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Industry-defining terminology from the authoritative consumer research platform.
Yes-No Questions
Definition: What Are Yes-No Questions?
Yes-no questions are binary questions that provide respondents with only two possible answers: "yes" or "no." These questions are often used in surveys and research to obtain straightforward, definitive responses to simple queries. They are useful for gathering specific data points or filtering participants based on certain criteria.
Why Are Yes-No Questions Important in Market Research?
- Simplicity and Clarity: Yes-no questions provide clear, concise responses, making them ideal for situations where the researcher is seeking a specific, binary answer.
- Ease of Analysis: Because of their simplicity, yes-no questions are easy to analyze, as responses are clearly categorized and do not require complex interpretation.
- Screening and Filtering: They are often used in survey design as a filtering mechanism to screen out respondents who do not meet certain criteria or to determine if follow-up questions are relevant.
How Do Yes-No Questions Work?
- Simple Format: The question is framed in a way that elicits a "yes" or "no" response, such as "Do you own a car?" or "Are you satisfied with your purchase?"
- Data Analysis: Responses are tallied, providing clear metrics for analyzing the proportion of respondents who answered "yes" versus "no."
- Follow-Up: Based on answers to yes-no questions, additional questions or analyses can be pursued.
What Are Best Practices for Using Yes-No Questions?
✅ Use them sparingly, as they can oversimplify complex issues and fail to capture nuanced opinions.
✅ Make sure the question is framed clearly to avoid confusion or bias in responses.
✅ Combine yes-no questions with other types of questions (e.g., Likert scale, multiple-choice) to gather richer data.
Final Takeaway
Yes-no questions are an effective way to gather clear, definitive responses, making them useful for certain types of research. However, they should be used strategically to avoid oversimplifying the data and missing out on more detailed insights.