research questions for qualitative studies

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How to write qualitative research questions.

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

What is qualitative research?

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

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

What are research questions?

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

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

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

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

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

Discover the fundamentals of qualitative research

Quantitative vs. qualitative research questions

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

Quantitative research questions

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

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

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

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

• What is the connection?

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

For example:

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

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

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

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

Qualitative research questions

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

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

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

A few example qualitative research questions:

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

Qualitative research question types

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

• Exploratory questions

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

• Explanatory questions

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

• Descriptive questions

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

• Predictive questions

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

Why are good qualitative research questions important?

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

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

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

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

Developing good qualitative research questions

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

1. Keep it specific

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

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

Weak example: Do people feel different about gender now?

2. Be original

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

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

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

3. Make it research-worthy

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

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

Weak example: What languages are spoken in India?

4. Focus your question

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

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

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

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

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

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

Weak example: How can hate crimes be prevented?

5. Ask something researchable

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

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

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

Related resources

Qualitative research design 12 min read, primary vs secondary research 14 min read, business research methods 12 min read, qualitative research interviews 11 min read, market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, request demo.

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83 Qualitative Research Questions & Examples

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Qualitative research questions help you understand consumer sentiment. They’re strategically designed to show organizations how and why people feel the way they do about a brand, product, or service. It looks beyond the numbers and is one of the most telling types of market research a company can do.

The UK Data Service describes this perfectly, saying, “The value of qualitative research is that it gives a voice to the lived experience .”

Read on to see seven use cases and 83 qualitative research questions, with the added bonus of examples that show how to get similar insights faster with Similarweb Research Intelligence.

What is a qualitative research question?

A qualitative research question explores a topic in-depth, aiming to better understand the subject through interviews, observations, and other non-numerical data. Qualitative research questions are open-ended, helping to uncover a target audience’s opinions, beliefs, and motivations.

How to choose qualitative research questions?

Choosing the right qualitative research questions can be incremental to the success of your research and the findings you uncover. Here’s my six-step process for choosing the best qualitative research questions.

• Start by understanding the purpose of your research. What do you want to learn? What outcome are you hoping to achieve?
• Consider who you are researching. What are their experiences, attitudes, and beliefs? How can you best capture these in your research questions ?
• Keep your questions open-ended . Qualitative research questions should not be too narrow or too broad. Aim to ask specific questions to provide meaningful answers but broad enough to allow for exploration.
• Balance your research questions. You don’t want all of your questions to be the same type. Aim to mix up your questions to get a variety of answers.
• Ensure your research questions are ethical and free from bias. Always have a second (and third) person check for unconscious bias.
• Consider the language you use. Your questions should be written in a way that is clear and easy to understand. Avoid using jargon , acronyms, or overly technical language.

Types of qualitative research questions

For a question to be considered qualitative, it usually needs to be open-ended. However, as I’ll explain, there can sometimes be a slight cross-over between quantitative and qualitative research questions.

Open-ended questions

These allow for a wide range of responses and can be formatted with multiple-choice answers or a free-text box to collect additional details. The next two types of qualitative questions are considered open questions, but each has its own style and purpose.

• Probing questions are used to delve deeper into a respondent’s thoughts, such as “Can you tell me more about why you feel that way?”
• Comparative questions ask people to compare two or more items, such as “Which product do you prefer and why?” These qualitative questions are highly useful for understanding brand awareness , competitive analysis , and more.

Closed-ended questions

These ask respondents to choose from a predetermined set of responses, such as “On a scale of 1-5, how satisfied are you with the new product?” While they’re traditionally quantitative, adding a free text box that asks for extra comments into why a specific rating was chosen will provide qualitative insights alongside their respective quantitative research question responses.

• Ranking questions get people to rank items in order of preference, such as “Please rank these products in terms of quality.” They’re advantageous in many scenarios, like product development, competitive analysis, and brand awareness.
• Likert scale questions ask people to rate items on a scale, such as “On a scale of 1-5, how satisfied are you with the new product?” Ideal for placement on websites and emails to gather quick, snappy feedback.

Qualitative research question examples

There are many applications of qualitative research and lots of ways you can put your findings to work for the success of your business. Here’s a summary of the most common use cases for qualitative questions and examples to ask.

Qualitative questions for identifying customer needs and motivations

These types of questions help you find out why customers choose products or services and what they are looking for when making a purchase.

• What factors do you consider when deciding to buy a product?
• What would make you choose one product or service over another?
• What are the most important elements of a product that you would buy?
• What features do you look for when purchasing a product?
• What qualities do you look for in a company’s products?
• Do you prefer localized or global brands when making a purchase?
• How do you determine the value of a product?
• What do you think is the most important factor when choosing a product?
• How do you decide if a product or service is worth the money?
• Do you have any specific expectations when purchasing a product?
• Do you prefer to purchase products or services online or in person?
• What kind of customer service do you expect when buying a product?
• How do you decide when it is time to switch to a different product?
• Where do you research products before you decide to buy?
• What do you think is the most important customer value when making a purchase?

Qualitative research questions to enhance customer experience

Use these questions to reveal insights into how customers interact with a company’s products or services and how those experiences can be improved.

• What aspects of our product or service do customers find most valuable?
• How do customers perceive our customer service?
• What factors are most important to customers when purchasing?
• What do customers think of our brand?
• What do customers think of our current marketing efforts?
• How do customers feel about the features and benefits of our product?
• How do customers feel about the price of our product or service?
• How could we improve the customer experience?
• What do customers think of our website or app?
• What do customers think of our customer support?
• What could we do to make our product or service easier to use?
• What do customers think of our competitors?
• What is your preferred way to access our site?
• How do customers feel about our delivery/shipping times?
• What do customers think of our loyalty programs?

Qualitative research question example for customer experience

• ‍♀️ Question: What is your preferred way to access our site?
• Insight sought: How mobile-dominant are consumers? Should you invest more in mobile optimization or mobile marketing?
• Challenges with traditional qualitative research methods: While using this type of question is ideal if you have a large database to survey when placed on a site or sent to a limited customer list, it only gives you a point-in-time perspective from a limited group of people.
• A new approach: You can get better, broader insights quicker with Similarweb Digital Research Intelligence. To fully inform your research, you need to know preferences at the industry or market level.
• ⏰ Time to insight: 30 seconds
• ✅ How it’s done: Similarweb offers multiple ways to answer this question without going through a lengthy qualitative research process. 

First, I’m going to do a website market analysis of the banking credit and lending market in the finance sector to get a clearer picture of industry benchmarks.

Here, I can view device preferences across any industry or market instantly. It shows me the device distribution for any country across any period. This clearly answers the question of how mobile dominate my target audience is , with 59.79% opting to access site via a desktop vs. 40.21% via mobile

I then use the trends section to show me the exact split between mobile and web traffic for each key player in my space. Let’s say I’m about to embark on a competitive campaign that targets customers of Chase and Bank of America ; I can see both their audiences are highly desktop dominant compared with others in their space .

Qualitative question examples for developing new products or services

Research questions like this can help you understand customer pain points and give you insights to develop products that meet those needs.

• What is the primary reason you would choose to purchase a product from our company?
• How do you currently use products or services that are similar to ours?
• Is there anything that could be improved with products currently on the market?
• What features would you like to see added to our products?
• How do you prefer to contact a customer service team?
• What do you think sets our company apart from our competitors?
• What other product or service offerings would like to see us offer?
• What type of information would help you make decisions about buying a product?
• What type of advertising methods are most effective in getting your attention?
• What is the biggest deterrent to purchasing products from us?

Qualitative research question example for service development

• ‍♀️ Question: What type of advertising methods are most effective in getting your attention?
• Insight sought: The marketing channels and/or content that performs best with a target audience .
• Challenges with traditional qualitative research methods: When using qualitative research surveys to answer questions like this, the sample size is limited, and bias could be at play.
• A better approach: The most authentic insights come from viewing real actions and results that take place in the digital world. No questions or answers are needed to uncover this intel, and the information you seek is readily available in less than a minute.
• ⏰ Time to insight: 5 minutes
• ✅ How it’s done: There are a few ways to approach this. You can either take an industry-wide perspective or hone in on specific competitors to unpack their individual successes. Here, I’ll quickly show a snapshot with a whole market perspective.

Using the market analysis element of Similarweb Digital Intelligence, I select my industry or market, which I’ve kept as banking and credit. A quick click into marketing channels shows me which channels drive the highest traffic in my market. Taking direct traffic out of the equation, for now, I can see that referrals and organic traffic are the two highest-performing channels in this market.

Similarweb allows me to view the specific referral partners and pages across these channels. 

Looking closely at referrals in this market, I’ve chosen chase.com and its five closest rivals . I select referrals in the channel traffic element of marketing channels. I see that Capital One is a clear winner, gaining almost 25 million visits due to referral partnerships.

Next, I get to see exactly who is referring traffic to Capital One and the total traffic share for each referrer. I can see the growth as a percentage and how that has changed, along with an engagement score that rates the average engagement level of that audience segment. This is particularly useful when deciding on which new referral partnerships to pursue.  

Once I’ve identified the channels and campaigns that yield the best results, I can then use Similarweb to dive into the various ad creatives and content that have the greatest impact.

These ads are just a few of those listed in the creatives section from my competitive website analysis of Capital One. You can filter this list by the specific campaign, publishers, and ad networks to view those that matter to you most. You can also discover video ad creatives in the same place too.

In just five minutes ⏰ 

• I’ve captured audience loyalty statistics across my market
• Spotted the most competitive players
• Identified the marketing channels my audience is most responsive to
• I know which content and campaigns are driving the highest traffic volume
• I’ve created a target list for new referral partners and have been able to prioritize this based on results and engagement figures from my rivals
• I can see the types of creatives that my target audience is responding to, giving me ideas for ways to generate effective copy for future campaigns

Qualitative questions to determine pricing strategies

Companies need to make sure pricing stays relevant and competitive. Use these questions to determine customer perceptions on pricing and develop pricing strategies to maximize profits and reduce churn.

• How do you feel about our pricing structure?
• How does our pricing compare to other similar products?
• What value do you feel you get from our pricing?
• How could we make our pricing more attractive?
• What would be an ideal price for our product?
• Which features of our product that you would like to see priced differently?
• What discounts or deals would you like to see us offer?
• How do you feel about the amount you have to pay for our product?

Get Faster Answers to Qualitative Research Questions with Similarweb Today

Qualitative research question example for determining pricing strategies.

• ‍♀️ Question: What discounts or deals would you like to see us offer?
• Insight sought: The promotions or campaigns that resonate with your target audience.
• Challenges with traditional qualitative research methods: Consumers don’t always recall the types of ads or campaigns they respond to. Over time, their needs and habits change. Your sample size is limited to those you ask, leaving a huge pool of unknowns at play.
• A better approach: While qualitative insights are good to know, you get the most accurate picture of the highest-performing promotion and campaigns by looking at data collected directly from the web. These analytics are real-world, real-time, and based on the collective actions of many, instead of the limited survey group you approach. By getting a complete picture across an entire market, your decisions are better informed and more aligned with current market trends and behaviors.
• ✅ How it’s done: Similarweb’s Popular Pages feature shows the content, products, campaigns, and pages with the highest growth for any website. So, if you’re trying to unpack the successes of others in your space and find out what content resonates with a target audience, there’s a far quicker way to get answers to these questions with Similarweb.

Here, I’m using Capital One as an example site. I can see trending pages on their site showing the largest increase in page views. Other filters include campaign, best-performing, and new–each of which shows you page URLs, share of traffic, and growth as a percentage. This page is particularly useful for staying on top of trending topics , campaigns, and new content being pushed out in a market by key competitors.

Qualitative research questions for product development teams

It’s vital to stay in touch with changing consumer needs. These questions can also be used for new product or service development, but this time, it’s from the perspective of a product manager or development team. 

• What are customers’ primary needs and wants for this product?
• What do customers think of our current product offerings?
• What is the most important feature or benefit of our product?
• How can we improve our product to meet customers’ needs better?
• What do customers like or dislike about our competitors’ products?
• What do customers look for when deciding between our product and a competitor’s?
• How have customer needs and wants for this product changed over time?
• What motivates customers to purchase this product?
• What is the most important thing customers want from this product?
• What features or benefits are most important when selecting a product?
• What do customers perceive to be our product’s pros and cons?
• What would make customers switch from a competitor’s product to ours?
• How do customers perceive our product in comparison to similar products?
• What do customers think of our pricing and value proposition?
• What do customers think of our product’s design, usability, and aesthetics?

Qualitative questions examples to understand customer segments

Market segmentation seeks to create groups of consumers with shared characteristics. Use these questions to learn more about different customer segments and how to target them with tailored messaging.

• What motivates customers to make a purchase?
• How do customers perceive our brand in comparison to our competitors?
• How do customers feel about our product quality?
• How do customers define quality in our products?
• What factors influence customers’ purchasing decisions ?
• What are the most important aspects of customer service?
• What do customers think of our customer service?
• What do customers think of our pricing?
• How do customers rate our product offerings?
• How do customers prefer to make purchases (online, in-store, etc.)?

Qualitative research question example for understanding customer segments

• ‍♀️ Question: Which social media channels are you most active on?
• Insight sought: Formulate a social media strategy . Specifically, the social media channels most likely to succeed with a target audience.
• Challenges with traditional qualitative research methods: Qualitative research question responses are limited to those you ask, giving you a limited sample size. Questions like this are usually at risk of some bias, and this may not be reflective of real-world actions.
• A better approach: Get a complete picture of social media preferences for an entire market or specific audience belonging to rival firms. Insights are available in real-time, and are based on the actions of many, not a select group of participants. Data is readily available, easy to understand, and expandable at a moment’s notice.
• ✅ How it’s done: Using Similarweb’s website analysis feature, you can get a clear breakdown of social media stats for your audience using the marketing channels element. It shows the percentage of visits from each channel to your site, respective growth, and specific referral pages by each platform. All data is expandable, meaning you can select any platform, period, and region to drill down and get more accurate intel, instantly.

This example shows me Bank of America’s social media distribution, with YouTube , Linkedin , and Facebook taking the top three spots, and accounting for almost 80% of traffic being driven from social media.

When doing any type of market research, it’s important to benchmark performance against industry averages and perform a social media competitive analysis to verify rival performance across the same channels.

Qualitative questions to inform competitive analysis

Organizations must assess market sentiment toward other players to compete and beat rival firms. Whether you want to increase market share , challenge industry leaders , or reduce churn, understanding how people view you vs. the competition is key.

• What is the overall perception of our competitors’ product offerings in the market?
• What attributes do our competitors prioritize in their customer experience?
• What strategies do our competitors use to differentiate their products from ours?
• How do our competitors position their products in relation to ours?
• How do our competitors’ pricing models compare to ours?
• What do consumers think of our competitors’ product quality?
• What do consumers think of our competitors’ customer service?
• What are the key drivers of purchase decisions in our market?
• What is the impact of our competitors’ marketing campaigns on our market share ? 10. How do our competitors leverage social media to promote their products?

Qualitative research question example for competitive analysis

• ‍♀️ Question: What other companies do you shop with for x?
• Insight sought: W ho are your competitors? Which of your rival’s sites do your customers visit? How loyal are consumers in your market?
• Challenges with traditional qualitative research methods:  Sample size is limited, and customers could be unwilling to reveal which competitors they shop with, or how often they around. Where finances are involved, people can act with reluctance or bias, and be unwilling to reveal other suppliers they do business with.
• A better approach: Get a complete picture of your audience’s loyalty, see who else they shop with, and how many other sites they visit in your competitive group. Find out the size of the untapped opportunity and which players are doing a better job at attracting unique visitors – without having to ask people to reveal their preferences.
• ✅ How it’s done: Similarweb website analysis shows you the competitive sites your audience visits, giving you access to data that shows cross-visitation habits, audience loyalty, and untapped potential in a matter of minutes.

Using the audience interests element of Similarweb website analysis, you can view the cross-browsing behaviors of a website’s audience instantly. You can see a matrix that shows the percentage of visitors on a target site and any rival site they may have visited.

With the Similarweb audience overlap feature, view the cross-visitation habits of an audience across specific websites. In this example, I chose chase.com and its four closest competitors to review. For each intersection, you see the number of unique visitors and the overall proportion of each site’s audience it represents. It also shows the volume of unreached potential visitors.

Here, you can see a direct comparison of the audience loyalty represented in a bar graph. It shows a breakdown of each site’s audience based on how many other sites they have visited. Those sites with the highest loyalty show fewer additional sites visited.

From the perspective of chase.com, I can see 47% of their visitors do not visit rival sites. 33% of their audience visited 1 or more sites in this group, 14% visited 2 or more sites, 4% visited 3 or more sites, and just 0.8% viewed all sites in this comparison. 

How to answer qualitative research questions with Similarweb

Similarweb Research Intelligence drastically improves market research efficiency and time to insight. Both of these can impact the bottom line and the pace at which organizations can adapt and flex when markets shift, and rivals change tactics.

Outdated practices, while still useful, take time . And with a quicker, more efficient way to garner similar insights, opting for the fast lane puts you at a competitive advantage.

With a birds-eye view of the actions and behaviors of companies and consumers across a market , you can answer certain research questions without the need to plan, do, and review extensive qualitative market research .

Wrapping up

Qualitative research methods have been around for centuries. From designing the questions to finding the best distribution channels, collecting and analyzing findings takes time to get the insights you need. Similarweb Digital Research Intelligence drastically improves efficiency and time to insight. Both of which impact the bottom line and the pace at which organizations can adapt and flex when markets shift.

Similarweb’s suite of digital intelligence solutions offers unbiased, accurate, honest insights you can trust for analyzing any industry, market, or audience.

• Methodologies used for data collection are robust, transparent, and trustworthy.
• Clear presentation of data via an easy-to-use, intuitive platform.
• It updates dynamically–giving you the freshest data about an industry or market.
• Data is available via an API – so you can plug into platforms like Tableau or PowerBI to streamline your analyses.
• Filter and refine results according to your needs.

Are quantitative or qualitative research questions best?

Both have their place and purpose in market research. Qualitative research questions seek to provide details, whereas quantitative market research gives you numerical statistics that are easier and quicker to analyze. You get more flexibility with qualitative questions, and they’re non-directional.

What are the advantages of qualitative research?

Qualitative research is advantageous because it allows researchers to better understand their subject matter by exploring people’s attitudes, behaviors, and motivations in a particular context. It also allows researchers to uncover new insights that may not have been discovered with quantitative research methods.

What are some of the challenges of qualitative research?

Qualitative research can be time-consuming and costly, typically involving in-depth interviews and focus groups. Additionally, there are challenges associated with the reliability and validity of the collected data, as there is no universal standard for interpreting the results.

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• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
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What’s in a Qualitative Research Question?

Qualitative research questions are driven by the need for the study. Ideally, research questions are formulated as a result of the problem and purpose, which leads to the identification of the methodology. When a qualitative methodology is chosen, research questions should be exploratory and focused on the actual phenomenon under study.

From the Dissertation Center, Chapter 1: Research Question Overview , there are several considerations when forming a qualitative research question. Qualitative research questions should

Below is an example of a qualitative phenomenological design. Note the use of the term “lived experience” in the central research question. This aligns with phenomenological design.

RQ1: “ What are the lived experiences of followers of mid-level managers in the financial services sector regarding their well-being on the job?”

If the researcher wants to focus on aspects of the theory used to support the study or dive deeper into aspects of the central RQ, sub-questions might be used. The following sub-questions could be formulated to seek further insight:

RQ1a.   “How do followers perceive the quality and adequacy of the leader-follower exchanges between themselves and their novice leaders?”

RQ1b.  “Under what conditions do leader-member exchanges affect a follower’s own level of well-being?”

Qualitative research questions also display the desire to explore or describe phenomena. Qualitative research seeks the lived experience, the personal experiences, the understandings, the meanings, and the stories associated with the concepts present in our studies.

We want to ensure our research questions are answerable and that we are not making assumptions about our sample. View the questions below:

How do healthcare providers perceive income inequality when providing care to poor patients?

In Example A, we see that there is no specificity of location or geographic areas. This could lead to findings that are varied, and the researcher may not find a clear pattern. Additionally, the question implies the focus is on “income inequality” when the actual focus is on the provision of care. The term “poor patients” can also be offensive, and most providers will not want to seem insensitive and may perceive income inequality as a challenge (of course!).

How do primary care nurses in outreach clinics describe providing quality care to residents of low-income urban neighborhoods?

In Example B, we see that there is greater specificity in the type of care provider. There is also a shift in language so that the focus is on how the individuals describe what they think about, experience, and navigate providing quality care.

Other Qualitative Research Question Examples

Vague : What are the strategies used by healthcare personnel to assist injured patients?

Try this : What is the experience of emergency room personnel in treating patients with a self-inflicted household injury?

The first question is general and vague. While in the same topic area, the second question is more precise and gives the reader a specific target population and a focus on the phenomenon they would have experienced. This question could be in line with a phenomenological study as we are seeking their experience or a case study as the ER personnel are a bounded entity.

Unclear : How do students experience progressing to college?

Try this : How do first-generation community members describe the aspects of their culture that promote aspiration to postsecondary education?

The first question does not have a focus on what progress is or what students are the focus. The second question provides a specific target population and provides the description to be provided by the participants. This question could be in line with a descriptive study.

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Qualitative Research Questions: Gain Powerful Insights + 25 Examples

We review the basics of qualitative research questions, including their key components, how to craft them effectively, & 25 example questions.

Einstein was many things—a physicist, a philosopher, and, undoubtedly, a mastermind. He also had an incredible way with words. His quote, "Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted," is particularly poignant when it comes to research. 

Some inquiries call for a quantitative approach, for counting and measuring data in order to arrive at general conclusions. Other investigations, like qualitative research, rely on deep exploration and understanding of individual cases in order to develop a greater understanding of the whole. That’s what we’re going to focus on today.

Qualitative research questions focus on the "how" and "why" of things, rather than the "what". They ask about people's experiences and perceptions , and can be used to explore a wide range of topics.

The following article will discuss the basics of qualitative research questions, including their key components, and how to craft them effectively. You'll also find 25 examples of effective qualitative research questions you can use as inspiration for your own studies.

Let’s get started!

What are qualitative research questions, and when are they used?

When researchers set out to conduct a study on a certain topic, their research is chiefly directed by an overarching question . This question provides focus for the study and helps determine what kind of data will be collected.

By starting with a question, we gain parameters and objectives for our line of research. What are we studying? For what purpose? How will we know when we’ve achieved our goals?

Of course, some of these questions can be described as quantitative in nature. When a research question is quantitative, it usually seeks to measure or calculate something in a systematic way.

For example:

• How many people in our town use the library?
• What is the average income of families in our city?
• How much does the average person weigh?

Other research questions, however—and the ones we will be focusing on in this article—are qualitative in nature. Qualitative research questions are open-ended and seek to explore a given topic in-depth.

According to the Australian & New Zealand Journal of Psychiatry , “Qualitative research aims to address questions concerned with developing an understanding of the meaning and experience dimensions of humans’ lives and social worlds.”

This type of research can be used to gain a better understanding of people’s thoughts, feelings and experiences by “addressing questions beyond ‘what works’, towards ‘what works for whom when, how and why, and focusing on intervention improvement rather than accreditation,” states one paper in Neurological Research and Practice .

Qualitative questions often produce rich data that can help researchers develop hypotheses for further quantitative study.

• What are people’s thoughts on the new library?
• How does it feel to be a first-generation student at our school?
• How do people feel about the changes taking place in our town?

As stated by a paper in Human Reproduction , “...‘qualitative’ methods are used to answer questions about experience, meaning, and perspective, most often from the standpoint of the participant. These data are usually not amenable to counting or measuring.”

Both quantitative and qualitative questions have their uses; in fact, they often complement each other. A well-designed research study will include a mix of both types of questions in order to gain a fuller understanding of the topic at hand.

If you would like to recruit unlimited participants for qualitative research for free and only pay for the interview you conduct, try using Respondent  today. 

Crafting qualitative research questions for powerful insights

Now that we have a basic understanding of what qualitative research questions are and when they are used, let’s take a look at how you can begin crafting your own.

According to a study in the International Journal of Qualitative Studies in Education, there is a certain process researchers should follow when crafting their questions, which we’ll explore in more depth.

1. Beginning the process 

Start with a point of interest or curiosity, and pose a draft question or ‘self-question’. What do you want to know about the topic at hand? What is your specific curiosity? You may find it helpful to begin by writing several questions.

For example, if you’re interested in understanding how your customer base feels about a recent change to your product, you might ask: 

• What made you decide to try the new product?
• How do you feel about the change?
• What do you think of the new design/functionality?
• What benefits do you see in the change?

2. Create one overarching, guiding question 

At this point, narrow down the draft questions into one specific question. “Sometimes, these broader research questions are not stated as questions, but rather as goals for the study.”

As an example of this, you might narrow down these three questions: 

into the following question: 

• What are our customers’ thoughts on the recent change to our product?

3. Theoretical framing 

As you read the relevant literature and apply theory to your research, the question should be altered to achieve better outcomes. Experts agree that pursuing a qualitative line of inquiry should open up the possibility for questioning your original theories and altering the conceptual framework with which the research began.

If we continue with the current example, it’s possible you may uncover new data that informs your research and changes your question. For instance, you may discover that customers’ feelings about the change are not just a reaction to the change itself, but also to how it was implemented. In this case, your question would need to reflect this new information: 

• How did customers react to the process of the change, as well as the change itself?

4. Ethical considerations 

A study in the International Journal of Qualitative Studies in Education stresses that ethics are “a central issue when a researcher proposes to study the lives of others, especially marginalized populations.” Consider how your question or inquiry will affect the people it relates to—their lives and their safety. Shape your question to avoid physical, emotional, or mental upset for the focus group.

In analyzing your question from this perspective, if you feel that it may cause harm, you should consider changing the question or ending your research project. Perhaps you’ve discovered that your question encourages harmful or invasive questioning, in which case you should reformulate it.

5. Writing the question 

The actual process of writing the question comes only after considering the above points. The purpose of crafting your research questions is to delve into what your study is specifically about” Remember that qualitative research questions are not trying to find the cause of an effect, but rather to explore the effect itself.

Your questions should be clear, concise, and understandable to those outside of your field. In addition, they should generate rich data. The questions you choose will also depend on the type of research you are conducting: 

• If you’re doing a phenomenological study, your questions might be open-ended, in order to allow participants to share their experiences in their own words.
• If you’re doing a grounded-theory study, your questions might be focused on generating a list of categories or themes.
• If you’re doing ethnography, your questions might be about understanding the culture you’re studying.

Whenyou have well-written questions, it is much easier to develop your research design and collect data that accurately reflects your inquiry.

In writing your questions, it may help you to refer to this simple flowchart process for constructing questions:

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25 examples of expertly crafted qualitative research questions

It's easy enough to cover the theory of writing a qualitative research question, but sometimes it's best if you can see the process in practice. In this section, we'll list 25 examples of B2B and B2C-related qualitative questions.

Let's begin with five questions. We'll show you the question, explain why it's considered qualitative, and then give you an example of how it can be used in research.

1. What is the customer's perception of our company's brand?

Qualitative research questions are often open-ended and invite respondents to share their thoughts and feelings on a subject. This question is qualitative because it seeks customer feedback on the company's brand. 

This question can be used in research to understand how customers feel about the company's branding, what they like and don't like about it, and whether they would recommend it to others.

2. Why do customers buy our product?

This question is also qualitative because it seeks to understand the customer's motivations for purchasing a product. It can be used in research to identify the reasons  customers buy a certain product, what needs or desires the product fulfills for them, and how they feel about the purchase after using the product.

3. How do our customers interact with our products?

Again, this question is qualitative because it seeks to understand customer behavior. In this case, it can be used in research to see how customers use the product, how they interact with it, and what emotions or thoughts the product evokes in them.

4. What are our customers' biggest frustrations with our products?

By seeking to understand customer frustrations, this question is qualitative and can provide valuable insights. It can be used in research to help identify areas in which the company needs to make improvements with its products.

5. How do our customers feel about our customer service?

Rather than asking why customers like or dislike something, this question asks how they feel. This qualitative question can provide insights into customer satisfaction or dissatisfaction with a company. 

This type of question can be used in research to understand what customers think of the company's customer service and whether they feel it meets their needs.

20 more examples to refer to when writing your question

Now that you’re aware of what makes certain questions qualitative, let's move into 20 more examples of qualitative research questions:

• How do your customers react when updates are made to your app interface?
• How do customers feel when they complete their purchase through your ecommerce site?
• What are your customers' main frustrations with your service?
• How do people feel about the quality of your products compared to those of your competitors?
• What motivates customers to refer their friends and family members to your product or service?
• What are the main benefits your customers receive from using your product or service?
• How do people feel when they finish a purchase on your website?
• What are the main motivations behind customer loyalty to your brand?
• How does your app make people feel emotionally?
• For younger generations using your app, how does it make them feel about themselves?
• What reputation do people associate with your brand?
• How inclusive do people find your app?
• In what ways are your customers' experiences unique to them?
• What are the main areas of improvement your customers would like to see in your product or service?
• How do people feel about their interactions with your tech team?
• What are the top five reasons people use your online marketplace?
• How does using your app make people feel in terms of connectedness?
• What emotions do people experience when they're using your product or service?
• Aside from the features of your product, what else about it attracts customers?
• How does your company culture make people feel?

As you can see, these kinds of questions are completely open-ended. In a way, they allow the research and discoveries made along the way to direct the research. The questions are merely a starting point from which to explore.

This video offers tips on how to write good qualitative research questions, produced by Qualitative Research Expert, Kimberly Baker.

Wrap-up: crafting your own qualitative research questions.

Over the course of this article, we've explored what qualitative research questions are, why they matter, and how they should be written. Hopefully you now have a clear understanding of how to craft your own.

Remember, qualitative research questions should always be designed to explore a certain experience or phenomena in-depth, in order to generate powerful insights. As you write your questions, be sure to keep the following in mind:

• Are you being inclusive of all relevant perspectives?
• Are your questions specific enough to generate clear answers?
• Will your questions allow for an in-depth exploration of the topic at hand?
• Do the questions reflect your research goals and objectives?

If you can answer "yes" to all of the questions above, and you've followed the tips for writing qualitative research questions we shared in this article, then you're well on your way to crafting powerful queries that will yield valuable insights.

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Introduction

Why are research questions so important?

Research question examples, types of qualitative research questions, writing a good research question, guiding your research through research questions.

• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Research questions

The research question plays a critical role in the research process, as it guides the study design, data collection , analysis , and interpretation of the findings.

A research paper relies on a research question to inform readers of the research topic and the research problem being addressed. Without such a question, your audience may have trouble understanding the rationale for your research project.

People can take for granted the research question as an essential part of a research project. However, explicitly detailing why researchers need a research question can help lend clarity to the research project. Here are some of the key roles that the research question plays in the research process:

Defines the scope and focus of the study

The research question helps to define the scope and focus of the study. It identifies the specific topic or issue that the researcher wants to investigate, and it sets the boundaries for the study. A research question can also help you determine if your study primarily contributes to theory or is more applied in nature. Clinical research and public health research, for example, may be more concerned with research questions that contribute to practice, while a research question focused on cognitive linguistics are aimed at developing theory.

Provides a rationale for the study

The research question provides a rationale for the study by identifying a gap or problem in existing literature or practice that the researcher wants to address. It articulates the purpose and significance of the study, and it explains why the study is important and worth conducting.

Guides the study design

The research question guides the study design by helping the researcher select appropriate research methods , sampling strategies, and data collection tools. It also helps to determine the types of data that need to be collected and the best ways to analyze and interpret the data because the principal aim of the study is to provide an answer to that research question.

Shapes the data analysis and interpretation

The research question shapes the data analysis and interpretation by guiding the selection of appropriate analytical methods and by focusing the interpretation of the findings. It helps to identify which patterns and themes in the data are more relevant and worth digging into, and it guides the development of conclusions and recommendations based on the findings.

Generates new knowledge

The research question is the starting point for generating new knowledge. By answering the research question, the researcher contributes to the body of knowledge in the field and helps to advance the understanding of the topic or issue under investigation.

Overall, the research question is a critical component of the research process, as it guides the study from start to finish and provides a foundation for generating new knowledge.

Supports the thesis statement

The thesis statement or main assertion in any research paper stems from the answers to the research question. As a result, you can think of a focused research question as a preview of what the study aims to present as a new contribution to existing knowledge.

Here area few examples of focused research questions that can help set the stage for explaining different types of research questions in qualitative research . These questions touch upon various fields and subjects, showcasing the versatility and depth of research.

• What factors contribute to the job satisfaction of remote workers in the technology industry?
• How do teachers perceive the implementation of technology in the classroom, and what challenges do they face?
• What coping strategies do refugees use to deal with the challenges of resettlement in a new country?
• How does gentrification impact the sense of community and identity among long-term residents in urban neighborhoods?
• In what ways do social media platforms influence body image and self-esteem among adolescents?
• How do family dynamics and communication patterns affect the management of type 2 diabetes in adult patients?
• What is the role of mentorship in the professional development and career success of early-career academics?
• How do patients with chronic illnesses experience and navigate the healthcare system, and what barriers do they encounter?
• What are the motivations and experiences of volunteers in disaster relief efforts, and how do these experiences impact their future involvement in humanitarian work?
• How do cultural beliefs and values shape the consumer preferences and purchasing behavior of young adults in a globalized market?
• How do individuals whose genetic factors predict a high risk for developing a specific medical condition perceive, cope with, and make lifestyle choices based on this information?

These example research questions highlight the different kinds of inquiries common to qualitative research. They also demonstrate how qualitative research can address a wide range of topics, from understanding the experiences of specific populations to examining the impact of broader social and cultural phenomena.

Also, notice that these types of research questions tend to be geared towards inductive analyses that describe a concept in depth or develop new theory. As such, qualitative research questions tend to ask "what," "why," or "how" types of questions. This contrasts with quantitative research questions that typically aim to verify an existing theory. and tend to ask "when," "how much," and "why" types of questions to nail down causal mechanisms and generalizable findings.

Whatever your research inquiry, turn to ATLAS.ti

Powerful tools to help turn your research question into meaningful analysis, starting with a free trial.

As you can see above, the research questions you ask play a critical role in shaping the direction and depth of your study. These questions are designed to explore, understand, and interpret social phenomena, rather than testing a hypothesis or quantifying data like in quantitative research. In this section, we will discuss the various types of research questions typically found in qualitative research, making it easier for you to craft appropriate questions for your study.

Descriptive questions

Descriptive research questions aim to provide a detailed account of the phenomenon being studied. These questions usually begin with "what" or "how" and seek to understand the nature, characteristics, or functions of a subject. For example, "What are the experiences of first-generation college students?" or "How do small business owners adapt to economic downturns?"

Comparative questions

Comparative questions seek to examine the similarities and differences between two or more groups, cases, or phenomena. These questions often include the words "compare," "contrast," or "differences." For example, "How do parenting practices differ between single-parent and two-parent families?" or "What are the similarities and differences in leadership styles among successful female entrepreneurs?"

Exploratory questions

Exploratory research questions are open-ended and intended to investigate new or understudied areas. These questions aim to identify patterns, relationships, or themes that may warrant further investigation. For example, "How do teenagers use social media to construct their identities?" or "What factors influence the adoption of renewable energy technologies in rural communities?"

Explanatory questions

Explanatory research questions delve deeper into the reasons or explanations behind a particular phenomenon or behavior. They often start with "why" or "how" and aim to uncover underlying motivations, beliefs, or processes. For example, "Why do some employees resist organizational change?" or "How do cultural factors influence decision-making in international business negotiations?"

Evaluative questions

Evaluative questions assess the effectiveness, impact, or outcomes of a particular intervention, program, or policy. They seek to understand the value or significance of an initiative by examining its successes, challenges, or unintended consequences. For example, "How effective is the school's anti-bullying program in reducing incidents of bullying?" or "What are the long-term impacts of a community-based health promotion campaign on residents' well-being?"

Interpretive questions

Interpretive questions focus on understanding how individuals or groups make sense of their experiences, actions, or social contexts. These questions often involve the analysis of language, symbols, or narratives to uncover the meanings and perspectives that shape human behavior. For example, "How do cancer survivors make sense of their illness journey?" or "What meanings do members of a religious community attach to their rituals and practices?"

There are mainly two overarching ways to think about how to devise a research question. Many studies are built on existing research, but others can be founded on personal experiences or pilot research.

Using the literature review

Within scholarly research, the research question is often built from your literature review . An analysis of the relevant literature reporting previous studies should allow you to identify contextual, theoretical, or methodological gaps that can be addressed in future research.

A compelling research question built on a robust literature review ultimately illustrates to your audience what is novel about your study's objectives.

Conducting pilot research

Researchers may conduct preliminary research or pilot research when they are interested in a particular topic but don't yet have a basis for forming a research question on that topic. A pilot study is a small-scale, preliminary study that is conducted in order to test the feasibility of a research design, methods, and procedures. It can help identify unresolved puzzles that merit further investigation, and pilot studies can draw attention to potential issues or problems that may arise in the full study.

One potential benefit of conducting a pilot study in qualitative research is that it can help the researcher to refine their research question. By collecting and analyzing a small amount of data, the researcher can get a better sense of the phenomenon under investigation and can develop a more focused and refined research question for the full study. The pilot study can also help the researcher to identify key themes, concepts, or variables that should be included in the research question.

In addition to helping to refine the research question, a pilot study can also help the researcher to develop a more effective data collection and analysis plan. The researcher can test different methods for collecting and analyzing data, and can make adjustments based on the results of the pilot study. This can help to ensure that the full study is conducted in the most effective and efficient manner possible.

Overall, conducting a pilot study in qualitative research can be a valuable tool for refining the research question and developing a more effective research design, methods, and procedures. It can help to ensure that the full study is conducted in a rigorous and effective manner, and can increase the likelihood of generating meaningful and useful findings.

When you write a research question for your qualitative study, consider which type of question best aligns with your research objectives and the nature of the phenomenon you are investigating. Remember, qualitative research questions should be open-ended, allowing for a range of perspectives and insights to emerge. As you progress in your research, these questions may evolve or be refined based on the data you collect, helping to guide your analysis and deepen your understanding of the topic.

Use ATLAS.ti for every step of your research project

From the research question to the key insights, ATLAS.ti is there for you. See how with a free trial.

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Qualitative Methods in Monitoring and Evaluation: Qualitative Research Questions

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Selecting your research topic and crafting a qualitative research question from it is the first, and possibly the hardest, step of qualitative research. You will likely start with a topic, and as you start reading and do exploratory research, hone that topic into a research question that can be answered using qualitative methods.

I suggest that students start big and then narrow their topics. As you review the literature and current events around your larger topic, you will likely discover what questions academics and policymakers are asking about that topic. You should identify your topic’s puzzles, those questions that have yet to be answered. Then you should choose one of these puzzles to meld into your research question.

Throughout this process, you should constantly remind yourself of the purpose of qualitative inquiry. As researchers, we use qualitative data collection techniques to gather rich, emic data around a topic. That data highlights experiences and perceptions that help to provide explanation. As you explore your larger topic, focus on those puzzles that need qualitative explanation. As you hone your topic into possible research questions, ask yourself why qualitative data collection techniques would be the best way to provide insight into your topic and answer your research question. This is actually harder than you might think, as many of us tend towards the quantitative. Usually, crafting a qualitative research question means asking a why or a what explains question, NOT a how or a descriptive question.

The best qualitative research questions are:

• Interesting to you. Depending on the purpose of your research and your research output, you will likely spend a lot of time on your topic. Pick a topic that you find interesting, so that you will be engaged throughout the research process.
• Original. When we conduct primary research, we are not summarizing the research of others. We are coming up with our own research question and qualitative design to answer it. Your qualitative research could identify a brand new topic, or it could take a new spin on an old topic, or look at a new topic in a different light.
• Answerable. Your research question should be answerable using qualitative methods. Not every research question can and should be answered using qualitative data collection techniques. You should craft a question that is best answered using qualitative research.
• Manageable. Your research question should be manageable within your time, space, and budget constraints. Craft a question that fits within the purpose and scope of your research. Some qualitative questions might take an article length paper to answer, and some may take a book! Some questions might require a longer time to answer, travel that you are not able to do, or a larger budget than you have to support your research. Craft your question with these constraints and parameters in mind.

Once you have a research question, you will need to draft your qualitative research design. Your design will need to provide specifics on the qualitative data collection techniques you intend to use to answer your research question. You should think in advance about what kinds of data you will need, and what qualitative data collection techniques would be most useful to gather it. You have a number of tools available in your qualitative data collection toolkit, and you need to figure out which is most appropriate for your data collection need. You might use observation, participant observation , interviews , focus groups , or participatory tools , for example. You also need to think through how you will address missing or incomplete data, and how you will manage and analyze the data that you collect.

Qualitative Questions and Evaluation

When we conduct an evaluation , we usually start by crafting a logic model or Logical Framework (LogFrame) . As evaluators, we usually ask qualitative questions that help us to understand an organization’s logic model or to populate its LogFrame. We might ask a broad question such as: What explains this organization’s theory of change? Such a broad question would also have support questions such as: What does this organization do? Why does it do it that way? What are some examples of projects? How are those projects managed? Who are the beneficiaries? What are this organization’s challenges? What are this organization’s risks and assumptions?

Good qualitative research questions that help us to craft an evaluation might include questions around program need, and program conceptualization and design (Rossi, Lipsey, and Freeman, 2004). Depending on the purpose of the evaluation and your evaluation design, you might ask process-focused questions such as who, what, when, where, why, and how; or you might ask outcome focused questions around changes, effects, and impacts.

Your qualitative research and the answers to all of these questions could help you to develop a LogFrame that you could use to guide a future evaluation that asks questions around program operations and service delivery, program outcomes, or program cost efficiency. Your evaluation design would include evaluation questions that likely have a mixed method element that uses a combination of qualitative and quantitative data and methods to help measure progress or change. Our evaluation questions are not necessarily qualitative in nature; they are often questions that require mixed methods or quantitative tools and analyses to answer. However, we often use qualitative research questions and data collection techniques to help us craft our evaluation questions, LogFrame, and evaluation design.

Rossi, Peter, Mark Lipsey, and Howard Freeman. Evaluation: A Systematic Approach. 7th edition. Thousand Oaks, SAGE, 2004.

About The Author

Dr. Beverly Peters has more than twenty years of experience teaching, conducting qualitative research, and managing community development, microcredit, infrastructure, and democratization projects in several countries in Africa. As a consultant, Dr. Peters worked on EU and USAID funded infrastructure, education, and microcredit projects in South Africa and Mozambique. She also conceptualized and developed the proposal for Darfur Peace and Development Organization’s women’s crisis center, a center that provides physical and economic assistance to women survivors of violence in the IDP camps in Darfur. Dr. Peters has a Ph.D. from the University of Pittsburgh. Learn more about Dr. Peters.

To learn more about American University’s online MS in Measurement & Evaluation or Graduate Certificate in Project Monitoring & Evaluation, request more information or call us toll free at 855-725-7614.

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Qualitative Research Design: Start

Qualitative Research Design

What is Qualitative research design?

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much . It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and analyzing numerical data for statistical analysis. Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Research Paradigms 

• Positivist versus Post-Positivist
• Social Constructivist (this paradigm/ideology mostly birth qualitative studies)

Events Relating to the Qualitative Research and Community Engagement Workshops @ CMU Libraries

CMU Libraries is committed to helping members of our community become data experts. To that end, CMU is offering public facing workshops that discuss Qualitative Research, Coding, and Community Engagement best practices.

The following workshops are a part of a broader series on using data. Please follow the links to register for the events. 

Qualitative Coding

Using Community Data to improve Outcome (Grant Writing)

Survey Design  

Upcoming Event: March 21st, 2024 (12:00pm -1:00 pm)

Community Engagement and Collaboration Event 

Join us for an event to improve, build on and expand the connections between Carnegie Mellon University resources and the Pittsburgh community. CMU resources such as the Libraries and Sustainability Initiative can be leveraged by users not affiliated with the university, but barriers can prevent them from fully engaging.

The conversation features representatives from CMU departments and local organizations about the community engagement efforts currently underway at CMU and opportunities to improve upon them. Speakers will highlight current and ongoing projects and share resources to support future collaboration.

Event Moderators:

Taiwo Lasisi, CLIR Postdoctoral Fellow in Community Data Literacy,  Carnegie Mellon University Libraries

Emma Slayton, Data Curation, Visualization, & GIS Specialist,  Carnegie Mellon University Libraries

Nicky Agate , Associate Dean for Academic Engagement, Carnegie Mellon University Libraries

Chelsea Cohen , The University’s Executive fellow for community engagement, Carnegie Mellon University

Sarah Ceurvorst , Academic Pathways Manager, Program Director, LEAP (Leadership, Excellence, Access, Persistence) Carnegie Mellon University

Julia Poeppibg , Associate Director of Partnership Development, Information Systems, Carnegie Mellon University 

Scott Wolovich , Director of New Sun Rising, Pittsburgh 

Additional workshops and events will be forthcoming. Watch this space for updates. 

Workshop Organizer

Qualitative Research Methods

What are Qualitative Research methods?

Qualitative research adopts numerous methods or techniques including interviews, focus groups, and observation. Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant observers to share the experiences of the subject or non-participant or detached observers.

What constitutes a good research question? Does the question drive research design choices?

According to Doody and Bailey (2014);

 We can only develop a good research question by consulting relevant literature, colleagues, and supervisors experienced in the area of research. (inductive interactions).

Helps to have a directed research aim and objective.

Researchers should not be “ research trendy” and have enough evidence. This is why research objectives are important. It helps to take time, and resources into consideration.

Research questions can be developed from theoretical knowledge, previous research or experience, or a practical need at work (Parahoo 2014). They have numerous roles, such as identifying the importance of the research and providing clarity of purpose for the research, in terms of what the research intends to achieve in the end.

Qualitative Research Questions

What constitutes a good Qualitative research question?

A good qualitative question answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. Qualitative research gathers participants' experiences, perceptions and behavior.

Examples of good Qualitative Research Questions:

What are people's thoughts on the new library? 

How does it feel to be a first-generation student attending college?

Difference example (between Qualitative and Quantitative research questions):

How many college students signed up for the new semester? (Quan) 

How do college students feel about the new semester? What are their experiences so far? (Qual)

• Qualitative Research Design Workshop Powerpoint

Foley G, Timonen V. Using Grounded Theory Method to Capture and Analyze Health Care Experiences. Health Serv Res. 2015 Aug;50(4):1195-210. [ PMC free article: PMC4545354 ] [ PubMed: 25523315 ]

Devers KJ. How will we know "good" qualitative research when we see it? Beginning the dialogue in health services research. Health Serv Res. 1999 Dec;34(5 Pt 2):1153-88. [ PMC free article: PMC1089058 ] [ PubMed: 10591278 ]

Huston P, Rowan M. Qualitative studies. Their role in medical research. Can Fam Physician. 1998 Nov;44:2453-8. [ PMC free article: PMC2277956 ] [ PubMed: 9839063 ]

Corner EJ, Murray EJ, Brett SJ. Qualitative, grounded theory exploration of patients' experience of early mobilisation, rehabilitation and recovery after critical illness. BMJ Open. 2019 Feb 24;9(2):e026348. [ PMC free article: PMC6443050 ] [ PubMed: 30804034 ]

Moser A, Korstjens I. Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. Eur J Gen Pract. 2018 Dec;24(1):9-18. [ PMC free article: PMC5774281 ] [ PubMed: 29199486 ]

Houghton C, Murphy K, Meehan B, Thomas J, Brooker D, Casey D. From screening to synthesis: using nvivo to enhance transparency in qualitative evidence synthesis. J Clin Nurs. 2017 Mar;26(5-6):873-881. [ PubMed: 27324875 ]

Soratto J, Pires DEP, Friese S. Thematic content analysis using ATLAS.ti software: Potentialities for researchs in health. Rev Bras Enferm. 2020;73(3):e20190250. [ PubMed: 32321144 ]

Zamawe FC. The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections. Malawi Med J. 2015 Mar;27(1):13-5. [ PMC free article: PMC4478399 ] [ PubMed: 26137192 ]

Korstjens I, Moser A. Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. Eur J Gen Pract. 2018 Dec;24(1):120-124. [ PMC free article: PMC8816392 ] [ PubMed: 29202616 ]

Saldaña, J. (2021). The coding manual for qualitative researchers. The coding manual for qualitative researchers, 1-440.

O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014 Sep;89(9):1245-51. [ PubMed: 24979285 ]

Palermo C, King O, Brock T, Brown T, Crampton P, Hall H, Macaulay J, Morphet J, Mundy M, Oliaro L, Paynter S, Williams B, Wright C, E Rees C. Setting priorities for health education research: A mixed methods study. Med Teach. 2019 Sep;41(9):1029-1038. [ PubMed: 31141390 ]

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Qualitative Research Questions: What it is and how to write it

Qualitative research questions are like a compass that points researchers in the right direction to find rich stories, untangle complicated social relationships, and get a clear picture of how people act in subtle ways. Unlike their quantitative counterparts, these questions go beyond numbers and figures to explore the subjective, contextual, and complex parts of the human experience.

It’s well-established that all forms of research come with their own theories and implementation methods. Qualitative research is much the same. Qualitative research is conducted to understand the thought process of both the respondents as well as researchers. It usually is conducted in a natural setup where respondents will be their true selves and would respond transparently. 

Results achieved from this research will not be generalized to the entire population but asked research questions , and their vocabulary gives away the researcher’s motive making it easier for respondents to participate in qualitative market research .

LEARN ABOUT: Research Process Steps

Qualitative research survey questions are created to understand a particular topic better or to inspect a new subject to understand the nerve of respondent experiences.

Content Index

What are qualitative research questions?

How to write qualitative research questions, types of qualitative research questions, how to choose qualitative research questions, what should be the process of forming qualitative research questions and questionnaires.

Qualitative research questions are the inquiries that lead to qualitative research studies and investigations. They are meant to help people explore and understand phenomena, experiences, meanings, and views from the participant’s point of view. 

Different from quantitative research questions, which often try to measure and quantify variables, qualitative research questions try to understand the richness and complexity of human experiences and social events.

Most qualitative research questions are open-ended and allow for in-depth study. They want more than simple yes/no answers but instead want people to talk about their thoughts, feelings, views, and experiences. These questions try to find deeper meanings, patterns, and connections in a given situation.

Here are some examples of qualitative study questions in different fields:

• In psychology: How do individuals experience and cope with traumatic events?
• In sociology: What factors influence a student’s decision to pursue higher education?
• In anthropology: How do cultural norms and values shape gender roles in a specific community?
• In education: What are the challenges faced by teachers in implementing project-based learning in the classroom?
• In healthcare: What are the experiences and perspectives of patients undergoing long-term treatment for a chronic illness?

Qualitative research questions should be straightforward, specific, and tailored to the research’s goals. They guide the process of gathering data through interviews, observations, or document analysis and give a method for analyzing and interpreting data.

Writing the right qualitative research questions requires careful thought about the research goals, the event being studied, and the wanted level of understanding. Here are some tips to help you write good qualitative research questions:

Begin with a broad research question

Start by posing an all-encompassing question that probes the subject or phenomenon of interest. Exploring and learning from the answer to this open-ended question should be possible.

Specify the research objectives

Clearly state the objectives and purposes of your research. What do you want your qualitative study to accomplish? What facets or dimensions of the subject do you wish to investigate?

Focus on the phenomenon

Decide on whatever specific subject or phenomenon you want to research. Any pertinent topic, including social behavior, cultural customs, personal experiences, and more, may be used.

Use open-ended and exploratory language

In qualitative research, open-ended questions should be used to enable participants to offer thorough and in-depth responses. Avoid yes/no questions and queries with a one-word answer. Use words like “how,” “what,” “why,” or “describe” instead to compel people to express their thoughts and experiences.

LEARN ABOUT: Qualitative Interview

Consider the context and participants

Consider your research’s background as well as the qualities of your subjects. Make sure your qualitative methods are specific to the people you will be studying so that they are pertinent and meaningful to them.

Incorporate theory and literature

Your research questions should be based on pertinent theories and available literature. This gives your investigation a theoretical foundation and places your study within the body of knowledge.

Balance breadth and depth

When formulating your research topics, try to strike a balance between depth and breadth. To fully understand the subject, you should investigate it broadly to get a variety of viewpoints and intensively delve into certain areas.

Avoid leading or biased questions

Ensure your questions are neutral and unbiased. Avoid leading participants towards a particular response. Instead, create questions that allow participants to express their thoughts and experiences freely.

Pilot test your questions

Pilot-test your research questions with a small group of people before finalizing them. This will make it easier to spot any possible problems, ambiguities, or places where clarity may be increased.

Revise and refine

Revise and clarify your research questions based on the comments and understandings received from the pilot testing. Aim for consistency, coherence, and congruence with your research goals.

Remember, qualitative market research questions should be flexible and adaptable throughout the research process. They serve as a guide but may evolve as you delve deeper into the data and discover new insights.

LEARN ABOUT: Steps in Qualitative Research

There are several types of qualitative research questions focus that can be used to guide qualitative studies. Here are some common types:

1. Descriptive questions

These questions aim to describe and understand a phenomenon or topic in detail. They focus on providing a comprehensive account of the subject matter. For example:

• What are the experiences of individuals living with chronic pain?
• How do employees perceive the organizational culture in a specific company?

2. Exploratory questions

These questions are used to explore new or under-researched areas. They seek to gain a deeper understanding of a topic or phenomenon. For example:

• What are the factors influencing consumers’ decision-making process when purchasing organic food?
• How do teachers perceive the implementation of project-based learning in the classroom?

3. Experiential questions

These questions focus on understanding individuals’ experiences, perspectives, and subjective meanings related to a particular phenomenon. They aim to capture personal experiences and emotions. For example:

• What are the challenges first-generation college students face during their transition to higher education?
• How do individuals with social anxiety disorder experience social interactions?

4. Comparative questions

These questions involve comparing and contrasting different groups, contexts, or perspectives to identify similarities, differences, or patterns. They explore variations in experiences or phenomena. For example:

• How do parenting practices differ between cultures A and B in terms of child discipline?
• What are the similarities and differences in the coping strategies used by individuals with individuals and depression questionnaire with anxiety disorders?

5. Process-oriented questions

These questions focus on understanding a phenomenon’s processes, mechanisms, or dynamics. They aim to uncover how and why certain outcomes or behaviors occur. For example:

• What are the processes by which teams in a workplace reach a consensus on decision-making?
• How does the negotiation process unfold during conflict resolution in interpersonal relationships?

6. Theoretical questions

These questions seek to generate or refine theory. They explore concepts, relationships, or theoretical frameworks to contribute to the existing body of knowledge. For example:

• How does the concept of “self-efficacy” manifest in the context of entrepreneurship?
• What underlying mechanisms explain the relationship between social support and mental health outcomes?

These are just a few examples of the types of qualitative research questions that can be used. The specific type of question you choose will depend on your research objectives, the phenomenon under investigation, and the depth of understanding you aim to achieve.

Explore Insightfully Contextual Inquiry in Qualitative Research

Choosing a good qualitative research question involves a thoughtful and systematic approach to ensure they align with the objectives of your study and allows for an in-depth exploration of the topic. Here are some steps to help you choose effective qualitative research questions:

Identify your research objectives

Clearly define the purpose of your study. What do you want to explore or understand? What specific insights or knowledge are you seeking to gain through your market research?

Review existing literature

Conduct a thorough review of relevant literature to identify existing research gaps or areas requiring further exploration. This will help you understand the current state of knowledge and inform the development of your research questions.

Brainstorm potential qualitative research question

Generate a list of potential research questions that address your research objectives. Consider different angles, perspectives, and dimensions of your topic. Creating open-ended questions that allow for in-depth exploration rather than simple yes/no answers is important.

Prioritize and refine the questions

Evaluate the generated questions based on their relevance to your research objectives, feasibility, and potential to yield meaningful insights. Prioritize the questions that are most likely to provide rich and valuable data. Refine and rephrase the questions as needed to ensure clarity and focus.

Consider the research design and methodology

Take into account the specific qualitative research design and methodology you plan to use. Different research approaches, such as ethnography, interviews, focus groups, or case studies, may require different types of research questions. Ensure that your questions align with your chosen methodology and will help you gather the desired data.

Pilot test the questions

Before finalizing your research questions, consider conducting a pilot test with a small group of participants. This will allow you to assess your questions’ clarity, appropriateness, and effectiveness. Make necessary revisions based on the feedback received.

Seek feedback

Share your research questions with colleagues, mentors, or experts in your field for feedback and suggestions. They can provide valuable insights and help you refine your questions further.

Finalize your research questions

Based on the steps above, select a set of research questions that are well-aligned with your research objectives, provide scope for exploration, and are feasible within the resources and time available for your study.

1. Mention the purpose of conducting qualitative research. It can be in the form of either of these sentences:

• This study will be on the topic of ….
• The reason for conducting this research is ….

2. Create qualitative statements with a defined objective that can be easily communicated to the target audience .

Keep these pointers in mind while designing this statement:

• Try and form single-sentence statements. Single statements can be much more effective than elaborate ones as they help in communicating important messages in an impactful manner in a short and succinct sentence.
• Clarify the purpose of conducting qualitative research in clear words so that respondents understand their contribution to this research.
• Mention the main topic of research that would prompt respondents to have a clearer idea about what they’re getting into.
• It’s the words that make all the difference. Use qualitative words that demonstrate the quality or feeling behind your purpose, such as understanding, describing, explore.
• Specify details that you would want to communicate to your respondents.
• Mention the name of the research website.

3. Other than the primary qualitative questions, you must create sub-questions so that the purpose is executed in a better manner.

• The main question might be – “What is the state of illiteracy in your state?”
• You can create sub-questions such as: “How does illiteracy hamper progress in your state?” or “How would you best describe your feelings about illiteracy?”

4. Highlight these questions using ‘qualitative’ words:

• Start the questions with “What” or “How” to make sure the respondents provide details about their feelings.
• Communicate what you’re trying to “understand,” “explore,” or “identify” using this Qualitative research online survey questionnaire.
• Questions such as “What happened” can be asked to develop a description of the topic.
• Questions about “how did respondents interpret the what happened question” can be asked to examine the outcome.
• Understand the entire qualitative research process by asking questions about “What happened to you with time?”

5. Develop a skeleton to design the primary questions and also the sub-questions. For example:

• Primary Qualitative research survey question: “How do you think _______ (the main topic of research) means?” or “Describe _____(the main topic of research) as you’ve experienced.”
• Sub-question for qualitative research: “What _________ (characteristic) does __________ (respondents) interest in as a _________ (main topic of research)?”

LEARN ABOUT: Structured Questionnaire

Qualitative research questions are key to giving research studies depth and breadth. These questions go into the details and complexities of human experiences, perceptions, and behaviors. This helps researchers get a full picture of a certain occurrence. 

Qualitative research questions are meant to explore, describe, and make sense of subjective truths. Most of the time, they are open-ended, so people can say what they think and feel in their own words. 

QuestionPro is an online poll and research platform with several tools and features that can make it easier to make and use qualitative research questions. Its easy-to-use design and variety of question types help researchers collect qualitative data quickly and easily, improving the whole research process.

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Criteria for Good Qualitative Research: A Comprehensive Review

• Regular Article
• Open access
• Published: 18 September 2021
• Volume 31 , pages 679–689, ( 2022 )

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• Drishti Yadav   ORCID: orcid.org/0000-0002-2974-0323 1  

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This review aims to synthesize a published set of evaluative criteria for good qualitative research. The aim is to shed light on existing standards for assessing the rigor of qualitative research encompassing a range of epistemological and ontological standpoints. Using a systematic search strategy, published journal articles that deliberate criteria for rigorous research were identified. Then, references of relevant articles were surveyed to find noteworthy, distinct, and well-defined pointers to good qualitative research. This review presents an investigative assessment of the pivotal features in qualitative research that can permit the readers to pass judgment on its quality and to condemn it as good research when objectively and adequately utilized. Overall, this review underlines the crux of qualitative research and accentuates the necessity to evaluate such research by the very tenets of its being. It also offers some prospects and recommendations to improve the quality of qualitative research. Based on the findings of this review, it is concluded that quality criteria are the aftereffect of socio-institutional procedures and existing paradigmatic conducts. Owing to the paradigmatic diversity of qualitative research, a single and specific set of quality criteria is neither feasible nor anticipated. Since qualitative research is not a cohesive discipline, researchers need to educate and familiarize themselves with applicable norms and decisive factors to evaluate qualitative research from within its theoretical and methodological framework of origin.

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Good Qualitative Research: Opening up the Debate

Beyond qualitative/quantitative structuralism: the positivist qualitative research and the paradigmatic disclaimer.

What is Qualitative in Research

Avoid common mistakes on your manuscript.

Introduction

“… It is important to regularly dialogue about what makes for good qualitative research” (Tracy, 2010 , p. 837)

To decide what represents good qualitative research is highly debatable. There are numerous methods that are contained within qualitative research and that are established on diverse philosophical perspectives. Bryman et al., ( 2008 , p. 262) suggest that “It is widely assumed that whereas quality criteria for quantitative research are well‐known and widely agreed, this is not the case for qualitative research.” Hence, the question “how to evaluate the quality of qualitative research” has been continuously debated. There are many areas of science and technology wherein these debates on the assessment of qualitative research have taken place. Examples include various areas of psychology: general psychology (Madill et al., 2000 ); counseling psychology (Morrow, 2005 ); and clinical psychology (Barker & Pistrang, 2005 ), and other disciplines of social sciences: social policy (Bryman et al., 2008 ); health research (Sparkes, 2001 ); business and management research (Johnson et al., 2006 ); information systems (Klein & Myers, 1999 ); and environmental studies (Reid & Gough, 2000 ). In the literature, these debates are enthused by the impression that the blanket application of criteria for good qualitative research developed around the positivist paradigm is improper. Such debates are based on the wide range of philosophical backgrounds within which qualitative research is conducted (e.g., Sandberg, 2000 ; Schwandt, 1996 ). The existence of methodological diversity led to the formulation of different sets of criteria applicable to qualitative research.

Among qualitative researchers, the dilemma of governing the measures to assess the quality of research is not a new phenomenon, especially when the virtuous triad of objectivity, reliability, and validity (Spencer et al., 2004 ) are not adequate. Occasionally, the criteria of quantitative research are used to evaluate qualitative research (Cohen & Crabtree, 2008 ; Lather, 2004 ). Indeed, Howe ( 2004 ) claims that the prevailing paradigm in educational research is scientifically based experimental research. Hypotheses and conjectures about the preeminence of quantitative research can weaken the worth and usefulness of qualitative research by neglecting the prominence of harmonizing match for purpose on research paradigm, the epistemological stance of the researcher, and the choice of methodology. Researchers have been reprimanded concerning this in “paradigmatic controversies, contradictions, and emerging confluences” (Lincoln & Guba, 2000 ).

In general, qualitative research tends to come from a very different paradigmatic stance and intrinsically demands distinctive and out-of-the-ordinary criteria for evaluating good research and varieties of research contributions that can be made. This review attempts to present a series of evaluative criteria for qualitative researchers, arguing that their choice of criteria needs to be compatible with the unique nature of the research in question (its methodology, aims, and assumptions). This review aims to assist researchers in identifying some of the indispensable features or markers of high-quality qualitative research. In a nutshell, the purpose of this systematic literature review is to analyze the existing knowledge on high-quality qualitative research and to verify the existence of research studies dealing with the critical assessment of qualitative research based on the concept of diverse paradigmatic stances. Contrary to the existing reviews, this review also suggests some critical directions to follow to improve the quality of qualitative research in different epistemological and ontological perspectives. This review is also intended to provide guidelines for the acceleration of future developments and dialogues among qualitative researchers in the context of assessing the qualitative research.

The rest of this review article is structured in the following fashion: Sect.  Methods describes the method followed for performing this review. Section Criteria for Evaluating Qualitative Studies provides a comprehensive description of the criteria for evaluating qualitative studies. This section is followed by a summary of the strategies to improve the quality of qualitative research in Sect.  Improving Quality: Strategies . Section  How to Assess the Quality of the Research Findings? provides details on how to assess the quality of the research findings. After that, some of the quality checklists (as tools to evaluate quality) are discussed in Sect.  Quality Checklists: Tools for Assessing the Quality . At last, the review ends with the concluding remarks presented in Sect.  Conclusions, Future Directions and Outlook . Some prospects in qualitative research for enhancing its quality and usefulness in the social and techno-scientific research community are also presented in Sect.  Conclusions, Future Directions and Outlook .

For this review, a comprehensive literature search was performed from many databases using generic search terms such as Qualitative Research , Criteria , etc . The following databases were chosen for the literature search based on the high number of results: IEEE Explore, ScienceDirect, PubMed, Google Scholar, and Web of Science. The following keywords (and their combinations using Boolean connectives OR/AND) were adopted for the literature search: qualitative research, criteria, quality, assessment, and validity. The synonyms for these keywords were collected and arranged in a logical structure (see Table 1 ). All publications in journals and conference proceedings later than 1950 till 2021 were considered for the search. Other articles extracted from the references of the papers identified in the electronic search were also included. A large number of publications on qualitative research were retrieved during the initial screening. Hence, to include the searches with the main focus on criteria for good qualitative research, an inclusion criterion was utilized in the search string.

From the selected databases, the search retrieved a total of 765 publications. Then, the duplicate records were removed. After that, based on the title and abstract, the remaining 426 publications were screened for their relevance by using the following inclusion and exclusion criteria (see Table 2 ). Publications focusing on evaluation criteria for good qualitative research were included, whereas those works which delivered theoretical concepts on qualitative research were excluded. Based on the screening and eligibility, 45 research articles were identified that offered explicit criteria for evaluating the quality of qualitative research and were found to be relevant to this review.

Figure  1 illustrates the complete review process in the form of PRISMA flow diagram. PRISMA, i.e., “preferred reporting items for systematic reviews and meta-analyses” is employed in systematic reviews to refine the quality of reporting.

PRISMA flow diagram illustrating the search and inclusion process. N represents the number of records

Criteria for Evaluating Qualitative Studies

Fundamental criteria: general research quality.

Various researchers have put forward criteria for evaluating qualitative research, which have been summarized in Table 3 . Also, the criteria outlined in Table 4 effectively deliver the various approaches to evaluate and assess the quality of qualitative work. The entries in Table 4 are based on Tracy’s “Eight big‐tent criteria for excellent qualitative research” (Tracy, 2010 ). Tracy argues that high-quality qualitative work should formulate criteria focusing on the worthiness, relevance, timeliness, significance, morality, and practicality of the research topic, and the ethical stance of the research itself. Researchers have also suggested a series of questions as guiding principles to assess the quality of a qualitative study (Mays & Pope, 2020 ). Nassaji ( 2020 ) argues that good qualitative research should be robust, well informed, and thoroughly documented.

Qualitative Research: Interpretive Paradigms

All qualitative researchers follow highly abstract principles which bring together beliefs about ontology, epistemology, and methodology. These beliefs govern how the researcher perceives and acts. The net, which encompasses the researcher’s epistemological, ontological, and methodological premises, is referred to as a paradigm, or an interpretive structure, a “Basic set of beliefs that guides action” (Guba, 1990 ). Four major interpretive paradigms structure the qualitative research: positivist and postpositivist, constructivist interpretive, critical (Marxist, emancipatory), and feminist poststructural. The complexity of these four abstract paradigms increases at the level of concrete, specific interpretive communities. Table 5 presents these paradigms and their assumptions, including their criteria for evaluating research, and the typical form that an interpretive or theoretical statement assumes in each paradigm. Moreover, for evaluating qualitative research, quantitative conceptualizations of reliability and validity are proven to be incompatible (Horsburgh, 2003 ). In addition, a series of questions have been put forward in the literature to assist a reviewer (who is proficient in qualitative methods) for meticulous assessment and endorsement of qualitative research (Morse, 2003 ). Hammersley ( 2007 ) also suggests that guiding principles for qualitative research are advantageous, but methodological pluralism should not be simply acknowledged for all qualitative approaches. Seale ( 1999 ) also points out the significance of methodological cognizance in research studies.

Table 5 reflects that criteria for assessing the quality of qualitative research are the aftermath of socio-institutional practices and existing paradigmatic standpoints. Owing to the paradigmatic diversity of qualitative research, a single set of quality criteria is neither possible nor desirable. Hence, the researchers must be reflexive about the criteria they use in the various roles they play within their research community.

Improving Quality: Strategies

Another critical question is “How can the qualitative researchers ensure that the abovementioned quality criteria can be met?” Lincoln and Guba ( 1986 ) delineated several strategies to intensify each criteria of trustworthiness. Other researchers (Merriam & Tisdell, 2016 ; Shenton, 2004 ) also presented such strategies. A brief description of these strategies is shown in Table 6 .

It is worth mentioning that generalizability is also an integral part of qualitative research (Hays & McKibben, 2021 ). In general, the guiding principle pertaining to generalizability speaks about inducing and comprehending knowledge to synthesize interpretive components of an underlying context. Table 7 summarizes the main metasynthesis steps required to ascertain generalizability in qualitative research.

Figure  2 reflects the crucial components of a conceptual framework and their contribution to decisions regarding research design, implementation, and applications of results to future thinking, study, and practice (Johnson et al., 2020 ). The synergy and interrelationship of these components signifies their role to different stances of a qualitative research study.

Essential elements of a conceptual framework

In a nutshell, to assess the rationale of a study, its conceptual framework and research question(s), quality criteria must take account of the following: lucid context for the problem statement in the introduction; well-articulated research problems and questions; precise conceptual framework; distinct research purpose; and clear presentation and investigation of the paradigms. These criteria would expedite the quality of qualitative research.

How to Assess the Quality of the Research Findings?

The inclusion of quotes or similar research data enhances the confirmability in the write-up of the findings. The use of expressions (for instance, “80% of all respondents agreed that” or “only one of the interviewees mentioned that”) may also quantify qualitative findings (Stenfors et al., 2020 ). On the other hand, the persuasive reason for “why this may not help in intensifying the research” has also been provided (Monrouxe & Rees, 2020 ). Further, the Discussion and Conclusion sections of an article also prove robust markers of high-quality qualitative research, as elucidated in Table 8 .

Quality Checklists: Tools for Assessing the Quality

Numerous checklists are available to speed up the assessment of the quality of qualitative research. However, if used uncritically and recklessly concerning the research context, these checklists may be counterproductive. I recommend that such lists and guiding principles may assist in pinpointing the markers of high-quality qualitative research. However, considering enormous variations in the authors’ theoretical and philosophical contexts, I would emphasize that high dependability on such checklists may say little about whether the findings can be applied in your setting. A combination of such checklists might be appropriate for novice researchers. Some of these checklists are listed below:

The most commonly used framework is Consolidated Criteria for Reporting Qualitative Research (COREQ) (Tong et al., 2007 ). This framework is recommended by some journals to be followed by the authors during article submission.

Standards for Reporting Qualitative Research (SRQR) is another checklist that has been created particularly for medical education (O’Brien et al., 2014 ).

Also, Tracy ( 2010 ) and Critical Appraisal Skills Programme (CASP, 2021 ) offer criteria for qualitative research relevant across methods and approaches.

Further, researchers have also outlined different criteria as hallmarks of high-quality qualitative research. For instance, the “Road Trip Checklist” (Epp & Otnes, 2021 ) provides a quick reference to specific questions to address different elements of high-quality qualitative research.

Conclusions, Future Directions, and Outlook

This work presents a broad review of the criteria for good qualitative research. In addition, this article presents an exploratory analysis of the essential elements in qualitative research that can enable the readers of qualitative work to judge it as good research when objectively and adequately utilized. In this review, some of the essential markers that indicate high-quality qualitative research have been highlighted. I scope them narrowly to achieve rigor in qualitative research and note that they do not completely cover the broader considerations necessary for high-quality research. This review points out that a universal and versatile one-size-fits-all guideline for evaluating the quality of qualitative research does not exist. In other words, this review also emphasizes the non-existence of a set of common guidelines among qualitative researchers. In unison, this review reinforces that each qualitative approach should be treated uniquely on account of its own distinctive features for different epistemological and disciplinary positions. Owing to the sensitivity of the worth of qualitative research towards the specific context and the type of paradigmatic stance, researchers should themselves analyze what approaches can be and must be tailored to ensemble the distinct characteristics of the phenomenon under investigation. Although this article does not assert to put forward a magic bullet and to provide a one-stop solution for dealing with dilemmas about how, why, or whether to evaluate the “goodness” of qualitative research, it offers a platform to assist the researchers in improving their qualitative studies. This work provides an assembly of concerns to reflect on, a series of questions to ask, and multiple sets of criteria to look at, when attempting to determine the quality of qualitative research. Overall, this review underlines the crux of qualitative research and accentuates the need to evaluate such research by the very tenets of its being. Bringing together the vital arguments and delineating the requirements that good qualitative research should satisfy, this review strives to equip the researchers as well as reviewers to make well-versed judgment about the worth and significance of the qualitative research under scrutiny. In a nutshell, a comprehensive portrayal of the research process (from the context of research to the research objectives, research questions and design, speculative foundations, and from approaches of collecting data to analyzing the results, to deriving inferences) frequently proliferates the quality of a qualitative research.

Prospects : A Road Ahead for Qualitative Research

Irrefutably, qualitative research is a vivacious and evolving discipline wherein different epistemological and disciplinary positions have their own characteristics and importance. In addition, not surprisingly, owing to the sprouting and varied features of qualitative research, no consensus has been pulled off till date. Researchers have reflected various concerns and proposed several recommendations for editors and reviewers on conducting reviews of critical qualitative research (Levitt et al., 2021 ; McGinley et al., 2021 ). Following are some prospects and a few recommendations put forward towards the maturation of qualitative research and its quality evaluation:

In general, most of the manuscript and grant reviewers are not qualitative experts. Hence, it is more likely that they would prefer to adopt a broad set of criteria. However, researchers and reviewers need to keep in mind that it is inappropriate to utilize the same approaches and conducts among all qualitative research. Therefore, future work needs to focus on educating researchers and reviewers about the criteria to evaluate qualitative research from within the suitable theoretical and methodological context.

There is an urgent need to refurbish and augment critical assessment of some well-known and widely accepted tools (including checklists such as COREQ, SRQR) to interrogate their applicability on different aspects (along with their epistemological ramifications).

Efforts should be made towards creating more space for creativity, experimentation, and a dialogue between the diverse traditions of qualitative research. This would potentially help to avoid the enforcement of one's own set of quality criteria on the work carried out by others.

Moreover, journal reviewers need to be aware of various methodological practices and philosophical debates.

It is pivotal to highlight the expressions and considerations of qualitative researchers and bring them into a more open and transparent dialogue about assessing qualitative research in techno-scientific, academic, sociocultural, and political rooms.

Frequent debates on the use of evaluative criteria are required to solve some potentially resolved issues (including the applicability of a single set of criteria in multi-disciplinary aspects). Such debates would not only benefit the group of qualitative researchers themselves, but primarily assist in augmenting the well-being and vivacity of the entire discipline.

To conclude, I speculate that the criteria, and my perspective, may transfer to other methods, approaches, and contexts. I hope that they spark dialog and debate – about criteria for excellent qualitative research and the underpinnings of the discipline more broadly – and, therefore, help improve the quality of a qualitative study. Further, I anticipate that this review will assist the researchers to contemplate on the quality of their own research, to substantiate research design and help the reviewers to review qualitative research for journals. On a final note, I pinpoint the need to formulate a framework (encompassing the prerequisites of a qualitative study) by the cohesive efforts of qualitative researchers of different disciplines with different theoretic-paradigmatic origins. I believe that tailoring such a framework (of guiding principles) paves the way for qualitative researchers to consolidate the status of qualitative research in the wide-ranging open science debate. Dialogue on this issue across different approaches is crucial for the impending prospects of socio-techno-educational research.

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Yadav, D. Criteria for Good Qualitative Research: A Comprehensive Review. Asia-Pacific Edu Res 31 , 679–689 (2022). https://doi.org/10.1007/s40299-021-00619-0

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations, authors’ contributions.

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

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100 Questions (and Answers) About Qualitative Research

• Lisa M. Given - Swinburne University, Australia, Charles Sturt University, Australia, RMIT University, Melbourne, Australia

“This is a great companion book for a course on qualitative methods and it is also a great resource as a ‘ready-reference,’ which should be a required companion for all graduate students who will be taking qualitative research methods.”

“It provides an overview of the subject on the nuances of qualitative research.”

“ Very precise in helping students determine if their study is appropriate for this type of research design.”

“The book appears to provide the right combination of breadth and depth . There are a lot of topics covered, but the book seems to provide a succinct, snapshot-like answer for each question.”

“A book like this can provide a useful supplement to major texts and be used as a reference.”

Lisa M. Given

Qualitative Study

Affiliations.

• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

Qualitative Research Approaches

Ethnography

Ethnography as a research design originates in social and cultural anthropology and involves the researcher being directly immersed in the participant’s environment. Through this immersion, the ethnographer can use a variety of data collection techniques to produce a comprehensive account of the social phenomena that occurred during the research period. That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc, through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded theory

Grounded Theory is the "generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior." Unlike quantitative research, which is deductive and tests or verifies an existing theory, grounded theory research is inductive and, therefore, lends itself to research aimed at social interactions or experiences. In essence, Grounded Theory’s goal is to explain how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is the "study of the meaning of phenomena or the study of the particular.” At first glance, it might seem that Grounded Theory and Phenomenology are pretty similar, but the differences can be seen upon careful examination. At its core, phenomenology looks to investigate experiences from the individual's perspective. Phenomenology is essentially looking into the "lived experiences" of the participants and aims to examine how and why participants behaved a certain way from their perspective. Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources. In contrast, Phenomenology focuses on describing and explaining an event or phenomenon from the perspective of those who have experienced it.

Narrative research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called a "thick" or "rich" description and is a strength of qualitative research. Narrative research is rife with the possibilities of "thick" description as this approach weaves together a sequence of events, usually from just one or two individuals, hoping to create a cohesive story or narrative. While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be "opportunities for innovation."

Research Paradigm

Research paradigms are the assumptions, norms, and standards underpinning different research approaches. Essentially, research paradigms are the "worldviews" that inform research. It is valuable for qualitative and quantitative researchers to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontologies and epistemologies. Ontology is defined as the "assumptions about the nature of reality,” whereas epistemology is defined as the "assumptions about the nature of knowledge" that inform researchers' work. It is essential to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a complete understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, researchers must understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist versus postpositivist

To further understand qualitative research, we must discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social and natural sciences. Essentially, positivist thinking insists that the social sciences should use natural science methods in their research. It stems from positivist ontology, that there is an objective reality that exists that is wholly independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained, but could be approximated. Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world,” and therefore, postpositivist philosophy is often associated with qualitative research. An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are also constructivist, meaning they think there is no objective external reality that exists but instead that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. "Constructivism contends that individuals' views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality.” constructivist thought focuses on how "reality" is not a fixed certainty and how experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike positivist views, that there is not necessarily an "objective"reality we all experience. This is the ‘relativist’ ontological view that reality and our world are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.”

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have. It can even change the role of the researchers. For example, is the researcher an "objective" observer, such as in positivist quantitative work? Or is the researcher an active participant in the research, as in postpositivist qualitative work? Understanding the philosophical base of the study undertaken allows researchers to fully understand the implications of their work and their role within the research and reflect on their positionality and bias as it pertains to the research they are conducting.

Data Sampling

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors. The following are examples of participant sampling and selection:

Purposive sampling- selection based on the researcher’s rationale for being the most informative.

Criterion sampling selection based on pre-identified factors.

Convenience sampling- selection based on availability.

Snowball sampling- the selection is by referral from other participants or people who know potential participants.

Extreme case sampling- targeted selection of rare cases.

Typical case sampling selection based on regular or average participants.

Data Collection and Analysis

Qualitative research uses several techniques, including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic, and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant-observers to share the experiences of the subject or non-participants or detached observers.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or the participants' environment, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed, which may then be coded manually or using computer-assisted qualitative data analysis software or CAQDAS such as ATLAS.ti or NVivo.

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. Results could also be in the form of themes and theory or model development.

Dissemination

The healthcare team can use two reporting standards to standardize and facilitate the dissemination of qualitative research outcomes. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a more comprehensive range of qualitative research.

Applications

Many times, a research question will start with qualitative research. The qualitative research will help generate the research hypothesis, which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data to better understand what the numbers truly mean and their implications. The qualitative techniques can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research, researchers can explore poorly studied subjects with quantitative methods. These include opinions, individual actions, and social science research.

An excellent qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure no omissions of part of the target population. A proper collection method should be selected that will help obtain the desired information without overly limiting the collected data because, often, the information sought is not well categorized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of why teens start to smoke and factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered "cool," and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current nonsmokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the survey results to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the primary factor that keeps teens from starting to smoke, and peer pressure was the primary factor that contributed to teens starting smoking. The researcher can go back to qualitative research methods to dive deeper into these for more information. The researcher wants to focus on keeping teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly in the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure to smoke. The researcher finds a local park where many local teenagers hang out and sees that the smokers tend to hang out in a shady, overgrown area of the park. The researcher notes that smoking teenagers buy their cigarettes from a local convenience store adjacent to the park, where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk populations their perceptions of the changes and what factors are still at play, and quantitative research that includes teen smoking rates in the community and the incidence of new teen smokers, among others.

Copyright © 2024, StatPearls Publishing LLC.

• Introduction
• Issues of Concern
• Clinical Significance
• Enhancing Healthcare Team Outcomes
• Review Questions

Publication types

• Study Guide

Managing Low Back Pain in Rural Uganda: A Qualitative Study Exploring the Perspectives and Practices of Frontline Health Workers regarding LBP Management in Primary Care.

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BACKGROUND AND AIMS: Low-back pain (LBP) is the main cause of years lived with disabilities (YLDs) worldwide and the second cause of YLDs in Uganda. In 2019, it was responsible for 7.4% of global YLDs and 5% of YLDs in Uganda. LBP takes a significant toll on people’s quality of life and disproportionately affects lower socioeconomic classes, elders, and women. In rural Uganda, LBP is managed in health centres by clinical officers and nurses with limited resources. This study aims to understand the perspectives and practices of these health workers. Method: A qualitative design using semi-structured focus-group discussions was employed. Purposive sampling allowed us to identify relevant participants based on their roles as healthcare professionals working in primary care context in rural South-West Uganda. Data was analysed using thematic analysis. Findings: LBP is a common and persistent complaint among patients presenting to rural health centres in Uganda. Manual labour and female specific health conditions were deemed to be common causes. There was a strong reliance on medication prescription, coupled with X-ray diagnosis, with little emphasis on education or exercise. Finally, findings highlighted major barriers for patients within the referral system to hospital care or rehabilitation. Discussion: Education and training of frontline clinicians in terms of appropriate prescribing and rehabilitation for LBP is crucial. Evidence-based rehabilitation interventions need to be developed and adapted so that they can be delivered within the time and resource constraints of the health workforce, ensuring that they are acceptable and effective to patients in the context of rural Uganda.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Author declarations.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study received ethical approval from the Mbarara University of Science and Technology Research Ethics Committee (MUST-2022-540) and from the Human Research Ethics Committee at University College Dublin (LS-LR-22-227-OSullivan).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

Data are not publicly available because participants did not give explicit consent for the data to be held in a public repository

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This paper is in the following e-collection/theme issue:

Published on 8.5.2024 in Vol 26 (2024)

This is a member publication of University of Toronto

A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study

Authors of this article:

Original Paper

• Jia Xue 1, 2 , PhD   ; 
• Micheal L Shier 1 , PhD   ; 
• Junxiang Chen 3 , PhD   ; 
• Yirun Wang 4 , MSc   ; 
• Chengda Zheng 4 , MI   ; 
• Chen Chen 4 , PhD  

1 Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada

2 Faculty of Information, University of Toronto, Toronto, ON, Canada

3 Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, United States

4 Artificial Intelligence for Justice Lab, University of Toronto, Toronto, ON, Canada

Corresponding Author:

Jia Xue, PhD

Factor-Inwentash Faculty of Social Work

University of Toronto

246 Bloor Street West

Toronto, ON, M5S 1V4

Phone: 1 4169465429

Email: [email protected]

Background: Nonprofit organizations are increasingly using social media to improve their communication strategies with the broader population. However, within the domain of human service nonprofits, there is hesitancy to fully use social media tools, and there is limited scope among organizational personnel in applying their potential beyond self-promotion and service advertisement. There is a pressing need for greater conceptual clarity to support education and training on the varied reasons for using social media to increase organizational outcomes.

Objective: This study leverages the potential of Twitter (subsequently rebranded as X [X Corp]) to examine the online communication content within a sample (n=133) of nonprofit sexual assault (SA) centers in Canada. To achieve this, we developed a typology using a qualitative and supervised machine learning model for the automatic classification of tweets posted by these centers.

Methods: Using a mixed methods approach that combines machine learning and qualitative analysis, we manually coded 10,809 tweets from 133 SA centers in Canada, spanning the period from March 2009 to March 2023. These manually labeled tweets were used as the training data set for the supervised machine learning process, which allowed us to classify 286,551 organizational tweets. The classification model based on supervised machine learning yielded satisfactory results, prompting the use of unsupervised machine learning to classify the topics within each thematic category and identify latent topics. The qualitative thematic analysis, in combination with topic modeling, provided a contextual understanding of each theme. Sentiment analysis was conducted to reveal the emotions conveyed in the tweets. We conducted validation of the model with 2 independent data sets.

Results: Manual annotation of 10,809 tweets identified seven thematic categories: (1) community engagement, (2) organization administration, (3) public awareness, (4) political advocacy, (5) support for others, (6) partnerships, and (7) appreciation. Organization administration was the most frequent segment, and political advocacy and partnerships were the smallest segments. The supervised machine learning model achieved an accuracy of 63.4% in classifying tweets. The sentiment analysis revealed a prevalence of neutral sentiment across all categories. The emotion analysis indicated that fear was predominant, whereas joy was associated with the partnership and appreciation tweets. Topic modeling identified distinct themes within each category, providing valuable insights into the prevalent discussions surrounding SA and related issues.

Conclusions: This research contributes an original theoretical model that sheds light on how human service nonprofits use social media to achieve their online organizational communication objectives across 7 thematic categories. The study advances our comprehension of social media use by nonprofits, presenting a comprehensive typology that captures the diverse communication objectives and contents of these organizations, which provide content to expand training and education for nonprofit leaders to connect and engage with the public, policy experts, other organizations, and potential service users.

Introduction

It has long been acknowledged that social media plays a significant role in facilitating stakeholder engagement between nonprofits and community members [ 1 - 3 ]. Human service nonprofits have recognized the potential of social media in securing donations; recruiting volunteers [ 4 - 7 ]; enhancing trust, accountability, and awareness [ 8 ]; and fostering partnerships [ 9 ]. However, research on the specific focus of social media engagement by human service nonprofits remains somewhat limited in the existing literature and practice [ 10 ]. Traditionally, social media in the nonprofit sector has been extensively explored in relation to its use for advocacy purposes [ 11 - 13 ]. Although investigation into the advocacy function of social media use is important within the human service nonprofit sector, as this is a key role played by human service nonprofits to promote and support social welfare development, the need of these organizations to engage with the wider community is much more expansive.

Human service organizations are complex entities involved in a wide range of activities to fulfill their missions, primarily focused on providing direct support to address negative social, economic, and political outcomes of social groups considered marginalized and to promote social welfare development through program development, public awareness, and advocacy efforts [ 14 ]. These human service nonprofits interact with diverse human resources (professional and volunteer), service users, and community groups; form partnerships across sectors (nonprofits, for-profit firms, and governments); and manage activities with for-profit (eg, social enterprises, social investors, and consumers), government (eg, contracting arrangements), and nonprofit (eg, through foundations) revenue sources and organizations. The use of social media is complicated further when considering its use for service user engagement, as there is an emerging body of literature on the use of social media for service delivery–related purposes [ 15 , 16 ].

To move beyond the advocacy-related function of social media by human service nonprofits, this research investigated in greater detail the various reasons why human service nonprofits are using social media within the sexual violence service delivery sector across Canada. The research merges social science, big data, and computer science to further enhance our knowledge and understanding of how human service nonprofit organizations use information communication technology. This study expands upon prior studies of nonprofit organizational communication research by using Twitter-based data (subsequently rebranded as X [X Corp]). Human-labeled tweets were used as training data, and a supervised machine learning approach was used to automatically predict content analytical themes in a Twitter corpus. The study builds a predictive classification model that uses a supervised machine learning algorithm to evaluate large social media data sets, resulting in a theoretical framework that categorizes the objectives of the sexual assault (SA) organization posts on social media. The overarching question that guides this research is as follows: “What are the different purposes of social media communication among SA centers in Canada?” This research is part of a greater effort to develop a strategic approach and educational information for training human service personnel and leaders on the use of social media to increase the capacity of human service nonprofits [ 17 , 18 ].

Literature Review

Current research indicates that nonprofit organizations are increasingly using social media to improve their communication strategies with the broader population. A primary focus of research in this area has been on the specific tangible ways of this type of engagement, including the volume of engagement and the focus of messaging, along with its directionality, and the emphasis of the posts being informative and practical [ 19 - 21 ]. For example, Guo and Saxton [ 22 ] have focused on the extent to which nonprofits are gaining attention and highlight that this is influenced by the size of an organization’s network, the frequency with which it communicates through social media, and the number of conversations an organization joins [ 22 ]. This research is important, as it highlights the mechanisms of social media use and the frequency; however, it does not provide sufficient insight into the various reasons for social media use and the outcomes of this communication strategy on different organizational functions or purposes, and particularly important within the realm of human service nonprofit organization, which may use social media to achieve a multitude of objectives.

In fact, research on social media use within human service nonprofits specifically has identified some hesitancy to use social media education or useful tools to focus on social media use [ 23 , 24 ], and there is limited scope among organizational personnel in applying its usefulness beyond promoting one’s organization and its services [ 25 ]. This lack of engagement has been determined to be influenced in part due to limited education and awareness of the utility of social media use in the human services sector and other key organizational dynamics such as organizational culture, funding, and size of the organization [ 6 , 24 , 26 , 27 ].

Furthermore, a strong focus within the literature has been on how social media has been impacted by market actors (such as donors), which has constrained the framing of social media messaging [ 20 , 28 - 30 ]. Likewise, challenges with social media use, such as breaches of confidentiality and its increased use for surveillance and accountability-related purposes [ 31 ], also act to constrain social media use. As a result, there is a need for greater conceptual clarity to support education and training on the varied reasons for using social media to increase organizational outcomes [ 32 - 34 ].

This research seeks to address these gaps by investigating the wider range of social media use by human service nonprofits, establishing a typology of reasons for social media use beyond advocacy-related purposes. By doing so, it also addresses concerns regarding limited education and training within the sector on leveraging social media for diverse organizational objectives. Through the incorporation of machine learning and content analysis, this study contributes to a deeper understanding of nonprofit communication strategies and offers practical implications for improved social media engagement within the human service context.

Aim of the Study

This study investigates the objectives of social media engagement and the contents posted by human service nonprofit organizations on the social media platform Twitter, with a particular focus on SA service delivery centers in Canada. To achieve this aim, this study addresses the following research questions: (1) What is the typology and theoretical framework that effectively captures and categorizes the diverse online organizational communication objectives of SA centers as they use Twitter as a strategic tool to achieve their organizational outcomes? (2) How do the sentiments and emotions expressed in Twitter posts by SA centers vary in relation to different categories in the typology of online organizational communication, such as advocacy or public awareness? (3) How can machine learning and content analysis categorize and analyze the social media posts of these organizations, providing insights into their communication strategies?

This study used mixed research methods, including qualitative content analysis, supervised machine learning, unsupervised machine learning, thematic analysis, and sentiment analysis. To classify the full set of tweets, we first manually coded a subset of the full data set (n=10,809 tweets) into 7 emergent categories ( Table 1 ). These human-labeled tweets were used as the training data set to train a supervised machine learning algorithm to classify the remaining tweets. Figure 1 illustrates the mixed methods approach.

To select SA centers in Canada, this study used a purposive sampling approach. Initially, a sampling frame was developed by combining the list of SA centers by province and territory from the Canadian Association of Sexual Assault Centres and the Sexual Assault Centres, Crisis Lines, and Support Services websites. After removing duplicates, the sample frame consisted of 350 SA centers across 10 provinces and 3 territories. The sample frame provided basic information about the centers, including their names, contact information (phone number and email), and website or URL. The inclusion criteria were twofold: (1) the SA center had an active Twitter account and (2) it had posted at least 1 tweet on its account. To verify the eligibility of these centers, the authors manually searched their home page and Twitter pages and conducted thorough Google searches. Ultimately, the Twitter accounts of 133 SA centers were included as the final sample for this study. These centers were from 9 provinces and the Northwest Territories (Prince Edward Island did not have any SA centers that used Twitter).

Data Collection

To collect tweets from SA centers, the authors followed the pipeline outlined in their papers, including acquiring Twitter handles, obtaining Twitter IDs, and collecting tweets via Twitter’s application programing interface (API) [ 35 - 43 ]. The collected tweets encompassed the period from March 12, 2009, to March 15, 2023. The data set consisted of 297,360 tweets from 133 SA centers in Canada. The data sets are available for use by researchers upon request. First, a total of 91 unique Twitter handles (ie, @name) were obtained from the 133 SA centers in the sample, with 26 duplicate Twitter handles. Second, the 91 Twitter handles were converted into 91 Twitter IDs using 3 websites: TweeterID, CodeOfaNinja, and Comment Picker. Third, Twitter’s premium search API and timeline end points (full-archive end point) were used to collect tweets posted by the sampled SA centers in Canada, starting from as early as 2006 (search tweets, 2019). Data collection concluded on March 15, 2023.

Manual Annotation

The purpose of manual annotation was to obtain human-labeled tweets categorized into different themes. These labeled tweets would serve as the training data set for classifying the entire corpus using a supervised machine learning approach. The coding protocol was developed based on prior literature on organizational communication research and adapted to suit the objectives of this study. Table 1 included the classification, labels, definitions, and sample tweets.

To ensure consistency, 2 authors (JX and MLS) provided training to the research assistants on the protocol and research goals. During the training phase, a random subset of 200 tweets was selected, and 2 research assistants were assigned to independently code them. This process was repeated 4 times (n=809 tweets) until an acceptable interrater reliability score of 0.7 was achieved for each of the 7 categories. Krippendorff α was used to determine the interrater reliability, which indicated substantial agreement.

Following the training phase, a subset of 10,000 tweets was randomly selected from the collected data. Research assistants were assigned to independently code a subset of 5000 tweets. The manual annotation data set consisted of a random subsample of 10,809 manually labeled tweets categorized into 7 themes from the full data set.

Construction of Predictive Classification Model

To create an accurate classification model for Twitter data, we used the BERT model [ 44 ]. BERT is a widely used natural language processing model that has been pretrained on various English language data sets, making it suitable for fine-tuning tasks such as sentence classification. To evaluate the performance of our model, we randomly selected 80% of the human-labeled tweets as training data, with the remaining 20% used as test data.

Due to the imbalanced distribution of classes in our data set, we used a 2-step strategy to train the machine learning model. First, we fine-tuned the BERT model with all the training data by minimizing the logistic loss [ 45 ]. Second, we applied a random undersampling process [ 46 ] to retrain the last layer of the BERT model (the classification layer) using this undersampled subset. The undersampling process randomly selected a subset of training data, ensuring an equal number of samples for each class. We chose the undersampling technique as it is less prone to overfitting the data compared to other methods such as oversampling [ 46 ].

In addition to using deep learning models in our study, we also used a range of traditional machine learning algorithms as benchmarks for performance comparison. Specifically, we trained models using linear regression, support vector machines with a radial basis function kernel, and support vector machine with a linear kernel. To represent features in these traditional algorithms, we chose the term frequency–inverse document frequency approach to convert our textual data into numerical vectors.

To evaluate the efficacy of these models, we computed the average sensitivity score based on the test data. The sensitivity score for a given class “k” denotes the probability that a sample will be classified by a model as belonging to class “k,” given that the sample truly belongs to that class. We calculated the mean of the sensitivity scores across all 7 classes as our final measurement. Following the training of the BERT model, we used it to classify the unlabeled 286,551 tweets into 7 categories.

Validation Data

To ensure the robust performance of our model across diverse contexts, we gathered 2 distinct independent data sets from Twitter and Facebook. Independent data set #1 was derived using the same sampling frame in this study. Our aim was to identify organizations active on Facebook but not on Twitter, thereby maintaining uniformity in organization type while varying the social media platform for further model validation. Using Apify software [ 47 ], we collected messages from 67 SA organizations and subsequently selected a random sample of 500 messages (n=2520). Independent data set #2 was obtained through a list of human service organizations (approximately 12,000) from the government of Canada’s list of charitable nonprofits (N=85,496). Of the approximately 86,000 charitable nonprofits in Canada, the list of human service organizations was developed through an assessment of the organizations’ website that shows an indication of providing some type of social service programing to a service user group. This frame enabled the identification of organizations with active Twitter accounts, thus ensuring consistency in the chosen social media platform while introducing variation in the type of organization for enhanced model validation. Following the collection of tweets via our API, a random sample of 500 tweets (n=15,696) was selected for data validation. We used the same manual annotation procedure for these 2 data sets to establish manual labels. This allowed us to directly compare the model’s predictions against these manual labels, serving as a method to assess the model’s effectiveness ( Multimedia Appendix 1 ).

Sentiment Analysis

Sentiment analysis, sometimes referred to as opinion mining, involves the classification and analysis of people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions concerning various entities, including products, services, organizations, individuals, issues, events, topics, and their associated attributes [ 48 ]. Sentiment analysis applied to social media content has been extensively studied, and Twitter has the capability to promptly gauge public sentiments and emotions regarding a given topic [ 49 ]. For this analysis, we used RoBERTa, a deep learning framework [ 50 ]. We used a pretrained model [ 51 ] that was fine-tuned specifically for sentiment analysis of social media data. The model categorized each tweet into 1 of the 3 sentiments: “positive,” “neutral,” or “negative.” We converted a significant amount of textual data into quantitative sentiment scores and calculated the percentage of each sentiment within every category.

Emotion Analysis

Emotion analysis primarily focuses on capturing nuanced emotions, which contrasts with sentiment analysis, primarily concerned with detecting simple attitudes such as positivity and negativity. Mohammad [ 52 ] indicated that machines can infer people’s emotions in a limited way but are useful. We need to hold automatic emotion recognition systems to high standards by incorporating ethical considerations associated with each step of the detection process. In our study, we delve into emotion analysis within 7 categorized groups to investigate and compare potential emotional variations across different categories. For this analysis, we used a pretrained RoBERTa model [ 53 ], optimized for emotion analysis of social media data. This model classifies each tweet into 1 of the 7 emotion categories: “anger,” “disgust,” “fear,” “joy,” “neutral,” “sadness,” or “surprise.” We then calculated the percentage of tweets associated with each emotion category for each of our designated categories. Subsequently, we determined the percentage distribution of each emotion within each category. This measurement across various goals and intentions in social media communication provides valuable insights into the perspectives of both the public and SA issues, enhancing our overall understanding of these topics.

Topic Modeling for Tweets Categorized Into 7 Classes

The objective of this unsupervised machine learning work was to extract latent topics within each theme after categorizing tweets into 7 different categories. To achieve this, we used the latent Dirichlet allocation approach for topic modeling, which allowed us to group tweets into different topics. The initial step in this analysis stage was preprocessing, which enhanced model performance by removing noisy data. We eliminated various elements, such as “mentions,” “emojis,” “hyperlinks,” “RT symbols,” and punctuations, and converted all tweets to lower case. Removing mention symbols eliminated irrelevant terms from the analysis, such as names of organizations and individuals. In addition, we removed stop words such as “the,” “is,” and “and” while retaining nouns, adjectives, and verbs related to events.

Once the data were preprocessed, we implemented latent Dirichlet allocation models using the Gensim library in Python. Our hyperparameter range was set from 1 to 30, and the similarity score served as our evaluation metric. By plotting the similarity score against the number of topics, we identified the turning point on the graph as our optimal hyperparameter. To further analyze the topics within each of the 7 categories, we extracted popular bigrams and reviewed a random sample of tweets. We used qualitative thematic analysis to assign underlying topic meanings to them.

Topic Evaluation

The team, consisting of domain experts and research assistants, summarized and evaluated the results of the topic modeling. Salient bigrams were used to summarize each topic, and similar topic themes were merged into higher-level categories, as per the machine learning approach described by Zhou et al [ 54 ].

Ethical Considerations

This study used publicly available Twitter data, eliminating the need for ethics approval or consent from organizations. The study data mentioned in this paper underwent processes of anonymization and deidentification. To guarantee full anonymity, all data that could potentially identify individuals or organizations, including users’ metadata and original tweets, have been carefully excluded from the data set.

Our data set included 297,360 tweets and retweets from 133 SA support organizations in Canada. These tweets were posted from March 12, 2009, to March 14, 2023. Multimedia Appendix 2 illustrates a bar plot that summarizes the number of tweets collected for each year.

Manual Annotation and Class Distribution of Tweets

Among the data set consisting of 297,360 tweets and retweets, 10,809 tweets were manually annotated by humans following the coding protocol. As shown in Table 2 , organization administration (5652/10,809, 52.29%) was the most frequent type of post, followed by community engagement (2322/10,809, 21.38%) and public awareness (1522/10,809, 14.08%). The smallest segment of tweets belonged to political advocacy (129/10,809, 1.19%) and partnerships (162/10,809, 1.5%). Multimedia Appendix 3 presents the histogram of class distributions of the human-labeled tweets. It is worth noting that the distribution of labels was imbalanced, with a smaller proportion of tweets (<5%) falling into the categories of political advocacy, support for others, and partnerships. Figure 2 presents a plot showing the percentage of each category plotted against the corresponding years.

Performance of the Supervised Machine Learning Model

We evaluated the performance of the supervised machine learning model using the test set (20% of the human-labeled tweets). The accuracy of machine learning classification achieved by our trained model was 63.4%, which was higher than that of the human coders who labeled the training data set. This indicates an improvement in the BERT model’s ability to accurately predict classifications compared to those made by human coders.

We analyzed and presented the confusion matrices in Tables 3 and 4 . Table 3 displays the confusion matrix for the initial model without undersampling, whereas Table 4 illustrates the confusion matrix with undersampling applied. These matrices provide insights into the percentage of samples with actual labels that were correctly classified into the predicted class by the model. The sensitivity scores for each class are represented on the diagonal of the matrices, and the average sensitivity score across the 7 classes serves as an indicator of the overall performance of the model. As observed in the tables, the average sensitivity scores improved from 48.3% to 53.1%. As demonstrated in tables, BERT’s performance surpassed that of traditional machine learning methods.

Results of Predictive Classification of Unlabeled Data Using Machine Learning

The remaining 286,551 tweets were classified into 7 classes using the supervised machine learning algorithms. The classification results are presented in Table 2 . The supervised machine learning model produced classifications that were similar to those of the human-annotated tweets in terms of the percentage of each category. The most frequent tweet class was public awareness (89,295/286,551, 31.2%), followed by community engagement (67,285/286,551, 23.5%) and organization administration (50,620/286,551, 23.5%). The 2 smallest categories of posts were partnerships (7144/286,551, 2.5%) and political advocacy (19,620/286,551, 6.8%).

Top Unigrams and Bigrams in the Tweets

We conducted an analysis to identify the most commonly used words and phrases in the tweets from the SA support organizations. To do this, we removed the stop words and generated a list of the top 30 most frequently occurring unigrams and bigrams, as presented in Multimedia Appendices 4 and 5 . We observed that >220,000 tweets included a URL link, which directed users to news or events related to SA. In addition, approximately 100,000 tweets were retweets, with “rt” in the messages. The terms “women,” “support,” “sexual assault,” “sexual violence,” and “crisis line” were among the most commonly used terms in these tweets.

Sentiment and Emotion Analysis Results

We conducted sentiment and emotion analysis, and the summarized results can be found in Tables 5 and 6 . The findings revealed that the neutral sentiment category surpassed both the negative and positive sentiment categories across all 7 classes.

Regarding the emotion analysis, most tweets from organizations were associated with the emotion of “fear.” In contrast, tweets discussing topics related to class 6 (partnerships) and class 7 (appreciation) exhibited the emotion of “joy.” Here are a few examples of tweets reflecting fear:

Every minute of every day, a Canadian woman or child is being sexually assaulted. #VAW

Salau’s story is so symbolic of how universally disregarded, disrespected, and unprotected Black women are, even in our most vulnerable moments. #EndVAW #JusticeForToyin.

Here is a tweet reflecting joy (appreciation):

We’ve seen that charity brings together amazing people to create great change and make meaningful impact in the lives of the people in their community. THANK YOU to the incredible supporters who make our work possible...

These examples illustrate the emotional tone associated with different tweet categories, with fear being prevalent among organizational tweets and joy being linked to discussions on partnerships and appreciation.

Topic Modeling Results

The coded data set yielded distinct topics within each of the classes or categories. The identified topics, bigrams, and representative tweet examples are presented in Table 7 . These themes provide insights into the prevalent topics and discussions within the data set, showcasing different aspects of the discourse surrounding SA and related issues.

a Not available.

Community Engagement

Approximately 20% of the tweets in the data set contained themes related to community engagement, generating 3 topics: “experience and awareness of abuse,” “support and information,” and “social media engagement.” Topic 1 focuses on discussions related to experiences of abuse and raising awareness about it. Topic 2 revolves around providing support and information and promoting human rights in relation to SA. Topic 3 highlights engagement on social media platforms, connecting with friends and family, and finding ways to stay informed and connected.

Organization Administration

In the organization administration class, “sexual assault support services,” “helplines,” and “support groups and emotional support” were the salient topics. The first topic revolves around providing support for survivors of SA, and the tweets likely contain information about available services and crisis lines; promote helpline numbers; and emphasize the availability of support services. Topic 2 centers on support groups and emotional support for individuals impacted by sexual violence. The tweets may discuss the importance of support networks, encourage individuals to join support groups, and highlight the emotional support available.

Public Awareness

In the public awareness class, we identified 3 topics. Topic 1 focuses on discussions related to gender-based violence, and the tweets likely highlight the need to raise awareness, advocate for survivors, and address issues surrounding gender-based violence. Topic 2 centers on SA awareness, support for survivors, and efforts to combat sexual violence. The tweets may highlight initiatives such as awareness months, support services, survivor empowerment, and the importance of ending sexual violence. Topic 3 focuses on violence against women, advocating for women’s rights, and addressing issues such as domestic violence and intimate partner violence. The tweets may discuss the importance of human rights, raise awareness about violence against women, and emphasize the need for support services.

Political Advocacy

This theme delves into the criminal justice system’s response to SA cases and advocates for changes and reforms. It discusses specific initiatives, such as signing petitions and calling for justice for communities considered marginalized. It also mentions the importance of advocacy and policy work for Francophone women. Topic 1 revolves around discussions related to SA, advocating for survivors, and seeking justice. The tweets may address issues such as domestic violence, support for survivors, legal cases, and the need for systemic change in addressing SA and violence against women. Topic 2 focuses on support services and resources available for survivors of SA, including centers providing assistance and legal aid. The tweets may mention crisis centers, justice systems, confidential services, and the importance of providing support and resources to survivors. Topic 3 highlights discussions surrounding Indigenous rights, reconciliation, and addressing SA and violence within Indigenous communities. The tweets may emphasize actions for truth and reconciliation, support for Indigenous women, and the need for systemic change to combat violence and promote gender equality. Topic 4 addresses sexual violence in general, including domestic violence and violence against women. The tweets may focus on the need for action, standing against violence, justice systems, systemic change, and the role of government in addressing sexual violence.

Support for Others

This theme included 2 topics, highlighting the importance of community involvement, support, and collective efforts to combat violence and abuse. Topic 1 emphasizes “support and advocacy for survivors of sexual assault,” and the tweets may mention initiatives, organizations, and individuals working to support and address their mental health needs, highlighting the importance of community efforts in making a positive difference. Topic 2 focuses on the “campaigns and events to raise awareness about sexual assault,” and the discussion within this topic often involves various campaigns and events, with tweets possibly mentioning actions such as wearing purple to demonstrate support as well as sharing information and promoting community campaigns.

Partnerships

This theme centers on the importance of partnerships, fundraising efforts, and collective efforts to address and prevent violence and abuse. We identified 4 topics within the theme. Topic 1 revolves around providing support and raising funds for survivors of sexual violence. The tweets may mention community partners, local businesses, and fundraising efforts aimed at supporting survivors. Topic 2 focuses on campaigns and initiatives to end violence against women. The tweets may mention supporting campaigns, raising funds, and providing support services for survivors of SA. The topic highlights the importance of community engagement and collective action in addressing violence against women. Topic 3 emphasizes programs and efforts aimed at supporting women and children who have experienced SA. The tweets may mention fundraising events, supporting local services, helplines, and providing assistance to survivors. Topic 4 revolves around community engagement and support related to sexual violence. The tweets may mention joining teams, spreading the word, and supporting survivors through initiatives such as silent auctions. The topic highlights the importance of community participation and collaboration in addressing sexual violence.

Appreciation

The least common theme revealed 3 topics, including “gratitude and appreciation for support,” “thanking supporters and donors,” and “gratitude for engagement and participation.” This theme revolves around expressing gratitude and appreciation for support, donations, and contributions. Topic 1 revolves around expressing gratitude and appreciation for the support received. The tweets thank individuals, organizations, and community members, and the topic emphasizes the importance of acknowledging and recognizing the contributions of supporters. Topic 2 focuses on thanking supporters and donors for their contributions. The tweets express gratitude toward individuals and organizations for their generous donations and ongoing support. The topic highlights the significance of recognizing and thanking those who have contributed to the cause. Topic 3 focuses on expressing gratitude for engagement and participation. The tweets thank individuals for sharing information, participating in events such as walks or auctions, and making a difference. The topic emphasizes the importance of community involvement and active participation.

Principal Findings

This study presents a comprehensive classification of Twitter messages that elucidate the reasons for social media use among SA support centers in Canada. Leveraging a data set of 297,360 tweets from 133 SA support organizations, the application of supervised machine learning enabled us to automatically predict content analytical themes within the Twitter corpus. First, we identified the emerging classifications of Twitter’s use by human service nonprofits. The results indicated that Twitter is used by SA centers across Canada for various purposes. The identified classifications include (1) community engagement, (2) organization administration, (3) public awareness, (4) political advocacy, (5) support for others, (6) partnerships, and (7) appreciation. These categories reflect the multifaceted nature of human service nonprofits’ communication strategies and their engagement with stakeholders. Second, the findings of this study contribute to the existing literature by expanding the understanding of social media use by human service nonprofits beyond the traditional focus on advocacy-related purposes. Although advocacy remains an important aspect, this research reveals that these organizations use social media to achieve a diverse range of objectives, such as raising public awareness, community engagement, and organization administration. Third, the sentiment and emotion analysis of tweets shed light on the emotional tone of different tweet categories. The prevalence of “fear” among organizational tweets underscores the gravity of the issues addressed by human service nonprofits. In contrast, the emotion of “joy” associated with the partnership and appreciation categories highlights the positive impact of community involvement and support. Fourth, the application of machine learning in this study has proven to be valuable in predicting content analytical themes in a large Twitter corpus. The predictive classification model outperformed human coders in terms of accuracy, indicating the potential of machine learning algorithms in analyzing social media data and gaining deeper insights into nonprofits communication strategies.

Typology and Theoretical Framework of Online Organizational Communication Objectives

A key discovery from our study pertains to the distribution of tweet categories. The analysis revealed that the most frequent type of posts falls under the “public awareness” category. Approximately one-third of the collected tweets were classified within this category, signifying that SA support organizations predominantly use social media platforms for advocating against issues related to intimate partner violence and sexual violence. These findings align with prior literature, highlighting how social media allows organizations to disseminate content aimed at increasing awareness while incurring minimal costs [ 8 ]. Our topic modeling results uncovered 3 salient themes within the public awareness category, which encompass tweets that emphasize the need to raise awareness, advocate for addressing gender-based violence issues, and support survivors of sexual violence and women’s rights. This emphasis on awareness-raising activities reflects the pivotal role social media plays in creating awareness for organizations and fostering interactions with donors and volunteers [ 55 , 56 ].

Community engagement emerges as the second most prominent reason for social media use by SA organizations in Canada, constituting approximately one-fourth of all collected tweets. This category generated 3 distinct topics through classifications, where community engagement entails nonprofits engaging with their community beyond their primary mission. These tweets include sharing well-wishes, updates on organization activities, quotes, and information about resources that the community may find valuable. Although some tweets do touch on providing information beyond their primary mission, the main focus remains on community engagement concerning sexual violence support. The nuanced examination of tweet contents provided by our topic modeling analysis sheds light on the multifaceted nature of community engagement efforts by SA support organizations. It is evident that these organizations recognize the significance of engaging with their communities regarding sexual violence and related matters through social media. Consistent with existing literature, our findings align with the view that social media offers an avenue for powerful participation and community engagement [ 57 ]. It also emphasizes the potential role of social media as a mechanism to raise awareness and inform the community of various initiatives and projects [ 58 ]. Although community engagement through social media remains an opportunity for SA support organizations to connect with their communities and market their services, further research should delve into the effectiveness of engagement across all aspects of design, delivery, and evaluation, particularly with regard to specific objectives such as supporting and combating sexual violence.

Sentiments and Emotions in Different Categories in the Typology

Sentiment analysis is used to examine the evaluative perspectives expressed within the text, with its importance stemming from its ability to comprehend the shifting dynamics, potential interventions, and predictive insights into public sentiment regarding trending events. It serves as a valuable tool for offering decision-making support to relevant authorities in the realms of public sentiment monitoring, intervention, and governance. Our sentiment and emotion analysis yielded valuable insights into the emotional tone of the tweets related to each identified online organizational communication objective. Most tweets exhibited a neutral sentiment, surpassing both the negative and positive categories across all 7 classes. This prevalence of neutrality could be attributed to the sensitive nature of the topic, as SA is a deeply distressing issue. However, it is worth noting that tweets related to partnerships and appreciation displayed a greater presence of joy as the dominant emotion, indicating positive sentiments associated with community engagement, support, and expressions of gratitude.

Machine Learning Classification

The evaluation of the machine learning model’s performance on the test set showcased promising results. The model demonstrated improvement in average sensitivity scores, from 48.3% to 53.1%, indicating its ability to accurately classify tweets into their respective categories and provide reliable predictions for content analytical themes. Nonetheless, it is essential to acknowledge that there is still room for improvement in the model’s performance, particularly in accurately classifying tweets related to partnerships and political advocacy, which constituted smaller segments of the data set.

Implications

Our research carries significant implications for both practitioners and policy makers in the field of SA support services. The typology we have developed represents a substantial advancement in research within this domain and provides a comprehensive framework for understanding how these organizations can effectively use Twitter to disseminate information and engage with the public at the message level. This typology empowers human service nonprofits to align their social media strategies with specific organizational goals. By understanding the different categories and topics of Twitter communication, these organizations can tailor their content and engagement strategies to maximize the impact and relevance of their online presence.

The study emphasizes the importance of social media as a potent communication tool for engaging with communities, raising public awareness, and providing essential information and support. Through an understanding of the diverse communication themes and strategies used by SA support organizations, practitioners can optimize their social media use to effectively reach and connect with their target audience.

On the basis of our research findings, we recommend that human service nonprofits invest in social media education and training for their personnel to enhance their understanding of how to use social media effectively. By building a strategic approach to social media use that aligns with organizational objectives, these nonprofits can maximize their impact and outreach, ultimately furthering their mission to support and advocate for survivors of SA.

Limitations

The study has certain limitations that need to be acknowledged. The predictive performance of the model is influenced by factors such as human annotation, interrater agreement, and the training data set. The trained model attempts to mimic the classification by the human coders, whose understanding of the tweet content and familiarity with the background and theoretical framework is critical to the study. In this regard, the study underwent 4 rounds of training to attain a satisfactory interrater reliability score. However, future studies could incorporate more human coders to enhance the accuracy of the results. Furthermore, our analysis focused solely on Twitter data from SA support organizations in Canada. This geographic and platform limitation may restrict the generalizability of our findings to other countries and social media platforms. Future research should consider expanding the scope to include a more diverse range of organizations and platforms. Finally, although our machine learning model demonstrated promising performance, there is still room for improvement. The accuracy of the model in classifying tweets related to partnerships and political advocacy was relatively lower compared to other categories. Further refinement and fine-tuning of the model could enhance its accuracy and reliability.

Conclusions

In conclusion, our study offers valuable insights into the application of machine learning to understand the message-level communication purposes of SA support organizations on Twitter in Canada. By combining social science and computer science, we effectively analyzed a large data set and identified content analytical themes, sentiments, emotions, and topics within tweets. These findings enrich our understanding of how SA support organizations use social media for community engagement, public awareness, and organizational administration purposes. The implications extend to practitioners, policy makers, and organizational personnel, emphasizing the significance of education and training to maximize the benefits of social media in achieving organizational goals within the realm of SA support services.

Acknowledgments

The authors would like to extend gratitude to Qiaoru Zhang and Yiding Jin for their contributions to the data validation process of our study. Qiaoru Zhang diligently identified organizations with active Facebook or Twitter accounts, thereby ensuring the reliability and relevance of our data sources. Yiding Jin collected a sample of the Facebook data set.

Data Availability

The data sets generated during and analyzed during this study are available from the corresponding author on reasonable request.

Conflicts of Interest

None declared.

Confusion matrix for the independent data sets.

The number of tweets collected each year.

The histograms of the categories in the human-labeled data: (1) community engagement, (2) organization administration, (3) public awareness, (4) political advocacy, (5) support for others, (6) partnerships, and (7) appreciation.

Top unigrams in the tweets.

Top bigrams in the tweets.

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Abbreviations

Edited by A Mavragani; submitted 08.08.23; peer-reviewed by MO Khursheed, N Hu; comments to author 31.08.23; revised version received 12.10.23; accepted 08.04.24; published 08.05.24.

©Jia Xue, Micheal L Shier, Junxiang Chen, Yirun Wang, Chengda Zheng, Chen Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.05.2024.

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