Clarity and Simplicity: Questions must use clear, unambiguous language that is appropriate for the target audience's reading level. Avoiding technical jargon and complex sentence structures prevents respondent frustration and misinterpretation.
Logical Sequencing: The questionnaire should follow a psychological flow, typically starting with simple, engaging questions to build rapport. Sensitive or demographic questions are usually placed at the end to prevent early drop-outs.
Pilot Testing: Before full implementation, a questionnaire should be tested on a small sample of the target population. This 'pre-test' identifies confusing wording, technical glitches in digital forms, or issues with the logical flow of the instrument.
Avoiding Bias: Researchers must avoid leading questions that nudge the respondent toward a specific answer. Neutral phrasing is critical to ensure that the data reflects the respondent's true opinion rather than the researcher's expectations.
| Feature | Closed-ended Questions | Open-ended Questions |
|---|---|---|
| Data Type | Quantitative (Numerical) | Qualitative (Textual) |
| Analysis | Statistical and rapid | Thematic and time-consuming |
| Respondent Effort | Low (selecting an option) | High (composing a response) |
| Flexibility | Limited to provided choices | High (unlimited expression) |
| Best Use | Testing hypotheses/trends | Exploring new ideas/reasons |
Questionnaire vs. Interview Schedule: While both involve a list of questions, a questionnaire is typically self-administered by the respondent, whereas an interview schedule is a script used by an interviewer to guide a verbal conversation.
Reliability vs. Validity: Reliability refers to the consistency of the questionnaire (getting the same result repeatedly), while validity refers to whether the questionnaire actually measures what it claims to measure.
Identify Double-Barreled Questions: Always check if a question asks two things at once (e.g., 'Do you like the price and the quality?'). These are invalid because a respondent might feel differently about each part.
Evaluate Response Exhaustiveness: Ensure that in multiple-choice questions, the options cover all possible answers. Including an 'Other' or 'Not Applicable' category is a common way to ensure exhaustiveness.
Check for Mutual Exclusivity: In numerical ranges (like age groups), ensure categories do not overlap (e.g., use 10-19 and 20-29, rather than 10-20 and 20-30).
Verify Scale Balance: If using a Likert scale, ensure there is an equal number of positive and negative options to avoid biasing the respondent toward one end of the spectrum.
Social Desirability Bias: Respondents often answer in a way they believe is socially acceptable rather than being honest. This is particularly common in questionnaires regarding health, ethics, or illegal activities.
Non-Response Bias: This occurs when the people who choose not to answer the questionnaire differ significantly from those who do. A low response rate can severely limit the generalizability of the findings.
The 'Neutral' Trap: Providing a middle/neutral option can lead to 'satisficing,' where respondents choose the easiest path rather than thinking deeply. However, forcing a choice can lead to inaccurate data if the respondent truly is neutral.
