what are the types of data analysis in qualitative research

Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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85 Comments

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Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

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Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

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Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

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choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

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Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

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do you have any material on Data collection

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Qualitative Data Analysis

Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories:

1. Content analysis . This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data.

2. Narrative analysis . This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher.

3. Discourse analysis . A method of analysis of naturally occurring talk and all types of written text.

4. Framework analysis . This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation.

5. Grounded theory . This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory.

Qualitative data analysis can be conducted through the following three steps:

Step 1: Developing and Applying Codes . Coding can be explained as categorization of data. A ‘code’ can be a word or a short phrase that represents a theme or an idea. All codes need to be assigned meaningful titles. A wide range of non-quantifiable elements such as events, behaviours, activities, meanings etc. can be coded.

There are three types of coding:

• Open coding . The initial organization of raw data to try to make sense of it.
• Axial coding . Interconnecting and linking the categories of codes.
• Selective coding . Formulating the story through connecting the categories.

Coding can be done manually or using qualitative data analysis software such as

 NVivo,  Atlas ti 6.0,  HyperRESEARCH 2.8,  Max QDA and others.

When using manual coding you can use folders, filing cabinets, wallets etc. to gather together materials that are examples of similar themes or analytic ideas. Manual method of coding in qualitative data analysis is rightly considered as labour-intensive, time-consuming and outdated.

In computer-based coding, on the other hand, physical files and cabinets are replaced with computer based directories and files. When choosing software for qualitative data analysis you need to consider a wide range of factors such as the type and amount of data you need to analyse, time required to master the software and cost considerations.

Moreover, it is important to get confirmation from your dissertation supervisor prior to application of any specific qualitative data analysis software.

The following table contains examples of research titles, elements to be coded and identification of relevant codes:

 Qualitative data coding

Step 2: Identifying themes, patterns and relationships . Unlike quantitative methods , in qualitative data analysis there are no universally applicable techniques that can be applied to generate findings. Analytical and critical thinking skills of researcher plays significant role in data analysis in qualitative studies. Therefore, no qualitative study can be repeated to generate the same results.

Nevertheless, there is a set of techniques that you can use to identify common themes, patterns and relationships within responses of sample group members in relation to codes that have been specified in the previous stage.

Specifically, the most popular and effective methods of qualitative data interpretation include the following:

• Word and phrase repetitions – scanning primary data for words and phrases most commonly used by respondents, as well as, words and phrases used with unusual emotions;
• Primary and secondary data comparisons – comparing the findings of interview/focus group/observation/any other qualitative data collection method with the findings of literature review and discussing differences between them;
• Search for missing information – discussions about which aspects of the issue was not mentioned by respondents, although you expected them to be mentioned;
• Metaphors and analogues – comparing primary research findings to phenomena from a different area and discussing similarities and differences.

Step 3: Summarizing the data . At this last stage you need to link research findings to hypotheses or research aim and objectives. When writing data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions.

It is important to note that the process of qualitative data analysis described above is general and different types of qualitative studies may require slightly different methods of data analysis.

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

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

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.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

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

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Qualitative Data – Types, Methods and Examples

Table of Contents

Qualitative Data

Definition:

Qualitative data is a type of data that is collected and analyzed in a non-numerical form, such as words, images, or observations. It is generally used to gain an in-depth understanding of complex phenomena, such as human behavior, attitudes, and beliefs.

Types of Qualitative Data

There are various types of qualitative data that can be collected and analyzed, including:

• Interviews : These involve in-depth, face-to-face conversations with individuals or groups to gather their perspectives, experiences, and opinions on a particular topic.
• Focus Groups: These are group discussions where a facilitator leads a discussion on a specific topic, allowing participants to share their views and experiences.
• Observations : These involve observing and recording the behavior and interactions of individuals or groups in a particular setting.
• Case Studies: These involve in-depth analysis of a particular individual, group, or organization, usually over an extended period.
• Document Analysis : This involves examining written or recorded materials, such as newspaper articles, diaries, or public records, to gain insight into a particular topic.
• Visual Data : This involves analyzing images or videos to understand people’s experiences or perspectives on a particular topic.
• Online Data: This involves analyzing data collected from social media platforms, forums, or online communities to understand people’s views and opinions on a particular topic.

Qualitative Data Formats

Qualitative data can be collected and presented in various formats. Some common formats include:

• Textual data: This includes written or transcribed data from interviews, focus groups, or observations. It can be analyzed using various techniques such as thematic analysis or content analysis.
• Audio data: This includes recordings of interviews or focus groups, which can be transcribed and analyzed using software such as NVivo.
• Visual data: This includes photographs, videos, or drawings, which can be analyzed using techniques such as visual analysis or semiotics.
• Mixed media data : This includes data collected in different formats, such as audio and text. This can be analyzed using mixed methods research, which combines both qualitative and quantitative research methods.
• Field notes: These are notes taken by researchers during observations, which can include descriptions of the setting, behaviors, and interactions of participants.

Qualitative Data Analysis Methods

Qualitative data analysis refers to the process of systematically analyzing and interpreting qualitative data to identify patterns, themes, and relationships. Here are some common methods of analyzing qualitative data:

• Thematic analysis: This involves identifying and analyzing patterns or themes within the data. It involves coding the data into themes and subthemes and organizing them into a coherent narrative.
• Content analysis: This involves analyzing the content of the data, such as the words, phrases, or images used. It involves identifying patterns and themes in the data and examining the relationships between them.
• Discourse analysis: This involves analyzing the language and communication used in the data, such as the meaning behind certain words or phrases. It involves examining how the language constructs and shapes social reality.
• Grounded theory: This involves developing a theory or framework based on the data. It involves identifying patterns and themes in the data and using them to develop a theory that explains the phenomenon being studied.
• Narrative analysis : This involves analyzing the stories and narratives present in the data. It involves examining how the stories are constructed and how they contribute to the overall understanding of the phenomenon being studied.
• Ethnographic analysis : This involves analyzing the culture and social practices present in the data. It involves examining how the cultural and social practices contribute to the phenomenon being studied.

Qualitative Data Collection Guide

Here are some steps to guide the collection of qualitative data:

• Define the research question : Start by clearly defining the research question that you want to answer. This will guide the selection of data collection methods and help to ensure that the data collected is relevant to the research question.
• Choose data collection methods : Select the most appropriate data collection methods based on the research question, the research design, and the resources available. Common methods include interviews, focus groups, observations, document analysis, and participatory research.
• Develop a data collection plan : Develop a plan for data collection that outlines the specific procedures, timelines, and resources needed for each data collection method. This plan should include details such as how to recruit participants, how to conduct interviews or focus groups, and how to record and store data.
• Obtain ethical approval : Obtain ethical approval from an institutional review board or ethics committee before beginning data collection. This is particularly important when working with human participants to ensure that their rights and interests are protected.
• Recruit participants: Recruit participants based on the research question and the data collection methods chosen. This may involve purposive sampling, snowball sampling, or random sampling.
• Collect data: Collect data using the chosen data collection methods. This may involve conducting interviews, facilitating focus groups, observing participants, or analyzing documents.
• Transcribe and store data : Transcribe and store the data in a secure location. This may involve transcribing audio or video recordings, organizing field notes, or scanning documents.
• Analyze data: Analyze the data using appropriate qualitative data analysis methods, such as thematic analysis or content analysis.
• I nterpret findings : Interpret the findings of the data analysis in the context of the research question and the relevant literature. This may involve developing new theories or frameworks, or validating existing ones.
• Communicate results: Communicate the results of the research in a clear and concise manner, using appropriate language and visual aids where necessary. This may involve writing a report, presenting at a conference, or publishing in a peer-reviewed journal.

Qualitative Data Examples

Some examples of qualitative data in different fields are as follows:

• Sociology : In sociology, qualitative data is used to study social phenomena such as culture, norms, and social relationships. For example, a researcher might conduct interviews with members of a community to understand their beliefs and practices.
• Psychology : In psychology, qualitative data is used to study human behavior, emotions, and attitudes. For example, a researcher might conduct a focus group to explore how individuals with anxiety cope with their symptoms.
• Education : In education, qualitative data is used to study learning processes and educational outcomes. For example, a researcher might conduct observations in a classroom to understand how students interact with each other and with their teacher.
• Marketing : In marketing, qualitative data is used to understand consumer behavior and preferences. For example, a researcher might conduct in-depth interviews with customers to understand their purchasing decisions.
• Anthropology : In anthropology, qualitative data is used to study human cultures and societies. For example, a researcher might conduct participant observation in a remote community to understand their customs and traditions.
• Health Sciences: In health sciences, qualitative data is used to study patient experiences, beliefs, and preferences. For example, a researcher might conduct interviews with cancer patients to understand how they cope with their illness.

Application of Qualitative Data

Qualitative data is used in a variety of fields and has numerous applications. Here are some common applications of qualitative data:

• Exploratory research: Qualitative data is often used in exploratory research to understand a new or unfamiliar topic. Researchers use qualitative data to generate hypotheses and develop a deeper understanding of the research question.
• Evaluation: Qualitative data is often used to evaluate programs or interventions. Researchers use qualitative data to understand the impact of a program or intervention on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population. Researchers use qualitative data to identify the most pressing needs of the population and develop strategies to address those needs.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail. Researchers use qualitative data to understand the context, experiences, and perspectives of the people involved in the case.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences. Researchers use qualitative data to gain insights into consumer attitudes, opinions, and motivations.
• Social and cultural research : Qualitative data is often used in social and cultural research to understand social phenomena such as culture, norms, and social relationships. Researchers use qualitative data to understand the experiences, beliefs, and practices of individuals and communities.

Purpose of Qualitative Data

The purpose of qualitative data is to gain a deeper understanding of social phenomena that cannot be captured by numerical or quantitative data. Qualitative data is collected through methods such as observation, interviews, and focus groups, and it provides descriptive information that can shed light on people’s experiences, beliefs, attitudes, and behaviors.

Qualitative data serves several purposes, including:

• Generating hypotheses: Qualitative data can be used to generate hypotheses about social phenomena that can be further tested with quantitative data.
• Providing context : Qualitative data provides a rich and detailed context for understanding social phenomena that cannot be captured by numerical data alone.
• Exploring complex phenomena : Qualitative data can be used to explore complex phenomena such as culture, social relationships, and the experiences of marginalized groups.
• Evaluating programs and intervention s: Qualitative data can be used to evaluate the impact of programs and interventions on the people who participate in them.
• Enhancing understanding: Qualitative data can be used to enhance understanding of the experiences, beliefs, and attitudes of individuals and communities, which can inform policy and practice.

When to use Qualitative Data

Qualitative data is appropriate when the research question requires an in-depth understanding of complex social phenomena that cannot be captured by numerical or quantitative data.

Here are some situations when qualitative data is appropriate:

• Exploratory research : Qualitative data is often used in exploratory research to generate hypotheses and develop a deeper understanding of a research question.
• Understanding social phenomena : Qualitative data is appropriate when the research question requires an in-depth understanding of social phenomena such as culture, social relationships, and experiences of marginalized groups.
• Program evaluation: Qualitative data is often used in program evaluation to understand the impact of a program on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail.

Characteristics of Qualitative Data

Here are some characteristics of qualitative data:

• Descriptive : Qualitative data provides a rich and detailed description of the social phenomena under investigation.
• Contextual : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena.
• Subjective : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation.
• Flexible : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question.
• Emergent : Qualitative data analysis is often an iterative process, where new themes and patterns emerge as the data is analyzed.
• Interpretive : Qualitative data analysis involves interpretation of the data, which requires the researcher to be reflexive and aware of their own biases and assumptions.
• Non-standardized: Qualitative data collection methods are often non-standardized, which means that the data is not collected in a standardized or uniform way.

Advantages of Qualitative Data

Some advantages of qualitative data are as follows:

• Richness : Qualitative data provides a rich and detailed description of the social phenomena under investigation, allowing for a deeper understanding of the phenomena.
• Flexibility : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question, allowing for a more nuanced exploration of social phenomena.
• Contextualization : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena and their cultural and social context.
• Subjectivity : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation, allowing for a more holistic understanding of the phenomena.
• New insights : Qualitative data can generate new insights and hypotheses that can be further tested with quantitative data.
• Participant voice : Qualitative data collection methods often involve direct participation by the individuals and communities under investigation, allowing for their voices to be heard.
• Ethical considerations: Qualitative data collection methods often prioritize ethical considerations such as informed consent, confidentiality, and respect for the autonomy of the participants.

Limitations of Qualitative Data

Here are some limitations of qualitative data:

• Subjectivity : Qualitative data is subjective, and the interpretation of the data depends on the researcher’s own biases, assumptions, and perspectives.
• Small sample size: Qualitative data collection methods often involve a small sample size, which limits the generalizability of the findings.
• Time-consuming: Qualitative data collection and analysis can be time-consuming, as it requires in-depth engagement with the data and often involves iterative processes.
• Limited statistical analysis: Qualitative data is often not suitable for statistical analysis, which limits the ability to draw quantitative conclusions from the data.
• Limited comparability: Qualitative data collection methods are often non-standardized, which makes it difficult to compare findings across different studies or contexts.
• Social desirability bias : Qualitative data collection methods often rely on self-reporting by the participants, which can be influenced by social desirability bias.
• Researcher bias: The researcher’s own biases, assumptions, and perspectives can influence the data collection and analysis, which can limit the objectivity of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Home Market Research

Qualitative Data – Definition, Types, Analysis, and Examples

For a market researcher, collecting qualitative data helps answer questions like who their customers are, what issues or problems they are facing, and where they need to focus their attention so problems or issues are resolved. Let’s talk about it.

Content Index

What is qualitative data?

Importance of qualitative data.

• Advantages of qualitative data analysis
• Disadvantages of qualitative data analysis
• Qualitative data analysis methods

Qualitative data analysis approaches

5 steps to qualitative data analysis, qualitative data examples.

• Do you want to create your own survey?n

Qualitative data is defined as data that approximates and characterizes. Qualitative data can be observed and recorded. 

This data type is non-numerical. This type of data is collected through methods of observations, one-to-one interviews, conducting focus groups , and similar methods. Qualitative data in statistics is also known as categorical data – data that can be arranged categorically based on the attributes and properties of a thing or a phenomenon.

Qualitative data is important in determining the particular frequency of traits or characteristics. It allows the statistician or the researchers to form parameters through which larger data sets can be observed. 

It provides how observers can quantify the world around them. Qualitative data is about the emotions or perceptions of people and what they feel. Qualitative analysis is key to getting useful insights from textual data, figuring out its rich context, and finding subtle patterns and themes. 

In qualitative data, these perceptions and emotions are documented. It helps market researchers understand their consumers’ language and solve the research problem effectively and efficiently. 

Advantages of qualitative data

Some advantages of qualitative data are given below:

It helps in-depth analysis

The data collected provide the qualitative researchers with a detailed analysis, like a thematic analysis of subject matters. While collecting it, the researchers tend to probe the participants and can gather ample information by asking the right kind of questions. The data collected is used to conclude a series of questions and answers. 

Understand what customers think

The data helps market researchers understand their customers’ mindsets. Using qualitative data gives businesses an insight into why a customer purchased a product. Understanding customer language helps market research infer the data collected more systematically.

Collected data can also be used to conduct future research. Since the questions asked to collect qualitative data are open-ended questions, respondents are free to express their opinions, leading to more information.

Disadvantages of qualitative data

Some disadvantages of qualitative data are given below:

Time-consuming

 As collecting this data is more time-consuming, fewer people study than collecting quantitative data. Unless time and budget allow, a smaller sample size is included.

Not easy to generalize

Since fewer people are studied, it is difficult to generalize the results of that population.

Dependent on the researcher’s skills

This type of data is collected through one-to-one interviews, observations, focus groups , etc. It relies on the researcher’s skills and experience to collect information from the sample.

It is typically descriptive analysis data and is more difficult to analyze than quantitative data. Now, you have to decide which is the best option for your research project; remember that to obtain and analyze this data, we need a little more time, so you should consider it in your planning.

Learn about: the 12 Best Tools for Researchers

Qualitative data collection methods

Qualitative data collection is exploratory; it involves in-depth analysis and research. Its collection methods mainly focus on gaining insights, reasoning, and motivations; hence, they go deeper into research . Since this data cannot be measured, researchers prefer methods or data collection tools that are structured to a limited extent.

Here are the qualitative data collection methods :

One-to-one interviews

It is one of the most commonly used data collection instruments for qualitative research questions, mainly because of its approach. The interviewer or the researcher collects data directly from the interviewee one-to-one. The interview method may be informal and unstructured – conversational. The open-ended questions are mostly asked spontaneously, with the interviewer letting the interview flow dictate the questions to be asked.

LEARN ABOUT: Best Data Collection Tools

Focus groups

This is done in a group discussion setting. The group is limited to 6-10 people, and a moderator is assigned to moderate the ongoing discussion.

Depending on the data which is sorted, the group members may have something in common. For example, a researcher conducting a study on track runners will choose athletes who are track runners or were track runners and have sufficient knowledge of the subject matter.

Record keeping

This method uses existing reliable documents and similar sources of information as the data source. This data can be used in the new research. It is similar to going to a library. There, one can go over books and other reference material to collect relevant data that can be used in the research.

Process of observation

In this data collection method, the researcher immerses himself/ herself in the setting where his respondents are, keeps a keen eye on the participants, and takes notes. This is known as the process of observation.

Besides taking notes, other documentation methods, such as video and audio recording, photography, and similar methods, can be used.

Longitudinal studies

This data collection method is repeatedly performed on the same data source over an extended period. It is an observational research method that goes on for a few years and sometimes can go on for even decades. Such data collection methods aim to find correlations through empirical studies of subjects with common traits.

Case studies

This method gathers data from an in-depth analysis of case studies . The versatility of this method is demonstrated in how this method can be used to analyze both simple and complex subjects. The strength of this method is how judiciously it uses a combination of one or more qualitative methods to draw inferences.

Learn more: Qualitative Research Methods .

Analyzing qualitative data is vital, as you have spent time and money collecting it. It is essential because you don’t want to find yourself in the dark even after putting in so much effort. However, there are no set ground rules for analyzing data; it all begins with understanding its two main approaches. 

Qualitative data analysis allows researchers to dig deep into research findings and reveal the complex meanings of qualitative data. Two main approaches to qualitative analysis:

Deductive approach

The deductive approach involves analyzing qualitative data based on a structure that the researcher predetermines. A researcher can use the questions as a guide for analyzing the data. This approach is quick and easy and can be used when a researcher has a fair idea about the likely responses that he/she is going to receive from the sample population.

Inductive approach

On the contrary, the inductive approach is not based on a predetermined structure or set ground rules/framework. It is a more time-consuming and thorough approach to the qualitative analysis process. An inductive approach is often used when a researcher has very little or no idea of the research phenomenon. 

Learn more: Data analysis in research .

Whether you want to analyze qualitative data collected through a one-to-one interview or a survey , these simple steps will ensure a robust data analysis .

Step 1: Arrange your data

Once you have collected all the data, it is largely unstructured and sometimes makes no sense when viewed at a glance. Therefore, it is essential that as a researcher, you first need to transcribe the data collected. 

The first step in analyzing your data is arranging it systematically. Arranging data means converting all the data into a text format. You can either export the data into a spreadsheet or manually type in the data, or choose from any of the computer-assisted qualitative data analysis tools.

LEARN ABOUT: Level of Analysis

Step 2: Organize all your data

After transforming and arranging your data, the immediate next step is to organize your data. You may have a large amount of information that still needs to be arranged in an orderly manner. One of the best ways to organize the data is by going back to your research objectives and then organizing the data based on the questions asked. 

Arrange your research objective in a table so it appears visually clear. At all costs, avoid the temptations of working with unorganized data. You will waste time, and no conclusive results will be obtained.

Step 3: Set a code to the data collected

Setting up proper codes for the collected data takes you a step ahead. The coding process is one of the best ways to compress a tremendous amount of information collected. Data coding means categorizing and assigning properties and patterns to the collected data.

Coding is important in this data analysis, as you can derive theories from relevant research findings. After assigning codes to your data, you can build on the patterns to gain in-depth insight into the data that will help make informed decisions.

Step 4: Validate your qualitative data

Validating data is one of the crucial steps of qualitative data analysis for successful research. Since data is quintessential for research, ensuring that the data is not flawed is imperative. Please note that data validation is not just one step in this analysis; this is a recurring step that needs to be followed throughout the research process. There are two sides to validating data:

• Accuracy of your research design or methods.
• Reliability is the extent to which the methods consistently produce accurate data.

Step 5: Concluding the analysis process

It is important to finally conclude your data, which means systematically presenting your data, a report that can be readily used. The report should state the method you used as a researcher to conduct the research studies, the positives and negatives, and the study limitations. In the report, you should also state the suggestions/inferences of your findings and any related areas for future research. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders.

LEARN ABOUT: Steps in Qualitative Research

Qualitative data is also called categorical data since this data can be grouped according to categories.

For example, think of a student reading a paragraph from a book during class sessions. A teacher listening to the reading gives feedback on how the child reads that paragraph. 

Suppose the teacher gives feedback based on fluency, intonation, word throwing, and pronunciation clarity without giving the child a grade. In that case, this is considered an example of qualitative data.

It’s pretty easy to understand the difference between qualitative and quantitative data. It does not include numbers in its traits definition, whereas quantitative data is all about numbers.

• The cake is orange, blue, and black in color (qualitative).
• Females have brown, black, blonde, and red hair (qualitative).

Quantitative data is any quantifiable information that can be used for mathematical calculation or statistical analysis . This form of data helps in making real-life decisions based on mathematical derivations. Quantitative data is used to answer questions like How many? How often? How much? This data can be validated and verified. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

LEARN ABOUT:   Statistical Analysis Methods

To better understand the concept of qualitative and quantitative data, it’s best to observe examples of particular datasets and how they can be defined. The following are examples of quantitative data .

• There are four cakes and three muffins kept in the basket (quantitative).
• One glass of fizzy drink has 97.5 calories (quantitative).

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Pass on the heavy lifting to QuestionPro and focus on what’s important. Our experienced teams deliver intuitive analysis to improve critical takeaways and help turn insight into action. From research design to complex scripting, from analytics management to reporting, work with a team of professionals that will be an extension of your brand.

Frequently Asking Questions (FAQ)

The ability to identify issues and opportunities from respondents is one of the main characteristics of an effective qualitative research question. of an open-ended nature. Simple to comprehend and absorb, with little need for more explanation.

Validity is the quality of research that relates to how effectively the conclusions acquired from examining the study participants’ data reflect genuine findings among similar individuals outside the study’s population.

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The Primary Methods of Qualitative Data Analysis

In academic research as well as in the business landscape, qualitative data analysis plays a crucial role in understanding and interpreting non-numerical data.

Qualitative data analysis helps us make sense of the stories and personal narratives. In the business context, qualitative data analysis turns customer feedback into an in-depth understanding of what matters to customers. Sharing the insights from this analysis with decision-makers helps them drive initiatives that improve customer experiences.

While quantitative data analysis focuses on numerical measurement and statistical analysis, qualitative data analysis delves into the rich and complex nature of human experiences and perceptions.  When analyzed effectively, customer feedback can be transformed into actionable insights for every team across the company.

This guide will provide an in-depth exploration of the different methods employed in qualitative data analysis, as well as the steps involved and challenges encountered. We’ll also have a look at what QDA means in the business context and how to turn it into a high-powered tool for CX and product teams.

Understanding Qualitative Data Analysis Methods

Definition and importance of qualitative data analysis.

Qualitative data analysis refers to the systematic process of examining and interpreting non-numerical data to gain meaningful insights and generate new knowledge. It’s what happens when you put a year’s worth of Amazon reviews into a thematic analysis engine, and end up with a thorough understanding of how users interact with your product (and half a dozen actionable insights to boot).

It involves dissecting text, images, videos, and other forms of qualitative data to identify patterns, themes, and relationships.

By capturing the nuances and depth of human experiences, the qualitative data analysis approach allows researchers to explore complex social phenomena that quantitative approaches cannot fully capture. It provides a rich and detailed understanding of social contexts, individual perspectives, and subjective experiences.

Qualitative data analysis methods offer an in-depth exploration of the hows and whys behind social phenomena, enabling researchers to gain a comprehensive understanding of complex social issues.  It is incredibly valuable in fields such as sociology, anthropology, psychology, and education, where human behavior and social interactions are studied.

In these fields, researchers often seek to understand the intricacies of human experiences, and qualitative data analysis allows them to capture the complexity of these phenomena.

In the world of business & product development, qualitative data analysis methods can work to improve user experiences. Suddenly, you’ve got the opportunity to reach a comprehensive understanding of just what your products mean on the social landscape.

User feedback gets transformed into big-picture knowledge that offers a 360-degree view of how a product performs in the real world.  Product teams get a solid, reliable basis on which to make decisions , and guesswork becomes a thing of the past.

Key Principles of Qualitative Data Analysis

Before delving into the various methods of qualitative data analysis, let’s look at the key principles that underpin these analysis techniques. Qualitative data analysis is guided by the following principles:

• Inductive Reasoning: Qualitative research focuses on specific observations and gradually develops broader interpretations and theories. It allows for the discovery of new patterns and relationships through an iterative process of data investigation.
• Contextual Understanding: Qualitative data analysis emphasizes the importance of understanding the research context and the social, cultural, and historical factors that shape it. Context provides meaning and helps researchers identify themes as well as interpret and make sense of the data.
• Subjectivity and Reflexivity: When research is human-led, the researchers acknowledge and critically reflect upon their own beliefs, biases, and experiences throughout the qualitative data analysis process. Where research is AI-driven, humans get a chance to view the actual data each insight is based on and check to see if it makes objective sense.
• Active Engagement: A qualitative data analysis method is an active and dynamic process that involves constant engagement with the data. Thematic analysis works most effectively as an ongoing process,  thoroughly examining and interpreting all available data, while continually questioning and refining the research questions and analysis as new data points are added.

Inductive reasoning is a fundamental principle of qualitative data analysis. It allows researchers to start with specific observations and gradually develop broader interpretations and theories. Through this iterative process of data investigation, new patterns and relationships can be discovered. When you’ve got AI-driven data analysis software, this inductive reasoning is going on under the hood.

Contextual understanding is another key principle of the qualitative analysis process. It emphasizes the importance of understanding the research context and the social, cultural, and historical factors that shape it.

By considering the context when analyzing qualitative data, researchers can gain a deeper understanding of the data and interpret it more accurately. Well-designed thematic analysis software has this built in.

Subjectivity and reflexivity are essential principles in qualitative data analysis. Qualitative data analysis research must be repeatable if it is to be relied on, and there should always be ways to check just what qualitative feedback particular trends and insights come from. When qualitative data analysis is done right, transparency and rigor can be maintained throughout the process, from the initial selection of research questions and gathering of raw data to final analysis techniques.

Active engagement is a crucial aspect of qualitative feedback interpretation. It involves constant engagement with the data, as researchers thoroughly examine and interpret it. This active and dynamic process allows researchers to continually question and refine their qualitative analysis, ensuring a comprehensive understanding of the data.

Different Qualitative Data Analysis Methods

Just how does qualitative analysis work out in practice? In this article, we will explore five commonly used qualitative analysis methods: content analysis, narrative analysis, discourse analysis,  grounded theory, and thematic analysis.

• Content Analysis

Content analysis is a systematic and objective approach to analyzing data by categorizing, coding, and quantifying specific words, themes, or concepts within a text. It involves identifying patterns, frequencies, and relationships in the content, which can be textual, visual, or auditory.

Researchers can employ content analysis techniques to examine interviews, focus group discussions, newspaper articles, social media posts, and other forms of textual data. By assigning codes to different segments of the text, researchers can identify recurring themes, sentiments, or messages.

This same qualitative data analysis approach can be used by CX and product teams to analyze customer feedback or support tickets.

For example, in an analysis of public response to a new product, a PX team might use content analysis to analyze social media posts discussing the topic.

By categorizing the posts based on their stance (e.g., positive, negative, neutral) and identifying recurring themes (e.g., user experience, look and feel), a company could gain insights into the dominant narratives and public perceptions surrounding the product launch.

• Narrative Analysis

Narrative analysis focuses on interpreting and understanding the stories and personal narratives shared by individuals. Researchers analyze the structure, content, and meaning of these narratives to gain insights into how individuals make sense of their experiences, construct identities, and communicate their perspectives.

Through narrative analysis techniques, qualitative researchers explore the plot, characters, setting, and themes within a narrative. They examine how the narrator constructs meaning, conveys emotions, and positions themselves within the story.

This same narrative analysis method is often used in psychology, sociology, and anthropology to understand identity formation, life histories, and personal narratives. It can be used in a business setting to analyze long-form responses and user interviews or descriptions of user behavior.

For instance, in a study on the experiences of cancer survivors, researchers may conduct narrative analysis on interviews with survivors. By examining the narratives, researchers can identify common themes such as coping strategies, support systems, and personal growth.

This qualitative analysis process can provide valuable insights into the lived experiences of cancer survivors and inform interventions and support programs.

• Discourse Analysis

Discourse analysis examines the social, cultural, and power relations that shape language use in different contexts. It focuses on the ways in which language constructs and reflects social reality, identities, and ideologies.

Researchers employing discourse analysis analyze data that includes spoken or written language, including interviews, speeches, media articles, and conversations.

They examine linguistic features such as metaphors, power dynamics, framing, and silences to uncover underlying social structures and processes.

For example, in a study on gender representation in media, researchers may use discourse analysis to analyze television advertisements. By examining the language, visual cues, and narratives used in the advertisements, researchers can identify how gender roles and stereotypes are constructed and reinforced.

It can shed light on the ways in which media perpetuates or challenges societal norms and expectations.

Another example might be using discourse analysis to analyze Tik Tok and YouTube videos to understand the societal responses to a rebranding; for instance, that from Twitter to X. Customer interviews are another good source for this analysis method.

• Grounded Theory

Grounded theory is an approach to qualitative analysis that aims to develop theories and concepts grounded in data. It involves iterative data collection and analysis to develop an inductive theory that emerges from the unstructured data itself.

Researchers using grounded theory analyze interviews, observations, and textual data to generate concepts and categories.

These concepts are continually refined and developed through theoretical sampling and constant comparison. Grounded theory analysis is particularly useful when exploring complex social phenomena where existing theories may be limited.

For instance, in a study on the experiences of individuals living with chronic pain, researchers may use grounded theory to analyze interviews with participants. Through iterative analysis, researchers can identify key concepts such as pain management strategies, social support networks, and psychological coping mechanisms.

These concepts can then be used to develop a theoretical framework within grounded theory that captures the multidimensional nature of living with chronic pain.

Although historically grounded theory analysis has been primarily used in the social sciences, grounded theory has also been used successfully for business inquiry.

• Thematic Analysis

Thematic analysis is a widely used method in qualitative data analysis that involves identifying, analyzing, and reporting patterns or themes within data. It is a flexible approach that can be applied across a variety of qualitative data, such as interview transcripts, survey responses, and observational notes.

When thematic analysis is done manually, researchers initially familiarize themselves with the raw data, reading through the material multiple times to gain a deep understanding.

Following this, they begin manual coding. The first step is to generate initial codes, which are tags or labels that identify important features of the data relevant to the research question.

These codes are then collated into potential themes, which are broader patterns that emerge across the data set.

Each theme is then reviewed and refined to ensure it accurately represents the coded data and the overall data set. The final step involves defining and naming the themes, during which researchers provide detailed analysis, including how themes relate to each other and to the research question.

Sound complicated? The great news is that advances in artificial intelligence mean we no longer have to do all that by hand.

Thematic analysis software can process thousands of pieces of consumer feedback in a matter of minutes, providing a user-friendly view of the themes and trends in the customer data pool.

What’s more, this type of software can be programmed to do content analysis, discourse analysis, and narrative analysis at the same time.   The best comprehensive business solution for thematic analysis today is Thematic; a comprehensive feedback analysis that is designed for customer-centric businesses. It makes qualitative user analysis accessible to anyone, and is able to process feedback at scale.

Across disciplines, thematic analysis is particularly valued for its ability to provide a rich and detailed, yet complex account of data. It's a method that is accessible to researchers across different levels of qualitative research experience and can be applied to a variety of theoretical and epistemological approaches, making it a versatile tool in qualitative work.

Steps in Qualitative Data Analysis

Data collection.

Data collection is the initial phase of qualitative research and data analysis. It involves selecting appropriate methods to gather data such as interviews, observations, focus groups, or archival research.

Researchers may employ various techniques to collect data. These can include developing interview protocols, conducting observations, or collecting data using audio-visual recording devices.

They may need to consider ethical considerations, ensure informed consent, and establish rapport with participants to obtain rich and reliable data. The goal is to gather qualitative data that is relevant, comprehensive, and representative of the research topic.

Qualitative research questions can be more open-ended than those used for gathering quantitative data, and the research findings have the potential to be far more extensive.

In a business context, much of the work is done for you by customers who provide feedback in reviews, on support tickets, and on social media. Customer interviews are another possible source of rich data.

Data Coding

Data coding is the process of categorizing and organizing qualitative data into meaningful segments. When this is done manually, researchers assign codes to different parts of the data based on the emerging patterns, themes, or concepts identified during analysis. This coding process helps researchers manage and make sense of large amounts of qualitative data.

There are different types of codes used in analyzing raw data, including descriptive codes, interpretive codes, and conceptual codes.

Descriptive codes capture the content and surface-level meaning of all the data, while interpretive codes delve deeper into the underlying meanings and interpretations. A conceptual coding system further abstracts the research data by identifying broader concepts or theories.

Data Interpretation

Data interpretation involves making sense of the coded data and exploring the relationships, themes, and patterns that emerge from the analysis. Researchers critically examine the data, compare different codes, and then identify themes and connections between categories and concepts.

During data interpretation, researchers may engage in constant comparison, where they continually compare new data to existing codes and categories. This iterative process helps refine the analysis and identify theoretical insights.

It involves synthesizing the findings of qualitative and quantitative data and crafting a narrative that presents a comprehensive understanding of the research phenomenon.

Both data coding and data interpretation can be done by your qualitative analytics software, either in a research or business setting. In a corporate setting, CX /PX teams and customer service can then use information gained through the data interpretation step to drive favourable outcomes.

Performing Qualitative Data Analysis with Generative AI and LLM

Running manual grounded theory analysis or content analysis on a large amount of consumer feedback has never been a practical option. But that doesn’t mean qualitative research doesn’t make sense in a business context.

Generative AI, based on large language models (LLMs) can work with qualitative data at scale, analyze it, and derive the themes, connections and insights that can inform business decisions.

An LLM is a powerful machine learning model, based on deep learning and neural networks. It’s able to process and identify the complex relationships in natural language, and it can also understand user questions and  moods and even generate text.

A natural languague processing LLM, trained on huge amounts of text data, could do all the work of a QDA researcher with the added benefits of easily verifiable, repeatable results.

Companies with extensive  in-house talent  may be able to build an in-house AI engine to analyze customer feedback and make sense of it— on a small scale. Those who are serious about getting real insights, though, will want to go with professional tools that have been trained on massive amounts of data and give reliable, dependable results.

Thematic is probably the best example of such a tool. Built to make sense of any amount of feedback data,  it works in a highly transparent way that will leave you confident in every insight you derive.

It’s also incredibly user-friendly, with helpful visualizations and an easy-to-use dashboard that enables you to keep constant tabs on exactly what your users feel about the company. It’s never been easier to transform your user experience.

Modern Methods of Qualitative Data Analysis in Action: A Case Study

Instacart is one example of a company that discovered the power of qualitative data analysis. This company has 10 million end users, 500,000 personal shoppers, and more than 40,000 retailers. Processing all this qualitative data the traditional way would have been impossible, but Ant Marty,  product operations team manager, found a method that worked.

Plugging data from the app into Thematic, she got real time information on everything happening among those millions of users: trends, themes, and deep understanding of what mattered to the people who made the company run.

Data collection is easy when you have an app with numerous feedback collection options.  Data coding is automated by Thematic. And Thematic makes the first move in interpretation as well, providing insights that can be transformed by product teams into action plans and even a long-term vision.

Challenges Facing Qualitative Data Analysis Methods

Ensuring data validity and reliability.

One of the main challenges to a qualitative approach is ensuring the validity and reliability of the findings. Validity refers to the accuracy, truthfulness, and credibility of the data collected and analysis, while reliability refers to the consistency and replicability of the research process and findings.

Researchers address these challenges by employing rigorous data collection methods, ensuring data saturation, conducting member checks, and establishing inter-rater reliability. They also maintain reflexivity by critically reflecting on their assumptions, biases, and interpretations throughout the analysis process.

If you are a business using software to conduct qualitative research, your data validation check may be somewhat different, but it’s just as important.  Some software, like Thematic, has validation built in, and the whole process is so transparent you can easily check and double-check where each insight comes from .

With other software options, you may have to run manual checks to ensure every piece of information provided has a firm basis.

Dealing with Subjectivity and Bias

Subjectivity and bias used to be considered inherent to qualitative research methods due to the interpretive nature of the process. Researchers bring their own perspectives, beliefs, and experiences, which can influence the analysis and interpretations.

To mitigate subjectivity and bias, researchers maintain transparency in their analytical processes by documenting their decision-making, providing detailed justifications for their interpretations, and engaging in peer debriefing and member checking. Using multiple researchers or an expert panel can also increase the credibility and reliability of the analysis.

Another way to decrease subjectivity is through thematic analysis software, which produces results that are repeatable and verifiable.

When it is all said and done, qualitative analysis offers a powerful and nuanced examination of human experiences and social phenomena. By employing diverse methods, adhering to key principles, and addressing potential limitations, researchers can harness the full potential of qualitative data to uncover rich insights and contribute to the advancement of knowledge.

Benefits of Qualitative Data Analysis Methods

Rich, in-depth insights.

A primary benefit of qualitative research techniques is their ability to provide rich, in-depth insights into complex phenomena. These methods delve deeply into human experiences, emotions, beliefs, and behaviors, offering a comprehensive understanding that is often unattainable through quantitative methods.

By exploring the nuances and subtleties of social interactions and personal experiences, qualitative analysis can uncover the layers of meaning that underpin human behavior. This depth of understanding is particularly valuable in fields like psychology, sociology, and anthropology, where the intricacies of human experience are central to the research question.

It is even more important for customer-focused businesses and enables them to create a product and a CX that meets their customer’s needs and desires. Quantitative analysis can provide a one-dimensional understanding of user behavior based on quantitative data, but when analysing qualitative data you get the why to every what.

Flexibility and Contextual Understanding

Another significant advantage of these analysis techniques is their inherent flexibility and capacity to provide contextual understanding. Unlike quantitative research, which relies on rigid structures and predefined hypotheses, qualitative research is adaptable to the evolving nature of the study.

This flexibility allows researchers to explore unexpected themes and patterns that emerge during the data collection process.  Qualitative analysis is how businesses like Atlassian have created infinite customer feedback loops and powered their own infinitely evolving products.

Additionally, qualitative methods are sensitive to the context in which the data is collected, acknowledging and incorporating the environmental, cultural, and social factors that influence the data. The context-rich approach used to collect qualitative data ensures a more holistic understanding of the subject matter, making it particularly useful in cross-cultural studies, community research, and exploratory investigations.

Your product may have global reach, and users in different areas may interact with it in different ways– but qualitative techniques can take all that into account.

This considered, it should be no surprise that qualitative analysis techniques have become powerful tools for researchers seeking to understand the complexities of human behavior and social phenomena. Their ability to provide depth, context, and rich narrative data makes them indispensable tools in the arsenal of social science research, and there’s no better way to gain solid information to guide your business decisions.

Whether you’re a researcher keen on analyzing and interpreting qualitative data or an entrepreneur keen on making your business more customer-centric, this research method is likely to become your next best friend.

If you’re in academia, you may want to do it all manually, and that’s totally okay. But if it’s business intelligence you’re after— try out Thematic. Your future self will thank you, as will everyone else who views the end-of-year reports.

What are the five methods to analyze qualitative data?

The five chief methods of qualitative data analysis are:

The right analysis method for your use case will depend on what context, your research questions, and the form of data available to you.

What are good sources of data for qualitative data analysis?

In a business context user reviews, support tickets, customer surveys and social media posts are all great sources of data for qualitative analysis. In a research project, gathering qualitative data may mean conducting interviews, surveys, or focus groups.

What are the benefits of qualitative data analysis?

Two big benefits of qualitative data analysis include:

• Rich, in-depth insights
• Flexibility and contextual understanding

In a business context, this translates into a loyal, well-satisfied user base, a successful product, and an upwards-ticking revenue curve. Research objectives for social sciences may include a better understanding of social dynamics or human relations.

What are the challenges of qualitative data analysis?

The two prime challenges of qualitative data analysis techniques are:

• Ensuring data validity and reliability
• Dealing with subjectivity and bias

What is the best tool for qualitative data analysis?

While a number of other options do exist, the best comprehensive software for qualitative data analysis in a business context today is Thematic.

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Qualitative vs Quantitative Research Methods & Data Analysis

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

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Qualitative Data Analysis Methods

In the following, we will discuss basic approaches to analyzing data in all six of the acceptable qualitative designs.

After reviewing the information in this document, you will be able to:

• Recognize the terms for data analysis methods used in the various acceptable designs.
• Recognize the data preparation tasks that precede actual analysis in all the designs.
• Understand the basic analytic methods used by the respective qualitative designs.
• Identify and apply the methods required by your selected design.

Terms Used in Data Analysis by the Six Designs

Each qualitative research approach or design has its own terms for methods of data analysis:

• Ethnography—uses modified thematic analysis and life histories.
• Case study—uses description, categorical aggregation, or direct interpretation.
• Grounded theory—uses open, axial, and selective coding (although recent writers are proposing variations on those basic analysis methods).
• Phenomenology—describes textures and structures of the essential meaning of the lived experience of the phenomenon
• Heuristics—patterns, themes, and creative synthesis along with individual portraits.
• Generic qualitative inquiry—thematic analysis, which is really a foundation for all the other analytic methods. Thematic analysis is the starting point for the other five, and the endpoint for generic qualitative inquiry. Because it is the basic or foundational method, we'll take it first.

Preliminary Tasks in Analysis in all Methods

In all the approaches—case study, grounded theory, generic inquiry, and phenomenology—there are preliminary tasks that must be performed prior to the analysis itself. For each, you will need to:

• Arrange for secure storage of original materials. Storage should be secure and guaranteed to protect the privacy and confidentiality of the participants' information and identities.
• Transcribe interviews or otherwise transform raw data into usable formats.
• Make master copies and working copies of all materials. Master copies should be kept securely with the original data. Working copies will be marked up, torn apart, and used heavily: make plenty.
• Arrange secure passwords or other protection for all electronic data and copies.
• When ready to begin, read all the transcripts repeatedly—at least three times—for a sense of the whole. Don't force it—allow the participants' words to speak to you.

These tasks are done in all forms of qualitative analysis. Now let's look specifically at generic qualitative inquiry.

Data Analysis in Generic Qualitative Inquiry: Thematic Analysis

The primary tool for conducting the analysis of data when using the generic qualitative inquiry approach is thematic analysis, a flexible analytic method for deriving the central themes from verbal data. A thematic analysis can also be used to conduct analysis of the qualitative data in some types of case study.

Thematic analysis essentially creates theme-statements for ideas or categories of ideas (codes) that the researcher extracts from the words of the participants.

There are two main types of thematic analysis:

• Inductive thematic analysis, in which the data are interpreted inductively, that is, without bringing in any preselected theoretical categories.
• Theoretical thematic analysis, in which the participants' words are interpreted according to categories or constructs from the existing literature.

Analytic Steps in Thematic Analysis: Reading

Remember that the last preliminary task listed above was to read the transcripts for a sense of the whole. In this discussion, we'll assume you're working with transcribed data, usually from interviews. You can apply each step, with changes, to any kind of qualitative data. Now, before you start analyzing, take the first transcript and read it once more, as often as necessary, for a sense of what this participant told you about the topic of your study. If you're using other sources of data, spend time with them holistically.

Thematic Analysis: Steps in the Process

When you have a feel for the data,

• Underline any passages (phases, sentences, or paragraphs) that appear meaningful to you. Don't make any interpretations yet! Review the underlined data.
• Decide if the underlined data are relevant to the research question and cross out or delete all data unrelated to the research question. Some information in the transcript may be interesting but unrelated to the research question.
• Create a name or "code" for each remaining underlined passage (expressions or meaning units) that focus on one single idea. The code should be:
• Briefer than the passage, should
• Sum up its meaning, and should be
• Supported by the meaning unit (the participant's words).
• Find codes that recur; cluster these together. Now begin the interpretation, but only with the understanding that the codes or patterns may shift and change during the process of analysis.
• After you have developed the clusters or patterns of codes, name each pattern. The pattern name is a theme. Use language supported by the original data in the language of your discipline and field.
• Write a brief description of each theme. Use brief direct quotations from the transcript to show the reader how the patterns emerged from the data.
• Compose a paragraph integrating all the themes you developed from the individual's data.
• Repeat this process for each participant, the "within-participant" analysis.
• Finally, integrate all themes from all participants in "across-participants" analysis, showing what general themes are found across all the data.

Some variation of thematic analysis will appear in most of the other forms of qualitative data analysis, but the other methods tend to be more complex. Let's look at them one at a time. If you are already clear as to which approach or design your study will use, you can skip to the appropriate section below.

Ethnographic Data Analysis

Ethnographic data analysis relies on a modified thematic analysis. It is called modified because it combines standard thematic analysis as previously described for interview data with modified thematic methods applied to artifacts, observational notes, and other non-interview data.

Depending on the kinds of data to be interpreted (for instance pictures and historical documents) Ethnographers devise unique ways to find patterns or themes in the data. Finally, the themes must be integrated across all sources and kinds of data to arrive at a composite thematic picture of the culture.

(Adapted from Bogdan and Taylor, 1975; Taylor and Bogdan, 1998; Aronson, 1994.)

Data Analysis in Grounded Theory

Going beyond the descriptive and interpretive goals of many other qualitative models, grounded theory's goal is building a theory. It seeks explanation, not simply description.

It uses a constant comparison method of data analysis that begins as soon as the researcher starts collecting data. Each data collection event (for example, an interview) is analyzed immediately, and later data collection events can be modified to seek more information on emerging themes.

In other words, analysis goes on during each step of the data collection, not merely after data collection.

The heart of the grounded theory analysis is coding, which is analogous to but more rigorous than coding in thematic analysis.

Coding in Grounded Theory Method

There are three different types of coding used in a sequential manner.

• The first type of coding is open coding, which is like basic coding in thematic analysis. During open coding, the researcher performs:
• A line-by-line analysis (or sentence or paragraph analysis) of the data.
• Labels and categorizes the dimensions or aspects of the phenomenon being studied.
• The researcher also uses memos to describe the categories that are found.
• The second type of coding is axial coding, which involves finding links between categories and subcategories found in the open coding.
• The open codes are examined for their relationships: cause and effect, co-occurrence, and so on.
• The goal here is to picture how the various dimensions or categories of data interact with one another in time and space.
• The third type of coding is selective coding, which identifies a core category and relates the categories subsidiary to this core.
• Selective coding selects the main phenomenon, (core category) around which subsidiary phenomena, (all other categories) are grouped, arranging the groupings, studying the results, and rearranging where the data require it.

The Final Stages of Grounded Theory Analysis, after Coding

From selective coding, the grounded theory researcher develops:

• A model of the process, which is the description of which actions and interactions occur in a sequence or series.
• A transactional system, which is the description of how the interactions of different events explain the phenomenon being investigated.
• Finally, A conditional matrix is diagrammed to help consider the conditions and consequences related to the phenomenon under study.

These three essentially tell the story of the outcome of the research, in other words, the description of the process by which the phenomenon seems to happen, the transactional system supporting it, and the conditional matrix that pictures the explanation of the phenomenon are the findings of a grounded theory study.

(Adapted from Corbin and Strauss, 2008; Strauss and Corbin, 1990, 1998.)

Data Analysis in Qualitative Case Study: Background

There are a few points to consider in analyzing case study data:

• Analysis can be:
• Holistic—the entire case.
• Embedded—a specific aspect of the case.
• Multiple sources and kinds of data must be collected and analyzed.
• Data must be collected, analyzed, and described about both:
• The contexts of the case (its social, political, economic contexts, its affiliations with other organizations or cases, and so on).
• The setting of the case (geography, location, physical grounds, or set-up, business organization, etc.).

Qualitative Case Study Data Analysis Methods

Data analysis is detailed in description and consists of an analysis of themes. Especially for interview or documentary analysis, thematic analysis can be used (see the section on generic qualitative inquiry). A typical format for data analysis in a case study consists of the following phases:

• Description: This entails developing a detailed description of each instance of the case and its setting. The words "instance" and "case" can be confusing. Let's say we're conducting a case study of gay and lesbian members of large urban evangelical Christian congregations in the Southeast. The case would be all such people and their congregations. Instances of the case would be any individual person or congregation. In this phase, all the congregations (the settings) and their larger contexts would be described in detail, along with the individuals who are interviewed or observed.
• Categorical Aggregation: This involves seeking a collection of themes from the data, hoping that relevant meaning about lessons to be learned about the case will emerge. Using our example, a kind of thematic analysis from all the data would be performed, looking for common themes.
• Direct Interpretation: By looking at the single instance or member of the case and drawing meaning from it without looking for multiple instances, direct interpretation pulls the data apart and puts it together in more meaningful ways. Here, the interviews with all the gay and lesbian congregation members would be subjected to thematic analysis or some other form of analysis for themes.
• Within-Case Analysis: This would identify the themes that emerge from the data collected from each instance of the case, including connections between or among the themes. These themes would be further developed using verbatim passages and direct quotation to elucidate each theme. This would serve as the summary of the thematic analysis for each individual participant.
• Cross-Case Analysis: This phase develops a thematic analysis across cases as well as assertions and interpretations of the meaning of the themes emerging from all participants in the study.
• Interpretive Phase: In the final phase, this is the creation of naturalistic generalizations from the data as a whole and reporting on the lesson learned from the case study.

(Adapted from Creswell, 1998; Stake, 1995.)

Data Analysis in Phenomenological Research

There are a few existing models of phenomenological research, and they each propose slightly different methods of data analysis. They all arrive at the same goal, however. The goal of phenomenological analysis is to describe the essence or core structures and textures of some conscious psychological experience. One such model, empirical, was developed at Duquesne University. This method of analysis consists of five essential steps and represents the other variations well. Whichever model is chosen, those wishing to conduct phenomenological research must choose a model and abide by its procedures. Empirical phenomenology is presented as an example.

• Sense of the whole. One reads the entire description in order to get a general sense of the whole statement. This often takes a few readings, which should be approached contemplatively.
• Discrimination of meaning units. Once the sense of the whole has been grasped, the researcher returns to the beginning and reads through the text once more, delineating each transition in meaning.
• The researcher adopts a psychological perspective to do this. This means that the researcher looks for shifts in psychological meaning.
• The researcher focuses on the phenomenon being investigated. This means that the researcher keeps in mind the study's topic and looks for meaningful passages related to it.
• The researcher next eliminates redundancies and unrelated meaning units.
• Transformation of subjects' everyday expressions (meaning units) into psychological language. Once meaning units have been delineated,
• The researcher reflects on each of the meaning units, which are still expressed in the concrete language of the participants, and describes the essence of the statement for the participant.
• The researcher makes these descriptions in the language of psychological science.
• Synthesis of transformed meaning units into a consistent statement of the structure of the experience.
• Using imaginative variation on these transformed meaning units, the researcher discovers what remains unchanged when variations are imaginatively applied, and
• From this develops a consistent statement regarding the structure of the participant's experience.
• The researcher completes this process for each transcript in the study.
• Final synthesis. Finally, the researcher synthesizes all of the statements regarding each participant's experience into one consistent statement that describes and captures [of] the essence of the experience being studied.

(Adapted from Giorgi, 1985, 1997; Giorgi and Giorgi, 2003.)

Data Analysis in Heuristics

Six steps typically characterize the heuristic process of data analysis, consisting of:

• Initial engagement.
• Incubation.
• Illumination.
• Explication.

To start, place all the material drawn from one participant before you (recordings, transcriptions, journals, notes, poems, artwork, and so on). This material may either be data gathered by self-search or by interviews with co-researchers.

• Immerse yourself fully in the material until you are aware of and understand everything that is before you.
• Incubate the material. Put the material aside for a while. Let it settle in you. Live with it but without particular attention or focus. Return to the immersion process. Make notes where they would enable you to remember or classify the material. Continue this rhythm of working with the data and resting until an illumination or essential configuration emerges. From your core or global sense, list the essential components or patterns and themes that characterize the fundamental nature and meaning of the experience. Reflectively study the patterns and themes, dwell inside them, and develop a full depiction of the experience. The depiction must include the essential components of the experience.
• Illustrate the depiction of the experience with verbatim samples, poems, stories, or other materials to highlight and accentuate the person's lived experience.
• Return to the raw material of your co-researcher (participant). Does your depiction of the experience fit the data from which you have developed it? Does it contain all that is essential?
• Develop a full reflective depiction of the experience, one that characterizes the participant's experience reflecting core meanings for the individuals as a whole. Include in the depiction, verbatim samples, poems, stories, and the like to highlight and accentuate the lived nature of the experience. This depiction will serve as the creative synthesis, which will combine the themes and patterns into a representation of the whole in an aesthetically pleasing way. This synthesis will communicate the essence of the lived experience under inquiry. The synthesis is more than a summary: it is like a chemical reaction, a creation anew.
• Return to the data and develop a portrait of the person in such a way that the phenomenon and the person emerge as real.

(Adapted from Douglass and Moustakas, l985; Moustakas, 1990.)

Bogdan, R., & Taylor, S. J. (1975). Introduction to qualitative research methods: A phenomenological approach (3rd ed.). New York, NY: Wiley.

Corbin, J., & Strauss, A. (2008). Basics of qualitative research: Techniques and procedures for developing grounded theory (3rd ed.). Los Angeles, CA: Sage.

Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions . Thousand Oaks, CA: Sage.

Douglass, B. G., & Moustakas, C. (1985). Heuristic inquiry: The internal search to know. Journal of Humanistic Psychology , 25(3), 39–55.

Giorgi, A. (Ed.). (1985). Phenomenology and psychological research . Pittsburgh, PA: Duquesne University Press.

Giorgi, A. (1997). The theory, practice and evaluation of phenomenological methods as a qualitative research procedure. Journal of Phenomenological Psychology , 28, 235–260.

Giorgi, A. P., & Giorgi, B. M. (2003). The descriptive phenomenological psychological method. In P. M. Camic, J. E. Rhodes, & L. Yardley (Eds.), Qualitative research in psychology: Expanding perspectives in methodology and design (pp. 243–273). Washington, DC: American Psychological Association.

Moustakas, C. (1990). Heuristic research: Design, methodology, and applications . Newbury Park, CA: Sage.

Stake, R. E. (1995). The art of case study research . Thousand Oaks, CA: Sage.

Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques . Newbury Park, CA: Sage.

Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Techniques and theory for developing grounded theory (2nd ed.). Thousand Oaks, CA: Sage.

Taylor, S. J., & Bogdan, R. (1998). Introduction to qualitative research methods: A guidebook and resource (3rd ed.). New York: Wiley.

Doc. reference: phd_t3_u06s6_qualanalysis.html

Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

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What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. 

As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

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A perfect digital customer experience is often the difference between company growth and failure. And the first step toward building that experience is quantifying who your customers are, what they want, and how to provide them what they need.

Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

Fullstory's DXI platform combines the quantitative insights of product analytics with picture-perfect session replay for complete context that helps you answer questions, understand issues, and uncover customer opportunities.

Start a free 14-day trial to see how Fullstory can help you combine your most invaluable quantitative and qualitative insights and eliminate blind spots.

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Introduction to qualitative research methods – Part I

Shagufta bhangu.

Department of Global Health and Social Medicine, King's College London, London, United Kingdom

Fabien Provost

Carlo caduff.

Qualitative research methods are widely used in the social sciences and the humanities, but they can also complement quantitative approaches used in clinical research. In this article, we discuss the key features and contributions of qualitative research methods.

INTRODUCTION

Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures. In this article, we describe the strengths and role of qualitative research methods and how these can be employed in clinical research.

Although frequently employed in the social sciences and humanities, qualitative research methods can complement clinical research. These techniques can contribute to a better understanding of the social, cultural, political, and economic dimensions of health and illness. Social scientists and scholars in the humanities rely on a wide range of methods, including interviews, surveys, participant observation, focus groups, oral history, and archival research to examine both structural conditions and lived experience [ Figure 1 ]. Such research can not only provide robust and reliable data but can also humanize and add richness to our understanding of the ways in which people in different parts of the world perceive and experience illness and how they interact with medical institutions, systems, and therapeutics.

Examples of qualitative research techniques

Qualitative research methods should not be seen as tools that can be applied independently of theory. It is important for these tools to be based on more than just method. In their research, social scientists and scholars in the humanities emphasize social theory. Departing from a reductionist psychological model of individual behavior that often blames people for their illness, social theory focuses on relations – disease happens not simply in people but between people. This type of theoretically informed and empirically grounded research thus examines not just patients but interactions between a wide range of actors (e.g., patients, family members, friends, neighbors, local politicians, medical practitioners at all levels, and from many systems of medicine, researchers, policymakers) to give voice to the lived experiences, motivations, and constraints of all those who are touched by disease.

PHILOSOPHICAL FOUNDATIONS OF QUALITATIVE RESEARCH METHODS

In identifying the factors that contribute to the occurrence and persistence of a phenomenon, it is paramount that we begin by asking the question: what do we know about this reality? How have we come to know this reality? These two processes, which we can refer to as the “what” question and the “how” question, are the two that all scientists (natural and social) grapple with in their research. We refer to these as the ontological and epistemological questions a research study must address. Together, they help us create a suitable methodology for any research study[ 1 ] [ Figure 2 ]. Therefore, as with quantitative methods, there must be a justifiable and logical method for understanding the world even for qualitative methods. By engaging with these two dimensions, the ontological and the epistemological, we open a path for learning that moves away from commonsensical understandings of the world, and the perpetuation of stereotypes and toward robust scientific knowledge production.

Developing a research methodology

Every discipline has a distinct research philosophy and way of viewing the world and conducting research. Philosophers and historians of science have extensively studied how these divisions and specializations have emerged over centuries.[ 1 , 2 , 3 ] The most important distinction between quantitative and qualitative research techniques lies in the nature of the data they study and analyze. While the former focus on statistical, numerical, and quantitative aspects of phenomena and employ the same in data collection and analysis, qualitative techniques focus on humanistic, descriptive, and qualitative aspects of phenomena.[ 4 ]

For the findings of any research study to be reliable, they must employ the appropriate research techniques that are uniquely tailored to the phenomena under investigation. To do so, researchers must choose techniques based on their specific research questions and understand the strengths and limitations of the different tools available to them. Since clinical work lies at the intersection of both natural and social phenomena, it means that it must study both: biological and physiological phenomena (natural, quantitative, and objective phenomena) and behavioral and cultural phenomena (social, qualitative, and subjective phenomena). Therefore, clinical researchers can gain from both sets of techniques in their efforts to produce medical knowledge and bring forth scientifically informed change.

KEY FEATURES AND CONTRIBUTIONS OF QUALITATIVE RESEARCH METHODS

In this section, we discuss the key features and contributions of qualitative research methods [ Figure 3 ]. We describe the specific strengths and limitations of these techniques and discuss how they can be deployed in scientific investigations.

Key features of qualitative research methods

One of the most important contributions of qualitative research methods is that they provide rigorous, theoretically sound, and rational techniques for the analysis of subjective, nebulous, and difficult-to-pin-down phenomena. We are aware, for example, of the role that social factors play in health care but find it hard to qualify and quantify these in our research studies. Often, we find researchers basing their arguments on “common sense,” developing research studies based on assumptions about the people that are studied. Such commonsensical assumptions are perhaps among the greatest impediments to knowledge production. For example, in trying to understand stigma, surveys often make assumptions about its reasons and frequently associate it with vague and general common sense notions of “fear” and “lack of information.” While these may be at work, to make such assumptions based on commonsensical understandings, and without conducting research inhibit us from exploring the multiple social factors that are at work under the guise of stigma.

In unpacking commonsensical understandings and researching experiences, relationships, and other phenomena, qualitative researchers are assisted by their methodological commitment to open-ended research. By open-ended research, we mean that these techniques take on an unbiased and exploratory approach in which learnings from the field and from research participants, are recorded and analyzed to learn about the world.[ 5 ] This orientation is made possible by qualitative research techniques that are particularly effective in learning about specific social, cultural, economic, and political milieus.

Second, qualitative research methods equip us in studying complex phenomena. Qualitative research methods provide scientific tools for exploring and identifying the numerous contributing factors to an occurrence. Rather than establishing one or the other factor as more important, qualitative methods are open-ended, inductive (ground-up), and empirical. They allow us to understand the object of our analysis from multiple vantage points and in its dispersion and caution against predetermined notions of the object of inquiry. They encourage researchers instead to discover a reality that is not yet given, fixed, and predetermined by the methods that are used and the hypotheses that underlie the study.

Once the multiple factors at work in a phenomenon have been identified, we can employ quantitative techniques and embark on processes of measurement, establish patterns and regularities, and analyze the causal and correlated factors at work through statistical techniques. For example, a doctor may observe that there is a high patient drop-out in treatment. Before carrying out a study which relies on quantitative techniques, qualitative research methods such as conversation analysis, interviews, surveys, or even focus group discussions may prove more effective in learning about all the factors that are contributing to patient default. After identifying the multiple, intersecting factors, quantitative techniques can be deployed to measure each of these factors through techniques such as correlational or regression analyses. Here, the use of quantitative techniques without identifying the diverse factors influencing patient decisions would be premature. Qualitative techniques thus have a key role to play in investigations of complex realities and in conducting rich exploratory studies while embracing rigorous and philosophically grounded methodologies.

Third, apart from subjective, nebulous, and complex phenomena, qualitative research techniques are also effective in making sense of irrational, illogical, and emotional phenomena. These play an important role in understanding logics at work among patients, their families, and societies. Qualitative research techniques are aided by their ability to shift focus away from the individual as a unit of analysis to the larger social, cultural, political, economic, and structural forces at work in health. As health-care practitioners and researchers focused on biological, physiological, disease and therapeutic processes, sociocultural, political, and economic conditions are often peripheral or ignored in day-to-day clinical work. However, it is within these latter processes that both health-care practices and patient lives are entrenched. Qualitative researchers are particularly adept at identifying the structural conditions such as the social, cultural, political, local, and economic conditions which contribute to health care and experiences of disease and illness.

For example, the decision to delay treatment by a patient may be understood as an irrational choice impacting his/her chances of survival, but the same may be a result of the patient treating their child's education as a financial priority over his/her own health. While this appears as an “emotional” choice, qualitative researchers try to understand the social and cultural factors that structure, inform, and justify such choices. Rather than assuming that it is an irrational choice, qualitative researchers try to understand the norms and logical grounds on which the patient is making this decision. By foregrounding such logics, stories, fears, and desires, qualitative research expands our analytic precision in learning about complex social worlds, recognizing reasons for medical successes and failures, and interrogating our assumptions about human behavior. These in turn can prove useful in arriving at conclusive, actionable findings which can inform institutional and public health policies and have a very important role to play in any change and transformation we may wish to bring to the societies in which we work.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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Data Analysis in Research: Types & Methods

Data analysis is a crucial step in the research process, transforming raw data into meaningful insights that drive informed decisions and advance knowledge. This article explores the various types and methods of data analysis in research, providing a comprehensive guide for researchers across disciplines.

Data Analysis in Research

Overview of Data analysis in research

Data analysis in research is the systematic use of statistical and analytical tools to describe, summarize, and draw conclusions from datasets. This process involves organizing, analyzing, modeling, and transforming data to identify trends, establish connections, and inform decision-making. The main goals include describing data through visualization and statistics, making inferences about a broader population, predicting future events using historical data, and providing data-driven recommendations. The stages of data analysis involve collecting relevant data, preprocessing to clean and format it, conducting exploratory data analysis to identify patterns, building and testing models, interpreting results, and effectively reporting findings.

• Main Goals : Describe data, make inferences, predict future events, and provide data-driven recommendations.
• Stages of Data Analysis : Data collection, preprocessing, exploratory data analysis, model building and testing, interpretation, and reporting.

Types of Data Analysis

1. descriptive analysis.

Descriptive analysis focuses on summarizing and describing the features of a dataset. It provides a snapshot of the data, highlighting central tendencies, dispersion, and overall patterns.

• Central Tendency Measures : Mean, median, and mode are used to identify the central point of the dataset.
• Dispersion Measures : Range, variance, and standard deviation help in understanding the spread of the data.
• Frequency Distribution : This shows how often each value in a dataset occurs.

2. Inferential Analysis

Inferential analysis allows researchers to make predictions or inferences about a population based on a sample of data. It is used to test hypotheses and determine the relationships between variables.

• Hypothesis Testing : Techniques like t-tests, chi-square tests, and ANOVA are used to test assumptions about a population.
• Regression Analysis : This method examines the relationship between dependent and independent variables.
• Confidence Intervals : These provide a range of values within which the true population parameter is expected to lie.

3. Exploratory Data Analysis (EDA)

EDA is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It helps in discovering patterns, spotting anomalies, and checking assumptions with the help of graphical representations.

• Visual Techniques : Histograms, box plots, scatter plots, and bar charts are commonly used in EDA.
• Summary Statistics : Basic statistical measures are used to describe the dataset.

4. Predictive Analysis

Predictive analysis uses statistical techniques and machine learning algorithms to predict future outcomes based on historical data.

• Machine Learning Models : Algorithms like linear regression, decision trees, and neural networks are employed to make predictions.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to forecast future trends.

5. Causal Analysis

Causal analysis aims to identify cause-and-effect relationships between variables. It helps in understanding the impact of one variable on another.

• Experiments : Controlled experiments are designed to test the causality.
• Quasi-Experimental Designs : These are used when controlled experiments are not feasible.

6. Mechanistic Analysis

Mechanistic analysis seeks to understand the underlying mechanisms or processes that drive observed phenomena. It is common in fields like biology and engineering.

Methods of Data Analysis

1. quantitative methods.

Quantitative methods involve numerical data and statistical analysis to uncover patterns, relationships, and trends.

• Statistical Analysis : Includes various statistical tests and measures.
• Mathematical Modeling : Uses mathematical equations to represent relationships among variables.
• Simulation : Computer-based models simulate real-world processes to predict outcomes.

2. Qualitative Methods

Qualitative methods focus on non-numerical data, such as text, images, and audio, to understand concepts, opinions, or experiences.

• Content Analysis : Systematic coding and categorizing of textual information.
• Thematic Analysis : Identifying themes and patterns within qualitative data.
• Narrative Analysis : Examining the stories or accounts shared by participants.

3. Mixed Methods

Mixed methods combine both quantitative and qualitative approaches to provide a more comprehensive analysis.

• Sequential Explanatory Design : Quantitative data is collected and analyzed first, followed by qualitative data to explain the quantitative results.
• Concurrent Triangulation Design : Both qualitative and quantitative data are collected simultaneously but analyzed separately to compare results.

4. Data Mining

Data mining involves exploring large datasets to discover patterns and relationships.

• Clustering : Grouping data points with similar characteristics.
• Association Rule Learning : Identifying interesting relations between variables in large databases.
• Classification : Assigning items to predefined categories based on their attributes.

5. Big Data Analytics

Big data analytics involves analyzing vast amounts of data to uncover hidden patterns, correlations, and other insights.

• Hadoop and Spark : Frameworks for processing and analyzing large datasets.
• NoSQL Databases : Designed to handle unstructured data.
• Machine Learning Algorithms : Used to analyze and predict complex patterns in big data.

Applications and Case Studies

Numerous fields and industries use data analysis methods, which provide insightful information and facilitate data-driven decision-making. The following case studies demonstrate the effectiveness of data analysis in research:

Medical Care:

• Predicting Patient Readmissions: By using data analysis to create predictive models, healthcare facilities may better identify patients who are at high risk of readmission and implement focused interventions to enhance patient care.
• Disease Outbreak Analysis: Researchers can monitor and forecast disease outbreaks by examining both historical and current data. This information aids public health authorities in putting preventative and control measures in place.
• Fraud Detection: To safeguard clients and lessen financial losses, financial institutions use data analysis tools to identify fraudulent transactions and activities.
• investing Strategies: By using data analysis, quantitative investing models that detect trends in stock prices may be created, assisting investors in optimizing their portfolios and making well-informed choices.
• Customer Segmentation: Businesses may divide up their client base into discrete groups using data analysis, which makes it possible to launch focused marketing efforts and provide individualized services.
• Social Media Analytics: By tracking brand sentiment, identifying influencers, and understanding consumer preferences, marketers may develop more successful marketing strategies by analyzing social media data.
• Predicting Student Performance: By using data analysis tools, educators may identify at-risk children and forecast their performance. This allows them to give individualized learning plans and timely interventions.
• Education Policy Analysis: Data may be used by researchers to assess the efficacy of policies, initiatives, and programs in education, offering insights for evidence-based decision-making.

Social Science Fields:

• Opinion mining in politics: By examining public opinion data from news stories and social media platforms, academics and policymakers may get insight into prevailing political opinions and better understand how the public feels about certain topics or candidates.
• Crime Analysis: Researchers may spot trends, anticipate high-risk locations, and help law enforcement use resources wisely in order to deter and lessen crime by studying crime data.

Data analysis is a crucial step in the research process because it enables companies and researchers to glean insightful information from data. By using diverse analytical methodologies and approaches, scholars may reveal latent patterns, arrive at well-informed conclusions, and tackle intricate research inquiries. Numerous statistical, machine learning, and visualization approaches are among the many data analysis tools available, offering a comprehensive toolbox for addressing a broad variety of research problems.

Data Analysis in Research FAQs:

What are the main phases in the process of analyzing data.

In general, the steps involved in data analysis include gathering data, preparing it, doing exploratory data analysis, constructing and testing models, interpreting the results, and reporting the results. Every stage is essential to guaranteeing the analysis’s efficacy and correctness.

What are the differences between the examination of qualitative and quantitative data?

In order to comprehend and analyze non-numerical data, such text, pictures, or observations, qualitative data analysis often employs content analysis, grounded theory, or ethnography. Comparatively, quantitative data analysis works with numerical data and makes use of statistical methods to identify, deduce, and forecast trends in the data.

What are a few popular statistical methods for analyzing data?

In data analysis, predictive modeling, inferential statistics, and descriptive statistics are often used. While inferential statistics establish assumptions and draw inferences about a wider population, descriptive statistics highlight the fundamental characteristics of the data. To predict unknown values or future events, predictive modeling is used.

In what ways might data analysis methods be used in the healthcare industry?

In the healthcare industry, data analysis may be used to optimize treatment regimens, monitor disease outbreaks, forecast patient readmissions, and enhance patient care. It is also essential for medication development, clinical research, and the creation of healthcare policies.

What difficulties may one encounter while analyzing data?

Answer: Typical problems with data quality include missing values, outliers, and biased samples, all of which may affect how accurate the analysis is. Furthermore, it might be computationally demanding to analyze big and complicated datasets, necessitating certain tools and knowledge. It’s also critical to handle ethical issues, such as data security and privacy.

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Qualitative vs. Quantitative: Key Differences in Research Types

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Let's say you want to learn how a group will vote in an election. You face a classic decision of gathering qualitative vs. quantitative data.

With one method, you can ask voters open-ended questions that encourage them to share how they feel, what issues matter to them and the reasons they will vote in a specific way. With the other, you can ask closed-ended questions, giving respondents a list of options. You will then turn that information into statistics.

Neither method is more right than the other, but they serve different purposes. Learn more about the key differences between qualitative and quantitative research and how you can use them.

What Is Qualitative Research?

What is quantitative research, qualitative vs. quantitative research: 3 key differences, benefits of combining qualitative and quantitative research.

Qualitative research aims to explore and understand the depth, context and nuances of human experiences, behaviors and phenomena. This methodological approach emphasizes gathering rich, nonnumerical information through methods such as interviews, focus groups , observations and content analysis.

In qualitative research, the emphasis is on uncovering patterns and meanings within a specific social or cultural context. Researchers delve into the subjective aspects of human behavior , opinions and emotions.

This approach is particularly valuable for exploring complex and multifaceted issues, providing a deeper understanding of the intricacies involved.

Common qualitative research methods include open-ended interviews, where participants can express their thoughts freely, and thematic analysis, which involves identifying recurring themes in the data.

Examples of How to Use Qualitative Research

The flexibility of qualitative research allows researchers to adapt their methods based on emerging insights, fostering a more organic and holistic exploration of the research topic. This is a widely used method in social sciences, psychology and market research.

Here are just a few ways you can use qualitative research.

• To understand the people who make up a community : If you want to learn more about a community, you can talk to them or observe them to learn more about their customs, norms and values.
• To examine people's experiences within the healthcare system : While you can certainly look at statistics to gauge if someone feels positively or negatively about their healthcare experiences, you may not gain a deep understanding of why they feel that way. For example, if a nurse went above and beyond for a patient, they might say they are content with the care they received. But if medical professional after medical professional dismissed a person over several years, they will have more negative comments.
• To explore the effectiveness of your marketing campaign : Marketing is a field that typically collects statistical data, but it can also benefit from qualitative research. For example, if you have a successful campaign, you can interview people to learn what resonated with them and why. If you learn they liked the humor because it shows you don't take yourself too seriously, you can try to replicate that feeling in future campaigns.

Types of Qualitative Data Collection

Qualitative data captures the qualities, characteristics or attributes of a subject. It can take various forms, including:

• Audio data : Recordings of interviews, discussions or any other auditory information. This can be useful when dealing with events from the past. Setting up a recording device also allows a researcher to stay in the moment without having to jot down notes.
• Observational data : With this type of qualitative data analysis, you can record behavior, events or interactions.
• Textual data : Use verbal or written information gathered through interviews, open-ended surveys or focus groups to learn more about a topic.
• Visual data : You can learn new information through images, photographs, videos or other visual materials.

Quantitative research is a systematic empirical investigation that involves the collection and analysis of numerical data. This approach seeks to understand, explain or predict phenomena by gathering quantifiable information and applying statistical methods for analysis.

Unlike qualitative research, which focuses on nonnumerical, descriptive data, quantitative research data involves measurements, counts and statistical techniques to draw objective conclusions.

Examples of How to Use Quantitative Research

Quantitative research focuses on statistical analysis. Here are a few ways you can employ quantitative research methods.

• Studying the employment rates of a city : Through this research you can gauge whether any patterns exist over a given time period.
• Seeing how air pollution has affected a neighborhood : If the creation of a highway led to more air pollution in a neighborhood, you can collect data to learn about the health impacts on the area's residents. For example, you can see what percentage of people developed respiratory issues after moving to the neighborhood.

Types of Quantitative Data

Quantitative data refers to numerical information you can measure and count. Here are a few statistics you can use.

• Heights, yards, volume and more : You can use different measurements to gain insight on different types of research, such as learning the average distance workers are willing to travel for work or figuring out the average height of a ballerina.
• Temperature : Measure in either degrees Celsius or Fahrenheit. Or, if you're looking for the coldest place in the universe , you may measure in Kelvins.
• Sales figures : With this information, you can look at a store's performance over time, compare one company to another or learn what the average amount of sales is in a specific industry.

Quantitative and qualitative research methods are both valid and useful ways to collect data. Here are a few ways that they differ.

• Data collection method : Quantitative research uses standardized instruments, such as surveys, experiments or structured observations, to gather numerical data. Qualitative research uses open-ended methods like interviews, focus groups or content analysis.
• Nature of data : Quantitative research involves numerical data that you can measure and analyze statistically, whereas qualitative research involves exploring the depth and richness of experiences through nonnumerical, descriptive data.
• Sampling : Quantitative research involves larger sample sizes to ensure statistical validity and generalizability of findings to a population. With qualitative research, it's better to work with a smaller sample size to gain in-depth insights into specific contexts or experiences.

You can simultaneously study qualitative and quantitative data. This method , known as mixed methods research, offers several benefits, including:

• A comprehensive understanding : Integration of qualitative and quantitative data provides a more comprehensive understanding of the research problem. Qualitative data helps explain the context and nuances, while quantitative data offers statistical generalizability.
• Contextualization : Qualitative data helps contextualize quantitative findings by providing explanations into the why and how behind statistical patterns. This deeper understanding contributes to more informed interpretations of quantitative results.
• Triangulation : Triangulation involves using multiple methods to validate or corroborate findings. Combining qualitative and quantitative data allows researchers to cross-verify results, enhancing the overall validity and reliability of the study.

This article was created in conjunction with AI technology, then fact-checked and edited by a HowStuffWorks editor.

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11 Types of qualitative research marketers navigate every day

Types of qualitative research methods, when to conduct qualitative research, get the best of both worlds with attest market research platform.

Is your marketing or product development a bit weak and under the weather, or isn’t it as punchy as it used to be? Qualitative research might just be the pick-me-up it needs. Now, not just any type of qualitative market research (it’s not some magic cure-all). You need to pick the right type of qualitative research — and we’re here to help you do that.

But what you need to know about qualitative research at its core, is that it’s about exploring the qualities and nuances of human behavior and preferences. Using discussions, observations, and analysis, you try to uncover not just what people do, but why they do it.

Conducting qualitative research provides you with rich, detailed feedback that gives depth to – and compliments – quantitative research, and can help you formulate direct actions to take. Here’s which qualitative methods we’ll be exploring today.

• Focus groups
• Observation
• Content analysis 
• Narrative analysis
• Historical records management and case studies
• Ethnographic research
• Phenomenological research
• Grounded theory method
• Action research

1. Qualitative research surveys

Surveys are great for tapping into the minds of your audience: you can ask direct questions to gather feedback on everything, in a variety of formats.

With the flexibility to reach a broad audience and the ability to tailor your questions for specific insights, surveys are one of the most used tools for gathering qualitative data at scale, and in record speed.

• Collect feedback from a wide range of participants quickly.
• Tailor surveys to explore various aspects of consumer behavior, from product preferences to brand perception.
• Compared to other qualitative methods, surveys are relatively low-cost and can be distributed widely with minimal resources.

Challenges and solutions:

• Formulating questions that get deep, meaningful responses can be tricky. Focus on open-ended questions and avoid leading or biased phrasing.
• Keeping respondents interested and encouraging thoughtful responses is tricky. Offer incentives and ensure the survey is quick and clear to boost engagement and completion rates.
• The pile of qualitative data from open-ended survey responses can be a lot to work through, xo make sure you’re prepped for your qualitative data analysis.

When to use:

Use surveys to explore consumer sentiments, identify unmet needs and pain points, and evaluate what drives brand loyalty.

Send out survey questions and collect written answers or even video responses with Attest . Our platform takes care of everything, from survey templates to get you started, to best-in-class research advice to help you run truly great research.

See how qual research with Attest works

You can get high-quality video responses from your target audiences with Attest, and our team of research pros is on hand to help you run awesome research

2. Interviews

If you want to go deep, and not necessarily get a lot of data from different participants, interviews are your thing. By sitting down for a one-on-one with people from your target audience you can gather detailed feedback and personal stories

• You can follow the conversation wherever it leads, asking follow-up questions that bring out detailed or surprising insights.
• Human-to-human interactions can lead to more genuine responses, giving you a clearer picture of your audience.
• Interviews take a lot of time to conduct and analyze. Using transcription software and focusing your questions can speed things up.
• People might tell you what they think you want to hear. Make sure you create a comfortable setting and assure anonymity to encourage brutal honesty and fight bias.
• Data from interviews can be hard to compare. Sticking to a set of core questions while allowing for (controlled) personal exploration can help.

Use Interviews for qualitative research when developing new products or features to deeply understand user needs and reactions, and for branding or campaigns to gather stories and emotions that tie people to your brand, enriching your next marketing initiative.

3. Focus groups

Learn to read the room. Focus groups bring together a small group of people from your target market to discuss their opinions and experiences regarding your product or service. The setup of these groups often encourages participants to share their thoughts and ideas.

• Bringing together a variety of viewpoints and hearing how they compare to each other helps you understand the nuances of your target audience.
• Group discussions can lead to surprising angles and new insights into consumer attitudes and perceptions that individual interviews may not capture.
• Participants might sway towards consensus opinions. Encouraging open dialogue and using a skilled moderator can help avoid this. And make sure your group is diverse enough as well.
• Individuals can be overlooked in group settings. Feel like some voices are overpowering? Complement focus groups with one-on-one interviews for deeper insights.
• Organizing focus groups is pretty resource-intensive. Virtual focus groups or streamlined in-person sessions are more flexible.

Use focus groups for brand perception studies to delve into group discussions about your brand and for concept testing to gather immediate reactions to new product ideas, packaging, or marketing strategies.

4. Observation

Watching how people interact with your product or service in their natural environment (in person or through video recordings), without interference, is a great way to get real-life insights into user behavior, preferences, and potential improvements that might not be revealed through direct questioning.

• Beat assumptions and get a contextual understanding of how people interact with your product or service in real-world settings.
• Body language and other non-verbal signals can tell you a lot about how consumers feel when handling your product.
• the presence of an observer might make people change their behavior. Unobtrusive methods like video recording can help avoid that.
• Observers might interpret actions through their own bias. Make sure they are well-trained to avoid this, and that you work with multiple observers to compare interpretations.
• Translating observations into actionable data can be challenging. Structured observation guides and analytical frameworks can streamline your analysis.

Use Observation for user experience research to see how people interact with your product in real settings and for environmental impact studies to understand how different environments influence consumer behavior towards your brand.

5. Content analysis 

The words, images or videos related to your brand or product that people create and share tell a story. With content analysis, you collect all these elements and try to find themes, patterns or issues that stand out.

• You don’t have to worry about getting brand-new data in, which also makes it a more cost-effective and sometimes faster qualitative research method.
• With social listening and content analysis, you can identify emerging trends early in. All you need to do is really zoom in.
• The amount of available content is probably going to be overwhelming, but there are plenty of software tools for sentiment analysis out there that do the heavy lifting for you.
• Unhappy customers might be louder than the happy ones, so the content might not represent the broader audience. Balance your content analysis with direct research methods like surveys or interviews to mitigate this bias.

Use content analysis for uncovering insights into brand perception and evaluating the impact of marketing campaigns on public sentiment through social media content analysis.

6. Narrative analysis

Narrative analysis delves into the stories people tell about their experiences with your product or service. It focuses on understanding the sequence of events, the context, and the emotional journeys described by consumers.

• Unpacks the emotional journey and personal experiences of consumers, offering a rich understanding of their relationship with your product or service.
• By analyzing stories, you capture not just the facts but the context around consumer decisions and experiences, revealing deeper motivations.
• Stories often reflect broader cultural and social influences, helping you see how these factors impact consumer behavior.
• Personal biases can influence how narratives are interpreted. Establishing a clear analytical framework and involving multiple analysts can reduce bias.
• Narrative analysis can be detail-oriented and time-consuming. Using software to assist in data coding and thematic analysis can streamline the process.
• It can be challenging to ensure that the narratives collected are directly relevant to your research questions. Carefully designing the prompt and selection criteria for participants can help focus the stories gathered.

Use narrative analysis to map out detailed consumer journeys from first awareness to loyalty and to craft compelling brand stories that resonate deeply with your audience.

7. Historical records management and case studies

This method involves analyzing existing documents and records related to your market or industry, and conducting case studies on specific examples within your field. You look at historical trends, previous campaigns, product launches, and customer feedback over time, providing a context for current market dynamics and guiding future strategies.

• Offers a perspective on how consumer behaviors and market trends have evolved, giving you context for current data.
• You can measure the impact of changes or interventions tend to make in your marketing strategy or product development.
• Historical records may be scattered or difficult to access, so digitize records and maintain a centralized database now for future researchers.
• Ensuring that historical data is still relevant to current contexts can be challenging, so regularly update your data collection and analysis methods to reflect current market conditions.

Use historical records management and case studies for analyzing long-term market trends, assessing the effectiveness of marketing campaigns over time, and understanding the evolution of product life cycles influenced by consumer preferences.

8. Ethnographic research

Ethnographic research immerses you in the everyday lives of your target audience, observing them in their natural settings to understand their behaviors, rituals, and the social context of product usage. This gives you culturally grounded insights into how and why your product fits into consumers’ lives.

• By observing people in their natural environments, you get to see how they genuinely interact with products or services, unfiltered by self-reporting biases.
• You get detailed descriptions of people’s lives and interactions, and much more nuanced insights than numbers and charts.
• You’ll need significant time in the field and enough resources to do it right. Streamlining focus areas and using digital tools for data collection can help manage the workload.
• Immersion in a community or culture can lead to biased perspectives. Regular reflection sessions and involving multiple researchers can help maintain a balanced viewpoint.

Use ethnographic research to understand how user environments and cultures affect product use, tailor offerings for specific markets or cultural groups, and innovate with designs centered on real-world user behavior.

9. Phenomenological research

Phenomenological research focuses on the lived experiences of individuals regarding a particular phenomenon. Through in-depth interviews and discussions, you gather detailed personal accounts, looking for the underlying meanings and emotions attached to experiences with your product or service.

• It centers on the lived experiences of users, giving you a true-to-life image of understanding their needs, desires, and motivations.
• Captures the essence of consumer experiences, delivering authentic insights that can guide more empathetic and effective marketing strategies.
• The depth of phenomenological data can make analysis challenging. Working with thematic analysis and seeking expert advice can make it more manageable.
• Finding participants willing to share deeply personal experiences may be difficult. Offer assurances of confidentiality and create a safe, respectful environment.

Use phenomenological research to dive deep into the emotions and experiences of new market segments, refine user experiences for greater satisfaction, and create brand messages that forge stronger emotional connections with your audience.

10. Grounded theory method

The grounded theory method starts with data collection without a predefined hypothesis, allowing theories to emerge from the data itself. Through continuous comparison of data from interviews, surveys, or observations, you develop a theory that explains a particular aspect of consumer behavior or market trends.

• Exploring data without preconceived theories is ideal for uncovering fresh insights and new perspectives on consumer behavior.
• Based on the data, you can develop theories that explain patterns and relationships within your market, setting up a strong foundation for strategic decisions.
• As data collection and analysis proceed in tandem, you can refine your research focus based on emerging insights, ensuring the relevance and depth of findings.
• The open-ended nature of grounded theory means you’ll get piles of data. Using software for data management and employing selective sampling techniques to focus the research.
• The iterative process of coding and recoding data to develop a theory is complex. Training in grounded theory methods and regular team discussions can help clarify the process.

Use the grounded theory method to innovate products, tackle complex consumer issues, and craft strategies that deeply align with consumer preferences and behaviors.

11. Action research

Action research is a participatory method where researchers work alongside participants to identify and solve problems or improve practices. In the context of market research, it could involve collaborating with consumers to co-create solutions or enhance product design.

• Findings and insights can be applied in real-time, allowing for fast adjustments to products, services, or marketing strategies.
• Active involvement from participants, leads to a deeper engagement with your brand and a sense of ownership over the solutions developed.
• Balancing the input and engagement of participants without overwhelming them can be challenging. Set clear expectations and provide structured feedback.
• The focus on immediate solutions might overlook deeper, underlying issues. Supplement with other qualitative methods to provide a more comprehensive understanding.
• The cyclical nature of action research, with its continuous cycles of planning, acting, observing, and reflecting, requires dedication and flexibility. Agile project management techniques can keep the project on track.

Use action research to develop products informed by user feedback, enhance customer experiences through targeted improvements, and strengthen relationships with communities or stakeholders through collaborative engagement.

Conduct qualitative research when you need in-depth understanding of consumer attitudes, feelings, or behaviors—areas where quantitative research’s numbers and statistics can’t provide the full picture.

Qualitative research is best used in tandem with quantitative research – they really do compliment each other. You can use qualitative research to help inspire you at the beginning of a project, or to flesh out ideas that emerge during preceding quantitative research.

It’s especially useful for exploring new concepts, enhancing product development, or deepening brand engagement, complementing quantitative data by adding context and depth to the insights gained.

With Attest’s market research platform, you can seamlessly blend qualitative and quantitative data, giving you the insights you need for smarter marketing and better product development. See how Attest is helping businesses in a variety of industries to better understand their audiences.

Andrada Comsa

Principal Customer Research Manager 

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• Open access
• Published: 02 January 2022

Co-designing implementation strategies for the WALK-Cph intervention in Denmark aimed at increasing mobility in acutely hospitalized older patients: a qualitative analysis of selected strategies and their justifications

• Jeanette Wassar Kirk 1 , 2 ,
• Per Nilsen 3 ,
• Ove Andersen 1 ,
• Byron J. Powell 4 ,
• Tine Tjørnhøj-Thomsen 5 ,
• Thomas Bandholm 1 , 6 , 7 &
• Mette Merete Pedersen 1  

BMC Health Services Research volume  22 , Article number:  8 ( 2022 ) Cite this article

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Selecting appropriate strategies to target barriers to implementing interventions represents a considerable challenge in implementation research and practice. The aim was to investigate what categories of implementation strategies were selected by health care practitioners and their managers in a co-design process and how they justified these strategies aimed at facilitating the implementation of the WALK-Cph intervention.

The study used a qualitative research design to explore what implementation strategies were selected and the justifications for selecting these strategies. Workshops were used because this qualitative method is particularly well suited for studying co-design processes that involve substantial attention to social interaction and the context. Data were 1) analyzed deductively based on the Proctor et al. taxonomy of implementation strategies, 2) categorized in accordance with the ERIC compilation of implementation strategies by Powell et al., and 3) analyzed to examine the justification for the selected strategies by the Proctor et al. framework for justifications of implementation strategies.

Thirteen different types of implementation strategies were chosen across two hospitals. The deductive analysis showed that selection of implementation strategies was based on pragmatic and theoretical justifications. The contents of the two types of justifications were thematized into nine subthemes.

This study contributes with knowledge about categories and justification of implementation strategies selected in a co-design process. In this study, implementation strategies were selected through pragmatic and theoretical justifications. This points to a challenge in balancing strategies based on practice-based and research-based knowledge and thereby selection of strategies with or without proven effectiveness.

Peer Review reports

Implementation strategies constitute the “how to” of getting evidence-based interventions (EBIs) into practice [ 1 , 2 , 3 , 4 ]. Powell et al. [ 5 ] define implementation strategies as “methods or techniques used to enhance the adoption, implementation, sustainment and scale-up” of interventions. Strategies can vary in complexity from single to multifaceted [ 3 , 5 ]. Selecting appropriate strategies to target barriers to implementing interventions represents a considerable challenge in implementation research and practice [ 4 , 5 , 6 , 7 , 8 , 9 ], e.g., choosing appropriate strategies to influence poor motivation or negative attitudes concerning the use of a new intervention. Systematic approaches are needed to design and tailor implementation strategies that integrate evidence, theory, and stakeholder perspectives [ 7 , 10 , 11 ]. Theories, models, and frameworks have been developed to facilitate the matching of relevant strategies to specific barriers [ 4 , 6 , 8 , 9 , 10 , 12 ]. However, the use of inconsistent terminology and inadequate descriptions of strategies [ 3 , 13 ] make it difficult to identify optimal strategies and to advance our understanding of how and when different implementation strategies are effective [ 14 , 15 , 16 ]. Implementation strategies tend to be entangled in the context, which can affect the effectiveness of the strategies [ 17 ]. Thus, if the identified barrier is insufficient skills training can have more impact on health care practitioners’ performance than a “default” strategy, which has always been selected in the past regardless of what barriers might have existed.

Developing and selecting implementation strategies can be achieved in different ways, e.g., through expert panels [ 18 ] that involve both implementation researchers and clinical experts or through stakeholder involvement in co-design processes to achieve more contextually adapted strategies to increase the likelihood of successful implementation of EBIs [ 19 , 20 , 21 ]. Co-design has been defined as “the creativity of designers and people not trained in design working together in the design development process” [ 22 ]. Co-design is used to develop solutions to complex problems [ 23 ] and is intended to be a social and democratic process [ 24 ]. Several advantages of accounting for stakeholder priorities in co-design processes have been described, including improved credibility of the results and optimization of the implementation of EBIs through better understanding of the intervention-context fit [ 7 , 11 , 25 , 26 , 27 ]. Some studies have investigated the use of co-design processes involving practitioners (i.e., non-researchers) together with researchers to develop and select implementation strategies for implementation of EBIs in health care [ 28 , 29 , 30 ].

This study was part of the WALK-Copenhagen (WALK-Cph) project [ 31 ], which was initiated to implement a multi-facetted intervention to increase mobility in older patients acutely admitted to two medical departments at two university hospitals in Denmark (Hospital X and Hospital Y). The project used a Hybrid II design, with a dual focus on the development and the implementation of the intervention [ 32 ]. This study report results from study 1b, 2d and 2 h in relation to the overall WALK-Cph project (Additional file  1 : Appendix S1). The intervention was co-designed in a collaborative process between researchers, one design architect employed at the hospital, health care practitioners, patients, and their relatives. The co-design of the strategies to implement the intervention involved only researchers and health care practitioners, who were to be responsible for implementing the intervention. The content of the intervention is shown in Additional file  2 : Appendix S2. In response to lack of empirical knowledge on the involvement of health care practitioners in co-designing implementation strategies, the aim of this study was to investigate what implementation strategies were selected by health care practitioners and their managers and how they justified these strategies aimed at facilitating the implementation of the WALK-Cph intervention.

Study design

The study used a qualitative research design, interactive workshops, which is particularly well suited for studying co-design processes that involve substantial attention to social interaction and the context [ 22 ]. The study is reported using the Standard for Reporting Implementation Studies (StaRI) checklist [ 33 ].

Study setting

The WALK-Cph project was carried out in Denmark. The health care system is funded by taxes and provides free treatment for all citizens for primary medical care, hospital care, and home-based care services. The project involved two hospitals (Hospital X and Hospital Y) represented by: Department X, a Department of Endocrinology and a Department of Occupational and Physical Therapy and the associated municipality (Municipality X), and Department Y, a Department of General Medicine including occupational therapists and physiotherapists and the associated municipality (Municipality Y). The departments were located at two public university hospitals in the Capital Region of Denmark and were similar in size and staff composition (Additional file  3 : Appendix S3). The municipalities, in which the hospitals are located, were also part of the project since they were recipients of patients discharged from the two intervention departments. The municipalities were represented by their rehabilitation units and the home care services. Thereby, in daily practice there was a close collaboration between the two medical departments and the two municipalities. Before designing the implementation plan, all stakeholders (staff from Hospital X and Y, the municipalities, patients and relatives) designed the interventions together (Kirk et al. in review).

Co-design workshops

The stakeholders worked together in two initial co-design workshops held in June 2018 with Hospital X participating in the 1st workshop and in December 2018 with Hospital Y participating in the 2nd workshop to develop the implementation plans (Fig.  1 , Workshops VI + VII). The workshops were followed-up by four co-design workshops (Fig. 1 , Workshops X-XIII), with the purpose to adapt the implementation plan. The workshops were inspired by Pavelin et al. [ 34 ], who defined interactive workshops as “a structured set of facilitated activities for groups of participants who work together to explore a problem and its solutions, over a specific period of time, in one location.” The co-design workshops were designed to encourage creativity and production of ideas among the participants for the proposed implementation process, e.g. by using coloured sticky notes and through the use of a Conjoint Analysis (CA) [ 35 ] to prioritize potential implementation strategies suggested by the stakeholders. Both sticky notes and CAs can function as mediating tools to generate ideas and thus increase the likelihood of implementation of an intervention. The first author (JWK) and the other project manager (MMP) had the overall responsibility for leading and facilitating the co-design process of the workshops (Fig. 1 ). Neither JWK nor MMP has a formal education in design, but JWK has experience with user-involvement, co-creation methods, facilitation and process competencies [ 36 ]. The hospital design architect, who participated in the design of the intervention, had a formal education in co-creation.

Process of initial and follow-up co-design workshops

In this study, facilitation is defined as helping a group to achieve a common goal and assist them in achieving the desired results. This is done without taking a stand or being prescriptive, but by focusing on the dialogue and context of the discussions [ 37 ]. Facilitation basically consists of two components: design of the process and the facilitation itself [ 37 ]. The design of the process is described below in form of involving stakeholders and the structure of the workshops.

In the facilitation situation, the facilitators functioned as neutral catalysts and ensured that all relevant perspectives were accounted for. For instance, the facilitators asked about the same issues across groups and observed and tried to interpret body language that signaled a positive or negative attitude. The dialogue was guided through open and simple questions and by taking responsibility for the energy in the workshop. This was achieved by means of alternating between common dialogue and allowing time for reflection and pauses [ 38 ].

The stakeholders were health care practitioners and frontline managers from the two intervention hospitals. From Hospital X they were one nurse, one ward physician, one frontline manager (nurse), three physiotherapists and two head managers (physiotherapists). From Hospital Y they were one nurse, one frontline manager (nurse), two physiotherapists, one frontline manager (physiotherapist) and one occupational therapist (Table  1 ). In Denmark, the frontline managers generally have expertise in quality improvement and have the daily responsibility for patient care and contact with the staff. They are usually responsible for implementing new interventions and monitoring the quality of existing processes. Head managers have the overall responsibility for implementing interventions. The research team participating in the workshops consisted of a professor, a postdoc researcher, and a research assistant with a background in physiotherapy, and a PhD student and a postdoc researcher with a nursing background.

Before the study started, JWK and a colleague held individual meetings with the head and frontline managers to discuss their department’s capacity regarding who and how many of their health care practitioners they could involve as implementation champions [ 39 ] (e.g., someone who is dedicated to promote the implementation of an intervention and thereby overcome resistance that the intervention may create in an organization) [ 39 ]. The researchers aimed for variation in inclusion of participants as the managers had knowledge and experience about who might be expected to have positive perceptions of the intervention and who would be more skeptical. This type of knowledge can be useful when implementing new initiatives [ 40 ]. In total, 15 people participated in the workshops (Table 1 ).

The two initial workshops took place in June 2018 (Department X) and December 2018 (Department Y) (Fig. 1 ). Each workshop lasted 2.5 h, and they were held in meeting rooms at the hospitals. The workshops were co-design sessions in which the health care practitioners (i.e., managers and implementation champions), developed the formal implementation plan (i.e., what strategies, when to perform the strategies, how to use the strategies, and the time required [ 39 ]). These workshops were followed by four follow-up workshops with the participants in December 2018 (Department X) and March (Department Y), September (Department X), and December 2019 (Department X). All stakeholders from Hospital X participated in the 1st, 3rd and 4th follow-up workshops. Stakeholders from Hospital Y participated in the 2nd follow-up workshop (Table 1 ). Only one follow-up workshop was completed with stakeholders from hospital Y because the department was scheduled to close down at the end of the year due to political reforms. The follow-up workshops aimed at refining the implementation strategies (Fig. 1 ) based on experience from practice and on testing the relevance and appropriateness of the chosen strategies.

The initial workshops were structured as follows: (1) group discussions about how to ensure capacity in relation to implementation (e.g., staff and finances); (2) discussions within and between the groups about perceived implementation barriers; (3) feedback loops whereby the research team presented barriers (Table  2 ) identified in an earlier ethnographic field study performed in the same setting. Three new barriers emerged in the barrier discussions: continuous management, information exchange and concern about lack of resources (barriers 8–10 in Table 2 ) [ 41 ]; (4) PowerPoint presentation of implementation strategies based on the ERIC compilation [ 39 ] and hand-out of written examples where several of the strategies were clearly described; (5) selection and tailoring of implementation strategies from the ERIC compilation to address barriers and define outcome targets of the implementation strategies; (6) a conjoint analysis of identified and prioritized barriers based on Farley et al. [ 35 ]; and (7) development of the final implementation plan [ 39 ]. The ERIC compilation is a well-established and widely applied taxonomy by Powell [ 13 ] that identifies no less than 68 implementation strategies, such as distributing educational materials to healthcare professionals. The use of the ERIC compilation was a way to visualize and make different types of implementation strategies apparent for the participants.

The co-design process itself made use of different implementation strategies, e.g. Develop an implementation glossary and Facilitation. These strategies mediated the overall progress of the project and supported the methodological design, including user involvement and co-design. An example of this was the strategy to organize implementation team meetings, which were operationalized as follow-up workshops where the research team continuously provided feedback on analyzed data via the strategy of Audit and feedback. In addition, the strategies were used as examples for the participants, enabling them to see how different types of implementation strategies could be used.

Participants from Hospital X were grouped in three groups within their own department or municipality, while participants from Hospital Y were in one group as there was only one participant from the municipality. By presenting data from the earlier ethnographic field study [ 41 ] and barrier screening [ 42 ], the assumption was that this would create transparency in the work, create trust between the research team and the participants, and support behavioral changes of the participants in the implementation of the intervention. After the workshops, all participants returned to their departments and municipalities, and they had 2 weeks to complete the final implementation plan, which was then sent to the research team.

The research team met with the health care practitioners four times in follow-up workshops, which lasted approximately 1.5 h each (Fig. 1 ). These were more unstructured workshops as the participants were placed randomly in a large group. The purpose was to discuss the participants’ experiences with the use of the implementation plan. I.e. what worked or did not work and why. The researchers wrote barriers, facilitators and adaptations of the implementation plan on a whiteboard, and before the end of the workshops, the participants adapted the implementation plan in collaboration. Again, the assumption was that group negotiation about implementation plan adjustments would create transparency and a joint ownership, which was assumed to be a prerequisite for a successful implementation.

Data collection

Data were collected the same way at all workshops and follow-up workshops. We divided the participants into small groups belonging to the same department (e.g., the municipality). JWK acted as a facilitator during the workshops and ensured that the same topics were discussed in all groups. The facilitator did not contribute with ideas but encouraged input from all participants. The researchers worked on the premise that participants could negotiate appropriate proposals themselves.

All groups were in the same room, which made it difficult to audiotape conversations all the time. Additionally, research team members took notes on: (1) interactions between members within and between groups; (2) justifications for the selected implementation strategies; and (3) contradictions between members within and between groups. All follow-up workshops were audiotaped because they were held in separate rooms in the two hospitals. In total, 8 h of conversations were captured on recordings, 30 pages of notes and 107 pages of transcribed data material were produced.

Data analysis

First, the first author (JWK) performed a deductive thematic analysis by reading the empirical material line-by-line. Then the meaning units was coded in three steps [ 43 ] using different frameworks [ 3 , 13 ]. The frameworks were used to ascertain consistent labeling and descriptions as well as to provide a theoretical justification for the findings. First, the content of the stakeholders’ proposed implementation strategies was categorized according to the framework of Proctor et al. [ 3 ] which emphasizes naming, defining and operationalizing implementation strategies. The operationalization encompasses the specification of the following dimensions: actors, actions, action target (or mechanism through which the strategy works), temporality, dose, and outcome affected. Second, the strategies were categorized in accordance with a taxonomy of strategies by Powell et al. [ 13 ]: planning, e.g. helping stakeholders gather data or select strategies, educating, e.g. presentation of strategies of various levels of intensity that can be used to inform stakeholders about the intervention and/or its implementation, financing, restructuring, managing quality, and attending to the policy context. Third, data were analyzed to examine the justifications for the selected implementation strategies by JWK reading and re-reading the field notes and the transcribed material to gain a deeper understanding of the underlying motives for the selected strategies. This data analysis was informed by Proctor et al.’s [ 3 ] distinction between empirical, pragmatic, and theoretical justifications. Empirical justifications are based on either research evidence or on individuals’ experience with the evidence-based strategies [ 44 ]. Pragmatic justifications specify a clear rationale about what factors need to be addressed and how selected strategies may address them, but do not provide an empirical or theoretical justification for the strategies [ 45 ]. Pragmatic justification is based on knowledge derived from “real-world” practice and on “practice-based evidence” [ 43 , 46 , 47 ] derived from the experience of the participants. Theoretical justifications are based on theoretical knowledge and rely on insights gained from a body of knowledge accumulated in a research field or concerning a specific subject [ 47 , 48 ].

The initial sub-themes for the justification of the implementation strategies were discussed with the research team before the condensation of the final themes. One issue discussed was to secure consensus understandings of the three categories, i.e. empirical, pragmatic and theoretical justifications.

Thirteen implementation strategies were selected across Hospitals X and Y (Table  3 and Additional file  4 : Appendix S4). In Hospital X, all 13 strategies were selected while Hospital Y, selected seven of the 13 strategies. The strategy “change physical structure and equipment” was not associated with a specific barrier but was based on previous experiences of all managers who emphasized the importance of having the equipment ready before the start of the project because this could otherwise become a barrier. Detailed information about each implementation strategy is presented in S4.

Categorization of the selected strategies

The results from the deductive analysis of the characteristics of the selected strategies are shown in Table 3 and a short summery can be found in Additional file  5 : Appendix S5. Most of the selected strategies could be mapped onto the planning and educating categories in the ERIC compilation of implementation strategies [ 13 ]. For example, participants from Hospital X selected the strategy ‘Identify early adopters’, whereas participants from Hospital Y chose the strategy ‘Develop educational materials’ (Table  4 ). Educating involve different strategies of various levels of intensity that can be used to inform stakeholders about the intervention and/or its implementation. No strategy was selected in the category “attend to policy context” because there was a lack of strategies targeting the institutional level (see the action targets in Table 4 ).

Justifications for the selected strategies

The deductive analysis of the data concerning the rationale for the selected strategies showed that selection was based on pragmatic and theoretical justifications. The contents of the two types of justifications were thematized into nine subthemes (Table  5 ).

Empirical justifications

The analysis showed that empirical justifications were not used to select implementation strategies. The participants had various practice-based experiences using some of the evidence-based strategies (e.g., audit and feedback). However, they were not concerned with whether these strategies were evidence-based or not, despite the fact that the research team described some of the strategies and told the participants about the degree to which the strategies were based on evidence.

Pragmatic justifications

Four types of pragmatic justifications were identified in the data: experience (knowledge gained through the participants’ own experiences in their professional practice); intentions (the participants’ goodwill to implement the intervention); habits (actions that were more or less automatically enacted, executed without much conscious or deliberate effort); and resource limitations (based on perceived lack of time and/or staff). Two additional types of pragmatic justifications were identified: a sense of shared responsibility for implementing the intervention that developed among the participants and unfamiliarity with systematic implementation work. Neither of these are a part of the taxonomy by Powell et al. [ 39 ] or Proctor et al. [ 3 ] and neither relied on experience.

The participants often explained how they had good experience from their clinical practice. They did not use the word strategy but talked about “actions” or “plans.” A frontline manager expressed, when addressing the strategy on local consensus discussions:

“We need to start a discussion in the staff group about the importance of getting patients out of bed and the importance of the intervention. Normally it helps to get the intervention implemented.” (Hospital X, notes).

A frontline manager argued when discussing the strategy on identifying early adopters and how and why certain staff should be informed before other staff:

“As soon as possible we need to talk to xx. She is always so positive and open to change. I have a good feeling about her, and I have good experiences with getting her involved.” (Hospital X, notes)

In these discussions, the participants did not relate their justifications to any form of evidence, evaluation or measure, but simply referred to everyday experiences that had become useful and significant to them in their daily practice.

A second category of pragmatic justification was selecting strategies based on intentions. These intentions were also grounded in experience, but were more hypothetical and/or normative, i.e., expressed in terms of what they think they “should do” or would be “best to do.” An implementation champion said:

“I think it is best if you [addressing the frontline manager] talk about the importance of implementing this intervention every day at board meetings. Or, maybe I can do it ?[pause] On the other hand, you have the mandate and they [the staff] listen to you.” (Hospital X, transcribed notes)

This statement indicates that the strategy on information on board meetings with all the staff was justified based on an idea that the implementation champion could perform this task even if it was new to her. The intentional justification was grounded in power and position based on experience, where the frontline manager and the implementation champions decided that the strategy should be carried out by a person with a mandate to ensure that the strategy is implemented (Table 3 ). This also became evident at the follow-up workshop, where it emerged that it had not been possible to implement the strategy due to oversight and lack of manager presence at the board meetings.

Some strategies were selected with the justifications that the staff were used to employing the strategies, which were more or less automatically enacted, i.e. a form of habit-based justification. An implementation champion described:

“We normally develop written material. I know I have to deliver the material to the patients and students, but I often forget to do it” (Hospital Y, notes)

Written material is an educational implementation strategy [ 39 ] often intended to increase knowledge. Despite the awareness that the participants often forgot to hand out written material and the fact that no barrier concerning lack of knowledge was identified, this was chosen as a strategy (Tables  2 and 4 ).

Similarly, the strategy of conducting ongoing training was addressed by the physiotherapist implementation champions and managers as something they always did, although no barrier indicated that the staff lacked relevant skills (Additional file 4 : Appendix S4).

At a follow-up workshop, it became clear for several frontline managers and implementation champions that they normally used implementation strategies at the outset of projects but did not employ them throughout the process. A frontline manager expressed:

“We are always so motivated in the beginning of a new innovation, but the hard part is to ensure focus along the way. I have learned that it is just as important to use implementation strategies along the way in the projects as at the start if we are to ensure that the interventions are implemented.” (Hospital X, transcribed notes)

The participants described how high motivation at the beginning of a project led to a focus on the use of implementation strategies. When the motivation decreased over time, the focus on implementation strategies declined, resulting in limited use of the strategies. The strong motivation at the outset of the project appeared to be a justification for predominantly selecting strategies in the category of ‘planning’ (Table 4 ).

Resource limitations

Resource limitations in the form of time and/or staff restrictions provided another pragmatic justification. As part of the intervention, the physiotherapists and the nurses should collaborate on deciding what level of walking plan should be delivered to the patients. For the therapists in X, it required new ways of collaborating and allocation of more time to get the intervention implemented. As a frontline manager said:

“It is not possible for me to allocate more time for my therapists in the departments. If the implementation of the intervention requires more resources, then I think financial resources must be dedicated so that I can free up time for my therapists.” (Hospital X, transcribed notes)

The quote reflected resource limitations, which could justify the selection of a financing strategy, such as ‘Access new or existing money to facilitate the implementation’. Resources were not discussed as a strategy in Y because the therapists were affiliated to the department. The way of organizing seemed important for selection and justification of implementation strategies. Despite discussions about resources, no financial implementation strategies were selected, as shown in Table 4 .

In general, these four categories of pragmatic justifications were characterized by a perception of importance for the participants as well as on intentions, habits, and resource limitations rather than on scientific evidence or systematic evaluations of what research has established works and what does not work. Two additional categories of pragmatic justifications were identified, but neither of these relied on any specified experience.

Sense of shared responsibility

The discussions in the workshops showed that the participants’ knowledge about each other’s work within and across health care sectors was limited, even though the municipalities and both medical departments had worked together for many years. They talked about how they had experiences with complicated collaborations across health care sectors in relation to implementing interventions (e.g., communication). They knew that there was not a one size fits all strategy to overcome these complications. During the discussions, one of the implementation champions suggested a visit across sectors as part of the preparation for the implementation of the intervention:

“It seems a good idea to visit each other before we start to test the intervention. I think it is important because it gives a better understanding and acceptance of how we work with the patients, also if there are problems getting it [the intervention] implemented.” (Hospital X, transcribed notes)

A head manager followed up:

“It also helps create responsibility between us, so everyone does their best getting the intervention implemented.” (Hospital X, notes)

None of the participants disagreed even though no one had previously taken the initiative to visit across health care sectors. The participants visited each other, which contributed to the strategy of building a coalition between the departments and the municipalities to create a sense of shared responsibility for the implementation of the intervention.

Unfamiliar with systematic work with implementation

Throughout the process of developing the implementation plan and selecting strategies to overcome barriers, it appeared that none of the participants had previously worked with implementation strategies and plans in a systematic way. A frontline manager commented:

“In my daily practice, I normally implement a lot of new innovations, big or small innovations. But I have never known many of the 'tools'[implementation strategies] that I have been introduced to in this process. It's actually scary.” (Hospital X, transcribed notes)

This statement was followed up by an observation by an implementation champion:

“It is interesting that implementation is a mandatory part of my job, but I have never received any education in implementation.” (Hospital X, notes)

In general, the importance of creating a shared responsibility between the participants led to the development of a new implementation strategy of a visit across sectors. This shared responsibility could have consequences for the trust or distrust between the participants, depending on the outcome of the implementation. Few participants had implementation science knowledge, but by participating in the project, they learned about the importance of working with implementation strategies in a systematic manner, which contributed to the selection of strategies on, for instance, conducting barrier screening and tailoring strategies. The sparse knowledge of implementation research also became apparent in the theoretical justifications.

Theoretical justifications

Three types of theoretical justifications were identified. These justifications were based on theoretical knowledge derived from three areas of research: quality improvement (QI), organizational theory, and professional knowledge.

Knowledge from quality improvement

QI knowledge was the basis for selecting the strategy of audit and feedback . Head and frontline managers are responsible for the quality in their departments and are acquainted with QI knowledge, which they referred to. For example, a frontline manager stated:

“I know a little about quality improvement, the PDSA circle or audit and feedback.”

Knowledge from organizational theory

In contrast to the frontline managers, both head managers selected strategies with a knowledge basis in organizational theory concerning implementation rather than QI knowledge. They had acquired this knowledge through other education. A head manager said:

“As part of my Master’s in public management, I have learned about implementation. I know the importance of creating an understanding of the importance of change through motivation. Try to create a positive burning platform [a difficult situation which it is crucial to change].” (Hospital X, transcribed notes)

The theoretical knowledge referred to by the head managers was derived from the field of organizational theory [ 49 ]. One of the head managers reflected on this:

“Although I seem to have a lot of theoretical knowledge about implementation due to my higher education, this project has nevertheless taught me some new and more concrete tools on how to approach implementation.” (Hospital X, transcribed notes)

Both head managers used their knowledge from organizational theory in choosing implementation strategies, e.g., information on board meetings with all the staff and choosing early adopters with motivational arguments and tools. The burning platform was an example of a theoretical organizational tool, which is about understanding the importance of change through motivation. Although the head managers had learned about implementation through their education, they experienced that implementation science contained new knowledge and tools, which they considered to be useful in implementing the intervention.

Profession-related knowledge

A third form of theoretical justification for strategies emanated from professional knowledge, i.e., clinical knowledge concerning the treatment and care of patients. The analysis shows that clinically relevant knowledge related to different aspects of patient treatment and care depending on the profession. For the physicians, this knowledge could relate to diagnostics, for the physiotherapists it was related to respiratory physiotherapy, and for the nurses, administration of medications was discussed as clinically relevant knowledge. Particularly for the physicians and the physiotherapists, professional knowledge was mentioned as important in justifying why the intervention should be implemented. A physician explained:

“If I am going to motivate my medical colleagues to be active in implementing this intervention, they need the professional relevance. I need to have the clinical arguments ready.” (Hospital X, notes)

For this physician, justifications other than those pertaining to clinical professional knowledge seemed irrelevant. Accordingly, the physician chose the strategy of information at the physician conferences, because this was a forum for physicians to hear about clinically relevant topics and thus about the project and its implementation. Even though the research team had explained that information can rarely be used alone to change behavior, no other strategies were chosen.

For the physiotherapists, the justifications based on professional knowledge also increased motivation. An implementation champion expressed:

“An intervention with focus on mobility motivates physiotherapists. I cannot imagine that implementation is going to be a problem. I think many of our colleagues will require professional training to be sure they are up to date with the latest knowledge. It increases their motivation even more.” (Hospital Y, transcribed notes)

The implementation strategy of conducting ongoing training was selected by the physiotherapists based on a motivation to ascertain up-to-date clinically relevant knowledge rather than recognizing training as a means to improve skills. For physicians and physiotherapists, professional knowledge was used as a justification, but in different ways. For the physicians, it was a central way of getting their physician colleagues interested and motivated in the project. For the physiotherapists, motivation was strong from the beginning of the project, and was signaled through quotes such as: “ It is great to be allowed to work with the implementation of physical activity” (Hospital X, notes) and: “ Physical activity is the core of our work” (Hospital Y, notes). Their motivation was further strengthened by the strategy of conducting ongoing training .

In general, the group of frontline managers referred only to QI knowledge while head managers used theory from other scientific fields. Theoretical justification was used to select implementation strategies that could support and increase the motivation of the staff.

This qualitative study investigated the strategies selected by health care practitioners and their managers for the implementation of the WALK-Cph intervention. We found that implementation champions and managers selected primarily implementation strategies classified as educating and planning in the taxonomy by Powell et al. [ 13 ]. There were also a few managing quality, restructuring and financing strategies. The justifications for the selection of these strategies were made by using pragmatic justifications (experience, intentions, habits, resource limitations, sense of shared responsibility and unfamiliarity with implementation) and theoretical justifications (QI knowledge, organizational theory knowledge, and professional knowledge). These results are consistent with the taxonomy developed by Proctor et al. [ 3 ] concerning different types of justifications. We did not identify any empirical justifications to motivate selections of strategies.

The use of pragmatic justifications in our study highlights a tension between using generalizable research-based knowledge and paying attention to the importance of the local context [ 50 ]. Research has shown that clinical experts largely make choices based on a tacit practice-based knowledge built up from practitioners’ experience, which is manifested in their craft expertise and skills; the source is often specific problems that require solutions [ 51 ]. In contrast, some authors have suggested that implementation experts should make decisions based on findings from implementation research, i.e., research-based knowledge [ 3 , 13 ]. However, there is a risk that research-based knowledge is difficult to apply in the local setting because it is not adapted to the specific context where implementation occurs, e.g., the type of leadership or culture in a particular hospital or department. On the other hand, a co-design process can produce more contextualized strategies and interventions, but the risk is that too much emphasis is put on pragmatic justifications of what clinical experts “believe” would work well because it may have worked in the past. It has been argued that unsuccessful implementation is more likely when strategies are chosen routinely or by habits rather than being based on purposefully addressing specific barriers [ 52 ].

Our study findings suggest the importance of finding a balance between research- and practice-based knowledge in co-design processes. Practice-based knowledge predominantly serves to solve the problems that occur in everyday life and work, but the subjective and context-bound nature of this knowledge limits its generalizability. On the other hand, research-based knowledge typically has ambitions for applicability beyond the immediate boundaries of the specific study, but this type of knowledge can rarely provide quick solutions to problems [ 52 ]. In practice, however, aspects of the two types of knowledge are intertwined, which means that it is rarely an either/or choice for health care practitioners, but more often a question of making sense of many sources of knowledge. Fitzgerald et al. [ 53 ] view the relationship as “circular”, with the two knowledge types reinforcing each other as they become woven together. An important point is that implementation is not only a science but also a practice since many implementers, as in this study, are not researchers. When implementation occurs in a real-world setting (and when not done by researchers) it requires judgment and skills to ensure adaptation to real-time changes and variations, as well as drawing on evidence from the field of implementation science. So, both types of knowledge are needed for practitioners to develop a high level of competence, i.e. the ability to act knowledgeably, effectively, deliberately, strategically and reflectively in a situation.

Many of the implementation strategies mapped onto the category of planning and educating [ 13 ], providing examples of selections based on practice-based evidence from past experiences. However, strategies like these tend to have a limited duration and may not support the implementation of an intervention throughout an entire project. The reason for choosing these short-term strategies may be due to the participants’ belief that implementation is an “introduction” of something new, i.e. implementation is something with a clear beginning and an end. However, if implementation is viewed in terms of a longer, more complex change process with no clear ending there is a need for strategies that can provide long-term support for implementation to be successful. Hence, the selection of strategies is dependent on the perspective of implementation. This finding highlights the relevance of selecting implementation strategies that can support the implementation process in a longer time perspective. One tool that could support the selection of strategies is for researchers to make a greater effort to relate these strategies to the participants and settings, so the participants become more familiar with the different types of implementation strategies and the underlying evidence. Evidence was not a factor that appeared to be significant to the participants in their justifications when no empirical justifications were used.

Education was a commonly selected category of implementation strategy by the stakeholders in our study. Despite the preference for this type of strategy there is rather weak evidence of effectiveness for educational strategies focusing on cognitive participation at the expense of collective action in changing healthcare professionals’ behaviours [ 54 ], which usually describes such strategies as ineffective for changing practice or achieving optimal care [ 44 ]. These results highlight the research-based knowledge versus practice-based knowledge dilemma, raising the question of whether a more research-led process would have ensured a stronger emphasis on strategies with proven effectiveness. Participatory design has seen a development from the users as subjects to users as partners, with a continuum of user-involvement methods in which the power to determine the outcome to a greater or lesser degree is placed with the researchers [ 22 ]. In the current study, the participants had full authority to influence the process and the outcome. Based on some of our results, including the choice of strategies being based only on pragmatic and theoretical justifications and the paucity of knowledge about implementation based on implementation research, a more equal distribution of power could possibly have been more appropriate. On the other hand, a more researcher-led process utilizing more research-based knowledge could challenge the basic premise of co-design, i.e., to ensure a high degree of user involvement and move decisional authority from the research team to the participants, also defined as “power from us to them” [ 55 ] and where the definition of power is the ability to influence an outcome [ 56 ].

The theoretical justifications in our study were not based on knowledge from implementation science [ 57 ]. This finding is not problematic per se, but the participants expressed a need to learn more about implementation research. Several of the participants were selected as implementation champions because they had previously dealt with QI tasks and implementation issues in their daily work. Our finding is consistent with Mosson et al. [ 58 ], who found that health care practitioners and their managers often lack skills for implementing evidence-based methods and that implementation often occurs without a structured approach [ 59 ].

These results suggest a need for expanding and/or improving training in implementation science for health care practitioners to facilitate the practical use of this knowledge. Westerlund et al. [ 60 ] and Lyon et al. [ 61 ] have highlighted a form of “implementation paradox,” wherein there is a risk of knowledge produced in implementation science not being used in real-world health care practice despite the fact that the field was borne out of ambitions to bridge the knowing-doing gap. Although implementation science is an applied science, the extent to which knowledge produced in this field is actually used by practitioners is not known [ 60 ]. There are few empirical studies concerning if or how knowledge on implementation is being applied in health care practice [ 62 , 63 ]. Meissner et al. [ 38 ] and Ramaswamy et al. [ 64 ] argue that more courses on training in implementation science nationally and internationally are needed to expand implementation capacity [ 65 ]. Our experiences working with the implementation of the WALK-Cph intervention top managers need to acknowledge implementation research as a scientific field with relevant knowledge to support real-life implementation if they are to prioritize staff resources for learning about implementation science in the form of skills, knowledge, and practice-based learning.

A pragmatic justification that occurred in our study was a shared sense of responsibility, which can be understood as a relational concept where a person has responsibility for causing something to happen where there can be implications of praise or blame [ 66 ]. Achieving collaboration across health care sectors in Denmark is a complex matter [ 67 , 68 , 69 ], and the participants in our study had previously tried coordination of communications across health care sectors without much success. From earlier experiences, the participants had learned that there was not a one size fits all implementation strategy that could ensure efficient collaboration across sectors and a shared responsibility when implementing interventions. These previous experiences entailed developing a new strategy, a visit across sectors, which none of the participants had tried before. This was justified by the idea that it could create a shared responsibility, which would have consequences for the trust or distrust between the participants depending on the outcome of the implementation of the WALK-Cph intervention.

It has become increasingly important for managers to gain the trust of followers which is needed to achieve effective leadership [75]. Balkrishnan et al. [ 70 ] have defined trust as the willingness to be vulnerable to the actions of another party, irrespective of the ability to monitor or control the other party. To propose an implementation strategy that was not tried or known by some of the participants could be a sign of trust between the participants across sectors but also between the implementation champions and the managers. By creating a relational “safe” place built on trust in their interactions with the implementation champions, the managers made the participants more open to trying something new even if it meant failing [ 71 ]. Building trust became an important factor for pragmatic justifications.

Strengths and limitations

A strength of the study was the design and methodological choices, which enabled us to follow the strategies from their selection to their implementation. This increased our knowledge about the “birth” and the “life” of implementation strategies in clinical practice. It was also a strength that we were present at the workshops to observe (gestures, mimics, etc.) the participants, which gave us a better understanding of the situational and contextual situation in which these strategies were selected. Another strength was that we described implementation strategies based on Proctor et al.’s [ 3 ] standards for characterizing implementation strategies, because this strengthened the labeling, increased comparability, and increased the knowledge of whether, which, who, and why when working with implementation strategies and health care practitioners.

A limitation of the study was that we could not comment on the effectiveness of the strategies selected. Further research is necessary to study whether there are correlations between the type of justification and management styles and effectiveness.

The way the co-design process was carried out may be considered a limitation. The researchers chose only to take on a productive role when contributing with implementation science knowledge and a part from this a facilitating role to ensure that ownership of the implementation plan would lie with the stakeholders. The ambition was to ensure that ownership of the implementation plan would lie with the stakeholders. Much emphasis was put on pragmatic justifications despite the fact that the research team had ensured that all relevant knowledge was present in the co-design team. Further, knowledge about implementation strategies was presented and discussed. Throughout the process, the facilitator asked challenging questions to the participants concerning their justifications of strategies.

The challenges faced in the co-design process may be difficult to generalize too broadly since many decisions taken were likely specific to the process and context of the studied case. Variability in co-design processes restricts generalizability and the ability to draw definitive or far-reaching conclusions.

A further limitation is the sole focus on health care practitioners’ training and learning implementation science instead of letting implementation researchers and health care practitioners train together as they could learn from each other, potentially making practice more research-informed and the science of implementation more practical and applicable.

A learning point for future co-design processes is to ascertain that expert knowledge and experience from the researchers, who were implementation researchers in this study, is afforded an equally central place in the co-design process. The goal would be to orchestrate the co-design process in a way that enables a synergistic combination of the different stakeholders’ expertise and experiences.

Transferability of the findings to other settings is possible, as thick descriptions were developed concerning the selection of implementation strategies and the justifications by selecting exemplary citations and describing the contexts of the data collection, including referring to the article focusing on reflective reflections on the use of co-design methods from a researcher perspective [ 36 ].

In this study, the implementation champions and managers came from medical departments and the municipalities. Further research should explore other settings and departments where implementation of evidence-based practice is more of a strategic imperative, which could yield a stronger focus on EBI. The study has shown that situational and relational factors are important for justification of implementation strategies, underscoring that justifications are highly sensitive to contextual factors such as culture, climate, and management.

This qualitative study of implementation strategies selected by health care practitioners and their managers to support the implementation of an intervention to promote mobility found that implementation champions and managers predominantly selected implementation strategies that focus on planning for the implementation of the intervention and education by means of informing stakeholders about the intervention and/or its implementation. The selected strategies were motivated through pragmatic justifications, i.e. a clear rationale about what factors need to be addressed and how selected strategies may address them, and theoretical justifications based on theoretical knowledge accumulated in a research field. The sources of the theoretical knowledge were QI knowledge, organizational theory knowledge and professional knowledge. No implementation strategies were motivated by means of empirical justifications based on research evidence or previous experience with the evidence-based strategies. Although co-design processes may be very process- and context-specific, our finding points to a challenge in balancing strategies derived from practice-based knowledge and research-based knowledge.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

WALK-Copenhagen

Evidence-based intervention

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Acknowledgments

The authors thank the managers and the implementation champions for taking the time to participate in the workshops. We also thank all the health care practitioners from the medical departments for allowing us to follow them in their daily practice. Finally, thanks to Rasmus Brødsgaard and Ditte Maria Sivertsen helping the collection of data.

Funding was granted by The Velux Foundations (grant number F-21835-01-04-03); the Association of Danish Physiotherapists (PD-2018-30-10); and the Capital Region of Denmark (P-2018-2-11). The funding bodies had no role in the design of the study and collection, analysis and interpretation of the data and in writing the manuscript.

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Department of Clinical Research, Copenhagen University Hospital, Amager and Hvidovre, Hvidovre, Denmark

Jeanette Wassar Kirk, Ove Andersen, Thomas Bandholm & Mette Merete Pedersen

Department of Public Health, Nursing, Aarhus University, Aarhus, Denmark

Jeanette Wassar Kirk

Department of Health, Medical and Caring Sciences, Linköping University, Linköping, Sweden

Brown School, Washington University in St. Louis, St. Louis, MO, USA

Byron J. Powell

Department of Health and Social Context, National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark

Tine Tjørnhøj-Thomsen

Department of Orthopedic Surgery, Physical Medicine & Rehabilitation Research-Copenhagen (PMR-C), Copenhagen University Hospital, Amager and Hvidovre, Hvidovre, Denmark

Thomas Bandholm

Department of Physical and Occupational Therapy, Physical Medicine & Rehabilitation Research-Copenhagen (PMR-C), Copenhagen University Hospital, Amager and Hvidovre, Hvidovre, Denmark

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Contributions

JWK, MMP, TB, OA, PN and TTT designed the study. JWK, MMP and TB collected the data. JWK drafted the manuscript. JWK and PN analysed the data. JWK interpreted the data and drafted the initial manuscript. PN, TTT, MMP, TB, OA and BP critically reviewed the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jeanette Wassar Kirk .

Ethics declarations

Ethics approval and consent to participate.

The study adhered to the Helsinki Declaration [ 72 ]. Before starting the workshops, the head managers from all departments gave permission for all participants to participate. Audiotaped oral informed content was obtained from the participants at the workshops. We choose oral informed consent, why written informed consent was not obtained for the following reasons: 1) the head of departments approved the health care professional’s participation in the study, 2) we did not expect the interviews to include personally identifiable topics (which they did not) and 3) as the head of departments approved the health care practitioners participation and that the participants gave audiotaped oral consent, informed consent was given. In the research notes, all names of participants were replaced by code names. All participants were informed that it was voluntary to participate and that they could withdraw from the study at any time. The Danish Data Protection Agency (AHH-2016-080, I-Suite no. 05078) approved the study.

Ethical approval was applied for but according to Danish law, ethical approval is not mandatory for studies not involving biomedical issues [I-Suite no. H-18027877], cf. section 1, subsection 4 of the Committee Act, and can be carried out without permission from the Research Ethics Committees of the Capital Region. In Denmark, the task of the research ethics committee is to evaluate research projects within health sciences. A health science research project is a project which involves research on live birth human subjects, human sex cells that are intended for fertilization, human fertilized eggs, embryos and fetuses, tissues, cells and hereditary components from humans, fetuses and the like or deceased. This includes clinical trials of pharmaceutical products on humans and clinical trials of medical equipment.

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Supplementary Information

Additional file 1: appendix s1..

WALK-Cph intervention and implementation study, Hybrid II design.

Additional file 2: Appendix S2.

Final WALK-Cph intervention.

Additional file 3: Appendix S3.

Size and staff composition of Departments.

Additional file 4: Appendix S4.

Selected implementation strategies.

Additional file 5: Appendix S5.

Summary of the selected strategies and outcome effected.

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Kirk, J.W., Nilsen, P., Andersen, O. et al. Co-designing implementation strategies for the WALK-Cph intervention in Denmark aimed at increasing mobility in acutely hospitalized older patients: a qualitative analysis of selected strategies and their justifications. BMC Health Serv Res 22 , 8 (2022). https://doi.org/10.1186/s12913-021-07395-z

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Received : 21 March 2021

Accepted : 08 December 2021

Published : 02 January 2022

DOI : https://doi.org/10.1186/s12913-021-07395-z

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