Although it is beyond the scope of the current paper to provide a comprehensive review of the many IRR statistics that are available, references will be provided to other IRR statistics suitable for designs not covered in this tutorial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.141 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".