jm-methods
GitHub辅助为《Journal of Marketing》稿件选择并辩护研究设计。根据实质性问题匹配实验、田野调查等“大帐篷”方法，强调因果识别、现场真实性和管理相关性，确保设计能支持实证主张。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jm-methods -g -y
SKILL.md
Frontmatter
{
"name": "jm-methods",
"description": "Use when choosing and defending the research design for a Journal of Marketing (JM) manuscript — matching a \"big tent\" method (experiment, field study, survey, secondary data, qualitative) to a substantive marketing question, with field realism and identification in mind. Designs the study; it does not run the estimation (jm-data-analysis) or frame the contribution (jm-contribution-framing)."
}
Research Design, Big-Tent (jm-methods)
When to trigger
- The question is set and you must choose a design that can actually answer it
- A reviewer will ask whether the method supports a causal or managerial claim
- You are deciding between a clean lab experiment and a messier but realer field study
- You have secondary (scanner/CRM/financial) data and need an identification strategy
JM's "big tent" — let the question pick the method
JM is methodologically pluralistic: it welcomes primary data (experiments, field studies, surveys, interviews, observational data) and secondary data, and champions empirics-first research grounded in real-world phenomena. No single method is privileged. The design rule at JM is therefore: choose the method that most credibly answers a substantive question and supports a managerially relevant claim — not the most sophisticated technique. Work centered on mathematical/statistical methods for their own sake is out of scope (route to Marketing Science / JMR); methods here are servants of the substantive insight.
Match design to claim
| Substantive claim / data situation | Design |
|---|---|
| Causal effect of a marketing action on consumer response | Randomized experiment (lab or online panel) |
| Causal effect in a real market with realism/external validity | Randomized field experiment with a firm/platform |
| Process / mechanism (why an effect occurs) | Experiment with mediation + moderation-of-process designs |
| Preferences, trade-offs, willingness-to-pay | Survey / choice-based conjoint / discrete-choice experiment |
| Market-level dynamics from observational data | Panel with FE; DiD / event study; synthetic control; IV |
| Customer-base behavior (CLV, churn, response) | Longitudinal CRM/transaction modeling |
| Meaning, emergent constructs, theory-building from practice | Qualitative (interviews, ethnography, archival text) |
Combine methods (multi-study or mixed) when one design cannot establish both internal validity (the effect is real) and external/managerial validity (it matters in the market).
Field realism and managerial validity
JM prizes evidence that travels to real decisions. Strengthen the design by: securing a field setting or firm partner where feasible; choosing outcomes managers act on (sales, CLV, conversion, welfare) over proxy attitudes alone; sampling a population the claim should generalize to; and documenting the real-world stimulus, market, and time frame so a practitioner recognizes the setting.
Design for transparency up front
JM requires a replication packet at conditional acceptance and encourages preregistration. Build this in now: preregister experiments (you will later supply anonymized links and an attestation), version-control analysis scripts, and log sample-construction and exclusion rules as you go — not retroactively.
Execution bridge (StatsPAI / Stata MCP)
For the empirical / causal lane, estimate and audit rather than only specify. Full
map: execution-with-mcp. JM is empirical marketing — field experiments, panel/CRM data, and quasi-experiments; randomization inference for experiments, DiD / IV for observational claims.
detect_design→recommend→ fit withas_handle=true→audit_resultto enumerate the checks the design owes.- Panel / staggered DiD:
callaway_santanna/sun_abraham+bacon_decompositionhonest_did_from_result. IV:effective_f_test+anderson_rubin_ci. RDD:rdrobust+mccrary_test.
- Experiments: randomization-based inference and
romano_wolffor the many-outcome family-wise correction reviewers expect.
Match the toolchain to the reviewer pool, and report the effect size the venue wants. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
Checklist
- Method chosen to fit the substantive claim, not for sophistication
- Internal validity (randomization/identification) addressed
- External/managerial validity (field realism, actionable outcomes) addressed
- Multi-study / mixed design where one method cannot do both
- Outcomes managers/policy makers care about are measured
- Preregistration planned (experiments); scripts and exclusion rules logged
- Power/sample-size justified a priori for experiments
Anti-patterns
- Method-driven paper: a clever estimator in search of a question (out of scope at JM).
- Lab-only causal claim asserted to hold in the market with no field/external evidence.
- Endogenous treatment in secondary data with no identification strategy.
- Attitude proxies standing in for outcomes managers actually move.
- Retrofitted transparency: no preregistration, exclusions documented after the fact.
Output format
【Substantive claim】[...]
【Design】experiment / field experiment / survey-conjoint / panel-DiD / qualitative / mixed
【Internal validity】randomization / identification: [...]
【External & managerial validity】field realism, actionable outcomes: [...]
【Multi-study plan】[...]
【Transparency】preregistration + script/exclusion logging: [...]
【Next step】jm-data-analysis
Version History
- 1839142 Current 2026-07-05 13:49
