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Qualitative study design: Surveys & questionnaires

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Surveys & questionnaires

Qualitative surveys use open-ended questions to produce long-form written/typed answers. Questions will aim to reveal opinions, experiences, narratives or accounts. Often a useful precursor to interviews or focus groups as they help identify initial themes or issues to then explore further in the research. Surveys can be used iteratively, being changed and modified over the course of the research to elicit new information. 

Structured Interviews may follow a similar form of open questioning.  

Qualitative surveys frequently include quantitative questions to establish elements such as age, nationality etc. 

Qualitative surveys aim to elicit a detailed response to an open-ended topic question in the participant’s own words.  Like quantitative surveys, there are three main methods for using qualitative surveys including face to face surveys, phone surveys, and online surveys. Each method of surveying has strengths and limitations.

Face to face surveys  

• Researcher asks participants one or more open-ended questions about a topic, typically while in view of the participant’s facial expressions and other behaviours while answering. Being able to view the respondent’s reactions enables the researcher to ask follow-up questions to elicit a more detailed response, and to follow up on any facial or behavioural cues that seem at odds with what the participants is explicitly saying.
• Face to face qualitative survey responses are likely to be audio recorded and transcribed into text to ensure all detail is captured; however, some surveys may include both quantitative and qualitative questions using a structured or semi-structured format of questioning, and in this case the researcher may simply write down key points from the participant’s response.

Telephone surveys

• Similar to the face to face method, but without researcher being able to see participant’s facial or behavioural responses to questions asked. This means the researcher may miss key cues that would help them ask further questions to clarify or extend participant responses to their questions, and instead relies on vocal cues.

Online surveys

• Open-ended questions are presented to participants in written format via email or within an online survey tool, often alongside quantitative survey questions on the same topic.
• Researchers may provide some contextualising information or key definitions to help ‘frame’ how participants view the qualitative survey questions, since they can’t directly ask the researcher about it in real time. 
• Participants are requested to responses to questions in text ‘in some detail’ to explain their perspective or experience to researchers; this can result in diversity of responses (brief to detailed).
• Researchers can not always probe or clarify participant responses to online qualitative survey questions which can result in data from these responses being cryptic or vague to the researcher.
• Online surveys can collect a greater number of responses in a set period of time compared to face to face and phone survey approaches, so while data may be less detailed, there is more of it overall to compensate.

Qualitative surveys can help a study early on, in finding out the issues/needs/experiences to be explored further in an interview or focus group. 

Surveys can be amended and re-run based on responses providing an evolving and responsive method of research. 

Online surveys will receive typed responses reducing translation by the researcher 

Online surveys can be delivered broadly across a wide population with asynchronous delivery/response. 

Limitations

Hand-written notes will need to be transcribed (time-consuming) for digital study and kept physically for reference. 

Distance (or online) communication can be open to misinterpretations that cannot be corrected at the time. 

Questions can be leading/misleading, eliciting answers that are not core to the research subject. Researchers must aim to write a neutral question which does not give away the researchers expectations. 

Even with transcribed/digital responses analysis can be long and detailed, though not as much as in an interview. 

Surveys may be left incomplete if performed online or taken by research assistants not well trained in giving the survey/structured interview. 

Narrow sampling may skew the results of the survey. 

Example questions

Here are some example survey questions which are open ended and require a long form written response:

• Tell us why you became a doctor? 
• What do you expect from this health service? 
• How do you explain the low levels of financial investment in mental health services? (WHO, 2007) 

Example studies

• Davey, L. , Clarke, V. and Jenkinson, E. (2019), Living with alopecia areata: an online qualitative survey study. British Journal of Dermatology, 180 1377-1389. Retrieved from https://onlinelibrary-wiley-com.ezproxy-f.deakin.edu.au/doi/10.1111%2Fbjd.17463    
• Richardson, J. (2004). What Patients Expect From Complementary Therapy: A Qualitative Study. American Journal of Public Health, 94(6), 1049–1053. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=s3h&AN=13270563&site=eds-live&scope=site  
• Saraceno, B., van Ommeren, M., Batniji, R., Cohen, A., Gureje, O., Mahoney, J., ... & Underhill, C. (2007). Barriers to improvement of mental health services in low-income and middle-income countries. The Lancet, 370(9593), 1164-1174. Retrieved from https://www-sciencedirect-com.ezproxy-f.deakin.edu.au/science/article/pii/S014067360761263X?via%3Dihub  

Below has more detail of the Lancet article including actual survey questions at: 

• World Health Organization. (2007.) Expert opinion on barriers and facilitating factors for the implementation of existing mental health knowledge in mental health services. Geneva: World Health Organization. https://apps.who.int/iris/handle/10665/44808
• Green, J. 1961-author., & Thorogood, N. (2018). Qualitative methods for health research. SAGE. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat00097a&AN=deakin.b4151167&authtype=sso&custid=deakin&site=eds-live&scope=site   
• JANSEN, H. The Logic of Qualitative Survey Research and its Position in the Field of Social Research Methods. Forum Qualitative Sozialforschung, 11(2), Retrieved from http://www.qualitative-research.net/index.php/fqs/article/view/1450/2946  
• Neilsen Norman Group, (2019). 28 Tips for Creating Great Qualitative Surveys. Retrieved from https://www.nngroup.com/articles/qualitative-surveys/     
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• Last Updated: Jul 3, 2024 11:46 AM
• URL: https://deakin.libguides.com/qualitative-study-designs

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Qualitative Surveys: What They Are, Benefits, and How to Conduct Them

Qualitative surveys have been an essential part of research as they help uncover aspects related to respondents’ emotions, behaviors, and perceptions beyond what numbers can convey.

This article will delve into qualitative surveys in detail, so you can effectively use them in your next study.

What are Qualitative Surveys?

Qualitative surveys are a research tool that employs open-ended questions to gather opinions, experiences, narratives, or accounts from respondents. 

These surveys are useful for generating information through a conversation that identifies initial topics or issues to explore further in research . 

Qualitative surveys seek comments, opinions, suggestions, and other types of responses that are not as easy to classify and quantify as numbers. Typically, fewer people may be surveyed compared to quantitative surveys, but richer data can be obtained.

Benefits of Qualitative Surveys

Opinions can change and evolve throughout a conversation; qualitative research can capture this. Here are some benefits of using qualitative surveys:

Capture Changing Attitudes

Researchers can quickly adapt questions, change the environment, or other variables to enhance responses if useful data isn’t obtained. Qualitative research can capture changing attitudes within a target group, such as consumers of a product or service or attitudes in the workplace.

Greater Flexibility

If responses don’t align with researcher expectations, qualitative data is equally useful for adding context and perhaps explaining something that numbers alone cannot reveal.

In-Depth Explanation

Qualitative research methods don’t have the same limitations as quantitative methods. When collecting non-numeric data, there’s the potential to provide explanations that reveal more about the data.

Explore Uncharted Areas

Qualitative surveys allow speculative research into areas that researchers find valuable. Capturing qualitative data empowers researchers to be more speculative about the areas they choose to investigate and how to do so.

Enhance Participation

Using qualitative surveys allows for a more direct approach to research participants , who may feel more listened to and motivated to complete the survey .

Types of Qualitative Surveys

There are numerous types of Qualitative Surveys, each offering a distinct approach to comprehending human experiences and perspectives. The selection of a method depends on research objectives, context, and available resources.

Some common types of qualitative surveys include:

Face-to-Face Surveys

In face-to-face surveys , the researcher asks participants one or more open-ended questions on a topic, usually observing participants’ facial expressions and other behaviors while they respond.

Being able to see participants’ reactions allows the researcher to ask follow-up questions for more detailed responses and record any facial or behavioral cues that seem contrary to what participants are explicitly saying.

Phone Surveys

Phone-based qualitative surveys are similar to face-to-face methods, but the researcher cannot see participants’ facial or behavioral responses to the questions asked. This means the researcher must rely on vocal clues.

Online Qualitative Surveys

Online surveys can collect more responses within a shorter time frame than in-person or phone surveys. Although the data may be less detailed, it is generally more abundant to compensate.

Open-ended questions are presented to participants in written format via email or online survey software , often alongside quantitative survey questions on the same topic.

Researchers may provide contextual information or key definitions to help “frame” how participants view qualitative survey questions, as they cannot directly ask the researcher about it in real-time.

Observational Studies

Observational studies involve systematically observing participants in their natural environments. This method provides insights into behaviors, interactions, and contextual factors.

Grounded Theory

Grounded theory aims to develop theories from the data itself, allowing researchers to derive concepts and relationships directly from participants’ responses.

Focus Groups

In a focus group, a small group of participants discuss a specific topic or issue under the guidance of a moderator. This method encourages participants to interact with each other, generating rich discussions.

How to Conduct a Qualitative Survey

Here’s a step-by-step guide on how to conduct a qualitative survey in 7 steps:

1. Set Clear Objectives for Your Survey

Determine the purpose of your survey and be clear about what you want to know and the information you expect to gather. Plan precisely how you’ll record response data, including using specific tables or charts that are useful for report generation.

2. Craft Questions that Probe “Why” and “How”

Qualitative research aims to take a concrete idea, delve into why it exists, and determine how it has come about. 

With that in mind, your survey questions should be phrased and sequenced to elicit these types of insights. For instance, use open-ended text questions.

3. Place Key Questions at the Start

If you have a set of questions that you deem more important than others, place these questions at the beginning of your survey. Respondents may become fatigued after answering multiple questions, and if respondents stop responding to the survey after partially completing it, their response data will be severely affected.

Ensuring each question serves a purpose can mitigate survey fatigue, and it’s also a good idea to place the most important questions at the beginning.

4. Be Concise in Each Question and the Number of Questions

Responses to survey questions should be intuitive and straightforward for respondents. Therefore, complex instructions shouldn’t be necessary. 

Furthermore, each additional question reduces response rates, decreases validity, and makes all results suspect. 

People are much more likely to participate in single-question surveys. Therefore, realistically estimate the time needed to complete the survey, as the more open-ended questions and complex classifications you ask people, the more respondents you’ll lose.

5. Test Your Survey

Before using your survey in the actual research, it’s important to conduct a test to determine if the questions you’ve developed yield the responses you expect. This involves creating a draft of the questions and obtaining feedback from collaborators.

Test the survey system’s format with a small group of testers from your target audience , collecting feedback on each page, and examine the results of the test survey to ensure that the collected data is in a useful and analyzable format.

6. Code Text Responses

Researchers often talk about coding data during analysis. This involves converting text responses into something countable so that the most important trends can be extracted and communicated in a way that makes sense to the report’s audience. 

Coding text responses allows you to capture rich textual data for understanding and quoting.

Create Your Qualitative Surveys with QuestionPro!

QuestionPro is currently the most comprehensive tool for conducting qualitative surveys due to its integration of open-ended questions and as software for qualitative data analysis.

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Your ultimate guide to qualitative research (with methods and examples).

16 min read You may be already using qualitative research and want to check your understanding, or you may be starting from the beginning. Learn about qualitative research methods and how you can best use them for maximum effect.

What is qualitative research?

Qualitative research is a research method that collects non-numerical data. Typically, it goes beyond the information that quantitative research provides (which we will cover below) because it is used to gain an understanding of underlying reasons, opinions, and motivations.

Qualitative research methods focus on the thoughts, feelings, reasons, motivations, and values of a participant, to understand why people act in the way they do .

In this way, qualitative research can be described as naturalistic research, looking at naturally-occurring social events within natural settings. So, qualitative researchers would describe their part in social research as the ‘vehicle’ for collecting the qualitative research data.

Qualitative researchers discovered this by looking at primary and secondary sources where data is represented in non-numerical form. This can include collecting qualitative research data types like quotes, symbols, images, and written testimonials.

These data types tell qualitative researchers subjective information. While these aren’t facts in themselves, conclusions can be interpreted out of qualitative that can help to provide valuable context.

Because of this, qualitative research is typically viewed as explanatory in nature and is often used in social research, as this gives a window into the behavior and actions of people.

It can be a good research approach for health services research or clinical research projects.

Free eBook: The qualitative research design handbook

Quantitative vs qualitative research

In order to compare qualitative and quantitative research methods, let’s explore what quantitative research is first, before exploring how it differs from qualitative research.

Quantitative research

Quantitative research is the research method of collecting quantitative research data – data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed .

Quantitative research methods deal with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data.

Quantitative research data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

The difference between quantitative and qualitative research methodology

While qualitative research is defined as data that supplies non-numerical information, quantitative research focuses on numerical data.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative research methods. If you want to explore ideas, thoughts, and meanings, use qualitative research methods.

While qualitative research helps you to properly define, promote and sell your products, don’t rely on qualitative research methods alone because qualitative findings can’t always be reliably repeated. Qualitative research is directional, not empirical.

The best statistical analysis research uses a combination of empirical data and human experience ( quantitative research and qualitative research ) to tell the story and gain better and deeper insights, quickly.

Where both qualitative and quantitative methods are not used, qualitative researchers will find that using one without the other leaves you with missing answers.

For example, if a retail company wants to understand whether a new product line of shoes will perform well in the target market:

• Qualitative research methods could be used with a sample of target customers, which would provide subjective reasons why they’d be likely to purchase or not purchase the shoes, while
• Quantitative research methods into the historical customer sales information on shoe-related products would provide insights into the sales performance, and likely future performance of the new product range.

Approaches to qualitative research

There are five approaches to qualitative research methods:

• Grounded theory: Grounded theory relates to where qualitative researchers come to a stronger hypothesis through induction, all throughout the process of collecting qualitative research data and forming connections. After an initial question to get started, qualitative researchers delve into information that is grouped into ideas or codes, which grow and develop into larger categories, as the qualitative research goes on. At the end of the qualitative research, the researcher may have a completely different hypothesis, based on evidence and inquiry, as well as the initial question.
• Ethnographic research : Ethnographic research is where researchers embed themselves into the environment of the participant or group in order to understand the culture and context of activities and behavior. This is dependent on the involvement of the researcher, and can be subject to researcher interpretation bias and participant observer bias . However, it remains a great way to allow researchers to experience a different ‘world’.
• Action research: With the action research process, both researchers and participants work together to make a change. This can be through taking action, researching and reflecting on the outcomes. Through collaboration, the collective comes to a result, though the way both groups interact and how they affect each other gives insights into their critical thinking skills.
• Phenomenological research: Researchers seek to understand the meaning of an event or behavior phenomenon by describing and interpreting participant’s life experiences. This qualitative research process understands that people create their own structured reality (‘the social construction of reality’), based on their past experiences. So, by viewing the way people intentionally live their lives, we’re able to see the experiential meaning behind why they live as they do.
• Narrative research: Narrative research, or narrative inquiry, is where researchers examine the way stories are told by participants, and how they explain their experiences, as a way of explaining the meaning behind their life choices and events. This qualitative research can arise from using journals, conversational stories, autobiographies or letters, as a few narrative research examples. The narrative is subjective to the participant, so we’re able to understand their views from what they’ve documented/spoken.

Qualitative research methods can use structured research instruments for data collection, like:

Surveys for individual views

A survey is a simple-to-create and easy-to-distribute qualitative research method, which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Qualitative research questions tend to be open questions that ask for more information and provide a text box to allow for unconstrained comments.

Examples include:

• Asking participants to keep a written or a video diary for a period of time to document their feelings and thoughts
• In-Home-Usage tests: Buyers use your product for a period of time and report their experience

Surveys for group consensus (Delphi survey)

A Delphi survey may be used as a way to bring together participants and gain a consensus view over several rounds of questions. It differs from traditional surveys where results go to the researcher only. Instead, results go to participants as well, so they can reflect and consider all responses before another round of questions are submitted.

This can be useful to do as it can help researchers see what variance is among the group of participants and see the process of how consensus was reached.

• Asking participants to act as a fake jury for a trial and revealing parts of the case over several rounds to see how opinions change. At the end, the fake jury must make a unanimous decision about the defendant on trial.
• Asking participants to comment on the versions of a product being developed , as the changes are made and their feedback is taken onboard. At the end, participants must decide whether the product is ready to launch .

Semi-structured interviews

Interviews are a great way to connect with participants, though they require time from the research team to set up and conduct, especially if they’re done face-to-face.

Researchers may also have issues connecting with participants in different geographical regions. The researcher uses a set of predefined open-ended questions, though more ad-hoc questions can be asked depending on participant answers.

• Conducting a phone interview with participants to run through their feedback on a product . During the conversation, researchers can go ‘off-script’ and ask more probing questions for clarification or build on the insights.

Focus groups

Participants are brought together into a group, where a particular topic is discussed. It is researcher-led and usually occurs in-person in a mutually accessible location, to allow for easy communication between participants in focus groups.

In focus groups , the researcher uses a set of predefined open-ended questions, though more ad-hoc questions can be asked depending on participant answers.

• Asking participants to do UX tests, which are interface usability tests to show how easily users can complete certain tasks

Direct observation

This is a form of ethnographic research where researchers will observe participants’ behavior in a naturalistic environment. This can be great for understanding the actions in the culture and context of a participant’s setting.

This qualitative research method is prone to researcher bias as it is the researcher that must interpret the actions and reactions of participants. Their findings can be impacted by their own beliefs, values, and inferences.

• Embedding yourself in the location of your buyers to understand how a product would perform against the values and norms of that society

Qualitative data types and category types

Qualitative research methods often deliver information in the following qualitative research data types:

• Written testimonials

Through contextual analysis of the information, researchers can assign participants to category types:

• Social class
• Political alignment
• Most likely to purchase a product
• Their preferred training learning style

Advantages of qualitative research

• Useful for complex situations: Qualitative research on its own is great when dealing with complex issues, however, providing background context using quantitative facts can give a richer and wider understanding of a topic. In these cases, quantitative research may not be enough.
• A window into the ‘why’: Qualitative research can give you a window into the deeper meaning behind a participant’s answer. It can help you uncover the larger ‘why’ that can’t always be seen by analyzing numerical data.
• Can help improve customer experiences: In service industries where customers are crucial, like in private health services, gaining information about a customer’s experience through health research studies can indicate areas where services can be improved.

Disadvantages of qualitative research

• You need to ask the right question: Doing qualitative research may require you to consider what the right question is to uncover the underlying thinking behind a behavior. This may need probing questions to go further, which may suit a focus group or face-to-face interview setting better.
• Results are interpreted: As qualitative research data is written, spoken, and often nuanced, interpreting the data results can be difficult as they come in non-numerical formats. This might make it harder to know if you can accept or reject your hypothesis.
• More bias: There are lower levels of control to qualitative research methods, as they can be subject to biases like confirmation bias, researcher bias, and observation bias. This can have a knock-on effect on the validity and truthfulness of the qualitative research data results.

How to use qualitative research to your business’s advantage?

Qualitative methods help improve your products and marketing in many different ways:

• Understand the emotional connections to your brand
• Identify obstacles to purchase
• Uncover doubts and confusion about your messaging
• Find missing product features
• Improve the usability of your website, app, or chatbot experience
• Learn about how consumers talk about your product
• See how buyers compare your brand to others in the competitive set
• Learn how an organization’s employees evaluate and select vendors

6 steps to conducting good qualitative research

Businesses can benefit from qualitative research by using it to understand the meaning behind data types. There are several steps to this:

• Define your problem or interest area: What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis: Ask yourself what could be the causes for the situation with those qualitative research data types.
• Plan your qualitative research: Use structured qualitative research instruments like surveys, focus groups, or interviews to ask questions that test your hypothesis.
• Data Collection: Collect qualitative research data and understand what your data types are telling you. Once data is collected on different types over long time periods, you can analyze it and give insights into changing attitudes and language patterns.
• Data analysis: Does your information support your hypothesis? (You may need to redo the qualitative research with other variables to see if the results improve)
• Effectively present the qualitative research data: Communicate the results in a clear and concise way to help other people understand the findings.

Qualitative data analysis

Evaluating qualitative research can be tough when there are several analytics platforms to manage and lots of subjective data sources to compare.

Qualtrics provides a number of qualitative research analysis tools, like Text iQ , powered by Qualtrics iQ, provides powerful machine learning and native language processing to help you discover patterns and trends in text.

This also provides you with:

• Sentiment analysis — a technique to help identify the underlying sentiment (say positive, neutral, and/or negative) in qualitative research text responses
• Topic detection/categorisation — this technique is the grouping or bucketing of similar themes that can are relevant for the business & the industry (eg. ‘Food quality’, ‘Staff efficiency’ or ‘Product availability’)

How Qualtrics products can enhance & simplify the qualitative research process

Even in today’s data-obsessed marketplace, qualitative data is valuable – maybe even more so because it helps you establish an authentic human connection to your customers. If qualitative research doesn’t play a role to inform your product and marketing strategy, your decisions aren’t as effective as they could be.

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting qualitative research. From survey creation and data collection to textual analysis and data reporting, it can help all your internal teams gain insights from your subjective and categorical data.

Qualitative methods are catered through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of qualitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Text IQ™ and Driver IQ™ make analyzing subjective and categorical data easy and simple. Choose to highlight key findings based on topic, sentiment, or frequency. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

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

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

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

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

Table of contents

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

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

Common uses of survey research include:

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

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

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

Populations

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

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

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

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

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

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

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

There are two main types of survey:

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

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

Questionnaires

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

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

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

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

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

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

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

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

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

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

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

Open-ended vs closed-ended questions

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

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

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

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

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

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

The content of the survey questions

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

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

Phrasing the survey questions

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

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

Ordering the survey questions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

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

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

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

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

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

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Qualitative Research: Surveys

• Getting Started
• Focus Groups
• Observation
• Case Studies
• Data Collection
• Cleaning Text
• Analysis Tools
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Survey Research Topics

DISM Tipsheets

What is surveying?

• Involves gathering information from individuals by using a questionnaire.
• Can generate standardized, quantifiable, empirical data as well as qualitative data.
• Can reach large number of respondents.
• Development involves creating questions and response categories; writing up background information and instructions; and determining organization, layout and design.

Duke Resources for help with Surveys

• Duke Initiative on Survey Methodology Provides advising on individual projects, funding opportunities, and training in survey research methods.  
• Qualtrics Survey tool that can create surveys with open-ended questions; free access with Duke NetID
• Encyclopedia of Survey Research Methods (e-book) Provides information about important topics across the entirety of survey methodology; serves as a “first place” to turn to learn about aspects of survey methodology.

Suggested Readings

• Fowler, F. J. (1995). Improving survey questions: Design and evaluation . Thousand Oaks: Sage Publications.
• Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., Tourangeau, R. (2004). Survey methodology . NY: John Wiley & Sons.
• Oppenheimer, A.N. (1999). Questionnaire design, interviewing, and attitude measurement . London: Pinter.
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• Last Updated: Mar 1, 2024 10:13 AM
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World Leaders in Research-Based User Experience

28 tips for creating great qualitative surveys.

September 25, 2016 2016-09-25

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Qualitative surveys ask open-ended questions to find out more, sometimes in preparation for doing quantitative surveys. Test surveys to eliminate problems.

Sooner or later, most UX professionals will need to conduct a survey. Survey science from the quantitative side can be intimidating because it’s a specialized realm full of statistics, random selection, and scary stories of people going wrong with confidence. Don’t be afraid of doing qualitative surveys, though. Sure, it’s important to learn from survey experts, but you don’t have to be a survey specialist to get actionable data. You do have to find and fix the bugs in your questions first, however.

In This Article:

Quantitative vs. qualitative surveys, tips for qualitative surveys.

Quantitative surveys count results : how many people do this vs. do that (or rather, how many say that they do this or that). Use quant surveys when you need to ask questions that can be answered by checkbox or radio button, and when you want to be sure your data is broadly applicable to a large number of people. Quantitative surveys follow standard methods for randomly selecting a large number of participants (from a target group) and use statistical analysis to ensure that the results are statistically significant and representative for the whole population.

Qualitative surveys ask open-ended questions . Use them when you need to generate useful information via a conversation rather than a vote, such as when you’re not sure what the right set of answers might include. Qualitative surveys ask for comments, feedback, suggestions, and other kinds of responses that aren’t as easily classified and tallied as numbers can be. You can survey fewer people than in a quantitative survey and get rich data.

It’s possible to mix the two kinds of surveys, and it’s especially useful to do small, primarily qualitative surveys first to help you generate good answers to count later in a bigger survey. This one-two-punch strategy is much preferable to going straight to a closed-ended question with response categories you and your colleagues thought up in your conference room. (Yes, you could add an “other” option, but don’t count on valid statistics for options left to a catch-all bucket.)

Unordered lists can be more time-consuming to look through than lists that have an obvious ordering principle, but unordered lists seem to yield better answers, especially if you can sort the list differently for different respondents.

• Draft questions and get feedback from colleagues.
• Draft survey and get colleagues to attempt to answer the questions. Ask for comments after each question to help you revise questions toward more clarity and usefulness.
• Revise survey and test iteratively on paper. We typically do 4 rounds of testing, with 1 respondent per round. At this stage, don’t rely on colleagues, but recruit participants from the target audience. Revise between each round. Run these tests as think-aloud studies ; do not send out the survey and rely on written comments — they will never be the same as a realtime stream of commentary.
• Randomize some sections and questions of the survey to help ensure that (1) people quitting partway through don’t affect the overall balance of data being collected, and (2) the question or section ordering doesn’t bias people’s responses.
• Test the survey-system format with a small set of testers from the target audience, again collecting comments on each page.
• Examine the output from the test survey to ensure the data gathered is in an analyzable, useful format.
• Revise the survey one more time.
• Don’t make your own tool for surveys if you can avoid it . Many solid survey platforms exist, and they can save you lots of time and money.
• Decide up front what the survey learning goals are . What do you want to report about? What kind of graphs and tables will you want to deliver?
• Write neutral questions that don’t imply particular answers or give away your expectations .
• Open vs. closed answers : Asking open-ended questions is the best approach, but it’s easy to get into the weeds in data analysis when every answer is a paragraph or two of prose. Plus, users quickly tire of answering many open-ended questions, which usually require a lot of typing and explanation. That being said, it’s best to ask open-ended questions during survey testing . The variability of the answers to these questions during the testing phase can help you decide whether the question should be open-ended in the final survey or could be replaced with a closed-ended question that would be easier to answer and analyze.
• Carefully consider how you will analyze and act on the data . The type of questions you ask will have everything to do with the kind of analysis you can make: multiple answers, single answers, open or closed sets, optional and required questions, ratings, rankings, and free-form answer fields are some of the choices open to you when deciding what kinds of answers to accept. (If you won’t act on the data, don’t ask that question. See guideline #12.)
• Multiple vs. single answers : Often multiple-answer questions are better than single-answer ones because people usually want to be accurate, and often several answers apply to them. Survey testing on paper can help you find multiple-answer questions, because people will mark several answers even when you ask them to mark only one (and they will complain about it). If you are counting answers, consider not only how many responses each answer got, but also how many choices people made.
• Front-load the most important questions, because people will quit partway through . Ensure that partial responses will be recorded anyway.
• Provide responses such as, “Not applicable” and “Don’t use” to prevent people skipping questions or giving fake answers. People get angry when asked questions they can’t answer honestly, and it skews your data if they try to do it anyway.
• People have trouble understanding required and optional signals on survey question/forms . It’s common practice to use a red asterisk “ * ” to mark required fields, but that didn’t work well enough, even in a survey of UX professionals — many of whom likely design such forms. People complained that required fields were not marked. Pages that stated at the top that all were required or optional also didn’t help, because many people ignore instruction text. Use “(Optional)” and/or “(Required)” after each question, to be sure people understand.
• When marking is not clear enough, many people feel obligated to answer optional questions . Practically speaking that means you don’t have to require every question, but you should be careful not to include so many questions that people quit the survey in the middle.
• Keep it short . Every extra question reduces your response rate, decreases validity, and makes all your results suspect. Better to administer 2 short surveys to 2 different subsamples of your audience than to lump everything you want to know into a long survey that won’t be completed by the average customer. 20 questions are too many unless you have a highly motivated set of participants. People are much more likely to participate in 1-question surveys. Be sensitive to what your pilot testers tell you, and realistically estimate the time to complete the survey. The more open-ended questions and complex ranking you ask people to do, the more you’ll lose respondents.
• People often overlook examples and instructions that are on the right , after questions. Move instructions and examples to the left margin instead (or the opposite side, for languages that read right to left), to put them in the scannability zone and place them closer to the person’s focus of attention, which is on the answer area.
• Use one-line directions if you can. Less is more. Just as in our original writing for the web studies , people read more text when there is a lot less of it. People complain about not getting enough information, but when it’s there they don’t read it because it’s too long.
• People tend not to read paragraphs or introductions . If you must use a paragraph, bold important ideas to help ensure that most people, who scan instead of reading , glean that information.
• Think carefully about using subjective terms , such as “essential,” “useful,” or “frequent.” Terms that cause people to make a judgment call may get at how they feel, but such questions can be confusing to evaluate logically. Ratings scales are more flexible. If you do need to know how participants perceive a certain aspect, indicate that’s what you want them to base their answer on (for example, instead of asking “Is X essential for Y?” say “Do you feel that X is essential for Y?”).
• Define terms as needed in order to start from a shared meaning. People might quibble about the definition, but it’s better than getting misleading answers because of a misinterpretation.
• Don’t ask about things that your analytics can tell you . Ask why and how questions.
• Include a survey professional in your test group . Your survey method may be criticized after the fact, so get expert advice before you conduct your survey.
• Items at the top and bottom of lists may attract more attention than items in the middle of long lists.
• Because people scan instead of read, the first words of items in lists can cause them to overlook the right choice, especially in alphabetical lists.
• Test where best to place page breaks. Sometimes it’s important for people to be able to see all the topic’s questions before they answer one. Otherwise they volunteer answers for the questions they have not yet seen and write, “see previous answer” later, which adds extra interpretation steps in data analysis. To find questions with these kinds of problems, you can test the survey with each question on its own page first, and then collocate the questions that need to be shown together on one page in the next test version. In some cases, simply forcing one question to come before another one can fix these problems.
• If possible, don’t annoy people by asking questions that don’t apply to them . When respondents choose a particular answer, show them one or two more questions about that topic that would be applicable in that case. Choose a survey platform that allows conditional questions, so you can avoid presenting nonapplicable questions and keep your list of questions as short as possible for each respondent. If most of your questions are conditional, you might be able to put a key conditional question early in the list, then branch to different versions of the survey for the rest of the questions.
• Take your data with a grain of salt . Unlike for quantitative surveys, qualitative survey metrics are rarely representative for the whole target audience; instead, they represent the opinions of the respondents. You can still present descriptive statistics (such as how many people selected a specific response to a multiple-choice question) to summarize the results of the survey, but, unless you use sound statistics tools, you cannot say whether these results are the result of noise or sample selection, as opposed to truly reflecting the attitudes of your whole user population.
• Count whatever you can count . Researchers often refer to coding and normalizing data during analysis. Coding data is the process of making text answers into something you can count, so you can extract the bigger trends and report them in a way that makes sense to your report audience. You can capture rich textual data for understanding and quoting, and code some types of responses as 0, 1, or 2 (no, partially, yes; or none, some, all) for example, or you may be able to define many different phrases as meaning the same thing (for example when people use synonyms or express the same ideas). This coding can be done after the data is collected, in a spreadsheet.
• Show, don’t tell . Use lots of graphs, charts, and tables, with an executive summary of key takeaways.
• Consider graphs before you decide on a spreadsheet layout . Unfortunately some spreadsheets won’t make reasonable graphs until you switch columns to rows or rows to columns. It’s easiest to plan for this necessity before you analyze your data. It’s also possible to take the chart data, put it on its own spreadsheet page, and then reorder it to make the charts. Just be careful not to make data transfer errors.
• Beware of disappearing chart data . Some spreadsheets hide data in charts silently when font-size changes or chart-size changes are made.
• Don’t embed data if you can screenshot it . Screenshots (PNG format is recommended) are lovely and robust over time, unlike embedded data, which tends to cause document corruption, become unlinked, or could be changed by mistake.

Qualitative surveys are tools for gathering rich feedback. They can also help you discover which questions you need to ask and the best way to ask them, for a later quantitative survey. Improve surveys through iterative testing with open-ended feedback. Test surveys on paper first to save time-consuming rework in the testing platform. Then test online to see the effects of page order and question randomization and to gauge how useful the automated results data may be.

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Qualitative Research Methods: Types, Examples, and Analysis

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

In a universe swarming with data, numbers, and algorithms, lies a lesser-known realm where emotions, stories, and intimate revelations take center stage. When you want to get inside your customers’ heads to understand their thoughts, feelings, perceptions, beliefs, and emotions, numbers are unlikely to provide a complete picture.

Let’s set the scene: picture a cozy bakery buzzing with conversations. People from different walks of life gather, each carrying a unique story to tell. You observe that your sale of pancakes is more than that of pastries, numerical data will tell you that much. But numbers won’t tell you why.

This is exactly where qualitative surveys come into play; they take you right to the heart of people’s minds and experiences – the “why” behind the statistics.

Quantitative data may offer a bird’s-eye view of the crowd, but qualitative surveys open the doorways to your audience’s individual tales. In this blog, we are going to explore qualitative research, its types, analytical procedures, positive and negative aspects, and examples.

Here we go!

What Is Qualitative Research?

Qualitative research is a branch of market research that involves collecting and analyzing qualitative data through open-ended communication. The primary purpose of conducting qualitative research is to understand the individual’s thoughts, feelings, opinions, and reasons behind these emotions.

It is used to gather in-depth and rich insights into a particular topic. Understanding how your audience feels about a specific subject helps make informed decisions in research.

As opposed to quantitative research, qualitative research does not deal with the collection of numerical data for statistical analysis. The application of this research method is usually found in humanities and social science subjects like sociology, history, anthropology, health science, education, etc.

Types of Qualitative Research Methods

Qualitative research methods are designed to understand the behavior and perception of the target audience about a particular subject.

Qualitative research methods include observations, one-on-one interviews, case study research, focus groups, ethnographic research, phenomenology, and grounded theory.

Let’s discuss them one by one.

1. Observations

Observation is one of the oldest qualitative methods of research used to collect systematic data using subjective methodologies. It is based on five primary sense organs – smell, sight, taste, touch, and hearing, and their functioning. This method focuses on characteristics and qualities rather than numbers.

The qualitative observation technique involves observing the interaction patterns in a particular situation. Researchers collect data by closely watching the behaviors of others. They rely on their ability to observe the target audience rather than communicating with people about their thoughts on a particular subject.

2. One-on-One Interviews

Conducting one-on-one interviews is one of the most common types of qualitative research methods. Although both open-ended and closed-ended questions can be a part of these interviews, open-ended conservation between researchers and participants related to a particular subject is still the preferred mode of communication. This is to gather in-depth qualitative data for the research purpose.

Here, the researcher asks pre-determined questions to the participants to collect specific information about their research topic. Interviews can be conducted face-to-face, by email, or by phone. The drawback of this method is that sometimes the participants feel uncomfortable sharing honest answers with the researcher.

3. Focus Groups

A Focus group involves collecting qualitative data by conducting a group discussion of 6-12 members along with a moderator related to a particular subject. Here the moderator asks respondents a set of predetermined questions so that they can interact with each other and form a group discussion. It helps researchers to collect rich qualitative data about their market research.

However, it is essential to ensure that the moderator asks open-ended questions like “how,” “what,” and “why” that will enable participants to share their thoughts and feelings.

Close-ended questions like “yes” and “no” should be avoided as they do not lead to engagement among participants.

4. Case Study Research

A case study is another example of qualitative research that involves a comprehensive examination of a particular subject, person, or event.

This method is used to obtain in-depth data and complete knowledge of the subject. The data is collected from various sources like interviews and observation to supplement the conclusion.

This qualitative approach is extensively used in the field of social sciences, law, business, and health. Many companies use this technique when marketing their products/services to new customers. It tells them how their business offerings can solve a particular problem. Let’s discuss an example of this method of qualitative research.

5. Digital Ethnography

This is an innovative form of qualitative research that focuses on understanding people and their cultures in the context of the digital realm. Digital ethnography aims to study individuals’ behavior, interactions, and social dynamics within online environments and digital communities.

In digital ethnography, the researcher acts as both an observer and a participant in these said online communities to gain firsthand insight into the lifestyles, cultures, and traditions of people navigating these digital landscapes.

Unlike traditional ethnography, digital ethnography is more efficient and accessible. The studies are conducted remotely, reducing the need for extended physical presence in a specific location, and the data collection process is often more streamlined.

6. Grounded Theory

This is another data collection method of qualitative research used across various disciplines. The Grounded Theory aims to provide the reasons, theories, and explanations behind an event. It focuses on why a course of action has happened the way it did.

The grounded theory model collects and analyzes the data to develop new theories about the subject. The data is collected using different techniques like observation, literature review, and document analysis.

This qualitative method is majorly used in business for conducting user satisfaction surveys to explain why a customer purchases a particular product or service. It helps companies in managing customer loyalty.

Watch: How to Create a Customer Satisfaction Survey

7. Phenomenology

Phenomenology is another qualitative research example that describes how an individual experiences or feels about a particular event. It also explores the experience of a specific event in a community.

Here, the researcher interviews people who have experienced a particular event to find similarities between their experiences. The researcher can also record what they learn from the target audience to maintain the credibility of the data.

Although this qualitative technique depends majorly on interviews, other data collection methods like observation, interviews, and survey questionnaires are also used to supplement the findings. The application of this method is found in psychology, philosophy, and education.

For example, to prompt a participant to share their experience around an event they encountered, you can ask:

“What was your experience like when you first encountered [a specific phenomenon or event]?”

Want to create a CSAT survey?

8. Record Keeping

This approach involves using existing trustworthy documents and other reliable sources as the basis of data for new research. It’s comparable to visiting a library, where you can explore books and reference materials to gather relevant data that might be helpful for your research.

How Do You Analyze Qualitative Data?

1. Arranging the Data

Qualitative data is collected in different forms like audio recordings, interviews, video transcriptions, etc. This step involves arranging all the collected data in the text format in the spreadsheet. This can be done either manually or with the help of data analysis tools.

2. Organizing the Data

Even after putting the data into a spreadsheet, the data is still messy and hard to read. Due to this, the data needs to be organized in a readable and understandable pattern.

For example, you can organize data based on questions asked. Organize your data in such a way that it appears visually clear. Data organization can be tedious, but it is essential for the next step.

3. Assigning Codes

Developing codes for the data helps simplify the data analysis methods in qualitative research. Assigning code implies categorizing and setting patterns and properties to the collected data. It helps in compressing the vast amount of information collected. By developing codes for your data, you can gather deep insight into the data to make informed business decisions.

4. Analyzing the Data

Qualitative data cannot be analyzed based on any universally accepted equation like quantitative data. Qualitative data analysis depends on the thinking and logical skill of the researcher.

However, there are a few techniques by which you can easily interpret data by identifying themes and patterns between sample responses:

• Checking the data for repetitive words and phrases commonly used by the audience in their answers.
• Comparing the primary and secondary data collection to find the difference between them.
• Scanning the data for expected information that has not been included in answers provided by respondents.

5. Summarizing the Data

The final stage is to link the qualitative data to the hypothesis. Highlight significant themes, patterns, and trends by using essential quotes from the data, as well as any possible contradictions.

One of the main things about qualitative data is that there isn’t a single, formal way to collect and analyze data. Each research project will have its own set of methods and techniques that it needs to use.

The key is to look at the specific needs of each project and change the research method accordingly.

Advantages and Limitations of Qualitative Research

Qualitative market research techniques offer a more comprehensive and complete picture of the subject than quantitative research, which focuses on specific and narrow areas. Other advantages of using qualitative research methods are:

• Explore the subject in-depth: Qualitative research is personal and offers a deep understanding of the respondent’s feelings, thoughts, and actions so that the researcher can perform an in-depth analysis of the subject.
• Promotes discussion: Qualitative research methods are open-ended in approach rather than rigorously following a predetermined set of questions. It adds context to the research rather than just numbers.
• More flexibility: The interviewer can study and ask questions on the subject they feel is pertinent or had not previously thought about during the discussions. Moreover, open-ended questions enable respondents to be free to share their thoughts, leading to more information.
• Capture trends as they change: Qualitative research can track how people’s feelings and attitudes change over time. Respondents’ opinions can change during the conversation, and qualitative research can show this.

With that being said, however, we do not mean that qualitative data is entirely devoid of flaws. Like most things, it, too, has its fair share of limitations, the prime among them being:

• Subjectivity: Qualitative data can be influenced by the researcher’s bias or interpretation, potentially affecting the objectivity of the findings. The absence of strict guidelines in qualitative research can lead to variations in data collection and analysis too.
• Time-Consuming & Resource-Intensive: Conducting qualitative research can be a lengthy process, from data collection through transcription and analysis. It also often requires skilled researchers, making it more resource-intensive compared to some quantitative methods.
• Difficulty in Analysis: Analyzing qualitative data can be complex, as it involves coding, categorizing, and interpreting open-ended responses. This data category often does not lend itself well to traditional statistical tests, limiting the depth of statistical analysis as well.
• Challenges in Replication: Replicating qualitative studies can be challenging due to the unique context and interactions involved.

Advantages of Using Website Surveys for Qualitative Research

The role of surveys and questionnaires in collecting quantitative data is pretty obvious, but how exactly would you use them to capture qualitative data, and why? Well, for starters, website surveys offer numerous advantages here, such as letting researchers explore diverse perspectives, collect rich and detailed data, conduct cost-effective and time-efficient studies, etc.

Let’s have a brief rundown of the significant benefits below:

Reach and Diversity: Website surveys enable researchers to engage with a diverse and global audience. They break geographical barriers, allowing participation from individuals residing in different regions, cultures, and backgrounds, leading to a richer pool of perspectives.

• Cost-Effectiveness: Conducting traditional face-to-face qualitative research can be expensive and time-consuming. In contrast, website surveys are cost-effective, as they eliminate the need for travel, venue rentals, and other logistical expenses.
• Convenience and Flexibility: Website surveys offer unparalleled convenience to both researchers and participants. Respondents can take part in the study at their own pace and preferred time, promoting higher response rates and reducing non-response bias.
• Anonymity and Honesty: Participants often feel more comfortable expressing themselves honestly in online surveys. Anonymity ensures confidentiality, encouraging candid responses, and allowing researchers to gain deeper insights into personal experiences and opinions.
• Rich Data Collection: Website surveys can accommodate various question types, including open-ended questions, allowing respondents to elaborate on their thoughts. This results in the collection of rich, detailed, and nuanced data, enriching the qualitative analysis.
• Time-Efficient Data Collection: Website surveys facilitate efficient data collection, reaching a large number of participants in a short span. Researchers can access real-time data, enabling quick analysis and timely decision-making.
• Ease of Analysis: Online survey platforms often provide tools for automated data analysis, simplifying the coding and categorization process. Researchers can swiftly identify themes and patterns, expediting the interpretation of qualitative findings.
• Longitudinal Studies: Website surveys are well-suited for longitudinal studies, as they allow researchers to follow up with the same participants over an extended period. This longitudinal approach enables the exploration of changes in attitudes or behaviors over time.
• Integration with Multimedia: Website surveys can seamlessly incorporate multimedia elements, such as images, videos, or audio clips, enabling respondents to provide more context and depth to their responses.
• Eco-Friendly Approach: By reducing the need for paper and physical materials, website surveys promote a sustainable and eco-friendly approach to data collection, aligning with responsible research practices.

Most website survey tools are equipped with features that efficiently collect and analyze diverse perspectives, ultimately furthering your data collection process. For example:

• Question Customization: These tools allow users to create and customize a wide range of questions, including open-ended, closed-ended, rating scale, and more. This flexibility allows participants to express their thoughts and feelings in their own words, paving the way for gathering diverse qualitative data.
• Anonymity and Confidentiality: Ensuring confidentiality in qualitative research is crucial for building trust and obtaining more accurate and sensitive data. Participants can often remain anonymous when using website survey tools, which can encourage them to provide honest and candid responses.
• Data Analysis Support: Many website survey tools offer built-in data analysis features, such as basic statistical summaries and visualizations. While these features are more suited for quantitative data, they can still aid in organizing and understanding qualitative responses, making the analysis process more manageable.
• Flexibility in Survey Design: Researchers can use skip logic and branching features in these tools to create dynamic surveys that adapt based on participants’ responses. This can be greatly valuable in qualitative research, where participants’ experiences might vary widely.
• Ease of Participation: Participants can access website surveys using various devices like computers, tablets, or smartphones, making it convenient and accessible for them to take part in the research. This ease of participation can contribute to a higher response rate and a more diverse participant pool.
• Data Storage and Security: Many website survey tools offer secure data storage and backup, ensuring the safety of the collected qualitative data. This feature is essential for maintaining the confidentiality and integrity of participants’ responses.

Examples of Website Survey Questions for Qualitative Research

These examples can greatly help in targeting customers through Click-to-WhatsApp Ads on various social media platforms. Crafting effective survey questions is crucial for qualitative research. Ensuring clarity, avoiding leading questions, and maintaining a balanced mix of question types is paramount if you are looking to gather comprehensive and valuable qualitative data.

With well-designed website survey questions, you can delve deep into participants’ thoughts, emotions, and experiences, providing a solid foundation for insightful qualitative analysis.

Let’s explore some of the prime examples:

1. Open-Ended Questions (Exploratory):

• “Please describe your experience with our product/service in your own words.”
• “What are the main challenges you face in your daily work?”

2. Multiple-Choice Questions (Categorization):

“Which age group do you belong to?”

• 18-25 years
• 26-35 years
• 36-45 years
• 46-55 years

3. Likert Scale Questions (Rating/Opinion): “On a scale of 1 to 5, how satisfied are you with our customer support?” 1 (Not satisfied at all) 2 (Slightly satisfied) 3 (Moderately satisfied) 4 (Very satisfied) 5 (Extremely satisfied)

4. Ranking Questions (Preference):

“Please rank the following factors in order of importance for choosing a smartphone:”

• Battery life
• Camera quality
• Processor speed
• Display resolution

5. Semantic Differential Questions (Contrast): “How would you describe our website’s user interface?”

• Difficult _ Easy Unattractive Attractive
• Confusing ___ Clear

6. Picture Choice Questions (Visual Feedback):

“Which logo do you find more appealing for our brand?”

• Option A (Image)
• Option B (Image)

7. Demographic Questions (Participant Profiling):

“Which of the following best describes your occupation?”

• Professional

8. Dichotomous Questions (Yes/No):

• “Have you ever purchased products from our online store?”

9. Follow-Up Probing Questions (In-depth Insight):

• “You mentioned facing challenges at work. Could you please elaborate on the specific challenges you encounter?”

10. Experience-Based Questions (Narrative):

• “Tell us about a memorable customer service experience you’ve had, whether positive or negative.”

Ready to Obtain Quality Data Using Qualitative Research?

So, there you have it all about qualitative research methods: their types, examples, use, and importance. Quantitative research is one of the most effective instruments to understand individuals’ thoughts and feelings or identify their needs and problems.

After figuring out the problem, quantitative research is used to make the conclusion and offer a reliable solution for business.

You can also supplement your qualitative market research with ProProfs Survey Maker to reach your target audience more effectively and in a shorter duration. Use the 15-day free trial to enhance your qualitative research – no commitment, no credit card details!

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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When are surveys qualitative or quantitative research: Learn the difference!

• December 9, 2019

Are surveys qualitative or quantitative methods of data collection?

What is a qualitative survey, what types of survey questions are analyzed qualitatively, benefits of using a qualitative survey, types of a survey questionnaire in qualitative research, what is a quantitative survey, benefits of using a quantitative survey, an example of a survey with quantitative data, qualitative versus quantitative survey question examples, when to use qualitative versus quantitative research.

If you are new to the world of creating surveys, you might have a few questions about what a survey is and the different types. At SurveyPlanet, we give you the tools you need to create any survey. We also want to help you understand how to create better surveys that serve your distinct purposes.

Interested in finding out when a survey is a part of qualitative versus quantitative research?

What is a survey and how to know if it is qualitative or quantitative?

A survey is a method of gathering information from a select sample of people. Responses can be used to gain insights and data that enable drawing conclusions about a subject. The sample size of a survey represents a larger population and there are two different types of research: qualitative and quantitative. The type of research determines which kind of questions to ask.

A survey can be qualitative or quantitative. If you create a questionnaire with answer options using a scale then it is quantitative. If you have questions that require detailed responses then it is qualitative. Mixed-method surveys involve both.

A qualitative survey collects data in order to describe a topic. In other words, the survey is more interested in learning about opinions, views, and impressions than numerical data. Qualitative surveys are less structured and offer insights into the way people think, their motivations, and attitudes toward a topic. Such surveys are more difficult to analyze but can supply much-needed depth to research. Qualitative surveys give answers to “why” and “how.”

Many of the most powerful surveys are qualitative. They collect data that enables an understanding of people’s attitudes, motivations, and experiences. Qualitative surveys provide a deeper level of insight into consumer behavior and preferences than quantitative surveys.

Qualitative research survey questions examples

Qualitative questions are a useful research method when the goal is describing certain phenomena rather than getting an exact answer. Therefore, instead of sitting down one-on-one with participants, survey questions have a short-answer box that respondents can use to express themselves. Qualitative research questions are open-ended and are useful for market research and other data collection purposes.

Read our “ How to Analyze Survey Data: Learn What to Do With Survey Responses? “ to optimize data collection and analysis.

For the most part, qualitative surveys are completely exploratory. Their main purpose is to understand the way a targeted group thinks—its opinions and attitudes about a particular topic. During the analysis phase, every word written by respondents can be analyzed to form a hypothesis.

Although this type of survey is great for learning more about personal opinions, it’s best suited for a small sample size. Conclusions aren’t necessarily representative of the targeted group, and instead only a small portion of it.

Despite small sample sizes, qualitative surveys are essential for identifying weak points in business operations. Once identified, create related questions to include in a quantitative survey, which often are not carried out without doing qualitative research beforehand.

Examples of qualitative research surveys

There are many different ways to use qualitative research, with qualitative questions often used in interviews that collect data from one person about one topic. If the plan is to send a qualitative research survey to employees about job satisfaction or company culture, interviewing a few employees first is a good start. This way, you have an idea of what topics to bring up as well as possible follow-up questions. Think of qualitative surveys as a way to gain insight that will help in the creation of a comprehensive quantitative survey down the line.

Another example of qualitative research is a case study, which is like interviews in that they collect data from one source and are primarily focused on opinions. If you want to use a case study as a marketing tool to attract more customers, conduct a one-on-one interview and ask participants a series of questions about your business that can be showcased on your website.

Expert opinions are another example of qualitative research in which an expert weighs in on a topic. Again, this is a way to gather insights from a single source about a specific topic.

Yet another example of qualitative research is focus groups, where A small sample size is asked for opinions on a certain subject. Focus groups allow the reactions of individuals to be gauged in a free-flowing setting. This is a great way to test a new product or marketing strategy.

You can also collect the same type of information by conducting a qualitative survey.

A quantitative survey collects facts and numbers from respondents. It’s most commonly used to prove or disprove a hypothesis after completing qualitative research. The analysis phase looks at the statistical data to draw conclusions, such as proving or disproving a specific hypothesis. Choosing the right type of survey to distribute depends on the ultimate goal of the research.

There are several benefits to using quantitative surveys to collect data. For one, they allow the testing and substantiation of conclusions previously developed. The analysis phase is usually straightforward since it involves looking at numbers. Short, written answers do not need to be analyzed. There are no opinions or detailed answers involved.

On the flip side, quantitative surveys aren’t always ideal because a large sample size is required to come to a credible conclusion. For example, a survey sent to 100 people to sample the operations of a business with millions of customers is not credible. It’s safe to say that the answers of 100 participants won’t represent the entire customer base. Read about analyzing survey data correctly here .

Quantitative research can be conducted by carrying out one of two types of surveys. The first is a cross-sectional survey, which gives multiple variables to analyze during a particular time period. It’s most common in the health care, retail, and small to medium-sized enterprise (SME) industries.

The other type is a longitudinal survey. This type of survey is commonly conducted over a certain amount of time, anywhere from days to years. The purpose is to observe changes in behavior or thought processes over time. For example, the buying habits of a teenager can be tracked through their adult years. This type of survey is ideal for long-term feedback on services or products, or when a certain sequence of events is important.

To sum up and better illustrate the theme, we prepared a table with quantitative and qualitative survey question examples that will aid in the writing of an excellent survey.

So, when do you use a qualitative survey as opposed to a quantitative survey? Use qualitative research when the main objective is to understand respondents’ motivations and opinions or gather insights with which to create a hypothesis. Use quantitative research to measure findings from qualitative research. The data gathered from quantitative research will usually allow for a conclusion to be drawn, while qualitative research only allows for the development of a hypothesis.

If you’re ready to test out either qualitative or quantitative research, it’s time to create a survey and get started. SurveyPlanet has many different themes and pre-made surveys to help. Sign up for a free account today! If you want to gather more insights and further expand your research, our Pro plan gives access to even more features (such as question branching).

Photo by John Schnobrich on Unsplash

surveys | August 27, 2020

The Guide to Qualitative Research: Methods, Types, and Examples

Daniel Ndukwu

Qualitative research is an important part of any project. It gives you insights that quantitative research can’t hope to match.

To receive the benefits that qualitative research can bring to the table, it’s essential to do it properly. That’s easier said than done.

This in-depth guide will give you a better understanding of qualitative research, how it can be used, the methods for carrying it out, and its limitations.

Table of Contents

What is qualitative research?

Qualitative research is the process of gathering non-numerical data that helps you understand the deeper meaning behind a topic. It can help you decipher the motivations, thought processes, and opinions of people who are experiencing the problem or situation.

For example, an entrepreneur wants to start a shoe brand targeted at a younger demographic. They know younger people spend more money on name-brand basketball shoes. Qualitative research will help them understand the motivations and thought processes behind why those shoes are appealing.

With the help of capable marketing teams and mentors , they can use this data to craft communication plans that will resonate with their audience.

The data gained helps develop better hypotheses, confirm or disprove theories, and informs quantitative research studies. There are multiple quantitative research methods that are ideal for certain situations and this guide delves deeper into those data collection processes .

Keep in mind that qualitative research gives you descriptive data that must then be analyzed and interpreted. This process is much more difficult than a quantitative analysis which is why many organizations opt to skip it entirely.

What’s the purpose of qualitative research?

Qualitative research was popularized by psychologists and sociologists who were unhappy with the scientific method in use.

In the legal industry, understanding qualitative insights can significantly enhance strategies for law firm SEO , helping firms to better align their services with client needs.

Traditional scientific methods were only able to tell what was happening but failed to understand why.

Qualitative research, on the other hand, seeks to find the deeper meaning behind actions and situations. For example, you may realize a relationship between two things exist like poverty and lower literacy rates. It’s qualitative data that can help you understand why this relationship exists.

In the diverse landscape of qualitative research its application extends beyond conventional fields offering valuable insights in specialized areas take for instance the legal sector where understanding nuanced human experiences is crucial a cerebral palsy lawyer leveraging qualitative research delves deeper into the multifaceted experiences of individuals and families impacted by cerebral palsy this methodical approach aids in comprehending the broader social emotional and economic ramifications thereby guiding more compassionate and effective legal representation.

When should qualitative research be used

There’s a simple stress test to understand whether qualitative research or quantitative research should be used. Ask yourself the following questions:

• Do you have a clear understanding of the problem? If not, use it;
• Do you understand the reasons that contribute to the problem or situation? If not, use it;
• Are the attitudes of the people who experience or display the behavior clear to you? If not, use it;
• Have you already analyzed first-person accounts or research related to the topic? If not, use it.

Qualitative research vs quantitative research

There’s a big difference between the two types of research. For the most part, qualitative research is exploratory. You’re trying to figure out the reasons behind situations and form a clearer hypothesis. Those hypotheses are then tested with further qualitative or quantitative research.

Quantitative research focuses on collecting numerical data that can be used to quantify the magnitude of a situation. The data gained can be organized and statistical analysis carried out.

For example, qualitative research may tell you that people in lower-income areas drop out of school and have lower literacy rates. Quantitative research can tell you the percentage of people that end up dropping out of school within a given population.

As you can see, they work together to give you a holistic understanding of a market or problem.

Qualitative research data collection Methods

We’ve written an in-depth guide about the data collection methods you can use for both quantitative and qualitative research. This section will give you a quick overview of the data collection methods available.

The first data collection method and the most common are surveys. More specifically, surveys with open-ended questions . These give your respondents the opportunity to explain things with their own words.

Another benefit of surveys, especially with online survey tools like KyLeads is that you can quickly distribute your survey to a huge audience. This can cut down on your costs while still giving you the insights you need.

There are two problems with surveys. The first one is that you’re unable to ask relevant clarifying questions. Some of the data you collect may be unclear and lead you to the wrong conclusions.

The second problem is that respondents, unless adequately incentivized, may abandon the survey or give inadequate answers. This is known as survey fatigue and is a challenge when you have longer surveys. You can mitigate the effects by placing the most important questions first.

Focus groups

A focus group involves 3 – 10 people and a specialized moderator. Groups larger than ten should be broken up and those fewer than three won’t be able to deliver the insights you need.

The benefits of a focus group come from the ability to recreate specific situations or test scenarios before they happen. To get the most out of the focus group, it’s important to carefully select the participants based on their demographic and psychographic profiles .

The advantage of a focus group is that the information is insightful and comes from multiple people within your target market. The disadvantage is that groupthink can be a real problem.

You can prevent groupthink by having people write their opinions down before voicing them and even assigning one person to play devil’s advocate. Don’t discourage divergent opinions or perspectives.

Another challenge is that focus groups are expensive compared to other methods listed here. The participants are usually paid for their time and it requires things like meeting space and specialized staff.

Interviews are an old staple of qualitative research and are almost as common as surveys. Interviews can be conducted over the phone, in person, or even through a video conference. The important part is that they’re real-time and you can ask clarifying questions so you don’t draw the wrong conclusions.

There are multiple types of interviews. You can use structured interviews, unstructured interviews, or semi-structured interviews. Keep in mind that the structured interview may not be the best option if you’re doing exploratory =research.

Observation/immersion

This is the process of observing the ongoing behavior of an individual or group. It’s most prevalent in social sciences and marketing applications. This data collection method is the most passive and may not be ideal when doing initial exploratory research. You may be drawing conclusions on incomplete information.

There is an option of participating actively in what you’re observing. Keep in mind that this is frowned upon because the researcher may accidentally introduce biases. The biggest disadvantage is that some things simply can’t be observed by a researcher without interaction.

Try to use team collaboration to cut down on the biases that will be introduced. Compare notes and, as much as possible, look at things objectively. A teammate is invaluable for this kind of exercise.

Pros and cons of qualitative research

Qualitative research is powerful and has many benefits but it also has multiple disadvantages you should be aware of before jumping in.

• Get a deep understanding of the behaviors and attitudes of your target group
• You can get those insights from smaller samples sizes
• As long as you choose the right aspects to focus on and groups to work with, the insights can have much wider applications.
• Helps reduce biases because you’re doing exploratory research to get a baseline of information
• Most qualitative research is fluid meaning it adapts to the inputs to get a better understanding of the overall situation
• The data itself is subjective because it’s based on the experiences and biases of the respondents
• It’s more expensive than quantitative research
• It can take much longer to go through the more involved data collection methods like focus groups and interviews
• It’s more difficult to analyze and often requires people with specialized skills
• It’s nonnumerical in nature so statistical analysis cannot be applied to the data
• Results can’t be easily replicated following the scientific method

Qualitative research can be a powerful tool in your arsenal but there are many things to take into consideration. It tends to take longer to collect the data and analyze it. It’s also more expensive than most quantitative research methods.

Before diving into a qualitative research strategy, define clear goals, a timeframe for completion, and the kind of information you need to solve your problem.

Let me know what you think in the comments and don’t forget to share.

what types of questionnaire appropriate to be use in qualitative research ?

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5 Qualitative Data Analysis Methods + When To Use Each

Qualitative data analysis is the work of organizing and interpreting descriptive data. Interview recordings, open-ended survey responses, and focus group observations all yield descriptive—qualitative—information. This is the opposite of quantitative data, which is all about numbers and statistics. 

Qualitative data can’t easily be cleaned, sliced, and diced like its numerical sister. So researchers use specific qualitative data analysis methods to understand the information they collect. 

The field of research recognizes five qualitative data analysis methods. We’ll quickly define each one. Then we’ll break down how to use them, when, and why.

#1. Content Analysis

Content analysis is when researchers categorize and organize words, concepts, patterns, and themes in their data. 

When You Should Use Content Analysis

Content analysis is useful for identifying trends and patterns in research. 

The trends could be literally anything. Market researchers could look at a large sample of contemporary ad campaigns to spot trends in how businesses use emotions to appeal to their customers.

Social researchers could study mommy blog posts and Instagram momfluencers from 2010 to 2020 to identify patterns about how motherhood changed in the span of a decade.

You get the idea. 

Content analysis hasis flexible, and to better understand it, get to know the two subtypes of content analysis: conceptual analysis and relational analysis. 

First, let’s look at conceptual analysis. 

When people talk about content analysis, they often mean conceptual analysis. This method involves picking a concept and then counting how often it appears in your data. The basic goal is to see how frequently certain terms come up. In general, conceptual analysis requires you to do three things: 

• Identify your question: Figure out what you want to learn from your research and choose your samples accordingly.
• Determine your level of analysis: Decide whether you want to analyze words, phrases, sentences, or themes.
• Code the text: Break the text down into manageable categories. In other words, figure out which specific words or patterns are relevant to your research question, and quantify them. This process, called coding, is made much easier with qualitative research software like QDA Miner and ATLAS.ti .

Relational analysis takes conceptual analysis a step further. It involves identifying patterns and focusing on the relationships between them, rather than studying the existence of the patterns themselves. 

There are three subtypes of relational analysis: 

• Affect extraction: Identifying and analyzing the emotions expressed within your qualitative data.
• Proximity analysis: Looking at how often and where words, phrases, or ideas appear close to each other in a text.
• Cognitive mapping: Using charts, graphs, or other visualization tools to explore the relationships between specific themes and trends. 

When To Use Content Analysis 

How do you know when to use cognitive vs. relational concept analysis?

It’s simple. 

• Use conceptual analysis when you want to quantify the presence and frequency of a specific concept. 
• Use relational analysis when you want to explore the relationships between concepts in a set of qualitative data. 

Say you run a survey to ask a group of dog owners whether they let their furry friends sleep in their beds with them. Conceptual content analysis can reveal how many times certain words are used, like “always,” “never,” “cuddle,” and “scratch.” It can also show you trends in the attitudes pup parents have about bedsharing (or not) with their furbabies. 

Relational analysis can help you explore themes among people who responded “yes” and people who said “no.” This, in turn, can help you understand the reasons behind the answers. 

#2. Narrative Analysis

Narrative analysis involves collecting stories or accounts and looking for underlying themes.  

When You Should Use Narrative Analysis

Use narrative analysis when you need to understand the stories behind client or customer experiences.

If your goal is to improve your customer journey, for instance, narrative analysis can reveal what your customers think about the journey as it is right now. What they love about it—and what they don’t.

The content you need for narrative analysis might already be out there. Do your customers or clients give reviews? Those count as narratives that can be analyzed.

But you can also be intentional and methodical about collecting the qualitative data you need for these reviews. Consider hosting one-on-one interviews with a small group of customers. Or running a focus group. Even surveys with space for long-form responses provide narratives you can dig through.

An Example of Narrative Analysis

Imagine a mid-sized retail company, Fashion ABC, is facing a decline in repeat customers. This is despite offering high-quality products—and even getting top influencers to be brand ambassadors. For some reason, though, Fashion ABC just can’t hang onto as many customers as they’d like to.

To get to the bottom of this, they decide to use narrative analysis on customer feedback collected over the past year.

Here’s how Fashion ABC runs the process, step by step.

• Gather stories : Fashion ABC collects customer stories from reviews, social media comments, and customer service call transcripts.
• Find the narratives : The team looks for personal stories within the data. They note the positive stories but focus more intensely on any narrative with a negative emotion, like anger or frustration.
• Identify themes : Next, Fashion ABC categorizes these stories to spot common themes, like “return process,” “customer service,” “product quality,” and “shopping experience.”
• Analyze patterns : In the “return process” category, the Fashion ABC team notices a trend: many customers are unhappy with the confusing return policies and especially the slow refunds.
• Draw insights : It becomes clear that although customers love the products, the complicated return process is a major pain point. Even returning something as simple as a too-big shirt or pair of pants is a major hassle. This issue shows up consistently across different feedback sources.
• Take action : Fashion ABC decides to simplify their return policy. They make it clearer and look to other companies, like Target and Costco, to figure out how to quickly refund customers. They roll out the new, easy-to-understand return policy on their website and streamline the return process.
• Check results : The narrative analysis isn’t done yet. After a few months, the Fashion ABC team gathers new customer feedback. Their goal? To see if the changes have made a positive impact on customer satisfaction and loyalty.

If the changes work, the team knows that the inefficient return process really was the culprit. If not, they can go back to square one and keep digging. 

#3. Discourse Analysis

Discourse analysis looks at how people structure and express language within a cultural context.

When You Should Use Discourse Analysis 

Discourse analysis is a great tool to use when you want to understand how language shapes and reflects us. It’s ideal for examining how different groups or institutions communicate—and helps measure the impact of language choices. 

Take political speeches, for example. Politicians hire speechwriters for a reason. They’re looking for people who can carefully use words to influence public opinion and persuade people to a specific point of view. (Hot tip: If you’ve never done discourse analysis before, these speeches are a great place to do some practice analysis.)

Like any type of concept analysis, though, discourse analysis is helpful in just about every field. 

In education, it can highlight how the language teachers use affects learning. In healthcare, it can help researchers understand how language between doctors and patients impacts the quality of care. 

It’s also really useful in media studies. You can use it to look at how news outlets frame specific stories, which can reveal hidden biases. All it takes is looking at one news story from the point of view of five different publications to see how the language used in each one might influence the reader to a specific viewpoint.

In short, discourse analysis doesn’t just help you understand what is being said. It also helps you understand why it’s being said the way it is. 

An Example of Discourse Analysis

Let’s pretend we’re analyzing an ad for a new herbal tea. Here’s the general process we’d take for discourse analysis.

First, we’d choose a specific ad promoting a brand of herbal tea.

Then we’d look for recurring themes within the images. Does the ad emphasize relaxation? Health benefits? Luxury? All three?

Let’s look at the image below, which shows a billboard for Pukka tea. Specifically, for a tea that’s meant to help people get ready for a good night’s sleep. 

Let’s look at what words and phrases the company uses to communicate these themes. 

There’s not a lot on the billboard, but what’s there is powerful: “Unwind with Nature.” Pukka’s signature twisty vines and flowers bookend the corners of the billboard, and the purple hues and steam rising from the cup help complete the ad. 

The language here is telling us that with Pukka tea, we can relax and look forward to a good night’s sleep. It also suggests that by drinking Pukka tea, we’re connected to nature—even in the middle of a city.

#4. Thematic Analysis

Thematic analysis involves looking at a set of qualitative data, like interview transcripts or survey responses, and extracting meanings and themes from it. 

When To Use Thematic Analysis

Thematic analysis is helpful for finding ideas—aka themes—in qualitative data. You can use it to analyze things like interview transcripts, open-ended survey responses, focus group discussions, and just about any type of qualitative data you collect or source from elsewhere.

And with today’s AI-powered tools like NVivo and ATLAS.ti , thematic analysis is easier than ever. (More on how to use AI for content analysis in a moment.)

Thematic analysis is especially useful early on in your research, especially when you’re trying to generate new ideas. Or when you’re exploring a topic without any specific hypotheses in mind.

For example, if you’re in healthcare research, you might use thematic analysis to understand how patients feel about a new treatment. In education, it can help you explore what teachers think about a new curriculum. In the business world, it’s useful for analyzing customer feedback to spot common complaints.

An Example of Thematic Analysis

Let’s say we’ve decided to use thematic analysis to analyze customer feedback for our new coffee shop. To begin, we collect and read through 50 customer reviews. Our goal is to look for recurring words or phrases that point to specific ideas—things like “friendly staff,” “cozy atmosphere,” and “long wait times.”

Next, we’ll group these elements into broader themes. “Friendly staff” and “cozy atmosphere” go in a “Positive Experiences” category. Points like “pricey menu” and “long wait times” go under a “Needs Work” category.

From there, we can summarize our findings and use them to make improvements to our new coffee shop. 

#5. Grounded Theory 

Grounded theory is a way of trying to understand the meanings of peoples’ actions—based on their own interpretations of those actions. 

When You Should Use Grounded Theory

You should use grounded theory when you’re trying to use your data to develop a theory, rather than the other way around. Grounded theory is particularly useful when existing theories about something don’t really fit, and you’re looking for a different angle or answer.

For instance, let’s say you’re studying how people adapt to remote work in a field like telehealth. With grounded theory, you can use participants’ actual experiences and interactions to generate theories and better understand the topic of your research. 

So why is it called grounded theory, anyway? 

The answer lies in this definition of grounded theory, which comes to us from a 2021 article titled, “Grounded theory: what makes a grounded theory study?” in the European Journal of Cardiovascular Nursing : “The focus of [grounded theory] is to generate theory that is grounded in data and shaped by the views of participants.”

In other words, it’s a qualitative research method that invites theory to originate from the ground up, instead of the other way around. 

An Example of Grounded Theory 

Imagine you want to develop software to help digital nomads keep track of their working hours and block distractions. (For reference, digital nomads are people who work remotely using technology and travel as they work.)

There’s not much existing research on how these folks manage their work-life balance or connect with other people. It must be hard, since they’re always on the go. How do they do it? What does it mean for their work-life balance? You want to use grounded theory to learn more about this growing segment of people so your product can support their needs.

To collect data, you start by surveying a variety of self-proclaimed digital nomads. You also spend time observing online forums like Reddit to get more of an insider perspective.

As you dig into the survey responses and your observation notes, you start the coding process. To do this, you identify concepts like “flexible work hours,” “isolation,” “community support,” and “travel logistics.”

Next, you connect these concepts into broader categories. For instance, “flexible work hours” and “travel logistics” might merge into a category called “lifestyle management,” while “isolation” and “community support” could come together under “social dynamics.”

Finally, you refine these categories into a new, core theory. You might come up with the idea that digital nomads thrive by balancing autonomy and flexible scheduling with community support. This theory highlights how digital nomads create routines that help them manage work and travel. At the same time, they rely on online and physical communities to fill their social cup. 

Your grounded theory suggests that the sustainability of the digital nomad lifestyle hinges on balancing personal freedom, structured but flexible work hours, and social connectedness.

At the end of this journey, you’ve decided to build software that provides digital nomads with a one-stop shop for logging work hours, blocking distractions, and connecting with other nomads. 

How to Use AI With Qualitative Data Analysis

AI is a hot-button topic, but there’s no question it can help with qualitative analysis. It can’t—and shouldn’t—do all the work for you, though. 

Here’s a quick round-up of Do’s and Don’ts when it comes to using AI in qualitative analysis.

• Use AI for data cleaning : AI tools can do the mindless tasks humans take forever to do, like transcribing interviews, removing duplicates, and organizing qualitative data.
• Use AI for initial coding : AI can also do a great job helping you with the coding process. How? By identifying frequent terms in the text. Some tools can help you organize those terms into broader theme groups, too.
• Use AI for visualizing data: AI can help you create graphs and other visual representations of data, which makes it easier to digest and study.

Don’t:

• Use AI for anything else. No, really. AI is not human, and it can’t make interpretations, draw conclusions, or basically do any of the heavy thinking necessary in qualitative analysis. It’s great for eliminating busywork, but you’ve got to use your own brain and experience as a human to do all the rest. 

Tools like ATLAS.ti, Nvivo, and Tableau can help you with the AI-friendly parts of qualitative research. Trust your experience as a human being to help you with the rest.

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Qualitative Survey Questions with Some Examples

Understanding your customers’ experiences and perceptions is crucial for enhancing your products and services. While quantitative research provides numerical insights, qualitative survey questions dive deeper, exploring the “why” behind customer behaviors.

This article answers what qualitative research questions focus on, their applications, types, and the advantages and disadvantages of using them.

Qualitative vs. Quantitative Questions

Surveys can include both qualitative and quantitative questions. Quantitative questions yield numerical data, easily measured and statistically analyzed, like “How many times have you used our app this week?” or “On a scale of 1-10, how satisfied are you with our service?” This data is invaluable for spotting trends, measuring performance, and making data-driven decisions.

Qualitative research questions, however, seek to understand the underlying reasons, opinions, and motivations behind customer actions. They often start with “why,” “how,” or “what.” For instance, “What features do you find most useful in our app?” or “Can you describe a challenge you faced while using our service?”

Our Tip: A Balanced Approach

These qualitative survey questions examples provide richer, more detailed data, invaluable for customer experience (CX) professionals aiming to boost user satisfaction and loyalty. They uncover insights that quantitative data alone can’t, such as emotional responses and personal stories, revealing deeper customer needs and preferences.

A balanced approach, blending qualitative and quantitative research, typically yields the best results. Quantitative data highlights areas needing attention, while qualitative data explains why these issues exist and suggests potential solutions. This combination offers a comprehensive understanding of customer experiences, guiding more effective improvements and innovations.

When Should We Use Qualitative Research Questions?

Qualitative research questions are especially useful in various scenarios. By asking these “why” questions, you uncover insights that shape strategies, conduct market research, and enhance customer experiences.

Exploring Experiences:

Understand how customers interact with your product or service. For instance, “Can you describe your overall experience with our customer support ?” This type of question helps you focus on uncovering the nuances of customer interactions, highlighting what works well and what needs improvement.

Investigating Processes:

Gain insights into how customers use your product. For example, “Can you walk us through how you typically use our app?” This helps identify pain points in the user journey and opportunities to streamline processes.

Addressing Sensitive Topics:

Delve into issues that might be uncomfortable or nuanced, such as “How do you feel about the privacy features of our app?” A careful approach ensures respondents feel safe and respected while sharing their thoughts.

Understanding Change:

Comprehend shifts in customer behavior or preferences, like “Why did you decide to upgrade to our premium plan?” Understanding these reasons guides strategic decisions and product development.

Uncovering Motivations:

Discover what drives customer decisions. For instance, “What motivated you to choose our product over competitors?” This helps understand the unique value propositions that attract customers to your brand.

Identifying Expectations:

Determine what customers expect from your service or product. For example, “What features would you like to see in future updates?” Knowing these expectations helps prioritize developments to enhance satisfaction and loyalty.

Evaluating Impact:

Assess the impact of changes or new features. For instance, “How has the new dashboard improved your workflow?” This provides direct feedback on recent updates, helping measure their effectiveness.

Try Ready-To-Use CX Surveys

Patient Satisfaction Survey

Net Promoter® Score (NPS) Survey

Hotel Survey

Service quality survey

Customer Needs Survey

Product Feedback Survey (NPS)

Types of Qualitative Research Questions – Examples

There are various types of qualitative research questions, each serving a distinct purpose. Here are some examples focused on customer experience and marketing:

Descriptive

Descriptive questions gather detailed information about specific aspects of your product or service, helping understand what customers notice and appreciate.

• “What specific design elements stand out in our app?”
• “What features do you find most helpful on our website?”
• “Which elements of our service do you value most, and why?”

These effective qualitative research questions uncover details often missed in broader surveys. Customers might highlight a feature’s simplicity or the aesthetic appeal of your app’s interface, offering actionable insights for your design team.

Predictive questions explore potential future behaviors or outcomes, helping anticipate customer needs and preferences for proactive improvements.

• “If we had a project management tool integration for our app, how often would you use it?”
• “How likely are you to recommend our service to a friend after using our new feature?”
• “What impact do you think adding live chat support would have on your overall satisfaction?”

Predictive questions gauge the potential success of new features or changes before implementation, allowing adjustments based on customer feedback and reducing the risk of investing in unwanted developments.

Experiential

Experiential questions focus on understanding customers’ personal experiences and emotions, providing insights into their journey and emotional responses at various touchpoints.

• “How would you describe your first impressions of our application?”
• “Can you share a time when our customer service exceeded your expectations?”
• “What emotions do you associate with using our product, and why?”

These questions help qualitative research methods identify emotional factors influencing customer satisfaction and loyalty, amplifying positive experiences and addressing negative ones to enhance overall perception.

Compar a tive

Comparative questions draw comparisons between different products, services, or experiences, helping understand your competitive position and identify areas for improvement.

• “Was the pricing clear and easy to understand compared to our competitors?”
• “How does our product compare to others you’ve used in terms of ease of use?”
• “In what ways do you think our service stands out from competitors?”

Comparative questions reveal strengths and weaknesses relative to competitors, guiding strategies to enhance unique selling points and address gaps in your offerings.

Process-oriented

Process-oriented questions explore the steps customers take when interacting with your product or service, identifying barriers and opportunities to optimize the customer journey.

• “What are your next steps when you encounter an issue with our product?”
• “How do you typically find information on our website?”
• “What process do you follow to decide to make a purchase on our platform?”

Each qualitative research question, together with its statistical analysis, provide insights into practical aspects of customer interactions, highlighting areas to streamline and make the UX more intuitive.

Advantages and Disadvantages of Using Qualitative Questions in Surveys

Qualitative methods in conducting online research have their strengths and weaknesses. Let’s take a brief look at them.

In-Depth Data Gathering:

Qualitative questions provide detailed insights into customer thoughts and feelings, helping understand the “why” behind their actions. This depth leads to more targeted and effective improvements.

Encouraging Customers to Speak Their Minds:

These questions invite open-ended responses, letting customers express their opinions and experiences in their own words. This uncovers insights that structured questions might miss, capturing the full range of customer sentiments.

Participant Engagement:

Qualitative questions make surveys more engaging, encouraging participants to spend more time providing thoughtful answers. Engaged participants offer richer data, leading to more valuable insights.

Flexibility in Responses:

Unlike quantitative questions, which limit answers to predefined options, qualitative questions let respondents answer in their own words. This flexibility reveals unexpected insights and nuances.

Contextual Understanding:

Qualitative responses include context that quantitative data lacks, providing a fuller picture of customer experiences and perceptions. This context is crucial for accurately interpreting feedback and making informed decisions.

Disadvantages

Sample Bias:

The open-ended nature of qualitative questions may attract responses from customers with strong opinions, potentially skewing the data. Ensure a diverse range of participants to mitigate this bias.

Privacy Issues:

Collecting detailed personal information can raise privacy concerns, requiring careful data handling to ensure confidentiality. Robust data protection measures are essential to maintain customer trust.

Time-Consuming Analysis:

Analyzing qualitative data is time-consuming, requiring a nuanced approach compared to quantitative data. This often involves coding responses, identifying themes, and interpreting meanings, which can be labor-intensive.

Subjectivity in Interpretation:

Qualitative data is inherently subjective, both in how respondents articulate their answers and in how researchers interpret them. Ensuring consistent, unbiased interpretation requires careful methodological rigor.

Limited Generalizability:

Qualitative data is detailed and specific, making it harder to generalize findings across a larger population. While valuable, these insights often need to be complemented with quantitative research to provide a broader perspective.

Good Qualitative Research Questions – Sum Up

Incorporating qualitative surveys into your research can unlock valuable insights that quantitative data alone can’t. By crafting and strategically using these questions, CX specialists can gain a deeper understanding of customer experiences, motivations, and preferences. Though challenges exist with qualitative data, its rich, detailed feedback is instrumental in shaping product and service strategies, enhancing customer satisfaction and loyalty.

Conducting qualitative research explore the nuanced aspects of CX in focus groups, laying the foundation for meaningful improvements. Whether you’re understanding customer behavior, addressing sensitive issues, or evaluating changes’ impact, qualitative questions offer the depth needed for informed decision-making. By balancing these with quantitative measures, you get a comprehensive view of your customers, enabling you to create more personalized, responsive, and effective CX strategies.

Tool For Quantitative and Qualitative Questions

As you choose qualitative research questions for your surveys, consider your research objective and your target audience’s unique contexts. Tailor questions to elicit detailed, thoughtful responses guiding your efforts to boost customer satisfaction and loyalty. Remember, the ultimate aim is to understand and meet your customers’ needs more effectively, fostering stronger relationships and driving long-term business success.

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Author: Dariusz Jaroń

Updated: 20 June 2024

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Diverse Methods of Marketing Research and

Their applications: a guide to business success.

In the previous article , we discussed the purpose and importance of marketing research in detail. By capturing market voices accurately and understanding consumer needs, you can build a foundation that supports your business growth. Marketing research is a powerful tool that underpins this process.

What marketing research methods are currently available? And how are they useful in different business scenarios? Marketing research can be broadly categorized into two main types: “qualitative research” and “quantitative research.” Each of these methods has its own strengths and applicable scenarios. Additionally, desk research is effective as a supplementary information-gathering method.

In this article, we will introduce the characteristics and benefits of each type of marketing research and specific applicable scenarios. This will help you choose the most suitable research method for your business challenges and develop more effective strategies.

Types of Marketing Research

Marketing research can be categorized primarily into two main types: "qualitative research" and "quantitative research." Each research method has unique characteristics and advantages, and it is crucial to use them appropriately, depending on the situation. The primary data obtained from "qualitative research" and "quantitative research" requires manual data collection, which can be time-consuming and costly. However, this effort can yield new unique information that only some know, allowing you to acquire valuable yet widely unavailable data.

In marketing research, it is efficient to review secondary data through desk research first and then collect any missing information as primary data.

Quantitative Research

Quantitative research is a method designed to gather data in the form of numbers or objective indicators by having respondents choose from predefined options. This method enables extensive data collection and works well when collecting objective facts. Below are some commonly known methods of quantitative research:

Internet Research : This method involves collecting data by having eligible respondents complete surveys online. It is cost-effective, requires less effort, and can be conducted in short-term research, making it the mainstream method for quantitative research today.

Mystery Shopping : This involves sending researchers disguised as customers to stores or other service locations to evaluate service quality, staff behavior, and other aspects. It offers the advantage of capturing actual service conditions.

Face-to-Face Interviews : This method involves researchers visiting respondents at their homes or workplaces to conduct interviews. The advantage of this approach is that it allows for the presentation and discussion of actual products or advertisements during the interview.

Mail Surveys : This involves sending questionnaires to respondents by mail, which they complete and return. It is beneficial for reaching older generations who may not use the internet.

Automated Telephone Surveys : This method uses an automated voice response system to conduct surveys. Respondents follow voice prompts and press keys to provide answers. It allows for rapid data collection without human intervention.

• Omnibus Surveys : This method involves multiple companies adding their questions to a shared survey, thus distributing the data collection costs between them. Each company receives responses to its specific questions only. It is cost-effective and allows for quick data collection.

Qualitative Research

Qualitative research is a method where respondents are allowed to express themselves freely, and their words are the data themselves. This approach works well when you want to understand consumers' emotions, opinions, and reasons for their behavior or when you want to explore complex issues that are difficult to quantify or the motivations behind consumer actions. Below are some representative methods of qualitative research.

Focus Groups : This method involves conducting a discussion session where participants talk about a specific theme, and opinions and ideas are collected. It typically involves 6 to 8 participants in a group with a moderator facilitating the discussion.

Depth Interviews : This method involves a one-on-one interview between the respondent and the interviewer. It allows them to dig into topics that may be difficult to discuss in front of a larger group.

Observational Research : This anthropological method involves observing the subjects' natural behavior and activities in their environment. Cameras and recording devices are set up in sales areas to conduct interviews while watching the footage or documenting and analyzing observed behaviors.

Specific Application Scenarios and Examples

Marketing research plays a crucial role in a wide range of business scenarios. Below are some specific application scenarios and examples.

ーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーー

Automotive Industry Marketing Research Case Study

• Research Objective: Measure the PR effectiveness of a taxi dispatch app
• Research Type: Quantitative research (20 screening questions + 5 main survey questions) with monthly fixed-point surveys
• Research Target: Men and women aged 20 and over. 2,000 responses for the main survey
• Research Period: 3 days

Screening questions are included in the questionnaire to group "taxi users" by area.

The survey examines first recall acquisition and usage of the dispatch app to investigate the effectiveness of PR activities by age and area. You can assess the promotional effects by comparing them with past results.

While some information cannot be conveyed fully through text alone in the questionnaire, including logo images, videos, and links to service sites ensures accurate data collection. ーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーー

IT Industry Marketing Research Case Study

• Research Objective: Brand image survey of a cloud service
• Research Type: Quantitative research (10 screening questions + 15 main survey questions)
• Research Target: Men and women aged 20 to 69, 1,000 responses for the main survey
• Research Period: 2 days

The survey aims to clarify the image of the product, brand, and company using a questionnaire, thereby understanding the accurate status of the company in the market. ーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーー

Toward Future Success

Net research is a highly effective method to deeply understand consumers and aid in product development and customer acquisition. GMO Research & AI supports strategic decision-making for your business challenges. For detailed information or consultations, please feel free to contact us .

Beginner's Guide to Successful Online Survey

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• Published: 05 August 2024

Climate change engagement of scientists

• Fabian Dablander   ORCID: orcid.org/0000-0003-2650-6491 1 , 2   na1 ,
• Maien S. M. Sachisthal   ORCID: orcid.org/0000-0002-9833-0723 3   na1 ,
• Viktoria Cologna   ORCID: orcid.org/0000-0003-3706-8669 4 , 5 ,
• Noel Strahm   ORCID: orcid.org/0000-0001-9837-8869 6 ,
• Anna Bosshard   ORCID: orcid.org/0000-0002-2095-1116 3 ,
• Nana-Maria Grüning   ORCID: orcid.org/0000-0002-1528-6625 7 ,
• Alison J. K. Green   ORCID: orcid.org/0000-0002-5216-0175 8 ,
• Cameron Brick   ORCID: orcid.org/0000-0002-7174-8193 3 , 9 ,
• Adam R. Aron 10 &
• Jonas M. B. Haslbeck   ORCID: orcid.org/0000-0001-9096-7837 11 , 12   na1  

Nature Climate Change ( 2024 ) Cite this article

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• Climate-change mitigation

Climate change is one of the biggest threats to humanity. Scientists are well positioned to help address it beyond conducting academic research, yet little is known about their wider engagement with the topic. We investigate scientists’ engagement with climate change using quantitative and qualitative analyses of a large-scale survey ( N  = 9,220) across 115 countries, all fields and all career stages. Many scientists already engage in individual lifestyle changes, but fewer engage in advocacy or activism. On the basis of our quantitative and qualitative results, we propose a two-step model of engagement to better understand why. Scientists must first overcome intellectual and practical barriers to be willing to engage, and then overcome additional barriers to actually engage. On the basis of this model, we provide concrete recommendations for increasing scientists’ engagement with climate change.

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Scientists’ identities shape engagement with environmental activism

Exposure to and learning from the IPCC special report on 1.5 °C global warming, and public support for climate protests and mitigation policies

Globally representative evidence on the actual and perceived support for climate action

Data availability.

We used the Scopus database 72 to identify scientists and academics who were subsequently invited to participate in the survey. The anonymized data used for analysis are available on Zenodo at https://doi.org/10.5281/zenodo.12187345 (ref. 74 ).

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Acknowledgements

This work was supported by NWO gravitation grant number 024.004.016 (to J.H.), Swiss National Science Foundation Postdoc Mobility Fellowship P500PS_202935 (to V.C.), the Harvard University Faculty Development Funds (to V.C.) and the Studienstiftung des deutschen Volkes (PhD scholarship) (to A.B.). We thank G. Gallo from New Environments and D. Mackey for help with Fig. 3 , and T. Fossen, K. Nielsen, A. Urai and C. van Eck for their helpful comments on an earlier version of this paper.

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These authors contributed equally: Fabian Dablander, Maien S. M. Sachisthal, Jonas M. B. Haslbeck.

Authors and Affiliations

Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands

Fabian Dablander

Institute for Advanced Study, University of Amsterdam, Amsterdam, the Netherlands

Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands

Maien S. M. Sachisthal, Anna Bosshard & Cameron Brick

Department of Communication and Media Research, University of Zürich, Zürich, Switzerland

Viktoria Cologna

Weather and Climate Risk Group, ETH Zürich, Zürich, Switzerland

Institute of Sociology, University of Bern, Bern, Switzerland

Noel Strahm

Institute of Biochemistry, Charite Universitätsmedizin Berlin, Berlin, Germany

Nana-Maria Grüning

Scientists’ Warning Foundation, Richmond, CA, USA

Alison J. K. Green

Department of Psychology, Inland Norway University of Applied Sciences, Elverum, Norway

Cameron Brick

Department of Psychology, University of California San Diego, La Jolla, CA, USA

Adam R. Aron

Department of Clinical Psychological Science, Maastricht University, Maastricht, the Netherlands

Jonas M. B. Haslbeck

Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands

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F.D. conceived the initial idea of the study and F.D., M.S.M.S., J.M.B.H., V.C., N.-M.G., C.B., A.J.K.G. and A.R.A. contributed to its conceptualization. F.D., M.S.M.S., J.M.B.H., N.S., N.M.G., A.B. and A.R.A. contributed to the formal analysis of the quantitative and/or qualitative data. F.D., M.S.M.S., J.M.B.H. and N.S. contributed to the methodology of the study, the data collection strategy, software used for the study administration and/or analysis. F.D., N.S., M.S.M.S., A.R.A. and A.B. contributed to the validation of the study results. F.D. and J.M.B.H. performed data visualization. F.D., M.S.M.S. and J.M.B.H. curated the data, administered the project and wrote the original draft. F.D., M.S.M.S., J.M.B.H., V.C., C.B., A.J.K.G. and A.R.A. reviewed and edited the original draft.

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Extended data fig. 1 number of observations per country in our data set..

Please note that Hong Kong (n = 32) is not included as a separate country on this map, but is modelled as a separate country in all other analyses that include country as a variable.

Extended Data Fig. 2 Distribution of Scopus and our sample on key variables.

The dashed lines indicate the median of the respective distributions.

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Dablander, F., Sachisthal, M.S.M., Cologna, V. et al. Climate change engagement of scientists. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-02091-2

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Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
• Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
• Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
• Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
• Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

• Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

• Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
• Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

• Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

Published: August 08, 2024

One of the most underrated skills you can have as a marketer is marketing research — which is great news for this unapologetic cyber sleuth.

From brand design and product development to buyer personas and competitive analysis, I’ve researched a number of initiatives in my decade-long marketing career.

And let me tell you: having the right marketing research methods in your toolbox is a must.

Market research is the secret to crafting a strategy that will truly help you accomplish your goals. The good news is there is no shortage of options.

How to Choose a Marketing Research Method

Thanks to the Internet, we have more marketing research (or market research) methods at our fingertips than ever, but they’re not all created equal. Let’s quickly go over how to choose the right one.

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1. Identify your objective.

What are you researching? Do you need to understand your audience better? How about your competition? Or maybe you want to know more about your customer’s feelings about a specific product.

Before starting your research, take some time to identify precisely what you’re looking for. This could be a goal you want to reach, a problem you need to solve, or a question you need to answer.

For example, an objective may be as foundational as understanding your ideal customer better to create new buyer personas for your marketing agency (pause for flashbacks to my former life).

Or if you’re an organic sode company, it could be trying to learn what flavors people are craving.

2. Determine what type of data and research you need.

Next, determine what data type will best answer the problems or questions you identified. There are primarily two types: qualitative and quantitative. (Sound familiar, right?)

• Qualitative Data is non-numerical information, like subjective characteristics, opinions, and feelings. It’s pretty open to interpretation and descriptive, but it’s also harder to measure. This type of data can be collected through interviews, observations, and open-ended questions.
• Quantitative Data , on the other hand, is numerical information, such as quantities, sizes, amounts, or percentages. It’s measurable and usually pretty hard to argue with, coming from a reputable source. It can be derived through surveys, experiments, or statistical analysis.

Understanding the differences between qualitative and quantitative data will help you pinpoint which research methods will yield the desired results.

For instance, thinking of our earlier examples, qualitative data would usually be best suited for buyer personas, while quantitative data is more useful for the soda flavors.

However, truth be told, the two really work together.

Qualitative conclusions are usually drawn from quantitative, numerical data. So, you’ll likely need both to get the complete picture of your subject.

For example, if your quantitative data says 70% of people are Team Black and only 30% are Team Green — Shout out to my fellow House of the Dragon fans — your qualitative data will say people support Black more than Green.

(As they should.)

Primary Research vs Secondary Research

You’ll also want to understand the difference between primary and secondary research.

Primary research involves collecting new, original data directly from the source (say, your target market). In other words, it’s information gathered first-hand that wasn’t found elsewhere.

Some examples include conducting experiments, surveys, interviews, observations, or focus groups.

Meanwhile, secondary research is the analysis and interpretation of existing data collected from others. Think of this like what we used to do for school projects: We would read a book, scour the internet, or pull insights from others to work from.

So, which is better?

Personally, I say any research is good research, but if you have the time and resources, primary research is hard to top. With it, you don’t have to worry about your source's credibility or how relevant it is to your specific objective.

You are in full control and best equipped to get the reliable information you need.

3. Put it all together.

Once you know your objective and what kind of data you want, you’re ready to select your marketing research method.

For instance, let’s say you’re a restaurant trying to see how attendees felt about the Speed Dating event you hosted last week.

You shouldn’t run a field experiment or download a third-party report on speed dating events; those would be useless to you. You need to conduct a survey that allows you to ask pointed questions about the event.

This would yield both qualitative and quantitative data you can use to improve and bring together more love birds next time around.

Best Market Research Methods for 2024

Now that you know what you’re looking for in a marketing research method, let’s dive into the best options.

Note: According to HubSpot’s 2024 State of Marketing report, understanding customers and their needs is one of the biggest challenges facing marketers today. The options we discuss are great consumer research methodologies , but they can also be used for other areas.

Primary Research

1. interviews.

Interviews are a form of primary research where you ask people specific questions about a topic or theme. They typically deliver qualitative information.

I’ve conducted many interviews for marketing purposes, but I’ve also done many for journalistic purposes, like this profile on comedian Zarna Garg . There’s no better way to gather candid, open-ended insights in my book, but that doesn’t mean they’re a cure-all.

What I like: Real-time conversations allow you to ask different questions if you’re not getting the information you need. They also push interviewees to respond quickly, which can result in more authentic answers.

What I dislike: They can be time-consuming and harder to measure (read: get quantitative data) unless you ask pointed yes or no questions.

Best for: Creating buyer personas or getting feedback on customer experience, a product, or content.

2. Focus Groups

Focus groups are similar to conducting interviews but on a larger scale.

In marketing and business, this typically means getting a small group together in a room (or Zoom), asking them questions about various topics you are researching. You record and/or observe their responses to then take action.

They are ideal for collecting long-form, open-ended feedback, and subjective opinions.

One well-known focus group you may remember was run by Domino’s Pizza in 2009 .

After poor ratings and dropping over $100 million in revenue, the brand conducted focus groups with real customers to learn where they could have done better.

It was met with comments like “worst excuse for pizza I’ve ever had” and “the crust tastes like cardboard.” But rather than running from the tough love, it took the hit and completely overhauled its recipes.

The team admitted their missteps and returned to the market with better food and a campaign detailing their “Pizza Turn Around.”

The result? The brand won a ton of praise for its willingness to take feedback, efforts to do right by its consumers, and clever campaign. But, most importantly, revenue for Domino’s rose by 14.3% over the previous year.

The brand continues to conduct focus groups and share real footage from them in its promotion:

What I like: Similar to interviewing, you can dig deeper and pivot as needed due to the real-time nature. They’re personal and detailed.

What I dislike: Once again, they can be time-consuming and make it difficult to get quantitative data. There is also a chance some participants may overshadow others.

Best for: Product research or development

Pro tip: Need help planning your focus group? Our free Market Research Kit includes a handy template to start organizing your thoughts in addition to a SWOT Analysis Template, Survey Template, Focus Group Template, Presentation Template, Five Forces Industry Analysis Template, and an instructional guide for all of them. Download yours here now.

3. Surveys or Polls

Surveys are a form of primary research where individuals are asked a collection of questions. It can take many different forms.

They could be in person, over the phone or video call, by email, via an online form, or even on social media. Questions can be also open-ended or closed to deliver qualitative or quantitative information.

A great example of a close-ended survey is HubSpot’s annual State of Marketing .

In the State of Marketing, HubSpot asks marketing professionals from around the world a series of multiple-choice questions to gather data on the state of the marketing industry and to identify trends.

The survey covers various topics related to marketing strategies, tactics, tools, and challenges that marketers face. It aims to provide benchmarks to help you make informed decisions about your marketing.

It also helps us understand where our customers’ heads are so we can better evolve our products to meet their needs.

Apple is no stranger to surveys, either.

In 2011, the tech giant launched Apple Customer Pulse , which it described as “an online community of Apple product users who provide input on a variety of subjects and issues concerning Apple.”

"For example, we did a large voluntary survey of email subscribers and top readers a few years back."

While these readers gave us a long list of topics, formats, or content types they wanted to see, they sometimes engaged more with content types they didn’t select or favor as much on the surveys when we ran follow-up ‘in the wild’ tests, like A/B testing.”  

Pepsi saw similar results when it ran its iconic field experiment, “The Pepsi Challenge” for the first time in 1975.

The beverage brand set up tables at malls, beaches, and other public locations and ran a blindfolded taste test. Shoppers were given two cups of soda, one containing Pepsi, the other Coca-Cola (Pepsi’s biggest competitor). They were then asked to taste both and report which they preferred.

People overwhelmingly preferred Pepsi, and the brand has repeated the experiment multiple times over the years to the same results.

What I like: It yields qualitative and quantitative data and can make for engaging marketing content, especially in the digital age.

What I dislike: It can be very time-consuming. And, if you’re not careful, there is a high risk for scientific error.

Best for: Product testing and competitive analysis

Pro tip:  " Don’t make critical business decisions off of just one data set," advises Pamela Bump. "Use the survey, competitive intelligence, external data, or even a focus group to give you one layer of ideas or a short-list for improvements or solutions to test. Then gather your own fresh data to test in an experiment or trial and better refine your data-backed strategy."

Secondary Research

8. public domain or third-party research.

While original data is always a plus, there are plenty of external resources you can access online and even at a library when you’re limited on time or resources.

Some reputable resources you can use include:

• Pew Research Center
• McKinley Global Institute
• Relevant Global or Government Organizations (i.e United Nations or NASA)

It’s also smart to turn to reputable organizations that are specific to your industry or field. For instance, if you’re a gardening or landscaping company, you may want to pull statistics from the Environmental Protection Agency (EPA).

If you’re a digital marketing agency, you could look to Google Research or HubSpot Research . (Hey, I know them!)

What I like: You can save time on gathering data and spend more time on analyzing. You can also rest assured the data is from a source you trust.

What I dislike: You may not find data specific to your needs.

Best for: Companies under a time or resource crunch, adding factual support to content

Pro tip: Fellow HubSpotter Iskiev suggests using third-party data to inspire your original research. “Sometimes, I use public third-party data for ideas and inspiration. Once I have written my survey and gotten all my ideas out, I read similar reports from other sources and usually end up with useful additions for my own research.”

9. Buy Research

If the data you need isn’t available publicly and you can’t do your own market research, you can also buy some. There are many reputable analytics companies that offer subscriptions to access their data. Statista is one of my favorites, but there’s also Euromonitor , Mintel , and BCC Research .

What I like: Same as public domain research

What I dislike: You may not find data specific to your needs. It also adds to your expenses.

Best for: Companies under a time or resource crunch or adding factual support to content

Which marketing research method should you use?

You’re not going to like my answer, but “it depends.” The best marketing research method for you will depend on your objective and data needs, but also your budget and timeline.

My advice? Aim for a mix of quantitative and qualitative data. If you can do your own original research, awesome. But if not, don’t beat yourself up. Lean into free or low-cost tools . You could do primary research for qualitative data, then tap public sources for quantitative data. Or perhaps the reverse is best for you.

Whatever your marketing research method mix, take the time to think it through and ensure you’re left with information that will truly help you achieve your goals.

Don't forget to share this post!

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ORIGINAL RESEARCH article

Understanding users of online energy efficiency counseling: comparison to representative samples in norway.

• 1 Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
• 2 Department of Psychology, University of Bergen, Bergen, Norway

Introduction: To achieve substantial energy efficiency improvements in the privately owned building stock, it is important to communicate with potential renovators at the right point in time and provide them with targeted information to strengthen their renovation ambitions. The European Union recommends using one-stop-shops (OSSs), which provide information and support throughout the whole process, from planning to acquisition of funding, implementation, and evaluation as a measure to remove unnecessary barriers.

Methods: For this paper, we invited visitors of two Norwegian websites with OSS characteristics to answer an online survey about their renovation plans and energy efficiency ambitions. The participants visited the websites out of their own interest; no recruitment for the websites was conducted as part of the study ( N = 437). They also rated a range of psychological drivers, facilitators, and barriers to including energy upgrades in a renovation project. Their answers were then compared to existing data from representative samples of Norwegian households regarding home renovation in 2014, 2018, and 2023, as well as data from a sample of people who were engaged in renovation projects in 2014, which was collected by the research team with a similar online survey. Furthermore, 78 visitors completed a brief follow-up online survey one year later to report the implemented measures.

Results: We found that visitors of the websites are involved in more comprehensive renovation projects and have substantially higher ambitions for the upgrade of energy efficiency compared to the representative samples. They also perceive stronger personal and social norms, as well as have a different profile of facilitators and barriers.

Discussion: The findings suggest to policymakers that OSSs should be marketed especially to people motivated to upgrade energy efficiency but lack information and are unable to implement their plans alone. Also, the construction industry might refer interested people to such low-threshold online solutions to assist informed and more ambitious decisions.

1 Introduction

Reducing energy use in the building sector by increasing energy efficiency is a key pillar of decarbonising Europe as formulated in the EU’s “Fit for 55” legislation ( Schlacke et al., 2022 , 4). On a global level, the residential sector is the third largest energy consumer, representing 27–30% of the energy consumption, almost at the same level as transportation and industry ( Nejat et al., 2015 , 843; IEA, 2023 ). Also in Europe, the residential sector stands for 26% of final energy consumption, being the second largest consumption sector after transportation ( Tsemekidi et al., 2019 , 1). Whereas the primary energy consumption in the residential sector decreased by 4.6% between 2000 and 2016 ( Tsemekidi et al., 2019 , 9), there is still a substantial untapped potential for further improvement of energy efficiency in the sector. This can be achieved through energy efficiency renovation of the existing building stock ( Pohoryles et al., 2020 , 11–12). Realizing this potential requires that also private house owners invest in energy efficiency measures. However, the annual rate of housing renovation in Europe is only about 1% ( Biere-Arenas and Marmolejo-Duarte, 2022 , 185), which is far too slow to reach the ambitious energy conservation targets. Besides, not all of those renovations include energy efficiency improvements. This raises the question of how property owners make decisions about renovating and energy efficiency measures and how they can be efficiently supported in these processes. To alleviate this problem, one-stop-shops (OSS), which are places where interested citizens can get counseling and support for the whole process of an energy retrofit, have gained a lot of attention lately as a means to support citizens in the matter of energy retrofits also from the European Union (as for example reflected in recently finished EU projects like “EUROPE one stop” or “ProRetro”).

1.1 One-stop-shops in energy counseling

Bertoldi et al. (2021 , 3–12) analysed the role of OSSs across Europe. They concluded that OSSs may be able to address some of the main barriers that households face when deciding about energy efficiency renovations. Often, these barriers can be categorized as economic (upfront costs, need for loan, split incentives between landlords and renters/disagreement between owners), information (information asymmetries, outcome uncertainties, incorrect beliefs), and decision-making (limited attention, social invisibility of the action, cognitive burden, loss aversion, status quo bias). Their analysis of 63 OSSs over Europe showed that the services the OSSs offer differ considerably, as do their business models. Some of them are public entities that often offer services for free, others are commercial enterprises. Their clients are usually homeowners living in relatively old buildings, and only a few of them work with social housing. Also Bagaini et al. (2022 , 3–4) analysed and categorized 29 OSS initiative around Europe and formulated five key elements on which the different OSS differed: (a) value proposition, (b) services, (c) partnership management, (d) revenue stream, and (e) shared value. Based on these dimensions, they destilled three archetypes for OSS models: They refer to them as the Facilitation Model (mostly focused on providing information to homeowners without a revenue generation model behind), the Coordination Model (also taking in a project management role with the contractors and generating revenue by fixed fees), and the Development Model (similar to the Coordination Model but with a revenue generated dynamically from the shared energy savings). Along similar lines, Pardalis et al. (2022) compared publicly and privately funded OSSs. In addition to the facilitation and the coordination model they separate the development model into “all inclusive models” (where the renovation process is fully managed by the OSS under one single contract, but energy savings are not guaranteed) and “ESCO models” (where Energy Service Companies−ESCOs−manage the whole renovation package and also guarantee energy savings). Whereas publicly funded OSSs are evaluated as providing homeowners with crucial services at the right time, privately funded OSSs struggle more with generating revenue and providing access to financing.

According to Bertoldi et al. (2021) , a key activity all of the surveyed OSSs cover is the assessment of the status quo, which is done in different ways (sometimes as a guided online self-assessment). Then, a stage of guidance toward possible measures is started, usually resulting in an individual renovation plan. In the next stage, financing is secured (either directly or indirectly, for example by supporting applications for subsidies). In the implementation stage, OSSs either manage the implementation themselves or recommend contractors who will do that. Often OSSs are involved in quality assurance of the implemented measures afterwards, sometimes certifying the result. Some OSSs also monitor the building after the energy upgrade to support the clients, often through a contract where financial benefits are shared between the OSS and the client (often in ESCO models). Finally, most OSSs also engage in campaigns for energy efficiency in buildings to increase awareness.

McGinley et al. (2020 , 355–57) formulate some key considerations for OSS design. They define OSS as offering full-service retrofitting, including initial building evaluation and thorough analysis, proposal of retrofitting solutions, retrofit execution, and quality assurance. However, they also state that little is known about characteristics and motivations of households that are drawn to OSS and how household decisions are impacted by OSSs, a research gap we aim to fill with this paper.

A number of recent EU projects have addressed the issue of OSSs in detail. In particular, the “EUROPA one stop” project (europaonestop.eu) is interesting as it created an online platform (SUNShINE−savehomesave.eu) to connect homeowners, facility managers, and contractors working on energy efficiency upgrades and provide them with easy access tools to online diagnose their renovation potential. This platform is structurally comparable with the platforms analysed in this paper and can be considered a concept following the facilitation model. However, to understand how homeowners may be affected by OSSs, it is important to take a look at decision-making processes.

1.2 Psychological drivers of implementing energy efficiency in renovation of privately owned dwellings

In a detailed study of decision-making about energy retrofits in Norwegian households data of which was also used as a comparison for this study, Klöckner and Nayum (2017 , 1014) found that an extended Theory of Planned Behaviour ( Ajzen, 1991 , 182; Klöckner, 2013 , 1032) formed a viable theoretical framework to structure these decision processes. They were able to show that personal norms, positive attitudes, and high self-efficacy were the decisive factors for forming intentions to include energy efficiency upgrades in renovation projects. Social norms were closely related to personal norms and an important trigger of these. More distal factors were problem awareness, value orientations, perceived consumer effectiveness, and innovativeness. The most central concepts are briefly introduced in the next paragraph.

In this context, personal norms are a feeling of moral obligation to invest in better energy efficiency. Positive attitudes are the overall evaluation of the pros and cons of the decision to invest. That is how good or bad this would be, all taken into account. Self-efficacy captures how capable one feels to implement the investment, a factor that most likely will be directly affected by engaging with an OSS. Following the theoretical framework as outlined and tested by Klöckner and Nayum (2017 , 1014), an intention to invest will thus be formed: (a) if people feel that they are morally obliged to do that because wasting energy is a bad thing which is more likely; (b) if other people who are important to them support this view. Furthermore, c) a positive attitude to energy efficiency investments d) and a high self-efficacy (i.e., knowing how to implement these measures and/or who to contract to do it) also contribute. As attitudes are a combination of positive and negative beliefs about the behavioral alternatives that people choose between ( Ajzen, 1996 , 385–403), a closer look at assumed barriers and facilitators underlying those alternatives could help in understanding the decision process further, as discussed in the next section.

1.3 Barriers and facilitators of energy efficiency measures in buildings

A number of studies analyzed facilitators of or barriers against implementing energy efficiency in a residential building from different theoretical and methodological perspectives. In his PhD thesis, Pardalis (2021 , 60) finds, based on an online survey with almost 1000 homeowners in Sweden, that the house age and time lived in a house but also energy concern trigger the decision to renovate. These factors are, again, influenced by sociodemographic factors of the occupants. Thus, structural aspects seem of importance as drivers of the retrofit decision.

Digging deeper into the decision process, Xue et al. (2022 , 5) conducted interviews with 39 professionals in the retrofit market to identify barriers to energy retrofitting from the perspective of the public sector, the private sector, and the owners who conduct the retrofit. They found financial issues as the most important barrier in all three groups. For owners who are supposed to implement energy efficiency measures, they further named lack of information, lack of creative models or cases, risks connected to the project, trust, and negative social influence as important barriers. Also, problems of reaching an agreement, time consuming processes, limited added value, and concerns about payback time were named.

Many of these aspects were also reflected in another qualitative study. Klöckner et al. (2013 , 406–408) interviewed 70 Norwegians on drivers and barriers regarding energy efficiency behaviour. They found that economic barriers (e.g., lack of investment money), motivational barriers (e.g., too much effort, loss of comfort, low perceived efficacy), structural barriers (e.g., building structure, ownership), and informational barriers (e.g., lack of trust, uncertainty, lack of specific information) were central.

Departing from practice theory in an ethnographic study of renovation projects, Judson and Maller (2014) interviewed 49 Australians involved in renovation projects and unraveled the process of renovation even more. They found that renovation projects, to a large degree, are shaped and reshaped by the existing or evolving practices people have within their buildings. Energy efficiency is traded off against other needs and meanings, negotiation between different household members occur, and focus shifts dynamically. Some parts of the home have a meaning for its inhabitants as part of their daily practices which cannot just be changed to enhance energy efficiency.

With a quantitative perspective, Klöckner and Nayum (2016 , 5) studied barriers in different stages of renovation processes in a representative sample of Norwegian households. Their findings indicate that facilitators like perceived increase in comfort, anticipated better living conditions or increased marked value were important in the early stages of decision making. Information about subsidy schemes or trustworthy information about the procedures came out as important at a later stage when planning was more advanced. Correspondingly, some barriers like building protection regulations, planning to move soon, or not owning the building were relevant already early in the process before people started even thinking about an energy retrofit, whereas barriers like too much disturbance of everyday life, contractors with a lack of competence, the need to supervise contractors, or a lack of economic resources were turned out to be relevant barriers later in the process. A particularly important barrier appeared to be the feeling that “the right point in time for a larger renovation project has not come, yet”.

In an economic modeling approach comparing expected utility theory (which assumes that decision makers chose the alternative with the best possible utility for them) and cumulative prospect theory (which assumes that decisions about investments are strongly affected by specific decision biases), Ebrahimigharehbaghi et al. (2022) found that cumulative prospect theory, which takes biases like “reference dependence” (utility changes are interpreted differently with respect to difference reference points), “loss aversion” (losses weigh higher than gains of the same size), “diminishing sensitivity” (avoiding risk for positive outcomes but taking risks for negative outcomes), and “probability weighting” (events with low probability but more extreme outcomes are overestimated) is much better equipped to predict homeowners investments in home energy efficiency in a large sample from the Netherlands than classical expected utility theory. This shows that people’s decision-making in such cases takes other aspects than economic utility into consideration to a large degree.

Studies such as the ones briefly mentioned above show that the selection of aspects that can interfere with or facilitate a decision-making process about energy retrofits is plentiful. In addition, they even have different importance depending on where in the process a decision-maker is. This makes it demanding to provide the most helpful support for decision-makers in the residential sector. It seems important to provide the right information at the right time to the right people, which underscores the need for careful targeting and timing of information provision. Flexible and interactive online counseling systems, which can take people through all stages of the process, similar OSSs, may be a way to find a good balance between resources needed and effects achieved in targeted energy counseling. Interestingly, Pardalis (2021 , 66) asked homeowners what would be most important for them with respect to OSSs, and guarantees for costs and quality, as well as having one contact and one contract and a preliminary check and counseling were on top of the list, directly addressing some of the issues identified as barriers in many of the studies above.

1.4 The present study

Summarizing what has been outlined in the introduction, energy efficiency upgrades of residential buildings are a major contributor to reaching the targets of the energy transition of the European Union. However, the private residential sector is lagging behind in this process. Renovation rates of the aging building stock are low. Even when the buildings are renovated, energy efficiency measures are not always implemented. In cases where some energy efficiency measures are included, they are often not to the standard that would be recommendable. One-stop-shops have been heavily promoted recently as a way of removing the burden of planning, financing, and implementing a deep renovation project from the individual house owners. Consequently, many such services have been implemented around Europe with differing business models, financing, and mandate. However, relatively little is known about who uses these services and what effect they have on their users. Especially, it is unknown to a large degree how interacting with a low-threshold digital OSS following a facilitation model shapes its users’ perception of barriers and facilitators of a retrofit decision, and if it affects their motivations and ambitions for this project. This research gap is addressed by the present study. More specifically, we are analysing if visitors of energy efficiency counceling websites differ in their engagement in retrofits, their energy efficiency ambitions, the profile of psychological variables, the drivers and barriers from representative samples of the population and a sample of home renovators.

Our study is, thus, contributing to the literature by providing new insights into how natural users of websites with OSS characteristics differ from the general population of homeowners on a number of psychological and socio-demographic characteristics. This helps on the one hand to identify who are the target group for such low-threshold website services, but on the other hand, we also provide an assessment if their renovation ambitions, and especially the level to which they intend to implement energy efficiency measures in these updates differs after they visited the service. Through a one-year follow-up, we can also provide an assessment of to which degree the planned measures were implemented. Taken together, the focus on primarily psychological drivers and barriers of energy efficiency investments in homes for a very specific target group in comparison to large, representative samples of homeowners paints a new, and informative picture of who the users of these websites are not only socio-demographically, but also psychologically, what they are looking for on these websites, and to which degree the websites support them in their pathway towards more energy efficient homes. Being able to run the comparisons of a relatively large sample of website users to several, large representative comparison samples which were surveyed with the same methodology in the same country over the course of 10 years provides an unique opportunity to understand the target group.

2 Materials and Methods

2.1 study design.

For this study, we collected responses from users of two online energy efficiency counseling websites, which have a similar structure that might be conceptualized as OSS following a facilitating model. These websites offer an analysis of the current energy standard of privately owned residential buildings (either as a guided self-assessment or based on data from the Norwegian building registry). They can also suggest a rough renovation plan and connect the homeowner to potential contractors who can implement energy efficiency measures. Moreover, they can provide information about costs, pay-off rates, subsidies (incl. information on how to apply), etc. Energismart.no is promoted by the environmental organization Friends of the Earth Norway, whereas energiportalen.no is promoted by Viken county. From January 2022 until January 2023, participants for the study were recruited from natural visitors of both websites by messages on the websites and pop-up windows, which promoted participation in our study and provided a link to the online questionnaire. We thus recruited people who visited the websites out of their own interest without promoting using the websites from our end. This sampling strategy was chosen to recruit a ecologically valid group of website users.

In the online survey, participants were then asked about their plans for retrofitting their homes, recently finished or ongoing retrofitting projects, the ambitions for energy efficiency upgrades as part of these retrofits, and psychological drivers and barriers of the decisions.

Since randomization of users of the websites was not possible, as people self-assigned to the websites, we chose a comparison group design, where we compared the means and distributions of key variables in our survey against representative homeowner data collected in 2014, 2018, and 2023 ( Klöckner and Nayum, 2016 , 2017 ; Egner and Klöckner, 2021 ; Egner et al., 2021 ; Peng and Klöckner, 2024 ) with the same survey instrument (see Table 1 for an overview of the survey samples). Because of that design, we are unable to draw causal conclusions, but we can get indications for differences between the samples (for a deeper discussion, see the limitations section below). We were also not able to survey our participants before they entered the websites. Thus, we do not know if the described differences were already there before they used the website, or which differences were caused by the website visit. It is likely that people visit such counseling websites when they already have developed an interest for the information presented there. Thus, some of the differences will have existed already pre-visit. Especially some of the drivers and barriers, but also some parts of the psychological profile might fall into that category and it is important to keep this in mind when interpreting the results. Furthermore, we do not know how long people stayed on the websites, what they read, and how much they used the information to adapt their renovation strategy, which would have given us more insights into their user experience. However, we believe that comparing the visitors to representative homeowners from different historical points in time in the same country surveyed with the same questionnaire can give us some relevant insights and at least input for generating new hypotheses.

Table 1. Overview of sample statistics in the different samples.

Differences between the samples were identified by comparing 95% confidence intervals for the means. Non-overlapping confidence intervals were interpreted as significant mean differences. Effect sizes for the differences are presented in Supplementary Appendix Table 1 .

One year after the participants answered the survey, we approached them again with a short survey asking if and which retrofitting measures had been implemented in the meantime and if not, why. The follow-up survey was sent to every participant who agreed to be contacted again.

The surveys conducted in all different studies compared here were collected through an online survey platform operated by the University of Oslo (Nettskjema.no). The questions used for the analyses presented in this paper composed only part of the questionnaires; we describe only the relevant questions below. The full survey can be found in the data repository together with the dataset. 1

2.2.1 Sociodemographic information

In the surveys, participants were asked about their gender, age, highest education level, gross household income (in the 2023 data collection, individual gross income was recorded), the type of house they lived in, and if they owned or rented etheir dwellings. The categories of these variables can be found in Table 1 .

2.2.2 Deep renovation

To capture if the participants were just finished, engaged in, or planning what we refer to as a “deep renovation” project, we asked them the following questions:

(1) Within the previous three years, were you involved in a renovation project that involved (a) substantial work on the roof like replacing all tiles, (b) replacing at least 50% of the outer walls, (c) replacing at least 50% of the window area, and/or (d) substantial work on the foundation? This definition was developed for the 2014 study in a collaboration of the researchers behind the studies and the Norwegian Energy Efficiency Agency Enova and used in the same form in all data collections since. The aim of this definition was to differentiate larger renovation projects from smaller, more cosmetic renovation projects.

(2) Are you currently involved in a renovation project according to the definition above or are you planning to engage in such a renovation project within the next three years?

However, the definition does not automatically assume that energy efficiency measures are included in the deep renovation project.

The ambition level of these renovation projects was measured by how many of the four components they (are planning to) implement, and it ranges from 1 to 4.

2.2.3 Energy efficiency upgrade

If participants answered “yes” to either or both of the questions presented in the previous section, they were asked if that renovation project included, includes or is planned to include (a) additional insulation of the roof of at least 10 cm, (b) adding additional insulation to the walls of at least 5 cm, (c) energy saving windows with a μ-value of 1.0 or lower, (d) at least 5 cm additional insulation to the foundation walls, (e) installation of mechanical ventilation, and/or (f) installation of balanced ventilation. Also here, the definition of these measures was agreed upon with Enova in 2014 to represent a substantial improvement in the energy standard of the respective building component. For our analyses, we counted the number of these measures that had been/were planned to be implemented in the deep renovation project. The number could thus be between 0 and 6.

2.2.4 Personal norms, social norms, attitudes, and efficiency

Based on the Theory of Planned Behaviour ( Ajzen, 1991 , 182) extended by personal norms from the Norm-Activation Model ( Schwartz and Howard, 1981 ), four psychological variables are central to understand people’s intentions: attitudes, social norms, perceived behavioral control or behavioral efficacy, and personal norms. Each of these variables was measured by two items in the surveys, with a 7-point Likert scale from −3 to +3. Higher values indicate stronger norms, attitudes, or efficacy.

The two items to measure social norms were “People who influence my decisions think I should insulate my home” and “People who are important to me think I should retrofit my home”. The two items to measure perceived efficacy were “I know which person or company I need to contact to have my home professionally insulated” and “I know what I need to do to insulate my home”. The two items to measure personal norms were “Because of my values/principles, I feel obliged to insulate my home” and “I feel personally obliged to retrofit my home”. For each pair of items, the mean score was calculated and used in subsequent analyses.

Attitudes were measured with four semantic differentials: “Increasing the energy standard of my home would be (a) useless−useful, (b) uncomfortable−comfortable, (c) unfavorable−favorable, and (d) bad−good”. Each pair has −3 as the anchor for the negative word and +3 as the anchor for the positive word. For further analyses, the mean of the four items was calculated.

All items had been used in an identical way since the first study in 2014, as documented elsewhere ( Klöckner and Nayum, 2016 , 2017 ). In the 2023 data collection, different answering scales were used, therefore the results are not comparable and are not reported here ( Peng and Klöckner, 2024 ).

2.2.5 Barriers and facilitators

Finally, a list of potential barriers and facilitators of energy efficiency upgrades was presented in random order to the participants, asking how much they agreed with each item. The items can be found in the Supplementary Appendix . These lists were derived from a qualitative study on reasons why Norwegians upgrade or decide not to upgrade energy standards of their dwellings ( Klöckner et al., 2013 ). In the 2023 data collection, different answering scales had been used, therefore the results are not comparable and are not reported here.

2.3 Sample and comparison groups

The sample of counseling website users was recruited from the first week of January 2022 to the first week of January 2023. In total, 437 answers were collected. These answers were not equally distributed over the year, however, as ( Figure 1 ) shows. Whereas relatively many responses were collected in winter and early spring 2022, the interest was reduced in late spring and summer before it skyrocketed after summer 2022, as well as in winter 2023. This coincided with electricity price peaks in Norway (especially in the South) and media discussions about that topic. Thus, a first conclusion can already be that the interest in using energy efficiency counseling websites clearly follows the pattern of the energy price fluctuation and accompanying societal discussion.

Figure 1. Number of participants recruited for the counseling website user survey per week in 2022 (the line is the moving average).

Table 1 below shows the sociodemographic statistics of the sample from the counseling websites in comparison to the existing samples in detail. As can be seen, the samples are comparable on most of the dimensions. All samples contain close to 50% males and females (with the most deviation in the sample of renovators from 2014). The average age is around 50 years in all samples, with the youngest average age in the 2023 population sample and the oldest average age in the sample of the users of the websites. Education varies quite strongly, with the population sample from 2014 being the outlier with far lower education level than all other samples. Participants recruited from the counseling websites had the highest education level. The median household gross income category is the same in most samples. However, it is lower in the 2014 population sample and higher in the sample of people who answered the one-year follow-up after the visit on the counseling websites. Income categories of the 2023 sample cannot be compared, as individual gross income was recorded in that data collection. However, it can be extrapolated that the average household income would be comparable to the other samples. The proportion of people living in detached houses is particularly high in the sample of website users and the renovator sample from 2014. Also, the level of people owning their dwelling is close to 100% in these groups and a little lower in all other groups. As a conclusion, it can be said that the samples are comparable on most dimensions. Meanwhile, the website users are most similar to the people who were recruited as being in a renovation project in 2014. That is, they are more likely better educated, more likely to live in a detached house, and more likely to own their dwelling than representative samples of Norwegian households.

In the following section, we present the results of the comparison of the counseling website users with the other available samples. To do this, we examine the 95% confidence intervals as displayed in the figures for overlaps between the group of website users and the other groups. As the data is partly in separate datasets, we did not calculate formal significance tests, but a non-overlapping 95% confidence interval corresponds to an assumed significant difference between the respective groups. The numbers for the website users are always highlighted in the figures. Effect sizes are reported in Supplementary Appendix Table 1 . An overview of all results can be found in Table 2 .

Table 2. Summary of the differences between the website visitors and the representative homeowner samples from 2014, 2018, and 2023, as well as the renovator sample from 2014.

3.1 Engagement in deep renovation

As can be seen in Figure 2 , the percentage of people who were involved in a deep renovation project is higher in the group of counseling website users than in all three population samples. The same can be said for the ongoing or planned deep renovation projects, which are also more common for people visiting the energy counseling websites. Only the group that was specifically recruited in 2014 to only contain respondents who either just had been, were still, and/or were planning a deep renovation project in the near future has higher numbers (which is not surprising). Interestingly, the number of finished and planned projects in the population sample is lower in 2023 than in 2018 and 2014, likely an effect of renovation saturation after COVID years.

Figure 2. Percentage of households per group who were, are or plan to be in a deep renovation project (see definition in the text). The columns with the bold lines are the users of the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

Among the users of the energy counseling websites, the ambition level is higher than in any other group, both for finished, ongoing and planned projects (see Figure 3 ). This means that they are engaged in slightly larger projects, involving more of the four different potential measures (walls, windows, roof, foundation). Thus, these people probably are or plan to be involved in more comprehensive renovation projects.

Figure 3. Ambition of the deep renovation (how many different measures are included of walls, windows, roof, and basement). The columns with the bold lines are the users of the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

3.2 Energy efficiency ambitions

When looking at the level of ambitions for integrating energy efficiency upgrades in the renovation projects, the picture is even more interesting (see Figure 4 ). Among the users of the energy counseling websites, the ambition level is substantially higher than in any other group, both for finished, ongoing, and planned projects. On a side note, even if the total percentage of people involved in deep renovation was lower in the population in 2023 than in 2014 and 2018, the degree to which energy efficiency measures are included is increasing as can be seen in Figures 2 , 4 . This may be an effect of the energy crisis in Europe in 2022.

Figure 4. Ambition of the energy retrofit as part of the renovation (how many different energy efficiency measures are included of more insulation of walls, better windows, more insulation of roof and basement, balanced ventilation system, and heat pump). The columns with the bold lines are the users of the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

3.3 Psychological drivers

When comparing the psychological profiles of the website users to the population profiles from 2014 and 2018, it can be seen that the website users have substantially higher personal norms. This indicates that they feel more moral pressure to increase the energy efficiency of their dwellings (see Figure 5 ). They also feel stronger social norms, meaning more social pressure from their peers to engage in such energy upgrades. For attitudes, the differences are smaller. Meanwhile, the attitudes are slightly more positive than for the population samples, on the same level as for the renovators in 2014. Interestingly, despite small differences, the website users have the lowest perceived self-efficacy, especially compared to the renovators in 2014. In contrast to renovators in 2014, they feel less convinced that they know how to go about for the renovations.

Figure 5. Means in key psychological variables driving the decision to renovate and energy upgrade. The bold black line is the sample from the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

3.4 Facilitators and barriers of energy efficiency upgrades

Figures 6 , 7 show how the website users perceive facilitators and barriers of energy efficiency upgrades of their dwellings in comparison to people in the other samples. For some facilitators and barriers, differences are substantial: counseling website users expect more comfort, a cost reduction, a house that is better to live in, increased property value, and less waste of energy as a result of the renovation. They score the lowest of all samples, though, on availability of information, payback time, and availability of subsidy.

Figure 6. Means in key facilitators for an energy upgrade. The bold black line is the sample from the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

Figure 7. Means in key barriers towards an energy upgrade. The bold black line is the sample from the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

For the barriers, they score particularly high on perceptions of the renovation taking too much time, on lack of money, difficulty of finding information, a lack of ability to decide what to do, and a lack of capable contractors. They score lower on perceptions of it not being the right time to act.

3.5 Implemented energy efficiency actions

In the one-year follow-up, the participants of the energy counseling website survey were contacted again and asked if they implemented the planned actions. 201 participants (46.0% of all participants) gave permission to be contacted a year after the initial survey was completed, and 78 (38.8% of all who were willing to be contacted) answered the short follow-up survey.

Of the 78 participants, 25 stated that they implemented the energy efficiency upgrades that they were planning to implement (32.1%). 29.2% of these changed at least 50% of the outer walls, 45.8% worked on the roof, 45.8% on the windows, and 37.5% on the foundation walls.

Of the 25 who implemented the measures, 15 added at least 5 cm insulation to the walls, 13 installed highly efficient windows (μ = 1.0 or smaller), 13 installed new mechanical ventilation, 12 insulated the roof with at least 10 cm additional insulation, 10 insulated the foundation walls with at least 5 additional cm of insulation, and 7 installed a balanced ventilation system. In addition to these measures, 11 installed heat pumps, 11 installed clean-burning wood stoves, and 5 installed solar panels on their houses. Overall, the measures taken were fairly ambitious.

The main reasons for not implementing the planned measures among the remaining participants of the follow-up were lack of economic funding (57.1%), lack of subsidies (42.9%), and that the time was not right, yet, to start the renovation, again reflecting some of the main barriers indicated in the introduction.

4 Discussion

The study conducted with the users of two energy efficiency counseling websites had three aims: (a) finding out if users of the website differed from representative samples of Norwegian households in terms of engagement in retrofits and have higher ambitions for their renovation projects and the energy efficiency measures embedded in them, (b) finding out if they differ in the psychological profile in central variables driving the decision-making process, and (c) finding out if they perceive facilitators and barriers in this process differently than representative samples of households. Furthermore, a follow-up study aimed to find out how many participants implement their ambitions up to a year later.

For all three main questions, we find substantial differences. Whereas the website users are mostly comparable to the general population of Norwegian households regarding socio-demographics (but have a higher education level and an even smaller percentage of people renting their dwelling, which reflects well the drivers for renovation projects as identified by Pardalis, 2021 ), their psychological profile differs in two important points. Compared to all other samples (also including the renovators studied in 2014), the website users have far higher levels of personal norms−they feel they really should do something about the energy standard of their homes−and also higher social norms. Considering the importance of these two factors for intentions to implement energy renovations ( Klöckner and Nayum, 2017 , 1014), this finding is relevant. Having such high levels of these two variables makes it more likely that people will form intentions to improve the energy standard of their homes. It also indicates that people like these are a prime target group for interventions like OSSs: They are already motivated to take action because they have high energy-related moral standards, and they feel the social pressure of their peer groups.

Since we could not survey these people before they went to the website, we do not know if they had such high personal and social norm values already before the visit to the website. On the other hand, since one of the websites is promoted by the environmental organization Friends of the Earth Norway, it can be assumed that this is the case. Interestingly, users of the counseling websites had a slightly lower level of self-efficacy, especially compared to the renovators from 2014. This implies that a lower level of self-efficacy might be a barrier to implement the intentions they form, and maybe also a reason for visiting the websites. Again, this means that this group is a very attractive target group for OSS-type interventions: Alleviating the low self-efficacy is something a well-designed OSS can achieve by reducing uncertainties, providing requested information, and not the least making the link between the urge to act on the side of the homeowners and the competence the homeowners are lacking provided by skilled and trustworthy contractors. This finding is, again, very much in line with what Pardalis (2021) found as being the most important features of OSSs from the perspective of potential users.

Also in terms of facilitators and barriers analysed, counseling website users had some values substantially different from the other groups. In particular, increased expected comfort levels, expected cost reductions, and expectations of having a better house to live in after the renovation were more important facilitators for website users than for the population samples or the renovators. Expecting an increased value of the house after the renovation was also higher than for the population samples, but at the same level as for the renovators. Perceiving the current energy standards a waste was standing out again for the website users. This indicates that they enter the process with a different, more energy interested perspective (or they get convinced of that by visiting the website). Interestingly, counseling website users score lower on perceptions that information is easy to find, and that access to subsidy is available. Maybe this is also a reason why they ended up on the websites in the first place.

Among the barriers, the website users mention a lot more often the time demand for supervision and the lack of money as the main barriers. They thereby raise the need to have a facilitator (or even a manager) of the renovation process, again a function OSSs typically fill. The websites we studied are following a facilitation model, but still leave the management of the project to the homeowners. From their answers, we can conclude that many of them would actually prefer a more comprehensive model. Also here, they reiterate that they consider information hard to find, that they cannot decide what to do, and that contractors lack competence. The latter three again might be reasons for being interested in the website services in the first place. The websites seem to partly satisfy their needs, as can be seen in that a significant amount of the website visitors implement their renovation plans within a year. However, some still sit with the same lack of support and the same barriers after a year. Maybe for them, a more comprehensive OSS model with a higher degree of process management would be more appropriate. In line with the renovators from 2014, the website users are to a lesser degree unsure if the right point in time for a renovation project has come. Overall, the order of importance of renovation facilitators and barriers to a large extent reproduces what has been found in earlier studies ( Klöckner et al., 2013 ; Klöckner and Nayum, 2016 , 2017 ; Bertoldi et al., 2021 ; Xue et al., 2022 ).

Most importantly, we found that the visitors of the websites had stronger ambitions for their renovation projects, and in particular for the implementation of energy efficiency measures as part of them. Of course, we do not know if this was caused by visiting the websites or if it was already higher before they visited. Nevertheless, we can assume that there is at least some mutual influence. People with a stronger motivation, but who are unsure about how to implement, visit the websites, which then confirm their motivations and provide hands-on counseling to remove the implementation barriers. This then eventually might result in higher ambitions. This is good news for the OSS concept, even the low-threshold version of it that these websites represent ( McGinley et al., 2020 ). However, not all visitors seem to receive from these websites what they need. For the future, it might be recommendable to use low-threshold OSSs like the ones studied here following a facilitating model as an entry point but implement an (automated, maybe AI-based) detection of who would benefit from more comprehensive OSS models to channel these people to the offers that better suit their needs.

Finally, we could at least tentatively show−even if based upon only relatively few cases and subject to large sample attrition−that about 1/3 of the participants manage to implement their energy upgrade intentions. These people usually combine several measures and implement a deep renovation. For these people, the websites seem to have pushed them in the right direction without too much effort. As such, these websites have their niche as gatekeepers for a deeper process for some people, as the final push and reassurance for others.

5 Limitations and future research needs

Even if the study presented here shows some interesting results in a field where more research is needed, there are a number of limitations that are mostly caused by the design we had to choose. The biggest limitation of this study is that the participants recruited among the website users were, for obvious reasons, not randomly assigned to use the website but self-selected, and they were not surveyed before the visit on the website, a limitation that was already discussed in the methodology section. In addition, the users of the website fall into a narrower sociodemographic category than the population samples, though they seem to be rather comparable with people engaged in renovation projects six years prior to our study. Furthermore, we do not know how long people stayed on the websites, what they read, and how much they used the information to adapt their renovation strategy.

To address these limitations, studies with more controlled experimental designs would be advisable. Assigning participants randomly to different conditions (including no OSS, and different models of OSS) would give a better understanding of what the effects of the OSS are and what differences people come with in the process. Such a study could also test, whether different forms of OSS interact with different sociodemographic and psychological profiles of homeowners. In simple words, it might answer the question, which form of OSS works for which type of homeowner.

6 Conclusion

One-stop-shops have been promoted as a measure to overcome the inertia in energy efficiency retrofitting, especially in the privately owned residential building stock. Results from our study on users of two Norwegian energy efficiency counseling websites, which offer services in many ways similar to an OSS following a facilitator model, show that the users of these websites clearly differ from representative samples of Norwegian households that were surveyed with similar instruments. Their profiles were more like a sample of people who were in the beginning or in the middle of a larger renovation project, which was surveyed in 2014. However, the results also show that they are scoring substantially lower on their perceived access to information and subsidy. Regarding the psychological profiles, they were much more strongly motivated by personal and social norms than average households. Most importantly, it appears that visitors of such low-threshold websites have substantially higher ambitions for the energy upgrades, which about 1/3 of them have implemented a year after they visited the websites. Interest in online energy efficiency counseling services seems to be impacted by societal discussions about energy and/or by energy prices, as suggested by the spike in recruitment to our survey coinciding with an energy price increase during 2022 (however, this intriguing possibility will need to be confirmed in future studies). From a policy perspective, the results are interesting because they indicate that low-threshold OSSs can be gateways capturing people who are motivated for energy efficiency upgrades but not able to make the decision for several reasons. For some of them, the services that these relatively simple online platforms can offer is already enough to reduce their uncertainty and make the missing connections. For those still not satisfied after visiting these platforms, future developments should explore whether they can be automatically directed to more comprehensive forms of OSSs.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/10453810 .

Ethics statement

The studies involving humans were approved by the Norwegian Agency for Shared Services in Education and Research (SIKT). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

CK: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing. AN: Data curation, Formal analysis, Writing–original draft, Writing–review and editing. SV: Conceptualization, Funding acquisition, Writing–original draft, Writing–review and editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of the article. This study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957115 as part of the ENCHANT project: www.enchant-project.eu. Data for three of the comparison groups for the analyses was extracted from two previous projects funded by the Norwegian Energy Efficiency Agency, and one comparison group was extracted from data from an ongoing project funded by the Research Council of Norway (BEHAVIOUR, Project No. 308772).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1364980/full#supplementary-material

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Keywords : energy efficiency, renovation, one-stop-shops, counseling, psychological drivers, theory of planned behaviour, personal norms, facilitators

Citation: Klöckner CA, Nayum A and Vesely S (2024) Understanding users of online energy efficiency counseling: comparison to representative samples in Norway. Front. Psychol. 15:1364980. doi: 10.3389/fpsyg.2024.1364980

Received: 03 January 2024; Accepted: 18 July 2024; Published: 06 August 2024.

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Copyright © 2024 Klöckner, Nayum and Vesely. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Christian A. Klöckner, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.