1. Identify the Research Problem
What is it? A specific issue, gap in knowledge, or real-world problem that needs investigation.
Why it’s important: Without a clear problem, your research will lack direction.
How to do it:
- Read existing literature
- Talk to experts
- Observe the community or field
- Look for inconsistencies, gaps, or unanswered questions
🧠 Example: High neonatal mortality rates in rural Zambia.
2. Review the Literature
What is it? A comprehensive overview of existing research relevant to your problem.
Why: To understand what is already known and what is not.
What to look for:
- Definitions and concepts
- Past findings
- Theories and models
- Methodologies used
- Limitations of previous studies
How:
- Use databases like PubMed, JSTOR, Google Scholar
- Use keywords and Boolean operators (AND, OR, NOT)
- Organize findings thematically or chronologically
- Write a critical synthesis (not just summaries)
📚 Example: Studies on factors influencing neonatal mortality.
3. Formulate the Research Question
What: A focused, answerable question that guides your study.
Why: It defines your objective and scope.
Types:
- Descriptive: What is happening?
- Analytical: Why or how is it happening?
- Comparative: What is the difference?
Tool: Use the PICO framework (Population, Intervention, Comparison, Outcome).
❓ Example: What are the maternal factors associated with neonatal mortality in rural Zambia?
4. Define Objectives and Hypotheses
What: Clear goals and testable predictions.
Why: They guide study design and analysis.
Types:
- General objective: Overall purpose
- Specific objectives: Measurable components
- Hypotheses: Statements to be tested (null & alternative)
🎯 Example: To determine the association between maternal education level and neonatal outcomes.
5. Choose a Study Design
What: The overall strategy for answering your research question.
Types:
- Descriptive (cross-sectional, case report)
- Analytical (case-control, cohort, RCT)
- Qualitative (interviews, focus groups)
Choose based on: Objective, resources, time, and ethical constraints.
🧪 Example: Cross-sectional study using clinic records.
6. Define the Population and Sampling
What: Whom you will study and how you’ll select them.
Key terms:
- Target population: Entire group of interest
- Study population: Accessible portion
- Sample: Actual participants
- Sampling method: Random, stratified, convenience
👥 Example: Mothers attending New Masala Clinic in 2024.
7. Determine Sample Size
Why: Too small = unreliable; too large = resource-wasteful.
How:
- Use software (e.g., OpenEpi, Epi Info)
- Base on expected prevalence, confidence level, margin of error
📏 Example: Minimum sample size of 246 calculated for 95% confidence, 5% margin of error.
8. Select Data Collection Methods
What: Tools to gather information.
Examples:
- Questionnaires (structured or semi-structured)
- Interviews
- Focus groups
- Medical records
Ensure: Validity, reliability, and cultural appropriateness.
📝 Example: Pre-tested questionnaire for mothers at the clinic.
9. Plan for Data Analysis
What: Deciding how to summarize and interpret data.
Steps:
- Data coding and entry
- Descriptive statistics (mean, frequency, %, etc.)
- Inferential statistics (chi-square, t-test, regression)
Tools: SPSS, Stata, R, Excel
📊 Example: Use chi-square test to assess relationship between education and neonatal outcomes.
10. Address Ethical Considerations
What: Ensuring respect, safety, and dignity of participants.
Include:
- Informed consent
- Confidentiality
- Right to withdraw
- Approval by ethics committee
🛡️ Example: Obtain ethics clearance from TDRC and informed consent from all participants.
11. Conduct a Pilot Study
What: A small-scale test run of your study tools and procedures.
Why: To refine tools and logistics before the actual study.
🔍 Example: Pilot 10 questionnaires to refine unclear questions.
12. Collect Data
What: Implement your data gathering as per your plan.
Tips:
- Train data collectors
- Supervise the process
- Ensure data quality checks
📥 Example: Administer surveys at the clinic over a 2-week period.
13. Analyze and Interpret Data
What: Process and make sense of the data.
Steps:
- Clean and verify data
- Run statistical tests
- Interpret in light of objectives and literature
📈 Example: Chi-square shows significant association between maternal age and neonatal outcomes.
14. Report and Disseminate Findings
What: Share your research with stakeholders and the academic community.
Formats: Thesis, journal article, conference, policy brief, community feedback
📣 Example: Present results at Copperbelt Medical Research Symposium and submit paper to ZMJ.