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name: jppm-methods description: Use when choosing and designing the evidence for a Journal of Public Policy & Marketing (JPP&M) manuscript — experiments with policy-realistic stimuli, quasi-experimental policy evaluation (DiD, RDD, synthetic control), surveys, field data, or meta-analysis. Designs the studies; it does not estimate them (jppm-data-analysis).

Methods & Evaluation Design (jppm-methods)

When to trigger

• Deciding whether the question needs an experiment, a policy evaluation, a survey, or a synthesis
• A policy rolled out and you must find a credible counterfactual before claiming impact
• Your label/disclosure experiment uses stimuli no agency could ever mandate
• The sample excludes the very population the policy is meant to protect
• You are combining lab and field evidence and need the pieces to carry distinct weight

Methodological pluralism, disciplined by the policy question

JPP&M accepts a wider methods palette than most marketing journals — randomized experiments, quasi-experimental evaluation, surveys, field and archival data, meta-analysis, qualitative work — but the choice must follow from the policy inference required. Two questions dominate: Would the proposed instrument work? (prospective — usually experiments with realistic stimuli) and Did the enacted instrument work? (retrospective — evaluation designs with explicit counterfactuals). A submission that answers the retrospective question with before/after trends, or the prospective one with fantasy stimuli, fails regardless of statistical polish. Missing counterfactual reasoning in an evaluation design is one of this journal's known desk-reject patterns.

Prospective designs: policy-realistic experiments

• Mandatable stimuli. Test warning/label/disclosure formats an agency could actually require — real estate on a package, formats from the live rulemaking (e.g., FDA front-of-package proposals), not a full-screen banner no rule would compel.
• Treatment contrasts = decision options. Conditions should map to the regulator's choice set (status quo vs. proposed rule vs. stricter alternative), so each pairwise contrast answers a decision.
• Consequential outcomes. Choice with real stakes, purchases, incentivized behavior — self-reported intentions alone are weak currency for a claim that a rule will change behavior.
• Policy-relevant samples. Recruit the protected population (smokers for tobacco warnings, low-income households for financial disclosures, parents for children's marketing). A student panel cannot carry a vulnerability claim; when using Prolific/CloudResearch, screen and quota accordingly.
• Marketplace noise. Add realistic competing information (cluttered shelf, competing claims); effects that survive noise are the ones that survive markets.

Retrospective designs: evaluation with a counterfactual

Design	Use when	JPP&M-specific cautions
Difference-in-differences	policy adopted in some states/markets/categories, not others	staggered adoption needs heterogeneity-robust estimators; argue parallel trends behaviorally, not just visually
Regression discontinuity	eligibility threshold or size cutoff assigns exposure	check manipulation at the cutoff (firms sort!); effects are local to the threshold
Synthetic control	one large unit treated (a city soda tax, a national ban)	pre-period fit and placebo runs are the argument
Event study	timing of enforcement/announcement is sharp	anticipation by firms and media coverage blur the event date
Interrupted time series	no untreated comparison exists at all	weakest option; state so and bound the claims

Firms' strategic responses are both a threat and a finding: reformulation, pre-emptive compliance, or channel-shifting can contaminate the comparison group — design to detect it (untreated outcomes, supply-side data) rather than assume it away.

Surveys, qualitative work, and synthesis

• Surveys earn their place for constructs no archive holds (perceived deception, privacy concern, financial anxiety) — use validated scales and probability or well-quota'd samples when claims are population-level.
• Qualitative designs are welcome for vulnerable populations whose experience frames the policy problem; document access, consent, and IRB care to a higher standard, and avoid designs that further burden participants.
• Meta-analysis suits mature streams (warning-label effects, disclosure formats); code moderators the regulator controls (format, placement, dose).

Checklist

• The design answers the paper's policy question (prospective vs. retrospective) directly
• Experimental stimuli are mandatable and conditions map to the regulator's choice set
• The sample includes the population the policy targets; vulnerable groups are powered, not token
• Evaluations name the counterfactual and the assignment mechanism explicitly
• Firm strategic response is measured or ruled out, not assumed absent
• Ethics/IRB treatment matches the sensitivity of the population studied
• Pre-registration or a pre-analysis plan is in place for confirmatory studies

Anti-patterns

• Before/after theater: a pre/post trend presented as policy impact with no comparison group
• Fantasy stimuli: disclosure formats no agency could mandate, generalized to regulation
• Convenience-sample vulnerability claims: conclusions about protected groups from panels that exclude them
• Intentions-only evidence for behavior-change claims
• One-method dogma: forcing an experiment onto a question that demands field variation, or vice versa
• Sorted cutoffs: an RDD where firms demonstrably manipulate the threshold, unexamined

Output format

【Policy question type】prospective (would it work) / retrospective (did it work)
【Design】experiment / DiD / RDD / synthetic control / survey / meta-analysis / qualitative
【Counterfactual】comparison group + assignment logic (retrospective) or control condition logic (prospective)
【Stimuli & sample】mandatable formats; policy-target population included
【Firm response plan】how strategic reactions are detected or bounded
【Next skill】jppm-data-analysis