gec-research-design

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Use when defending the research design of a Global Environmental Change (GEC) manuscript — quantitative causal inference, qualitative case work and process tracing, experiments and surveys, or mixed-methods integration. GEC welcomes methodological pluralism but judges each tradition on its own terms. Strengthens the design; it does not write code.

brycewang-stanford By brycewang-stanford schedule Updated 6/10/2026

name: gec-research-design description: Use when defending the research design of a Global Environmental Change (GEC) manuscript — quantitative causal inference, qualitative case work and process tracing, experiments and surveys, or mixed-methods integration. GEC welcomes methodological pluralism but judges each tradition on its own terms. Strengthens the design; it does not write code.

Research Design (gec-research-design)

GEC is methodologically pluralist: quantitative, qualitative, and mixed-methods work are all welcome, provided each is rigorous and connects the conceptual framework (gec-conceptual-framework) to evidence about the human dimensions of environmental change. 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, sampling, or instrument design
  • A reviewer questioned causal claims, case choice, generalization, scale mismatch, or a confound
  • Designing a mixed-methods study and needing to justify the integration
  • Justifying how the design tests the framework's mechanism

Quantitative / causal inference

  • Estimand first. State what you are estimating and the assumptions that license a causal reading (ignorability, parallel trends, exclusion, continuity). Defend them, don't assert them.
  • Designs: panel/DID and event study (use modern staggered-adoption estimators, not naive TWFE), IV (first-stage strength, exclusion), RDD, matching/weighting with balance + sensitivity, multilevel models for nested social-ecological data.
  • Inference: cluster at the level of treatment/assignment; address spatial autocorrelation where relevant; report sensitivity to unobserved confounding.

Qualitative / case-based

  • Case selection justified by design logic (typical, deviant, most/least-likely, paired) — not convenience. Say what the case is a case of and at what scale.
  • Process tracing with explicit tests (hoop, smoking-gun, straw-in-the-wind); state what evidence would have disconfirmed the argument.
  • Source transparency: interviews, archives, fieldnotes, participatory data — plan how they are documented and cited (see gec-submission).

Experiments & surveys

  • Preregister design and primary analyses; report power/MDE; pre-specify subgroups.
  • For survey / choice experiments: sampling frame, treatment realism, and honest generalization claims.
  • Address attention/manipulation checks, attrition, and ethics/IRB and consent.

Mixed-methods integration

  • State the integration logic (sequential, concurrent, embedded) and why it answers the question better than either strand alone — do not staple a few interviews onto a regression.
  • Use joint displays / triangulation; say how convergence or divergence is interpreted.

The scale & adjudication test (GEC-specific)

State the scale at which your design operates (local, regional, global) and how it connects to the multi-scale nature of environmental change. Then, for the single strongest rival explanation, write one sentence: "If the rival were true rather than my argument, the evidence would look like ___; instead it looks like ___."

Anti-patterns

  • Naive TWFE on staggered treatment; clustering at the wrong level; ignoring spatial dependence
  • "Causal" language on a design that only supports association
  • Convenience case selection dressed up as theory-driven
  • Mixed methods where the strands never actually integrate
  • A scale mismatch between the claim and the design (local data, global claim)

Design objections by mode, and the GEC fix

GEC judges each tradition on its own terms, but every mode faces a scale-and-human-dimensions test on top of the usual methodological one. These are the objections that recur and how to disarm them.

Mode The objection a referee writes The fix that holds at GEC
Quant-causal "Causal language, associational design" / "naive TWFE on staggered rollout" State the estimand and license; switch to a modern staggered-adoption estimator; cluster correctly
Qualitative "Convenience case dressed as theory" Justify case selection by design logic and say what the case is a case of, at what scale
Experiment / survey "Treatment realism and generalisation unaddressed" Preregister, report MDE, and bound the generalisation claim to the sampling frame
Mixed methods "Interviews stapled to a regression" Give the integration logic and show why neither strand alone answers the question
Any mode "Scale mismatch — local data, global claim" Match the claim's scale to the design's, or theorise the link across scales explicitly

Worked micro-example (illustrative — mixed-methods coastal study)

A team pairs a household survey on adaptation with process-tracing interviews on why a district fund disbursed unevenly.

  • Weak design: the survey runs a cross-sectional regression labelled "the effect of governance," and a handful of interviews are appended as colour.
  • GEC-defensible design: the estimand is the association between tenure and adaptation uptake at equal exposure (no causal label it cannot support), clustered at the district; the qualitative strand uses smoking-gun tests on the eligibility-rule mechanism. The adjudication sentence: "If exposure rather than tenure drove uptake, low-tenure uptake would track surge depth; instead it tracks tenure at constant depth (0.37 vs 0.62, illustrative)." The integration logic is explanatory-sequential: the interviews explain why the survey pattern arises.
  • Payoff: each strand is rigorous on its own terms and the scale (district, with a stated link to the multi-scale nature of coastal change) matches the claim.

Calibration anchors (hedged)

  • Adjudication bar: a GEC design should rule out the single strongest rival in one written sentence; if it cannot, the design is underspecified.
  • Pluralism bar: quantitative, qualitative, and mixed work are equally welcome — the question is rigour and fit, not method fashion.
  • Confirm any human-subjects, ethics, and preregistration expectations against the journal's current author guidelines, since requirements evolve.

Output format

【Mode】quant-causal / qualitative / experiment-survey / mixed-methods
【Estimand or claim】what is being identified/shown, at what scale
【Key assumption(s)】and how each is defended
【Rival ruled out】the adjudication sentence
【Integration logic】(mixed methods) why both strands
【Next】gec-data-analysis

Supplementary resources

Install via CLI
npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills --skill gec-research-design
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