jpsp-data-analysis

Data Analysis & Internal Meta-Analysis (jpsp-data-analysis)

JPSP reviewers are methodologically sophisticated and the journal requires JARS reporting and TOP Level 2 transparency. Two things set JPSP analysis apart from short-report work: (1) you are analyzing a set of studies, and (2) the section expects you to integrate across them — an internal meta-analysis is a core move, explicitly prioritized in IRGP guidance. This skill covers execution and reporting; design lives in jpsp-study-design.

When to trigger

• Analyzing studies and building the results sections
• Pooling effects across your own studies (internal meta-analysis)
• A reviewer asked for robustness, mechanism, or alternative-explanation analyses
• Reconciling preregistered vs. exploratory analyses

Analysis norms JPSP expects

1. Effect sizes with uncertainty. Report standardized effect sizes and confidence (or credible) intervals, not just p-values/stars; state the substantive magnitude, per JARS.
2. Internal meta-analysis across studies. Pool the comparable effects from all studies (including any reported only in the supplement) into a random-effects estimate with a forest plot; report heterogeneity. This is the package's strongest summary and an explicit section expectation.
3. Accessible results sections. Write results for readers with general statistical expertise; relegate complex justification to tables, notes, or supplements (IRGP house rule; verify).
4. Honest robustness. Show specifications that could break an effect (alternative measures, exclusions, estimators); report what held and what did not.
5. Mechanism done right. Report mediation/process with bootstrapped indirect effects and CIs; do not over-claim causal mediation from cross-sectional data.
6. Alternative explanations. Provide the focused alternative-explanation analyses the section asks for (construct validity, confounds, alternative mediators/causal models).
7. Registered vs. exploratory. Clearly separate confirmatory (preregistered) from exploratory analyses; reconcile and justify any deviations from the plan.
8. Measurement. Report reliability and, where relevant, measurement invariance (especially PPID individual-differences and dyadic work).

Reproducibility while you work (not at the end)

• A master script regenerates every table and figure from the raw/constructed data, per study.
• Set and report seeds for bootstrap, simulation, and any stochastic step.
• Pin software/package versions (renv.lock, requirements.txt).
• Post data/code/materials to a trusted repository with a persistent identifier (TOP Level 2).

Anti-patterns

• Stars-only tables with no effect sizes or intervals
• Reporting each study in isolation and skipping the internal meta-analysis
• p-hacking / fishing for a significant interaction; HARKing exploratory results into hypotheses
• Over-claiming causal mediation from correlational designs
• Burying a failed study instead of pooling it honestly into the meta-analysis

Reviewer-pushback patterns and the analysis-side fix

These are the recurring post-credibility-revolution objections a JPSP section reviewer raises about results, and the move that defuses each. The fix is an analysis-and-reporting fix — design lives in jpsp-study-design.

Reviewer says	What they distrust	The JPSP-fit fix
"Stars only — where are the effect sizes?"	You hid magnitude behind significance	Report d/r/β with 95% CIs and a sentence on substantive meaning, per JARS
"Each study reads in isolation"	No cumulative claim	Add the internal meta-analysis: pooled estimate + forest plot + heterogeneity (I²)
"This could be a confound"	Alternative causal account	A focused alternative-explanation analysis or a study that manipulates the confound
"Mediation is over-claimed"	Causal language on cross-sectional data	Bootstrapped indirect effect with CI, hedged: "consistent with, not proof of, the path"
"Was this predicted?"	Suspected HARKing	A confirmatory/exploratory table mapping each test to the preregistration; flag deviations

Worked micro-example: pooling a preregistered three-study package

Illustrative numbers — invented to show the reporting logic, not real results.

Three ASC studies estimate the same gratitude→construal effect. Per-study d (95% CI): S1 = 0.34 [0.10, 0.58], S2 = 0.21 [−0.02, 0.44], S3 = 0.40 [0.16, 0.64]. S2 alone is "non-significant" — the trap that invites a reviewer to call the package inconsistent. The internal meta-analysis instead reports a random-effects pooled d ≈ 0.31, 95% CI [0.18, 0.45], low heterogeneity (I² ≈ 12%). That one sentence — "across three preregistered studies, pooled d = 0.31 [0.18, 0.45]" — is the strongest summary in the paper and exactly what an ASC reviewer means by integrative analysis. Report S2 inside the pool, not as a footnote.

Output format

【Per-study effects】effect size + CI + substantive meaning
【Internal meta-analysis】pooled random-effects estimate + heterogeneity + forest plot
【Mechanism】indirect effect + bootstrapped CI (design caveats)
【Robustness】specs that could break it → what held
【Registered vs exploratory】clearly separated?
【JARS + repository】reported to standard + data/code/materials posted? [Y/N]
【Next】jpsp-tables-figures

Supplementary resources

• ../../resources/external_tools.md — metafor (internal meta-analysis), lavaan/PROCESS, effect-size + reliability tools
• ../../resources/official-source-map.md — JARS, TOP Level 2, and IRGP integrative-analysis expectation