Review Research

Review Research

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

When to Use

• Reviewing a manuscript, preprint, or internal research report
• Evaluating a research proposal or study protocol
• Assessing the quality of evidence behind a claim or recommendation
• Providing feedback on a colleague's research design before data collection
• Reviewing a 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)

Procedure

Step 1: First Pass — Scope and Structure

Read the entire document once to understand:

1. Research question: Is it clearly stated and specific?
2. Contribution claim: What is novel or new?
3. Overall structure: Does it follow the expected format (IMRaD, or venue-specific)?
4. Scope match: Is the work appropriate for the 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 the paper's claims and contribution. If fail: If the research question is unclear after a full read, note this as a major concern and proceed.

Step 2: Evaluate Methodology

Assess the research design against standards for the field:

Quantitative Research

• Study design appropriate for the 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 the 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: If critical methodology information is missing, flag as a major concern rather than assuming.

Step 3: Assess Statistical and Analytical Choices

• Statistical methods appropriate for the 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 the 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: If the reviewer lacks expertise in a specific method, acknowledge this and recommend a 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:

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

Step 5: Identify Potential Biases

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

Got: Potential biases identified with specific examples from the manuscript. If fail: If biases cannot be assessed from the available information, recommend that the authors address this explicitly.

Step 6: Write the Review

Structure the 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 is specific, constructive, and references exact locations in the manuscript. If fail: If the review is running long, prioritize major concerns and note minor issues in a summary list.

Validation

• Every major concern references a specific section, figure, or claim
• Feedback is constructive — problems are paired with suggestions
• Positive aspects acknowledged alongside concerns
• Statistical assessment matches the analysis methods used
• Reproducibility is explicitly evaluated
• The recommendation is consistent with the severity of concerns raised
• The tone is professional, respectful, and collegial

Pitfalls

• Vague criticism: "The methodology is weak" is unhelpful. Specify what is weak and why.
• Demanding a different study: Review the research that was done, not the research you would have done.
• Ignoring scope: A conference paper has different expectations than a 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 the conclusions.

Related Skills

• 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