Bjps research design

Research Design (bjps-research-design)

BJPS accepts many methodologies but is demanding about each. The design must credibly connect the argument (bjps-theory-building) to evidence, and — because BJPS is international and cross-subfield — make the case generalize beyond a single setting. This skill is mode-aware: pick the section that matches your work and defend it against the strongest alternative explanation.

When to trigger

• Specifying identification, case selection, or experimental design
• A reviewer questioned causal claims, case choice, external validity, or a confound
• Preparing a pre-analysis plan for an experiment or observational study
• Justifying why your design adjudicates the rival account from bjps-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. survey/conjoint), DID/event study (use modern staggered-adoption estimators, not naive TWFE), IV (first-stage strength, exclusion, weak-IV-robust inference), RDD (density/manipulation tests, bandwidth robustness), matching/weighting with balance + sensitivity.
• Inference: cluster at the level of treatment assignment; randomization inference for experiments; multiple-comparison adjustment when testing many implications.
• Sensitivity: how strong must an unobserved confounder be to overturn the result?

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, and what it generalizes to.
• Process tracing with explicit tests (hoop, smoking-gun, straw-in-the-wind); state what evidence would have disconfirmed the argument.
• Source transparency: archives, interviews, fieldnotes — plan how they will be documented and cited (see bjps-transparency-and-data).

Experiments (lab / survey / field)

• Preregister the design and primary analyses; report power/MDE; pre-specify subgroups.
• Address attention/manipulation checks, attrition, and ethics/consent.
• For survey experiments: sampling frame, treatment realism, and the generalization claim — BJPS reviewers ask whether a single-country experiment speaks to a general mechanism.

Formal-empirical linkage

• Make the empirical test follow from the model's comparative statics, not a loose analogy.
• Distinguish predictions that are unique to your model from those shared with rivals.

The adjudication test (BJPS-specific)

For the single strongest rival explanation, write one sentence: "If the rival were true rather than my argument, the data would look like ___; instead they look like ___." Then add the generalization sentence: "This design speaks beyond my case because ___." If you cannot write both, the design does not yet identify a contribution of general interest.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. BJPS is comparative/IR-heavy — cross-country panels with confounded institutions; emphasize fixed effects, clustering, and weak-IV-robust inference.

• 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.

Anti-patterns

• Naive TWFE on staggered treatment; clustering at the wrong level
• "Causal" language on a design that only supports association
• Convenience case selection dressed up as theory-driven
• A single-country experiment over-generalized to "people" with no caveat about context
• A design that cannot distinguish your argument from the leading alternative

Output format

【Mode】quant-causal / qualitative / experiment / formal-empirical
【Estimand or claim】what is being identified/shown
【Key assumption(s)】and how each is defended
【Rival ruled out】the adjudication sentence
【Generalizes because】the cross-case generalization sentence
【Robustness/sensitivity】planned checks
【Next】bjps-data-analysis

What BJPS reviewers ask of each design mode

Mode	The decisive design question	The move that satisfies it
Quant-causal	Does the design license the causal word, and does it travel?	Estimand + assumption + sensitivity, plus the generalization sentence
Qualitative	Is case selection design-driven, and a case of what?	Justify selection logic; state the population the case speaks to
Experiment	Is a single-country result framed as a general mechanism?	Pre-register; report MDE; caveat context; argue the mechanism travels
Formal-empirical	Do the tests follow the comparative statics?	Map each prediction to a parameter the model moves

Calibration anchors (hedged)

• BJPS judges each tradition on its own terms — do not force a regression template onto qualitative, formal, or interpretive work, and do not excuse a weak design by appeal to pluralism.
• The international remit adds a second bar beyond identification: a clean design that cannot speak past its single setting is a positioning weakness as well as a generalization one.

Supplementary resources

• ../../resources/external_tools.md — design/identification packages (R/Stata/Python) and CAQDAS for qualitative work
• ../../resources/code/ — modern DiD/IV/RDD/DML command chain to adapt
• ../../resources/official-source-map.md — preregistration and transparency notes