---

name: review-research locale: caveman-ultra source_locale: en source_commit: 82c77053 fence_basis_commit: 82c77053 translator: "Julius Brussee homage — caveman" translation_date: "2026-04-26" description: > Peer review research methodology, experimental design, manuscript quality. Methodology eval, statistical appropriateness, reproducibility assess, bias ID, constructive feedback. Use → review manuscript/preprint/internal report, eval research proposal/protocol, assess evidence quality behind claim, review 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

Structured peer review of research, eval methodology, statistical choices, reproducibility, overall scientific rigour.

Use When

• Review manuscript, preprint, internal research report
• Eval research proposal or study protocol
• Assess evidence quality behind claim/recommendation
• Provide feedback on colleague's design before data collection
• Review thesis chapter or dissertation section

In

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

Do

Step 1: First Pass — Scope + Structure

Read entire doc once → understand:

1. Research q: Clearly stated + specific?
2. Contribution claim: What is novel?
3. Overall structure: Follows expected format (IMRaD, venue-specific)?
4. Scope match: 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]

→ Clear understanding of paper's claims + contribution. If err: research q unclear after full read → note as major concern + proceed.

Step 2: Eval Methodology

Assess design vs std for field:

Quantitative Research

• Study design appropriate for q (experimental, quasi-experimental, observational, survey)
• Sample size justified (power analysis or practical rationale)
• Sampling method described + appropriate (random, stratified, convenience)
• Vars clearly defined (independent, dependent, control, confounding)
• Measurement instruments validated + 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 + 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 quant + qual components meet respective stds

→ Methodology checklist completed w/ specific obs each item. If err: critical methodology missing → flag major concern not assume.

Step 3: Statistical + Analytical Choices

• Stats appropriate for data type + research q
• Assumptions checked + reported (normality, homoscedasticity, independence)
• Effect sizes reported alongside p-values
• Confidence intervals provided where appropriate
• Multi comparison corrections applied when needed (Bonferroni, FDR, etc.)
• Missing data handling described + appropriate
• Sensitivity analyses for key assumptions
• Results interpretation consistent w/ analysis (no overstating)

Common stat red flags:

• p-hacking indicators (many comparisons, selective reporting, "marginally significant")
• Inappropriate tests (t-test on non-normal w/o justification, parametric on ordinal)
• Confusing statistical significance w/ practical
• No effect size reporting
• Post-hoc hypotheses presented as a priori

→ Stat choices eval'd w/ specific concerns documented. If err: reviewer lacks expertise in specific method → ack + recommend specialist reviewer.

Step 4: Reproducibility

• Data availability stated (open data, repo link, on req)
• Analysis code availability stated
• Software vers + envs documented
• Random seeds or reproducibility mechanisms described
• Key params + hyperparameters reported
• Computational env described (hardware, OS, deps)

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

→ Reproducibility tier assigned w/ justification. If err: data can't be shared (privacy, proprietary) → synthetic data or detailed pseudocode acceptable alt — note if provided.

Step 5: Identify Biases

• Selection bias: Participants representative of target pop?
• Measurement bias: Could measurement systematically distort results?
• Reporting bias: All outcomes reported, including non-significant?
• Confirmation bias: Did authors only look for evidence supporting hypothesis?
• Survivorship bias: Dropouts, excluded data, failed exps accounted for?
• Funding bias: Funding source disclosed + could influence findings?
• Publication bias: Complete picture or might negative results be missing?

→ Potential biases ID'd w/ specific examples from manuscript. If err: biases can't be assessed from available info → recommend authors address explicitly.

Step 6: Write Review

Structure constructive:

## 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]

→ Review specific, constructive, refs exact locations in manuscript. If err: review running long → prioritize major concerns + note minor in summary list.

Check

• Every major concern refs specific section, figure, claim
• Feedback constructive — problems paired w/ suggestions
• Positive aspects ack'd alongside concerns
• Stat assessment matches analysis methods used
• Reproducibility explicitly eval'd
• Recommendation consistent w/ severity of concerns raised
• Tone professional, respectful, collegial

Traps

• Vague criticism: "Methodology is weak" unhelpful. Specify what + why.
• Demand diff study: Review research done, not research you would have done.
• Ignore scope: Conference paper has diff expectations than journal article.
• Ad hominem: Review work, not authors. Never ref author identity.
• Perfectionism: No study perfect. Focus on concerns changing conclusions.

→

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