Skill type: ANALYSIS MODULE. Codes qualitative data and reports agreement correctly. The central discipline is a branch: a reliability coefficient is right for codebook/content-analytic work, but for reflexive thematic analysis the criterion is consensus and reflexivity, not a statistic. The skill does not oversell the number.
Core Mission
KRIPPENDORFF'S ALPHA IS THE PRIMARY COEFFICIENT — BUT A COEFFICIENT IS THE WRONG TOOL FOR REFLEXIVE TA.
BRANCH BY DESIGN.
When to Use This Skill
- "Two RAs coded my interviews with a codebook — compute intercoder reliability."
- "Build/refine a codebook and report agreement."
- "I used an LLM to code open-ended responses — does it agree with my human coding?"
- "Which reliability statistics do I report for a content analysis?"
Does NOT Trigger
| The request is really about… | Route to | Why not this skill |
|---|---|---|
| Topic modeling / embeddings / supervised text classification | alterlab-text-as-data |
Computational text, not human-coding reliability. |
| Whether the study is trustworthy (positionality, Lincoln & Guba) | alterlab-ssci-reflexivity-gate |
Trustworthiness gate, not the coding itself. |
| Choosing the qualitative method (grounded theory, phenomenology) | alterlab-qualitative-methods |
Methodology selection, upstream. |
| Whether a scale is reliable/valid (quantitative) | alterlab-ssci-measurement-gate |
Psychometrics, different problem. |
The branch (this is the core discipline)
| Design | Reliability criterion |
|---|---|
| Codebook / content-analytic coding (fixed categories, replicable) | compute an ICR coefficient (Krippendorff's α) with a CI and threshold |
| Reflexive thematic analysis (interpretive, researcher-as-instrument) | consensus + reflexivity — collaborative discussion to understand differences; a coefficient misrepresents the epistemology and is NOT required. Route trustworthiness to alterlab-ssci-reflexivity-gate. |
Do not force α onto reflexive TA, and do not skip α on content-analytic coding.
Correct coefficient choice (verified)
- Krippendorff's α — primary. Handles ≥2 coders, any measurement level (nominal/ordinal/
interval/ratio), and missing data. Python
krippendorff.alpha(reliability_data=<coders × units>, level_of_measurement="nominal")returns a point estimate only — pair it with a bootstrap CI (the bundledscripts/icr.pydoes nominal α + a bootstrap CI + Cohen's κ in pure stdlib; RirrCAC::krippen.alpha.raworicr::krippalpha(..., bootstrap=TRUE)give CIs for all levels). - Cohen's κ (2 coders) / Fleiss' κ (>2, fixed number):
statsmodels.stats.inter_ratercohens_kappa,fleiss_kappa(build the count table withaggregate_raters); sklearncohen_kappa_scoreas a cross-check. - NOT valid ICR measures: chi-square, Cronbach's alpha, and Pearson's r — they measure covariation/internal consistency, not chance-corrected agreement. Refuse these.
Thresholds (Krippendorff): α ≥ .80 reliable; .667 ≤ α < .80 tentative conclusions only; α < .667 unreliable. Report α with a 95% CI, never the bare point estimate.
LLM-assisted coding (supported, never free)
Treat an LLM as another coder: human↔LLM double-coding, compute Krippendorff's α between each LLM and a human gold standard, and use the CI to judge whether the LLM is statistically indistinguishable from human coders. Disclose the model, the prompt, and the agreement — never present LLM coding as uncontrolled or cost-free.
Reporting checklist (put in every ICR report)
BRANCH: content-analytic (coefficient) | reflexive TA (consensus+reflexivity)
ICR MEASURE: Krippendorff alpha (level) | Cohen/Fleiss kappa — and why
CODERS: number of coders; human / LLM
DOUBLE-CODED: % of the data double-coded
ALPHA: point estimate + 95% CI + threshold verdict (.80 / .667)
RESOLUTION: how disagreements were resolved (adjudication / consensus)
References
references/icr_and_reporting.md— coefficient math, verified APIs, LLM-benchmarking, the reporting standard.references/reflexive_vs_codebook.md— when a statistic applies vs. when it does not.scripts/icr.py— stdlib nominal Krippendorff's α + bootstrap CI + Cohen's κ (no numpy / no deps).
Part of the AlterLab Academic Skills suite.