A strong research idea and well-defined research question are the foundation of any successful academic project. Here’s a guide to developing both, including how to start with a broad topic and narrow it down into a manageable research question.
1. Start with a Broad Topic
A broad topic is a general area of interest. It provides a starting point for exploration but is often too wide to study directly. Examples include:
- Artificial intelligence in education
- Climate change and biodiversity
- Mental health and social media
Tip: Choose a topic that aligns with your interests, expertise, and the needs of your field (Creswell & Creswell, 2018).
2. Narrowing the Topic
To make your topic manageable, focus on specific aspects by considering:
- Population: Who or what are you studying? (e.g., teenagers, urban ecosystems)
- Context: Where or when? (e.g., U.S. cities, post-COVID era)
- Variables: What are the key factors or relationships? (e.g., algorithms, mental health outcomes)
- Scope: What is feasible given your time and resources?
Example of Narrowing:
- Broad Topic: Social media and mental health
- Narrowed Topic: The impact of Instagram on body image dissatisfaction among teenage girls
3. Develop a Research Question
A research question is a focused, specific query that guides your study. It should:
- Be clear and concise.
- Be feasible to answer within your constraints.
- Have relevance to your field or society.
- Be original (not already exhaustively studied).
Example:
“How do Instagram’s recommendation algorithms influence body image dissatisfaction among teenage girls aged 13–18 in urban areas?”
4. Characteristics of a Strong Research Question
| Aspect | Checklist |
|---|---|
| Clarity | Could someone unfamiliar with the topic understand it? |
| Scope | Not too broad (“Does social media cause depression?”) or too narrow. |
| Methodology | Can it be answered through qualitative/quantitative/mixed methods? |
| Significance | Why does this matter to stakeholders (policymakers, educators, etc.)? |
5. Pitfalls to Avoid
- Overly ambitious questions: E.g., “How can we solve global poverty?”
- Dual/multiple questions: Split into sub-questions if needed.
- Assumptive phrasing: Avoid bias (e.g., “Why are algorithms harmful?” assumes harm).
6. Template for Refining Your Question
Use the PICOT framework (Population, Intervention, Comparison, Outcome, Time) or similar models to structure your question:
- Population: Who/What are you studying? (e.g., teenagers, urban ecosystems)
- Intervention/Exposure: What is the key variable? (e.g., algorithm use, policy change)
- Outcome: What are you measuring? (e.g., mental health, biodiversity)
- Context: Where/When? (e.g., U.S. cities, post-COVID)
Example:
“How does [intervention] affect [outcome] in [population] within [context]?”
7. Example Evolution
- Broad Topic: AI in education.
- Narrowed Focus: AI-powered tutoring systems in high schools.
- Research Question: “Does the use of AI-driven math tutoring apps improve test scores for low-income high school students compared to traditional methods?”
8. Evaluate Your Question
Ask:
- Is there existing literature to build on?
- What data or methods would answer it?
- Could the answer lead to actionable insights?
9. Next Steps
- Conduct a preliminary literature review to refine your question (Hart, 2018).
- Consult with advisors or peers for feedback.
- Draft hypotheses or sub-questions to guide your methodology.
References
- Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications.
- Hart, C. (2018). Doing a Literature Review: Releasing the Research Imagination (2nd ed.). Sage Publications.
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