Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
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crim-data-analysis
GitHub指导犯罪学稿件的数据分析与报告规范，确保通过专家审查。涵盖模型选择（计数、面板、轨迹、生存分析）、不确定性诚实报告、稳健性检验及可重复性实践，避免结果造假。
Trigger Scenarios
运行主分析和支持性分析
回复审稿人关于稳健性或替代模型的询问
拟合轨迹、固定效应面板、计数或生存模型
在数据存档前确保分析可复现
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill crim-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "crim-data-analysis",
"description": "Use when executing and reporting the analysis for a Criminology (ASC \/ Wiley) manuscript so it survives expert review — honest uncertainty, robustness, and methods appropriate to crime counts, longitudinal panels, trajectory models, and recidivism survival. Guides analysis norms; it does not fabricate results."
}
Data Analysis (crim-data-analysis)
Criminology reviewers are methodologically sophisticated and increasingly expect that your results
can be reproduced from deposited materials (see crim-data-and-transparency). Analyze as if both are
true. This skill covers execution and reporting norms; design decisions live in crim-research-design.
When to trigger
- Running main and supporting analyses; building the results section
- A reviewer asked for robustness, heterogeneity, or alternative specifications
- Fitting a trajectory model, fixed-effects panel, count model, or survival model
- Making the analysis reproducible before deposit
Analysis norms Criminology expects
- Report uncertainty honestly. Confidence/credible intervals, not just stars; the magnitude and substantive meaning (e.g., incident-rate ratios, predicted counts, change in offending), not just significance.
- Right model for crime data. Counts are over-dispersed and zero-heavy — prefer negative binomial / zero-inflated / hurdle over OLS on raw counts; rates need exposure offsets; rare-event cautions apply.
- Within- vs. between-person. When the theory is developmental, isolate within-individual change (fixed effects / hybrid models); do not interpret a between-person association as a life-course effect.
- Trajectory models with discipline. Report BIC across solutions, group shares, average posterior probabilities (AvePP ≥ 0.7), and odds of correct classification; do not over-interpret the group count.
- Survival / recidivism. Handle right-censoring and competing risks; report the relevant hazard, not just a binary "recidivated."
- Robustness that probes, not decorates. Show specs that could break the result (alternative crime measures, samples, estimators, fixed effects) and say what you learn.
- Right inference. Cluster at the assignment/sampling level (often place or agency); randomization inference for experiments; few-cluster corrections when clusters are sparse.
Crime-measurement specifics
- State whether the outcome is reported crime, victimization, or self-report, and how the dark figure, reporting, and recording changes (e.g., UCR→NIBRS transition) could bias trends.
- Validate scales (self-report delinquency, legitimacy, collective efficacy); report reliability.
Reproducibility while you work (not at the end)
- One master script regenerates every table and figure from the (raw or constructed) data.
- Set and report seeds for bootstrap, randomization inference, EM-based trajectory fitting, simulation.
- Pin software/package versions (
renv.lock,requirements.txt, recordedssc/net/trajinstalls). - Keep table/figure numbers matched to script outputs; document restricted-data steps that others can't rerun.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. Criminology is observational — place/person panels where selection is pervasive; foreground DiD/IV/RDD and the selection objection.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the supplement. See the executed chain in the JF execution walkthrough.
Anti-patterns
- OLS on raw, over-dispersed crime counts; ignoring exposure/offsets
- Stars-only tables with no rate ratios, effect sizes, or intervals
- Treating trajectory groups as literal offender types; cherry-picking the group count
- Reading a between-person coefficient as within-individual desistance
- "Robustness" that only reruns near-identical specs; p-hacking a significant interaction
Estimator choice keyed to the crime outcome (Criminology decision table)
Criminology reviewers are quantitatively literate and will name a mismatch between model and the data-generating process for offending. Use the outcome to pick the estimator, then defend the assumption the reviewer will probe.
| Outcome shape | Default estimator | Reviewer will probe |
|---|---|---|
| Over-dispersed offense counts | negative binomial w/ exposure offset | dispersion test, offset justification |
| Excess-zero counts (most offend zero times) | zero-inflated / hurdle | what the inflation stage means theoretically |
| Repeated within-person offending | fixed-effects / hybrid panel | within vs. between separation |
| Time-to-recidivism, censored | Cox / competing-risks | proportional hazards, censoring mechanism |
| Developmental offending paths | GBTM / growth mixture | BIC, AvePP ≥ 0.7, group shares not reified |
Worked micro-example: reading a within-person estimate (illustrative)
Suppose a hybrid model returns an incident-rate ratio of 0.62 on the within-person "employed" indicator (illustrative): entering employment maps to roughly a 38% lower offending rate for the same person, 95% CI [0.49, 0.78], net of stable traits. The between-person column, IRR 0.80, is weaker and reading it as desistance would conflate selection (people prone to desist also find work) with the within-person change the life-course claim requires. Report both; tell the reader which identifies the mechanism.
Analysis-stage referee pushback (with the Criminology fix)
- "Official-records bias is unaddressed." Fix: state whether the outcome is arrest, victimization, or self-report, and how the dark figure and UCR→NIBRS recording shifts could bias it.
- "Between-person read as within-person." Fix: report and interpret only the fixed-effects/hybrid within column for developmental claims.
- "Robustness only decorates." Fix: show a spec that could break the result; say what held.
Output format
【Main estimate】magnitude (IRR / predicted count / hazard) + interval + substantive meaning
【Crime measure】reported / victimization / self-report + dark-figure caveat
【Within vs between】isolated correctly? [Y/N/NA]
【Model fit】counts: dispersion handled? trajectory: BIC/AvePP reported? [Y/N/NA]
【Robustness】specs that could break it → what held
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】crim-tables-figures
Supplementary resources
../../resources/external_tools.md— count, trajectory, survival, and spatial packages../../resources/official-source-map.md— transparency expectations and crime-data sources
Version History
- 1839142 Current 2026-07-05 12:48
