Asked by helen
Which data methods (2-3) do you need to use for your study and explain why.
All Answers 1
Answered by
GPT-5 mini
AI
I don’t have details about your topic, aims, or constraints — but here are clear, practical options (each 2–3 methods) you can pick from depending on your study type, with reasons why they’re useful.
1) Quantitative/descriptive study (measure prevalence, test hypotheses)
- Methods: Structured survey + secondary (administrative/archival) data
- Why: The survey gives standardized, generalizable measures from your target population; secondary data (e.g., company records, national statistics) provides objective outcomes, increases sample size/power and allows triangulation. This combo supports statistical analysis and causal modeling when appropriate.
2) Qualitative/exploratory study (understand experiences, meanings, processes)
- Methods: Semi‑structured interviews + participant observation (or document analysis)
- Why: Interviews produce rich, detailed accounts of participants’ perspectives; observation reveals behaviour and context that participants may not report; documents (policies, emails, logs) add corroboration and historical context. Together they yield depth, context, and credibility through triangulation.
3) Mixed-methods evaluative study (explain quantitative findings or evaluate an intervention)
- Methods: Pre/post surveys or routine outcome data + focus groups or semi‑structured interviews
- Why: Quantitative measures show magnitude and direction of change; qualitative follow-up explains why changes occurred, uncovers mechanisms, and surfaces unintended effects. This combination strengthens validity and provides actionable recommendations.
How to choose between them
- If your research question asks “how many/what relationship?” → quantitative methods.
- If it asks “how/why/what is the experience?” → qualitative methods.
- If you need both breadth and depth or want to explain numbers → mixed methods.
If you tell me your research question, sample, and constraints (time, budget, access), I’ll recommend the best 2–3 specific methods and a brief plan for sampling and analysis.
1) Quantitative/descriptive study (measure prevalence, test hypotheses)
- Methods: Structured survey + secondary (administrative/archival) data
- Why: The survey gives standardized, generalizable measures from your target population; secondary data (e.g., company records, national statistics) provides objective outcomes, increases sample size/power and allows triangulation. This combo supports statistical analysis and causal modeling when appropriate.
2) Qualitative/exploratory study (understand experiences, meanings, processes)
- Methods: Semi‑structured interviews + participant observation (or document analysis)
- Why: Interviews produce rich, detailed accounts of participants’ perspectives; observation reveals behaviour and context that participants may not report; documents (policies, emails, logs) add corroboration and historical context. Together they yield depth, context, and credibility through triangulation.
3) Mixed-methods evaluative study (explain quantitative findings or evaluate an intervention)
- Methods: Pre/post surveys or routine outcome data + focus groups or semi‑structured interviews
- Why: Quantitative measures show magnitude and direction of change; qualitative follow-up explains why changes occurred, uncovers mechanisms, and surfaces unintended effects. This combination strengthens validity and provides actionable recommendations.
How to choose between them
- If your research question asks “how many/what relationship?” → quantitative methods.
- If it asks “how/why/what is the experience?” → qualitative methods.
- If you need both breadth and depth or want to explain numbers → mixed methods.
If you tell me your research question, sample, and constraints (time, budget, access), I’ll recommend the best 2–3 specific methods and a brief plan for sampling and analysis.
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