how to make a quantitative research questionnaire

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How to write qualitative research questions.

11 min read Here’s how to write effective qualitative research questions for your projects, and why getting it right matters so much.

What is qualitative research?

Qualitative research  is a blanket term covering a wide range of research methods and theoretical framing approaches. The unifying factor in all these types of qualitative study is that they deal with data that cannot be counted. Typically this means things like people’s stories, feelings, opinions and  emotions , and the meanings they ascribe to their experiences.

Qualitative study  is one of two main categories of research, the other being quantitative research. Quantitative research deals with numerical data – that which can be counted and quantified, and which is mostly concerned with trends and patterns in large-scale datasets.

What are research questions?

Research questions are questions you are trying to answer with your research. To put it another way, your research question is the reason for your study, and the beginning point for your research design. There is normally only one research question per study, although if your project is very complex, you may have multiple research questions that are closely linked to one central question.

A good qualitative research question sums up your research objective. It’s a way of expressing the central question of your research, identifying your particular topic and the central issue you are examining.

Research questions are quite different from survey questions, questions used in focus groups or interview questions. A long list of questions is used in these types of study, as opposed to one central question. Additionally, interview or survey questions are asked of participants, whereas research questions are only for the researcher to maintain a clear understanding of the research design.

Research questions are used in both  qualitative and quantitative research , although what makes a good research question might vary between the two.

In fact, the type of research questions you are asking can help you decide whether you need to take a quantitative or qualitative approach to your research project.

Discover the fundamentals of qualitative research

Quantitative vs. qualitative research questions

Writing research questions is very important in  both  qualitative and quantitative research, but the research questions that perform best in the two types of studies are quite different.

Quantitative research questions

Quantitative research questions  usually relate to quantities, similarities and differences.

It might reflect the researchers’ interest in determining whether relationships between variables exist, and if so whether they are statistically significant. Or it may focus on establishing differences between things through comparison, and using statistical analysis to determine whether those differences are meaningful or due to chance.

• How much? This kind of research question is one of the simplest. It focuses on quantifying something. For example:

How many Yoruba speakers are there in the state of Maine?

• What is the connection?

This type of quantitative research question examines how one variable affects another.

For example:

How does a low level of sunlight affect the mood scores (1-10) of Antarctic explorers during winter?

• What is the difference? Quantitative research questions in this category identify two categories and measure the difference between them using numerical data.

Do white cats stay cooler than tabby cats in hot weather?

If your research question fits into one of the above categories, you’re probably going to be doing a quantitative study.

Qualitative research questions

Qualitative research questions focus on exploring phenomena, meanings and experiences.

Unlike quantitative research, qualitative research isn’t about finding causal relationships between variables. So although qualitative research questions might touch on topics that involve one variable influencing another, or looking at the difference between things, finding and quantifying those relationships isn’t the primary objective.

In fact, you as a qualitative researcher might end up studying a very similar topic to your colleague who is doing a quantitative study, but your areas of focus will be quite different. Your research methods will also be different – they might include focus groups, ethnography studies, and other kinds of qualitative study.

A few example qualitative research questions:

• What is it like being an Antarctic explorer during winter?
• What are the experiences of Yoruba speakers in the USA?
• How do white cat owners describe their pets?

Qualitative research question types

Marshall and Rossman (1989) identified 4 qualitative research question types, each with its own typical research strategy and methods.

• Exploratory questions

Exploratory questions are used when relatively little is known about the research topic. The process researchers follow when pursuing exploratory questions might involve interviewing participants, holding focus groups, or diving deep with a case study.

• Explanatory questions

With explanatory questions, the research topic is approached with a view to understanding the causes that lie behind phenomena. However, unlike a quantitative project, the focus of explanatory questions is on qualitative analysis of multiple interconnected factors that have influenced a particular group or area, rather than a provable causal link between dependent and independent variables.

• Descriptive questions

As the name suggests, descriptive questions aim to document and record what is happening. In answering  descriptive questions , researchers might interact directly with participants with surveys or interviews, as well as using observational studies and ethnography studies that collect data on how participants interact with their wider environment.

• Predictive questions

Predictive questions  start from the phenomena of interest and investigate what ramifications it might have in the future. Answering predictive questions may involve looking back as well as forward, with content analysis, questionnaires and studies of non-verbal communication (kinesics).

Why are good qualitative research questions important?

We know research questions are very important. But what makes them so essential? (And is that question a qualitative or quantitative one?)

Getting your qualitative research questions right has a number of benefits.

• It defines your qualitative research project Qualitative research questions definitively nail down the research population, the thing you’re examining, and what the nature of your answer will be.This means you can explain your research project to other people both inside and outside your business or organization. That could be critical when it comes to securing funding for your project, recruiting participants and members of your research team, and ultimately for publishing your results. It can also help you assess right the ethical considerations for your population of study.
• It maintains focus Good qualitative research questions help researchers to stick to the area of focus as they carry out their research. Keeping the research question in mind will help them steer away from tangents during their research or while they are carrying out qualitative research interviews. This holds true whatever the qualitative methods are, whether it’s a focus group, survey, thematic analysis or other type of inquiry.That doesn’t mean the research project can’t morph and change during its execution – sometimes this is acceptable and even welcome – but having a research question helps demarcate the starting point for the research. It can be referred back to if the scope and focus of the project does change.
• It helps make sure your outcomes are achievable

Because qualitative research questions help determine the kind of results you’re going to get, it helps make sure those results are achievable. By formulating good qualitative research questions in advance, you can make sure the things you want to know and the way you’re going to investigate them are grounded in practical reality. Otherwise, you may be at risk of taking on a research project that can’t be satisfactorily completed.

Developing good qualitative research questions

All researchers use research questions to define their parameters, keep their study on track and maintain focus on the research topic. This is especially important with qualitative questions, where there may be exploratory or inductive methods in use that introduce researchers to new and interesting areas of inquiry. Here are some tips for writing good qualitative research questions.

1. Keep it specific

Broader research questions are difficult to act on. They may also be open to interpretation, or leave some parameters undefined.

Strong example: How do Baby Boomers in the USA feel about their gender identity?

Weak example: Do people feel different about gender now?

2. Be original

Look for research questions that haven’t been widely addressed by others already.

Strong example: What are the effects of video calling on women’s experiences of work?

Weak example: Are women given less respect than men at work?

3. Make it research-worthy

Don’t ask a question that can be answered with a ‘yes’ or ‘no’, or with a quick Google search.

Strong example: What do people like and dislike about living in a highly multi-lingual country?

Weak example: What languages are spoken in India?

4. Focus your question

Don’t roll multiple topics or questions into one. Qualitative data may involve multiple topics, but your qualitative questions should be focused.

Strong example: What is the experience of disabled children and their families when using social services?

Weak example: How can we improve social services for children affected by poverty and disability?

4. Focus on your own discipline, not someone else’s

Avoid asking questions that are for the politicians, police or others to address.

Strong example: What does it feel like to be the victim of a hate crime?

Weak example: How can hate crimes be prevented?

5. Ask something researchable

Big questions, questions about hypothetical events or questions that would require vastly more resources than you have access to are not useful starting points for qualitative studies. Qualitative words or subjective ideas that lack definition are also not helpful.

Strong example: How do perceptions of physical beauty vary between today’s youth and their parents’ generation?

Weak example: Which country has the most beautiful people in it?

Related resources

Ethnographic research 11 min read, business research methods 12 min read, qualitative research design 12 min read, business research 10 min read, qualitative research interviews 11 min read, video in qualitative research 10 min read, descriptive research 8 min read, request demo.

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How to Do a Quantitative Research Questionnaire

How to Figure Survey Percentages

Research questionnaires are one of the primary methods for conducting quantitative research. They are inexpensive, and you can give a questionnaire in person, on the phone, by email, or mail. Quantitative surveys ask questions with specific, usually numerical answers so that you can analyze the data quickly. They are useful for gathering large amounts of data, but they are not designed to gather descriptive information.

Identify the objective of your research. This will guide you through the questionnaire writing process. Your objective should be as clear as possible, and highlight specific information you want to discover. For example, an objective such as "to identify how satisfied people are in their relationships," is not a clear objective because too much is left open for interpretation. A better objective would be, "to identify the level of satisfaction that couples who have been married for at least 1-5 years have in the communication aspect of their relationship."

Identify your sampling group. Your objective will determine what group(s) you will want to sample. In the example objective, you would want to focus your attention on married couples.

Determine the number of people you want to answer your questionnaire; this is your sample size. This will depend on the amount of time and money you can spend on research, but you should pick a target sample size.

Develop a numerical scale for your quantitative research questions. You will need to explain the scale to your participants. Popular research scales go from 1 to 5, or 1 to 10. You will need to explain your scale to your participants. For example, if you used the 1 to 10 scale to measure satisfaction, you would explain that answering with "1" means "Not Satisfied," while answering "10," would mean "very satisfied."

Write quantitative research questions that fit the scale you created. For example, you could ask, "On a scale of 1 to 10, how satisfied are you with the amount of verbal communication between you and your spouse?"

Review your questionnaire. Check that your questions are clear, and achieve the overall objective of your research. You can also ask friends, peers, and coworkers to preview your questionnaire before you officially give it out to give you an idea of its effectiveness.

There are many quantitative questions that don't require you to develop your own scale. For example, if you ask, "How tall are you," you're looking for a specific value.

Keep your questionnaire as short as possible. The easier something is to finish, the more likely people are to finish it.

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How to Write Quantitative Research Questions: Types With Examples

“Research is creating new knowledge.” – Neil Armstrong

For research to be effective, it becomes crucial to properly formulate the quantitative research questions in a correct way. Otherwise, you will not get the answers you were looking for.

Has it ever happened that you conducted a quantitative research study and found out the results you were expecting are quite different from the actual results?

This could happen due to many factors like the unpredictable nature of respondents, errors in calculation, research bias, etc. However, your quantitative research usually does not provide reliable results when questions are not written correctly.

We get it! Structuring the quantitative research questions can be a difficult task.

Hence, in this blog, we will share a few bits of advice on how to write good quantitative research questions. We will also look at different types of quantitative research questions along with their examples.

Let’s start:

How to Write Quantitative Research Questions?

When you want to obtain actionable insight into the trends and patterns of the research topic to make sense of it, quantitative research questions are your best bet.

Being objective in nature, these questions provide you with detailed information about the research topic and help in collecting quantifiable data that can be easily analyzed. This data can be generalized to the entire population and help make data-driven and sound decisions.

Respondents find it easier to answer quantitative survey questions than qualitative questions. At the same time, researchers can also analyze them quickly using various statistical models.

However, when it comes to writing the quantitative research questions, one can get a little overwhelmed as the entire study depends on the types of questions used.

There is no “one good way” to prepare these questions. However, to design well-structured quantitative research questions, you can follow the 4-steps approach given below:

1. Select the Type of Quantitative Question

The first step is to determine which type of quantitative question you want to add to your study. There are three types of quantitative questions:

• Descriptive
• Comparative 
• Relationship-based

This will help you choose the correct words and phrases while constructing the question. At the same time, it will also assist readers in understanding the question correctly.

2. Identify the Type of Variable

The second step involves identifying the type of variable you are trying to measure, manipulate, or control. Basically, there are two types of variables:

• Independent variable (a variable that is being manipulated)
• Dependent variable (outcome variable)

If you plan to use descriptive research questions, you have to deal with a number of dependent variables. However, where you plan to create comparative or relationship research questions, you will deal with both dependent and independent variables.

3. Select the Suitable Structure

The next step is determining the structure of the research question. It involves:

• Identifying the components of the question. It involves the type of dependent or independent variable and a group of interest (the group from which the researcher tries to conclude the population).
• The number of different components used. Like, as to how many variables and groups are being examined.
• Order in which these are presented. For example, the independent variable before the dependent variable or vice versa.

4. Draft the Complete Research Question

The last step involves identifying the problem or issue that you are trying to address in the form of complete quantitative survey questions . Also, make sure to build an exhaustive list of response options to make sure your respondents select the correct response. If you miss adding important answer options, then the ones chosen by respondents may not be entirely true.

Want to create a quantitative research survey hassle-free? Explore our library of 1,000,000+ readymade questions.

Types of Quantitative Research Questions With Examples

Quantitative research questions are generally used to answer the “who” and “what” of the research topic. For quantitative research to be effective, it is crucial that the respondents are able to answer your questions concisely and precisely. With that in mind, let’s look in greater detail at the three types of formats you can use when preparing quantitative market research questions.

1. Descriptive

Descriptive research questions are used to collect participants’ opinions about the variable that you want to quantify. It is the most effortless way to measure the particular variable (single or multiple variables) you are interested in on a large scale. Usually, descriptive research questions begin with “ how much,” “how often,” “what percentage,” “what proportion,” etc.

Examples of descriptive research questions include:

2. Comparative

Comparative research questions help you identify the difference between two or more groups based on one or more variables. In general, a comparative research question is used to quantify one variable; however, you can use two or more variables depending on your market research objectives.

Comparative research questions examples include:

3. Relationship-based

Relationship research questions are used to identify trends, causal relationships, or associations between two or more variables. It is not vital to distinguish between causal relationships, trends, or associations while using these types of questions. These questions begin with “What is the relationship” between independent and dependent variables, amongst or between two or more groups.

Relationship-based quantitative questions examples include:

Ready to Write Your Quantitative Research Questions?

So, there you have it. It was all about quantitative research question types and their examples. By now, you must have figured out a way to write quantitative research questions for your survey to collect actionable customer feedback.

Now, the only thing you need is a good survey maker tool , like ProProfs Survey Maker , that will glide your process of designing and conducting your surveys . You also get access to various survey question types, both qualitative and quantitative, that you can add to any kind of survey along with professionally-designed survey templates .

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 structure quantitative research questions

There is no "one best way" to structure a quantitative research question. However, to create a well-structured quantitative research question, we recommend an approach that is based on four steps : (1) Choosing the type of quantitative research question you are trying to create (i.e., descriptive, comparative or relationship-based); (2) Identifying the different types of variables you are trying to measure, manipulate and/or control, as well as any groups you may be interested in; (3) Selecting the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved; and (4) Writing out the problem or issues you are trying to address in the form of a complete research question. In this article, we discuss each of these four steps , as well as providing examples for the three types of quantitative research question you may want to create: descriptive , comparative and relationship-based research questions .

• STEP ONE: Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create
• STEP TWO: Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in
• STEP THREE: Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved
• STEP FOUR: Write out the problem or issues you are trying to address in the form of a complete research question

STEP ONE Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create

The type of quantitative research question that you use in your dissertation (i.e., descriptive , comparative and/or relationship-based ) needs to be reflected in the way that you write out the research question; that is, the word choice and phrasing that you use when constructing a research question tells the reader whether it is a descriptive, comparative or relationship-based research question. Therefore, in order to know how to structure your quantitative research question, you need to start by selecting the type of quantitative research question you are trying to create: descriptive, comparative and/or relationship-based.

STEP TWO Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in

Whether you are trying to create a descriptive, comparative or relationship-based research question, you will need to identify the different types of variable that you are trying to measure , manipulate and/or control . If you are unfamiliar with the different types of variable that may be part of your study, the article, Types of variable , should get you up to speed. It explains the two main types of variables: categorical variables (i.e., nominal , dichotomous and ordinal variables) and continuous variables (i.e., interval and ratio variables). It also explains the difference between independent and dependent variables , which you need to understand to create quantitative research questions.

To provide a brief explanation; a variable is not only something that you measure , but also something that you can manipulate and control for. In most undergraduate and master's level dissertations, you are only likely to measure and manipulate variables. You are unlikely to carry out research that requires you to control for variables, although some supervisors will expect this additional level of complexity. If you plan to only create descriptive research questions , you may simply have a number of dependent variables that you need to measure. However, where you plan to create comparative and/or relationship-based research questions , you will deal with both dependent and independent variables . An independent variable (sometimes called an experimental or predictor variable ) is a variable that is being manipulated in an experiment in order to observe the effect this has on a dependent variable (sometimes called an outcome variable ). For example, if we were interested in investigating the relationship between gender and attitudes towards music piracy amongst adolescents , the independent variable would be gender and the dependent variable attitudes towards music piracy . This example also highlights the need to identify the group(s) you are interested in. In this example, the group of interest are adolescents .

Once you identifying the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in, it is possible to start thinking about the way that the three types of quantitative research question can be structured . This is discussed next.

STEP THREE Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved

The structure of the three types of quantitative research question differs, reflecting the goals of the question, the types of variables, and the number of variables and groups involved. By structure , we mean the components of a research question (i.e., the types of variables, groups of interest), the number of these different components (i.e., how many variables and groups are being investigated), and the order that these should be presented (e.g., independent variables before dependent variables). The appropriate structure for each of these quantitative research questions is set out below:

Structure of descriptive research questions

• Structure of comparative research questions
• Structure of relationship-based research questions

There are six steps required to construct a descriptive research question: (1) choose your starting phrase; (2) identify and name the dependent variable; (3) identify the group(s) you are interested in; (4) decide whether dependent variable or group(s) should be included first, last or in two parts; (5) include any words that provide greater context to your question; and (6) write out the descriptive research question. Each of these steps is discussed in turn:

Choose your starting phrase

Identify and name the dependent variable

Identify the group(s) you are interested in

Decide whether the dependent variable or group(s) should be included first, last or in two parts

Include any words that provide greater context to your question

Write out the descriptive research question

FIRST Choose your starting phrase

You can start descriptive research questions with any of the following phrases:

How many? How often? How frequently? How much? What percentage? What proportion? To what extent? What is? What are?

Some of these starting phrases are highlighted in blue text in the examples below:

How many calories do American men and women consume per day?

How often do British university students use Facebook each week?

What are the most important factors that influence the career choices of Australian university students?

What proportion of British male and female university students use the top 5 social networks?

What percentage of American men and women exceed their daily calorific allowance?

SECOND Identify and name the dependent variable

All descriptive research questions have a dependent variable. You need to identify what this is. However, how the dependent variable is written out in a research question and what you call it are often two different things. In the examples below, we have illustrated the name of the dependent variable and highlighted how it would be written out in the blue text .

The first two examples highlight that while the name of the dependent variable is the same, namely daily calorific intake , the way that this dependent variable is written out differs in each case.

THIRD Identify the group(s) you are interested in

All descriptive research questions have at least one group , but can have multiple groups . You need to identify this group(s). In the examples below, we have identified the group(s) in the green text .

What are the most important factors that influence the career choices of Australian university students ?

The examples illustrate the difference between the use of a single group (e.g., British university students ) and multiple groups (e.g., American men and women ).

FOURTH Decide whether the dependent variable or group(s) should be included first, last or in two parts

Sometimes it makes more sense for the dependent variable to appear before the group(s) you are interested in, but sometimes it is the opposite way around. The following examples illustrate this, with the group(s) in green text and the dependent variable in blue text :

Group 1st; dependent variable 2nd:

How often do British university students use Facebook each week ?

Dependent variable 1st; group 2nd:

Sometimes, the dependent variable needs to be broken into two parts around the group(s) you are interested in so that the research question flows. Again, the group(s) are in green text and the dependent variable is in blue text :

How many calories do American men and women consume per day ?

Of course, you could choose to restructure the question above so that you do not have to split the dependent variable into two parts. For example:

How many calories are consumed per day by American men and women ?

When deciding whether the dependent variable or group(s) should be included first or last, and whether the dependent variable should be broken into two parts, the main thing you need to think about is flow : Does the question flow? Is it easy to read?

FIFTH Include any words that provide greater context to your question

Sometimes the name of the dependent variable provides all the explanation we need to know what we are trying to measure. Take the following examples:

In the first example, the dependent variable is daily calorific intake (i.e., calories consumed per day). Clearly, this descriptive research question is asking us to measure the number of calories American men and women consume per day. In the second example, the dependent variable is Facebook usage per week. Again, the name of this dependent variable makes it easy for us to understand that we are trying to measure the often (i.e., how frequently; e.g., 16 times per week) British university students use Facebook.

However, sometimes a descriptive research question is not simply interested in measuring the dependent variable in its entirety, but a particular component of the dependent variable. Take the following examples in red text :

In the first example, the research question is not simply interested in the daily calorific intake of American men and women, but what percentage of these American men and women exceeded their daily calorific allowance. So the dependent variable is still daily calorific intake, but the research question aims to understand a particular component of that dependent variable (i.e., the percentage of American men and women exceeding the recommend daily calorific allowance). In the second example, the research question is not only interested in what the factors influencing career choices are, but which of these factors are the most important.

Therefore, when you think about constructing your descriptive research question, make sure you have included any words that provide greater context to your question.

SIXTH Write out the descriptive research question

Once you have these details ? (1) the starting phrase, (2) the name of the dependent variable, (3) the name of the group(s) you are interested in, and (4) any potential joining words ? you can write out the descriptive research question in full. The example descriptive research questions discussed above are written out in full below:

In the section that follows, the structure of comparative research questions is discussed.

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What Are Quantitative Survey Questions? Types and Examples

Table of contents: 

• Types of quantitative survey questions - with examples 
• Quantitative question formats
• How to write quantitative survey questions 
• Examples of quantitative survey questions 

Leveraging quantilope for your quantitative survey 

In a quantitative research study brands will gather numeric data for most of their questions through formats like numerical scale questions or ranking questions. However, brands can also include some non-quantitative questions throughout their quantitative study - like open-ended questions, where respondents will type in their own feedback to a question prompt. Even so, open-ended answers can be numerically coded to sift through feedback easily (e.g. anyone who writes in 'Pepsi' in a soda study would be assigned the number '1', to look at Pepsi feedback as a whole).  One of the biggest benefits of using a quantitative research approach is that insights around a research topic can undergo statistical analysis; the same can’t be said for qualitative data like focus group feedback or interviews. Another major difference between quantitative and qualitative research methods is that quantitative surveys require respondents to choose from a limited number of choices in a close-ended question - generating clear, actionable takeaways. However, these distinct quantitative takeaways often pair well with freeform qualitative responses - making quant and qual a great team to use together.  The rest of this article focuses on quantitative research, taking a closer look at quantitative survey question types and question formats/layouts. 

Back to table of contents 

Types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions - with examples 

Quantitative questions come in many forms, each with different benefits depending on dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139784">your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research objectives. Below we’ll explore some of these dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139785">survey question dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139785" data-dropdown-placement-param="top" data-term-id="281139785"> types, which are commonly used together in a single survey to keep things interesting for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents . The style of questioning used during dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139739">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139750">data dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139750" data-dropdown-placement-param="top" data-term-id="281139750"> collection is important, as a good mix of the right types of questions will deliver rich data, limit dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent fatigue, and optimize the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139757">response rate . dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">Questionnaires should be enjoyable - and varying the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139755">types of dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139755" data-dropdown-placement-param="top" data-term-id="281139755">quantitative research dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139755"> questions used throughout your survey will help achieve that. 

Descriptive survey questions

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139763">Descriptive research questions (also known as usage and attitude, or, U&A questions) seek a general indication or prediction about how a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139773">group of people behaves or will behave, how that group is characterized, or how a group thinks.

For example, a business might want to know what portion of adult men shave, and how often they do so. To find this out, they will survey men (the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience ) and ask descriptive questions about their frequency of shaving (e.g. daily, a few times a week, once per week, and so on.) Each of these frequencies get assigned a numerical ‘code’ so that it’s simple to chart and analyze the data later on; daily might be assigned ‘5’, a few times a week might be assigned ‘4’, and so on. That way, brands can create charts using the ‘top two’ and ‘bottom two’ values in a descriptive question to view these metrics side by side.

Another business might want to know how important local transit issues are to residents, so dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions will allow dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to indicate the degrees of opinion attached to various transit issues. Perhaps the transit business running this survey would use a sliding numeric scale to see how important a particular issue is.

Comparative survey questions

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139782">Comparative research questions are concerned with comparing individuals or groups of people based on one or more variables. These questions might be posed when a business wants to find out which segment of its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience might be more profitable, or which types of products might appeal to different sets of consumers.

For example, a business might want to know how the popularity of its chocolate bars is spread out across its entire customer base (i.e. do women prefer a certain flavor? Are children drawn to candy bars by certain packaging attributes? etc.). Questions in this case will be designed to profile and ‘compare’ segments of the market.

Other businesses might be looking to compare coffee consumption among older and younger consumers (i.e. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic segments), the difference in smartphone usage between younger men and women, or how women from different regions differ in their approach to skincare.

Relationship-based survey questions

As the name suggests, relationship-based survey questions are concerned with the relationship between two or more variables within one or more dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic groups. This might be a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139759">causal link between one thing and the other - for example, the consumption of caffeine and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents ’ reported energy levels throughout the day. In this case, a coffee or energy drink brand might be interested in how energy levels differ between those who drink their caffeinated line of beverages and those who drink decaf/non-caffeinated beverages.

Alternatively, it might be a case of two or more factors co-existing, without there necessarily being a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139759">causal link - for example, a particular type of air freshener being more popular amongst a certain dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic (maybe one that is controlled wirelessly via Bluetooth is more popular among younger homeowners than one that’s plugged into the wall with no controls). Knowing that millennials favor air fresheners which have options for swapping out scents and setting up schedules would be valuable information for new product development.

Advanced method survey questions

Aside from descriptive, comparative, and relationship-based survey questions, brands can opt to include advanced methodologies in their quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire for richer depth. Though advanced methods are more complex in terms of the insights output, quantilope’s Consumer Intelligence Platform automates the setup and analysis of these methods so that researchers of any background or skillset can leverage them with ease.

With quantilope’s pre-programmed suite of 12 advanced methodologies , including MaxDiff , TURF , Implicit , and more, users can drag and drop any of these into a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire and customize for their own dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research objectives.

For example, consider a beverage company that’s looking to expand its flavor profiles. This brand would benefit from a MaxDiff which forces dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to make tradeoff decisions between a set of flavors. A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent might say that coconut is their most-preferred flavor, and lime their least (when in a consideration set with strawberry), yet later on in the MaxDiff that same dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent may say Strawberry is their most-preferred flavor (over black cherry and kiwi). While this is just one example of an advanced method, instantly you can see how much richer and more actionable these quantitative metrics become compared to a standard usage and attitude question .

Advanced methods can be used alongside descriptive, comparison, or relationship questions to add a new layer of context wherever a business sees fit. Back to table of contents 

Quantitative question formats  

So we’ve covered the kinds of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139736">quantitative research questions you might want to answer using dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research , but how do these translate into the actual format of questions that you might include on your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire ?

Thinking ahead to your reporting process during your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire setup is actually quite important, as the available chart types differ among the types of questions asked; some question data is compatible with bar chart displays, others pie charts, others in trended line graphs, etc. Also consider how well the questions you’re asking will translate onto different devices that your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents might be using to complete the survey (mobile, PC, or tablet).

Single Select questions

Single select questions are the simplest form of quantitative questioning, as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents are asked to choose just one answer from a list of items, which tend to be ‘either/or’, ‘yes/no’, or ‘true/false’ questions. These questions are useful when you need to get a clear answer without any qualifying nuances.

Multi-select questions

Multi-select questions (aka, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139767">multiple choice ) offer more flexibility for responses, allowing for a number of responses on a single question. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents can be asked to ‘check all that apply’ or a cap can be applied (e.g. ‘select up to 3 choices’).

For example:

Aside from asking text-based questions like the above examples, a brand could also use a single or multi-select question to ask respondents to select the image they prefer more (like different iterations of a logo design, packaging options, branding colors, etc.). 

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139749">Likert dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139766">scale dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139766" data-dropdown-placement-param="top" data-term-id="281139766"> questions

A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139749">Likert scale   is widely used as a convenient and easy-to-interpret rating method. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents find it easy to indicate their degree of feelings by selecting the response they most identify with.

Slider scales

Slider scales are another good interactive way of formatting questions. They allow dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to customize their level of feeling about a question, with a bit more variance and nuance allowed than a numeric scale:

One particularly common use of a slider scale in a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139770">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139770" data-dropdown-placement-param="top" data-term-id="281139770"> study is known as a NPS (Net Promoter Score) - a way to measure dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139775">customer experience and loyalty . A 0-10 scale is used to ask customers how likely they are to recommend a brand’s product or services to others. The NPS score is calculated by subtracting the percentage of ‘detractors’ (those who respond with a 0-6) from the percentage of promoters (those who respond with a 9-10). dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents who select 7-8 are known as ‘passives’.

For example: 

Drag and drop questions

Drag-and-drop question formats are a more ‘gamified’ approach to survey capture as they ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to do more than simply check boxes or slide a scale. Drag-and-drop question formats are great for ranking exercises - asking dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to place answer options in a certain order by dragging with their mouse. For example, you could ask survey takers to put pizza toppings in order of preference by dragging options from a list of possible answers to a box displaying their personal preferences:

Matrix questions

Matrix   questions are a great way to consolidate a number of questions that ask for the same type of response (e.g. single select yes/no, true/false, or multi-select lists). They are mutually beneficial - making a survey look less daunting for the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent , and easier for a brand to set up than asking multiple separate questions.

Items in a matrix question are presented one by one, as respondents cycle through the pages selecting one answer for each coffee flavor shown. 

While the above example shows a single-matrix question - meaning a respondent can only select one answer per element (in this case, coffee flavors), a matrix setup can also be used for multiple-choice questions - allowing respondents to choose multiple answers per element shown, or for rating questions - allowing respondents to assign a rating (e.g. 1-5) for a list of elements at once.  Back to table of contents 

How to write dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions  

We’ve reviewed the types of questions you might ask in a quantitative survey, and how you might format those questions, but now for the actual crafting of the content.

When considering which questions to include in your survey, you’ll first want to establish what your research goals are and how these relate to your business goals. For example, thinking about the three types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions explained above - descriptive, comparative, and relationship-based - which type (or which combination) will best meet your research needs? The questions you ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents may be phrased in similar ways no matter what kind of layout you leverage, but you should have a good idea of how you’ll want to analyze the results as that will make it much easier to correctly set up your survey.

Quantitative questions tend to start with words like ‘how much,’ ‘how often,’ ‘to what degree,’ ‘what do you think of,’ ‘which of the following’ - anything that establishes what consumers do or think and that can be assigned a numerical code or value. Be sure to also include ‘other’ or ‘none of the above’ options in your quant questions, accommodating those who don’t feel the pre-set answers reflect their true opinion. As mentioned earlier, you can always include a small number of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139748">open-ended questions in your quant survey to account for any ideas or expanded feedback that the pre-coded questions don’t (or can’t) cover. Back to table of contents 

Examples of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions  

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">Quantitative survey questions impose limits on the answers that dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents can choose from, and this is a good thing when it comes to measuring consumer opinions on a large scale and comparing across dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents . A large volume of freeform, open-ended answers is interesting when looking for themes from qualitative studies, but impractical to wade through when dealing with a large dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139756">sample size , and impossible to subject to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139774">statistical analysis .

For example, a quantitative survey might aim to establish consumers' smartphone habits. This could include their frequency of buying a new smartphone, the considerations that drive purchase, which features they use their phone for, and how much they like their smartphone.

Some examples of quantitative survey questions relating to these habits would be:

Q. How often do you buy a new smartphone?

[single select question]

More than once per year

Every 1-2 years

Every 3-5 years

Every 6+ years

Q. Thinking about when you buy a smartphone, please rank the following factors in order of importance:

[drag and drop ranking question]

screen size

storage capacity

Q. How often do you use the following features on your smartphone?

[matrix question]

Q. How do you feel about your current smartphone?

[sliding scale]

I love it <-------> I hate it

Answers from these above questions, and others within the survey, would be analyzed to paint a picture of smartphone usage and attitude trends across a population and its sub-groups. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139738">Qualitative research might then be carried out to explore those findings further - for example, people’s detailed attitudes towards their smartphones, how they feel about the amount of time they spend on it, and how features could be improved. Back to table of contents 

quantilope’s Consumer Intelligence Platform specializes in automated, advanced survey insights so that researchers of any skill level can benefit from quick, high-quality consumer insights. With 12 advanced methods to choose from and a wide variety of quantitative question formats, quantilope is your one-stop-shop for all things dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research (including its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139776">in-depth dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139738">qualitative research solution - inColor ).

When it comes to building your survey, you decide how you want to go about it. You can start with a blank slate and drop questions into your survey from a pre-programmed list, or you can get a head start with a survey dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139765">template for a particular business use case (like concept testing ) and customize from there. Once your survey is ready to launch, simply specify your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience , connect any panel (quantilope is panel agnostic), and watch as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139783">answer questions in your survey in real-time by monitoring the fieldwork section of your project. AI-driven dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139764">data analysis takes the raw data and converts it into actionable findings so you never have to worry about manual calculations or statistical testing.

Whether you want to run your quantitative study entirely on your own or with the help of a classically trained research team member, the choice is yours on quantilope’s platform. For more information on how quantilope can help with your next dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139736">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139768">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139768" data-dropdown-placement-param="top" data-term-id="281139768"> project , get in touch below!

Get in touch to learn more about quantitative research with quantilope!

Related posts, quantilope academy is now open to the broader insights community, quantilope & greenbook webinar: tapping into consumers' subconscious through implicit research, master the art of tracking with quantilope's certification course, van westendorp price sensitivity meter questions.

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• How to ask quantitative survey questions: types & examples

Eren Eltemur

Are you looking for a way to collect objective data for your research? You can use quantitative questions to get objective data. You can use forms.app to create and customize your surveys with ready-made templates. This article will discuss creating quantitative survey questions with forms.app and the key principles of creating quantitive survey questions.

• What is a quantitative question?

A quantitive question is an objective question about any kind of research topic. You can use these types of questions in your research surveys, and you can create a research repor t that is produced using the quantitative data based on the analysis of the responses to these quantitative survey questions.

It offers assistance when you need to generalize your study and make predictions about the future. Surveys are an excellent instrument for quantitative research because they are flexible, affordable, and allow data collection from many respondents. Quantitive questions allow researchers to collect numeric data , and it is a method to gather statistical results . 

The definition of a quantitative question

Quantitative and qualitative survey questions

The goal of quantitative research is to gather data that can be represented statistically. Researchers frequently use it to compare information about particular groups . Quantitative research can be directed towards a particular audience, generally identified by demographic data like age, gender, and region , even though the survey audience is relatively large. 

Qualitative research focuses on individuals' unique behavior , including their routines or the reasons behind their choices. To understand more about sentiments, attitudes, and behaviors that are harder to measure but provide crucial extra context to quantitive research.

• 3 Types of quantitative survey questions

Quantitive questions and their equıvelent of survey questions can be separated on a basis. The branches of quantitative questions are methods, but when you want to include these principles in your surveys, you use form builder features such as the Likert scale, open-ended questions, opinion scale, multiple choice, short text, and long text . In the end, what gives meaning to these types of questions is their way of gathering answers . Here are the types of quantitive questions and their branches: 

• Descriptive research questions : To make a general assumption about a group of people, such as their age, sex, and maybe ethnicity, or commonly used products, researchers can use these types of questions. There are common types of descriptive research questions: 
• Frequency questions : To understand how often a particular event occurs.
• Percentage questions : To determine the proportion of a group, such as customers.
• Range questions : To find the highest and the lowest point of something, such as price.
• Profile questions : To describe a characteristic of a particular group.
• Case study questions : To get a detailed understanding of a specific topic.
• Comparative research questions : These types of questions are used to compare individuals or groups and can be classified as experimental or casual research.
• Experimental : Used to test the cause-and-effect relationship of a hypothesis by interventions and manipulations.
• Causal : To comprehend how variations in one variable affect another.
• Relationship-based research questions : These types of questions are used to understand the link between two groups or topics. Here are some types of relationship-based research questions:
• Correlation questions : Used to test the cause-and-effect relationship of a hypothesis without any interventions and manipulations.
• Meta-analysis : The combined result of multiple similar studies to find patterns and inconsistencies.
• Cross-sectional : The relationship between two things at a particular time to find a correlation.
• Case-control : Regardless of the outcome, the relationship between particular outcomes to find patterns.
• How to write better quantitative survey questions

Creating a better quantitative survey can be a complicated task because of the survey's nature. The intention of the questions must be chosen first to get the desired result. Clear, effective and unbiased survey questions are essential for quantitive surveys. For this reason, here is the three-step you must follow to get the desired result:

1 - Select the objective and type

You must select the type of question you want to ask. What is your intention? Are they descriptive, comparative, or relationship-based questions? By choosing your intention, you can ask the right questions and select the right words, which is the key element of your survey .

2 - Identify the variable

The dependent and independent variables, as well as the target audiences , should be decided by researchers. The many variables you seek to analyze, manipulate, or control must be identified regardless of whether you are trying to develop a descriptive, comparative, or relationship-based research question. Here are some examples for 

variables: Number of books read per year, level of education, average working hours, and time spent on social media .

You can control a variable in addition to something you can measure. You might need to assess a few dependent variables if you merely intend to develop descriptive research questions. However, you will deal with dependent and independent variables in situations where you intend to create comparative and relationship-based research questions. In an experiment, an independent variable is a variable that is changed to observe the effect.

3 - Select the appropriate structure

The aims of the questions, the types of variables, the number of variables and the groups engaged all have an influence on the structure of the three different types of quantitative research questions.

a.   Select your lead phrase.

b.   Specify the dependent variable.

c.   List the organizations in which you are interested.

d.   Choose whether to include the dependent variable or groups.

• 16 great quantitative survey question examples

To make the steps and types clear as forms.app, we have gathered 16 quantitive question examples in surveys. Below you will see comparative, descriptive, and relationship-based research questions with specified variables and groups.

1  - What is the average life expectancy of individuals living in urban areas compared to the average life expectancy of individuals living in rural areas? 

• Question type : Comparative
• Variable : life expectancy
• Groups :  "urban" and "rural"

2  - What is the average number of sick days taken by employees who work more than 40 hours per week compared to employees who work less than 40 hours per week? 

• Variable : Number of sick days taken
• Group : employees who work more than 40 hours per week and employees who work less than 40 hours per week

3  - What is the average height of adults in [ Asians ]? 

• Question type : Descriptive
• Variable : Average height
• Group : Asians

4  - What is the average number of books read per year by people aged 18-24 compared to people aged 25-34? 

• Variable : Number of books read per year
• Group : people aged 18-24, people aged 25-34

  5  - What is the average number of cars per household in a [ specific country ]? 

• Variable : average number of cars per household
• Group : specific country

6  - What is the average temperature [ in a specific city ] during the month of July? 

• Variable : average temperature
• Group : specific city, the month of July

 7  - Is there a relationship between exercise frequency and weight loss? 

• Question type : Relationship
• Variable : Exercise frequency, Weight loss
• Group : n/a

  8  - Is there a relationship between air pollution and lung cancer? 

• Variable : Air pollution, Lung cancer

  9  - Is there a relationship between the level of education and average credit score? 

• Variable : Level of education, Average credit score

10  - What is the average number of customers served per hour at fast food restaurants in the city center compared to fast food restaurants in the suburbs? 

• Variable : Number of customers served per hour
• Group : fast food restaurants in the city center, fast food restaurants in the suburbs

  11  - What is the average age of business owners in a specific region? 

• Variable : average age
• Group : business owners in a specific region

12  - What is the average commute time for residents who use public transportation compared to residents who drive alone? 

• Variable : Commute time
• Group : Residents who use public transportation and residents who drive alone

13  - Is there a relationship between social media usage and academic performance among college students? 

• Variable : Social media usage, academic performance
• Group : College students

  14  - What is the average time spent on social media per day among teenagers compared to adults? 

• Variable : Time spent on social media per day
• Group : Teenagers and Adults

  15  - What is the average revenue per year for small business owners compared to large business owners? 

• Variable : Revenue per year
• Group : Small business owners and large business owners

16  - Is there a relationship between gender and the likelihood of receiving a promotion in a specific company? 

• Variable : Gender, the likelihood of receiving a promotion
• Group : specific company
• How to design a quantitative survey

Since the elements are clear now, you must clarify your topic and style, then create your survey considering your aim. For example , let's say we are creating a survey intended to learn about gender and its relationship with receiving a promotion. Our variable is gender and the likelihood of receiving a promotion, and our group is a particular company. Here are some example questions with suitable form fields you can use:

• How satisfied are you with the current position? (Opinion scale)
• My skills and abilities find use in my job position. (Star rating)
• I am satisfied with my current working hours.  (Star rating)
• When did you get hired by this company? (Short text)
• Are there any inequalities between male and female workers? (Yes/no)
• How likely will you be working for this organization a year from now? (Opinion scale)
• How stressed do you feel on a regular day at work? (Opinion scale)
• I feel that my work is seen and appreciated within my organization. (Yes/no)
• My job allows me to grow and develop new skills. (Yes/no)
• Do you think there is a relationship between gender and the likelihood of receiving a promotion in this company? (Yes/no)
• Create your quantitative survey today

It can be hard to create quantitive research questions if you are unfamiliar with the method. For this reason, forms.app gathered the detailed key method of creating quantitive survey questions. You can use ready-made templates to start or just from scratch since you now know how to create your quantitive survey questions. If you want to get started quickly, take a look at these:

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Adding Quantitative Research Questions in Online Surveys

• Survey Tips

One of the things that makes Alchemer a powerful online survey and research platform is the sheer number of question types you have access to as a user. This flexibility also allows you to add different question types to any survey, so you don’t have to choose between quantitative and qualitative questions in your survey. You can have both.

If you’re unsure of the difference between quantitative and qualitative, read the article, Does your Consumer Survey Data Paint The Whole Picture . This blog explores the differences between the two question types but here is the short version:

• Quantitative questions will tell you Who and What.
• Qualitative questions will tell you Why.

Quantitative questions are easier to measure and easier for survey takers to answer. Qualitative questions, on the other hand, are subjective and harder to measure. They are also harder for survey-takers to answer and too many can lead to survey fatigue.

Qualitative questions (like open textboxes or essay questions) are great for the exploratory phase of your research project or to delve deeper into a matter, but you want to use them sparingly. Don’t tire your survey-takers or yourself. Trying to analyze essay question answers to find a common theme can be arduous and time-consuming.

One way to make qualitative questions easier on both of you is to use Video Feedback questions, which allow people to respond with a video, rather than writing out their answers.

If you need hard statistics or quantifiable numbers, use quantitative questions. You can assign numeric values for easy, objective measurement and comparison.

Quantitative questions are close-ended which makes them easy to answer. You can ask a lot of these questions without tiring survey respondents. But you’ll want to mix up the question types to keep your survey interesting and your respondents engaged.

In this article we will explore the different ways to ask quantitative questions in your online survey.

How to Phrase Quantitative Questions

Quantitative questions typically start with how or what. Some common leading phrases include:

• How frequently?
• What percentage?
• What proportion?
• To what extent?

Here are some quantitative question examples:

• How many text messages do you send a day?
• How frequently do you text while driving?
• How often do you send text messages while at work?

Be sure to identify all of the variables that might affect the outcome. Also be sure to include all of the groups you are interested in. Neglecting to recognize variables and groups involved will create gaps in your data that will make it hard for you to base sound decisions on.

In the example above, work and driving are variables that likely alter texting behavior. In this example, you could also collect demographic information such as age, gender, and job function so you can compare texting habits between these groups.

Quantitative Question Types

Most online survey tools offer an array of answer formats. This is good news, as these various options will engage your customers and reduce survey fatigue.

Mix up these close-ended question types to increase your response rate:

Radio Button Example:

Checkbox Example:

Drop Down Menu Example:

Drag and Drop Example:

Likert Scale Example:

Sliding Scale Example:

Star Ranking Example:

Net Promoter Score Example:

Image Select Example:

Matrix Example:

Considerations When Choosing Quantitative Question Types

While it is nice to vary your question types to keep respondents interested, it is important to consider the reporting options. Some question types report in bar and pie charts where others may not. Always test your survey and check the reports to ensure you are collecting the data in the format that best suits your needs.

Also consider the type of device your respondents will be using. Interactive question types are engaging but may not be reliable on all mobile devices. Long matrix tables can be frustrating on a mobile device since the radio buttons or checkboxes are small. Image select questions may not render properly or take too long to load.

Use “Other” as Answer Option When Necessary

Hopefully you have considered all of the relevant answer options when crafting your quantitative question. Of course, it is now always possible to include every answer option.

If you are fearful of not including an answer option, use an “Other” answer choice and provide a textbox so respondents can specify the alternative. These are easy to setup when using a radio button or checkbox question type.

If your question is well designed, the “Other” answer option should be the exception rather than the rule. Analyzing the textbox information should not be too arduous since there are likely only a few of them. If more than 50% selected “Other “ as the answer option than perhaps you needed to do some exploratory research.

Quantifiable Results

So there you have it; 10 different quantitative question types that will keep your survey interesting and your respondents engaged. But the best part is that you will have quantifiable data that you can act on! Related Articles: Does You Consumer Survey Data Paint The Whole Picture: When to Use Qualitative Vs. Quantitative Research Questions Quantitative Vs. Qualitative Research – When to Use Which Using Qualitative Exploration To Create Quantitative Surveys Using Highly Interactive Questions In Online Surveys

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Quantitative Survey Questions: Definition, Types and Examples

Content Index

Quantitative Survey Questions: Definition

Types of quantitative survey questions with examples, how to design quantitative survey questions.

Quantitative survey questions are defined as objective questions used to gain detailed insights from respondents about a survey research topic. The answers received for these quantitative survey questions are analyzed and a research report is generated on the basis of this

 data . These questions form the core of a survey and are used to gather numerical data to determine statistical results.

The primary stage before conducting an online survey will be to decide the objective of the survey. Every research should have an answer to this integral question: “What are the expected results of your survey?”. Once the answer to this question is figured out, the secondary stage will be deciding the type of required data: quantitative or qualitative data .

LEARN ABOUT: Survey Mistakes And How to Avoid

Deciding the data type indicates the type of information required from the research process. While qualitative data provides detailed information about the subject, quantitative data will provide effective and precise information.

Quantitative survey questions are thus, channels for collecting quantitative data . Feedback received to quantitative survey questions is related to, measured by or measuring a “quantity” or a statistic and not the “quality” of the parameter.   

Learn more: Survey Questions

Quantitative survey questions should be such that they offer respondents a medium to answer accurately. On the basis of this factor, quantitative survey questions are divided into three types:

1. Descriptive Survey Questions: Descriptive survey questions are used to gain information about a variable or multiple variables to associate a quantity to the variable.

It is the simplest type of quantitative survey questions and helps researchers in quantifying the variables by surveying a large sample of their target market.

LEARN ABOUT: Survey Sample Sizes

Most widely implemented descriptive analysis questions start with “What is this..”, “How much..”, “What is the percentage of..” and such similar questions. A popular example of a descriptive survey is an exit poll as it contains a question: “What is the percentage of candidate X winning this election?” or in a demographic segmentation survey: “How many people between the age of 18-25 exercise daily?”

Learn more: Demographic Survey Questions

Other examples of descriptive survey questions are:

• Variable: Cuisine
• Target Group: Mexicans
• Variable: Facets that transform career decisions
• Target Group: Indian students
• Variable: Number of citizens looking for better opportunities
• Target Group: Chinese citizens

In every example mentioned above, researchers should focus on quantifying the variable. The only factor that changes is the parameter of measurement. Every example mentions a different quantitative sample question which needs to be measured by different parameters.

LEARN ABOUT: Testimonial Questions

The answers for descriptive survey questions are definitional for the research topic and they quantify the topics of analysis. Usually, a descriptive research will require a long list of descriptive questions but experimental research or relationship-based research will be effective with a couple of descriptive survey questions.

Learn more: Quantitative Market Research & Descriptive Research vs Correlational Research

2. Comparative Survey Questions: Comparative survey questions are used to establish a comparison between two or more groups on the basis of one or more dependable variables. These quantitative survey questions begin with “What is the difference in” [dependable variable] between [two or more groups]?. This question will be enough to realize that the main objective of comparative questions is to form a comparative relationship between the groups under consideration.

LEARN ABOUT:   Structured Question & Structured Questionnaire

Comparative survey question examples:

• Dependable Variable: Cuisine preferences
• Comparison Groups: Mexican adults and children
• Dependable Variable: Factors that transform career decisions
• Comparison Groups: Indian and Australian students
• Dependable Variable: Political notions
• Comparison Groups: Asian and American citizens

The various groups mentioned in the above-mentioned options indicate independent variables (Mexican people or country of students). These independent variables could be based on gender questions , ethnicity or education. It is the dependable variable that determines the complexity of comparative survey questions.

LEARN ABOUT: Average Order Value

3. Relationship Survey Questions: Relationship survey questions are used to understand the association, trends and causal comparative research  relationship between two or more variables. When discussing research topics, the term relationship/causal survey questions should be carefully used since it is a widely used type of research design , i.e., experimental research – where the cause and effect between two or more variables. These questions start with “What is the relationship” [between or amongst] followed by a string of independent [gender or ethnicity] and dependent variables [career, political beliefs etc.]?

• Dependent Variable: Food preferences
• Independent Variable: Age
• Relationship groups: Mexico
• Dependent Variable: University admission
• Independent Variable: Family income
• Relationship groups: American students
• Dependent Variable: Lifestyle
• Independent Variable: Socio-economic class, ethnicity, education
• Relationship groups: China

Learn more: What is Research?

There are four critical steps to follow while designing quantitative survey questions:

1. Select the type of quantitative survey question: The objective of the research is reflected in the chosen type of quantitative survey question. For the respondents to have a clear understanding of the survey, researchers should select the desired type of quantitative survey question.  

2. Recognize the filtered dependent and independent variables along with the target group/s: Irrespective of the type of selected quantitative survey question (descriptive, comparative or relationship based), researchers should decide on the dependent and independent variables and also the target audiences .

LEARN ABOUT: Product Survey Questions

There are four levels of measurement variables – one of which can be chosen for creating quantitative survey questions. Nominal variables indicate the names of variables, Ordinal variables indicate names and order of variables, Interval variables indicate name, order and an established interval between ordered variables and Ratio variables indicate the name, order, an established interval and also an absolute zero value.

A variable can not only be calculated but also can be manipulated and controlled. For descriptive survey questions, there can be multiple variables for which questions can be formed. In the other two types of quantitative survey questions (comparative and relationship-based), dependent and independent variables are to be decided. Independent variables are those which are manipulated in order to observe the change in the dependent variables.

Learn more: Quantitative Observation

3. Choose the right structure according to the decided type of quantitative survey question: As discussed in the previous section, appropriate structures have to be chosen to create quantitative survey questions. The intention of creating these survey questions should align with the structure of the question.

LEARN ABOUT: Level of Analysis

This structure indicates – 1) Variables 2) Groups and 3) Order in which the variables and groups should appear in the question.

4. Note the roadblocks you are trying to solve in order to create a thorough survey question: Analyze the ease of reading these questions once the right structure is in place. Will the respondents be able to easily understand the questions? – Ensure this factor before finalizing the quantitative survey questions.

Learn more:

• Nominal Scale
• Ordinal Scale
• Interval Scale
• Ratio Scale
• Nominal Data

You can use QuestionPro for survey questions like income survey questions , Quantitative survey questions, Ethnicity survey questions, and life survey questions.

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Quantitative Research: Examples of Research Questions and Solutions

Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.

Understanding Quantitative Research Questions

Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:

• What is the relationship between class size and student academic performance?
• Does the use of technology in the classroom improve learning outcomes?
• How does parental involvement affect student achievement?
• What is the effect of a new drug treatment on reducing blood pressure?
• Is there a correlation between physical activity levels and the risk of cardiovascular disease?
• How does socioeconomic status influence access to healthcare services?
• What factors influence consumer purchasing behavior?
• Is there a relationship between advertising expenditure and sales revenue?
• How do demographic variables affect brand loyalty?

Stats Camp: Your Solution to Mastering Quantitative Research Methodologies

At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.

Bringing Your Own Data

One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.

Courses Offered at StatsCamp.org

• Latent Profile Analysis Course : Learn how to identify subgroups, or profiles, within a heterogeneous population based on patterns of responses to multiple observed variables.
• Bayesian Statistics Course : A comprehensive introduction to Bayesian data analysis, a powerful statistical approach for inference and decision-making. Through a series of engaging lectures and hands-on exercises, participants will learn how to apply Bayesian methods to a wide range of research questions and data types.
• Structural Equation Modeling (SEM) Course : Dive into advanced statistical techniques for modeling complex relationships among variables.
• Multilevel Modeling Course : A in-depth exploration of this advanced statistical technique, designed to analyze data with nested structures or hierarchies. Whether you’re studying individuals within groups, schools within districts, or any other nested data structure, multilevel modeling provides the tools to account for the dependencies inherent in such data.

As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!

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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.

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• Questionnaire Design | Methods, Question Types & Examples

Questionnaire Design | Methods, Question Types & Examples

Published on 6 May 2022 by Pritha Bhandari . Revised on 10 October 2022.

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

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

Table of contents

Questionnaires vs surveys, questionnaire methods, open-ended vs closed-ended questions, question wording, question order, step-by-step guide to design, frequently asked questions about questionnaire design.

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

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

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

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

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

Self-administered questionnaires

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

Self-administered questionnaires can be:

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

But they may also be:

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

Researcher-administered questionnaires

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

Researcher-administered questionnaires can:

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

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

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

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

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

Closed-ended questions

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

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

Examples of closed-ended questions for different variables

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

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

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

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

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

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

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

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

Pros and cons of closed-ended questions

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

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

Open-ended questions

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

• How do you feel about open science?
• How would you describe your personality?
• In your opinion, what is the biggest obstacle to productivity in remote work?

Open-ended questions have a few downsides.

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

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

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

Use clear language

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

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

Use balanced framing

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

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

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

Avoid leading questions

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

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

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

Keep your questions focused

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

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

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

Strongly Agree Agree Undecided Disagree Strongly Disagree

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

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

Logical flow

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

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

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

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

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

Randomisation

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

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

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

Step 1: Define your goals and objectives

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

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

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

Step 2: Use questions that are suitable for your sample

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

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

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

Step 3: Decide on your questionnaire length and question order

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

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

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

Step 4: Pretest your questionnaire

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

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

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

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

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

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

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

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

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

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

Questionnaires can be self-administered or researcher-administered.

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

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Hands-on guide to questionnaire research

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

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

Trisha Greenhalgh

Associated data, short abstract.

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

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

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

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

What information are you trying to collect?

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

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

Is a questionnaire appropriate?

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

Box 1: Pitfalls of designing your own questionnaire

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

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

Could you use an existing instrument?

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

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

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

Is the questionnaire valid and reliable?

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

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

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

How should you present your questions?

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

Examples of formats for presenting questionnaire items

Box 2: A closed ended design that produced misleading information

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

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

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

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

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

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

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

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

Apart from questions, what else should you include?

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

What should the questionnaire look like?

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

How should you select your sample?

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

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

What approvals do you need before you start?

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

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

Box 3: Don't let layout let you down

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

Summary points

Questionnaire studies often fail to produce high quality generalisable data

When possible, use previously validated questionnaires

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

Good explanations and design will improve response rates

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

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

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

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

Supplementary Material

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

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

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

Competing interests: None declared.

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Home » Questionnaire – Definition, Types, and Examples

Questionnaire – Definition, Types, and Examples

Table of Contents

Questionnaire

Definition:

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

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

History of Questionnaire

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

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

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

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

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

Types of Questionnaire

Types of Questionnaires are as follows:

Structured Questionnaire

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

Unstructured Questionnaire

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

Open-ended Questionnaire

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

Close-ended Questionnaire

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

Mixed Questionnaire

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

Pictorial Questionnaire:

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

Types of Questions in Questionnaire

The types of Questions in Questionnaire are as follows:

Multiple Choice Questions

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

• a. Red b . Blue c. Green d . Yellow

Rating Scale Questions

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

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

Open-Ended Questions

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

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

Likert Scale Questions

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

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

“I enjoy exercising regularly.”

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

Demographic Questions

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

• What is your age?

Yes/No Questions

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

Have you ever traveled outside of your home country?

Ranking Questions

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

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

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

Matrix Questions

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

Dichotomous Questions

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

Do you support the death penalty?

How to Make a Questionnaire

Step-by-Step Guide for Making a Questionnaire:

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

Questionnaire Administration Modes

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

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

Example of Questionnaire

Title of the Survey: Customer Satisfaction Survey

Introduction:

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

Instructions:

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

1. How satisfied are you with our product quality?

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

2. How satisfied are you with our customer service?

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

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

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

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

• Somewhat easy
• Somewhat difficult
• Very difficult

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

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

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

Conclusion:

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

Applications of Questionnaire

Some common applications of questionnaires include:

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

Purpose of Questionnaire

Some common purposes of questionnaires include:

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

When to use Questionnaire

Here are some situations when questionnaires might be used:

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

Characteristics of Questionnaire

Here are some of the characteristics of questionnaires:

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

Advantage of Questionnaire

Some Advantage of Questionnaire are as follows:

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

Limitations of Questionnaire

Limitations of Questionnaire are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Best practices for your quantitative survey

Gain the best possible insights from your user research with this guide to quantitative survey success.

What is a quantitative survey? 

A quantitative survey is a form of quantitative user research . 

While qualitative user research often gives you the why behind people's actions, you don’t get the data. Quantitative user research gives you data from a much larger audience. It allows you to take an informed and data-driven approach to your design decision making.

Surveys also give you the statistical significance of the results (something we’ll get into later) which allows you to have more confidence in the results, 

A survey typically takes the form of a set of questions. For example, you might ask participants to use a sliding scale to rank how they feel about something. Or to select an answer from a predetermined set. In some cases, a survey will include open-ended questions too, which can be really insightful.

Quantitative survey examples and use cases

Quantitative surveys can be used to measure a range of different things, but the most common use cases are to:

• Gauge customer satisfaction or an opinion
• Assess opportunities for improvement or redesign
• Acquire data to support your decision making and other research sources 
• Gauge demand for new digital product features

The advantages of quantitative user research

Surveys have several advantages over other methods of user research.

• Relatively inexpensive
• Straightforward to set up
• Fast (you can set up a survey, QA it, and get all your responses within two weeks)
• Easy to distribute across a broad range of channels (for example email, social, websites etc.)
• Suitable for a broad range of use cases

How to determine your target audience

Just like qualitative research, quantitative user research surveys need to have a clearly defined audience.

It’s important to align on this with key stakeholders at the start of your project. The last thing you want is to skew your results by inviting participants who aren’t relevant to what you’re trying to learn. As a broad example, you wouldn’t want feedback on a new product feature from people who don’t use the product.  

Some examples of your target audiences could be:

• Existing users
• New, potential, or prospective users
• Specific user segments (for example, inviting highly engaged or loyal users to comment on a particular feature) 

How to recruit participants for your user research survey

Once you’ve decided your research objectives – and the audience that you want to target – it’s time to think about recruitment. 

Firstly you need a screener to make sure you get the right people to answer your survey. A screener is a set of questions carefully designed and worded to ensure that each participant meets the criteria of your target audience – without asking potential participants to self qualify whether they fit the criteria.

Calculate your quantitative questionnaire sample

You will need to determine how big your sample size needs to be and a sample size calculator can do the work for you.

Sample size calculators are based on an equation that considers:

• Population size: the total number of people in a group whose opinions you want to gauge
• Confidence level: this shows how confident you can be that the results represent the attitude of all your users. The industry standard is 95%
• Margin of error (%): the percentage that tells you how much of your results reflect the views of the overall population. The smaller the margin or error the better, as this means you’re closer to having the exact answer at the given confidence level

Your sample range should be a minimum of 30-100. Going for 300 is ideal, but your user population size will determine whether that’s possible.

You can learn more about sample size calculators here . 

Tip: remember the times you declined to take part in a survey, because you didn’t have time, or just didn’t want to? This is something to consider when looking at sample sizes. You’ll inevitably get participants who don’t respond. A good rule of thumb is to plan for a loss estimate of 10% (participants won’t complete the survey). You can minimise the loss estimate by offering an incentive relative to the survey effort i.e. the longer the survey takes to fill in, the larger the incentive.

How to make your user research survey inclusive

Demographics questions ask participants to share information around things like sex, gender identity, ethnicity, age, location, education, and marital status. 

These questions provide good insight into your sample, but they’re potentially a very personal thing to ask participants. 

If you need to know this information, ask it in a blank (open) field and allow participants (if they feel comfortable doing so) to enter their own option. 

If you’re asking which gender a participant identifies as, allow them to enter their answer, rather than choose from a predefined list. It's a personal topic for some. So be clear, give context, and tell participants why you want this information (and how you'll use it).

Whichever survey tool you decide to use (we’ll explore some examples in a moment), make sure that tool is accessible. Your survey should meet AA web content accessibility guidelines (WCAG) standards or higher. This is to ensure that you’re meeting the diverse accessibility needs of your participants (bear in mind that 1 in 5 people in the UK have a disability, for example).

Write with accessibility in mind. Make sure your questions are clear and concise. If you need to use images, make sure that questions explain or give context to the images. And ensure your colour contrasts are accessible too.

9 tips for writing quantitative survey questions

The quality of your survey questions impacts the quality of your research insights.

Follow these steps to improve the outputs from your quantitative user research:

• Inspire participation. Consider how you can encourage people to take your survey. Consider the wording of your email subject line. Does it entice and encourage people to open the email? How can you use social media to encourage participation?
• Keep it simple. Keep your survey short to maintain attention span and stop drop offs. Only ask the questions you need to ask.
• Keep questions short. Make questions as short as possible. Use short words where possible, and keep questions clear, concise, and unambiguous.
• Avoid jargon. Use common terminology. Try and use the language of your audience, and provide explainers where needed. 
• Start simple. Start your survey with your most straightforward questions to encourage engagement. Leave trickier or more controversial questions to the end, and make them optional if possible.
• One topic per question. Only address one topic at a time in a question. For example, don’t ask whether a participant finds a website ‘quick and easy to use’ in a single question. Because these are two distinct topics.
• Avoid leading questions. For example, instead of asking: How concerned are you about the current fuel shortage situation? ask: How are you finding the current fuel shortage situation?
• Important words first. Put important words at the start of questions. For example, if you want to know how many times someone went to buy fuel this week ask: In the past week, how many times have you tried to buy fuel?
• Use neutral language . For example, rather than ask: Do you suffer from...? ask: Do you have…?

How to QA your survey

One of the main downsides of using surveys is that once it’s out there, there’s no way to edit it. That’s why quality assurance is key. 

After you’ve written your survey, you need to thoroughly check it. This process will depend on the nature of your survey and your sample size, but it ideally involves these key stages: 

• Test your survey with colleagues to tease out any immediate issues
• Do a trial run with a sample approximately similar to your target sample
• Do a trial run with an actual sample size
• Address any issues that come up and get ready to hit send! 

Then, when you’re ready, send it off and stand by for your results! 

How to analyse your survey findings

Most survey platforms include standard analytics for free. Based on the analytics you can draw findings and, with deeper analysis, you can then prioritise your next steps.  

Look out for false entries when analysing your results. Some participants complete a survey in the fastest possible way to get to the reward or incentive. These false entries can affect your results if they’re left unchecked.

Tell tale signs of false entries are things like: 

• Short survey completion times. Look out for instances where the survey was completed much faster than the average time. If you see that people typically complete the survey in 2-3 mins, but one individual took only 30 seconds, that might warrant a closer look.
• Look for patterns in the answers . If people select the first answer of every question, that might be a red flag.

Statistical significance

Statistical significance works out the probability of the results happening randomly. The probability needs to be no higher than 5%, which tends to be the cut off point. 

This is indicated by the ‘p’ value in the survey analytics. If the p value for your results is less than 0.05, your results can be considered statistically significant.

Survey tools (like SurveyMonkey) provide the statistical significance of your results and highlight ones with strong statistical significance. This allows you to be confident that the results are accurate rather than random.

User research survey tools

There are a ton of survey tools out there, with varying levels of complexity and cost. 

Here are some popular ones:

• Google forms . It’s free to use and gives very basic analysis for completed surveys.
• Typeform . This paid tool is more sophisticated than Google Forms and provides more in-depth analysis, with lots of integrations. 
• SurveyMonkey . This is one of the biggest paid survey tools on the market, with detailed analysis and lots of resources for guidance.

The value of quantitative surveys

User research surveys provide insightful quantitative data – at an affordable price point. Used and analysed correctly, they’re a very effective way to better understand your audience and replace your assumptions with evidence, in the form of data.

It’s important to remember that surveys don’t often give you the ‘why’ behind the questions you’re looking to answer.

That’s why the best customer insights come from a powerful blend of qualitative and quantitative research.

If you enjoyed this guide, do check out our user testing FAQ and our post on user research recruitment.

If you need support with your user research or recruitment, or you’re looking to grow your team’s research capabilities, do drop us a line . We’ll connect you with one of our user research specialists.