introduction in research survey

How to Write a Survey Introduction [+Examples]

Published: August 25, 2021

Writing a survey introduction probably isn't something you think about very often. That is until you're looking at the first screen of your almost finalized survey thinking "I should put something here. But what?"

Customer takes a customer service survey

While a potentially overlooked piece of the survey creation process, a good survey introduction is critical to improving survey completion rates and ensuring that the responses you receive are accurate. Taking the time to think about what information to include in your introduction can have a big impact on the success of your survey.

What is a Survey Introduction?

A survey introduction is the block of text that precedes the questions of your survey. It might be included at the top of an email requesting feedback or be the first slide in a series of questions. The survey introduction serves to set the stage for what the survey is, why the recipient should take the time to complete it, and what you're going to do with the information you collect. It should be compelling, informative, and reassuring.

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How to Write a Survey Introduction

Start by thinking about the purpose of this survey. Who will be taking the survey? What information do you need for the project to be successful? Distill this information down into a sentence or two for your audience. Some examples may include:

We're looking for feedback on our new product line for men.
Tell us about your recent customer service experience.
We're revamping our spring menu! What do you want for dinner?

Secondly, follow up with any logistical information they need to know about the survey. How many questions is it? When does the survey end? Who should they contact if they have additional questions? This might sound something like:

This 5 question survey will take around 10 minutes to complete.
Click below to access the short, two-question survey. For further information or feedback, please contact our support team at [email protected].
This survey will be open until April 24th, 2022. Please take 5 minutes to provide your feedback before that time.

Finally, reassure the survey participants that their data is safe, and offer any information about how the survey data will be used:

Your answers are anonymous and will be used to improve our future customer service strategy.
Responses will be anonymized and analyzed for our upcoming report on consumer perception of insurance companies in the US. Please leave your email address if you'd like to receive a copy of the finished report.
We read every response to our customer happiness surveys, and follow-up to make sure you're left with a positive experience.

No matter what you include in your survey introduction, make sure to keep it concise and as short as possible. Too long, and you risk readers dropping off and not completing your survey. It's also important to keep your survey messaging on-brand. If you typically use a brand voice that's quite corporate, switching to a conversational tone in your survey introduction will feel out of place. It might even make some readers question if the survey is truly coming from your company - causing distrust in its authenticity.

Finally, thank your respondents for their time. Even if their responses are negative, the fact that they're engaging with your survey is a great indicator of their loyalty . Customers will not take the time to provide feedback to companies they don't care about. Here are some phrases you can use to show your appreciation:

This feedback is very helpful for our team in developing new features. Thank you so much for taking the time to complete this survey.
We read every comment you leave on these surveys, so thank you for your feedback!
We truly appreciate your insight and your time.

Want to make sure you've got it all covered? Save this checklist of the most important aspects to include in the survey introduction:

How long will it take? (Minutes or number of questions)
Why are you doing this survey?
Why should they fill it out? Is there a giveaway for respondents (such as a draw for a $50 Amazon card) or another incentive to complete it?
What are you going to do with the results? Are they anonymous?
When does the survey close? What is the overall timeline?
Are there any definitions or things they need to know before filling out the survey?
Where should they go if they have questions or more feedback?
Thank your participants for their time and feedback.
Any additional information they need to fill out the survey with good, accurate data

Good Survey Introduction Examples

These survey introductions hit all the right notes. Read on for inspiration and additional tricks on how to write your own!

1. Squamish Off-Road Cycling Association (SORCA)

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Survey & Questionnaire Introduction: Examples + [5 Types]

Whether online or offline, you need to politely approach survey respondents and get them excited to fill your questionnaire when carrying out a research survey. Therefore, before going into the questions you want to ask, you need to kickstart your data collection process with a compelling survey or questionnaire introduction.

Generally, only a few people would even listen to you if you shoved your survey in their faces without a simple introduction first. Survey introductions in online questionnaires help you prepare the minds of your respondents ahead of time and gather the best responses.

What is a Survey Introduction?

A survey introduction is a concise description with relevant information about a survey. It is the first part of the survey that prospective respondents interact with and it helps them decide whether to fill your questionnaire or not.

Think of survey introductions as abstracts that communicate the entire essence of the data collection process. Without a good abstract, your thesis gets delayed or unapproved.

Following through with this thought means that the more exciting your survey introduction is, the higher your chances of collecting the right number of quality survey responses.

Features of a Survey Introduction

A good survey introduction must answer these 5 questions:

Who is conducting the survey?

Here, you should include the name of the person or organization that is carrying out the research.

What is the research about?

Survey respondents need to understand the aims and objectives of your research. This shows them why your survey is important and why they need to be part of it.

How long will the survey take?

Prepare their minds ahead of time by adding an estimated survey-completion time. While shorter surveys are likely to have more respondents, don’t give a false estimation to bait people to fill your survey.

Is my data safe?

Data privacy and protection is a huge concern for everyone. Since you plan to collect data from respondents, you need to tell them how you will use this information. You can include a link to your company’s privacy policy.

How will I fill the survey?

Include instructions on how to fill the survey. Include information about relevant documents for the survey too.

Your survey should be written in simple language your audience understands. It should be friendly, human and show the respondents how much impact they’ll make by taking part in the survey. Always include a nice “thank you” note in your survey introduction.

Types of Survey Introduction

Market survey introduction.

If you’re conducting market research using a survey , then you need a market survey introduction. To get more information about your customers/ target market, you need to conduct a market research survey. A market survey introduction gives your target audience a clear picture of what you want to achieve and how their participation is an important part of it.

Market research serves multiple purposes—sometimes, it is all about getting real-time data to inform product launches. Other times, it is for business expansion or product improvement. With a market survey introduction, you can get your audience on the same page and let them know the exact information you need from them.

A market survey introduction should answer all the questions we looked at when we discussed the features of a survey introduction. After naming your organization, you should also introduce your product or product idea for brand awareness.

Because of the type of information, market surveys are longer than other types of surveys ; sometimes, they have multiple sections. So, in your market survey introduction, give respondents a heads-up and let them know completing your survey will take more time than the average. You can add a nice reward they can claim after filling the survey.

Example of Market Survey Introduction

At Formplus, we are working to improve online data collection for you. We’d really like to know what you feel about online data gathering tools . Take this 20-minute survey and win a free 1-month Formplus premium subscription. Your data will be collected anonymously and only used for this research. Thank You!

Student Survey Introduction

A student survey is a method of sampling students’ opinions about the school, teachers, and overall learning experiences. From measuring student satisfaction to evaluating courses, student surveys help you to make the right changes to your school.

A student survey introduction is the first step in getting the best responses from your students. Encourage students to provide objective feedback and let them know how the information will be used.

In the survey introduction, indicate that all responses will be recorded anonymously. Students need to be sure that they can provide honest feedback in the survey without getting harassed or victimized.

Example of Student Survey Introduction

Thank you for being one of our students at Salthill College. Please complete this short 3-minutes survey to let us know how satisfied you are with your overall student experience at our college. All responses are recorded anonymously so feel free to provide honest feedback. Your responses will help us improve our teaching and learning environment.

Research Questionnaire Introduction

You need a good research questionnaire introduction during the data-collection phase of your research. People are more likely to fill your questionnaire when they clearly understand what you want to achieve and why your research is important.

In the research questionnaire introduction, you can include facts, data, or statistics about the research problem. Then, show how the data collected via the questionnaire will contribute to solving the problem. The introduction should also address data privacy, data protection, and participant’s consent.

Even if you plan to share the questionnaire physically, a good research questionnaire introduction will help collect responses faster and save time.

Example of Research Questionnaire Introduction

Hello, I am a postgraduate researcher at the London School of Tropical Medicine. I am conducting a study on effective treatment options for communicable diseases in West Africa and I would like to know your experiences with the signs, symptoms, and treatment of communicable diseases. Please complete this 30-minute survey. Your responses are anonymous and you can skip any questions you are not comfortable with. Thank you for your participation.

Customer Satisfaction Survey Introduction

Your customer satisfaction survey introduction should communicate 2 things—appreciation and brevity. First, you should let your customers know how much you love their patronage. Next, tell them that the survey will take just a few minutes.

Throw in an honorary mention of your brand and then, go through some of the information you’ll need from them in the survey. To increase response rates, you can reward respondents with a gift, discount, or special offer.

Example of Customer Satisfaction Survey Introduction

Thank you for shopping at Wreaths and Flowers! We’ll like to ask you a few questions about your shopping experience. Your responses will help us make shopping more enjoyable for you. This will only take 1 minute and you get 30% off your next order when you complete the survey!

Importance of Survey Introduction

It outlines the most important information about your survey

People need to know what they are getting into before filling your survey or questionnaire, and that’s exactly why you need a great survey introduction.

It’s a great way to welcome respondents

You wouldn’t just walk up to someone to ask for something without a proper introduction so why would you want to do this with your survey or questionnaire ? A questionnaire welcome page sets the mood for requesting responses from your respondents.

Quality survey introductions help you gain respondents’ trust

Many people are not excited about filling surveys and questionnaires, which is why they need a push. A survey or questionnaire introduction helps respondents to trust you and heightens their interest in filling your survey.

A survey introduction answers all the questions participants may have about the questionnaire. Think of it as some sort of FAQs that allows respondents to have a full grasp of your data collection process.

A questionnaire welcome page boosts survey participation and reduces survey dropout rates.

It helps survey participants to feel like an important part of the overall data gathering process. Survey introductions show participants that you value their opinions.

Survey introductions build the participants’ interest in your survey or questionnaire.

Why Use Formplus to Create Surveys?

Pre and Post Submission Page

Formplus allows you to add exciting survey introductions to your questionnaire. On the form’s intro page, you can provide a brief description of your survey, information on data privacy, and any other thing they need to know before filling the form.

You can also customize the form’s post-submission page and include a nice “thank you” note for respondents after they complete the survey or questionnaire. Learn more about our intro and post-submission pages here:

Intuitive Easy to Use Survey Maker

The Formplus builder is easy to use and you can build surveys and questionnaires from scratch in no time without writing a single line of code. It has a drag-and-drop feature that allows you to add more than 30 different fields to your form seamlessly.

Conditional Logic

Survey participants do not have to see or fill out all the fields in your form. With conditional logic, you can show or hide form fields and pages based on answers provided by respondents. This means survey respondents only have to fill the fields that are relevant to them.

Conditional logic helps you collect the right type of information from different survey participants. This way, you can avoid extra clutter and collect as much data as you want.

Offline Surveys

Formplus supports offline data collection and this means you can collect data in areas with poor or no internet access. Survey participants can fill and submit your questionnaire when they are offline. The data they provide will be automatically synced with our servers or your preferred cloud storage when internet access is restored.

Customized Surveys and Questionnaires

Formplus allows you to create beautiful and unique surveys with zero design experience. With the flexible design options, you can change the questionnaire’s background, colors, fonts, and create visually appealing designs. You can also add images and your organization’s logo.

Share Forms Easily

With multiple form-sharing options, you can send out your survey and collect responses in many ways. Apart from adding your questionnaire to your website, you can also share it using the social media direct sharing buttons and via email invitations.

Google Sheets Integration

With Google sheets integration, you can automatically update form responses in your spreadsheet and keep all form collaborators up to date. This makes it easy for you to import and export data, and collaborate with multiple people at the same time.

Custom Subdomain

Sharing your questionnaire via a custom subdomain adds an air of professionalism to your overall data collection process. When creating your custom URL, you can include the name of your organization as a means of promoting your brand.

Custom subdomains are simple and easy to remember too. Hosting your survey on a custom subdomain also serves as an extra layer of security; especially when you share the link via email.

Autoresponder Emails

After receiving a new response to your questionnaire, you can send out an automated automatic confirmation email to the survey participant in the form of autoresponder messages. In your autoresponder email, you should include a thank you message and any links to special offers and rewards.

Mobile-Friendly Forms

Many people fill out surveys and questionnaires on their mobile devices and this is why all Formplus forms are mobile-friendly. Participants can complete the survey right on their mobile devices without having to bother about pinching out or zooming in on your form. Formplus forms can be viewed and filled out on any smartphone, tablet, or internet-enabled mobile device.

In this article, we’ve looked at different survey introductions for different types of questionnaires and surveys including customer satisfaction surveys and research questionnaires. Whether you are collecting data online or offline, the right survey introduction will boost participants’ interest in completing your survey.

With Formplus, you can add unique survey introductions to your form before sharing it with respondents. On the post-submission page, you can include a beautiful “thank you” note for respondents who complete your survey. Try out the pre and post-submission page option as well as other exciting features when you sign up for a free Formplus account.

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How to write a survey introduction that will inspire people to participate

11 min read

What is a survey introduction—and what is its purpose?

1. the importance of a compelling introduction, 2. understand the audience, 3. personalization, 4. clear and concise language, 5. survey timing, 6. incentives and rewards, 7. privacy and data security, 8. contact information, 9. testing and feedback, 10. adapting to different survey types, 11. visual appeal, 12. a/b testing, 13. follow-up surveys, 14. compliance with ethical guidelines, 15. analyzing introduction performance, 16. continuous improvement, survey introduction example: a template for any type of research, introduction to a customer satisfaction survey, introduction to a market survey, student survey introduction sample, introduction to an employee survey, introduction for a research paper survey, introduction to a survey report, additional tips for creating the best survey introduction.

Creating a good introduction for a survey is a crucial part of successful research. Its quality will greatly impact the process. It will improve the end result, including survey completion rates and response accuracy.

A questionnaire introduction provides the chance to introduce yourself and the topic being explored to respondents. It is also a chance to assure them that their personal information will be kept safe and explain how they will benefit from completing the survey.

This article explores how to write a survey introduction, discusses its importance, and provides valuable, ready-to-use questionnaire introduction examples.

A questionnaire introduction is a short body of text appearing on the user’s screen at the beginning of a survey. It is the first contact point between you and potential participants prior to respondents seeing any of the survey questions .

This block of text sets up the level of cooperation that will be forthcoming from the person reading it. You need to convince them to participate by providing valuable information about the survey.

This includes the research topic, the expected time it will take to complete the survey, how responses will be processed, and why it’s in someone’s interest to take the time to complete it. The survey introduction can be in the body of an email or on the first slide of the survey.

Based on the introduction, potential respondents will decide whether to participate in the survey. It is an overall description of the survey, the equivalent of the abstract in a dissertation or other research paper.

How to write survey introduction text well

After spending days or even months making the perfect survey , you probably know it like the palm of your hand. However, it’s important to take time to better understand a respondent’s initial reaction to it—they may not be familiar with the topic at all.

As with every stage of the survey-making process, respondents’ perspectives have to be kept in mind and efforts undertaken to make their experience easy and worthwhile.

Here are 16 simple steps on how to write a survey introduction text to make it engaging.

The introduction in survey questionnaires serves as the gateway to a successful survey. A compelling one not only grabs the attention of respondents but also sets the tone for the entire surveying process. A well-framed introduction ensures that respondents understand the purpose and relevance of the survey, making them more inclined to complete it. Essentially, a thoughtful introduction can heavily influence the overall response rate and the quality of data collected.

Every survey is designed for a specific demographic or audience. Understanding them, and what drives them, allows for a tailored introduction that resonates. For instance, a survey meant for teenagers requires a different tone and approach than one aimed at senior citizens. By empathizing with the audience’s perspective, one can craft an introduction that speaks directly to their interests and motivations.

In today’s digital age, consumers appreciate distinctive touches. Personalizing a survey introduction, whether through addressing the respondent by name or referring to past interactions, adds a layer of authenticity. It gives the respondent a feeling of being valued and recognized, which can translate into a higher likelihood of participation.

Clarity is paramount in any communication. A good introduction for a questionnaire is vital in ensuring that respondents immediately understand the survey’s purpose and what’s expected of them. Avoid industry jargon or overly complex sentences. Instead, opt for straightforward and concise language that communicates the essentials without overwhelming or confusing respondents.

Timing can be a determining factor in the success of a survey. For instance, sending a customer satisfaction survey immediately after a purchase or service experience ensures the interaction is fresh in the respondent’s mind, leading to more accurate and detailed feedback. On the other hand, ill-timed surveys may come across as irrelevant or intrusive.

Motivation is a powerful tool. Offering respondents a tangible incentive—whether it’s a discount, gift card, or entry into a prize draw—can significantly boost participation rates. However, it’s essential that these incentives are relevant and appealing to the target audience and then delivered as promised.

With increasing concerns about data privacy, assuring respondents that their information’s safety is non-negotiable is vital. An introduction should clearly outline the measures taken to protect personal information and how the data being collected in the survey will be used. Being transparent about compliance with regulations like GDPR will instill confidence and trust in respondents.

Including contact details in the survey introduction can be a game-changer. It not only offers a channel for respondents to voice concerns or seek clarifications but also communicates transparency and openness. This proactive approach can lead to increased trust and a willingness to participate.

Like any piece of content, an introduction for a questionnaire benefits from testing. Running it by a small group—preferably from the target demographic—and seeking feedback can highlight potential areas for improvement. This iterative process ensures the introduction is optimized for its main audience.

Different surveys serve different purposes and their introductions should reflect this variance. An employee feedback survey will require a different tone and set of assurances than a market research questionnaire. Tailoring the introduction to the survey’s unique context ensures that it will resonate with potential respondents.

The aesthetics of a survey introduction can influence a respondent’s decision to proceed. Utilizing a clean, intuitive design incorporating on-brand colors and images can create an inviting and professional first impression. It’s essential to ensure the design enhances the content—as opposed to distracting from it.

Refinement is the key to perfection. A/B testing, in which two different introductions are presented to separate groups of respondents, can provide insights into which one performs better. This data-driven approach ensures that the introduction is continually optimized based on real-world feedback.

Gathering feedback is an ongoing process. Follow-up surveys, sent after the initial one, can delve deeper into specific topics or measure changes in opinions over time. However, their introduction needs to acknowledge the prior interaction and explain the rationale for a subsequent survey.

Conducting surveys isn’t just about gathering data, it’s about doing so ethically and responsibly. Ethical considerations, including informed consent and participant rights, should be highlighted in the introduction. This ensures participants are aware of their privileges and fosters a culture of respect.

After deploying a survey, it’s crucial to evaluate the introduction’s efficacy. By examining metrics like response rate, drop-off rate, and feedback, insights can be gained regarding the introduction’s strengths and the areas needing improvement. This analysis forms the foundation for future refinements.

The art of crafting survey introductions is one of continuous learning. As markets evolve and respondents’ preferences change, so should the survey approach. By staying adaptive and open to feedback, researchers can ensure their introductions remain effective and engaging.

Based on the checklist above, here is a survey introduction email example that fulfills all the requirements that will act as the perfect first contact with potential respondents.

Hey there, we would like to hear about your recent customer service experience!
At [company name], your satisfaction is what we value most. By participating in our survey, you will help us improve our products and offer you even better service.
This five-question survey takes only ten minutes to complete and is available until the 28th of November.
It is anonymous. The data gathered will only be used internally to improve our future customer service strategies.
Click below to access the survey. If you have any additional questions, feel free to contact us at support@company.com . We appreciate your feedback!

The wording of a questionnaire introduction and the information that is included can differ based on the field of research. Check out our survey introduction examples and choose an introduction sample best suited to your needs.

A customer satisfaction survey introduction is likewise an important part of customer experience research. The wording will have a huge impact on whether customers will take the time to answer—or just ignore it.

If surveying recent customer experience, send a survey shortly after customers purchased a product or had contact with the customer support team while the experience is still fresh in their mind.

Stay true to your company’s tone of voice and let respondents know that you appreciate their patronage. An incentive that encourages them to participate can also be offered. Here is a survey intro example:

Thank you for shopping at [company name]! We would like to ask you a few questions to learn about your shopping experience.

This survey will take only a couple of minutes and will be very valuable for improving the services we offer to you. The responses you give will stay anonymous.

Click below to participate, which will get you 30 percent off your next order!

Market research surveys are conducted to get more information about the situation in a specific economic sector and provide valuable real-time insights into the needs of a target audience and how the competition is doing.

Conducting product surveys can help improve existing products or make adjustments before releasing new products or services. Simply put, market research surveys help expand and grow a business.

When doing this kind of research, it is important to determine the target audience. If they are not yet customers, they may not be familiar with your brand, so make sure to introduce it properly and explain why they have been chosen for this research. Here is an example:

Nice to meet you! We are [company name], and we are working on bringing affordable [your products] to the world.
Our company aims to develop the best possible products for our customers, and we need your opinion to make this happen.
Wondering why we chose you? We are looking for [describe your target audience], which makes you a perfect fit.
We would appreciate it if you took the time to answer this five-minute survey. It is anonymous, and your data will be used only for this research.
Click below to fill out our survey and get 10 percent off our newest collection!

Student surveys are an important part of education surveys . With them, feedback is garnered from students regarding teachers, courses, curriculum, extracurricular activities, and much more.

Measuring students’ satisfaction levels helps highlight the strengths and weaknesses of a school, which in turn helps improve decision-making. However, in order to get accurate responses, certain steps are required, including how the introduction is written.

When making surveys for students, ensure they are anonymous. Many students may be afraid of retaliation, which will make them reluctant to give honest opinions.

Emphasize their anonymity in the introduction. Explain why this research is being carried out and how the gathered data will be used. Here is an example of a student questionnaire survey introduction:

Thank you for being one of our students at [name of your school]. Please take the time to answer this short five-minute survey and let us know how satisfied you are with your chosen courses from this semester.
This survey is anonymous, so feel free to answer honestly. It will very much improve the accuracy of our data and help us improve the curriculum as best as possible.

Conducting human resource surveys can greatly improve a workplace, which will result in happier and more productive employees. Find out about the work-life balance of employees and the overall company culture, measure the motivation and engagement of employees, and learn how satisfied they are with their jobs.

When writing the survey introduction, focus on the same aspects as above. Emphasize that the survey is anonymous and communicate this openly to employees. This will encourage them to share their honest opinions and help gather valuable and accurate responses.

Some research papers require conducting surveys on a particular topic. Writing a research questionnaire introduction for a research paper is no different than writing one for the previously mentioned purposes.

Introduce yourself and the topic to respondents and explain the purpose of the research and the benefit to them for participating. Include other information about the survey that you think is needed, though make sure to not overdo it. Keep it short and simple for high survey completion rates.

Writing a survey report is one of the seven steps of conducting survey research . It is the last one after the data analysis and is crucial to presenting findings.

A survey report introduction is very important for the success of a report. Its purpose is to introduce readers or listeners to the topic and the ultimate findings of the research.

The same advice applies: keep it short, use simple language, and incorporate only the most important information.

And above all, put yourself in the shoes of the audience. Unlike you, they have not been spending months with the survey and supporting material.

Good survey introductions help increase response rates and gain respondents’ trust. They are a perfect way for respondents to get to know you better, as well as the research topic and the ways they can benefit from it.

Here are some additional tips to create the best survey introductions, regardless of the topic of your research:

Make the survey anonymous and make sure respondents are aware of that.
Add a logo to the survey to increase brand recognition.
Don’t forget to keep the tone of voice on-brand.
If brand identity allows it, use a familiar tone.
Offer incentives for survey completion.
Thank the respondents for completing the survey.

Of course, before writing a survey introduction, you need to create the survey. With our help, amazing questionnaires can be made in no time. Sign up to Survey Planet today, create a survey for free, and add a well-written introduction using our tips!

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How to write a compelling survey introduction—get your response rates to soar

You only get one chance to engage your potential respondents with an online survey—and it all hinges on the introduction. Read on to find out how to persuade people to click through to your first question.

What to include in your survey introduction

There are some important details you really have to include in an introduction.

Before you set your mind to details of your writing, make sure you have the basics set in stone. There are five points you need to include in your survey introduction:

Your organization

The goal of the survey

How much time this will take

Anonymity/privacy of personal information (link to your privacy statement)

Relevant instructions

Now let’s take a look at each of them in more detail.

You wouldn’t grab someone in the street and ask them to fill out a survey with no introduction—so don’t do it online with your survey software.

It’s important for the people answering your survey to know who you are before they start, or you’ll be left with a bunch of nonresponses.

Improving your brand recognition is always a good idea. Especially if you want people to do more surveys for you in the future.

But don’t go on and on about yourselves. The more you talk about your own company and how great it is, the more biased the survey will feel.

Simply let people know who the survey is coming from—with a quick explanation of who you are. If they want to know more, you can leave a link to your site—or add it on your Thank You Screen at the end.

Be honest about why you are sending this survey research.

If you’re being vague about your objectives or failing to mention them altogether—your readers will find it hard to trust you.

Try to be as transparent as possible. Not only will this improve your responses, but people deserve to know what they’re taking part of.

People are busy—and time is money. Give an estimated time for completion upfront e.g. “This will only take a few minutes of your time.”

Don’t leave your readers in the dark. If they don’t know if there are five or five hundred questions left, they’ll get bored and impatient halfway through and hit the dreaded X button—or write half-hashed, inaccurate responses. And nonresponses are good for no one.

Let the people taking your survey know how far they are from the finishing line before they even start giving answers.

This is a big deal. You have to be clear and honest about what privacy rights people have.

If the responses you get from a survey are going to be anonymous, then let people know. Emphasize it—’cause you’ll get more honest answers if people understand that their answers remain confidential once they finish.

If you can’t offer anonymity to respondents—then they have the right to know that too.

Be fair with the people taking your survey. No one wants to give personal answers to something—only to have their answers used against them in the future.

Want to keep it short and sweet? You can always include a link to the privacy statement of your company. Give a very quick outline of the policy in the introduction, and give people the chance to learn more if they want.

Be clear about what your readers actually have to do in the survey or questionnaire .

Let’s say that you ask:

On a scale of 1-10, was this the tastiest type of cake?

Is “1” the tastiest score, because it’s number one? Does “10” represent the best cake, because it has a 10/10 flavor?

Who knows? Well, you will—but your readers won’t.

Asking questions like this means you’ll get answers from people in both camps. This means the answers you get will be worthless and your data can’t be used by your research team.

Keep the instructions as clear as possible. Ask someone to take the survey first. If they are confused by the questions, then the people taking your survey definitely will be too

Survey introduction writing tips

So now the necessary parts are taken care of, you need to focus on standing out from the crowd.

A perfect survey introduction is more than just a jumble of details and instructions. It’s the first contact you have with the people you will be relying on—so it’s important to start things off on the right foot. Make sure you:

The robots haven’t taken over yet—you’re not interviewing androids. You’re asking real people questions, so speak to them like, well, a human.

Keep the jargon for the boardroom. Speaking in formal, academic or technical language will just confuse most people.

If you open up with “Our company is looking for 100 respondents to answer a market research study on…” , then the survey will seem like some long, dry, serious read.

Try to humanize your speech in your survey intro.

Turn that cold, corporate speech into “We have a few questions to ask about…” .

Simple. Friendly. Human.

Always say thank you—you’re getting something from your readers with this survey.

Besides, if the people taking your survey feel their answers are valued, they’re much more likely to give genuine and thought-out answers

Your readers will appreciate it. Plus you’d make your grandmother proud.

A good introduction is a quick summary of the content that’s about to come up.

It’s the same in any medium, really.

So the best time to write your introduction is at the end of the writing process.

Why? Well, if you’ve gone through the entire process of planning and creating a survey, you’ll have a deep understanding of the content, hopefully.

Writing an introduction early on means you’ll be constantly editing if you make changes to the rest of the study.

If you do it at the end, you’ll have all the other parts ready to go—so this is the simplest time to put everything in a nutshell.

What’s the point of this study? Why should the people answering your questions care about them at all? Why should they spend five minutes on this survey instead of on Buzzfeed?

The best way to do this is to explain how these surveys had made a difference in the past.

Whether you’ve carried out research that led to policy change or simply asked employees about how they felt at work—then adapted the environment to suit them better, it’s certainly worth adding that info.

If you can show your survey isn’t meaningless research for some faceless organization, but rather information that can lead to positive change—then your readers have much more incentive to give thoughtful answers.

Encourage people to be happy to be part of the process.

Let’s take a look at a couple of good survey introduction examples from our fictional companies that follow this advice.

Customer surveys: feedback form introduction

Imagine that a customer has made a purchase from your shop, Absolutely Amazing Shoes , and you’d really like some customer feedback . Let’s look at how to introduce a survey you’d send to customers. First off, this person just bought something. So be gracious right off the bat. You’re happy they are a customer, right? Well, let them know.

The shop’s name is included, and stylised as their brand name. But the reader knows exactly who you are–so we can keep this to an absolute minimum here and still boost your brand recognition.

Readers know how quick this is going to be, and why they should give an answer.

Market research survey introduction

Undertaking market research is certainly different to customer feedback. But the same rules apply. Take a look at our example from Enough Plastic, a global anti-plastic NGO.

Let’s check this against our list from before.

Since this is possibly the first time the reader has heard of Enough Plastic, it’s important to add a short explanation. Readers get another way to learn more if they want, but you get the idea of this organization in a single sentence.

Letting your readers know this is a longer survey is important. For people who don’t want to take ten minutes on a survey, they see this right away and inaccurate answers don’t get included.

Readers are told about this study and are encouraged to give honest replies. Littering and wasteful behavior can be embarrassing to admit, so anonymity will be very important to anyone taking this survey.

The introduction ends with a sincere thank you and represents the global nature of not just the organization, but the planet as a whole.

How you ask is everything.

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How to Create a Survey

How to write a survey introduction (plus examples)

The first thing participants should see when they open a survey is a survey introduction. It’s a crucial piece of the survey puzzle, providing information about the survey topic, who’s conducting the survey, the goal of the research, and any other pertinent details that may encourage people to participate.

In other words, a survey introduction is your opportunity to make a good first impression — and to make a strong case for why recipients should help you out.

The tone of your introduction can be warm, funny, formal — even somber — depending on the goal of your survey. Done right, a good introduction can warm participants up to your request and make them more willing to respond.

Just so you know

Learn how to make a survey with our complete guide or get started today with Jotform’s free online survey maker !

The importance of survey introductions

Most people don’t want to spend their time filling out a survey if they can help it. Even if you offer them an incentive, say, a gift card or other reward, people may not take the time to fill out your survey thoughtfully and accurately.

A good survey introduction can solve these issues by providing important context for the participant. Consider the introduction as not just an informative piece, but also a light sales pitch — you’re essentially trying to convince participants to dedicate their time and energy to completing your survey as accurately and honestly as possible.

When done right, a survey introduction can do a few important things:

Boost response rates: A clearly stated, convincing objective is more likely to make participants believe that the survey is worth their time.
Set clear expectations: Informing participants on what the topic is, how much time the survey will take, and how you’ll use their answers helps build trust and reduces survey abandonment.
Increase engagement: A well-crafted introduction can make any topic — even a dry or technical one — more intriguing. It can also help make participants more invested in completing the survey.
Improve response quality: An introduction that clearly explains why participants’ input matters sets the stage for more thoughtful responses to your questions.

Keep reading to learn how to craft a perfect survey welcome message.

The ideal survey introduction: Important details to include

The name of your company or organization.

If you met a stranger on the street, you wouldn’t ask them for a favor without properly introducing yourself, would you? The same principle applies to an online survey — people want to know the person or organization behind it. Your survey introduction should identify who you are and include your company’s logo and name for brand recognition.

If your survey is intended for an audience that’s familiar with your organization, your logo and name will help respondents feel more confident about filling it out. If your audience isn’t acquainted with your organization, then it’s even more important to identify yourself. If you fall into the latter group, give a brief description of your organization, explain what you do, and include a link to your website or another place where they can find more information.

The goal of the survey

Make your objectives clear. State the goal of your research and what it will help you achieve. The more your readers understand your goal for the survey, the more likely you’ll be to get responses. Being transparent about the purpose of your research will engender trust and make people more willing to complete your survey.

Privacy and confidential information

Privacy is a big deal — people want to know how you’ll process the information they provide and how you’ll handle their personal information. Let everyone know up front whether their responses will be anonymous or not.

If you’ll be collecting personal information, let your respondents know what you intend to do (or not do) with that information. For instance, if you need their email address to follow up with them, make sure they know they can opt out at a later date if they decide not to continue with the survey.

The time required to complete the survey

The length of your survey can be a deal-breaker — people are busy, and they want to know right away how much time your survey will take to complete. This is why you should ask only the most essential questions pertaining to your goal.

So give a realistic estimate of how long it might take to complete your survey. For a more accurate estimate, take the survey yourself (or ask someone to do it for you) so you can see how long it takes to reasonably answer the questions.

Other relevant information or instructions

Each survey is different, so use your best judgment about any information you should disclose. Your goal is to be as transparent as possible so your respondents don’t find any unpleasant surprises ahead. If you think something could pose a problem, then it’s probably a good idea to state it in your welcome message.

Examples of good survey introductions

Let’s look at a few real-life examples of surveys that have great introductions or welcome messages, and see what we can learn from them.

Example 1: Keep it short

This survey by Resco Products shows respect for the user’s time. It gives the exact number of questions in the survey and the approximate time needed to complete it. In Jotform Cards , you can display the question count as part of your survey introduction.

Example 2: Make the terms clear

This survey about cycling habits lets participants know how the organization will use the data the survey collects. It also explicitly asks people to agree to the terms before giving them access to the survey.

Example 3: Say it with video

This survey uses an introductory video instead of a written introduction to get participants’ attention. Videos are far more engaging than text. In fact, one study showed that social video gets a lot more shares than text or images — a shocking 1,200 percent more for video than the other two combined.

If your introductory video hits the right note with your participants, you could see a lot more shares and responses.

Example 4: Provide necessary details

Survey introductions are best when they’re short and sweet, but there are occasions when you need to get into the details.

This COVID-19 rapid assessment survey explains the reasoning behind the survey in a comprehensive way, and here’s why: Not only does the survey ask for personal health information, but the organizers also want to follow up on the participants as appropriate. In this instance, participants need all the information they can get about the survey’s importance and how the organization will handle their personal data.

Example 5: Stay upbeat

How to write a survey introduction (plus examples) Image-1

This survey from the National Bicycle Organization projects an enthusiastic and positive tone. It also tries to promote a sense of “we’re all in this together” by using inspirational language: “Be part of the 1st 1,000 responders who will change history.” If your survey situation calls for it, include some inspirational words of your own to encourage people to respond.

Create the perfect survey introduction with Jotform

A well-crafted survey introduction will, in many cases, encourage users to fill out your survey. Using the Jotform Form Builder , you can customize a welcome screen that includes your survey introduction. Jotform’s flexible features allow you to customize your survey introduction and your survey to meet your needs.

Learn how to create a survey introduction in Jotform below using either the card or classic form types. Classic forms are the more traditional, scrollable versions of forms, while cards present questions or prompts one at a time.

How to create a survey introduction for a Jotform Cards form

Survey introductions — called “welcome pages” in Jotform — are built into Jotform Cards, so all you need to do is customize them to suit your needs.

Follow these steps:

Select a survey template. We’ll use this software survey form as an example.
Click Edit Welcome Page.

A screenshot of Jotform's form builder interface showing an in-progress software survey creation

Upload a new image that corresponds with the survey. This may be your company’s logo or an image related to the survey topic. If you want to use an icon instead, Jotform offers a large library to choose from.

You can also simply remove the image altogether if you don’t want to include one.

A pop-up window displaying a variety of icons to select for a 'Welcome Image Properties' setting

Change the headline and subheading to fit your needs.
Show or hide the question count, then click another form element or click in any open space to save your changes.

The image shows a user interface for a software survey form with options to start or edit the survey

How to create a survey introduction for a classic Jotform form

Classic forms don’t have built-in welcome pages, so you’ll need to create one. No worries — it’s a super simple, quick process!

Select a survey template. We’ll use this customer service survey as an example.
At the top of the form, click + Add Welcome Page (or + Edit Welcome Page if the template already includes a welcome page).

A screenshot showing the Jotform interface for editing a Customer Service Survey form

Build your welcome page into a proper survey introduction by adding form elements such as a survey title, logo or other image, and your introductory text. You can also select Click to add question count to display the number of questions in the survey.

See how easy it is to create your survey introduction? All it takes is a few steps — so you can start building forms with Jotform today!

Thank you for helping improve the Jotform Blog. 🎉

Data Collection
Data Management
Form Templates

Survey introduction examples that actually work

Written June 22, 2017
by Rens Deckers

Writing a solid survey introduction is not a waste of time.

You’ve created a survey and you need answers. Preferably without respondents going “NOPE” at seeing the word “survey” in their mailbox.

It’s your one window to get in, create a feeling of sympathy and make people feel like “Mmkay, I want to help these guys out with my answers”.

In fact, it’ll be the same trigger that pushes you to read this article or not. (No hard pushes though, just a gentle nudge. OK?) There are a few essentials that make up an irresistible survey introduction. We’ve collected all of them for you in a useful list and written up a good (copyable!) example of each approach. And one PERFECT template that combines them all.

4 Aspects of writing a solid survey introduction

1. provide all (relevant) necessary information.

Based on what you want to ask and who, choose from the items below. Don’t write an endless introduction for the sake of being correct.

Limit yourself to giving the essential information, and the trigger that your audience needs to take action. Let people know:

– What’s your purpose?

If I asked you a bunch of questions, you’d want to know why I’m asking and why I’m asking YOU specifically.

Being clear and transparent about your purpose will help people see you as “someone they can help”. Give them a chance to be a hero for one day. 😉

Simply state the reason for your survey and what you want to achieve with it, will persuade more people to complete it.

Use this template : We’d really like to find out how you feel about [research topic]

Saying what your purpose is, helps people value your research.

But actually showing what you do with your research , that’ll just draw them right in!

If you have previous results you can refer to and tie this to how people can help you now.

Don’t think, do it!

– Is it private?

Privacy is tricky as it is. With people becoming more and more aware of the importance of their personal information, it’s necessary that you are up front.

Always be clear about what you plan to do (or not do) with the collected information and you’ll ease respondents into answering more truthfully.

Use this template : No! We don’t want to spam you in any way. So don’t worry, you will remain completely anonymous.

If your respondents aren’t anonymous, be clear to state what will happen to their information and why:

Use this template: We appreciate you letting us know who you are. We don’t want to spam you in any way, the data we collect will be used only for this research.

Extra tip: There’s only so much room in an introduction screen. If you want to be thorough, you can always add a link to your privacy policy in one of the slides before you ask your respondent to submit their answers.

Just in case you do plan to reach out to them afterwards. Always give respondents the option to “opt out” :

Use this template: If you want to be informed about the results of this survey and receive other news from [company name], enter your email here.

– How long will it take?

This is THE most frequently asked question when people take a survey. Try to give your respondents a realistic estimate of the time it’ll take to answer your questionnaire.

Now let’s be honest!

We both know that your 10 open-ended questions will not get answered if you promised that the survey would only take a minute.

It’s up to you, not only to be honest about the duration, but also to make sure that you limit your survey to the essential questions and no more.

Respondents can and will abandon you for this.

When we send out surveys through email we will always write something along the lines of “This survey will only take 49 seconds of your time. Really! We timed it 😉 “.

(We actually do time it!)

Use this template: We realize how precious your time is. That’s why we made sure this survey will only take a quick [amount of time – preferably in seconds].

– What’s in it for me?

People want to win stuff. It’s simple.

Winning does not necessarily mean a big prize. People like to get knowledge in return, have a little fun with a game, get a coupon, …

Incentives are the perfect and easiest way to boost your participation rates. (Aside from the introduction examples, make sure the incentive is relevant and useful. Big and expensive does not equal relevance!)

Even people who are initially uninterested can be won over with a well-chosen incentive. Add more power and a little tease by adding a picture or video of the incentive.

Use this template: Think we were going to let you leave empty handed? 🙂 Take this survey and have a chance at WINNING our big prize [that you should specify here]!

– What audience am I in? Of course, your survey will have a certain target audience. So when approaching potential respondents about a survey, make sure to inform them about this chosen target audience.

Quickly let them know why you chose this segment and how your respondent fits in. It’ll increase their recognition of how they can help you personally.

Use this template: Wondering why we chose you? We’re looking for [that which specifies your target audience]. And that’s why you’re a perfect fit!

– Who am I answering these questions for?

Knowing from which company the survey originates is another way of convincing your target audience.

Give some basic information about yourself as participants will be more reluctant to share any data with an unknown company.

Use this template: Nice to meet you! We are [company name] and [a little bit more about your company, or jump straight to the purpose of your research]

2. A simple thank you

Power up your survey introduction with a thank you note.

Hey, it’s not only a sign of appreciation, it’s the least you can do!

Your participants are giving up their time for you to benefit from.

They are not gaining anything from doing this (except maybe your super relevant incentive). Try your best to make this experience as human and “spontaneous” as possible by adding a personal touch, especially by thanking your respondents.

(Thank you, by the way. You’re doing an awesome job reading all the way to the end… )

Use this template: We personally want to thank YOU for every second invested in our research. You rock!

If your company and research allows it, make this even more personal. Give your brand a face. Adding a real person’s name works just as well in surveys as it does in a newsletter, blog post or podcast.

Use this template: Thanks for helping us out. From all of us at [company name], [your name] [your title (optional)]

3. Less is more

Take everything you’ve learned in the previous steps… And now scratch that!

We recommend to always write your survey introduction text as short as possible.

???? … Sorry, I know!

People – especially those in a hurry – don’t want to waste their time reading page-long introductions before finally being able to start your survey. Use only the essentials from the above tips. Then go ahead and just make your point.

By writing up your survey introduction as short as possible, you force yourself to only focus on the most important message . And you don’t waste respondents’ time even before they’ve taken the survey. Getting them in is what’s most important.

Tip: If you want to analyze the way users read your surveys in order to predict their performance more accurately, maybe one of the AI consulting tools we looked at, Neurons, has what you need.

4. Inviting atmosphere is key

The first step for your introduction is making sure people will enter your survey and answer questions. Your second priority, is making sure they’re honest.

Here’s how to get that done:

Honesty: “You get what you give. What you put into things is what you get out of them.” – Jennifer Lopez.

Don’t expect your participants to blindly answer in a truthful way if they don’t know the full picture surrounding your study. Offer all corresponding information from the very beginning to avoid sloppy data which could lead to “brand dilution”.

Neutrality: Try to remain neutral throughout your entire survey, not just your introduction. You often see companies using one liners like “leading company in our niche” or “Bringing you the best service”. Don’t do this! As it will only create confusion and prejudice instead of confidence and reliable data.

Now let’s summarize, and get to the good stuff:

The perfect survey introduction example

Followed all of the steps above? Nice! You will have a survey introduction that is perfect and by the book!

Does it look like it got a bit too long? Too much info? Simply not as appealing as you thought it would be?

We wrote up a short, generic and to-the-point version for you to use. The perfect survey introduction example:

Use the ultimate template:

Hey, glad to see you around here! First of all, let me thank you for taking our [survey duration] survey. You are a great help! 🙂 We at [company name] are on a daring quest to collect the right data about [survey subject]. Our target audience involves everyone who [target audience]. This is why we chose you! And don’t worry, your data is just for [where you will use it] ]so [be clear about their privacy]. We promise! – Get started and take your chance to WIN [a grand prize]

It’s most important to make this introduction represent you as a brand, organization or person. It’s the first step of starting up a conversation.

And don’t be afraid to entertain: Don’t bore, get more. 😉

Extra tips & inspiring introduction examples

1. increase brand recognition.

Hey it’s you!

By adding your logo at the top of your intro screen , you’ll increase brand recognition without having to push it forward during the entire survey.

Make people feel like they’re talking to an old friend.

2. Use a conversational tone

Most people still associate surveys with these boring tasks that are basically – let’s face it – a waste of their time.

Spice up your language and bring some humanity into your questions .

Making your surveys more conversational will benefit your participation and completion rate tremendously!

EXTRA: If your brand and tone of voice allow it, throw in an interjection here and there. A “yee-hah” to show joy or an “ooh-la-la” to let respondents know they can win a prize?

Works like a charm.

Take a look at this list of interjections , for exclamations in every kind of situation.

3. Turn a frown upside down with emoticons

Go back 15 years in time and nobody would even THINK of using a smiley face. Well, that period is over and now it’s totally fine.

In fact, did you know that the use of emoticons in your communication increases the empathy towards your brand ? Perfect to express the mood of your survey, if your message is a playful one.

4. What’s in a name? Don’t use the word “survey”

Long, boring, difficult, too much work, … the word “survey” brings out some awful associations. We cannot blame our respondents. Instead, ask people to answer “a few questions” or to “spare a minute of their time”.

By avoiding the actual word you’ll see an increase in clicks and actual responses. This works in the introduction screen of your questionnaire, but in your email invites as well for example.

Keep in mind though that you should stick to just 4 questions if that’s what you said!

5. Show off the (incentive) goodies

A reward for answering a couple of questions lowers the threshold tremendously! Even more so if there’s a game or contest connected to the reward . The thrill of playing and possibly winning something is a perfect addition to your survey introduction.

6. In all seriousness

There’s a time and place for everything, so if your questionnaire is much too serious for smileys or “whoopees”, you can still write up a longer introduction that eases the respondent into the setup.

A competency assessment, like the above example, is something that requires more information. They managed to put quite a lot of it in the introduction, but decided to refer to an informative page via a hyperlink in case an employee would like to read up before getting started.

Create your own assessment for free!

About the author:.

Rens Deckers

10 responses.

Great tips to help get engagement and not sound like one more boring request for info

Thank you for taking the time to offer this guidance.

One of the great article! Precise and Clear Information, Kudos!!!

I should have read this in the past. I could have a great format. Thank you!

Thanks a lot. This will really help me.

Fruitful & eye opening. Thank you very much.

Thank you for your fantastic tips.

Great Tips. Took something away. Always good.

Quite helpful for a lively sourcing of needed information. Thank you lots.

Thanks for sharing!

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How to Write a Survey Introduction? Guide with Examples

Surveys can be instrumental in gathering insights, but their success often depends on the introduction. Crafting a well-written survey introduction can lead to higher response rates and ensure that the survey data collected is highly relevant. This guide will cover all the essential elements that a compelling survey introduction should have, provide examples for different types of surveys, and offer additional tips to help you optimize your response rates to gather honest feedback.

Survey Introduction “Must Have”

Conducting research or an online survey can be a daunting task. As well as… completing one.

When you think back to the last time you completed one, you may remember what motivated you to do it. Most likely, you found it worthwhile and understood its purpose.

A survey is usually introduced with a welcome message that explains why your feedback is essential and how it will be used. Introductions are like sales pitches; the survey author must persuade the respondent to believe them. By doing so, the study can gain valuable insights.

Let’s explore how to create an effective survey introduction and collect customer feedback .

Surveys for Master and Bachelor Degree Thesis

This template will help you get information on how parents can provide support for their children’s educational development.

How to Write a Survey Introduction

Here are five proven ways to write a good welcome message to survey participants.

Identify Yourself Clearly and Professionally

Transparency is critical in the world of surveys. The first element of a compelling introduction is identifying yourself or your organization. Ensure your introduction reflects a professional tone, aligning with your brand image. A professional introduction establishes credibility and assures respondents of the survey’s legitimacy.

Explain the Survey’s Purpose

When participants comprehend the reason behind their selection for a survey, it brings in a level of customization. Whether it’s because they are esteemed customers, respected employees, or belong to a specific group, clarifying the criteria for selection establishes a bond. This bond, in turn, nurtures a feeling of purpose and enhances the likelihood of sincere responses.

Provide an Estimate of Completion Time with Accuracy

It’s vital to respect respondents’ time when conducting surveys. Providing an accurate estimate of the time required to complete a survey can help set clear expectations and ensure valuable feedback. Misleading forecasts can lead to frustration and negatively impact data quality.

Reassure Respondents About Privacy

Protecting privacy is a top priority. Communicate data collection process: how data is collected, stored, and used to ensure trust and confidence from potential respondents.

Express Sincere Gratitude

Expressing gratitude is an essential art that should never be underestimated in survey introductions. A simple phrase like “thank you” at the end of your intro can go a long way in acknowledging the time and effort your respondents are about to invest. This small gesture can significantly influence their willingness to provide feedback.

Survey Introduction Examples

We already know what a good survey introduction should include. Now, we’ll examine real-life examples demonstrating how to use such messages; we’ll see that they are easier to write than they seem.

Employee Satisfaction Survey Introduction

“Dear [Participant Name],

Your commitment to Startquestion is the bedrock of our success. This survey is a testament to our dedication to ensuring your satisfaction. Your insights will shape the future of our workplace. Anticipated completion time: 5 minutes. Rest assured, your privacy is safeguarded.

Thank you for being the heart of our Company.”

Customer Satisfaction Survey Introduction

“Hello [Participant Name],

At Pied Piper, your satisfaction is our driving force. Your recent experience matters to us. This survey, taking approximately 7 minutes, is a direct path to enhancing your future interactions with us. Your responses are confidential and secure.

Thank you for being an integral part of our journey!”

Patient Satisfaction Survey

If you run a clinic or a facility providing professional healthcare, our survey template will help you quickly gain key knowledge about patient satisfaction.

Research Survey Introduction

Your role in our research is invaluable. This survey delves into [research topic], aiming for a comprehensive understanding. Estimated completion time: 10 minutes. Your privacy and confidentiality are paramount.

Thank you for advancing knowledge alongside us!”

Student Survey Introduction

Your perspective is instrumental in shaping the future of [School/College]. This survey, focusing on student experiences, will take approximately 6 minutes. Your responses are entirely anonymous, preserving your privacy.

Thank you for contributing to the evolution of our learning environment!”

Market Research Survey Introduction

Your expertise is crucial for our ongoing market research. You’ve been selected for your insights in [industry]. This survey, tailored for professionals like you, will take about 8 minutes. Your insightful responses will shape industry trends, and your privacy is our commitment.

Thank you for contributing to our collective knowledge!”

Crafting a Good Introduction: Additional Tips

A survey introduction example such as the ones above will encourage respondents to participate in your survey. The more personalized it is and better suited to the situation (e.g., customer satisfaction surveys sent after finalizing the purchase asking for the evaluation of the transaction in the online store), the greater the chance for a satisfactory response rate.

Here are more relevant instructions to help you with this task.

Infuse Brand Personality

Aligning tone, language, and visuals with your brand recognition will infuse your survey with your brand’s personality, creating a memorable brand interaction and reinforcing your identity.

Tailor Your Tone to Your Audience

Consider your audience when writing your survey introduction. For example, use a professional tone for professionals and a casual tone for a younger demographic. It will improve relatability and engagement.

Offer Tangible Incentives

Incentives can be powerful motivators. Consider offering respondents tangible benefits, such as discounts, exclusive access, or the chance to win a prize. It acknowledges their time and transforms the survey into a mutually beneficial activity.

Ensure Clear and Concise Instructions

Ambiguous survey instructions can cause confusion and incomplete responses. Ensure clarity and simplicity for accurate and thoughtful feedback.

Optimize for Mobile Devices

It’s essential to optimize surveys for mobile devices due to increasing reliance on smartphones. It ensures accessibility for respondents who prefer taking surveys on their mobile devices, expanding your reach and participation.

Writing a Survey Introduction: Sum Up

The introduction to a survey is a crucial component that builds trust and transparency and sets the tone for the entire experience.

Being transparent makes participants more likely to share their honest opinions, leading to valuable insights and better participant engagement. Providing an estimate of the completion time for the survey shows respect for the respondents’ time and manages expectations, creating a positive atmosphere that influences the quality of responses.

The Better Experience, the More Thoughtful Responses

By creating a compelling introduction, you make a strategic decision that can lead to richer insights, increased participant satisfaction, and better outcomes.

Remember that the survey introduction is the foundation of the entire survey experience and can impact the engagement and quality of the data collected. Focusing on composing a well-crafted introduction is the first step toward building a successful survey that yields valuable results.

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Author: Dariusz Jaroń

Updated: 02 February 2024

12 Post Event Survey Questions to Ask

After your meticulously planned event concludes, there’s one crucial step left: gathering feedback. Post-event surveys are invaluable tools for understanding attendee experiences, identifying areas for improvement, and maintaining attendee satisfaction.

Yes or No Questions in Online Surveys

This article will discuss the benefits of using yes or no questions, explore common examples, and provide practical tips for using them effectively in your surveys.

How to Write Good Survey Questions

In this comprehensive guide, we will explore the impact of poorly designed survey questions, offer tips for crafting excellent and unbiased questionnaires, and provide examples of valid survey questions.

[email protected]

214-257-8909

How to Write a Survey Introduction

What is the Purpose of a Survey Introduction? The survey introduction provides important information to participants regarding the objective and duration of the survey, how long the survey will take to complete, whether the responses are anonymous, and how the survey results will be utilized and shared. The primary purpose of introducing a survey properly is to make sure that the person taking the survey understands the purpose of the research. It has to be engaging and well written that the more respondents believe that their participation is going to help you with your research, the more they will be willing to complete the survey and provide relevant feedback. Components of a Survey Introduction

It’s important that you pay attention to your language, and make sure that the objectives of your research are clear for the person taking it. It may be a good idea to stress honesty, although doing so may cause its own biases.
You will also have to make sure that the introduction includes information privacy. Make sure that the person taking the survey fully understands that the survey is anonymous ; their information is going to be kept private and that it will not be shared with anyone or used to identify them. You may also want to explain how you received their contact information in the first place.
Finally, integrate something that allows them to know both how long the survey will take, how many questions there are, and how they will know when it is closer to completion. If they are rewarded for finishing the survey, it may be a good idea to explain the incentive and what they need to do to earn it. If you are going to share your results with them later, make sure they know. Seeing the results of the survey is a great incentive.

Getting Respondents to Take the Survey

The key here is to make sure that your respondents take the survey and answer the questions as honestly as possible, and anything you can place in your introduction to motivate them to completing it is valuable. Focus on what you will gain if respondents complete the survey and provide relevant feedback, while also making sure that they understand how it benefits them, and whether it is to improve products, services, or processes. Remember, the survey introduction is not a place for you to try to market to the customer. It is a place to get them to fill out the survey as accurately as possible.

Don’t miss! How Valuable are Shorter Introductions ?

Our details

When you visit our website, when you use our website, marketing communications, information obtained from third parties, disclosure and additional uses of your information, how long we retain your information, how we secure your information, transfers of your information outside the european economic area, your rights in relation to your information, changes to our privacy policy, children’s privacy.

This section summarises how we obtain, store and use information about you. It is intended to provide a very general overview only. It is not complete in and of itself and it must be read in conjunction with the corresponding full sections of this Privacy Policy.

Data controller: SurveyMethods
How we collect or obtain information about you: When you provide it to us, e.g. by contacting us, placing an order on our website, completing registration forms, signing up for free trials, submitting and completing surveys, posting blogs, or signing up for content such as newsletters. From your use of our website, using cookies and occasionally, from third parties such as mailing list providers.
Information we collect: Email address, password, first name, last name, job function, demographic information such as age, date of birth, gender, education, company name, phone, billing address, country, state/province/region, city, zip/postal code, and very limited credit card details (the cardholder’s name, only the last 4 digits of the credit card number, and the expiration date) for authentication (we do not store the CVV number), IP address, information from cookies, information about your computer or device (e.g. device and browser type), information about how you use our website (e.g. which pages you have viewed, the time when you view them and what you clicked on, the geographical location from which you accessed our website (based on your IP address), VAT number (if applicable), engagement history and transaction history).
How we use your information: For administrative and business purposes (particularly to contact you and process orders you place on our website, to post and respond to surveys, to improve our business and website, to fulfill our contractual obligations, to advertise our and other’s goods and services, to analyse your use of our website, and in connection with our legal rights and obligations).
Disclosure of your information to third parties: User information can be shared with partners for specific types of content and events where a user has registered their information. Other disclosures are only to the extent necessary to run our business, to our service providers, to fulfill any contracts we enter into with you and where required by law or to enforce our legal rights.
Do we sell your information to third parties (other than in the course of a business sale or purchase or similar event): No, SurveyMethods does not sell data. However, when you register or sign up for certain types of content, your registration data can be shared with sponsors and partners. Examples of where we do this include event registrations, webinar signups or whitepaper downloads. We will always make it clear where any information provided will be shared with other parties.
How long we retain your information: For no longer than necessary, taking into account any legal obligations we have (e.g. to maintain records for tax purposes), any other legal basis we have for using your information (e.g. your consent, performance of a contract with you or our legitimate interests as a business) and certain additional factors described in the main section below entitled 'How long we retain your information'. For specific retention periods in relation to certain information which we collect from you, please see the main section below entitled 'How long we retain your information'.
How we secure your information: Using appropriate technical and organisational measures such as storing your information on secure servers, encrypting transfers of data to or from our servers using Secure Sockets Layer (SSL) technology, encrypting payments you make on or via our website using Secure Sockets Layer (SSL) technology and only granting access to your information where necessary.
Use of cookies and similar technologies: We use cookies and similar information-gathering technologies such as marketing automation tracking on our website including essential, functional, analytical and targeting cookies. For more information, please view our cookie policy .
Transfers of your information outside the European Economic Area: By using our website, your information may be transferred outside of the European Economic Area. We take personal data seriously and as such we ensure appropriate safeguards are in place, including, for example, that the third parties we use who transfer your information outside the European Economic Area have self-certified themselves as compliant with the EU-U.S. Privacy Shield.
Use of profiling: We use profiling to understand our users better through web and marketing analytics, provide targeted advertising and deliver a personalised user experience.
to access your information and to receive information about its use
to have your information corrected and/or completed
to have your information deleted
to restrict the use of your information
to receive your information in a portable format
to object to the use of your information
to withdraw your consent to the use of your information
to complain to a supervisory authority
Sensitive personal information: We do collect what is commonly referred to as ‘sensitive personal information’, however we will only capture essential minimum data where it is strictly necessary. Any sensitive data will be held on our secure servers and will be transferred securely using SSL 256-bit encryption.

If you have any questions about this Privacy Policy, please contact the data controller.

The data controller in respect of our website is SurveyMethods and can be contacted at 800-601-2462 or 214-257-8909 .

You can also contact the data controller by emailing our data protection officer at [email protected] .

We collect and use information from website visitors in accordance with this section and the section entitled 'Disclosure and additional uses of your information'.

Web server log information

We use a third party server to host our website called Google Cloud the privacy policy of which is available here: https://policies.google.com/

Our website server automatically logs the IP address you use to access our website as well as other information about your visit such as the pages accessed, information requested, the date and time of the request, the source of your access to our website (e.g. the website or URL (link) which referred you to our website), and your browser version and operating system.

Use of website server log information for IT security purposes

We collect and store server logs to ensure network and IT security and so that the server and website remain uncompromised. This includes analysing log files to help identify and prevent unauthorised access to our network, the distribution of malicious code, denial of services attacks and other cyber-attacks, by detecting unusual or suspicious activity.

Unless we are investigating suspicious or potential criminal activity, we do not make, nor do we allow our hosting provider to make, any attempt to identify you from the information collected via server logs.

Legal basis for processing: Compliance with a legal obligation to which we are subject (Article 6(1)(c) of the General Data Protection Regulation).

Legal obligation: We have a legal obligation to implement appropriate technical and organisational measures to ensure a level of security appropriate to the risk of our processing of information about individuals. Recording access to our website using server log files is such a measure.

Legal basis for processing: Our legitimate interests (Article 6(1)(f) of the General Data Protection Regulation).

Legitimate interests: We have a legitimate interest in using your information for the purposes of ensuring network and information security.

Use of website server log information to analyse website use and improve our website

We use the information collected by our website server logs to analyse how our website users interact with our website and its features. For example, we analyse the number of visits and unique visitors we receive, the time and date of the visit, the location of the visit and the operating system and browser use.

We use the information gathered from the analysis of this information to improve our website. For example, we use the information gathered to change the information, content and structure of our website and individual pages based according to what users are engaging most with and the duration of time spent on particular pages on our website.

Legal basis for processing: our legitimate interests (Article 6(1)(f) of the General Data Protection Regulation).

Legitimate interest: improving our website for our website users and getting to know our website users’ preferences so our website can better meet their needs and desires.

Cookies are data files which are sent from a website to a browser to record information about users for various purposes.

We use cookies on our website, including essential, functional, analytical and targeting cookies. For further information on how we use cookies, please see our cookie policy .

You can reject some or all of the cookies we use on or via our website by changing your browser settings or non-essential cookies by using a cookie control tool, but doing so can impair your ability to use our website or some or all of its features. For further information about cookies, including how to change your browser settings, please visit www.allaboutcookies.org or see our cookie policy .

When you contact us

We collect and use information from individuals who contact us in accordance with this section and the section entitled 'Disclosure and additional uses of your information'.

When you send an email to the email address displayed on our website we collect your email address and any other information you provide in that email (such as your name, telephone number and the information contained in any signature block in your email).

Legitimate interest(s): Responding to enquiries and messages we receive and keeping records of correspondence.

Legal basis for processing: Necessary to perform a contract or to take steps at your request to enter into a contract (Article 6(1)(b) of the General Data Protection Regulation).

Reason why necessary to perform a contract: Where your message relates to us providing you with goods or services or taking steps at your request prior to providing you with our goods and services (for example, providing you with information about such goods and services), we will process your information in order to do so).

Enquiry forms

When you contact us using an enquiry form, we collect your personal details and match this to any information we hold about you on record. Typical personal information collected will include your name and contact details. We will also record the time, date and the specific form you completed.

If you do not provide the mandatory information required by our contact form, you will not be able to submit the contact form and we will not receive your enquiry.

We will also use this information to tailor any follow up sales and marketing communications with you. For further information, see the section of this privacy policy titled 'Marketing communications'.

Messages you send to us via our contact form may be stored outside the European Economic Area on our contact form provider’s servers.

When you contact us by phone, we collect your phone number and any information provide to us during your conversation with us.

We may record phone calls with customers for training and customer service purposes.

Legal basis for processing: Our legitimate interests (Article 6(1)(f) of the General Data Protection Regulation)

If you contact us by post, we will collect any information you provide to us in any postal communications you send us.

Legal basis for processing: Necessary to perform a contract or to take steps at your request to enter into a contract (Article 6(1)(b) of the General Data Protection Regulation).

We collect and use information from individuals who interact with particular features of our website in accordance with this section and the section entitled 'Disclosure and additional uses of your information'.

Social Media Tools

We have a wide range of social media tools to be able to use on our website. These tools include (but are not limited to); Sharing, Likes, comments and submitting content both on and off our website. By using these tools, you are providing your consent to store and use the submitted data, whether personal information or general information, both on and off our website.

Legal basis for processing: Your consent (Article 6(1)(a) of the General Data Protection Regulation). Consent: You give your consent to us storing and using submitted content using the steps described above.

We may also use this information to tailor any follow up sales and marketing communications with you. For further information, see the section of this privacy policy titled 'Marketing Communications'.

Information you submit may be stored both inside and outside the European Economic Area on our servers as well as third-party servers such as Facebook.

Registered Users

When you register as a user on our website:

We collect and store one or more of the following: Your email address, password, first name, last name, job function, company name, phone, billing address, country, state/province/region, city, zip/postal code, and very limited credit card details (the cardholder’s name, only the last 4 digits of the credit card number, and the expiration date) for authentication. We do not store the CVV number.
We use this data to provide you with customer support and other services, bill you for our services, collect feedback, send you account-related notifications, and keep you informed about our key features, important feature updates, and latest releases.
We store data related to your surveys, polls, and newsletters in your account that you access using your login-id and password. This includes questions, responses, images, email lists, data you enter while configuring or customizing any settings, etc. This data is processed by SurveyMethods to enable you to perform functions like design and distribution of surveys, polls, newsletters, and analysis & reporting.
We do not share any personally identifiable and account-related data with a third party without your explicit consent. However, if you use the SurveyMethods API or 3rd Party Integrations, you will need to share your SurveyMethods login-id and the “API Key” with the 3rd party for authentication. For more on our API Terms of Use, click here .
We may display your organization’s name and/or logo on our customer listing (unless agreed upon otherwise by both parties herein).
Your data will be visible to those with whom you share your published reports or extracted data/reports.
If you collaborate your surveys with other Registered Users, all collaborated data and your login-id will be visible to them.
If you are a Child User on an Enterprise account, the Enterprise Master User (Administrator) will be able to see the SurveyMethods login-id, first name, last name, phone number, account type, and expiration date of the Enterprise Child Accounts (Member Accounts). The Enterprise Child Accounts can view the SurveyMethods login-id, first name, last name, phone number, job title, job function, country, state/province/region, and city of the Enterprise Master User.
Troubleshoot problems and fix bugs (issues).
Ensure website compatibility across different devices and browsers.
Identify trends and patterns in the usage of our Services.
Gain insights for adding or improving the functionality and usability of our website.
Monitor and prevent abuse.
To prevent any undesirable, abusive, or illegal activities, we have automated processes in place that check your data for malicious activities, spam, and fraud.
We may use your data if required by law, court orders, subpoenas, or to enforce our agreements.
We collect information using cookies. Cookies are digital files that allow websites to recognize returning users. While most browsers allow users to refuse cookies or request permission on a case-by-case basis, our site will not function properly without them. SurveyMethods uses cookies primarily to enable the smooth functioning of its Services.
While accessing SurveyMethods, you may be able to access links that take you to websites external to SurveyMethods. SurveyMethods is not responsible for the content, policies, or terms of these websites.

GDPR Legal Classification for registered users

Legitimate interest: Registering and administering accounts on our website to provide access to content, allows you to buy goods and services and facilitates the running and operation of our business.

Transfer and storage of your information

Information you submit to us via the registration form on our website may be stored outside the European Economic Area on our third-party hosting provider’s servers.

When you register as an end user;

SurveyMethods’ Surveys and Polls sent by Registered Users
Newsletters from SurveyMethods Newsletter module and sent by Registered Users
When responding to a survey or a poll, End Users may provide personal data such as first name, last name, phone number, email address, demographic data like age, date of birth, gender, education, income, marital status, and any other sensitive data that directly or indirectly identifies them. SurveyMethods does not use or share any data of End Users in any way. The Registered User is solely responsible for ensuring that collection and sharing of any End User data, personal or otherwise, is done with the End User’s consent and in accordance with applicable data protection laws.
Since the Registered User controls and manages all data of their surveys, polls, and newsletters, End Users may contact the Registered User for any concerns regarding consent, privacy and protection of their data, or if they wish to access, modify, or delete their data.

GDPR Legal Classification for End Users

Visitors to our website

When you visit our website:

SurveyMethods may record your personal data (such as your name, email address, phone, company, and the reason you are contacting us) when you visit the SurveyMethods website and contact us using our online form. Any consent for the collection and use of your data in this case is entirely voluntary.
We may use your contact information to respond to you. We do not share any personally identifiable information with a third party without your explicit consent.

GDPR Legal Classification for Visitors

When you place an order

We collect and use information from individuals who place an order on our website in accordance with this section and the section entitled 'Disclosure and additional uses of your information'.

Information collected when you place an order

Mandatory information

When you place an order for goods or services on our website, we collect your name, email address, billing address.

If you do not provide this information, you will not be able to purchase goods or services from us on our website or enter into a contract with us.

Legal basis for processing: Compliance with a legal obligation (Article 6(1)(c) of the General Data Protection Regulation).

Legal obligation: We have a legal obligation to issue you with an invoice for the goods and services you purchase from us where you are VAT registered and we require the mandatory information collected by our checkout form for this purpose. We also have a legal obligation to keep accounting records, including records of transactions.

Additional information

We can also collect additional information from you, such as your phone number, full name, address etc.

We use this information to manage and improve your customer experience with us.

If you do not supply the additional information requested at checkout, you will not be able to complete your order as we will not have the correct level of information to adequately manage your account.

Legitimate interests: The ability to provide adequate customer service and management of your customer account.

Our content, goods and services

When signing up for content, registering on our website or making a payment, we will use the information you provide in order to contact you regarding related content, products and services.

We will continue to send you marketing communications in relation to similar goods and services if you do not opt out from receiving them.

You can opt-out from receiving marketing communications at any time by emailing [email protected] .

Legitimate interests: Sharing relevant, timely and industry-specific information on related business services, in order to help your organisation achieve its goals.

Third party goods and services

In addition to receiving information about our products and services, you can opt in to receiving marketing communications from us in relation third party goods and services by email by ticking a box indicating that you would like to receive such communications.

Legal basis for processing: Consent (Article 6(1)(a) of the General Data Protection Regulation).

Consent: You give your consent to us sending you information about third party goods and services by signing up to receive such information in accordance with the steps described above.

Information for marketing campaigns will be stored outside the European Economic Area on our third-party mailing list provider’s servers in the United States.

For further information about the safeguards used when your information is transferred outside the European Economic Area, see the section of this privacy policy below entitled 'Transfers of your information outside the European Economic Area'.

Use of tracking in emails

We use technologies such as tracking pixels (small graphic files) and tracked links in the emails we send to allow us to assess the level of engagement our emails receive by measuring information such as the delivery rates, open rates, click through rates and content engagement that our emails achieve.

This section sets out how we obtain or collect information about you from third parties.

Information received from third parties

We can often receive information about you from third parties. The third parties from which we receive information about you can include partner events within the marketing industry and other organisations that we have a professional affiliation with.

It is also possible that third parties with whom we have had no prior contact may provide us with information about you.

Information we obtain from third parties will generally be your name and contact details but will include any additional information about you which they provide to us.

Reason why necessary to perform a contract: Where a third party has passed on information about you to us (such as your name and email address) in order for us to provide services to you, we will process your information in order to take steps at your request to enter into a contract with you and perform a contract with you (as the case may be).

Consent: Where you have asked a third party to share information about you with us and the purpose of sharing that information is not related to the performance of a contract or services by us to you, we will process your information on the basis of your consent, which you give by asking the third party in question to pass on your information to us.

Legitimate interests: Where a third party has shared information about you with us and you have not consented to the sharing of that information, we will have a legitimate interest in processing that information in certain circumstances.

For example, we would have a legitimate interest in processing your information to perform our obligations under a sub-contract with the third party, where the third party has the main contract with you. Our legitimate interest is the performance of our obligations under our sub-contract.

Similarly, third parties may pass on information about you to us if you have infringed or potentially infringed any of our legal rights. In this case, we will have a legitimate interest in processing that information to investigate and pursue any such potential infringement.

Information obtained by us from third parties

In certain circumstances (for example, to verify the information we hold about you or obtain missing information we require to provide you with a service) we will obtain information about you from certain publicly accessible sources, both EU and non-EU, such as Companies House, online customer databases, business directories, media publications, social media, and websites (including your own website if you have one).

In certain circumstances will also obtain information about you from private sources, both EU and non-EU, such as marketing data services.

Legitimate interests: Sharing relevant, timely and industry-specific information on related business services.

Where we receive information about you in error

If we receive information about you from a third party in error and/or we do not have a legal basis for processing that information, we will delete your information.

This section sets out the circumstances in which will disclose information about you to third parties and any additional purposes for which we use your information.

Disclosure of your information to service providers

We use a number of third parties to provide us with services which are necessary to run our business or to assist us with running our business.

These include the following: Internet services, IT service providers and web developers.

Our third-party service providers are located both inside and outside of the European Economic Area.

Your information will be shared with these service providers where necessary to provide you with the service you have requested, whether that is accessing our website or ordering goods and services from us.

We do not display the identities of our service providers publicly by name for security and competitive reasons. If you would like further information about the identities of our service providers, however, please contact us directly by email and we will provide you with such information where you have a legitimate reason for requesting it (where we have shared your information with such service providers, for example).

Legal basis for processing: Legitimate interests (Article 6(1)(f) of the General Data Protection Regulation).

Legitimate interest relied on: Where we share your information with these third parties in a context other than where is necessary to perform a contract (or take steps at your request to do so), we will share your information with such third parties in order to allow us to run and manage our business efficiently.

Legal basis for processing: Necessary to perform a contract and/or to take steps at your request prior to entering into a contract (Article 6(1)(b) of the General Data Protection Regulation).

Reason why necessary to perform a contract: We may need to share information with our service providers to enable us to perform our obligations under that contract or to take the steps you have requested before we enter into a contract with you.

Disclosure and use of your information for legal reasons

Indicating possible criminal acts or threats to public security to a competent authority.

If we suspect that criminal or potential criminal conduct has occurred, we will in certain circumstances need to contact an appropriate authority, such as the police. This could be the case, for instance, if we suspect that fraud or a cyber-crime has been committed or if we receive threats or malicious communications towards us or third parties.

We will generally only need to process your information for this purpose if you were involved or affected by such an incident in some way.

Legitimate interests: Preventing crime or suspected criminal activity (such as fraud).

In connection with the enforcement or potential enforcement our legal rights

We will use your information in connection with the enforcement or potential enforcement of our legal rights including, for example, sharing information with debt collection agencies if you do not pay amounts owed to us when you are contractually obliged to do so. Our legal rights may be contractual (where we have entered into a contract with you) or non-contractual (such as legal rights that we have under copyright law or tort law).

Legitimate interest: Enforcing our legal rights and taking steps to enforce our legal rights.

In connection with a legal or potential legal dispute or proceedings

We may need to use your information if we are involved in a dispute with you or a third party for example, either to resolve the dispute or as part of any mediation, arbitration or court resolution or similar process.

Legitimate interest(s): Resolving disputes and potential disputes.

This section sets out how long we retain your information. We have set out specific retention periods where possible. Where that has not been possible, we have set out the criteria we use to determine the retention period.

Retention periods

Server log information: We retain information on our server logs for 3 months.

Order information: When you place an order for goods and services, we retain that information for seven years following the end of the financial year in which you placed your order, in accordance with our legal obligation to keep records for tax purposes.

Correspondence and enquiries: When you make an enquiry or correspond with us for any reason, whether by email or via our contact form or by phone, we will retain your information for as long as it takes to respond to and resolve your enquiry, and for 36 further months, after which we will archive your information.

Newsletter: We retain the information you used to sign up for our newsletter for as long as you remain subscribed (i.e. you do not unsubscribe).

Registration: We retain the information you used to register for as long as you remain subscribed (i.e. you do not unsubscribe).

Criteria for determining retention periods

In any other circumstances, we will retain your information for no longer than necessary, taking into account the following:

the purpose(s) and use of your information both now and in the future (such as whether it is necessary to continue to store that information in order to continue to perform our obligations under a contract with you or to contact you in the future);
whether we have any legal obligation to continue to process your information (such as any record-keeping obligations imposed by relevant law or regulation);
whether we have any legal basis to continue to process your information (such as your consent);
how valuable your information is (both now and in the future);
any relevant agreed industry practices on how long information should be retained;
the levels of risk, cost and liability involved with us continuing to hold the information;
how hard it is to ensure that the information can be kept up to date and accurate; and
any relevant surrounding circumstances (such as the nature and status of our relationship with you).

We take appropriate technical and organisational measures to secure your information and to protect it against unauthorised or unlawful use and accidental loss or destruction, including:

only sharing and providing access to your information to the minimum extent necessary, subject to confidentiality restrictions where appropriate, and on an anonymised basis wherever possible;
using secure servers to store your information;
verifying the identity of any individual who requests access to information prior to granting them access to information;
using Secure Sockets Layer (SSL) software to encrypt any payment transactions you make on or via our website;
only transferring your information via closed system or encrypted data transfers;

Transmission of information to us by email

Transmission of information over the internet is not entirely secure, and if you submit any information to us over the internet (whether by email, via our website or any other means), you do so entirely at your own risk.

We cannot be responsible for any costs, expenses, loss of profits, harm to reputation, damages, liabilities or any other form of loss or damage suffered by you as a result of your decision to transmit information to us by such means.

Your information may be transferred and stored outside the European Economic Area (EEA) in the circumstances set out earlier in this policy.

We will also transfer your information outside the EEA or to an international organisation in order to comply with legal obligations to which we are subject (compliance with a court order, for example). Where we are required to do so, we will ensure appropriate safeguards and protections are in place.

Subject to certain limitations on certain rights, you have the following rights in relation to your information, which you can exercise by writing to the data controller using the details provided at the top of this policy.

to request access to your information and information related to our use and processing of your information;
to request the correction or deletion of your information;
to request that we restrict our use of your information;
to receive information which you have provided to us in a structured, commonly used and machine-readable format (e.g. a CSV file) and the right to have that information transferred to another data controller (including a third-party data controller);
to object to the processing of your information for certain purposes (for further information, see the section below entitled 'Your right to object to the processing of your information for certain purposes'); and
to withdraw your consent to our use of your information at any time where we rely on your consent to use or process that information. Please note that if you withdraw your consent, this will not affect the lawfulness of our use and processing of your information on the basis of your consent before the point in time when you withdraw your consent.

In accordance with Article 77 of the General Data Protection Regulation, you also have the right to lodge a complaint with a supervisory authority, in particular in the Member State of your habitual residence, place of work or of an alleged infringement of the General Data Protection Regulation.

Further information on your rights in relation to your personal data as an individual

You can find out further information about your rights, as well as information on any limitations which apply to those rights, by reading the underlying legislation contained in Articles 12 to 22 and 34 of the General Data Protection Regulation, which is available here: http://ec.europa.eu/justice/data-protection/reform/files/regulation_oj_en.pdf

Verifying your identity where you request access to your information

Where you request access to your information, we are required by law to use all reasonable measures to verify your identity before doing so.

These measures are designed to protect your information and to reduce the risk of identity fraud, identity theft or general unauthorised access to your information.

How we verify your identity

Where we possess appropriate information about you on file, we will attempt to verify your identity using that information.

If it is not possible to identity you from such information, or if we have insufficient information about you, we may require original or certified copies of certain documentation in order to be able to verify your identity before we are able to provide you with access to your information.

We will be able to confirm the precise information we require to verify your identity in your specific circumstances if and when you make such a request.

Your right to object

You have the following rights in relation to your information, which you may exercise in the same way as you may exercise by writing to the data controller using the details provided at the top of this policy.

to object to us using or processing your information where we use or process it in order to carry out a task in the public interest or for our legitimate interests , including ‘profiling’ (i.e. analysing or predicting your behaviour based on your information) based on any of these purposes; and
to object to us using or processing your information for direct marketing purposes (including any profiling we engage in that is related to such direct marketing).

You may also exercise your right to object to us using or processing your information for direct marketing purposes by:

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How to Write a Survey Introduction: Tips & Free Template

Do you know what the last step of composing an article is? Writing the introduction!

Many editors do this at the end because, by the time they get there, they know what the article is about, what it contains, and who exactly it’s for.

That’s precisely the kind of information you should put in an article introduction. And the same goes for surveys.

A survey introduction is a place where you can let respondents know who you are, why you want their responses, and what you will do with them. Adding an introduction to a survey is one of the best ways to boost your response rate .

In this article, I’ll guide you through how to write a good introduction that will make respondents more eager to answer your questions. I’ll give you a survey introduction example and a template ready to fill out as well. Let’s begin.

What Is a Survey Introduction?

A survey introduction is a short text that outlines what a survey respondent can expect from the people/company running the survey. A survey introduction is also known as a survey disclaimer.

A good survey introduction teaches the respondents everything they need to know about your company and why you’re looking to gather information. It acts as a guiding reference and will put you at the front of all other companies looking to gain customer feedback .

No matter the content, each survey should begin with a survey introduction. You have spent your time creating a survey and now, you need answers. Having the right survey introduction will set you on track to catch your customers at ‘hello.’

For a more academic take on the subject, you might also like a breakdown by Dartmouth College of what a survey introduction should look like.

You should aim to add an introduction to each survey type you run, whether it's the universal and globally recognized Net Promoter Score (see the template below) or a customized survey created to suit your individual needs.

How to Introduce a Survey

The following five pieces of information are great places to start when writing a survey introduction. If you are unsure how to welcome your respondents, simply answer these five questions.

#1. Who are you?

If you’re running a survey outside your web page or social media, remember that your respondents might not know who you are.

Even if it’s quick, make sure you give your target audience an introduction to yourself and your company.

#2. Why are you asking for input?

Let’s face it, we’ve all filled out surveys and wondered if anyone was going to look at them. Make sure your respondents feel like their input is valued.

Explain why you collect data on this topic and why your respondents should care.

It probably isn’t a good enough reason that the survey will satisfy your curiosity. It needs to really have an impact on your company’s products and services.

#3. How will you use the gathered data?

It’s one thing to tell your potential respondents why you need to know this information but another thing to tell them how their personal information and insights will be handled.

Highlighting that your goal is not to gather responses for responses’ sake, but to take them into consideration and improve your product or service .

You may also want to mention your privacy policy. In the day and age of data leaks and the illegal use and purchase of data, keep in mind that you should be compliant with GDPR and other local policies.

Always use your clients’ information responsibly. You’ll benefit from the insights they give you and they can receive a better quality of products and services.

#4. How long will it take to complete the survey?

Have you ever started a survey, couldn’t see how long it would take to finish, got 20 questions in, and clicked out of it?

This is pretty common. If you don’t know how long a survey will take, it’s hard to commit to it. Taking an hour-long survey isn’t that rewarding for a client unless they’re invested in the outcome.

Tell your clients how long the survey will take , how many and what type of questions they can expect, and the average length of time it takes to complete. Remember, 10 closed-ended survey questions versus open-ended questions can take quite a different amount of time.

Adding a progress bar is another similar tactic that can help fight survey fatigue .

#5. Any other insights?

Conclude your intro with something that makes the respondents remember you are important.

Don’t forget to say goodbye and thank you as well! Make your respondents see that you’re reflecting on their insights and will include them in your future interactions.

Survey Introduction Example

Still wondering what your survey introduction might look like? Here’s a survey introduction sample. It helps to introduce clients to the company, explain the goal of the survey, and get your customers on board.

We’re [your company name] and we just launched a survey to find out how you feel about [your survey topic].

Once you submit your responses, we’ll make sure the data reaches the right teams. Our goal is to [improve our product/service, smooth out struggles, find out more about you, etc].

We are fully GDPR-compliant and will not share your sensitive data with anyone else.

There are [number of questions] [open-ended, closed-ended] questions in this survey and it shouldn’t take you more than [number of minutes] minutes to complete the survey .

There’s also room for your insights at the end if you’d like to let us know about anything not covered by the questions. We can even reach out to you and let you know when your issue issolved.

We greatly appreciate the time you take to let us know how we can do better!

If you are having trouble getting inspired, use this survey introduction template . You can send it within the email where you invite potential participants to complete a survey such as the one below:

Or, some survey tools allow you to embed the introduction right inside the survey.

How to Add an Introduction to Your Survicate Surveys

If you are using a good customer feedback tool like Survicate , you don’t have to worry about including introductions in emails, pop-ups , or anywhere outside of the platform itself.

Once you begin making a survey, you can add an introduction with a single click.

adding a survey introduction with Survicate

Click “welcome message” at any point during your survey creation and simply type it in. You can even add a separate introduction for each question.

Survicate let's you introduce each question

And don’t forget to include a thank-you screen as well!

Automate your surveys with Survicate

A good survey starts with a solid and clear introduction. Get your consumers at hello with a survey introduction that tells them exactly what’s coming.

Who are you?
Why are they taking this survey?
How long will it take them?
How will you protect their privacy?

These are all crucial questions and answering them all can improve your response rate in no time.

Stay ahead of your competition and begin crafting your survey introduction!

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Mastering Survey Success: Crafting Effective Introductions

Survey research is a powerful tool for gathering insights and feedback from your target audience. But if your survey introduction is lackluster or confusing, it can significantly impact the quality of the data you collect. That’s why it’s essential to master the art of crafting effective survey introductions that engage your audience and set the tone for the rest of the survey.

A successful survey introduction is like a firm handshake; it establishes trust and communicates the value of the survey to the participant. It sets expectations for what the survey will cover, why it matters, and how their feedback will be used. An effective introduction should grab the respondent’s attention, motivate them to participate, and guide them through the survey questions.

In this blog, we’ll explore the key elements of an effective survey introduction, including how to hook your audience, avoid common pitfalls, and ensure your survey questions align with your research objectives. With these tips, you can create introductions that set the stage for collecting high-quality data and actionable insights. So, let’s dive in and master the art of crafting survey introductions that deliver results.

What Is a Survey Introduction?

A survey introduction is the opening section of a survey questionnaire that serves as an introduction to the survey itself. It typically includes a brief explanation of the purpose and importance of the survey, as well as an invitation to participate. This is a critical part of the questionnaire as it sets the tone for the rest of the survey, establishes expectations, and motivates the respondent to participate.

An effective introduction to a survey should be clear, concise, and engaging. It should communicate the value of the survey to the respondent and explain why their feedback is important. The introduction should also provide any necessary context or background information that will help the respondent understand the survey questions and provide accurate answers.

A well-crafted introduction can increase the response rate and ensure that the data collected is accurate and actionable.

What Is the Importance of Survey Introduction and Its Benefits?

The survey introduction is a critical component of a survey questionnaire as it serves several important purposes and offers several benefits.

First, the introduction is important because it sets the tone for the rest of the survey. It provides the first impression of the survey to the respondent, and a well-crafted introduction can help engage the respondent and increase their motivation to complete the survey.

Second, the introduction provides context and establishes expectations for the survey respondent. It communicates the purpose of the survey, the type of questions that will be asked, and the value of the data being collected. This helps to ensure that respondents understand the survey and are more likely to provide accurate and useful responses.

Finally, an effective introduction to survey can help increase response rates. By clearly explaining the value of the survey and why the respondent’s input is important, the introduction can motivate respondents to participate and provide thoughtful responses.

Overall, a well-crafted survey introduction is essential for engaging respondents, gathering accurate data, and increasing response rates.

But how do you write a good introduction for surveys?

Let’s delve into it!

What Do We Explain in Introduction to Surveys?

In an introduction to the survey, you should explain several key elements to the survey respondent to help them understand the purpose and value of the survey. These elements include:

1. The purpose of the survey

Clearly explain why the survey is being conducted and what insights you hope to gain from it. This helps to give respondents a clear understanding of what they are contributing to.

2. The type of questions that will be asked

Explain the topics that the survey will cover and the types of questions that will be asked. This can help to set expectations and give respondents an idea of the level of detail that is required.

3. The target audience

Let respondents know who the survey is targeted towards and who will be using the data collected.

Read More: How to Find Survey Participants & Respondents

4. Confidentiality and anonymity

Explain how the data collected will be used and how it will be kept confidential and anonymous. This can help to assure respondents that their input is valuable and that their privacy will be respected.

5. The benefits of participation

Convey the benefits of participating in the survey and how the data collected will be used. This can help motivate respondents to complete the survey and provide thoughtful responses.

Overall, a survey introduction should provide enough information to help respondents understand the survey’s purpose and value, set expectations, and motivate them to provide accurate responses.

6 Key Features of a Powerful Survey Introduction

The features of a survey introduction should include:

1. Brief and concise

The introduction should be brief and concise, ideally no more than a few sentences or a short paragraph. It should clearly and succinctly convey the purpose of the survey and why the respondent’s input is valuable.

2. Engaging

The introduction should be engaging and encourage the respondent to participate in the survey. This can be achieved by highlighting the benefits of participating or by using an attention-grabbing statement.

3. Clear and easy to understand

A well-crafted introduction should be clear and easy to understand. Avoid using technical jargon or complex language that might confuse the respondent.

4. Honest and transparent

The introduction should be honest and transparent about the purpose of the survey, how the data will be used, and the expected duration of the survey.

5. Relevant and specific

The introduction should be relevant and specific to the survey topic. It should explain why the survey is being conducted, what topics it will cover, and what type of feedback is being sought.

6. Personalized

If possible, the introduction should be personalized to the respondent. This can be achieved by addressing the respondent by name or by using information about their demographics or past interactions with the organization.

To sum up, a well-crafted survey introduction should be engaging, clear, and honest. It should provide enough information to help respondents understand the purpose of the survey.

7 Tips for Writing a Better Introduction for Survey

Here are some tips for writing a better survey introduction:

1. Start with a compelling statement

Starting a survey with a compelling statement can be a game-changer. For example, “ Did you know that 90% of Americans struggle with time management? ” is a statement that would grab the attention of many respondents who can relate to the struggle of managing their time.

Another example could be, “ Are you tired of constantly worrying about your financial future? ” This statement could resonate with respondents concerned about their financial well-being. By starting with a statement that addresses a common pain point or concern, you can engage your audience and encourage them to provide thoughtful and insightful responses to your survey questions.

2. Clearly state the purpose

It’s crucial to clearly state the purpose of a survey in the introduction to help respondents understand what you’re trying to achieve. For example, “ We’re surveying to understand how our customers perceive our brand and products to improve their experience. ” This statement highlights the purpose of the survey and how the results will be used to enhance customer satisfaction.

Another example could be, “ We’re seeking employee feedback to improve our workplace culture and create a more productive and enjoyable work environment. ” This statement conveys the purpose of the survey and emphasizes the importance of employee feedback in shaping workplace culture. By clearly stating the purpose of the survey, you can ensure that respondents are motivated to provide relevant and meaningful feedback.

3. Keep it concise

When it comes to survey introductions, less is often more. Keeping your introduction concise ensures that respondents don’t lose interest before they even begin.

For example, “ We need your input to improve our customer service. Please take a few minutes to complete this survey. ” This statement is straightforward and to the point, conveying the purpose of the survey and the expected time commitment. Keeping your survey introduction concise can increase the chances of getting a higher response rate and more accurate feedback.

4. Be transparent

Transparency is essential in survey introductions to build trust with respondents. For example, “ We’re conducting research on behalf of our organization to evaluate employee satisfaction and identify areas for improvement. ” This statement is transparent about the survey’s purpose and who will use the results. By being transparent in your survey introduction, you can demonstrate that you value the respondent’s time and opinion, leading to more accurate and honest feedback.

5. Highlight the benefits

Highlighting the benefits of a survey can help motivate respondents to participate and provide valuable feedback. For example, “ By completing this survey, you’ll have the opportunity to enter a drawing for a $100 gift card and help shape the future of our products. ” This statement highlights the benefits of potentially winning a gift card and contributing to product development.

Another example could be, “ Your participation in this survey will help us understand your needs and preferences, so we can improve our services and better serve you. ” This statement highlights the benefit of improved services and a better customer experience.

6. Test your introduction

Testing your survey introduction before distributing it can help ensure that it is effective and engaging for respondents. For example, ask a small group of people to review the introduction and provide feedback on whether it conveys the purpose of the survey and is compelling enough to motivate them to participate.

Another approach is to conduct a pilot survey with a subset of your target audience to test the introduction and identify any areas that need improvement. By testing your introduction, you can refine it to maximize its effectiveness and increase the likelihood of obtaining valuable feedback from your respondents.

Here’s an example of how your introduction page can look like when created using an advanced survey software like ProProfs Survey Maker .

Survey Introduction Examples

Here are some simple yet effective survey introduction examples to inspire you!

1. Customer Satisfaction Survey

“Thank you for choosing our product/service! We value your feedback and would love to hear about your experience. Your feedback will help us improve our products/services and better meet your needs.”

2. Market Research Survey

“Your opinion matters! We are conducting a market research survey to better understand our target audience and improve our products/services. Your input will help us make informed decisions and deliver a better customer experience.”

3. Employee Engagement Survey

“We care about your experience at work! We are conducting an employee engagement survey to understand how we can better support and engage our employees. Your feedback will help us create a more positive and productive work environment.”

4. Product Feedback Survey

“We want to hear from you! We are conducting a product feedback survey to learn about your experience with our product. Your feedback will help us identify areas for improvement and make necessary changes to deliver a better product.”

5. Website Usability Survey

“We want to make our website more user-friendly! We are conducting a website usability survey to better understand how our users interact with our website. Your feedback will help us improve the user experience and make our website easier to navigate.”

These sample survey introduction examples work as clear and concise introductions for various types of surveys . These introductions efficiently demonstrate the purpose of the surveys, the type of feedback that is being sought, and the value of the respondent’s input.

Drive More Responses Using a Compelling Survey Introduction

The survey introduction is a critical component of any survey. A well-crafted introduction should be clear, concise, engaging, and honest. It should provide enough information to help the respondent understand the purpose and value of the survey and encourage them to participate.

By following the tips outlined above, you can create an effective introduction to a survey that engages your target audience and yields high-quality feedback.

Remember to keep your introduction relevant and personalized and to be transparent about how the data will be used.

If you’re looking for a robust tool to help you create strong surveys, ProProfs Survey Maker is your best bet. It requires no training, and your team will instantly adapt to creating surveys within minutes. The best part is that the tool lets you add a welcome page to your survey that you can personalize with a text or image of your choice. You can even reuse these messages to save significant time and effort.

So get set to create your survey and achieve your research goals head-on!

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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How to write a good survey introduction

Karolina Konopka

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Surveys are a powerful tool for gathering valuable information and insights, but their effectiveness hinges on your participants’ engagement. The survey introduction is crucial in setting the tone for the entire experience. A well-crafted introduction encourages respondents to participate and establishes transparency and trust. In this guide, we’ll explore the key elements of writing a good survey introduction that captivates your audience and maximizes the quality of your data.

How to title the survey?

Surveys often have a title that reads “Customer satisfaction survey”. This makes sense for the person creating the survey. Unfortunately, the user doesn’t see it that way anymore. The title of the survey must be engaging for the person completing it. “Share your opinion”, and “Help us improve the product you use.” Such a title says much more to the respondent, it informs about the type and what the survey is intended for. Additionally, we appreciate the customer’s opinion.

What to include in survey introduction

Clarity is Key: The survey introduction is the first point of contact between you and your participants. It should be clear, concise, and easy to understand. Clearly outline the purpose of the survey, making sure participants know why their input is valuable and how it will be used. Ambiguity can lead to confusion and, ultimately, affect the quality of the responses.
Establish Trust and Confidentiality: Participants are more likely to provide honest and accurate responses when they trust the survey process. Assure them of the confidentiality of their responses and explain how their data will be used. Clearly communicate any privacy measures you have in place, such as anonymous response collection, to build trust and encourage open participation.
Highlight the Importance: Convey the significance of the survey by emphasizing its relevance to the participants. Clearly articulate how their responses will contribute to meaningful insights, inform decision-making, or drive positive change. When participants understand the impact of their input, they are more likely to engage thoughtfully.
Set Expectations: Let participants know what to expect regarding the time commitment and the nature of the questions . If the survey is brief, mention it. If it covers specific topics, provide a brief overview. Setting clear expectations helps participants allocate the necessary time and mental energy to complete the survey accurately.
Personalize and Engage: Make the introduction more personable by addressing participants directly. Use inclusive language to create a connection. Consider incorporating a brief thank-you message to express appreciation for their time and participation. Personal touches can enhance engagement and foster a positive survey experience.
Pilot Test Your Introduction: Before launching the survey, conduct a pilot test with a small group to gather feedback on the introduction. Pay attention to participants’ comprehension, and make adjustments based on their suggestions. This iterative process can help you fine-tune the introduction for maximum clarity and effectiveness.
Provide Contact Information: Include contact information for any inquiries or concerns. Knowing they can reach out with questions can boost participants’ confidence in the survey process. Responding promptly to queries reinforces your commitment to a transparent and participant-friendly survey experience.

Customer surveys: feedback form introduction

The introduction to a customer experience survey feedback form is a crucial first step in inviting customers to share their insights. It’s a brief yet impactful message expressing gratitude for their time and emphasizing the significance of their feedback in shaping the company’s offerings. This introduction assures customers of the confidentiality of their responses, sets expectations regarding the survey’s length, and may highlight incentives for participation. For instance, a retail company could convey appreciation by offering participants a chance to win a gift card, fostering a positive and engaged response from customers. Overall, the feedback form introduction serves as a friendly invitation for customers to contribute their thoughts and experiences, creating a collaborative feedback loop that benefits both the customer and the business.

An example of a survey introduction

Dear [Customer Name],

Thank you for choosing [Your Company Name]! Your insights are invaluable in helping us improve. Please take a moment to share your thoughts through this brief survey.

Rest assured, your responses are confidential. Complete the survey to win a [Example: $50 Gift Card].

Thank you for shaping [Your Company Name]’s journey.

A well-crafted survey introduction is the cornerstone of a successful data collection process. By prioritizing clarity, trust, and engagement, you can create an introduction that encourages meaningful participation and yields high-quality responses. Remember, the effort invested in designing an effective introduction will pay off in valuable insights and a more successful survey overall.

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What is the survey introduction?

Keep it short, add an incentive, use clear and concise language, keep an eye on aesthetics, add a consent statement or privacy notice, define the required time, limit your target audience, leave contact details, include useful information, say “thank you”, wrapping up.

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How to write a survey introduction that motivates respondents to fill it out

The first impression matters, especially when you wish to encourage people to take your survey. The survey introduction is the first your respondent sees before proceeding to questions. What should you include? How to approach the reader? A professional writing service has shared some helpful principles and tips in the following article.

The paragraph of text that comes before your survey questions is called the survey introduction. It describes the survey's purpose, why the target audience should take the time to respond, and explains what you plan to do with the data you get.

An introduction is an essential part of any survey as it helps to set the tone for the survey, make participants aware of why they are being asked to participate, and provide any necessary background information. An introduction can provide a brief overview of the survey's purpose and goals, helping to set expectations for both the respondent and the survey conductor.

Depending on your audience and the survey purpose, your introduction should take from several sentences to several short paragraphs. Write the text of your introduction, and then think about how to make it shorter and rewrite it.

Mostly, your respondents do you a favor by filling in your survey, and the best way to show your respect is to save their time. The more attentive you will be with the word choice and phrases, the more likely your respondent will fill in your survey.

For example:

"We want to hear from our users to ensure we have the best possible products and services for them."

The statistics indicate that an incentive offer can be a very effective tool in increasing survey response rates. To encourage the respondent, you can provide a gift card, discount, free limited access to the service, gift, or any other incentive. Clearly state your offer or highlight it, so the reader will quickly see the opportunity. Also, make sure that your company can provide this action, as the number of participants may be huge.

The reader should easily understand your introduction. Use simple words and logical structures. Try to use a conversational and informal tone. Make sure the text is clear and concise, and establish your survey's topic in a way that will capture the reader's attention.

Also, it is important to explain any technical terms, unfamiliar concepts, and particular facts your reader needs to know to understand the questions you incorporate in the survey. It will create an impression that you care about the reader and give additional information right at the beginning.

A nice and friendly design of the first page of the survey may catch the respondent's attention and encourage them to participate in the survey. Adding eye-catching and lovely graphics is a powerful tool. Also, you can add a company's logo so that the respondent will stay more aware of the company's brand.

If your respondents live in countries (like the European Union residents) with strict data laws, you need to add a Privacy notice. Your response should know whether you can use their personal data and transfer it to another country. Providing such information and asking whether the respondent accepts it will help you set the right tone and keep the legal obligations.

In addition to respecting the respondent's time, you should also indicate the time needed to respond to your survey. Define an approximate amount of time the respondent needs to help them define whether they have time for your survey. Also, you can specify the number of questions the respondent should answer.

“Our survey consists of 10 questions and will take not more than 5 minutes to complete.”

If you want to be sure that the respondent fits your target audience, you can start the survey introduction with several sentences addressing the special group of respondents.

Add a contact number or email address for those respondents who have questions about the survey. Such information builds a trustful relationship with respondents as it shows that you are interested in collaboration and that a real person created a survey.

Mention the company's name and brief information about it.

Set the goal of the survey and why you have done it.

Explain how you will use the survey results and how they may provide benefits.

Mention whether the survey is anonymous and confidential or not.

Provide additional instructions on the study if necessary to avoid possible misunderstandings.

Define whether the respondent can edit the answers and the deadline.

Provide an option to send a copy of the completed survey to the respondent's email.

Even if your prospective respondent is negative about filling in your survey, a "thank you" will leave positive emotions. Staying loyal to your readers and thanking them ahead of time will be appreciated by those engaged in your survey.

"We are grateful to have received such insightful feedback."

"By hearing the opinions of our customers, we can make sure that we create the best possible product and add features that our users will find useful."

"We are very grateful for your time and input."

Respondents should be gratefully acknowledged for any input they offer. They made an effort to communicate with you and provide helpful information by taking time out of their busy schedules. Consider this when writing a survey introduction. Good luck!

How to write a survey introduction that motivates respondents to fill it out FAQ

What is the purpose of a survey introduction, the purpose of a survey introduction is to explain the purpose and context of the survey, provide clear instructions for completing the survey, and ensure that respondents understand how their information will be used., what should be included in a survey introduction, a good survey introduction should include a brief description of the survey, the purpose of the survey, the expected length of the survey, and any other relevant information. additionally, it should contain clear instructions for how to complete the survey, and explain how the respondent's information will be used., how can i ensure that my survey introduction is effective, to make sure your survey introduction is effective, make sure it is clear, concise, and easy to understand. additionally, it should provide enough information to give respondents an idea of what to expect and what is expected of them., like what you see share with a friend..

Wilson Bright

Wilson Bright is the co-founder of BlockSurvey. He is an avid crypto enthusiast, and his vision is to make BlockSurvey a go-to infrastructure for data collection with a focus on privacy and security.

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Survey Introduction Examples and Best Practices

Nemanja Jovancic

Jan 31 2020

No comments

With online surveys, as well as in life, first impressions are often all that matters. Whether you’ve received a survey via email, stumbled upon it on social media, or were required to take it on a particular website, you made the decision whether to take it based on the first screen you saw, right?

That first step is often referred to as the survey introduction and it’s your best shot at persuading a potential respondent to set a few minutes aside for completing your survey.

Unlike online quizzes, where a compelling quiz title is your best chance of getting the potential takers to pay attention, with surveys it all depends on your survey introduction.

That’s partly due to the fact that surveys are usually more “serious” in nature and there’s often not much room for playing with alluring titles, especially if you’re questioning people about a sensitive or socially important topic.

That being said, you need to make sure your survey introductions are good enough to make your potential respondents click through to question number one.

In this post, I’ll share some of the best survey introduction examples and explain how you can come up with similar ones. As an introduction to this post, here are some of the most important things you need to include in your survey introduction.

5 key elements of a good survey introduction

So far, we have used our survey builder to create hundreds of surveys for our customers and ourselves and have learned a thing or two in the process. There are 5 vital pieces of information every good survey introduction should contain:

1. Your company/organization name

2. The aim of the survey – why are you collecting their information?

3. How long it will take

4. How will you use their data? People often need reassurance that their data will not be abused so make sure to address any anonymity/privacy related issues

5. Give the necessary instructions for taking the survey

Survey introduction writing tips and tricks

Once you’ve made sure your survey introduction contains most of the basics presented above (not all of them are always absolutely necessary), there are still some things you can do to make your introduction more compelling and get people to actually take it.

Even if you have all the right ingredients, you need to make sure they are present in good proportion and in such a form that your potential respondents will devour your survey!

This is NOT what a good survey introduction looks like:

To learn what it should look like, keep on reading. Here are some tips to keep in mind when writing a survey introduction:

1. Use plain everyday language

In other words, write as if you’re addressing a real human being. Try not to sound robotic, too formal or overly corporate. Avoid jargon.

Your goal is not to confuse people and make them guess what you’re trying to say – it should require little to no effort on their end to understand what you’re trying to say.

So instead of starting with something dull and dry like “Our organization is seeking people who have previously indulged in purchasing products from our assortment to answer a 12-question market survey…” you could simply start with “We have a few questions about your recent shopping experience with us…”

Much friendlier and more human, right?

2. A simple “thank you” can go a long way

Even if you’re offering some kind of a survey incentive , you still probably need your respondents more than they need you.

Remember that you’re getting something from them and remember to say thank you. Our experience has shown that if people feel that their responses are valued, they are more likely to complete the entire survey and provide genuine responses.

3. Write the introduction last

Yes, you read that right. Even though the introduction is the first thing people see when they get a survey, you should write it only after you’ve written all the questions and answers.

Why is that? Well, a quality survey introduction should act as a summary of what follows. And only after you’ve gone through the entire survey creation process will you have a strong grasp of everything that’s in it.

4. Tell them more about the impact they’re making

Ok, you might have shared the aim of your survey, but have you convinced your potential respondents that they should actually care about it?

There are two ways to go about this. You can show and tell how similar surveys have previously made a difference or you can explain what actions you plan to take based on the survey results.

Whether it’s a major policy change or a new coffee maker for your employees, they should know that their answers can and will make a difference.

The survey introduction example below does a great job at this:

If you would like to check out the entire survey, here’s a free Job Satisfaction Survey Template available to all LeadQuizzes users.

How to add an introduction to your survey

First, log in to your LeadQuizzes account.

Click on Create New Content, then click on Create From Scratch, choose Outcome or Scoring logic (depending on whether or not you want your survey questions to be scored), set Content Name, and you’ll be taken to our intuitive Content Builder where you can add all the necessary elements to your survey.

Move the Cover Page element from the left side of the builder to the right – this will serve as an introduction page for your survey.

Here, you’ll have the option to enter your survey title and description, add a CTA button, and add an image or video. The description part of the cover page is where you will write the copy for your survey introduction.

After you’re completely satisfied with your survey introduction, you can proceed with adding all the questions and answer options using our simple and intuitive survey builder.

As already mentioned above, after you’re finished with the survey creation, you should go back to your introduction and make sure it reflects the content of the entire survey and contains most of the 5 elements listed at the beginning of this post.

Survey introduction examples

Product feedback survey.

Do you know the easiest way to retain your current customers and acquire new ones?

It’s very simple, actually. All you have to do is find out what they think about your products and services and use that knowledge to improve your offering.

There’s no better way to do it than to conduct a product feedback survey.

Below you can see a survey introduction selected from our survey template list . What makes it good is that it addresses some of the key concerns we listed above – explains the aim of the survey, clearly states what’s expected from the respondent, and relates how the data obtained will be used.

Extra tip – If your survey is anonymous, you should always clearly state that as it may increase your response rates.

If you would like to check out the entire survey, here’s a free Product Satisfaction Survey Template available to all LeadQuizzes users.

Patient satisfaction survey

A patient satisfaction survey can be an extremely valuable asset if you’re in any way involved in the healthcare industry. Getting honest feedback from your patients can help you understand your strengths and weaknesses and learn how you can improve your services.

Below you can see an example of a patient satisfaction survey introduction. This is a good example because it clearly states the name of the organization, gives clear instructions, defines the aim of the survey and what actions will be taken based on the data, reassures the respondents about the privacy issues, and shows gratitude for their help.

If you would like to check out the entire survey, here’s a free Patient Satisfaction Survey Template available to all LeadQuizzes users.

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How To Write Survey Introductions: Tips + Free Template

Author Survey Point Team
Published June 16, 2023

So you’ve created your first survey. After extensive hours of thinking and research, you’ve put everything together.

Now, what next?

You’ll want as many of your customers or potential respondents to engage with it as possible so you can have a strong response rate and quality, reliable data.

To acquire all that, you need a powerful survey introduction.

You only get a small window to grab the readers’ attention, hook them in, and persuade them to click on the first question.

So not only should your introduction be convincing, but it should also contain other aspects that will help you improve your survey response rate .

So, how do you write that perfect survey introduction that will get the responses you wish for?

This blog will help you with that. But let’s not get ahead of ourselves.

First, let us check out the definition of a survey introduction, its importance, and some examples. And then, we’ll go through some tips that will help improve your survey game.

Let’s get started!

Table of Contents

Survey Introduction: What is the Purpose?

A survey introduction is a short description that appears on the users’ screens before the survey’s first question. You may also call it a survey disclaimer.

It introduces the potential participant to your brand for the first time. An introduction tells the user what to expect from the survey and why they should undertake it.

A good survey introduction acts as a guiding force. It puts you at the forefront of other companies interested in acquiring customer feedback.

Irrespective of your survey topic, questions, or content, you should always start your survey with an introduction. Based on the survey introduction, potential respondents will decide whether to proceed with the survey.

If you want maximum response to your survey, its introduction should feature the essential information.

Survey Introductions Essentials

A good survey introduction should cover the following bare minimum points:

Introduce Who You Are

People value their privacy the most. Hence, they hesitate when sharing personal information with an unknown entity.

Hence, you must share as many details about yourself and your company as possible.

State What The Research Is About

Be honest about your research and value the respondents’ time.

If you’re transparent and clear, the respondent will perceive you as someone they can trust, thus making them more likely to help.

Explain How Long Will It Take

Time is money, and people have multiple commitments. Prepare your respondents’ minds by providing an estimated completion time, e.g., “This will only take 3 minutes of your time.”

Usually, people prefer shorter surveys, so ensure you only add the essential questions to the questionnaire. You can also let people know how far they are from completing the questionnaire.

Reassure Them About Anonymity

Always emphasize whether the responses will be anonymous. If yes, let the people know; you might receive even more responses and honest answers.

If you can’t provide anonymity, be upfront about it. Nobody wants to have their personal opinions used against them in the future.

To keep your survey introduction to a minimum, add a link to the privacy statement of your company.

Give Proper Instructions

Explain clearly what you want the readers to do. Don’t just assume. Make it as easy as possible for the respondents to answer your questions.

And once you’ve received all the answers, make sure you thank the respondents. You can also bribe your users with some incentive to sweeten the deal.

Types of Survey Introductions

Check out some of our survey introduction examples and pick one that best suits you:

Market Research Survey Introduction

A market research survey details the customers/ target market’s situation. A survey introduction will give your target audience a clear idea about the survey and why their participation is significant.

Market research surveys help you stay updated with the current market requirements and monitor the competition. It also helps improve your offerings. However, before conducting market research, you should be well aware of your target audience.

The audience needs to be introduced to your brand if they are unfamiliar with it. Here are a few illustrations that should help:

Hello there, We are [company name], and we help manufacture affordable [your products] in the world
We’d appreciate you taking the time to answer this three-minute survey. Your responses will be anonymous, and the data will only be used for this research.
Why did we choose you? We are looking for someone who [describes your target audience], which makes you a perfect fit!

Survey Introduction Sample For Students

Student surveys make up an essential part of education surveys. They offer critical feedback on courses, teaching quality, curriculum, extracurricular activities, etc.

When designing surveys for students, always ensure they are anonymous. Often, students fear retaliation, which will cause them to give false responses. Here are some sample survey introductions for students:

Thank you for enrolling in the (XYZ) course at (ABC) school. Kindly share your enrollment experience by answering this 5-question survey.
This survey is anonymous, so do answer frankly. It will help improve our data accuracy and allow us to tailor our curriculum as per the responses.

Customer Satisfaction

How you structure your introduction will determine whether customers will respond to or ignore your survey. Make sure you share it with the customers at the right time. You can share it right after the customer makes a purchase or after he contacts the support team.

Understand your company’s tone and stick to it. Start by thanking and appreciating your customers for their business, then gently direct them to your survey. You can even offer some incentives to encourage participation.

Here are some examples:

Thank you for choosing [company name]! We want to learn more about your shopping experience. It will only take a few minutes!
Share your shopping experience and get 25% off your next purchase!

You can’t compel your customers to share their opinions.

Any feedback they share regarding your business is a gift. They didn’t just take time out of their schedule to buy your product; they interacted with you to give you invaluable data.

Remember it when you’re creating a survey. Keep the introduction persuasive, and give the readers a reason to open your survey.

A decently crafted survey introduction will be vital to your survey strategy. But before you start curating introductions, you must create a survey.

SurveyPoint will help you with it! Create short and stunning surveys in no time.

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Methodology
Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

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

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

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

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

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

Common uses of survey research include:

Social research: Investigating the experiences and characteristics of different social groups
Market research: Finding out what customers think about products, services, and companies
Health research: Collecting data from patients about symptoms and treatments
Politics: Measuring public opinion about parties and policies
Psychology: Researching personality traits, preferences, and behaviours

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

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

Populations

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

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

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

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

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

There are many sampling methods that allow you to generalise to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

There are two main types of survey:

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

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

Questionnaires

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

You can easily access a large sample.
You have some control over who is included in the sample (e.g., residents of a specific region).
The response rate is often low.

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

You can quickly access a large sample without constraints on time or location.
The data is easy to process and analyse.
The anonymity and accessibility of online surveys mean you have less control over who responds.

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

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

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

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

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

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

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

Open-ended vs closed-ended questions

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

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

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

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

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

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

The content of the survey questions

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

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

Phrasing the survey questions

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

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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.

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Conducting Survey Research

Surveys represent one of the most common types of quantitative, social science research. In survey research, the researcher selects a sample of respondents from a population and administers a standardized questionnaire to them. The questionnaire, or survey, can be a written document that is completed by the person being surveyed, an online questionnaire, a face-to-face interview, or a telephone interview. Using surveys, it is possible to collect data from large or small populations (sometimes referred to as the universe of a study).

Different types of surveys are actually composed of several research techniques, developed by a variety of disciplines. For instance, interview began as a tool primarily for psychologists and anthropologists, while sampling got its start in the field of agricultural economics (Angus and Katona, 1953, p. 15).

Survey research does not belong to any one field and it can be employed by almost any discipline. According to Angus and Katona, "It is this capacity for wide application and broad coverage which gives the survey technique its great usefulness..." (p. 16).

Types of Surveys

Surveys come in a wide range of forms and can be distributed using a variety of media.

Mail Surveys

Group administered questionnaires, drop-off surveys, oral surveys, electronic surveys.

An Example Survey

Example Survey

General Instructions: We are interested in your writing and computing experiences and attitudes. Please take a few minutes to complete this survey. In general, when you are presented with a scale next to a question, please put an X over the number that best corresponds to your answer. For example, if you strongly agreed with the following question, you might put an X through the number 5. If you agreed moderately, you might put an X through number 4, if you neither agreed nor disagreed, you might put an X through number 3.

Example Question:

As is the case with all of the information we are collecting for our study, we will keep all the information you provide to us completely confidential. Your teacher will not be made aware of any of your responses. Thanks for your help.

Your Name: ___________________________________________________________

Your Instructor's Name: __________________________________________________

Written Surveys

Imagine that you are interested in exploring the attitudes college students have about writing. Since it would be impossible to interview every student on campus, choosing the mail-out survey as your method would enable you to choose a large sample of college students. You might choose to limit your research to your own college or university, or you might extend your survey to several different institutions. If your research question demands it, the mail survey allows you to sample a very broad group of subjects at small cost.

Strengths and Weaknesses of Mail Surveys

Cost: Mail surveys are low in cost compared to other methods of surveying. This type of survey can cost up to 50% less than the self-administered survey, and almost 75% less than a face-to-face survey (Bourque and Fielder 9). Mail surveys are also substantially less expensive than drop-off and group-administered surveys.

Convenience: Since many of these types of surveys are conducted through a mail-in process, the participants are able to work on the surveys at their leisure.

Bias: Because the mail survey does not allow for personal contact between the researcher and the respondent, there is little chance for personal bias based on first impressions to alter the responses to the survey. This is an advantage because if the interviewer is not likeable, the survey results will be unfavorably affected. However, this could be a disadvantage as well.

Sampling--internal link: It is possible to reach a greater population and have a larger universe (sample of respondents) with this type of survey because it does not require personal contact between the researcher and the respondents.

Low Response Rate: One of the biggest drawbacks to written survey, especially as it relates to the mail-in, self-administered method, is the low response rate. Compared to a telephone survey or a face-to-face survey, the mail-in written survey has a response rate of just over 20%.

Ability of Respondent to Answer Survey: Another problem with self-administered surveys is three-fold: assumptions about the physical ability, literacy level and language ability of the respondents. Because most surveys pull the participants from a random sampling, it is impossible to control for such variables. Many of those who belong to a survey group have a different primary language than that of the survey. They may also be illiterate or have a low reading level and therefore might not be able to accurately answer the questions. Along those same lines, persons with conditions that cause them to have trouble reading, such as dyslexia, visual impairment or old age, may not have the capabilities necessary to complete the survey.

Imagine that you are interested in finding out how instructors who teach composition in computer classrooms at your university feel about the advantages of teaching in a computer classroom over a traditional classroom. You have a very specific population in mind, and so a mail-out survey would probably not be your best option. You might try an oral survey, but if you are doing this research alone this might be too time consuming. The group administered questionnaire would allow you to get your survey results in one space of time and would ensure a very high response rate (higher than if you stuck a survey into each instructor's mailbox). Your challenge would be to get everyone together. Perhaps your department holds monthly technology support meetings that most of your chosen sample would attend. Your challenge at this point would be to get permission to use part of the weekly meeting time to administer the survey, or to convince the instructors to stay to fill it out after the meeting. Despite the challenges, this type of survey might be the most efficient for your specific purposes.

Strengths and Weaknesses of Group Administered Questionnaires

Rate of Response: This second type of written survey is generally administered to a sample of respondents in a group setting, guaranteeing a high response rate.

Specificity: This type of written survey can be very versatile, allowing for a spectrum of open and closed ended types of questions and can serve a variety of specific purposes, particularly if you are trying to survey a very specific group of people.

Weaknesses of Group Administered Questionnaires

Sampling: This method requires a small sample, and as a result is not the best method for surveys that would benefit from a large sample. This method is only useful in cases that call for very specific information from specific groups.

Scheduling: Since this method requires a group of respondents to answer the survey together, this method requires a slot of time that is convenient for all respondents.

Imagine that you would like to find out about how the dorm dwellers at your university feel about the lack of availability of vegetarian cuisine in their dorm dining halls. You have prepared a questionnaire that requires quite a few long answers, and since you suspect that the students in the dorms may not have the motivation to take the time to respond, you might want a chance to tell them about your research, the benefits that might come from their responses, and to answer their questions about your survey. To ensure the highest response rate, you would probably pick a time of the day when you are sure that the majority of the dorm residents are home, and then work your way from door to door. If you don't have time to interview the number of students you need in your sample, but you don't trust the response rate of mail surveys, the drop-off survey might be the best option for you.

Strengths and Weaknesses of Drop-off Surveys

Convenience: Like the mail survey, the drop-off survey allows the respondents to answer the survey at their own convenience.

Response Rates: The response rates for the drop-off survey are better than the mail survey because it allows the interviewer to make personal contact with the respondent, to explain the importance of the survey, and to answer any questions or concerns the respondent might have.

Time: Because of the personal contact this method requires, this method takes considerably more time than the mail survey.

Sampling: Because of the time it takes to make personal contact with the respondents, the universe of this kind of survey will be considerably smaller than the mail survey pool of respondents.

Response: The response rate for this type of survey, although considerably better than the mail survey, is still not as high as the response rate you will achieve with an oral survey.

Oral surveys are considered more personal forms of survey than the written or electronic methods. Oral surveys are generally used to get thorough opinions and impressions from the respondents.

Oral surveys can be administered in several different ways. For instance, in a group interview, as opposed to a group administered written survey, each respondent is not given an instrument (an individual questionnaire). Instead, the respondents work in groups to answer the questions together while one person takes notes for the whole group. Another more familiar form of oral survey is the phone survey. Phone surveys can be used to get short one word answers (yes/no), as well as longer answers.

Strengths and Weaknesses of Oral Surveys

Personal Contact: Oral surveys conducted either on the telephone or in person give the interviewer the ability to answer questions from the participant. If the participant, for example, does not understand a question or needs further explanation on a particular issue, it is possible to converse with the participant. According to Glastonbury and MacKean, "interviewing offers the flexibility to react to the respondent's situation, probe for more detail, seek more reflective replies and ask questions which are complex or personally intrusive" (p. 228).

Response Rate: Although obtaining a certain number of respondents who are willing to take the time to do an interview is difficult, the researcher has more control over the response rate in oral survey research than with other types of survey research. As opposed to mail surveys where the researcher must wait to see how many respondents actually answer and send back the survey, a researcher using oral surveys can, if the time and money are available, interview respondents until the required sample has been achieved.

Cost: The most obvious disadvantage of face-to-face and telephone survey is the cost. It takes time to collect enough data for a complete survey, and time translates into payroll costs and sometimes payment for the participants.

Bias: Using face-to-face interview for your survey may also introduce bias, from either the interviewer or the interviewee.

Types of Questions Possible: Certain types of questions are not convenient for this type of survey, particularly for phone surveys where the respondent does not have a chance to look at the questionnaire. For instance, if you want to offer the respondent a choice of 5 different answers, it will be very difficult for respondents to remember all of the choices, as well as the question, without a visual reminder. This problem requires the researcher to take special care in constructing questions to be read aloud.

Attitude: Anyone who has ever been interrupted during dinner by a phone interviewer is aware of the negative feelings many people have about answering a phone survey. Upon receiving these calls, many potential respondents will simply hang up.

With the growth of the Internet (and in particular the World Wide Web) and the expanded use of electronic mail for business communication, the electronic survey is becoming a more widely used survey method. Electronic surveys can take many forms. They can be distributed as electronic mail messages sent to potential respondents. They can be posted as World Wide Web forms on the Internet. And they can be distributed via publicly available computers in high-traffic areas such as libraries and shopping malls. In many cases, electronic surveys are placed on laptops and respondents fill out a survey on a laptop computer rather than on paper.

Strengths and Weaknesses of Electronic Surveys

Cost-savings: It is less expensive to send questionnaires online than to pay for postage or for interviewers.

Ease of Editing/Analysis: It is easier to make changes to questionnaire, and to copy and sort data.

Faster Transmission Time: Questionnaires can be delivered to recipients in seconds, rather than in days as with traditional mail.

Easy Use of Preletters: You may send invitations and receive responses in a very short time and thus receive participation level estimates.

Higher Response Rate: Research shows that response rates on private networks are higher with electronic surveys than with paper surveys or interviews.

More Candid Responses: Research shows that respondents may answer more honestly with electronic surveys than with paper surveys or interviews.

Potentially Quicker Response Time with Wider Magnitude of Coverage: Due to the speed of online networks, participants can answer in minutes or hours, and coverage can be global.

Sample Demographic Limitations: Population and sample limited to those with access to computer and online network.

Lower Levels of Confidentiality: Due to the open nature of most online networks, it is difficult to guarantee anonymity and confidentiality.

Layout and Presentation issues: Constructing the format of a computer questionnaire can be more difficult the first few times, due to a researcher's lack of experience.

Additional Orientation/Instructions: More instruction and orientation to the computer online systems may be necessary for respondents to complete the questionnaire.

Potential Technical Problems with Hardware and Software: As most of us (perhaps all of us) know all too well, computers have a much greater likelihood of "glitches" than oral or written forms of communication.

Response Rate: Even though research shows that e-mail response rates are higher, Opermann (1995) warns that most of these studies found response rates higher only during the first few days; thereafter, the rates were not significantly higher.

Designing Surveys

Initial planning of the survey design and survey questions is extremely important in conducting survey research. Once surveying has begun, it is difficult or impossible to adjust the basic research questions under consideration or the tool used to address them since the instrument must remain stable in order to standardize the data set. This section provides information needed to construct an instrument that will satisfy basic validity and reliability issues. It also offers information about the important decisions you need to make concerning the types of questions you are going to use, as well as the content, wording, order and format of your survey questionnaire.

Overall Design Issues

Four key issues should be considered when designing a survey or questionnaire: respondent attitude, the nature of the items (or questions) on the survey, the cost of conducting the survey, and the suitability of the survey to your research questions.

Respondent attitude: When developing your survey instrument, it is important to try to put yourself into your target population's shoes. Think about how you might react when approached by a pollster while out shopping or when receiving a phone call from a pollster while you are sitting down to dinner. Think about how easy it is to throw away a response survey that you've received in the mail. When developing your instrument, it is important to choose the method you think will work for your research, but also one in which you have confidence. Ask yourself what kind of survey you, as a respondent, would be most apt to answer.

Nature of questions: It is important to consider the relationship between the medium that you use and the questions that you ask. For instance, certain types of questions are difficult to answer over the telephone. Think of the problems you would have in attempting to record Likert scale responses, as in closed-ended questions, over the telephone--especially if a scale of more than five points is used. Responses to open-ended questions would also be difficult to record and report in telephone interviews.

Cost: Along with decisions about the nature of the questions you ask, expense issues also enter into your decision making when planning a survey. The population under consideration, the geographic distribution of this sample population, and the type of questionnaire used all affect costs.

Ability of instrument to meet needs of research question: Finally, there needs to be a logical link between your survey instrument and your research questions. If it is important to get a large number of responses from a broad sample of the population, you obviously will not choose to do a drop-off written survey or an in-person oral survey. Because of the size of the needed sample, you will need to choose a survey instrument that meets this need, such as a phone or mail survey. If you are interested in getting thorough information that might need a large amount of interaction between the interviewer and respondent, you will probably pick in-person oral survey with a smaller sample of respondents. Your questions, then, will need to reflect both your research goals and your choice of medium.

Creating Questionnaire Questions

Developing well-crafted questionnaires is more difficult than it might seem. Researchers should carefully consider the type, content, wording, and order of the questions that they include. In this section, we discuss the steps involved in questionnaire development and the advantages and disadvantages of various techniques.

Open-ended vs. Closed-ended Questions

All researchers must make two basic decisions when designing a survey--they must decide: 1) whether they are going to employ an oral, written, or electronic method, and 2) whether they are going to choose questions that are open or close-ended.

Closed-Ended Questions: Closed-ended questions limit respondents' answers to the survey. The participants are allowed to choose from either a pre-existing set of dichotomous answers, such as yes/no, true/false, or multiple choice with an option for "other" to be filled in, or ranking scale response options. The most common of the ranking scale questions is called the Likert scale question. This kind of question asks the respondents to look at a statement (such as "The most important education issue facing our nation in the year 2000 is that all third graders should be able to read") and then "rank" this statement according to the degree to which they agree ("I strongly agree, I somewhat agree, I have no opinion, I somewhat disagree, I strongly disagree").

Open-Ended Questions: Open-ended questions do not give respondents answers to choose from, but rather are phrased so that the respondents are encouraged to explain their answers and reactions to the question with a sentence, a paragraph, or even a page or more, depending on the survey. If you wish to find information on the same topic as asked above (the future of elementary education), but would like to find out what respondents would come up with on their own, you might choose an open-ended question like "What do you think is the most important educational issue facing our nation in the year 2000?" rather than the Likert scale question. Or, if you would like to focus on reading as the topic, but would still not like to limit the participants' responses, you might pose the question this way: "Do you think that the most important issue facing education is literacy? Explain your answer below."

Note: Keep in mind that you do not have to use close-ended or open-ended questions exclusively. Many researchers use a combination of closed and open questions; often researchers use close-ended questions in the beginning of their survey, then allow for more expansive answers once the respondent has some background on the issue and is "warmed-up."

Rating scales: ask respondents to rate something like an idea, concept, individual, program, product, etc. based on a closed ended scale format, usually on a five-point scale. For example, a Likert scale presents respondents with a series of statements rather than questions, and the respondents are asked to which degree they disagree or agree.

Ranking scales: ask respondents to rank a set of ideas or things, etc. For example, a researcher can provide respondents with a list of ice cream flavors, and then ask them to rank these flavors in order of which they like best, with the rank of "one" representing their favorite. These are more difficult to use than rating scales. They will take more time, and they cannot easily be used for phone surveys since they often require visual aids. However, since ranking scales are more difficult, they may actually increase appropriate effort from respondents.

Magnitude estimation scales: ask respondents to provide numeric estimation of answers. For example, respondents might be asked: "Since your least favorite ice cream flavor is vanilla, we'll give it a score of 10. If you like another ice cream 20 times more than vanilla, you'll give it a score of 200, and so on. So, compared to vanilla at a score of ten, how much do you like rocky road?" These scales are obviously very difficult for respondents. However, these scales have been found to help increase variance explanations over ordinal scaling.

Split or unfolding questions: begin by asking respondents a general question, and then follow up with clarifying questions.

Funneling questions: guide respondents through complex issues or concepts by using a series of questions that progressively narrow to a specific question. For example, researchers can start asking general, open-ended questions, and then move to asking specific, closed-ended, forced-choice questions.

Inverted funneling questions: ask respondents a series of questions that move from specific issues to more general issues. For example, researchers can ask respondents specific, closed-ended questions first and then ask more general, open-ended questions. This technique works well when respondents are not expected to be knowledgeable about a content area or when they are not expected to have an articulate opinion regarding an issue.

Factorial questions: use stories or vignettes to study judgment and decision-making processes. For example, a researcher could ask respondents: "You're in a dangerous, rapidly burning building. Do you exit the building immediately or go upstairs to wake up the other inhabitants?" Converse and Presser (1986) warn that little is known about how this survey question technique compares with other techniques.

The wording of survey questions is a tricky endeavor. It is difficult to develop shared meanings or definitions between researchers and the respondents, and among respondents.

In The Practice of Social Research , Keith Crew, a professor of Sociology at the University of Kentucky, cites a famous example of a survey gone awry because of wording problems. An interview survey that included Likert-type questions ranging from "very much" to "very little" was given in a small rural town. Although it would seem that these items would accurately record most respondents' opinions, in the colloquial language of the region the word "very" apparently has an idiomatic usage which is closer to what we mean by "fairly" or even "poorly." You can just imagine what this difference in definition did to the survey results (p. 271).

This, however, is an extreme case. Even small changes in wording can shift the answers of many respondents. The best thing researchers can do to avoid problems with wording is to pretest their questions. However, researchers can also follow some suggestions to help them write more effective survey questions.

To write effective questions, researchers need to keep in mind these four important techniques: directness, simplicity, specificity, and discreteness.

Questions should be written in a straightforward, direct language that is not caught up in complex rhetoric or syntax, or in a discipline's slang or lingo. Questions should be specifically tailored for a group of respondents.
Questions should be kept short and simple. Respondents should not be expected to learn new, complex information in order to answer questions.
Specific questions are for the most part better than general ones. Research shows that the more general a question is the wider the range of interpretation among respondents. To keep specific questions brief, researchers can sometimes use longer introductions that make the context, background, and purpose of the survey clear so that this information is not necessary to include in the actual questions.
Avoid questions that are overly personal or direct, especially when dealing with sensitive issues.

When considering the content of your questionnaire, obviously the most important consideration is whether the content of the questions will elicit the kinds of questions necessary to answer your initial research question. You can gauge the appropriateness of your questions by pretesting your survey, but you should also consider the following questions as you are creating your initial questionnaire:

Does your choice of open or close-ended questions lead to the types of answers you would like to get from your respondents?
Is every question in your survey integral to your intent? Superfluous questions that have already been addressed or are not relevant to your study will waste the time of both the respondents and the researcher.
Does one topic warrant more than one question?
Do you give enough prior information/context for each set of questions? Sometimes lead-in questions are useful to help the respondent become familiar and comfortable with the topic.
Are the questions both general enough (they are both standardized and relevant to your entire sample), and specific enough (avoid vague generalizations and ambiguousness)?
Is each question as succinct as it can be without leaving out essential information?
Finally, and most importantly, try to put yourself in your respondents' shoes. Write a survey that you would be willing to answer yourself, and be polite, courteous, and sensitive. Thank the responder for participating both at the beginning and the end of the survey.

Order of Questions

Although there are no general rules for ordering survey questions, there are still a few suggestions researchers can follow when setting up a questionnaire.

Pretesting can help determine if the ordering of questions is effective.
Which topics should start the survey off, and which should wait until the end of the survey?
What kind of preparation do my respondents need for each question?
Do the questions move logically from one to the next, and do the topics lead up to each other?

The following general guidelines for ordering survey questions can address these questions:

Use warm-up questions. Easier questions will ease the respondent into the survey and will set the tone and the topic of the survey.
Sensitive questions should not appear at the beginning of the survey. Try to put the responder at ease before addressing uncomfortable issues. You may also prepare the reader for these sensitive questions with some sort of written preface.
Consider transition questions that make logical links.
Try not to mix topics. Topics can easily be placed into "sets" of questions.
Try not to put the most important questions last. Respondents may become bored or tired before they get to the end of the survey.
Be careful with contingency questions ("If you answered yes to the previous question . . . etc.").
If you are using a combination of open and close-ended questions, try not to start your survey with open-ended questions. Respondents will be more likely to answer the survey if they are allowed the ease of closed-questions first.

Borrowing Questions

Before developing a survey questionnaire, Converse and Presser (1986) recommend that researchers consult published compilations of survey questions, like those published by the National Opinion Research Center and the Gallup Poll. This will not only give you some ideas on how to develop your questionnaire, but you can even borrow questions from surveys that reflect your own research. Since these questions and questionnaires have already been tested and used effectively, you will save both time and effort. However, you will need to take care to only use questions that are relevant to your study, and you will usually have to develop some questions on your own.

Advantages of Closed-Ended Questions

Closed-ended questions are more easily analyzed. Every answer can be given a number or value so that a statistical interpretation can be assessed. Closed-ended questions are also better suited for computer analysis. If open-ended questions are analyzed quantitatively, the qualitative information is reduced to coding and answers tend to lose some of their initial meaning. Because of the simplicity of closed-ended questions, this kind of loss is not a problem.
Closed-ended questions can be more specific, thus more likely to communicate similar meanings. Because open-ended questions allow respondents to use their own words, it is difficult to compare the meanings of the responses.
In large-scale surveys, closed-ended questions take less time from the interviewer, the participant and the researcher, and so is a less expensive survey method. The response rate is higher with surveys that use closed-ended question than with those that use open-ended questions.

Advantages of Open-Ended Questions

Open-ended questions allow respondents to include more information, including feelings, attitudes and understanding of the subject. This allows researchers to better access the respondents' true feelings on an issue. Closed-ended questions, because of the simplicity and limit of the answers, may not offer the respondents choices that actually reflect their real feelings. Closed-ended questions also do not allow the respondent to explain that they do not understand the question or do not have an opinion on the issue.
Open-ended questions cut down on two types of response error; respondents are not likely to forget the answers they have to choose from if they are given the chance to respond freely, and open-ended questions simply do not allow respondents to disregard reading the questions and just "fill in" the survey with all the same answers (such as filling in the "no" box on every question).
Because they allow for obtaining extra information from the respondent, such as demographic information (current employment, age, gender, etc.), surveys that use open-ended questions can be used more readily for secondary analysis by other researchers than can surveys that do not provide contextual information about the survey population.

Potential Problems with Survey Questions

While designing questions for a survey, researchers should to be aware of a few problems and how to avoid them:

"Everyone has an opinion": It is incorrect to assume that each respondent has an opinion regarding every question. Therefore, you might offer a "no opinion" option to avoid this assumption. Filters can also be created. For example, researchers can ask respondents if they have any thoughts on an issue, to which they have the option to say "no."

Agree and disagree statements: according to Converse and Presser (1986), these statements suffer from "acquiescence" or the tendency of respondents to agree despite question content (p.35). Researchers can avoid this problem by using forced-choice questions with these statements.

Response order bias: this occurs when a respondent loses track of all options and picks one that comes easily to mind rather than the most accurate. Typically, the respondent chooses the last or first response option. This problem might occur if researchers use long lists and/or rating scales.

Response set: this problem can occur when using a close-ended question format with response options like yes/no or agree/disagree. Sometimes respondents do not consider each question and just answer no or disagree to all questions.

Telescoping: occurs when respondents report that an event took place more recently than it actually did. To avoid this problem, Frey and Mertens (1995) say researchers can use "aided recall"-using a reference point or landmark, or list of events or behaviors (p. 101).

Forward telescoping: occurs when respondents include events that have actually happened before the time frame established. This results in overreporting. According to Converse and Presser (1986), researchers can use "bounded recall" to avoid this problem (p.21). Bounded recall is when researchers interview respondents several months or so after the initial interview to inquire about events that have happened since then. This technique, however, requires more resources. Converse and Presser said that researchers can also just try to narrow the reference points used, which has been shown to reduce this problem too.

Fatigue effect: happens when respondents grow bored or tired during the interview. To avoid this problem, Frey and Mertens (1995) say researchers can use transitions, vary questions and response options, and they can put easy to answer questions at the end of the questionnaire.

Types of Questions to Avoid

Double-barreled questions- force respondents to make two decisions in one. For example, a question like: "Do you think women and children should be given the first available flu shots?" does not allow the responder to choose whether women or children should be given the first shots.
Double negative questions-for example: "Please tell me whether or not you agree or disagree with this statement. Graduate teaching assistants should not be required to help students outside of class." Respondents may confuse the meaning of the disagree option.
Hypothetical questions- are typically too difficult for respondents since they require more scrutiny. For example, "If there were a cure for cancer, would you still support euthanasia?"
Ambiguous questions- respondents might not understand the question.
Biased questions- For example, "Don't you think that suffering terminal cancer patients should be allowed to be released from their pain?" Researchers should never try to make one response option look more suitable than another.
Questions with long lists-these questions may tire respondents or respondents may lose track of the question.

Pretesting the Questionnaire

Ultimately, designing the perfect survey questionnaire is impossible. However, researchers can still create effective surveys. To determine the effectiveness of your survey questionnaire, it is necessary to pretest it before actually using it. Pretesting can help you determine the strengths and weaknesses of your survey concerning question format, wording and order.

There are two types of survey pretests: participating and undeclared .

Participating pretests dictate that you tell respondents that the pretest is a practice run; rather than asking the respondents to simply fill out the questionnaire, participating pretests usually involve an interview setting where respondents are asked to explain reactions to question form, wording and order. This kind of pretest will help you determine whether the questionnaire is understandable.
When conducting an undeclared pretest , you do not tell respondents that it is a pretest. The survey is given just as you intend to conduct it for real. This type of pretest allows you to check your choice of analysis and the standardization of your survey. According to Converse and Presser (1986), if researchers have the resources to do more than one pretest, it might be best to use a participatory pretest first, then an undeclared test.

General Applications of Pretesting:

Whether or not you use a participating or undeclared pretest, pretesting should ideally also test specifically for question variation, meaning, task difficulty, and respondent interest and attention. Your pretests should also include any questions you borrowed from other similar surveys, even if they have already been pretested, because meaning can be affected by the particular context of your survey. Researchers can also pretest the following: flow, order, skip patterns, timing, and overall respondent well-being.

Pretesting for reliability and validity:

Researchers might also want to pretest the reliability and validity of the survey questions. To be reliable, a survey question must be answered by respondents the same way each time. According to Weisberg et. al (1989), researchers can assess reliability by comparing the answers respondents give in one pretest with answers in another pretest. Then, a survey question's validity is determined by how well it measures the concept(s) it is intended to measure. Both convergent validity and divergent validity can be determined by first comparing answers to another question measuring the same concept, then by measuring this answer to the participant's response to a question that asks for the exact opposite answer.

For instance, you might include questions in your pretest that explicitly test for validity: if a respondent answers "yes" to the question, "Do you think that the next president should be a Republican?" then you might ask "What party do you think you might vote for in the next presidential election?" to check for convergent validity, then "Do you think that you will vote Democrat in the next election?" to check the answer for divergent validity.

Conducting Surveys

Once you have constructed a questionnaire, you'll need to make a plan that outlines how and to whom you will administer it. There are a number of options available in order to find a relevant sample group amongst your survey population. In addition, there are various considerations involved with administering the survey itself.

Administering a Survey

This section attempts to answer the question: "How do I go about getting my questionnaire answered?"

For all types of surveys, some basic practicalities need to be considered before the surveying begins. For instance, you need to find the most convenient time to carry out the data collection (this becomes particularly important in interview surveying and group-administered surveys), how long the data collection is likely to take. Finally, you need to make practical arrangements for administering the survey. Pretesting your survey will help you determine the time it takes to administer, process, and analyze your survey, and will also help you clear out some of the bugs.

Administering Written Surveys

Written surveys can be handled in several different ways. A research worker can deliver the questionnaires to the homes of the sample respondents, explain the study, and then pick the questionnaires up on a later date (or, alternately, ask the respondent to mail the survey back when completed). Another option is mailing questionnaires directly to homes and having researchers pick up and check the questionnaires for completeness in person. This method has proven to have higher response rates than straightforward mail surveys, although it tends to take more time and money to administer.

It is important to put yourself into the role of respondent when deciding how to administer your survey. Most of us have received and thrown away a mail survey, and so it may be useful to think back to the reasons you had for not filling it out and returning it. Here are some ideas for boosting your response rate:

Include in each questionnaire a letter of introduction and explanation, and a self-addressed, stamped envelope for returning the questionnaire.
Oftentimes, when it fits the study's budget, the envelope might also include a monetary "reward" (usually a dollar to five dollars) as an incentive to fill out the survey.
Another method for saving the responder time is to create a self-mailing questionnaire that requires no envelope but folds easily so that the return address appears on the outside. The easier you make the process of completing and returning the survey, the better your survey results will be.
Follow up mailings are an important part of administering mail surveys. Nonrespondents can be sent letters of additional encouragement to participate. Even better, a new copy of the survey can be sent to nonresponders. Methodological literature suggests that three follow up letters are adequate, and two to three weeks should be allowed between each mailing.

Administering Oral Surveys

Face-To-Face Surveys

Oftentimes conducting oral surveys requires a staff of interviewers; to control this variable as much as possible, the presentation and preparation of the interviewer is an important consideration.

In any face-to-face interview, the appearance of the interviewer is important. Since the success of any survey relies on the interest of the participants to respond to the survey, the interviewer should take care to dress and act in such a way that would not offend the general sample population.
Of equal importance is the preparedness of the interviewer. The interviewer should be well acquainted with the questions, and have ample practice administering the survey with mock interviews. If several interviewers will be used, they should be trained as a group to ensure standardization and control. Interviewers also need to carry a letter of identification/authentication to present at in-person surveys.

When actually administering the survey, you need to make decisions about how much of the participants' responses need to be recorded, how much the interviewer will need to "probe" for responses, and how much the interviewer will need to account for context (what is the respondent's age, race, gender, reaction to the study, etc.) If you are administering a close-ended question survey, these may not be considerations. On the other hand, when recording more open-ended responses, the researcher needs to decide beforehand on each of these factors:

It depends on the purpose of the study whether the interview should be recorded word for word, or whether the interviewer should record general impressions and opinions. However, for the sake of precision, the former approach is preferred. More information is always better than less when it comes to analyzing the results.
Sometimes respondents will respond to a question with an inappropriate answer; this can happen with both open and close-question surveys. Even if you give the participant structured choices like "I agree" or "I disagree," they might respond "I think that is true," which might require the interviewer to probe for an appropriate answer. In an open-question survey, this probing becomes more challenging. The interviewer might come with a set of potential questions if the respondent does not elaborate enough or strays from the subject. The nature of these probes, however, need to be constructed by the researcher rather than ad-libbed by the interviewers, and should be carefully controlled so that they do not lead the respondent to change answers.

Phone Surveys

Phone surveys certainly involve all of the preparedness of the face-to-face surveys, but encounter new problems because of their reputation. It is much easier to hang-up on a phone surveyor than it is to slam the door in someone's face, and so the sheer number of calls needed to complete a survey can be baffling. Computer innovation has tempered this problem a bit by allowing more for quick and random number dialing and the ability for interviewers to type answers programs that automatically set up the data for analysis. Systems like CATI (Computer-assisted survey interview) have made phone surveys a more cost and time effective method, and therefore a popular one, although respondents are getting more and more reluctant to answer phone surveys because of the increase in telemarketing.

Before conducting a survey, you must choose a relevant survey population. And, unless a survey population is very small, it is usually impossible to survey the entire relevant population. Therefore, researchers usually just survey a sample of a population from an actual list of the relevant population, which in turn is called a sampling frame . With a carefully selected sample, researchers can make estimations or generalizations regarding an entire population's opinions, attitudes or beliefs on a particular topic.

Sampling Procedures and Methods

There are two different types of sampling procedures-- probability and nonprobability . Probability sampling methods ensure that there is a possibility for each person in a sample population to be selected, whereas nonprobability methods target specific individuals. Nonprobability sampling methods include the following:

Purposive samples: to purposely select individuals to survey.
Volunteer subjects: to ask for volunteers to survey.
Haphazard sampling: to survey individuals who can be easily reached.
Quota sampling: to select individuals based on a set quota. For example, if a census indicates that more than half of the population is female, then the sample will be adjusted accordingly.

Clearly, there can be an inherent bias in nonprobability methods. Therefore, according to Weisberg, Krosnick, and Bowen (1989), it is not surprising that most survey researchers prefer probability sampling methods. Some commonly used probability sampling methods for surveys are:

Simple random sample: a sample is drawn randomly from a list of individuals in a population.
Systematic selection procedure sample: a variant of a simple random sample in which a random number is chosen to select the first individual and so on from there.
Stratified sample: dividing up the population into smaller groups, and randomly sampling from each group.
Cluster sample: dividing up a population into smaller groups, and then only sampling from one of the groups. Cluster sampling is " according to Lee, Forthofer, and Lorimer (1989), is considered a more practical approach to surveys because it samples by groups or clusters of elements rather than by individual elements" (p. 12). It also reduces interview costs. However, Weisberg et. al (1989) said accuracy declines when using this sampling method.
Multistage sampling: first, sampling a set of geographic areas. Then, sampling a subset of areas within those areas, and so on.

Sampling and Nonsampling Errors

Directly related to sample size are the concepts of sampling and nonsampling errors. According to Fox and Tracy (1986), surveys are subject to both sampling errors and nonsampling errors.

A sampling error arises from the fact that inevitably samples differ from their populations. Therefore, survey sample results should be seen only as estimations. Weisberg et. al. (1989) said sampling errors cannot be calculated for nonprobability samples, but they can be determined for probability samples. First, to determine sample error, look at the sample size. Then, look at the sampling fraction--the percentage of the population that is being surveyed. Thus, the more people surveyed, the smaller the error. This error can also be reduced, according to Fox and Tracy (1986), by increasing the representativeness of the sample.

Then, there are two different kinds of nonsampling error--random and nonrandom errors. Fox and Tracy (1986) said random errors decrease the reliability of measurements. These errors can be reduced through repeated measurements. Nonrandom errors result from a bias in survey data, which is connected to response and nonresponse bias.

Confidence Level and Interval

Any statement of sampling error must contain two essential components: the confidence level and the confidence interval. These two components are used together to express the accuracy of the sample's statistics in terms of the level of confidence that the statistics fall within a specified interval from the true population parameter. For example, a researcher may be "95 percent confident" that the sample statistic (that 50 percent favor candidate X) is within plus or minus 5 percentage points of the population parameter. In other words, the researcher is 95 percent confident that between 45 and 55 percent of the total population favor candidate X.

Lauer and Asher (1988) provide a table that gives the confidence interval limits for percentages based upon sample size (p. 58):

Sample Size and Confidence Interval Limits

(95% confidence intervals based on a population incidence of 50% and a large population relative to sample size.)

Confidence Limits and Sample Size

When selecting a sample size, one can consider that a higher number of individuals surveyed from a target group yields a tighter measurement, a lower number yields a looser range of confidence limits. The confidence limits may need to be corrected if, according to Lauer and Asher (1988), "the sample size starts to approach the population size" or if "the variable under scrutiny is known to have a much [original emphasis] smaller or larger occurrence than 50% in the whole population" (p. 59). For smaller populations, Singleton (1988) said the standard error or confidence interval should be multiplied by a correction factor equal to sqrt(1 - f), where "f" is the sampling fraction, or proportion of the population included in the sample.

Lauer and Asher (1988) give a table of correction factors for confidence limits where sample size is an important part of population size (p. 60) and also a table of correction factors for where the percentage incidence of the parameter in the population is not 50% (p. 61).

Tables for Calculating Confidence Limits vs. Sample Size

Correction Factors for Confidence Limits When Sample Size (n) Is an Important Part of Population Size (N >= 100)

(For n over 70% of N, take all of N)

From Lauer and Asher (1988, p. 60)

Correction Factors for Rare and Common Percentage of Variables

From Lauer and Asher (1988, p. 61)

Analyzing Survey Results

After creating and conducting your survey, you must now process and analyze the results. These steps require strict attention to detail and, in some cases, knowledge of statistics and computer software packages. How you conduct these steps will depend on the scope of your study, your own capabilities, and the audience to whom you wish to direct the work.

Processing the Results

It is clearly important to keep careful records of survey data in order to do effective work. Most researchers recommend using a computer to help sort and organize the data. Additionally, Glastonbury and MacKean point out that once the data has been filtered though the computer, it is possible to do an unlimited amount of analysis (p. 243).

Jolliffe (1986) believes that editing should be the first step to processing this data. He writes, "The obvious reason for this is to ensure that the data analyzed are correct and complete . At the same time, editing can reduce the bias, increase the precision and achieve consistency between the tables [regarding those produced by social science computer software] (p. 100). Of course, editing may not always be necessary, if for example you are doing a qualitative analysis of open-ended questions, or the survey is part of a larger project and gets distributed to other agencies for analysis. However, editing could be as simple as checking the information input into the computer.

All of this information should be used to test for statistical significance. See our guide on Statistics for more on this topic.

Information may be recorded in any number of ways. Charts and graphs are clear, visual ways to record findings in many cases. For instance, in a mail-out survey where response rate is an issue, you might use a response rate graph to make the process easier. The day the surveys are mailed out should be recorded first. Then, every day thereafter, the number of returned questionnaires should be logged on the graph. Be sure to record both the number returned each day, and the cumulative number, or percentage. Also, as each completed questionnaire is returned, each should be opened, scanned and assigned an identification number.

Analyzing the Results

Before actually beginning the survey the researcher should know how they want to analyze the data. As stated in the Processing the Results section, if you are collecting quantifiable data, a code book is needed for interpreting your data and should be established prior to collecting the survey data. This is important because there are many different formulas needed in order to properly analyze the survey research and obtain statistical significance. Since computer programs have made the process of analyzing data vastly easier than it was, it would be sensible to choose this route. Be sure to pick your program before you design your survey - - some programs require the data to be laid out in different ways.

After the survey is conducted and the data collected, the results must be assembled in some useable format that allows comparison within the survey group, between groups, or both. The results could be analyzed in a number of ways. A T-test may be used to determine if scores of two groups differ on a single variable--whether writing ability differs among students in two classrooms, for instance. A matched T-Test could also be applied to determine if scores of the same participants in a study differ under different conditions or over time. An ANOVA could be applied if the study compares multiple groups on one or more variables. Correlation measurements could also be constructed to compare the results of two interacting variables within the data set.

Secondary Analysis

Secondary analysis of survey data is an accepted methodology which applies previously collected survey data to new research questions. This methodology is particularly useful to researchers who do not have the time or money to conduct an extensive survey, but may be looking at questions for which some large survey has already collected relevant data. A number of books and chapters have been written about this methodology, some of which are listed in the annotated bibliography under "Secondary Analysis."

Advantages and Disadvantages of Using Secondary Analysis

Considerably cheaper and faster than doing original studies
You can benefit from the research from some of the top scholars in your field, which for the most part ensures quality data.
If you have limited funds and time, other surveys may have the advantage of samples drawn from larger populations.
How much you use previously collected data is flexible; you might only extract a few figures from a table, you might use the data in a subsidiary role in your research, or even in a central role.
A network of data archives in which survey data files are collected and distributed is readily available, making research for secondary analysis easily accessible.

Disadvantages

Since many surveys deal with national populations, if you are interested in studying a well-defined minority subgroup you will have a difficult time finding relevant data.
Secondary analysis can be used in irresponsible ways. If variables aren't exactly those you want, data can be manipulated and transformed in a way that might lessen the validity of the original research.
Much research, particularly of large samples, can involve large data files and difficult statistical packages.

Data-entry Packages Available for Survey Data Analysis

SNAP: Offers simple survey analysis, is able to help with the survey from start to finish, including the designing of questions and questionnaires.

SPSS: Statistical package for social sciences; can cope with most kinds of data.

SAS: A flexible general purpose statistical analysis system.

MINITAB: A very easy-to-use and fairly limited general purpose package for "beginners."

STATGRAPHS: General interactive statistical package with good graphics but not very flexible.

Reporting Survey Results

The final stage of the survey is to report your results. There is not an established format for reporting a survey's results. The report may follow a pattern similar to formal experimental write-ups, or the analysis may show up in pitches to advertising agencies--as with Arbitron data--or the analysis may be presented in departmental meetings to aid curriculum arguments. A formal report might contain contextual information, a literature review, a presentation of the research question under investigation, information on survey participants, a section explaining how the survey was conducted, the survey instrument itself, a presentation of the quantified results, and a discussion of the results.

You can choose to graphically represent your data for easier interpretation by others outside your research project. You can use, for example, bar graphs, histograms, frequency polygrams, pie charts and consistency tables.

Commentary on Survey Research

In this section, we present several commentaries on survey research.

Strengths and Weaknesses of Surveys

Surveys are relatively inexpensive (especially self-administered surveys).
Surveys are useful in describing the characteristics of a large population. No other method of observation can provide this general capability.
They can be administered from remote locations using mail, email or telephone.
Consequently, very large samples are feasible, making the results statistically significant even when analyzing multiple variables.
Many questions can be asked about a given topic giving considerable flexibility to the analysis.
There is flexibilty at the creation phase in deciding how the questions will be administered: as face-to-face interviews, by telephone, as group administered written or oral survey, or by electonic means.
Standardized questions make measurement more precise by enforcing uniform definitions upon the participants.
Standardization ensures that similar data can be collected from groups then interpreted comparatively (between-group study).
Usually, high reliability is easy to obtain--by presenting all subjects with a standardized stimulus, observer subjectivity is greatly eliminated.

Weaknesses:

A methodology relying on standardization forces the researcher to develop questions general enough to be minimally appropriate for all respondents, possibly missing what is most appropriate to many respondents.
Surveys are inflexible in that they require the initial study design (the tool and administration of the tool) to remain unchanged throughout the data collection.
The researcher must ensure that a large number of the selected sample will reply.
It may be hard for participants to recall information or to tell the truth about a controversial question.
As opposed to direct observation, survey research (excluding some interview approaches) can seldom deal with "context."

Reliability and Validity

Surveys tend to be weak on validity and strong on reliability. The artificiality of the survey format puts a strain on validity. Since people's real feelings are hard to grasp in terms of such dichotomies as "agree/disagree," "support/oppose," "like/dislike," etc., these are only approximate indicators of what we have in mind when we create the questions. Reliability, on the other hand, is a clearer matter. Survey research presents all subjects with a standardized stimulus, and so goes a long way toward eliminating unreliability in the researcher's observations. Careful wording, format, content, etc. can reduce significantly the subject's own unreliability.

Ethical Considerations of Using Electronic Surveys

Because electronic mail is rapidly becoming such a large part of our communications system, this survey method deserves special attention. In particular, there are four basic ethical issues researchers should consider if they choose to use email surveys.

Sample Representatives: Since researchers who choose to do surveys have an ethical obligation to use population samples that are inclusive of race, gender, educational and income levels, etc., if you choose to utilize e-mail to administer your survey you face some serious problems. Individuals who have access to personal computers, modems and the Internet are not necessarily representative of a population. Therefore, it is suggested that researchers not use an e-mail survey when a more inclusive research method is available. However, if you do choose to do an e-mail survey because of its other advantages, you might consider including as part of your survey write up a reminder of the limitations of sample representativeness when using this method.

Data Analysis: Even though e-mail surveys tend to have greater response rates, researchers still do not necessarily know exactly who has responded. For example, some e-mail accounts are screened by an unintended viewer before they reach the intended viewer. This issue challenges the external validity of the study. According to Goree and Marszalek (1995), because of this challenge, "researchers should avoid using inferential analysis for electronic surveys" (p. 78).

Confidentiality versus Anonymity: An electronic response is never truly anonymous, since researchers know the respondents' e-mail addresses. According to Goree and Marszalek (1995), researchers are ethically required to guard the confidentiality of their respondents and to assure respondents that they will do so.

Responsible Quotation: It is considered acceptable for researchers to correct typographical or grammatical errors before quoting respondents since respondents do not have the ability to edit their responses. According to Goree and Marszalek (1995), researchers are also faced with the problem of "casual language" use common to electronic communication (p. 78). Casual language responses may be difficult to report within the formal language used in journal articles.

Response Rate Issues

Each year, nonresponse and response rates are becoming more and more important issues in survey research. According to Weisberg, Krosnick and Bowen (1989), in the 1950s it was not unusual for survey researchers to obtain response rates of 90 percent. Now, however, people are not as trusting of interviewers and response rates are much lower--typically 70 percent or less. Today, even when survey researchers obtain high response rates, they still have to deal with many potential respondent problems.

Nonresponse Issues

Nonresponse Errors Nonresponse is usually considered a source of bias in a survey, aptly called nonresponse bias . Nonresponse bias is a problem for almost every survey as it arises from the fact that there are usually differences between the ideal sample pool of respondents and the sample that actually responds to a survey. According to Fox and Tracy (1986), "when these differences are related to criterion measures, the results may be misleading or even erroneous" (p. 9). For example, a response rate of only 40 or 50 percent creates problems of bias since the results may reflect an inordinate percentage of a particular demographic portion of the sample. Thus, variance estimates and confidence intervals become greater as the sample size is reduced, and it becomes more difficult to construct confidence limits.

Nonresponse bias usually cannot be avoided and so inevitably negatively affects most survey research by creating errors in a statistical measurement. Researchers must therefore account for nonresponse either during the planning of their survey or during the analysis of their survey results. If you create a larger sample during the planning stage, confidence limits may be based on the actual number of responses themselves.

Household-Level Determinants of Nonresponse

According to Couper and Groves (1996), reductions in nonresponse and its errors should be based on a theory of survey participation. This theory of survey participation argues that a person's decision to participate in a survey generally occurs during the first moments of interaction with an interviewer or the text. According to Couper and Groves, four types of influences affect a potential respondent's decision of whether or not to cooperate in a survey. First, potential respondents are influenced by two factors that the researcher cannot control: by their social environments and by their immediate households. Second, potential respondents are influenced by two factors the researcher can control: the survey design and the interviewer.

To minimize nonresponse, Couper and Groves suggest that researchers manipulate the two factors they can control--the survey design and the interviewer.

Response Issues

Not only do survey researchers have to be concerned about nonresponse rate errors, but they also have to be concerned about the following potential response rate errors:

Response bias occurs when respondents deliberately falsify their responses. This error greatly jeopardizes the validity of a survey's measurements.
Response order bias occurs when a respondent loses track of all options and picks one that comes easily to mind rather than the most accurate.
Response set bias occurs when respondents do not consider each question and just answer all the questions with the same response. For example, they answer "disagree" or "no" to all questions.

These response errors can seriously distort a survey's results. Unfortunately, according to Fox and Tracy (1986), response bias is difficult to eliminate; even if the same respondent is questioned repeatedly, he or she may continue to falsify responses. Response order bias and response set errors, however, can be reduced through careful development of the survey questionnaire.

Satisficing

Related to the issue of response errors, especially response order bias and response bias, is the issue of satisficing. According to Krosnick, Narayan, and Smith (1996) satisficing is the notion that certain survey response patterns occur as respondents "shortcut the cognitive processes necessary for generating optimal answers" (p. 29). This theoretical perspective arises from the belief that most respondents are not highly motivated to answer a survey's questions, as reflected in the declining response rates in recent years. Since many people are reluctant to be interviewed, it is presumptuous to assume that respondents will devote a lot of effort to answering a survey.

The theoretical notion of satisficing can be further understood by considering what respondents must do to provide optimal answers. According to Krosnick et. al. (1996), "respondents must carefully interpret the meaning of each question, search their memories extensively for all relevant information, integrate that information carefully into summary judgments, and respond in ways that convey those judgments' meanings as clearly and precisely as possible"(p. 31). Therefore, satisficing occurs when one or more of these cognitive steps is compromised.

Satisficing takes two forms: weak and strong . Weak satisficing occurs when respondents go through all of the cognitive steps necessary to provide optimal answers, but are not as thorough in their cognitive processing. For example, respondents can answer a question with the first response that seems acceptable instead of generating an optimal answer. Strong satisficing, on the other hand, occurs when respondents omit the steps of judgment and retrieval altogether.

Even though they believe that not enough is known yet to offer suggestions on how to increase optimal respondent answers, Krosnick et. al. (1996) argue that satisficing can be reduced by maximizing "respondent motivation" and by "minimizing task difficulty" in the survey questionnaire (p. 43).

Annotated Bibliography

General Survey Information:

Allan, Graham, & Skinner, Chris (eds.) (1991). Handbook for Research Students in the Social Sciences. The Falmer Press: London.

This book is an excellent resource for anyone studying in the social sciences. It is not only well-written, but it is clear and concise with pertinent research information.

Alreck, P. L., & Settle, R. B. (1995 ). The survey research handbook: Guidelines and strategies for conducting a survey (2nd). Burr Ridge, IL: Irwin.

Provides thorough, effective survey research guidelines and strategies for sponsors, information seekers, and researchers. In a very accessible, but comprehensive, format, this handbook includes checklists and guidelists within the text, bringing together all the different techniques and principles, skills and activities to do a "really effective survey."

Babbie, E.R. (1973). Survey research methods . Belmont, CA: Wadsworth.

A comprehensive overview of survey methods. Solid basic textbook on the subject.

Babbie, E.R. (1995). The practice of social research (7th). Belmont, CA: Wadsworth.

The reference of choice for many social science courses. An excellent overview of question construction, sampling, and survey methodology. Includes a fairly detailed critique of an example questionnaire. Also includes a good overview of statistics related to sampling.

Belson, W.A. (1986). Validity in survey research . Brookvield, VT: Gower.

Emphasis on construction of survey instrument to account for validity.

Bourque, Linda B. & Fiedler, Eve P. (1995). How to Conduct Self-Administered and Mail Surveys. Sage Publications: Thousand Oaks.

Contains current information on both self-administered and mail surveys. It is a great resource if you want to design your own survey; there are step-by-step methods for conducting these two types of surveys.

Bradburn, N.M., & Sudman, S. (1979). Improving interview method and questionnaire design . San Francisco: Jossey-Bass Publishers.

A good overview of polling. Includes setting up questionnaires and survey techniques.

Bradburn, N. M., & Sudman, S. (1988). Polls and Surveys: Understanding What They Tell Us. San Francisco: Jossey-Bass Publishers.

These veteran survey researchers answer questions about survey research that are commonly asked by the general public.

Campbell, Angus, A., &and; Katona, Georgia. (1953). The Sample Survey: A Technique for Social Science Research. In Newcomb, Theodore M. (Ed). Research Methods in the Behavioral Sciences. The Dryden Press: New York. p 14-55.

Includes information on all aspects of social science research. Some chapters in this book are outdated.

Converse, J. M., & Presser, S. (1986). Survey questions: Handcrafting the standardized questionnaire . Newbury Park, CA: Sage.

A very helpful little publication that addresses the key issues in question construction.

Dillman, D.A. (1978). Mail and telephone surveys: The total design method . New York: John Wiley & Sons.

An overview of conducting telephone surveys.

Frey, James H., & Oishi, Sabine Mertens. (1995). How To Conduct Interviews By Telephone and In Person. Sage Publications: Thousand Oaks.

This book has a step-by-step breakdown of how to conduct and design telephone and in person interview surveys.

Fowler, Floyd J., Jr. (1993). Survey Research Methods (2nd.). Newbury Park, CA: Sage.

An overview of survey research methods.

Fowler, F. J. Jr., & Mangione, T. W. (1990). Standardized survey interviewing: Minimizing interviewer-related error . Newbury Park, CA: Sage.

Another aspect of validity/reliability--interviewer error.

Fox, J. & Tracy, P. (1986). Randomized Response: A Method for Sensitive Surveys . Beverly Hills, CA: Sage.

Authors provide a good discussion of response issues and methods of random response, especially for surveys with sensitive questions.

Frey, J. H. (1989). Survey research by telephone (2nd). Newbury Park, CA: Sage.

General overview to telephone polling.

Glock, Charles (ed.) (1967). Survey Research in the Social Sciences. New York: Russell Sage Foundation.

Although fairly outdated, this collection of essays is useful in illustrating the somewhat different ways in which different disciplines regard and use survey research.

Hoinville, G. & Jowell, R. (1978). Survey research practice . London: Heinemann.

Practical overview of the methods and procedures of survey research, particularly discussing problems which may arise.

Hyman, H. H. (1972). Secondary Analysis of Sample Surveys. New York: John Wiley & Sons.

This source is particularly useful for anyone attempting to do secondary analysis. It offers a comprehensive overview of this research method, and couches it within the broader context of social scientific research.

Hyman, H. H. (1955). Survey design and analysis: Principles, cases, and procedures . Glencoe, IL: Free Press.

According to Babbie, an oldie but goodie--a classic.

Jones, R. (1985). Research methods in the social and behavioral sciences . Sunderland, MA: Sinauer.

General introduction to methodology. Helpful section on survey research, especially the discussion on sampling.

Kalton, G. (1983). Compensating for missing survey data . Ann Arbor, MI: Survey Research Center, Institute for Social Research, the University of Michigan.

Addresses a problem often encountered in survey methodology.

Kish, L. (1965). Survey sampling . New York: John Wiley & Sons.

Classic text on sampling theories and procedures.

Lake, C.C., & Harper, P. C. (1987). Public opinion polling: A handbook for public interest and citizen advocacy groups . Washington, D.C.: Island Press.

Clearly written easy to read and follow guide for planning, conducting and analyzing public surveys. Presents material in a step-by-step fashion, including checklists, potential pitfalls and real-world examples and samples.

Lauer, J.M., & Asher, J. W. (1988). Composition research: Empirical designs . New York: Oxford UP.

Excellent overview of a number of research methodologies applicable to composition studies. Includes a chapter on "Sampling and Surveys" and appendices on basic statistical methods and considerations.

Monette, D. R., Sullivan, T. J, & DeJong, C. R. (1990). Applied Social Research: Tool for the Human Services (2nd). Fort Worth, TX: Holt.

A good basic general research textbook which also includes sections on minority issues when doing research and the analysis of "available" or secondary data..

Rea, L. M., & Parker, R. A. (1992). Designing and conducting survey research: A comprehensive guide . San Francisco: Jossey-Bass.

Written for the social and behavioral sciences, public administration, and management.

Rossi, P.H., Wright, J.D., & Anderson, A.B. (eds.) (1983). Handbook of survey research . New York: Academic Press.

Handbook of quantitative studies in social relations.

Salant, P., & Dillman, D. A. (1994). How to conduct your own survey . New York: Wiley.,

A how-to book written for the social sciences.

Sayer, Andrew. (1992). Methods In Social Science: A Realist Approach. Routledge: London and New York.

Gives a different perspective on social science research.

Schuldt, Barbara A., & Totter, Jeff W. (1994, Winter). Electronic Mail vs. Mail Survey Response Rates. Marketing Research, 6. 36-39.

An article with specific information for electronic and mail surveys. Mainly a technical resource.

Schuman, H. & Presser, S. (1981). Questions and answers in attitude surveys . New York: Academic Press.

Detailed analysis of research question wording and question order effects on respondents.

Schwartz, N. & Seymour, S. (1996) Answering Questions: Methodology for Determining Cognitive and Communication Processes in Survey Research. San Francisco: Josey-Bass.

Authors provide a summary of the latest research methods used for analyzing interpretive cognitive and communication processes in answering survey questions.

Seymour, S., Bradburn, N. & Schwartz, N. (1996) Thinking About Answers: The Application of Cognitive Processes to Survey Methodology. San Francisco: Josey-Bass.

Explores the survey as a "social conversation" to investigate what answers mean in relation to how people understand the world and communicate.

Simon, J. (1969). Basic research methods in social science: The art of empirical investigation. New York: Random .

An excellent discussion of survey analysis. The definitions and descriptions begin from a fairly understandable (simple) starting point, then the discussion unfolds to cover some fairly complex interpretive strategies.

Singleton, R. Jr., et. al. (1988). Approaches to social research . New York: Oxford UP.

Has a very accessible chapter on sampling as well as a chapter on survey research.

Smith, Robert B. (Ed.) (1982). A Handbook of Social Science Methods, Volume 3. Prayer: New York.

There is a series of handbooks, each one with specific topics in social science research. A good technical resource, yet slightly dated.

Sul Lee, E., Forthofer, R.N.,& Lorimor, R.J. (1989). Analyzing complex survey data . Newbury Park, CA: Sage Publications.

Details on the statistical analysis of survey data.

Singer, E., & Presser, S., eds. (1989). Survey research methods: A reader . Chicago: U of Chicago P.

The essays in this volume originally appeared in various issues of Public Opinion Quarterly.

Survey Research Center (1983). Interviewer's manual . Ann Arbor, MI: University of Michigan Press.

Very practical, step-by-step guide to conducting a survey and interview with lots of examples to illustrate the process.

Pearson, R.W., &Borouch, R.F. (Eds.) (1986). Survey Research Design: Towards a Better Understanding of Their Costs and Benefits. Springer-Verag: Berlin.

Explains, in a technical fashion, the financial aspects of research design. Somewhat of a cost-analysis book.

Weissberg, H.F., Krosnick , J.A., & Bowen, B.D. (1989). An introduction to survey research and data analysis . Glenview, IL: Scott Foresman.

A good discussion of basic analysis and statistics, particularly what statistical applications are appropriate for particular kinds of data.

Anderson, B., Puur, A., Silver, B., Soova, H., & Voormann, R. (1994). Use of a lottery as an incentive for survey participation: a pilot survey in Estonia. International Journal of Public Opinion Research, 6 , 64-71.

Looks at return results in a study that offers incentives, and recommends incentive use to increase response rates.

Bare, J. (1994). Truth about daily fluctuations in 1992 pre-election polls. Newspaper Research Journal, 15, 73-81.

Comparison of variations between daily poll results of the major polls used during the 1992 American Presidential race.

Chi, S. (1993). Computer knowledge, interests, attitudes, and uses among faculty in two teachers' universities in China. DAI-A, 54/12 , 4412-4623.

Survey indicating a strong link between subject area and computer usage.

Cowans, J. (1994). Wielding the people: Opinion polls and the problem of legitimacy in France since 1944. DAI-A, 54/12 , 4556-5027.

Study looks at how the advent of opinion polling has affected the legitimacy of French governments since World War II.

Crewe, I. (1993). A nation of liars? Opinion polls and the 1992 election. Journal of the Market Research Society, 35 , 341-359.

Poses possible reasons the British polls were so wrong in predicting the outcomes of the 1992 national elections.

Daly, J., & Miller, M. (1975). The empirical development of an instrument to measure writing apprehension. Research in the teaching of English , 9 (3), 242-249.

Discussion of basics in question development and data analysis. Also includes some sample questions.

Daniell, S. (1993). Graduate teaching assistants' attitudes toward and responses to academic dishonesty. DAI-A,54/06, 2065- 2257.

Study explores the ethical and academic responses to cheating, using a large survey tool.

Mittal, B. (1994). Public assessment of TV advertising: Faint praise and harsh criticism. Journal of Advertising Research, 34, 35-53.

Results of a survey of Southern U.S. television viewers' perceptions of television advertisements.

Palmquist, M., & Young, R.E. (1992). Is writing a gift? The impact on students who believe it is. Reading empirical research studies: The rhetoric of research . Hayes et al. eds. Hillsdale NJ: Erlbaum.

This chapter presents results of a study of student beliefs about writing. Includes sample questions and data analysis.

Serow, R. C., & Bitting, P. F. (1995). National service as educational reform: A survey of student attitudes. Journal of research and development in education , 28 (2), 87-90.

This study assessed college students' attitude toward a national service program.

Stouffer, Samuel. (1955). Communism, Conformity, and Civil Liberties. New York: John Wiley & Sons.

This is a famous old survey worth examining. This survey examined the impact of McCarthyism on the attitudes of both the general public and community leaders, a asking whether the repression of the early 1950s affected support for civil liberties.

Wanta, W. & Hu, Y. (1993). The agenda-setting effects of international news coverage: An examination of differing news frames. International Journal of Public Opinion Research, 5, 250-264.

Discusses results of Gallup polls on important problems in relation to the news coverage of international news.

Worcester, R. (1992). The performance of the political opinion polls in the 1992 British general election. Marketing and Research Today, 20, 256-263.

A critique of the use of polls in an attempt to predict voter actions.

Yamada, S, & Synodinos, N. (1994). Public opinion surveys in Japan. International Journal of Public Opinion Research, 6 , 118-138.

Explores trends in opinion poll usage, response rates, and refusals in Japanese polls from 1975 to 1990.

Criticism/Critique/Evaluation:

Bangura, A. K. (1992). The limitations of survey research methods in assessing the problem of minority student retention in higher education . San Francisco: Mellen Research UP.

Case study done at a Maryland university addressing an aspect of validity involving intercultural factors.

Bateson, N. (1984). Data construction in social surveys. London: Allen & Unwin.

Tackles the theory of the method (but not the methods of the method) of data construction. Deals with validity of the data by validizing the process of data construction.

Braverman, M. (1996). Sources of Survey Error: Implications for Evaluation Studies. New Directions for Evaluation: Advances in Survey Research ,70, 17-28.

Looks at how evaluations using surveys can benefit from using survey design methods that reduce various survey errors.

Brehm, J. (1994). Stubbing our toes for a foot in the door? Prior contact, incentives and survey response. International Journal of Public Opinion Research, 6 , 45-63.

Considers whether incentives or the original contact letter lead to increased response rates.

Bulmer, M. (1977). Social-survey research. In M. Bulmer (ed.), Sociological research methods: An introduction . London: Macmillan.

The section includes discussions of pros and cons of survey research findings, inferences and interpreting relationships found in social-survey analysis.

Couper, M. & Groves, R. (1996). Household-Level Determinants of Survey Nonresponse. . New Directions for Evaluation: Advances in Survey Research , 70, 63-80.

Authors discuss their theory of survey participation. They believe that decisions to participate are based on two occurences: interactions with the interviewer, and the sociodemographic characteristics of respondents.

Couto, R. (1987). Participatory research: Methodology and critique. Clinical Sociology Review, 5 , 83-90.

Criticism of survey research. Addresses knowledge/power/change issues through the critique.

Dillman, D., Sangster, R., Tarnai, J., & Rockwood, T. (1996) Understanding Differences in People's Answers to Telephone and Mail Surveys. New Directions for Evaluation: Advances in Survey Research , 70, 45-62.

Explores the issue of differences in respondents' answers in telephone and mail surveys, which can affect a survey's results.

Esaiasson, P. & Granberg, D. (1993). Hidden negativism: Evaluation of Swedish parties and their leaders under different survey methods. International Journal of Public Opinion Research, 5, 265-277.

Compares varying results of mailed questionnaires vs. telephone and personal interviews. Findings indicate methodology affected results.

Guastello, S. & Rieke, M. (1991). A review and critique of honesty test research. Behavioral Sciences and the Law, 9, 501-523.

Looks at the use of honesty, or integrity, testing to predict theft by employees, questioning further use of the tests due to extremely low validity. Social and legal implications are also considered.

Hamilton, R. (1991). Work and leisure: On the reporting of poll results. Public Opinion Quarterly, 55 , 347-356.

Looks at methodology changes that affected reports of results in the Harris poll on American Leisure.

Juster, F. & Stanford, F. (1991). Comment on work and leisure: On reporting of poll results. Public Opinion Quarterly, 55 , 357-359.

Rebuttal of the Hamilton essay, cited above. The rebuttal is based upon statistical interpretation methods used in the cited survey.

Krosnick, J., Narayan, S., & Smith, W. (1996). Satisficing in Surveys: Initial Evidence. New Directions in Evaluation: Advances in Survey Research , 70, 29-44.

Authors discuss "satisficing," a cognitive approach to survey response, which they believe helps researchers understand how survey respondents arrive at their answers.

Lindsey, J.K. (1973). Inferences from sociological survey data: A unified approach . San Francisco: Jossey-Bass.

Examines the statistical analysis of survey data.

Morgan, F. (1990). Judicial standards for survey research: An update and guidelines. Journal of Marketing, 54 , 59-70.

Looks at legal use of survey information as defined and limited in recent cases. Excellent definitions.

Pottick, K. (1990). Testing the underclass concept by surveying attitudes and behavior. Journal of Sociology and Social Welfare, 17, 117-125.

Review of definitional tests constructed to define "underclass."

Rohme, N. (1992). The state of the art of public opinion polling worldwide. Marketing and Research Today, 20, 264-271.

A quick review of the use of polling in several countries, concluding that the use of polling is on the rise worldwide.

Sabatelli, R. (1988). Measurement issues in marital research: A review and critique of contemporary survey instruments. Journal of Marriage and the Family, 55 , 891-915.

Examines issues of methodology.

Schriesheim, C. A.,& Denisi, A. S. (1980). Item Presentation as an Influence on Questionnaire Validity: A Field Experiment. Educational-and-Psychological-Measurement ; 40 (1), 175-82.

Two types of questionnaire formats measuring leadership variables were examined: one with items measuring the same dimensions grouped together and the second with items measuring the same dimensions distributed randomly. The random condition showed superior validity.

Smith, T. (1990). "A critique of the Kinsey Institute/Roper organization national sex knowledge survey." Public Opinion Quarterly, Vol. 55 , 449-457.

Questions validity of the survey based upon question selection and response interpretations. A rejoinder follows, defending the poll.

Smith, Tom W. (1990). "The First Straw? A Study of the Origins of Election Polls," Public Opinion Quarterly, Vol. 54 (Spring: 21-36).

This article offers a look at the early history of American political polling, with special attention to media reactions to the polls. This is an interesting source for anyone interested in the ethical issues surrounding polling and survey.

Sniderman, P. (1986). Reflections on American racism. Journal of Social Issues, 42 , 173-187.

Rebuttal of critique of racism research. Addresses issues of bias and motive attribution.

Stanfield, J. H. II, & Dennis, R. M., eds (1993). Race and Ethnicity in Research Methods . Newbury Park, CA: Sage.

The contributions in this volume examine the array of methods used in quantitative, qualitative, and comparative and historical research to show how research sensitive to ethnic issues can best be conducted.

Stapel, J. (1993). Public opinion polling: Some perspectives in response to 'critical perspectives.' International Journal of Public Opinion Research, 5, 193-194.

Discussion of the moral power of polling results.

Wentland, E. J., & Smith, K. W. (1993). Survey responses: An evaluation of their validity . San Diego: Academic Press.

Reviews and analyzes data from studies that have, through the use of external criteria, assessed the validity of individuals' responses to questions concerning personal characteristics and behavior in a wide variety of areas.

Williams, R. M., Jr. (1989). "The American Soldier: An Assessment, Several Wars Later." Public Opinion Quarterly. Vol. 53 (Summer: 155-174).

One of the classic studies in the history of survey research is reviewed by one of its authors.

Secondary Analysis:

Jolliffe, F.R. (1986). Survey Design and Analysis. Ellis Horwood Limited: Chichester.

Information about survey design as well as secondary analysis of surveys.

Kiecolt, K. J., & Nathan, L. E. (1985). Secondary analysis of survey data . Beverly Hills, CA: Sage.

Discussion of how to use previously collected survey data to answer a new research question.

Monette, D. R., Sullivan, T. J, & DeJong, C. R. (1990). Analysis of available data. In Applied Social Research: Tool for the Human Services (2nd ed., pp. 202-230). Fort Worth, TX: Holt.

Gives some existing sources for statistical data as well as discussing ways in which to use it.

Rubin, A. (1988). Secondary analyses. In R. M. Grinnell, Jr. (Ed.), Social work research and evaluation. (3rd ed., pp. 323-341). Itasca, IL: Peacock.

Chapter discusses inductive and deductive processes in relation to research designs using secondary data. It also discusses methodological issues and presents a case example.

Dale, A., Arber, S., & Procter, M. (1988). Doing Secondary Analysis . London: Unwin Hyman.

A whole book about how to do secondary analysis.

Electronic Surveys:

Carr, H. H. (1991). Is using computer-based questionnaires better than using paper? Journal of Systems Management September, 19, 37.

Reference from Thach.

Dunnington, Richard A. (1993). New methods and technologies in the organizational survey process. American Behavioral Scientist , 36 (4), 512-30.

Asserts that three decades of technological advancements in communications and computer techhnology have transformed, if not revolutionized, organizational survey use and potential.

Goree, C. & Marszalek, J. (1995). Electronic Surveys: Ethical Issues for Researchers. The College Student Affairs Journal , 15 (1), 75-79.

Explores how the use of electronic surveys challenge existing ethical standards of survey research, and how that researchers need to be aware of these new ethical issues.

Hsu, J. (1995). The Development of Electronic Surveys: A Computer Language-Based Method. The Electronic Library , 13 (3), 195-201.

Discusses the need for a markup language method to properly support the creation of survey questionnaires.

Kiesler, S. & Sproull, L. S. (1986). Response effects in the electronic survey. Public Opinion Quarterly, 50 , 402-13.

Opperman, M. (1995) E-Mail Surveys--Potentials and Pitfalls. Marketing Research, 7 (3), 29-33.

A discussion of the advantages and disadvantages of using E-Mail surveys.

Sproull, L. S. (1986). Using electronic mail for data collection in organizational research. Academy of Management Journal, 29, 159-69.

Synodinos, N. E., & Brennan, J. M. (1988). Computer interactive interviewing in survey research. Psychology & Marketing, 5 (2), 117-137.

Thach, Liz. (1995). Using electronic mail to conduct survey research. Educational Technology, 35, 27-31.

A review of the literature on the topic of survey research via electronic mail concentrating on the key issues in design, implementation, and response using this medium.

Walsh, J. P., Kiesler, S., Sproull, L. S., & Hesse, B. W. (1992). Self-selected and randomly selected respondents in a computer network survey. Public Opinion Quarterly, 56, 241-244.

Further Investigation

Bery, David N., & Smith , Kenwyn K. (eds.) (1988). The Self in Social Inquiry: Researching Methods. Sage Publications: Newbury Park.

Has some ethical issues about the role of researcher in social science research.

Barribeau, Paul, Bonnie Butler, Jeff Corney, Megan Doney, Jennifer Gault, Jane Gordon, Randy Fetzer, Allyson Klein, Cathy Ackerson Rogers, Irene F. Stein, Carroll Steiner, Heather Urschel, Theresa Waggoner, & Mike Palmquist. (2005). Survey Research. Writing@CSU . Colorado State University. https://writing.colostate.edu/guides/guide.cfm?guideid=68

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Writing Survey Questions

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

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

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

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

Question development

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

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

Measuring change over time

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

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

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

Open- and closed-ended questions

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

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

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

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

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

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

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

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

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

Question wording

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

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

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

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

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

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

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

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

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

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

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

Question order

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

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

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

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

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

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

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

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

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

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

U.S. Surveys

Other research methods.

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PMC11134238

Impact of Incentives on Physician Participation in Research Surveys: Randomized Experiment

Saadiya hawa.

1 Graduate Medical Education, Department of Internal Medicine, Weiss Memorial Hospital, Chicago, IL, United States

Shalmali Bane

2 Department of Epidemiology and Population Health, Stanford School of Medicine, Stanford, CA, United States

Kayla Kinsler

3 Brown University School of Public Health, Providence, RI, United States

Amadeia Rector

Yashaar chaichian.

4 Division of Immunology and Rheumatology, Department of Medicine, Stanford School of Medicine, Stanford, CA, United States

Titilola Falasinnu

Julia f simard, associated data.

The data sets generated or analyzed during this study are available in a project folder on Open Science Framework. Please contact the corresponding author for necessary details to access the data.

Web-based surveys can be effective data collection instruments; however, participation is notoriously low, particularly among professionals such as physicians. Few studies have explored the impact of varying amounts of monetary incentives on survey completion.

This study aims to conduct a randomized study to assess how different incentive amounts influenced survey participation among neurologists in the United States.

We distributed a web-based survey using standardized email text to 21,753 individuals randomly divided into 5 equal groups (≈4351 per group). In phase 1, each group was assigned to receive either nothing or a gift card for US $10, $20, $50, or $75, which was noted in the email subject and text. After 4 reminders, phase 2 began and each remaining individual was offered a US $75 gift card to complete the survey. We calculated and compared the proportions who completed the survey by phase 1 arm, both before and after the incentive change, using a chi-square test. As a secondary outcome, we also looked at survey participation as opposed to completion.

For the 20,820 emails delivered, 879 (4.2%) recipients completed the survey; of the 879 recipients, 622 (70.8%) were neurologists. Among the neurologists, most were male (412/622, 66.2%), White (430/622, 69.1%), non-Hispanic (592/622, 95.2%), graduates of American medical schools (465/622, 74.8%), and board certified (598/622, 96.1%). A total of 39.7% (247/622) completed their neurology residency more than 20 years ago, and 62.4% (388/622) practiced in an urban setting. For phase 1, the proportions of respondents completing the survey increased as the incentive amount increased (46/4185, 1.1%; 76/4165, 1.8%; 86/4160, 2.1%; 104/4162, 2.5%; and 119/4148, 2.9%, for US $0, $10, $20, $50, and $75, respectively; P <.001). In phase 2, the survey completion rate for the former US $0 arm increased to 3% (116/3928). Those originally offered US $10, $20, $50, and $75 who had not yet participated were less likely to participate compared with the former US $0 arm (116/3928, 3%; 90/3936, 2.3%; 80/3902, 2.1%; 88/3845, 2.3%; and 74/3878, 1.9%, for US $0, $10, $20, $50, and $75, respectively; P =.03). For our secondary outcome of survey participation, a trend similar to that of survey completion was observed in phase 1 (55/4185, 1.3%; 85/4165, 2%; 96/4160, 2.3%; 118/4162, 2.8%; and 135/4148, 3.3%, for US $0, $10, $20, $50, and $75, respectively; P <.001) and phase 2 (116/3928, 3%; 90/3936, 2.3%; 80/3902, 2.1%; 88/3845, 2.3%; and 86/3845, 2.2%, for US $0, $10, $20, $50, and $75, respectively; P =.10).

Conclusions

As expected, monetary incentives can boost physician survey participation and completion, with a positive correlation between the amount offered and participation.

Introduction

When conducting biomedical research, input from health care providers is critical in identifying barriers and facilitators to high-quality care. Such feedback occurs through multiple forums, including focus groups, interviews, and surveys. For survey research especially, participation among physicians is often low, including for web-based surveys versus postal mail [ 1 ]. While the influence of the mode of distribution, timing, and type of incentive offered has been evaluated, few studies have explored the impact of varying amounts of monetary incentives on survey completion among physicians [ 2 - 4 ].

We conducted a randomized study to determine to what extent the incentive amount influenced participation among neurologists participating in a case-vignette internet-based survey. For convenience, we examined neurologists as we were already conducting a larger study aimed at neurologists and could easily integrate the randomization.

Study Population

A mailing list of US-based specialists was obtained from SPAN Global Services or LakeMedia Group, a medical marketing company. The neurologist list (received June 1, 2022) included 22,085 email addresses. Duplicates (n=332) were removed; the remaining 21,753 individuals were randomly divided into 5 groups of ≈4351. We assigned each group to receive either US $0, $10, $20, $50, or $75 as the participation incentive.

Survey Dissemination

For phase 1, we distributed the survey through Qualtrics (Qualtrics) using individualized email links. The surveys with the randomized incentives were sent out on August 16, 2022, followed by reminder emails on August 22, August 29, August 31, and September 9. For phase 2, we concluded the randomization of the incentives and offered each participant US $75 to complete the survey among those who had not completed the survey nor opted out. We initiated phase 2 on October 12, 2022, with reminders on October 24 and November 9. We closed the survey on November 15, 2022.

To provide different incentive amounts, we created 5 identical Qualtrics surveys with informed consent at the start informing participants of the randomly assigned incentive amounts: US $0, $10, $20, $50, and $75. Email text and subject lines were identical, and only the incentive amount varied. Gift card payments were managed by Tango Rewards and integrated into the survey. For the group randomized to receive no incentive (US $0), we offered a surprise US $10 gift card at the end of the survey to those who completed the survey.

Qualtrics automatically reports participation as follows: email bounced, email sent, started survey, and finished survey. Our primary outcome was survey completion, defined as a “finished survey” in Qualtrics. As a secondary outcome, we considered survey participation, which included both started surveys and finished surveys.

Statistical Analysis

We summarized self-reported characteristics among all participants who self-identified as neurologists (completed either phase 1 or 2) and then separately for phase 1 participants stratified by randomized group using descriptive statistics. Among each of the 5 randomized groups, both with (phase 1) and without (phase 2) randomized incentives, we calculated the group attrition due to bounced emails, to estimate an appropriate denominator. For phase 2, the denominator also excluded those who opted out in phase 1. For our primary outcome, we calculated the proportion who completed the survey among the respondents who presumably had the email delivered. We used chi-square tests to determine whether the proportion who completed the survey (primary outcome) and survey participation (secondary outcome) varied based on the offered incentive amount. All analyses were performed in SAS (version 9.4; SAS institute Inc).

Ethical Considerations

The study was reviewed and approved by the Institutional Review Boards of Stanford University (approval 42909). Consent was obtained from the participants prior to beginning the survey by informing them about the format of the survey and expectations should they choose to participate. All participants were eligible to receive an incentive, including those whose initial invitation randomized them to no incentive. The amount was randomly assigned. The survey data are deidentified; however, personal information to disperse the incentives was collected and stored in a separate survey unlinked to the survey responses.

For the 20,820 emails delivered, complete responses were received from 879 (4.2%) individuals; of the 879 recipients, 622 (70.8%) were neurologists. Another 70 neurologists started the survey but did not complete it. Most participating neurologists were male (412/622, 66.2%), White (430/622, 69.1%), non-Hispanic (592/622, 95.2%), graduates of American medical schools (465/622, 74.8%), and board certified (598/622, 96.1%). Overall, 39.7% (247/622) completed their neurology residency over 20 years ago, and 62.4% (388/622) practiced in an urban setting. When restricting to phase 1 neurologist participants, we noted some modest variability in several characteristics ( Table 1 ).

Characteristics of all neurologist survey respondents (overall column) and restricted to those who completed the survey during the randomized incentive component (phase 1) stratified by the initial randomized incentive amount offered.

In phase 1, the proportion who completed the survey increased as the amount of incentive increased ( P <.001; Table 2 ). In phase 2, an increasing proportion completed the survey in the former US $0 and $10 arms, while the US $20 arm showed no change, and the US $50 and $75 arms showed a decrease ( P =.03; Table 2 ).

The proportion of surveys completed by neurologists during randomly assigned incentive amounts (phase 1) and after all were offered US $75 to participate (phase 2) by the initial randomized incentive amount.

For our secondary outcome of survey participation, a trend similar to that of survey completion was observed in phase 1 (55/4185, 1.3%; 85/4165, 2%; 96/4160, 2.3%; 118/4162, 2.8%; and 135/4148, 3.3%, for US $0, $10, $20, $50, and $75, respectively; P <.001) and phase 2 (116/3928, 3%; 90/3936, 2.3%; 80/3902, 2.1%; 88/3845, 2.3%; and 86/3845, 2.2%, for US $0, $10, $20, $50, and $75, respectively; P =.10).

Principal Findings

In this randomized study, survey completion increased as the incentive amount increased. Offering any monetary incentive was associated with survey completion, compared with offering no incentive, and those offered US $75 at outset were most likely to complete. However, participation increased disproportionately with incentive amount. Upon offering US $75 to all the groups, participation increased in the former US $0 and $10 arms, while it decreased in the other arms compared with participation during phase 1. This could occur if those likely to take the survey with any incentive offered did so during the initial distribution, which included multiple reminder emails. Another reason could be that the phase 2 incentive increase was the most substantial for the US $0 and $10 arms. Nevertheless, we saw an increasing trend across the 2 phases, with overall survey completion being 3.7% (162/4350), 3.8% (166/4351), 3.8% (166/4350), 4.4% (192/4351), and 4.4% (193/4351) for the US $0, $10, $20, $50, and $75 groups, respectively (based on phase 1 randomization). A similarly increasing trend was observed for survey participation (vs survey completion), with 3.9% (171/4350), 4% (175/4351), 4% (176/4350), 4.7% (206/4351), and 5.1% (221/4351) of individuals having started the survey for the US $0, $10, $20, $50, and $75 groups, respectively.

Comparison With Prior Work

Researchers have studied methods of increasing physician response rates by using different strategies [ 2 ]. When paper surveys have been used, sending prenotification letters, sending stamped return envelopes, varying survey length, and packaging using hospital or medical school envelopes have been tried with some success [ 5 - 8 ]. Recently, a study found that the response rate for a survey was significantly higher when offered a US $50 versus US $20 check (67.8% vs 52.1%; P <.001) [ 3 ]. Others showed that up-front cash rewards (90/263, 34%) generate a higher response rate than an immediate check (50/255, 20%), a promised check (26/265, 10%), or a promised check with a Social Security Number requirement (20/266, 8%; P <.001) [ 9 ]. Our findings are consistent with previous studies of a similar nature. One recent study found that participants offered Starbucks gift cards for US $50 were more likely to respond than those offered US $25 [ 4 ]. Given the impersonal nature of an emailed survey link as in our study, lower response rates are expected compared with previous smaller studies where the physicians belonged to the same institute as the investigators. Although prepayment was shown to more than triple the participation [ 9 ] with massive distribution lists such as ours, prepayment would be cost prohibitive.

Limitations

Our study has some limitations. The distribution list included affiliated professionals such as neurophysiologists and neurosurgeons. Some of these individuals contacted study staff for clarification about their participation, ignored the survey, or specified that they were not a neurologist in the survey (in the first question asked). This last group remained in the analysis as they were also likely to be physicians from neurology-related disciplines and our goal was to study the impact of incentive amount on survey participation among physicians. We reasoned that neurologists are a subset of physicians and, therefore, unlikely to differ from physicians as a whole. Another common limitation faced by web-based surveys distributed by email is diversion to the spam folder. To minimize this, the survey was sent from the primary investigator’s work email using a legitimate reply-to address and followed recommendations for email content and quality (eg, avoiding attachments). These limitations likely did not differ by incentive arms, given our large sample size and randomization. Unique strengths of our study were the range of incentive amounts offered; the ability to offer them immediately upon survey completion; and the ability to look at survey completion and engagement, a measure of starting the survey. Further boosting the phase 2 incentive examined the impact of visibly increasing incentives on survey completion. We found that an initial lower incentive amount followed by an increase did boost participation and may be an effective approach for studies with limited budgets. Given the large number of reminders distributed in phase 1, we do not anticipate that this was solely due to the invitation to participate with the increased amount serving as a reminder.

Incentivizing physician surveys with monetary rewards can increase participation and completion. As expected, we found a positive correlation between incentive amount, participation, and survey completion. However, the increase in participation and completion observed was not proportional to the incentive amount.

Acknowledgments

This study was funded by the National Institutes of Health (NIH) National Institute of Allergy and Infectious Diseases (NIAID [R01 AI154533]).

Data Availability

Conflicts of Interest: None declared.

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Use of Menthol-Flavored Tobacco Products Among US Middle and High School Students: National Youth Tobacco Survey, 2022

ORIGINAL RESEARCH — Volume 21 — May 30, 2024

Monica E. Cornelius, PhD 1 ; Andrea S. Gentzke, PhD 1 ; Caitlin G. Loretan, MPH 1 ; Nikki A. Hawkins, PhD 1 ; Ahmed Jamal, MBBS 1 ( View author affiliations )

Suggested citation for this article: Cornelius ME, Gentzke AS, Loretan CG, Hawkins NA, Jamal A. Use of Menthol-Flavored Tobacco Products Among US Middle and High School Students: National Youth Tobacco Survey, 2022. Prev Chronic Dis 2024;21:230305. DOI: http://dx.doi.org/10.5888/pcd21.230305 .

PEER REVIEWED

Introduction

Acknowledgments, author information.

What is already known on this topic?

Middle and high school students who currently use tobacco products report using a variety of flavors, including menthol.

What is added by this report?

In 2022, 23.8% of students who currently used any tobacco product and 39.5% who currently used flavored tobacco products reported using menthol-flavored tobacco products. Students who exhibited characteristics of addiction to tobacco product use had a higher prevalence of menthol-flavored tobacco product use.

What are the implications for public health practice?

Menthol and other characterizing flavors or additives in tobacco products may contribute to first-time tobacco use and sustained use among young people. Understanding this association can inform public health policy aimed at preventing and reducing tobacco product use in this population.

Menthol cigarettes have been associated with increased smoking initiation. Although numerous studies have focused on correlates of menthol cigarette smoking among youths, fewer studies have assessed the prevalence and correlates of overall menthol-flavored tobacco product use among middle and high school students.

We analyzed 2022 National Youth Tobacco Survey data to estimate the prevalence of menthol-flavored tobacco product use among US middle and high school students who used tobacco products within the past 30 days. Characteristics associated with menthol-flavored tobacco product use were also examined.

Use of menthol-flavored tobacco products was reported by 23.8% of students who currently used any tobacco product and by 39.5% of students who currently used any flavored tobacco product. Among students who reported past 30-day use of a flavored tobacco product, characteristics associated with a higher prevalence of menthol-flavored tobacco product use included non-Hispanic White race and ethnicity, frequent tobacco product use, use of multiple tobacco products, wanting to use a tobacco product within the first 30 minutes of awakening, and craving tobacco products within the past 30 days.

Unlike results of prior research focused on cigarette smoking among young people, prevalence of use of any menthol-flavored tobacco product was highest among non-Hispanic White youths. Any use of menthol-flavored tobacco products of any type (alone or in combination with other flavors) among young people may be associated with continued product use and symptoms of dependence.

Menthol, an additive in commercial tobacco products, creates a cooling sensation when inhaled (1–3). Menthol has both flavor and sensation properties (1–3). The effects of menthol can make cigarette smoke or e-cigarette aerosol seem less irritating and can enhance the product-user’s experience (1–4). Menthol flavoring is not limited to cigarettes and e-cigarettes; most types of commercial tobacco products are available in menthol flavor (3). Menthol cigarettes have been associated with increased smoking initiation, nicotine dependence, and lower smoking cessation success (1,3,5). Results from modeling studies suggest that prohibiting menthol cigarettes in the US could result in a 15% reduction in smoking prevalence and prevent an estimated 324,000 to 654,000 deaths over the next 40 years (6–8).

Disparities among population groups that use menthol cigarettes are well-documented. Marketing directed at certain population groups has been associated with a higher prevalence of menthol cigarette smoking in these groups (1,3,9,10). Population groups most likely to smoke menthol cigarettes are non-Hispanic Black people, women, sexual minority groups, people identifying as transgender, people residing in low-income communities, people with mental health conditions, youths, and young adults (3).

Smoking initiation usually begins in adolescence (4) when use of nicotine can have negative consequences on brain development and may increase the risk for nicotine dependence (11). Middle and high school students often use a variety of commercial tobacco products available in flavors, including menthol (12). E-cigarettes are the most commonly used tobacco product among middle and high school students — with 9.4% reporting e-cigarette use in 2022 — followed by cigars (1.9%) and cigarettes (1.6%) (12,13). Almost 4 of 5 (79.1%) middle and high school students who reported current use of 1 or more tobacco products used a flavored tobacco product (12). Furthermore, among middle and high school students who currently used any flavored tobacco product, 38.8% reported smoking menthol cigarettes (12). Non-Hispanic Black, Hispanic, and female middle and high school students have reported a higher prevalence of smoking menthol cigarettes (14).

Although numerous studies have focused on correlates of menthol cigarette smoking among youths, fewer studies have assessed the prevalence of using both cigarette and noncigarette menthol-flavored tobacco products in this population (14,15). Such information is important because, although the prevalence of cigarette smoking among youths has declined, use of e-cigarettes has increased, and new tobacco product types (eg, heated tobacco products) continue to become available (13,14). To examine whether previously observed characteristics associated with menthol cigarette smoking (eg, higher prevalence among Black, Hispanic, and female adolescent populations) are similar for use of any menthol-flavored tobacco product among adolescents, our study will 1) provide updated estimates of menthol-flavored tobacco product use among middle and high school students and 2) assess correlates of use of any menthol-flavored tobacco products in this population. Assessing correlates of menthol-flavored tobacco product use among youths can further identify populations that may benefit from public health strategies recognizing the effects of flavored tobacco products in reducing tobacco product use by young people.

Data sample

We analyzed data from the 2022 National Youth Tobacco Survey (NYTS), a cross-sectional, school-based, voluntary, self-administered survey of US middle and high school students in grades 6 to 12 (12,13). A stratified 3-stage cluster sampling procedure generated a nationally representative sample of US students attending public and private schools (16). We collected data from January through May 2022 from 28,291 middle and high school students (overall response rate: 45.2%) by using a web-based survey with 99.3% of respondents completing the survey on a school campus. The analytic sample consisted of middle and high school students who reported use of 1 or more tobacco products within the past 30 days. The 2022 NYTS was approved by the institutional review boards of the data collectors, the CDC institutional review board (45 C.F.R. part 46; 21 C.F.R. part 56), and the Office of Management and Budget.

We assessed current use of menthol-flavored tobacco products among students who indicated past 30-day use of any tobacco product (use of ≥1 tobacco products: e-cigarettes, cigarettes, cigars, smokeless tobacco [chewing tobacco, snuff, dip, snus], dissolvable tobacco products, waterpipes or hookahs, pipe tobacco, bidis, heated tobacco products, or nicotine pouches). We also assessed use of menthol-flavored tobacco products among students who indicated past 30-day use of any flavored tobacco products. Menthol-flavored tobacco product use was defined as using any menthol-flavored tobacco product within the past 30 days, regardless of whether other flavors of tobacco products were used. Responses of “yes” to questions about flavored tobacco product use and “menthol” to the type(s) of flavor used were categorized as menthol-flavored tobacco use. For cigarettes, respondents who, within the past 30 days 1) indicated using only 1 cigarette brand and indicated that the brand was a menthol-flavored brand (Kool, Newport), 2) responded that they smoked Kool or Newport brands to the question “During the past 30 days, what brand of cigarettes did you usually smoke? (Choose only one answer)” (asked among respondents who used multiple brands in the past 30 days), or 3) who answered yes to “During the past 30 days, were the cigarettes that you usually smoked menthol?” were considered to have used menthol-flavored tobacco products (12). Students indicating no use of menthol-flavored tobacco products were categorized as using nonmenthol tobacco products.

Among students who used a flavored tobacco product in the past 30 days, tobacco product use was categorized as follows: 1) e-cigarettes only; 2) combustible tobacco products (cigarettes, cigars, bidis, hookahs, or pipes) only; 3) other tobacco products (smokeless tobacco products [chewing tobacco, snuff, dip, snus], dissolvables, heated tobacco products, or nicotine pouches) only; and 4) any combination of the preceding 3 categories.

Covariates examined included sex (male/female), race and ethnicity (Hispanic, non-Hispanic Black, non-Hispanic White, non-Hispanic Other), sexual orientation (heterosexual, lesbian, gay, bisexual, not sure), transgender identity (yes, no, not sure, don’t know what question is asking), family affluence (scores of low [0–5], medium [6,7], high [8,9] on a 4-item scale), tobacco product advertising exposure (yes [most of the time/always/sometimes], no [rarely/none]), frequent use (≥20 of the past 30 days) of a tobacco product, use of multiple tobacco products (≥2 products), time to wanting to use a tobacco product after awakening (<30 minutes, ≥30 minutes), craving tobacco products within the past 30 days (yes, no), past-year quit attempts, and quit intentions. Categorization of family affluence, advertising exposure, and cessation behaviors were consistent with previous analyses (12).

Respondents who indicated seeing advertisements or promotions for e-cigarettes, cigarettes, and other tobacco products “sometimes,” “most of the time,” or “always” on the internet, in newspapers or magazines, at a store (convenience store, supermarket, gas station, kiosk/storefront, or shopping center), or on television or streaming services were categorized as having been exposed to tobacco product advertising. Those who responded “never” or “rarely” were categorized as unexposed. Those who reported “I do not use the internet,” “I do not read newspapers or magazines,” “I never go to a convenience store, supermarket, or gas station,” or “I do not watch television or streaming services or go to the movies” were excluded.

Respondents who indicated 1 or more for the number of times they had stopped using all tobacco products for 1 day or longer because they were trying to quit were categorized as having a past-year quit attempt. Those who indicated “I did not try to quit all tobacco products during the past 12 months” were categorized as not having made a past-year quit attempt. Respondents who indicated they were seriously thinking about quitting the use of all tobacco products were categorized as having quit intentions; those that responded “No, I am not thinking about quitting the use of all tobacco products” were categorized as not having quit intentions.

We computed the weighted prevalence and 95% CIs separately for menthol-flavored and nonmenthol-flavored tobacco product use among students who used 1) 1 or more tobacco products within the past 30 days (n = 3,334) and 2) 1 or more flavored tobacco products within the past 30 days (n = 2,020), overall and by sociodemographic characteristics, tobacco use characteristics, cessation behaviors, and advertising exposure. We also computed the weighted percentage of menthol use by type of tobacco product. Additionally, we computed the percentage of each characteristic by menthol and nonmenthol tobacco product use among students who used flavored tobacco products (n = 2,020), which is the primary focus of our study. Chi-square tests of independence were used to test for differences in the proportions of each characteristic among menthol- and nonmenthol-flavored tobacco product use, with a P value of <.05 indicating significance. Nested logistic regression models (unadjusted models and models adjusted for sex, racial or ethnic group, and grade level) were used to estimate associations between each characteristic of interest and current use of menthol-flavored tobacco products among students who used 1 or more flavored tobacco products within the past 30 days. Model-adjusted prevalence ratios (APRs) with predicted marginals and Wald χ 2 statistics were computed. Models were adjusted to control for confounding in the associations between each covariate of interest and menthol-flavored tobacco product use. All analyses were performed using SAS-callable SUDAAN software, version 11.0.3 (RTI International).

Prevalence of menthol-flavored and nonmenthol-flavored tobacco product use

Nonmenthol- and menthol-flavored tobacco product use among students who used any tobacco products. In 2022, 3.1 million middle and high school students (11.3%) reported currently using any tobacco product. Most of these students reported using nonmenthol tobacco products (76.2%), ranging from 56.0% (those indicating a time of wanting to use a tobacco product after awakening of <30 min) to 92.2% (non-Hispanic Black students) ( Table 1 ). Among middle and high school students who reported current use of any tobacco product, 23.8% (an estimated 730,000 students) reported use of a menthol-flavored tobacco product; prevalence of menthol-flavored tobacco product use was 25.6% among males and 22.2% among females ( Table 1 ). Prevalence of menthol-flavored tobacco product use by race or ethnicity ranged from 7.8% among non-Hispanic Black students to 30.1% among non-Hispanic White students. Prevalence was 19.6% among middle school students and 24.3% among high school students. Prevalence of menthol-flavored tobacco product use across sexual orientation categories ranged from 24.4% to 26.5%. Prevalence of menthol-flavored tobacco product use by transgender identity ranged from 20.5% among students who didn’t know what the question was asking to 37.7% among students who identified as transgender. Prevalence of menthol-flavored tobacco use among students with characteristics indicative of tobacco addiction (frequent use of tobacco, craving tobacco products, use of multiple tobacco products, and time after awakening to wanting to use a tobacco product) ranged from 38.0% to 44.0% compared with 13.8% to 23.5% among students who did not report characteristics indicative of tobacco addiction. Prevalence of menthol-flavored tobacco use was 26.5% among students with exposure to tobacco product advertising, 24.8% among students who intended to quit using all tobacco products, and 26.2% among students who reported a past-year quit attempt.

Nonmenthol- and menthol-flavored tobacco product use among students who used flavored tobacco products. Most students who currently used any flavored tobacco product reported using nonmenthol tobacco products (60.5%), ranging from 41.2% (those indicating “not sure” if they were transgender) to 84.5% (non-Hispanic Black students) ( Table 1 ). Among students who reported current use of a flavored tobacco product, 39.5% reported use of menthol-flavored tobacco products ( Table 1 ) ( Figure ). Among middle and high school students who currently used any flavored tobacco products, prevalence of menthol-flavored tobacco product use by sex was 43.7% among males and 35.9% among females ( Table 1 ). Prevalence of menthol-flavored tobacco product use ranged from 15.5% among non-Hispanic Black students to 47.1% among non-Hispanic White students. Among middle school students, the prevalence was 34.7% compared with 39.9% among high school students and ranged from 39.4% to 44.3% across sexual orientation categories. Prevalence of menthol-flavored tobacco product use by transgender identity ranged from 37.6% among those who identified as not transgender to 58.8% among those who were not sure. Prevalence of menthol-flavored tobacco use among students with characteristics indicative of addiction (craving tobacco products, use of multiple tobacco products, frequent use of tobacco, and time after awakening to wanting to use a tobacco product) ranged from 50.7% to 57.9% compared with 26.4% to 36.5% among students who did not report characteristics indicative of tobacco addiction. Prevalence of menthol-flavored tobacco use was 41.2% among students with exposure to tobacco product advertising, 38.3% among students who intended to quit using all tobacco products, and 40.6% among students who reported a past-year quit attempt.

Menthol-flavored tobacco use by type of flavored tobacco product. Approximately 53.9% of students who used a combination of types of flavored tobacco products, including e-cigarettes, combustible tobacco products, and other types of tobacco product, indicated use of at least 1 menthol-flavored tobacco product ( Figure ). Among students who exclusively used e-cigarettes, 30.6% reported using menthol-flavored products, and 29.6% of students who exclusively used combustible tobacco products reported using menthol-flavored products. The estimate for prevalence of use of menthol-flavored tobacco products among students who exclusively used other types of tobacco products was not statistically reliable and is not presented.

Characteristics of middle and high school students who use menthol- and nonmenthol-flavored tobacco products among students who use flavored tobacco products. Among students who used any flavored tobacco products, those who used menthol-flavored products differed from those who used nonmenthol-flavored products ( Table 2 ). Compared with students who used nonmenthol-flavored tobacco products, a higher proportion of students who used menthol-flavored tobacco products were male (50.4% among menthol vs 42.2% among nonmenthol, P = .04) or non-Hispanic White, Hispanic, or non-Hispanic Other (96.2% menthol vs 86.5% nonmenthol, P < .001, not shown in table). In contrast, compared with students who used nonmenthol-flavored products, a lower proportion of students who used menthol-flavored products were non-Hispanic Black (3.8% menthol vs 13.5% nonmenthol, P < .001). A higher proportion of students who used menthol-flavored tobacco products (compared with students who used nonmenthol-flavored products) used tobacco products frequently (66.0% vs 38.1%, P < .001); used multiple tobacco products (54.0% vs 31.3%, P < .001); wanted to use a tobacco product within less than 30 minutes of awakening (48.1% vs 27.9%, P < .001); craved tobacco products within the past 30 days (44.8% vs 28.3%, P < .001); and did not intend to quit using tobacco products (39.9% vs 33.1%, P = .03).

Characteristics associated with menthol-flavored tobacco product use among students who use flavored tobacco products. We examined correlates of menthol-flavored tobacco product use among middle and high school students who reported current use of any flavored product. Except for sex and intending to quit using all tobacco products, significant associations between covariates and use of menthol-flavored tobacco products remained after adjustment for grade level, sex, and race and ethnicity, although some changes existed in the strengths of association. Compared with non-Hispanic White students, the prevalence of menthol-flavored tobacco product use was lower among Hispanic students (APR, 0.59; 95% CI, 0.45–0.77) and non-Hispanic Black students (APR, 0.34; 95% CI, 0.22–0.53) ( Table 3 ). Compared with students who were not transgender, current prevalence of menthol-flavored tobacco product use was also higher among students who were transgender (APR, 1.45; 95% CI, 1.03–2.03) and those who were not sure if they were transgender (APR, 1.55; 95% CI, 1.14–2.12). Current prevalence of menthol-flavored tobacco product use was also higher among students who indicated frequent tobacco product use (APR: 1.88; 95% CI, 1.59–2.22); use of multiple tobacco products (APR, 1.68; 95% CI, 1.36–2.05); wanting to use a tobacco product within 30 minutes of awakening (APR, 1.55; 95% CI, 1.27–1.88); and craving tobacco products within the past 30 days (APR, 1.34; 95% CI, 1.08–1.66), compared with respective reference categories.

We found that more than 1 in 5 students who reported current use of at least 1 tobacco product reported use of a menthol-flavored tobacco product. Among students who reported use of at least 1 flavored tobacco product, nearly 2 in 5 reported current use of a menthol-flavored tobacco product. Additionally, 3 in 10 students who reported currently using only flavored e-cigarettes reported using a menthol-flavored product; more than 3 in 10 students who currently only used flavored combustible tobacco products reported using a menthol-flavored product; and more than half of all students who currently used a combination of flavored e-cigarettes, combustible tobacco products, and noncombustible tobacco products reported use of a menthol-flavored product. Differences in sociodemographic characteristics, tobacco product use behavior, and cessation indicators were found among middle and high school students who used menthol-flavored tobacco products.

The prevalence of menthol-flavored tobacco product use was highest among non-Hispanic White students and lowest among non-Hispanic Black students — a result that is contrary to studies focused on menthol cigarette smoking among youths and adults (14,15). At the time of our writing, we found no studies focused on prevalence of any menthol-flavored tobacco product use among youths by race or ethnicity; most studies focused on menthol cigarette smoking or any flavored tobacco product use or did not distinguish between menthol and mint flavors (14,15,17,18). Although our results contrast with some previous studies of cigarette smoking among young people, these findings align with recent research on menthol cigarette smoking that reported a similar pattern (14,19). Miech et al reported that Black adolescents had a lower prevalence of menthol cigarette smoking than adolescents of other races and ethnicities, although results from modeling showed that Black adolescents who smoked cigarettes were more likely to smoke menthol cigarettes compared with White adolescents (19). The results from our study and the Miech study could be partially attributable to a lower prevalence of cigarette smoking in general among young people (12,13) and later-age onset of cigarette smoking among non-Hispanic Black people (20,21). The higher prevalence of e-cigarette use compared with other tobacco products among youths may also play a role. E-cigarettes account for a large proportion of prevalence of any tobacco product use in this population, and fruit- and candy-flavored e-cigarettes are popular in this population (12,13). Populations of young people with a high prevalence of e-cigarette use differ from adult populations with a high prevalence of cigarette smoking relative to other tobacco products. We saw differences by race and ethnicity and among any menthol-flavored tobacco product use (15).

Among students who reported past 30-day use of flavored tobacco products, we saw no association between sexual orientation and menthol-flavored tobacco product use. This is in contrast with previous literature among adults who smoke menthol cigarettes (3). This could be due partly to the high proportion of youths using e-cigarettes and nonmenthol-flavored noncigarette tobacco products (12).

Similar to results from previous studies focused on menthol cigarette smoking (17,22), our study’s results show that, among students who used menthol-flavored tobacco products within the past 30 days, use was associated with behaviors that indicated tobacco dependence. These behaviors include frequent tobacco product use, use of multiple tobacco products, wanting to use tobacco products within 30 minutes of awakening, and craving a tobacco product within the past 30 days. These results suggest use of any menthol-flavored tobacco product (alone or in combination with other flavors) among students who use any flavored tobacco products may be associated with symptoms of dependence, which in turn, can contribute to continued use.

We also found that in 2022, 30.6% of students who currently used only flavored e-cigarettes used menthol e-cigarettes. To our knowledge, our study is one of a few studies focused on the prevalence of menthol-flavored tobacco product use among middle and high school students who currently use any flavored tobacco product, although at least 1 study assessed this among all youths (not just those who currently use tobacco products) (18). Most studies have focused exclusively on the prevalence of menthol cigarette smoking (14,17,19). Thus, our study expands the knowledge base on use by young people of menthol flavor across multiple tobacco product types.

Findings of this study are subject to at least 4 limitations. First, the sample size was not large enough to present characteristics of menthol-flavored product use by exclusive use of individual tobacco product types (eg, cigarette smoking only, cigar use only). Second, NYTS data are cross-sectional, and identified associations reflect tobacco use patterns at the time of survey completion. Third, NYTS data are subject to response bias. However, the validity of self-reported tobacco product use in population-based studies has been shown to be high (23). Finally, our results are generalizable only to middle and high school students in public and private schools in the US.

As of July 2023, menthol is the only nontobacco flavoring allowed in cigarettes sold in the US since the 2009 Family Smoking Prevention and Tobacco Control Act, which prohibited the sale of all characterizing flavors of cigarettes except menthol and tobacco (24). Additionally, in early 2020, the US Food and Drug Administration (FDA) prohibited the use of characterizing flavors in cartridge-based e-cigarettes, excluding menthol (25). In 2022, FDA proposed standards to prohibit menthol as a characterizing flavor in cigarettes and all flavored cigars (6).

Although prohibiting sales of flavors can have a significant effect on reducing tobacco product use among young people, the continued availability of menthol could mitigate the effects of policies prohibiting flavors (26). For example, immediately following the FDA’s announcement of prioritized enforcement of sales of prefilled e-cigarette cartridges in flavors other than tobacco and menthol, increases occurred in the market share of menthol-flavored, prefilled, cartridge-based e-cigarettes and nonmenthol-flavored (including fruit, candy, and alcohol flavored) disposable e-cigarettes (27,28). How this affected overall e-cigarette use among young people is currently unknown. However, a recent study in Minnesota reported changes in tobacco product use in this population after a flavor ban that included menthol was implemented in the Twin Cities (Minneapolis and St. Paul) (26). The study reported that any tobacco product use and e-cigarette use among youths increased to a greater extent in the rest of the state of Minnesota when compared with the increase in the Twin Cities (26). Additionally, use of noncigarette tobacco products with flavors other than mint or menthol by youths increased by 5% in the Twin Cities compared with 10.2% in the rest of the state (26). This shows that the inclusion of menthol in prohibitions of tobacco product flavor can further reduce overall tobacco product use among youths.

As new product types continue to be added to the tobacco landscape, examining the role of menthol and other characterizing flavors or additives in all tobacco products will be important to determine factors that may contribute to initiation and sustained use of tobacco products. Future studies are needed of menthol-flavored tobacco product use with sufficient sample sizes to assess use of specific tobacco products by demographic groups. Continued surveillance of the use of all characterizing flavored tobacco products (including menthol) and the effectiveness of restrictions on flavored tobacco product sales are needed to inform public health policy and tobacco prevention and control efforts.

This research did not receive funding from agencies in the public, commercial, or not-for-profit sectors. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Prevention and Control. The authors received no external financial support for the research, authorship, or publication of this article. The authors declared no potential conflicts of interest with respect to the research, authorship, or publication of this article. No copyrighted material, surveys, instruments, or tools were used in this research.

Corresponding Author: Monica E. Cornelius, PhD, Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Hwy, MS S107-7, Atlanta, GA 30341 ( [email protected] ).

Author Affiliations: 1 Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.

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a Current menthol-flavored tobacco product use was assessed among students who indicated past 30-day tobacco product use (use of ≥1 tobacco products including e-cigarettes, cigarettes, cigars, smokeless tobacco [chewing tobacco, snuff, dip, snus], dissolvable tobacco products, waterpipes/hookahs, pipe tobacco, bidis, heated tobacco products, and nicotine pouches). Those responding “yes” to using a flavored product and “menthol” to type of flavor were categorized as having used menthol-flavored tobacco products. For cigarettes, respondents who, within the past 30 days, indicated 1) using only 1 cigarette brand and indicated that the brand was a menthol-flavored brand (Kool, Newport); 2) responded that they smoked Kool or Newport brands to the question “During the past 30 days, what brand of cigarettes did you usually smoke? (Choose only 1 answer)” (asked among respondents who used multiple brands in the past 30 days); or 3) who answered “yes” to “During the past 30 days, were the cigarettes that you usually smoked menthol?” were considered as having using menthol-flavored tobacco products. b Estimated weighted total numbers were rounded to the nearest 10,000 persons. Overall population estimates might not sum to corresponding subgroup population estimates because of rounding or inclusion of students who did not self-report sex, race and ethnicity, or grade level. c Family affluence was assessed with a composite scale that comprised 4 questions: 1) “Does your family own a vehicle (such as a car, van, or truck)?”; 2) “Do you have your own bedroom?”; 3) “How many computers (including laptops and tablets, not including game consoles and smartphones) does your family own?”; and 4) “During the past 12 months, how many times did you travel on vacation with your family?” Complete data from all 4 questions (n=2,619 among students who currently use tobacco products; n = 1,617 among students who currently used flavored tobacco products) were summed (range, 0–9) and categorized into approximate tertiles based on the sample’s weighted distribution of scores. d Exposure to tobacco product marketing (advertisements or promotions) was assessed separately for e-cigarettes, cigarettes, and other tobacco products for 4 sources: retail stores; internet; television, streaming services, or movies; and newspapers or magazines. Respondents were asked, “When you [are using the Internet; read newspapers or magazines; go to a convenience store, supermarket, or gas station; watch television or streaming services (such as Netflix, Hulu, or Amazon Prime), or go to the movies], how often do you see ads or promotions for [e-cigarettes; cigarettes or other tobacco products]?” Respondents were categorized as exposed if they responded “sometimes,” “most of the time,” or “always” or unexposed if they responded “never” or “rarely.” Those who reported “I do not use the internet,” “I do not read newspapers or magazines,” “I never go to a convenience store, supermarket, or gas station,” or “I do not watch television or streaming services or go to the movies” were excluded from the analysis. There were 476 respondents excluded among students reporting current tobacco product use and 262 respondents excluded among students reporting current flavored tobacco product use. e People who used tobacco products in the past 30 days who indicated use of any product on 20 or more days in the past 30 days were categorized as using tobacco products frequently; otherwise, if all tobacco products were reported as being used less than 20 days out of the last 30, they were categorized as not having frequent tobacco product use. f Based on the question “During the past 30 days, have you had a strong craving or felt like you really needed to use a tobacco product of any kind?” Those answering “yes” were categorized as craving tobacco products within the past 30 days. g Based on the question, “During the past 12 months, how many times have you stopped using all tobacco products for 1 day or longer because you were trying to quit tobacco products for good?” Responses other than “I did not try to quit all tobacco products during the past 12 months” were considered having made 1 or more quit attempts. Respondents missing data on this outcome (n = 619 among students reporting current tobacco product use; n = 286 among students reporting current flavored tobacco product use) were excluded from the analysis. h Based on the question, “Are you seriously thinking about quitting the use of all tobacco products?” Responses of “Yes, during the next 30 days,” “Yes, during the next 6 months,” “Yes, during the next 12 months,” and “Yes, but not during the next 12 months” indicated having quit intentions. The response, “No, I am not thinking about quitting the use of all tobacco products” indicated not having quit intentions. Respondents missing data on this outcome (n = 578 among students reporting current tobacco product use; n = 265 among students reporting current flavored tobacco product use) were excluded from the analysis.

a Current use of menthol-flavored tobacco products was assessed among students who indicated past 30-day tobacco product use (use of ≥1 tobacco products including e-cigarettes, cigarettes, cigars, smokeless tobacco [chewing tobacco, snuff, dip, snus], dissolvable tobacco products, waterpipes/hookahs, pipe tobacco, bidis, heated tobacco products, and nicotine pouches). Those responding “Yes” to using a flavored product and “menthol” to the type of flavor were categorized as using menthol-flavored tobacco products. For cigarettes, respondents who, within the past 30 days, indicated 1) using only one cigarette brand and indicated that the brand was a menthol-flavored brand (Kool, Newport); 2) responded that they smoked Kool or Newport brands to the question “During the past 30 days, what brand of cigarettes did you usually smoke? (Choose only 1 answer)” (asked among respondents who used multiple brands in the past 30 days); or 3) who answered “Yes” to “During the past 30 days, were the cigarettes that you usually smoked menthol?” were categorized as using menthol-flavored tobacco products. b Estimated weighted total numbers were rounded to the nearest 10,000 people. Overall population estimates might not sum to corresponding subgroup population estimates because of rounding or inclusion of students who did not self-report sex, race and ethnicity, or grade level. c P value calculated by using the χ 2 test of independence and indicates whether there are differences between use of menthol-flavored and nonmenthol-flavored tobacco products for each characteristic. d Unstable estimate is not presented because of a relative SE of ≥0.3 or unweighted denominators less than 50. e Family affluence was assessed with a composite scale that comprised 4 questions: 1) “Does your family own a vehicle (such as a car, van, or truck)?”; 2) “Do you have your own bedroom?”; 3) “How many computers (including laptops and tablets; not including game consoles and smartphones) does your family own?”; and 4) “During the past 12 months, how many times did you travel on vacation with your family?” Complete data from all 4 questions (n = 1,617 among students who currently used flavored tobacco products) were summed (range = 0–9) and categorized into approximate tertiles based on the sample’s weighted distribution of scores. f Exposure to tobacco product marketing (advertisements or promotions) was assessed separately for e-cigarettes, cigarettes, and other tobacco products for 4 sources: retail stores; internet; television, streaming services, or movies; and newspapers or magazines. Respondents were asked, “When you [are using the Internet; read newspapers or magazines; go to a convenience store, supermarket, or gas station; watch television or streaming services (such as Netflix, Hulu, or Amazon Prime); or go to the movies], how often do you see ads or promotions for [e-cigarettes; cigarettes or other tobacco products]?” Respondents were categorized as exposed if they responded “sometimes,” “most of the time,” or “always” or unexposed if they responded “never” or “rarely.” Those who reported “I do not use the internet,” “I do not read newspapers or magazines,” “I never go to a convenience stores, supermarket, or gas station,” or “I do not watch television or streaming services or go to the movies” were excluded from the analysis. There were 262 respondents excluded. g Persons who used tobacco products in the past 30 days who indicated use of any product on 20 or more days in the past 30 days were categorized as using tobacco products frequently; otherwise, if all tobacco products were reported as being used less than 20 days out of the last 30, then persons were categorized as not having frequent tobacco product use. h Based on the question “During the past 30 days, have you had a strong craving or felt like you really needed to use a tobacco product of any kind?” those answering “yes” were categorized as craving tobacco products within the past 30 days. i Based on the question, “During the past 12 months, how many times have you stopped using all tobacco products for 1 day or longer because you were trying to quit tobacco products for good?” responses other than “I did not try to quit all tobacco products during the past 12 months” were considered having made 1 or more quit attempts. Respondents (n = 286) missing data on this outcome were excluded from the analysis. j Based on the question, “Are you seriously thinking about quitting the use of all tobacco products?” Responses of “Yes, during the next 30 days,” “Yes, during the next 6 months,” “Yes, during the next 12 months,” and “Yes, but not during the next 12 months” indicated having quit intentions. The response, “No, I am not thinking about quitting the use of all tobacco products” indicated not having quit intentions. Respondents (n = 265) missing data on this outcome were excluded from the analysis.

Abbreviations: APR, adjusted prevalence ratio; PR, prevalence ratio. a Current menthol-flavored tobacco product use was assessed among students who indicated past 30-day tobacco product use (use of ≥1 tobacco products including e-cigarettes, cigarettes, cigars, smokeless tobacco [chewing tobacco, snuff, dip, snus], dissolvable tobacco products, waterpipes/hookahs, pipe tobacco, bidis, heated tobacco products, and nicotine pouches). Those responding “Yes” to using a flavored product and “menthol” to the type of flavor were categorized as using menthol tobacco products. For cigarettes, respondents who, within the past 30 days, indicated 1) using only 1 cigarette brand and indicated that the brand was a menthol-flavored brand (Kool, Newport); 2) responded that they smoked Kool or Newport brands to the question “During the past 30 days, what brand of cigarettes did you usually smoke? (Choose only 1 answer)” (asked among respondents who used multiple brands in the past 30 days), or 3) who answered “Yes” to “During the past 30 days, were the cigarettes that you usually smoked menthol?” were considered as having used menthol-flavored tobacco products. b Prevalence ratios adjusted for sex, race, and grade level for all variables except sex, race, and grade. APR for sex adjusted for race and grade; APR for race adjusted for sex and grade; APR for grade adjusted for sex and race. c P value was calculated by using the Wald χ 2 and tests for differences between menthol status groups (menthol flavors, nonmenthol flavor tobacco product use) for each characteristic. d Family affluence was assessed with a composite scale that comprised 4 questions: 1) “Does your family own a vehicle (such as a car, van, or truck)?”; 2) “Do you have your own bedroom?”; 3) “How many computers (including laptops and tablets, not including game consoles and smartphones) does your family own?”; and 4) “During the past 12 months, how many times did you travel on vacation with your family?” Complete data from all 4 questions (n = 1,617) were summed (range = 0–9) and categorized into approximate tertiles based on the sample’s weighted distribution of scores. e Exposure to tobacco product marketing (advertisements or promotions) was assessed separately for e-cigarettes and cigarettes or other tobacco products for 4 sources: retail stores; internet; television, streaming services, or movies; and newspapers or magazines. Respondents were asked, “When you [are using the Internet; read newspapers or magazines; go to a convenience store, supermarket, or gas station; watch television or streaming services (such as Netflix, Hulu, or Amazon Prime), or go to the movies], how often do you see ads or promotions for [e-cigarettes; cigarettes or other tobacco products]?” Respondents were categorized as exposed if they responded “sometimes,” “most of the time,” or “always” or unexposed if they responded “never” or “rarely.” Those who reported “I do not use the internet,” “I do not read newspapers or magazines,” “I never go to a convenience stores, supermarkets, or gas stations,” or “I do not watch television or streaming services or go to the movies” were excluded from the analysis. There were 262 respondents excluded. f Students who used tobacco products within the past 30 days who indicated use of any product on 20 or more days in the past 30 days were categorized as using tobacco products frequently; otherwise, if all tobacco products were reported as being used less than 20 days out of the last 30, then students who used tobacco product within the past 30 days were categorized as not using tobacco products frequently. g Based on the question “During the past 30 days, have you had a strong craving or felt like you really needed to use a tobacco product of any kind?” Those answering “yes” were categorized as craving tobacco products within the past 30 days. h Based on the question, “During the past 12 months, how many times have you stopped using all tobacco products for 1 day or longer because you were trying to quit tobacco products for good?” Responses other than “I did not try to quit all tobacco products during the past 12 months” indicated having made 1 or more quit attempts. Respondents (n = 286) missing data on this outcome were excluded from the analysis. i Based on the question, “Are you seriously thinking about quitting the use of all tobacco products?” Responses of “Yes, during the next 30 days,” “Yes, during the next 6 months,” “Yes, during the next 12 months,” and “Yes, but not during the next 12 months” indicated quit intentions. The response, “No, I am not thinking about quitting the use of all tobacco products” indicated not having quit intentions. Respondents (n = 265) missing data on this outcome were excluded from the analysis.

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Open Access

Peer-reviewed

Research Article

Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017–18

Contributed equally to this work with: Najma Begum, Mohammad Omar Faruk

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Statistics, Noakhali Science and Technology University, Noakhali, Bangladesh

Roles Conceptualization, Methodology, Writing – review & editing

Affiliation Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh

Roles Conceptualization, Data curation, Formal analysis, Methodology

Najma Begum,
Mohd. Muzibur Rahman,
Mohammad Omar Faruk

Published: May 31, 2024
https://doi.org/10.1371/journal.pone.0304389
Reader Comments

Malnutrition in pregnant women significantly affects both mother and child health. This research aims to identify the best machine learning (ML) techniques for predicting the nutritional status of pregnant women in Bangladesh and detect the most essential features based on the best-performed algorithm.

This study used retrospective cross-sectional data from the Bangladeshi Demographic and Health Survey 2017–18. Different feature transformations and machine learning classifiers were applied to find the best transformation and classification model.

This investigation found that robust scaling outperformed all feature transformation methods. The result shows that the Random Forest algorithm with robust scaling outperforms all other machine learning algorithms with 74.75% accuracy, 57.91% kappa statistics, 73.36% precision, 73.08% recall, and 73.09% f1 score. In addition, the Random Forest algorithm had the highest precision (76.76%) and f1 score (71.71%) for predicting the underweight class, as well as an expected precision of 82.01% and f1 score of 83.78% for the overweight/obese class when compared to other algorithms with a robust scaling method. The respondent’s age, wealth index, region, husband’s education level, husband’s age, and occupation were crucial features for predicting the nutritional status of pregnant women in Bangladesh.

The proposed classifier could help predict the expected outcome and reduce the burden of malnutrition among pregnant women in Bangladesh.

Citation: Begum N, Rahman MM, Omar Faruk M (2024) Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017–18. PLoS ONE 19(5): e0304389. https://doi.org/10.1371/journal.pone.0304389

Editor: Benojir Ahammed, Khulna University, BANGLADESH

Received: January 24, 2024; Accepted: May 12, 2024; Published: May 31, 2024

Copyright: © 2024 Begum et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data and Code availability Statement: The code and data of this study can be found online at: https://www.kaggle.com/datasets/faruk268/nutritional-status-of-the-pregnant-women .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Nutritional status is the outcome of the biological phenomenon of food utilization and is a vital aspect of health. Good nutrition is linked to better health outcomes in infants, children, and mothers, more robust immune systems, reduced risk of non-communicable diseases (like diabetes and cardiovascular disease), and safer pregnancies and childbirths [ 1 ]. In contrast, malnutrition can lead to a range of issues, including low work productivity, higher chances of miscarriage, stillbirth, low birth weight, infant mortality, and fatal complications during pregnancy, delivery, and postpartum periods [ 2 ]. Malnutrition poses significant health risks. Globally, nearly 1.9 billion adults are either overweight or obese, and approximately 462 million adults are underweight [ 3 ]. Regarding nutrition, Bangladesh is experiencing a decline in underweight individuals but an upward trend in overweight and obese individuals [ 3 ]. Rural-urban disparities in unhealthy body mass index (BMI) categories are also a significant concern. According to the Bangladesh Demographic and Health Survey (BDHS) from 2017–2018, 13% of rural women are underweight, while 9% of urban women are underweight. In contrast, 43% of urban women are overweight or obese, compared to 28% of rural women who are obese [ 3 , 4 ].

Approximately 200 million women become pregnant yearly, most residing in developing countries [ 5 ]. The nutrition of mothers during pregnancy is critical for the short- and long-term health of both the mother and her growing fetus [ 6 ]. A healthy pregnancy outcome is contingent upon good nutritional status before and during pregnancy. Maternal malnutrition poses significant health risks for both the pregnant mother and her children [ 7 ]. In Ethiopia, pregnant women’s undernutrition ranges from 21.8 to 43.1%. Rural women exhibit a higher prevalence of undernutrition [ 8 , 9 ]. Malnutrition in pregnant mothers often goes unnoticed and unreported, resulting in insufficient attention given to the extent, consequences, and causes of this health issue [ 10 ]. Extensive research has been conducted on malnutrition’s impact on pregnant women’s health. Numerous factors contribute to malnutrition, including demographic, household, physical, socioeconomic, and cultural factors [ 11 ]. Previous studies have shown that individuals with a lower wealth index and less education are at a higher risk of being underweight, but the threat of being overweight is lower [ 12 ].

Machine learning is an intersection of artificial intelligence and statistical learning that explores large data sets to uncover unknown patterns or relationships [ 13 ]. Various studies have been conducted to identify the most informative risk factors and predict nutritional status using machine learning models, such as child malnutrition [ 14 , 15 ] and malnutrition among women [ 2 , 3 ], based on different demographic and health survey (DHS) datasets. Islam et al. (2022) utilized the Bangladesh Demographic Health and Survey (BDHS) 2014 dataset with 15,464 respondents and employed five different algorithms–NB, DT, SVM, ANN, and RF–to predict malnourished women. The RF classifier was found to have the highest accuracy (81.4%) and AUC (0.837) for underweight and accuracy (82.4%) and AUC (0.853) for overweight/obese [ 2 ]. Moreover, Mukuku et al. (2019) conducted a cross-sectional study with 263 children and employed an LR-based algorithm to predict nutritional status, revealing an AUC of 0.969, sensitivity of 93.5%, and specificity of 93.1% [ 16 ]. Another study by Hossain et al. (2022) applied six different machine learning algorithms to predict unintended pregnancies among married women in Bangladesh using the pregnancy intention of 1129 respondents. Among them, the elastic net regression (ENR) algorithm gained a higher AUC of 74.67% [ 17 ].

Researchers have recently used various machine-learning algorithms to study prediction performance [ 18 ]. All in all, machine learning is now being used everywhere in the research sector. Nowadays, machine learning is prevalent in health-related fields [ 13 , 14 , 19 ]. However, no research has considered machine learning algorithms to evaluate the nutritional status of pregnant women. The main objective of this study was to use various well-known machine learning algorithms to predict the nutritional status of pregnant women in Bangladesh and to identify the critical features of the best model with more accurate prediction.

Methodology

Data source and sampling design.

The nutritional status of currently pregnant women data was extracted from the Bangladesh Demographic and Health Survey (BDHS), conducted in 2017–18, which is accessible online [ 4 ]. This study only included women currently pregnant and excluded all women who did not fall into the inclusion criteria. BDHS 2017–18 data comprise 20,127 ever-married women aged 15–49 who were interviewed. Among them, 18,895 were married women. However, only 1,129 currently pregnant women were included in this study. The purpose of BDHS was to collect household data to monitor and evaluate the health status of mothers and children, including nutrition, causes of death, newborn care, women’s empowerment, and more. United States Agency for International Development (USAID) provided financial assistance for this investigation in Bangladesh. Demographic Health Survey Authority employed a two-step stratified sampling procedure in the 2017–18 BDHS, where data was collected from eight divisions: Barisal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet. The survey used a list of enumerated areas (EAs) from Bangladesh’s population and housing census 2011 provided by the Bangladesh Statistics Office (BBS). In the 1 st sampling stage, 675 Eas were selected, of which 425 were from rural areas and 250 were from urban areas, with a probability proportional to the EA scale. In the second sampling stage, a complete household listing procedure was carried out in all selected Eas to provide a sampling frame for the systematic selection of 30 households per EA. This allowed for statistically accurate estimates of key demographic and health variables for the nation, rural, and urban areas separately [ 4 ].

Study variables and measurement

Dependent variable..

The study primarily focused on assessing the nutritional status of pregnant women by utilizing the body mass index (BMI) as a measure. According to the World Health Organization (WHO), BMI was categorized as underweight (BMI<18.5 kg/m2), normal weight (18.5≤BMI≤24.9 kg/m2), overweight (25.0≤ BMI<30.0 kg/m2), and obese (BMI≥30.0 kg/m2) [ 20 ]. However, for this study, overweight and obese women were classified as a single category.

This study tried to recommend the nutritional status of pregnant women. To begin with, we consider the current weight of the pregnant women. During normal pregnancy, women gain weight 11.5–16 kg, and it is essential to note that usual weight gain (UWG) is not affected by the height of the pregnant women [ 21 , 22 ]. To calculate the pre-pregnancy weight of pregnant women, we have deduced the usual weight gain (UWG) during pregnancy from their current weight.

In the first trimester (1–13 weeks), the UWG is 0.5–2 kg and 0.35–0.5 kg/week for the second and third trimesters [ 21 – 25 ]. UWG for the first trimester is 0.5kg, with little weight gain experienced during the first trimester [ 21 , 26 ]. In the second trimester, we divided it into two equal parts: the first half (14–20 weeks) and the second half (21–27 weeks). It is mentioned that the weight gain is typically lower during the first half of the second trimester [ 27 ]. Therefore, we consider UWG for this part to be 0.35 kg/week. It is also mentioned that the weight gain in the second half of the second trimester is comparatively higher than in the first half [ 23 ]. So, we consider the UWG 0.5kg/week for this part. In the third trimester, UWG is also 0.5kg/week [ 28 ].

So, at a glance, UWG during the first trimester or first three months is 0.5kg, and up to 20 weeks of gestation or during the first five months (0.5+0.35*7), it’s approximately 3 kg. For the subsequent months, it’s considered 2 kg per month. Using this information, we can calculate the pre-pregnancy weight of the pregnant women = current weight—UWG during pregnancy.

For example, if a pregnant woman is seven months into her pregnancy and her current weight is 80kg, and according to our discussion, UWG of 7kg, her pre-pregnancy weight would be 73kg. BIM calculated for this pregnant woman is = (73kg/ height in meters squared). Calculate the BMI for each respondent in the study using the formula (pre-pregnancy weight in kg/height in meters squared), which categorizes as underweight (<18.5 kg/m2), normal weight (18.5≤BMI≤24.9 kg/m2), and overweight (≥25.0 kg/m2). Previous literature conducted in Asian countries has used the BMI categories recommended by the World Health Organization (WHO) [ 2 , 29 – 33 ]. Following this literature, we have used the WHO-recommended BMI categories in our analysis.

Independent variable.

Table 1 presents the predictor names, types, descriptions, and categorizations based on previous relevant works [ 2 , 3 , 14 , 15 ] The predictors included the respondent’s age, place of residence, region, religion, educational attainments, current employment status, wealth index, total number of children, number of living children, current pregnancy wanted, currently breastfeeding, access to mass media, age, occupation, and educational attainment of the partner, toilet facility, and sources of drinking water.

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Data pre-processing

The BDHS 2017–18 data has been used for this study. First, reviewing the literature, we made a list of variables and extracted the selected variables from the BDHS data. Most of the features considered in this study were categorical, and a few were numeric. The numeric variable was also converted into categorical for the convenience of the study. Before model training, an extensive exploratory analysis was conducted. The categorical features of the dataset were encoded for numerical values. First, all variables’ frequency was calculated to check anomalies such as inconsistent values, missing observations, and outliers. The conflicting values were removed or replaced with consistent values. The missing values and outliers were deleted from the dataset.

Feature selection.

We proceeded with variable or feature selection after removing any missing values. Variable selection aims to reduce data dimensions to minimize processing time and computation costs [ 34 ]. To enhance the overall predictive performance of the classification, we chose a subset of variables that significantly contributed to the target class. Identifying these by performing the chi-square (χ2) test between nutritional status (BMI) with each of the variables primary, which was adjusted for the complex survey design using second-order Rao–Scott corrections [ 35 , 36 ]. And included those with a p-value < 0.05. Thirteen features met these criteria and were selected for developing the classification model. These features included the respondent’s age, region, place of residence, highest educational level, wealth index, total children ever born, number of living children, current pregnancy wanted, access to mass media, husband’s age, husband’s education level, husband’s occupation, and toilet facility. The S1 Table shows the features list from the chi-square test results, adjusted using second-order Rao–Scott corrections.

Dealing with imbalanced datasets.

In this study on the BMI data of pregnant women, we noted a class imbalance, which could result in inaccurate or biased estimates of measures such as accuracy and precision. The percentage of overweight/obese pregnant women in Bangladesh was 15%, which may create an imbalanced distribution of the underlying classes and lead to biased and unreliable results while using ML. To overcome this issue, an oversampling approach named the Synthetic Minority Oversampling Technique (SMOTE) was implemented. This technique was developed by Nitesh Chawla [ 37 ].

Model validation.

For ML approaches, the dataset is randomly divided into two distinct datasets: a training dataset that comprises 70% of the data and a test dataset that predicts the response variable and checks whether the expected outcome is similar to the actual outcomes, which include 30% of the primary dataset. All models were trained based on 10-fold cross-validation, designed to assess performance and optimize prediction models using ML techniques. The Statistical Package for Social Science (SPSS) 26 version and Python version 3.9.13 were used for data management and analysis.

Feature transformation (FT)

Four feature selection techniques were applied to decrease the datasets’ spread equality, skewness, and linear and additive relationships (see details in Table 2 ). From these transformations, we evaluated the best one for which the best ML model can be extracted. The transformations we applied are Standardization, Min-Max Scaling, Log Scaling, and Robus Scaling. A brief description of the transformation has been presented in Table 2 .

https://doi.org/10.1371/journal.pone.0304389.t002

Machine learning algorithms

This research utilized ten machine learning algorithms to predict the nutritional status (underweight, normal weight, overweight/obese) of pregnant women in Bangladesh. The performance of these algorithms was evaluated based on model evaluation parameters. The ML algorithms used in this investigation include logistic regression (LR), decision trees (DT), random forest (RF), k-nearest neighbors (k-NN), support vector machine (SVM), Naïve Bayes (NB), adaptive boosting (ADB), extreme gradient boosting (XGB), gradient boost, and bagging were included in this analysis. A brief description of ML algorithms used in this study is provided in supplement A in the S1 File .

Performance evaluation

Research supports using a variety of measures to assess and summarize a model’s performance, as no single measure can fully capture all aspects of a model. Methods such as accuracy, f1 score, precision, recall (sensitivity), and the area under the receiver operating characteristic curve should be employed to evaluate a model. Supplement B in the S1 File will discuss each performance evaluation parameter.

Feature importance

Identifying important features is crucial to machine learning prediction. Feature importance rates illustrate the significance of each feature for decision-making purposes. We have utilized two distinct feature importance methods, namely (a) Mean Decrease Impurity (MDI) and (b) Permutation Importance (PI), to identify the significant features from the datasets. After analyzing these datasets, we determined the algorithm that yielded the best results.

Ethical approval

BDHS 2017–2018 provided the publicly available secondary data for this study, which was conducted with ethical approval from the Institutional Review Boards of ICF Macro in Calverton, MD, USA, and Bangladesh Medical Research Council. All participants were informed of the study’s purpose, risks and benefits, future use of data, confidentiality, and anonymity, and they provided informed consent. We removed all identifier information before downloading the data from the BDHS website [ 4 ].

Baseline characteristics

S2 Table depicts the background characteristics of the pregnant women participating in this study. The most significant percentage of respondents are from the Chittagong and Dhaka divisions (15.4%) and (15.3%), respectively. The highest proportion of mothers belongs to the 20–24 age group, accounting for 34.4%, while most of the respondents’ husbands aged between 20–30 years (77%), but there were still some pregnant women who were less than 20 years old and over 35 years. Besides, (48.5%) of pregnant women are in secondary education, and only (4.3%) of the respondents could not read and write. The education level of the partners of the respondents is distributed as follows: 12.6% have no education, 33.2% have primary education, 35.3% have secondary education, and 19% have higher education. The majority of pregnant women (67.2%) are not currently working, and most of the respondents’ husbands (35.4%) work as employees. Large numbers of pregnant women (50.5%) had 1–2 children. Most participants come from poor and rich wealth statuses (approximately 20% each), with only 18.8% belonging to middle-class families. Most pregnant women (64.7%) were involved in mass media, and 35.3% were not. 75% of women’s pregnancies had Intended, and only 6% were breastfeeding. Most of the respondents were from rural areas (64.6%) and had improved drinking water sources at home.

Machine learning algorithm specifications

This study used specific machine learning algorithms, summarized in Table 3 . To help prevent errors, 10-fold cross-validation was used to determine the best parameters for these algorithms.

https://doi.org/10.1371/journal.pone.0304389.t003

Machine learning algorithms performance evaluation

This study applied four different feature transformation (FT) methods—Standardization, Min-Max, log, and Robust (referred to as FT1, FT2, FT3, and FT4)—along with ten machine learning (ML) algorithms to classify the nutritional status of pregnant women. The algorithms were evaluated based on various performance parameters, including accuracy, kappa statistics, precision, recall, f1 score, and AUC value. Tables 4 – 8 present each algorithm’s classification accuracy, kappa statistics, precision, f1 score, and recall. Tables 9 – 12 also show prediction results for underweight and overweight/obese classes, including AUC, precision, f1 score, and recall. The study also evaluated the performance of these ML algorithms without any transformation techniques, and the results showed that using FT methods improved the accuracy of the classification and other performance parameters. These results are reported in S3 Table .

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Variable importance from best performing algorithm

After evaluating machine learning, two different feature importance approaches, such as MDI and PI, were implemented for the RF algorithm with robust scaling to utilize and rank the significant features of the datasets. The factors, including respondent’s current age, wealth index, region, husband’s education level, husband’s age, and occupation, were the most important features of the nutritional status of pregnant women. In contrast, variables such as total number of children ever born, religion, number of living children, and toilet facility were found to be the least predictive based on the all-features importance methods ( Fig 5 ). S4 Table represents the important features rank of robust transformed datasets for the RF algorithm.

https://doi.org/10.1371/journal.pone.0304389.g005

To date, many prediction models can identify the nutritional status of children and women in Bangladesh [ 2 , 3 , 13 ]. However, there is a lack of research on the potential use of machine learning techniques to predict the nutritional status of pregnant women in Bangladesh. The main aim of this study is to predict the nutritional status (underweight, overweight/obese) of currently pregnant women in Bangladesh. This study applied four different feature transformation (FT) methods and then ten well-known machine learning algorithms such as decision tree, logistic regression, random forest, support vector machine, k-nearest neighbor, naïve Bayes, adaptive boosting (ADB), eXtreme Gradient Boosting (XGB), gradient boost and bagging. All models were trained using 10-fold cross-validation on the training data set.

The results of this study revealed that the FT4 or robust transformation is the best in the case of pregnant women’s nutritional status as it achieved the highest performance parameter for all classifiers. RF algorithm gained the highest accuracy (74.75%), kappa statistics (57.91%), precision (73.36%), recall (73.08%), and f1 score (73.09%) among all algorithms applied in the investigation with FT4 or robust scaling. The RF classifier had a high precision of 76.76% and an f1 score of 71.71% for the underweight class, while for the overweight class, the precision was 82.01%, and the f1 score was 83.78%.

In a study conducted by Balabaeva et al. [ 41 ], the impact of various feature scaling methods on heart failure patient datasets was examined and used LR, XGB, DT, and RF algorithms with scaling methods such as Standard Scaler, Max Abs Scaler, MinMax Scaler, Quantile Transformer, and Robust scaler. The study found that RF demonstrated better performance with Standard Scaler and Robust Scaler, which is consistent with our findings. A study conducted by M. Ahsan on a dataset with heart disease patients to evaluate eleven machine learning (ML) algorithms and six different data scaling methods such as Normalization, MinMax, Standscale, MaxAbs, Quantile Transformer, and Robust Scaler and gained that CART algorithm, along with Quantile Transformer, or Robust Scaler, outperforms all other ML algorithms [ 40 ].

Islam et al. discovered that the RF algorithm has the best prediction accuracy and the highest AUC score compared to other machine learning algorithms for health issues, including women’s nutritional status [ 2 ]. Khudri et al. conducted a study that found the ADB, RF, and XGB algorithms were the most effective at predicting women of childbearing age’s nutritional status [ 3 ], supporting this study’s findings. J. Ali et al. [ 42 ] developed a nutritional prediction model for Pakistani women using a Support Vector Machine, Logistic Regression, Random Forest, K-nearest neighbor, and Naïve Bayes algorithms. They found that Random Forest had the highest accuracy. B. Alamma et al. [ 43 ] used Random Forest (RF) and Decision Tree (DT) classifiers to analyze risk factors for obesity and overweight women in their research. They found that the Random Forest algorithm produced the best results with an accuracy and f1 score of 77% and 75%, respectively. Dunstan conducted a study on predicting nationwide obesity from food sales and found that RF had the best performance, which supports the findings of this study [ 44 ]. Talukder and Ahammed applied RF, LR, SVM, k-NN, and LDA algorithms to predict malnutrition in under-five Bangladeshi children. They found that the RF algorithm performed the best, with a specificity of 69.76%, sensitivity of 94.66%, and accuracy of 68.51% [ 14 ]. Another study to predict under-five malnutrition in Bangladesh, conducted by S. Ahmed et al., showed the best performance by the RF algorithm with an accuracy of 70.1% and 72.4% and AUC of 69.8% and 70% for stunting and underweight, respectively [ 45 ]. In a recent study on predicting childhood anemia in Bangladesh, Khan and colleagues showed that the RF algorithm achieved a height accuracy of 68.53% with a specificity of 66.41%, sensitivity of 70.73%, and AUC of 0.6857 [ 13 ]. Rahman et al. discovered that the RF algorithm achieved the highest AUC of 0.6590, accuracy of 0.8890, specificity of 0.9789, sensitivity of 0.0480, f1 score of 0.0771, and precision of 0.1960 for infant mortality in Bangladesh compared to other algorithms [ 46 ]. The Random Forest algorithm outperformed all other algorithms (total accuracy: 95%; area under ROC curve: 93%; Kappa Coefficient: 66%) in Ahmadi’s study on predicting low birth weight [ 47 ]. S. Rahman et al. [ 48 ] implemented three ML classifiers (support vector machine, LR, and random forest) to predict malnutrition in children. They achieved the maximum accuracy of 87.7% for wasted, 88.3% for stunted, and 85.7% for underweight, obtained by the RF algorithm. Random Forest performed better than other algorithms in Chilyabanyama’s research on predicting stunting among children under five in Zambia, which supports the current investigation’s findings [ 49 ].

In addition to identifying the best predictive models, this study also determined the essential features predicting nutritional status among currently pregnant women in Bangladesh based on the best algorithm found in this study. Based on the important feature score for RF, algorithms suggested that the wealth index, respondent age, region, husband education level, and husband’s age and occupation are the six most important features for predicting the nutritional status of pregnant women in Bangladesh. Household wealth status is a significant factor in determining maternal health care. As per the findings of this study, mothers with poor socioeconomic status face a greater risk of being underweight than those with high socioeconomic status, which is consistent with a previous study [ 50 ]. This research aligns with previous studies that have linked wealth index and working women to maternal underweight and overweight/obesity [ 51 ]. Respondent age is a vital indicator of the nutritional status of pregnant women. Some previous studies revealed that respondent age during the third trimester of pregnancy is a risk factor for developing malnutrition [ 52 , 53 ]. The husband’s age is also an important feature in the nutritional status of pregnant women. A study found that being overweight is more prevalent among women whose husbands are aged 31 years or above (29%) [ 3 ]. The current study also revealed that pregnant women whose husband’s education level is a significant factor related to nutrition, which is consistent with the former studies done by M. Fite [ 54 ] and Hossain [ 32 ]. According to a study conducted in a rural area of Assam, India, it was observed that the incidence of malnutrition among pregnant women was significantly associated with the occupation of their husbands. The study reported a strong positive relationship between BMI and the husband’s occupation, which supports this study’s results [ 55 ]. Another study by M. Fite et al. showed that pregnant women’s nutritional status and dietary practices can significantly impact their husbands’ occupation [ 54 ]. According to a study of women living in Bangladesh, the location of residence is the most important factor in pregnant women’s health status. This study’s findings align with previous research conducted by M. Islam [ 2 ] and another nationally representative study, which used BDHS data [ 56 ].

Despite their usefulness, ML models may have limitations, such as not providing odds ratios or coefficients to indicate the direction of the relationship between important features. Knowing the direction of the association of each feature’s importance would improve the design and implementation of interventions to prevent malnutrition among pregnant women in Bangladesh.

Strengths and limitations

It is important to note that the study has limitations as it relies on cross-sectional data, which restricts its ability to access supplementary information about other related factors. However, it has been suggested that by combining these factors, the predictive power and AUC of the algorithms could potentially increase. Another limitation is that the study’s analysis did not adjust for the sampling weight. Despite these limitations, the study’s strength lies in identifying the best ML algorithm using various performance evaluation techniques, which is a significant contribution to the field of research.

Conclusions

Malnutrition is a significant concern for the health of developing nations. This paper aims to conduct a comprehensive study that compares and assesses the effectiveness of various machine learning (ML) algorithms in predicting the nutritional status of pregnant women in Bangladesh. To summarize, we applied FT methods to the datasets and utilized various algorithms to analyze the transformed data and evaluate their performance. The best performance was found in this study of the RF algorithm for a robust scaling method. According to the RF algorithm, the most important features that determine the nutritional status of pregnant women in Bangladesh are the respondent’s age, wealth index, region, husband’s education level, husband’s age, and occupation. This research will assist healthcare providers and policymakers develop a framework for implementing necessary interventions and care practices to prevent severe complications and reduce the burden of nutritional status concerns.

Supporting information

S1 table. association between pregnant women’s nutritional status (bmi) and demographic and socio-economic characteristics..

https://doi.org/10.1371/journal.pone.0304389.s001

S2 Table. Background characteristics of the pregnant women in Bangladesh.

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S3 Table. Evaluation of prediction performance (%) of different ML Algorithms for Overall nutritional status, underweight, and overweight/Obese without any FT methods.

https://doi.org/10.1371/journal.pone.0304389.s003

S4 Table. Feature importance ranking for the best-performing algorithm.

https://doi.org/10.1371/journal.pone.0304389.s004

https://doi.org/10.1371/journal.pone.0304389.s005

Acknowledgments

The authors thank the Demographic Health Survey for allowing us to use open-access data for their study.

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Open access
Published: 01 June 2024

Comprehensive knowledge of mother-to-child HIV/AIDS transmission, prevention, and associated factors among reproductive-age women in East Africa: insights from recent demographic and national health surveys

Bewuketu Terefe 1 ,
Mahlet Moges Jembere 2 &
Bikis Liyew 2

BMC Women's Health volume 24 , Article number: 318 ( 2024 ) Cite this article

Metrics details

More than 90% of babies acquire HIV/AIDS through vertical transmission, primarily due to low maternal comprehensive knowledge about Mother-To-Child Transmission (MTCT) of HIV/AIDS and its prevention, which is a cornerstone for eliminating MTCT of HIV/AIDS. However, there are limitations in terms of population data and literature evidence based on recent Demographic and Health Surveys (DHS) reports in East Africa. Therefore, this study aims to assess the comprehensive knowledge and PMTCT of HIV/AIDS among women, as well as the associated factors in East Africa.

Our data was obtained from the most recent DHS conducted in East African countries between 2011 and 2022. For our research, we included DHS data from ten nations, resulting in a total weighted sample of 133,724 women for our investigation. A generalized linear model (GLM) with a log link and binomial family to directly estimate prevalence ratios (PR) and 95% confidence intervals (CI) for the association between the independent variables, and the outcome variable. Finally, we reported the adjusted prevalence ratios along with their corresponding 95% CIs. Factors with p-values ≤ 0.2 for univariate logistic regression and < 0.05 were considered statistically significant factors of HIV/AIDS knowledge and prevention in the final model.

In this study, 59.41% (95% CI: 59.15–59.67) of respondents had a comprehensive knowledge about MTCT of HIV/AIDS and its prevention among reproductive-age women in East Africa. Being in the older age group, better education level, being from a rich household, employment status, having ANC follow up, institutional delivery, and modern contraception usage were associated with higher prevalence ratios of comprehensive knowledge about MTCT of HIV/AIDS and its prevention. However, being single in marital status, rural women, and traditional contraception utilization were associated with lower ratios of comprehensive knowledge about MTCT of HIV/AIDS and its prevention.

Our findings indicate a significant deficiency in comprehensive knowledge and prevention of HIV/AIDS MTCT among women in East Africa. These results emphasize the need for significant improvements in maternal-related health services. It is crucial to effectively target high-risk populations during interventions, raise awareness about this critical public health issue, and address the catastrophic consequences associated with MTCT. By implementing these measures, we can make substantial progress in reducing the transmission of HIV/AIDS from mother to child and ensuring better health outcomes for both mothers and their children.

Peer Review reports

Introduction

Vertical transmission of Human Immunodeficiency Virus (HIV) from mother to child during pregnancy, birth, and breast feeding remains a serious public health concern and is the leading source of HIV infection in children under the age of 15 worldwide [ 1 , 2 ]. Morbidity and mortality from HIV infection have declined globally over the last decade as a result of preventive measures such as greater coverage of Antiretroviral Therapy (ART) and prevention of HIV/AIDS transmission from mother to child (PMTCT) [ 3 , 4 ]. However, over 90% of new infections of HIV in babies and young children are transmitted from mother to child still [ 5 ]. In 2022, there was around 39 million HIV-positive people worldwide [ 6 ]. Among these, about 37.5 million, and 1.5 million were adults and children (15 and under), 53% were women and girls [ 6 , 7 ]. Similarly, the USAIDS, in 2023 estimated, more than 39 million individuals were infected with HIV, and lived with the virus [ 8 ]. Additionally, AIDS-related illnesses claimed the lives of almost 630 thousand people this year [ 8 ]. However, Eastern and Southern Africa making over half of that number [ 9 , 10 ]. Using the above references, it is obvious that the number of people getting infected with HIV is increasing over time, and rigorous research related to it is expected from various individuals and organizations [ 6 ].

According to the UNAIDS 2023 report, in terms of women and girls in 2022, women and girls of all ages accounted for 46% of all new HIV infections worldwide [ 11 ]. Women and girls (of all ages) accounted for 63% of all new HIV infections in Sub-Saharan Africa (SSA) [ 6 , 11 ]. In all other geographical regions, men and boys accounted for more than 70% of new HIV infections in 2022. In 2022, 4000 adolescent girls and young women aged 15–24 years would be infected with HIV per week over the world. SSA was responsible for 3100 of these illnesses [ 12 ].

In 2017, approximately 50% of the 180,000 new pediatric HIV infections occurred during breastfeeding, and it is estimated that in the absence of any intervention to prevent MTCT, the risk of transmission ranges from 15 to 45% (5–10% during pregnancy, 10–20% during childbirth, and 10–20% via mixed infant feeding) [ 13 ]. This rate, however, can be reduced to less than 5% with appropriate interventions [ 13 ]. The recent 2023 UNAIDS reports indicated that, each day, HIV infection affects 4,000 individuals, including 1,100 young people aged 15 to 24. If present patterns persist, it is projected that 1.2 million individuals will acquire HIV in 2025, which is three times higher than the targeted number of 370,000 new infections for that year [ 14 ].

Knowledge of MTCT and PMTCT for HIV/AIDS is associated with characteristics such as maternal age, maternal education, wealth level, occupation, marital status, media exposure, and domicile [ 15 , 16 , 17 , 18 , 19 , 20 ]. Maternal awareness of HIV/AIDS MTCT and prevention is essential for HIV MTCT elimination. Despite the fact that the majority of the population in SSA lives in rural areas with limited availability and accessibility of health facilities, the majority of studies on HIV/AIDS knowledge and prevention were conducted among available women, such as those who came to the health facility for their antenatal care follow up [ 17 , 21 , 22 , 23 , 24 , 25 ]. Since East Africa is the second most affected region by HIV/AIDS, women are the primary vulnerable group among the population in the region, and no current study has revealed the situation utilizing nationally representative data from recent DHS surveys that this study aims to investigate. Hence, studying women’s comprehensive knowledge about HIV/AIDS will help reduce stigma and discrimination, improve health outcomes for mothers and children, and decrease MTCT [ 26 ]. Furthermore, by understanding the factors involved, the findings of this study can provide valuable insights for policymakers, healthcare providers, and public health practitioners in East Africa. Therefore, using the recent national demographic health survey data, this study aimed to assess the comprehensive knowledge and PMTCT of HIV/AIDS among women, as well as its associated factors in East Africa.

Data sources and study population

Our data was obtained from the most recent Demographic and Health Surveys (DHS) conducted in East African countries between 2011 and 2022. This study included DHS data from 10 countries as shown in Table 1 . To conduct our research, we incorporated DHS data from these 10 nations using the corresponding Stata command. The survey utilized stratified, two-stage cluster sampling. In the first step, enumeration areas (EAs) were selected with a probability proportional to their size within each sampling stratum. Subsequently, households were sampled in the second step. The source population consisted of mothers of reproductive age. Consequently, classical logistic regression was deemed more appropriate. Ultimately, our study utilized a weighted sample of 133,724 women of reproductive age.

Data management and statistical analysis

Stata version 17 is used to extract, recode, and analyze data. Weighting was used throughout the study to ensure representativeness and non-response rate, as well as to obtain a suitable statistical estimate (robust standard error) [ 27 ]. In the univariate analysis, variables with a p-value of ≤ 0.2 were considered for the multivariable analysis. The multivariable logistic model provided the adjusted prevalence ratio (APR) with a 95% confidence interval to identify the associated factors of knowledge of PMTCT use. We used generalized linear models (GLM) with a log link and binomial family to directly estimate prevalence ratios (PR) and 95% confidence intervals (CI) for the association between the independent variables and the binary outcome of comprehensive knowledge of PMTCT. This approach allows for the estimation of PRs without the common issue of overestimation that can occur when using logistic regression to estimate odds ratios for common outcomes. We specified robust standard errors to account for potential heteroscedasticity in the model. The log-binomial GLM allowed us to directly estimate prevalence ratios, which are more readily interpretable than odds ratios for this cross-sectional study with a relatively common outcome. The use of robust standard errors ensures valid statistical inferences in the presence of any violation of model assumptions.

Since the data had a potential hierarchical structure, we assessed it to determine if multilevel model analysis could be conducted by calculating the intra-class correlation (ICC) coefficient. However, the ICC coefficient was found to be only approximately 1.7%, which did not meet the minimum criterion for conducting multilevel analysis. Descriptive data were summarized using measures such as frequency count and proportion for categorical variables. To examine multicollinearity among the independent variables, a logistic regression was fitted using the variance inflation factor. The Hosmer and Lemeshow test were also used to evaluate the overall fitness of the final regression model. The statistical significance for the final model was set at p < 0.05.

Variables of the study

The outcome variable.

The outcome variable of this study was the comprehensive knowledge of PMTCT among women of reproductive age. This outcome was measured using two percentages: the percentage of women who were aware that HIV can be transmitted from mother to child during pregnancy, delivery, and breastfeeding, and in all three ways; and the percentage of women who knew that the risk of mother-to-child transmission can be reduced by the mother taking special drugs. Women who responded “Yes” to both questions were considered knowledgeable about PMTCT, whereas those who missed either of them were classified as not knowledgeable. The study population included all women of reproductive age, specifically those aged 15–49 years old, as determined by the IR file, and the time period was defined by the current status at the time of the survey interview. The outcome variable was subsequently recategorized as “Yes = 1” if the women knew the correct answers to both questions, and “No = 0” if they missed either of them. All classifications and analyses were conducted following the guidelines provided in the DHS statistics book [ 28 ].

The independent variables

Independent variables: Various maternal-related factors were included, such as maternal age, educational status, types of places of residence, marital status, household wealth index, current employment status, mass media exposure, ANC follow-up, place of delivery, number of health visits in the past 12 months, under-five children, contraceptive utilization, distance to the health facility, knowledge of HIV/AIDS, sex of the household head, country, and breastfeeding status.

Sociodemographic characteristics of the study participant

In this study, a total weighted sample of 133,724 women of reproductive age were enrolled in East African countries. Nearly half of them, 53,712 (40.17%), fell within the 15–24 years age group. In terms of marital status, approximately half of the mothers, 66,037 (49.38%), were married. Regarding place of residence, educational status, wealth index, place of delivery, and ANC follow-up, the majority of mothers, 97,636 (73.01%), 97,637 (46.81%), 34,309 (25.66%), 120,494 (90.11%), and 129,855 (97.11%), respectively, were from rural areas, had primary educational status, belonged to the richest households, opted for institutional delivery, and had at least one ANC follow-up during their pregnancies. Similarly, approximately 67,551 (50.52%) and 79,879 (59.73%) of women did not have access to any form of mass media exposure (such as radio, television, or magazines/newspapers) and were unemployed, respectively. However, more than half of the mothers, 88,376 (66.09%), and 51,509 (38.52%), did not utilize any contraceptive methods and reported facing challenges related to the distance to the health facility. Furthermore, around 107,992 (80.76%) participants had only one health facility visit per year, and 93,094 (69.62%) reported having male household heads (Table 2 ).

Knowledge of women about PMTCT of HIV/AIDS

The overall comprehensive knowledge of PMTCT of HIV/AIDS was about 79,447(59.41%). The transmission of HIV/AIDS during pregnancy 110,349(82.52%), during delivery 120,735(90.29%), during breastfeeding 119,955(89.70%), and about a special drug to avoid HIV during pregnancy 108,782(81.35%) was replied correctly (Table 3 ).

Factors associated with comprehensive knowledge of PMTCT of HIV/AIDS among women in East Africa

The adjusted prevalence ratio (APR) of having comprehensive knowledge about PMTCT of HIV increased by 1.09 times (APR = 1.09, 95% CI: 1.07, 1.11) and 1.05 times (APR = 1.05, 95% CI: 1.03, 1.08) among women aged 25–34 years and 35–49 years, respectively, compared to women aged 15–24 years. Similarly, compared to participants with no education, mothers who had completed primary education and secondary/higher education had higher prevalence ratios of being knowledgeable about PMTCT of HIV, with prevalence ratios of 1.08 (APR = 1.08, 95% CI: 1.05, 1.10) and 1.06 (APR = 1.06, 95% CI: 1.03, 1.13) respectively. Regarding the household wealth index, mothers from middle, richer, and richest households showed higher ratios of having comprehensive knowledge of PMTCT of HIV compared to mothers from the poorest households, with prevalence ratios of 1.06 (APR = 1.06, 95% CI: 1.02, 1.11), (APR = 1.09, 95% CI: 1.04, 1.13), and (APR = 1.08, 95% CI: 1.05, 1.11) respectively. The prevalence ratio of comprehensive knowledge about HIV were 1.04 times higher among employed mothers (APR = 1.04, 95% CI: 1.03, 1.06) compared to unemployed mothers. The ratios of knowledge about HIV among married and divorced/widowed women were (APR = 1.19, 95% CI: 1.15, 1.26) and (APR = 1.16, 95% CI: 1.14, 1.19) times higher, respectively, when compared to never married women. Women who gave birth at health institutions had 1.25 times higher ratios of (APR = 1.25, 95% CI: 1.23, 1.28) of being knowledgeable about PMTCT of HIV compared to those who gave birth at home. Moreover, women who had at least one ANC visit showed more comprehensive knowledge about PMTCT, with a prevalence ratio of 1.22 (95% CI: 1.17, 1.27) compared to those who did not have an ANC visit. On the other hand, regarding contraceptive method types, mothers who utilized traditional methods had 0.13 times lower ratios (APR = 0.87, 95% CI: 0.84, 0.91), while those who used modern methods had 1.09 times higher ratios (APR = 1.09, 95% CI: 1.07, 1.10), of being knowledgeable about PMTCT of HIV compared to mothers who did not use any type of contraceptives. Finally, women from rural areas showed less comprehensive knowledge about PMTCT, with a prevalence ratio of 0.98 (95% CI: 0.97, 0.99) compared to urban residential women (Table 4 ).

The purpose of this study was to examine comprehensive knowledge regarding HIV/AIDS transmission from mother to child, as well as its prevention and associated factors, among reproductive-age women in East Africa using recent DHS data. In this survey, about 59.41% of respondents were comprehensively knowledgeable with HIV/AIDS MTCT and its prevention. This result is lower than in previous studies conducted in Zimbabwe [ 16 ], Tanzania [ 29 ], and Nigeria [ 30 ]. However, our study’s findings are slightly higher than those of research conducted in SSA [ 19 ], Ethiopia [ 17 ], and Uganda [ 31 ]. Firstly, the disparity may be due to the fact that the study conducted a pooled analysis that included data from multiple East African countries. Since each country may have different contexts, healthcare systems, and population characteristics, the combined analysis might have introduced variations in the results. Secondly, differences in the study time, sample size, outcome ascertainment criteria, approach of analysis, and the study population could contribute to the observed disparity. These methodological variations can influence the findings and interpretations. For example, if the studies were conducted at different time points, there could have been changes in healthcare policies, interventions, or awareness campaigns that could impact the knowledge levels about the specific topic being studied. Additionally, differences in sample sizes, criteria for determining the outcome, analytical approaches, and characteristics of the study population (e.g., age groups, socioeconomic status) can all introduce variations in the results. Overall, the observed disparity in the findings may be due to a combination of factors related to the diverse nature of the pooled analysis, as well as differences in study methodology and population characteristics. These factors need to be considered when interpreting and comparing the results of studies conducted in different settings or at different time points. In the multiple logistic regression analysis, older age, attendance at primary and secondary school, coming from a wealthy family, marital status, at least one ANC follow-up, institutional delivery, and contraception use were associated with a higher likelihood of knowing about HIV/AIDS MTCT and prevention.

The study found that older age groups had higher ratios of knowing about MTCT of HIV/AIDS and its prophylaxis than younger age groups (women aged 15–24 years). This is consistent with research conducted in SSA, Ethiopia, and Zimbabwe [ 16 , 19 , 20 ]. This could be linked to older women’s proximity to various maternal health services during each consecutive pregnancy. Furthermore, this could imply that initiatives to support younger women (adolescents) in raising HIV awareness, reducing MTCT, and promoting ART adherence and viral suppression are insufficient [ 13 ]. As a result, more attention should be placed on HIV/AIDS and MTCT ideas for those young moms in order to prevent HIV transmission from mother to child. The study’s findings regarding the association between age groups and knowledge about MTCT of HIV/AIDS align with the Social Cognitive Theory (SCT) proposed by Bandura (1986) [ 32 ]. According to SCT, individuals acquire knowledge and behavior through observational learning and social interactions. In this context, older women’s higher ratios of knowing about MTCT and its prophylaxis could be attributed to their increased exposure to maternal health services, which provide opportunities for information exchange and learning from healthcare professionals. This finding supports the notion that access to healthcare services and exposure to educational interventions play a crucial role in knowledge acquisition and behavior change. Furthermore, the paragraph suggests that the lack of sufficient initiatives targeting younger women, particularly adolescents, raises questions about the effectiveness of current interventions based on the Theory of Planned Behavior (TPB). According to TPB, individuals’ attitudes, subjective norms, and perceived behavioral control influence their intentions and subsequent behaviors [ 33 ].

Similarly, when compared to uneducated participants, women with primary and secondary/higher educational attainment had significantly higher likelihood of being knowledgeable about HIV PMTCT. This is consistent with prior research done elsewhere SSA [ 19 ], and Ethiopia [ 15 , 20 , 34 ]. This could be because educated women have better access to health-related information and can grasp HIV/AIDS and associated MTCT. The findings regarding the association between educational attainment and knowledge about HIV PMTCT align with several theoretical perspectives. One such framework is the Health Belief Model (HBM), which suggests that individuals’ health-related beliefs and perceptions influence their adoption of preventive behaviors. In this context, educated women may have a higher level of perceived susceptibility to HIV/AIDS and recognize the significance of PMTCT knowledge in protecting their own health and that of their children [ 35 , 36 , 37 ]. Education can also enhance their perceived benefits of adopting preventive measures, such as adhering to antiretroviral therapy and practicing safe delivery methods, leading to a higher likelihood of being knowledgeable about PMTCT [ 35 ]. Furthermore, the findings resonate with the Diffusion of Innovations theory, which posits that knowledge and new ideas are more readily adopted by individuals with higher education levels [ 36 , 38 ].

In terms of the household wealth index, and employment status, the current study discovered that mothers from the middle, richer, and richest households were more likely to have comprehensive knowledge of HIV PMTCT than mothers from the worst household wealth index, and unemployed mothers respectively. This is consistent with research undertaken in SSA [ 19 ], Ethiopia [ 15 ], and Tanzania [ 39 ]. The higher degree of awareness among women from well-off households could be attributed to their easy access to maternal health services such as PMTCT programs and mass media exposure. Employed mothers may have more social interaction and independence than unemployed mothers.

In terms of marital status, married and divorced/widowed women were more educated about HIV PMTCT than never married women. Women who were married or divorced were more likely to have comprehensive understanding about MTCT and its eradication. This conclusion is similar with findings from Rwanda [ 40 ], Nigeria [ 41 , 42 ], and Ethiopia [ 15 , 43 ]. The most obvious explanation is that married and divorced women obtain health information at health care centers during ANC visits and related family planning services [ 15 ]. Women who gave birth in health facilities, those who used modern contraception, and those who had ANC follow-up during their pregnancy periods had a higher likelihood of understanding HIV PMTCT than their counterparts. This could be because women who have a history of ANC follow-up may have the opportunity to learn from health experts, and this information may improve women’s knowledge of PMTCT. Similarly, women with a history of institutional delivery and contemporary contraception use may be eligible for PMTCT services from health experts at a health facility. This finding is similar to the findings of an Ethiopian investigation [ 18 , 44 ].

Women from rural areas in developing countries and Sub-Saharan Africa tend to exhibit lower comprehensive knowledge about Prevention of Mother-to-Child Transmission (PMTCT) of HIV compared to urban residential women. Research indicates that various factors influence this disparity in PMTCT knowledge among women in different settings. Studies have shown that women with access to mass media, formal education, and occupation are more likely to have correct knowledge of MTCT and PMTCT [ 15 , 45 ]. Urban areas often provide better access to health information and education through media and workplaces, contributing to higher knowledge levels among urban women. Women’s decision-making power, wealth index, and occupation type play a significant role in their PMTCT knowledge [ 46 , 47 ]. Women with decision-making power, manual occupations, and higher wealth status are more likely to have better PMTCT knowledge.

Factors like ANC follow-up and utilization of maternal health services are associated with higher PMTCT knowledge among women [ 45 , 48 ]. Women who engage in ANC services have increased opportunities to learn about PMTCT from health professionals. Rural residents face challenges in accessing PMTCT services due to limited infrastructure and media coverage, contributing to lower knowledge levels compared to urban areas [ 45 , 48 ]. Efforts are needed to intensify health education and PMTCT services in rural and emerging regions.

This study relied on nationally representative data, as well as adequate statistical analysis and a large number of factors. As a result, it can assist policymakers, as well as governmental and non-governmental groups, in making appropriate actions. However, the study had certain shortcomings. First, because it was based on survey data, some characteristics that may be related with the outcome variable, such as the quality and availability of health care and knowledge about HIV/AIDS, were not addressed. Second, because it is based on survey data, we are unable to demonstrate the temporal relationship between the result variable and the independent variables that were included. Furthermore, we used DHS from the preceding ten years, and there may have been changes in MTCT and ART regimen awareness, as well as ART uptake before to and during pregnancy (Option B+) over time. As a result, due to time constraints, caution is advised when interpreting study findings.

Conclusions, and implications

The study findings reveal that HIV/AIDS MTCT and preventive knowledge among reproductive-age women in East Africa is rated as low. However, certain factors were identified to be associated with a higher likelihood of knowledge about MTCT of HIV/AIDS and its prevention. These factors include older age, attending primary and secondary school, coming from a wealthy family and rural areas, being married, having at least one antenatal care (ANC) follow-up, opting for institutional delivery, and using contraception.

These findings have important implications for addressing the knowledge gap and improving the prevention of HIV/AIDS MTCT among reproductive-age women in East Africa. The study highlights the need for targeted interventions and educational programs that focus on improving knowledge and awareness of HIV/AIDS transmission and prevention methods. Specifically, efforts should be directed towards younger women, those with limited education, and those from lower socioeconomic backgrounds, as they are more likely to have lower levels of knowledge.

Furthermore, the study underscores the importance of ANC utilization and institutional delivery, as these factors were associated with higher knowledge levels. Strengthening and expanding ANC services, particularly in terms of HIV/AIDS education and counseling, can enhance women’s understanding of MTCT and its prevention. Similarly, promoting contraception use among reproductive-age women can serve as an additional avenue to disseminate information on MTCT prevention.

Policy makers, healthcare providers, and public health practitioners in East Africa should consider incorporating these findings into their strategies and interventions. By addressing the identified factors and tailoring interventions to the specific needs of different subgroups, it is possible to improve knowledge levels, reduce stigma and discrimination, enhance health outcomes for mothers and children, and ultimately reduce the incidence of HIV/AIDS MTCT in the region. As a result, it is preferable to prioritize high-risk populations during the intervention in order to raise awareness about this critical public health issue and address its catastrophic consequences. Improving maternal-related services such as ANC, institutional delivery, and family planning are examples of good possibilities for women to have a more thorough understanding of HIV/AIDS vertical transmission.

Data availability

All data concerning this study are accommodated and presented in this document. The detailed data set can be freely accessible from the www.dhsprogram.com website.

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Acknowledgements

We would like to acknowledge the DHS program for providing permission for this study following research ethics.

This study was not supported financially by anyone.

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Department of Community Health Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

Bewuketu Terefe

Department of Emergency and Critical Care Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

Mahlet Moges Jembere & Bikis Liyew

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BT was involved in conceptualization, design, data extraction, statistical analysis, language editing, and original manuscript writing. MMJ reviewed the study’s design and the draft manuscript, checked the analysis, and made a significant contribution. BL data interpretation, data curation, article review, and validation, critical revision for intellectual substance, and article review. The authors approved the final version of the manuscript.

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Correspondence to Bewuketu Terefe .

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The study was conducted after obtaining a permission letter from www.dhsprogram.com on an online request to access East African DHS data after reviewing the submitted brief descriptions of the survey to the DHS program. The datasets were treated with the utmost confidence. This study was done based on secondary data from East Africa DHS. Issues related to informed consent, confidentiality, anonymity, and privacy of the study participants are already done ethically by the DHS office. We did not manipulate and apply the microdata other than in this study. There was no patient or public involvement in this study.

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Terefe, B., Jembere, M.M. & Liyew, B. Comprehensive knowledge of mother-to-child HIV/AIDS transmission, prevention, and associated factors among reproductive-age women in East Africa: insights from recent demographic and national health surveys. BMC Women's Health 24 , 318 (2024). https://doi.org/10.1186/s12905-024-03173-1

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Received : 26 August 2023

Accepted : 29 May 2024

Published : 01 June 2024

DOI : https://doi.org/10.1186/s12905-024-03173-1

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