crim-research-design
GitHub用于辩护犯罪学手稿的研究设计，涵盖因果推断、纵向/生命历程、空间实验及定性案例选择。针对ASC/Wiley期刊标准，协助识别假设、处理数据偏差并反驳竞争理论，强化设计逻辑而非编写代码。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill crim-research-design -g -y
SKILL.md
Frontmatter
{
"name": "crim-research-design",
"description": "Use when defending the research design of a Criminology (ASC \/ Wiley) manuscript — causal identification for quantitative work, longitudinal and life-course designs, criminal-career and trajectory methods, place-based and experimental designs, or case selection and process tracing for qualitative work. Criminology judges each tradition on its own terms. Strengthens the design; it does not write code."
}
Research Design (crim-research-design)
Criminology accepts many methodologies but is demanding about each. The design must credibly connect
the mechanism (crim-theory-building) to crime evidence. This skill is mode-aware: pick the section
that matches your work and defend it against the strongest rival explanation.
When to trigger
- Specifying identification, a longitudinal design, case selection, or an experiment
- A reviewer questioned causal claims, selection, the dark figure, or a confound
- Choosing between a trajectory model, fixed-effects panel, or survival design
- Justifying why your design adjudicates the rival theory from
crim-literature-positioning
Quantitative / causal inference
- Identification first. State the estimand and the assumptions that license a causal reading (ignorability, parallel trends, exclusion, continuity). Defend them; don't assert them.
- Designs: experiments (incl. randomized policing/hot-spot trials), DID/event study (use modern staggered-adoption estimators, not naive TWFE), IV (first-stage strength, exclusion), RDD (density/manipulation tests, bandwidth robustness), matching/weighting with balance + sensitivity.
- Inference: cluster at the level of treatment assignment (often place or agency); randomization inference for experiments; few-cluster corrections (wild-cluster bootstrap).
- Crime-data validity: state which construct you measure — reported crime (UCR/NIBRS), victimization (NCVS), or self-report — and how the dark figure and reporting/recording bias affect inference.
Longitudinal / life-course / criminal careers
- Within- vs. between-person: use fixed effects or hybrid models to isolate within-individual change when the theory is about turning points or desistance.
- Trajectory / group-based models (GBTM, growth mixture): justify the number of groups (BIC, AvePP ≥ 0.7, group shares, classification odds); treat groups as a summary, not literal types.
- Survival / recidivism: handle right-censoring and competing risks; distinguish timing from prevalence.
- Criminal-career parameters: separate onset, frequency (λ), seriousness, and desistance; do not let prevalence masquerade as incidence.
Place-based & experimental
- Randomized field trials (patrol, deterrence, reentry): report power/MDE, attrition, fidelity, ethics/IRB.
- Spatial designs: address displacement vs. diffusion of benefits; near-repeat and hot-spot logic.
Qualitative / case-based
- Case selection justified by design logic (typical, deviant, most/least-likely, paired comparison), not convenience. Say what the case is a case of.
- Process tracing / life-history with explicit tests; state what evidence would have disconfirmed
the argument. Plan source documentation (see
crim-data-and-transparency).
The adjudication test (Criminology-specific)
For the single strongest rival theory, write one sentence: "If the rival mechanism were operating instead of mine, the crime data would look like ___; instead they look like ___." If you cannot, the design does not yet identify the contribution.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. Criminology is observational — place/person panels where selection is pervasive; foreground DiD/IV/RDD and the selection objection.
detect_design→recommend→ fit withas_handle=true→audit_result.- Observational causal claims: staggered DiD (
callaway_santanna/sun_abraham+bacon_decomposition+honest_did_from_result); IV (effective_f_test+anderson_rubin_ci); RDD (rdrobust+mccrary_test). - Experiments: randomization-based inference,
romano_wolffor many-outcome family-wise control, andmediatefor mediation (not naive controlling-away). - Sensitivity:
oster_delta/sensemakrfor observational claims.
Report the effect size in interpretable units; route the full battery to the appendix/supplement. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
Anti-patterns
- Naive TWFE on staggered policy adoption; clustering below the assignment level
- "Causal" language on a design that only supports association
- Reading trajectory groups as real, fixed offender types
- Ignoring the dark figure / reporting bias when using official counts
- Convenience case selection dressed up as theory-driven
Identification expectations by design (Criminology calibration table)
A defensible Criminology design names the threat reviewers are trained to raise and the move that neutralizes it. Selection into offending and into treatment is the recurring worry.
| Design | Identifying assumption | Threat a referee names | Defensive move |
|---|---|---|---|
| Hot-spot / policing RCT | randomization, no spillover | displacement contaminates controls | measure diffusion vs. displacement |
| Staggered deterrence-policy DID | parallel trends across adopters | bad-comparison TWFE | staggered estimator + pre-trends |
| Life-course turning point | within-person change isolates effect | selection into marriage/work | fixed-effects/hybrid + sensitivity |
| RDD at a sentencing threshold | continuity at the cutoff | manipulation at the line | McCrary density + bandwidth robustness |
Worked micro-example: a deterrence-policy quasi-experiment (illustrative)
A state raises a sentencing penalty in some counties before others. A naive TWFE gives a 9% drop (illustrative); a referee flags invalid comparisons among staggered adopters. Refit with a heterogeneity-robust staggered estimator: flat pre-trends and a credibly identified 4% first-year drop. Cluster at the county (assignment) level; with 14 treated counties add a wild-cluster bootstrap, and note a NIBRS transition could inflate pre-period UCR counts.
Design-stage referee pushback (with the Criminology fix)
- "Selection into treatment/offending." Fix: isolate within-person change or use a quasi-experiment with a stated continuity/parallel-trends defense.
- "Association, not causation." Fix: write the estimand and the licensing assumption; soften prose if the design only supports correlation.
- "Official-records bias unaddressed." Fix: name reported vs. victimization vs. self-report and the dark-figure bias.
- "Clustering below assignment." Fix: cluster at place/agency; few-cluster corrections when units are sparse.
Output format
【Mode】quant-causal / longitudinal-life-course / place-experiment / qualitative
【Estimand or claim】what is being identified/shown (and within- vs. between-person)
【Crime measure】reported / victimization / self-report + dark-figure note
【Key assumption(s)】and how each is defended
【Rival ruled out】the adjudication sentence
【Robustness/sensitivity】planned checks
【Next】crim-data-analysis
Supplementary resources
../../resources/external_tools.md— trajectory/survival/spatial packages and longitudinal datasets../../resources/official-source-map.md— Criminology scope and methodological breadth
Version History
- 1839142 Current 2026-07-05 12:48
