jmr-methods

name: jmr-methods description: Use when matching the research design to the claim for a Journal of Marketing Research (JMR) manuscript — experimental design (lab and field), causal identification (IV/DiD/RDD/matching), or structural/analytical estimation. Adapts to JMR's dominant genres and to its journal-level rigor and replication expectations. It designs; jmr-data-analysis executes and reports.

Research Design & Identification (jmr-methods)

When to trigger

The design may not actually support the causal, behavioral, or structural claim
You must choose between a lab experiment, a field experiment, and observational identification
A structural model needs an identification and estimation plan
Reviewers will probe confounds, internal/external validity, or "what identifies this?"

Match design to the claim by genre

Behavioral (lab and field experiments)

Manipulation: a clean operationalization of the cause, with manipulation and attention checks; pretests to validate stimuli.
Design: random assignment; factorial designs for interactions; process-by-moderation or measured-vs-manipulated mediation to test the mechanism (not just the effect).
Field experiments: a randomized intervention with a real marketing outcome (purchase, click, retention) strengthens external validity; pre-register where feasible.
Power: a priori power analysis sized for the interaction, not just the main effect; plan multiple studies (lab establishes mechanism; field shows it in market).

Modeling / econometric (observational and structural)

Causal identification: choose the strategy the variation supports — IV/2SLS, difference-in-differences (modern estimators for staggered adoption), regression discontinuity, matching, or control-function approaches — and defend the exclusion/parallel-trends/continuity assumptions.
Structural estimation: random-coefficient (BLP-style) demand, dynamic/discrete-choice, or hierarchical-Bayes models; state what data variation identifies each parameter and the estimator (GMM/MLE/MCMC).
Data: scanner/panel (NielsenIQ-IRI), clickstream, platform logs, or field-collaboration data; document sample construction and selection.

Journal-level expectations that shape design

The eventual report must carry exact p-values (three digits), standard errors, and effect sizes — design and power your studies so these are meaningful, not borderline.
Plan the Web Appendix from the start: full stimuli, additional studies, estimation details, and robustness go there ('W'-prefixed), keeping the print paper within 50 pages.
Plan replication: per AMA transparency policy you must be able to share code, instruments/stimuli, and materials, and provide data/materials before final acceptance — build clean, documented pipelines now.

Execution bridge (StatsPAI / Stata MCP)

For the empirical / causal lane, estimate and audit rather than only specify. Full map: execution-with-mcp. JMR mixes experiments, structural models, and quasi-experiments; the chain below serves the experimental and reduced-form lanes, while structural demand estimation uses its own toolkit.

detect_design → recommend → fit with as_handle=true → audit_result to enumerate the checks the design owes.
Panel / 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 and romano_wolf for 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.

Anti-patterns

A single-cell or confounded manipulation that cannot isolate the cause.
Claiming causality from cross-sectional correlation with no identification strategy.
A structural model with an unstated or hand-waved identification argument.
Underpowered interaction tests; optional-stopping / unreported flexibility.
Designing studies that cannot meet the exact-statistics or replication mandates.

Methods pass for Journal of Marketing Research

Run this as a concrete capability pass. First lock the marketing construct, data or study design, inference threat, and managerial or consumer implication; then test whether the manuscript addresses marketing reviewers who expect measurement, experiments, consumer behavior, or empirical strategy to answer a marketing question.

Primary move: Name the estimand or objective, assumptions, diagnostics, robustness checks, and failure modes before accepting the method as venue-ready.
Decision ledger: return claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.
Sibling comparison: compare against Marketing Science for quantitative modeling, Journal of Marketing for strategic managerial contribution, Journal of Consumer Research for consumer-theory depth; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
Verification floor: before submission-ready advice, re-open resources/official-source-map.md for volatile rules and name the one unresolved fact that could change the recommendation.

Output format

[Target] JMR
[Genre] behavioral / modeling-econometric
[Claim] causal / structural / descriptive
[Design] experiment(lab/field) / IV-DiD-RDD-matching / structural
[Identification] assumption + the variation that identifies it
[Power & studies] sized for interaction? lab+field plan?
[Web Appendix / replication] planned
[Next skill] jmr-data-analysis