jcp-data-analysis

Research

Use when analyzing experimental data for a Journal of Consumer Psychology (JCP) manuscript — ANOVA/regression on the effect, measured and experimental mediation, moderation and moderated mediation, measurement of the process, and the rigor-era reporting standards. Analyzes the studies; it does not design them (jcp-methods).

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Data Analysis (jcp-data-analysis)

When to trigger

Your effect is significant but the process evidence does not yet hold up
You ran mediation but a reviewer calls it correlational or under-powered
A moderation is predicted but the interaction is messy or the simple effects are not probed
You need to report results to JCP's post-rigor-reform standards (effect sizes, CIs, exclusions)
The measure of your psychological process is noisy or its validity is in question

Analyze the process, not just the p-value

JCP's contribution is a mechanism, so the analysis must make the process visible and defensible. The headline test of the effect (typically ANOVA or regression with the manipulated IV) is necessary but not sufficient; the paper lives or dies on whether the mediation/moderation evidence supports the proposed psychological process and rules out rivals. Report estimates with effect sizes and confidence intervals, exact statistics, and full Ns before and after pre-specified exclusions. APA reporting style is the house norm.

The analysis toolkit by link in the chain

Link	Standard analysis	What reviewers look for
Existence of effect	t-test / ANOVA / OLS with the manipulated IV	clean cells, effect size (d, η²), CI, no covariate fishing
Measured mediation	bootstrapped indirect effect (e.g., PROCESS / lavaan), bias-corrected CI	indirect effect with CI excluding 0; honesty that this is correlational evidence on the mediator
Experimental mediation	causal-chain design or manipulated-mediator analysis	the manipulation of M moves Y as the theory predicts
Moderation	regression interaction; ANOVA factorial	interaction term + probed simple effects (spotlight/floodlight), not just a significant interaction
Moderated mediation	conditional indirect effects (index of moderated mediation)	the index, with CI, and conditional indirect effects by moderator level

Prefer experimental/causal-chain mediation and moderation-of-process over measured-mediator-only inference: JCP reviewers now treat a bootstrapped indirect effect on a self-reported mediator as suggestive, not dispositive, because it cannot establish the causal direction of M → Y.

Measuring the psychological process

Validate the mediator measure: report reliability (α/ω) for multi-item scales; show the measure captures the intended construct and discriminates from confounds (mood, arousal, difficulty).
Rule out alternative mediators statistically: include rival process measures and show the focal mediator carries the effect when they are modeled together.
Avoid mediator-as-manipulation-check confusion: a manipulation check is not a mediator; the mediator is the downstream mental state.

Rigor-era reporting (post-2010s consumer-psych reforms)

Report exact test statistics, p-values, effect sizes, and CIs — not just "p < .05."
Disclose all conditions and measures collected; do not hide arms (the disclosure norm).
Report sample size determination and adherence to (or deviation from) the pre-registration.
State exclusions and their rule transparently, with Ns before/after.
Avoid asterisk-only tables; report the numbers a reader needs to assess the process.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JCP is experimental consumer psychology; randomization inference, mediation done right (mediate, not naive controlling-away), and family-wise corrections matter most.

Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg — report the adjusted threshold.
OVB sensitivity: oster_delta / sensemakr.
Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley; multilevel data → cluster at the right level.
Re-fit off one handle: audit_result(result_id) lists the missing checks and the exact suggest_function for each.
Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.

Checklist

Effect reported with exact stats, effect size, and CI; cells and Ns clear
Mediation uses bootstrapped/bias-corrected CIs; measured-only mediation is labeled correlational
At least one stronger-than-Baron-Kenny process test where the claim is causal
Moderation: interaction reported and simple effects probed (spotlight/floodlight)
Moderated mediation: index of moderated mediation + conditional indirect effects
Mediator measure reliability reported; rival mediators modeled and ruled out
Exclusions pre-specified; all conditions/measures disclosed; preregistration deviations noted

Anti-patterns

Indirect-effect worship: a significant bootstrapped indirect effect treated as proof of causal process
Interaction without simple effects: a significant interaction with no spotlight/floodlight probing
Covariate fishing: adding controls until the effect appears, undisclosed
Hidden arms: dropping conditions or DVs that didn't work without reporting them
p-only reporting: asterisks instead of effect sizes and CIs
Mediator confound: a "mediator" that is just mood/difficulty the manipulation also moved

Output format

【Effect】test, stat, effect size, CI, cell Ns
【Mediation】measured / experimental; indirect effect + CI; correlational caveat if measured-only
【Moderation】interaction + probed simple effects (spotlight/floodlight)
【Moderated mediation】index + conditional indirect effects (if applicable)
【Process measure】reliability + rival mediators ruled out
【Rigor disclosures】exclusions, all conditions/measures, preregistration deviations
【Next skill】jcp-contribution-framing