| name | jams-methods |
| description | Use when matching the research design to the claim for a Journal of the Academy of Marketing Science (JAMS) manuscript — construct validity and measurement, survey/SEM design, secondary-data identification, experiments, or meta-analysis. Designs the study and stress-tests validity; jams-data-analysis executes and reports the estimates. |
Research Design, Measurement & Identification (jams-methods)
When to trigger
- The design may not actually support the theoretical claim
- Constructs are measured but scale validity (reliability, convergent, discriminant) is unestablished
- A causal claim rests on a cross-sectional survey or OLS-with-controls
- Reviewers will probe common method variance, endogeneity, manipulation validity, or coding reliability
Match design to claim by genre
JAMS publishes several empirical genres; the validity question is genre-specific. Pick the genre, then clear its bar.
Survey + SEM/PLS (strategy, B2B, services, branding)
- Construct validity is the gate. Report reliability (composite reliability / Cronbach's α), convergent validity (AVE ≥ .50, loadings), and discriminant validity (Fornell–Larcker and/or the HTMT ratio — JAMS reviewers increasingly expect HTMT).
- Common method variance (CMV): design against it (temporal/source separation, marker variable) and test for it (Harman is weak — prefer a marker-variable or CFA-marker approach). CMV is a top reason survey papers stall at JAMS.
- Measurement before structure: establish the measurement model (CFA) before interpreting the structural model; report fit (χ²/df, CFI, TLI, RMSEA, SRMR).
- Formative vs. reflective: justify the specification; do not run a reflective CFA on a formative construct.
- Endogeneity in survey models: a clean SEM does not buy causality — address it (instruments, Gaussian-copula control, panel design) where the claim is causal.
Secondary-data econometrics (scanner, CRM, marketing–finance)
- Identification is the gate. Name the strategy the variation supports — DiD (modern staggered estimators), IV/2SLS, RDD, matching, control function — and defend the exclusion / parallel-trends / continuity assumption explicitly.
- Address endogeneity of marketing actions (price, advertising, entry are chosen, not random); a lagged regressor is not identification.
- Cluster inference at the assignment level; report first-stage strength / pre-trends as relevant.
Behavioral experiment
- Manipulation validity: clean operationalization, manipulation and attention checks, pretested stimuli.
- Mechanism, not just effect: measured-or-manipulated mediation and process-by-moderation; power sized for the interaction, not the main effect.
- Multi-study logic: lab establishes the mechanism; a field study or a consequential outcome adds external validity (a JAMS strength).
Meta-analysis
- Pre-specified sampling frame and search protocol; transparent inclusion/exclusion.
- Inter-coder reliability reported; effect-size metric and artifact corrections justified.
- Moderator analysis that tests the theory, plus publication-bias diagnostics.
Construct validity is JAMS's most-policed area
Because so many JAMS papers are survey-based, the measurement model is where reviewers concentrate fire. Make the chain airtight: each construct has a conceptual definition first, then a measure whose items match that definition (content validity), then evidence of reliability (CR/α), convergent validity (AVE ≥ .50, significant loadings), and discriminant validity. For discriminant validity, report HTMT (threshold typically .85/.90) in addition to Fornell–Larcker — reviewers increasingly treat Fornell–Larcker alone as insufficient. If you adapt an existing scale, justify the changes and re-validate; if you create a new scale, follow a recognized scale-development procedure (item generation, purification, validation on a fresh sample). A reflective construct measured with formative items (or vice versa) is a fatal mismatch.
Tie the design back to the claim and the manager
A method is "JAMS-ready" only when it supports both the theoretical claim and the managerial reading. After choosing the design, write one line: the variation / manipulation that identifies the focal effect, and one line: the managerial quantity the estimates will produce. If the design cannot deliver a managerially interpretable magnitude (e.g., a standardized path with no translatable unit), plan now to add a study, an elasticity, or a scenario analysis — discovering this after data collection is expensive. Hand the executed plan to jams-data-analysis, which carries the same managerial-magnitude discipline into reporting.
Sample, power, and data provenance
- Sample frame and response. For surveys, justify the sampling frame, report the response rate, and test for non-response bias (e.g., early-vs-late respondents) and informant quality (key-informant competence for B2B/firm-level constructs).
- Power. Size the study for the effect that carries the contribution — usually an interaction or an indirect effect, which needs more power than a main effect. State the a priori power analysis.
- Provenance. Name the data source (panel/scanner such as NielsenIQ/Circana, CRM, a field partner, a Prolific/Qualtrics panel) and document sample construction, screening, and any exclusions — JAMS reviewers and the data-availability policy both expect a clear data trail.
- Multi-source / multi-wave designs strengthen both causal credibility and the CMV defense; flag where a single-source cross-section limits the causal claim and adjust the language accordingly.
Pre-registration and replicability
For experiments and field studies, pre-registration (AsPredicted / OSF) strengthens the inference and pre-empts a HARKing or p-hacking critique; report any deviations from the plan. Across all genres, design the data and analysis pipeline now so it can satisfy the Springer data/code availability policy at acceptance — keep raw data, cleaning scripts, and estimation code organized and documented from the start rather than reconstructing them under deadline. A clean, shareable pipeline is also the cheapest insurance against a reviewer who asks to see a specific robustness check.
Execution bridge (StatsPAI / Stata MCP)
For the empirical / causal lane, estimate and audit rather than only specify. Full
map: execution-with-mcp. JAMS is empirical marketing with much survey-based SEM; the chain below serves causal / quasi-experimental designs and many-outcome corrections.
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.
Checklist
Anti-patterns
- Treating a good-fitting SEM as evidence of causality
- Discriminant validity by Fornell–Larcker only when HTMT would fail
- Harman's single-factor test offered as the whole CMV defense
- Endogenous marketing regressors with a lagged variable passed off as a fix
- A single-cell or confounded manipulation that cannot isolate the cause
- A meta-analysis with no inter-coder reliability or publication-bias check
Output format
【Genre】survey-SEM / secondary-data / experiment / meta-analysis
【Claim】causal / structural / descriptive
【Construct validity】reliability + AVE + discriminant (FL/HTMT): pass/fix
【CMV (survey)】design + test (marker/CFA-marker): pass/fix/NA
【Identification (secondary)】strategy + key assumption: [...] / NA
【Experiment】manipulation + mediation + moderation + power: pass/fix/NA
【Meta】frame + coding reliability + bias checks: pass/fix/NA
【Next skill】jams-data-analysis
