crim-research-design

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](../../../shared-resources/empirical-methods/execution-with-mcp.md). Criminology is observational — place/person panels where selection is pervasive; foreground DiD/IV/RDD and the selection objection.

• detect_design → recommend → fit with as_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_wolf for many-outcome

family-wise control, and mediate for mediation (not naive controlling-away).

• Sensitivity: oster_delta / sensemakr for 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

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](../../resources/external_tools.md) — trajectory/survival/spatial packages and longitudinal datasets
• [../../resources/official-source-map.md](../../resources/official-source-map.md) — Criminology scope and methodological breadth