Hello

1. 1. What is the main purpose of interpreting data in a qualitative study?
   

• A. To apply statistics
  

•  B. To summarize and make sense of the data
  

•  C. To create numerical charts
  

•  D. To prove a hypothesis
  

2. 2. Which question helps guide the interpretation process?
   

•  A. How much is the standard deviation?
  

• B. What is the lesson learned?
  

•  C. Who paid for the research?
  
  

• D. How many variables are there?

3. 3. What does ‘validity’ in qualitative research refer to?
   

• A. The number of participants
  

•  B. The researcher's academic title
  

•  C. The accuracy and credibility of findings
  
  

• D. The financial cost of the study

4. 4. What method involves asking participants to confirm the results?
   
   

• 4. A. Coding

• 4. B. Peer debriefing

• 4. C. Member checking

• 4. D. Data mining

5. 5. Which of the following supports validity by providing detailed context?
   
   

• 5. A. Cross-tabulation

• 5. B. Rich, thick description

• 5. C. Descriptive statistics

•  D. Abstract summarization
  

6. 6. Which term is also used to describe ‘reliability’ in qualitative research?
   
   

• 6. A. Authenticity

• 6. B. Dependability

• 6. C. Reflexivity

• 6. D. Theory

7. 7. What helps improve reliability by preventing coding drift over time?
   
   

• 7. A. Writing a conclusion

• 7. B. Periodically checking the transcript

• 7. C. Comparing codes regularly

• 7. D. Running a t-test

8. 8. Which technique helps ensure that coding is consistent across researchers?
   
   

• 8. A. Intercoder agreement

• 8. B. Likert scale

• 8. C. Participant observation

• 8. D. Sampling bias

9. 9. Why do researchers clarify their own bias in qualitative studies?
   
   

• 9. A. To hide their opinion

• 9. B. To improve survey results

• 9. C. To increase transparency and trustworthiness

• 9. D. To impress peer reviewers

10. 10. Using software in qualitative research helps to:
    

• 10. A. Replace interviews

• 10. B. Increase the sample size

• 10. C. Enhance consistency in data analysis
      

• 10. D. Lower the budget