| name | bjps-research-design |
| description | Use when defending the research design of a British Journal of Political Science (BJPS) manuscript — causal identification for quantitative work, case selection and process tracing for qualitative work, experimental and survey-experimental design, or formal-empirical linkage. BJPS judges each tradition on its own terms. Strengthens the design; it does not write code. |
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
