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name: review-research locale: caveman source_locale: en source_commit: 82c77053 fence_basis_commit: 82c77053 translator: "Julius Brussee homage — caveman" translation_date: "2026-04-26" description: > Conduct peer review of research methodology, experimental design, manuscript quality. Covers methodology evaluation, statistical appropriateness, reproducibility assessment, bias identification, constructive feedback. Use when reviewing manuscript, preprint, or internal research report; evaluating research proposal or study protocol; assessing evidence quality behind a claim; or reviewing thesis chapter or dissertation section. license: MIT allowed-tools: Read Grep Glob WebFetch metadata: author: Philipp Thoss version: "1.0" domain: review complexity: advanced language: natural tags: peer-review, methodology, research, reproducibility, bias, manuscript

Review Research

Perform structured peer review of research work. Evaluate methodology, statistical choices, reproducibility, overall scientific rigour.

When Use

• Reviewing manuscript, preprint, or internal research report
• Evaluating research proposal or study protocol
• Assessing quality of evidence behind a claim or recommendation
• Providing feedback on colleague research design before data collection
• Reviewing thesis chapter or dissertation section

Inputs

• Required: Research document (manuscript, report, proposal, or protocol)
• Required: Field/discipline context (affects methodology standards)
• Optional: Journal or venue guidelines (if reviewing for publication)
• Optional: Supplementary materials (data, code, appendices)
• Optional: Prior reviewer comments (if reviewing a revision)

Steps

Step 1: First Pass — Scope and Structure

Read entire document once to understand:

1. Research question: Clearly stated and specific?
2. Contribution claim: What is novel or new?
3. Overall structure: Does it follow expected format (IMRaD, or venue-specific)?
4. Scope match: Work appropriate for target audience/venue?

## First Pass Assessment
- **Research question**: [Clear / Vague / Missing]
- **Novelty claim**: [Stated and supported / Overstated / Unclear]
- **Structure**: [Complete / Missing sections: ___]
- **Scope fit**: [Appropriate / Marginal / Not appropriate]
- **Recommendation after first pass**: [Continue review / Major concerns to flag early]

Got: Clear understanding of paper claims and contribution. If fail: Research question unclear after full read? Note this as major concern and proceed.

Step 2: Evaluate Methodology

Assess research design against standards for the field:

Quantitative Research

• Study design appropriate for research question (experimental, quasi-experimental, observational, survey)
• Sample size justified (power analysis or practical rationale)
• Sampling method described and appropriate (random, stratified, convenience)
• Variables clearly defined (independent, dependent, control, confounding)
• Measurement instruments validated and reliability reported
• Data collection procedure reproducible from description
• Ethical considerations addressed (IRB/ethics approval, consent)

Qualitative Research

• Methodology explicit (grounded theory, phenomenology, case study, ethnography)
• Participant selection criteria and saturation discussed
• Data collection methods described (interviews, observations, documents)
• Researcher positionality acknowledged
• Trustworthiness strategies reported (triangulation, member checking, audit trail)
• Ethical considerations addressed

Mixed Methods

• Rationale for mixed design explained
• Integration strategy described (convergent, explanatory sequential, exploratory sequential)
• Both quantitative and qualitative components meet their respective standards

Got: Methodology checklist completed with specific observations for each item. If fail: Critical methodology information missing? Flag as major concern rather than assume.

Step 3: Assess Statistical and Analytical Choices

• Statistical methods appropriate for data type and research question
• Assumptions of statistical tests checked and reported (normality, homoscedasticity, independence)
• Effect sizes reported alongside p-values
• Confidence intervals provided where appropriate
• Multiple comparison corrections applied when needed (Bonferroni, FDR, etc.)
• Missing data handling described and appropriate
• Sensitivity analyses conducted for key assumptions
• Results interpretation consistent with analysis (not overstating findings)

Common statistical red flags:

• p-hacking indicators (many comparisons, selective reporting, "marginally significant")
• Inappropriate tests (t-test on non-normal data without justification, parametric tests on ordinal data)
• Confusing statistical significance with practical significance
• No effect size reporting
• Post-hoc hypotheses presented as a priori

Got: Statistical choices evaluated with specific concerns documented. If fail: Reviewer lacks expertise in specific method? Acknowledge this and recommend specialist reviewer.

Step 4: Evaluate Reproducibility

• Data availability stated (open data, repository link, available on request)
• Analysis code availability stated
• Software versions and environments documented
• Random seeds or reproducibility mechanisms described
• Key parameters and hyperparameters reported
• Computational environment described (hardware, OS, dependencies)

Reproducibility tiers:

Tier	Description	Evidence
Gold	Fully reproducible	Open data + open code + containerized environment
Silver	Substantially reproducible	Data available, analysis described in detail
Bronze	Potentially reproducible	Methods described but no data/code sharing
Opaque	Not reproducible	Insufficient method detail or proprietary data

Got: Reproducibility tier assigned with justification. If fail: Data cannot be shared (privacy, proprietary)? Synthetic data or detailed pseudocode acceptable alternative — note whether this provided.

Step 5: Identify Potential Biases

• Selection bias: Were participants representative of target population?
• Measurement bias: Could measurement process have systematically distorted results?
• Reporting bias: Are all outcomes reported, including non-significant ones?
• Confirmation bias: Did authors only look for evidence supporting their hypothesis?
• Survivorship bias: Were dropouts, excluded data, or failed experiments accounted for?
• Funding bias: Is funding source disclosed and could it influence findings?
• Publication bias: Is this complete picture or might negative results be missing?

Got: Potential biases identified with specific examples from manuscript. If fail: Biases cannot be assessed from available information? Recommend authors address this explicit.

Step 6: Write the Review

Structure review constructively:

## Summary
[2-3 sentences summarizing the paper's contribution and your overall assessment]

## Major Concerns
[Issues that must be addressed before the work can be considered sound]

1. **[Concern title]**: [Specific description with reference to section/page/figure]
   - *Suggestion*: [How the authors might address this]

2. ...

## Minor Concerns
[Issues that improve quality but are not fundamental]

1. **[Concern title]**: [Specific description]
   - *Suggestion*: [Recommended change]

## Questions for the Authors
[Clarifications needed to complete the evaluation]

1. ...

## Positive Observations
[Specific strengths worth acknowledging]

1. ...

## Recommendation
[Accept / Minor revision / Major revision / Reject]
[Brief rationale for the recommendation]

Got: Review specific, constructive, references exact locations in manuscript. If fail: Review running long? Prioritize major concerns and note minor issues in summary list.

Checks

• Every major concern references specific section, figure, or claim
• Feedback constructive — problems paired with suggestions
• Positive aspects acknowledged alongside concerns
• Statistical assessment matches analysis methods used
• Reproducibility explicit evaluated
• Recommendation consistent with severity of concerns raised
• Tone professional, respectful, collegial

Pitfalls

• Vague criticism: "The methodology is weak" is unhelpful. Specify what is weak and why.
• Demand a different study: Review research that was done, not research you would have done.
• Ignore scope: Conference paper has different expectations than journal article.
• Ad hominem: Review the work, not the authors. Never reference author identity.
• Perfectionism: No study is perfect. Focus on concerns that would change conclusions.

See Also

• review-data-analysis — deeper focus on data quality and model validation
• format-apa-report — APA formatting standards for research reports
• generate-statistical-tables — publication-ready statistical tables
• validate-statistical-output — statistical output verification