how to write questionnaire for research

• PRO Courses Guides New Tech Help Pro Expert Videos About wikiHow Pro Upgrade Sign In
• EDIT Edit this Article
• EXPLORE Tech Help Pro About Us Random Article Quizzes Request a New Article Community Dashboard This Or That Game Happiness Hub Popular Categories Arts and Entertainment Artwork Books Movies Computers and Electronics Computers Phone Skills Technology Hacks Health Men's Health Mental Health Women's Health Relationships Dating Love Relationship Issues Hobbies and Crafts Crafts Drawing Games Education & Communication Communication Skills Personal Development Studying Personal Care and Style Fashion Hair Care Personal Hygiene Youth Personal Care School Stuff Dating All Categories Arts and Entertainment Finance and Business Home and Garden Relationship Quizzes Cars & Other Vehicles Food and Entertaining Personal Care and Style Sports and Fitness Computers and Electronics Health Pets and Animals Travel Education & Communication Hobbies and Crafts Philosophy and Religion Work World Family Life Holidays and Traditions Relationships Youth
• Browse Articles
• Learn Something New
• Quizzes Hot
• Happiness Hub
• This Or That Game
• Train Your Brain
• Explore More
• Support wikiHow
• About wikiHow
• Log in / Sign up
• Education and Communications

How to Develop a Questionnaire for Research

Last Updated: July 21, 2024 Fact Checked

This article was co-authored by Alexander Ruiz, M.Ed. . Alexander Ruiz is an Educational Consultant and the Educational Director of Link Educational Institute, a tutoring business based in Claremont, California that provides customizable educational plans, subject and test prep tutoring, and college application consulting. With over a decade and a half of experience in the education industry, Alexander coaches students to increase their self-awareness and emotional intelligence while achieving skills and the goal of achieving skills and higher education. He holds a BA in Psychology from Florida International University and an MA in Education from Georgia Southern University. There are 12 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 592,110 times.

A questionnaire is a technique for collecting data in which a respondent provides answers to a series of questions. [1] X Research source To develop a questionnaire that will collect the data you want takes effort and time. However, by taking a step-by-step approach to questionnaire development, you can come up with an effective means to collect data that will answer your unique research question.

Designing Your Questionnaire

• Come up with a research question. It can be one question or several, but this should be the focal point of your questionnaire.
• Develop one or several hypotheses that you want to test. The questions that you include on your questionnaire should be aimed at systematically testing these hypotheses.

• Dichotomous question: this is a question that will generally be a “yes/no” question, but may also be an “agree/disagree” question. It is the quickest and simplest question to analyze, but is not a highly sensitive measure.
• Open-ended questions: these questions allow the respondent to respond in their own words. They can be useful for gaining insight into the feelings of the respondent, but can be a challenge when it comes to analysis of data. It is recommended to use open-ended questions to address the issue of “why.” [2] X Research source
• Multiple choice questions: these questions consist of three or more mutually-exclusive categories and ask for a single answer or several answers. [3] X Research source Multiple choice questions allow for easy analysis of results, but may not give the respondent the answer they want.
• Rank-order (or ordinal) scale questions: this type of question asks your respondent to rank items or choose items in a particular order from a set. For example, it might ask your respondents to order five things from least to most important. These types of questions forces discrimination among alternatives, but does not address the issue of why the respondent made these discriminations. [4] X Research source
• Rating scale questions: these questions allow the respondent to assess a particular issue based on a given dimension. You can provide a scale that gives an equal number of positive and negative choices, for example, ranging from “strongly agree” to “strongly disagree.” [5] X Research source These questions are very flexible, but also do not answer the question “why.”

• Write questions that are succinct and simple. You should not be writing complex statements or using technical jargon, as it will only confuse your respondents and lead to incorrect responses.
• Ask only one question at a time. This will help avoid confusion
• Asking questions such as these usually require you to anonymize or encrypt the demographic data you collect.
• Determine if you will include an answer such as “I don’t know” or “Not applicable to me.” While these can give your respondents a way of not answering certain questions, providing these options can also lead to missing data, which can be problematic during data analysis.
• Put the most important questions at the beginning of your questionnaire. This can help you gather important data even if you sense that your respondents may be becoming distracted by the end of the questionnaire.

• Only include questions that are directly useful to your research question. [8] X Trustworthy Source Food and Agricultural Organization of the United Nations Specialized agency of the United Nations responsible for leading international efforts to end world hunger and improve nutrition Go to source A questionnaire is not an opportunity to collect all kinds of information about your respondents.
• Avoid asking redundant questions. This will frustrate those who are taking your questionnaire.

• Consider if you want your questionnaire to collect information from both men and women. Some studies will only survey one sex.
• Consider including a range of ages in your target demographic. For example, you can consider young adult to be 18-29 years old, adults to be 30-54 years old, and mature adults to be 55+. Providing the an age range will help you get more respondents than limiting yourself to a specific age.
• Consider what else would make a person a target for your questionnaire. Do they need to drive a car? Do they need to have health insurance? Do they need to have a child under 3? Make sure you are very clear about this before you distribute your questionnaire.

• Consider an anonymous questionnaire. You may not want to ask for names on your questionnaire. This is one step you can take to prevent privacy, however it is often possible to figure out a respondent’s identity using other demographic information (such as age, physical features, or zipcode).
• Consider de-identifying the identity of your respondents. Give each questionnaire (and thus, each respondent) a unique number or word, and only refer to them using that new identifier. Shred any personal information that can be used to determine identity.
• Remember that you do not need to collect much demographic information to be able to identify someone. People may be wary to provide this information, so you may get more respondents by asking less demographic questions (if it is possible for your questionnaire).
• Make sure you destroy all identifying information after your study is complete.

Writing your questionnaire

• My name is Jack Smith and I am one of the creators of this questionnaire. I am part of the Department of Psychology at the University of Michigan, where I am focusing in developing cognition in infants.
• I’m Kelly Smith, a 3rd year undergraduate student at the University of New Mexico. This questionnaire is part of my final exam in statistics.
• My name is Steve Johnson, and I’m a marketing analyst for The Best Company. I’ve been working on questionnaire development to determine attitudes surrounding drug use in Canada for several years.

• I am collecting data regarding the attitudes surrounding gun control. This information is being collected for my Anthropology 101 class at the University of Maryland.
• This questionnaire will ask you 15 questions about your eating and exercise habits. We are attempting to make a correlation between healthy eating, frequency of exercise, and incidence of cancer in mature adults.
• This questionnaire will ask you about your recent experiences with international air travel. There will be three sections of questions that will ask you to recount your recent trips and your feelings surrounding these trips, as well as your travel plans for the future. We are looking to understand how a person’s feelings surrounding air travel impact their future plans.

• Beware that if you are collecting information for a university or for publication, you may need to check in with your institution’s Institutional Review Board (IRB) for permission before beginning. Most research universities have a dedicated IRB staff, and their information can usually be found on the school’s website.
• Remember that transparency is best. It is important to be honest about what will happen with the data you collect.
• Include an informed consent for if necessary. Note that you cannot guarantee confidentiality, but you will make all reasonable attempts to ensure that you protect their information. [11] X Research source

• Time yourself taking the survey. Then consider that it will take some people longer than you, and some people less time than you.
• Provide a time range instead of a specific time. For example, it’s better to say that a survey will take between 15 and 30 minutes than to say it will take 15 minutes and have some respondents quit halfway through.
• Use this as a reason to keep your survey concise! You will feel much better asking people to take a 20 minute survey than you will asking them to take a 3 hour one.

• Incentives can attract the wrong kind of respondent. You don’t want to incorporate responses from people who rush through your questionnaire just to get the reward at the end. This is a danger of offering an incentive. [12] X Research source
• Incentives can encourage people to respond to your survey who might not have responded without a reward. This is a situation in which incentives can help you reach your target number of respondents. [13] X Research source
• Consider the strategy used by SurveyMonkey. Instead of directly paying respondents to take their surveys, they offer 50 cents to the charity of their choice when a respondent fills out a survey. They feel that this lessens the chances that a respondent will fill out a questionnaire out of pure self-interest. [14] X Research source
• Consider entering each respondent in to a drawing for a prize if they complete the questionnaire. You can offer a 25$ gift card to a restaurant, or a new iPod, or a ticket to a movie. This makes it less tempting just to respond to your questionnaire for the incentive alone, but still offers the chance of a pleasant reward.

• Always proof read. Check for spelling, grammar, and punctuation errors.
• Include a title. This is a good way for your respondents to understand the focus of the survey as quickly as possible.
• Thank your respondents. Thank them for taking the time and effort to complete your survey.

Distributing Your Questionnaire

• Was the questionnaire easy to understand? Were there any questions that confused you?
• Was the questionnaire easy to access? (Especially important if your questionnaire is online).
• Do you feel the questionnaire was worth your time?
• Were you comfortable answering the questions asked?
• Are there any improvements you would make to the questionnaire?

• Use an online site, such as SurveyMonkey.com. This site allows you to write your own questionnaire with their survey builder, and provides additional options such as the option to buy a target audience and use their analytics to analyze your data. [18] X Research source
• Consider using the mail. If you mail your survey, always make sure you include a self-addressed stamped envelope so that the respondent can easily mail their responses back. Make sure that your questionnaire will fit inside a standard business envelope.
• Conduct face-to-face interviews. This can be a good way to ensure that you are reaching your target demographic and can reduce missing information in your questionnaires, as it is more difficult for a respondent to avoid answering a question when you ask it directly.
• Try using the telephone. While this can be a more time-effective way to collect your data, it can be difficult to get people to respond to telephone questionnaires.

• Make your deadline reasonable. Giving respondents up to 2 weeks to answer should be more than sufficient. Anything longer and you risk your respondents forgetting about your questionnaire.
• Consider providing a reminder. A week before the deadline is a good time to provide a gentle reminder about returning the questionnaire. Include a replacement of the questionnaire in case it has been misplaced by your respondent.

Community Q&A

You Might Also Like

• ↑ https://www.questionpro.com/blog/what-is-a-questionnaire/
• ↑ https://www.hotjar.com/blog/open-ended-questions/
• ↑ https://www.questionpro.com/a/showArticle.do?articleID=survey-questions
• ↑ https://surveysparrow.com/blog/ranking-questions-examples/
• ↑ https://www.lumoa.me/blog/rating-scale/
• ↑ http://www.sciencebuddies.org/science-fair-projects/project_ideas/Soc_survey.shtml
• ↑ http://www.fao.org/docrep/W3241E/w3241e05.htm
• ↑ http://managementhelp.org/businessresearch/questionaires.htm
• ↑ https://www.surveymonkey.com/mp/survey-rewards/
• ↑ http://www.ideafit.com/fitness-library/how-to-develop-a-questionnaire
• ↑ https://www.surveymonkey.com/mp/take-a-tour/?ut_source=header

About This Article

To develop a questionnaire for research, identify the main objective of your research to act as the focal point for the questionnaire. Then, choose the type of questions that you want to include, and come up with succinct, straightforward questions to gather the information that you need to answer your questions. Keep your questionnaire as short as possible, and identify a target demographic who you would like to answer the questions. Remember to make the questionnaires as anonymous as possible to protect the integrity of the person answering the questions! For tips on writing out your questions and distributing the questionnaire, keep reading! Did this summary help you? Yes No

• Send fan mail to authors

Reader Success Stories

Abdul Bari Khan

Nov 11, 2020

Did this article help you?

Jul 25, 2023

Iman Ilhusadi

Nov 26, 2016

Jaydeepa Das

Aug 21, 2018

Atefeh Abdollahi

Jan 3, 2017

Featured Articles

Trending Articles

Watch Articles

• Terms of Use
• Privacy Policy
• Do Not Sell or Share My Info
• Not Selling Info

Get all the best how-tos!

Sign up for wikiHow's weekly email newsletter

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Writing Survey Questions

Perhaps the most important part of the survey process is the creation of questions that accurately measure the opinions, experiences and behaviors of the public. Accurate random sampling will be wasted if the information gathered is built on a shaky foundation of ambiguous or biased questions. Creating good measures involves both writing good questions and organizing them to form the questionnaire.

Questionnaire design is a multistage process that requires attention to many details at once. Designing the questionnaire is complicated because surveys can ask about topics in varying degrees of detail, questions can be asked in different ways, and questions asked earlier in a survey may influence how people respond to later questions. Researchers are also often interested in measuring change over time and therefore must be attentive to how opinions or behaviors have been measured in prior surveys.

Surveyors may conduct pilot tests or focus groups in the early stages of questionnaire development in order to better understand how people think about an issue or comprehend a question. Pretesting a survey is an essential step in the questionnaire design process to evaluate how people respond to the overall questionnaire and specific questions, especially when questions are being introduced for the first time.

For many years, surveyors approached questionnaire design as an art, but substantial research over the past forty years has demonstrated that there is a lot of science involved in crafting a good survey questionnaire. Here, we discuss the pitfalls and best practices of designing questionnaires.

Question development

There are several steps involved in developing a survey questionnaire. The first is identifying what topics will be covered in the survey. For Pew Research Center surveys, this involves thinking about what is happening in our nation and the world and what will be relevant to the public, policymakers and the media. We also track opinion on a variety of issues over time so we often ensure that we update these trends on a regular basis to better understand whether people’s opinions are changing.

At Pew Research Center, questionnaire development is a collaborative and iterative process where staff meet to discuss drafts of the questionnaire several times over the course of its development. We frequently test new survey questions ahead of time through qualitative research methods such as  focus groups , cognitive interviews, pretesting (often using an  online, opt-in sample ), or a combination of these approaches. Researchers use insights from this testing to refine questions before they are asked in a production survey, such as on the ATP.

Measuring change over time

Many surveyors want to track changes over time in people’s attitudes, opinions and behaviors. To measure change, questions are asked at two or more points in time. A cross-sectional design surveys different people in the same population at multiple points in time. A panel, such as the ATP, surveys the same people over time. However, it is common for the set of people in survey panels to change over time as new panelists are added and some prior panelists drop out. Many of the questions in Pew Research Center surveys have been asked in prior polls. Asking the same questions at different points in time allows us to report on changes in the overall views of the general public (or a subset of the public, such as registered voters, men or Black Americans), or what we call “trending the data”.

When measuring change over time, it is important to use the same question wording and to be sensitive to where the question is asked in the questionnaire to maintain a similar context as when the question was asked previously (see  question wording  and  question order  for further information). All of our survey reports include a topline questionnaire that provides the exact question wording and sequencing, along with results from the current survey and previous surveys in which we asked the question.

The Center’s transition from conducting U.S. surveys by live telephone interviewing to an online panel (around 2014 to 2020) complicated some opinion trends, but not others. Opinion trends that ask about sensitive topics (e.g., personal finances or attending religious services ) or that elicited volunteered answers (e.g., “neither” or “don’t know”) over the phone tended to show larger differences than other trends when shifting from phone polls to the online ATP. The Center adopted several strategies for coping with changes to data trends that may be related to this change in methodology. If there is evidence suggesting that a change in a trend stems from switching from phone to online measurement, Center reports flag that possibility for readers to try to head off confusion or erroneous conclusions.

Open- and closed-ended questions

One of the most significant decisions that can affect how people answer questions is whether the question is posed as an open-ended question, where respondents provide a response in their own words, or a closed-ended question, where they are asked to choose from a list of answer choices.

For example, in a poll conducted after the 2008 presidential election, people responded very differently to two versions of the question: “What one issue mattered most to you in deciding how you voted for president?” One was closed-ended and the other open-ended. In the closed-ended version, respondents were provided five options and could volunteer an option not on the list.

When explicitly offered the economy as a response, more than half of respondents (58%) chose this answer; only 35% of those who responded to the open-ended version volunteered the economy. Moreover, among those asked the closed-ended version, fewer than one-in-ten (8%) provided a response other than the five they were read. By contrast, fully 43% of those asked the open-ended version provided a response not listed in the closed-ended version of the question. All of the other issues were chosen at least slightly more often when explicitly offered in the closed-ended version than in the open-ended version. (Also see  “High Marks for the Campaign, a High Bar for Obama”  for more information.)

Researchers will sometimes conduct a pilot study using open-ended questions to discover which answers are most common. They will then develop closed-ended questions based off that pilot study that include the most common responses as answer choices. In this way, the questions may better reflect what the public is thinking, how they view a particular issue, or bring certain issues to light that the researchers may not have been aware of.

When asking closed-ended questions, the choice of options provided, how each option is described, the number of response options offered, and the order in which options are read can all influence how people respond. One example of the impact of how categories are defined can be found in a Pew Research Center poll conducted in January 2002. When half of the sample was asked whether it was “more important for President Bush to focus on domestic policy or foreign policy,” 52% chose domestic policy while only 34% said foreign policy. When the category “foreign policy” was narrowed to a specific aspect – “the war on terrorism” – far more people chose it; only 33% chose domestic policy while 52% chose the war on terrorism.

In most circumstances, the number of answer choices should be kept to a relatively small number – just four or perhaps five at most – especially in telephone surveys. Psychological research indicates that people have a hard time keeping more than this number of choices in mind at one time. When the question is asking about an objective fact and/or demographics, such as the religious affiliation of the respondent, more categories can be used. In fact, they are encouraged to ensure inclusivity. For example, Pew Research Center’s standard religion questions include more than 12 different categories, beginning with the most common affiliations (Protestant and Catholic). Most respondents have no trouble with this question because they can expect to see their religious group within that list in a self-administered survey.

In addition to the number and choice of response options offered, the order of answer categories can influence how people respond to closed-ended questions. Research suggests that in telephone surveys respondents more frequently choose items heard later in a list (a “recency effect”), and in self-administered surveys, they tend to choose items at the top of the list (a “primacy” effect).

Because of concerns about the effects of category order on responses to closed-ended questions, many sets of response options in Pew Research Center’s surveys are programmed to be randomized to ensure that the options are not asked in the same order for each respondent. Rotating or randomizing means that questions or items in a list are not asked in the same order to each respondent. Answers to questions are sometimes affected by questions that precede them. By presenting questions in a different order to each respondent, we ensure that each question gets asked in the same context as every other question the same number of times (e.g., first, last or any position in between). This does not eliminate the potential impact of previous questions on the current question, but it does ensure that this bias is spread randomly across all of the questions or items in the list. For instance, in the example discussed above about what issue mattered most in people’s vote, the order of the five issues in the closed-ended version of the question was randomized so that no one issue appeared early or late in the list for all respondents. Randomization of response items does not eliminate order effects, but it does ensure that this type of bias is spread randomly.

Questions with ordinal response categories – those with an underlying order (e.g., excellent, good, only fair, poor OR very favorable, mostly favorable, mostly unfavorable, very unfavorable) – are generally not randomized because the order of the categories conveys important information to help respondents answer the question. Generally, these types of scales should be presented in order so respondents can easily place their responses along the continuum, but the order can be reversed for some respondents. For example, in one of Pew Research Center’s questions about abortion, half of the sample is asked whether abortion should be “legal in all cases, legal in most cases, illegal in most cases, illegal in all cases,” while the other half of the sample is asked the same question with the response categories read in reverse order, starting with “illegal in all cases.” Again, reversing the order does not eliminate the recency effect but distributes it randomly across the population.

Question wording

The choice of words and phrases in a question is critical in expressing the meaning and intent of the question to the respondent and ensuring that all respondents interpret the question the same way. Even small wording differences can substantially affect the answers people provide.

[View more Methods 101 Videos ]

An example of a wording difference that had a significant impact on responses comes from a January 2003 Pew Research Center survey. When people were asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule,” 68% said they favored military action while 25% said they opposed military action. However, when asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule  even if it meant that U.S. forces might suffer thousands of casualties, ” responses were dramatically different; only 43% said they favored military action, while 48% said they opposed it. The introduction of U.S. casualties altered the context of the question and influenced whether people favored or opposed military action in Iraq.

There has been a substantial amount of research to gauge the impact of different ways of asking questions and how to minimize differences in the way respondents interpret what is being asked. The issues related to question wording are more numerous than can be treated adequately in this short space, but below are a few of the important things to consider:

First, it is important to ask questions that are clear and specific and that each respondent will be able to answer. If a question is open-ended, it should be evident to respondents that they can answer in their own words and what type of response they should provide (an issue or problem, a month, number of days, etc.). Closed-ended questions should include all reasonable responses (i.e., the list of options is exhaustive) and the response categories should not overlap (i.e., response options should be mutually exclusive). Further, it is important to discern when it is best to use forced-choice close-ended questions (often denoted with a radio button in online surveys) versus “select-all-that-apply” lists (or check-all boxes). A 2019 Center study found that forced-choice questions tend to yield more accurate responses, especially for sensitive questions.  Based on that research, the Center generally avoids using select-all-that-apply questions.

It is also important to ask only one question at a time. Questions that ask respondents to evaluate more than one concept (known as double-barreled questions) – such as “How much confidence do you have in President Obama to handle domestic and foreign policy?” – are difficult for respondents to answer and often lead to responses that are difficult to interpret. In this example, it would be more effective to ask two separate questions, one about domestic policy and another about foreign policy.

In general, questions that use simple and concrete language are more easily understood by respondents. It is especially important to consider the education level of the survey population when thinking about how easy it will be for respondents to interpret and answer a question. Double negatives (e.g., do you favor or oppose  not  allowing gays and lesbians to legally marry) or unfamiliar abbreviations or jargon (e.g., ANWR instead of Arctic National Wildlife Refuge) can result in respondent confusion and should be avoided.

Similarly, it is important to consider whether certain words may be viewed as biased or potentially offensive to some respondents, as well as the emotional reaction that some words may provoke. For example, in a 2005 Pew Research Center survey, 51% of respondents said they favored “making it legal for doctors to give terminally ill patients the means to end their lives,” but only 44% said they favored “making it legal for doctors to assist terminally ill patients in committing suicide.” Although both versions of the question are asking about the same thing, the reaction of respondents was different. In another example, respondents have reacted differently to questions using the word “welfare” as opposed to the more generic “assistance to the poor.” Several experiments have shown that there is much greater public support for expanding “assistance to the poor” than for expanding “welfare.”

We often write two versions of a question and ask half of the survey sample one version of the question and the other half the second version. Thus, we say we have two  forms  of the questionnaire. Respondents are assigned randomly to receive either form, so we can assume that the two groups of respondents are essentially identical. On questions where two versions are used, significant differences in the answers between the two forms tell us that the difference is a result of the way we worded the two versions.

One of the most common formats used in survey questions is the “agree-disagree” format. In this type of question, respondents are asked whether they agree or disagree with a particular statement. Research has shown that, compared with the better educated and better informed, less educated and less informed respondents have a greater tendency to agree with such statements. This is sometimes called an “acquiescence bias” (since some kinds of respondents are more likely to acquiesce to the assertion than are others). This behavior is even more pronounced when there’s an interviewer present, rather than when the survey is self-administered. A better practice is to offer respondents a choice between alternative statements. A Pew Research Center experiment with one of its routinely asked values questions illustrates the difference that question format can make. Not only does the forced choice format yield a very different result overall from the agree-disagree format, but the pattern of answers between respondents with more or less formal education also tends to be very different.

One other challenge in developing questionnaires is what is called “social desirability bias.” People have a natural tendency to want to be accepted and liked, and this may lead people to provide inaccurate answers to questions that deal with sensitive subjects. Research has shown that respondents understate alcohol and drug use, tax evasion and racial bias. They also may overstate church attendance, charitable contributions and the likelihood that they will vote in an election. Researchers attempt to account for this potential bias in crafting questions about these topics. For instance, when Pew Research Center surveys ask about past voting behavior, it is important to note that circumstances may have prevented the respondent from voting: “In the 2012 presidential election between Barack Obama and Mitt Romney, did things come up that kept you from voting, or did you happen to vote?” The choice of response options can also make it easier for people to be honest. For example, a question about church attendance might include three of six response options that indicate infrequent attendance. Research has also shown that social desirability bias can be greater when an interviewer is present (e.g., telephone and face-to-face surveys) than when respondents complete the survey themselves (e.g., paper and web surveys).

Lastly, because slight modifications in question wording can affect responses, identical question wording should be used when the intention is to compare results to those from earlier surveys. Similarly, because question wording and responses can vary based on the mode used to survey respondents, researchers should carefully evaluate the likely effects on trend measurements if a different survey mode will be used to assess change in opinion over time.

Question order

Once the survey questions are developed, particular attention should be paid to how they are ordered in the questionnaire. Surveyors must be attentive to how questions early in a questionnaire may have unintended effects on how respondents answer subsequent questions. Researchers have demonstrated that the order in which questions are asked can influence how people respond; earlier questions can unintentionally provide context for the questions that follow (these effects are called “order effects”).

One kind of order effect can be seen in responses to open-ended questions. Pew Research Center surveys generally ask open-ended questions about national problems, opinions about leaders and similar topics near the beginning of the questionnaire. If closed-ended questions that relate to the topic are placed before the open-ended question, respondents are much more likely to mention concepts or considerations raised in those earlier questions when responding to the open-ended question.

For closed-ended opinion questions, there are two main types of order effects: contrast effects ( where the order results in greater differences in responses), and assimilation effects (where responses are more similar as a result of their order).

An example of a contrast effect can be seen in a Pew Research Center poll conducted in October 2003, a dozen years before same-sex marriage was legalized in the U.S. That poll found that people were more likely to favor allowing gays and lesbians to enter into legal agreements that give them the same rights as married couples when this question was asked after one about whether they favored or opposed allowing gays and lesbians to marry (45% favored legal agreements when asked after the marriage question, but 37% favored legal agreements without the immediate preceding context of a question about same-sex marriage). Responses to the question about same-sex marriage, meanwhile, were not significantly affected by its placement before or after the legal agreements question.

Another experiment embedded in a December 2008 Pew Research Center poll also resulted in a contrast effect. When people were asked “All in all, are you satisfied or dissatisfied with the way things are going in this country today?” immediately after having been asked “Do you approve or disapprove of the way George W. Bush is handling his job as president?”; 88% said they were dissatisfied, compared with only 78% without the context of the prior question.

Responses to presidential approval remained relatively unchanged whether national satisfaction was asked before or after it. A similar finding occurred in December 2004 when both satisfaction and presidential approval were much higher (57% were dissatisfied when Bush approval was asked first vs. 51% when general satisfaction was asked first).

Several studies also have shown that asking a more specific question before a more general question (e.g., asking about happiness with one’s marriage before asking about one’s overall happiness) can result in a contrast effect. Although some exceptions have been found, people tend to avoid redundancy by excluding the more specific question from the general rating.

Assimilation effects occur when responses to two questions are more consistent or closer together because of their placement in the questionnaire. We found an example of an assimilation effect in a Pew Research Center poll conducted in November 2008 when we asked whether Republican leaders should work with Obama or stand up to him on important issues and whether Democratic leaders should work with Republican leaders or stand up to them on important issues. People were more likely to say that Republican leaders should work with Obama when the question was preceded by the one asking what Democratic leaders should do in working with Republican leaders (81% vs. 66%). However, when people were first asked about Republican leaders working with Obama, fewer said that Democratic leaders should work with Republican leaders (71% vs. 82%).

The order questions are asked is of particular importance when tracking trends over time. As a result, care should be taken to ensure that the context is similar each time a question is asked. Modifying the context of the question could call into question any observed changes over time (see  measuring change over time  for more information).

A questionnaire, like a conversation, should be grouped by topic and unfold in a logical order. It is often helpful to begin the survey with simple questions that respondents will find interesting and engaging. Throughout the survey, an effort should be made to keep the survey interesting and not overburden respondents with several difficult questions right after one another. Demographic questions such as income, education or age should not be asked near the beginning of a survey unless they are needed to determine eligibility for the survey or for routing respondents through particular sections of the questionnaire. Even then, it is best to precede such items with more interesting and engaging questions. One virtue of survey panels like the ATP is that demographic questions usually only need to be asked once a year, not in each survey.

U.S. Surveys

Other research methods.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center

Creating a Questionnaire

Create the perfect questionnaire and collect actionable data using our online guide!

Table of Contents

• How to Create

Questionnaire Types

• Collecting Responses
• Analyzing Results
• Getting Started

What is a Questionnaire?

Definition: A questionnaire is a convenient way to collect feedback. A questionnaire can be used to measure customer satisfaction, capture employee feedback, or even conduct product research. Responses can be collected via email, web link, QR code, or using a survey panel.

The term "survey" and "questionnaire" are commonly used interchangeably. A questionnaire refers to the questions used to collect feedback (the form itself). A survey relates to the entire research process, including summarizing and analyzing questionnaire data.

Getting Started + Tips

How to make a questionnaire: Keep questions short and focused on one topic at a time. Use multiple-choice questions to fit answers into a specific category. Use an open-ended question to capture comments. A Likert scale or MaxDiff question can be used for market research. Collect responses for your questionnaire using an email collector, an anonymous link, or even a QR code.

The following 6 tips will help you create the perfect questionnaire:

1) Use 10 Questions or Less

The shorter you keep your survey, the higher your completion rates. Longer questionnaires usually tend to have a high drop-off percentage. Keeping your surveys to 10 questions or fewer forces you to draft a study that only includes important questions; you should remove trivial questions during the draft process.

2) One Idea Per Question

Make sure each question only covers one topic. Try to include only one topic at a time. For example, in an employee survey, you would not want to ask, "Do you feel satisfied with your compensation and career advancement?". Instead, you would like to separate "compensation" and "career advancement" into two questions or use a Likert scale , putting each question on a separate row.

3) Group Similar Questions Together

Suppose the survey is more than ten questions; similar questions should be grouped on separate pages. If you don't want to use more than one page, add extra spacing between groups of the question; extra white space can increase the increase the readability of your questionnaire.

4) Use Skip/Display Logic

If you have questions that only apply to certain people, consider using skip or display logic to show those questions conditionally. This will help reduce the length of your survey and boost response rates.

If you have questions that only apply to certain people, consider using skip or display logic to show those questions conditionally. This will help reduce the length of your survey and boost response rates. For example, if you asked, "Are you currently looking for new employment opportunities?". If the answer were "yes," a follow-up question would ask, "Why?"

5) Use Research Questions Like MaxDiff

Research questions are an excellent tool for customer or product questionnaires. Instead of asking multiple questions on which features are essential or what price is desirable, question types like MaxDiff and Conjoint will provide you with high-quality, actionable data that can be used for feature prioritization and product pricing. In addition, these question types will reduce the length of your questionnaire.

6) Keep the Audience in Mind

An employee questionnaire should use an anonymous link to collect responses; this will help boost trust and increase honest answers. If doing a customer study, consider adding custom data to the weblink to help identify responses. A survey panel and current customers can lend fresh perspectives for general market research.

Questionnaire Templates

Adding customer surveys to your Google review strategy will add additional data points to improve customer satisfaction. In addition, surveys are a valuable tool to identify ways to improve, establish internal benchmarks, and conduct pricing and product research to improve your company's products.

While there are numerous types of questionnaires (or survey types), these are the five most common general categories:

1) Customer Satisfaction

Capturing customer feedback is one of the most common uses of questionnaires. A good customer satisfaction survey will always revolve around a Net Promoter Score question. When the Net Promoter Score question results are tallied, one number from -100 is 100 is displayed. This number is ideal for benchmarks. Net Promoter provides quick and actionable feedback when combined with an open-ended text question.

2) Customer Effort

Measuring how easily customers can complete a purchase or take a specific action is crucial for the customer experience strategy. A customer effort score question is a rating scale from 1 to 7 (disagree to agree). Results for this question are averaged; the higher the score, the easier it is for your customers to complete tasks.

3) Employee Satisfaction & Engagement

Employee satisfaction and engagement are often used interchangeably but measure different things. Both types of surveys often use opinion scales to ask questions.

Employee satisfaction measures how satisfied employees are with their job and work environment. Standard measures of employee satisfaction include salary, benefits, and co-worker relationships.

Employee engagement relates to the emotional commitment employees have to an organization. It goes beyond simple satisfaction. Standard measures of engagement include belief in the company mission, opportunities for career growth, and being inspired to perform at a high level.

4) Employee Exit Interviews

When employees leave for new opportunities, sending a questionnaire is a great way to understand why that employee is leaving. The feedback obtained here can be used to improve the workplace and reduce employee turnover.

5) Product Research

MaxDiff is used to identify what is most important to your audience. For example, if building a new mobile application, asking a group of users what they think is least and most important will help guide product strategy; your team should only focus on the important areas.

For pricing a new product, Van Westendorp will give you a range of prices the market is willing to expect. You could price your product too high or too low without a question like this, reducing your market penetration.

Collecting Responses For Your Questionnaire

There are a few different ways to collect feedback for questionnaires. Depending on your needs, each one could have an advantage.

With email distribution, you would upload a list of email addresses, and the platform would automatically place a link to your questionnaire inside the email body. One advantage is sending email reminders to respondents who still need to complete your survey. In addition, the email links are unique for each respondent, so you can track email open and click rates. As a result, email surveys are ideal for customer research.

A web link is a convenient way to collect feedback at your convenience. You can place a web link on social media, your website, or even inside your CRM email program (instead of an email collector with a unique link to each person). Custom data can be included in the link, such as store location. This custom data can be used to segment and filter results.

Anonymous Link

When you want to protect your respondents' identities, you use an anonymous link . Anonymous inks do not store respondent information, IP address, or email address. Because of this, anonymous survey links are perfect for employee surveys.

QR code Surveys

QR code surveys can be placed on paper receipts, product packaging, or flyers. In addition, QR codes are a great way to collect feedback after or during an event or even during in-person focus groups.

Survey Panels

If you're conducting market research and need access to a customer base, using a survey panel will get you the responses required. A good survey panel will allow you to target specific demographics, job titles, or interest levels (such as car enthusiasts). When using survey panels, you'll want to double-check and clean your data for low-quality responses. People who speed through your survey or mark the first answer for all questions should be removed.

How to Analyze Questionnaire Data

When analyzing the data from a questionnaire, consider a few advanced techniques like the ones below. These techniques will give you better insights than just simple graphs and charts.

Creating a segment or a cross-tabulation is the easiest way to dive deeper into your results. For example, if you conducted an employee satisfaction survey, the overall scores for the company could be high. But that might only tell part of the story. For example, if your company has multiple departments, you should create a cross-tabulation for each department. You might notice that there is one department with low scores. or one department with high scores.

If your company conducted its first Net Promoter Score survey and the results were -10, that score would be your benchmark. Each subsequent customer survey you run should be compared against that initial number to improve it each time.

TURF Analysis

This is an advanced research technique but very valuable. TURF analysis analysis stands for "Total Unduplicated Reach and Frequency" and is used to find the combination of items that would provide the highest reach level. For example, suppose you ask, "Which of the following flavor of ice cream would you buy?" If you run a TURF analysis on the results, you could find the top 3 or 4 combinations of flavors that would result in the highest sales.

Unsure Where to Start?

Creating a questionnaire can be a challenging process. However, these three suggestions can help you with the perfect questionnaire strategy.

1) Talk With Your Team

Some departments might want to conduct pricing research and do simple Net Promoter Score surveys. Having your organization aligned on strategy will simplify the process and eliminate any possibility of re-work. An aligned strategy will also mean a shorter study with fewer overlapping questions.

2) Start with a Template

A pre-made template will show you how to format and word questions. Next, try multiple templates to understand the various question types.

3) Look at Competitor Surveys

You might notice competitors asking specific questions - this would be a sign that those questions provide valuable metrics. If you can incorporate the great things your competition does while making it more efficient for respondents, your questionnaire campaigns will have a greater chance of success.

Get Started Now

We have you covered on anything from customer surveys, employee surveys, to market research. Get started and create your first survey for free.

• Utility Menu

Harvard University Program on Survey Research

• Questionnaire Design Tip Sheet

This PSR Tip Sheet provides some basic tips about how to write good survey questions and design a good survey questionnaire.

PSR Resources

• Managing and Manipulating Survey Data: A Beginners Guide
• Finding and Hiring Survey Contractors
• How to Frame and Explain the Survey Data Used in a Thesis
• Overview of Cognitive Testing and Questionnaire Evaluation
• Sampling, Coverage, and Nonresponse Tip Sheet
• Introduction to Surveys for Honors Thesis Writers
• PSR Introduction to the Survey Process
• Related Centers/Programs at Harvard
• General Survey Reference
• Institutional Review Boards
• Select Funding Opportunities
• Survey Analysis Software
• Professional Standards
• Professional Organizations
• Major Public Polls
• Survey Data Collections
• Major Longitudinal Surveys
• Other Links

28 Questionnaire Examples, Questions, & Templates to Survey Your Clients

Published: May 15, 2023

The adage "the customer is always right" has received some pushback in recent years, but when it comes to conducting surveys , the phrase is worth a deeper look. In the past, representatives were tasked with solving client problems as they happened. Now, they have to be proactive by solving problems before they come up.

Salesforce found that 63% of customers expect companies to anticipate their needs before they ask for help. But how can a customer service team recognize these customer needs in advance and effectively solve them on a day-to-day basis?

A customer questionnaire is a tried-and-true method for collecting survey data to inform your customer service strategy . By hearing directly from the customer, you'll capture first-hand data about how well your service team meets their needs. In this article, you'll get free questionnaire templates and best practices on how to administer them for the most honest responses.

Table of Contents:

Questionnaire Definition

Survey vs. questionnaire, questionnaire templates.

• Questionnaire Examples

Questionnaire Design

Survey question examples.

• Examples of Good Survey Questions

How to Make a Questionnaire

A questionnaire is a research tool used to conduct surveys. It includes specific questions with the goal to understand a topic from the respondents' point of view. Questionnaires typically have closed-ended, open-ended, short-form, and long-form questions.

The questions should always stay as unbiased as possible. For instance, it's unwise to ask for feedback on a specific product or service that’s still in the ideation phase. To complete the questionnaire, the customer would have to imagine how they might experience the product or service rather than sharing their opinion about their actual experience with it.

Ask broad questions about the kinds of qualities and features your customers enjoy in your products or services and incorporate that feedback into new offerings your team is developing.

What makes a good questionnaire?

Define the goal, make it short and simple, use a mix of question types, proofread carefully, keep it consistent.

A good questionnaire should find what you need versus what you want. It should be valuable and give you a chance to understand the respondent’s point of view.

Make the purpose of your questionnaire clear. While it's tempting to ask a range of questions simultaneously, you'll get more valuable results if you stay specific to a set topic.

According to HubSpot research , 47% of those surveyed say their top reason for abandoning a survey is the time it takes to complete.

So, questionnaires should be concise and easy to finish. If you're looking for a respondent’s experience with your business, focus on the most important questions.

5 Free Customer Satisfaction Survey Templates

Easily measure customer satisfaction and begin to improve your customer experience.

• Net Promoter Score
• Customer Effort Score

Download Free

All fields are required.

You're all set!

Click this link to access this resource at any time.

5 Customer Survey Templates

Featured resource.

Your questionnaire should include a combination of question types, like open-ended, long-form, or short-ended questions.

Open-ended questions give users a chance to share their own answers. But closed-ended questions are more efficient and easy to quantify, with specific answer choices.

If you're not sure which question types are best, read here for more survey question examples .

While it's important to check spelling and grammar, there are two other things you'll want to check for a great questionnaire.

First, edit for clarity. Jargon, technical terms, and brand-specific language can be confusing for respondents. Next, check for leading questions. These questions can produce biased results that will be less useful to your team.

Consistency makes it easier for respondents to quickly complete your questionnaire. This is because it makes the questions less confusing. It can also reduce bias.

Being consistent is also helpful for analyzing questionnaire data because it makes it easier to compare results. With this in mind, keep response scales, question types, and formatting consistent.

In-Depth Interviews vs. Questionnaire

Questionnaires can be a more feasible and efficient research method than in-depth interviews. They are a lot cheaper to conduct. That’s because in-depth interviews can require you to compensate the interviewees for their time and give accommodations and travel reimbursement.

Questionnaires also save time for both parties. Customers can quickly complete them on their own time, and employees of your company don't have to spend time conducting the interviews. They can capture a larger audience than in-depth interviews, making them much more cost-effective.

It would be impossible for a large company to interview tens of thousands of customers in person. The same company could potentially get feedback from its entire customer base using an online questionnaire.

When considering your current products and services (as well as ideas for new products and services), it's essential to get the feedback of existing and potential customers. They are the ones who have a say in purchasing decisions.

A questionnaire is a tool that’s used to conduct a survey. A survey is the process of gathering, sampling, analyzing, and interpreting data from a group of people.

The confusion between these terms most likely stems from the fact that questionnaires and data analysis were treated as very separate processes before the Internet became popular. Questionnaires used to be completed on paper, and data analysis occurred later as a separate process. Nowadays, these processes are typically combined since online survey tools allow questionnaire responses to be analyzed and aggregated all in one step.

But questionnaires can still be used for reasons other than data analysis. Job applications and medical history forms are examples of questionnaires that have no intention of being statistically analyzed. The key difference between questionnaires and surveys is that they can exist together or separately.

Below are some of the best free questionnaire templates you can download to gather data that informs your next product or service offering.

What makes a good survey question?

Have a goal in mind, draft clear and distinct answers and questions, ask one question at a time, check for bias and sensitivity, include follow-up questions.

To make a good survey question, you have to choose the right type of questions to use. Include concise, clear, and appropriate questions with answer choices that won’t confuse the respondent and will clearly offer data on their experience.

Good survey questions can give a business good data to examine. Here are some more tips to follow as you draft your survey questions.

To make a good survey, consider what you are trying to learn from it. Understanding why you need to do a survey will help you create clear and concise questions that you need to ask to meet your goal. The more your questions focus on one or two objectives, the better your data will be.

You have a goal in mind for your survey. Now you have to write the questions and answers depending on the form you’re using.

For instance, if you’re using ranks or multiple-choice in your survey, be clear. Here are examples of good and poor multiple-choice answers:

Poor Survey Question and Answer Example

California:

• Contains the tallest mountain in the United States.
• Has an eagle on its state flag.
• Is the second-largest state in terms of area.
• Was the location of the Gold Rush of 1849.

Good Survey Question and Answer Example

What is the main reason so many people moved to California in 1849?

• California's land was fertile, plentiful, and inexpensive.
• The discovery of gold in central California.
• The East was preparing for a civil war.
• They wanted to establish religious settlements.

In the poor example, the question may confuse the respondent because it's not clear what is being asked or how the answers relate to the question. The survey didn’t fully explain the question, and the options are also confusing.

In the good example above, the question and answer choices are clear and easy to understand.

Always make sure answers and questions are clear and distinct to create a good experience for the respondent. This will offer your team the best outcomes from your survey.

It's surprisingly easy to combine multiple questions into one. They even have a name — they’re called "double-barreled" questions. But a good survey asks one question at a time.

For example, a survey question could read, "What is your favorite sneaker and clothing apparel brand?" This is bad because you’re asking two questions at once.

By asking two questions simultaneously, you may confuse your respondents and get unclear answers. Instead, each question should focus on getting specific pieces of information.

For example, ask, "What is your favorite sneaker brand?" then, "What is your favorite clothing apparel brand?" By separating the questions, you allow your respondents to give separate and precise answers.

Biased questions can lead a respondent toward a specific response. They can also be vague or unclear. Sensitive questions such as age, religion, or marital status can be helpful for demographics. These questions can also be uncomfortable for people to answer.

There are a few ways to create a positive experience with your survey questions.

First, think about question placement. Sensitive questions that appear in context with other survey questions can help people understand why you are asking. This can make them feel more comfortable responding.

Next, check your survey for leading questions, assumptions, and double-barreled questions. You want to make sure that your survey is neutral and free of bias.

Asking more than one survey question about an area of interest can make a survey easier to understand and complete. It also helps you collect more in-depth insights from your respondents.

1. Free HubSpot Questionnaire Template

HubSpot offers a variety of free customer surveys and questionnaire templates to analyze and measure customer experience. Choose from five templates: net promoter score, customer satisfaction, customer effort, open-ended questions, and long-form customer surveys.

2. Client Questionnaire Template

It's a good idea to gauge your clients' experiences with your business to uncover opportunities to improve your offerings. That will, in turn, better suit their lifestyles. You don't have to wait for an entire year to pass before polling your customer base about their experience either. A simple client questionnaire, like the one below, can be administered as a micro survey several times throughout the year. These types of quick survey questions work well to retarget your existing customers through social media polls and paid interactive ads.

1. How much time do you spend using [product or service]?

• Less than a minute
• About 1 - 2 minutes
• Between 2 and 5 minutes
• More than 5 minutes

2. In the last month, what has been your biggest pain point?

• Finding enough time for important tasks
• Delegating work
• Having enough to do

3. What's your biggest priority right now?

• Finding a faster way to work
• Problem-solving
• Staff development

3. Website Questionnaire Template

Whether you just launched a brand new website or you're gathering data points to inform a redesign, you'll find customer feedback to be essential in both processes. A website questionnaire template will come in handy to collect this information using an unbiased method.

1. How many times have you visited [website] in the past month?

• More than once

2. What is the primary reason for your visit to [website]?

• To make a purchase
• To find more information before making a purchase in-store
• To contact customer service

3. Are you able to find what you're looking for on the website homepage?

4. Customer Satisfaction Questionnaire Template

If you've never surveyed your customers and are looking for a template to get started, this one includes some basic customer satisfaction questions. These will apply to just about any customer your business serves.

1. How likely are you to recommend us to family, friends, or colleagues?

• Extremely unlikely
• Somewhat unlikely
• Somewhat likely
• Extremely likely

2. How satisfied were you with your experience?

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10

3. Rank the following items in terms of their priority to your purchasing process.

• Helpful staff
• Quality of product
• Price of product
• Ease of purchase
• Proximity of store
• Online accessibility
• Current need
• Appearance of product

4. Who did you purchase these products for?

• Family member
• On behalf of a business

5. Please rate our staff on the following terms:

• Friendly __ __ __ __ __ Hostile
• Helpful __ __ __ __ __ Useless
• Knowledgeable __ __ __ __ __ Inexperienced
• Professional __ __ __ __ __ Inappropriate

6. Would you purchase from our company again?

7. How can we improve your experience for the future?

________________________________.

5. Customer Effort Score Questionnaire Template

The following template gives an example of a brief customer effort score (CES) questionnaire. This free template works well for new customers to measure their initial reaction to your business.

1. What was the ease of your experience with our company?

• Extremely difficult
• Somewhat difficult
• Somewhat easy
• Extremely easy

2. The company did everything it could to make my process as easy as possible.

• Strongly disagree
• Somewhat disagree
• Somewhat agree
• Strongly agree

3. On a scale of 1 to 10 (1 being "extremely quickly" and 10 being "extremely slowly"), how fast were you able to solve your problem?

4. How much effort did you have to put forth while working with our company?

• Much more than expected
• Somewhat more than expected
• As much as expected
• Somewhat less than expected
• Much less than expected

6. Demographic Questionnaire Template

Here's a template for surveying customers to learn more about their demographic background. You could substantiate the analysis of this questionnaire by corroborating the data with other information from your web analytics, internal customer data, and industry data.

1. How would you describe your employment status?

• Employed full-time
• Employed part-time
• Freelance/contract employee
• Self-employed

2. How many employees work at your company?

3. How would you classify your role?

• Individual Contributor

4. How would you classify your industry?

• Technology/software
• Hospitality/dining
• Entertainment

Below, we have curated a list of questionnaire examples that do a great job of gathering valuable qualitative and quantitative data.

4 Questionnaire Examples

1. customer satisfaction questions.

Learn more about HubSpot's Customer Survey software.

Multiple-Choice

Multiple-choice questions offer respondents several answers to choose from. This is a popular choice of questionnaire format since it's simple for people to fill out and for companies to analyze.

Multiple-choice questions can be in single-answer form (respondents can only choose one response) or multiple-answer form (respondents can choose as many responses as necessary).

Multiple-choice survey question examples : "Which of the following social media platforms do you use most often?"

Image Source

Rating Scale

Rating scale questions offer a scale of numbers and ask respondents to rate topics based on the sentiments assigned to that scale. This is effective when assessing customer satisfaction.

Rating scale survey question examples : "Rate your level of satisfaction with the customer service you received today on a scale of 1-10."

Yes or no survey questions are a type of dichotomous question. These are questions that only offer two possible responses. They’re useful because they’re quick to answer and can help with customer segmentation.

Yes or no survey questions example : "Have you ever used HubSpot before?"

Likert Scale

Likert scale questions assess whether a respondent agrees with the statement, as well as the extent to which they agree or disagree.

These questions typically offer five or seven responses, with sentiments ranging from items such as "strongly disagree" to "strongly agree." Check out this post to learn more about the Likert scale .

Likert scale survey question examples : “How satisfied are you with the service from [brand]?”

Open-ended questions ask a broader question or offer a chance to elaborate on a response to a close-ended question. They're accompanied by a text box that leaves room for respondents to write freely. This is particularly important when asking customers to expand on an experience or recommendation.

Open-ended survey question examples : "What are your personal goals for using HubSpot? Please describe."

Matrix Table

A matrix table is usually a group of multiple-choice questions grouped in a table. Choices for these survey questions are usually organized in a scale. This makes it easier to understand the relationships between different survey responses.

Matrix table survey question examples : "Rate your level of agreement with the following statements about HubSpot on a scale of 1-5."

Rank Order Scaling

These questions ask respondents to rank a set of terms by order of preference or importance. This is useful for understanding customer priorities.

Rank order scaling examples : "Rank the following factors in order of importance when choosing a new job."

Semantic Differential Scale

This scale features pairs of opposite adjectives that respondents use for rating, usually for a feature or experience. This type of question makes it easier to understand customer attitudes and beliefs.

Semantic differential scale question examples : "Rate your overall impression of this brand as friendly vs. unfriendly, innovative vs. traditional, and boring vs. exciting."

Side-By-Side Matrix

This matrix table format includes two sets of questions horizontally for easy comparison. This format can help with customer gap analysis.

Side-by-side matrix question examples : "Rate your level of satisfaction with HubSpot's customer support compared to its ease of use."

Stapel Scale

The Stapel rating scale offers a single adjective or idea for rating. It uses a numerical scale with a zero point in the middle. This survey question type helps with in-depth analysis.

Stapel scale survey question examples : "Rate your overall experience with this product as +5 (excellent) to -5 (terrible)."

Constant Sum Survey Questions

In this question format, people distribute points to different choices based on the perceived importance of each point. This kind of question is often used in market research and can help your team better understand customer choices .

Constant sum survey question examples : "What is your budget for the following marketing expenses: Paid campaigns, Events, Freelancers, Agencies, Research."

Image Choice

This survey question type shows several images. Then, it asks the respondent to choose the image that best matches their response to the question. These questions are useful for understanding your customers’ design preferences.

Image choice survey questions example : "Which of these three images best represents your brand voice?"

Choice Model

This survey question offers a hypothetical scenario, then the respondent must choose from the presented options. It's a useful type of question when you are refining a product or strategy.

Choice model survey questions example : "Which of these three deals would be most appealing to you?"

Click Map Questions

Click map questions offer an image click on specific areas of the image in response to a question. This question uses data visualization to learn about customer preferences for design and user experience.

Click map question examples : "Click on the section of the website where you would expect to find pricing information."

Data Upload

This survey question example asks the respondent to upload a file or document in response to a question. This type of survey question can help your team collect data and context that might be tough to collect otherwise.

Data upload question examples : "Please upload a screenshot of the error you encountered during your purchase."

Benchmarkable Questions

This question type asks a respondent to compare their answers to a group or benchmark. These questions can be useful if you're trying to compare buyer personas or other customer groups.

Benchmarkable survey questions example : "Compare your company's marketing budget to other companies in your industry."

Good Survey Questions

• What is your favorite product?
• Why did you purchase this product?
• How satisfied are you with [product]?
• Would you recommend [product] to a friend?
• Would you recommend [company name] to a friend?
• If you could change one thing about [product], what would it be?
• Which other options were you considering before [product or company name]?
• Did [product] help you accomplish your goal?
• How would you feel if we did not offer this product, feature, or service?
• What would you miss the most if you couldn't use your favorite product from us?
• What is one word that best describes your experience using our product?
• What's the primary reason for canceling your account?
• How satisfied are you with our customer support?
• Did we answer all of your questions and concerns?
• How can we be more helpful?
• What additional features would you like to see in this product?
• Are we meeting your expectations?
• How satisfied are you with your experience?

1. "What is your favorite product?"

This question is a great starter for your survey. Most companies want to know what their most popular products are, and this question cuts right to the point.

It's important to note that this question gives you the customer's perspective, not empirical evidence. You should compare the results to your inventory to see if your customers' answers match your actual sales. You may be surprised to find your customers' "favorite" product isn't the highest-selling one.

2. "Why did you purchase this product?"

Once you know their favorite product, you need to understand why they like it so much. The qualitative data will help your marketing and sales teams attract and engage customers. They'll know which features to advertise most and can seek out new leads similar to your existing customers.

3. "How satisfied are you with [product]?"

When you have a product that isn't selling, you can ask this question to see why customers are unhappy with it. If the reviews are poor, you'll know that the product needs reworking, and you can send it back to product management for improvement. Or, if these results are positive, they may have something to do with your marketing or sales techniques. You can then gather more info during the questionnaire and restrategize your campaigns based on your findings.

4. "Would you recommend [product] to a friend?"

This is a classic survey question used with most NPS® surveys. It asks the customer if they would recommend your product to one of their peers. This is extremely important because most people trust customer referrals more than traditional advertising. So, if your customers are willing to recommend your products, you'll have an easier time acquiring new leads.

5. "Would you recommend [company name] to a friend?"

Similar to the question above, this one asks the customer to consider your business as a whole and not just your product. This gives you insight into your brand's reputation and shows how customers feel about your company's actions. Even if you have an excellent product, your brand's reputation may be the cause of customer churn . Your marketing team should pay close attention to this question to see how they can improve the customer experience .

6. "If you could change one thing about [product], what would it be?"

This is a good question to ask your most loyal customers or ones that have recently churned. For loyal customers, you want to keep adding value to their experience. Asking how your product can improve helps your development team find flaws and increases your chances of retaining a valuable customer segment.

For customers that have recently churned, this question gives insight into how you can retain future users that are unhappy with your product or service. By giving these customers a space to voice their criticisms, you can either reach out and offer solutions or relay feedback for consideration.

7. "Which other options were you considering before [product or company name]?"

If you're operating in a competitive industry, customers will have more than one choice when considering your brand. And if you sell variations of your product or produce new models periodically, customers may prefer one version over another.

For this question, you should offer answers to choose from in a multiple-selection format. This will limit the types of responses you'll receive and help you get the exact information you need.

8. "Did [product] help you accomplish your goal?"

The purpose of any product or service is to help customers reach a goal. So, you should be direct and ask them if your company steered them toward success. After all, customer success is an excellent retention tool. If customers are succeeding with your product, they're more likely to stay loyal to your brand.

9. "How would you feel if we did not offer this product, feature, or service?"

Thinking about discontinuing a product? This question can help you decide whether or not a specific product, service, or feature will be missed if you were to remove it.

Even if you know that a product or service isn't worth offering, it's important to ask this question anyway because there may be a certain aspect of the product that your customers like. They'll be delighted if you can integrate that feature into a new product or service.

10. "If you couldn't use your favorite product from us, what would you miss the most about it?"

This question pairs well with the one above because it frames the customer's favorite product from a different point of view. Instead of describing why they love a particular product, the customer can explain what they'd be missing if they didn't have it at all. This type of question uncovers "fear of loss," which can be a very different motivating factor than "hope for gain."

11. "What word best describes your experience using our product?"

Your marketing team will love this question. A single word or a short phrase can easily sum up your customers’ emotions when they experience your company, product, or brand. Those emotions can be translated into relatable marketing campaigns that use your customers’ exact language.

If the responses reveal negative emotions, it's likely that your entire customer service team can relate to that pain point. Rather than calling it "a bug in the system," you can describe the problem as a "frustrating roadblock" to keep their experience at the forefront of the solution.

12. "What's the primary reason for canceling your account?"

Finding out why customers are unhappy with your product or service is key to decreasing your churn rate . If you don't understand why people leave your brand, it's hard to make effective changes to prevent future turnover. Or worse, you might alter your product or service in a way that increases your churn rate, causing you to lose customers who were once loyal supporters.

13. "How satisfied are you with our customer support?"

It's worth asking customers how happy they are with your support or service team. After all, an excellent product doesn't always guarantee that customers will stay loyal to your brand. Research shows that one in six customers will leave a brand they love after just one poor service experience.

14. "Did we answer all of your questions and concerns?"

This is a good question to ask after a service experience. It shows how thorough your support team is and whether they're prioritizing speed too much over quality. If customers still have questions and concerns after a service interaction, your support team is focusing too much on closing tickets and not enough on meeting customer needs .

15. "How can we be more helpful?"

Sometimes it's easier to be direct and simply ask customers what else you can do to help them. This shows a genuine interest in your buyers' goals which helps your brand foster meaningful relationships with its customer base. The more you can show that you sincerely care about your customers' problems, the more they'll open up to you and be honest about how you can help them.

16. What additional features would you like to see in this product?

With this question, your team can get inspiration for the company's next product launch. Think of the responses as a wish list from your customers. You can discover what features are most valuable to them and whether they already exist within a competitor's product.

Incorporating every feature suggestion is nearly impossible, but it's a convenient way to build a backlog of ideas that can inspire future product releases.

17. "Are we meeting your expectations?"

This is a really important question to ask because customers won't always tell you when they're unhappy with your service. Not every customer will ask to speak with a manager when they're unhappy with your business. In fact, most will quietly move on to a competitor rather than broadcast their unhappiness to your company. To prevent this type of customer churn, you need to be proactive and ask customers if your brand is meeting their expectations.

18. "How satisfied are you with your experience?"

This question asks the customer to summarize their experience with your business. It gives you a snapshot of how the customer is feeling in that moment and their perception of your brand. Asking this question at the right stage in the customer's journey can tell you a lot about what your company is doing well and where you can stand to improve.

Next, let's dig into some tips for creating your own questionnaire.

Start with templates as a foundation. Know your question types. Keep it brief when possible. Choose a simple visual design. Use a clear research process. Create questions with straightforward, unbiased language. Make sure every question is important. Ask one question at a time. Order your questions logically. Consider your target audience. Test your questionnaire.

1. Use questionnaire templates.

Rather than build a questionnaire from scratch, consider using questionnaire templates to get started. HubSpot's collection of customer-facing questionnaire templates can help you quickly build and send a questionnaire to your clients and analyze the results right on Google Drive.

Download Now

2. Know your question types.

A simple "yes" or "no" doesn't cut it. To get feedback that actually matters, you need to give customers options that go in-depth. There's a method to getting accurate feedback from your questionnaire, and it starts by choosing the appropriate types of questions for the information you want to know.

Vrnda LeValley , customer training manager at HubSpot, recommends starting with an alignment question like, "Does this class meet your expectations?" because it gives more context to any positive or negative scores that follow. She continues, "If it didn't meet expectations, then there will potentially be negative responses across the board (as well as the reverse)."

3. Keep it brief, when possible.

Most questionnaires don't need to be longer than a page. For routine customer satisfaction surveys, it's unnecessary to ask 50 slightly varied questions about a customer's experience when those questions could be combined into 10 solid questions.

The shorter your questionnaire is, the more likely a customer will complete it. Plus a shorter questionnaire means less data for your team to collect and analyze. Based on the feedback, it will be a lot easier for you to get the information you need to make the necessary changes in your organization and products.

4. Choose a simple visual design.

There's no need to make your questionnaire a stunning work of art. As long as it's clear and concise, it will be attractive to customers. When asking questions that are important to furthering your company, it's best to keep things simple. Select a font that’s common and easy to read, like Helvetica or Arial. Use a text size that customers of all abilities can navigate.

A questionnaire is most effective when all the questions are visible on a single screen. The layout is important. If a questionnaire is even remotely difficult to navigate, your response rate could suffer. Make sure that buttons and checkboxes are easy to click and that questions are visible on both computer and mobile screens.

5. Use a clear research process.

Before planning questions for your questionnaire, you'll need to have a definite direction for it. A questionnaire is only effective if the results answer an overarching research question. After all, the research process is an important part of the survey, and a questionnaire is a tool that's used within the process.

In your research process, you should first come up with a research question. What are you trying to find out? What's the point of this questionnaire? Keep this in mind throughout the process.

After coming up with a research question, it's a good idea to have a hypothesis. What do you predict the results will be for your questionnaire? This can be structured in a simple "If … then …" format. A structured experiment — yes, your questionnaire is a type of experiment — will confirm that you're only collecting and analyzing data necessary to answer your research question. Then, you can move forward with your survey .

6. Create questions with straightforward, unbiased language.

When crafting your questions, it's important to structure them to get the point across. You don't want any confusion for your customers because this may influence their answers. Instead, use clear language. Don't use unnecessary jargon, and use simple terms in favor of longer-winded ones.

You may risk the reliability of your data if you try to combine two questions. Rather than asking, "How was your experience shopping with us, and would you recommend us to others?" separate it into two separate questions. Customers will be clear on your question and choose a response most appropriate for each one.

You should always keep the language in your questions unbiased. You never want to sway customers one way or another because this will cause your data to be skewed. Instead of asking, "Some might say that we create the best software products in the world. Would you agree or disagree?" it may be better to ask, "How would you rate our software products on a scale of 1 to 10?" This removes any bias and confirms that all the responses are valid.

7. Ask only the most important questions.

When creating your questionnaire, keep in mind that time is one of the most valuable commodities for customers. Most aren't going to sit through a 50-question survey, especially when they're being asked about products or services they didn't use. Even if they do complete it, most of these will be half-hearted responses from fatigued customers who simply want to be finished with it.

If your questionnaire has five or 55 questions, make sure each has a specific purpose. Individually, they should be aimed at collecting certain pieces of information that reveal new insights into different aspects of your business. If your questions are irrelevant or seem out of place, your customers will be easily derailed by the survey. And, once the customer has lost interest, it'll be difficult to regain their focus.

8. Ask one question at a time.

Since every question has a purpose, ask them one at a time. This lets the customer focus and encourages them to share a thoughtful response. This is particularly important for open-ended questions where customers need to describe an experience or opinion.

By grouping questions together, you risk overwhelming busy customers who don't have time for a long survey. They may think you're asking them too much, or they might see your questionnaire as a daunting task. You want your survey to appear as painless as possible. Keeping your questions separated will make it more user-friendly.

9. Order your questions logically.

A good questionnaire is like a good book. The beginning questions should lay the framework, the middle ones should cut to the core issues, and the final questions should tie up all loose ends. This flow keeps customers engaged throughout the entire survey.

When creating your questionnaire, start with the most basic questions about demographics. You can use this information to segment your customer base and create different buyer personas.

Next, add in your product and services questions. These are the ones that offer insights into common customer roadblocks and where you can improve your business's offerings. Questions like these guide your product development and marketing teams looking for new ways to enhance the customer experience.

Finally, you should conclude your questionnaire with open-ended questions to understand the customer journey. These questions let customers voice their opinions and point out specific experiences they've had with your brand.

10. Consider your target audience.

Whenever you collect customer feedback, you need to keep in mind the goals and needs of your target audience. After all, the participants in this questionnaire are your active customers. Your questions should be geared toward the interests and experiences they've already had with your company.

You can even create multiple surveys that target different buyer personas. For example, if you have a subscription-based pricing model, you can personalize your questionnaire for each type of subscription your company offers.

11. Test your questionnaire.

Once your questionnaire is complete, it's important to test it. If you don't, you may end up asking the wrong questions and collecting irrelevant or inaccurate information. Start by giving your employees the questionnaire to test, then send it to small groups of customers and analyze the results. If you're gathering the data you're looking for, then you should release the questionnaire to all of your customers.

How Questionnaires Can Benefit Your Customer Service Strategy

Whether you have one customer or 1000 customers, their opinions matter when it comes to the success of your business. Their satisfaction with your offerings can reveal how well or how poorly your customer service strategy and business are meeting their needs. A questionnaire is one of the most powerful, cost-effective tools to uncover what your customers think about your business. When analyzed properly, it can inform your product and service launches.

Use the free questionnaire templates, examples, and best practices in this guide to conduct your next customer feedback survey.

Now that you know the slight difference between a survey and a questionnaire, it’s time to put it into practice with your products or services. Remember, a good survey and questionnaire always start with a purpose. But, a great survey and questionnaire give data that you can use to help companies increase the way customers respond to their products or services because of the questions.

Net Promoter, Net Promoter System, Net Promoter Score, NPS, and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Fred Reichheld, and Satmetrix Systems, Inc.

Editor's note: This post was originally published in July 2018 and has been updated for comprehensiveness.

Don't forget to share this post!

Related articles.

Nonresponse Bias: What to Avoid When Creating Surveys

How to Make a Survey with a QR Code

50 Catchy Referral Slogans & How to Write Your Own

How Automated Phone Surveys Work [+Tips and Examples]

Online Panels: What They Are & How to Use Them Effectively

The Complete Guide to Survey Logic (+Expert Tips)

Focus Group vs. Survey: Which One Should You Use?

Leading Questions: What They Are & Why They Matter [+ Examples]

What are Survey Sample Sizes & How to Find Your Sample Size

24 Diversity, Equity, and Inclusion Survey Questions to Ask Your Employees

5 free templates for learning more about your customers and respondents.

Service Hub provides everything you need to delight and retain customers while supporting the success of your whole front office

• Privacy Policy

Home » Questionnaire – Definition, Types, and Examples

Questionnaire – Definition, Types, and Examples

Table of Contents

Questionnaire

Definition:

A Questionnaire is a research tool or survey instrument that consists of a set of questions or prompts designed to gather information from individuals or groups of people.

It is a standardized way of collecting data from a large number of people by asking them a series of questions related to a specific topic or research objective. The questions may be open-ended or closed-ended, and the responses can be quantitative or qualitative. Questionnaires are widely used in research, marketing, social sciences, healthcare, and many other fields to collect data and insights from a target population.

History of Questionnaire

The history of questionnaires can be traced back to the ancient Greeks, who used questionnaires as a means of assessing public opinion. However, the modern history of questionnaires began in the late 19th century with the rise of social surveys.

The first social survey was conducted in the United States in 1874 by Francis A. Walker, who used a questionnaire to collect data on labor conditions. In the early 20th century, questionnaires became a popular tool for conducting social research, particularly in the fields of sociology and psychology.

One of the most influential figures in the development of the questionnaire was the psychologist Raymond Cattell, who in the 1940s and 1950s developed the personality questionnaire, a standardized instrument for measuring personality traits. Cattell’s work helped establish the questionnaire as a key tool in personality research.

In the 1960s and 1970s, the use of questionnaires expanded into other fields, including market research, public opinion polling, and health surveys. With the rise of computer technology, questionnaires became easier and more cost-effective to administer, leading to their widespread use in research and business settings.

Today, questionnaires are used in a wide range of settings, including academic research, business, healthcare, and government. They continue to evolve as a research tool, with advances in computer technology and data analysis techniques making it easier to collect and analyze data from large numbers of participants.

Types of Questionnaire

Types of Questionnaires are as follows:

Structured Questionnaire

This type of questionnaire has a fixed format with predetermined questions that the respondent must answer. The questions are usually closed-ended, which means that the respondent must select a response from a list of options.

Unstructured Questionnaire

An unstructured questionnaire does not have a fixed format or predetermined questions. Instead, the interviewer or researcher can ask open-ended questions to the respondent and let them provide their own answers.

Open-ended Questionnaire

An open-ended questionnaire allows the respondent to answer the question in their own words, without any pre-determined response options. The questions usually start with phrases like “how,” “why,” or “what,” and encourage the respondent to provide more detailed and personalized answers.

Close-ended Questionnaire

In a closed-ended questionnaire, the respondent is given a set of predetermined response options to choose from. This type of questionnaire is easier to analyze and summarize, but may not provide as much insight into the respondent’s opinions or attitudes.

Mixed Questionnaire

A mixed questionnaire is a combination of open-ended and closed-ended questions. This type of questionnaire allows for more flexibility in terms of the questions that can be asked, and can provide both quantitative and qualitative data.

Pictorial Questionnaire:

In a pictorial questionnaire, instead of using words to ask questions, the questions are presented in the form of pictures, diagrams or images. This can be particularly useful for respondents who have low literacy skills, or for situations where language barriers exist. Pictorial questionnaires can also be useful in cross-cultural research where respondents may come from different language backgrounds.

Types of Questions in Questionnaire

The types of Questions in Questionnaire are as follows:

Multiple Choice Questions

These questions have several options for participants to choose from. They are useful for getting quantitative data and can be used to collect demographic information.

• a. Red b . Blue c. Green d . Yellow

Rating Scale Questions

These questions ask participants to rate something on a scale (e.g. from 1 to 10). They are useful for measuring attitudes and opinions.

• On a scale of 1 to 10, how likely are you to recommend this product to a friend?

Open-Ended Questions

These questions allow participants to answer in their own words and provide more in-depth and detailed responses. They are useful for getting qualitative data.

• What do you think are the biggest challenges facing your community?

Likert Scale Questions

These questions ask participants to rate how much they agree or disagree with a statement. They are useful for measuring attitudes and opinions.

How strongly do you agree or disagree with the following statement:

“I enjoy exercising regularly.”

• a . Strongly Agree
• c . Neither Agree nor Disagree
• d . Disagree
• e . Strongly Disagree

Demographic Questions

These questions ask about the participant’s personal information such as age, gender, ethnicity, education level, etc. They are useful for segmenting the data and analyzing results by demographic groups.

• What is your age?

Yes/No Questions

These questions only have two options: Yes or No. They are useful for getting simple, straightforward answers to a specific question.

Have you ever traveled outside of your home country?

Ranking Questions

These questions ask participants to rank several items in order of preference or importance. They are useful for measuring priorities or preferences.

Please rank the following factors in order of importance when choosing a restaurant:

• a. Quality of Food
• c. Ambiance
• d. Location

Matrix Questions

These questions present a matrix or grid of options that participants can choose from. They are useful for getting data on multiple variables at once.

Dichotomous Questions

These questions present two options that are opposite or contradictory. They are useful for measuring binary or polarized attitudes.

Do you support the death penalty?

How to Make a Questionnaire

Step-by-Step Guide for Making a Questionnaire:

• Define your research objectives: Before you start creating questions, you need to define the purpose of your questionnaire and what you hope to achieve from the data you collect.
• Choose the appropriate question types: Based on your research objectives, choose the appropriate question types to collect the data you need. Refer to the types of questions mentioned earlier for guidance.
• Develop questions: Develop clear and concise questions that are easy for participants to understand. Avoid leading or biased questions that might influence the responses.
• Organize questions: Organize questions in a logical and coherent order, starting with demographic questions followed by general questions, and ending with specific or sensitive questions.
• Pilot the questionnaire : Test your questionnaire on a small group of participants to identify any flaws or issues with the questions or the format.
• Refine the questionnaire : Based on feedback from the pilot, refine and revise the questionnaire as necessary to ensure that it is valid and reliable.
• Distribute the questionnaire: Distribute the questionnaire to your target audience using a method that is appropriate for your research objectives, such as online surveys, email, or paper surveys.
• Collect and analyze data: Collect the completed questionnaires and analyze the data using appropriate statistical methods. Draw conclusions from the data and use them to inform decision-making or further research.
• Report findings: Present your findings in a clear and concise report, including a summary of the research objectives, methodology, key findings, and recommendations.

Questionnaire Administration Modes

There are several modes of questionnaire administration. The choice of mode depends on the research objectives, sample size, and available resources. Some common modes of administration include:

• Self-administered paper questionnaires: Participants complete the questionnaire on paper, either in person or by mail. This mode is relatively low cost and easy to administer, but it may result in lower response rates and greater potential for errors in data entry.
• Online questionnaires: Participants complete the questionnaire on a website or through email. This mode is convenient for both researchers and participants, as it allows for fast and easy data collection. However, it may be subject to issues such as low response rates, lack of internet access, and potential for fraudulent responses.
• Telephone surveys: Trained interviewers administer the questionnaire over the phone. This mode allows for a large sample size and can result in higher response rates, but it is also more expensive and time-consuming than other modes.
• Face-to-face interviews : Trained interviewers administer the questionnaire in person. This mode allows for a high degree of control over the survey environment and can result in higher response rates, but it is also more expensive and time-consuming than other modes.
• Mixed-mode surveys: Researchers use a combination of two or more modes to administer the questionnaire, such as using online questionnaires for initial screening and following up with telephone interviews for more detailed information. This mode can help overcome some of the limitations of individual modes, but it requires careful planning and coordination.

Example of Questionnaire

Title of the Survey: Customer Satisfaction Survey

Introduction:

We appreciate your business and would like to ensure that we are meeting your needs. Please take a few minutes to complete this survey so that we can better understand your experience with our products and services. Your feedback is important to us and will help us improve our offerings.

Instructions:

Please read each question carefully and select the response that best reflects your experience. If you have any additional comments or suggestions, please feel free to include them in the space provided at the end of the survey.

1. How satisfied are you with our product quality?

• Very satisfied
• Somewhat satisfied
• Somewhat dissatisfied
• Very dissatisfied

2. How satisfied are you with our customer service?

3. How satisfied are you with the price of our products?

4. How likely are you to recommend our products to others?

• Very likely
• Somewhat likely
• Somewhat unlikely
• Very unlikely

5. How easy was it to find the information you were looking for on our website?

• Somewhat easy
• Somewhat difficult
• Very difficult

6. How satisfied are you with the overall experience of using our products and services?

7. Is there anything that you would like to see us improve upon or change in the future?

…………………………………………………………………………………………………………………………..

Conclusion:

Thank you for taking the time to complete this survey. Your feedback is valuable to us and will help us improve our products and services. If you have any further comments or concerns, please do not hesitate to contact us.

Applications of Questionnaire

Some common applications of questionnaires include:

• Research : Questionnaires are commonly used in research to gather information from participants about their attitudes, opinions, behaviors, and experiences. This information can then be analyzed and used to draw conclusions and make inferences.
• Healthcare : In healthcare, questionnaires can be used to gather information about patients’ medical history, symptoms, and lifestyle habits. This information can help healthcare professionals diagnose and treat medical conditions more effectively.
• Marketing : Questionnaires are commonly used in marketing to gather information about consumers’ preferences, buying habits, and opinions on products and services. This information can help businesses develop and market products more effectively.
• Human Resources: Questionnaires are used in human resources to gather information from job applicants, employees, and managers about job satisfaction, performance, and workplace culture. This information can help organizations improve their hiring practices, employee retention, and organizational culture.
• Education : Questionnaires are used in education to gather information from students, teachers, and parents about their perceptions of the educational experience. This information can help educators identify areas for improvement and develop more effective teaching strategies.

Purpose of Questionnaire

Some common purposes of questionnaires include:

• To collect information on attitudes, opinions, and beliefs: Questionnaires can be used to gather information on people’s attitudes, opinions, and beliefs on a particular topic. For example, a questionnaire can be used to gather information on people’s opinions about a particular political issue.
• To collect demographic information: Questionnaires can be used to collect demographic information such as age, gender, income, education level, and occupation. This information can be used to analyze trends and patterns in the data.
• To measure behaviors or experiences: Questionnaires can be used to gather information on behaviors or experiences such as health-related behaviors or experiences, job satisfaction, or customer satisfaction.
• To evaluate programs or interventions: Questionnaires can be used to evaluate the effectiveness of programs or interventions by gathering information on participants’ experiences, opinions, and behaviors.
• To gather information for research: Questionnaires can be used to gather data for research purposes on a variety of topics.

When to use Questionnaire

Here are some situations when questionnaires might be used:

• When you want to collect data from a large number of people: Questionnaires are useful when you want to collect data from a large number of people. They can be distributed to a wide audience and can be completed at the respondent’s convenience.
• When you want to collect data on specific topics: Questionnaires are useful when you want to collect data on specific topics or research questions. They can be designed to ask specific questions and can be used to gather quantitative data that can be analyzed statistically.
• When you want to compare responses across groups: Questionnaires are useful when you want to compare responses across different groups of people. For example, you might want to compare responses from men and women, or from people of different ages or educational backgrounds.
• When you want to collect data anonymously: Questionnaires can be useful when you want to collect data anonymously. Respondents can complete the questionnaire without fear of judgment or repercussions, which can lead to more honest and accurate responses.
• When you want to save time and resources: Questionnaires can be more efficient and cost-effective than other methods of data collection such as interviews or focus groups. They can be completed quickly and easily, and can be analyzed using software to save time and resources.

Characteristics of Questionnaire

Here are some of the characteristics of questionnaires:

• Standardization : Questionnaires are standardized tools that ask the same questions in the same order to all respondents. This ensures that all respondents are answering the same questions and that the responses can be compared and analyzed.
• Objectivity : Questionnaires are designed to be objective, meaning that they do not contain leading questions or bias that could influence the respondent’s answers.
• Predefined responses: Questionnaires typically provide predefined response options for the respondents to choose from, which helps to standardize the responses and make them easier to analyze.
• Quantitative data: Questionnaires are designed to collect quantitative data, meaning that they provide numerical or categorical data that can be analyzed using statistical methods.
• Convenience : Questionnaires are convenient for both the researcher and the respondents. They can be distributed and completed at the respondent’s convenience and can be easily administered to a large number of people.
• Anonymity : Questionnaires can be anonymous, which can encourage respondents to answer more honestly and provide more accurate data.
• Reliability : Questionnaires are designed to be reliable, meaning that they produce consistent results when administered multiple times to the same group of people.
• Validity : Questionnaires are designed to be valid, meaning that they measure what they are intended to measure and are not influenced by other factors.

Advantage of Questionnaire

Some Advantage of Questionnaire are as follows:

• Standardization: Questionnaires allow researchers to ask the same questions to all participants in a standardized manner. This helps ensure consistency in the data collected and eliminates potential bias that might arise if questions were asked differently to different participants.
• Efficiency: Questionnaires can be administered to a large number of people at once, making them an efficient way to collect data from a large sample.
• Anonymity: Participants can remain anonymous when completing a questionnaire, which may make them more likely to answer honestly and openly.
• Cost-effective: Questionnaires can be relatively inexpensive to administer compared to other research methods, such as interviews or focus groups.
• Objectivity: Because questionnaires are typically designed to collect quantitative data, they can be analyzed objectively without the influence of the researcher’s subjective interpretation.
• Flexibility: Questionnaires can be adapted to a wide range of research questions and can be used in various settings, including online surveys, mail surveys, or in-person interviews.

Limitations of Questionnaire

Limitations of Questionnaire are as follows:

• Limited depth: Questionnaires are typically designed to collect quantitative data, which may not provide a complete understanding of the topic being studied. Questionnaires may miss important details and nuances that could be captured through other research methods, such as interviews or observations.
• R esponse bias: Participants may not always answer questions truthfully or accurately, either because they do not remember or because they want to present themselves in a particular way. This can lead to response bias, which can affect the validity and reliability of the data collected.
• Limited flexibility: While questionnaires can be adapted to a wide range of research questions, they may not be suitable for all types of research. For example, they may not be appropriate for studying complex phenomena or for exploring participants’ experiences and perceptions in-depth.
• Limited context: Questionnaires typically do not provide a rich contextual understanding of the topic being studied. They may not capture the broader social, cultural, or historical factors that may influence participants’ responses.
• Limited control : Researchers may not have control over how participants complete the questionnaire, which can lead to variations in response quality or consistency.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Triangulation in Research – Types, Methods and...

Basic Research – Types, Methods and Examples

Qualitative Research Methods

One-to-One Interview – Methods and Guide

Mixed Methods Research – Types & Analysis

Research Methods – Types, Examples and Guide

How to Design Effective Research Questionnaires for Robust Findings

As a staple in data collection, questionnaires help uncover robust and reliable findings that can transform industries, shape policies, and revolutionize understanding. Whether you are exploring societal trends or delving into scientific phenomena, the effectiveness of your research questionnaire can make or break your findings.

In this article, we aim to understand the core purpose of questionnaires, exploring how they serve as essential tools for gathering systematic data, both qualitative and quantitative, from diverse respondents. Read on as we explore the key elements that make up a winning questionnaire, the art of framing questions which are both compelling and rigorous, and the careful balance between simplicity and depth.

Table of Contents

The Role of Questionnaires in Research

So, what is a questionnaire? A questionnaire is a structured set of questions designed to collect information, opinions, attitudes, or behaviors from respondents. It is one of the most commonly used data collection methods in research. Moreover, questionnaires can be used in various research fields, including social sciences, market research, healthcare, education, and psychology. Their adaptability makes them suitable for investigating diverse research questions.

Questionnaire and survey  are two terms often used interchangeably, but they have distinct meanings in the context of research. A survey refers to the broader process of data collection that may involve various methods. A survey can encompass different data collection techniques, such as interviews , focus groups, observations, and yes, questionnaires.

Pros and Cons of Using Questionnaires in Research:

While questionnaires offer numerous advantages in research, they also come with some disadvantages that researchers must be aware of and address appropriately. Careful questionnaire design, validation, and consideration of potential biases can help mitigate these disadvantages and enhance the effectiveness of using questionnaires as a data collection method.

Structured vs Unstructured Questionnaires

Structured questionnaire:.

A structured questionnaire consists of questions with predefined response options. Respondents are presented with a fixed set of choices and are required to select from those options. The questions in a structured questionnaire are designed to elicit specific and quantifiable responses. Structured questionnaires are particularly useful for collecting quantitative data and are often employed in surveys and studies where standardized and comparable data are necessary.

Advantages of Structured Questionnaires:

• Easy to analyze and interpret: The fixed response options facilitate straightforward data analysis and comparison across respondents.
• Efficient for large-scale data collection: Structured questionnaires are time-efficient, allowing researchers to collect data from a large number of respondents.
• Reduces response bias: The predefined response options minimize potential response bias and maintain consistency in data collection.

Limitations of Structured Questionnaires:

• Lack of depth: Structured questionnaires may not capture in-depth insights or nuances as respondents are limited to pre-defined response choices. Hence, they may not reveal the reasons behind respondents’ choices, limiting the understanding of their perspectives.
• Limited flexibility: The fixed response options may not cover all potential responses, therefore, potentially restricting respondents’ answers.

Unstructured Questionnaire:

An unstructured questionnaire consists of questions that allow respondents to provide detailed and unrestricted responses. Unlike structured questionnaires, there are no predefined response options, giving respondents the freedom to express their thoughts in their own words. Furthermore, unstructured questionnaires are valuable for collecting qualitative data and obtaining in-depth insights into respondents’ experiences, opinions, or feelings.

Advantages of Unstructured Questionnaires:

• Rich qualitative data: Unstructured questionnaires yield detailed and comprehensive qualitative data, providing valuable and novel insights into respondents’ perspectives.
• Flexibility in responses: Respondents have the freedom to express themselves in their own words. Hence, allowing for a wide range of responses.

Limitations of Unstructured Questionnaires:

• Time-consuming analysis: Analyzing open-ended responses can be time-consuming, since, each response requires careful reading and interpretation.
• Subjectivity in interpretation: The analysis of open-ended responses may be subjective, as researchers interpret and categorize responses based on their judgment.
• May require smaller sample size: Due to the depth of responses, researchers may need a smaller sample size for comprehensive analysis, making generalizations more challenging.

Types of Questions in a Questionnaire

In a questionnaire, researchers typically use the following most common types of questions to gather a variety of information from respondents:

1. Open-Ended Questions:

These questions allow respondents to provide detailed and unrestricted responses in their own words. Open-ended questions are valuable for gathering qualitative data and in-depth insights.

Example: What suggestions do you have for improving our product?

2. Multiple-Choice Questions

Respondents choose one answer from a list of provided options. This type of question is suitable for gathering categorical data or preferences.

Example: Which of the following social media/academic networking platforms do you use to promote your research?

• ResearchGate
• Academia.edu

3. Dichotomous Questions

Respondents choose between two options, typically “yes” or “no”, “true” or “false”, or “agree” or “disagree”.

Example: Have you ever published in open access journals before?

4. Scaling Questions

These questions, also known as rating scale questions, use a predefined scale that allows respondents to rate or rank their level of agreement, satisfaction, importance, or other subjective assessments. These scales help researchers quantify subjective data and make comparisons across respondents.

There are several types of scaling techniques used in scaling questions:

i. Likert Scale:

The Likert scale is one of the most common scaling techniques. It presents respondents with a series of statements and asks them to rate their level of agreement or disagreement using a range of options, typically from “strongly agree” to “strongly disagree”.For example: Please indicate your level of agreement with the statement: “The content presented in the webinar was relevant and aligned with the advertised topic.”

• Strongly Agree
• Strongly Disagree

ii. Semantic Differential Scale:

The semantic differential scale measures respondents’ perceptions or attitudes towards an item using opposite adjectives or bipolar words. Respondents rate the item on a scale between the two opposites. For example:

• Easy —— Difficult
• Satisfied —— Unsatisfied
• Very likely —— Very unlikely

iii. Numerical Rating Scale:

This scale requires respondents to provide a numerical rating on a predefined scale. It can be a simple 1 to 5 or 1 to 10 scale, where higher numbers indicate higher agreement, satisfaction, or importance.

iv. Ranking Questions:

Respondents rank items in order of preference or importance. Ranking questions help identify preferences or priorities.

Example: Please rank the following features of our app in order of importance (1 = Most Important, 5 = Least Important):

• User Interface
• Functionality
• Customer Support

By using a mix of question types, researchers can gather both quantitative and qualitative data, providing a comprehensive understanding of the research topic and enabling meaningful analysis and interpretation of the results. The choice of question types depends on the research objectives , the desired depth of information, and the data analysis requirements.

Methods of Administering Questionnaires

There are several methods for administering questionnaires, and the choice of method depends on factors such as the target population, research objectives , convenience, and resources available. Here are some common methods of administering questionnaires:

Each method has its advantages and limitations. Online surveys offer convenience and a large reach, but they may be limited to individuals with internet access. Face-to-face interviews allow for in-depth responses but can be time-consuming and costly. Telephone surveys have broad reach but may be limited by declining response rates. Researchers should choose the method that best suits their research objectives, target population, and available resources to ensure successful data collection.

How to Design a Questionnaire

Designing a good questionnaire is crucial for gathering accurate and meaningful data that aligns with your research objectives. Here are essential steps and tips to create a well-designed questionnaire:

1. Define Your Research Objectives : Clearly outline the purpose and specific information you aim to gather through the questionnaire.

2. Identify Your Target Audience : Understand respondents’ characteristics and tailor the questionnaire accordingly.

3. Develop the Questions :

• Write Clear and Concise Questions
• Avoid Leading or Biasing Questions
• Sequence Questions Logically
• Group Related Questions
• Include Demographic Questions

4. Provide Well-defined Response Options : Offer exhaustive response choices for closed-ended questions.

5. Consider Skip Logic and Branching : Customize the questionnaire based on previous answers.

6. Pilot Test the Questionnaire : Identify and address issues through a pilot study .

7. Seek Expert Feedback : Validate the questionnaire with subject matter experts.

8. Obtain Ethical Approval : Comply with ethical guidelines , obtain consent, and ensure confidentiality before administering the questionnaire.

9. Administer the Questionnaire : Choose the right mode and provide clear instructions.

10. Test the Survey Platform : Ensure compatibility and usability for online surveys.

By following these steps and paying attention to questionnaire design principles, you can create a well-structured and effective questionnaire that gathers reliable data and helps you achieve your research objectives.

Characteristics of a Good Questionnaire

A good questionnaire possesses several essential elements that contribute to its effectiveness. Furthermore, these characteristics ensure that the questionnaire is well-designed, easy to understand, and capable of providing valuable insights. Here are some key characteristics of a good questionnaire:

1. Clarity and Simplicity : Questions should be clear, concise, and unambiguous. Avoid using complex language or technical terms that may confuse respondents. Simple and straightforward questions ensure that respondents interpret them consistently.

2. Relevance and Focus : Each question should directly relate to the research objectives and contribute to answering the research questions. Consequently, avoid including extraneous or irrelevant questions that could lead to data clutter.

3. Mix of Question Types : Utilize a mix of question types, including open-ended, Likert scale, and multiple-choice questions. This variety allows for both qualitative and quantitative data collections .

4. Validity and Reliability : Ensure the questionnaire measures what it intends to measure (validity) and produces consistent results upon repeated administration (reliability). Validation should be conducted through expert review and previous research.

5. Appropriate Length : Keep the questionnaire’s length appropriate and manageable to avoid respondent fatigue or dropouts. Long questionnaires may result in incomplete or rushed responses.

6. Clear Instructions : Include clear instructions at the beginning of the questionnaire to guide respondents on how to complete it. Explain any technical terms, formats, or concepts if necessary.

7. User-Friendly Format : Design the questionnaire to be visually appealing and user-friendly. Use consistent formatting, adequate spacing, and a logical page layout.

8. Data Validation and Cleaning : Incorporate validation checks to ensure data accuracy and reliability. Consider mechanisms to detect and correct inconsistent or missing responses during data cleaning.

By incorporating these characteristics, researchers can create a questionnaire that maximizes data quality, minimizes response bias, and provides valuable insights for their research.

In the pursuit of advancing research and gaining meaningful insights, investing time and effort into designing effective questionnaires is a crucial step. A well-designed questionnaire is more than a mere set of questions; it is a masterpiece of precision and ingenuity. Each question plays a vital role in shaping the narrative of our research, guiding us through the labyrinth of data to meaningful conclusions. Indeed, a well-designed questionnaire serves as a powerful tool for unlocking valuable insights and generating robust findings that impact society positively.

Have you ever designed a research questionnaire? Reflect on your experience and share your insights with researchers globally through Enago Academy’s Open Blogging Platform . Join our diverse community of 1000K+ researchers and authors to exchange ideas, strategies, and best practices, and together, let’s shape the future of data collection and maximize the impact of questionnaires in the ever-evolving landscape of research.

Frequently Asked Questions

A research questionnaire is a structured tool used to gather data from participants in a systematic manner. It consists of a series of carefully crafted questions designed to collect specific information related to a research study.

Questionnaires play a pivotal role in both quantitative and qualitative research, enabling researchers to collect insights, opinions, attitudes, or behaviors from respondents. This aids in hypothesis testing, understanding, and informed decision-making, ensuring consistency, efficiency, and facilitating comparisons.

Questionnaires are a versatile tool employed in various research designs to gather data efficiently and comprehensively. They find extensive use in both quantitative and qualitative research methodologies, making them a fundamental component of research across disciplines. Some research designs that commonly utilize questionnaires include: a) Cross-Sectional Studies b) Longitudinal Studies c) Descriptive Research d) Correlational Studies e) Causal-Comparative Studies f) Experimental Research g) Survey Research h) Case Studies i) Exploratory Research

A survey is a comprehensive data collection method that can include various techniques like interviews and observations. A questionnaire is a specific set of structured questions within a survey designed to gather standardized responses. While a survey is a broader approach, a questionnaire is a focused tool for collecting specific data.

The choice of questionnaire type depends on the research objectives, the type of data required, and the preferences of respondents. Some common types include: • Structured Questionnaires: These questionnaires consist of predefined, closed-ended questions with fixed response options. They are easy to analyze and suitable for quantitative research. • Semi-Structured Questionnaires: These questionnaires combine closed-ended questions with open-ended ones. They offer more flexibility for respondents to provide detailed explanations. • Unstructured Questionnaires: These questionnaires contain open-ended questions only, allowing respondents to express their thoughts and opinions freely. They are commonly used in qualitative research.

Following these steps ensures effective questionnaire administration for reliable data collection: • Choose a Method: Decide on online, face-to-face, mail, or phone administration. • Online Surveys: Use platforms like SurveyMonkey • Pilot Test: Test on a small group before full deployment • Clear Instructions: Provide concise guidelines • Follow-Up: Send reminders if needed

Thank you, Riya. This is quite helpful. As discussed, response bias is one of the disadvantages in the use of questionnaires. One way to help limit this can be to use scenario based questions. These type of questions may help the respondents to be more reflective and active in the process.

Thank you, Dear Riya. This is quite helpful.

Great insights there Doc

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

Ensuring Academic Integrity and Transparency in Academic Research: A comprehensive checklist for researchers

Academic integrity is the foundation upon which the credibility and value of scientific findings are…

• Publishing Research
• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

• Industry News
• Trending Now

Breaking Barriers: Sony and Nature unveil “Women in Technology Award”

Sony Group Corporation and the prestigious scientific journal Nature have collaborated to launch the inaugural…

Achieving Research Excellence: Checklist for good research practices

Academia is built on the foundation of trustworthy and high-quality research, supported by the pillars…

• Promoting Research

Plain Language Summary — Communicating your research to bridge the academic-lay gap

Science can be complex, but does that mean it should not be accessible to the…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

In your opinion, what is the most effective way to improve integrity in the peer review process?

How to write survey questions for research – with examples

• Post author: Marta Costa
• Post published: April 5, 2023
• Post category: Data Collection & Data Quality

A good survey can make or break your research. Learn how to write strong survey questions, learn what not to do, and see a range of practical examples.

The accuracy and relevance of the data you collect depend largely on the quality of your survey questions . In other words, good questions make for good research outcomes.  It makes sense then, that you should put considerable thought and planning into writing your survey or questionnaire.

In this article, we’ll go through what a good survey question looks like, talk about the different kinds of survey questions that exist, give you some tips for writing a good survey question, and finally, we’ll take a look at some examples. 

What is a good survey question?

A good survey question should contain simple and clear language. It should elicit responses that are accurate and that help you learn more about your target audience and their experiences. It should also fit in with the overall design of your survey project and connect with your research objective. There are many different types of survey questions. Let’s take a look at some of them now. 

New to survey data collection? Explore SurveyCTO for free with a 15-day trial.

Types of survey questions

Different types of questions are used for different purposes. Often questionnaires or surveys will combine several types of questions. The types you choose will depend on the overall design of your survey and your aims.  Here is a list of the most popular kinds of survey questions:  

These questions can’t be answered with a simple yes or no. They require the respondent to use more descriptive language to share their thoughts and answer the question. These types of questions result in qualitative data.

Closed-ended

A closed-ended question is the opposite of an open-ended question. Here the respondent’s answers are normally restricted to a yes or no, true or false, or multiple-choice answer. This results in quantitative data.

Dichotomous

This is a type of closed-ended question. The defining characteristic of these questions is that they have two opposing fields. For example, a question that can only be answered with a yes/no answer is a dichotomous question. 

Multiple choice

These are another type of closed-ended question. Here you give the respondent several possible ways, or options, in which they can respond. It’s also common to have an “other” section with a text box where the respondent can provide an unlisted answer.

Rating scale

This is again another type of close-ended question. Here you would normally present two extremes and the respondent has to choose between these extremes or an option placed along the scale.

Likert scale

A Likert scale is a form of a rating scale. These are generally used to measure attitudes towards something by asking the respondent to agree or disagree with a statement. They are commonly used to measure satisfaction. 

Ranking scale 

Here the respondents are given a few options and they need to order these different options in terms of importance, relevance, or according to the instructions.  

Demographic questions

These are often personal questions that allow you to better understand your respondents and their backgrounds. They normally cover questions related to age, race, marital status, education level, etc.

Ready to start creating your surveys? Sign up for a free 15-day trial.

7 Tips for writing a good survey question

The following 7 tips will help you to write a good survey question: 

1. Use clear, simple language

Your survey questions must be easy to understand. When they’re straight to the point, it’s more likely that your respondent will understand what you are asking of them and be able to respond accurately, giving you the data you need. 

2. Keep your questions (and answers) concise

When sentences or questions are convoluted or confusing, respondents might misunderstand the question. If your questions are too long, they may also get bored by the questions. And in your lists of answers for multiple choice questions, make sure your choice lists are concise as well.  If your questions are too long, or if you’ve provided too many options, you may receive responses that are inaccurate or that are not a true representation of how the respondent feels. To limit the number of options a respondent sees, you can use a survey platform like SurveyCTO to filter choice lists and make it easy for respondents to answer quickly. If you have an exceptionally long list of possible responses, like countries, implement search functionality in your list of choices so your respondents can quickly search for their selection.

3. Don’t add bias to your question

You should avoid leading your respondent in any particular direction with your questions, you want their response to be 100% their thoughts without being unduly influenced.  An example of a question that could lead the respondent in a particular direction would be:  How happy are you to live in this amazing area?  By adding the adjective amazing before area, you are putting the idea in the respondent’s head that the area is amazing. This could cloud their judgment and influence the way they answer the question. The word happy together with amazing may also be problematic. A better, less loaded way to ask this question might be something like this:  How satisfied are you living in this area?

4. Ask one question at a time

Asking multiple things in one question is confusing and will lead to inaccuracies in the answer. When you write your question you should know exactly what you want to achieve. This will help you to avoid combining two questions in one. Here is an example of a double-barrelled question that would be difficult for a respondent to answer: Please answer yes or no to the following question: Do you drive to work and do you carry any passengers? In this question, the respondent is being asked two things, yet they only have the opportunity to respond to one. Even then, they don’t know which one they should respond to. Avoid this kind of questioning to get clearer, more accurate data.

5. Account for all possible answer choices

You should give your respondent the ability to answer a question accurately. For instance, if you are asking a demographic question you’ll need to provide options that accurately reflect their experience. Below, you can see there is an “other” option with space where the respondent can answer how they see fit, in the case that they don’t fit into any of the other options. Which gender do you most identify with:

• Prefer not to say
• Other [specify]

6. Plan the question flow and choose your questions carefully

Question writing goes hand-in-hand with questionnaire design. So, when writing survey questions, you should consider the survey as a whole. For example, if you write a close-ended question like:  Were you satisfied with the customer service you received when you bought x product? You might want to follow it up with an open-ended question such as:   Please explain the reason for your answer: This will help you draw out more information from your respondent that can help you assess the strengths and weaknesses of your customer service team.  Making sure your questions flow in a logical order is also important. 

For instance, if you ask a question regarding the total cost of a person’s childcare arrangements, but you’re unaware if they have children, you should first ask if they have children and how many.  It’s also a good idea to start your survey with short, easy-to-answer, non-sensitive questions before moving on to something more complex. This way there is more chance you’ll engage your audience early on and make it more likely that they’ll continue with the survey. You should also consider whether you need qualitative or quantitative data for your research outcomes or a mix of the two. This will help you decide the balance of closed-ended and open-ended questions you use.   With close-ended questions, you get quantitative data. This data will be fairly conclusive and simple to analyze. It can be useful when you need to measure specific variables or metrics like population sizes, education levels, literacy levels, etc. 

On the other hand, qualitative data gained by open-ended questions can be full of insights. However, these questions can be more laborious for the respondent to complete making it more likely for them to skip through or give a token answer. They’re also more complex to analyze.

7. Test your surveys

Before a questionnaire goes anywhere near a respondent, it needs to be checked over. Mistakes in your survey questions can give inaccurate results. They can also waste time and resources.  Having an impartial person check your questions can also help prevent bias. So, not only should you check your work, but you should also share it with colleagues for them to check.  After checking your survey questions, make sure to check the functionality and flow of your survey. If you’re building your form in SurveyCTO, you can use our form testing interface to catch errors, make quick fixes, and test your workflows with real data.

Examples of good survey questions

Now that we’ve gone through some dos and don’ts for writing survey questions, we can move on to more practical examples of how a good survey question should look. To keep these specific to the research world we’ll look at three categories of questions. 

• Household survey questions 
• Monitoring and evaluation survey questions 
• Impact evaluation survey questions

1. Household Survey Questions

2. monitoring and evaluation survey questions , 3. impact evaluation questions .

Strong survey questions lead to better research outcomes

Writing good survey questions is essential if you want to achieve your research aims.  A good survey question should be clear, concise, and contain simple language. They should be free of bias and not lead the respondent in any direction. Your survey questions need to complement each other, engage your audience and connect back to the overall objectives of your research.  Creating survey questions and survey designs is a large part of your research, however, is just a part of the puzzle. When your questions are ready, you’ll need to conduct your survey and then find a way to manage your data and workflow. Take a look at this post to see more ways SurveyCTO can help you beyond writing your research survey questions. 

Your next steps: Explore more resources

To keep reading about how SurveyCTO can help you design better surveys, take a look at these resources:  

• Sign up here to get notified about our monthly webinars, where organizations like IDinsight  share best practices for effective surveys.
• Check out previous webinars from SurveyCTO about survey forms, like this one on high-frequency checks for monitoring surveys. 
• Sign up for a free trial of SurveyCTO for your next survey project.

To see how SurveyCTO can help you with your survey needs, start a free 15-day trial today. No credit card required. 

Marta Costa

Senior Product Specialist

Marta is a member of the Customer Success team for Dobility. She helps users working at NGOs, nonprofits, survey firms, universities and research institutes achieve their objectives using SurveyCTO, and works on new ways to help users get the most out of the platform.

Marta has worked in international development consultancy and research, supporting and coordinating impact evaluations, monitoring and evaluation projects, and data collection processes at the national level in areas such as education, energy access, and financial inclusion.

You Might Also Like

SurveyCTO Desktop: No WiFi? No problem! Use powerful tools to work offline

SurveyCTO Desktop: Create your own workspace

Android App: Getting Started with SurveyCTO Collect

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• Product Demos
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories
• Artificial Intelligence

Market Research

• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Salt Lake City.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO

How to create an effective survey in 15 simple tips

Updated August 15, 2023

You don’t have to be an expert to create a survey, but by following a few survey best practices you can make sure you’re collecting the best data possible.

Access 50+ expert-designed survey templates with a free Qualtrics Surveys account

From working out what you want to achieve to providing incentives for respondents, survey design can take time.

But when you don’t have hours to devote to becoming a survey-creation guru, a quick guide to the essentials is a great way to get started.

In this article, we’re going to reveal how to create a survey that’s easy to complete, encourages collecting feedback, hits the research questions you’re interested in, and produces data that’s easy to work with at the analysis stage .

15 Tips when creating surveys

1. define the purpose of the survey.

Before you even think about your survey questions , you need to define their purpose.

The survey’s purpose should be a clear, attainable, and relevant goal. For example, you might want to understand why customer engagement is dropping off during the middle of the sales process.

Your goal could then be something like: “I want to understand the key factors that cause engagement to dip at the middle of the sales process, including both internal and external elements.”

Or maybe you want to understand customer satisfaction post-sale. If so, the goal of your survey could be: “I want to understand how customer satisfaction is influenced by customer service and support post-sale, including through online and offline channels.”

The idea is to come up with a specific, measurable, and relevant goal for your survey. This way you ensure that your questions are tailored to what you want to achieve and that the data captured can be compared against your goal.

2. Make every question count

You’re building your survey questionnaire to obtain important insights, so every question should play a direct role in hitting that target.

Make sure each question adds value and drives survey responses that relate directly to your research goals. For example, if your participant’s precise age or home state is relevant to your results, go ahead and ask. If not, save yourself and your respondents some time and skip it.

It’s best to plan your survey by first identifying the data you need to collect and then writing your questions.

You can also incorporate multiple-choice questions to get a range of responses that provide more detail than a solid yes or no. It’s not always black and white.

For a deeper dive into the art and science of question-writing and survey best practices, check out Survey questions 101 .

3. Keep it short and simple

Although you may be deeply committed to your survey, the chances are that your respondents... aren’t.

As a survey designer, a big part of your job is keeping their attention and making sure they stay focused until the end of the survey.

Respondents are less likely to complete long surveys or surveys that bounce around haphazardly from topic to topic. Make sure your survey follows a logical order and takes a reasonable amount of time to complete.

Although they don’t need to know everything about your research project, it can help to let respondents know why you’re asking about a certain topic. Knowing the basics about who you are and what you’re researching means they’re more likely to keep their responses focused and in scope.

Access 50+ expert-designed survey templates now

4. Ask direct questions

Vaguely worded survey questions confuse respondents and make your resulting data less useful. Be as specific as possible, and strive for clear and precise language that will make your survey questions easy to answer.

It can be helpful to mention a specific situation or behavior rather than a general tendency. That way you focus the respondent on the facts of their life rather than asking them to consider abstract beliefs or ideas .

See an example:

Good survey design isn’t just about getting the information you need, but also encouraging respondents to think in different ways.

Get access to the top downloaded survey templates here

5. Ask one question at a time

Although it’s important to keep your survey as short and sweet as possible, that doesn’t mean doubling up on questions. Trying to pack too much into a single question can lead to confusion and inaccuracies in the responses.

Take a closer look at questions in your survey that contain the word “and” – it can be a red flag that your question has two parts. For example: “Which of these cell phone service providers has the best customer support and reliability?” This is problematic because a respondent may feel that one service is more reliable, but another has better customer support.

Also, if you want to go beyond surveys and develop a multi-faceted listening approach to drive meaningful change and glean actionable insights, make sure to download our guide .

6. Avoid leading and biased questions

Although you don’t intend them to, certain words and phrases can introduce bias into your questions or point the respondent in the direction of a particular answer.

As a rule of thumb, when you conduct a survey it’s best to provide only as much wording as a respondent needs to give an informed answer. Keep your question wording focused on the respondent and their opinions, rather than introducing anything that could be construed as a point of view of your own.

In particular, scrutinize adjectives and adverbs in your questions. If they’re not needed, take them out.

7. Speak your respondent's language

This tip goes hand in hand with many others in this guide – it’s about making language only as complex or as detailed as it needs to be when conducting great surveys.

Create surveys that use language and terminology that your respondents will understand. Keep the language as plain as possible, avoid technical jargon and keep sentences short. However, beware of oversimplifying a question to the point that its meaning changes.

8. Use response scales whenever possible

Response scales capture the direction and intensity of attitudes, providing rich data. In contrast, categorical or binary response options, such as true/false or yes/no response options, generally produce less informative data.

If you’re in the position of choosing between the two, the response scale is likely to be the better option.

Avoid using scales that ask your target audience to agree or disagree with statements, however. Some people are biased toward agreeing with statements , and this can result in invalid and unreliable data.

9. Avoid using grids or matrices for responses

Grids or matrices of answers demand a lot more thinking from your respondent than a scale or multiple choice question. They need to understand and weigh up multiple items at once, and oftentimes they don’t fill in grids accurately or according to their true feelings .

Another pitfall to be aware of is that grid question types aren’t mobile-friendly. It’s better to separate questions with grid responses into multiple questions in your survey with a different structure such as a response scale.

See an example using our survey tool:

10. Rephrase yes/no questions if possible in online survyes

As we’ve described, yes/no questions provide less detailed data than a response scale or multiple-choice, since they only yield one of two possible answers.

Many yes/no questions can be reworked by including phrases such as “How much,” “How often,” or “How likely.” Make this change whenever possible and include a response scale for richer data.

By rephrasing your questions in this way, your survey results will be far more comprehensive and representative of how your respondents feel.

Next? Find out how to write great questions .

11. Start with the straightforward stuff

Ease your respondent into the survey by asking easy questions at the start of your questionnaire, then moving on to more complex or thought-provoking elements once they’re engaged in the process.

This is especially valuable if you need to cover any potentially sensitive topics in your survey. Never put sensitive questions at the start of the questionnaire where they’re more likely to feel off-putting.

Your respondent will probably become more prone to fatigue and distraction towards the end of the survey, so keep your most complex or contentious questions in the middle of the survey flow rather than saving them until last.

12. Use unbalanced scales with care

Unbalanced response scales and poorly worded questions can mislead respondents.

For example, if you’ve asked them to rate a product or service and you provide a scale that includes “poor”, “satisfactory”, “good” and “excellent”, they could be swayed towards the “excellent” end of the scale because there are more positive options available.

Make sure your response scales have a definitive, neutral midpoint (aim for odd numbers of possible responses) and that they cover the whole range of possible reactions to the question .

13. Consider adding incentives

To increase the number of responses, incentives — discounts, offers, gift cards, or sweepstakes — can prove helpful.

Of course, while the benefits of offering incentives sound appealing (more respondents), there’s the possibility of attracting the opinions of the wrong audiences, such as those who are only in it for the incentive.

With this in mind, make sure you limit your surveys to your target population and carefully assess which incentives would be most valuable to them.

14. Take your survey for a test drive

Want to know how to make a survey a potential disaster? Send it out before you pre-test .

However short or straightforward your questionnaire is, it’s always a good idea to pre-test your survey before you roll it out fully so that you can catch any possible errors before they have a chance to mess up your survey results.

Share your survey with at least five people, so that they can test your survey to help you catch and correct problems before you distribute it.

15. Let us help you

Survey design doesn’t have to be difficult — even less so with the right expertise, digital solutions, and survey templates.

At Qualtrics, we provide survey software that’s used by more than 11,000 of the top brands and 99 of the top business schools worldwide.

Furthermore, we have a library of high-quality, ready-to-use, and easy-to-configure survey templates that can improve your surveys significantly.

You can check out our template marketplace here . As a free or existing customer, you have access to the complete collection and can filter by the core experiences you want to drive.

As for our survey software , it’s completely free to use and powers more than 1 billion surveys a year. Using it, you can get answers to your most important brand, market, customer, and product questions, build your own surveys, get insights from your audience wherever they are, and much, much more.

If you want to learn more about how to use our survey tool to create a survey, as well as what else it can do — check out our blog on how to create a free online survey using Qualtrics .

See instant results with our online free survey maker

Sarah Fisher

Related Articles

May 20, 2024

Best strategy & research books to read in 2024

May 13, 2024

Experience Management

X4 2024 Strategy & Research Showcase: Introducing the future of insights generation

November 7, 2023

Brand Experience

The 4 market research trends redefining insights in 2024

June 27, 2023

The fresh insights people: Scaling research at Woolworths Group

June 20, 2023

Bank less, delight more: How Bankwest built an engine room for customer obsession

April 1, 2023

Academic Experience

How to write great survey questions (with examples)

March 21, 2023

Sample size calculator

November 18, 2022

Statistical analysis software: your complete guide to getting started

Stay up to date with the latest xm thought leadership, tips and news., request demo.

Ready to learn more about Qualtrics?

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.328(7451); 2004 May 29

Hands-on guide to questionnaire research

Selecting, designing, and developing your questionnaire, petra m boynton.

1 Department of Primary Care and Population Sciences, University College London, Archway Campus, London N19 5LW

Trisha Greenhalgh

Associated data, short abstract.

Anybody can write down a list of questions and photocopy it, but producing worthwhile and generalisable data from questionnaires needs careful planning and imaginative design

The great popularity with questionnaires is they provide a “quick fix” for research methodology. No single method has been so abused. 1

Questionnaires offer an objective means of collecting information about people's knowledge, beliefs, attitudes, and behaviour. 2 , 3 Do our patients like our opening hours? What do teenagers think of a local antidrugs campaign and has it changed their attitudes? Why don't doctors use computers to their maximum potential? Questionnaires can be used as the sole research instrument (such as in a cross sectional survey) or within clinical trials or epidemiological studies.

Randomised trials are subject to strict reporting criteria, 4 but there is no comparable framework for questionnaire research. Hence, despite a wealth of detailed guidance in the specialist literature, 1 - 3 , 5 w1-w8 elementary methodological errors are common. 1 Inappropriate instruments and lack of rigour inevitably lead to poor quality data, misleading conclusions, and woolly recommendations. w8 In this series we aim to present a practical guide that will enable research teams to do questionnaire research that is well designed, well managed, and non-discriminatory and which contributes to a generalisable evidence base. We start with selecting and designing the questionnaire. ​ questionnaire.

What information are you trying to collect?

You and your co-researchers may have different assumptions about precisely what information you would like your study to generate. A formal scoping exercise will ensure that you clarify goals and if necessary reach an agreed compromise. It will also flag up potential practical problems—for example, how long the questionnaire will be and how it might be administered.

As a rule of thumb, if you are not familiar enough with the research area or with a particular population subgroup to predict the range of possible responses, and especially if such details are not available in the literature, you should first use a qualitative approach (such as focus groups) to explore the territory and map key areas for further study. 6

Is a questionnaire appropriate?

People often decide to use a questionnaire for research questions that need a different method. Sometimes, a questionnaire will be appropriate only if used within a mixed methodology study—for example, to extend and quantify the findings of an initial exploratory phase. Table A on bmj.com gives some real examples where questionnaires were used inappropriately. 1

Box 1: Pitfalls of designing your own questionnaire

Natasha, a practice nurse, learns that staff at a local police station have a high incidence of health problems, which she believes are related to stress at work. She wants to test the relation between stress and health in these staff to inform the design of advice services. Natasha designs her own questionnaire. Had she completed a thorough literature search for validated measures, she would have found several high quality questionnaires that measure stress in public sector workers. 8 Natasha's hard work produces only a second rate study that she is unable to get published.

Research participants must be able to give meaningful answers (with help from a professional interviewer if necessary). Particular physical, mental, social, and linguistic needs are covered in the third article of this series. 7

Could you use an existing instrument?

Using a previously validated and published questionnaire will save you time and resources; you will be able to compare your own findings with those from other studies, you need only give outline details of the instrument when you write up your work, and you may find it easier to get published (box 1).

Increasingly, health services research uses standard questionnaires designed for producing data that can be compared across studies. For example, clinical trials routinely include measures of patients' knowledge about a disease, 9 satisfaction with services, 10 or health related quality of life. 11 - 13 w3 w9 The validity (see below) of this approach depends on whether the type and range of closed responses reflects the full range of perceptions and feelings that people in all the different potential sampling frames might hold. Importantly, health status and quality of life instruments lose their validity when used beyond the context in which they were developed. 12 , 14 , 15 w3 w10-12

If there is no “off the peg” questionnaire available, you will have to construct your own. Using one or more standard instruments alongside a short bespoke questionnaire could save you the need to develop and validate a long list of new items.

Is the questionnaire valid and reliable?

A valid questionnaire measures what it claims to measure. In reality, many fail to do this. For example, a self completion questionnaire that seeks to measure people's food intake may be invalid because it measures what they say they have eaten, not what they have actually eaten. 16 Similarly, responses on questionnaires that ask general practitioners how they manage particular clinical conditions differ significantly from actual clinical practice. w13 An instrument developed in a different time, country, or cultural context may not be a valid measure in the group you are studying. For example, the item “I often attend gay parties” may have been a valid measure of a person's sociability level in the 1950s, but the wording has a very different connotation today.

Reliable questionnaires yield consistent results from repeated samples and different researchers over time. Differences in results come from differences between participants, not from inconsistencies in how the items are understood or how different observers interpret the responses. A standardised questionnaire is one that is written and administered so all participants are asked the precisely the same questions in an identical format and responses recorded in a uniform manner. Standardising a measure increases its reliability.

Just because a questionnaire has been piloted on a few of your colleagues, used in previous studies, or published in a peer reviewed journal does not mean it is either valid or reliable. The detailed techniques for achieving validity, reliability, and standardisation are beyond the scope of this series. If you plan to develop or modify a questionnaire yourself, you must consult a specialist text on these issues. 2 , 3

How should you present your questions?

Questionnaire items may be open or closed ended and be presented in various formats ( figure ). Table B on bmj.com examines the pros and cons of the two approaches. Two words that are often used inappropriately in closed question stems are frequently and regularly. A poorly designed item might read, “I frequently engage in exercise,” and offer a Likert scale giving responses from “strongly agree” through to “strongly disagree.” But “frequently” implies frequency, so a frequency based rating scale (with options such as at least once a day, twice a week, and so on) would be more appropriate. “Regularly,” on the other hand, implies a pattern. One person can regularly engage in exercise once a month whereas another person can regularly do so four times a week. Other weasel words to avoid in question stems include commonly, usually, many, some, and hardly ever. 17 w14

Examples of formats for presenting questionnaire items

Box 2: A closed ended design that produced misleading information

Customer: I'd like to discontinue my mobile phone rental please.

Company employee: That's fine, sir, but I need to complete a form for our records on why you've made that decision. Is it (a) you have moved to another network; (b) you've upgraded within our network; or (c) you can't afford the payments?

Customer: It isn't any of those. I've just decided I don't want to own a mobile phone any more. It's more hassle than it's worth.

Company employee: [after a pause] In that case, sir, I'll have to put you down as “can't afford the payments.”

Closed ended designs enable researchers to produce aggregated data quickly, but the range of possible answers is set by the researchers not respondents, and the richness of potential responses is lower. Closed ended items often cause frustration, usually because researchers have not considered all potential responses (box 2). 18

Ticking a particular box, or even saying yes, no, or maybe can make respondents want to explain their answer, and such free text annotations may add richly to the quantitative data. You should consider inserting a free text box at the end of the questionnaire (or even after particular items or sections). Note that participants need instructions (perhaps with examples) on how to complete free text items in the same way as they do for closed questions.

If you plan to use open ended questions or invite free text comments, you must plan in advance how you will analyse these data (drawing on the skills of a qualitative researcher if necessary). 19 You must also build into the study design adequate time, skills, and resources for this analysis; otherwise you will waste participants' and researchers' time. If you do not have the time or expertise to analyse free text responses, do not invite any.

Some respondents (known as yea sayers) tend to agree with statements rather than disagree. For this reason, do not present your items so that strongly agree always links to the same broad attitude. For example, on a patient satisfaction scale, if one question is “my GP generally tries to help me out,” another question should be phrased in the negative, such as “the receptionists are usually impolite.”

Apart from questions, what else should you include?

A common error by people designing questionnaires for the first time is simply to hand out a list of the questions they want answered. Table C on bmj.com gives a checklist of other things to consider. It is particularly important to provide an introductory letter or information sheet for participants to take away after completing the questionnaire.

What should the questionnaire look like?

Researchers rarely spend sufficient time on the physical layout of their questionnaire, believing that the science lies in the content of the questions and not in such details as the font size or colour. Yet empirical studies have repeatedly shown that low response rates are often due to participants being unable to read or follow the questionnaire (box 3). 3 w6 In general, questions should be short and to the point (around 12 words or less), but for issues of a sensitive and personal nature, short questions can be perceived as abrupt and threatening, and longer sentences are preferred. w6

How should you select your sample?

Different sampling techniques will affect the questions you ask and how you administer your questionnaire (see table D on bmj.com ). For more detailed advice on sampling, see Bowling 20 and Sapsford. 3

If you are collecting quantitative data with a view to testing a hypothesis or assessing the prevalence of a disease or problem (for example, about intergroup differences in particular attitudes or health status), seek statistical advice on the minimum sample size. 3

What approvals do you need before you start?

Unlike other methods, questionnaires require relatively little specialist equipment or materials, which means that inexperienced and unsupported researchers sometimes embark on questionnaire surveys without completing the necessary formalities. In the United Kingdom, a research study on NHS patients or staff must be:

• Formally approved by the relevant person in an organisation that is registered with the Department of Health as a research sponsor (typically, a research trust, university or college) 21 ;
• Consistent with data protection law and logged on the organisation's data protection files (see next article in series) 19
• Accordant with research governance frameworks 21
• Approved by the appropriate research ethics committee (see below).

Box 3: Don't let layout let you down

Meena, a general practice tutor, wanted to study her fellow general practitioners' attitudes to a new training scheme in her primary care trust. She constructed a series of questions, but when they were written down, they covered 10 pages, which Meena thought looked off putting. She reduced the font and spacing of her questionnaire, and printed it double sided, until it was only four sides in length. But many of her colleagues refused to complete it, telling her they found it too hard to read and work through. She returned the questionnaire to its original 10 page format, which made it easier and quicker to complete, and her response rate increased greatly.

Summary points

Questionnaire studies often fail to produce high quality generalisable data

When possible, use previously validated questionnaires

Questions must be phrased appropriately for the target audience and information required

Good explanations and design will improve response rates

In addition, if your questionnaire study is part of a formal academic course (for example, a dissertation), you must follow any additional regulations such as gaining written approval from your supervisor.

A study is unethical if it is scientifically unsound, causes undue offence or trauma, breaches confidentiality, or wastes people's time or money. Written approval from a local or multicentre NHS research ethics committee (more information at www.corec.org.uk ) is essential but does not in itself make a study ethical. Those working in non-NHS institutions or undertaking research outside the NHS may need to submit an additional (non-NHS) ethical committee application to their own institution or research sponsor.

The committee will require details of the study design, copies of your questionnaire, and any accompanying information or covering letters. If the questionnaire is likely to cause distress, you should include a clear plan for providing support to both participants and researchers. Remember that just because you do not find a question offensive or distressing does not mean it will not upset others. 6

As we have shown above, designing a questionnaire study that produces usable data is not as easy as it might seem. Awareness of the pitfalls is essential both when planning research and appraising published studies. Table E on bmj.com gives a critical appraisal checklist for evaluating questionnaire studies. In the following two articles we will discuss how to select a sample, pilot and administer a questionnaire, and analyse data and approaches for groups that are hard to research.

Supplementary Material

This is the first in a series of three articles on questionnaire research

Susan Catt supplied additional references and feedback. We also thank Alicia O'Cathain, Jill Russell, Geoff Wong, Marcia Rigby, Sara Shaw, Fraser MacFarlane, and Will Callaghan for feedback on earlier versions. Numerous research students and conference delegates provided methodological questions and case examples of real life questionnaire research, which provided the inspiration and raw material for this series. We also thank the hundreds of research participants who over the years have contributed data and given feedback to our students and ourselves about the design, layout, and accessibility of instruments.

Contributors and sources: PMB and TG have taught research methods in a primary care setting for the past 13 years, specialising in practical approaches and using the experiences and concerns of researchers and participants as the basis of learning. This series of papers arose directly from questions asked about real questionnaire studies. To address these questions we explored a wide range of sources from the psychological and health services research literature.

Competing interests: None declared.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

Published on August 20, 2019 by Shona McCombes . Revised on June 22, 2023.

Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyze the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyze the survey results, step 6: write up the survey results, other interesting articles, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research : investigating the experiences and characteristics of different social groups
• Market research : finding out what customers think about products, services, and companies
• Health research : collecting data from patients about symptoms and treatments
• Politics : measuring public opinion about parties and policies
• Psychology : researching personality traits, preferences and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and in longitudinal studies , where you survey the same sample several times over an extended period.

Prevent plagiarism. Run a free check.

Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• US college students
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18-24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalized to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

Several common research biases can arise if your survey is not generalizable, particularly sampling bias and selection bias . The presence of these biases have serious repercussions for the validity of your results.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every college student in the US. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalize to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions. Again, beware of various types of sampling bias as you design your sample, particularly self-selection bias , nonresponse bias , undercoverage bias , and survivorship bias .

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by mail, online or in person, and respondents fill it out themselves.
• An interview , where the researcher asks a set of questions by phone or in person and records the responses.

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by mail is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g. residents of a specific region).
• The response rate is often low, and at risk for biases like self-selection bias .

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyze.
• The anonymity and accessibility of online surveys mean you have less control over who responds, which can lead to biases like self-selection bias .

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping mall or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g. the opinions of a store’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations and is at risk for sampling bias .

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data: the researcher records each response as a category or rating and statistically analyzes the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analyzed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g. yes/no or agree/disagree )
• A scale (e.g. a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g. age categories)
• A list of options with multiple answers possible (e.g. leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analyzed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an “other” field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic. Avoid jargon or industry-specific terminology.

Survey questions are at risk for biases like social desirability bias , the Hawthorne effect , or demand characteristics . It’s critical to use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no indication that you’d prefer a particular answer or emotion.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by mail, online, or in person.

There are many methods of analyzing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also clean the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analyzing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analyzed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion , you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 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 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Survey Research | Definition, Examples & Methods. Scribbr. Retrieved July 22, 2024, from https://www.scribbr.com/methodology/survey-research/

Is this article helpful?

Shona McCombes

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, questionnaire design | methods, question types & examples, what is a likert scale | guide & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Survey Questionnaire

Questionnaire generator.

Businesses and other organizations find various means to communicate with their clients and find ways to achieve customer satisfaction . One way is through a survey questionnaire as it is to be able to help interact with individuals while also gaining essential information that can help improve operations. This article will show you ways on how to create one suitable for your needs.

32+ Survey Questionnaire Examples

Survey questionnaire example.

• Google Docs

Size: A4, US

Survey Questionnaire for Food Business

Basic Survey Questionnaire Template

Size: A4 & US

Health Survey Questionnaire

Student health survey questionnaire.

Size: 348 KB

Health Survey Questionnaire Example

Size: 157 KB

Health and Wellness Questionnaire

Housing Survey Questionnaire

Housing needs survey questionnaire.

Size: 67 KB

Affordable Housing Questionnaire

Size: 53 KB

Market Survey Questionnaire Example

Real estate market survey questionnaire.

Size: 203 KB

Market Survey Questionnaire For Product

Size: 47 KB

Customer Survey Questionnaire

Customer satisfaction survey questionnaire.

Size: 123 KB

Customer Loyalty Survey Questionnaire

Size: 504 kB

Restaurant Survey Questionnaire

Restaurant food survey questionnaire.

Size: 977 kB

New Restaurant Survey Questionnaire

Size: 121 kB

Employee Survey Questionnaire Example

Employee satisfaction survey in pdf.

Size: 63 kB

Job Survey Questionnaire

Job satisfaction survey questionnaire.

Pre-Survey Questionnaires

Pre-event survey questionnaire.

Size: 155 kB

Pre-Training Survey Questionnaire

Size: 48 kB

Pre-Survey Questionnaire for Accreditation

Size: 37 kB

Insurance Survey Questionnaires

Health insurance survey questionnaire.

Commercial Insurance Survey Questionnaire

Size: 99 kB

School Survey Questionnaire

Private school survey questionnaire.

Size: 92 kB

School Travel Questionnaire

Size: 279 kB

Business Survey Questionnaire Example

Small business survey questionnaire.

Size: 482 kB

Annual Business Questionnaire

Size: 130 kB

Family Business Survey Questionnaire

Size: 65 kB

Energy Survey Questionnaires

Preliminary energy survey questionnaire.

Size: 176 kB

Household Survey Questionnaire

Size: 254 kB

Coach Survey Questionnaire

Size: 38 kB

IT Survey Questionnaire Example

Size: 136 kB

Food Survey Questionnaire

Size: 755 kB

Demographic Survey Questionnaire

Size: 20 kB

Thematic Survey Questionnaire

Size: 62 kB

What Is a Survey Questionnaire?

It is a collection of questions used in a survey . It is a system of gathering data utilized to collect, analyze, and interpret various views of a group of people from a specific population. In such questionnaires , respondents have to provide answers to the questions asked. Situations that involve its use include school projects or quantitative research .

What Is the Purpose of a Survey Questionnaire?

Conducting a survey requires a good amount of time and effort. It involves constant research to come up with one. You also need the right resources to reach a particular amount of people. So no, a survey questionnaire isn’t normally created for the heck of it nor should it deem as a waste of one’s time.

Formal questionnaires, such as a business questionnaire or a student questionnaire , all have a definite purpose. This may be to gain important data on one’s feedback or suggestions or to simply understand the minds of the respondents to draw a conclusion for a study.

The data collected may evoke discussions on topics that have never been considered before. It will serve as a basis for future business decisions as well. A survey questionnaire will also promote proper communication between two entities to better understand common issues that need solutions.

Is it necessary to use survey questionnaires for research? For the most part, yes. You can’t base your study solely on data from online and print sources. You need to know if it is indeed true by simply going out on the field and collecting the information yourself. You may also like student questionnaire examples .

How To Create a Survey Questionnaire

Whatever you include in a survey questionnaire can impact the value of your results. An inquiry that’s complex and confusing will only garner misleading and inaccurate responses. To avoid doing so, here are some tips in writing the complete survey questionnaire.

1. State The Survey’s Purpose

The respondents have the right to know why they should answer something; you may explain this at the beginning of the survey. For example, if you are a restaurant seeking to find out whether customers enjoyed the food and the employee ‘s services, then state it at the beginning of your survey questionnaire.

2. Ask Questions Clearly

It’s important to ask questions that are understandable without a second thought to avoid confusion. Don’t ask questions that have two different answers. This will help your respondents answer the survey as best as they can.

3. Make Your Content Relevant

When creating the questions for your survey questionnaire, make sure they are all relevant to your topic. Avoid asking questions that have nothing to do with your research, as it will only take up space. For example, if your survey is about student feedback on college education, then make sure to ask questions about that matter alone.

4. Write Your Questions Considerably

Some questions might seem intrusive to some respondents, so for these types of problems, it’s wise to provide an option for respondents not to answer. Depending on your topic, some questions that center on beliefs and practices can be a sensitive topic to some. But if it’s necessary, be considerate with the way you phrase these questions.

What are the advantages of using a questionnaire?

They are easy to analyze and are one of the most familiar ways of gathering data. You can contact a large sample of the population at once, at a lower cost. The format is familiar to most respondents, which makes it easy to administer.

Who makes the survey questionnaire?

Depending on the subject matter, researchers tend to create these surveys in academic settings while the HR and marketing department does it in a corporate environment.

Why is choosing survey questionnaires not the best option always?

Respondents may not feel encouraged to provide accurate answers. The number of respondents who choose to respond may be different from those who decided not to, hence creating a bias. This is why a survey is not always the right option, but that depends on your target population.

A well-made survey questionnaire can let you gain a lot of insight from your target. According to a Forbes article, if you want to get the most of this tool , you can use social media and other online channels to gain more information. If you need any more ideas on how to create your survey questionnaire, check out our other research and analysis templates .

Text prompt

• Instructive
• Professional

Create a fun quiz to find out which historical figure you're most like in your study habits

Design a survey to discover students' favorite school subjects and why they love them.

• Writing Center

Beginner’s Guide to Research

Click here to download a .pdf copy of our Beginner’s Guide to Research !

Last updated : July 18, 2024

Consider keeping a printed copy to have when writing and revising your resume!  If you have any additional questions, make an appointment or email us at [email protected] !

Most professors will require the use of academic (AKA peer-reviewed) sources for student writing. This is because these sources, written for academic audiences of specific fields, are helpful for developing your argument on many topics of interest in the academic realm, from history to biology. While popular sources like news articles also often discuss topics of interest within academic fields, peer-reviewed sources offer a depth of research and expertise that you cannot find in popular sources. Therefore, knowing how to (1) identify popular vs. academic sources, (2) differentiate between primary and secondary sources, and (3) find academic sources is a vital step in writing research. Below are definitions of the two ways scholars categorize types of sources based on when they were created (i.e. time and place) and how (i.e. methodology):

Popular vs. academic sources:

• Popular sources are publicly accessible periodicals–newspapers, magazines, and blogs–such as The Washington Post or The New Yorker . These sources are most often written for non-academic audiences, but can be helpful for finding general information and a variety of opinions on your topic.
• Academic sources , known also as peer reviewed or scholarly articles, are those that have undergone peer review before being published. Typically, these articles are written for other scholars in the field and are published in academic journals, like Feminist Studies or The American Journal of Psychology . Literature reviews, research projects, case studies, and notes from the field are common examples.

Primary vs. secondary sources:

• Primary sources are articles written by people directly involved in what they were writing about, including: News reports and photographs, diaries and novels, films and videos, speeches and autobiographies, as well as original research and statistics.
• Secondary sources , on the other hand, are second hand accounts written about a topic based on primary sources. Whether a journal article or other academic publication is considered a secondary source depends on how you use it.

How to Find Academic Sources

Finding appropriate academic sources from the hundreds of different journal publications can be daunting. Therefore, it is important to find databases –digital collections of articles–relevant to your topic to narrow your search. Albertson’s Library has access to several different databases, which can be located by clicking the “Articles and Databases” tab on the website’s homepage, and navigating to “Databases A-Z” to refine your search. Popular databases include: Academic Search Premier and Proquest Central (non-specific databases which include a wide variety of articles), JSTOR (humanities and social sciences, from literature to history), Web of Science (formal sciences and natural sciences such as biology and chemistry), and Google Scholar (a web search engine that searches scholarly literature and academic sources). If you are unable to access articles from other databases, make sure you’re signed in to Alberton’s Library through Boise State!

Performing a Database Search

Databases include many different types of sources besides academic journals, however, including book reviews and other periodicals. Using the search bar , you can limit search results to those containing specific keywords or phrases like “writing center” or “transfer theory.” Utilizing keywords in your search–names of key concepts, authors, or ideas–rather than questions is the most effective way to find articles in databases. When searching for a specific work by title, placing the title in quotation marks will ensure your search includes only results in that specific word order. In the example below, search terms including the author (“Virginia Woolf”) and subject (“feminism”) are entered into the popular database EBSCOhost:

Refining Your Search Results

Many databases have a bar on the left of the screen where you can further refine your results. For example, if you are only interested in finding complete scholarly articles, or peer-reviewed ones, you can toggle these different options to further limit your search. These options are located under the “Refine Results” bar in EBSCOhost, divided into different sections, with a display of currently selected search filters and filter options to refine your search based on your specific needs, as seen in the figure below:

Search results can also be limited by subject : If you search “Romeo and Juliet” on Academic Search Premier to find literary analysis articles for your English class, you’ll find a lot of other sources that include this search term, such as ones about theater production or ballets based on Shakespeare’s play. However, if you’re writing a literary paper on the text of the play itself, you might limit your search results to “fiction” to see only articles that discuss the play within the field of literature. Alternatively, for a theater class discussing the play, you might limit your search results to “drama.”

The Writing Center

Learning how to develop a research question throughout the PhD process: training challenges, objectives, and scaffolds drawn from doctoral programs for students and their supervisors

• Open access
• Published: 15 July 2024

Cite this article

You have full access to this open access article

• Nathalie Girard   ORCID: orcid.org/0000-0003-1036-0010 1 ,
• Aurélie Cardona 2 &
• Cécile Fiorelli 3  

394 Accesses

Explore all metrics

With the higher education reform putting forward the professionalization of doctoral students, doctoral education has been strongly focused on generic transferable skills to ensure employability. However, doctoral training should not forget core skills of research and especially the ability to formulate research questions, which are the key to original research and difficult to develop at the same time. Learning how to develop a research question is traditionally seen as a one-to-one learning process and an informal daily transmission between a novice and a senior researcher. The objective of this paper is to offer a framework to design doctoral programs aimed at supporting the process of development of research questions for doctoral candidates guided by their supervisors. We base our proposal on two doctoral training programs designed with a pedagogical strategy based on dialogs with peers, whether they be students, supervisors, or trainers from a diversity of scientific backgrounds. The resulting framework combines three learning challenges faced by doctoral students and their supervisors when developing their research question, as well as training objectives corresponding to what they should learn and that are illustrated by the scaffolds we have used in our training programs. Finally, we discuss the conditions and originality of our pedagogical strategy based on the acquisition of argumentation skills, taking both the subjective dimensions of PhD work and the added value of interactions with a diversity and heterogeneity of peers into account.

Similar content being viewed by others

Factors in a professional learning program to support a teacher’s growth in mathematical reasoning and its pedagogy

The Potential of Communities of Learning for Dual Career PhD Programs – A Case Study

Promoting the asking of research questions in a high-school biotechnology inquiry-oriented program.

Avoid common mistakes on your manuscript.

Introduction

With the higher education reform ongoing in the Western world, doctoral education has undergone “a shift from the master–apprentice model to the professional model” (Poyatos Matas, 2012 ), focusing doctoral education on doctoral graduate employability (Cardoso et al., 2022 ) and thus on generic transferable skills (Christensen, 2005 ). However, Poyatos Matas ( 2012 ) warns doctoral educators of the danger of reducing doctoral education to a business or team skills approach, arguing the “importan[ce of maintaining] an adequate balance between skill-based and knowledge-based approaches to doctoral education.” Along the same line, Christensen ( 2005 ) argues that training in transferable skills “should not be overemphasised with respect to original research.” Nevertheless, Poyatos Matas ( 2012 ) does not explicitly explain what the core skills of research, grouped into a broad category referred to as “research skills,” are among seven other skills listed by the European Universities Association’s Salzburg principles.

Among research skills, the way the research question is formulated is critical. As Einstein and Infeld expressed it in 1971 , “the formulation of a problem is often more essential than its solution […]. To raise new questions, new possibilities, to regard old questions from a new angle, requires creative imagination and marks real advance in science.” In this article, we consider the development of a research question as a process that consists of determining and reducing the identified problems, whether scientific or socio-economic, and translating them into a relevant and treatable question (Callon, 1984 ). We assume that it is a key process for research activities and a skill that PhD students have to acquire during their PhD experience. However, learning how to develop a research question is far from being easy, as revealed by the multiplication of methodological guides and tutorials on this topic. As researchers and human resource advisors working in a multidisciplinary research institute (INRAE) Footnote 1 , we have also observed many PhD students struggling to formulate their research question, which may seriously inhibit the writing of the final manuscript, whether it be a thesis by publication or not. Some authors have pointed out that the current graduate school education system has largely focused on producing better learners and problem solvers, thus neglecting problem-finding or creativity development in doctoral education (Whitelock et al., 2008 ). Preparing a “research proposal” and developing a researchable question is even recognized as a critical step for doctoral students (Zuber-Skerrit & Knight, 1986 ), becoming a “threshold to cross” during the PhD journey (Chatterjee-Padmanabhan & Nielsen, 2018 ). It thus appears essential to explore the challenges of research question development and how doctoral training programs can contribute to its learning.

The objective of our article is to offer a framework to think about and design doctoral training programs that support the development of research questions for doctoral candidates guided by their supervisors. Our proposal is grounded in two doctoral training programs designed with a pedagogical strategy based on dialogs with peers, whether they be other students, supervisors, or trainers from a diversity of scientific backgrounds. This article is structured into four sections. We present our theoretical background in order to explore the diversity of approaches to develop a research question, laying out our vision of doctoral experience and education, and the way in which the concept of scaffolding has been used in the learning processes that underlie the development of research questions (“ Theoretical background ” section). We then present our methodology, combining an analysis of the literature, our experience in conducting research, supervising and training doctoral students and their supervisors, and our case studies (“ Materials and methods ” section). Our results consist of a framework that combines three learning challenges and the corresponding training objectives, illustrated by scaffoldings we have used in our training programs (“ Results: scaffolding learning challenges for the development of the research question within a thesis ” section). Finally, we discuss the conditions and originality of our proposal based on the acquisition of argumentation skills, with the consideration of the subjective dimensions of PhD work and the added value of interactions with a diversity and heterogeneity of peers (“ Discussion: Enriching peer-learning scaffolding to support the development of a research question as a dialogical process ” section).

Theoretical background

Opening up the process of research question development: a diversity of approaches.

According to the literature about the development of research questions, it is a task that is difficult to formalize and for which several approaches coexist. It may differ according to the disciplines (Xypas & Robin, 2010 ) as well as according to the practical context of the doctoral thesis (i.e., participative research, methodological or fundamental research, financial support). We identified four approaches to research question development:

Gap-spotting (e.g., Locke & Golden-Biddle, 1997 ), the more classical approach, which consists in identifying gaps in existing literature that need to be filled.

Challenging the assumptions underlying existing theory in order to develop and evaluate alternative assumptions. Such an approach aims at coming up “with novel research questions through a dialectical interrogation of one’s own familiar position, other stances, and the domain of literature targeted for assumption challenging” (Alvesson & Sandberg, 2013 ). These authors explicitly adopt a critical perspective of gap-spotting, which they consider as a form of “underproblematization.”

Expressing a contrastive stance to create dialogical space, presented as critical in order to develop a convincing research question (Mei, 2006 ). This approach has addressed the research question formulation by focusing on the writing process.

Problem-solving study based on a negotiation about the “problem framing” involving scientists and stakeholders, and which focuses on practical problem-solving (Archbald, 2008 ).

The literature and our experience show that these different approaches coexist, but do not fall within the same temporality. For example, gap-spotting can be an operation that takes place at the beginning of the research process and which is limited in time, whereas the negotiation of problems between scientists and stakeholders can be much longer and can arise at different stages of the research process. In the same way, challenging existing theories can be a long and incremental process that evolves as the doctoral student acquires new knowledge from scientific literature along the doctoral path or due to an unexpected observation in the field. Trafford and Leshem ( 2009 ) also explain how research begins with a gap in knowledge or professional practices and how research questions evolve with new inputs from the literature, fieldwork, and the progressive establishment of a conceptual framework and theoretical perspectives, to finally end up by proposing a “justifiable contribution to knowledge”. In this perspective, the formulation of a research question can be considered as an incremental path that continues during the doctoral journey.

The knowledge and know-how involved in research question development are thus of a very specific nature (metacognition, implicit, diversity of thinking, etc.), rendering it impossible to design doctoral training programs focused on this complex task as a simple “knowledge transfer”. Moreover, beyond the cognitive learning required, it also refers to more developmental challenges, both for doctoral students and their supervisors, since it is embedded in their specific epistemological and social working situation.

Our vision of the PhD experience and doctoral education

We consider research and, thus, doctoral experience as an activity involving affects, interests, and social networks (Shapin, 2010 ). In line with other scholars (Lonka et al., 2019 ; Sun & Cheng, 2022 ; Xypas & Robin, 2010 ), we argue that doctoral education should rely on a person-centered approach. This means paying attention to doctoral students’ profiles, their perceptions of the academic environment and their professional aims, i.e., the individual contexts of each PhD thesis and the diversity of PhD researchers’ needs and goals (Inouye, 2023 ), as well as their conceptions of research or epistemological backgrounds (Charmillot, 2023 ). We thus consider the PhD process as a professional experience with its multidimensional nature and the distinct quests of PhD students (quest for the self; intellectual quest; professional quest) when navigating their doctoral paths (Skakni, 2018 ).

This type of view leads to a developmental approach of the PhD journey, with doubts, uncertainties, and paradoxes in becoming doctoral researchers, and a “transformation of understanding and of self” (Rennie & Kinsella, 2020 ). Influenced by their personal trajectories and post-PhD goals, doctoral students may thus adopt various approaches in the yearly phase of the PhD process when developing their research projects, whether writing a research proposal constitutes or not a formal step to becoming a full doctoral candidate Footnote 2 . We also consider the PhD experience as a transformative process of a bidirectional nature, for both doctoral students and their supervisors (Halse & Malfroy, 2010 ; Kobayashi, 2014 ).

When it comes to doctoral education, this point of view implies the necessity to combine both generic support and individual guidance, to tailor training and to take each of the doctoral student’s stage of development into account. It also requires that trainers take on the role of facilitators more than those “who know”, in a socio-constructivist approach to learning. Nevertheless, designing doctoral training dedicated to research question development throughout the doctoral journey opens up questions on how to promote such learning in the workplace.

Scaffolding as an adaptive support of learning

In line with Vygotsky’s approach to learning, we consider that the concept of scaffolding can be beneficial to understanding how PhD supervisors can assist their doctoral students in learning how to develop their research question. Firstly defined by Wood et al. ( 1976 ) as a process similar to parents helping infants to solve a problem, this concept has proven to be an efficient pedagogical strategy to support learning in science (Lin et al., 2012 ). It can then be connected to Vygotski’s Zone of Proximal Development (ZPD) ( 1978 ), consisting of tasks that students cannot yet carry out on their own, but which they can accomplish with assistance. Scaffolding has been specified by Belland ( 2014 ) in instructional settings as a “just-in-time support provided by a teacher/parent (tutor) that allows students (tutees) to meaningfully participate in and gain skill at problem solving”. Beyond this use within formal instruction, it has been put forward as “a central educational arrangement in workplace learning”, considered as a “socially-shared situation between master and apprentice” (Nielsen, 2008 ). Scholars argued that scaffolding could also be used to improve higher-order thinking abilities through social interaction, such as argumentation when solving ill-structured problems or when building dialectical arguments.

Three critical features are central to successful scaffolding:

Firstly, the notion of a shared understanding of the goal of the activity is crucial (Puntambekar & Hübscher, 2005 ), requiring an “intersubjectivity” between the tutor and the tutee (Belland, 2014 ), which is reached when they collaboratively redefine the task. The stake here is to make sure that learners are invested in the task, as well as to help sustain this motivation, encouraging them to be informed participants who understand the point of the activity, the value and use of the strategies and “making it worthwhile for the learner to risk the next step” (Wood et al., 1976 ).

Secondly, the tutor should provide the tutee with a graduated assistance based on an ongoing diagnosis of the tutee’s current level of skill, which Belland ( 2014 ) sums up by “providing just the right amount of support at just the right time, and backing off as students gained skill”. Therefore, scaffolding is highly contingent on both the task and the learner’s characteristics, thus being “dynamically adjusted according to tutee ability” (Belland, 2014 ) and requiring the tutor to manage a careful calibration of support (Puntambekar & Hübscher, 2005 ).

Thirdly, scaffolding is successful when the learner controls and takes responsibility for the task, thus moving towards autonomous activity. Scaffolding should then promote this transfer of responsibility, as well as including its own fadeout as internalization progresses.

First focused on the interactions between individuals, the scaffolding concept is now being more broadly applied to artifacts, resources, and environments designed as scaffolds (Puntambekar & Hübscher, 2005 ), with three main “scaffolding modalities”:

One-to-one scaffolding, which “consists of a teacher’s contingent support of students within their respective ZPDs”, considered as the ideal modality with a tailored scaffolding;

Peer scaffolding, which goes beyond the original idea of assistance by a more capable individual (Wood et al., 1976 ) and which hypothesizes that peers can also provide such support;

Computer/paper/artifact-based scaffolding, which emerged as a solution to the dilemma that teachers cannot provide adequate one-to-one scaffolding to all students in a classroom.

Beyond the advantages and limitations of each scaffolding modality, various scholars have discussed the challenges of designing scaffolding in complex environments. It can be a question of taking the heterogeneity of learners into consideration when designing tools (Puntambekar & Hübscher, 2005 ), of building dynamic assessments and fading into the whole environment (Puntambekar & Hübscher, 2005 ; Belland, 2014 ), or of considering the learning environment by combining tools and agents (Puntambekar & Hübscher, 2005 ) in a system of “distributed scaffolding” (Tabak, 2004 ). Lastly, beyond the dyadic relationship between the master and the apprentice, many authors have shown the distributed and collective nature of scaffolding at the workplace (Filliettaz, 2011 ), pointing out the role of “the entire work community” in workplace learning. This enlargement of the concept of scaffolding appears to be especially relevant for the learning of research question development, which is a long process that results from a diversity of interactions, as shown in the previous sub-section.

Existing scaffoldings to support the learning of research question development

In her report of the Bologna seminar on Doctoral Programs for the European Knowledge Society, Christensen ( 2005 ) argues that only training by doing research can provide doctoral candidates with core skills such as “problem solving, innovative, creative and critical thinking”. Until now, the traditional model of doctoral education was based on a supervisor-centered model and a transmission model “where the apprenticeship learns from the master by observation” (Poyatos Matas, 2012 ). Such informal learning thus takes place in private spaces, pointing out the lack of explicit knowledge on “what the academic career involves, the norms, values, and ethics embedded in their disciplines, and the expectations of work habits that they would be expected to meet” (Austin, 2009 ).

Even if this master-apprenticeship model was previously adequate, it turns out to be outdated because of the evolution of doctoral conditions. The increasing control and limitation of PhD duration and the obligation of regular reporting about the progress of the PhD leave less room and time for mimetic and trial-and-error learning. This is especially true in the case of specific doctoral education models such as the PhD by publication, the professional doctorate, the practice-based doctorate (Poyatos Matas, 2012 ), and the case of traditional PhDs. However, most of the time, doctoral students remain “without fully learning how to frame their own questions and design and conduct their own studies” (Austin, 2009 ). It is thus not surprising that the offer of learning supports for PhD students has greatly increased, with a wide diversity of options (handbooks, YouTube channels, writing courses or groups, etc.). Among the diverse training programs offered to doctoral students and sometimes supervisors, some doctoral schools and universities have also created specific training programs to support research question development, while some authors like Inouye ( 2020 ) put forward that training and supervision should include explicit training on the Research Proposal as a “threshold to cross” (see footnote n°2). On the basis of this diversity of offers, we identified three main scaffoldings corresponding to the three main modalities identified in the previous section: artifacts, peer-learning groups (e.g., Chatterjee-Padmanabhan & Nielsen, 2018 ; Poyatos Matas, 2012 ; Zuber-Skerritt & Knight, 1986 ), and supervisors (e.g., Manathunga et al., 2006 ; Whitelock et al., 2008 ).

Following a developmental approach to the PhD process, the present study aims at offering a generic framework to think about and design doctoral programs that scaffold the learning of the development of research questions.

Materials and methods

Building a framework by combining our experiences with the literature.

This research was based on two distinct doctoral training programs that we designed and independently ran over a period of 10 years. Having reflected together on our department’s doctoral training policy, we then progressively formalized the issues at stake in doctoral training and analyzed how our programs responded to them. The importance and difficulties of learning how to develop research questions during doctoral studies then became crucial, leading us to formalize what we had learned from our two programs. In this article, these programs are our case studies, i.e., the situation where we conducted an empirical inquiry to investigate the scaffolding of research question development and from which we can expand and generalize theories on doctoral training (Yin, 2018 ).

For each case study, we combined several methods to collect data:

We used ethnographic techniques (Parker-Jenkins, 2018 ) with a participant observer stance. As researchers conducting research and supervising doctoral students, as HR advisors supporting doctoral students and researchers at INRAE, and as trainers and coordinators in two doctoral training programs, we are involved in prolonged and repeated periods of observation. We thus documented detailed field notes that were revisited as research data.

We built a corpus of pre-existing documents presenting the two doctoral programs (brochures, Website contents, scientific articles, time schedules and targeted objectives at each sequence). For each document, we carried out an open-coding operation to identify the narratives about research question development.

We gathered feedback spontaneously expressed by the trainees during the training courses, the hot debriefs occurring at the end of each course, and training assessments one month after the course, as well as in the course of our activity (in individual HR interventions or in reading the acknowledgements of a PhD thesis).

In parallel with data collection, we carried out a review of the literature on the evolution of doctoral education and the emerging learning challenges for doctoral students and their supervisors, some epistemological articles on research question development and the process of doctoral experience, empirical articles describing training for research question development and seminal articles, and reviews on scaffolding in education sciences. We undertook a cross-reading of this literature to build a conceptual framework identifying the key concepts to study training for research question development: scaffolds, scaffolding objectives, learning challenges, and scaffolding practices. We then analyzed our data to identify the scaffolds mobilized in each case study, the objectives of this scaffolding, and the learning challenges of research question development considered as a scaffolding system. Finally, we characterized our scaffolding practices, i.e., the way in which we, as trainers, concretely support the learning required to achieve the challenges of research question development. Both training programs result from a continuous improvement process based on the feedback of the trainees: with such feedback and our own observations, we were thus able to identify and select the most effective teaching methods in line with our objectives to support the learning of research question development. Behind the classical scaffolding modalities identified in the literature, we chose to identify the diversity of very contextual scaffolding practices and devices used, which we then linked to our training objectives. For each program, we also detailed how these objectives relate to the larger learning challenges of research question development. This led us to formalize a generic grid, which was tested and improved by using it to describe each of our programs.

Two doctoral training programs as case studies for cross-analysis

As a public research institute, the main goals of INRAE are to produce and disseminate scientific knowledge, with a specific focus on the contribution to education and training. Given the broad field of competences within INRAE devoted to the development of agriculture, food and the environment, and its inherent multidisciplinary nature, the thesis defended may draw from extremely various disciplines, ranging from molecular biology to sociology, with a dominance of life and environmental sciences. Moreover, INRAE is a targeted research institute that works with and for various partners in higher education and research, industry, and the agricultural sector and regional governments. This means that many research projects, including doctoral research, are designed and carried out within partnerships with these various stakeholders. INRAE doctoral students are supervised by INRAE researchers, mainly within complex multidisciplinary supervision teams together with French or international academic partners.

In this context, we have developed our vision of research activity and doctoral experience (see the “ Our vision of the PhD experience and doctoral education ” section) and have been designing, improving, and leading two doctoral training programs for more than 10 years (Table  1 ), which share common postulates such as the following:

Considering the PhD process as a part of the professional trajectory.

Aiming at supporting autonomy of doctoral students through the enhancement of their capacity to defend the choices they have made to build research questions, thus also aiming at helping supervisors to adopt a companionship stance.

Considering research question development as an activity, which implies the choice of pedagogical principles based on action learning rather than knowledge transfer.

Considering diversity as an asset, we base our training programs on multidisciplinary workshops.

Nevertheless, they differ in terms of the training audience and times of training in the PhD process:

Course A is only open to doctoral students of the ACT Footnote 3 division of INRAE, whereas course B trains both doctoral students and their supervisors belonging to the different divisions of INRAE.

Doctoral students may attend course A three times during their thesis, whereas course B is designed to train doctoral students once during their thesis, at the end of the first year.

Results: scaffolding learning challenges for the development of the research question within a thesis

In this section, we present a generic framework to think about and design doctoral training programs with the aim of scaffolding the learning of research question development. It combines learning challenges (LC) faced by doctoral students and their supervisors when formulating their research question and training objectives (TO) corresponding to what the participants should learn. We also illustrate how each of these TO can be scaffolded, drawing on some examples from our training programs.

First challenge: to empower doctoral students in the development of their research question, guided by their supervisors

As a professionalization period, the PhD process is considered as a peer-learning process (Boud & Lee, 2005 ) that relies on a mentoring relationship that aims at developing the autonomy of the young researcher (Willison and O’Regan, 2007 ). Developing doctoral agency (Inouye, 2023 ) and, more specifically, promoting a subject-centered approach (Sun & Cheng, 2022 ) to research question development is the first learning challenge that we identified. We then consider that the doctoral student is the one who makes the subject evolve, who reflects and chooses the components of the research question. We divide this first learning challenge into three training objectives and various sub-objectives (see Fig.  1 ), one focused on the doctoral student, one on the supervisor, and one on their relative roles.

Training objectives set out for the challenge: “to empower doctoral students in their research question development”

First, the doctoral student needs to understand the expectations, nature, and difficulties of PhD research and, specifically, of research question development (TO1). This encompasses the sub-objective of understanding the iterative and unplanned nature of the research process as well as making it clear with their supervisor(s) how their creativity can be expressed regarding institutional or financial constraints. For many authors, problem finding or identifying and describing a research question is part of doctoral subjective creativity and a key for an original contribution to knowledge. At the same time, we observe, as other scholars (Brodin, 2018 ; Frick, 2011 ; Whitelock et al., 2008 ) have, that there is a lack of explicit expectations on creativity in doctoral education, which is then limited by scholarly traditions and institutional requirements. During research question development, “standing at the border between the known and the unknown” Footnote 4 can put doctoral students in a situation of uncertainty about their identity and purpose (Trafford & Leshem, 2009 ). For Frick ( 2011 ), doctoral becoming requires an alignment between “how students view themselves in relation to the research process of becoming a scholar (ontology), how they relate to different forms of knowledge (epistemology), how they know to obtain and create such knowledge (methodology), and how they frame their interests in terms of their values and ethics within the discipline (axiology)”. At the crossroads between these four dimensions, research question development is thus a key process that stimulates doctoral student becoming and that requires the support of supervisors so that their students can understand what is expected of them. Knowing that this can be a source of stress for doctoral students, we put the subject of “what is a research process” up for discussion between supervisors and students in course B. After discussing with other students on their perception of creativity in their thesis, students are invited to watch, together with their supervisors, a video calling for scientists to stop thinking of research as a linear process from question to answer but, instead, as a creative and eventually sinuous path (see footnote n°4). Students often express a sense of relief later on when they work with their supervisors on the second reformulation of the thesis subject. In this way, doctoral students become aware that a formulation is likely to evolve during the thesis and feel more comfortable about formulating one that is in no way definitive at the end of the course. In the same way, in course A, we invite the second-year PhD students to work on the transformation of their research subject in order to illustrate its evolution. We ask them to write the formulation of their subject as worded in the PhD offer or initial PhD contract and the formulation that they would use today to describe it. We then collectively work with the other PhD students at various stages in their thesis to identify the differences between the two formulations, so that the concerned second-year PhD students may explain their choices, eventualities, or constraints that led to the transformation of the subject. During debriefs, trainees express that this exercise helped them to understand that this transformation is an integral part of the research process.

This learning challenge also implies that doctoral students and their supervisors clarify their respective roles regarding research question development (TO2). The degree to which supervisors encourage doctoral students to think and act autonomously has been shown to be associated with students’ supervision satisfaction and greater research self-efficacy (Overall et al., 2011 ). This can be done firstly by clarifying the distinction between the supervisor(s)’s research project, professional career issues and those of the PhD. In course B, asking the doctoral students and their supervisors to describe and discuss the thesis supervision ecosystem has been observed as one of the crucial steps in this clarification of their respective roles in research question development. For doctoral students, research question development also implies that they take ownership of the subject, whereas it was often initially written by the supervisors. In course B, the rule “letting the student speak first” has been expressed by doctoral students as very useful for taking on the role, especially during the three workshops focused on the formulation of the thesis subject. In course A, we ask the doctoral students to present the professional context of their PhD (research project, subsidy, disciplines of the supervisors, proximity of the supervisors to the subject, etc.). This presentation helps the trainees to clarify the contextual framing of the PhD students’ theses, as well as the margin of freedom. For their part, supervisors need to let the PhD students develop their research question by themselves and find the right stance, with a careful balance between “hands-off” and “hands-on” (Gruzdev et al., 2020 ). In course B, supervisors first exchange between themselves about what it means to supervise a thesis and their role in the PhD process. The three reformulation workshops are then practical opportunities to take on this role: experiencing this role of being a support and not the leader of the PhD project is sometimes seen as difficult by supervisors who are used to being research project leaders, but they also admit that it is a necessary step to experience the supervision stance.

Supervisors also need to understand the challenges faced by PhD candidates in the development of research questions (TO3) by first abandoning the assumption of the already autonomous student (Manathunga & Goozée, 2007 ). According to Halse and Malfroy ( 2010 ), the supervisor is “responsible for recognizing and responding to the needs of different students”, within a “learning alliance” with the student. When it comes to formulating their research question, it becomes important to be able to situate their own role with their values and desires in the research process, in general, and, in particular, in the development of the research question, which is not just made up of rational intellectual choices. For this objective, supervisors have to be able to clearly identify the doctoral student’s state of progress in the development of the research question within the thesis and, more broadly, the doctoral student’s values and desires in doing research (Skakni, 2018 ). In course B, we ask them to step back and remain silent (even stolid!) when their doctoral students present their subject. While listening and writing down their observations, they foster their understanding of the states of progress and the orientations chosen by the students. With this rule, we then observe that most of them are able to adopt the correct stance for later workshops when they are asked to work with students on their research question.

Second challenge: to be aware of the various forms and processes of research question development within a diversity of ways of doing research and to be able to situate oneself in this diversity.

The second learning challenge focuses on making the PhD students (and their supervisors) aware of the diversity of ways of doing research and especially various forms and processes of research question development (see the “ Opening up the process of research question development: a diversity of approaches ” section) and situating oneself in this diversity. Many authors argue that doctoral education should highlight scientific pluralism (Pallas, 2001 ), opening the epistemological doctoral experience in order to question the implicit norm of neutrality of the positivist ideal (Charmillot, 2023 ). This is particularly true when it comes to the development of research questions for “wicked problems” (Rittel & Webber, 1973 ), i.e., economic, political, and environmental issues involving many stakeholders with different values and priorities. In this context, developing research questions often requires analysis at the crossroads between several disciplines (Bosque-Perez et al., 2016 ) and between different social stakes (Manathunga et al., 2006 ). It requires reinforcing a scientific culture favorable to this practice of multi-/inter-/transdisciplinarity (Kemp & Nurius, 2015 ), then making interdisciplinary research skills a part of graduate education (Pallas, 2001 ; Bosque-Perez et al., 2016 ). Doctoral students then have to develop their awareness about the diversity of forms and processes of research question development, requiring that they are able to understand this diversity, to know how they themselves relate to different forms of knowledge (Frick, 2011 ), and to acknowledge their performativity in the world.

Within this second learning challenge, we distinguish four training objectives (Fig.  2 ), all concerning doctoral students and their supervisors.

Training objectives for the challenge: “to be aware of the diversity of ways of doing research, to be able to situate oneself in this diversity”

Both of them need to understand and respect the diversity of research stances (TO4). In both of our case studies, we ensure that a diversity of disciplines is represented in each working group, and we guarantee the mutual respect among them. We facilitate the expression of all doctoral students about how they are developing their research question, thus illustrating the diversity of research stances. During the hot debrief of course A, trainees regularly point out the discovery of this diversity as a positive outcome, which helps them to situate their own work. Moreover, discussing research question development within small and heterogeneous groups in terms of disciplines is experienced by participants as a strength “to take a step back and clarify key points” (student, course B, 2017), acknowledging that “working with other disciplines, it helped us to refocus and clarify the subject” (supervisor, course B, 2023).

Doctoral students and their supervisors also need to be able to formulate questions and clearly explain the doctoral research project, especially the way they develop their research question, whatever their discipline may be (TO5). This is why active participation is required in the workshops in both case studies, putting doctoral students and supervisors in the position of an active learner, not a passive trainee. Since such workshops may be very demanding for the PhD student and might be emotionally intense, it is of utmost importance that the trainers carefully manage the collective discussion, guaranteeing trust, mutual respect, and achieving balance in speaking. In particular, doctoral students and their supervisors are the ones who know the scientific community(ies) to which they will contribute and are the only ones who can assess the relevance of the subject. Participants are then asked to question the PhD students without calling the relevance of their theses into question. When aiming at promoting the expression of PhD students as human subjects , trainers have to pay particular attention to the fact that participants do not reformulate the subject for the students but, on the contrary, help them to open up the possibilities, to sort out, and to clarify the status of the elements presented. Trainers also use expression modes such as the questioning forms (open/closed questions), the subject pronouns used (I/we), and the origin of the arguments or events expressed by the PhD student as points of vigilance for managing the group discussion and as levers to go deeper into the questioning and analysis of the PhD students’ thinking about their research questions.

They both have to examine (in their own research and that of others) the place of stakeholders in the development of the research question (TO6). In course A, we use the conceptual framework of translation from Callon ( 1984 ) to analyze how a social problem can be translated into a research question. In course B, the framework given to trainees to develop their research question specifically points out the distinction to be made between the academic research stakes and the stakes for society. They also have to understand how the diversity of ways of scientific knowledge production perform or do not perform in problematic situations (TO7).

Third learning challenge: to know how to develop their research question throughout the research process

The third learning challenge concerns the staggered process of formulation of the research question throughout the PhD process. For many authors, the formulation of a “researchable question” or “research conceptualization” (Badenhorst, 2021 ) by the doctoral student is the first step in the doctoral research process with the writing, and sometimes formal presentation, of a “research proposal”. It is often seen as a threshold in the doctoral journey (Chatterjee-Padmanabhan & Nielsen, 2018 ) and a key feature of “doctorateness”, combining gaps in knowledge, contributions to knowledge, research questions, conceptual frameworks, and research design (Trafford & Leshem, 2009 ). For Frick ( 2011 ), the preparation of a proposal requires background reading and “demarcation of the research question”. It consists in knowing to which scientific issues the thesis will contribute and in identifying the relevant disciplinary concepts. Mastering the various modes of communication in the development of a research question is of utmost importance for PhD students, enabling them to accurately formulate their research question (Lim, 2014 ), as well as to take most of their supervisors’ or other researchers’ (colleagues, reviewers) feedback into consideration (Carter & Kumar, 2017 ). More widely, knowing how to formulate their research question is not sufficient without being able to step back from their own formulation. Boch ( 2023 ) expresses it as a necessary reflexivity in research writing, which means becoming aware of oneself in research and integrating this experience into the writing in an argumentative and convincing way. Stepping back from their research question also puts forward the need for doctoral students to be clear about the translations and reductions made (Callon, 1984 ), their research strategies (Inouye, 2023 ), or research stances (Hazard et al., 2020 ).

This learning challenge includes three training objectives (Fig.  3 ), two of them concerning the doctoral student and the third one concerning the students and their supervisors.

Training objectives for the challenge: “to know how to express their research question throughout the research process”

Doctoral students must clearly lay out the research stakes (both academic and for society) throughout their thesis process (TO8). In course B, we give learners a framework to think about and discuss research question development as a combination of three main ingredients (operational and scientific stakes, research question, strategy), requiring that students make the difference between the scientific stakes and the thesis objective clear, while defining the scope of the thesis within broader issues (European project, lab project). In course A, the conceptual framework of the translation from Callon is useful to recognize the driving forces of the reductions and translations in order to identify them and their consequences on the formulation of the research question. It helps clarify their research practices and understand how they contribute to the development of the research strategy, beyond what has been done so far. In course A, we use a trajectory to identify the consistency and the sense of the various research practices of the 3 rd year PhD students. In course B, the “research strategy,” viewed both as a “realized” and “planned” one (Mintzberg, 1987 ), is useful as both a hindsight (what have been my choices so far?) and planning tool (how to reach my research objective as I can express it today?), allowing students to put the weight of their thesis schedule into perspective.

In order to progress in their reflection, the doctoral students need to understand the importance of different oral and written (scientific or not) communications for making the formulation of their research question evolve (TO9). In course A, when designing the trajectory of the 3 rd year PhD students, we question them about their scientific communications or articles and about the consequences they had on the evolution of the formulation of their research question. We also ask them about the impact of the different feedback they had at the time of these communications and articles (from peers, from supervisors and other researchers, and from stakeholders) on the development of their research question. In course B, there are three exercises focused on the research question. While being considered as difficult, these exercises are also seen by trainees as effective for training themselves in expressing (orally and then on a written basis) their own subject and receiving feedback and questions from other students and their supervisors. We can observe that research questions and soundness of argumentation deeply evolve throughout the week, to the great satisfaction of students and their supervisors.

Doctoral students, as well as their supervisors for the research carried out under their responsibility, have to understand and explain the consequences of research question choices on the ways knowledge produced in the thesis could be used in the real world (TO10). In course A, we use a heuristic tool to help PhD students to understand the relevance for action of the knowledge they generate (Hazard et al., 2020 ).

Discussion: enriching peer-learning scaffolding to support the development of a research question as a dialogical process

Learning how to build a research question is traditionally seen as a one-to-one learning process based on informal and daily transmission between a novice and a senior researcher. In order to open up this informal process, we have grounded our pedagogical strategy in multiple opportunities for dialog with peers, whether it be other students, supervisors, or trainers. Taken as a whole, it thus combines interdisciplinarity, peer-learning, and dialogical principles that result in the construction of an “overall distributed scaffolding strategy” (Belland, 2014 ) and that create synergy between peer scaffolding, one-to-one and media scaffolding (Belland, 2014 ).

Firstly, our case studies emphasize speaking and argumentation skills rather than writing competencies. Many research works like Zuber-Skerritt and Knight ( 1986 ), Maher et al. ( 2013 ), Kumar and Aitchison ( 2018 ), and Badenhorst ( 2021 ) have explored the needs and modalities of doctoral education in terms of writing, even from the supervisor’s perspective (González-Ocampo & Castelló, 2018 ). Our pedagogical choice contrasts with this focus on doctoral writing since we give trainees many dialogical opportunities to train themselves to orally express and defend their intellectual autonomy. Doing so, we join Cahusac de Caux et al. ( 2017 ) who argue, “peer feedback and discussion benefits students by helping them verbalise their internal reflective thinking, fostering reflective practice skills development”. Even if we use some media-based scaffoldings, tools are not at the core of our case studies: our objective is instead to help trainees to put their thoughts into words, in line with the cognitive apprenticeship of Austin ( 2009 ), referring to a specific kind of apprenticeship for the less easily observed processes of thinking.

Secondly, our training programs make the most of the diversity and heterogeneity of peers, whether they be more or less experienced in supervision, from various disciplines, or at different stages of their thesis, thus enriching peer-learning scaffolding. All the participants, in their capacity as scientists, are considered as peers who are able to understand the work of other researchers, regardless of the discipline and the thesis subject. It is also by striving to understand and question subjects that are sometimes far from their field of research that researchers acquire the capacity for analysis, synthesis, and hindsight that is necessary in research work. By setting up dialogical spaces to help inexperienced researchers hone their argumentation skills, our training programs implement our view of research in practical terms as a collective process and of doctoral education as a professional socialization process, thus requiring that research organizations facilitate collective practices in the workplace (Malfroy, 2005 ). Moreover, with the inherent heterogeneity of participants, these workshops also constitute places where the multidisciplinarity and plurality of the sciences are experienced firsthand, convergent with Manathunga et al. ( 2006 ) or Bosque-Perez et al. ( 2016 ). Doing so, we are taking part in the debate of whether scaffolds need to contain domain-specific knowledge (Belland, 2014 ) by saying that there is no need for discipline or domain-specific scaffolds. Moreover, being active on one’s own case as well as on others’ situations is an efficient training strategy to move away from the objects and routines of a discipline or community when expressing ideas between specialists. Such collective reflexivity, sometimes turning into an analysis of professional practices, is a classic vocational training principle known to enhance the development of professionalization in the long term. What we add in our training sessions is the heterogeneity of participants, which is a resource for reflexivity, but that has to be carefully managed.

Thirdly, trainees are considered as human subjects engaged in their PhD with their various motivations and professional projects, which can strongly impact the way they see their thesis and envision their research work (Skakni, 2018 ), as well as their affinities and values, their doubts, and fears. Thanks to our focus on oral exchanges, we are then able to reveal and deal with these subjective dimensions of PhD work, which are often hidden when training PhD students in scientific writing. More precisely, expressing one’s doctoral experience and professional situations experienced is known as an efficient scaffolding practice within the collaborative reflective writing of “learning journals” with peer feedback (Boldrini & Cattaneo, 2014 ). We have shown how to implement such scaffolding in small groups of doctoral students with the facilitation of experienced researchers.

However, our proposal requires that some binding conditions be met:

Learning to formulate a research question through dialog with peers requires spending time, in our case, 4 full days, within small groups to ensure that everyone can take part in it and take advantage of the feedback of others.

This dialog is made possible and emphasized by the diversity of participants (either in terms of discipline, stage of the thesis, experience, etc.).

Managing both the human and scientific conditions of this dialog requires reflexive and open-minded trainers that adopt a facilitating stance.

As a result, our perspective on scaffolding is not merely an issue of training technique but, on the contrary, a situated perspective that echoes the view of Nielsen ( 2008 ) on training “both as part of a social practice and as part of the learner’s trajectory of participation”, within an expansive process inspired by Engeström’s work. With this developmental view on doctoral experience, we acknowledge that research question development is a process that goes beyond the limited time of a 4-day training program. Trainee feedback collected after their participation in course A or B revealed that they continue the work begun during the training programs, on the basis of the given scaffolding (e.g., “I feel that we familiarized ourselves with these tools [referring to the concepts of translation and reduction] because we work on them and I started to think. […] I know these tools will remain in my head until I write my thesis and that I really learned a lot” Hot debrief, course A, 2016). It is also not rare that trainees mention their participation in course A or B to their PhD steering committees as having helped to frame/define their research question. Course A or B is also frequently mentioned as an essential support in acknowledgement of their PhD thesis. Although limited in time, the training programs studied in this article act as an accelerator in research question development (e.g. course B “we saved several months”, supervisor, 2017, “In just 2 days, everything became much clearer and more focused”, student, 2021). We thus assume that they contribute to awareness and reflexivity on research activity and to the professional development of trainees, which is particularly crucial in France with the pressure put on thesis duration and the absence of formal recognition of the research proposal stage.

Our experience puts forward two avenues for future research. Firstly, bringing together doctoral students at different stages of their thesis and then offering them the opportunity to participate each year of their PhD process opens a window on to their intellectual trajectory and a situated adjustment of our scaffolding practices. Secondly, training doctoral supervisors—and trainers involved in these doctoral programs—remains of utmost importance to make scaffolding last and be adapted throughout the next months and years.

This study examined the learning challenges and objectives required for the task of research question development throughout the PhD process, both for doctoral students and their supervisors. We have drawn some lessons for the scaffolding of these challenges and objectives from two different doctoral training programs that we have been designing and leading for more than 10 years.

Considering the development of a research question as a dialogical process, we suggest three conditions to scaffold these learnings: firstly, offering many dialogical opportunities is an effective way for students to train themselves to express their intellectual autonomy and to defend their research project; secondly, making the most of the diversity and heterogeneity of peers, whether they be more or less experienced in supervision, from various disciplines, or at different stages of their thesis, thus enriching peer-learning scaffolding, proved to be beneficial when the multidisciplinarity and plurality of the sciences are experienced firsthand; and finally, giving priority to oral communication allows trainers and trainees to reveal and deal with the subjective dimensions of PhD work and their various motivations and professional projects that always underlie the development of a research question. Taken as a whole, our work seriously rises to the challenge of training reflexive researchers with an acute awareness of the collective nature of research and an intellectual openness to the plurality of sciences.

INRAE, the French public research institute devoted to the development of agriculture, food and the environment ( https://www.inrae.fr/en ), continuously hosts some 2000 PhD students.

For example, in the UK, writing and defending a research proposal allows a Transfer of Status from an initial probationary status to that of a full doctoral candidate (Inouye, 2020 ), whereas in France, there is no such formal assessment.

The ACT research division of INRAE aims at understanding and supporting transformative changes in socio-ecosystems and agrifood systems, which take actors’ practices and strategies into account in order to promote sustainable innovations and transitions, particularly at the territorial level.

As Uri Alon puts it in his TED video: “Why science demands a leap into the unknown” https://www.ted.com/talks/uri_alon_why_science_demands_a_leap_into_the_unknown .

Alvesson M., & Sandberg, J. (2013). Constructing research questions: Doing interesting research . (1st ed). SAGE Publications Ltd.

Archbald, D. (2008). Research versus problem solving for the education leadership doctoral thesis: Implications for form and function. Educational Administration Quarterly, 44 (5), 704–739. https://doi.org/10.1177/0013161X07313288

Article   Google Scholar  

Austin, A. E. (2009). Cognitive apprenticeship theory and its implications for doctoral education: A case example from a doctoral program in higher and adult education. International Journal for Academic Development, 14 (3), 173–183. https://doi.org/10.1080/13601440903106494

Badenhorst, C. (2021). Research conceptualization in doctoral and master’s research writing. Writing & Pedagogy, 12 (2–3), 423–444. https://doi.org/10.1558/wap.19542

Belland, B. R. (2014). Scaffolding: Definition, current debates, and future directions. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Éds.), Handbook of Research on Educational Communications and Technology (4th ed., pp. 505–518). Springer-Verlag.

Boch, F. (2023). Quelles conceptions de la réflexivité dans l’écriture de recherche? Savoir de quel je on parle. Revue internationale de pédagogie de l’enseignement supérieur, 39(1). https://journals.openedition.org/ripes/4552

Boldrini, E., & Cattaneo, A. (2014). Scaffolding collaborative reflective writing in a VET curriculum. Vocations and Learning, 7 (2), 145–165. https://doi.org/10.1007/s12186-014-9110-3

Bosque-Pérez, N. A., Klos, P. Z., Force, J. E., Waits, L. P., Cleary, K., Rhoades, P., Galbraith, S. M., Brymer, A. L. B., O’Rourke, M., Eigenbrode, S. D., Finegan, B., Wulfhorst, J. D., Sibelet, N., & Holbrook, J. D. (2016). A pedagogical model for team-based, problem-focused interdisciplinary doctoral education. BioScience, 66 (6), 477–488. https://doi.org/10.1093/biosci/biw042

Boud, D., & Lee, A. (2005). ‘Peer learning’ as pedagogic discourse for research education. Studies in Higher Education, 30 (5), 501–516. https://doi.org/10.1080/03075070500249138

Brodin, E. M. (2018). The stifling silence around scholarly creativity in doctoral education: Experiences of students and supervisors in four disciplines. Higher Education, 75 (4), 655–673. https://doi.org/10.1007/s10734-017-0168-3

Cahusac de Caux, B. K. C. D., Lam, C. K. C., Lau, R., Hoang, C. H., & Pretorius, L. (2017). Reflection for learning in doctoral training: Writing groups, academic writing proficiency and reflective practice. Reflective Practice, 18 (4), 463–473. https://doi.org/10.1080/14623943.2017.1307725

Callon, M. (1984). Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St Brieuc Bay. The Sociological Review, 32 (1), 196–233. https://doi.org/10.1111/j.1467-954X.1984.tb00113.x

Cardoso, S., Santos, S., Diogo, S., Soares, D., & Carvalho, T. (2022). The transformation of doctoral education : A systematic literature review. Higher Education, 84 (4), 885–908. https://doi.org/10.1007/s10734-021-00805-5

Carter, S., & Kumar, V. (2017). ‘Ignoring me is part of learning’: Supervisory feedback on doctoral writing. Innovations in Education and Teaching International, 54 (1), 68–75. https://doi.org/10.1080/14703297.2015.1123104

Charmillot, M. (2023). Les implications éthiques de l’accompagnement doctoral. Revue internationale de pédagogie de l’enseignement supérieur, 39(1). https://journals.openedition.org/ripes/4454

Chatterjee-Padmanabhan, M., & Nielsen, W. (2018). Preparing to cross the research proposal threshold: A case study of two international doctoral students. Innovations in Education and Teaching International, 55 (4), 417–424. https://doi.org/10.1080/14703297.2016.1251331

Christensen, K. K. (2005). Bologna seminar doctoral programmes for the European Knowledge Society . Salzburg, 3–5 February 2005, General Rapporteur’s Report, pp. 10.  https://www.ehea.info/cid102053/doctoral-degree-salzburg-2005.html

Einstein, A., & Infeld, L. (1971). The evolution of physics . Cambridge University Press.

Google Scholar  

Filliettaz, L. (2011). Collective guidance at work : A resource for apprentices? Journal of Vocational Education & Training., 63 (3), 485–504. https://doi.org/10.1080/13636820.2011.580359

Frick, L. (2011). Facilitating creativity in doctoral education : A resource for supervisors. In V. Kumar & A. Lee (Eds.), Doctoral Education in International Context : Connecting Local, Regional, and Global Perspectives (pp. 123–137). Penerbit Universiti Putra Malaysia Press.

González-Ocampo, G., & Castelló, M. (2018). Writing in doctoral programs: Examining supervisors’ perspectives. Higher Education, 76 (3), 387–401. https://doi.org/10.1007/s10734-017-0214-1

Gruzdev, I., Terentev, E., & Dzhafarova, Z. (2020). Superhero or hands-off supervisor? An empirical categorization of PhD supervision styles and student satisfaction in Russian universities. Higher Education, 79 (5), 773–789. https://doi.org/10.1007/s10734-019-00437-w

Halse, C., & Malfroy, J. (2010). Retheorizing doctoral supervision as professional work. Studies in Higher Education, 35 (1), 79–92. https://doi.org/10.1080/03075070902906798

Hazard, L., Cerf, M., Lamine, C., Magda, D., & Steyaert, P. (2020). A tool for reflecting on research stances to support sustainability transitions. Nature Sustainability, 3 , 89–95. https://doi.org/10.1038/s41893-019-0440-x

Inouye, K. (2020). Becoming a scholar : Feedback, writing, and the doctoral research proposal . University of Oxford.

Inouye, K. (2023). Developing the PhD thesis project in relation to individual contexts A multiple case study of five doctoral researchers. Higher Education, 85, 1143–1160. https://doi.org/10.1007/s10734-022-00882-0

Kemp, S. P., & Nurius, P. S. (2015). Preparing emerging doctoral scholars for transdisciplinary research : A developmental approach. Journal of Teaching in Social Work, 35 (1–2), 131–150. https://doi.org/10.1080/08841233.2014.980929

Kobayashi, S. (2014). Learning dynamics in doctoral supervision . Doctoral thesis, Copenhague, Faculty of Science, Department of Science Education.  https://www.ind.ku.dk/publikationer/inds_skriftserie/2014-38/SofieKobayashi_Thesis.pdf

Kumar, V., & Aitchison, C. (2018). Peer facilitated writing groups: A programmatic approach to doctoral student writing. Teaching in Higher Education, 23 (3), 360–373. https://doi.org/10.1080/13562517.2017.1391200

Lim, J. (2014). Formulating research questions in experimental doctoral dissertations on Applied Linguistics. English for Specific Purposes, 35 , 66–88. https://doi.org/10.1016/j.esp.2014.02.003

Lin, T.-C., Hsu, Y.-S., Lin, S.-S., Changlai, M.-L., Yang, K.-Y., & Lai, T.-L. (2012). A review of empirical evidence on scaffolding for science education. International Journal of Science and Mathematics Education, 10 (2), 437–455. https://doi.org/10.1007/s10763-011-9322-z

Locke, K., & Golden-Biddle, K. (1997). Constructing opportunities for contribution: Structuring intertextual coherence and ‘problematizing’ in organizational studies. The Academy of Management Journal, 40 (5), 1023–1062. https://doi.org/10.2307/256926

Lonka, K., Ketonen, E., Vekkaila, J., Cerrato Lara, M., & Pyhältö, K. (2019). Doctoral students’ writing profiles and their relations to well-being and perceptions of the academic environment. Higher Education, 77 (4), 587–602. https://doi.org/10.1007/s10734-018-0290-x

Maher, M., Fallucca, A., & MulhernHalasz, H. (2013). Write on! Through to the PhD: Using writing groups to facilitate doctoral degree progress. Studies in Continuing Education, 35 (2), 193–208. https://doi.org/10.1080/0158037X.2012.736381

Malfroy, J. (2005). Doctoral supervision, workplace research and changing pedagogic practices. Higher Education Research & Development, 24 (2), 165–178. https://doi.org/10.1080/07294360500062961

Manathunga, C., & Goozée, J. (2007). Challenging the dual assumption of the ‘always/already’ autonomous student and effective supervisor. Teaching in Higher Education, 12 (3), 309–322. https://doi.org/10.1080/13562510701278658

Manathunga, C., Lant, P., & Mellick, G. (2006). Imagining an interdisciplinary doctoral pedagogy. Teaching in Higher Education, 11 (3), 365–379. https://doi.org/10.1080/13562510600680954

Mei, W. S. (2006). Creating a contrastive rhetorical stance: Investigating the strategy of problematization in students’ argumentation. RELC Journal, 37 (3), 329–353. https://doi.org/10.1177/0033688206071316

Mintzberg, H. (1987). The strategy concept I: Fives Ps for strategy. California Management Journal, 30 (1), 11–24. https://doi.org/10.2307/41165263

Nielsen, K. (2008). Scaffold instruction at the workplace from a situated perspective. Studies in Continuing Education, 30 (3), 247–261. https://doi.org/10.1080/01580370802439888

Overall, N. C., Deane, K. L., & Peterson, E. R. (2011). Promoting doctoral students’ research self-efficacy: Combining academic guidance with autonomy support. Higher Education Research & Development, 30 (6), 791–805. https://doi.org/10.1080/07294360.2010.535508

Pallas, A. M. (2001). Preparing education doctoral students for epistemological diversity. Educational researcher, 30 (5), 1–6. https://www.jstor.org/stable/3594455 .

Parker-Jenkins, M. (2018). Problematising ethnography and case study: Reflections on using ethnographic techniques and researcher positioning. Ethnography and Education, 13 (1), 18–33. https://doi.org/10.1080/17457823.2016.1253028

PoyatosMatas, C. (2012). Doctoral education and skills development An international perspective. REDU Revista de Docencia Universitaria, 10 (2), 163–191. https://doi.org/10.4995/redu.2012.6102

Puntambekar, S., & Hubscher, R. (2005). Tools for scaffolding students in a complex learning environment : What have we gained and what have we missed? Educational Psychologist, 40 (1), 1–12. https://doi.org/10.1207/s15326985ep4001_1

Rennie, K., & Kinsella, N. (2020). Supporting transformational learning processes for person-centred healthcare research in doctoral education A critical creative reflection. International Practice Development Journal, 10 (1), 10. https://doi.org/10.19043/ipdj.101.010

Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4 , 155–169. https://doi.org/10.1007/BF01405730

Shapin, S. (2010). Never pure: Historical studies of science as if it was produced by people with bodies, situated in time, space, culture, and society, and struggling for credibility and authority . Johns Hopkins University Press.

Skakni, I. (2018). Reasons, motives and motivations for completing a PhD : A typology of doctoral studies as a quest. Studies in Graduate and Postdoctoral Education, 9 (2), 197–212. https://doi.org/10.1108/SGPE-D-18-00004

Sun, X., & Cheng, M. W. T. (2022). Conceptualising manifestations and shapers of doctoral student agency : A subject-centered approach. Journal of Further and Higher Education, 46 (7), 1–15. https://doi.org/10.1080/0309877X.2021.2020222

Tabak, I. (2004). Synergy : A complement to emerging patterns of distributed scaffolding. The journal of the Learning Sciences, 13 (3), 305–335. https://www.jstor.org/stable/1466940 .

Trafford, V., & Leshem, S. (2009). Doctorateness as a threshold concept. Innovations in Education and Teaching International, 46 (3), 305–316. https://doi.org/10.1080/14703290903069027

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes . Harvard University Press.

Whitelock, D., Faulkner, D., & Miell, D. (2008). Promoting creativity in PhD supervision : Tensions and dilemmas. Thinking Skills and Creativity, 3 (2), 143–153. https://doi.org/10.1016/j.tsc.2008.04.001

Willison, J., & O’Regan, K. (2007). Commonly known, commonly not known, totally unknown : A framework for students becoming researchers. Higher Education Research & Development, 26 (4), 393–409. https://doi.org/10.1080/07294360701658609

Wood, D., Bruner, J., & Ross, G. (1976). The role of tutoring in problem-solving. Journal of Child Psychology and Psychiatry, 17 , 89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x

Xypas, C., & Robin, J.-Y. (2010). La dimension existentielle dans la construction du problème en recherche doctorale. Recherches en éducation, 9. https://doi.org/10.4000/ree.4670

Yin, R. K. (2018). Case study research and applications: design and methods. (6th ed). Los Angeles: SAGE.

Zuber-Skerritt, O., & Knight, N. (1986). Problem definition and thesis writing. Higher Education, 15 (1), 89–103. https://doi.org/10.1007/BF00138094

Download references

Author information

Authors and affiliations.

ACT Department, INRAE, UMR AGIR, Chemin de Borde Rouge, Castanet-Tolosan, 31326, France

Nathalie Girard

ACT Department, INRAE, UR Ecodéveloppement, Avignon, France

Aurélie Cardona

ACT Department, INRAE, Clermont-Ferrand, France

Cécile Fiorelli

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Nathalie Girard .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Girard, N., Cardona, A. & Fiorelli, C. Learning how to develop a research question throughout the PhD process: training challenges, objectives, and scaffolds drawn from doctoral programs for students and their supervisors. High Educ (2024). https://doi.org/10.1007/s10734-024-01258-2

Download citation

Accepted : 24 June 2024

Published : 15 July 2024

DOI : https://doi.org/10.1007/s10734-024-01258-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Doctoral training
• Research question
• Problem finding
• Supervision
• Scaffolding
• Peer-learning
• Find a journal
• Publish with us
• Track your research

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Questionnaire Design | Methods, Question Types & Examples

Questionnaire Design | Methods, Question Types & Examples

Published on 6 May 2022 by Pritha Bhandari . Revised on 10 October 2022.

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.

Table of contents

Questionnaires vs surveys, questionnaire methods, open-ended vs closed-ended questions, question wording, question order, step-by-step guide to design, frequently asked questions about questionnaire design.

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 .

Prevent plagiarism, run a free check.

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

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

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.

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

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.

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

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.

• How do you feel about open science?
• How would you describe your personality?
• 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 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.

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.

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.

• 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?
• 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.

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.

Strongly Agree Agree Undecided Disagree Strongly Disagree

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

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.

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

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

Randomisation

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.

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 .

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.

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.

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.

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.

Questionnaires can be self-administered or researcher-administered.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, October 10). Questionnaire Design | Methods, Question Types & Examples. Scribbr. Retrieved 22 July 2024, from https://www.scribbr.co.uk/research-methods/questionnaire-design/

Is this article helpful?

Pritha Bhandari

Other students also liked, doing survey research | a step-by-step guide & examples, what is a likert scale | guide & examples, reliability vs validity in research | differences, types & examples.

• Turnitin Guides
• Administrator hub
• Release notes and known issues
• Welcome to Turnitin Guides

Welcome to Turnitin’s new website for guidance!

In 2024, we migrated our comprehensive library of guidance from https://help.turnitin.com to this site, guides.turnitin.com. During this process we have taken the opportunity to take a holistic look at our content and how we structure our guides.

This page is here to help you orientate yourself with these changes and update your resources

What's new?

We have restructured the content to help you navigate it more efficiently.

We are consolidating numerous pages to make our individual guides more valuable as well as removing duplicated content.

For example, our Similarity Report guidance on help.turnitin is repeated in numerous places to cater for each individual integration and license type. On guides.turnitin this content will exist in a single place to allow for users of all integrations and licenses to find it easily. We have made slight modifications to these guides to help you understand which guides are pertinent to you and your institution.

Our guidance search has greatly improved

As a result of our content restructure, the search functionality for guides.turnitin has improved. Use the search bar at the top of any page to locate the guidance you’re searching for.

Dedicated student and administrator guidance hubs

Visit the Student hub area to locate student guidance. For students who access Turnitin via an LMS or VLE, check out the subsection Submitting to Turnitin .

Visiting the Administrator hub area to locate administrator guidance and release notes. 

iThenticate and Crossref Similarity Check guidance is now located on a separate site

To improve the experience for our iThenticate and Crossref Similiarity Check customers we have move their help content onto a separate help site, guides.ithenticate.com . This will improve the search for all users.

We have also created an orientation page for this site to help users become acclimatised.

Some guidance is no longer grouped within the LMS umbrella

Some guidance which was previously provided under each LMS has been moved to sections that reflect those workflows’ outcomes. Use the table below as a cheatsheet to quickly locate guidance.

Articles in this section

• Turnitin release notes
• Integrations release notes

• Resume and Cover Letter
• 47 Accomplishment Examples...

47 Accomplishment Examples for Your Resume: Expert Picks

13 min read · Updated on July 04, 2024

The right accomplishments for your resume can make all the difference in the world

When employers look at your resume, what do they see? Do they see a dependable, run-of-the-mill employee with a track record of fulfilling responsibilities, or a dynamic, results-oriented worker with real, measurable accomplishments? Truly compelling resumes will always showcase you as that second type of worker - and for good reason. That's why it's so important to know how to create great accomplishments for your resume.

In this post, we'll explain what resume accomplishments are designed to do and why it's so important to include good accomplishments in your resume. We'll also offer some advice about where and how you should include them and provide tips to help you highlight your own accomplishments. Finally, we'll examine 47 examples of job accomplishments for a resume.

What are resume accomplishments?

So, what are resume accomplishments, anyway? Put simply, they're things that you've achieved in your previous jobs. In fact, achievement is another word for accomplishments on a resume. However, it's important to understand that achievements are not simply things that you did. Instead, they are quantifiable - measurable - accomplishments that added real value to your team and employer.

Of course, many people simply list their job duties in their resume and, for some professions, that may be all you need to land the job. However, most employers are less interested in reading some dry recitation of your previous job responsibilities than they are in seeing concrete examples of how you can benefit their company. Using the right accomplishments for your resume can help to illustrate that benefit.

The reality is that every job candidate with work experience has had job responsibilities. If that's all you list on your resume, then you'll always struggle to stand out from your competition. By including the right accomplishments for your resume, you can deliver a more compelling narrative that showcases you as the best person for the job.

Use numbers to showcase value

You might be wondering what we mean by “quantifiable” or “measurable” accomplishments. The easiest way to understand this concept is to think in terms of real numbers that demonstrate value. For example, you could list an accomplishment that looks like this:

Led a team of salespeople

Any hiring manager who looked at that achievement might be impressed that you were in some type of leadership role, but they would also likely wonder what you did with that responsibility. What impact did you have on the team's success? Now consider this example:

Successfully led a 12-person sales team that increased quarterly sales by 12%, reduced client attrition by 32%, and enhanced division profitability by 19%

If you were a hiring manager, which one of those examples would capture your interest? The second one, right? And the reason why is easy to understand; by including real numbers that demonstrate real value, you can help the employer better understand the benefits you can provide to their company.

Why is it important to include accomplishments in your resume?

To fully understand why accomplishments for your resume are so important, let's summarize some of the key benefits that they can provide for your job search efforts. For example:

Using real numbers is the best way to demonstrate your impact

While some accomplishments may not always be easy to quantify in this way, any achievement that illustrates real value is always going to grab attention. Those real numbers that you use to measure your results are a powerful way to showcase the positive impact you've made throughout your career.

Quantifiable achievements are more likely to make a real impression

It's also important to ensure that your resume makes a great first impression - and one that lasts longer than the time it takes to read your resume summary. Carefully crafted accomplishments for your resume can help you to ensure that you make the right impression, so that employers who read your resume walk away thinking about the type of value you can add to their enterprise.

Including these types of accomplishments showcases your professionalism

Of course, the simple act of taking the time to write down your measurable accomplishments will say a lot about your commitment to professionalism. When an employer reads these types of resume achievements, they will recognize you as someone who is committed to results. They'll also have confidence that you understand the importance of producing real, measurable value.

Where and how do you include resume achievements?

Before we look at our tips for creating accomplishments for your resume, it might be a good idea to focus on the best place to list these achievements and the right format to use. There are only two good places to incorporate these accomplishments in your resume. The first place is within the body of your resume summary paragraph. The second is in your work experience section.

Including accomplishments for your resume in your summary

Your summary statement is basically an elevator pitch that goes right below your contact information on the resume. This short statement should be designed to highlight your major qualifications and provide a “hook” that entices employers to read through the rest of your resume. By including a great measurable achievement in this paragraph, you can immediately capture that employer's attention. For example:

Dynamic Human Resources Manager with 9 years of experience in mid-size and large enterprise environments. Successfully managed employee relations in a 100-employee financial firm, reducing employee onboarding time by 15%, achieving a 92% issue-resolution rate, and reducing attrition by 22%.

As you can see, the inclusion of a measurable achievement within that summary paragraph can provide the employer with some immediate and concrete information about your capabilities and potential value. It's just enough to make any hiring manager want to learn more about the type of benefits you can provide as an employee.

How to include accomplishments for your resume in your summary

Including this type of achievement in your summary paragraph is simple. You just take one of your most notable achievements and add it to the summary section. Unlike most of the achievements listed on your resume, however, this one should just be included within the paragraph. There's no need to separate it with a bullet point. Save that for the work experience accomplishments!

Including accomplishments for your resume in your work experience section

Of course, most of the accomplishments for your resume are going to be located within your work experience section. In fact, you should plan to include several achievements for each job you list in that section. While there's no hard and fast rule for how many accomplishments you can list, it is typically a good idea to include at least three or four for each position.

How to include accomplishments for your resume in your work experience section

The process for including measurable accomplishments for your resume in your work experience section is not as difficult as it might seem. Simply add a series of achievements for each job that you've held over the last ten years, right below the basic details you provide about that position (company name, job title, dates of employment).

Make sure that you draw attention to these accomplishments by listing them in bullet point form. That will enable hiring managers to focus on each achievement and ensures that the information on your resume is easy to follow.

Tips for creating accomplishments for your resume

By now, you're probably ready to learn how to craft powerful accomplishments for your resume. We've compiled some simple tips to help you get started.

Make a list of your achievements

Before you can write a resume-ready accomplishment bullet point, you need to identify your achievements. Suitable accomplishments for your resume can include:

Making or saving the company money

Exceeding expectations

Improving customer experience

Introducing innovation

Leading a team that achieved positive, measurable results

Reducing inefficiencies

While it's tempting to focus on monetary value, it's vital to recognize that there are many ways to provide benefits to an employer. For example, you may have accomplished something that saved time, or reduced costs. These achievements can all be enticing for prospective employers.

Remember also that there are different ways to measure the value of your achievements. You may want to quantify some achievements in terms of monetary value by citing specific dollar amounts. Other achievements are best measured as a function of time or as a percentage of increase or decrease.

Include context

Each accomplishment also needs to provide enough information to give needed context to the achievement. If you simply say that you saved the company $10,000, that's not enough context to make any sort of positive impression on an employer. However, if you say that you reorganized the sales process to reduce inefficiencies, saving the company $10,000 a year, that explains not only what you did but how you did it.

Add keywords

Your accomplishments can also be a great place to include relevant keywords in your resume. You can find those keywords in the job posting you're targeting, since they are primarily related to things like skills and other qualifications. 

Make sure that you use those keywords exactly as you find them in the job posting. That can help to ensure that any applicant tracking system, or ATS , that the company is using will find your resume. This automated screening process will scan your resume in search of those keywords, so including them is one of the easiest ways to improve your likelihood of success.

Challenge, Action, Result

To further simplify this stage, you can use a simple step-by-step process that is often used in job interviews - the CAR method. CAR stands for Challenge, Action, and Result. Simply think about the problem you were tasked with resolving, the action you took, and the results you obtained.

Accomplishments for your resume: 47 examples

And now for the main event: our sample list of accomplishments for a resume. Below are 47 examples of achievements you can use in your resume, separated by job role. Feel free to modify and adapt any of them to your situation!

1.     Student examples

Increased readership by 23% while serving as Editor-in-Chief of university newspaper

Acquired real-world experience in an internship at XYZ Corp during senior year of college, with management praising commitment and attitude 

Established and ran a successful student events society that attracted 150 members in its first 6 months

Volunteered at a local animal hospital part-time, while maintaining 3.9 GPA

2.     Customer service examples

Resolved customer complaints with a reported 98% satisfaction rate.

Led effort to increase upsell rates by 10% in 2022 and 2023

Revitalized retention outreach program, increasing customer retention by 11% over two quarters

Implemented a customer response program that reduced response time by 20%, resulting in 22% improvement in client retention

3.     Teacher and educational examples

Introduced a knowledge-based learning program that increased class GPA by an average of 13%

Managed classrooms of 25+ students, maintaining 92% overall attendance rate

Led a school-wide effort to focus on individualized learning, which increased pass rate by 20%

Created an innovative parent-teacher online interface that improved parent engagement by 30%, with a 20% boost in student performance

4.     Marketing examples

Led a social media reorganization that increased online customer engagement by 40%

Introduced marketing campaigns that increased market share by 12%, while enhancing reported brand loyalty by 15%

Re-energized online marketing by doubling company's digital content output with new landing pages, regular blog postings, and social media interactions.

Redesigned the company website, increasing customer engagement and sales conversions by 30%

Deployed an email engagement strategy that expanded customer lists by 300% in six months, followed by a rewards program that increased sales and customer interactions by 23%

5.     Finance and accounting examples

Oversaw a team of Accountants tasked with managing a $2 million budget

Modernized the company's financial reporting processes, reducing redundancies by 23% and lowering labor input by 12%

Managed a financial team responsible for overseeing project budgets valued at more than $400,000

Led a training process that onboarded more than 40 new Bank Tellers over a five-year period

6.     Project manager examples

Oversaw implementation of a performance management process that boosted productivity by 22%

Led a workplace safety overhaul effort that reduced site injuries by 33% over six months

Successfully completed 9 projects worth more than $4,000,000 over the last two years

Delivered a business-critical IT initiative within a challenging 3-month deadline, 6% under budget

7.     IT examples

Led an effort to integrate a new network system that reduced downtime and repairs by 18%

Implemented a  Lean training program that reduced labor costs by 8% while increasing profitability by 9%

Oversaw updates to obsolete equipment, replaced energy-inefficient machines, and lowered yearly energy bills by 11%

Efficiently managed the departmental budget, negotiating with vendors to reduce annual supply costs by 10%

8.     Software Programmer examples

Led a team that migrated legacy systems to new technology, improving sustainability and scalability, while reducing downtime and IT troubleshooting calls

Successfully managed troubleshooting efforts to eliminate a critical bug responsible for software crashes, reducing failures by 90% and boosting program stability

Reorganized development processes, increasing productivity by 19% through improved Developer collaboration

Managed a 12-person team of Programmers in the development of an innovative customer relationship management platform

9.     Engineer examples

Streamlined project management processes to reduce costs and increase efficiency, resulting in 22% enhancement in program turnaround times

Collaborated with a 10-person team of Junior Engineers to redesign HVAC systems for commercial real estate clients

Managed more than two-dozen projects valued at $19 million, ensuring 100% technical and regulatory compliance

Optimized workflow and worker utilization to reduce inefficiencies by 19% and increase annual production by 11%

10.  Managerial examples

Implemented workplace changes that refocused company culture on customer satisfaction and employee morale, resulting in a 28% boost in worker retention and 18% increase in revenue

Oversaw three departments during a company-wide reorganization, minimizing employee turnover and increasing profits by 22% over three quarters.

Created hybrid remote work policies and procedures that resulted in a 19% increase in retention, 17% boost in productivity, and 33% reduction in sick time

Successfully incorporated new daily shift safety meetings that resulted in a 22% increase in employee engagement, 12% boost in productivity, and 24% reduction in accidents

Led 12 design team projects to modernize the company, increasing efficiency and profitability by 17%

11.  Sales examples

Successfully led a 20-person sales team that boosted company growth by more than 24% over six months, by focusing on larger accounts and increased attention to client relationships

Consistently exceeded sales goals by 20%, while increasing client retention by 23%

Created and implemented a new training program for new sales personnel, reducing onboarding time by 42% and boosting overall sales production by 24%

Expanded company client base by 22% in six months, building profitable relationships with mid-size clients and increasing sales revenues by more than $3 million

Achieved recognition by XYZ Inc. as its Top Producer for three straight years, with client accounts valued at more than $60 million

The bottom line

Though there was a time long ago when employers would be satisfied with you listing your job duties on your resume, those days are gone. Today's companies are more interested in what you achieved for your past employers. Fortunately, that can provide you with a golden opportunity to illustrate your potential value by including the right accomplishments in your resume, showcasing measurable results that are sure to make a powerful first impression !

Need help creating and organizing those powerful accomplishments for your resume? Get your free resume review from our team of experts today and learn how they can help you to get the effective resume you need to land more interviews.

Recommended reading:

Make the Perfect Resume for a Career Change

How to Write a Resume Outline that Can Simplify the Resume Creation Process

How to Answer, “What Motivates You?” - With Examples

Related Articles:

Do Hiring Managers Actually Read Cover Letters?

How to Create a Resume With No Education

Why You Lose When You Lie on Your Resume: Learning From Mina Chang

See how your resume stacks up.

Career Advice Newsletter

Our experts gather the best career & resume tips weekly. Delivered weekly, always free.

Thanks! Career advice is on its way.

Share this article:

Let's stay in touch.

Subscribe today to get job tips and career advice that will come in handy.

Your information is secure. Please read our privacy policy for more information.

• Election 2024
• Entertainment
• Newsletters
• Photography
• AP Buyline Personal Finance
• AP Buyline Shopping
• Press Releases
• Israel-Hamas War
• Russia-Ukraine War
• Global elections
• Asia Pacific
• Latin America
• Middle East
• Election Results
• Delegate Tracker
• AP & Elections
• 2024 Paris Olympic Games
• Auto Racing
• Movie reviews
• Book reviews
• Financial Markets
• Business Highlights
• Financial wellness
• Artificial Intelligence
• Social Media

Nearly two-thirds of Democrats want Biden to withdraw, new AP-NORC poll finds

Nearly two-thirds of Democrats say President Joe Biden should withdraw from the presidential race and let his party nominate a different candidate, according to a new poll from The Associated Press-NORC Center for Public Affairs Research.

President Joe Biden speaks at a 2024 Prosperity Summit, July 16, 2024, in North Las Vegas, Nevada. (AP Photo/Ronda Churchill, File)

• Copy Link copied

FILE - President Joe Biden speaks at a 2024 Prosperity Summit, July 16, 2024, in North Las Vegas, Nev. Nearly two-thirds of Democrats say President Joe Biden should withdraw from the presidential race and let his party select a different candidate, according to a new poll by the AP-NORC Center for Public Affairs Research. It sharply undercuts his post-debate claim that “average Democrats” are still with him even if some “big names” are turning on him. (AP Photo/Ronda Churchill, File)

Follow AP’s live coverage of the 2024 presidential race.

WASHINGTON (AP) — Nearly two-thirds of Democrats say President Joe Biden should withdraw from the presidential race and let his party nominate a different candidate, according to a new poll, sharply undercutting his post-debate claim that “average Democrats” are still with him even if some “big names” are turning on him.

The new survey by the AP-NORC Center for Public Affairs Research , conducted as Biden works to salvage his candidacy two weeks after his debate flop, also found that only about 3 in 10 Democrats are extremely or very confident that he has the mental capability to serve effectively as president, down slightly from 40% in an AP-NORC poll in February .

The findings underscore the challenges the 81-year-old president faces as he tries to silence calls from within his own party to leave the race and tries to convince Democrats that he’s the best candidate to defeat Donald Trump. The poll was conducted mostly before Saturday’s assassination attempt on Trump at a campaign rally in Pennsylvania. It’s unclear whether the shooting influenced people’s views of Biden, but the small number of poll interviews completed after the shooting provided no early indication that his prospects improved.

Meanwhile, as Vice President Kamala Harris receives additional scrutiny amid the talk about whether Biden should bow out, the poll found that her favorability rating is similar to his — but the share of Americans who have an unfavorable opinion of her is slightly lower.

The poll provides some evidence that Black Democrats are among Biden’s strongest supporters, with roughly half in the survey saying he should continue running, compared to about 3 in 10 white and Hispanic Democrats. Overall, seven in 10 Americans think Biden should drop out, with Democrats only slightly less likely than Republicans and independents to say that he should make way for a new nominee.

“I do have genuine concerns about his ability to hold the office,” said Democrat Andrew Holcomb, 27, of Denver. “I think he’s frankly just too old for the job.”

AP AUDIO: Nearly two-thirds of Democrats want Biden to withdraw, new AP-NORC poll finds

AP Washington correspondent Sagar Meghani reports a new poll sharply undercuts President Biden’s claim that ‘average Democrats’ are still with him after his debate debacle.

Janie Stapleton, a 50-year-old lifelong Democrat from Walls, Mississippi, held the opposite view, saying Biden is the “best candidate” for president.

People aren’t just sour on Biden on as they size up their choices this election season.

What to know about the 2024 Election

• Read the latest: Follow AP’s live coverage of this year’s election .
• Democracy: American democracy has overcome big stress tests since 2020. More challenges lie ahead in 2024.
• AP’s Role: The Associated Press is the most trusted source of information on election night, with a history of accuracy dating to 1848. Learn more.
• Stay informed. Keep your pulse on the news with breaking news email alerts. Sign up here .

About 6 in 10 Americans want Trump to withdraw -- but relatively few Republicans are in that camp.

As for Biden, younger Democrats are especially likely to want to see him bow out – and to say they’re dissatisfied with him. Three-quarters of Democrats under the age of 45 want Biden to drop out, compared to about 6 in 10 of those who are older.

“I just feel like these two individuals are a sad choice,” said Alexi Mitchell, 35, a civil servant who lives in Virginia. She identifies as a Democratic-leaning independent, and while she thinks Biden is probably still mentally up to the job, she worries that the past few weeks’ unraveling of support makes him a weak candidate, no matter what happens next. “If he doesn’t have control over his own party, that’s a fatal flaw,” she said. “He’s put us in a bad position where Trump might win.”

Despite bullish talk from the Biden campaign heading into the debate, the faceoff only left the president in a deeper hole. Democrats are slightly more likely to say they’re dissatisfied with Biden as their nominee now than they were before his halting performance. About half are dissatisfied, an uptick from about 4 in 10 in an AP-NORC poll from June .

By contrast, most Republicans – about 6 in 10 – came out of the debate very or somewhat satisfied with Trump as their candidate. Too few interviews were conducted after the assassination attempt to provide a clear indication of whether Republicans or Americans overall have rallied further around Trump since then.

David Parrott, a Democrat from Soddy-Daisy, Tennessee, was willing to give Biden the benefit of the doubt given the president’s age, but he still voiced concerns about a potential second term.

“I don’t know if he can make it another four years or not,” said Parrott, a 58-year-old retiree. “Shouldn’t he be sitting at his beach house taking it easy?”

All of the recent churn has left Americans much more likely to think Trump is capable of winning the 2024 election than is Biden – 42% to 18%. About a quarter thought the the two men equally capable of winning.

Even Democrats are relatively dour about their party’s prospects come November.

Only about a third of Democrats believe Biden is more capable of winning than is Trump. About 3 in 10 think the two are equally capable of winning and 16% say victory is more likely to go to the Republican. By contrast, Republicans are overwhelmingly convinced that Trump is in the best position to win.

Trump also has the edge on Biden when Americans consider who is most capable of handling a crisis, 38% to 28%. And people are about equally divided on which candidate has the better vision for the country, with 35% saying Biden and 34% Trump.

For all of the disenchantment Biden is up against, the president insists it’s not too late to turn things around, saying past presidents have come back from a deficit at this stage in the campaign. In an interview Tuesday with BET News , he said many voters haven’t focused yet, adding, “The point is, we’re just getting down to gametime right now.”

The poll did also offer a bright spot for Biden: 40% of adults say he’s more honest than Trump, while about 2 in 10 think the opposite.

Most Democrats — around 6 in 10 — say that Vice President Harris would make a good president, while 22% think not and 2 in 10 don’t know enough to say. The poll showed that 43% of U.S. adults have a favorable opinion of her, while 48% have an unfavorable opinion. Somewhat more have a negative view of Biden: approximately 6 in 10 Americans.

The survey was conducted before Trump selected freshman Sen. JD Vance of Ohio as his running mate. It showed that for most Americans, Vance is still an unknown. Six in 10 don’t know enough about him to form an opinion, while 17% have a favorable view and 22% view him negatively.

The poll of 1,253 adults was conducted July 11-15, 2024, using a sample drawn from NORC’s probability-based AmeriSpeak Panel, which is designed to be representative of the U.S. population. The margin of sampling error for all respondents is plus or minus 3.8 percentage points.

BREAKING: Secret Service Director Kimberly Cheatle resigns after assassination attempt on Donald Trump, two sources say

Poll: Debate aftermath damages Biden and Democratic Party — but one-on-one matchup with Trump is unchanged

CORRECTION (July 14, 8:46 p.m. ET): Due to an error with the original polling documents, a previous version of this article misstated the vote shares and margins for the multicandidate ballot test. Former President Donald Trump leads President Joe Biden by 3 points in the six-way ballot test, not the other way around.

A new national NBC News poll — conducted after President Joe Biden ’s bad debate performance and before a gunman fired at former President Donald Trump and rallygoers in Pennsylvania on Saturday — found the   presidential contest remaining stable and competitive, with Biden trailing former President   Donald Trump by 2 points in the survey.

The result was well within the poll’s margin of error and had the same margin as April’s survey.

Still, the survey showed the toll the debate and its aftermath took on the president and his party — though it's unclear how public sentiment in a shocked nation will shift in the wake of Saturday's shooting.

In the poll, more than 60% of Democrats said they would   prefer someone else as the party’s presidential nominee. Almost 80% of all voters reported   having concerns about Biden’s mental and physical fitness.

And the popularity of the Democratic Party declined, matching its all-time low in the three-decade history of the NBC News poll.

Yet   the 2024 head-to-head matchup was relatively unchanged, at least   for that moment — partly because of voter sentiment about not just Biden but also Trump. That included   a majority of voters continuing to   hold negative views of the former president, while   Trump faced deficits versus Biden   on the questions of   temperament and being honest and trustworthy.

Another reason for the unchanged race is the degree to which political partisans remained locked in on their choices, with 71% of respondents saying the debate made no difference in how they will vote in November.

And a third explanation is that the debate only confirmed voters’ previous perceptions of Biden, says Republican pollster Bill McInturff of Public Opinion Strategies, who conducted this survey with Democrat Jeff Horwitt of Hart Research Associates.

“Numbers change when new information is presented,” McInturff said. “The voters have been trying to tell us for a very long time that they have concerns about Biden serving a second term.”

Horwitt notes that the lack of change in the race is a “consequential” story — and one that’s not favorable to Biden’s campaign.

“[Biden’s] numbers were already low. And coming out of the debate, the trajectory of the race has not changed,” Horwitt said. “That’s the bigger story coming out of the debate.”

Most Democrats — 62% — would prefer another party leader

This poll, conducted July 7-9, came as a number of prominent   Democrats and Hollywood personalities called for the president to exit the race, though many Democrats and key segments of the party’s base are   standing behind Biden.

(The poll was also conducted before Biden’s news conference Thursday and rally Friday in Detroit.)

In the survey, just 33% of Democratic voters and Democratic-leaning independents said they were satisfied with Biden as their party’s presidential nominee, versus 62% who say they would have preferred someone else.

By contrast, 71% of Republican voters and GOP-leaning independents say they were satisfied with Trump as the Republican nominee, compared with 27% who would have preferred someone else.

Additionally, a combined 79% of all voters — including 61% of Democrats — say they had "major" or "moderate" concerns that Biden, at age 81, might not have the necessary mental and physical health to be president. That is up from the 74% of all voters who said that in the September 2023 News poll and from the 76% who said it in January.

That compares with 58% of voters who said they had major or moderate concerns about Trump being convicted on 34 felony counts in New York, and with 50% who had concerns about Trump, at age 78, not having the mental and physical health to be president.

And the poll found Biden losing ground to Trump on key presidential attributes since the debate.

Trump held a 29-point lead over Biden on which candidate was better seen as having the necessary mental and physical health to be president — up from Trump’s 19-point advantage on this question in the April NBC News poll.

By a 49%-31% margin, voters picked Trump as better than Biden on being competent and effective. In April’s poll, Trump’s edge here was 11 points. (And in NBC’s 2020 polling, it was Biden with a 9-point lead on this question.)

Biden, meanwhile, held a 16-point advantage over Trump on having the right temperament to be president, as well as a 15-point lead on being honest and trustworthy. (But in 2020, Biden held larger leads over Trump on these qualities, especially on temperament.)

The stable Biden-Trump (and Harris-Trump) race

Despite those changes since the debate, the poll showed a continuation of a stable race between Biden and Trump.

In a head-to-head matchup, Trump led Biden by 2 points among registered voters, 45% to 43%, which is within the poll’s margin of error of plus or minus 3.5 percentage points.

In April’s poll, the contest stood at Trump 46%, Biden 44%.

A combined 12% of voters in the current survey said they prefer a different candidate, wouldn’t vote or are undecided between Biden and Trump — higher than in any other NBC News poll this election cycle.

Horwitt, the Democratic pollster, said it’s noteworthy that Trump’s numbers hadn't changed since the debate. “While the focus is on Biden and his struggles at this stage, Donald Trump has not moved the race more to his advantage either,” he said.

In the matchup, Trump enjoyed advantages among men (52% to 36%), white voters (52% to 38%) and white voters without college degrees (62% to 29%).

Biden was ahead among Black voters (69% to 12%), Latinos (54% to 38%), whites with college degrees (52% to 36%), women (50% to 39%) and independents (39% to 30%).

The candidates were essentially tied among voters ages 18 to 34 (Trump 43%, Biden 41%).

In a hypothetical matchup featuring Trump running against Vice President Kamala Harris — if Biden were no longer the presumptive Democratic nominee — the former Republican president also led Harris by a 2-point margin, 47% to 45%.

(See here for a breakdown of where Harris runs stronger and weaker than Biden does among certain demographics.)

And in a contest featuring third-party candidates, the NBC News poll found Trump at 40% among registered voters, Biden at 37%, independent Robert F. Kennedy Jr. at 10%, Jill Stein at 3%, Libertarian nominee Chase Oliver at 2% and Cornel West at   1%.

The close congressional ballot was essentially unchanged, with 47% of voters preferring a Democratic-controlled Congress and 46% wanting Republicans in charge. (In April, it was 47% GOP, 46% Democratic.)

Biden’s approval rating drops to 40%

The poll found Biden’s job rating standing at 40% approval, 58% disapproval — down slightly from 42% approval, 56% disapproval in April, though the movement is within the margin of error.

In the history of the NBC News poll, that 40% approval rating for Biden was lower than every other recent first-term president had in the summer before their re-election — except for George H.W. Bush, whose approval rating stood   at 34% at this point in 1992 before he lost.

Biden’s rating was 1 point lower than Trump’s 41%   approval rating   in the July 2020 NBC poll, before Trump's failed   re-election bid.

“Biden looks more like the two incumbents who lost rather than any incumbent who won,” said McInturff, the GOP pollster.

Unpopular candidates, unpopular parties — even an unpopular first lady

What also stood out in the poll was the unpopularity of all political figures and institutions the survey measured — from Biden and Trump to the U.S. Supreme Court and even the first lady.

The most unpopular figure or institution in the new NBC News poll, in terms of net positive-negative rating, was the Democratic Party, with 31% of voters holding positive views of the party, versus 50% who holding negative views.

That net rating for the Democratic Party (-19) tied the party’s all-time low mark in the history of the NBC News poll. The party’s rating was 37% positive, 47% negative (-10) in April.

That was followed by Harris (-18), Biden (-17), Trump (-15), the Republican Party (-14), Kennedy (-12) and the U.S. Supreme Court (-12).

Also holding a net-negative rating in the poll was first lady Jill Biden, with 31% of voters seeing her positively, compared with 41% viewing her negatively (-10).

That’s a precipitous drop for the first lady from the only other time the NBC News poll measured her, in January 2021 right before her husband took office.

In that poll, she held a 40% positive, 26% negative rating (+14).

The NBC News poll of 800 registered voters — 660 reached via cellphone — was conducted July 7-9, and it has an overall margin of error of plus-minus 3.5 percentage points.

Mark Murray is a senior political editor at NBC News.