jppm-methods

Research

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).

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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