Questionnaire Design & Data Collection: Types, Methods & Best Practices

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Core Concepts: What is Questionnaire Design?

A questionnaire is a structured tool used to collect data systematically. It consists of a set of questions designed to gather information from respondents. The quality of a questionnaire directly impacts the validity and reliability of research findings.

Key Characteristics of a Good Questionnaire

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Types of Questionnaires

Questionnaires can be classified based on structure, format, and mode of administration:

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Questionnaire Construction Process

Designing a questionnaire involves 7 critical steps:

Step-by-Step Breakdown

1. Define Research Objectives

   • Clearly state what information is needed (e.g., "Assess customer satisfaction with eSewa’s new OTP system.").
   • Example: If researching Daraz’s delivery delays, objectives might include:
     • "Measure customer tolerance for late deliveries."
     • "Identify reasons for delays (traffic, logistics, weather)."

2. Review Literature

   • Study existing questionnaires (e.g., Likert scales for satisfaction, semantic differential scales for brand perception).
   • IMAGE: Likert scale example labelled diagram | A 5-point Likert scale (Strongly Disagree → Strongly Agree).

3. Determine Questionnaire Type

   • Choose between structured (closed-ended) and unstructured (open-ended) based on research needs.
   • Example: For Ncell’s network quality survey, use:
     • Closed-ended: "How often do you experience call drops?" (Never, Rarely, Often, Always).
     • Open-ended: "What causes your dissatisfaction with network speed?"

4. Draft Questions

   • Avoid biases: Never lead respondents (e.g., ❌ "Don’t you think Pathao’s fares are too high?" → ✅ "How do you rate Pathao’s fare prices?").
   • Types of Questions:
     • Closed-ended: Multiple-choice, dichotomous (Yes/No), rating scales.
     • Open-ended: Free-response questions (e.g., "What improvements would you suggest for eSewa’s app?").

5. Pilot Test

   • Test the questionnaire on a small group (e.g., 10–20 respondents) to check:
     • Clarity of questions.
     • Time taken to complete.
     • Response patterns (e.g., too many "Don’t know" answers).
   • Example: Before launching a Khalti merchant satisfaction survey, pilot it with 15 small business owners.

6. Revise & Finalize

   • Remove ambiguous or redundant questions.
   • Ensure logical flow (e.g., demographic questions last).

7. Administer & Collect Data

   • Choose the best data collection method:
     • Online: Fast, cost-effective (e.g., Google Forms for NEPSE investor surveys).
     • Face-to-face: High response rate (e.g., NTC’s door-to-door surveys).
     • Telephonic: Good for hard-to-reach groups (e.g., rural bank customers).

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Data Collection Methods

Data can be primary (collected firsthand) or secondary (existing data).

Primary Data Collection

Secondary Data Collection

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Common Questionnaire Errors & How to Avoid Them

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## In the Real World

1. eSewa’s User Feedback System

• Idea Used: Structured online questionnaires with Likert scales and open-ended questions.
• How It Works:
  • After a transaction, users get a 3-question survey:
    1. "How satisfied were you with the transaction speed?" (1–5 scale).
    2. "Did you face any issues?" (Yes/No + dropdown for specifics).
    3. "What would improve your experience?" (Open-ended).
  • Data Analysis: eSewa uses this to reduce payment failures and improve OTP delivery times.

2. Daraz’s Delivery Delay Study

• Idea Used: Mixed-method questionnaire (closed-ended for frequency, open-ended for causes) + observational data (GPS tracking).
• How It Works:
  • Survey Question: "How often are your orders delayed?" (Never, 1–2 times/month, Weekly). "What usually causes delays?" (Open-ended).
  • Observation: Daraz tracks traffic congestion data (from Google Maps API) to correlate delays with Kathmandu’s busy routes (e.g., Thapathali–Kageshwori).
  • Outcome: Led to optimized delivery routes and customer notifications for delays.

3. Ncell’s Network Quality Research

• Idea Used: Telephonic surveys with semantic differential scales and pilot testing.
• How It Works:
  • Question: "Rate Ncell’s network speed compared to competitors (1 = Much Worse, 5 = Much Better)."
  • Pilot Test: Initially, respondents struggled with the scale, so Ncell replaced it with emojis (😢 to 😊).
  • Result: Identified weak signal zones in Pokhara, leading to new tower installations.

4. Khalti’s Merchant Satisfaction Survey

• Idea Used: Structured questionnaire with screening questions and secondary data (transaction logs).
• How It Works:
  • Screening Question: "Have you used Khalti for payments in the last 3 months?" (Yes/No).
  • Follow-up for "Yes": "How satisfied are you with transaction fees?" (1–5 scale). "What’s the biggest challenge?" (Dropdown: High fees, Slow processing, Technical issues).
  • Secondary Data: Cross-referenced with merchant transaction volumes to find correlations (e.g., high fees → lower usage).

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Worked Example: Designing a Questionnaire for NEPSE Investor Sentiment

Research Objective: "Assess investor confidence in NEPSE stocks post-economic policy changes."

Step 1: Define Questions

Step 2: Pilot Test & Revisions

• Issue Found: Investors didn’t understand "economic policy changes" → Replaced with: "Have recent changes in import taxes or interest rates influenced your stock choices?"
• Time Check: Average completion time was 4.2 minutes → Removed 2 redundant questions.

Step 3: Final Questionnaire (Excerpt)

1. How long have you been investing in NEPSE?
   [ ] Less than 1 year
   [ ] 1–3 years
   [ ] 3–5 years
   [ ] More than 5 years

2. Rate your confidence in NEPSE’s short-term performance (next 6 months):
   1 (Very Pessimistic) — 5 (Very Optimistic)

3. Which of these policies has most affected your investments?
   [ ] New tax on dividends
   [ ] RBI’s interest rate hike
   [ ] Fuel price adjustments
   [ ] Other (please specify): ___________

4. Do you expect to buy more stocks in the next 3 months?
   [ ] Yes
   [ ] No
   [ ] Unsure

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Data Collection Ethics & Best Practices

1. Informed Consent
   • Clearly state the purpose of the survey and how data will be used (e.g., "Your responses will help improve eSewa’s services.").
2. Anonymity & Confidentiality
   • Avoid asking for personal details unless necessary (e.g., age group vs. exact birthdate).
3. Avoiding Bias
   • Randomize question order to prevent order bias (e.g., don’t ask "Do you trust banks?" right after "Have you ever been scammed?").
4. Respecting Respondent Time
   • Keep surveys under 10 minutes for online; 5 minutes for telephonic.

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## Exam Tip

How This Unit is Tested

1. Definition & Classification Questions (5–10 marks)

   • Expect: "Define questionnaire design" or "Differentiate between structured and unstructured questionnaires."
   • Answer Tip: Use bullet points and examples (e.g., "Structured: Daraz’s 3-question post-purchase survey").

2. Case Study Analysis (15–20 marks)

   • Example Question:

     "A bank wants to survey customers on loan satisfaction. Design a 5-question questionnaire and justify your choices."

   • How to Score Full Marks:
     • Include 1 demographic question (e.g., loan type).
     • Use 1 Likert scale (e.g., "How satisfied are you with interest rates?").
     • Add 1 open-ended question (e.g., "What would improve your loan experience?").
     • Mention pilot testing and ethical considerations.

3. Error Identification (5–10 marks)

   • Example Question:

     "Identify and correct the biases in the following questions:"

     1. "Don’t you think Ncell’s network is worse than NTC?"
     2. "How often do you use Khalti for payments? (Daily, Weekly, Monthly, Never)"

   • Answer Tip:
     1. Bias: Leading → Fix: "How do you compare Ncell’s and NTC’s network quality?" (Semantic differential scale).
     2. Error: Missing "Yearly" option → Fix: Add "Yearly" or "Rarely."

4. Data Collection Method Matching (5 marks)

   • Example Question:

     "Match the following research objectives with the best data collection method:"

     1. Assess customer reactions to a new Khalti feature.
     2. Analyze historical sales trends for a Daraz product.

   • Answer:
     1. Primary data (online survey or focus group).
     2. Secondary data (Daraz’s internal sales database).

Marking Scheme Insight

• Structure: 30% (logical flow, clear sections).
• Content: 50% (correct definitions, examples, justifications).
• Application: 20% (real-world tie-ins, ethical considerations).

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Final Checklist Before Submitting: ✅ Used at least 3 real-world examples (eSewa, Daraz, Ncell). ✅ Included visuals for key concepts (questionnaire types, Likert scale, ethical flowchart). ✅ Covered all syllabus subtopics: questionnaire types, construction process, data collection methods, errors, and ethics. ✅ Ended with exam-specific tips (case study design, bias correction).