Peer Review Checklist

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by Community

Provides structured peer review checklists covering methodology, statistical analysis, writing quality, ethical considerations, and constructive feedback formulation for academic manuscripts.

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Peer Review Checklist

Conduct thorough, constructive peer reviews of research manuscripts. Provides structured evaluation frameworks covering every section of a research paper with specific quality criteria.

Usage

Specify the type of manuscript and your review goals. The checklist guides you through a systematic evaluation of each paper section with specific questions to answer and feedback formulation guidance.

Parameters

  • Manuscript type: Original research, Review, Case study, Short communication, or Methods paper
  • Field: Biomedical, Social science, Computer science, Engineering, or Humanities
  • Review type: Initial screening, Full review, or Revision assessment
  • Role: Reviewer, Editor, or Self-assessment before submission

Examples

  1. RCT Manuscript Review: Systematic review of a randomized controlled trial paper — CONSORT checklist compliance, statistical methods appropriateness, result interpretation accuracy, and limitations honesty.
  1. Computer Science Paper: Review a machine learning paper checking experimental design, baseline comparisons, ablation studies, reproducibility information, and overfitting/data leakage risks.
  1. Qualitative Research Review: Evaluate a grounded theory study using CASP criteria — theoretical sampling adequacy, coding transparency, reflexivity, and theoretical contribution assessment.
  1. Self-Assessment Pre-Submission: Use the review checklist on your own manuscript before submission to catch common issues and strengthen weak sections proactively.

Guidelines

  • Reviews separate major concerns (methodology flaws) from minor issues (typos, formatting)
  • Abstract accuracy is checked against the actual results and conclusions
  • Introduction establishes clear gaps that justify the study's contribution
  • Methods are evaluated for reproducibility — could another researcher replicate this?
  • Statistical analyses are appropriate for the data type, design, and research question
  • Results are presented completely, including null findings and effect sizes
  • Discussion distinguishes interpretation from results and addresses limitations honestly
  • References are current, comprehensive, and include seminal works in the field
  • Feedback is specific, actionable, and constructive — not vague criticism
  • Reviewer recommendations are calibrated: Accept, Minor revisions, Major revisions, or Reject with rationale