lang-data-analysis
GitHub用于规划或审计语言学手稿分析，确保证据可信地支持理论主张。涵盖R混合效应模型、语音测量、语料库统计及从数据到概括的分析链条审查，强调正确处理不确定性和收敛性证据。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill lang-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "lang-data-analysis",
"description": "Use when planning or auditing the analysis of a Language (LSA) manuscript so the evidence credibly supports the theoretical claim. Covers quantitative modeling (mixed-effects in R), phonetic measurement, corpus statistics, and the analytic trail from glossed data or judgments to the generalization. Improves the analysis chain; it does not fabricate results."
}
Data Analysis (lang-data-analysis)
At Language the analysis exists to make the theoretical claim credible — not to display technique or notation. A cross-subfield, double-anonymous reviewer will ask whether the evidence actually warrants the generalization and whether uncertainty is handled honestly. Where the work is quantitative, Language now expects properly specified models (typically mixed-effects models in R) rather than by-subject t-tests or raw counts; where it is analytic, it expects the pattern to be demonstrable from the glossed data. This skill stress-tests the analysis chain in the idiom of your work.
When to trigger
- Planning the analysis, or auditing it before writing up
- A reader doubts the statistics, the evidence-to-claim link, or the treatment of variability
- Reconciling multiple data sources (corpus + experiment, judgments + text) into one argument
- Deciding which analyses are confirmatory vs. exploratory
Analysis norms (by mode)
Quantitative (experiment / corpus)
- Fit mixed-effects models with the random-effects structure the design justifies (crossed by-subject and by-item random effects; random slopes for within-cluster predictors). Report the model, not just p-values.
- Report effect sizes and intervals, not stars alone; state the coding/contrasts and the convergence status; keep seeds and pinned package versions.
- Distinguish preregistered/confirmatory from exploratory analyses where applicable.
Phonetic
- State measurement settings (windowing, formant ceilings, alignment) and how outliers/mis-tracks were handled; show the effect is not an artifact of the measurement pipeline.
Analytic (formal)
- Demonstrate the generalization directly from numbered, glossed examples; show the analysis derives the attested cases and blocks the unattested ones.
Historical / typological
- Make the inferential logic explicit (implicational universals, reconstruction, statistical tendencies); guard against non-independence of the sample.
Convergent evidence (a Language strength)
Language rewards a generalization shown through more than one window — e.g., an experimental effect corroborated by a corpus trend, or judgments backed by text frequencies. When windows disagree, say so and explain the discrepancy rather than hiding the inconvenient one.
Referee-pushback patterns on the evidence chain (Language fixes)
| Referee writes… | The Language-specific fix |
|---|---|
| "No random effects / pseudoreplication." | fit the justified mixed model; cluster by subject and item |
| "Significance without effect size." | report estimates + intervals in interpretable units |
| "The stat model doesn't match the design." | align random-effects structure with the sampling |
| "Analysis doesn't rule out the alternative." | show it derives attested and blocks unattested cases |
Calibration (Language appetite, hedged)
Orienting heuristics; confirm against the current author pages. Language increasingly expects that a quantitative claim rests on a model appropriate to the clustered, repeated-measures nature of linguistic data — the modal avoidable failure is pseudoreplication (ignoring by-speaker or by-item structure). Illustrative: a paper claims a durational contrast "is significant (p < .01)" from 1,200 tokens produced by 8 speakers, analyzed as if independent. A referee writes "pseudoreplication." The fix refits a mixed-effects model with by-speaker and by-word random intercepts and slopes, reports the estimate (an illustrative 12 ms, 95% CI ~4–20), and notes two speakers who show no effect — turning a fragile claim into a credible, bounded one.
Anti-patterns
- Treating repeated measures as independent (pseudoreplication); stars-only reporting
- A statistical model whose random structure ignores the sampling design
- Phonetic effects that are artifacts of measurement settings, not language
- Cherry-picked examples that ignore counterexamples in the same corpus/elicitation
- Presenting exploratory results as if confirmatory
- Notation or technique foregrounded over the generalization it is meant to support
Evidence pass for Language
Treat this skill as an executable review pass, not a prose hint. First lock the empirical generalization, evidence base, warrant, and theoretical payoff; then judge whether the manuscript answers the venue's real reader: linguists across subfields who value grounded analysis, transparent and checkable evidence, and careful, appropriately scoped generalizations.
- Do the pass: audit the analysis before polishing prose — unit of analysis, random-effects structure, effect sizes, measurement pipeline, exclusions, and reproducibility must be visible.
- Return a ledger: give
claim / evidence / risk / manuscript locationrows so the next agent can edit rather than rediscover the issue. - Sibling guard: compare against Laboratory Phonology, Journal of Memory and Language, Language Variation and Change; if a sibling owns the contribution, recommend re-routing before polishing.
- Stop condition: do not give submission-ready advice until
resources/official-source-map.mdhas been checked and the manuscript has one concrete fix for the largest venue-specific risk.
Output format
【Claim under test】from theory-building
【Primary evidence】the analysis that carries the claim
【Model】mixed-effects structure matches the design? [Y/N/NA]
【Uncertainty】effect sizes + intervals reported? [Y/N]
【Convergence】corroborated across windows? [Y/N/NA]
【Confirmatory vs. exploratory】labeled where relevant? [Y/N]
【Next】lang-data-and-transparency
Supplementary resources
../../resources/external_tools.md— R / lme4 / brms, Praat, corpus tooling../../resources/official-source-map.md— Language quantitative and evidence expectations
Version History
- 9f86f09 Current 2026-07-19 16:57
