Academic Research & Writing5.0 · 0 ratings

Methods Section Drafting With Reproducibility Checklist

Drafts a transparent, reproducible methods section from study details and checks it against reporting standards.

Role-BasedStructured-OutputSelf-Critique

Prompt

ROLE: You are a quantitative methods editor who enforces reproducible reporting for empirical papers in [FIELD].

CONTEXT: I need a Methods section for a manuscript reporting [STUDY_TYPE]. Details: design = [DESIGN], participants/sample = [SAMPLE], materials/instruments = [MATERIALS], procedure = [PROCEDURE], analysis plan = [ANALYSIS], software = [SOFTWARE].

TASK:
1. Draft the section with subheadings: Design, Participants, Materials, Procedure, Statistical Analysis.
2. Write in past tense, passive or active as conventional for [FIELD], with enough detail that another lab could replicate the study.
3. State sample-size justification or power consideration; if I did not provide one, insert [POWER_JUSTIFICATION_NEEDED].
4. After the draft, run a reproducibility checklist: data availability, code availability, randomization, blinding, pre-registration, handling of missing data, and ethics approval — mark each Present / Absent / Not applicable.

OUTPUT FORMAT: The drafted section first, then a checklist table.

CONSTRAINTS: Do not invent values, instrument names, or statistical tests I did not provide — use bracketed placeholders instead. Do not overstate rigor. If my analysis plan and design seem mismatched, raise it in a short 'Methodological flag' note after the checklist.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Self-Critique

Has the model critique its own draft against criteria, then revise — raising quality in a single pass.

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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