MATRIX KAPPA: A PROPOSAL FOR A CARD SORT STATISTIC FOR IS SURVEY INSTRUMENT DEVELOPMENT
Bibliographic record
Abstract
The card sort is a key scale development tool that is frequently used in IS survey instrument development. Cohen's Kappa is a recommended measure of inter-rater agreement in this process, however one of its underlying statistical assumptions is violated when it is used in open card sorts. To address this issue, Matrix Kappa is proposed as a complement to other card sort analysis techniques, reframing constructs in terms of item relationships and representing inter-rater agreement in terms of matrices. Matrix Kappa has the benefit of meeting Cohen’s Kappa assumptions for open card sorts and can be used to differentiate both open and closed card sort results that Cohen’s Kappa cannot.
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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 | 0.286 | 0.572 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".