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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.

Qualitative data analysis methods

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 .

qualitative data analysis vs quantitative data analysis

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?

method of analysis in qualitative research

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.

Thematic analysis takes bodies of data and groups them according to similarities (themes), which help us make sense of the content.

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 .

Grounded theory is used to create a new theory (or theories) by using the data at hand, as opposed to existing theories and frameworks.

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.

IPA can help you understand the personal experiences of a person or group concerning a major life event, an experience or a situation.

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.

No single analysis method is perfect, so it can often make sense to adopt more than one  method (this is called triangulation).

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.

method of analysis in qualitative research

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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84 Comments

Richard N

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netaji

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Mariam Jaiyeola

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Nzube

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Lee

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Derek Jansen

Great to hear that. Good luck with your qualitative data analysis, Pramod!

Adam Zahir

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Golit,F.

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Emmanuel

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Shahzada

Precise explanation of method.

Alyssa

Hi, may we use 2 data analysis methods in our qualitative research?

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

Dr. Manju Pandey

You explained it in very simple language, everyone can understand it. Thanks so much.

Phillip

Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands

Anne

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

Rev. Osadare K . J

This is my first time to come across a well explained data analysis. so helpful.

Tina King

I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!

Bromie

Thank you very much, this is well explained and useful

udayangani

i need a citation of your book.

khutsafalo

Thanks a lot , remarkable indeed, enlighting to the best

jas

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

M

Keep writing useful artikel.

Adane

It is important concept about QDA and also the way to express is easily understandable, so thanks for all.

Carl Benecke

Thank you, this is well explained and very useful.

Ngwisa

Very helpful .Thanks.

Hajra Aman

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Hillary Mophethe

The session was very helpful and insightful. Thank you

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Catherine

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Keep up the good work Grad Coach you are unmatched with quality content for sure.

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Emanuela

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Noble Naade

Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?

Karen

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amirhossein

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Tebogo

What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Catherine Shimechero

Informative video, explained in a clear and simple way. Kudos

Van Hmung

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.

BRIAN ONYANGO MWAGA

This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

Livhuwani Reineth

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Storm Erlank

This was incredibly helpful.

Jack Kanas

Very helpful.

catherine

very educative

Wan Roslina

Nicely written especially for novice academic researchers like me! Thank you.

Talash

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?

ramesh

that was very helpful for me. because these details are so important to my research. thank you very much

Kumsa Desisa

I learnt a lot. Thank you

Tesfa NT

Relevant and Informative, thanks !

norma

Well-planned and organized, thanks much! 🙂

Dr. Jacob Lubuva

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Nyi Nyi Lwin

Clear explanation on qualitative and how about Case study

Ogobuchi Otuu

This was helpful. Thank you

Alicia

This was really of great assistance, it was just the right information needed. Explanation very clear and follow.

Wow, Thanks for making my life easy

C. U

This was helpful thanks .

Dr. Alina Atif

Very helpful…. clear and written in an easily understandable manner. Thank you.

Herb

This was so helpful as it was easy to understand. I’m a new to research thank you so much.

cissy

so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

Ayo

Thank you for the great content, I have learnt a lot. So helpful

Tesfaye

precise and clear presentation with simple language and thank you for that.

nneheng

very informative content, thank you.

Oscar Kuebutornye

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!

NG

Brilliant Delivery. You made a complex subject seem so easy. Well done.

Ankit Kumar

Beautifully explained.

Thanks a lot

Kidada Owen-Browne

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.

Mathewos Damtew

content analysis can be qualitative research?

Hend

THANK YOU VERY MUCH.

Dev get

Thank you very much for such a wonderful content

Kassahun Aman

do you have any material on Data collection

Prince .S. mpofu

What a powerful explanation of the QDA methods. Thank you.

Kassahun

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

BORA SAMWELI MATUTULI

very helpful, thank you so much

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method of analysis in qualitative research

Home Market Research

Qualitative Data Analysis: What is it, Methods + Examples

Explore qualitative data analysis with diverse methods and real-world examples. Uncover the nuances of human experiences with this guide.

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods, and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

  • Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
  • Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
  • Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
  • Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
  • Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

  • Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
  • Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
  • Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

  • Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
  • Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
  • Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
  • Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

  • Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
  • Complexity: Analyzing huge data sets requires time and great attention to detail.
  • Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

  • Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
  • Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
  • Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

  • Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
  • Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
  • Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

  • Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
  • Time-consuming: The study can be time-consuming, especially with large data sets.
  • Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

  • Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
  • Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
  • Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

  • Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
  • Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

  • Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
  • Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

  • Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
  • Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
  • Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

  • Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
  • In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
  • Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

  • Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
  • Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
  • Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

  • Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
  • Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
  • Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

  • Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
  • Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
  • Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

  • Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
  • Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
  • Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

  • Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
  • Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
  • Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
  • Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
  • Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
  • Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
  • Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
  • Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

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method of analysis in qualitative research

Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

  • Interview transcripts
  • Surveys with open-ended questions
  • Contact center transcripts
  • Texts and documents
  • Audio and video recordings
  • Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

Overwhelming quantity of feedback

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

  • Gathering and collecting your qualitative data
  • Organizing and connecting into your qualitative data
  • Coding your qualitative data
  • Analyzing the qualitative data for insights
  • Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Methods of qualitative data analysis; approaches and techniques to qualitative data analysis

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

  • Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
  • Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
  • Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
  • Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
  • Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
  • Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

  • Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
  • Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
  • Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
  • Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
  • Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
  • Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

G2.com reviews of the product Airtable. You could pull reviews from G2 for your analysis.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

  • Assists in the organizing of your data
  • Opens you up to exploring different interpretations of your data analysis
  • Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

The user interface of CAQDAS software 'NVivo'

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

  • Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
  • EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Some of qualitative data integrations offered by Thematic

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

  • Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
  • Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
  • Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
  • Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
  • Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Example of a hierarchical coding frame in qualitative data analysis

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

  • CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
  • Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
  • IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
  • Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

Example of sub-codes

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

  • Demographic
  • And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

  • Calculate overall NPS
  • Calculate NPS in the subset of responses that do not contain that theme
  • Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

Visualizing qualitative data: Calculating the impact of a code on your score

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

Analyzing qualitative data: Calculate which codes are linked to increases or decreases in my score

The visualization could look like this:

Visualizing qualitative data trends over time

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Codes displayed by volume within Thematic. You can 'manage themes' to introduce human input.

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Two examples of qualitative data visualizations within Thematic

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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How to use and assess qualitative research methods

  • Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
  • Wolfgang Wick 1 , 2 &
  • Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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

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

What is qualitative research?

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

Why conduct qualitative research?

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

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

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

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

figure 1

Iterative research process

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

Data collection

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

Document study

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

Observations

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

Semi-structured interviews

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

Focus groups

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

Choosing the “right” method

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

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

figure 2

Possible combination of data collection methods

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

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

Data analysis

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

figure 3

From data collection to data analysis

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

How to report qualitative research?

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

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

figure 4

Three common mixed methods designs

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

How to assess qualitative research?

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

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

Reflexivity

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

Sampling and saturation

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

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

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

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

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

Member checking

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

Stakeholder involvement

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

How not to assess qualitative research

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

Protocol adherence

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

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

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

Interrater reliability, variability and other “objectivity checks”

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

Not being quantitative research

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

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

Availability of data and materials

Not applicable.

Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

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Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z

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13 Qualitative analysis

Qualitative analysis is the analysis of qualitative data such as text data from interview transcripts. Unlike quantitative analysis, which is statistics driven and largely independent of the researcher, qualitative analysis is heavily dependent on the researcher’s analytic and integrative skills and personal knowledge of the social context where the data is collected. The emphasis in qualitative analysis is ‘sense making’ or understanding a phenomenon, rather than predicting or explaining. A creative and investigative mindset is needed for qualitative analysis, based on an ethically enlightened and participant-in-context attitude, and a set of analytic strategies. This chapter provides a brief overview of some of these qualitative analysis strategies. Interested readers are referred to more authoritative and detailed references such as Miles and Huberman’s (1984) [1] seminal book on this topic.

Grounded theory

How can you analyse a vast set of qualitative data acquired through participant observation, in-depth interviews, focus groups, narratives of audio/video recordings, or secondary documents? One of these techniques for analysing text data is grounded theory —an inductive technique of interpreting recorded data about a social phenomenon to build theories about that phenomenon. The technique was developed by Glaser and Strauss (1967) [2] in their method of constant comparative analysis of grounded theory research, and further refined by Strauss and Corbin (1990) [3] to further illustrate specific coding techniques—a process of classifying and categorising text data segments into a set of codes (concepts), categories (constructs), and relationships. The interpretations are ‘grounded in’ (or based on) observed empirical data, hence the name. To ensure that the theory is based solely on observed evidence, the grounded theory approach requires that researchers suspend any pre-existing theoretical expectations or biases before data analysis, and let the data dictate the formulation of the theory.

Strauss and Corbin (1998) describe three coding techniques for analysing text data: open, axial, and selective. Open coding is a process aimed at identifying concepts or key ideas that are hidden within textual data, which are potentially related to the phenomenon of interest. The researcher examines the raw textual data line by line to identify discrete events, incidents, ideas, actions, perceptions, and interactions of relevance that are coded as concepts (hence called in vivo codes ). Each concept is linked to specific portions of the text (coding unit) for later validation. Some concepts may be simple, clear, and unambiguous, while others may be complex, ambiguous, and viewed differently by different participants. The coding unit may vary with the concepts being extracted. Simple concepts such as ‘organisational size’ may include just a few words of text, while complex ones such as ‘organizational mission’ may span several pages. Concepts can be named using the researcher’s own naming convention, or standardised labels taken from the research literature. Once a basic set of concepts are identified, these concepts can then be used to code the remainder of the data, while simultaneously looking for new concepts and refining old concepts. While coding, it is important to identify the recognisable characteristics of each concept, such as its size, colour, or level—e.g., high or low—so that similar concepts can be grouped together later . This coding technique is called ‘open’ because the researcher is open to and actively seeking new concepts relevant to the phenomenon of interest.

Next, similar concepts are grouped into higher order categories . While concepts may be context-specific, categories tend to be broad and generalisable, and ultimately evolve into constructs in a grounded theory. Categories are needed to reduce the amount of concepts the researcher must work with and to build a ‘big picture’ of the issues salient to understanding a social phenomenon. Categorisation can be done in phases, by combining concepts into subcategories, and then subcategories into higher order categories. Constructs from the existing literature can be used to name these categories, particularly if the goal of the research is to extend current theories. However, caution must be taken while using existing constructs, as such constructs may bring with them commonly held beliefs and biases. For each category, its characteristics (or properties) and the dimensions of each characteristic should be identified. The dimension represents a value of a characteristic along a continuum. For example, a ‘communication media’ category may have a characteristic called ‘speed’, which can be dimensionalised as fast, medium, or slow . Such categorisation helps differentiate between different kinds of communication media, and enables researchers to identify patterns in the data, such as which communication media is used for which types of tasks.

The second phase of grounded theory is axial coding , where the categories and subcategories are assembled into causal relationships or hypotheses that can tentatively explain the phenomenon of interest. Although distinct from open coding, axial coding can be performed simultaneously with open coding. The relationships between categories may be clearly evident in the data, or may be more subtle and implicit. In the latter instance, researchers may use a coding scheme (often called a ‘coding paradigm’, but different from the paradigms discussed in Chapter 3) to understand which categories represent conditions (the circumstances in which the phenomenon is embedded), actions/interactions (the responses of individuals to events under these conditions), and consequences (the outcomes of actions/interactions). As conditions, actions/interactions, and consequences are identified, theoretical propositions start to emerge, and researchers can start explaining why a phenomenon occurs, under what conditions, and with what consequences.

The third and final phase of grounded theory is selective coding , which involves identifying a central category or a core variable, and systematically and logically relating this central category to other categories. The central category can evolve from existing categories or can be a higher order category that subsumes previously coded categories. New data is selectively sampled to validate the central category, and its relationships to other categories—i.e., the tentative theory. Selective coding limits the range of analysis, and makes it move fast. At the same time, the coder must watch out for other categories that may emerge from the new data that could be related to the phenomenon of interest (open coding), which may lead to further refinement of the initial theory. Hence, open, axial, and selective coding may proceed simultaneously. Coding of new data and theory refinement continues until theoretical saturation is reached—i.e., when additional data does not yield any marginal change in the core categories or the relationships.

The ‘constant comparison’ process implies continuous rearrangement, aggregation, and refinement of categories, relationships, and interpretations based on increasing depth of understanding, and an iterative interplay of four stages of activities: comparing incidents/texts assigned to each category to validate the category), integrating categories and their properties, delimiting the theory by focusing on the core concepts and ignoring less relevant concepts, and writing theory using techniques like memoing, storylining, and diagramming. Having a central category does not necessarily mean that all other categories can be integrated nicely around it. In order to identify key categories that are conditions, action/interactions, and consequences of the core category, Strauss and Corbin (1990) recommend several integration techniques, such as storylining, memoing, or concept mapping, which are discussed here. In storylining , categories and relationships are used to explicate and/or refine a story of the observed phenomenon. Memos are theorised write-ups of ideas about substantive concepts and their theoretically coded relationships as they evolve during ground theory analysis, and are important tools to keep track of and refine ideas that develop during the analysis. Memoing is the process of using these memos to discover patterns and relationships between categories using two-by-two tables, diagrams, or figures, or other illustrative displays. Concept mapping is a graphical representation of concepts and relationships between those concepts—e.g., using boxes and arrows. The major concepts are typically laid out on one or more sheets of paper, blackboards, or using graphical software programs, linked to each other using arrows, and readjusted to best fit the observed data.

After a grounded theory is generated, it must be refined for internal consistency and logic. Researchers must ensure that the central construct has the stated characteristics and dimensions, and if not, the data analysis may be repeated. Researcher must then ensure that the characteristics and dimensions of all categories show variation. For example, if behaviour frequency is one such category, then the data must provide evidence of both frequent performers and infrequent performers of the focal behaviour. Finally, the theory must be validated by comparing it with raw data. If the theory contradicts with observed evidence, the coding process may need to be repeated to reconcile such contradictions or unexplained variations.

Content analysis

Content analysis is the systematic analysis of the content of a text—e.g., who says what, to whom, why, and to what extent and with what effect—in a quantitative or qualitative manner. Content analysis is typically conducted as follows. First, when there are many texts to analyse—e.g., newspaper stories, financial reports, blog postings, online reviews, etc.—the researcher begins by sampling a selected set of texts from the population of texts for analysis. This process is not random, but instead, texts that have more pertinent content should be chosen selectively. Second, the researcher identifies and applies rules to divide each text into segments or ‘chunks’ that can be treated as separate units of analysis. This process is called unitising . For example, assumptions, effects, enablers, and barriers in texts may constitute such units. Third, the researcher constructs and applies one or more concepts to each unitised text segment in a process called coding . For coding purposes, a coding scheme is used based on the themes the researcher is searching for or uncovers as they classify the text. Finally, the coded data is analysed, often both quantitatively and qualitatively, to determine which themes occur most frequently, in what contexts, and how they are related to each other.

A simple type of content analysis is sentiment analysis —a technique used to capture people’s opinion or attitude toward an object, person, or phenomenon. Reading online messages about a political candidate posted on an online forum and classifying each message as positive, negative, or neutral is an example of such an analysis. In this case, each message represents one unit of analysis. This analysis will help identify whether the sample as a whole is positively or negatively disposed, or neutral towards that candidate. Examining the content of online reviews in a similar manner is another example. Though this analysis can be done manually, for very large datasets—e.g., millions of text records—natural language processing and text analytics based software programs are available to automate the coding process, and maintain a record of how people’s sentiments fluctuate with time.

A frequent criticism of content analysis is that it lacks a set of systematic procedures that would allow the analysis to be replicated by other researchers. Schilling (2006) [4] addressed this criticism by organising different content analytic procedures into a spiral model. This model consists of five levels or phases in interpreting text: convert recorded tapes into raw text data or transcripts for content analysis, convert raw data into condensed protocols, convert condensed protocols into a preliminary category system, use the preliminary category system to generate coded protocols, and analyse coded protocols to generate interpretations about the phenomenon of interest.

Content analysis has several limitations. First, the coding process is restricted to the information available in text form. For instance, if a researcher is interested in studying people’s views on capital punishment, but no such archive of text documents is available, then the analysis cannot be done. Second, sampling must be done carefully to avoid sampling bias. For instance, if your population is the published research literature on a given topic, then you have systematically omitted unpublished research or the most recent work that is yet to be published.

Hermeneutic analysis

Hermeneutic analysis is a special type of content analysis where the researcher tries to ‘interpret’ the subjective meaning of a given text within its sociohistoric context. Unlike grounded theory or content analysis—which ignores the context and meaning of text documents during the coding process—hermeneutic analysis is a truly interpretive technique for analysing qualitative data. This method assumes that written texts narrate an author’s experience within a sociohistoric context, and should be interpreted as such within that context. Therefore, the researcher continually iterates between singular interpretation of the text (the part) and a holistic understanding of the context (the whole) to develop a fuller understanding of the phenomenon in its situated context, which German philosopher Martin Heidegger called the hermeneutic circle . The word hermeneutic (singular) refers to one particular method or strand of interpretation.

More generally, hermeneutics is the study of interpretation and the theory and practice of interpretation. Derived from religious studies and linguistics, traditional hermeneutics—such as biblical hermeneutics —refers to the interpretation of written texts, especially in the areas of literature, religion and law—such as the Bible. In the twentieth century, Heidegger suggested that a more direct, non-mediated, and authentic way of understanding social reality is to experience it, rather than simply observe it, and proposed philosophical hermeneutics , where the focus shifted from interpretation to existential understanding. Heidegger argued that texts are the means by which readers can not only read about an author’s experience, but also relive the author’s experiences. Contemporary or modern hermeneutics, developed by Heidegger’s students such as Hans-Georg Gadamer, further examined the limits of written texts for communicating social experiences, and went on to propose a framework of the interpretive process, encompassing all forms of communication, including written, verbal, and non-verbal, and exploring issues that restrict the communicative ability of written texts, such as presuppositions, language structures (e.g., grammar, syntax, etc.), and semiotics—the study of written signs such as symbolism, metaphor, analogy, and sarcasm. The term hermeneutics is sometimes used interchangeably and inaccurately with exegesis , which refers to the interpretation or critical explanation of written text only, and especially religious texts.

Finally, standard software programs, such as ATLAS.ti.5, NVivo, and QDA Miner, can be used to automate coding processes in qualitative research methods. These programs can quickly and efficiently organise, search, sort, and process large volumes of text data using user-defined rules. To guide such automated analysis, a coding schema should be created, specifying the keywords or codes to search for in the text, based on an initial manual examination of sample text data. The schema can be arranged in a hierarchical manner to organise codes into higher-order codes or constructs. The coding schema should be validated using a different sample of texts for accuracy and adequacy. However, if the coding schema is biased or incorrect, the resulting analysis of the entire population of texts may be flawed and non-interpretable. However, software programs cannot decipher the meaning behind certain words or phrases or the context within which these words or phrases are used—such sarcasm or metaphors—which may lead to significant misinterpretation in large scale qualitative analysis.

  • Miles, M. B., & Huberman, A. M. (1984). Qualitative data analysis: A sourcebook of new methods . Newbury Park, CA: Sage Publications. ↵
  • Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research . New York: Aldine Pub Co. ↵
  • Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques , Beverly Hills: Sage Publications. ↵
  • Schiling, J. (2006). On the pragmatics of qualitative assessment: Designing the process for content analysis. European Journal of Psychological Assessment , 22(1), 28–37. ↵

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Research-Methodology

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

Qualitative Data Analysis

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5 qualitative data analysis methods

Qualitative data uncovers valuable insights that help you improve the user and customer experience. But how exactly do you measure and analyze data that isn't quantifiable?

There are different qualitative data analysis methods to help you make sense of qualitative feedback and customer insights, depending on your business goals and the type of data you've collected.

Before you choose a qualitative data analysis method for your team, you need to consider the available techniques and explore their use cases to understand how each process might help you better understand your users. 

This guide covers five qualitative analysis methods to choose from, and will help you pick the right one(s) based on your goals. 

Content analysis

Thematic analysis

Narrative analysis

Grounded theory analysis

Discourse analysis

5 qualitative data analysis methods explained

Qualitative data analysis ( QDA ) is the process of organizing, analyzing, and interpreting qualitative research data—non-numeric, conceptual information, and user feedback—to capture themes and patterns, answer research questions, and identify actions to improve your product or website.

Step 1 in the research process (after planning ) is qualitative data collection. You can use behavior analytics software—like Hotjar —to capture qualitative data with context, and learn the real motivation behind user behavior, by collecting written customer feedback with Surveys or scheduling an in-depth user interview with Engage .

Use Hotjar’s tools to collect feedback, uncover behavior trends, and understand the ‘why’ behind user actions.

1. Content analysis

Content analysis is a qualitative research method that examines and quantifies the presence of certain words, subjects, and concepts in text, image, video, or audio messages. The method transforms qualitative input into quantitative data to help you make reliable conclusions about what customers think of your brand, and how you can improve their experience and opinion.

Conduct content analysis manually (which can be time-consuming) or use analysis tools like Lexalytics to reveal communication patterns, uncover differences in individual or group communication trends, and make broader connections between concepts.

#Benefits and challenges of using content analysis

How content analysis can help your team

Content analysis is often used by marketers and customer service specialists, helping them understand customer behavior and measure brand reputation.

For example, you may run a customer survey with open-ended questions to discover users’ concerns—in their own words—about their experience with your product. Instead of having to process hundreds of answers manually, a content analysis tool helps you analyze and group results based on the emotion expressed in texts.

Some other examples of content analysis include:

Analyzing brand mentions on social media to understand your brand's reputation

Reviewing customer feedback to evaluate (and then improve) the customer and user experience (UX)

Researching competitors’ website pages to identify their competitive advantages and value propositions

Interpreting customer interviews and survey results to determine user preferences, and setting the direction for new product or feature developments

Content analysis was a major part of our growth during my time at Hypercontext.

[It gave us] a better understanding of the [blog] topics that performed best for signing new users up. We were also able to go deeper within those blog posts to better understand the formats [that worked].

2. Thematic analysis

Thematic analysis helps you identify, categorize, analyze, and interpret patterns in qualitative study data , and can be done with tools like Dovetail and Thematic .

While content analysis and thematic analysis seem similar, they're different in concept: 

Content analysis can be applied to both qualitative and quantitative data , and focuses on identifying frequencies and recurring words and subjects

Thematic analysis can only be applied to qualitative data, and focuses on identifying patterns and themes

#The benefits and drawbacks of thematic analysis

How thematic analysis can help your team

Thematic analysis can be used by pretty much anyone: from product marketers, to customer relationship managers, to UX researchers.

For example, product teams use thematic analysis to better understand user behaviors and needs and improve UX . Analyzing customer feedback lets you identify themes (e.g. poor navigation or a buggy mobile interface) highlighted by users and get actionable insight into what they really expect from the product. 

💡 Pro tip: looking for a way to expedite the data analysis process for large amounts of data you collected with a survey? Try Hotjar’s AI for Surveys : along with generating a survey based on your goal in seconds, our AI will analyze the raw data and prepare an automated summary report that presents key thematic findings, respondent quotes, and actionable steps to take, making the analysis of qualitative data a breeze.

3. Narrative analysis

Narrative analysis is a method used to interpret research participants’ stories —things like testimonials , case studies, focus groups, interviews, and other text or visual data—with tools like Delve and AI-powered ATLAS.ti .

Some formats don’t work well with narrative analysis, including heavily structured interviews and written surveys, which don’t give participants as much opportunity to tell their stories in their own words.

#Benefits and challenges of narrative analysis

How narrative analysis can help your team

Narrative analysis provides product teams with valuable insight into the complexity of customers’ lives, feelings, and behaviors.

In a marketing research context, narrative analysis involves capturing and reviewing customer stories—on social media, for example—to get in-depth insight into their lives, priorities, and challenges. 

This might look like analyzing daily content shared by your audiences’ favorite influencers on Instagram, or analyzing customer reviews on sites like G2 or Capterra to gain a deep understanding of individual customer experiences. The results of this analysis also contribute to developing corresponding customer personas .

💡 Pro tip: conducting user interviews is an excellent way to collect data for narrative analysis. Though interviews can be time-intensive, there are tools out there that streamline the workload. 

Hotjar Engage automates the entire process, from recruiting to scheduling to generating the all-important interview transcripts you’ll need for the analysis phase of your research project.

4. Grounded theory analysis

Grounded theory analysis is a method of conducting qualitative research to develop theories by examining real-world data. This technique involves the creation of hypotheses and theories through qualitative data collection and evaluation, and can be performed with qualitative data analysis software tools like MAXQDA and NVivo .

Unlike other qualitative data analysis techniques, this method is inductive rather than deductive: it develops theories from data, not the other way around.

#The benefits and challenges of grounded theory analysis

How grounded theory analysis can help your team

Grounded theory analysis is used by software engineers, product marketers, managers, and other specialists who deal with data sets to make informed business decisions. 

For example, product marketing teams may turn to customer surveys to understand the reasons behind high churn rates , then use grounded theory to analyze responses and develop hypotheses about why users churn, and how you can get them to stay. 

Grounded theory can also be helpful in the talent management process. For example, HR representatives may use it to develop theories about low employee engagement, and come up with solutions based on their research findings.

5. Discourse analysis

Discourse analysis is the act of researching the underlying meaning of qualitative data. It involves the observation of texts, audio, and videos to study the relationships between information and its social context.

In contrast to content analysis, this method focuses on the contextual meaning of language: discourse analysis sheds light on what audiences think of a topic, and why they feel the way they do about it.

#Benefits and challenges of discourse analysis

How discourse analysis can help your team

In a business context, this method is primarily used by marketing teams. Discourse analysis helps marketers understand the norms and ideas in their market , and reveals why they play such a significant role for their customers. 

Once the origins of trends are uncovered, it’s easier to develop a company mission, create a unique tone of voice, and craft effective marketing messages.

Which qualitative data analysis method should you choose?

While the five qualitative data analysis methods we list above are all aimed at processing data and answering research questions, these techniques differ in their intent and the approaches applied.  

Choosing the right analysis method for your team isn't a matter of preference—selecting a method that fits is only possible once you define your research goals and have a clear intention. When you know what you need (and why you need it), you can identify an analysis method that aligns with your research objectives.

Gather qualitative data with Hotjar

Use Hotjar’s product experience insights in your qualitative research. Collect feedback, uncover behavior trends, and understand the ‘why’ behind user actions.

FAQs about qualitative data analysis methods

What is the qualitative data analysis approach.

The qualitative data analysis approach refers to the process of systematizing descriptive data collected through interviews, focus groups, surveys, and observations and then interpreting it. The methodology aims to identify patterns and themes behind textual data, and other unquantifiable data, as opposed to numerical data.

What are qualitative data analysis methods?

Five popular qualitative data analysis methods are:

What is the process of qualitative data analysis?

The process of qualitative data analysis includes six steps:

Define your research question

Prepare the data

Choose the method of qualitative analysis

Code the data

Identify themes, patterns, and relationships

Make hypotheses and act

Qualitative data analysis guide

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How to Use Intersectional Analysis in Qualitative Research

  • By: Safaa Charafi Edited by: Anna CohenMiller
  • Product: Sage Research Methods: Diversifying and Decolonizing Research
  • Publisher: SAGE Publications Ltd
  • Publication year: 2024
  • Online pub date: March 21, 2024
  • Discipline: Sociology , Education , Psychology , Health , Anthropology , Social Policy and Public Policy , Social Work , Political Science and International Relations , Geography , Criminology and Criminal Justice , Nursing , Business and Management , Communication and Media Studies , Counseling and Psychotherapy , History , Economics , Marketing , Science , Technology , Engineering , Mathematics , Computer Science , Medicine
  • Methods: Sensitive topics , Qualitative data analysis , Intersectionality
  • DOI: https:// doi. org/10.4135/9781529689327
  • Keywords: ethical considerations , identity , intersectionality , privilege , social identity , social inequality , social justice , social power Show all Show less
  • Academic Level: Advanced Undergraduate Online ISBN: 9781529689327 More information Less information

This how-to guide explores the application of intersectional analysis in qualitative research, addressing the methodologic and practical challenges involved. Intersectionality, a framework examining social identities’ interconnectedness, has garnered attention in social sciences research. However, effectively incorporating it into qualitative research is complex. This guide provides researchers with a comprehensive understanding of intersectionality and offers insights on how to employ intersectional analysis in their research, focusing on a broader perspective beyond conventional data analysis. First, it introduces the concept and its relevance in diverse research settings. It emphasizes the need to consider intersecting social identities and their impact on individuals’ experiences. Real-world examples are used to illustrate the significance of intersectional analysis in uncovering power dynamics and exploring complex social phenomena. Intersectional analysis is not solely about data analysis but also serves as a broader research lens or framework. To clarify this distinction, this guide provides contextual information to differentiate intersectional analysis from conventional data-analysis methods. Then a step-by-step approach is outlined, covering important considerations in research design, including research questions, sampling strategies, and data-collection methods. This guide also explores data-analysis techniques, highlighting the importance of identifying intersecting identities within the data and analyzing their interplay. Other considerations are addressed, including reflexivity, confidentiality, and participant empowerment. Researchers are encouraged to engage in critical self-reflection to navigate their own positionality and biases. This how-to guide empowers them to effectively incorporate intersectional analysis into their research, which enhances the depth and richness of research findings and promotes more nuanced understandings of society.

Learning Outcomes

By the end of this guide, readers should be able to

  • understand the concept of intersectionality and its relevance in diverse, intersectional, and decolonial research settings.
  • apply intersectional analysis as a methodologic approach in qualitative research to explore the interconnectedness of social identities and power dynamics.
  • implement a step-by-step approach to incorporating intersectional analysis in research design, including formulating research questions, selecting appropriate sampling strategies, and employing effective data-collection methods.
  • analyze qualitative data through an intersectional lens, identifying intersecting social identities and examining their interplay within the data.
  • navigate practical and ethical considerations when conducting intersectional analysis, including reflexivity, confidentiality, and participant empowerment.

Introduction

Qualitative research plays a foundational role in understanding complex social phenomena and exploring individuals’ lived experiences. However, traditional qualitative methodologies often overlook the intricate interplay of intersecting social identities and power dynamics that shape these experiences. To address this limitation, intersectional analysis has emerged as a crucial framework that acknowledges and examines the interconnections among social identities and systems of power. This guide aims to provide students with a comprehensive understanding of effectively incorporating intersectional analysis into qualitative research.

The primary objective is to explore the methodologic and practical challenges associated with integrating intersectional analysis into research and equip students with the necessary knowledge and skills to apply it in their own work. To ensure the relevance and applicability of intersectional analysis across diverse fields, including sociology, real-life examples will be used to exemplify its use and shed light on critical societal aspects within real-world contexts.

The following sections delve into the key concepts and steps used for an intersectional analysis within qualitative research. This guide explores the significance of intersectionality within diverse, intersectional, and decolonial research settings and provides practical guidance on research design, data collection, data analysis, and ethical considerations related to the application of intersectional analysis.

By equipping students with the necessary tools and knowledge, this guide aims to empower them to conduct research that is more inclusive, nuanced, and attuned to the intricate complexities of intersecting social identities and power dynamics. Ultimately, students will be able to effectively integrate intersectional analysis into their research, enabling a more comprehensive and nuanced understanding of social phenomena.

What Is Intersectionality in the First Place?

Defining intersectionality.

To begin, it is essential to understand the concept of intersectionality and its relevance to qualitative research. The notion of intersectionality has a dual nature, originating from its political origins deeply rooted in the history of Black feminism and its subsequent academic conceptualization by Crenshaw ( 1989 ), which prompted a significant scholarly discourse. It emphasizes the interrelatedness of social identities and acknowledges that individuals simultaneously embody multiple interconnected categories, including race, gender, sexuality, class, ethnicity, ability, and age ( Crenshaw, 1991 ; Hankivsky, 2014 ). The term was coined to address the limitations of understanding discrimination solely through a single-axis framework. Crenshaw highlighted that the experiences of individuals facing intersecting oppressions, such as race and gender, cannot be fully grasped by analyzing each factor independently. Instead of considering these categories as discrete and mutually exclusive, intersectionality recognizes their overlapping and mutually constitutive nature, revealing how they intersect and interact to shape individuals’ experiences and perpetuate social inequalities ( Collins, 2015 ).

By examining the interplay of multiple social categories, intersectionality provides a more comprehensive understanding of the complexities of individuals’ lives and the systems of power that influence them ( Rice, Harrison, & Friedman, 2019 ). It moves beyond simplistic analyses that isolate single dimensions of identity, enabling a more nuanced examination of how social hierarchies and forms of discrimination intersect and compound each other. Far from being just an exercise in semantics—despite taking the experiences of historically oppressed or marginalized communities as its vantage point—it calls for the need to consider various dimensions of privilege and disadvantage that intersect within individuals’ lives, recognizing that privilege and oppression operate simultaneously. In other words, individuals can experience both privilege and marginalization based on different social categories ( Shields, 2008 ). For example, a White woman may experience gender-based discrimination but also benefit from racial privilege compared with women of color. A poor lesbian Black woman will be discriminated against due to her sexual orientation, social class, and race—making her experience different from that of a rich Black woman or man.

As such, Crenshaw’s foundational work emphasized the experiences of Black women within legal frameworks and highlighted how their intersectional identities rendered them invisible within dominant narratives of discrimination ( Crenshaw, 1989 ). Shortly after, by emphasizing the interconnectedness of systems of power and the importance of centering marginalized voices, scholars such as Patricia Hill Collins, with her “Matrix of Domination,” expanded the concept and led to its development ( Collins, 1998 , p. 543). Her contribution extends the foundational concept by revealing the complex web of privilege and oppression individuals navigate across various facets of their identities. Collins’ framework offers a nuanced perspective, going beyond singular dimensions of discrimination to reveal the intricate and dynamic nature of power structures and providing a comprehensive lens to analyze the multidimensional manifestations of privilege and oppression within society.

Since then, the concept has evolved, with scholars applying it to various contexts to uncover the complex ways in which multiple systems of oppression intersect. It has been employed to examine issues related to race, gender, sexuality, disability, and class, revealing the intertwined nature of these systems ( Hancock, 2016 ). Its interdisciplinary nature also encouraged dialogue across academic disciplines and shaped social justice movements, contributing to a broader understanding of social inequalities.

Yet, while this concept has been a valuable analytical tool, it is not exempt from criticism. Some scholars argue that the concept risks essentializing identities, reducing individuals to fixed categories and undermining their agency ( Collins, 2015 ), others contend that it has been coopted and depoliticized, losing sight of its radical roots and becoming a diluted concept ( Buchanan & Wiklund, 2021 ; Dhamoon, 2011 ), whereas others argue that operationalizing it in practice presents challenges, hindering its potential to create meaningful change in society ( Banks, 2018 ).

Despite its limitations, the concept played a significant role in advancing social justice agendas. By highlighting the interconnectedness of systems of oppression, it contributed to more inclusive approaches in policymaking, activism, and social research. Ultimately, it calls for a comprehensive understanding of the complex dynamics of power and privilege, urging scholars and practitioners to consider the intersections of social identities in their analyses and interventions.

Relevance of Intersectionality in Research

The relevance of intersectionality in research is significant because it provides a framework for understanding and analyzing the complex and intersecting factors that contribute to social inequalities and oppression ( Grzanka et al., 2020 ). One of intersectionality’s greatest strengths is its emphasis on centering marginalized individuals’ voices, particularly those who are often overlooked or historically oppressed within existing research frameworks ( Watson-Singleton, Lewis, & Dworkin, 2023 a).

The category of oppression encompasses the integration of inequality, power, and social justice, thereby encompassing the examination of power dynamics and social inequalities. In research, the exploration of complexity and context is achieved through comparative analysis and the identification of the inherent instability of categories, or the process of deconstruction ( Misra, Curington, & Green, 2021 ). As Mary Romero ( 2023 ) posits, “the category ‘relational’ goes beyond recognizing social identities to acknowledge that one’s subordination is related to another’s privilege” (p. 3). Intersectionality’s core focus is historically oppressed or marginalized identities, yet, despite this, it does not presume that all interlocking identities are equally disadvantaged. By incorporating intersectionality into qualitative research, researchers can uncover the nuances and complexities of individuals’ lived experiences and the social structures that contribute to their marginalization or privilege ( Harris, 2016 ). This approach allows for a more nuanced and comprehensive understanding of social phenomena and can contribute to the development of more inclusive and equitable research practices ( Love, Booysen, & Essed, 2018 ).

However, it is important to note that incorporating intersectionality into qualitative research is not without challenges. Researchers must navigate the complexities of multiple intersecting identities and power dynamics, ensuring that their research is sensitive to the experiences and perspectives of marginalized individuals ( Logie et al., 2022 ). They also must critically reflect on their own positionality and biases, recognizing the potential for their own identities and social locations to influence the research process ( Maxwell et al., 2016 ). Additionally, researchers must consider ethical considerations and the potential for harm when exploring sensitive topics related to intersectionality, as I will dive into later.

Section Summary

  • Intersectionality is a theoretical framework that recognizes the interconnectedness of social identities.
  • It was introduced by Kimberlé Crenshaw in 1989 to address the limitations of single-axis approaches to social justice.
  • It allows researchers to understand how different social identities intersect and shape individuals’ experiences and social structures.
  • It challenges essentialist and simplified understandings of identity and power.

Intersectional Analysis in Qualitative Research

Intersectionality recognizes that individuals are characterized by multiple social categories that are interconnected and intertwined and that these categories are embedded with dimensions of inequality or power ( Else-Quest & Hyde, 2016 ). It also acknowledges that these categories are fluid and dynamic and that they are both properties of the individual and characteristics of the social context ( Davis, 2008 a).

Selecting Research Questions

Crafting intersectional research questions necessitates a critical and contextually sensitive approach informed by theoretical frameworks. Researchers should explicitly consider multiple dimensions of identity and power: this means going beyond studying isolated single social categories and instead examining how these categories intersect and interact with each other to shape individuals’ experiences of privilege and oppression. For example, instead of asking how gender or race alone influences participants’ experiences, researchers should aim to uncover how the intersection of the two, along with other relevant social categories, shapes participants’ experiences ( Atewologun, Sealy, & Vinnicombe, 2016 ; Banks, 2018 ). To do so effectively, researchers should apply an intersectionality framework at each stage of the research ( Grabe, 2020 ). This includes generating hypotheses, sampling, operationalization, analysis, and interpretation of findings. Moreover, engaging in a reflexive and iterative process of dialogue with diverse stakeholders, including individuals with lived experiences, can further enhance the development of research questions that are sensitive, responsive, and socially relevant.

However, it is important to note that this exercise can be challenging. Available methodologic tools and traditional research designs may impede a comprehensive understanding of intersectionality and hinder its meaningful incorporation ( Shields, 2008 ). For example, quantitative methods often focus on additive processes and independent factors, which may not fully capture the interdependent nature of intersecting social categories ( McCormick-Huhn et al., 2019 ). As a result, intersectional approaches have been used predominantly in qualitative research, whereas quantitative research often uses components of the approach without explicitly framing them as intersectional ( Else-Quest & Hyde, 2016 ).

Sampling Strategies

When conducting intersectional research analysis, there are various sampling strategies to include diverse perspectives and experiences. One approach is purposeful sampling , also known as selective or criterion sampling , where participants are intentionally selected based on specific characteristics or experiences related to intersecting identities ( Jackson, Mohr, & Kindahl, 2021 ; Robinson, 2014 ). This method can be used to intentionally select participants who represent different intersections of social identities relevant to the research topic. For example, if the research focuses on the experiences of women of color in leadership positions, the sampling strategy should aim to include participants who identify as women, belong to racial or ethnic minority groups, and hold leadership positions.

Another similar approach is stratified purposeful sampling , which involves the selection of participants from diverse social strata based on intersecting identities crucial to the research inquiry. This approach aims to critically capture a spectrum of individuals representing various dimensions of privilege and oppression, such as race, gender, class, and other pertinent categories. By intentionally curating a sample that reflects these intersecting identities, the method enables a more nuanced exploration of how power dynamics operate at the crossroads of multiple social identities, fostering a critical understanding of the complexities shaping individuals’ experiences within the research framework ( Bauer et al., 2021 ). In essence, purposeful sampling concentrates on selecting participants based on their relevance to the research, whereas stratified purposeful sampling specifically targets diverse categories or strata, particularly in studies emphasizing intersectional analysis. While intersectional approaches traditionally have been associated with qualitative methods, quantitative researchers can incorporate an intersectional approach by understanding key features that define quantitative intersectionality analyses and improving reporting practices ( Else-Quest & Hyde, 2016 ).

Researchers have proposed various methodologic approaches and frameworks for conducting intersectional research, including the use of stratification and latent class analysis to derive classes of intersectional social status. Other approaches include using regression with interactions, multilevel analysis, and decision trees to examine the interaction among social positions and their effects on health outcomes ( Hancock, 2007 ; Zhang, Chang, & Du, 2021 a). These approaches go beyond considering single indicators and help capture the complex ways in which intersecting identities influence health outcomes. Ideally, researchers should approach the design of intersectional quantitative research by treating complex differences and inequalities between groups as assumptions or hypotheses. This ensures that the research design accounts for the multifaceted nature of social inequality and captures the nuances of intersecting identities. Despite this, there are certain limitations associated with using multiplicative approaches in intersectional research. One major limitation pertains to the interpretation of variable interactions alongside main effects within regression models. To prevent statistical misspecification, it is necessary to include the variables separately and jointly as an interaction term, as emphasized in studies conducted by Bailey et al. ( 2019 ) and Bowleg ( 2012 ).

Moreover, maintaining a diverse and representative sample is crucial ( Bauer et al., 2021 ), yet researchers must be mindful of avoiding tokenism or reductionism while achieving such a sample. By employing latent variable and clustering methods, they can further engage with intersectionality by providing definitions and citing foundational sources ( Bauer et al., 2021 ). In this way, they enhance the analysis of intersectional dynamics and provide a deeper understanding of the complex relationships between intersecting identities.

Data-Collection Methods

Qualitative methods such as in-depth interviews, focus groups, participant observation, ethnography, photo voice, and participatory action research provide rich data on intersectional dynamics:

  • The use of in-depth interviews as a prevalent method for gathering intersectional data involves conducting individualized sessions where participants share detailed narratives about their experiences. These interviews, characterized by open-ended conversations, allow researchers to delve into the intricate navigation and negotiation of intersecting identities by individuals, unveiling the challenges they encounter within systems of oppression ( Robinson, 2014 ). Further academic exploration could encompass specific interview techniques, such as empathetic interviewing or employing various question types such as role playing and grand or mini tours, which could encourage and elicit intersectional dynamics during the interview process. These methodologies have shown promise in facilitating a more profound exploration of the multifaceted dimensions of participants’ experiences within the intersectional framework.
  • Focus groups are another method for collecting intersectional data. They involve bringing together a group of individuals with similar intersecting identities to discuss their experiences and perspectives. Focus groups provide a space for participants to share their stories, engage in dialogue, and explore commonalities and differences in their experiences, which allow them to capture the collective experiences of a group and identify shared themes and patterns.
  • Participant observation involves researchers immersing themselves in the natural settings of participants to observe and document their behaviors, interactions, and experiences. This method allows researchers to gain a deep understanding of the social context in which individuals with intersecting identities navigate their lives. By actively participating in the lives of participants, researchers can gain insights into the daily challenges, interactions, and dynamics that shape their experiences ( Bonu, 2022 ).
  • Ethnography involves the systematic study of a particular culture or social group ( Small, 2009 ). It allows researchers to immerse themselves in the community or group being studied, gaining an in-depth understanding of their experiences, practices, and beliefs. Ethnographic research is particularly useful for exploring the intersectional experiences of marginalized communities and understanding how intersecting identities shape their lives ( Collins, 2023 ).
  • Art-based methods offer a diverse range of approaches within participatory research, facilitating creative expression and narrative sharing among participants. One specific technique within this spectrum is participatory photography, often referred to as PhotoVoice , which amalgamates photography and storytelling to empower individuals to convey their experiences and viewpoints ( Miller & Kurth, 2022 ). Participants are given cameras and are encouraged to use them to document visual representations of their daily experiences. They then hold group conversations about the images and the stories behind them. This technique provides participants with a one-of-a-kind platform to graphically express their intersecting identities and experiences, providing for a compelling and distinct capture of their narratives.
  • Participatory action research (PAR) is an approach that emphasizes collaboration between researchers and the participants being studied. When used in intersectional research, researchers actively involve participants from marginalized communities in the process, ensuring that their voices and perspectives are central to the study ( Pittaway, Bartolomei, & Hugman, 2010 ). It challenges traditional power dynamics by involving participants as co-researchers and agents of change ( Fine & Torre, 2019 ) and as such uses methods such as community-based participatory research, narrative inquiry, mapping, and PhotoVoice ( Moffitt, Juang, & Syed, 2020 ). One of its benefits is to generate more relevant, meaningful, and applicable knowledge. By involving participants in the research process, PAR ensures that the research questions, methods, and outcomes are aligned with their needs and priorities ( Bennett, 2020 ). It also promotes community capacity building and empowerment because it enables them to develop research skills, critical-thinking abilities, and a sense of ownership over the research process ( Rogers & Kelly, 2011 ). However, it also has limitations. These include the time and resources required to establish and maintain collaborative partnerships with communities, potential power imbalances and conflicts, and the need to navigate ethical considerations and ensure the protection of participants’ rights and well-being ( Brabeck et al., 2015 ). As Fine et al. ( 2021 ) noted, the tenet of “no research on us without us” is valid yet raises some questions: “Who holds the vision? With whom and for whom is the project designed? . . . Coresearchers: Who constitutes the research team? . . . Recruiting an inclusive sample: Who is being interviewed, surveyed, engaged in the inquiry? . . . Speaking to/with varied audiences” (p. 348). Additionally, its participatory nature may limit the generalizability of findings because the focus is often on specific contexts and communities ( Bailey et al., 2019 ).

To illustrate the application of intersectional analysis in qualitative research, consider a study on the experiences of Muslim women with disabilities in accessing healthcare services. The research design may involve conducting semistructured interviews with Muslim women who identify as having disabilities. The interviews would explore the intersections of their religious identity, gender, and disability and how these intersections shape their experiences with healthcare. The data analysis would involve identifying themes related to the barriers and facilitators these women encounter in accessing healthcare services, considering the intersections of their social identities. The findings could contribute to a better understanding of the unique challenges faced by Muslim women with disabilities and inform the development of more inclusive and culturally sensitive healthcare practices.

In the realm of education, intersectional analysis has been instrumental in examining how the intersectionality of social identities such as race, class, and gender influences educational opportunities and outcomes within specific educational institutions or communities. Similarly, in the context of health, intersectional analysis has provided valuable insights into how intersecting social identities contribute to disparities in health outcomes, access to healthcare services, and experiences of healthcare discrimination among marginalized populations ( Bowleg, 2012 ; Rogers & Kelly, 2011 ).

  • 1. Intersectional analysis in qualitative research acknowledges the complex interplay of multiple dimensions of identity and power, going beyond studying single social categories in isolation.
  • 2. Incorporating intersectionality in research design involves selecting an appropriate theoretical lens and methodologic framework, considering the timing and approach to incorporating intersectionality.
  • 3. Sampling strategies should ensure the inclusion of diverse perspectives and experiences, using techniques such as purposive sampling or incorporating intersectionality into quantitative research.
  • 4. Data-collection methods such as in-depth interviews, focus groups, participant observation, ethnography, PhotoVoice, and PAR are valuable for capturing the complex experiences of individuals with intersecting identities.

Ethical Considerations

Reflexivity.

Reflexivity in intersectional research significantly extends beyond ethical considerations not only because it crucially demands that researchers critically reflect on their positionality and biases but also because it fosters relational accountability and bolsters the trustworthiness of the collected data ( Adams, 2021 ; Palaganas et al., 2017 ). Guillemin and Gillam’s ( 2004 ) exploration distinguishes between procedural ethics and “ethics in practice” within qualitative research, shedding light on the impact of reflexivity on research conduct. This self-reflective approach aids in navigating potential power imbalances inherent in studying intersecting identities and marginalized communities.

To adopt reflexive practices in research, researchers could consider implementing strategies such as regular journaling or maintaining reflective logs to document their evolving thoughts, personal biases, and the influence of their social location on the research process. Additionally, peer debriefing or forming reflexive research groups offers a platform for researchers to discuss their reflections and receive constructive feedback, further enhancing the depth and accuracy of the research outcomes ( Rodriguez & Ridgway, 2023 ). Engaging in such reflexive strategies not only encourages a more transparent and accountable research practice but also nurtures a deeper comprehension of the intricate dynamics present within intersectional studies.

Confidentiality

Confidentiality is a vital ethical consideration when conducting intersectional research, particularly when working with sensitive data. Researchers must prioritize participant confidentiality and ensure that information shared by participants remains secure and anonymous ( Brabeck et al., 2015 ). This is especially critical when studying individuals who belong to multiple marginalized groups because they may face heightened risks of discrimination and harm if their identities are revealed (e.g., Muslim LGBTQI+ asylum seekers can be at risk if outed to their Muslim communities). Consent processes should address the potential risks associated with intersecting identities, clearly outlining measures taken to protect participant confidentiality. Researchers should be transparent about data storage, protection, and any limitations to confidentiality that may exist while also respecting participants’ right to privacy and anonymity. Upholding confidentiality not only safeguards participants but also promotes trust, enhancing the validity and integrity of the research.

Participant Empowerment

Researchers should strive to involve participants as active contributors to the research process and as agents of change and ensure that they are not “used” in the research ( Collins, 2015 ). This can be achieved through feedback sessions, collaborative analysis, and coauthorship opportunities, when possible. Engaging participants in these ways acknowledges their agency, promotes ownership over the research, and ensures that their voices are central to the interpretation and dissemination of findings ( Grbich, 2013 ). As such, researchers can challenge traditional power dynamics inherent in research relationships and work toward a more equitable and inclusive research practice. Furthermore, participant empowerment can lead to research outcomes that are more meaningful and applicable to the communities being studied, fostering social change and promoting the well-being of marginalized groups ( Naples, 2013 ; Rice, Harrison, & Friedman, 2019 ).

Avoiding Homogenization and Essentialization

One of the critical considerations in intersectional research is the avoidance of homogenization and essentialization of identities. Homogenization occurs when researchers overlook the diverse experiences and perspectives within a particular social category, treating individuals as a monolithic group ( Hancock, 2016 ). Essentialization, in contrast, means reducing complex and multifaceted identities to simplified stereotypes or fixed characteristics ( McCall, 2005 ). Both homogenization and essentialization can perpetuate stereotypes and reinforce oppressive systems.

To avoid homogenization, researchers must recognize and acknowledge the diversity within each social category they study. Intersectionality highlights the intersecting and unique experiences of individuals with multiple marginalized identities, emphasizing the need to explore the nuances and variations within these groups ( Collins, 2015 ). Researchers also should be mindful of the intersectional complexity of individuals’ lives, accounting for how multiple identities interact and shape their experiences. People’s identities are not monolithic. Essentialization can be avoided by recognizing the fluidity and complexity of social identities. Researchers should be cautious not to reduce individuals to a single dimension of their identity because this overlooks how various social categories intersect and influence their lived experiences ( McCall, 2005 ).

The principle of do no harm is a central ethical consideration in intersectional research. Researchers have a responsibility to minimize any potential harm that may arise from their research process and outcomes, particularly when studying individuals with intersecting marginalized identities. This principle necessitates careful attention to the potential risks and vulnerabilities faced by participants and the broader communities they represent ( Rice, Harrison, & Friedman, 2019 ). For example, Corbin and Morse ( 2003 ) discuss the ethical considerations in unstructured interactive interviews, highlighting the need for researchers to possess interviewing skills and adhere to a rigid code of ethics; they acknowledge that qualitative interviews may cause emotional distress but suggest that this distress is not necessarily greater than what individuals experience in everyday life.

To uphold this principle, researchers should conduct a thorough risk assessment before initiating their research, which involves identifying potential physical, psychological, and social risks participants may encounter because of their involvement in the research ( Grbich, 2013 ). For instance, discussing sensitive topics related to intersecting identities (e.g., migration status, drug use, family trauma, health issues, and abuse) may evoke emotional distress or trigger trauma responses in participants. Researchers should proactively address these risks through informed consent, providing participants with detailed information about the nature of the study, potential risks, and available support resources. Ideally, researchers should create a safe and respectful research environment by asking for informed consent, maintaining open lines of communication with participants, actively listening to their concerns, and providing opportunities for them to withdraw their participation without consequences ( Mackenzie, McDowell, & Pittaway, 2007 ). Researchers also should establish clear boundaries and ethical guidelines for data collection, storage, and dissemination to protect participants’ confidentiality and privacy as well as ensure their well-being and autonomy.

Additionally, researchers should be mindful of potential power imbalances and the potential for the research process to reproduce or exacerbate existing inequalities. Engaging in reflexive practices, as discussed earlier, enables them to critically examine their own positions of power and privilege and take proactive measures to mitigate any negative impacts ( Logie et al., 2022 ). As a matter of fact, they should aim to actively challenge oppressive systems and practices by centering the voices and perspectives of marginalized individuals and communities throughout the research process.

  • Researchers must navigate the complexities of multiple intersecting identities, avoiding essentialization and homogenization.
  • Researchers should prioritize ethical principles, including informed consent, participant confidentiality, and privacy and upholding the do-no-harm principle.
  • Reciprocity and long-term engagement with participants ensure that research outcomes contribute to meaningful change and empowerment.
  • Reflexivity and contextual sensitivity are essential for respecting individual agency and diversity.

This comprehensive how-to guide addresses the methodologic and practical challenges associated with using intersectional analysis in qualitative research. By engaging with it, readers gain valuable insights and practical guidance to enhance their research in terms of inclusivity, rigor, and social relevance.

Intersectional analysis is a critical framework for understanding social phenomena because traditional qualitative methodologies often overlook interconnected social identities and power dynamics, limiting the depth of analysis. Intersectional analysis enables researchers to go beyond single-axis approaches and examine the complex interplay of intersecting social identities such as race, gender, class, sexuality, and disability and how they shape individuals’ experiences.

This guide provides guidance on research design, sampling strategies, and data-collection methods to ensure the inclusion of diverse perspectives and the exploration of intersectional experiences. It also highlights the ethical considerations researchers should uphold while conducting intersectional research, including obtaining informed consent, safeguarding participant confidentiality, and protecting privacy. Moving beyond transactional relationships, researchers are encouraged to foster reciprocity, long-term engagement, collaboration, and active involvement of participants and make sure to engage in reflexivity and contextual sensitivity to respect individual agency and acknowledge the diversity within intersecting identities. Researchers also must critically reflect on their biases, assumptions, and positionality throughout the research process.

In conclusion, embracing an intersectional perspective allows researchers to capture the multidimensionality and contextual nature of individuals’ identities, enabling a more nuanced analysis. By doing so, they can better capture the multidimensionality and contextual nature of individuals’ identities, moving beyond simplistic categorizations and allowing for a more nuanced analysis.

Multiple-Choice Quiz Questions

1. Intersectionality acknowledges that identities and social categories are

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Correct Answer

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2. Qualitative research is primarily focused on

3. Which of the following is an example of an intersectional approach in qualitative research?

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4. When conducting intersectional research, it is important to consider

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5. What is the main goal of intersectionality in qualitative research?

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

  • Qualitative vs. quantitative : Will your data take the form of words or numbers?
  • Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
  • Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

  • For quantitative data, you can use statistical analysis methods to test relationships between variables.
  • For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

  • From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
  • Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

  • During an experiment .
  • Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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 test of independence
  • Statistical power
  • Descriptive statistics
  • Degrees of freedom
  • Pearson correlation
  • Null hypothesis
  • Double-blind study
  • Case-control study
  • Research ethics
  • Data collection
  • Hypothesis testing
  • Structured interviews

Research bias

  • Hawthorne effect
  • Unconscious bias
  • Recall bias
  • Halo effect
  • Self-serving bias
  • Information bias

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

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

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

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
  • If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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PESTLE Analysis

SWOT and Business Analysis Tools

Qualitative Analysis Made Easy: Your Comprehensive Step-by-Step Manual

Mar 18, 2024 by Abdul Momin

You must have heard the term “Qualitative” previously. It means comparing two or more things based on quality instead of quantity. Businesses often use numerical records, reports, and statistics to monitor operations.

Besides quantitative data, qualitative data is equally important for businesses to measure their success. Qualitative data is significant for businesses because it provides a deep analysis and unfolds many mysteries.

For example, imagine a well-established business suddenly starts observing losses. In this case, if the business managers only look at the quantitative data, they will see their business sales dropping drastically. 

By looking at the quantitative data, they will learn that their business sold 25,000 fewer products this month than the previous month. However, they won’t be able to tell the reason for poor sales.

Managers will have to focus on the qualitative data to determine the reason that led to poor sales. The qualitative data may be in surveys, questionnaires, interviews, etc. By analysing the qualitative data, managers can tell why the sales dropped suddenly in the current month.

Hence, qualitative analysis is very significant in problem-solving. Now let’s proceed and discuss qualitative analysis in detail.

What Is Qualitative Analysis?

Qualitative analysis in business refers to examining and interpreting non-numerical data to understand the underlying reasons, motivations, and attitudes of individuals or groups.

This type of analysis can involve reviewing information gathered through interviews, focus groups, observations, surveys, or other qualitative data sources.

Qualitative analysis is often used in business to gain insights into customer needs and preferences . Other than that, businesses also use qualitative data for risk management. For example, qualitative risk analysis estimates the risk in projects and other business activities.

For example, a company may conduct a focus group to gather qualitative data on customers’ feelings about a particular product or service. The data collected in this way can then be analyzed to identify patterns and themes that can inform business decisions and strategies .

Qualitative analysis is a complementary approach to quantitative analysis involving numerical data and statistical methods. Together, these two approaches can provide a more complete understanding of a business problem or opportunity and help inform decision-making more comprehensively.

To sum it up, qualitative analysis can provide valuable insights that quantitative data alone cannot provide. By collecting and analyzing non-numerical data, businesses can better understand their customers, employees, and markets and make informed decisions that drive success.

Now that we have discussed the purpose of qualitative analysis in detail, let’s proceed and look at how to conduct the qualitative analysis.

How To Do Qualitative Analysis?

Businesses conduct qualitative analysis using various methods, typically gathering and analyzing non-numerical data. Following are some common steps that businesses follow to conduct qualitative analysis.

Define The Research Question

Defining the research question is a crucial first step in qualitative analysis. It helps businesses focus their efforts and ensure that the data they collect is relevant and useful for answering the research question.

To define the research question, businesses need to consider the research purpose and what they hope to achieve by conducting it. This involves identifying a problem that needs to be solved.

For example, a company may want to explore why customers are not buying a particular product. To define the research question, the company needs to consider what information they need to answer it. This might involve asking questions such as:

  • What are the factors that influence customers’ purchasing decisions?
  • What are customers’ attitudes towards the product?
  • What do customers perceive to be the benefits and drawbacks of the product?

Once the research question is defined, businesses can design their data collection methods, select appropriate participants, and analyze the data collected to generate insights.

Selecting A Sample

An important aspect of qualitative research is the selection of participants. Qualitative research often involves selecting a small sample of participants representative of the studied population.

The goal of selecting a representative sample is to ensure that the insights gained from the study can be generalized to the larger population. Therefore, participants are selected based on various criteria, such as demographic characteristics, geographic location, or other relevant factors.

For example, suppose the study is about the impact of marketing campaigns on children. In that case, participants may be selected based on their age group, exposure to marketing campaigns, etc.

Businesses may use different sampling techniques in selecting participants, such as purposive, snowball, or convenience.

Choose A Method For Data Collection

Collecting qualitative data can be done using various methods, and the choice of method depends on the research question and the target audience.

For example, businesses can collect qualitative data from case studies since they analyze a specific person, group, or organization in detail. Moreover, they are useful for exploring complex issues in-depth and understanding specific situations deeply.

Besides that, if the data needed is specific, the business must obtain primary data through other means, such as interviews, surveys, and observations.

Since interviews are one-on-one conversations between the researcher and the participant, businesses can directly put their query to their customers and get specific answers.

Other than that, if the data is to be obtained from a large group of people, then the business can use surveys to get the response from the targeted group.

Businesses can also simply observe the targeted group and record its behaviors. Each method has pros and cons . Businesses must choose the most appropriate method to ensure they get the most valuable insights.

Coding The Data

Coding is a critical step in qualitative analysis. Businesses systematically identify, categorize, and label key themes, concepts, and patterns within the data. This process helps to organize the data and make sense of the complex and often subjective information collected in the study.

Researchers use software programs or tools to assist with coding and data management. The coding process is often iterative, where researchers review the data repeatedly to identify additional themes or refine the existing ones.

This helps ensure that the analysis is comprehensive and accurate and that all relevant information has been captured. In addition, the coding process is often iterative, and the researcher must review the data repeatedly to identify additional themes or refine the existing ones.

Data analysis

After collecting the data through coding, it is essential to analyze it to make sense of it and draw meaningful conclusions.

One way to analyze the data is to identify patterns, trends, and themes. These patterns can help businesses understand how the data is distributed, and the trends help businesses determine if there are any changes in the data over time.

Themes refer to recurring ideas or concepts that emerge from the data. Businesses use software to help them identify and visualise patterns.

Draw Conclusions

Once the data has been collected, coded, and analyzed, the next step is to draw conclusions and report the findings. Drawing conclusions involves making sense of the patterns, themes, and insights that emerge from the analysis.

Researchers use their expertise and judgment to interpret the data and draw conclusions based on the research question and the available evidence.

For businesses, this might involve identifying key customer needs or preferences, understanding how their products or services are perceived in the market, or gaining insights into the effectiveness of their marketing campaigns.

Reporting the findings involves communicating the analysis’s conclusions clearly and concisely. The goal is to make the insights accessible and actionable for stakeholders, such as executives, managers, or frontline staff.

Qualitative Data Analysis Methods

In the previous sections, we discussed the significance of qualitative data analysis and how to carry out qualitative data analysis. This section will discuss the qualitative data analysis techniques that are commonly used by businesses for data analysis .

Interviews involve asking people questions in a structured or semi-structured way to gather information about their experiences, opinions, beliefs, or behaviors. Conduction interviews provide primary data to businesses. Interviews are preferred if the sample size is small. 

One of the main advantages of conducting interviews is that the data obtained is highly specific. Therefore, qualitative data analysis involves transcribing the interviews, coding the data into categories, and analyzing the themes and patterns that emerge from the data.

Focus Groups

Focus groups involve bringing a small group together to discuss a particular topic or issue. Businesses use focus groups as a qualitative research method to gain insights into their target market or customers’ opinions, attitudes, and behaviors.

Focus groups allow businesses to gather rich, in-depth data about their products, services, or brand perceptions in a structured way. This method helps businesses understand what features or attributes their customers seek in a new product or service.

Businesses use focus groups to test prototypes or mock-ups of new products, get feedback on packaging or branding, and gather ideas for new product concepts.

Businesses use surveys as a qualitative research method to gather data from their customers or target market. Surveys are used to gather qualitative data by asking open-ended questions that allow respondents to express their opinions and experiences in their own words.

For example, businesses can use surveys to allow customers to express their opinions and experiences in their own words, providing rich, qualitative data to help the business identify areas for improvement.

Ethnography

Businesses can use ethnography as a qualitative research method to gain a deeper understanding of the behavior and culture of their target market or customers.

Ethnography involves conducting participant observation and field research, which allows researchers to immerse themselves in the culture they are studying and observe behavior in context.

How is Qualitative Data Analysis Different than Quantitative?

Both qualitative and quantitative analysis provides insight into businesses. They are both equally helpful and complementary to each other. However, there are some key differences between these two data analysis techniques .

Numerical Vs. Non-Numerical Data

Quantitative analysis concerns measurement, statistical analysis, and numerical modeling. It involves collecting data through standardized instruments such as surveys, experiments, or statistical databases.

Qualitative analysis, on the other hand, deals with non-numerical data, such as text, images, or sound recordings. It involves collecting data through open-ended instruments, such as interviews, focus groups, or observation.

Generalisation Vs. Detailed Description

Quantitative analysis involves using statistical and numerical methods to analyze large data sets. This approach allows researchers to make generalizations about a population based on the characteristics of a sample drawn from that population.

On the other hand, qualitative analysis involves analysing non-numerical data, such as text, images, or sound recordings. This approach allows researchers to provide detailed descriptions of a particular case or context without necessarily making generalizations about a larger population.

Testing Hypotheses Vs. Generating Hypotheses

Quantitative analysis is often used to test hypotheses. In quantitative research, researchers formulate hypotheses or research questions and collect numerical data to test or answer these hypotheses.

However, qualitative analysis is often used to generate new hypotheses. In qualitative research, researchers often start with a broad research question and gain a deeper understanding of complex phenomena, explore new research questions, and generate new ideas and theories.

Qualitative Analysis: Final Word

Businesses use qualitative analysis to investigate their operations. This analysis uses qualitative data, which is non-numerical data, mainly in the form of text, video, audio, etc. Therefore, qualitative analysis is as important and significant for businesses as quantitative analysis.

Businesses conduct qualitative analysis by defining the research question. According to that, they select the targeted audience and choose one of the qualitative analysis techniques. After that, they code and analyze the data, which provides them with results.

Businesses use qualitative techniques such as Interviews, focal groups, surveys, and ethnography for data analysis. Although both qualitative and quantitative analysis are used by businesses, they differ from each other in many ways.

The most obvious difference between the two is the data type. Qualitative analysis deals with non-numerical data, whereas quantitative analysis deals with numerical data.

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

Published on 15.3.2024 in Vol 26 (2024)

This is a member publication of University of Cambridge (Jisc)

Exploring the Types of Social Support Exchanged by Survivors of Pediatric Stroke and Their Families in an Online Peer Support Community: Qualitative Thematic Analysis

Authors of this article:

Author Orcid Image

Original Paper

  • William J A Wright 1 , MB, BChir, MA   ; 
  • Charlotte Howdle 1   ; 
  • Neil S Coulson 2 , PhD   ; 
  • Anna De Simoni 1, 3 , PhD  

1 School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom

2 Medical School, Nottingham City Hospital, University of Nottingham, Nottingham, United Kingdom

3 Centre for Primary Care, Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom

Corresponding Author:

Anna De Simoni, PhD

Centre for Primary Care

Wolfson Institute of Population Health

Queen Mary University of London

58 Turner Street

London, E1 2AB

United Kingdom

Phone: 44 882 2520

Fax:44 882 2520

Email: [email protected]

Background: Pediatric stroke is relatively rare and underresearched, and there is little awareness of its occurrence in wider society. There is a paucity of literature on the effectiveness of interventions to improve rehabilitation and the services available to survivors. Access to online health communities through the internet may be a means of support for patients with pediatric stroke and their families during recovery; however, little research has been done in this area.

Objective: This study aims to identify the types of social support provided by an online peer support group to survivors of pediatric stroke and their families.

Methods: This was a qualitative thematic analysis of posts from a pediatric stroke population on a UK online stroke community active between 2004 and 2011. The population was split into 2 groups based on whether stroke survivors were aged ≤18 years or aged >18 years at the time of posting. The posts were read by 2 authors who used the adapted Social Support Behavior Code to analyze the types of social support exchanged.

Results: A total of 52 participants who experienced a pediatric stroke were identified, who posted a total of 425 messages to the community. About 41 survivors were aged ≤18 years at the time of posting and were written about by others (31/35 were mothers), while 11 were aged >18 years and were writing about themselves. Survivors and their families joined together in discussion threads. Support was offered and received by all participants, regardless of age. Of all 425 posts, 193 (45.4%) contained at least 1 instance of social support. All 5 types of social support were identified: informational, emotional, network, esteem support, and tangible aid. Informational and emotional support were most commonly exchanged. Emotional support was offered more often than informational support among participants aged ≤18 years at the time of posting; this finding was reversed in the group aged >18 years. Network support and esteem support were less commonly exchanged. Notably, the access subcategory of network support was not exchanged with the community. Tangible aid was the least commonly offered type of support. The exchanged social support provided insight into rehabilitation interventions and the unmet needs of pediatric stroke survivors.

Conclusions: We found evidence of engagement of childhood stroke survivors and their families in an online stroke community, with peer support being exchanged between both long- and short-term survivors of pediatric stroke. Engagement of long-term survivors of pediatric stroke through the online community was key, as they were able to offer informational support from lived experience. Further interventional research is needed to assess health and rehabilitation outcomes from engagement with online support groups. Research is also needed to ensure safe, nurturing online communities.

Introduction

More than 400 children are diagnosed with stroke in the United Kingdom each year [ 1 ]. Despite this sizeable number, diagnosis is often a shock to parents because there is little awareness that strokes can affect children [ 2 ]. Recovery from pediatric stroke is a long process, with impairments lasting decades after the event [ 3 ]. Novel deficits may present many years after the stroke itself, and existing issues evolve and become more numerous as the survivor grows, with as many as three-quarters of families experiencing at least 1 unmet need after stroke [ 4 ]. Pediatric stroke is underresearched [ 5 ], and as a result, little is known and available to survivors and their families to support them during recovery [ 2 , 4 , 6 , 7 ]. Finally, during the recovery process, survivors and their families feel lonely and isolated from other members of their family, previous friends, peers, and other survivors [ 3 ]. Peer support groups may be well placed to alleviate these barriers by providing a space where users can share information, advice, and support each other.

A survey of internet access in the United Kingdom conducted in 2020 revealed that the internet is available in the homes of 93% of the population [ 8 ]. This provides an opportunity for individuals to connect online and has led to the formation of online peer-support groups for health care conditions. The advantages of having peer support on an online platform have been outlined by a number of studies [ 9 - 12 ]. These include transcending geographic or temporal boundaries to communication, allowing users to interact regardless of where they live and their time commitments. In addition, posts are written anonymously and asynchronously, allowing participants to speak frankly about their experience without fear of being recognized and, furthermore, spend time composing their posts rather than rushing into a response.

A systematic review found that the roles of online peer support groups for long term conditions consist of a shared social identity, learning from the experiences of others, fostering personal growth, and supporting others [ 13 ]. Emerging quantitative data shows that online social support groups may have a therapeutic benefit for some users [ 14 ], and a systematic review of social networking site interventions for health care conditions found that they had a positive effect on health-related behaviors [ 15 ]. A quantitative study on the use of an online peer support group for women with breast cancer found that the group membership had a moderate effect on reducing depression scores, perceived stress, and cancer-related trauma [ 16 ].

The role of in-person peer support groups for adult stroke survivors has been evaluated. A systematic review found they offered a platform for shared experience, social comparison, vicarious learning, and mutual gain [ 17 ]. Qualitative reviews additionally found that peer support groups for adult stroke were a place where people shared knowledge [ 18 ], felt they belonged [ 18 , 19 ], found a purpose in mentoring each other [ 18 , 19 ], and formed and maintained friendships outside the group setting [ 18 , 19 ]. A study looking at the uses of an online forums for stroke survivors found that users shared a story, requested information and/or support, and provided information and/or support [ 20 ]. Adult stroke survivors and their families received information and support on an individual basis from an online forum with 95% of user intentions being met [ 20 ].

There is a growing literature evaluating the types of peer support offered in a variety of patient groups with chronic conditions using the Social Support Behavior Code (SSBC) framework and the adapted SSBC [ 10 , 21 , 22 ]. The SSBC measures 5 types of social support: informational support, emotional support, esteem support (expressing respect and confidence in others), network support (sharing a feeling of belonging to the group), and tangible assistance (providing or offering help). Studies using the SSBC to look at the types of support offered on online forums found the same broad pattern. Emotional and informational support were most commonly exchanged. Esteem and network support were less common, and tangible assistance was rare [ 9 , 10 , 22 - 24 ]. Online health communities allow larger degree of personal disclosure, possibly due to anonymity when posting [ 10 ].

Exploring the support given by peers in an online stroke community could provide insight about unmet needs with regard to rehabilitation services. Indeed, the “Stroke in Childhood Clinical Guidelines,” published in May 2017, requested research that aimed to identify “rehabilitation interventions in line with the emerging evidence of motor, social, behavioral, and communication sequelae following stroke” [ 5 ]. This study aims to analyze whether online peer support was exchanged among survivors of pediatric stroke and what type of peer support was exchanged. A secondary aim is to examine whether time (number of years) since the pediatric stroke was related to engagement and the type of support offered.

A qualitative analysis using the adapted SSBC framework [ 10 ] on posts by a pediatric stroke population in an online community.

The analysis used posts from the 2004-2011 Talkstroke online forum, a UK-based moderated online forum hosted by the Stroke Association website. The forum was set up as part of the charity website to facilitate communication between stroke survivors and caregivers. In total, the archives included 22,173 posts, written by 2583 unique usernames. A total of 58 participants who had a pediatric stroke were identified from the characterization of individual community users, as reported in a previous study [ 20 ]. We further excluded 2 users when analysis revealed their age at stroke was 18 years or older and 4 users because their age at the time of posting was unknown. A sample of 52 users remained, who produced a total of 425 posts that were collected in an Excel (Microsoft Corporation) spreadsheet. Characteristics of stroke survivors, including demographics, employment, education, stroke type, initial impairments as well as impairments at the time of posting, support needs, and independence, were retrieved from within the posts in a previous study [ 20 ].

Data Analysis

WJAW read through all posts to become familiar with the data and patient narratives. Considering the potentially different perspectives of survivors who wrote as adults on their experience of rehabilitation from pediatric stroke and survivors who were written about by their parents, posts were split into 2 categories: whether the pediatric stroke survivor was older than 18 years or aged 18 years or younger at the time of posting. This assessed whether the time since the stroke was related to the amount and type of social support given. Rather than excluding posts that were not relevant (ie, did not include instances of social support), all posts were taken into account for the analysis to give an idea of how prevalent social support exchange was within the discussion threads. WJAW analyzed all posts, and CH and ADS independently coded a random 10% of posts. Posts were analyzed individually, not in the context of other posts in the forum thread. Deductive thematic analysis was applied, as described by Braun and Clarke [ 25 ]. The adapted SSBC ( Textbox 1 ) was used as a framework to identify the types of social support provided through engagement with the online forums [ 10 ], and results have been reported according to the 5 types of social support: informational, emotional, esteem, network support, and tangible assistance. Coding was discussed with CH and ADS until agreement was reached.

Support types and definitions

  • Advice: provides ideas or suggestions for action.
  • Referral: refers the recipient to other sources of information or help.
  • Situational appraisal: helps reassess or redefine the situation being faced by the recipient.
  • Teaching: offers detailed information, facts, or news.
  • Relationship: conveys the importance of closeness.
  • Physical affection: offers physical contact, such as hugs and kisses.
  • Confidentiality: keeps the recipient’s problem in confidence.
  • Sympathy: sorrow or regret for the situation faced by the recipient.
  • Understanding or empathy: expressions of understanding of the situation or discloses similar experience in a way that conveys understanding.
  • Encouragement: provides the recipient with hope and confidence.
  • Prayer: offers prayer for the recipient.
  • Compliment: says positive things about the recipient.
  • Validation: provides agreement with the views of the recipient.
  • Relief of blame: alleviates any feelings of guilt the recipient has about the situation.
  • Access: provides the recipient with access to new people.
  • Presence: offers to be there.
  • Companions: reminds the recipient that there are others who share similar experiences and are available.
  • Loan: lend money to the recipient.
  • Direct task: offers to do a direct task.
  • Indirect task: offers to take over a task from the recipient while they are stressed.
  • Active participation: offers to join the recipient in an activity.
  • Willingness: offers or expressions of willingness to help.

Patient and Public Involvement

A survivor of pediatric stroke aged 22 years (aged 0 years at the time of stroke and not a member of an online stroke community) was contacted after the initial analysis was completed and read the first draft of the results, providing insightful comments that helped to finalize the analysis and informed the discussion.

Ethical Considerations

The Stroke Association provided access to the archived forums and gave permission for the data to be used for this research purpose. The data from Talkstroke were stored and accessed through the University of Cambridge Clinical School Secure Data Hosting Service. Users of the forums had previously agreed that their data would become public upon registration within the forums, and there is consensus that internet data that are freely and publicly accessible can be used for research without needing ethics committee approval [ 20 ]. In order to protect the identity and intellectual property of forums participants, direct quotes have not been used, despite this being normal practice in qualitative research. Summative descriptions of quotes will instead be used throughout the paper. De Simoni et al [ 20 ] report a detailed description of the ethics linked to the research in the Talkstroke archives.

Participants’ Characteristics

A total of 41 survivors were aged ≤18 years at the time of participation, contributing a total of 273 posts; 11 survivors were aged >18 years and contributed 152 posts. Most survivors in the group aged ≤18 years took part in the community less than 1 year after their stroke, with the majority of content contributed indirectly through third-party users (31/35 were mothers, 89%). Content from the group aged >18 years was reported firsthand by adult survivors of pediatric stroke ( Table 1 ). The time between stroke and participation in the online community for both groups ranged from 2 weeks to 46 years. Further information about our population can be found in Howdle et al [ 3 ].

Types of Support Requested by Participants

Posts written by participants at the start of a discussion thread followed a similar structure: an introduction followed by an account of their stroke and recovery up until the time of writing. Participants would then request help or ask for general or specific advice. Within the group aged ≤18 years, nearly half of the posts involved sharing their experience of pediatric stroke.

One parent wrote that they were not sure where to start and explained that their child was born with a congenital heart defect. They described the location of (stroke) damage in the child’s brain. They explained that the progress the child has made has amazed everyone, describing the regaining of functions. The user then requested general help and advice as it was all new (p1, stroke aged 0, 0 years poststroke, mother).

Some posts in the group aged >18 years described their experience with pediatric stroke. In general, rather than sharing their whole story, they shared parts that were relevant to the topic of discussion within a thread. Their aim when participating typically seemed to be to give hope and motivation to other survivors.

A survivor aged older than 18 years wrote, first, to say they felt sorry for the original poster’s loss. They then explained their own story about how their childhood stroke was missed. Since then, they have had many tests in the hope of finding a cause; however, the cause of the stroke was still unknown. They went on to complete their General Certificate of Secondary Education and A-level examinations and were currently studying at university. They finished by writing that strokes do happen in young people and about the need for more societal awareness. They then told the survivor that they too struggled to find anyone to help support their rehabilitation, however, reassuring them they were not alone and to not hesitate to ask any questions (p43, stroke aged 13, 7 years poststroke, survivor).

Reaching out for help occurred in posts by those aged ≤18 years and >18 years. These posts tended to involve asking for advice on dealing with a specific type of symptom.

A survivor aged ≤18 years wrote that her headaches were seriously affecting her everyday life. She followed the doctor’s advice to rest, drink water, and take painkillers, but she found she was resting all the time, and it was affecting her sleep. She apologized for “moaning” and then asked for any ideas (p47, stroke aged 15, 1 year poststroke, survivor).

A user aged >18 years wrote that after 30 years, their eyes were “dizzy.” They then subsequently asked if anyone had medication or other advice to stop this (p15, stroke aged 11, 30 years poststroke).

SSBC Analysis

About half of the posts contained instances of social support. It was common for posts to contain more than one form of support, for example, a user relating their situation to the recipient’s to show understanding and then providing advice based on their experience of a similar situation.

Informational Support

All 4 subcategories of informational support were represented (advice, referral, situation appraisal, and teaching). Comparing the 2 age categories, informational support was proportionally offered more by the group aged >18 years than the group aged ≤18 years. Advice was offered on a range of topics. In the group aged ≤18 years, these included therapy types, disability allowance, travel, and not giving up and carving out time as a caregiver. Survivors aged >18 years also gave advice on therapy types and financial aid, but additionally discussed driving and how to be a good caregiver.

One user aged ≤18 years told another participant to look into claiming disability living allowance for children aged 16 years, saying it could save them 17% of the cost of alterations to their home (p6, stroke aged 0, 14 years poststroke, mother).

One user aged >18 years advised a caregiver to take their child out of their comfort zone and make them realize that there is more to life than sitting in a chair. They advised that this would motivate the survivor and be lots of fun (p52, stroke aged 17, 21 years poststroke, survivor).

In the referral subcategory, participants recommended research groups and organizations to each other for support and to aid financial claims.

One user wrote that one good organization for their child’s one-sided weakness was “Hemihelp” (p3, stroke aged 0, 11 years poststroke, mother).

Situation appraisal was common among participants in the group aged ≤18 years. This occurred to help others understand that pediatric strokes occurred more commonly than widely perceived, that the recovery process was different in children compared to adults, and to normalize the grief that parents were feeling for their “lost child.”

A user wrote not to be discouraged about hearing recovery stories of adult survivors of stroke, and that recovery for a child was different, as the plasticity of the brain is a childhood phenomenon (p12, stroke aged 1, 0 years poststroke, mother).

There were several instances of users teaching each other in both groups. Topics involved included how specific medications worked and their dosage, the meaning of medical tests, appointments, and the different types of strokes the disability support available and how to access it, and how to deal with the side effects of stroke.

One user aged >18 years advised another to start a diary to write appointments and other important things in. The user said that this piece of information was invaluable, as it would keep their mind on the stroke and not worrying about forgetting things (p52, stroke aged 17, 21 years poststroke, survivor).

A user explained what a magnetic resonance angiography scan is, saying that it is basically a magnetic resonance imaging scan, but that then they add a dye to show how well the blood is flowing through veins and arteries (p22, stroke aged 5, 4 years poststroke, mother).

Emotional Support

Emotional support was offered proportionally more by participants aged ≤18 years than >18 years. Support was offered to survivors of stroke in both age categories and to caregivers who wrote on behalf of the survivors. About 5 of the 7 subcategories were seen relationship, physical affection, confidentiality, sympathy, understanding or empathy, and encouragement.

Sympathy was the most displayed subcategory. Messages expressed sorrow for participants’ stroke events, bad news since the stroke, bad experiences, ongoing symptoms, and the struggle to find a cause.

One user wrote that she was sorry to hear about another user’s stroke (p4, stroke aged 0, 0 years poststroke, mother).

Understanding and empathy were displayed in the context of loneliness, fighting the urge to give up, acknowledging similar experiences, symptoms experienced, and difficulty finding travel insurance.

One user wrote they knew what another user meant when saying they felt alone as they had that most of their life. (p36, stroke aged 0, 35 years poststroke, survivor).

A user empathized with another user, writing that they too understood the challenges of dealing with a young child (with stroke) as a single parent (p12, stroke aged 1, 0 years poststroke, mother).

Messages of encouragement were present, often in the form of remarks like “good luck,” “chin up,” or “it gets easier over time.” Most encouragement occurred in the context of helping people start their recovery journey, inspiring people not to give up, or wishing good fortune when attending health care institutions.

A health care worker who previously had a stroke as a child shared her story in the hope it would inspire and encourage people that life does not have to end following a stroke and that with determination you can get on and find other ways of doing things. They finished by stating not to let the stroke win (p39, stroke aged 8, 23 years poststroke, survivor).

The relationship subcategory describes the closeness felt between members of the online community. Users told others they did the best thing by joining the community and expressed joy in finding people who were going through similar experiences.

One user wrote how sorry they were to hear another person’s story but that it was lovely to hear from them as it was difficult to find people in a similar situation. They wanted to be kept up to date about how the other survivors were getting on (p2, stroke aged 0, 2 years poststroke, mother).

Physical affection was displayed through affectionate language and symbols. The most common symbol used was an x to send kisses.

One user wrote that they hoped another’s child was well and that their heart truly went to their family, then signed off with their name (p11, stroke aged 1, 1 year poststroke, mother).

Another user wished someone a happy new year, followed by 3 kisses (p49, stroke aged 17, 2 years poststroke, survivor).

Network Support

Around 2 out of 3 subcategories of network support were identified within the data: presence and companions. The most common incidences of presence, a member offering to be there for another member, were related to 2 users finding themselves in a similar position. This prompted them to stay in touch, especially when somebody was offering advice, to see if it worked. Adult survivors of childhood stroke also offered to keep in touch with younger participants in the long term, to keep answering questions related to their recovery.

A survivor aged >18 years wrote that a user could contact them with any questions and they would do their best to answer them (p49, stroke aged 17, 2 years poststroke, survivor).

A mother of a survivor wrote to another to stay in touch; a few mothers had found each other, and it did help (p12, stroke aged 1, 0 years poststroke, mother).

The community offered a lot in the way of companionship. Users reminded each other that everyone on the site had been through similar experiences and were all there to support each other. Users were often complimentary about others in the community.

One user stated that if another felt down, then they could just talk to those on the site, as everyone was there for each other (p36, stroke aged 0, 35 years poststroke, survivor).

A user prompted another to keep posting because the people on the site were brilliant (p26, stroke aged 9, 0 years poststroke, mother).

Esteem Support

All subcategories of esteem support were present: compliments, validation, and relief of blame.

Compliments were exchanged to thank people for advice and recovery ideas, for sharing their stories, to motivate caregivers, and to congratulate people for joining the community.

One user wrote to another that they admired their confidence and how they wanted to thank them for always being on the site with advice and support (p26, stroke aged 9, 0 years poststroke, mother).

Another user complimented another by saying they thought the rehabilitation measures they had in place were a brilliant idea (p22, stroke aged 5, 4 years poststroke, mother).

Validation was given when survivors had been through similar experiences. Notable themes within validation were missed or delayed diagnoses, poor awareness of pediatric stroke, isolation, feeling tired, and wanting live chatrooms.

One user agreed with another about the difficulty of convincing doctors to do investigations for stroke. They said their child had all the signs of stroke, but they only obtained a computed tomography scan by pushing the consultant hard, who then agreed to get a radiologist out of bed (p12, stroke aged 1, 0 years poststroke, mother).

Another user agreed that previous friends found it difficult to comprehend how difficult recovery was for stroke survivors (p28, stroke aged 9, 2 years poststroke, parents).

There was a single account of relief of blame, reassuring a survivor that they were not a burden and that everyone needs support at some point (p12, stroke aged 1, 0 years poststroke, mother).

Tangible Aid

Tangible aid was the least common type of social support offered. Loans, direct tasks, indirect tasks, and active participation were not identified within the data set. However, members displayed a willingness to help wherever possible.

One user wrote that they would be more than happy to help if required (p49, 17 years, 4 years poststroke, survivor).

The analysis was read by a survivor of multiple childhood strokes while aged <1 year, who is now studying at university. She commented that she and her family had never had any experience with peer support, whether in person or online; support had only been provided by medical professionals. However, she thought peer support would have been useful for her and her family. She especially valued the teaching and situational appraisal subcategories in informational support, emotional support, and the validation subcategory in esteem support. Some extracts of her comments are reported in Textbox 2 .

Regarding situational appraisal, the survivor said:

  • “I know my family were told this a lot while we were waiting to see if my stroke at <0 years would affect me in my first decade. It was emphasised to be aware of the difference between childhood and adult stroke and not to compare the speed of recovery.”

Regarding teaching, the survivor said:

  • “I am sure this support would have been invaluable to my family if these forums existed when I was born. Instead informational support was given by doctors only.”

Regarding emotional, the survivor said:

  • “Again I’m sure this emotional support would have been of great help especially in the uncertain times following a stroke, especially since my family were unaware and unable to connect with any of other families who had experienced pediatric stroke. Additionally, emotional support would have been a great help during the early years when there was uncertainty about the future implications of the stroke as it’s apparently hard to judge the extent of the effects, they wait to see if the child walks, talks or can communicate and learn at school.”

Validation with regards to medical professionals being less experienced with diagnosing pediatric strokes:

  • “I’m sure my family can sympathise with this, it took one very persistent doctor/nurse who was adamant I was having a stroke to investigate further.”

This study provides qualitative evidence that online peer support, facilitated by the online community, played an important role in meeting the rehabilitation needs of patients with pediatric stroke. Most posts analyzed displayed social support. Discussion threads engaged a range of people: those who were recently recovering from a pediatric stroke, their caregivers and families, and adult survivors of pediatric stroke. Users appreciated finding others they could share their lived experience of stroke with and relate to similar incidences, particularly regarding the difficulty in getting a diagnosis and rehabilitation. They additionally discussed both the lack of specific local support and available resources and support services. All 5 categories of social support in the SSBC were evidenced; emotional support was the most common, followed by informational support. The group aged ≤18 years provided more emotional support than informational support; however, the group aged >18 years provided more informational support than emotional support. This difference may arise as members of the group aged >18 years engage with the community in order to provide informational support based on their own lived experience of decades of rehabilitation. There were instances of network support and esteem support, and tangible support was exchanged the least. There were no instances of subcategory access support. It was noticeable that survivors in the group aged >18 years displayed empathy less often than survivors in the group aged ≤18 years.

There are 3 strengths to this study. First, the online community facilitated communication and discussion between participants who recently had a stroke and users who had a stroke many years before. This brought unprecedented insight into the long-term lived experiences of pediatric stroke recovery and survivors’ unmet needs. Second, the methodological design of analyzing posts on a forums meant that messages were viewed and assessed directly by the researcher. This is a nontraditional method that helps to avoid potential problems such as retrospective self-reports, recall bias, and researchers’ bias. Third, the population that uses the forums might include people who do not partake in traditional research studies, allowing the needs of an underrepresented patient population to be studied [ 11 ].

Limitations of the study include the analysis of a single UK-based online peer-support community dated 2004-2011, decreasing the generalizability of the study. Additionally, although messages imply that users appreciated support from others, the impact of the support given was hard to measure; performing interviews with participants would have been more successful in measuring the effect of comments on users. Moreover, the study population may not be representative of survivors who do not have access to a computer, do not know how to use the online forums, and therefore may not be able to participate in the online community, resulting in a potential patient population being missed from this study [ 16 ]. Further to this, participants who were adult survivors needed to be able to read and type, abilities sometimes affected by stroke and potentially acting as barriers to participation. This study was not set to explore the drawbacks of using online forums as support for health conditions, as posts were analyzed individually and not in the context of the thread to which they belonged. This meant we could not analyze how a post affected other users taking part in that thread. Nevertheless, a previous qualitative study of the same stroke online community found that users would promptly counter inappropriate medical information or health behavior [ 20 ].

The ability to access support is a form of social medical capital, defined as the “advantages that any user (patient or caregiver) can gain from participation in the social networks provided by online health communities” [ 26 ]. This confirms findings from similar studies across a variety of chronic conditions [ 9 , 10 , 23 ].

Only half of the posts analyzed in the study contained instances of social support. This was much lower than previous studies, which found rates between 83.8% and 98.9% [ 9 , 24 ]. This could be for a number of reasons: there is a paucity of literature for survivors of pediatric stroke to access, resulting in families and survivors having many questions during their recovery. It is possible that participants were asking more questions than they were able to answer. Also, the forum was for survivors of both adult and pediatric stroke, and it is possible that the adult stroke survivors provided support, which was not included in this study sample. Similar to other studies, informational and emotional support were the 2 most commonly exchanged categories of support; network and esteem support were the next most common, with tangible aid being the least common form of support received [ 9 , 10 , 22 - 24 ]. This may be reflective of the actual online platform as the means for providing support, where the geographical limitations of the group mean that the ability to perform tasks to help others is limited.

Informational support involved participants giving each other practical and recovery advice. In addition, participants taught each other strategies for dealing with things referred them to resources and helped them appraise situations. Emotional support was commonly offered; participants exhibited affection for each other, and the forums appeared to provide a safe space to share experiences where users could sympathize, empathize, or encourage each other. Network support was present on the forums; there were many messages that expressed a sense of comradery, reiterating that members of the site were there to support each other. This could be particularly useful in the context of pediatric stroke, where the incidence is relatively low and the chances of meeting another survivor who lives close by are low. Esteem support was provided to thank people for their contribution to the site as well as validate people’s recovery processes.

Notably, there were no examples of the access subcategory. Access is defined as providing “the recipient with access to new people.” This is contrary to other studies [ 9 , 10 , 22 , 23 ]. This may be another illustration of the low incidence of pediatric stroke, and so not many survivors and families are well known. In addition, medical and public awareness of pediatric stroke in society is low [ 2 , 3 , 7 ] and as a result, there are very few people and sources of literature available for survivors. There were no examples of confidentiality, a finding found in other similar studies on online forums; this was as expected as posts were made on a public platform [ 9 , 10 ].

A new finding from this study is that posts made by users whose child was aged ≤18 years at the time of posting offered emotional support more commonly than informational support. However, survivors who were aged >18 years at the time of posting gave more informational support than emotional support. This finding can be interpreted in the context of the theory of optimal matching [ 27 ], which hypothesizes that specific types of social support may be beneficial in aiding specific types of stress. According to this theory, the controllability of a situation plays an important role in determining what kind of social support will be most beneficial to the individual. Informational support is a type of action-facilitating support that fosters behaviors designed to mitigate a stressor. Emotional support is a nurturing support that helps individuals cope with the emotional consequences of a stressor [ 22 ]. It proposes that individuals with controllable problems should benefit most from informational support because they can use this information, advice, and guidance to help them deal with the cause of their difficulties. However, those with uncontrollable problems should benefit more from emotional support because this will help them cope with unpleasant emotions and the stressful negative effects of being in an uncontrollable situation. In the context of pediatric stroke, survivors who are closer to their stroke event may perceive to have less control over events in their life and so offer emotional support. In contrast, survivors aged >18 years may view their stroke event as more controllable and therefore may have perceived that providing informational support from decades of lived experience was more useful. In addition, socioemotional selectivity theory [ 28 ] suggests that the degree to which an illness is relatively more chronic or acute could influence the support seeker’s time perspective, whereby chronic illness facilitates problem-focused coping that favors action-facilitating types of support [ 22 ]. Pediatric stroke is an illness with both an acute and chronic phase. As predicted by the social-emotional selectivity theory, emotional support is more prevalent in the group aged ≤18 years coping with acute illness, whereas informational support is more common in the group aged >18 years.

Conclusions

This study brings qualitative evidence that a nation-wide online peer support group was beneficial in drawing support to survivors of pediatric stroke and their families. The community also represented an opportunity for adult survivors of pediatric stroke to validate their experiences many decades post stroke, share information gained through their rehabilitation journeys, provide insight about unmet needs with regard to rehabilitation services, and provide hope to families with more acute stroke incidences. The study additionally highlights that recovery from stroke is a long process, with adult survivors reaching out for advice. More research is needed, in particular interventional research studies, to evaluate the effectiveness of online peer support groups for survivors of pediatric stroke, as well as research to ensure online communities are safe and nurtured.

Acknowledgments

The authors are grateful to the Stroke Association for sharing the archive file of Talkstroke Online with us. We thank our patient and public involvement representative for her comments on the manuscript. ADS was partly funded by a National Institute for Health and Care Research (NIHR) Programme Grant for Applied Research (NIHR202037). Views are those of the authors and not necessarily those of the National Health Service, NIHR, or the Department of Health and Social Care.

Conflicts of Interest

None declared.

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Abbreviations

Edited by T Leung; submitted 29.05.23; peer-reviewed by S Atanasova, D Riggins; comments to author 19.09.23; revised version received 07.11.23; accepted 29.01.24; published 15.03.24.

©William J A Wright, Charlotte Howdle, Neil S Coulson, Anna De Simoni. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

2 Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

3 Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, 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. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

Biographies

is professor of sociology at the Department of Sociology, Uppsala University and Universität St. Gallen. His main focus is economic sociology, and in particular, markets. He has published numerous articles and books, including Orderly Fashion (Princeton University Press 2010), Markets (Polity Press 2011) and Re-Imagining Economic Sociology (edited with N. Dodd, Oxford University Press 2015). His book Ethnographic Methods (in Swedish) has already gone through several editions.

is associate professor of sociology at the Department of Media and Social Sciences, University of Stavanger. His research has been published in journals such as Social Psychology Quarterly, Sociological Theory, Teaching Sociology, and Music and Arts in Action. As an ethnographer he is working on a book on he social world of big-wave surfing.

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Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

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IMAGES

  1. Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic

    method of analysis in qualitative research

  2. Understanding Qualitative Research: An In-Depth Study Guide

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  3. Qualitative Research: Definition, Types, Methods and Examples

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  4. 6 Types of Qualitative Research Methods

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  5. Qualitative Research

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  6. Qualitative Research

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VIDEO

  1. Qualitative Research Analysis Approaches

  2. introduction to Preliminary analysis in qualitative analysis

  3. Introduction to Thematic Analysis in Qualitative Research

  4. Five Types of Data Analysis

  5. Qualitative Research Method

  6. Qualitative and Quantitative Data Analysis in Social Sciences

COMMENTS

  1. Qualitative Data Analysis Methods: Top 6 + Examples

    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.

  2. Qualitative Research

    Qualitative Research. Qualitative research is a type of research methodology that focuses on exploring and understanding people's beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus ...

  3. What Is Qualitative 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. Qualitative research question examples

  4. Learning to Do Qualitative Data Analysis: A Starting Point

    For many researchers unfamiliar with qualitative research, determining how to conduct qualitative analyses is often quite challenging. Part of this challenge is due to the seemingly limitless approaches that a qualitative researcher might leverage, as well as simply learning to think like a qualitative researcher when analyzing data. From framework analysis (Ritchie & Spencer, 1994) to content ...

  5. Qualitative Data Analysis: What is it, Methods + Examples

    Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights. In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos.

  6. Qualitative Data Analysis: Step-by-Step Guide (Manual vs ...

    Qualitative Data Analysis methods. Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you've gathered. Common qualitative data analysis methods include: Content Analysis. This is a popular approach to qualitative data ...

  7. How to use and assess qualitative research methods

    How to conduct qualitative research? Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [13, 14].As Fossey puts it: "sampling, data collection, analysis and interpretation are related to each other in a cyclical ...

  8. Planning Qualitative Research: Design and Decision Making for New

    While many books and articles guide various qualitative research methods and analyses, there is currently no concise resource that explains and differentiates among the most common qualitative approaches. We believe novice qualitative researchers, students planning the design of a qualitative study or taking an introductory qualitative research course, and faculty teaching such courses can ...

  9. How to use and assess qualitative research methods

    Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [13, 14].As Fossey puts it: "sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one ...

  10. Sage Research Methods

    The wide range of approaches to data analysis in qualitative research can seem daunting even for experienced researchers. This handbook is the first to provide a state-of-the art overview of the whole field of QDA; from general analytic strategies used in qualitative research, to approaches specific to particular types of qualitative data, including talk, text, sounds, images and virtual data.

  11. PDF The SAGE Handbook of Qualitative Data Analysis

    of the analysis in qualitative research, in general, a kind of stocktaking of the various approaches to qualitative analysis and of the challenges it faces seems necessary. Anyone interested in the current state and develop-ment of qualitative data analysis will find a field which is constantly growing and becom - ing less structured.

  12. Introduction to 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.

  13. Units of Analysis and Methodologies for Qualitative Studies

    This post is excerpted and adapted from Chapter 2 of Doing Qualitative Research Online (2022). Use the code COMMUNITY3 for a 20% discount on the book, valid worldwide until March 31, 2024. Please note that the first edition of this book, as well as some of my other writings about online research, are available in the SAGE Research Methods database.

  14. Qualitative analysis

    Content analysis is the systematic analysis of the content of a text—e.g., who says what, to whom, why, and to what extent and with what effect—in a quantitative or qualitative manner. Content analysis is typically conducted as follows. First, when there are many texts to analyse—e.g., newspaper stories, financial reports, blog postings ...

  15. (PDF) Data Analysis Methods for Qualitative Research: Managing the

    Thematic analysis is a method of data analysis in qualitative research that most researchers use, and it is flexible because it can be applied and utilized broadly across various epistemologies ...

  16. Data Analysis in Qualitative Research: A Brief Guide to Using Nvivo

    In some cases, qualitative data can also include pictorial display, audio or video clips (e.g. audio and visual recordings of patients, radiology film, and surgery videos), or other multimedia materials. Data analysis is the part of qualitative research that most distinctively differentiates from quantitative research methods.

  17. Qualitative Data Analysis

    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.

  18. 5 Qualitative Data Analysis Methods to Reveal User Insights

    5 qualitative data analysis methods explained. Qualitative data analysis is the process of organizing, analyzing, and interpreting qualitative research data—non-numeric, conceptual information, and user feedback—to capture themes and patterns, answer research questions, and identify actions to improve your product or website.Step 1 in the research process (after planning) is qualitative ...

  19. Qualitative Analysis: 5 Qualitative Research Methods

    Qualitative Analysis: 5 Qualitative Research Methods. Written by MasterClass. Last updated: Jan 23, 2023 • 2 min read. Learn about qualitative analysis and examples of different modalities and strategies within this research method.

  20. How to Use Intersectional Analysis in Qualitative Research

    Then a step-by-step approach is outlined, covering important considerations in research design, including research questions, sampling strategies, and data-collection methods. This guide also explores data-analysis techniques, highlighting the importance of identifying intersecting identities within the data and analyzing their interplay.

  21. Qualitative Study

    Qualitative research is a type of research that explores and provides deeper insights into real-world problems.[1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants ...

  22. Research Methods

    Qualitative analysis methods. Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected: From open-ended surveys and interviews, literature reviews, case studies, ethnographies, and other sources that use text rather than numbers. Using non-probability sampling methods.

  23. QUALITATIVE RESEARCH: Recent Developments in Case Study Methods

    Abstract This article surveys the extensive new literature that has brought about a renaissance of qualitative methods in political science over the past decade. It reviews this literature's focus on causal mechanisms and its emphasis on process tracing, a key form of within-case analysis, and it discusses the ways in which case-selection criteria in qualitative research differ from those ...

  24. Qualitative Analysis Made Easy: Your Comprehensive Step-by-Step Manual

    Businesses conduct qualitative analysis using various methods, typically gathering and analyzing non-numerical data. Following are some common steps that businesses follow to conduct qualitative analysis. Define The Research Question. Defining the research question is a crucial first step in qualitative analysis. It helps businesses focus their ...

  25. Qualitative Research: Data Collection, Analysis, and Management

    Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of ...

  26. Journal of Medical Internet Research

    Methods: This was a qualitative thematic analysis of posts from a pediatric stroke population on a UK online stroke community active between 2004 and 2011. The population was split into 2 groups based on whether stroke survivors were aged ≤18 years or aged >18 years at the time of posting.

  27. Clinical Characteristics and Treatment Efficacy for Co-Morbid Insomnia

    Unfortunately, research about clinical characteristics and management of COMISA based on quantitative evidence is lacking. Method Standard procedures for literature retrieval, selection and quality assessment, data extraction, analysis, and interpretation were conducted step by step.

  28. What is Qualitative in Qualitative Research

    A fourth issue is that the "implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm" (Goertz and Mahoney 2012:9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving ...