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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.

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.

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, and review analysis is a great place to start. 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.

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.

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.

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 .

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.

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 .

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:

The visualization could look like this:

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 .

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.

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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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

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

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

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

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

What (exactly) is qualitative data analysis?

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

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

So, if it’s not numbers, what is it?

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

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

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

So, qualitative analysis is easier than quantitative, right?

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

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

Need a helping hand?

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

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

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

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

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

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

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

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

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

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

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

QDA Method #2: Narrative Analysis 

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

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

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

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

QDA Method #3: Discourse Analysis 

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

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

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

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

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

QDA Method #4: Thematic Analysis

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

Let’s take a look at an example.

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

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

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

QDA Method #5: Grounded theory (GT) 

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

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

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

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

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

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

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

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

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

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

Let’s just stick with IPA, okay?

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

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

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

How to choose the right analysis method

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

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

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

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

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

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

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

Let’s recap on QDA methods…

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

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

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

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

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86 Comments

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Precise explanation of method.

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.

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

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

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

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

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

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

content analysis can be qualitative research?

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do you have any material on Data collection

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalizability

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

• Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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Data & Finance for Work & Life

Data Analysis for Qualitative Research: 6 Step Guide

Data analysis for qualitative research is not intuitive. This is because qualitative data stands in opposition to traditional data analysis methodologies: while data analysis is concerned with quantities, qualitative data is by definition unquantified . But there is an easy, methodical approach that anyone can take use to get reliable results when performing data analysis for qualitative research. The process consists of 6 steps that I’ll break down in this article:

• Perform interviews(if necessary )
• Gather all documents and transcribe any non-paper records
• Decide whether to either code analytical data, analyze word frequencies, or both
• Decide what interpretive angle you want to take: content analysis , narrative analysis, discourse analysis, framework analysis, and/or grounded theory
• Compile your data in a spreadsheet using document saving techniques (windows and mac)
• Identify trends in words, themes, metaphors, natural patterns, and more

To complete these steps, you will need:

• Microsoft word
• Microsoft excel
• Internet access

You can get the free Intro to Data Analysis eBook to cover the fundamentals and ensure strong progression in all your data endeavors.

What is qualitative research?

Qualitative research is not the same as quantitative research. In short, qualitative research is the interpretation of non-numeric data. It usually aims at drawing conclusions that explain why a phenomenon occurs, rather than that one does occur. Here’s a great quote from a nursing magazine about quantitative vs qualitative research:

“A traditional quantitative study… uses a predetermined (and auditable) set of steps to confirm or refute [a] hypothesis. “In contrast, qualitative research often takes the position that an interpretive understanding is only possible by way of uncovering or deconstructing the meanings of a phenomenon. Thus, a distinction between explaining how something operates (explanation) and why it operates in the manner that it does (interpretation) may be [an] effective way to distinguish quantitative from qualitative analytic processes involved in any particular study.” (bold added) (( EBN ))

Learn to Interpret Your Qualitative Data

This article explain what data analysis is and how to do it. To learn how to interpret the results, visualize, and write an insightful report, sign up for our handbook below.

Step 1a: Data collection methods and techniques in qualitative research: interviews and focus groups

Step 1 is collecting the data that you will need for the analysis. If you are not performing any interviews or focus groups to gather data, then you can skip this step. It’s for people who need to go into the field and collect raw information as part of their qualitative analysis.

Since the whole point of an interview and of qualitative analysis in general is to understand a research question better, you should start by making sure you have a specific, refined research question . Whether you’re a researcher by trade or a data analyst working on one-time project, you must know specifically what you want to understand in order to get results.

Good research questions are specific enough to guide action but open enough to leave room for insight and growth. Examples of good research questions include:

• Good : To what degree does living in a city impact the quality of a person’s life? (open-ended, complex)
• Bad : Does living in a city impact the quality of a person’s life? (closed, simple)

Once you understand the research question, you need to develop a list of interview questions. These questions should likewise be open-ended and provide liberty of expression to the responder. They should support the research question in an active way without prejudicing the response. Examples of good interview questions include:

• Good : Tell me what it’s like to live in a city versus in the country. (open, not leading)
• Bad : Don’t you prefer the city to the country because there are more people? (closed, leading)

Some additional helpful tips include:

• Begin each interview with a neutral question to get the person relaxed
• Limit each question to a single idea
• If you don’t understand, ask for clarity
• Do not pass any judgements
• Do not spend more than 15m on an interview, lest the quality of responses drop

Focus groups

The alternative to interviews is focus groups. Focus groups are a great way for you to get an idea for how people communicate their opinions in a group setting, rather than a one-on-one setting as in interviews.

In short, focus groups are gatherings of small groups of people from representative backgrounds who receive instruction, or “facilitation,” from a focus group leader. Typically, the leader will ask questions to stimulate conversation, reformulate questions to bring the discussion back to focus, and prevent the discussion from turning sour or giving way to bad faith.

Focus group questions should be open-ended like their interview neighbors, and they should stimulate some degree of disagreement. Disagreement often leads to valuable information about differing opinions, as people tend to say what they mean if contradicted.

However, focus group leaders must be careful not to let disagreements escalate, as anger can make people lie to be hurtful or simply to win an argument. And lies are not helpful in data analysis for qualitative research.

Step 1b: Tools for qualitative data collection

When it comes to data analysis for qualitative analysis, the tools you use to collect data should align to some degree with the tools you will use to analyze the data.

As mentioned in the intro, you will be focusing on analysis techniques that only require the traditional Microsoft suite programs: Microsoft Excel and Microsoft Word . At the same time, you can source supplementary tools from various websites, like Text Analyzer and WordCounter.

In short, the tools for qualitative data collection that you need are Excel and Word , as well as web-based free tools like Text Analyzer and WordCounter . These online tools are helpful in the quantitative part of your qualitative research.

Step 2: Gather all documents & transcribe non-written docs

Once you have your interviews and/or focus group transcripts, it’s time to decide if you need other documentation. If you do, you’ll need to gather it all into one place first, then develop a strategy for how to transcribe any non-written documents.

When do you need documentation other than interviews and focus groups? Two situations usually call for documentation. First , if you have little funding , then you can’t afford to run expensive interviews and focus groups.

Second , social science researchers typically focus on documents since their research questions are less concerned with subject-oriented data, while hard science and business researchers typically focus on interviews and focus groups because they want to know what people think, and they want to know today.

Non-written records

Other factors at play include the type of research, the field, and specific research goal. For those who need documentation and to describe non-written records, there are some steps to follow:

• Put all hard copy source documents into a sealed binder (I use plastic paper holders with elastic seals ).
• If you are sourcing directly from printed books or journals, then you will need to digitalize them by scanning them and making them text readable by the computer. To do so, turn all PDFs into Word documents using online tools such as PDF to Word Converter . This process is never full-proof, and it may be a source of error in the data collection, but it’s part of the process.
• If you are sourcing online documents, try as often as possible to get computer-readable PDF documents that you can easily copy/paste or convert. Locked PDFs are essentially a lost cause .
• Transcribe any audio files into written documents. There are free online tools available to help with this, such as 360converter . If you run a test through the system, you’ll see that the output is not 100%. The best way to use this tool is as a first draft generator. You can then correct and complete it with old fashioned, direct transcription.

Step 3: Decide on the type of qualitative research

Before step 3 you should have collected your data, transcribed it all into written-word documents, and compiled it in one place. Now comes the interesting part. You need to decide what you want to get out of your research by choosing an analytic angle, or type of qualitative research.

The available types of qualitative research are as follows. Each of them takes a unique angle that you must choose to get what information you want from the analysis . In addition, each of them has a different impact on the data analysis for qualitative research (coding vs word frequency) that we use.

Content analysis

Narrative analysis, discourse analysis.

• Framework analysis, and/or

Grounded theory

From a high level, content, narrative, and discourse analysis are actionable independent tactics, whereas framework analysis and grounded theory are ways of honing and applying the first three.

• Definition : Content analysis is identify and labelling themes of any kind within a text.
• Focus : Identifying any kind of pattern in written text, transcribed audio, or transcribed video. This could be thematic, word repetition, idea repetition. Most often, the patterns we find are idea that make up an argument.
• Goal : To simplify, standardize, and quickly reference ideas from any given text. Content analysis is a way to pull the main ideas from huge documents for comparison. In this way, it’s more a means to an end.
• Pros : The huge advantage of doing content analysis is that you can quickly process huge amounts of texts using simple coding and word frequency techniques we will look at below. To use a metaphore, it is to qualitative analysis documents what Spark notes are to books.
• Cons : The downside to content analysis is that it’s quite general. If you have a very specific, narrative research question, then tracing “any and all ideas” will not be very helpful to you.
• Definition : Narrative analysis is the reformulation and simplification of interview answers or documentation into small narrative components to identify story-like patterns.
• Focus : Understanding the text based on its narrative components as opposed to themes or other qualities.
• Goal : To reference the text from an angle closer to the nature of texts in order to obtain further insights.
• Pros : Narrative analysis is very useful for getting perspective on a topic in which you’re extremely limited. It can be easy to get tunnel vision when you’re digging for themes and ideas from a reason-centric perspective. Turning to a narrative approach will help you stay grounded. More importantly, it helps reveal different kinds of trends.
• Cons : Narrative analysis adds another layer of subjectivity to the instinctive nature of qualitative research. Many see it as too dependent on the researcher to hold any critical value.
• Definition : Discourse analysis is the textual analysis of naturally occurring speech. Any oral expression must be transcribed before undergoing legitimate discourse analysis.
• Focus : Understanding ideas and themes through language communicated orally rather than pre-processed on paper.
• Goal : To obtain insights from an angle outside the traditional content analysis on text.
• Pros : Provides a considerable advantage in some areas of study in order to understand how people communicate an idea, versus the idea itself. For example, discourse analysis is important in political campaigning. People rarely vote for the candidate who most closely corresponds to his/her beliefs, but rather for the person they like the most.
• Cons : As with narrative analysis, discourse analysis is more subjective in nature than content analysis, which focuses on ideas and patterns. Some do not consider it rigorous enough to be considered a legitimate subset of qualitative analysis, but these people are few.

Framework analysis

• Definition : Framework analysis is a kind of qualitative analysis that includes 5 ordered steps: coding, indexing, charting, mapping, and interpreting . In most ways, framework analysis is a synonym for qualitative analysis — the same thing. The significant difference is the importance it places on the perspective used in the analysis.
• Focus : Understanding patterns in themes and ideas.
• Goal : Creating one specific framework for looking at a text.
• Pros : Framework analysis is helpful when the researcher clearly understands what he/she wants from the project, as it’s a limitation approach. Since each of its step has defined parameters, framework analysis is very useful for teamwork.
• Cons : It can lead to tunnel vision.
• Definition : The use of content, narrative, and discourse analysis to examine a single case, in the hopes that discoveries from that case will lead to a foundational theory used to examine other like cases.
• Focus : A vast approach using multiple techniques in order to establish patterns.
• Goal : To develop a foundational theory.
• Pros : When successful, grounded theories can revolutionize entire fields of study.
• Cons : It’s very difficult to establish ground theories, and there’s an enormous amount of risk involved.

Step 4: Coding, word frequency, or both

Coding in data analysis for qualitative research is the process of writing 2-5 word codes that summarize at least 1 paragraphs of text (not writing computer code). This allows researchers to keep track of and analyze those codes. On the other hand, word frequency is the process of counting the presence and orientation of words within a text, which makes it the quantitative element in qualitative data analysis.

Video example of coding for data analysis in qualitative research

In short, coding in the context of data analysis for qualitative research follows 2 steps (video below):

• Reading through the text one time
• Adding 2-5 word summaries each time a significant theme or idea appears

Let’s look at a brief example of how to code for qualitative research in this video:

Click here for a link to the source text. 1

Example of word frequency processing

And word frequency is the process of finding a specific word or identifying the most common words through 3 steps:

• Decide if you want to find 1 word or identify the most common ones
• Use word’s “Replace” function to find a word or phrase
• Use Text Analyzer to find the most common terms

Here’s another look at word frequency processing and how you to do it. Let’s look at the same example above, but from a quantitative perspective.

Imagine we are already familiar with melanoma and KITs , and we want to analyze the text based on these keywords. One thing we can do is look for these words using the Replace function in word

• Locate the search bar
• Click replace
• Type in the word
• See the total results

Here’s a brief video example:

Another option is to use an online Text Analyzer. This methodology won’t help us find a specific word, but it will help us discover the top performing phrases and words. All you need to do it put in a link to a target page or paste a text. I pasted the abstract from our source text, and what turns up is as expected. Here’s a picture:

Step 5: Compile your data in a spreadsheet

After you have some coded data in the word document, you need to get it into excel for analysis. This process requires saving the word doc as an .htm extension, which makes it a website. Once you have the website, it’s as simple as opening that page, scrolling to the bottom, and copying/pasting the comments, or codes, into an excel document.

You will need to wrangle the data slightly in order to make it readable in excel. I’ve made a video to explain this process and places it below.

Step 6: Identify trends & analyze!

There are literally thousands of different ways to analyze qualitative data, and in most situations, the best technique depends on the information you want to get out of the research.

Nevertheless, there are a few go-to techniques. The most important of this is occurrences . In this short video, we finish the example from above by counting the number of times our codes appear. In this way, it’s very similar to word frequency (discussed above).

A few other options include:

• Ranking each code on a set of relevant criteria and clustering
• Pure cluster analysis
• Causal analysis

We cover different types of analysis like this on the website, so be sure to check out other articles on the home page .

How to analyze qualitative data from an interview

To analyze qualitative data from an interview , follow the same 6 steps for quantitative data analysis:

• Perform the interviews
• Transcribe the interviews onto paper
• Decide whether to either code analytical data (open, axial, selective), analyze word frequencies, or both
• Compile your data in a spreadsheet using document saving techniques (for windows and mac)
• Source text [ ↩ ]

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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Mastering Qualitative Data Analysis: The Step-by-Step Process & 5 Essential Methods

12 min read

Wondering how to analyze qualitative data and get actionable insights? Search no further!

This article will help you analyze qualitative data and fuel your product growth . We’ll walk you through the following steps:

• 5 Qualitative data analysis methods.
• 5 Steps to analysing qualitative data.
• How to act on research findings.

Let’s get started!

• Qualitative data analysis turns non-numerical data into insights, including customer feedback , surveys, and interviews.
• Qualitative data provides rich insights for refining strategies and uncovering growth opportunities.
• The benefits of qualitative data analysis include deep insight, flexibility, contextual understanding, and amplifying participant voices.
• Challenges include data overload, reliability, and validity concerns, as well as time-intensive nature.
• Qualitative and quantitative data analysis differ in analyzing numerical vs. non-numerical data.
• Qualitative data methods include content analysis, narrative analysis, discourse analysis, thematic analysis, and grounded theory analysis.
• Content analysis involves systematically analyzing text to identify patterns and themes.
• Narrative analysis interprets stories to understand customer feelings and behaviors.
• The thematic analysis identifies patterns and themes in data.
• Grounded theory analysis generates hypotheses from data.
• Choosing a method depends on research questions, data type, context, expertise, and resources.
• The qualitative data analysis process involves defining questions, gathering data, organizing, coding, and making hypotheses.
• Userpilot facilitates qualitative data collection through surveys and offers NPS dashboard analytics.
• Building in-app experiences based on qualitative insights enhances user experience and drives satisfaction.
• The iterative qualitative data analysis process aims to refine understanding of the customer base.
• Userpilot can automate data collection and analysis, saving time and improving customer understanding. Book a demo to learn more!

Try Userpilot and Take Your Qualitative Research to the Next Level

• 14 Day Trial
• No Credit Card Required

What is a qualitative data analysis?

Qualitative data analysis is the process of turning qualitative data — information that can’t be measured numerically — into insights.

This could be anything from customer feedback, surveys , website recordings, customer reviews, or in-depth interviews.

Qualitative data is often seen as more “rich” and “human” than quantitative data, which is why product teams use it to refine customer acquisition and retention strategies and uncover product growth opportunities.

Benefits of qualitative data analysis

Here are the key advantages of qualitative data analysis that underscore its significance in research endeavors:

• Deep Insight: Qualitative data analysis allows for a deep understanding of complex patterns and trends by uncovering underlying meanings, motivations, and perspectives.
• Flexibility: It offers flexibility in data interpretation, allowing researchers to explore emergent themes and adapt their analysis to new insights.
• Contextual Understanding: Qualitative analysis enables the exploration of contextual factors, providing rich context to quantitative findings and uncovering hidden dynamics.
• Participant Voice: It amplifies the voices of participants, allowing their perspectives and experiences to shape the analysis and resulting interpretations.

Challenges of qualitative data analysis

While qualitative data analysis offers rich insights, it comes with its challenges:

• Data Overload and Management: Qualitative data often comprises large volumes of text or multimedia, posing challenges in organizing, managing, and analyzing the data effectively.
• Reliability and Validity: Ensuring the reliability and validity of qualitative findings can be complex, as there are fewer standardized measures compared to quantitative analysis, requiring meticulous attention to methodological rigor.
• Time-Intensive Nature: Qualitative data analysis can be time-consuming, involving iterative processes of coding, categorizing, and synthesizing data, which may prolong the research timeline and increase resource requirements.

Quantitative data analysis vs. Qualitative data analysis

Here let’s understand the difference between qualitative and quantitative data analysis.

Quantitative data analysis is analyzing numerical data to locate patterns and trends. Quantitative research uses numbers and statistics to systematically measure variables and test hypotheses.

Qualitative data analysis , on the other hand, is the process of analyzing non-numerical, textual data to derive actionable insights from it. This data type is often more “open-ended” and can be harder to conclude from.

However, qualitative data can provide insights that quantitative data cannot. For example, qualitative data can help you understand how customers feel about your product, their unmet needs , and what motivates them.

Other differences include:

What are the 5 qualitative data analysis methods?

There are 5 main methods of qualitative data analysis. Which one you choose will depend on the type of data you collect, your preferences, and your research goals.

Content analysis

Content analysis is a qualitative data analysis method that systematically analyses a text to identify specific features or patterns. This could be anything from a customer interview transcript to survey responses, social media posts, or customer success calls.

The data is first coded, which means assigning it labels or categories.

For example, if you were looking at customer feedback , you might code all mentions of “price” as “P,” all mentions of “quality” as “Q,” and so on. Once manual coding is done, start looking for patterns and trends in the codes.

Content analysis is a prevalent qualitative data analysis method, as it is relatively quick and easy to do and can be done by anyone with a good understanding of the data.

The advantages of content analysis process

• Rich insights: Content analysis can provide rich, in-depth insights into how customers feel about your product, what their unmet needs are, and their motives.
• Easily replicable: Once you have developed a coding system, content analysis is relatively quick and easy because it’s a systematic process.
• Affordable: Content analysis requires very little investment since all you need is a good understanding of the data, and it doesn’t require any special software.

The disadvantages of content analysis process

• Time-consuming: Coding the data is time-consuming, particularly if you have a large amount of data to analyze.
• Ignores context: Content analysis can ignore the context in which the data was collected which may lead to misinterpretations.
• Reductive approach: Some people argue that content analysis is a reductive approach to qualitative data because it involves breaking the data down into smaller pieces.

Narrative analysis

Analysing qualitative data with narrative analysis involves identifying, analyzing, and interpreting customer or research participants’ stories. The input can be in the form of customer interviews, testimonials, or other text data.

Narrative analysis helps product managers to understand customers’ feelings toward the product identify trends in customer behavior and personalize their in-app experiences .

The advantages of narrative analysis

• Provide a rich form of data: The stories people tell give a deep understanding of customers’ needs and pain points.
• Collects unique, in-depth data based on customer interviews or testimonials.

The disadvantages of narrative analysis

• Hard to implement in studies of large numbers.
• Time-consuming: Transcribing customer interviews or testimonials is labor-intensive.
• Hard to reproduce since it relies on unique customer stories.

Discourse analysis

Discourse analysis is about understanding how people communicate with each other. It can be used to analyse written or spoken language. For instance, product teams can use discourse analysis to understand how customers talk about their products on the web.

The advantages of discourse analysis

• Uncovers motivation behind customers’ words.
• Gives insights into customer data.

The disadvantages of disclosure analysis

• Takes a large amount of time and effort as the process is highly specialized and requires training and practice. There’s no “right” way to do it.
• Focuses solely on language.

Thematic analysis

Thematic analysis is a popular qualitative data analysis method that identifies patterns and themes in data. The process of thematic analysis involves coding the data, which means assigning it labels or categories.

It can be paired with sentiment analysis to determine whether a piece of writing is positive, negative, or neutral. This can be done using a lexicon (i.e., a list of words and their associated sentiment scores).

A common use case for thematic analysis in SaaS companies is customer feedback analysis with NPS surveys and NPS tagging to identify patterns among your customer base.

The advantages of thematic analysis

• Doesn’t require training: Anyone with little training on how to label the data can perform thematic analysis.
• It’s easy to draw important information from raw data: Surveys or customer interviews can be easily converted into insights and quantitative data with the help of labeling.
• An effective way to process large amounts of data if done automatically: you will need AI tools for this.

The disadvantages of thematic analysis

• Doesn’t capture complex narratives: If the data isn’t coded correctly, it can be difficult to identify themes since it’s a phrase-based method.
• Difficult to implement from scratch because a perfect approach must be able to merge and organize themes in a meaningful way, producing a set of themes that are not too generic and not too large.

Grounded theory analysis

Grounded theory analysis is a method that involves the constant comparative method, meaning qualitative researchers analyze and code the data on the fly.

The grounded theory approach is useful for product managers who want to understand how customers interact with their products . It can also be used to generate hypotheses about how customers will behave in the future.

Suppose product teams want to understand the reasons behind the high churn rate , they can use customer surveys and grounded theory to analyze responses and develop hypotheses about why users churn and how to reengage inactive ones .

You can filter the disengaged/inactive user segment to make analysis easier.

The advantages of ground theory analysis

• Based on actual data, qualitative analysis is more accurate than other methods that rely on assumptions.
• Analyse poorly researched topics by generating hypotheses.
• Reduces the bias in interpreting qualitative data as it’s analyzed and coded as it’s collected.

The disadvantages of ground theory analysis

• Overly theoretical
• Requires a lot of objectivity, creativity, and critical thinking

Which qualitative data analysis method should you choose?

We have covered different qualitative data analysis techniques with their pros and cons but choosing the appropriate qualitative data analysis method depends on various factors, including:

• Research Question : Different qualitative methods are suitable for different research questions.
• Nature of Data : Consider the type of data you have collected—interview transcripts, reviews, or survey responses—and choose a method that aligns with the data’s characteristics. For instance, thematic analysis is versatile and can be applied to various types of qualitative data, while narrative analysis focuses specifically on stories and narratives.
• Research Context : Take into account the broader context of your research. Some qualitative methods may be more prevalent or accepted in certain fields or contexts.
• Researcher Expertise : Consider your own skills and expertise in qualitative analysis techniques. Some methods may require specialized training or familiarity with specific software tools. Choose a method that you feel comfortable with and confident in applying effectively.
• Research Goals and Resources : Evaluate your research goals, timeline, and resources available for analysis. Some methods may be more time-consuming or resource-intensive than others. Consider the balance between the depth of analysis and practical constraints.

How to perform qualitative data analysis process in steps

With all that theory above, we’ve decided to elicit the essential steps of qualitative research methods and designed a super simple guide for gathering qualitative data.

Let’s dive in!

Step 1: Define your qualitative research questions

The qualitative analysis research process starts with defining your research questions . It’s important to be as specific as possible, as this will guide the way you choose to collect qualitative research data and the rest of your analysis.

Examples are:

• What are the primary reasons customers are dissatisfied with our product?
• How does X group of users feel about our new feature?
• What are our customers’ needs, and how do they vary by segment?
• How do our products fit into our customers’ lives?
• What factors influence the low feature usage rate of the new feature ?

Step 2: Gather your qualitative customer data

Now, you decide what type of data collection to use based on previously defined goals. Here are 5 methods to collect qualitative data for product companies:

• User feedback

• NPS follow-up questions

• Review sites

• User interviews
• Focus groups

We recommend using a mix of in-app surveys and in-person interviews. The former helps to collect rich data automatically and on an ongoing basis. You can collect user feedback through in-product surveys, NPS platforms, or use Zoom for live interviews.

The latter enables you to understand the customer experience in the business context as you can ask clarifying questions during the interviews.

Try Userpilot and Easily Collect Qualitative Customer Data

Step 3: organize and categorize collected data.

Before analyzing customer feedback and assigning any value, unstructured feedback data needs to be organized in a single place. This will help you detect patterns and similar themes more easily.

One way to do this is to create a spreadsheet with all the data organized by research questions. Then, arrange the data by theme or category within each research question.

You can also organize NPS responses with Userpilot . This will allow you to quickly calculate scores and see how many promoters, passives, and detractors there are for each research question.

Step 4: Use qualitative data coding to identify themes and patterns

Themes are the building blocks of analysis and help you understand how your data fits together.

For product teams, an NPS survey might reveal the following themes: product defect, pricing, and customer service. Thus, the main themes in SaaS will be around identifying friction points, usability issues, UI issues, UX issues, missing features, etc.

You need to define specific themes and then identify how often they occur. In turn, the pattern is a relationship between 2 or multiple elements (e.g. users who have specific JTBD complain of a specific missing feature).

You can detect those patterns from survey analytics.

Pair themes with in-app customer behavior and product usage data to understand whether different user segments fall under specific feedback themes.

Following this step, you will get enough data to improve customer loyalty .

Step 5: Make hypotheses and test them

The last step in qualitative research is to analyze the data collected to find insights. Segment your users based on in-app behavior, user type, company size, or job to be done to draw meaningful decisions.

For instance, you may notice that negative feedback stems from the customer segment that recently engaged with XYZ features. Just like that, you can pinpoint friction points and the strongest sides of your product to capitalize on.

How to perform qualitative data analysis with Userpilot

Userpilot is a product growth platform that helps product managers collect and analyze qualitative data. It offers a suite of features to make it easy to understand how users interact with your product, their needs, and how you can improve user experience.

When it comes to performing qualitative research, Userpilot is not a qualitative data analysis software but it has some very useful features you could use.

Collect qualitative feedback from users with in-app surveys

Userpilot facilitates the collection of qualitative feedback from users through in-app surveys.

These surveys can be strategically placed within your application to gather insights directly from users while they interact with your product.

By leveraging Userpilot’s in-app survey feature, you can gather valuable feedback on user experiences, preferences, pain points , and suggestions for improvement.

Benefit from NPS dashboard and survey analytics

With Userpilot, you can harness the power of the NPS (Net Promoter Score) dashboard and survey analytics to gain valuable insights into user sentiment and satisfaction levels.

The NPS dashboard provides a comprehensive overview of your NPS scores over time, allowing you to track changes and trends in user loyalty and advocacy.

Additionally, Userpilot’s survey analytics offer detailed insights into survey responses, enabling you to identify common themes, uncover actionable feedback, and prioritize areas for improvement.

Build different in-app experiences based on the insights from qualitative data analysis

By analyzing qualitative feedback collected through in-app surveys, you can segment users based on these insights and create targeted in-app experiences designed to address specific user concerns or enhance key workflows.

Whether it’s guiding users through new features, addressing common user challenges, or personalizing the user journey based on individual preferences, Userpilot empowers you to deliver a more engaging and personalized user experience that drives user satisfaction and product adoption.

The qualitative data analysis process is iterative and should be revisited as new data is collected. The goal is to constantly refine your understanding of your customer base and how they interact with your product.

Want to get started with qualitative analysis? Get a Userpilot Demo and automate the data collection process. Save time on mundane work and understand your customers better!

Try Userpilot and Take Your Qualitative Data Analysis to the Next Level

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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.

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

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.

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.

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.

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 conduct qualitative data analysis

Last updated

21 February 2023

Reviewed by

Tanya Williams

This is where qualitative data analysis comes into play. It helps organizations identify and understand the underlying patterns and meanings of data. As a result, numerous fields, including research, customer experience, user experience design, and product design, use qualitative data analysis.

By understanding the underlying meanings and patterns in qualitative data, you can gain valuable insights that can help your business grow.

Read on to learn more about qualitative data analysis, appropriate methods, and how to do qualitative data analysis.

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• What is qualitative data analysis?

Qualitative data analysis is a research method that helps identify relevant themes and patterns in data sets. 

It involves organizing, coding, and interpreting data to understand how it connects to its subject. Such subjects may be people, products, or behaviors. Qualitative research approaches are generally used to explore questions that call for an explanation of why or how something happens.

• Importance of qualitative data

Qualitative data analysis can yield valuable insights often missed by quantitative approaches. Qualitative research generally provides an in-depth understanding of a person's motivations, beliefs, and behaviors. 

It can help you better understand how people perceive their experiences and the environment around them. This is because qualitative research focuses on exploring a person's beliefs, values, and actual behaviors, not just responses to multiple choice questions.

Through qualitative analysis, you can uncover underlying meanings in data sets that are not easily captured by numbers. It focuses on the "why" behind decisions, providing organizations with an understanding of consumer behavior that helps inform decision-making.

• Qualitative data examples

You can find qualitative data in a range of sources, including:

Text: Transcripts from interviews, open-ended survey questions , newspaper articles, etc.

Audio recordings : Podcasts, audio diaries, etc. 

Video recordings: Instructional videos, film footage, etc. 

Images: Photographs, illustrations, etc. 

Documents: Memos, reports, legal documents, etc.

• Qualitative data analysis methods

There are different methods of performing qualitative data analysis. These include content analysis, narrative analysis, discourse analysis, and thematic analysis. Let's take a look at each of these in more detail:

1. Content analysis

Content analysis is a research method used to identify and categorize information in data sets. It involves examining the text for "themes" or patterns that emerge from the data set. 

This method is often used when studying large volumes of textual material, such as newspaper articles, survey responses, and blog posts.

2. Narrative analysis

You can use narrative analysis to identify, analyze, and interpret narrative elements in data sets. This method focuses on the stories or experiences of subjects within the data set. 

Narrative analysis is often used when studying communication between people or groups, such as interviews or focus groups.

3. Discourse analysis

Discourse analysis is a research method used to interpret data sets by examining the language (how it's used and what it means) structure, and context in conversations between people. The objective is to understand how different social groups use language and what they mean.

This method is also often used when studying communication between people or groups, such as interviews or focus groups .

4. Thematic analysis

You can use thematic analysis to identify and interpret patterns in data sets. Thematic analysis involves breaking down the data set into smaller "themes" or categories and analyzing the relationship between them. 

This method is also often used when studying large volumes of textual material, such as newspaper articles, survey responses, and blog posts.

• How to do qualitative data analysis

An organization's in-depth understanding of the internal and external business environment is essential for growth. Qualitative data analysis provides tools to make sense of otherwise random and meaningless data.

But in the age of big data, it's not just about gathering and analyzing data. You must determine the right data to collect and the appropriate collection channels to get maximum value. And more importantly, you must have clarity about what you're researching and why.

For instance, if your objective is to understand how customers perceive your brand, the approach will differ from what you'd do if your objective was to discover customer sentiment about a particular product.

So, before you begin qualitative data analysis, set out the objectives. These objectives will help you determine how to conduct the process and the data to focus on.

Understandably, performing qualitative data analysis may be intimidating, as the process is complex. However, you'll get the insight you need with the right approach.

Here are the steps you should follow:

Step 1: Gather your qualitative data and conduct research

Gathering the data you need for analysis is the first step. Your approach here should be guided by the objectives you set. Make sure to document your data collection process and sources.

Depending on your objectives, you can use different data collection methods .

1. Traditional methods of collecting data

With technology advancing, there are new and faster methods of collecting data, such as text analytics. However, traditional methods like surveys and focus groups are still relevant and very effective for qualitative data analysis.

This is why many organizations still rely on traditional methods to collect data for qualitative analysis. Such methods include:

Surveys: allow you to collect data from large numbers of people and include open-ended questions to gather detailed feedback 

Focus groups : great for collecting data from small groups of people in a controlled environment,  allowing for discussion in groups which can provide opportunities for people to share opinions and build on ideas and feedback together

Interviews: allow you to collect detailed information  from individuals or key informants about topics and/or behaviors being studied

2. Leverage existing qualitative data

Sometimes, you don't need to collect new data. You can leverage existing qualitative data already in your organization's public domain. With numerous contact points with customers, you can access tons of solicited and unsolicited customer feedback .

You can access such data from support ticketing systems, emails, chatbots, and other sources. Analyzing such data can give you insights into customer sentiment, CX gaps, and other information that can help you understand your customers better.

Data from such sources is incredible because not only does it provide a lot of information, but it's easily accessible. Instead of wasting time and resources on creating new research studies or focus groups every time you have a question about your customers, simply review data you already have. It will most likely hold the answers you're seeking.

3. Untapped qualitative data channels

Data that is relevant to your research can be found in unexpected places.

For example, if you're looking for customer sentiment regarding a product, you may want to check out comments on YouTube or Reddit. If you're researching consumer behavior, look at reviews of your product on Amazon or Yelp.

These unexpected channels can offer insights that traditional methods cannot provide. Qualitative data in these places is usually unstructured and difficult to analyze, but they are invaluable, unsolicited sources of intelligence.

Step 2: Connect & organize all your qualitative data

After collecting the data, you need to ensure it's in a suitable format for analysis. Qualitative data is usually unstructured and scattered across different channels, so sorting them into usable chunks can be time-consuming. 

To make it easier to summarize, draw insight, and make decisions from collected data, it has to be easily accessible.

Some of the methods you can use to organize and make your data more accessible include:

1. Organize data manually

This method involves the use of spreadsheets to organize quantitative feedback. While organizations and departments used this method to analyze data separately, it's inefficient.

This approach can be very cumbersome, time-consuming, and does not allow you to gain insights at scale. It also requires a significant effort to ensure data accuracy. 

2. Organize data using qualitative data analysis software

Technology has made it easier to organize qualitative data. Qualitative analysis software helps you to organize quickly and analyze large volumes of qualitative data visually. Such tools allow you to create different categories for the responses and even generate sentiment scores for each response to draw insights from the data.

Qualitative data analysis software also makes it easier to share insights with the rest of your team by creating visual dashboards and reports. With qualitative data analysis software, you can save time and effort while deriving more accurate insights from your data. 

3. Use feedback repositories

Feedback repositories are online databases where you can store customer feedback . They make accessing and analyzing qualitative data easier across different channels, as they provide a platform that consolidates all your data into one place.

These platforms also facilitate collaboration, making it easy for teams to collaborate on research projects and gain insights. With feedback repositories, everyone can access the same data, analyze it, and share insights for further discussion.

Using qualitative data analysis software, feedback repositories, and manual methods to organize your qualitative data can help you make sense of your collected feedback. It also makes it easier to identify trends in customer behavior and draw meaningful insights from the data. This is an important step in the qualitative data analysis process. 

Step 3: Coding your qualitative data

The next stage of qualitative data analysis is coding. This involves assigning codes to each response you have collected for easy analysis and categorization.

Codes are short descriptions or labels used to identify common themes and topics in each response. For example, you can assign codes such as "Product Quality" or "Customer Service" to customer feedback to categorize them.

Coding qualitative data helps you categorize and organize the responses into different areas of interest, making them easier to analyze. It also makes it possible to identify patterns and trends in customer behavior and allows you to draw meaningful insights from the data.

In order to code your qualitative data, you need to define a set of codes that represent the different topics discussed in the responses. After that, you can assign these codes to each response. This will help you organize them into categories to do further analysis.

Step 4: Analyze your data and find meaningful insights

Once you have coded your qualitative data, the next step is to analyze it. Qualitative data analysis involves looking for patterns and trends in customer behavior and drawing meaningful insights from the data. 

You can use qualitative data analysis tools to help you with this process. These tools use different methods, such as content analysis, narrative analysis, and thematic analysis, to help you identify key themes in the responses.

Qualitative data analysis tools can help you make sense of large amounts of data and gain insights that are not immediately obvious. With qualitative data analysis software, you can save time and effort while deriving more accurate insights from your data. 

Step 5: Report on your data and tell the story

Once you have analyzed your qualitative data, the next step is to report on it. Qualitative data analysis reports provide a way to convey the insights you have gained from your data in an easily understandable format. 

• Which qualitative data analysis method should you choose?

When it comes to qualitative data analysis, there is no one-size-fits-all approach. Different methods are suitable for different kinds of customer feedback and research projects. 

Content analysis and thematic analysis are suitable for customer feedback and surveys, while narrative analysis can be used to analyze stories and narratives in customer feedback. Qualitative data analysis software can help you decide which method is right for your project.

• Advantages of qualitative data

Qualitative data analysis provides insights into customer behaviors, opinions, and experiences that quantitative analysis cannot obtain. Qualitative data can help you understand customer motivations, identify areas of improvement, and gain a deeper understanding of customer feedback. 

• Disadvantages of qualitative data

One major limitation of qualitative data analysis is that it does not provide statistically significant results. This is because the samples used to collect data are not representative of the population.  

As such, measuring the accuracy of qualitative data analysis and drawing quantitative conclusions from it is difficult. Qualitative data also tends to be more subjective, as it focuses on individual opinions rather than hard facts. 

• How Dovetail can help you

Qualitative data analysis is a powerful tool for gaining insights into customer experiences and behaviors. It can help identify areas of improvement, uncover customer motivations, and provide a deeper understanding of customer feedback.

Dovetail helps you quickly uncover meaningful insights from customer feedback. Our qualitative data analysis tools make it easy to analyze customer feedback , identify key themes, and create compelling reports to share with your stakeholders.

Try Dovetail and unlock the power of your qualitative data.

What are qualitative analysis tools?

Qualitative analysis tools are software programs that help analyze customer feedback and open-ended survey responses. These tools use different qualitative data analysis methods such as content analysis, narrative analysis, and thematic analysis to help identify key themes in customer responses.

What are the 3 main components of qualitative data analysis?

The three main types of qualitative data analysis are content analysis, narrative analysis, and thematic analysis. Content analysis involves looking for keywords and phrases frequently appearing in customer feedback.

In contrast, narrative analysis is used to analyze stories and narratives, and thematic analysis is used to group responses with common themes and topics. Qualitative data analysis software can help you choose the right method for your project.

What is the difference between qualitative and quantitative data analysis?

The main difference between qualitative and quantitative data analysis is that qualitative data analysis focuses on understanding customer behavior, opinions, and experiences to get at the 'why' and 'how,' whereas quantitative data analysis is concerned with measuring numerical results and statistics.

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Qualitative Data Analysis

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

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

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

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

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

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

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

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

There are three types of coding:

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

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

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

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

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

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

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

 Qualitative data coding

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

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

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

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

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

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

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

Harnessing The Power Of Qualitative Data Analysis

Unlock the stories in your data! Learn how qualitative data analysis helps you understand the “why” behind the numbers.

In today’s data-driven world, qualitative data analysis stands as a cornerstone for understanding the intricacies of human experiences, behaviors, and perspectives. While quantitative methods provide numerical insights, qualitative analysis delves deeper, unraveling the richness of narratives, contexts, and meanings embedded within the data.

This guide serves as a compass through the terrain of qualitative data analysis, offering clarity amidst the complexities of interpreting non-numerical data. Whether you’re a seasoned researcher or a novice navigating the realm of qualitative analysis, this resource aims to illuminate pathways to insightful interpretations and meaningful discoveries.

Throughout this guide, various approaches, techniques, and best practices essential for conducting robust qualitative data analysis will be explored. From understanding the fundamentals of qualitative inquiry to employing advanced methods for data interpretation, each section is designed to equip readers with the tools needed to unlock the stories hidden within their data.

Understanding Qualitative Data

Qualitative data comprises non-numerical information that seeks to understand the quality or nature of phenomena. Unlike quantitative data, which deals with quantities and measurements, qualitative data focuses on exploring and interpreting meanings, experiences, and behaviors within their natural contexts. It often involves gathering rich, descriptive data through methods such as interviews, observations, and textual analysis.

What Is Qualitative Data?

Qualitative data encompasses a wide range of information that is collected and analyzed to gain insights into the complexities of human behavior, perceptions, and social phenomena. This type of data is typically descriptive and subjective, allowing researchers to explore nuances, patterns, and underlying meanings that quantitative data alone may not capture. Qualitative data can take various forms, including text, images, audio recordings, and video footage.

Qualitative data can be categorized into several types based on the sources from which it is gathered and the methods used for collection:

• Interviews : Qualitative interviews involve in-depth conversations between researchers and participants to explore their perspectives, experiences, and opinions on a particular topic. These interviews can be structured, semi-structured, or unstructured, depending on the level of guidance provided by the researcher.
• Observations : Qualitative observations involve systematically watching and recording behaviors, interactions, and events in natural settings. Researchers may adopt participant observation, where they immerse themselves in the environment being studied, or non-participant observation, where they observe from a distance.
• Focus Groups : Focus groups involve bringing together a small group of participants to discuss specific topics or issues in a group setting. This method allows researchers to gather collective insights, uncover group dynamics, and explore shared experiences and perspectives.
• Textual Analysis : Qualitative textual analysis involves examining written or textual data, such as documents, articles, social media posts, or literature, to identify themes, patterns, and meanings. Researchers may use techniques such as content analysis, thematic analysis, or discourse analysis to interpret the data.
• Visual Data : Qualitative research may also involve the analysis of visual data, including photographs, drawings, paintings, or videos. Visual data can provide rich insights into cultural norms, social practices, and individual experiences, complementing other qualitative methods.

By utilizing a combination of these qualitative data types, researchers can gain a comprehensive understanding of complex phenomena, explore diverse perspectives, and generate rich, nuanced insights that contribute to knowledge advancement and social change.

Differences Between Qualitative And Quantitative Data

In summary, qualitative and quantitative data differ in their nature, measurement, and analysis approach. Qualitative data is subjective, descriptive, and focuses on understanding meanings and contexts, while quantitative data is objective, measurable, and emphasizes statistical analysis. The choice between qualitative and quantitative data depends on the research objectives, the nature of the phenomena under investigation, and the depth of understanding required.

Key Differences

• Qualitative Data : Qualitative data consists of non-numerical information that focuses on understanding the quality or nature of phenomena. It involves descriptions, narratives, and interpretations of experiences, behaviors, and perceptions.
• Quantitative Data : Quantitative data comprises numerical information that quantifies and measures phenomena. It deals with quantities, frequencies, and statistical analyses to identify patterns and relationships.
• Qualitative Data : Qualitative data is subjective and descriptive, often gathered through methods such as interviews, observations, or textual analysis. It explores meanings, contexts, and underlying perceptions.
• Quantitative Data : Quantitative data is objective and measurable, typically collected through surveys, experiments, or structured observations. It involves precise measurements, numerical values, and statistical analyses.
• Qualitative Data : Qualitative data analysis focuses on exploring, interpreting, and making sense of the richness and complexity of data. It involves techniques such as thematic analysis, content analysis, or grounded theory, emphasizing in-depth understanding and context.
• Quantitative Data : Quantitative data analysis involves statistical methods to analyze numerical data and test hypotheses. It employs techniques such as descriptive statistics, inferential statistics, and regression analysis to quantify relationships and patterns.

When to Use Each Type of Data

Qualitative Data:

• Use qualitative data when exploring complex phenomena that require in-depth understanding, such as human behavior, perceptions, and social processes.
• Use qualitative data when seeking rich, descriptive insights into individuals’ experiences, motivations, and cultural contexts.
• Use qualitative data when initial exploration and hypothesis generation are needed before quantitative investigations.

Quantitative Data:

• Use quantitative data when aiming to quantify relationships, patterns, or trends in phenomena through statistical analysis.
• Use quantitative data when conducting large-scale surveys or experiments that require numerical measurements and precise comparisons.
• Use quantitative data when seeking to generalize findings to a larger population and make predictions based on statistical models.

Related article: What’s the Difference: Qualitative vs Quantitative Research?

Fundamentals Of Qualitative Data Analysis

Understanding the fundamentals of qualitative data analysis is essential for researchers seeking to explore the complexities of human experiences, behaviors, and phenomena. 

Principles Of Qualitative Analysis

• Interpretive Understanding : Qualitative analysis aims to interpret and understand the meanings, perspectives, and experiences embedded within the data. Researchers approach analysis with an open mind, acknowledging the subjective nature of human experiences and seeking to capture the richness and complexity of the phenomena under investigation.
• Contextualization : Context plays a crucial role in qualitative analysis. Researchers consider the broader social, cultural, and historical contexts surrounding the data to provide depth and nuance to their interpretations. Understanding the context helps researchers uncover patterns, themes, and relationships that may influence the phenomena being studied.
• Reflexivity : Qualitative researchers reflect on their own biases, assumptions, and perspectives throughout the analysis process. Reflexivity involves critically examining the researcher’s role in shaping data collection, interpretation, and representation. By acknowledging their subjectivity, researchers strive for transparency and rigor in their analytical approach.
• Iterative Process : Qualitative analysis is often iterative and cyclical, involving repeated cycles of data collection, coding, analysis, and interpretation. Researchers continuously refine their understanding and insights through ongoing engagement with the data, allowing for flexibility and adaptation as new perspectives emerge.

Stages In Qualitative Analysis

• Data Collection : The qualitative analysis process begins with data collection, which may involve methods such as interviews, observations, focus groups, or document analysis. Researchers gather rich, descriptive data that capture the nuances and diversity of participants’ experiences and perspectives.
• Data Organization : Once data is collected, researchers organize and prepare it for analysis. This stage may involve transcribing interviews, organizing field notes, or categorizing textual or visual data for easier retrieval and management.
• Data Coding : Coding is a fundamental aspect of qualitative analysis, involving the systematic labeling and categorization of data based on themes, patterns, or concepts. Researchers use inductive or deductive approaches to identify and code segments of data relevant to their research questions.
• Theme Development : After coding the data, researchers identify recurring themes, patterns, or concepts that emerge from the analysis. Themes represent meaningful patterns or insights within the data and provide a framework for organizing and interpreting findings.
• Data Interpretation : In this stage, researchers analyze and interpret the coded data to generate insights and conclusions. Interpretation involves synthesizing the findings, exploring relationships between themes, and examining how they relate to the research objectives and broader theoretical frameworks.
• Validation and Triangulation : Qualitative researchers strive for validity and reliability by employing strategies such as member checking, peer debriefing, and triangulation. Validation ensures the trustworthiness and credibility of the findings by seeking multiple perspectives and sources of evidence.
• Presentation of Results : Finally, researchers present their findings in a clear, coherent manner that effectively communicates the key insights and implications of the study. This may involve writing narrative descriptions, creating visual representations, or presenting quotes and excerpts to illustrate key themes and findings.

Methods Of Qualitative Data Analysis

By understanding the methods of qualitative data analysis, researchers can navigate the complexities of data interpretation and uncover meaningful insights that contribute to knowledge advancement and social change. Check out some of these methods below:

Manual Analysis

​​Manual analysis involves the systematic examination and interpretation of qualitative data without the aid of specialized software tools. Here’s an overview of how to manually analyze qualitative data:

• Data Familiarization : Begin by immersing yourself in the data, such as transcribed interviews, field notes, or textual documents. Read and re-read the data to gain a thorough understanding of the content, context, and themes.
• Coding : Identify meaningful segments or units of data and assign descriptive labels or codes to them. Coding involves categorizing data based on themes, concepts, or patterns that emerge during analysis.
• Theme Development : Group related codes into overarching themes or categories. Look for connections, patterns, and variations within the data to develop a comprehensive understanding of the phenomena under investigation.
• Interpretation : Analyze the coded data to uncover insights, interpretations, and implications. Explore how themes relate to the research questions, theoretical frameworks, and broader contexts, and consider alternative perspectives and interpretations.

Computer-assisted Analysis

• Flexibility : Manual analysis allows researchers to adapt their approach to suit the unique characteristics of the data and research context.
• Deep Engagement : Manual analysis promotes in-depth immersion and understanding of the data, enabling researchers to uncover rich insights and nuances.
• Transparency : Manual analysis fosters transparency and reflexivity, as researchers are actively involved in the coding and interpretation process.

Key Techniques In Qualitative Data Analysis

Understanding qualitative data analysis techniques is crucial for researchers aiming to unravel the intricacies of human experiences, behaviors, and phenomena. This section delves into three key methods: Content Analysis, Grounded Theory, and Phenomenological Analysis. These approaches offer distinct pathways for interpreting qualitative data, each with its own merits and applications. 

Content Analysis

Content analysis is a systematic method for analyzing qualitative data that involves categorizing and interpreting textual, visual, or audio content. Researchers identify themes, patterns, and meanings within the data by coding and categorizing segments based on predetermined criteria or emergent themes.

• Content analysis is best used when researchers aim to explore patterns, trends, or representations within textual, visual, or audio data.
• It is particularly useful for analyzing large datasets, such as media content, archival materials, or online discussions.
• Researchers typically employ a deductive or inductive approach to coding, depending on whether they start with predetermined categories or allow themes to emerge from the data.

Grounded Theory

Grounded theory is an inductive approach to qualitative analysis aimed at developing theories or explanations grounded in the data itself. Researchers collect and analyze data iteratively, constantly comparing new data with existing insights to refine and develop theoretical frameworks.

• Grounded theory is best used when researchers seek to generate new theories or explanations based on empirical data.
• It is particularly suited for exploratory research where the phenomena under investigation are complex and not well understood.
• Researchers typically engage in constant comparison, iteratively coding data, identifying categories, and refining theoretical concepts until saturation is reached.

Related article: Unlock New Insights with Grounded Theory Qualitative Research

Phenomenological Analysis

Phenomenological analysis is a qualitative method focused on understanding individuals’ lived experiences and subjective perspectives. Researchers explore the essence of phenomena by bracketing preconceptions and assumptions, aiming to uncover the underlying meanings and structures of lived experiences.

• Phenomenological analysis is best used when researchers aim to explore and understand the subjective experiences of individuals within a particular phenomenon.
• It is particularly suited for research areas such as psychology, sociology, and healthcare, where understanding lived experiences is central to the research questions.
• Researchers typically engage in in-depth interviews or phenomenological reflections, seeking to uncover common themes, patterns, and structures across participants’ experiences.

Challenges In Qualitative Data Analysis

This section delves into the challenges inherent in qualitative data analysis, focusing on two primary areas: data management and ensuring validity and reliability . Effective management of qualitative data presents various hurdles, including organizing, categorizing, and safeguarding diverse data types. 

Common issues encountered in data management will be discussed alongside strategies to overcome them. Additionally, the concepts of validity and reliability in qualitative analysis will be explored, essential for establishing the trustworthiness and credibility of research findings. Understanding and addressing these challenges are crucial for researchers to enhance the rigor and impact of their qualitative data analyses. 

By addressing challenges in data management and implementing strategies to ensure validity and reliability, qualitative researchers can enhance the rigor and trustworthiness of their analyses, ultimately contributing to the credibility and impact of their research findings.

Data Management

Data management poses significant challenges in qualitative analysis, primarily due to the volume, complexity, and diversity of qualitative data. Here are common issues and strategies for managing data effectively:

Common Issues

• Organizing and categorizing large volumes of qualitative data, including transcripts, field notes, and multimedia content.
• Ensuring consistency and accuracy in data coding and categorization across multiple researchers or coding sessions.
• Maintaining data confidentiality and security, particularly when dealing with sensitive or personal information.
• Handling different data formats and sources, such as text, audio, video, and images, and integrating them into the analysis process.

Tips and Strategies

• Develop a systematic data management plan at the outset of the research project, outlining procedures for data collection, storage, and analysis.
• Use software tools or databases designed for qualitative data management, such as NVivo, Dedoose, or MAXQDA, to facilitate organization, coding, and retrieval of data.
• Establish clear naming conventions and file structures to ensure consistency and ease of access to data files.
• Regularly back up data and maintain version control to prevent loss or corruption of data files.
• Implement protocols for data sharing and collaboration, including documentation of coding decisions and regular team meetings to discuss data management issues.

Ensuring Validity And Reliability

Validity and reliability are fundamental concepts in qualitative data analysis, ensuring the trustworthiness and credibility of research findings. Here’s an explanation of these concepts and strategies for enhancing validity and reliability:

Explanation of Concepts

• Validity refers to the extent to which qualitative findings accurately reflect the phenomena under investigation. It involves assessing whether the data collection and analysis methods capture the intended constructs and meanings.
• Reliability refers to the consistency and stability of qualitative findings over time and across different researchers or contexts. It involves ensuring that the research process produces consistent results that can be replicated or corroborated.
• Triangulation : Use multiple data sources, methods, or researchers to corroborate findings and enhance the credibility of qualitative interpretations.
• Member Checking : Involve participants in the research process by validating interpretations or findings to ensure they accurately represent their experiences or perspectives.
• Reflexivity : Maintain awareness of your own biases, assumptions, and perspectives throughout the research process, documenting and reflecting on their potential impact on data collection and analysis.
• Audit Trails : Keep detailed records of coding decisions, analytic memos, and data transformations to enhance transparency and traceability of the analysis process.
• Peer Debriefing : Seek feedback and validation from colleagues or experts in qualitative research to review interpretations, challenge assumptions, and improve the credibility of findings.

Science Figures, Graphical Abstracts, And Infographics For Your Research

Mind the Graph is a user-friendly online platform designed to assist scientists in creating visually engaging scientific figures, graphical abstracts, and infographics. With customizable templates, a drag-and-drop interface, and a focus on scientific accuracy, researchers can efficiently communicate their findings with clarity and precision. The platform enables collaboration, sharing, and accessibility, empowering scientists to enhance the impact and accessibility of their research.

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Jessica Abbadia is a lawyer that has been working in Digital Marketing since 2020, improving organic performance for apps and websites in various regions through ASO and SEO. Currently developing scientific and intellectual knowledge for the community's benefit. Jessica is an animal rights activist who enjoys reading and drinking strong coffee.

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

MSc, PhD, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Qualitative data is often subjective, rich, and consists of in-depth information normally presented in the form of words. Analysing qualitative data entails reading a large amount of transcripts looking for similarities or differences, and subsequently finding themes and developing categories. Traditionally, researchers ‘cut and paste’ and use coloured pens to categorise data. Recently, the use of software specifically designed for qualitative data management greatly reduces technical sophistication and eases the laborious task, thus making the process relatively easier. A number of computer software packages has been developed to mechanise this ‘coding’ process as well as to search and retrieve data. This paper illustrates the ways in which NVivo can be used in the qualitative data analysis process. The basic features and primary tools of NVivo which assist qualitative researchers in managing and analysing their data are described.

QUALITATIVE RESEARCH IN MEDICINE

Qualitative research has seen an increased popularity in the last two decades and is becoming widely accepted across a wide range of medical and health disciplines, including health services research, health technology assessment, nursing, and allied health. 1 There has also been a corresponding rise in the reporting of qualitative research studies in medical and health related journals. 2

The increasing popularity of qualitative methods is a result of failure of quantitative methods to provide insight into in-depth information about the attitudes, beliefs, motives, or behaviours of people, for example in understanding the emotions, perceptions and actions of people who suffer from a medical condition. Qualitative methods explore the perspective and meaning of experiences, seek insight and identify the social structures or processes that explain people”s behavioural meaning. 1 , 3 Most importantly, qualitative research relies on extensive interaction with the people being studied, and often allows researchers to uncover unexpected or unanticipated information, which is not possible in the quantitative methods. In medical research, it is particularly useful, for example, in a health behaviour study whereby health or education policies can be effectively developed if reasons for behaviours are clearly understood when observed or investigated using qualitative methods. 4

ANALYSING QUALITATIVE DATA

Qualitative research yields mainly unstructured text-based data. These textual data could be interview transcripts, observation notes, diary entries, or medical and nursing records. 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. It is not a technical exercise as in quantitative methods, but more of a dynamic, intuitive and creative process of inductive reasoning, thinking and theorising. 5 In contrast to quantitative research, which uses statistical methods, qualitative research focuses on the exploration of values, meanings, beliefs, thoughts, experiences, and feelings characteristic of the phenomenon under investigation. 6

Data analysis in qualitative research is defined as the process of systematically searching and arranging the interview transcripts, observation notes, or other non-textual materials that the researcher accumulates to increase the understanding of the phenomenon. 7 The process of analysing qualitative data predominantly involves coding or categorising the data. Basically it involves making sense of huge amounts of data by reducing the volume of raw information, followed by identifying significant patterns, and finally drawing meaning from data and subsequently building a logical chain of evidence. 8

Coding or categorising the data is the most important stage in the qualitative data analysis process. Coding and data analysis are not synonymous, though coding is a crucial aspect of the qualitative data analysis process. Coding merely involves subdividing the huge amount of raw information or data, and subsequently assigning them into categories. 9 In simple terms, codes are tags or labels for allocating identified themes or topics from the data compiled in the study. Traditionally, coding was done manually, with the use of coloured pens to categorise data, and subsequently cutting and sorting the data. Given the advancement of software technology, electronic methods of coding data are increasingly used by qualitative researchers.

Nevertheless, the computer does not do the analysis for the researchers. Users still have to create the categories, code, decide what to collate, identify the patterns and draw meaning from the data. The use of computer software in qualitative data analysis is limited due to the nature of qualitative research itself in terms of the complexity of its unstructured data, the richness of the data and the way in which findings and theories emerge from the data. 10 The programme merely takes over the marking, cutting, and sorting tasks that qualitative researchers used to do with a pair of scissors, paper and note cards. It helps to maximise efficiency and speed up the process of grouping data according to categories and retrieving coded themes. Ultimately, the researcher still has to synthesise the data and interpret the meanings that were extracted from the data. Therefore, the use of computers in qualitative analysis merely made organisation, reduction and storage of data more efficient and manageable. The qualitative data analysis process is illustrated in Figure 1 .

Qualitative data analysis flowchart

USING NVIVO IN QUALITATIVE DATA ANALYSIS

NVivo is one of the computer-assisted qualitative data analysis softwares (CAQDAS) developed by QSR International (Melbourne, Australia), the world’s largest qualitative research software developer. This software allows for qualitative inquiry beyond coding, sorting and retrieval of data. It was also designed to integrate coding with qualitative linking, shaping and modelling. The following sections discuss the fundamentals of the NVivo software (version 2.0) and illustrates the primary tools in NVivo which assist qualitative researchers in managing their data.

Key features of NVivo

To work with NVivo, first and foremost, the researcher has to create a Project to hold the data or study information. Once a project is created, the Project pad appears ( Figure 2 ). The project pad of NVivo has two main menus: Document browser and Node browser . In any project in NVivo, the researcher can create and explore documents and nodes, when the data is browsed, linked and coded. Both document and node browsers have an Attribute feature, which helps researchers to refer the characteristics of the data such as age, gender, marital status, ethnicity, etc.

Project pad with documents tab selected

The document browser is the main work space for coding documents ( Figure 3 ). Documents in NVivo can be created inside the NVivo project or imported from MS Word or WordPad in a rich text (.rtf) format into the project. It can also be imported as a plain text file (.txt) from any word processor. Transcripts of interview data and observation notes are examples of documents that can be saved as individual documents in NVivo. In the document browser all the documents can be viewed in a database with short descriptions of each document.

Document browser with coder and coding stripe activated

NVivo is also designed to allow the researcher to place a Hyperlink to other files (for example audio, video and image files, web pages, etc.) in the documents to capture conceptual links which are observed during the analysis. The readers can click on it and be taken to another part of the same document, or a separate file. A hyperlink is very much like a footnote.

The second menu is Node explorer ( Figure 4 ), which represents categories throughout the data. The codes are saved within the NVivo database as nodes. Nodes created in NVivo are equivalent to sticky notes that the researcher places on the document to indicate that a particular passage belongs to a certain theme or topic. Unlike sticky notes, the nodes in NVivo are retrievable, easily organised, and give flexibility to the researcher to either create, delete, alter or merge at any stage. There are two most common types of node: tree nodes (codes that are organised in a hierarchical structure) and free nodes (free standing and not associated with a structured framework of themes or concepts). Once the coding process is complete, the researcher can browse the nodes. To view all the quotes on a particular Node, select the particular node on the Node Explorer and click the Browse button ( Figure 5 ).

Node explorer with a tree node highlighted

Browsing a node

Coding in NVivo using Coder

Coding is done in the document browser. Coding involves the desegregation of textual data into segments, examining the data similarities and differences, and grouping together conceptually similar data in the respective nodes. 11 The organised list of nodes will appear with a click on the Coder button at the bottom of document browser window.

To code a segment of the text in a project document under a particular node, highlight the particular segment and drag the highlighted text to the desired node in the coder window ( Figure 3 ). The segments that have been coded to a particular node are highlighted in colours and nodes that have attached to a document turns bold. Multiple codes can be assigned to the same segment of text using the same process. Coding Stripes can be activated to view the quotes that are associated with the particular nodes. With the guide of highlighted text and coding stripes, the researcher can return to the data to do further coding or refine the coding.

Coding can be done with pre-constructed coding schemes where the nodes are first created using the Node explorer followed by coding using the coder. Alternatively, a bottom-up approach can be used where the researcher reads the documents and creates nodes when themes arise from the data as he or she codes.

Making and using memos

In analysing qualitative data, pieces of reflective thinking, ideas, theories, and concepts often emerge as the researcher reads through the data. NVivo allows the user the flexibility to record ideas about the research as they emerge in the Memos . Memos can be seen as add-on documents, treated as full status data and coded like any other documents. 12 Memos can be placed in a document or at a node. A memo itself can have memos (e.g. documents or nodes) linked to it, using DocLinks and NodeLinks .

Creating attributes

Attributes are characteristics (e.g. age, marital status, ethnicity, educational level, etc.) that the researcher associates with a document or node. Attributes have different values (for example, the values of the attribute for ethnicity are ‘Malay’, ‘Chinese’ and ‘Indian’). NVivo makes it possible to assign attributes to either document or node. Items in attributes can be added, removed or rearranged to help the researcher in making comparisons. Attributes are also integrated with the searching process; for example, linking the attributes to documents will enable the researcher to conduct searches pertaining to documents with specified characteristics ( Figure 6 ).

Document attribute explorer

Search operation

The three most useful types of searches in NVivo are Single item (text, node, or attribute value), Boolean and Proximity searches. Single item search is particularly important, for example, if researchers want to ensure that every mention of the word ‘cure’ has been coded under the ‘Curability of cervical cancer’ tree node. Every paragraph in which this word is used can be viewed. The results of the search can also be compiled into a single document in the node browser and by viewing the coding stripe. The researcher can check whether each of the resulting passages has been coded under a particular node. This is particularly useful for the researcher to further determine whether conducting further coding is necessary.

Boolean searches combine codes using the logical terms like ‘and’, ‘or’ and ‘not’. Common Boolean searches are ‘or’ (also referred to as ‘combination’ or ‘union’) and ‘and’ (also called ‘intersection’). For example, the researcher may wish to search for a node and an attributed value, such as ‘ever screened for cervical cancer’ and ‘primary educated’. Search results can be displayed in matrix form and it is possible for the researcher to perform quantitative interpretations or simple counts to provide useful summaries of some aspects of the analysis. 13 Proximity searches are used to find places where two items (e.g. text patterns, attribute values, nodes) appear near each other in the text.

Using models to show relationships

Models or visualisations are an essential way to describe and explore relationships in qualitative research. NVivo provides a Modeler designated for visual exploration and explanation of relationships between various nodes and documents. In Model Explorer, the researcher can create, label and connect ideas or concepts. NVivo allows the user to create a model over time and have any number of layers to track the progress of theory development to enable the researcher to examine the stages in the model-building over time ( Figure 7 ). Any documents, nodes or attributes can be placed in a model and clicking on the item will enable the researcher to inspect its properties.

Model explorer showing the perceived risk factors of cervical cancer

NVivo has clear advantages and can greatly enhance research quality as outlined above. It can ease the laborious task of data analysis which would otherwise be performed manually. The software certainly removes the tremendous amount of manual tasks and allows more time for the researcher to explore trends, identify themes, and make conclusions. Ultimately, analysis of qualitative data is now more systematic and much easier. In addition, NVivo is ideal for researchers working in a team as the software has a Merge tool that enables researchers that work in separate teams to bring their work together into one project.

The NVivo software has been revolutionised and enhanced recently. The newly released NVivo 7 (released March 2006) and NVivo 8 (released March 2008) are even more sophisticated, flexible, and enable more fluid analysis. These new softwares come with a more user-friendly interface that resembles the Microsoft Windows XP applications. Furthermore, they have new data handling capacities such as to enable tables or images embedded in rich text files to be imported and coded as well. In addition, the user can also import and work on rich text files in character based languages such as Chinese or Arabic.

To sum up, qualitative research undoubtedly has been advanced greatly by the development of CAQDAS. The use of qualitative methods in medical and health care research is postulated to grow exponentially in years to come with the further development of CAQDAS.

More information about the NVivo software

Detailed information about NVivo’s functionality is available at http://www.qsrinternational.com . The website also carries information about the latest versions of NVivo. Free demonstrations and tutorials are available for download.

ACKNOWLEDGEMENT

The examples in this paper were adapted from the data of the study funded by the Ministry of Science, Technology and Environment, Malaysia under the Intensification of Research in Priority Areas (IRPA) 06-02-1032 PR0024/09-06.

TERMINOLOGY

Attributes : An attribute is a property of a node, case or document. It is equivalent to a variable in quantitative analysis. An attribute (e.g. ethnicity) may have several values (e.g. Malay, Chinese, Indian, etc.). Any particular node, case or document may be assigned one value for each attribute. Similarities within or differences between groups can be identified using attributes. Attribute Explorer displays a table of all attributes assigned to a document, node or set.

CAQDAS : Computer Aided Qualitative Data Analysis. The CAQDAS programme assists data management and supports coding processes. The software does not really analyse data, but rather supports the qualitative analysis process. NVivo is one of the CAQDAS programmes; others include NUDIST, ATLAS-ti, AQUAD, ETHNOGRAPH and MAXQDA.

Code : A term that represents an idea, theme, theory, dimension, characteristic, etc., of the data.

Coder : A tool used to code a passage of text in a document under a particular node. The coder can be accessed from the Document or Node Browser .

Coding : The action of identifying a passage of text in a document that exemplifies ideas or concepts and connecting it to a node that represents that idea or concept. Multiple codes can be assigned to the same segment of text in a document.

Coding stripes : Coloured vertical lines displayed at the right-hand pane of a Document ; each is named with title of the node at which the text is coded.

DataLinks : A tool for linking the information in a document or node to the information outside the project, or between project documents. DocLinks , NodeLinks and DataBite Links are all forms of DataLink .

Document : A document in an NVivo project is an editable rich text or plain text file. It may be a transcription of project data or it may be a summary of such data or memos, notes or passages written by the researcher. The text in a document can be coded, may be given values of document attributes and may be linked (via DataLinks ) to other related documents, annotations, or external computer files. The Document Explorer shows the list of all project documents.

Memo : A document containing the researcher”s commentary flagged (linked) on any text in a Document or Node. Any files (text, audio or video, or picture data) can be linked via MemoLink .

Model : NVivo models are made up of symbols, usually representing items in the project, which are joined by lines or arrows, designed to represent the relationship between key elements in a field of study. Models are constructed in the Modeller .

Node : Relevant passages in the project”s documents are coded at nodes. A Node represents a code, theme, or idea about the data in a project. Nodes can be kept as Free Nodes (without organisation) or may be organised hierarchically in Trees (of categories and subcategories). Free nodes are free-standing and are not associated to themes or concepts. Early on in the project, tentative ideas may be stored in the Free Nodes area. Free nodes can be kept in a simple list and can be moved to a logical place in the Tree Node when higher levels of categories are discovered. Nodes can be given values of attributes according to the features of what they represent, and can be grouped in sets. Nodes can be organised (created, edited) in Node Explorer (a window listing all the project nodes and node sets). The Node Browser displays the node”s coding and allow the researcher to change the coding.

Project : Collection of all the files, documents, codes, nodes, attributes, etc. associated with a research project. The Project pad is a window in NVivo when a project is open which gives access to all the main functions of the programme.

Sets : Sets in NVivo hold shortcuts to any nodes or documents, as a way of holding those items together without actually combining them. Sets are used primarily as a way of indicating items that in some way are related conceptually or theoretically. It provides different ways of sorting and managing data.

Tree Node : Nodes organised hierarchically into trees to catalogue categories and subcategories.

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

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Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

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 groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

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Qualitative Data Analysis

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After introducing basic statistical methods analyzing quantitative data this chapter turns to analyzing qualitative data, such as open-ended survey questions, planning documents, and narrative data collected from storytelling, planning workshops, public meetings, public hearings, planning forums, or focus groups. Practicing planners collect these types of data regularly and they are often the foundation of community needs analysis. Analyzing these data requires specialized methods. This chapter introduces methods to analyze qualitative data and conduct content analysis. Identifying trends and patterns of the data is the key to analyzing qualitative data. Related software, such as Atlas.ti, will be briefly explored to help researchers analyze complex qualitative data with complicated content or a large number of observations.

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Li, Y., Zhang, S. (2022). Qualitative Data Analysis. In: Applied Research Methods in Urban and Regional Planning. Springer, Cham. https://doi.org/10.1007/978-3-030-93574-0_8

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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 &
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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.

Iterative research process

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

Data collection

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

Document study

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

Observations

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

Semi-structured interviews

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

Focus groups

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

Choosing the “right” method

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

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

Possible combination of data collection methods

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

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

Data analysis

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

From data collection to data analysis

Attributions for icons: see Fig. 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 .

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.

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Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

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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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Level up your expertise with our growing collection of guides to qualitative research and analysis.

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

Learn the essential building blocks of qualitative research. This section covers everything from key definitions to research design, data collection methods, and important ethical considerations.

• Understand what qualitative research is and how it differs from quantitative research
• Get an overview of qualitative data collection methods like interviews, observations, and surveys
• Leverage best practices for conducting ethical qualitative research

The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

Get hands-on guidance for working with qualitative data. Discover how to transcribe, organize, code, and analyze textual, verbal, and visual data.

• Transform raw qualitative data into organized, coded sets ready for analysis
• Master qualitative analysis approaches like thematic analysis, content analysis, and grounded theory
• Leverage qualitative data analysis software to streamline the process

The Ultimate Guide to Qualitative Research - Part 3: Presenting Qualitative Data

Finish strong by effectively presenting qualitative findings. Learn how to visualize data, write up analyses, and meet standards for transparency and rigor.

• Communicate insights through effective qualitative data visualization
• Write a compelling qualitative research paper from start to finish
• Ensure research transparency, quality, and impact through rigorous practices

The Guide to Thematic Analysis

Master the art of thematic analysis, a powerful method for identifying patterns and themes in qualitative data. This guide covers everything from the basics to advanced techniques.

• Understand the purpose, advantages, and unique features of thematic analysis
• Learn how to apply thematic analysis to various qualitative data collection methods
• Discover best practices for presenting results and ensuring research rigor

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

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

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

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

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

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

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

What is quantitative data?

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

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

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

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

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

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

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

Quantitative Data 101: What is quantitative data?

Take a deeper dive into what quantitative data is, how it works, how to analyze it, collect it, use it, and more.

Learn more about quantitative data →

What is qualitative data?

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

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

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

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

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

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

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What are the differences between qualitative vs. quantitative data?

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

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

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

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

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

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

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

More examples of quantitative and qualitative data

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

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

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

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

The eggs were delicious.

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

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

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

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

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

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

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

Disadvantages of quantitative data

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

With choppy information, it can be inconclusive.

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

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

Advantages of qualitative data

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

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

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

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

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

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

Let's get into the research.

Quantitative and qualitative research

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

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

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

What is quantitative research?

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

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

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

Some examples of quantitative research: 

What is the amount of money invested into this service?

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

How many customers are actually clicking this button?

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

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

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

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

Quantitative data collection methods

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

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

Experiments

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

What is qualitative research?

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

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

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

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

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

The man had blonde hair with a blue hat.

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

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

Qualitative data collection methods

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

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

Focus groups

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

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So which type of data is better for data analysis?

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

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

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

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

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Qualitative vs. Quantitative: Key Differences in Research Types

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

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

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

What Is Qualitative Research?

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

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

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

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

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

Examples of How to Use Qualitative Research

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

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

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

Types of Qualitative Data Collection

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

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

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

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

Examples of How to Use Quantitative Research

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

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

Types of Quantitative Data

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

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

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

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

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

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

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

Please copy/paste the following text to properly cite this HowStuffWorks.com article:

• Open access
• Published: 28 March 2022

Unprofessional behaviours experienced by hospital staff: qualitative analysis of narrative comments in a longitudinal survey across seven hospitals in Australia

• Antoinette Pavithra 1 ,
• Neroli Sunderland 1 ,
• Joanne Callen 1 &
• Johanna Westbrook 1  

BMC Health Services Research volume  22 , Article number:  410 ( 2022 ) Cite this article

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Unprofessional behaviours of healthcare staff have negative impacts on organisational outcomes, patient safety and staff well-being. The objective of this study was to undertake a qualitative analysis of narrative responses from the Longitudinal Investigation of Negative Behaviours survey (LION), to develop a comprehensive understanding of hospital staff experiences of unprofessional behaviours and their impact on staff and patients. The LION survey identified staff experiences and perceptions related to unprofessional behaviours within hospitals.

Two open-ended questions within the LION survey invited descriptions of unprofessional staff behaviours across seven hospitals in three Australian states between December 2017 and November 2018. Respondents were from medical, nursing, allied health, management, and support services roles in the hospitals. Data were qualitatively analysed using Directed Content Analysis (DCA).

From 5178 LION survey responses, 32% ( n  = 1636) of participants responded to the two open-ended questions exploring staff experiences of unprofessional behaviours across the hospital sites surveyed. Three primary themes and 11 secondary themes were identified spanning, i) individual unprofessional behaviours, ii) negative impacts of unprofessional behaviours on staff well-being, psychological safety, and employee experience, as well as on patient care, well-being, and safety, and iii) organisational factors associated with staff unprofessional behaviours.

Unprofessional behaviours are experienced by hospital staff across all professional groups and functions. Staff conceptualise, perceive and experience unprofessional behaviours in diverse ways. These behaviours can be understood as enactments that either negatively impact other staff, patients or the organisational outcomes of team cohesion, work efficiency and efficacy. A perceived lack of organisational action based on existing reporting and employee feedback appears to erode employee confidence in hospital leaders and their ability to effectively address and mitigate unprofessional behaviours.

Peer Review reports

A growing body of literature has presented evidence demonstrating the negative impact that unprofessional behaviours amongst healthcare staff has on organisational outcomes, patient safety, and staff well-being [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. Waterson et al. examined the enactment of patient safety culture across hospitals and highlighted the need to further explore the complex range of factors that impact patient safety culture within healthcare systems [ 11 ]. The relationship between human resource management practices, staff shortages, employee performance and patient satisfaction has also been explored by international studies, demonstrating the implications of organisational practices on employee and patient outcomes at hospitals [ 12 ]. Studies that have sought to demonstrate a link between staff well-being, patient safety and quality of care have called for further evidence to verify and elaborate how the metrics of staff well-being are associated with improved safety and quality [ 6 , 13 ]. In studying the psychometric properties of teamwork and patient safety, researchers have also pointed out that future research needs to address how the “fault lines” in healthcare teams and leadership impact patient care and outcomes [ 14 , 15 ].

Existing efforts to create comprehensive categorisation of unprofessional behaviour for medical professionals thus far, have largely focused on professional sub-groups such as medical students [ 16 ]. Recent scholarship has identified the need for better definition and conceptual clarity, and the need for categorisation of unprofessional behaviour in healthcare that is relevant to multiple professional groups [ 16 , 17 , 18 ]. Rogers and Ballantyne characterise professionalism within medical practice as spanning ethical and behavioural aspects in practical aspects of healthcare provision [ 19 ]. The current commonly accepted understanding of professional behaviour is characterised as being enshrined within institutional, local, and international codes of conducts that demand values-based behaviours anchored in respect, compassion, justice, integrity, and excellence [ 20 ]. Therefore, the converse of professional behaviour can be conceptualised as any manner of being, behaving or belonging within a healthcare organisation that negatively impacts other internal or external individual stakeholders, relationships, the cohesive environment at any level within the organisation, or organisational outcomes [ 18 , 21 ]. While a growing body of work has identified the prevalence of unprofessional behaviours as experienced by groups of staff, such as nurses, physicians or medical students, further work is warranted to examine how the phenomenon of unprofessional behaviour unfolds amongst all staff groups across multiple contexts in healthcare organisations [ 2 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Growing awareness of the negative impacts of unprofessional behaviours within the workplace, including within healthcare organisations, has led to developments in policy and regulation within Australia over the last two years to ensure psychosocial safety is considered a crucial factor within workplace health and safety management frameworks [ 35 , 36 ]. As healthcare organisations implement these changes, there is a need for ongoing assessments of how staff experience unprofessional behaviour, its impacts and organisational factors that contribute to these experiences [ 37 ].

A recent survey was designed to understand the experience of unprofessional behaviours among hospital staff in Australia [ 38 , 39 , 40 , 41 ]. The survey invited participation from all staff at seven Australian hospitals enquired about the prevalence of 26 unprofessional behaviours, from rudeness to physical assault between staff [ 42 ]. In total 39% ( n  = 2009) of staff reported experiencing one or more of these behaviours in the previous week [ 42 ]. The survey also enquired about staff perceptions of the impact and organisational factors associated with both reporting and reducing these behaviours. In addition to these structured questions, the survey contained two opened-ended questions inviting staff to comment on their experiences of unprofessional behaviour in their hospital. The aim of this paper was to assess the descriptions of unprofessional behaviours provided by all staff groups within hospitals to understand the types of i) negative behaviours enacted by staff, ii) negative impacts on wellbeing and safety of human stakeholders, and iii) organisational factors that contribute to the prevalence of unprofessional behaviours.

Study design, scope and setting

The Longitudinal Investigation of Negative behaviour (LION) survey was administered to staff across seven hospitals in three Australian states between December 2017 and November 2018 to determine the baseline prevalence of unprofessional behaviours prior to an organisational intervention [ 42 ]. Two open-ended questions were included in the LION survey: 1) “Are there any specific instances of unprofessional staff behaviour that you would like to describe?”, and 2) “Are there any other comments you would like to make about staff behaviour in this hospital?”.

Data synthesis and analysis

Narrative responses to the open-ended questions were imported into NVivo 12 (QSR International) to enable qualitative analysis using the Directed Content Analysis (DCA) method. DCA uses constructivist grounded theory to determine a preliminary coding scheme [ 43 , 44 ]. The analysis was also informed by the themes presented within the closed-ended questions of the LION survey that offered definitions for types of unprofessional behaviours and perceived impacts on respondents [ 42 ]. The closed-ended questions in the LION survey spanned the following categories: (i) employee demographics, ii) types of negative behaviours experienced and frequency, (iii) degree and type of impact of the unprofessional behaviours experienced, (iv) self-identified speaking up skills, and (v) perceptions related to organisational factors. This part of the data analysis formed the deductive coding scaffold for the study of the narrative responses collected through the survey as outlined in Fig.  1 . Inductive coding of narrative comments was performed by identifying descriptions about unprofessional behaviours provided by staff within narrative responses. Inductive coding was informed by the dimensions set out by the closed-ended survey sections, where questions covered the experience and perception of respondents in relation to 26 types of negative behaviours and impact types categorised based on staff, patient and organisational impacts [ 37 , 38 ]. Deductive coding served to elaborate on the granular details pertaining to context and content of behaviours that have been reported as sub themes within the results. Systems-thinking-based approaches, particularly in relation to living systems and understanding work-related violence in hospital settings in Australia have informed the themes used to categorise the descriptions of behaviours provided by respondents [ 21 ]. Increased regulatory focus on the impacts of psychosocial risks on workplace wellbeing and safety have also informed our analysis and the need to highlight the linkages between themes – particularly the impact of organisational factors on employee psychosocial safety and wellbeing [ 45 , 46 , 47 ].

Directed Content Analysis performed on narrative responses to the LION survey

Two authors performed the qualitative analysis, where AP coded the entire set of narrative comments and NS independently coded over 20% of the narrative comments. Inter-rater agreement was over 90% and achieved through discussion till consensus was reached around the themes and sub-themes used to classify the comments. Coding was performed on all usable comments till themes were saturated, and all authors approved the final set of themes reported in the results. Quotes from narrative comments that have been provided within this article have been modified to de-identify professional groups, proprietary software names and individuals where necessary with asterisks (e.g., “***”).

Respondent characteristics

From 5,178 LION survey responses, 32% ( n  = 1636) of respondents provided narrative comments to the two open-ended questions. Of these, 72% ( n  = 1183) worked in clinical roles, and 26% ( n  = 453) in non-clinical roles (Table 1 ). The profile of staff who provided comments to the opened-ended questions was very similar to the overall survey population (Table 1 ). For example, 11% of survey respondents were doctors, and 11% of comments were provided by doctors. Female respondents were slightly more likely than males to provide comments (e.g., 75% of surveys were completed by women, and 78% of the open-ended questions were provided by women). The high proportion of female respondents reflects the overall healthcare workforce which is 75% female [ 42 ].

Thematic findings about unprofessional behaviours

Participants provided a range of descriptions of unprofessional behaviours based on their lived experience and observed unprofessional behaviours within their workplaces in response to the two open-ended questions within the LION survey. These accounts were categorised according to three primary themes and the related sub-themes (Tables 2 , 3 , 4 and 5 ):

Theme 1: Individual unprofessional behaviours.

Theme 2: Negative impacts of unprofessional behaviours.

2a: Impact on staff well-being, safety, and employee experience.

2b: Impact on patient care and patient safety.

Theme 3: Organisational factors associated with staff unprofessional behaviours.

Theme 1: Types of individual unprofessional behaviours

The range of unprofessional behaviours described in Table 2 were behaviours that respondents stated had occurred between two individuals and were identified by verbal, non-verbal interactions, or behaviours demonstrative of a lack of positive values, such as unethical and discriminatory behaviours.

“Doctors yelling at nurses for no reason. Rudeness from doctors towards nurses such as sarcasm and belittling behaviours. Doctors showing sexually explicit photos / pictures on phones. Doctors being rude to nurses when called after hours with concerns about patients. Doctors calling nurses on their mobile phones whilst they are driving home after their shift and yelling at them bringing them to tears.”

In addition to overtly negative behaviours, comments also mentioned a wide range of behaviours that demonstrated lower grade negative behaviours encompassed within the concept of “incivility”, such as “working with headphones on”, hostility, not engaging in commonly expected socio-cultural niceties such as greeting colleagues, or other general forms of negative demeanours that were perceived as unprofessional.

“Just general incivility. People coming into work in a bad mood. Or people being passive aggressive about things not being done or not being done right…”

The normalisation of verbal and non-verbal incivility through facial expressions and body language within regular interactions was mentioned as a factor that contributed to creating a negative work environment.

"…Personal interactions with some team members and mangers can feel arduous as their reaction will be unnecessarily critical and feel like you are being constantly judged. Facial expressions and body language can be perceived that your performance will never be enough to satisfy...Witnessing other people get treated this way as well erodes the confidence of the team and affects team morale..."

Experiences of unprofessional behaviours such as eye-rolling and sarcasm within the context of performing work in high-risk work units were also described.

“I regularly experienced aggressive "over-ruling" and "eye-rolling" type behaviour when expressing clinical concerns about patient conditions in the ICU. It is very detrimental to confidence in reporting concerns when you are met with an offhand or sarcastic response. I feel it creates an environment where you are reluctant to report concerns for fear of being embarrassed.”

Extreme interpersonal negative behaviours that spanned harassment, bullying, assault, and discrimination appear to be well-understood and characterised as widely unacceptable. It is likely that well-developed policy, regulations, explicit directives across Human Resources (HR) and existing risk management systems contribute to this common understanding. Some of these types of behaviours described by respondents spanned themes related to sexism, sexual harassment and disrespect based on gender.

“I feel gender discrimination is part of everyday life at my institution. I am not offered the same level of respect or opportunities as my male counterparts. Comments about gender and pregnancy are a regular occurrence. Many of my female colleagues have experienced sexual harassment from prominent male colleagues and have never spoken out in fear for their careers.”

In addition to discriminatory behaviours based on sex and gender, a combination of other unprofessional behaviours such as unprofessional humour with discriminatory themes were also noted.

“… ‘Jokes’ in meetings about patients' ethnicity, social status, education level. – frequently…”

Descriptions of unprofessional behaviours extended beyond the experience of the primary behaviours themselves and went on to describe the flow-on effects when inadequate or inconsistent organisational action (or inaction) appeared to be perceived as unprofessional as well, resulting in compounding the impacts of individual interpersonal unprofessional behaviours, and making them a more pronounced phenomenon, at the organisational level.

“I hear a lot about the inappropriate behaviour of surgeons to patients and staff. One surgeon that we had as a patient, a few years ago now, was sexually verbally abusive to many staff members, including me, but nothing was done about it as the attitude was that he is a surgeon and therefore must be treated like a god. This hospital deals well with unprofessional behaviour if it is occurring from a general worker (they will be promptly taken to a disciplinary hearing), but it is very different if it is a manager or surgeon in which case that's ok, that's expected, no action required.”

Theme 2: Negative impacts of unprofessional behaviours on staff and patients

The negative impacts of unprofessional behaviours described were clustered across staff (Theme 2a – Table 3 ) and patients (Theme 2b – Table 4 ). Direct impacts of experiencing unprofessional behaviours spanned negative outcomes related to psycho-social well-being, safety, and employee experience.

"I've seen people bullied and confidence squashed to the point they resign as an anxious mess. This seems like a popular strategy to move along the unwanted/unpopular… I've been told of reporting sexually inappropriate conduct, and because the perpetrator was a senior surgeon, nothing was done, and the complainant was disregarded…”

The negative effects resulting from being exposed to unprofessional behaviours were described as being amplified by the unspoken codes of silence [ 48 ] surrounding widespread tolerance of negative behaviours, a sense of learned helplessness and disempowerment as a result of occupying a position that exposed victims to being abused by those in power, and conditional or inconsistent support [ 49 ]. Some respondents described the toll that being exposed to such behaviours has on their health and sense of well-being.

“My boss has left now - she used to hang up on me if I called in sick…Scream at me if I asked for a day off, refused to give me long service leave. I was scared of her; I wasn't the only one. Reporting would not have worked, being a boss!”

The effects, that staff who were exposed to these behaviours spoke of, included increased presenteeism (which refers to professionals being physically present and working, but under-functioning within their roles), ineffective teamwork, and an erosion of individual psycho-social and physical well-being over time.

"At this moment I am suffering from anxiety because of bullying: accusing…intimidating, abusing and ignoring… I have almost four months of sick leaves because I’m refused sick (leave)…As a victim I feel like no one cares… Some morning(s), I said to my (partner), ‘I don’t want to live…anymore, I want to disappear from all this negativity…for good. Enough is enough’, and my (partner) is very (concerned) because he is the only one (who) can see the (effect this has had) on me…”

Instances of negative behaviours, that had a direct or indirect impact on patient care (Theme 2b), ranged from staff treating patients and their families rudely and proving inadequate care to posting details about patients on social media. Examples of unprofessional behaviours in front of patients or in the process of providing patient care were provided.

“On one occasion in the last 12 months during a procedure with a patient, the staff member threw drapes onto the floor when they realised the solution used was something that the patient was allergic to. Rather than apologise and explain to the patient, wash the solution off and calmy recommence the procedure, the staff member picked the drapes off the patient and threw them across the room and on the floor. The patient was conscious and could hear and see this behaviour.”

Blatant violation of processes and procedures compromising patient safety and confidentiality were also described.

"I have seen staff uploading pictures in social media describing about the sick patient they looked after and including the pictures of the pump and patient they cared for.

The themes of these impacts on patient care and safety resulting from these behaviours are listed in Table 4 . Comments indicated direct negative impacts on quality, manner, and safety of patient care, as well as indirect impacts as a result of poor teamwork and communication on the quality of patient care.

“… (unprofessional behaviours) this does have an impact on the quality of patient care employees are able to provide and can lead to poor staff behaviour, communication breakdown and further issues.”

Ultimately, cumulative episodes of unprofessional behaviours displayed by staff and their negative impacts over time appear to coalesce into a culture of blame and normalisation of negative behaviours within organisational cultures at hospitals.

“I was…yelled at by a doctor after being falsely accused of not doing my job- when the job was actually the responsibility of another staff member- which I politely explained to the doctor. The doctor then proceeded to make two other staff members cry as a result of verbally aggressive behaviour. This type of behaviour is unfortunately, not uncommon.”

Working within environments where normalised incivility was prevalent in addition to high degrees of professional stress was mentioned as a contributing factor to undermining staff well-being, performance, and safety.

“…It’s just making work a depressing place to be hence why there’s so many staff constantly taking sick leave because they’re over it.”

In describing the experience of a colleague who had resorted to taking stress leave resulting from a combination of negative workplace interactions and work-related stress, one respondent also added:

“…It is also noted that there have been a number of suicides of staff…it is very concerning if work stressors are a significant contributor."

“Repeat offenders” and senior staff were mentioned as perpetuating a culture that was disrespectful, and therefore diminishing efficiency of individuals, teams, and consequently impacting patient safety.

"I have witnessed a physician being highly unprofessional both over the phone and in person to several nurses on the floor I work. It has left the nurses shaken and I believe it will have an effect on patient safety if nurses don't wish to call the physician when needed.”

Theme 3: Organisational factors associated with unprofessional behaviours

Organisational factors as a theme emerged repeatedly in response to descriptions of unprofessional behaviours, indicating that contextual institutional factors were perceived as inextricably linked to the positive or negative experience and perceptions of employees (Table 5 ). Inter-group unprofessional behaviours or instances were also included within this theme. Inter-group unprofessional behaviours are defined as behaviours enacted implicitly or explicitly by a group and not a single individual, which have a negative impact or are perceived negatively by staff. These behaviours were often associated with organisational factors that had contributed to unprofessional behaviours and were therefore categorised under this theme. The main sub-themes that emerged within this theme were:

Process, Performance and Practice

Leadership and Management

Learning and Development

Remediation

Negative Clustering

Organisational factors related to process, performance, and practice

The complexity of multiple process or practice-based factors that influenced staff behaviour negatively were acknowledged and described by respondents. Lack of clarity in workflow processes and management appeared to contribute to work-related discontent.

“…Roles are blurred. Instructions are unclear and when you try to sort something out you are verbally attacked (or emailed) if something is not done 'correctly' even though you tried to seek out the 'correct' process…”

Conflicting priorities and diffused responsibility and the impact of these dynamics on the efficiency and quality of work was also noted.

“…Some enquiries to some departments/individuals go unanswered/ignored regularly. Not only does this impair my work efficiency, but also signals to me that I am not viewed as a customer/client of that department, or I and my work are seen as too far down…their priority list to bother with.”

A lack of accountability for the negative work-related dynamics appeared to further accentuate the normalisation of the interpersonal and group dynamics that were perceived as unprofessional.

“Power relations within each silo of disciplines…(e.g.) a clinician raising unprofessional behaviour of another clinician from a different discipline, reports up through line manager/stream manager, only for the reporting clinician to be made 'the problem'… shunt off to EAP (employee assistance services) …unprofessional behaviour continues… Line manager and line manager's manager continually demonstrate unprofessional behaviour. HR dept also demonstrates unprofessional behaviour, no one else to report to…”

Some other factors that contributed to the challenges that staff mentioned included issues related to risk management and personnel management practices.

“High staff turnover due to poor management by upper management. Particularly a concern when issues raised but no assurance that the issue(s) will be addressed.”

These challenges appeared to extend to issues such dealing with change, and the lack of sufficient resources for staff to perform work well, while being supportive of each other. This dynamic seemed to create low morale and possibly flow-on effects like individual unprofessional behaviours.

“I feel that there is a high level of stress among staff members at this institution related to increasing workload, need for change, poor implementation of new systems and poor communication. There seems to be less capacity for people to be supportive and low morale.”

Organisational factors related to leadership and management

Respondents mentioned organisational factors impacting their ability to deliver appropriate care due to goals at the inter-personal, professional, service, and organisational levels being at odds. Comments noted the tension between what were perceived to be organisational goals such as remaining financially viable or profitable against the conditions they believed were required to provide person-centred care.

“As frontline workers, we value patient-centred care and the patient experience over the cost-cutting. Management are intent on viewing any staff who challenge such decisions as recalcitrant and therefore need discipline.”

Financial models and operational factors found multiple mentions within the comments.

“The work environment is toxic with male surgeons who bring in high revenue streams to the hospital (and) seem to be allowed to treat staff as they please despite the fact that staff have raised concerns, along with the fact that (some staff) seem willing to capitulate to these abusive ‘quirks’ of particular individuals for fear of losing their revenue streams.”

Profitability and financial viability of the hospital appeared to present a fault-line along which power is enacted, and solidified through the enforcement, or lack thereof, of protective policies and regulations.

“…Staff are not made accountable for their actions; policies are not enforced which promote best and safe practices. At times it appears that bad behaviour is rewarded rather than managed by the department managers.”

Organisational factors related to learning and development

Teaching and mentoring junior staff was mentioned contextually in relation to multiple negative interactions.

“A senior nurse reported me to the NUM (nursing unit manager) for not knowing the entire instrument (set) instead of her going through each instrument so I can learn them while using it. (The) same staff nurse pick(ed) another nurse to do a count with her (theatre set-up) while I was already doing the count with her… Teaching must be done while doing the task and unnecessary reporting (of junior staff) without addressing or educating the person involved makes learning hard and the experience unpleasant.”

Comments that described interactions such as these drew attention to the efficiency of the clinical process and patient safety protocols that were being followed. However, uncivil behaviours that were enacted in the interest of preserving or improving efficiency appeared to in some instances to undermine the confidence and ability of junior staff to learn because of the manner of communication displayed.

“The culture here is poor, but largely because from my point of view there is excessive pressure placed on trainees to make up for shortfalls in hospital systems and processes…For example, Theatre late starts being ‘blamed’ on a registrar, or incorrect operative bookings being ‘blamed’ on a junior surgical trainee. Often these problems are out of control of the junior staff member, but they receive the burden of blame, and it leads to an intimidating culture.”

Organisational factors related to remediation

Combined with job-based and organisational factors, unprofessional behaviours appear to be tolerated and internalised as normalised behaviour, creating a self-perpetuating cycle of negative behaviours, negative sub-cultures, and self-isolation among victims.

“I've had a staff member say she will only deal with me, not my colleague because of his inappropriateness toward her and his comments about her body. HR are aware of this, but she was not comfortable enough to make a formal complaint. She did not feel supported.”

The perceived incentivisation of unprofessional behaviours by the organisation, an unequal distribution of power, flow-on effects of internalised acceptance of unprofessional behaviours within certain contexts, and what appeared to be organisational inaction in stemming negative behaviours were considered causal to cementing unspoken norms and expectations related to unprofessional behaviours among staff.

"…comments from ***(professional group) about staff that are unfair and unprofessional…Constant passive aggressive comments that are uttered to demean and shame staff with management well aware of this behaviour and has been happening for years…Despite staff bringing concerns to management staff seldom see a positive outcome and are rarely informed if their concerns are being addressed. With staff being reduced to tears for fear of working (with)*** surgeon… The environment is toxic.”

The “seriousness” of behaviours appeared to be a factor in determining whether staff would report these incidents using formal mechanisms. This appeared to be the case particularly when hierarchical dynamics had also informed these interactions.

“If very senior staff are perpetrators of problematic behaviour and they are a manager or a person the junior staff member reports to, it appears very difficult for reporting to be initiated unless it has become very serious.”

Negative clustering behaviours within specific work environments and roles

The design of organisational structures and distribution of power invested within certain professional roles also appeared to trigger aggressive or abusive behaviours from certain staff within some environments repeatedly. Behaviours on the lower spectrum of severity such as incivility appeared to intersect with negative intergroup behaviours as well, thus contributing to negative sub-cultures over time because these behaviours seem to be considered more commonplace, diffuse, and thus, more difficult to address. Operating theatres were mentioned as environments that foster a climate that lent itself to higher degrees of unprofessional behaviours, as well as more extreme forms of unprofessional behaviours.

“I often find the operating theatres an unpleasant place to work due to the behaviour of several staff members that seems to be fostered by within an insular unit such as operating theatres.”

Professional stressors related to surgical processes and procedures appear to activate or elicit authoritarian and hierarchical modes of interacting between inter-professional groups. This may be associated with staff lapsing into verbal or physical unprofessional behaviours.

“Offensive language and threatening language (used)… ‘I'm reporting this to your boss’, ‘Do you know who I am?’ used by surgeons when they do not get their way or there are difficulties with examination requests or in theatre cases... Nursing staff can be very rude and aggressive to radiographers when asking for crucial patient safety questions to be completed or filled out properly.”

Such instances may be associated with self-perpetuating cycles of negative climate within specific environments and entrenched practices that may come to be seen as reflective of professional sub-groups.

“Some (surgical medical staff) in theatres are still very rude to nurses - every week I would witness a surgeon raising his voice, yelling, throwing things out of anger and impatience…”

Further, within these professional sub-groups, certain sub-specialties were named by multiple commenters, noting that professionals who belonged to these clusters appeared to demonstrate a greater pre-disposition to displaying unprofessional behaviours.

"Aggressive swearing from (a) surgeon in theatre if (there was a) perception of difficulty, raised theatre temperature, or) having to wait for anything…Belittling negative and passive aggressive commentary from surgeon (was directed) to (the) entire team of specialty. (The) staff (were) so demoralised they were unable to work in (the) theatre. Some staff reduced (their) hours and changed workdays to avoid this individual. Others refused to work with them.”

Spatial configurations that involve multiple staff at varying levels of experience and expertise may introduce conflicting roles and expectations that increase the complexity of the primary task and patient care.

“(A) ***(sub-speciality) surgeon (was) yelling at theatre tech for being unable to find (the) correct piece of equipment. Another *** surgeon (was) yelling at scrub nurses and throwing instruments because certain equipment (was) unavailable – (the) surgeon did not check availability before set up. A third *** surgeon (was) putting pressure on theatre staff to rush starting a case because he had another list in another hospital to start…In my experience the worst behaved staff at *** are the surgeons…”

The associating between negative clusters and patterns based on clinical environments such as operating theatres or working within surgical specialties by extensions were seen as contributing to a psychological and physically unsafe working environment.

“…I felt unsafe in that environment… In the theatre environment at this hospital, I have been sexually harassed verbally and bullied by senior staff…”

Ultimately, descriptions of negative behaviours noted the intrinsic link between unprofessional behaviours and the challenging circumstances inherent to working in healthcare. Positive acknowledgments of exemplary behaviour were mentioned within comments specific to certain hospital sites and work groups. These comments were notable as respondents commented that despite exposure to negative behaviours, ultimately, positive experiences did contribute to employee satisfaction and engagement at work.

“I have been blessed to work on a ward with an amazing and supportive...staff. I am happy the minute I walk through the doors no matter what’s going on in my life. I love my work and the people who work there in all the different jobs about the place.”

Our results offer insights into a variety of views from a broad range of hospital staff across varying roles, departments, and diverse employment arrangements. Using an approach based on systems theory is appropriate to understanding the narrative comments because this approach has allowed us to preserve and report on the complexity of interactions between elements that result in individuals’ experience of organisational culture and unprofessional behaviour, rather than reducing these lived experiences to statistical artefacts that can be neatly delineated. The need for localisation and customisation of training and remediation mechanisms may be warranted owing to the heterogeneity in how unprofessional behaviours are experienced across professional roles and specialities as well as by those with different demographic characteristics [ 50 ]. The narrative comments analysed within this study offer a rich insight into the complex relationships between individual staff behaviours, group structures, structural inadequacies, and implicit expectations that contribute to the phenomenon of unprofessional behaviours among hospital staff. These findings further elucidate results presented by previous researchers related to the complex inter-relationships between multiple inter-personal, individual, and organisational factors in giving rise to the phenomenon of unprofessional behaviour [ 15 , 23 , 26 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ]. Organisational and sector-related socio-cultural and contextual factors (such as negative, internalised sub-cultures and pervasive incivility) also seem to have a significant influence on the experience and under-reporting of unprofessional behaviours that has been identified by previous studies [ 5 , 6 , 60 , 61 , 62 ].

While confirming the findings of previous studies that have identified the complex interactions between organisational and individual staff factors in the emergence of unprofessional behaviours among hospital staff, our study also provides further clarity around these themes. The identification and categorisation of specific behaviours displayed by individual hospital staff members, the prevalence and proliferation of these behaviours within specific spatial environments, as well as their relationships to organisational structure, leadership and management factors are a significant addition to the literature related to healthcare organisational behaviour and culture. Consequently, the range of perceived negative impacts of these behaviours on staff satisfaction, well-being and safety, and not just patient safety and well-being has not previously been reported across a similar range and scale of multiple staff roles, groups, and hospital services, to the best of our knowledge. Our study presents a synthesis of clinical as well as non-clinical staff from across a large-scale multi-site cohort, demonstrating the spread of unprofessional behaviour among hospital staff as a pervasive problem with several common features. This presents a promising avenue for ongoing culture change interventions to focus efforts.

Existing literature does not appear to have sufficiently elaborated the prevalence of divergent experiences of unprofessional behaviours, therefore, making the process of designing suitable interventions challenging. From our findings, training staff to collectively understand what unprofessional behaviours mean and establishing consistency in expectations and interventions across professional groups and hospital environments might be a foundational step towards achieving improvements in staff perceptions of positive organisational culture. In addition, hospital policies and governing mechanisms as interpreted by employees appear to lack articulation around the challenges that can give rise to, as well as result from, unprofessional staff behaviours. An erosion of employee confidence in hospital leaders and their ability to effectively address and mitigate unprofessional behaviours, associated with the organisational factors reported by respondents, could undermine hospital management efforts to improve staff working conditions and eventually, efforts to improve organisational culture. Solutions such as implementing responsive feedback loops between staff and management, and co-constructed and cross-sectional modules for all hospital staff to understand acceptable and inappropriate behaviours may present practical approaches for hospital management to gain employee confidence and remedy the widespread problem of unprofessionalism among their staff.

Limitations

Pre-defined categories within the closed-ended questions of our survey may have limited the emphasis of responses by pre-determining the categories of behaviours that we sought to understand from responses to the open-ended questions. Further, the broad phrasing of the open-ended questions resulted in a wide range of comments that reflected multiple aspects of organisational culture, unprofessional behaviours as well as impacts, and the interactions of all these elements with each other. A gap in our study design may have resulted from the decision to perform qualitative analysis on the comments after primary quantitative analysis had been conducted, rather than including more specific open-ended questions that corresponded against each of the categories we have investigated and reported on. In addition, the use of directed content analysis methods may have resulted in positive bias in coding themes within the data. Finally, the qualitative findings reported within this article are based only on textual responses to the open-ended questions within the LION survey. Therefore, further elaboration of our findings is recommended through interviews and focus groups. These additional research methods are recommended across multiple sites and professional groups to test the validity of our insights against other contexts.

Unprofessionalism in hospital settings is diverse in terms of how it is perceived, understood, and experienced by hospital staff, across professional and personal demographic categories. Perceived lack of organisational action to contain and address unprofessional behaviours appears to have a significant effect on the internalisation of unprofessional behaviours as professional norms, resulting in underreporting and ineffective remediation of these behaviours.

Complex factors that include internal sub-cultures because of the manner of power distribution across the organisation may play a major role in how prevalent unprofessional behaviours are among certain staff groups and within certain environments. The challenge of addressing these factors is complex, but not insurmountable. The phenomenon of unprofessional behaviours among medical professionals requires further study to identify effective tools that do not merely address behavioural challenges among individuals, but also address the systemic, structural, and organisational gaps that have led to these behaviours. Some such gaps are perceived organisational inaction, inconsistent enforcement of remediation, and unequal distribution of power, and the lack of integration of expectations for respectful behaviours at the level of human resource management and career progression. Concerted efforts not just by researchers, but more importantly, by professional bodies, and hospital administration and management, are required to ensure that flow-on negative effects on patients and staff can be stemmed sustainably. The benefits from such improvements would have far-ranging positive effects across all stakeholder groups including staff, patients and wider healthcare organisations and networks.

Availability of data and materials

The data generated and analysed during this study are not publicly available to protect the privacy of survey respondents. All relevant de-identified supporting material reported within this study has been provided within the manuscript.

Abbreviations

Longitudinal Investigation Of Negative behaviour survey

Directed content analysis

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Acknowledgements

We would like to acknowledge and thank the staff at the hospitals who participated in this survey, for their time and valuable comments.

This investigation was supported by a National Health and Medical Research Council Partnership Project Grant (1134459) in partnership with St Vincent’s Health Australia. The corresponding author is also supported by the Research Training Program (RTP) scholarship allocation number 2018378/ 20191504. The scholarship is offered by the Australian Government and administered by Macquarie University. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Antoinette Pavithra, Neroli Sunderland, Joanne Callen & Johanna Westbrook

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NS and JW conceptualized the study and led data collection. AP led the qualitative data analysis and results interpretation with input from NS, JW and JC. AP drafted the manuscript and all authors reviewed and approved the final version of the manuscript.

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Correspondence to Antoinette Pavithra .

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Human Research Ethics approval was granted by St Vincent’s Hospital Melbourne Human Research Ethics Committee for a multi-site study (HREC/17/SVHM/237). All participants were over the age of 18, employed at one or more of the study sites, and provided informed consent by agreeing to participate in the LION survey. All methods were carried out in accordance with relevant guidelines and regulations.

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Pavithra, A., Sunderland, N., Callen, J. et al. Unprofessional behaviours experienced by hospital staff: qualitative analysis of narrative comments in a longitudinal survey across seven hospitals in Australia. BMC Health Serv Res 22 , 410 (2022). https://doi.org/10.1186/s12913-022-07763-3

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Received : 09 March 2021

Accepted : 07 March 2022

Published : 28 March 2022

DOI : https://doi.org/10.1186/s12913-022-07763-3

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• Unprofessional behaviour
• Employee satisfaction
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• Speaking up
• Organisational culture
• Culture change
• Patient safety
• Staff well-being

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