Last updated on Sep 1, 2024

How do you avoid leading or loaded questions that influence the answers?

Survey research is a powerful tool to gather data and insights from your target audience, but it also comes with some challenges. One of the most common pitfalls is asking leading or loaded questions that influence the answers and create survey bias. In this article, you will learn how to avoid this mistake and design more neutral and effective questions for your survey.

1 What are leading or loaded questions?

Leading or loaded questions are questions that suggest a desired or expected answer, or that imply a value judgment or assumption about the topic. For example, "Do you agree that our product is the best in the market?" is a leading question that implies a positive opinion and encourages agreement. "How often do you waste time on social media?" is a loaded question that assumes a negative behavior and induces guilt or defensiveness. These types of questions can skew your results and reduce the validity and reliability of your survey.

Add your perspective

Ankesh Saxena

32x Top Voices | Podcaster @ The Collaborative Canvas | Do you also hear what people say? | Learn and Grow | Market Research | Personal Branding | Consumer Insights | Sales | ESOMAR member | MRSI Member |
Report contribution
To avoid leading or loaded questions, use neutral, clear, and concise language. Ensure questions are balanced without implying a preferred answer. Avoid emotionally charged or biased wording. Test questions in a pretest to identify and correct any subtle biases. Seek feedback from diverse reviewers to ensure fairness. Focus on objective phrasing and offer balanced response options to accurately capture respondents' true opinions.

Like
Dr. Blake Curtis, Sc.D

Cybersecurity Governance Advisor | Research Scientist | CISSP, CISM, CISA, CRISC, CGEIT, CDPSE, COBIT, COSO | 🛡️ Top 25 Cybersecurity Leaders in 2024 | Speaker | Author | Editor | Licensed Skills Consultant | Educator
Report contribution
Leading questions encourage respondents to answer in a specific manner & often contain language that subtly influences them to agree with the statement. Example: "Do you agree that our product is the best in the market?" This implies that the product is the best, leading respondents toward agreement, even if they might not fully share that opinion. These questions distort data by pushing respondents toward specific answers, which may not accurately reflect their true opinions. This results in data that is not representative of the population. Word your questions neutrally without implying any judgment or expectation. Avoid adjectives or adverbs that carry connotations, such as "best," "worst," "waste," or "important."

Like

2 How to spot leading or loaded questions?

One way to spot leading or loaded questions is to check if they contain any words or phrases that indicate a preference, imply a norm, evoke an emotion, suggest a cause or consequence, introduce a comparison, or limit the range of options. Examples include words like "obviously", "should", "sad", "because", "better", and "only".

Add your perspective

3 How to avoid leading or loaded questions?

To avoid leading or loaded questions, it is important to use neutral and precise language that does not imply any bias or expectation. Open-ended questions that allow respondents to express their own opinions or experiences are preferable to closed-ended questions that force them to choose from a predefined set of options. Balanced scales with equal numbers of positive and negative options should be used, and extreme or vague terms such as "always", "never", "very", or "somewhat" should be avoided. Factual and specific questions that focus on observable and measurable aspects of the topic should be asked, rather than subjective and general questions that rely on interpretations and judgments. Additionally, clear and simple questions that avoid ambiguity, confusion, or double meanings should be used, as well as multiple questions that cover different aspects of the topic instead of single questions that combine several issues or dimensions.

Add your perspective

Ding Wang

Program Developer | Helping innovators apply LLM in their survey and reporting projects
Report contribution
This is all great advice! One thing I'd add is to think about the order of your questions. Sometimes, even a well-worded question can influence how someone answers later questions. For example, if you ask a bunch of questions about how great a company is before asking about their customer service, people might be more likely to rate the customer service higher. It's like you've already put them in a positive mood. Mixing up the order of your questions can help with this.

Like

4 How to test your questions for bias?

Even if you follow the guidelines, you may still have some questions that are unintentionally leading or loaded. To test for bias, you can pretest your questions with a small sample of your target population and ask for feedback on their clarity, neutrality, and relevance. Additionally, piloting your survey with a larger sample can help analyze the distribution, variability, and reliability of responses. Furthermore, comparing your questions to similar ones from other sources can reveal any significant differences or discrepancies. Lastly, it is beneficial to review your questions with an expert or peer familiar with the topic and survey design to identify any potential sources of bias or influence.

Add your perspective

5 How to deal with bias in your results?

Even if you avoid leading or loaded questions in your survey, you may still encounter bias in your results due to sampling errors, response errors, nonresponse errors, or measurement errors. To reduce this bias, adjust the sample size and selection criteria to ensure that the sample is representative of the population and has enough power and precision to detect meaningful differences or relationships. Improve response rate and quality by using appropriate incentives, reminders, modes, and formats for your survey. Additionally, reduce measurement error by using valid and reliable instruments, scales, and indicators for your survey. Finally, report results with transparency and honesty by acknowledging any limitations, assumptions, or uncertainties in your survey and providing appropriate confidence intervals, error margins, or significance tests for estimates or findings.

Add your perspective

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

Add your perspective

How do you avoid leading or loaded questions that influence the answers?

1

2

3

4

5

6

1 What are leading or loaded questions?

2 How to spot leading or loaded questions?

3 How to avoid leading or loaded questions?

4 How to test your questions for bias?

5 How to deal with bias in your results?

6 Here’s what else to consider

Survey Research

Rate this article

Thanks for your feedback

More articles on Survey Research

More relevant reading