Jams Methods

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

• Genre named; design matched to the causal/behavioral/structural claim
• Survey: reliability + AVE + discriminant validity (Fornell–Larcker / HTMT) reported
• Survey: CMV designed against and tested (not Harman alone)
• Measurement model validated before the structural model; fit indices reported
• Secondary data: identification strategy named and its key assumption defended
• Experiment: manipulation/attention checks; mediation + moderation; power for interaction
• Meta: coding reliability + moderators + publication-bias checks
• Causal language never exceeds what the design identifies

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

Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-the-Academy-of-Marketing-Science-Skills/skills/jams-methods/SKILL.md