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arpsych-transparency-and-reproducibility

Use when documenting the literature search and making any embedded meta-analysis reproducible for an Annual Review of Psychology (ARPsych) review. C…

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技能内容

Transparency & Reproducibility (arpsych-transparency-and-reproducibility)

When to trigger

  • The review documents a systematic search and you must report it reproducibly
  • The review embeds a meta-analysis or any new quantitative synthesis
  • You are deciding what to deposit (search log, coding sheet, effect-size data, code)
  • A reader or the Committee should be able to verify how the literature was selected

A review reports no new data — so transparency bites elsewhere

A pure narrative review has no dataset of its own, so the transparency obligation does not look like a primary-paper replication package. It bites on two things:

  1. How the literature was found and selected — the search protocol from arpsych-literature-synthesis, written up so a reader could reproduce the coverage.
  2. Any quantitative synthesis the review itself contributes — if you compute pooled effects, that is original analysis, and it must be reproducible (检索于 2026-06;以官网为准).

Post-replication-crisis, ARPsych readers expect both, and a review that asserts "the literature shows…" with no documented basis reads as less authoritative.

If the review is narrative (no meta-analysis)

  • Report the search: databases, terms, date range, inclusion/exclusion, and the stopping rule — a short, near-PRISMA-style account suffices.
  • State selection logic: why these studies and not others (especially when the field is large and you are selective).
  • Be explicit about replication status of contested effects (this is part of transparency, not just balance).

If the review embeds a meta-analysis

Then you have run original analysis and must meet quantitative-synthesis standards:

| Requirement | What to provide |

|-------------|-----------------|

| PRISMA-style flow | search → screening → included, with counts at each step |

| Coding protocol | how effects were extracted/coded; inter-coder reliability |

| Effect-size dataset | the extracted effects + moderators, deposited |

| Analysis code | scripts reproducing the pooled estimates and plots |

| Heterogeneity + bias | I², moderators, funnel/publication-bias diagnostics |

| Preregistration (if applicable) | protocol/PROSPERO registration where the synthesis was prospective |

Deposit data and code in a public repository (e.g., OSF) and cite the DOI in the review.

Required declarations (检索于 2026-06;以官网为准)

Annual Reviews requires authors to disclose potential sources of bias / conflicts of interest and to state funding; prepare these per the author pages. AI tools are not authors. Re-confirm the exact disclosure format on the live Annual Reviews pages.

Checklist

  • [ ] Search protocol written up reproducibly (databases, terms, dates, in/out, stopping rule)
  • [ ] Selection logic stated where coverage is selective
  • [ ] Replication status of contested effects made explicit
  • [ ] If meta-analytic: PRISMA-style flow with counts
  • [ ] If meta-analytic: coding protocol + inter-coder reliability reported
  • [ ] If meta-analytic: effect-size data + analysis code deposited (OSF DOI cited)
  • [ ] If meta-analytic: heterogeneity and publication-bias diagnostics reported
  • [ ] COI / potential-bias disclosure + funding prepared; AI not listed as author

Anti-patterns

  • "The literature shows…" with no documented search behind the claim
  • Reporting pooled effects with no deposited data or code (irreproducible meta-analysis)
  • A meta-analysis with no heterogeneity or publication-bias assessment
  • Treating a review's transparency like a primary-paper replication package (wrong object)
  • Omitting the conflict-of-interest / potential-bias disclosure Annual Reviews requires
  • Listing an AI tool as an author or hiding its use where disclosure is required

Output format

【Review type】narrative | embedded-meta-analysis
【Search transparency】protocol documented reproducibly? Y/N
【If meta-analysis】PRISMA flow + coding + reliability? Y/N
【Open materials】effect data + code deposited (OSF DOI)? Y/N | N/A
【Heterogeneity / bias】I² + funnel/pub-bias reported? Y/N | N/A
【Declarations】COI / bias disclosure + funding prepared; AI not author? Y/N
【Next step】→ arpsych-editor-strategy (align scope/timeline with the Editor)

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原文件路径Annual-Review-of-Psychology-Skills/skills/arpsych-transparency-and-reproducibility/SKILL.md

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