Questionnaire Design | Methods, Question Types & Examples

A questionnaire is a list of questions or items used to gather data from respondents about their attitudes, experiences, or opinions. Questionnaires can be used to collect quantitative and/or qualitative information.

Questionnaires are commonly used in market research as well as in the social and health sciences. For example, a company may ask for feedback about a recent customer service experience, or psychology researchers may investigate health risk perceptions using questionnaires.

Questionnaires vs surveys

A survey is a research method where you collect and analyse data from a group of people. A questionnaire is a specific tool or instrument for collecting the data.

Designing a questionnaire means creating valid and reliable questions that address your research objectives, placing them in a useful order, and selecting an appropriate method for administration.

But designing a questionnaire is only one component of survey research. Survey research also involves defining the population you’re interested in, choosing an appropriate sampling method, administering questionnaires, data cleaning and analysis, and interpretation.

Sampling is important in survey research because you’ll often aim to generalise your results to the population. Gather data from a sample that represents the range of views in the population for externally valid results. There will always be some differences between the population and the sample, but minimising these will help you avoid sampling bias.

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Questionnaire methods

Questionnaires can be self-administered or researcher-administered. Self-administered questionnaires are more common because they are easy to implement and inexpensive, but researcher-administered questionnaires allow deeper insights.

Self-administered questionnaires

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Self-administered questionnaires can be:

  • Cost-effective
  • Easy to administer for small and large groups
  • Anonymous and suitable for sensitive topics
  • Self-paced

But they may also be:

  • Unsuitable for people with limited literacy or verbal skills
  • Susceptible to a nonreponse bias (most people invited may not complete the questionnaire)
  • Biased towards people who volunteer because impersonal survey requests often go ignored

Researcher-administered questionnaires

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents.

Researcher-administered questionnaires can:

  • Help you ensure the respondents are representative of your target audience
  • Allow clarifications of ambiguous or unclear questions and answers
  • Have high response rates because it’s harder to refuse an interview when personal attention is given to respondents

But researcher-administered questionnaires can be limiting in terms of resources. They are:

  • Costly and time-consuming to perform
  • More difficult to analyse if you have qualitative responses
  • Likely to contain experimenter bias or demand characteristics
  • Likely to encourage social desirability bias in responses because of a lack of anonymity

Open-ended vs closed-ended questions

Your questionnaire can include open-ended or closed-ended questions, or a combination of both.

Using closed-ended questions limits your responses, while open-ended questions enable a broad range of answers. You’ll need to balance these considerations with your available time and resources.

Closed-ended questions

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. Closed-ended questions are best for collecting data on categorical or quantitative variables.

Categorical variables can be nominal or ordinal. Quantitative variables can be interval or ratio. Understanding the type of variable and level of measurement means you can perform appropriate statistical analyses for generalisable results.

Examples of closed-ended questions for different variables

Nominal variables include categories that can’t be ranked, such as race or ethnicity. This includes binary or dichotomous categories.

It’s best to include categories that cover all possible answers and are mutually exclusive. There should be no overlap between response items.

In binary or dichotomous questions, you’ll give respondents only two options to choose from.

Example: Nominal variables
What is your race?

Black or African American
American Indian or Alaska Native
Native Hawaiian or Other Pacific Islander

Are you satisfied with the current work-from-home policies?

Ordinal variables include categories that can be ranked. Consider how wide or narrow a range you’ll include in your response items, and their relevance to your respondents.

Example: Ordinal variables
What is your age?

15 or younger
76 or older

Likert-type questions collect ordinal data using rating scales with five or seven points.

Example: Likert-type questions
How satisfied or dissatisfied are you with your online shopping experience today?

Very dissatisfied
Somewhat dissatisfied
Neither satisfied nor dissatisfied
Somewhat satisfied
Very satisfied

When you have four or more Likert-type questions, you can treat the composite data as quantitative data on an interval scale. Intelligence tests, psychological scales, and personality inventories use multiple Likert-type questions to collect interval data.

With interval or ratio data, you can apply strong statistical hypothesis tests to address your research aims.

Pros and cons of closed-ended questions

Well-designed closed-ended questions are easy to understand and can be answered quickly. However, you might still miss important answers that are relevant to respondents. An incomplete set of response items may force some respondents to pick the closest alternative to their true answer. These types of questions may also miss out on valuable detail.

To solve these problems, you can make questions partially closed-ended, and include an open-ended option where respondents can fill in their own answer.

Open-ended questions

Open-ended, or long-form, questions allow respondents to give answers in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. For example, respondents may want to answer ‘multiracial’ for the question on race rather than selecting from a restricted list.

Example: Open-ended questions
  1. How do you feel about open science?
  2. How would you describe your personality?
  3. In your opinion, what is the biggest obstacle to productivity in remote work?

Open-ended questions have a few downsides.

They require more time and effort from respondents, which may deter them from completing the questionnaire.

For researchers, understanding and summarising responses to these questions can take a lot of time and resources. You’ll need to develop a systematic coding scheme to categorise answers, and you may also need to involve other researchers in data analysis for high reliability.

Question wording

Question wording can influence your respondents’ answers, especially if the language is unclear, ambiguous, or biased. Good questions need to be understood by all respondents in the same way (reliable) and measure exactly what you’re interested in (valid).

Use clear language

You should design questions with your target audience in mind. Consider their familiarity with your questionnaire topics and language and tailor your questions to them.

For readability and clarity, avoid jargon or overly complex language. Don’t use double negatives because they can be harder to understand.

Use balanced framing

Respondents often answer in different ways depending on the question framing. Positive frames are interpreted as more neutral than negative frames and may encourage more socially desirable answers.

Example: Positive vs negative frames
Positive frame Negative frame
Should protests of pandemic-related restrictions be allowed? Should protests of pandemic-related restrictions be forbidden?

Use a mix of both positive and negative frames to avoid bias, and ensure that your question wording is balanced wherever possible.

Unbalanced questions focus on only one side of an argument. Respondents may be less likely to oppose the question if it is framed in a particular direction. It’s best practice to provide a counterargument within the question as well.

Example: Unbalanced vs balanced frames
Unbalanced Balanced
Do you favour …? Do you favour or oppose …?
Do you agree that …? Do you agree or disagree that …?

Avoid leading questions

Leading questions guide respondents towards answering in specific ways, even if that’s not how they truly feel, by explicitly or implicitly providing them with extra information.

It’s best to keep your questions short and specific to your topic of interest.

Example: Leading questions
  1. The average daily work commute in the US takes 54.2 minutes and costs $29 per day. Since 2020, working from home has saved many employees time and money. Do you favour flexible work-from-home policies even after it’s safe to return to offices?
  2. Experts agree that a well-balanced diet provides sufficient vitamins and minerals, and multivitamins and supplements are not necessary or effective. Do you agree or disagree that multivitamins are helpful for balanced nutrition?

    Keep your questions focused

    Ask about only one idea at a time and avoid double-barrelled questions. Double-barrelled questions ask about more than one item at a time, which can confuse respondents.

    Example: Double-barrelled question
    Do you agree or disagree that the government should be responsible for providing clean drinking water and high-speed internet to everyone?

    Strongly Agree
    Strongly Disagree

    This question could be difficult to answer for respondents who feel strongly about the right to clean drinking water but not high-speed internet. They might only answer about the topic they feel passionate about or provide a neutral answer instead – but neither of these options capture their true answers.

    Instead, you should ask two separate questions to gauge respondents’ opinions.

    Example: Asking about one topic at a time
    Do you agree or disagree that the government should be responsible for providing clean drinking water to everyone?

    Strongly Agree
    Strongly Disagree

    Do you agree or disagree that the government should be responsible for providing high-speed internet to everyone?

    Strongly Agree
    Strongly Disagree

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    Question order

    You can organise the questions logically, with a clear progression from simple to complex. Alternatively, you can randomise the question order between respondents.

    Logical flow

    Using a logical flow to your question order means starting with simple questions, such as behavioural or opinion questions, and ending with more complex, sensitive, or controversial questions.

    The question order that you use can significantly affect the responses by priming them in specific directions. Question order effects, or context effects, occur when earlier questions influence the responses to later questions, reducing the validity of your questionnaire.

    While demographic questions are usually unaffected by order effects, questions about opinions and attitudes are more susceptible to them.

    Example: Order effects
    Presidential approval ratings (US) are often influenced by any related previous questions or references to political situations. These ratings are lower if they follow relevant questions.

    1. How knowledgeable are you about Joe Biden’s executive orders in his first 100 days?
    2. Are you satisfied or dissatisfied with the way Joe Biden is managing the economy?
    3. Do you approve or disapprove of the way Joe Biden is handling his job as president?

    For this reason, presidential approval ratings questions are asked at the start of surveys to measure opinions more accurately.

    It’s important to minimise order effects because they can be a source of systematic error or bias in your study.


    Randomisation involves presenting individual respondents with the same questionnaire but with different question orders.

    When you use randomisation, order effects will be minimised in your dataset. But a randomised order may also make it harder for respondents to process your questionnaire. Some questions may need more cognitive effort, while others are easier to answer, so a random order could require more time or mental capacity for respondents to switch between questions.

    Step-by-step guide to design

    Follow this step-by-step guide to design your questionnaire.

    Step 1: Define your goals and objectives

    The first step of designing a questionnaire is determining your aims.

    • What topics or experiences are you studying?
    • What specifically do you want to find out?
    • Is a self-report questionnaire an appropriate tool for investigating this topic?

    Once you’ve specified your research aims, you can operationalise your variables of interest into questionnaire items. Operationalising concepts means turning them from abstract ideas into concrete measurements. Every question needs to address a defined need and have a clear purpose.

    Step 2: Use questions that are suitable for your sample

    Create appropriate questions by taking the perspective of your respondents. Consider their language proficiency and available time and energy when designing your questionnaire.

    • Are the respondents familiar with the language and terms used in your questions?
    • Would any of the questions insult, confuse, or embarrass them?
    • Do the response items for any closed-ended questions capture all possible answers?
    • Are the response items mutually exclusive?
    • Do the respondents have time to respond to open-ended questions?

    Consider all possible options for responses to closed-ended questions. From a respondent’s perspective, a lack of response options reflecting their point of view or true answer may make them feel alienated or excluded. In turn, they’ll become disengaged or inattentive to the rest of the questionnaire.

    Step 3: Decide on your questionnaire length and question order

    Once you have your questions, make sure that the length and order of your questions are appropriate for your sample.

    If respondents are not being incentivised or compensated, keep your questionnaire short and easy to answer. Otherwise, your sample may be biased with only highly motivated respondents completing the questionnaire.

    Decide on your question order based on your aims and resources. Use a logical flow if your respondents have limited time or if you cannot randomise questions. Randomising questions helps you avoid bias, but it can take more complex statistical analysis to interpret your data.

    Step 4: Pretest your questionnaire

    When you have a complete list of questions, you’ll need to pretest it to make sure what you’re asking is always clear and unambiguous. Pretesting helps you catch any errors or points of confusion before performing your study.

    Ask friends, classmates, or members of your target audience to complete your questionnaire using the same method you’ll use for your research. Find out if any questions were particularly difficult to answer or if the directions were unclear or inconsistent, and make changes as necessary.

    If you have the resources, running a pilot study will help you test the validity and reliability of your questionnaire. A pilot study is a practice run of the full study, and it includes sampling, data collection, and analysis.

    You can find out whether your procedures are unfeasible or susceptible to bias and make changes in time, but you can’t test a hypothesis with this type of study because it’s usually statistically underpowered.

    Frequently asked questions about questionnaire design

    What’s the difference between a questionnaire and a survey?

    A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

    What’s the difference between closed-ended and open-ended questions?

    Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

    Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

    What is the definition of a Likert scale?

    A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

    To use a Likert scale in a survey, you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

    How do you order a questionnaire?

    You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

    How do you administer questionnaires?

    Questionnaires can be self-administered or researcher-administered.

    Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

    Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

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    Pritha Bhandari

    Pritha has an academic background in English, psychology and cognitive neuroscience. As an interdisciplinary researcher, she enjoys writing articles explaining tricky research concepts for students and academics.