Review Process (devpsych-review-process)

SkillMedia

Use when you need to understand how Developmental Psychology (APA) evaluates a manuscript, masked peer review, editorial weighting of developmental significance, design rigor (age/cohort, invariance, attrition), JARS reporting, and TOP transparency. Use when stress-testing a paper before submission or interpreting a decision letter. Sets expectations and shapes the paper to survive review; it does not contact editors.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Review Process (devpsych-review-process) skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Developmental-Psychology-Skills/skills/devpsych-review-process/SKILL.md and read by ahel’s review.

Developmental Psychology combines theory-advancing selectivity with rigorous developmental-method scrutiny. The journal offers masked peer review, and editors and reviewers weigh not only whether the finding is interesting, but whether the developmental claim is credible (age vs. cohort, measurement invariance, attrition), whether reporting meets JARS, and whether transparency meets TOP. Knowing this lets you pre-empt the common rejection reasons.

When to trigger

  • Before submitting, to stress-test the manuscript
  • Deciding whether to request masked review and how to anonymize
  • Interpreting a decision letter and setting expectations

How review works

  1. Masked (anonymized) review available. Authors may request masked review; if so, keep author identity out of the manuscript and out of shared links (see devpsych-submission). Masked review is intended to reduce bias against (e.g.) early-career or under-represented authors.
  2. Editorial triage. The handling editor assesses developmental significance, theory advance, design rigor, and fit; weak-fit or "not-developmental" papers may be declined without full review.
  3. External review assesses the theoretical contribution, the credibility of the change claim (age/cohort, invariance, attrition, power), analysis and JARS disclosure, and transparency.
  4. Transparency and reporting are weighed. Under TOP and JARS, missing data-availability statements, unjustified opacity, or non-JARS reporting weaken a paper.
  5. Decisions. Reject, revise and resubmit, or accept; expect substantive revision and frequent requests for invariance tests, attrition analyses, added robustness, or fuller disclosure.

What developmental reviewers reliably probe

  • Is it change, or a snapshot? A cross-sectional age difference framed as development draws fire.
  • Age vs. cohort. Could the "age effect" be a cohort/era effect? Reviewers ask routinely.
  • Measurement invariance. Is the construct the same across the ages you compare?
  • Attrition. Is the trajectory biased by who dropped out, and is missingness modeled?
  • Power for the change parameter and JARS-complete reporting.

Desk-reject and decline-without-review patterns

Confirm current categories against the official guidelines, but recognize these shapes:

Pattern an editor seesLikely outcomePre-empt it by
Single-age finding framed as developmentaldesk reject (fit)add an age contrast/wave or reframe
Cross-sectional age effect, no cohort discussionreview with heavy R&Raddress age vs. cohort up front
Trajectory interpreted with no invariance testR&R or rejectreport configural→metric→scalar first
Listwise deletion with non-trivial attritionR&Rrefit with FIML/MI; add attrition analysis
"Data available on request," no statementreturned for complianceadd a data-availability statement + DOIs

Worked micro-example (illustrative triage)

Manuscript: preregistered three-wave growth study (N = 300; ages 4-8),
            invariance established, FIML for attrition, open de-identified
            data + scripts, identifiable video via Databrary.
Editor read: developmental significance (within-person growth + mechanism),
            rigor (invariance + attrition handled), transparency (TOP-strong).
Likely route: external review, probable R&R for added robustness/disclosure.
Counter-case: same finding, one age group, cross-sectional, no invariance,
            data on request → likely declined or not-developmental.

How reviewers weigh the evidence (calibration anchors)

  • A credible within-person change claim — invariant measure, modeled attrition, powered slope — is the strongest signal; it converts "interesting correlation" into "developmental finding."
  • Transparency under TOP is judged in context: a candid, justified restriction on minors' data with an access path reads better than silent opacity or unethical open posting.
  • Masked review is a tool against bias, not a guarantee — anonymize the manuscript and the repository links, or it is undone.

Anti-patterns

  • A single-age or purely cross-sectional finding sold as developmental
  • Ignoring age-vs-cohort, measurement invariance, or attrition
  • Exploratory trajectory shapes dressed as confirmatory (JARS violation)
  • Weak or absent transparency / data-availability statement
  • Expecting acceptance without an invariance/attrition/disclosure-heavy R&R

Output format

【Developmental significance】clear + theory-advancing? [Y/N]
【Change claim credible】age/cohort + invariance + attrition + power? [Y/N]
【JARS reporting】complete (ES+CI, exclusions, missing data)? [Y/N]
【Transparency (TOP)】data-availability + preregistration handled? [Y/N]
【Masked review】manuscript + links anonymized? [Y/N]
【Realistic outcome】reject / R&R / accept
【Next】devpsych-submission (or devpsych-rebuttal if decided)

Supplementary resources

Signals

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devpsych-review-process
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github.com/brycewang-stanford/awesome-journal-skills