Research Question Refinement Blueprint
Turn a broad topic into a defensible sociology research question, sub-questions, boundaries, and contribution statement.
Match the question to real data
Secondary Dataset Feasibility Scanner
Find datasets, assess fit, and build a realistic secondary-data strategy for a sociology project.
When to use this
- Use this when you need a feasible project quickly and cannot collect primary data.
- Use this when you know the topic but not the best dataset.
- Use this before locking in an analysis plan or methods chapter.
What you should prepare first
- Research topic or question: [state clearly]
- Population and geography of interest: [country, region, city, cross-national, institutional setting, etc.]
- Preferred data type: [survey, panel, census, administrative, digital trace, content archive, mixed]
- Software comfort level: [R, Stata, SPSS, Python, qualitative software, beginner or advanced]
- Project scale and deadline: [course paper, thesis, article, urgent deadline, long project]
Paste this prompt
You are an elite sociology research and writing partner with strong expertise in sociological theory, research design, data analysis, academic communication, and publication standards. Primary task: Recommend the most suitable existing datasets or data repositories for my sociology topic and explain what analyses they make possible. Project details I will provide or you should ask me to clarify: - Research topic or question: [state clearly] - Population and geography of interest: [country, region, city, cross-national, institutional setting, etc.] - Preferred data type: [survey, panel, census, administrative, digital trace, content archive, mixed] - Software comfort level: [R, Stata, SPSS, Python, qualitative software, beginner or advanced] - Project scale and deadline: [course paper, thesis, article, urgent deadline, long project] How you should work: 1. Identify 3 to 6 plausible datasets or repositories and summarize what each contains. 2. Explain which key variables, populations, time spans, and access conditions matter for my question. 3. Show the strongest possible research design using each dataset and note its limitations. 4. Rank the options by feasibility, accessibility, conceptual fit, and analytic depth. 5. Recommmend the best one and give me the first concrete steps to acquire, inspect, and prepare it. Output requirements: - A ranked shortlist of datasets or repositories. - A design memo linking question, variables, and analysis possibilities. - A first-week data access and familiarization plan. Quality guardrails: - Do not recommend datasets that clearly cannot answer the stated question. - Separate publicly available data from restricted or application-only data. - Be honest about when secondary data can only partially address the topic. If crucial information is missing, make the minimum reasonable assumptions, label them clearly, and show me what extra details would improve the result. Use headings, compact tables, bullet points, and examples where they improve clarity.
What this should give you
- A ranked shortlist of datasets or repositories.
- A design memo linking question, variables, and analysis possibilities.
- A first-week data access and familiarization plan.
Good inputs improve this prompt
- Name the region or population precisely because dataset fit depends heavily on scope.
- If you need longitudinal analysis, say so up front.
- If your analysis skills are still basic, mention that to keep the recommendation realistic.
AI handoff
Paste the full prompt into ChatGPT, Claude, or Gemini after replacing the placeholders with your topic, setting, constraints, and target output. If the first answer drifts, tighten the missing inputs before running it again.
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