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• Questionnaire Design | Methods, Question Types & Examples

Questionnaire Design | Methods, Question Types & Examples

Published on July 15, 2021 by Pritha Bhandari . Revised on June 22, 2023.

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, other interesting articles, frequently asked questions about questionnaire design.

A survey is a research method where you collect and analyze 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 cleansing and analysis, and interpretation.

Sampling is important in survey research because you’ll often aim to generalize 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 minimizing these will help you avoid several types of research bias , including sampling bias , ascertainment bias , and undercoverage bias .

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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 through mail. All questions are standardized 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 nonresponse 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 analyze 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 generalizable 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 scale questions collect ordinal data using rating scales with 5 or 7 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 scales , 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 for 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 summarizing responses to these questions can take a lot of time and resources. You’ll need to develop a systematic coding scheme to categorize 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 research 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 counter argument 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 favor 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-barreled questions. Double-barreled 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?

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

Logical flow

Using a logical flow to your question order means starting with simple questions, such as behavioral 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 minimize order effects because they can be a source of systematic error or bias in your study.

Randomization

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

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

Step 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 operationalize your variables of interest into questionnaire items. Operationalizing 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 incentivized 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 randomize questions. Randomizing 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 .

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.

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 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.

You can organize 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. Randomization can minimize 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.

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Questionnaires

Questionnaires can be classified as both, quantitative and qualitative method depending on the nature of questions. Specifically, answers obtained through closed-ended questions (also called restricted questions) with multiple choice answer options are analyzed using quantitative methods. Research findings in this case can be illustrated using tabulations, pie-charts, bar-charts and percentages.

Answers obtained to open-ended questionnaire questions (also known as unrestricted questions), on the other hand, are analyzed using qualitative methods. Primary data collected using open-ended questionnaires involve discussions and critical analyses without use of numbers and calculations.

There are following types of questionnaires:

Computer questionnaire . Respondents are asked to answer the questionnaire which is sent by mail. The advantages of the computer questionnaires include their inexpensive price, time-efficiency, and respondents do not feel pressured, therefore can answer when they have time, giving more accurate answers. However, the main shortcoming of the mail questionnaires is that sometimes respondents do not bother answering them and they can just ignore the questionnaire.

Telephone questionnaire .  Researcher may choose to call potential respondents with the aim of getting them to answer the questionnaire. The advantage of the telephone questionnaire is that, it can be completed during the short amount of time. The main disadvantage of the phone questionnaire is that it is expensive most of the time. Moreover, most people do not feel comfortable to answer many questions asked through the phone and it is difficult to get sample group to answer questionnaire over the phone.

In-house survey .  This type of questionnaire involves the researcher visiting respondents in their houses or workplaces. The advantage of in-house survey is that more focus towards the questions can be gained from respondents. However, in-house surveys also have a range of disadvantages which include being time consuming, more expensive and respondents may not wish to have the researcher in their houses or workplaces for various reasons.

Mail Questionnaire . This sort of questionnaires involve the researcher to send the questionnaire list to respondents through post, often attaching pre-paid envelope. Mail questionnaires have an advantage of providing more accurate answer, because respondents can answer the questionnaire in their spare time. The disadvantages associated with mail questionnaires include them being expensive, time consuming and sometimes they end up in the bin put by respondents.

Questionnaires can include the following types of questions:

Open question questionnaires . Open questions differ from other types of questions used in questionnaires in a way that open questions may produce unexpected results, which can make the research more original and valuable. However, it is difficult to analyze the results of the findings when the data is obtained through the questionnaire with open questions.

Multiple choice question s. Respondents are offered a set of answers they have to choose from. The downsize of questionnaire with multiple choice questions is that, if there are too many answers to choose from, it makes the questionnaire, confusing and boring, and discourages the respondent to answer the questionnaire.

Dichotomous Questions .  Thes type of questions gives two options to respondents – yes or no, to choose from. It is the easiest form of questionnaire for the respondent in terms of responding it.

Scaling Questions . Also referred to as ranking questions, they present an option for respondents to rank the available answers to questions on the scale of given range of values (for example from 1 to 10).

For a standard 15,000-20,000 word business dissertation including 25-40 questions in questionnaires will usually suffice. Questions need be formulated in an unambiguous and straightforward manner and they should be presented in a logical order.

Questionnaires as primary data collection method offer the following advantages:

• Uniformity: all respondents are asked exactly the same questions
• Cost-effectiveness
• Possibility to collect the primary data in shorter period of time
• Minimum or no bias from the researcher during the data collection process
• Usually enough time for respondents to think before answering questions, as opposed to interviews
• Possibility to reach respondents in distant areas through online questionnaire

At the same time, the use of questionnaires as primary data collection method is associated with the following shortcomings:

• Random answer choices by respondents without properly reading the question.
• In closed-ended questionnaires no possibility for respondents to express their additional thoughts about the matter due to the absence of a relevant question.
• Collecting incomplete or inaccurate information because respondents may not be able to understand questions correctly.
• High rate of non-response

Survey Monkey represents one of the most popular online platforms for facilitating data collection through questionnaires. Substantial benefits offered by Survey Monkey include its ease to use, presentation of questions in many different formats and advanced data analysis capabilities.

Survey Monkey as a popular platform for primary data collection

There are other alternatives to Survey Monkey you might want to consider to use as a platform for your survey. These include but not limited to Jotform, Google Forms, Lime Survey, Crowd Signal, Survey Gizmo, Zoho Survey and many others.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of quantitative methods. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words.

John Dudovskiy

Questionnaire Method In Research

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A questionnaire is a research instrument consisting of a series of questions for the purpose of gathering information from respondents. Questionnaires can be thought of as a kind of written interview . They can be carried out face to face, by telephone, computer, or post.

Questionnaires provide a relatively cheap, quick, and efficient way of obtaining large amounts of information from a large sample of people.

Data can be collected relatively quickly because the researcher would not need to be present when completing the questionnaires. This is useful for large populations when interviews would be impractical.

However, a problem with questionnaires is that respondents may lie due to social desirability. Most people want to present a positive image of themselves, and may lie or bend the truth to look good, e.g., pupils exaggerate revision duration.

Questionnaires can effectively measure relatively large subjects’ behavior, attitudes, preferences, opinions, and intentions more cheaply and quickly than other methods.

Often, a questionnaire uses both open and closed questions to collect data. This is beneficial as it means both quantitative and qualitative data can be obtained.

Closed Questions

A closed-ended question requires a specific, limited response, often “yes” or “no” or a choice that fit into pre-decided categories.

Data that can be placed into a category is called nominal data. The category can be restricted to as few as two options, i.e., dichotomous (e.g., “yes” or “no,” “male” or “female”), or include quite complex lists of alternatives from which the respondent can choose (e.g., polytomous).

Closed questions can also provide ordinal data (which can be ranked). This often involves using a continuous rating scale to measure the strength of attitudes or emotions.

For example, strongly agree / agree / neutral / disagree / strongly disagree / unable to answer.

Closed questions have been used to research type A personality (e.g., Friedman & Rosenman, 1974) and also to assess life events that may cause stress (Holmes & Rahe, 1967) and attachment (Fraley, Waller, & Brennan, 2000).

• They can be economical. This means they can provide large amounts of research data for relatively low costs. Therefore, a large sample size can be obtained, which should represent the population from which a researcher can then generalize.
• The respondent provides information that can be easily converted into quantitative data (e.g., count the number of “yes” or “no” answers), allowing statistical analysis of the responses.
• The questions are standardized. All respondents are asked exactly the same questions in the same order. This means a questionnaire can be replicated easily to check for reliability . Therefore, a second researcher can use the questionnaire to confirm consistent results.

Limitations

• They lack detail. Because the responses are fixed, there is less scope for respondents to supply answers that reflect their true feelings on a topic.

Open Questions

Open questions allow for expansive, varied answers without preset options or limitations.

Open questions allow people to express what they think in their own words. Open-ended questions enable the respondent to answer in as much detail as they like in their own words. For example: “can you tell me how happy you feel right now?”

Open questions will work better if you want to gather more in-depth answers from your respondents. These give no pre-set answer options and instead, allow the respondents to put down exactly what they like in their own words.

Open questions are often used for complex questions that cannot be answered in a few simple categories but require more detail and discussion.

Lawrence Kohlberg presented his participants with moral dilemmas. One of the most famous concerns a character called Heinz, who is faced with the choice between watching his wife die of cancer or stealing the only drug that could help her.

Participants were asked whether Heinz should steal the drug or not and, more importantly, for their reasons why upholding or breaking the law is right.

• Rich qualitative data is obtained as open questions allow respondents to elaborate on their answers. This means the research can determine why a person holds a certain attitude .
• Time-consuming to collect the data. It takes longer for the respondent to complete open questions. This is a problem as a smaller sample size may be obtained.
• Time-consuming to analyze the data. It takes longer for the researcher to analyze qualitative data as they have to read the answers and try to put them into categories by coding, which is often subjective and difficult. However, Smith (1992) has devoted an entire book to the issues of thematic content analysis that includes 14 different scoring systems for open-ended questions.
• Not suitable for less educated respondents as open questions require superior writing skills and a better ability to express one’s feelings verbally.

Questionnaire Design

With some questionnaires suffering from a response rate as low as 5%, a questionnaire must be well designed.

There are several important factors in questionnaire design.

Pilot Study

Question order.

Questions should progress logically from the least sensitive to the most sensitive, from the factual and behavioral to the cognitive, and from the more general to the more specific.

The researcher should ensure that previous questions do not influence the answer to a question.

Question order effects

• Question order effects occur when responses to an earlier question affect responses to a later question in a survey. They can arise at different stages of the survey response process – interpretation, information retrieval, judgment/estimation, and reporting.
• Types of question order effects include: unconditional (subsequent answers affected by prior question topic), conditional (subsequent answers depend on the response to the prior question), and associational (correlation between two questions changes based on order).
• Question order effects have been found across different survey topics like social and political attitudes, health and safety studies, vignette research, etc. Effects may be moderated by respondent factors like age, education level, knowledge and attitudes about the topic.
• To minimize question order effects, recommendations include avoiding judgmental dependencies, separating potentially reactive questions, randomizing questions, following good survey design principles, considering respondent characteristics, and intentionally examining question context and order.

Terminology

• There should be a minimum of technical jargon. Questions should be simple, to the point, and easy to understand. The language of a questionnaire should be appropriate to the vocabulary of the group of people being studied.
• Use statements that are interpreted in the same way by members of different subpopulations of the population of interest.
• For example, the researcher must change the language of questions to match the social background of the respondent’s age / educational level / social class/ethnicity, etc.

Presentation

Ethical issues.

• The researcher must ensure that the information provided by the respondent is kept confidential, e.g., name, address, etc.
• This means questionnaires are good for researching sensitive topics as respondents will be more honest when they cannot be identified.
• Keeping the questionnaire confidential should also reduce the likelihood of psychological harm, such as embarrassment.
• Participants must provide informed consent before completing the questionnaire and must be aware that they have the right to withdraw their information at any time during the survey/ study.

Problems with Postal Questionnaires

At first sight, the postal questionnaire seems to offer the opportunity to get around the problem of interview bias by reducing the personal involvement of the researcher. Its other practical advantages are that it is cheaper than face-to-face interviews and can quickly contact many respondents scattered over a wide area.

However, these advantages must be weighed against the practical problems of conducting research by post. A lack of involvement by the researcher means there is little control over the information-gathering process.

The data might not be valid (i.e., truthful) as we can never be sure that the questionnaire was completed by the person to whom it was addressed.

That, of course, assumes there is a reply in the first place, and one of the most intractable problems of mailed questionnaires is a low response rate. This diminishes the reliability of the data

Also, postal questionnaires may not represent the population they are studying. This may be because:

• Some questionnaires may be lost in the post, reducing the sample size.
• The questionnaire may be completed by someone not a member of the research population.
• Those with strong views on the questionnaire’s subject are more likely to complete it than those without interest.

Benefits of a Pilot Study

A pilot study is a practice / small-scale study conducted before the main study.

It allows the researcher to try out the study with a few participants so that adjustments can be made before the main study, saving time and money.

It is important to conduct a questionnaire pilot study for the following reasons:

• Check that respondents understand the terminology used in the questionnaire.
• Check that emotive questions are not used, as they make people defensive and could invalidate their answers.
• Check that leading questions have not been used as they could bias the respondent’s answer.
• Ensure the questionnaire can be completed in an appropriate time frame (i.e., it’s not too long).

Frequently Asked Questions 

How do psychological researchers analyze the data collected from questionnaires.

Psychological researchers analyze questionnaire data by looking for patterns and trends in people’s responses. They use numbers and charts to summarize the information.

They calculate things like averages and percentages to see what most people think or feel. They also compare different groups to see if there are any differences between them.

By doing these analyses, researchers can understand how people think, feel, and behave. This helps them make conclusions and learn more about how our minds work.

Are questionnaires effective in gathering accurate data?

Yes, questionnaires can be effective in gathering accurate data. When designed well, with clear and understandable questions, they allow individuals to express their thoughts, opinions, and experiences.

However, the accuracy of the data depends on factors such as the honesty and accuracy of respondents’ answers, their understanding of the questions, and their willingness to provide accurate information. Researchers strive to create reliable and valid questionnaires to minimize biases and errors.

It’s important to remember that while questionnaires can provide valuable insights, they are just one tool among many used in psychological research.

Can questionnaires be used with diverse populations and cultural contexts?

Yes, questionnaires can be used with diverse populations and cultural contexts. Researchers take special care to ensure that questionnaires are culturally sensitive and appropriate for different groups.

This means adapting the language, examples, and concepts to match the cultural context. By doing so, questionnaires can capture the unique perspectives and experiences of individuals from various backgrounds.

This helps researchers gain a more comprehensive understanding of human behavior and ensures that everyone’s voice is heard and represented in psychological research.

Are questionnaires the only method used in psychological research?

No, questionnaires are not the only method used in psychological research. Psychologists use a variety of research methods, including interviews, observations , experiments , and psychological tests.

Each method has its strengths and limitations, and researchers choose the most appropriate method based on their research question and goals.

Questionnaires are valuable for gathering self-report data, but other methods allow researchers to directly observe behavior, study interactions, or manipulate variables to test hypotheses.

By using multiple methods, psychologists can gain a more comprehensive understanding of human behavior and mental processes.

What is a semantic differential scale?

The semantic differential scale is a questionnaire format used to gather data on individuals’ attitudes or perceptions. It’s commonly incorporated into larger surveys or questionnaires to assess subjective qualities or feelings about a specific topic, product, or concept by quantifying them on a scale between two bipolar adjectives.

It presents respondents with a pair of opposite adjectives (e.g., “happy” vs. “sad”) and asks them to mark their position on a scale between them, capturing the intensity of their feelings about a particular subject.

It quantifies subjective qualities, turning them into data that can be statistically analyzed.

Ayidiya, S. A., & McClendon, M. J. (1990). Response effects in mail surveys. Public Opinion Quarterly, 54 (2), 229–247. https://doi.org/10.1086/269200

Fraley, R. C., Waller, N. G., & Brennan, K. A. (2000). An item-response theory analysis of self-report measures of adult attachment. Journal of Personality and Social Psychology, 78, 350-365.

Friedman, M., & Rosenman, R. H. (1974). Type A behavior and your heart . New York: Knopf.

Gold, R. S., & Barclay, A. (2006). Order of question presentation and correlation between judgments of comparative and own risk. Psychological Reports, 99 (3), 794–798. https://doi.org/10.2466/PR0.99.3.794-798

Holmes, T. H., & Rahe, R. H. (1967). The social readjustment rating scale. Journal of psychosomatic research, 11(2) , 213-218.

Schwarz, N., & Hippler, H.-J. (1995). Subsequent questions may influence answers to preceding questions in mail surveys. Public Opinion Quarterly, 59 (1), 93–97. https://doi.org/10.1086/269460

Smith, C. P. (Ed.). (1992). Motivation and personality: Handbook of thematic content analysis . Cambridge University Press.

Further Information

• Questionnaire design and scale development
• Questionnaire Appraisal Form

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Questionnaire: The ultimate guide, advantages & examples

What is a Questionnaire?

A questionnaire is a research instrument that consists of a set of questions or other types of prompts that aims to collect information from a respondent. A research questionnaire is typically a mix of close-ended questions  and  open-ended questions .

Open-ended, long-form questions offer the respondent the ability to elaborate on their thoughts. Research questionnaires were developed in 1838 by the Statistical Society of London.

LEARN ABOUT: Candidate Experience Survey

The data collected from a data collection questionnaire can be both  qualitative  as well as  quantitative  in nature. A questionnaire may or may not be delivered in the form of a  survey , but a survey always consists of a questionnaire.

LEARN ABOUT: Testimonial Questions

Advantages of a good questionnaire design

• With a survey questionnaire, you can gather a lot of data in less time.
• There is less chance of any bias(like selection bias ) creeping if you have a standard set of questions to be used for your target audience. You can apply logic to questions based on the respondents’ answers, but the questionnaire will remain standard for a group of respondents that fall in the same segment.
• Surveying online survey software is quick and cost-effective. It offers you a rich set of features to design, distribute, and analyze the response data.
• It can be customized to reflect your brand voice. Thus, it can be used to reinforce your brand image.
• The responses can be compared with the historical data and understand the shift in respondents’ choices and experiences.
• Respondents can answer the questionnaire without revealing their identity. Also, many survey software complies with significant data security and privacy regulations.

LEARN ABOUT: Structured Questionnaire

Characteristics of a good questionnaire

Your survey design depends on the type of information you need to collect from respondents. Qualitative questionnaires are used when there is a need to collect exploratory information to help prove or disprove a hypothesis. Quantitative questionnaires are used to validate or test a previously generated hypothesis. However, most questionnaires follow some essential characteristics:

• Uniformity:  Questionnaires are very useful to collect demographic information, personal opinions, facts, or attitudes from respondents. One of the most significant attributes of a research form is uniform design and standardization. Every respondent sees the same questions. This helps in  data collection  and  statistical analysis  of this data. For example, the  retail store evaluation questionnaire template  contains questions for evaluating retail store experiences. Questions relate to purchase value, range of options for product selections, and quality of merchandise. These questions are uniform for all customers.

LEARN ABOUT: Research Process Steps

• Exploratory:  It should be exploratory to collect qualitative data. There is no restriction on questions that can be in your questionnaire. For example, you use a data collection questionnaire and send it to the female of the household to understand her spending and saving habits relative to the household income. Open-ended questions give you more insight and allow the respondents to explain their practices. A very structured question list could limit the data collection.

LEARN ABOUT: Best Data Collection Tools

• Question Sequence:  It typically follows a structured flow of questions to increase the number of responses. This sequence of questions is screening questions , warm-up questions, transition questions, skip questions, challenging questions, and classification questions. For example, our  motivation and buying experience questionnaire template  covers initial demographic questions and then asks for time spent in sections of the store and the rationale behind purchases.

Types & Definitions

As we explored before, questionnaires can be either structured or free-flowing. Let’s take a closer look at what that entails for your surveys.

• Structured Questionnaires:  Structured questionnaires collect  quantitative data . The questionnaire is planned and designed to gather precise information. It also initiates a formal inquiry, supplements data, checks previously accumulated data, and helps validate any prior hypothesis.
• Unstructured Questionnaires:  Unstructured questionnaires collect  qualitative data . They use a basic structure and some branching questions but nothing that limits the responses of a respondent. The questions are more open-ended to collect specific data from participants.

Types of questions in a questionnaire

You can use multiple question types in a questionnaire. Using various question types can help increase responses to your research questionnaire as they tend to keep participants more engaged. The best customer satisfaction survey templates are the most commonly used for better insights and decision-making.

Some of the widely used  types of questions  are:

• Open-Ended Questions:   Open-ended questions  help collect qualitative data in a questionnaire where the respondent can answer in a free form with little to no restrictions.
• Dichotomous Questions:  The  dichotomous question  is generally a “yes/no”  close-ended question . This question is usually used in case of the need for necessary validation. It is the most natural form of a questionnaire.
• Multiple-Choice Questions:   Multiple-choice questions  are a close-ended question type in which a respondent has to select one (single-select multiple-choice question) or many (multi-select multiple choice question) responses from a given list of options. The multiple-choice question consists of an incomplete stem (question), right answer or answers, incorrect answers, close alternatives, and distractors. Of course, not all multiple-choice questions have all of the answer types. For example, you probably won’t have the wrong or right answers if you’re looking for customer opinion.
• Scaling Questions:  These questions are based on the principles of the four measurement scales –  nominal, ordinal, interval, and ratio . A few of the question types that utilize these scales’ fundamental properties are  rank order questions ,  Likert scale questions ,  semantic differential scale questions , and  Stapel scale questions .

LEARN ABOUT: System Usability Scale

• Pictorial Questions:  This question type is easy to use and encourages respondents to answer. It works similarly to a multiple-choice question. Respondents are asked a question, and the answer choices are images. This helps respondents choose an answer quickly without over-thinking their answers, giving you more accurate data.

Types of Questionnaires

Questionnaires can be administered or distributed in the following forms:

• Online Questionnaire : In this type, respondents are sent the questionnaire via email or other online mediums. This method is generally cost-effective and time-efficient. Respondents can also answer at leisure. Without the pressure to respond immediately, responses may be more accurate. The disadvantage, however, is that respondents can easily ignore these questionnaires. Read more about online surveys .
• Telephone Questionnaire:  A researcher makes a phone call to a respondent to collect responses directly. Responses are quick once you have a respondent on the phone. However, a lot of times, the respondents hesitate to give out much information over the phone. It is also an expensive way of conducting research. You’re usually not able to collect as many responses as other types of questionnaires, so your sample may not represent the broader population.
• In-House Questionnaire:  This type is used by a researcher who visits the respondent’s home or workplace. The advantage of this method is that the respondent is in a comfortable and natural environment, and in-depth data can be collected. The disadvantage, though, is that it is expensive and slow to conduct.

LEARN ABOUT: Survey Sample Sizes

• Mail Questionnaire:  These are starting to be obsolete but are still being used in some  market research studies. This method involves a researcher sending a physical data collection questionnaire request to a respondent that can be filled in and sent back. The advantage of this method is that respondents can complete this on their own time to answer truthfully and entirely. The disadvantage is that this method is expensive and time-consuming. There is also a high risk of not collecting enough responses to make actionable insights from the data.

How to design a Questionnaire

Questionnaire design is a multistep process that requires attention to detail at every step.

Researchers are always hoping that the responses received for a survey questionnaire yield useable data. If the questionnaire is too complicated, there is a fair chance that the respondent might get confused and will drop out or answer inaccurately.

LEARN ABOUT: Easy Test Maker

As a  survey creator , you may want to pre-test the survey by administering it to a focus group during development. You can try out a few different questionnaire designs to determine which resonates best with your target audience. Pre-testing is a good practice as the survey creator can comprehend the initial stages if there are any changes required in the survey .

Steps Involved in Questionnaire Design

1. identify the scope of your research:.

Think about what your questionnaire is going to include before you start designing the look of it. The clarity of the topic is of utmost importance as this is the primary step in creating the questionnaire. Once you are clear on the purpose of the questionnaire, you can begin the design process.

LEARN ABOUT:  Social Communication Questionnaire

2. Keep it simple:

The words or phrases you use while writing the questionnaire must be easy to understand. If the questions are unclear, the respondents may simply choose any answer and skew the data you collect.

3. Ask only one question at a time:

At times, a researcher may be tempted to add two similar questions. This might seem like an excellent way to consolidate answers to related issues, but it can confuse your respondents or lead to inaccurate data. If any of your questions contain the word “and,” take another look. This question likely has two parts, which can affect the quality of your data.

4. Be flexible with your options:

While designing, the survey creator needs to be flexible in terms of “option choice” for the respondents. Sometimes the respondents may not necessarily want to choose from the answer options provided by the survey creator. An “other” option often helps keep respondents engaged in the survey.

5. The open-ended or closed-ended question is a tough choice:

The survey creator might end up in a situation where they need to make distinct choices between open or close-ended questions. The question type should be carefully chosen as it defines the tone and importance of asking the question in the first place.

If the questionnaire requires the respondents to elaborate on their thoughts, an  open-ended q u estion  is the best choice. If the surveyor wants a specific response, then close-ended questions should be their primary choice. The key to asking closed-ended questions is to generate data that is easy to analyze and spot trends.

6. It is essential to know your audience:

A researcher should know their target audience. For example, if the target audience speaks mostly Spanish, sending the questionnaire in any other language would lower the response rate and accuracy of data. Something that may seem clear to you may be confusing to your respondents. Use simple language and terminology that your respondents will understand, and avoid technical jargon and industry-specific language that might confuse your respondents.

For efficient market research, researchers need a representative sample collected using one of the many  sampling techniques , such as a sample questionnaire. It is imperative to plan and define these target respondents based on the demographics  required.

7. Choosing the right tool is essential: 

QuestionPro is a simple yet advanced survey software platform that the surveyors can use to create a questionnaire or choose from the already existing 300+ questionnaire templates.

Always save personal questions for last. Sensitive questions may cause respondents to drop off before completing. If these questions are at the end, the respondent has had time to become more comfortable with the interview and are more likely to answer personal or demographic questions.

Differences between a Questionnaire and a Survey

Read more: Difference between a survey and a questionnaire

Questionnaire Examples

The best way to understand how questionnaires work is to see the types of questionnaires available. Some examples of a questionnaire are:

USE THIS FREE TEMPLATE

The above survey questions are typically easy to use, understand, and execute. Additionally, the standardized answers of a survey questionnaire instead of a person-to-person conversation make it easier to compile useable data.

The most significant limitation of a data collection questionnaire is that respondents need to read all of the questions and respond to them. For example, you send an invitation through email asking respondents to complete the questions on social media. If a target respondent doesn’t have the right social media profiles, they can’t answer your questions.

Learn More: 350+ Free Survey Examples and Templates

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Your ultimate guide to questionnaires and how to design a good one

The written questionnaire is the heart and soul of any survey research project. Whether you conduct your survey using an online questionnaire, in person, by email or over the phone, the way you design your questionnaire plays a critical role in shaping the quality of the data and insights that you’ll get from your target audience. Keep reading to get actionable tips.

What is a questionnaire?

A questionnaire is a research tool consisting of a set of questions or other ‘prompts’ to collect data from a set of respondents.

When used in most research, a questionnaire will consist of a number of types of questions (primarily open-ended and closed) in order to gain both quantitative data that can be analyzed to draw conclusions, and qualitative data to provide longer, more specific explanations.

A research questionnaire is often mistaken for a survey - and many people use the term questionnaire and survey, interchangeably.

But that’s incorrect.

Which is what we talk about next.

Get started with our free survey maker with 50+ templates

Survey vs. questionnaire – what’s the difference?

Before we go too much further, let’s consider the differences between surveys and questionnaires.

These two terms are often used interchangeably, but there is an important difference between them.

Survey definition

A survey is the process of collecting data from a set of respondents and using it to gather insights.

Survey research can be conducted using a questionnaire, but won’t always involve one.

Questionnaire definition

A questionnaire is the list of questions you circulate to your target audience.

In other words, the survey is the task you’re carrying out, and the questionnaire is the instrument you’re using to do it.

By itself, a questionnaire doesn’t achieve much.

It’s when you put it into action as part of a survey that you start to get results.

Advantages vs disadvantages of using a questionnaire

While a questionnaire is a popular method to gather data for market research or other studies, there are a few disadvantages to using this method (although there are plenty of advantages to using a questionnaire too).

Let’s have a look at some of the advantages and disadvantages of using a questionnaire for collecting data.

Advantages of using a questionnaire

1. questionnaires are relatively cheap.

Depending on the complexity of your study, using a questionnaire can be cost effective compared to other methods.

You simply need to write your survey questionnaire, and send it out and then process the responses.

You can set up an online questionnaire relatively easily, or simply carry out market research on the street if that’s the best method.

2. You can get and analyze results quickly

Again depending on the size of your survey you can get results back from a questionnaire quickly, often within 24 hours of putting the questionnaire live.

It also means you can start to analyze responses quickly too.

3. They’re easily scalable

You can easily send an online questionnaire to anyone in the world and with the right software you can quickly identify your target audience and your questionnaire to them.

4. Questionnaires are easy to analyze

If your questionnaire design has been done properly, it’s quick and easy to analyze results from questionnaires once responses start to come back.

This is particularly useful with large scale market research projects.

Because all respondents are answering the same questions, it’s simple to identify trends.

5. You can use the results to make accurate decisions

As a research instrument, a questionnaire is ideal for commercial research because the data you get back is from your target audience (or ideal customers) and the information you get back on their thoughts, preferences or behaviors allows you to make business decisions.

6. A questionnaire can cover any topic

One of the biggest advantages of using questionnaires when conducting research is (because you can adapt them using different types and styles of open ended questions and closed ended questions) they can be used to gather data on almost any topic.

There are many types of questionnaires you can design to gather both quantitative data and qualitative data - so they’re a useful tool for all kinds of data analysis.

Disadvantages of using a questionnaire

1. respondents could lie.

This is by far the biggest risk with a questionnaire, especially when dealing with sensitive topics.

Rather than give their actual opinion, a respondent might feel pressured to give the answer they deem more socially acceptable, which doesn’t give you accurate results.

2. Respondents might not answer every question

There are all kinds of reasons respondents might not answer every question, from questionnaire length, they might not understand what’s being asked, or they simply might not want to answer it.

If you get questionnaires back without complete responses it could negatively affect your research data and provide an inaccurate picture.

3. They might interpret what’s being asked incorrectly

This is a particular problem when running a survey across geographical boundaries and often comes down to the design of the survey questionnaire.

If your questions aren’t written in a very clear way, the respondent might misunderstand what’s being asked and provide an answer that doesn’t reflect what they actually think.

Again this can negatively affect your research data.

4. You could introduce bias

The whole point of producing a questionnaire is to gather accurate data from which decisions can be made or conclusions drawn.

But the data collected can be heavily impacted if the researchers accidentally introduce bias into the questions.

This can be easily done if the researcher is trying to prove a certain hypothesis with their questionnaire, and unwittingly write questions that push people towards giving a certain answer.

In these cases respondents’ answers won’t accurately reflect what is really happening and stop you gathering more accurate data.

5. Respondents could get survey fatigue

One issue you can run into when sending out a questionnaire, particularly if you send them out regularly to the same survey sample, is that your respondents could start to suffer from survey fatigue.

In these circumstances, rather than thinking about the response options in the questionnaire and providing accurate answers, respondents could start to just tick boxes to get through the questionnaire quickly.

Again, this won’t give you an accurate data set.

Questionnaire design: How to do it

It’s essential to carefully craft a questionnaire to reduce survey error and optimize your data . The best way to think about the questionnaire is with the end result in mind.

How do you do that?

Start with questions, like:

• What is my research purpose ?
• What data do I need?
• How am I going to analyze that data?
• What questions are needed to best suit these variables?

Once you have a clear idea of the purpose of your survey, you’ll be in a better position to create an effective questionnaire.

Here are a few steps to help you get into the right mindset.

1. Keep the respondent front and center

A survey is the process of collecting information from people, so it needs to be designed around human beings first and foremost.

In his post about survey design theory, David Vannette, PhD, from the Qualtrics Methodology Lab explains the correlation between the way a survey is designed and the quality of data that is extracted.

“To begin designing an effective survey, take a step back and try to understand what goes on in your respondents’ heads when they are taking your survey.

This step is critical to making sure that your questionnaire makes it as likely as possible that the response process follows that expected path.”

From writing the questions to designing the survey flow, the respondent’s point of view should always be front and center in your mind during a questionnaire design.

2. How to write survey questions

Your questionnaire should only be as long as it needs to be, and every question needs to deliver value.

That means your questions must each have an individual purpose and produce the best possible data for that purpose, all while supporting the overall goal of the survey.

A question must also must be phrased in a way that is easy for all your respondents to understand, and does not produce false results.

To do this, remember the following principles:

Get into the respondent's head

The process for a respondent answering a survey question looks like this:

• The respondent reads the question and determines what information they need to answer it.
• They search their memory for that information.
• They make judgments about that information.
• They translate that judgment into one of the answer options you’ve provided. This is the process of taking the data they have and matching that information with the question that’s asked.

When wording questions, make sure the question means the same thing to all respondents. Words should have one meaning, few syllables, and the sentences should have few words.

Only use the words needed to ask your question and not a word more .

Note that it’s important that the respondent understands the intent behind your question.

If they don’t, they may answer a different question and the data can be skewed.

Some contextual help text, either in the introduction to the questionnaire or before the question itself, can help make sure the respondent understands your goals and the scope of your research.

Use mutually exclusive responses

Be sure to make your response categories mutually exclusive.

Consider the question:

What is your age?

Respondents that are 31 years old have two options, as do respondents that are 40 and 55. As a result, it is impossible to predict which category they will choose.

This can distort results and frustrate respondents. It can be easily avoided by making responses mutually exclusive.

The following question is much better:

This question is clear and will give us better results.

Ask specific questions

Nonspecific questions can confuse respondents and influence results.

Do you like orange juice?

• Like very much
• Neither like nor dislike
• Dislike very much

This question is very unclear. Is it asking about taste, texture, price, or the nutritional content? Different respondents will read this question differently.

A specific question will get more specific answers that are actionable.

How much do you like the current price of orange juice?

This question is more specific and will get better results.

If you need to collect responses about more than one aspect of a subject, you can include multiple questions on it. (Do you like the taste of orange juice? Do you like the nutritional content of orange juice? etc.)

Use a variety of question types

If all of your questionnaire, survey or poll questions are structured the same way (e.g. yes/no or multiple choice) the respondents are likely to become bored and tune out. That could mean they pay less attention to how they’re answering or even give up altogether.

Instead, mix up the question types to keep the experience interesting and varied. It’s a good idea to include questions that yield both qualitative and quantitative data.

For example, an open-ended questionnaire item such as “describe your attitude to life” will provide qualitative data – a form of information that’s rich, unstructured and unpredictable. The respondent will tell you in their own words what they think and feel.

A quantitative / close-ended questionnaire item, such as “Which word describes your attitude to life? a) practical b) philosophical” gives you a much more structured answer, but the answers will be less rich and detailed.

Open-ended questions take more thought and effort to answer, so use them sparingly. They also require a different kind of treatment once your survey is in the analysis stage.

3. Pre-test your questionnaire

Always pre-test a questionnaire before sending it out to respondents. This will help catch any errors you might have missed. You could ask a colleague, friend, or an expert to take the survey and give feedback. If possible, ask a few cognitive questions like, “how did you get to that response?” and “what were you thinking about when you answered that question?” Figure out what was easy for the responder and where there is potential for confusion. You can then re-word where necessary to make the experience as frictionless as possible.

If your resources allow, you could also consider using a focus group to test out your survey. Having multiple respondents road-test the questionnaire will give you a better understanding of its strengths and weaknesses. Match the focus group to your target respondents as closely as possible, for example in terms of age, background, gender, and level of education.

Note: Don't forget to make your survey as accessible as possible for increased response rates.

Questionnaire examples and templates

There are free questionnaire templates and example questions available for all kinds of surveys and market research, many of them online. But they’re not all created equal and you should use critical judgement when selecting one. After all, the questionnaire examples may be free but the time and energy you’ll spend carrying out a survey are not.

If you’re using online questionnaire templates as the basis for your own, make sure it has been developed by professionals and is specific to the type of research you’re doing to ensure higher completion rates. As we’ve explored here, using the wrong kinds of questions can result in skewed or messy data, and could even prompt respondents to abandon the questionnaire without finishing or give thoughtless answers.

You’ll find a full library of downloadable survey templates in the Qualtrics Marketplace , covering many different types of research from employee engagement to post-event feedback . All are fully customizable and have been developed by Qualtrics experts.

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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.

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Surveys & Questionnaires

Surveys involve asking a series of questions to participants. They can be administered online, in person, or remotely (e.g. by post/mail). The data collected can be analysed quantitatively or qualitatively (or both). Researchers might carry out statistical surveys to make statistical inferences about the population being studied. Such inferences depend strongly on the survey questions used (Solomon, 2001) meaning that getting the wording right is crucial. For this reason, many test out surveys in pilot studies with smaller populations and use the results to refine their survey instrument.

Sampling for surveys can range between self-selection (e.g. where a link is shared with members of a target population in the hope they and others contribute data and share the survey) through to the use of specialised statistical techniques (“probability sampling”) that analyse results from a carefully selected sample to draw statistical conclusions about the wider population. Survey methodologies therefore cover a range of considerations including sampling, research instrument design, improving response rates, ensuring quality in data, and methods of analysis (Groves et al., 2011).

One common question format is to collect quantitative data alongside qualitative questions. This allows a more detailed description or justification for the answer given to be provided. Collecting ordinal data (e.g. ranking of preferences through a Likert scale) can be a way to make qualitative data more amenable to quantitative analysis. But there is no one superior approach: the crucial thing is that the survey questions and their phrasing aligns with the research question(s) correctly.

Surveys are widely used in education science and in the social sciences more generally. Surveys are highly efficient (both in terms of time and money) compared with other methods, and can be administered remotely. They can provide a series of data points on a subject which can be compared across the sample group(s). This provides a considerable degree of flexibility when it comes to analysing data as several variables may be tested at once. Surveys also work well when used alongside other methods, perhaps to provide a baseline of data (such as demographics) for the first step in a research study. They are also commonly used in evaluations of teaching & learning (i.e. after an intervention to assess the impact). However, there are some noteworthy disadvantages to using surveys. Respondents may not feel encouraged to provide accurate answers, or may not feel comfortable providing answers that present themselves in a unfavourable manner (particularly if the survey is not anonymous). “Closed” questions may have a lower validity rate than other question types as they might be interpreted differently. Data errors due to question non-responses may exist creating bias. Survey answer options should be selected carefully because they may be interpreted differently by respondents (Vehovar & Katja Lozar, 2008).

Surveys & Questionnaires: GO-GN Insights

Marjon Baas collected quantitative data through a questionnaire among teachers within an OER Community of Practice to explore the effect of the activities undertaken to encourage the use of the community on teachers’ behaviour in relation to OER.

“I used several theoretical models (Clements and Pawlowski, 2012; Cox and Trotter, 2017; Armellini and Nie, 2013) to conceptualise different aspects (that relate to) OER adoption. This enabled me as a researcher to design my specific research instruments.”

Judith Pete had a deliberate selection of twelve Sub-Saharan African universities across Kenya, Ghana and South Africa with randomly sampled students and lecturers to develop a representative view of OER. Separate questionnaires were used for students (n=2249) and lecturers (n=106).

“We used surveys to collect data across three continents. Online survey tools were very helpful in online data collection and, where that was not possible, local coordinators used physical copies of the survey and later entered the information into the database. This approach was cost-effective, versatile and quick and easy to implement. We were able to reach a wide range of respondents in a short time. Sometimes we wondered, though, whether all those who responded had enough time to fully process and understand the questions that they were being asked. We had to allocate a significant amount of time to curating the data afterwards.”

Samia Almousa adopted Unified Theory of Acceptance and Use of Technology (UTAUT) survey questionnaire, along with additional constructs (relating to information quality and culture) as a lens through which her research data is analysed.

“In my research, I have employed a Sequential Explanatory Mixed Methods Design (online questionnaires and semi-structured interviews) to examine the academics’ perceptions of OERs integration into their teaching practices, as well as to explore the motivations that encourage them to use and reuse OERs, and share their teaching materials in the public domain. The online questionnaire was an efficient and fast way to reach a large number of academics. I used the online survey platform, which does not require entering data or coding as data is input by the participants and answers are saved automatically (Sills & Song, 2002). Using questionnaires as a data collection tool has some drawbacks. In my study, the questionnaire I developed was long, which made some participants choose their answers randomly. In addition, I have received many responses from academics in other universities although the questionnaire was sent to the sample university. Since I expected this to happen, I required the participants to write the name of their university in the personal information section of the questionnaire, then excluded the responses from outside the research sample. My advice for any researcher attempting to use questionnaires as a data collection tool is to ensure that their questionnaire is as short and clear as possible to help the researcher in analysing the findings and the participants in answering all questions accurately. Additionally, personal questions should be as few as possible to protect the identity and privacy of the participants, and to obtain the ethical approval quickly.”

Olawale Kazeeem Iyikolakan adopted a descriptive survey of the correlational type. The author research design examines the relationship among the key research variables (technological self-efficacy, perception, and use of open educational resources) and to identify the most significant factors that influence academic performance of LIS undergraduates without a causal connection.

“The descriptive research design is used as a gathering of information about prevailing conditions or situations for the purpose of description and interpretation (Aggarwal, 2008). My research design examines the relationship among the key research variables (technological self-efficacy, perception, and use of open educational resources) to identify the most significant factors that influence academic performance of Library & Information Science undergraduates without a causal connection. Ponto (2015) describes that descriptive survey research is a useful and legitimate  approach to research that has clear benefits in helping to describe and explore variables and constructs of interest by using quantitative research strategies (e.g., using a survey with numerically rated items. “The reason for the choice of descriptive survey research instead of ex-post-facto quasi-experimental design is that this type of research design is used to capture people’s perceptions, views, use, about a current issue, current state of play or movements such as perception and use of OER. This research design comes with several merits as it enables the researcher to obtain the needed primary data directly from the respondents. Other advantages include: (1) Using this method, the researcher has no control over the variable; (2) the researcher can only report what has happened or what is happening. One of the demerits of this type of research design is that research results may reflect a certain level of bias due to the absence of statistical tests.”

Useful references for Surveys & Questionnaires: Aggarwal (2008); Fowler (2014); Groves et al., 2011); Lefever, Dal & Matthíasdóttir (2007); Ponto (2015); Sills & Song (2002); Solomon (2001); Vehovar & Manfreda (2008); Vehovar, Manfreda, & Berzelak (2018)

Research Methods Handbook Copyright © 2020 by Rob Farrow; Francisco Iniesto; Martin Weller; and Rebecca Pitt is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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This chapter discusses (1) the important role of research questions for descriptive, predictive, and causal studies across the three research paradigms (i.e., quantitative, qualitative, and mixed methods); (2) characteristics of quality research questions, and (3) three frameworks to support the development of research questions and their dissemination within scholarly work. For the latter, a description of the P opulation/ P articipants, I ntervention/ I ndependent variable, C omparison, and O utcomes (PICO) framework for quantitative research as well as variations depending on the type of research is provided. Second, we discuss the P articipants, central Ph enomenon, T ime, and S pace (PPhTS) framework for qualitative research. The combination of these frameworks is discussed for mixed-methods research. Further, templates and examples are provided to support the novice health scholar in developing research questions for applied and theoretical studies. Finally, we discuss the Create a Research Space (CARS) model for introducing research questions as part of a research study, to demonstrate how scholars can apply their knowledge when disseminating research.

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Ibrahim, A., Bryant, C.L. (2023). Designing a Research Question. In: Fitzgerald, A.S., Bosch, G. (eds) Education Scholarship in Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-38534-6_4

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Home » Survey Research – Types, Methods, Examples

Survey Research – Types, Methods, Examples

Table of Contents

Survey Research

Definition:

Survey Research is a quantitative research method that involves collecting standardized data from a sample of individuals or groups through the use of structured questionnaires or interviews. The data collected is then analyzed statistically to identify patterns and relationships between variables, and to draw conclusions about the population being studied.

Survey research can be used to answer a variety of questions, including:

• What are people’s opinions about a certain topic?
• What are people’s experiences with a certain product or service?
• What are people’s beliefs about a certain issue?

Survey Research Methods

Survey Research Methods are as follows:

• Telephone surveys: A survey research method where questions are administered to respondents over the phone, often used in market research or political polling.
• Face-to-face surveys: A survey research method where questions are administered to respondents in person, often used in social or health research.
• Mail surveys: A survey research method where questionnaires are sent to respondents through mail, often used in customer satisfaction or opinion surveys.
• Online surveys: A survey research method where questions are administered to respondents through online platforms, often used in market research or customer feedback.
• Email surveys: A survey research method where questionnaires are sent to respondents through email, often used in customer satisfaction or opinion surveys.
• Mixed-mode surveys: A survey research method that combines two or more survey modes, often used to increase response rates or reach diverse populations.
• Computer-assisted surveys: A survey research method that uses computer technology to administer or collect survey data, often used in large-scale surveys or data collection.
• Interactive voice response surveys: A survey research method where respondents answer questions through a touch-tone telephone system, often used in automated customer satisfaction or opinion surveys.
• Mobile surveys: A survey research method where questions are administered to respondents through mobile devices, often used in market research or customer feedback.
• Group-administered surveys: A survey research method where questions are administered to a group of respondents simultaneously, often used in education or training evaluation.
• Web-intercept surveys: A survey research method where questions are administered to website visitors, often used in website or user experience research.
• In-app surveys: A survey research method where questions are administered to users of a mobile application, often used in mobile app or user experience research.
• Social media surveys: A survey research method where questions are administered to respondents through social media platforms, often used in social media or brand awareness research.
• SMS surveys: A survey research method where questions are administered to respondents through text messaging, often used in customer feedback or opinion surveys.
• IVR surveys: A survey research method where questions are administered to respondents through an interactive voice response system, often used in automated customer feedback or opinion surveys.
• Mixed-method surveys: A survey research method that combines both qualitative and quantitative data collection methods, often used in exploratory or mixed-method research.
• Drop-off surveys: A survey research method where respondents are provided with a survey questionnaire and asked to return it at a later time or through a designated drop-off location.
• Intercept surveys: A survey research method where respondents are approached in public places and asked to participate in a survey, often used in market research or customer feedback.
• Hybrid surveys: A survey research method that combines two or more survey modes, data sources, or research methods, often used in complex or multi-dimensional research questions.

Types of Survey Research

There are several types of survey research that can be used to collect data from a sample of individuals or groups. following are Types of Survey Research:

• Cross-sectional survey: A type of survey research that gathers data from a sample of individuals at a specific point in time, providing a snapshot of the population being studied.
• Longitudinal survey: A type of survey research that gathers data from the same sample of individuals over an extended period of time, allowing researchers to track changes or trends in the population being studied.
• Panel survey: A type of longitudinal survey research that tracks the same sample of individuals over time, typically collecting data at multiple points in time.
• Epidemiological survey: A type of survey research that studies the distribution and determinants of health and disease in a population, often used to identify risk factors and inform public health interventions.
• Observational survey: A type of survey research that collects data through direct observation of individuals or groups, often used in behavioral or social research.
• Correlational survey: A type of survey research that measures the degree of association or relationship between two or more variables, often used to identify patterns or trends in data.
• Experimental survey: A type of survey research that involves manipulating one or more variables to observe the effect on an outcome, often used to test causal hypotheses.
• Descriptive survey: A type of survey research that describes the characteristics or attributes of a population or phenomenon, often used in exploratory research or to summarize existing data.
• Diagnostic survey: A type of survey research that assesses the current state or condition of an individual or system, often used in health or organizational research.
• Explanatory survey: A type of survey research that seeks to explain or understand the causes or mechanisms behind a phenomenon, often used in social or psychological research.
• Process evaluation survey: A type of survey research that measures the implementation and outcomes of a program or intervention, often used in program evaluation or quality improvement.
• Impact evaluation survey: A type of survey research that assesses the effectiveness or impact of a program or intervention, often used to inform policy or decision-making.
• Customer satisfaction survey: A type of survey research that measures the satisfaction or dissatisfaction of customers with a product, service, or experience, often used in marketing or customer service research.
• Market research survey: A type of survey research that collects data on consumer preferences, behaviors, or attitudes, often used in market research or product development.
• Public opinion survey: A type of survey research that measures the attitudes, beliefs, or opinions of a population on a specific issue or topic, often used in political or social research.
• Behavioral survey: A type of survey research that measures actual behavior or actions of individuals, often used in health or social research.
• Attitude survey: A type of survey research that measures the attitudes, beliefs, or opinions of individuals, often used in social or psychological research.
• Opinion poll: A type of survey research that measures the opinions or preferences of a population on a specific issue or topic, often used in political or media research.
• Ad hoc survey: A type of survey research that is conducted for a specific purpose or research question, often used in exploratory research or to answer a specific research question.

Types Based on Methodology

Based on Methodology Survey are divided into two Types:

Quantitative Survey Research

Qualitative survey research.

Quantitative survey research is a method of collecting numerical data from a sample of participants through the use of standardized surveys or questionnaires. The purpose of quantitative survey research is to gather empirical evidence that can be analyzed statistically to draw conclusions about a particular population or phenomenon.

In quantitative survey research, the questions are structured and pre-determined, often utilizing closed-ended questions, where participants are given a limited set of response options to choose from. This approach allows for efficient data collection and analysis, as well as the ability to generalize the findings to a larger population.

Quantitative survey research is often used in market research, social sciences, public health, and other fields where numerical data is needed to make informed decisions and recommendations.

Qualitative survey research is a method of collecting non-numerical data from a sample of participants through the use of open-ended questions or semi-structured interviews. The purpose of qualitative survey research is to gain a deeper understanding of the experiences, perceptions, and attitudes of participants towards a particular phenomenon or topic.

In qualitative survey research, the questions are open-ended, allowing participants to share their thoughts and experiences in their own words. This approach allows for a rich and nuanced understanding of the topic being studied, and can provide insights that are difficult to capture through quantitative methods alone.

Qualitative survey research is often used in social sciences, education, psychology, and other fields where a deeper understanding of human experiences and perceptions is needed to inform policy, practice, or theory.

Data Analysis Methods

There are several Survey Research Data Analysis Methods that researchers may use, including:

• Descriptive statistics: This method is used to summarize and describe the basic features of the survey data, such as the mean, median, mode, and standard deviation. These statistics can help researchers understand the distribution of responses and identify any trends or patterns.
• Inferential statistics: This method is used to make inferences about the larger population based on the data collected in the survey. Common inferential statistical methods include hypothesis testing, regression analysis, and correlation analysis.
• Factor analysis: This method is used to identify underlying factors or dimensions in the survey data. This can help researchers simplify the data and identify patterns and relationships that may not be immediately apparent.
• Cluster analysis: This method is used to group similar respondents together based on their survey responses. This can help researchers identify subgroups within the larger population and understand how different groups may differ in their attitudes, behaviors, or preferences.
• Structural equation modeling: This method is used to test complex relationships between variables in the survey data. It can help researchers understand how different variables may be related to one another and how they may influence one another.
• Content analysis: This method is used to analyze open-ended responses in the survey data. Researchers may use software to identify themes or categories in the responses, or they may manually review and code the responses.
• Text mining: This method is used to analyze text-based survey data, such as responses to open-ended questions. Researchers may use software to identify patterns and themes in the text, or they may manually review and code the text.

Applications of Survey Research

Here are some common applications of survey research:

• Market Research: Companies use survey research to gather insights about customer needs, preferences, and behavior. These insights are used to create marketing strategies and develop new products.
• Public Opinion Research: Governments and political parties use survey research to understand public opinion on various issues. This information is used to develop policies and make decisions.
• Social Research: Survey research is used in social research to study social trends, attitudes, and behavior. Researchers use survey data to explore topics such as education, health, and social inequality.
• Academic Research: Survey research is used in academic research to study various phenomena. Researchers use survey data to test theories, explore relationships between variables, and draw conclusions.
• Customer Satisfaction Research: Companies use survey research to gather information about customer satisfaction with their products and services. This information is used to improve customer experience and retention.
• Employee Surveys: Employers use survey research to gather feedback from employees about their job satisfaction, working conditions, and organizational culture. This information is used to improve employee retention and productivity.
• Health Research: Survey research is used in health research to study topics such as disease prevalence, health behaviors, and healthcare access. Researchers use survey data to develop interventions and improve healthcare outcomes.

Examples of Survey Research

Here are some real-time examples of survey research:

• COVID-19 Pandemic Surveys: Since the outbreak of the COVID-19 pandemic, surveys have been conducted to gather information about public attitudes, behaviors, and perceptions related to the pandemic. Governments and healthcare organizations have used this data to develop public health strategies and messaging.
• Political Polls During Elections: During election seasons, surveys are used to measure public opinion on political candidates, policies, and issues in real-time. This information is used by political parties to develop campaign strategies and make decisions.
• Customer Feedback Surveys: Companies often use real-time customer feedback surveys to gather insights about customer experience and satisfaction. This information is used to improve products and services quickly.
• Event Surveys: Organizers of events such as conferences and trade shows often use surveys to gather feedback from attendees in real-time. This information can be used to improve future events and make adjustments during the current event.
• Website and App Surveys: Website and app owners use surveys to gather real-time feedback from users about the functionality, user experience, and overall satisfaction with their platforms. This feedback can be used to improve the user experience and retain customers.
• Employee Pulse Surveys: Employers use real-time pulse surveys to gather feedback from employees about their work experience and overall job satisfaction. This feedback is used to make changes in real-time to improve employee retention and productivity.

Survey Sample

Purpose of survey research.

The purpose of survey research is to gather data and insights from a representative sample of individuals. Survey research allows researchers to collect data quickly and efficiently from a large number of people, making it a valuable tool for understanding attitudes, behaviors, and preferences.

Here are some common purposes of survey research:

• Descriptive Research: Survey research is often used to describe characteristics of a population or a phenomenon. For example, a survey could be used to describe the characteristics of a particular demographic group, such as age, gender, or income.
• Exploratory Research: Survey research can be used to explore new topics or areas of research. Exploratory surveys are often used to generate hypotheses or identify potential relationships between variables.
• Explanatory Research: Survey research can be used to explain relationships between variables. For example, a survey could be used to determine whether there is a relationship between educational attainment and income.
• Evaluation Research: Survey research can be used to evaluate the effectiveness of a program or intervention. For example, a survey could be used to evaluate the impact of a health education program on behavior change.
• Monitoring Research: Survey research can be used to monitor trends or changes over time. For example, a survey could be used to monitor changes in attitudes towards climate change or political candidates over time.

When to use Survey Research

there are certain circumstances where survey research is particularly appropriate. Here are some situations where survey research may be useful:

• When the research question involves attitudes, beliefs, or opinions: Survey research is particularly useful for understanding attitudes, beliefs, and opinions on a particular topic. For example, a survey could be used to understand public opinion on a political issue.
• When the research question involves behaviors or experiences: Survey research can also be useful for understanding behaviors and experiences. For example, a survey could be used to understand the prevalence of a particular health behavior.
• When a large sample size is needed: Survey research allows researchers to collect data from a large number of people quickly and efficiently. This makes it a useful method when a large sample size is needed to ensure statistical validity.
• When the research question is time-sensitive: Survey research can be conducted quickly, which makes it a useful method when the research question is time-sensitive. For example, a survey could be used to understand public opinion on a breaking news story.
• When the research question involves a geographically dispersed population: Survey research can be conducted online, which makes it a useful method when the population of interest is geographically dispersed.

How to Conduct Survey Research

Conducting survey research involves several steps that need to be carefully planned and executed. Here is a general overview of the process:

• Define the research question: The first step in conducting survey research is to clearly define the research question. The research question should be specific, measurable, and relevant to the population of interest.
• Develop a survey instrument : The next step is to develop a survey instrument. This can be done using various methods, such as online survey tools or paper surveys. The survey instrument should be designed to elicit the information needed to answer the research question, and should be pre-tested with a small sample of individuals.
• Select a sample : The sample is the group of individuals who will be invited to participate in the survey. The sample should be representative of the population of interest, and the size of the sample should be sufficient to ensure statistical validity.
• Administer the survey: The survey can be administered in various ways, such as online, by mail, or in person. The method of administration should be chosen based on the population of interest and the research question.
• Analyze the data: Once the survey data is collected, it needs to be analyzed. This involves summarizing the data using statistical methods, such as frequency distributions or regression analysis.
• Draw conclusions: The final step is to draw conclusions based on the data analysis. This involves interpreting the results and answering the research question.

Advantages of Survey Research

There are several advantages to using survey research, including:

• Efficient data collection: Survey research allows researchers to collect data quickly and efficiently from a large number of people. This makes it a useful method for gathering information on a wide range of topics.
• Standardized data collection: Surveys are typically standardized, which means that all participants receive the same questions in the same order. This ensures that the data collected is consistent and reliable.
• Cost-effective: Surveys can be conducted online, by mail, or in person, which makes them a cost-effective method of data collection.
• Anonymity: Participants can remain anonymous when responding to a survey. This can encourage participants to be more honest and open in their responses.
• Easy comparison: Surveys allow for easy comparison of data between different groups or over time. This makes it possible to identify trends and patterns in the data.
• Versatility: Surveys can be used to collect data on a wide range of topics, including attitudes, beliefs, behaviors, and preferences.

Limitations of Survey Research

Here are some of the main limitations of survey research:

• Limited depth: Surveys are typically designed to collect quantitative data, which means that they do not provide much depth or detail about people’s experiences or opinions. This can limit the insights that can be gained from the data.
• Potential for bias: Surveys can be affected by various biases, including selection bias, response bias, and social desirability bias. These biases can distort the results and make them less accurate.
• L imited validity: Surveys are only as valid as the questions they ask. If the questions are poorly designed or ambiguous, the results may not accurately reflect the respondents’ attitudes or behaviors.
• Limited generalizability : Survey results are only generalizable to the population from which the sample was drawn. If the sample is not representative of the population, the results may not be generalizable to the larger population.
• Limited ability to capture context: Surveys typically do not capture the context in which attitudes or behaviors occur. This can make it difficult to understand the reasons behind the responses.
• Limited ability to capture complex phenomena: Surveys are not well-suited to capture complex phenomena, such as emotions or the dynamics of interpersonal relationships.

Following is an example of a Survey Sample:

Welcome to our Survey Research Page! We value your opinions and appreciate your participation in this survey. Please answer the questions below as honestly and thoroughly as possible.

1. What is your age?

• A) Under 18
• G) 65 or older

2. What is your highest level of education completed?

• A) Less than high school
• B) High school or equivalent
• C) Some college or technical school
• D) Bachelor’s degree
• E) Graduate or professional degree

3. What is your current employment status?

• A) Employed full-time
• B) Employed part-time
• C) Self-employed
• D) Unemployed

4. How often do you use the internet per day?

•  A) Less than 1 hour
• B) 1-3 hours
• C) 3-5 hours
• D) 5-7 hours
• E) More than 7 hours

5. How often do you engage in social media per day?

6. Have you ever participated in a survey research study before?

7. If you have participated in a survey research study before, how was your experience?

• A) Excellent
• E) Very poor

8. What are some of the topics that you would be interested in participating in a survey research study about?

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

9. How often would you be willing to participate in survey research studies?

• A) Once a week
• B) Once a month
• C) Once every 6 months
• D) Once a year

10. Any additional comments or suggestions?

Thank you for taking the time to complete this survey. Your feedback is important to us and will help us improve our survey research efforts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• 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.

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Methodology, overview of survey methodologies.

The analysis of changes in party identification over time is based on a compilation of survey estimates conducted by telephone between 1994 and 2018 and online surveys conducted on Pew Research Center’s American Trends Panel between 2019 and 2023. Data from telephone surveys is adjusted to account for differences in survey mode; for details on the adjustment process, refer to Appendix A .

The data from 1994 to 2018 is based on 262 telephone surveys and more than 355,000 interviews among registered voters conducted by Pew Research Center from January 1994 to December 2018. These surveys are combined into one large data file that can be sorted according to a range of demographic characteristics, with comparisons made across different time periods. Yearly totals are calculated by combining all surveys for the calendar year, with appropriate weights applied. The table shows the number of surveys and interviews conducted each year as well as the margin of error for each yearly sample.

The data from 2019 to 2023 is based on American Trends Panel annual profile surveys conducted each summer. These surveys included at least 10,000 respondents and were conducted online. The methodology for the 2023 American Trends Panel profile survey is below.

The 2023 American Trends Panel profile survey methodology

The American Trends Panel (ATP), created by Pew Research Center, is a nationally representative panel of randomly selected U.S. adults. Panelists participate via self-administered web surveys. Panelists who do not have internet access at home are provided with a tablet and wireless internet connection. Interviews are conducted in both English and Spanish. The panel is being managed by Ipsos.

The most current data in this report is drawn from ATP Wave 133, conducted from Aug. 7 to Aug 27, 2023. A total of 11,945 panelists responded out of 12,925 who were sampled, for a response rate of 92%. The cumulative response rate accounting for nonresponse to the recruitment surveys and attrition is 4%. The break-off rate among panelists who logged on to the survey and completed at least one item is less than 1%. The margin of sampling error for the full sample of 11,945 respondents is plus or minus 1.4 percentage points. The survey interviewed 10,124 registered voters, for a sampling error of plus or minus 1.3 percentage point.

Panel recruitment

The ATP was created in 2014, with the first cohort of panelists invited to join the panel at the end of a large, national, landline and cellphone random-digit-dial survey that was conducted in both English and Spanish. Two additional recruitments were conducted using the same method in 2015 and 2017, respectively. Across these three surveys, a total of 19,718 adults were invited to join the ATP, of whom 9,942 (50%) agreed to participate.

In August 2018, the ATP switched from telephone to address-based sampling (ABS) recruitment. A study cover letter and a pre-incentive are mailed to a stratified, random sample of households selected from the U.S. Postal Service’s Delivery Sequence File. This Postal Service file has been estimated to cover as much as 98% of the population, although some studies suggest that the coverage could be in the low 90% range. 1 Within each sampled household, the adult with the next birthday is asked to participate. Other details of the ABS recruitment protocol have changed over time but are available upon request. 2

We have recruited a national sample of U.S. adults to the ATP approximately once per year since 2014. In some years, the recruitment has included additional effort (known as an “oversample”) to boost sample size with underrepresented groups. For example, Hispanic adults, Black adults and Asian adults were oversampled in 2019, 2022 and 2023, respectively.

Across the six address-based recruitments, a total of 23,862 adults were invited to join the ATP, of whom 20,917 agreed to join the panel and completed an initial profile survey. Of the 30,859 individuals who have ever joined the ATP, 12,925 remained active panelists and continued to receive survey invitations at the time this survey was conducted.

The American Trends Panel never uses breakout routers or chains that direct respondents to additional surveys.

Sample design

The overall target population for this survey was noninstitutionalized persons ages 18 and older living in the U.S., including Alaska and Hawaii. All active panel members were invited to participate in this wave.

Questionnaire development and testing

The questionnaire was developed by Pew Research Center in consultation with Ipsos. The web program was rigorously tested on both PC and mobile devices by the Ipsos project management team and Pew Research Center researchers. The Ipsos project management team also populated test data that was analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey.

All respondents were offered a post-paid incentive for their participation. Respondents could choose to receive the post-paid incentive in the form of a check or a gift code to Amazon.com or could choose to decline the incentive. Incentive amounts ranged from $5 to $20 depending on whether the respondent belongs to a part of the population that is harder or easier to reach. Differential incentive amounts were designed to increase panel survey participation among groups that traditionally have low survey response propensities.

Data collection protocol

The data collection field period for this survey was Aug. 7 to Aug. 27, 2023. Postcard notifications were mailed to all ATP panelists with a known residential address on Aug. 7.

Invitations were sent out in two separate launches: soft launch and full launch. Ninety panelists were included in the soft launch, which began with an initial invitation sent on Aug. 7. The ATP panelists chosen for the initial soft launch were known responders who had completed previous ATP surveys within one day of receiving their invitation. All remaining English- and Spanish-speaking sampled panelists were included in the full launch and were sent an invitation on Aug. 8.

All panelists with an email address received an email invitation and up to five email reminders if they did not respond to the survey. All ATP panelists who consented to SMS messages received an SMS invitation and up to five SMS reminders. On Aug. 25, interactive voice recording reminder calls were made to 39 tablet households that previously provided consent to receive these reminders.

Data quality checks

To ensure high-quality data, the Center’s researchers performed data quality checks to identify any respondents showing clear patterns of satisficing. This includes checking for very high rates of leaving questions blank, as well as always selecting the first or last answer presented. As a result of this checking, two ATP respondents were removed from the survey dataset prior to weighting and analysis.

The ATP data is weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. First, each panelist begins with a base weight that reflects their probability of selection for their initial recruitment survey. These weights are then rescaled and adjusted to account for changes in the design of ATP recruitment surveys from year to year. Finally, the weights are calibrated to align with the population benchmarks in the accompanying table to correct for nonresponse to recruitment surveys and panel attrition. If only a subsample of panelists was invited to participate in the wave, this weight is adjusted to account for any differential probabilities of selection.

Among the panelists who completed the survey, this weight is then calibrated again to align with the population benchmarks identified in the accompanying table and trimmed at the 1st and 99.5th percentiles to reduce the loss in precision stemming from variance in the weights. Sampling errors and tests of statistical significance take into account the effect of weighting.

The following table shows the unweighted sample sizes and the error attributable to sampling that would be expected at the 95% level of confidence for different groups in the survey.

Sample sizes and sampling errors for other subgroups are available upon request. In addition to sampling error, one should bear in mind that question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls.

Dispositions and response rates

How family income tiers are calculated

Family income data reported in this study is adjusted for household size and cost-of-living differences by geography. Panelists then are assigned to income tiers that are based on the median adjusted family income of all American Trends Panel members. The process uses the following steps:

• First, panelists are assigned to the midpoint of the income range they selected in a family income question that was measured on either the most recent annual profile survey or, for newly recruited panelists, their recruitment survey. This provides an approximate income value that can be used in calculations for the adjustment.
• Next, these income values are adjusted for the cost of living in the geographic area where the panelist lives. This is calculated using price indexes published by the U.S. Bureau of Economic Analysis. These indexes, known as Regional Price Parities (RPP), compare the prices of goods and services across all U.S. metropolitan statistical areas as well as non-metro areas with the national average prices for the same goods and services. The most recent available data at the time of the annual profile survey is from 2021. Those who fall outside of metropolitan statistical areas are assigned the overall RPP for their state’s non-metropolitan area.
• Family incomes are further adjusted for the number of people in a household using the methodology from Pew Research Center’s previous work on the American middle class . This is done because a four-person household with an income of say, $50,000, faces a tighter budget constraint than a two-person household with the same income.
• Panelists are then assigned an income tier:
• Lower-income adults are in families with adjusted family incomes that are less than half the median adjusted family income for the full ATP at the time of the most recent annual profile survey.
• Lower middle income indicates adjusted family incomes from half to less than two-thirds of the median.
• Middle income indicates adjusted incomes from two-thirds to less than double the median.
• Upper middle income indicates adjusted family incomes from double to less than triple the median.
• Upper income indicates adjusted family incomes that are at least triple the median.

The median adjusted family income for the panel is roughly $71,800. Using this median income, the middle-income range is about $47,900 to $143,600. Lower-income families have adjusted incomes less than $35,900 and lower-middle-income families have adjusted incomes from $35,900 to less than $47,900. Meanwhile, upper-middle-income families have adjusted incomes from $143,600 to less than $215,400, and upper-income families have adjusted incomes $215,400 or greater. (All figures expressed in 2022 dollars and scaled to a household size of three.) If a panelist did not provide their income and/or their household size, they are assigned “no answer” in the income tier variable.

Two examples of how a given area’s cost-of-living adjustment was calculated are as follows: The Anniston-Oxford metropolitan area in Alabama is a relatively inexpensive area, with a price level that is 16.2% less than the national average. The San Francisco-Oakland-Berkeley metropolitan area in California is one of the most expensive areas, with a price level that is 19.8% higher than the national average. Income in the sample is adjusted to make up for this difference. As a result, a family with an income of $41,900 in the Anniston-Oxford area is as well off financially as a family of the same size with an income of $59,900 in San Francisco. 

© Pew Research Center, 2024

• AAPOR Task Force on Address-based Sampling. 2016. “ AAPOR Report: Address-based Sampling .” ↩
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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Voice of the US farmer 2023–24: Farmers seek path to scale sustainably

Since 2018, McKinsey has gone into the field to better understand farmers’ mindsets. Following our publication of “Global Farmer Insights 2022,” 1 “ Global Farmer Insights 2022 ,” McKinsey, accessed March 29, 2024. we surveyed nearly 500 US farmers in 2023 to learn about their profitability, outlook on purchasing decisions, and adoption of sustainability practices and agtech solutions. 2 Survey conducted in fourth quarter 2023. There were 485 responses through computer-assisted web interviewing. Farmers surveyed included row- and specialty-crop operations with a farm-size distribution of 60 percent with less than 1,000 acres, 33 percent with 1,000 to 5,000 acres, and 7 percent with more than 5,000 acres. The 2023 survey found that despite recent supply chain disruptions, price volatility, and inflation, many farmers are investing in adopting sustainable-farming practices and technology solutions.

The sustainable-farming environment is rapidly evolving

Sustainable-farming practices are necessary to meet decarbonization goals and broader environmental targets. 3 The US Department of Agriculture (USDA) defines sustainable agriculture as “farming in such a way to protect the environment, aid and expand natural resources, and to make the best use of nonrenewable resources.” “Sustainability agriculture,” National Agricultural Library, accessed March 29, 2024. Agriculture accounts for nearly a quarter of global emissions, 4 Climate change 2022: Mitigation of climate change: Summary for policymakers , Intergovernmental Panel on Climate Change, 2022. and it was identified as the industry that contributes the most to exceeding planetary boundaries in McKinsey’s 2022 report Nature in the balance . 5 Nature in the balance: What companies can do to restore natural capital , McKinsey, December 5, 2022.

About the authors

Governments, investors, and companies are taking action to promote change. For example, the US Department of Agriculture’s mandatory budget in 2023 included an estimated $7 billion dedicated to conservation, with an additional $17 billion in conservation funding mandated by the Inflation Reduction Act through 2031. 6 “CBO’s February 2023 baseline for farm programs,” Congressional Budget Office, February 2023; FY 2024 budget summary , US Department of Agriculture, 2024. Furthermore, many agriculture players and consumer goods companies have committed to regenerative farming and deforestation-free supply chains.

However, average 2021–22 global finance flows for agriculture were only $43 billion, compared with $515 billion for energy systems and $336 billion for transport. 7 Barbara Buchner et al., “Global landscape of climate finance 2023,” Climate Policy Initiative, November 2, 2023. And adoption of sustainable-farming practices is not increasing fast enough to meet the sustainability goals of food processors and consumer goods companies.

In 2022, we released The agricultural transition: Building a sustainable future , 8 The agricultural transition: Building a sustainable future , McKinsey, June 27, 2023. a report that examined on-farm decarbonization measures and laid out a path to meet decarbonization targets. As a part of our fourth quarter 2023 survey, we explored a selection of these and other practices to better understand farmer adoption and opportunities to scale it in the United States.

Adoption of sustainable-farming practices is growing, but penetration remains low

Although 90 percent of farmers expressed awareness of the selected sustainable-farming practices, holistic adoption remains low. While more than 68 percent of farmers surveyed have adopted reduced- or no-till practices, only about half are using variable-rate fertilizer application and 35 percent are using controlled-irrigation practices.

Sustainable-farming practices that require behavioral changes in agriculture lead the way in adoption (for example, reducing or eliminating tillage), followed by practices that require product changes, such as nitrogen stabilizers or inhibitors. Practices that require changes in equipment tend to have the lowest adoption levels (Exhibit 1).

Many farmers are adopting sustainable-farming practices, but they are implementing them on only a small share of their acreage—generally less than 30 percent. There is an opportunity to increase acreage penetration among current users, especially for practices such as cover cropping and use of biologicals.

There are further opportunities to increase overall adoption by educating farmers on newer practices and convincing farmers who have heard of but not used the practices to give them a try. For example, 74 percent of farmers said they had heard of on-farm renewable energy, but only 13 percent have adopted it and only 7 percent are planning to adopt it in the next two years. Furthermore, 16 percent of farmers said they had not heard of biologicals, and 46 percent had not heard of biochar as a fertilizer.

Specialty-crop farmers are leading the way on adoption of most sustainability practices (Exhibit 2). Adoption is at least 20 percent greater among specialty-crop farmers for half of the practices surveyed. For example, biologicals adoption is 11 percentage points higher for specialty crops than for row crops; 20 percent of specialty farmers said they have adopted on-farm renewable-energy generation compared with 11 percent of row-crop farmers. Moreover, a greater percentage of specialty-crop farmers said they plan to implement sustainability practices in the next two years compared with row-crop farmers.

Practice adoption is correlated with perceived ROI

Farmers adopting sustainable-farming practices say they expect a positive ROI. Adoption of practices is highly correlated with farmers’ perceived ROI. Practices with the highest perceived ROI, such as applying fertilizer based on soil sampling, reducing or eliminating tillage, and implementing variable-rate fertilization, also have the highest rates of adoption (Exhibit 3).

Farmers expect many of the practices to have positive long-term benefits, such as a 3 to 5 percent yield rise and higher land value. However, farmers said they expect costs to remain 1 to 3 percent higher for most practices after more than five years of adoption. The ROI on adoption of sustainable-farming practices is complex and depends on a combination of factors including crop yield, crop prices, land value, and input, labor, and equipment costs. Although farmers said they generally expect the use of sustainable-farming practices to raise their costs, they also expect this increase to pay dividends in higher crop yield, land value appreciation, and better crop pricing.

For example, surveyed farmers who grow cover crops said they are experiencing or expect to experience a 3 percent increase in crop yield, a 2 percent increase in crop prices, and a 3 percent increase in land value on average. They are also experiencing or expect to experience a 2 percent increase in input, labor, and equipment costs on average, but they expect the benefits to outweigh these costs. Notably, for some practices, such as reducing or eliminating tillage, farmers expect both revenue increases (through increased yields) and cost reductions.

There are a variety of barriers to adoption

Small and large farms face different barriers in adopting sustainable-farming practices. However, compensation is a major obstacle to implementation for farms of all sizes (Exhibit 4). For example, 51 percent of medium-size and large farms with more than 1,000 acres and 39 percent of small farms identified obtaining a market premium (higher price) for sustainably grown crops or commodities as a top barrier to adoption. Moreover, 45 percent of medium-size and large farms see generating additional revenue from sustainability assets (for example, carbon credits) as their biggest barrier to adoption. Farmers with less than 1,000 acres are far more likely than those with bigger holdings to face challenges in implementing sustainable-farming practices (for example, operational barriers).

The most attractive incentives to unlock the transition to sustainable-farming practices vary by farm size (Exhibit 5). Representatives of medium-size and large farms with more than 1,000 acres said that certainty on operational benefits and reliable information on expected ROI are key. On the other hand, small farms with less than 1,000 acres highlighted the availability of financial incentives and assurance of a green premium (higher price) as the top two factors necessary to promote adoption. Overall, farmers are focused on gaining confidence in the economic benefits of sustainable-farming practices before making the full transition.

Across the board, both small and large farmers highlighted crop and commodity premiums from buyers of sustainable crops as the most attractive financial lever to increase adoption of sustainable-farming practices. Generating long-term and reliable sources of economic benefit may be key to driving additional adoption.

Government programs have seen greater participation than industry programs

Government-led programs designed to boost adoption of sustainable practices have substantially greater participation among farmers than industry programs do. These initiatives, such as the Environmental Quality Incentives Program (EQIP), are helping to spur adoption among farmers.

Of surveyed farmers, 57 percent said they were participating in a government program, while only 4 percent were participating in an industry-sponsored program, including the more than 15 carbon programs launched since 2016. 9 Lisa Moore et al., Agricultural carbon programs: From program chaos to systems change , American Farmland Trust, August 19, 2023. Adoption of sustainable-farming practices was at least 20 percent greater among government program participants than among those not enrolled in any program for ten of the 13 surveyed practices (Exhibit 6). This could indicate that government programs are driving adoption and that continued programmatic support from governments and industry players may encourage more farmers to transition to sustainable-farming practices.

Industry players can take steps to support farmers

As the importance of sustainable-farming grows, winning players will partner with farmers to support them in growth and innovation. To do so, industry participants could consider the following:

• reassessing the share of value captured by the farmer and bringing transparency to consumers about the “true cost of food”
• building sustainability programs and making them accessible; practice adoption is higher among participants in programs, but most of these are small scale today
• investing in education to help farmers overcome operational challenges and generate further data points on yield and cost benefits, especially for less-adopted practices
• collaborating with smaller and specialty-crop farmers in the near term, as these farmers are more willing to adopt most practices in the next two years
• continuing to evolve and grow nutrient-related programs, such as applying variable-rate fertilizer; most farmers view these practices as ROI positive, but only about half have adopted them and only in a part of their acreage

Many agriculture players are making commitments to sustainable farming in response to the sector’s large emissions footprint. But the transition to sustainably produced food depends on changing farmers’ behaviors and operational decisions. Our survey suggests that agriculture ecosystem players should consider how they can meet farmers’ expressed need for compensation for investments in sustainable-farming practices and provide reliable information about practice implementation and operational benefits.

David Fiocco is a senior partner in McKinsey’s Minneapolis office, Vasanth Ganesan is a partner in the New York office, Maria Garcia de la Serrana Lozano is an associate partner in the San Francisco office, and Julia Kalanik and Wilson Roen are consultants in the Chicago office.

The authors wish to thank Rui Chen, Raissa Dantas, Emma Galeucia, David Greenawalt, Emmanuel Mwaka, and Asha Sharma for their contributions to this article.

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