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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: What is it, Methods + Examples

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

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

What is Qualitative Data Analysis?

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

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

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

Importance of Qualitative Data Analysis

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

In-Depth Understanding

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

Contextual Insight

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

Theory Development

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

Participant Perspectives

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

Exploratory Research

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

Types of Qualitative Data

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

Interviews and Focus Groups

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

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

Observations and Field Notes

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

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

Textual and Visual Data

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

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

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

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

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

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

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

Qualitative Data Analysis Methods and Examples

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

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

Method 1: Content Analysis

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

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

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

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

Benefits & Challenges

There are various advantages to using content analysis:

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

However, keep in mind the challenges that arise:

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

Example of Content Analysis

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

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

Method 2: Thematic Analysis

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

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

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

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

Thematic analysis has various benefits:

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

However, challenges may arise, such as:

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

Example of Thematic Analysis

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

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

Method 3: Narrative Analysis

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

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

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

There are various advantages to narrative analysis:

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

However, difficulties may arise, such as:

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

Example of Narrative Analysis

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

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

Method 4: Grounded Theory Analysis

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

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

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

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

Grounded theory analysis has various benefits:

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

However, challenges might arise with:

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

Example of Grounded Theory Analysis

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

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

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

Method 5: Discourse Analysis

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

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

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

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

There are various advantages of using discourse analysis:

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

However, the following challenges may arise:

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

Example of Discourse Analysis

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

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

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

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

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

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

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

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

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

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

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

What (exactly) is qualitative data analysis?

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

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

So, if it’s not numbers, what is it?

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

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

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

So, qualitative analysis is easier than quantitative, right?

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

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

Need a helping hand?

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

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

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

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

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

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

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

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

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

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

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

QDA Method #2: Narrative Analysis 

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

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

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

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

QDA Method #3: Discourse Analysis 

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

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

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

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

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

QDA Method #4: Thematic Analysis

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

Let’s take a look at an example.

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

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

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

QDA Method #5: Grounded theory (GT) 

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

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

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

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

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

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

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

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

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

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

Let’s just stick with IPA, okay?

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

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

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

How to choose the right analysis method

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

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

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

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

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

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

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

Let’s recap on QDA methods…

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

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

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

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

Psst... there’s more!

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

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86 Comments

This has been very helpful. Thank you.

Thank you madam,

Thank you so much for this information

I wonder it so clear for understand and good for me. can I ask additional query?

Very insightful and useful

Good work done with clear explanations. Thank you.

Thanks so much for the write-up, it’s really good.

Thanks madam . It is very important .

thank you very good

Great presentation

This has been very well explained in simple language . It is useful even for a new researcher.

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

This is very useful information. And it was very a clear language structured presentation. Thanks a lot.

Thank you so much.

very informative sequential presentation

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.

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

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

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

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

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

Thank you very much, this is well explained and useful

i need a citation of your book.

Thanks a lot , remarkable indeed, enlighting to the best

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

Keep writing useful artikel.

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

Thank you, this is well explained and very useful.

Very helpful .Thanks.

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

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

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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.

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 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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Qualitative Data Analysis

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

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

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

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

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

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

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

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

There are three types of coding:

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

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

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

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

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

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

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

 Qualitative data coding

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

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

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

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

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

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

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

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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

Previous chapter

QDA challenges

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

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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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Article Contents

Introduction, challenging some common methodological assumptions about online qualitative surveys, ten practical tips for designing, implementing and analysing online qualitative surveys, acknowledgements, conflict of interest statement, data availability, ethical approval.

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Methodological and practical guidance for designing and conducting online qualitative surveys in public health

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Samantha L Thomas, Hannah Pitt, Simone McCarthy, Grace Arnot, Marita Hennessy, Methodological and practical guidance for designing and conducting online qualitative surveys in public health, Health Promotion International , Volume 39, Issue 3, June 2024, daae061, https://doi.org/10.1093/heapro/daae061

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Online qualitative surveys—those surveys that prioritise qualitative questions and interpretivist values—have rich potential for researchers, particularly in new or emerging areas of public health. However, there is limited discussion about the practical development and methodological implications of such surveys, particularly for public health researchers. This poses challenges for researchers, funders, ethics committees, and peer reviewers in assessing the rigour and robustness of such research, and in deciding the appropriateness of the method for answering different research questions. Drawing and extending on the work of other researchers, as well as our own experiences of conducting online qualitative surveys with young people and adults, we describe the processes associated with developing and implementing online qualitative surveys and writing up online qualitative survey data. We provide practical examples and lessons learned about question development, the importance of rigorous piloting strategies, use of novel techniques to prompt detailed responses from participants, and decisions that are made about data preparation and interpretation. We consider reviewer comments, and some ethical considerations of this type of qualitative research for both participants and researchers. We provide a range of practical strategies to improve trustworthiness in decision-making and data interpretation—including the importance of using theory. Rigorous online qualitative surveys that are grounded in qualitative interpretivist values offer a range of unique benefits for public health researchers, knowledge users, and research participants.

Public health researchers are increasingly using online qualitative surveys.

There is still limited practical and methodological information about the design and implementation of these studies.

Building on Braun and Clarke (2013) , Terry and Braun (2017) and Braun et al . (2021) , we reflect on the methodological and practical lessons we have learnt from our own experience with conducting online qualitative surveys.

We provide guidance and practical examples about the design, implementation and analysis processes.

We argue that online qualitative surveys have rich potential for public health researchers and can be an empowering and engaging way to include diverse populations in qualitative research.

Public health researchers mostly engage in experiential (interpretive) qualitative approaches ( Braun and Clarke, 2013 ). These approaches are ‘centred on the exploration of participants’ subjective experiences and sense-making’ [( Braun and Clarke, 2021c ), p. 39]. Given the strong focus in public health on social justice, power and inequality, researchers proactively use the findings from these qualitative studies—often in collaboration with lived experience experts and others who are impacted by key decisions ( Reed et al ., 2024 )—to advocate for changes to public health policy and practice. There is also an important level of theoretical, methodological and empirical reflection that is part of the public health researcher’s role. For example, as qualitative researchers actively construct and interpret meaning from data, they constantly challenge their assumptions, their way of knowing and their way of ‘doing’ research ( Braun and Clarke, 2024 ). This reflexive practice also includes considering how to develop more inclusive opportunities for people to participate in research and to share their opinions and experiences about the issues that matter to them.

While in-depth interviews and focus groups provide rich and detailed narratives that are central to understanding people’s lives, these forms of data collection may sometimes create practical barriers for both researchers and participants. For example, they can be time consuming, and the power dynamics associated with face-to-face interviews (even in online settings) may make them less accessible for groups that are marginalized or stigmatized ( Edwards and Holland, 2020 ). While some population subgroups (and contexts) may suit (or require) face-to-face qualitative data collection approaches, others may lend themselves to different forms of data collection. Young people, for example, may be keen to be civically involved in research about the issues that matter to them, such as the climate crisis, but they may find it more convenient and comfortable using anonymized digital technologies to do so ( Arnot et al ., 2024b ). As such, part of our reflexive practice as public health researchers must be to explore, and be open to, a range of qualitative methodological approaches that could be more convenient, less intimidating and more engaging for a diverse range of population subgroups. This includes thinking about pragmatic ways of operationalizing qualitative data collection methods. How can we develop methods and engagement strategies that enable us to gain insights from a diverse range of participants about new issues or phenomenon that may pose threats to public health, or look at existing issues in new ways?

Advancements in online data collection methods have also created new options for researchers and participants about how they can be involved in qualitative studies ( Hensen et al ., 2021 ; Chen, 2023 ; Fan et al ., 2024 ). Online qualitative surveys—those surveys that prioritize qualitative values and questions—have rich potential for qualitative researchers. Braun and Clarke (2013 , p. 135) state that qualitative surveys:

…consist of a series of open-ended questions about a topic, and participants type or hand-write their responses to each question. They are self-administered; a researcher-administered qualitative survey would basically be an interview.

While these types of studies are increasingly utilized in public health, researchers have highlighted that there is still relatively limited discussion about the methodological and practical implications of these surveys ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al ., 2021 ). This poses challenges for qualitative public health researchers, funders, ethics committees and peer reviewers in assessing the purpose, rigour and contribution of such research, and in deciding the appropriateness of the method for answering different research questions.

Using examples from online qualitative surveys that we have been involved in, this article discusses a range of methodological and practical lessons learnt from developing, implementing and analysing data from these types of surveys. While we do not claim to have all the answers, we aim to develop and extend on the methodological and practical guidance from Braun and Clarke (2013) , Terry and Braun (2017) and Braun et al . (2021) about the potential for online qualitative surveys. This includes how they can provide a rigorous ‘wide-angle picture’ [( Toerien and Wilkinson, 2004 ), p. 70] from a diverse range of participants about contemporary public health phenomena.

Figure 1 aims to develop and extend on the key points made by Braun and Clarke (2013) , Terry and Braun (2017) and Braun et al . (2021) , which provide the methodological and empirical foundation for our article.

: Methodological considerations in conducting online qualitative surveys.

Harnessing interpretivist approaches and qualitative values in online qualitative surveys

Online qualitative surveys take many forms. They may be fully qualitative or qualitative dominant—mostly qualitative with some quantitative questions ( Terry and Braun, 2017 ). There are also many different ways of conducting these studies—from using a smaller number of questions that engage specific population groups or knowledge users in understanding detailed experiences  ( Hennessy and O’Donoghue, 2024 ), to a larger number of questions (which may use market research panel providers to recruit participants), that seek broader opinions and attitudes about public health issues ( Marko et al ., 2022a ; McCarthy et al ., 2023 ; Arnot et al ., 2024a ). However, based on our experiences of applying for grant funding and conducting, publishing and presenting these studies, there are still clear misconceptions and uncertainties about these types of  surveys.

One of the concerns raised about online qualitative surveys is how they are situated within broader qualitative values and approaches. This includes whether they can provide empirically innovative, rigorous, rich and theoretically grounded qualitative contributions to knowledge. Our experience is that online qualitative surveys have the most potential when they harness the values of interpretivist ‘Big Q’ approaches to collect information from a diverse range of participants about their experiences, opinions and practices ( Braun et al ., 2021 ). The distinction between positivist (small q) and interpretivist (Big Q) approaches to online qualitative surveys is an important one that requires some initial methodological reflection, particularly in considering the (largely unhelpful) critiques that are made about the rigour and usefulness of these surveys. These critiques often overlook the theoretical underpinnings and qualitative values inherent in such surveys. For example, while there may be a tendency to think of surveys and survey data as atheoretical and descriptive, the use of theory is central in informing online qualitative surveys. For example, Varpio and Ellaway (2021 , p. 343) explain that theory can ‘offer explanations and detailed premises that we can wrestle with, agree with, disagree with, reject and/or accept’. This includes the research design, the approach to data collection and analysis, the interpretation of findings and the conclusions that are drawn. Theory is also important in helping researchers to engage in reflexive practice. The use of theory is essential in progressing online qualitative surveys beyond description and towards in-depth interpretation and explanations—thus facilitating a deeper understanding of the studied phenomenon ( Collins and Stockton, 2018 ; Jamie and Rathbone, 2022 ).

Considering the assumptions that online qualitative surveys can only collect ‘thin’ data

The main assumptions about online qualitative surveys are that they can only collect ‘thin’ textual data, and that they are not flexible enough as a data collection tool for researchers to prompt or ask follow-up questions or to co-create detailed and rich data with participants ( Braun and Clarke, 2013 ; Terry and Clarke, 2017 ; Braun et al ., 2021 ). While we acknowledge that the type of data that is collected in these types of studies is different from those in in-depth interview studies, these surveys may be a more accessible and engaging way to collect rich insights from a diverse range of participants who may otherwise not participate in qualitative research ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al ., 2021 ). Despite this, peer reviewers can question the depth of information that may be collected in these studies. Assumptions about large but ‘thin’ datasets may also mean that researchers, funders and reviewers take (and perhaps expect) a more positivist approach to the design and analytical processes associated with these surveys. For example, the multiple topics and questions, larger sample sizes, and the generally smaller textual responses that online qualitative surveys generate may lead researchers to approach these surveys using more descriptive and atheoretical paradigms. This approach may focus on ‘measuring’ phenomena, using variables, developing thinner analytical description and adding numerical values to the number of responses for different categories or themes.

We have found that assumptions can also impact the review processes associated with these types of studies, receiving critiques from those with both positivist and interpretivist positions. Positivist critiques focus on matters associated with whether the samples are ‘representative’, and the flaws associated with ‘self-selecting convenience’ samples. Critiques from interpretivist colleagues question why such large sample sizes are needed for qualitative studies, seeing surveys as a less rigorous method for gaining rich and meaningful data. For example, we have had reviewers query the scope and depth of the analysis of the data that we present from these studies because they are concerned that the type of data collected lacks depth and does not fully contextualize and explain how participants think about issues. We have also had reviewers request that we should return to the study to collect quantitative data to supplement the qualitative findings of the survey. They also question how ‘representative’ the samples are of population groups. These comments, of course, are not unique to online qualitative surveys but do highlight the difficulty that reviewers may have in placing and situating these types of studies in broader qualitative approaches. With this in mind, we have also found that some reviewers can ask for additional information to justify both the use of online qualitative surveys and why we have chosen these over other qualitative approaches. For example, reviewers have asked us to justify why we have chosen an online qualitative survey and also to explain what we may have missed out on by not conducting in-depth interviews or quantitative or mixed methods surveys instead.

Requests for ‘numbers’ and ‘strategies to minimize bias’

While there is now a general understanding that attributing ‘numbers’ to qualitative data is largely unhelpful and inappropriate ( Chowdhury, 2015 ), there may be expectations that the larger sample sizes associated with online qualitative surveys enable researchers to provide numerical indicators of data. Rather than focusing on the ‘artfully interpretive’ techniques used to analyse and construct themes from the data ( Finlay, 2021 ), we have found that reviewers often ask us to provide numerical information about how many people provided different responses to different questions (or constructed themes), and the number at which ‘saturation’ was determined. Reviewer feedback that we have received about analytical processes has asked for detailed explanations about why attempts to ‘minimize bias’ (including calculations of inter-rater reliability and replicability of data quality) were not used. This demonstrates that peer reviewers may misinterpret the interpretivist values that guide online qualitative surveys, asking for information that is essentially ‘meaningless’ in qualitative paradigms in which researchers’ subjectivity ‘sculpts’ the knowledge that is produced ( Braun and Clarke, 2021a ).

The benefits and limitations of online qualitative surveys for participants, researchers and knowledge users

As well as a ‘wide-angle picture’ [( Toerien and Wilkinson, 2004 ), p. 70] on phenomenon, online qualitative surveys can also: (i) generate both rich and focused data about perceptions and practices, and (ii) have multiple participatory and practical advantages—including helping to overcome barriers to research participation ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al ., 2021 ). For researchers , online qualitative surveys can be a more cost-effective alternative ( Braun and Clarke, 2013 ; Terry and Braun, 2017 )—they are generally more time-efficient and less labour-intensive (particularly if working with market research companies to recruit panels). They are also able to reach a broad range of participants—such as those who are geographically dispersed ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ), and those who may not have internet connectivity that is reliable enough to complete online interviews (a common issue for individuals living in regional or rural settings) ( de Villiers et al ., 2022 ). We are also more able to engage young people in qualitative research through online surveys, perhaps partly due to extensive panel company databases but also because they may be a more accessible and familiar way for young people to participate in research. The ability to quickly investigate new public health threats from the perspective of lived experience can also provide important information for researchers, providing justification for new areas of research focus, including setting agendas and advocating for the need for funding (or policy attention). Collecting data from a diverse range of participants—including from those who hold views that we may see as less ‘politically acceptable’, or inconsistent with our own public health reasoning about health and equity—is important in situating and contextualizing community attitudes towards particular issues.

For participants , benefits include having a degree of autonomy and control over their participation, including completing the survey at a time and place that suits them, and the anonymous nature of participation (that may be helpful for people from highly stigmatized groups). Participants can take time to reflect on their responses or complete the survey, and may feel more able to ‘talk back’ to the researcher about the framing of questions or the purpose of the research ( Braun et al ., 2021 ). We would also add that a benefit of these types of studies is that participants can also drop out of the study easily if the survey does not interest them or meet their expectations—something that we think might be more onerous or uncomfortable for participants in an interview or focus group.

For knowledge users, including advocates, service providers and decision-makers, qualitative research provides an important form of evidence, and the ‘wide-angle picture' [( Toerien and Wilkinson, 2004 ), p. 70] on issues from a diverse range of individuals in a community or population can be a powerful advocacy tool. Online qualitative surveys can also provide rapid insights into how changes to policy and practice may impact population subgroups in different ways.

There are, of course, some limitations associated with online qualitative surveys ( Braun et al ., 2021 ; Marko et al ., 2022b ). For example, there is no ability to engage individuals in a ‘traditional’ conversation or to prompt or probe meaning in the interactive ways that we are familiar with in interview studies. There is less ability to refine the questions that we ask participants in an iterative way throughout a study based on participant responses (particularly when working with market research panel companies). There may also be barriers associated with written literacy, access to digital technologies and stable internet connections ( Braun et al ., 2021 ). They may also not be the most suitable for individuals who have different ways of ‘knowing, being and doing’ qualitative research—including Indigenous populations [( Kennedy et al ., 2022 ), p. 1]. All of these factors should be taken into consideration when deciding whether online qualitative surveys are an appropriate way of collecting data. Finally, while these types of surveys can collect data quickly ( Marko et al ., 2022b ), there can also be additional decision-making processes related to data preparation and inclusion that can be time-consuming.

There are a range of practical considerations that can improve the rigour, trustworthiness and quality of online qualitative survey data. Again, developing and expanding on ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al ., 2021 ), Figure 2 gives an overview of some key practical considerations associated with the design, implementation and analysis of these surveys. We would also note that before starting your survey design, you should be aware that people may use different types of technology to complete the survey, and in different spaces. For example, we cannot assume that people will be sitting in front of a computer or laptop at home or in the office, with people more likely to complete surveys on a mobile phone, perhaps on a train or bus on the way to work or school.

: Top ten practical tips for conducting online qualitative surveys.

Survey design

Creating an appropriate and accessible structure

The first step in designing an online qualitative survey is to plan the structure of your survey. This step is important because the structure influences the way that participants interact with and participate through the survey. The survey structure helps to create an ‘environment’ that helps participants to share their perspectives, prompt their views and develop their ideas ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ). Similar to an interview study, the structure of the survey guides participants from one set of questions (and topics) to the next. It is important to consider the ordering of topics to enable participants to complete a survey that has a logical flow, introduces participants to concepts and allows them to develop their depth of responses.

Before participants start the survey, we provide a clear and simple lay language summary of the survey. Because many individuals will be familiar with completing quantitative surveys, we include a welcoming statement and reiterate the qualitative nature of the survey, stating that their answers can be about their own experiences:

Thank you for agreeing to take part in this survey about [topic] . This survey involves writing responses to questions rather than checking boxes.

We then clearly reiterate the purpose of the survey, providing a short description of the topic that we are investigating. We state that we do not seek to collect any data that is identifiable, that we are interested in participants perspectives, that there are no right or wrong answers, and that participants can withdraw from the survey at any time without giving a reason.

Similar to Braun et al . (2021) , we start our surveys with questions about demographic and related characteristics (which we often call ‘ participant/general characteristics ’). These can be discrete choice questions, but can also utilize open text—for example, in relation to gender identity. We have found that there is always a temptation with surveys to ask many questions about the demographic characteristics of participants. However, we caution that too many questions can be intrusive for participants and can take away valuable time from open-text questions, which are the core focus of the survey. We recommend asking participant characteristic and demographic questions that situate and contextualize the sample ( Elliott et al ., 1999 ).

We generally start the open-text sections of these surveys by asking broad introductory questions about the topic. This might include questions such as: ‘Please describe the main reasons you drink alcohol ’, and ‘W hat do you think are the main impacts of climate change on the world? ’ We have found that these types of questions get participants used to responding to open-text questions relevant to the study’s research questions and aims. For each new topic of investigation (which are based on our theoretical concepts and overall study aims and research questions), we provide a short explanation about what we will ask participants. We also use tools and text to signpost participant progress through the survey. This can be a valuable way to avoid high attrition rates where participants exit the survey because they are getting fatigued and are unclear when the survey will end:

Great! We are just over half-way through the survey.

We ask more detailed questions that are more aligned with our theoretical concepts in the middle of the survey. For example, we may start with broad questions about a harmful industry and their products (such as gambling, vaping or alcohol) and then in the middle of the survey ask more detailed questions about the commercial determinants of health and the specific tactics that these industries use (for example, about product design, political tactics, public relations strategies or how these practices may influence health and equity). In relation to these more complex questions, it is particularly important that we reiterate that there are no wrong answers and try to include encouraging text throughout the survey:

There are no right or wrong answers—we are curious to hear your opinions .

We always try to end the survey on a positive. While these types of questions depend on the study, we try to ask questions which enable participants to reflect on what could be done to address or improve an issue. This might include their attitudes about policy, or what they would say to those in positions of power:

What do you think should be done to protect young people from sports betting advertising on social media? If there was one thing that could be done to prevent young people from being exposed to the risks associated with alcohol, cigarettes, vaping, or gambling, what would it be? If you could say one thing to politicians about climate change, what would it be?

Finally, we ask participants if there is anything we have missed or if they have anything else to add, sometimes referred to as a ‘clean-up’ question ( Braun and Clarke, 2013 ). The following provides a few examples of how we have framed these questions in some of our studies:

Is there anything you would like to say about alcohol, cigarettes, vaping, and gambling products that we have not covered? Is there anything we haven’t asked you about the advertising of alcohol to women that you would like us to know?

Considering the impact of the length of the survey on responses

The length of the survey (both the number of questions and the time it takes an individual to complete the survey) is guided by a range of methodological and practical considerations and will vary between studies ( Braun and Clarke, 2013 ). Many factors will influence completion times. We try to give individuals a guide at the start of the survey about how long we think it will take to complete the survey (for example, between 20 and 30 minutes). We highlight that it may take people a little longer or shorter and that people are able to leave their browser open or save the survey and come back to finish it later. For our first few online qualitative surveys, we found that we asked lots of questions because we felt less in control of being able to prompt or ask follow-up questions from participants. However, we have learned that less is more! Asking too many questions may lead to more survey dropouts, and may significantly reduce the textual quality of the information that you receive from participants ( Braun and Clarke, 2013 ; Terry and Clarke, 2017 ). This includes considering how the survey questions might lead to repetition, which may be annoying for participants, leading to responses such as ‘like I’ve already said’ , ‘I’ve already answered that’ or ‘see above’ .

Providing clear and simple guidance

When designing an online qualitative survey, we try to think of ways to make participation in the survey engaging. We do not want individuals to feel that we are ‘mining’ them for data. Rather we want to demonstrate that we are genuinely interested in their perspectives and views. We use a range of mechanisms to do this. Because there is no opportunity to verbally explain or clarify concepts to participants, there is a particular need to ensure that the language used is clear and accessible ( Braun and Clarke, 2013 ; Terry and Clarke, 2017 ). If language or concepts are complex, you are more likely to receive ‘I don’t know’ responses to your questions. We need to remember that participants have a range of written and comprehension skills, and inclusive and accessible language is important. We also never try to assume a level of knowledge about an issue (unless we have specifically asked for participants who are aware and engaged in an issue—such as women who drink alcohol) ( Pitt et al ., 2023 ). This includes avoiding highly technical or academic language and not making assumptions that the individuals completing the survey will understand concepts in the same way that researchers do ( Braun and Clarke, 2013 ). Clearly explaining concepts or using text or images to prompt memories can help to overcome this:

Some big corporations (such as the tobacco, vaping, alcohol, junk food, or gambling industries) sponsor women's sporting teams or clubs, or other events. You might see sponsor logos on sporting uniforms, or at sporting grounds, or sponsoring a concert or arts event.

At all times, we try to centre the language that we use with the population from which we are seeking responses. Advisory groups can be particularly helpful in framing language for different population subgroups. We often use colloquial language, even if it might not be seen as the ‘correct’ academic language or terminology. Where possible, we also try to define theoretical concepts in a clear and easy to understand way. For example, in our study investigating parent perceptions of the impact of harmful products on young people, we tried to clearly define ‘normalization’:

In this section we ask you about some of the perceived health impacts of the above products on young people. We also ask you about the normalisation of these products for young people. When we talk about normalisation, we are thinking about the range of factors that might make these products more acceptable for young people to use. These factors might include individual factors, such as young people being attracted to risk, the influence of family or peers, the accessibility and availability of these products, or the way the industry advertises and promotes these products.

Using innovative approaches to improve accessibility and prompt responses

Online qualitative surveys can include features beyond traditional question-and-answer formats ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ). For example, we often use a range of photo elicitation techniques (using images or videos) to make surveys more accessible to participate in, address different levels of literacy, and overcome the assumption that we are not able to ‘prompt’ responses. These types of visual methodologies enable a collaborative and creative research experience by asking the participant to reflect on aspects of the visual materials, such as symbolic representations, and discuss these in relation to the research objectives ( Glaw et al ., 2017 ). The combination of visual images and clear descriptions helps to provide a focus for responses about different issues, as well as prompting nuanced information such as participant memories and emotions ( Glaw et al ., 2017 ). We use different types of visuals in our studies, such as photographs (including of the public health issues we’re investigating); screenshots from websites and social media posts (including newspaper headlines) and videos (including short videos from social media sites such as TikTok) ( Arnot et al ., 2024b ). For example, when talking about government responses to the climate crisis, we used a photograph of former Australian Prime Minister Scott Morrison holding a piece of coal in the Australian parliament to prompt participants’ thinking about the government’s relationship with fossil fuels and to provide a focal point for their answer. However, we would caution against using any images that may be confronting for participants or deliberately provocative. The purpose of using visuals must always be in the interests of the participants—to clarify, prompt and reflect on concepts. Ethics committees should carefully review the images used in surveys to ensure that they have a clear purpose and are unlikely to cause any discomfort.

Survey implementation

Thinking carefully about your criteria for recruitment

Determining the sample size of online qualitative studies is not an exact science. The sample sizes for recent studies have ranged from n = 46 in a study about pregnancy loss ( Hennessy and O’Donoghue, 2024 ), to n = 511 in a study with young people about the climate crisis ( Arnot et al ., 2023b ). We follow ‘rules of thumb’ [( Braun and Clarke, 2021b ), p. 211] which try to balance the needs of the research and data richness with key practical considerations (such as funding and time constraints), funder expectations, discipline-specific norms and our knowledge and experience of designing and implementing online qualitative surveys. However, we have found that peer reviewers expect much more justification of sample sizes than they do for other types of qualitative research. Robust justification of sample sizes are often needed to prevent any ‘concerns’ that reviewers may raise. Our response to these reviews often reiterates that our focus (as with all qualitative research) is not to produce a ‘generalisable’ or ‘representative’ sample but to recruit participants who will help to provide ‘rich, complex and textured data’ [( Terry and Braun, 2017 ), p. 15] about an issue. Instead of focusing on data saturation, a contested concept which is incongruent with reflexive thematic analysis in particular ( Braun and Clarke, 2021b ), we find it useful to consider information power to determine the sample size for these surveys ( Malterud et al ., 2016 ). Information power prioritizes the adequacy, quality and variability of the data collected over the number of participants.

Recruitment for online qualitative surveys can be influenced by a range of factors. Monetary and time constraints will impact the size and, if using market research company panels, the specificity of participant quotas. Recruitment strategies must be developed to ensure that the data provides enough information to answer the research questions of the study. For our research purposes, we often try to ensure that participants with a range of socio-demographic characteristics are invited to participate in the sample. We set soft quotas for age, gender and geographic location to ensure some diversity. We have found that some population subgroups may also be recruited more easily than others—although this may depend on the topic of the survey. For example, we have found that quotas for women and those living in metropolitan areas may fill more quickly. In these scenarios, the research team must weigh up the timelines associated with recruitment and data collection (e.g. How long do we want to run data collection for? How much of our budget can be spent on achieving a more equally split sample? Are quotas necessary?) versus the purpose and goals of the research (i.e. to generate ideas rather than data representativeness), and the study-specific aims and research questions.

There are, of course, concerns about not being able to ‘see’ the people that are completing these surveys. There is an increasing focus in the academic literature on ‘false’ respondents, particularly in quantitative online surveys ( Levi et al ., 2021 ; Wang et al ., 2023 ). This will be an important ongoing discussion for qualitative researchers, and we do not claim to have the answers for how to overcome these issues. For example, some individuals may say that they meet the inclusion criteria to access the survey, while others may not understand or misinterpret the inclusion criteria. There is also a level of discomfort about who and how we judge who may be a ‘legitimate’ participant or not. However, we can talk practically about some of the strategies that we use to ensure the rigour of data. For example, we find that screening questions can provide a ‘double-check’ in relation to inclusion criteria and can also help with ensuring that there is consistency between the information an individual provides about how they meet the inclusion criteria and subsequent responses. For example, in a recent survey of parents of young people, a participant stated that they were 18 years old and were a parent to a 16-year-old and 15-year-old. Their overall responses were inconsistent with being a parent of children these ages. Similarly, in our gambling studies, people may tick that they have gambled in the last year but then in subsequent questions say they have not gambled at all. This highlights the importance of checking data across all questions, although it should be noted that time and cost constraints associated with comprehensively scanning the data for such responses are not always feasible and can result in overlooking these participants.

Ensuring that there are strategies to create agency and engage participants in the research

One of the benefits of online qualitative surveys compared to traditional quantitative surveys is the scope for participants to explain their answers and to disagree with the research team’s position. An indication that participants are feeling able to do this is when they are asked for any additional comments at the end of the survey. For example, in a survey about women’s attitudes towards alcohol marketing, the following participant concluded the survey by writing: ‘I think you have covered everything. I think that you need to stop shaming women for having fun’. Other participants demonstrate their engagement and interest in the survey by reaffirming the perspectives they have shared throughout the survey. For example, in a study with young people on climate, participants responded at the end that ‘it’s one of the few things I actually care about’ , while another commented on the quality of the survey questions, stating, ‘I think this survey did a great job with probing questions to prompt all the thoughts I have on it’ .

We also think that online qualitative surveys may lead to less social desirability in participants’ responses. Participants seem less wary about communicating less politically correct opinions than they may do in a face-to-face interview. For example, at times, participants communicate attitudes that may not align with public health values (e.g. supporting personal responsibility, anti-nanny state, and neoliberal ideologies of health and wellbeing), that we rarely see communicated to us in in-depth interview or focus group studies. We would argue that these perspectives are valuable for public health researchers because they capture a different community voice that may not otherwise be represented in research. This may show where there is a lack of support for health interventions and policy reforms and may indicate where further awareness-raising needs to occur. These types of responses also contribute to reflexive practice by challenging our assumptions and positions about how we think people should think or feel about responses to particular public health issues. Examples of such responses from our surveys include:

"Like I have already said, if you try to hide it you will only make it more attractive. This nanny-state attitude of the elite drives me crazy. People must be allowed to decide for themselves."

Ethical issues for participants and researchers

Researchers should also be aware that some of the ethical issues associated with online qualitative surveys may be different from those in in-depth interviews—and it is important that these are explained in any ethical consideration of the study. Providing a clear and simply worded Plain Language Statement (in written or video form) is important in establishing informed consent and willingness to participate. While participants are given information about who to contact if they have further questions about the study, this may be an extra step for participants, and they may not feel as able to ask for clarification about the study. Because of this, we try to provide multiple examples of the types of questions that we will ask, as well as providing downloadable support details (for example, for mental health support lines). A positive aspect of surveys is that participants are able to easily ignore recruitment notices to participate in the study. They are also able to stop the survey at any time by exiting out of the browser if they feel discomfort without having to give a reason in person to a researcher.

While the anonymous nature of the survey may be empowering for some participants ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al. , 2021 ), it can also make it difficult for researchers to ascertain if people need any further support after completing the survey. Participants may also fill in surveys with someone else and may be influenced about how they should respond to questions (with the exception of some studies in which people may require assistance from someone to type their responses). Because of the above, some researchers, ethics committees and funders may be more cautious about using these studies for highly sensitive subjects. However, we would argue that the important point is that the studies follow ethical principles and take the lack of direct contact with participants into the ethical considerations of the study. It is also important to ensure that platforms used to collect survey data are trusted and secure. Here, we would argue that universities have an obligation to investigate and, where possible, approve survey providers to ensure that researchers are using platforms that meet rigorous standards for data and privacy.

It is also important to note that there may be responses from participants that may be challenging ( Terry and Braun, 2017 ; Braun and Clarke, 2021 ). Online spaces are rife with trolling due to their anonymous nature, and online surveys are not immune to this behaviour. Naturally, this leads to some silly responses—‘ Deakin University is responsible for all of this ’, but researchers should also be aware that the anonymity of surveys can (although in our experience not often) lead to responses that may cause discomfort for the researchers. For example, when asked if participants had anything else to add to a climate survey ( Arnot et al ., 2024c ), one responded ‘ nope, but you sure asked a lot of dumbass questions’ . Just as with interview-based studies, there must be processes built into the research for debriefing—particularly for students and early career researchers—as well as clear decisions about whether to include or exclude these types of responses when preparing the dataset for analysis and in writing up the results from the survey.

The importance of piloting the survey

Because of the lack of ability to explain and clarify concepts, piloting is particularly important ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al. , 2021 ) to ensure that: (i) the technical aspects of the survey work as intended; (ii) the survey is eliciting quality responses (with limited ‘nonsensical’ responses such as random characters); (iii) the survey responses indicate comprehension of the survey questions; and (vi) there is not a substantial number of people who ‘drop-out’ of the study. Typically, we pilot our survey with 10% of the intended sample size. After piloting, we often change question wording, particularly to address questions that elicit very small text responses, the length of the survey and sometimes refine definitions or language to ensure increased comprehension. Researchers should remember that changes to the survey questions may need to be reviewed by ethics committees before launching the full survey. It is important to build in time for piloting and the revision of the survey to ensure you get this right as once you launch the full survey, there is no going back!

Survey analysis and write-up

Preparing the dataset

Once launching the full survey, the quality of data and types of responses you receive in these types of surveys can vary. There is very limited transparency around how the dataset was prepared (more familiar to some as ‘data cleaning’) in published papers, including the decisions about which (if any) participants (or indeed responses) were excluded from the dataset and why. Nonsensical responses can be common—and can take a range of forms ( Figure 3 ). These can include random numbers or letters, a chunk of text that has been copied and pasted from elsewhere, predictive text or even repeat emojis. In one study, we had a participant quote the script of The Bee Movie in response to questions.

: Visual examples of nonsensical responses in online qualitative surveys.

Part of our familiarization with the dataset [Phase One in Braun and Clarke’s reflexive approach to thematic analysis ( Braun and Clarke, 2013 ; Braun et al ., 2021 )] includes preparing the dataset for analysis. We use this phase to help make decisions about what to include and exclude from the final dataset. While a row of emojis in the data file can easily be spotted and removed from the dataset, sometimes responses can look robust until you read, become familiar and engage with the data. For example, when asked about what they thought about collective climate action ( Arnot et al ., 2023a , 2024c ), some participants entered random yet related terms such as ‘ plastic ’, or repeated similar phrases across multiple questions:

“ why do we need paper straws ”, “ paper straws are terrible ”, “ papers straws are bad for you ”, “ paper straws are gross .”

Participants can also provide comprehensive answers for the first few questions and then nonsensical responses for the rest, which may also be due to question fatigue [( Braun and Clarke, 2013 ), p. 138]. Therefore, it is important to closely go through each participant’s response to ensure they have attempted to provide bone-fide responses. For example, in one of our young people and climate surveys ( Arnot et al ., 2023a , 2024c ), one participant responded genuinely to the first half of the survey before their quality dropped dramatically:

“I can’t even be bothered to read that question ”, “ why so many questions ”, “ bro too many sections. ”

Some market research panel providers may complete an initial quality screen of data. However, this does not replace the need for the research teams’ own data preparation processes. Researchers should ensure they are checking that responses are coherent—for example, not giving information that contradicts or is not credible. In our more recent studies, we have increasingly seen responses cut and pasted from ChatGPT and other AI tools—providing a new challenge in assessing the quality of responses. If you are seeing these types of responses, it might be an opportunity to think about the style and suitability of the questions being asked. For example, the use of AI tools might suggest that people are finding it difficult to answer questions or may feel that they have to present a ‘correct’ answer. We would also note that because of the volume of data in these surveys, the preparation of data involves multiple members of the team. In many cases, decisions need to be made about participants who may not have provided authentic responses across the survey. The research team should make clear in any paper their decisions about their choices to include or exclude participants from the study. There is a careful balancing act that can require assessing the quality of the participants’ responses across the whole dataset to determine if the overall quality of responses contributes to the research.

Navigating the volume of data and writing up results

Finally, discussions about how to navigate the volume of data that these types of studies produce could be a standalone paper. In general, principles of reflexive practices apply to the analysis of data from these studies. However, as a starting point, here are a few considerations when approaching these datasets.

We would argue that online qualitative surveys lend themselves to some types of analytical approaches over others—for example, reflexive thematic analysis, as compared to grounded theory or interpretive phenomenological analysis (though it can be used with these) ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ).

While initial familiarization, coding and analysis can focus on specific questions and associated responses, it is important to analyse the dataset as a whole (or as clusters associated with particular topics) as participants may provide relevant data to a topic under multiple questions ( Terry and Braun, 2017 ). We initially focus our coding on specific questions or a group of survey questions under a topic of investigation. Once we have developed and constructed preliminary themes from the data associated with these clusters of questions, we then move to looking at responses across the dataset as we review themes further.

Researchers should think carefully about how to manage the data—which may not be available as ‘individual participant transcripts’ but rather as a ‘whole’ dataset in an Excel spreadsheet. Some may prefer qualitative data analysis software (QDAS) to manage and navigate data. However, many of us find that Excel (and particularly the use of labelled Tabs) is useful in grouping data and moving from codes to constructing themes.

As with all rigorous qualitative research, coding and theme development should be guided by the research questions. A clear record of decision-making about analytical choices (and being reflexive about these) should be kept. In any write-up, we would recommend that researchers are clear about which survey questions they used in the analysis [researchers could consider providing a supplementary file of some or all of the survey questions—see, for example Hennessy and O’Donoghue (2024) ].

In writing up the results, researchers should still seek to present a rich description of the data, as demonstrated in the presentation of results in the following papers ( Marko et al ., 2022a , 2022b ; McCarthy et al ., 2023 ; Pitt et al ., 2023 ; Hennessy and O’Donoghue, 2024 ). We have found the use of tables with additional examples of quotes as they relate to themes and subthemes can be a practical way of providing the reader with further examples of the data, particularly when constrained by journal word count limits [see, for example, Table 2 in Arnot et al ., (2024c) ]. However, these tables do not replace a full and complete presentation of the interpretation of the data.

This article offers methodological reflections and practical guidance around online qualitative survey design, implementation and analysis. While online qualitative surveys engage participants in a different type of conversation, they have design features that enable the collection of rich data. We recognize that we have much to learn and that while no survey of ours has been perfect, each new experience with developing and conducting online qualitative surveys has brought new understandings and lessons for future studies. In recognizing that we are learning, we also feel that our experience to date could be valuable for progressing the conversation about the rigour of online qualitative surveys and maximizing this method for public health gains.

H.P. is funded through a VicHealth Postdoctoral Research Fellowship. S.M. is funded through a Deakin University Faculty of Health Deans Postdoctoral Fellowship. G.A. is funded by an Australian Government Research Training Program Scholarship. M.H. is funded through an Irish Research Council Government of Ireland Postdoctoral Fellowship Award [GOIPD/2023/1168].

The pregnancy loss study was funded by the Irish Research Council through its New Foundations Awards and in partnership with the Irish Hospice Foundation as civil society partner [NF/2021/27123063].

S.T. is Editor in Chief of Health Promotion International, H.P. is a member of the Editorial Board of Health Promotion International, S.M. and G.A. are Social Media Coordinators for Health Promotion International, M.H. is an Associate Editor for Health Promotion International. They were not involved in the review process or in any decision-making on the manuscript.

The data used in this study are not available.

Ethical approval for studies conducted by Deakin University include the climate crisis (HEAG-H 55_2020, HEAG-H 162_2021); parents perceptions of harmful industries on young people (HEAG-H 158_2022); women and alcohol marketing (HEAG-H 123_2022) and gambling (HEAG 227_2020).

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Data Analysis in Research

Ai generator.

Data analysis in research involves systematically applying statistical and logical techniques to describe, illustrate, condense, and evaluate data. It is a crucial step that enables researchers to identify patterns, relationships, and trends within the data, transforming raw information into valuable insights. Through methods such as descriptive statistics, inferential statistics, and qualitative analysis, researchers can interpret their findings, draw conclusions, and support decision-making processes. An effective data analysis plan and robust methodology ensure the accuracy and reliability of research outcomes, ultimately contributing to the advancement of knowledge across various fields.

What is Data Analysis in Research?

Data analysis in research involves using statistical and logical techniques to describe, summarize, and compare collected data. This includes inspecting, cleaning, transforming, and modeling data to find useful information and support decision-making. Quantitative data provides measurable insights, and a solid research design ensures accuracy and reliability. This process helps validate hypotheses, identify patterns, and make informed conclusions, making it a crucial step in the scientific method.

Examples of Data analysis in Research

• Survey Analysis : Researchers collect survey responses from a sample population to gauge opinions, behaviors, or characteristics. Using descriptive statistics, they summarize the data through means, medians, and modes, and then inferential statistics to generalize findings to a larger population.
• Experimental Analysis : In scientific experiments, researchers manipulate one or more variables to observe the effect on a dependent variable. Data is analyzed using methods such as ANOVA or regression analysis to determine if changes in the independent variable(s) significantly affect the dependent variable.
• Content Analysis : Qualitative research often involves analyzing textual data, such as interview transcripts or open-ended survey responses. Researchers code the data to identify recurring themes, patterns, and categories, providing a deeper understanding of the subject matter.
• Correlation Studies : Researchers explore the relationship between two or more variables using correlation coefficients. For example, a study might examine the correlation between hours of study and academic performance to identify if there is a significant positive relationship.
• Longitudinal Analysis : This type of analysis involves collecting data from the same subjects over a period of time. Researchers analyze this data to observe changes and developments, such as studying the long-term effects of a specific educational intervention on student achievement.
• Meta-Analysis : By combining data from multiple studies, researchers perform a meta-analysis to increase the overall sample size and enhance the reliability of findings. This method helps in synthesizing research results to draw broader conclusions about a particular topic or intervention.

Data analysis in Qualitative Research

Data analysis in qualitative research involves systematically examining non-numeric data, such as interviews, observations, and textual materials, to identify patterns, themes, and meanings. Here are some key steps and methods used in qualitative data analysis:

• Coding : Researchers categorize the data by assigning labels or codes to specific segments of the text. These codes represent themes or concepts relevant to the research question.
• Thematic Analysis : This method involves identifying and analyzing patterns or themes within the data. Researchers review coded data to find recurring topics and construct a coherent narrative around these themes.
• Content Analysis : A systematic approach to categorize verbal or behavioral data to classify, summarize, and tabulate the data. This method often involves counting the frequency of specific words or phrases.
• Narrative Analysis : Researchers focus on the stories and experiences shared by participants, analyzing the structure, content, and context of the narratives to understand how individuals make sense of their experiences.
• Grounded Theory : This method involves generating a theory based on the data collected. Researchers collect and analyze data simultaneously, continually refining and adjusting their theoretical framework as new data emerges.
• Discourse Analysis : Examining language use and communication patterns within the data, researchers analyze how language constructs social realities and power relationships.
• Case Study Analysis : An in-depth analysis of a single case or multiple cases, exploring the complexities and unique aspects of each case to gain a deeper understanding of the phenomenon under study.

Data analysis in Quantitative Research

Data analysis in quantitative research involves the systematic application of statistical techniques to numerical data to identify patterns, relationships, and trends. Here are some common methods used in quantitative data analysis:

• Descriptive Statistics : This includes measures such as mean, median, mode, standard deviation, and range, which summarize and describe the main features of a data set.
• Inferential Statistics : Techniques like t-tests, chi-square tests, and ANOVA (Analysis of Variance) are used to make inferences or generalizations about a population based on a sample.
• Regression Analysis : This method examines the relationship between dependent and independent variables. Simple linear regression analyzes the relationship between two variables, while multiple regression examines the relationship between one dependent variable and several independent variables.
• Correlation Analysis : Researchers use correlation coefficients to measure the strength and direction of the relationship between two variables.
• Factor Analysis : This technique is used to identify underlying relationships between variables by grouping them into factors based on their correlations.
• Cluster Analysis : A method used to group a set of objects or cases into clusters, where objects in the same cluster are more similar to each other than to those in other clusters.
• Hypothesis Testing : This involves testing an assumption or hypothesis about a population parameter. Common tests include z-tests, t-tests, and chi-square tests, which help determine if there is enough evidence to reject the null hypothesis.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to identify trends, cycles, and seasonal variations.
• Multivariate Analysis : Techniques like MANOVA (Multivariate Analysis of Variance) and PCA (Principal Component Analysis) are used to analyze data that involves multiple variables to understand their effect and relationships.
• Structural Equation Modeling (SEM) : A multivariate statistical analysis technique that is used to analyze structural relationships. This method is a combination of factor analysis and multiple regression analysis and is used to analyze the structural relationship between measured variables and latent constructs.

Data analysis in Research Methodology

Data analysis in research methodology involves the process of systematically applying statistical and logical techniques to describe, condense, recap, and evaluate data. Here are the key components and methods involved:

• Data Preparation : This step includes collecting, cleaning, and organizing raw data. Researchers ensure data quality by handling missing values, removing duplicates, and correcting errors.
• Descriptive Analysis : Researchers use descriptive statistics to summarize the basic features of the data. This includes measures such as mean, median, mode, standard deviation, and graphical representations like histograms and pie charts.
• Inferential Analysis : This involves using statistical tests to make inferences about the population from which the sample was drawn. Common techniques include t-tests, chi-square tests, ANOVA, and regression analysis.
• Qualitative Data Analysis : For non-numeric data, researchers employ methods like coding, thematic analysis, content analysis, narrative analysis, and discourse analysis to identify patterns and themes.
• Quantitative Data Analysis : For numeric data, researchers apply statistical methods such as correlation, regression, factor analysis, cluster analysis, and time series analysis to identify relationships and trends.
• Hypothesis Testing : Researchers test hypotheses using statistical methods to determine whether there is enough evidence to reject the null hypothesis. This involves calculating p-values and confidence intervals.
• Data Interpretation : This step involves interpreting the results of the data analysis. Researchers draw conclusions based on the statistical findings and relate them back to the research questions and objectives.
• Validation and Reliability : Ensuring the validity and reliability of the analysis is crucial. Researchers check for consistency in the results and use methods like cross-validation and reliability testing to confirm their findings.
• Visualization : Effective data visualization techniques, such as charts, graphs, and plots, are used to present the data in a clear and understandable manner, aiding in the interpretation and communication of results.
• Reporting : The final step involves reporting the results in a structured format, often including an introduction, methodology, results, discussion, and conclusion. This report should clearly convey the findings and their implications for the research question.

Types of Data analysis in Research

• Purpose : To summarize and describe the main features of a dataset.
• Methods : Mean, median, mode, standard deviation, frequency distributions, and graphical representations like histograms and pie charts.
• Example : Calculating the average test scores of students in a class.
• Purpose : To make inferences or generalizations about a population based on a sample.
• Methods : T-tests, chi-square tests, ANOVA (Analysis of Variance), regression analysis, and confidence intervals.
• Example : Testing whether a new teaching method significantly affects student performance compared to a traditional method.
• Purpose : To analyze data sets to find patterns, anomalies, and test hypotheses.
• Methods : Visualization techniques like box plots, scatter plots, and heat maps; summary statistics.
• Example : Visualizing the relationship between hours of study and exam scores using a scatter plot.
• Purpose : To make predictions about future outcomes based on historical data.
• Methods : Regression analysis, machine learning algorithms (e.g., decision trees, neural networks), and time series analysis.
• Example : Predicting student graduation rates based on their academic performance and demographic data.
• Purpose : To provide recommendations for decision-making based on data analysis.
• Methods : Optimization algorithms, simulation, and decision analysis.
• Example : Suggesting the best course of action for improving student retention rates based on various predictive factors.
• Purpose : To identify and understand cause-and-effect relationships.
• Methods : Controlled experiments, regression analysis, path analysis, and structural equation modeling (SEM).
• Example : Determining the impact of a specific intervention, like a new curriculum, on student learning outcomes.
• Purpose : To understand the specific mechanisms through which variables affect one another.
• Methods : Detailed modeling and simulation, often used in scientific research to understand biological or physical processes.
• Example : Studying how a specific drug interacts with biological pathways to affect patient health.

How to write Data analysis in Research

Data analysis is crucial for interpreting collected data and drawing meaningful conclusions. Follow these steps to write an effective data analysis section in your research.

1. Prepare Your Data

Ensure your data is clean and organized:

• Remove duplicates and irrelevant data.
• Check for errors and correct them.
• Categorize data if necessary.

2. Choose the Right Analysis Method

Select a method that fits your data type and research question:

• Quantitative Data : Use statistical analysis such as t-tests, ANOVA, regression analysis.
• Qualitative Data : Use thematic analysis, content analysis, or narrative analysis.

3. Describe Your Analytical Techniques

Clearly explain the methods you used:

• Software and Tools : Mention any software (e.g., SPSS, NVivo) used.
• Statistical Tests : Detail the statistical tests applied, such as chi-square tests or correlation analysis.
• Qualitative Techniques : Describe coding and theme identification processes.

4. Present Your Findings

Organize your findings logically:

• Use Tables and Figures : Display data in tables, graphs, and charts for clarity.
• Summarize Key Results : Highlight the most significant findings.
• Include Relevant Statistics : Report p-values, confidence intervals, means, and standard deviations.

5. Interpret the Results

Explain what your findings mean in the context of your research:

• Compare with Hypotheses : State whether the results support your hypotheses.
• Relate to Literature : Compare your results with previous studies.
• Discuss Implications : Explain the significance of your findings.

6. Discuss Limitations

Acknowledge any limitations in your data or analysis:

• Sample Size : Note if the sample size was small.
• Biases : Mention any potential biases in data collection.
• External Factors : Discuss any factors that might have influenced the results.

7. Conclude with a Summary

Wrap up your data analysis section:

• Restate Key Findings : Briefly summarize the main results.
• Future Research : Suggest areas for further investigation.

Importance of Data analysis in Research

Data analysis is a fundamental component of the research process. Here are five key points highlighting its importance:

• Enhances Accuracy and Reliability Data analysis ensures that research findings are accurate and reliable. By using statistical techniques, researchers can minimize errors and biases, ensuring that the results are dependable.
• Facilitates Informed Decision-Making Through data analysis, researchers can make informed decisions based on empirical evidence. This is crucial in fields like healthcare, business, and social sciences, where decisions impact policies, strategies, and outcomes.
• Identifies Trends and Patterns Analyzing data helps researchers uncover trends and patterns that might not be immediately visible. These insights can lead to new hypotheses and areas of study, advancing knowledge in the field.
• Supports Hypothesis Testing Data analysis is vital for testing hypotheses. Researchers can use statistical methods to determine whether their hypotheses are supported or refuted, which is essential for validating theories and advancing scientific understanding.
• Provides a Basis for Predictions By analyzing current and historical data, researchers can develop models that predict future outcomes. This predictive capability is valuable in numerous fields, including economics, climate science, and public health.

FAQ’s

What is the difference between qualitative and quantitative data analysis.

Qualitative analysis focuses on non-numerical data to understand concepts, while quantitative analysis deals with numerical data to identify patterns and relationships.

What is descriptive statistics?

Descriptive statistics summarize and describe the features of a data set, including measures like mean, median, mode, and standard deviation.

What is inferential statistics?

Inferential statistics use sample data to make generalizations about a larger population, often through hypothesis testing and confidence intervals.

What is regression analysis?

Regression analysis examines the relationship between dependent and independent variables, helping to predict outcomes and understand variable impacts.

What is the role of software in data analysis?

Software like SPSS, R, and Excel facilitate data analysis by providing tools for statistical calculations, visualization, and data management.

What are data visualization techniques?

Data visualization techniques include charts, graphs, and maps, which help in presenting data insights clearly and effectively.

What is data cleaning?

Data cleaning involves removing errors, inconsistencies, and missing values from a data set to ensure accuracy and reliability in analysis.

What is the significance of sample size in data analysis?

Sample size affects the accuracy and generalizability of results; larger samples generally provide more reliable insights.

How does correlation differ from causation?

Correlation indicates a relationship between variables, while causation implies one variable directly affects the other.

What are the ethical considerations in data analysis?

Ethical considerations include ensuring data privacy, obtaining informed consent, and avoiding data manipulation or misrepresentation.

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Article contents

Use of qualitative methods in evaluation studies.

• Namita Ranganathan Namita Ranganathan University of Delhi
•  and  Toolika Wadhwa Toolika Wadhwa Shyama Prasad Mukherji College for Women
• https://doi.org/10.1093/acrefore/9780190264093.013.378
• Published online: 26 April 2019

Evaluation studies typically comprise research endeavors that are undertaken to investigate and gauge the effectiveness of a program, an institution, or individuals working in educational contexts, such as teachers, students, administrators, and other stakeholders in education. Usually, research studies in this genre use empirical methods to evaluate educational practices and systems. Alternatively, they may take up theoretical reflections on new policies, programs, and systems. An evaluation study requires a rigorous design and method of assessment to focus on the specific context and set of issues that it targets. In general, research studies that attempt to evaluate a program, an individual, or an institution place emphasis on checking their efficacy. They do not seek to find explanations that have led to the level of efficacy that the variables under study may have achieved. Thus, quite often, they are contested as not being full-fledged research.

Evaluation studies use a variety of methods. The choice of method depends on the area of study as well as the research questions. An evaluation study may thus fall within the qualitative or quantitative paradigms. Often, a mixed method approach is used. The purpose of the study plays a significant role in deciding the method of inquiry and analysis. Establishing the probability, plausibility, and adequacy of the program can be some of the main aims of evaluation studies. This implies as well that the programs, institutions, or individuals under study would have an impact on the course and direction of future programs and practices. An evaluation study is thus of vital importance to ensure that appropriate decisions can be made about efficacy, transferability to different contexts, and difficulties and challenges to be faced in subsequent applications.

Evaluation studies in India have been done in a vast range of areas that include program evaluation, impact studies, evaluations of specific interventions, performance outcome assessments, and the like. Some examples of studies undertaken by the government and the development sector in this regard are the following: assessment of interventions for adolescence education; impact studies of interventions, programs, and policies launched for education of minorities, including girls; and evaluation of performance outcomes stemming from programs for education of the marginalized.

The key challenges in evaluation studies are to gather accurate data in order to establish reliable outcomes, to establish clear relationships between the outcomes and the interventions being studied, and to safeguard against researcher bias.

• evaluation studies
• program evaluation
• qualitative evaluation
• outcome-based evaluation
• project evaluation
• inferring qualitative trends

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Introduction to qualitative research methods – Part I

Shagufta bhangu.

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

Fabien Provost

Carlo caduff.

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

INTRODUCTION

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

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

Examples of qualitative research techniques

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

PHILOSOPHICAL FOUNDATIONS OF QUALITATIVE RESEARCH METHODS

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

Developing a research methodology

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

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

KEY FEATURES AND CONTRIBUTIONS OF QUALITATIVE RESEARCH METHODS

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

Key features of qualitative research methods

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

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

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

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

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

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

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• Open access
• Published: 01 July 2024

Understanding the challenges of identifying, supporting, and signposting patients with alcohol use disorder in secondary care hospitals, post COVID-19: a qualitative analysis from the North East and North Cumbria, England

• Katherine Jackson 1 ,
• Rosie Baker 2 ,
• Amy O’Donnell 1 ,
• Iain Loughran 3 ,
• William Hartrey 4 &
• Sarah Hulse 5  

BMC Health Services Research volume  24 , Article number:  772 ( 2024 ) Cite this article

Metrics details

Alcohol-related mortality and morbidity increased during the COVID-19 pandemic in England, with people from lower-socioeconomic groups disproportionately affected. The North East and North Cumbria (NENC) region has high levels of deprivation and the highest rates of alcohol-related harm in England. Consequently, there is an urgent need for the implementation of evidence-based preventative approaches such as identifying people at risk of alcohol harm and providing them with appropriate support. Non-alcohol specialist secondary care clinicians could play a key role in delivering these interventions, but current implementation remains limited. In this study we aimed to explore current practices and challenges around identifying, supporting, and signposting patients with Alcohol Use Disorder (AUD) in secondary care hospitals in the NENC through the accounts of staff in the post COVID-19 context.

Semi-structured qualitative interviews were conducted with 30 non-alcohol specialist staff (10 doctors, 20 nurses) in eight secondary care hospitals across the NENC between June and October 2021. Data were analysed inductively and deductively to identify key codes and themes, with Normalisation Process Theory (NPT) then used to structure the findings.

Findings were grouped using the NPT domains ‘implementation contexts’ and ‘implementation mechanisms’. The following implementation contexts were identified as key factors limiting the implementation of alcohol prevention work: poverty which has been exacerbated by COVID-19 and the prioritisation of acute presentations (negotiating capacity); structural stigma (strategic intentions); and relational stigma (reframing organisational logics). Implementation mechanisms identified as barriers were: workforce knowledge and skills (cognitive participation); the perception that other departments and roles were better placed to deliver this preventative work than their own (collective action); and the perceived futility and negative feedback cycle (reflexive monitoring).

Conclusions

COVID-19, has generated additional challenges to identifying, supporting, and signposting patients with AUD in secondary care hospitals in the NENC. Our interpretation suggests that implementation contexts, in particular structural stigma and growing economic disparity, are the greatest barriers to implementation of evidence-based care in this area. Thus, while some implementation mechanisms can be addressed at a local policy and practice level via improved training and support, system-wide action is needed to enable sustained delivery of preventative alcohol work in these settings.

Peer Review reports

Alcohol is now the leading risk factor for ill-health, early mortality, and disability amongst working age adults (aged 15 to 49) in England, and the fifth leading risk factor for ill-health across all age groups [ 1 ]. Evidence also shows significant socioeconomic inequalities in alcohol-related harm [ 2 ]. Over half of the one million hospital admissions relating to alcohol in England each year occur in the lowest three socioeconomic deciles [ 3 ] and rates of alcohol-related deaths increase with decreasing socioeconomic status [ 4 ]. In 2020 people under 75 years living in the most deprived areas in England had a 4.8 times greater likelihood of premature mortality from alcohol-related liver disease than those living in the most affluent areas [ 5 ].

Although globally, there is mixed evidence about the impact of the COVID-19 pandemic and associated social and economic restrictions on alcohol consumption [ 6 ], some studies suggest that people who were already drinking alcohol heavily increased their intake during this period [ 7 , 8 ]. Latest data for England show that the total number of deaths from conditions that were wholly attributed to alcohol rose by 20% in a single year in 2020, the largest increase on record [ 9 ]. In England, and elsewhere, it has been argued that COVID-19 should be regarded as a syndemic rather than a pandemic, as it has interacted with, and most adversely affected those in the most deprived social groups who were already experiencing the greatest inequalities [ 10 ]. In the case of alcohol use, COVID-19 may have interacted with and exacerbated the social conditions associated with alcohol use such as poverty, and loneliness and isolation [ 11 , 12 ]. Moreover, with evidence that alcohol-related harms will continue to increase, there is concern this will further widen health inequalities for those communities and regions who are likely to be most affected [ 8 , 13 ]. Thus, there is an urgent need for the implementation of evidence-based preventative strategies to reduce alcohol harm and associated inequalities, as part of a wider system level approach that includes primary, secondary and specialist care settings [ 8 ]. From here we use the term Alcohol Use Disorder (AUD), to refer to a spectrum of alcohol use from harmful to dependent alcohol use [ 14 ].

In secondary care hospitals, the UK government prioritised the implementation of Alcohol Care Teams (ACTs) in England in the National Health Service (NHS) Long Term Plan with the aim of improving care and reducing alcohol-related harms [ 15 ]. ACTs are clinician-led, multidisciplinary teams designed to support provision of integrated alcohol treatment pathways across primary, secondary and community care, and have been shown to reduce alcohol harms through reductions in avoidable bed days; readmissions; Accident and Emergency Department (AED) attendances; and ambulance call outs [ 16 ]. However, the non-specialist secondary care workforce also has an essential role in identifying and managing people at risk, using evidence-based approaches such as screening patients for excessive alcohol use and the provision brief advice [ 17 ]. Given that people may not always present primarily with alcohol-related concerns, routine screening provides an important opportunity to identify people at an earlier stage in their drinking and thereby prevent escalation of alcohol-related problems. Current NHS clinical guidance [ 18 ] requires that non-specialist healthcare staff ‘should be competent to identify harmful drinking (high-risk drinking) and alcohol dependence’ (p46). This includes having the skills to assess the need for an intervention or to provide an appropriate referral.

Despite this guidance however, evidence from prior to the pandemic suggests a range of barriers exist in the delivery and widespread implementation of alcohol prevention work by non-specialist secondary care staff. These include time pressures, limited knowledge and awareness of AUD, and a lack of training, skills, and financial support [ 19 , 20 , 21 , 22 ]. Many studies also highlight that the delivery of preventative support for AUD in secondary care is hampered by wider social cultural challenges such as the stigma of heavy alcohol use and widespread belief that problematic alcohol use is a personal responsibility and represents moral failing, leading to an emphasis on individuals to manage their own care [ 22 ]. Additionally, as AUD frequently co-occurs with other physical and mental health conditions [ 23 ], non-specialist healthcare staff can find themselves ill-equipped to provide the best standard of care for these patients who have multiple and complex needs [ 24 ]. Moreover, in England, as in other health systems, the impact of COVID-19 has created additional pressures and challenges for the whole NHS, including secondary hospitals. There are more people visiting AED than before the pandemic, with longer waiting lists for treatment and fewer hospital beds [ 25 ]. There is also record dissatisfaction amongst the workforce, with more doctors now stating they want to leave the NHS than before the pandemic [ 26 ].

Given the clear need for preventive work to reduce inequalities in alcohol-related harm and the current challenges within secondary care in a post-COVID-19 context, there is value in exploring the views of secondary care staff about supporting patients with AUD since the pandemic. Moreover, the low levels of delivery of preventative support for AUD across different sites suggest there is merit in using implementation science theory [ 27 ] to support improved explanation and understanding of this situation [ 27 , 28 ]. Normalisation Process Theory [ 29 ] has been used extensively in studies conducted in other health settings to understand and evaluate past and future implementation efforts e.g. [ 28 , 30 , 31 , 48 , 33 ], including in relation to alcohol screening and brief intervention in England and Australia [ 30 , 31 ]. NPT is a sociological implementation theory that identifies three domains as shaping the implementation of a new intervention or practice: contexts; mechanisms; and outcomes. Contexts refer to the ‘events in systems unfolding over time within and between settings in which implementation work is done.’ [ 34 ]; mechanisms are factors that ‘motivate and shape the work that people do when they participate in implementation processes’ [ 34 ]; outcomes refer to what changes occur when interventions are implemented. NPT is a conceptual tool and can be used at different stages of the research process [ 29 ]. In this study NPT has been used retrospectively during the analysis stage.

The aim of the present study is to use NPT to elucidate possible explanations for why the preventative practice of identifying, supporting, and referring patients with AUD to appropriate support is not consistently taking place in secondary care in the NENC in the post COVID-19 context. We also aim to make recommendations for areas that should be targeted by policy and practice initiatives.

Study setting

We conducted a qualitative study with health care professionals working in eight secondary care hospitals in the eight NHS Trusts in the North East and North Cumbria (NENC) region of England. The NENC experiences significant health inequalities [ 35 ], including health inequalities in alcohol-related harm. In 2021, the region had the highest reported alcohol specific and alcohol related mortality and the most alcohol related and alcohol specific admissions in England [ 36 ].

The data collection was carried out between June and October 2021. At this time, most COVID-19 restrictions had just been lifted in the NENC [ 37 ] but the impacts of COVID-19 on patients, staff and health care delivery were still ongoing.

As such, the study was planned to contribute to a baseline understanding of support for AUD in secondary care in the NENC conducted as part of a wider regional alcohol health needs assessment (2022) which would inform and direct strategic action and resource allocation in secondary care to improve alcohol-related outcomes post-COVID-19. The Principal Investigator (PI) for the study was the alcohol lead for the NENC Integrated Care System (SH), and the wider study team included representation from Primary Care, Secondary Care, Public Health, and Academia.

We used the method of qualitative semi-structured interviews to enable us to focus on issues that we wanted to explore, as well as allowing the participants flexibility to discuss the issues that were important to them [ 38 ]. We adopted a critical realist approach to the interpretation of data which purports that data can be taken as evidence for ‘real phenomena and processes’, but also recognises that the knowledge generated through qualitative research is situated and partial [ 39 ].

As part of a wider ambition to build research capacity in the study region, a novel aspect of the study design is that six junior doctors from the Gastroenterology Research and Audit through North Trainees, were trained in qualitative interview skills by a qualitative methodologist from the NIHR Applied Research Collaboration (ARC) North East and North Cumbria (NENC) and supported by members of the study team to recruit staff and carry out the interviews with secondary care clinicians.

Participants

We used a form of stratified purposive sampling [ 40 ] as the recruitment of healthcare professionals was structured to provide insights across all the NHS Trusts in the study region, a range of clinical specialities, and a range of points across the clinical pathway, with both medical and nursing staff. As such, professionals working in AED, Medical specialties, Psychiatric Liaison (PL), Gastroenterology or Surgical specialties were eligible to participate. Junior doctor interviewers or the PI contacted potential participants either by email or face-to-face and explained the purpose of the study. People who expressed an interest were then provided with the study participant information sheet and consent form. The sampling was deemed complete when the quota of participants was met for each trust.

Data collection involved semi-structured interviews based on a topic guide. The topic guide was developed by the study team and was informed by the National Institute for Clinical Excellence – Quality Standard 11 [ 41 ], which contains guidance about identifying and supporting adults and young people who may have an AUD and caring for people with alcohol-related health problems (see Additional file 1 ).

All interviews were conducted via Microsoft Teams, lasted an average of 33 min, were audio recorded and transcribed by professional transcriptionists before being fully anonymised by KJ and IL.

Data analysis involved three stages:

Stage 1: Generating descriptive codes from each area of the data set

In the first stage of analysis, once all transcripts were available, in order to generate insights that could contribute to the baseline understanding of the current situation with regards to support for AUD in secondary care, one researcher (IL) used a method of thematic analysis [ 42 ] and drew on deductive and inductive reasoning to identify descriptive codes against each focus question area of the interview topic guide. This researcher read and re-read the full data set, allowing them to identify descriptive codes across staff accounts.

Stage 2: Generating descriptive and interpretive codes and themes from across the full data set

Following this, to generate insights which went beyond the question areas of the topic guide a second researcher (KJ) familiarised themselves with the data. In contrast to Stage 1, they were less restricted by the original topic guide and through a process of constant comparison began to identify both descriptive and interpretive broad thematic topic areas and codes, across the different areas of the interviews. After the first half of the interview transcripts were coded by the researcher in this way, the broad thematic topic areas were discussed with the wider study team in two meetings. In these meetings the broad topic areas and associated coding framework were refined. This refined framework was applied to future transcripts, with flexibility to add further codes as the analysis progressed. At the end of this process, a decision was made by the team to focus the interpretation for this paper on current practices around identifying, supporting, and signposting patients with AUD in secondary care hospitals because it was felt that this focus could make a meaningful contribution to the existing literature in a post-pandemic context.

Stage 3: Applying Normalisation Process Theory retrospectively to data to generate the final interpretation

To ensure the usefulness of the findings of the current analysis to support the design and delivery of future policy and practice to reduce inequalities in alcohol related harm, academic members of the team suggested using an appropriate implementation theory, namely NPT, to guide our interpretation and understanding of data from this point in the analysis [ 34 ]. NPT had not been used in the study to this point and has been used retrospectively as a sensitising, and partial structuring, device, as seen in previous comparable research e.g. [ 28 , 43 ].

[ 29 , 34 ]. First, when applying NPT, we returned to the codes identified at Stage 2 to identify those that related to the practice of identifying, supporting, and signposting patients with AUD to explore how they may fit alongside the domains of NPT. At this point it was evident that most of the codes related to how implementation contexts and mechanisms were felt to adversely affect provision of support for patients with AUD. In contrast, we found negligible data related to the third NPT domain of outcomes (i.e. what changes occur when interventions are implemented). It was therefore agreed that applying the context and mechanisms domains could be valuable to show how contexts and mechanisms limit the implementation of the phenomena of interest. For transparency however, data not included at this stage is indicated in Additional file 2 .

Next, we separated the codes generated in Stage 2 into overarching thematic areas, these were then labelled as either contexts or mechanisms. For example, poverty and austerity were labelled as contexts, and workforce skills and knowledge were labelled as mechanisms. Details of each stage of the analysis and where the codes generated at Stage 2 of the analysis were mapped, against the NPT context and mechanism domains are shown in Additional file 2 .

Following this we endeavoured to align the thematic topic areas in each NPT domain into its associated constructs. It should be noted that our initial researcher-generated thematic areas aligned easily with three of the four NPT mechanism constructs. Conversely, as the NPT context constructs are a new addition to NPT theory, there were few practical examples of how these should be operationalised meaning it took more interpretive work to understand how our data mapped to these constructs. Through reflective discussions as a team, however, we identified that the researcher-generated themes aligned with three of the four context constructs. Table  1 below summarises the implementation context and mechanism constructs and identifies where our data do and do not map to these constructs. COVID-19 provides an overarching context to the study however as the timing of the interviews meant it penetrated almost all the data.

In keeping with the critical realist approach which recognises the situatedness of knowledge, we see researcher positionality as important to consider in the interpretation of qualitative data. Research can never be value free but, it is necessary to be explicit about where positionality might have affected the interactions [ 45 ]. The junior doctor interviewers and the PI who collected the data had experience of clinical work on the topic of the research. Indeed, the transcripts indicated that there were times when the interviewers aligned themselves or discussed their own experiences in the interviews. Some of the junior doctor interviewers recorded reflexive notes about the interviews, these were used during Stages 1 and 2 of the analysis to support interpretation, but have not been used as data. The researcher who conducted Stage 1 of the analysis has a professional background in healthcare but no direct experience of the topic area. The researcher who led the rest of the analysis has experience of carrying out research about AUD, but no clinical experience of working with people experiencing AUD. Other members of the project team have direct experience of working in hospital settings with patients experiencing AUD. Agreement amongst this heterogeneous research team about the final interpretation gives us confidence that it is grounded in the data. Moreover, this agreement amongst the research team about the final interpretation, and the congruence of findings with the existing literature on the topic of the research prior to COVID-19, gives us confidence that the insider researchers did not compromise the quality of the original empirical data.

In total, 30 staff in the study region were interviewed across the eight NHS Trusts, including 20 nurses and 10 doctors (see Table  2 ) based in five departments: AED; PL; Medical; Surgical; and Gastroenterology ( n  = 6 each). Information related to participant gender and ethnicity are not available and we have not analysed the data with these as a focus. The absence of this data also helps to preserve the anonymity of participants because the geographical region of the study is named.

Overall, participants’ accounts suggested that they were not consistently trying to identify AUD or assessing the need for intervention in the patients they worked with. Where any identification of AUD did take place, this appeared to often be through informal questioning rather than utilising formal, validated screening questionnaires. The following response was typical:

We’ll just ask about units a week. I know that there is a screening tool, there is a chart of some sort and it’s a physical thing that I think the alcohol and drugs nurses use on medications. So we don’t use that on a regular basis. As of now, there’s still a paper–based documenting system, but we don’t use that necessarily. (Participant 14 – Doctor, Trust 4, AED)

Conversely, some staff working in PL teams suggested they more commonly tried to identify AUD. Although again, validated screening questionnaires appeared to be used inconsistently:

Substance misuse is always an integral part of the assessment that we do. . We do have specific packs that we are trained to carry out our assessments to. I think in practice, we often don’t follow those verbatim and we will just do a free form assessment and substances are always part of that… .: “Do you consider that’s an issue for you, is it something that you want help with?” We’re always having those conversations. (Participant 8 – Nurse, Trust 2, PL)

Many staff’s accounts suggested they did not consistently signpost patients with identified AUD to a service that could provide an assessment of need or provide further care. Using NPT to frame our interpretation, in the next section we aim to highlight current practice around these phenomena and identify areas that appeared to be key barriers to implementation.

Implementation contexts

The successful implementation of interventions requires supportive implementation environments both within and outside the settings in which they are delivered. Our data highlighted several key aspects of the implementation context/s that are barriers to the widespread implementation of asking about, supporting, and signposting patients with AUD in secondary care in the study region. As the data collection was conducted very soon after COVID-19 restrictions ended, COVID-19 was an overarching context of the staffs’ accounts.

Widespread poverty, austerity, and the prioritisation of acute conditions – negotiating capacity

Negotiating capacity refers to how contexts shape the extent to which interventions can fit into existing ways of working [ 34 ]. Through the participants’ accounts we identified two aspects of context which appear to limit negotiating capacity: widespread poverty and austerity within the study region; and the focus of secondary care hospitals on the acute and presenting health needs of patients.

Most staff accounts suggested they perceived AUD to be common in the communities their hospitals covered and the patients they saw. Many staff linked the prevalence of AUD in the region to the high rates of poverty. To illustrate, Participant 23 commented that the basic provision for patients with AUD in the hospital, was in stark contrast to the apparent need in the community:

The demographic for around here, people are poor, they do drink, people do smoke,. . people take drugs a lot around here and the help, there isn’t [anything for them] it’s absolutely crazy. (Participant 23 - Nurse, Trust 6, Surgical)

While the need to support patients with AUD was perceived to have been high prior to the COVID-19 pandemic, many staff noted that they had seen a rise in patients presenting with or showing signs of AUD following the pandemic, with some suggesting that they felt that the presentations of alcohol-related morbidity and mortality were likely to increase in the future:

Our numbers [of patients with AUD] have gone up by 100% in five years. . So it’s not going anywhere, and I predict that at the beginning of next year we’re going to see huge influence on alcoholic dependence. Because we’ve already seen people who are having fits, first fits, people who were drinking prior to COVID or probably drinking too much, at high risk, not necessarily dependent and then, furloughed, have begun to drink every day and developed alcohol dependence. (Participant 25 - Nurse, Trust 7, Gastroenterology)

A small number of participants mentioned that because of the observed high levels of AUD in the study region it was harder to decide how to prioritise who to ask about alcohol. They indicated that they were unlikely to ask patients about alcohol if they were drinking at what they saw as lower levels, as they perceived most people were drinking a lot. For example, Participant 7 said:

If they were a binge drinker or they drank more than was recommended, it’s kind of like, where do you take that? How do I talk to my patients about that? Thinking about where we live, our demographic of the type of patients that we see, it’s very common that patients would drink more alcohol than the recommended. So, I guess that is the challenge of how you would approach that to the patient, without coming across like you were being judgmental or self-righteous when you’re trying to give them this advice. And actually asking them; ‘do you even see it as a problem?’ A lot of patients that you would speak to you wouldn’t even say that that is a problem. (Participant 7 - Nurse, Trust 2, Surgical)

Thus, these accounts indicated that the normalisation and prevalence of heavy drinking in some communities actively constrained the extent to which staff could integrate asking about and supporting patients with alcohol use into their day to day work .

Conversely, and illustrating how contexts can be barriers to implementation in one setting but facilitate it in others [ 44 ], some staff working in PL described how they had recently begun doing more systematic screening for AUD because it was recognised as being so prevalent in the patients they saw.

[Previously] unless alcohol was kind of front and centre and was an issue that was discussed from the get-go, it wasn’t always something that was really looked into in great detail as part of our assessments. Whereas now that we do the AUDIT, there’s an AUDIT-C tool with all patients. (Participant 4 – Nurse, Trust 1, PL)

Nonetheless, staff accounts more commonly focused on the need to tackle severe alcohol harm rather than preventative work. In-keeping with other research studies and clinical knowledge, the participants’ suggested that a key reason that patients aren’t routinely being asked about AUD in secondary care is because staff need to prioritise the presenting acute condition/s. Something which is colloquially termed ‘the rule of rescue’. Thus, any identification of AUD, where it did happen, was primarily focused on managing patients whose alcohol use was already affecting, or had the potential to affect, the treatment of their acute physical or mental illness. Participants almost always linked this to the pressurised setting and the restricted time they had to work with patients, as further limiting their capacity to address a patient’s drinking. This context is illustrated in the following quotes:

‘I’m asking [about alcohol] because it effects how I care for that patient and not necessarily about educating them’ (Participant 15 – Doctor, Trust 4, Medical). . .I think asking about the preventative problems, and screening for problems, is something that we just don’t do. If someone comes in and they’re alcohol dependent, realistically the thing you think about most is, right well we need to make sure that we’ve got the right things for if they withdraw, you don’t think, oh well shall we see if there’s anything we can do and to be fair, you don’t really have the time, I don’t think. (Participant 6 - Doctor, Trust 2, AED)

Overall, time and the focus on acute conditions, were commonly cited by staff as key contextual factors, that limited their negotiating capacity to ask patients about alcohol and to provide follow-up support.

Stigma at a structural level – strategic intentions

Strategic intentions refers to how contexts shape the formulation and planning of interventions. Many staff accounts suggested that they perceived there was little visible commitment to the prevention of AUD within their NHS trust or at a national NHS level. Many staff suggested they had seen no communications about providing preventative support to patients with AUD from their trust:

There’s nothing to my knowledge, Trust–wide, of how we help this cohort of patients. There doesn’t seem to be anything written in stone, on the help that we provide. (Participant 21 – Nurse, Trust 6, AED)

Others emphasised that although they had seen some communications about alcohol from their trust, these were limited. Some participants’ accounts indicated a sense of frustration that alcohol was not being prioritised by the NHS and moreover that any care offered to patients with AUD was voluntary rather than a designated part of their core work. For example, in one trust it was noted that the role of the Alcohol Lead was not formalised:

At the moment it’s almost voluntary and there’s always something else that comes along that’s more immediate, more important or seems that way. People aren’t taking the longer view that if we don’t address this problem now then the tsunami of liver disease will just continue. (Participant 10 - Doctor, Trust 3, Gastroenterology)

Relational stigma – reframing organisational logic

Reframing organisational logic refers to the extent to which social structural and social cognitive resources shape the implementation environment [ 34 ]. The stigma which was evident at a structural level was also directly perceived to impact the care of patients with AUD at a relational level. Many staff mentioned that the identification of AUD and subsequent signposting for patients who drink heavily are obstructed because some staff perceive that heavy alcohol use is a personal failing and individual problem. Indeed, judgement or stigma was explicitly proposed by participants as one of the key reasons that AUD prevention and treatment interventions were not implemented, or attempts weren’t made to help people with AUD:

People find them incredibly frustrating and [like] they’re not real patients or people who need [help]. (Participant 4 - Nurse, Trust 4, PL)

This judgement was also seen to be compounded by austerity and the increased demands on health and social care post COVID-19, meaning those who were more challenging or difficult to help were often the easiest group to not manage.

Relational stigma appeared evident in the reluctance of some staff to speak to patients about alcohol. For example, a few participants expressed concern about how patients would respond if they were to ask them about their alcohol use because heavy alcohol consumption can sometimes be perceived by patients and wider society as a personal failing or as evidence of a lack of control:

It’s quite a personal conversation to have with somebody and you’ve got a small thin curtain between every single patient and having those conversations when everybody hears the conversation that you have in the bay, so I think that sometimes contributes to it. (Participant 24 – Nurse, Trust 7, Medicine)

Moreover, the effects of stigma seemed evident in the extent to which staff perceived people would be honest about or disclose their heavy drinking and the extent to which would subsequently make adaptions to investigate further. Some staff said that they did not have the time to build rapport with patients to generate a context where they perceived patients might be more likely to be truthful about their drinking:

It comes down to them being honest. If they say that they don’t drink a lot then we wouldn’t give any advice. (Participant 26 – Nurse, Trust 7, Surgical)

The data also suggests that the extent to which staff appeared willing to identify or support patients with AUD is related to them not seeing it as relevant to the presenting problem which relates to the prioritisation of acute conditions and the negotiating capacity.

Implementation mechanisms

Alongside contexts, we identified a number of mechanisms that appeared to be barriers to implementation across our participants’ accounts.

Workforce knowledge and skills – cognitive participation

All participants’ accounts suggested that there was no mandatory training within trusts to support staff to deliver alcohol prevention work. While participants acknowledged there was indeed very little mandatory training about most conditions, many staff suggested they had not been trained post-University in how to have conversations with patients about alcohol, to assess need, or how to refer and signpost on:

. . we’ve got team days where we go through mandatory training and do little courses and do all our training, but there’s nothing about alcohol on there whereas it might be quite useful because we do get a lot of patients with alcohol issues so that would be beneficial. . we’ve had no training or updates on what’s out there in the community. (Participant 9 – Nurse, Trust 2, Medical)

In a small number of trusts, some staff with a specific remit around alcohol stated they were in the process of developing training about identification within their teams and appeared optimistic about the spread and impact of this.

Where staff did ask about alcohol, a barrier to referring people with AUD to appropriate services was their limited awareness of relevant services within the community. Indeed, a few participants conveyed the sentiment of Participant 11 who described their perception of asking about alcohol in their hospital as a ‘ tick box exercise rather than purposeful tool .’ (Nurse, Trust 3, Medical). Only a small number of participants seemed very knowledgeable about local community services; like Participant 9 above, most staff accounts suggested a lack of awareness of relevant organisations they could refer patients to. Some staff indicated that knowledge of appropriate services was made more challenging because of the frequent change in service provision and cuts and short-term commissioning of relevant voluntary and community sector services:

It is a bit vague at the moment as to exactly what they are going to do with the provider changing over. . when the Covid stuff started, they stopped coming in and just did electronic stuff. But I think they’ve started coming in again. But I don’t quite know what hours they are planning to come in, with the new changeover of people. (Participant 1 – Doctor, Trust 1, Gastroenterology)

In a context of frequent service changeovers and decommissioning, widespread poverty and austerity, and limited awareness of appropriate local services, there appeared to be a heavy reliance on referrals to primary care by staff, even when they didn’t know what primary care would offer patients. This is illustrated by this quote from Participant 15:

Sometimes if people ask me, or if I’ve found that they’ve got like deranged liver functions, I’ll often just sort of say to them, if it fits with an alcohol picture, I would say: “It does look like your alcohol use is affecting your liver, it might be something you think about cutting down,” but at that point I’m not always sure where to refer them to, so I usually end up saying you can get support from your GP. Yes. (Participant 15 – Doctor, Trust 4, Medical)

Role legitimacy – collective action

When asked directly in the interviews about whether they felt that managing AUD was their responsibility most participants stated that it was. However, their wider accounts indicated that many participants and their colleagues relied heavily on calling on staff in other departments to manage patients with AUD who they saw as better placed to address these patients’ needs. In particular, the participants commonly suggested that alcohol nurses or other staff in gastroenterology were most able to help:

In our trust, I’m not sure if it’s the same as any others, when we do the nurse’s admission, we ask how many units they’ve had and if they score over ten then they automatically get pinged to the alcohol nurses who will come and see them. Or we refer them and call the alcohol nurses here. . (Participant 28 – Nurse, Trust 8, AED)

Staff in the site where an ACT had recently been set-up suggested that the introduction of this service had significantly improved the care that they could offer people with visible presentations of AUD and provided a clearer route for signposting. However, the reliance on this service also served to illustrate the limited support prior to this in these sites and the significant care gap at other sites who did not have this provision. Moreover, the accounts of a few participants suggested that due to the high level of need for alcohol dependent support, the ACTs appeared to have little capacity to do preventative work:

The alcohol care team nurses are building up good relationships with some of our more frequent members that are coming on ward. And then they’re able to get permission off them to do more like referrals to [community alcohol service], discussions about tapering down or alcohol reduction therapy, discussions about cognitive behavioural therapies, discussions with housing officers and things, discussions with safeguarding. . having said that, like I say they are getting an abundance of referrals daily now and I think unfortunately it’s ended up a lot bigger than they were expecting, a bit of a mammoth task. (Participant 2 – Nurse, Trust 1, Medical)

In contrast to staff in other departments, as mentioned above, staff from PL teams suggested that identifying patients’ patterns of alcohol use, usually through formalised screening, had relatively recently become part of their core work. Nonetheless, the focus was still on management of AUD rather than prevention, as most indicated that the implementation of this was in response to the prevalence of heavy drinking in the patients they saw. Here the mechanism of collective action appears to be shaped by the context of poverty and austerity.

Perceived futility and negative feedback cycle – reflexive monitoring

Participants’ accounts indicated that they had little information about the outcomes of the people that they saw with AUD. Some staff mentioned that the only time they saw patients again, whether or not they delivered an intervention, was when they re-attended. The following response was typical:

We put them on file with the GP letter, and we don’t know what happens after that. (Participant 26 – Nurse, Trust 7, Surgical)

In the context of this perceived futility, staff appeared to find it difficult to have hope for patients when they experienced only negative reinforcement. Compounding this it was also evident that the recording of information about alcohol use and any advice or signposting were limited in most departments. Although some PL services and some trusts seemed to be trying to record screening more systematically at the time of the research, it was still not mandatory and was not always prioritised as the following quote illustrates:

[We] have the AUDIT -C put on e-records, and that provided some challenges as well. . there’s a lot of things that are recorded, you get a lot of alerts, we know that. . staff just tap off them, if they’re not mandatory, So, it was about trying to sell it is an important message. (Participant 25 - Nurse, Trust 7, Gastroenterology)

Here again we see the link between contexts and mechanisms whereby the lack of systematic recording of patients’ alcohol use is likely to be influenced by the context of structural stigma and its impact on strategic intentions.

This paper reports the findings of a collaborative study between practitioners, policy makers, and academics which aimed to explore the challenges to the delivery of identification, support, and subsequent signposting for AUD in the secondary care settings in the NENC region post- COVID-19. Our findings broadly concur with what was already known about the challenges of implementing identification and support for AUD in secondary care hospitals prior to the COVID-19 pandemic. For example, the persistent contextual challenge of time pressures, and the lack of key enabling mechanisms, such as having a workforce with the skills and knowledge to confidently ask about alcohol and signpost patients appropriately [ 22 ]. However, our findings extend existing evidence by highlighting some additional barriers to alcohol prevention work in secondary care in the post-COVID-19 context. Moreover, the use of theory, specifically NPT domains, enables us to illuminate the interplay of context and mechanisms which make implementation of AUD care especially difficult in this setting.

A key contribution of this study to the extant literature is that it provides empirical evidence of how COVID-19 has served to amplify the challenges already experienced by secondary care staff trying to delivery preventative alcohol work in hospital settings. Many staff indicated that the sheer scale of people presenting with possible AUD since COVID-19, meant they did not have the time to ask people or to prioritise asking people about alcohol. Where people were identified as experiencing AUD, provision of effective signposting and support for patients was adversely affected by lack of staff awareness about relevant care providers and lack of capacity in local services due to the impact of austerity and cuts to public services. Two trusts in the study region had ACTs in place at the time of the interviews, as part of the wider NHS commitment to reduction alcohol harm in England [ 16 ]. This appeared to have increased the capacity of the non-specialist workforce at these two sites to refer patients identified as experiencing AUD onto appropriate specialist support. However, a tentative, but notable, finding of this study was that while ACTs were making a difference in these trusts for those with existing alcohol dependence, they were limited in their capacity to deliver more preventative work around AUD (initially part of their remit) due to the high level of need amongst the dependent patient population. This warrants further exploration, with further insights potentially to come via the wider programme of work around ACTs that is currently ongoing in England [ 46 ]. Overall, the study provides empirical evidence that the implementation of the preventative practices to support a reduction in AUD may be particularly difficult in areas of deprivation such as the NENC meaning that inequalities are likely to be widening with other more affluent regions.

Stigma, the process of marking certain groups as being somehow contagious or of less value than others [ 47 ], is internationally recognised as a significant constraining factor to the delivery of compassionate and appropriate healthcare for patients with AUD and other substance use in secondary care and other health and social care settings [ 47 , 48 ]. In this study we chose to approach stigma as a structural and relational concept, seeing relational stigma as developing from structural stigma [ 49 ]. The role of structural stigma for limiting the implementation of identifying, supporting, and signposting patients with AUD was striking, as our data highlighted that the prevention of heavy alcohol use does not appear to be a visible priority within individual trusts, and arguably the wider NHS. Limited resources were perceived available for this area of care, and little visible commitment to support patients with AUD despite the scale of the problem. Stigma was also evident at a relational level in our participants accounts of the interactions between staff and patients, notably staff’s reluctance to ask about alcohol use and their perception that patients did not want to disclose their AUD. However, it should be noted that many of the staff who took part in the study suggested that they did not perceive patients in this way yet continued to struggle to provide alcohol prevention care. Thus, this relational stigma is likely an important, but only partial explanation for limited care provision. Nonetheless, our findings suggest that structural stigma is one of the main barriers to the identification of alcohol use and care in secondary care settings in the NENC. This echoes the damning findings of the ‘Remeasuring the Units’ report, also published since the pandemic, that argued that stigma contributes to the missed opportunities in secondary care for patients who ultimately die from alcohol-related liver disease [ 5 ].

This study was conducted primarily as a vehicle to understand and bring about change in workforce practice around the prevention of alcohol harm in NENC secondary care services. It was an integral component of a broader Health Care Needs Assessment (2022) on alcohol undertaken in response to increasing levels of alcohol harm in this region of the UK, which led to recommendations over four overarching themes: service delivery; workforce; data; and leadership from the healthcare system. The results of the study have directly shaped the regional strategy for the reduction of alcohol harm, a key element of which is the integrated alcohol workforce strategy for the NENC which aims to better support the NHS workforce to prevent alcohol harm through: increased awareness of the Chief Medical Officer alcohol guidance; improved pathways to community-based alcohol treatment and recovery support; workforce training and development; and support for staff to address their own drinking. The evidence highlighting the importance of stigma have additionally led to a strategic drive for senior leaders to acknowledge the impact alcohol has on their organisation and the communities they serve, and to take action to work in partnership to reduce this. There is also cross-system support to tackle relational stigma, initially though a co-ordinated multi-agency media campaign.

Overall, our interpretation has signalled areas of policy and practice which can be targeted to try to increase the uptake of these preventive strategies in the secondary care settings. However, ultimately the findings illustrate that the challenge for implementation of these evidence based preventative measures is not just upskilling the workforce or increasing resources. It also indicates that we need to address the complex interplay of contextual factors and implementation mechanisms which have been compounded by the pandemic and contribute to reinforcing and increasing existing inequalities. The works contributes to calls for a multi-layered response to reducing alcohol harm and wider cultural change for how alcohol use and substance use is perceived.

Study strengths and limitations

A strength of the study is that it was undertaken in an area experiencing some of the greatest inequalities from the COVID-19 pandemic. This allowed us to see the challenges to delivering preventative work in these contexts, which might be similar in other regions. A further strength is that mapping the empirical data onto an evidence-based implementation theory, which has been widely use in different settings, enabled us to focus on the aspects of the implementation, that are likely to be important across other settings too. Framing the interpretation using the NPT domains has helped us to emphasise how contexts and mechanisms interact to make the implementation at this particular time and place difficult. A key limitation of the study is that as it was based in one region of England, we cannot know for sure if these insights are transferrable beyond this context.

Secondary care hospitals are an important setting for the delivery of preventative care for AUD, due to the frequency with which AUD co-occurs with other physical and mental health conditions. Prior to the pandemic there was evidence that non-specialist healthcare staff can find caring for patients with alcohol-related presentations difficult, meaning that identifying, supporting, and that signposting patients was happening inconsistently. In this study, we highlight the additional challenges facing secondary care staff due to post-pandemic pressures and the significant rise in alcohol-related harm in some regions such as the NENC. Thus, whilst the mechanisms for implementing alcohol prevention work in secondary care need attention, our findings suggest that the greatest barrier is contextual, including widespread structural stigma.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Normalisation Process Theory

Alcohol Care Teams

North East and North Cumbria

Alcohol Use Disorder

Accident and Emergency Department

Psychiatric Liaison Teams

Alcohol Use Disorders Identification Test

Alcohol Use Disorders Identification Test Consumption

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Acknowledgements

In addition to co-authors WH and RB we are grateful to the four junior doctors Jamie Catlow, Rebecca Dunn, Sarah Manning and Satyasheel Ramful from the Gastroenterology Research and Audit through North Trainees who collected data for the study. We are grateful to Dr Matthew Breckons the qualitative methodologist who co-trained (with AOD and KJ) the junior doctors in qualitative interview skills. We are especially grateful to the thirty staff who gave up their time to participate in the research.

The project was funded by the North East and North Cumbria Integrated Care System Prevention Programme.

AO is Deputy Theme Lead – Prevention, Early Intervention and Behaviour Change within the NIHR Applied Research Collaboration (ARC) North East and North Cumbria (NENC) (NIHR200173). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. AO and KJ are also part-funded by a NIHR Advanced Fellowship (ADEPT: Alcohol use disorder and DEpression Prevention and Treatment, Grant: NIHR300616). The NIHR have not had any role in the design, implementation, analysis, write-up and/or dissemination of this research.

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SH and RB designed the study; SH, RB and WH were involved in the data collection; IL and KJ analysed and interpreted the data with support from AOD, SH, RB and WH; KJ drafted the manuscript with support from SH, AOD, RB, IL and WH. All authors approved the submitted version.

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Jackson, K., Baker, R., O’Donnell, A. et al. Understanding the challenges of identifying, supporting, and signposting patients with alcohol use disorder in secondary care hospitals, post COVID-19: a qualitative analysis from the North East and North Cumbria, England. BMC Health Serv Res 24 , 772 (2024). https://doi.org/10.1186/s12913-024-11232-4

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Sule S , DaCosta MC , DeCou E , Gilson C , Wallace K , Goff SL. Communication of COVID-19 Misinformation on Social Media by Physicians in the US. JAMA Netw Open. 2023;6(8):e2328928. doi:10.1001/jamanetworkopen.2023.28928

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Communication of COVID-19 Misinformation on Social Media by Physicians in the US

• 1 Department of Health Promotion and Policy, School of Public Health and Health Sciences, University of Massachusetts, Amherst
• Correction Errors in Quotes and Dates JAMA Network Open

Question   What types of COVID-19 misinformation have been propagated online by US physicians and through what channels?

Findings   In this mixed-methods study of high-use social media platforms, physicians from across the US and representing a range of medical specialties were found to propagate COVID-19 misinformation about vaccines, treatments, and masks on large social media and other online platforms and that many had a wide reach based on number of followers.

Meaning   This study’s findings suggest a need for rigorous evaluation of harm that may be caused by physicians, who hold a uniquely trusted position in society, propagating misinformation; ethical and legal guidelines for propagation of misinformation are needed.

Importance   Approximately one-third of the more than 1 100 000 confirmed COVID-19–related deaths as of January 18, 2023, were considered preventable if public health recommendations had been followed. Physicians’ propagation of misinformation about COVID-19 on social media and other internet-based platforms has raised professional, public health, and ethical concerns.

Objective   To characterize (1) the types of COVID-19 misinformation propagated by US physicians after vaccines became available, (2) the online platforms used, and (3) the characteristics of the physicians spreading misinformation.

Design, Setting, and Participants   Using US Centers for Disease Control and Prevention guidelines for the prevention and treatment of COVID-19 infection during the study window to define misinformation, structured searches of high-use social media platforms (Twitter, Facebook, Instagram, Parler, and YouTube) and news sources ( The New York Times , National Public Radio) were conducted to identify COVID-19 misinformation communicated by US-based physicians between January 2021 and December 2022. Physicians’ state of licensure and medical specialty were identified. The number of followers for each physician on 4 major platforms was extracted to estimate reach and qualitative content analysis of the messages was performed.

Main Outcomes and Measures   Outcome measures included categories of COVID-19 misinformation propagated, the number and traits of physicians engaged in misinformation propagation, and the type of online media channels used to propagate misinformation and potential reach.

Results   The propagation of COVID-19 misinformation was attributed to 52 physicians in 28 different specialties across all regions of the country. General misinformation categories included vaccines, medication, masks, and other (ie, conspiracy theories). Forty-two physicians (80.8%) posted vaccine misinformation, 40 (76.9%) propagated information in more than 1 category, and 20 (38.5%) posted misinformation on 5 or more platforms. Major themes identified included (1) disputing vaccine safety and effectiveness, (2) promoting medical treatments lacking scientific evidence and/or US Food and Drug Administration approval, (3) disputing mask-wearing effectiveness, and (4) other (unsubstantiated claims, eg, virus origin, government lies, and other conspiracy theories).

Conclusions and Relevance   In this mixed-methods study of US physician propagation of COVID-19 misinformation on social media, results suggest widespread, inaccurate, and potentially harmful assertions made by physicians across the country who represented a range of subspecialties. Further research is needed to assess the extent of the potential harms associated with physician propagation of misinformation, the motivations for these behaviors, and potential legal and professional recourse to improve accountability for misinformation propagation.

As of May 11, 2023, an estimated 1 128 000 COVID-19 deaths had occurred in the US, 1 and nearly 14% of people infected by the COVID-19 virus have experienced the post–COVID-19 condition. 2 , 3 As of December 2022, estimated death rates for unvaccinated persons in the US were 271 per 100 000 compared with 82 per 100 000 for those fully vaccinated, yet only 69.2% of eligible people had received the full primary vaccine series, and 15.5% had received the bivalent booster. 1 Vaccination rates have varied by region throughout the pandemic despite widespread availability, with southeastern states having lower full primary series rates (52%) compared with northeastern states (80%). 1 Other preventive behaviors, such as mask wearing and social distancing, have varied similarly by geographic region. 4 , 5

Individual health behaviors related to COVID-19 have been attributed to complex social phenomena, including inconsistent recommendations by government entities early in the pandemic, mistrust of the scientific community, political polarization, and unclear or incorrect guidance from other sources. 6 - 8 COVID-19 misinformation, defined as false, inaccurate, or misleading information according to the best evidence available at the time, and disinformation, defined as having an intentionally malicious purpose, have been ubiquitous on social media, despite major platforms’ COVID-19 misinformation policies. 9 Medical misinformation was propagated long before the COVID-19 pandemic, 10 but the internet increases reach and speed of dissemination, potentially exacerbating misinformation consequences during an unparalleled public health threat that has killed more than 7 million people across the globe. 11 - 13

COVID-19 misinformation has been spread by many people on social medial platforms, 14 but misinformation spread by physicians may be particularly pernicious. 15 Physicians are often considered credible sources of medical and public health information, increasing the potential negative impact of physician-initiated misinformation. The US Food and Drug Administration (FDA) and others have called for action to limit the potential harm of physician-propagated COVID-19 misinformation. 15 , 16 Despite the rising concerns voiced in news articles and opinion pieces, physician-propagated COVID-19 misinformation and its associated outcomes remain understudied.

This study aimed to address this gap in knowledge by examining COVID-19 misinformation communicated on social media platforms and other online sources by US physicians after vaccines were made available. Understanding the extent of this phenomenon, its potential impact, and associated professional, ethical, and legal ramifications may help to better understand the role that physician-propagated COVID-19 misinformation may have played in preventable COVID-19 deaths and mistrust in institutions.

This mixed-methods study sought to characterize the (1) type of COVID-19 misinformation physicians communicated online between January 1, 2021, and May 1, 2022; (2) social media and other online platforms where misinformation appeared; and (3) characteristics of the physicians. Physician age, sex, and race and ethnicity were not available on social media or other online postings. A decision was made to not infer these data from pictures or other means to avoid potential bias and misclassification. We defined COVID-19 misinformation as assertions unsupported by or contradicting US Centers for Disease Control and Prevention (CDC) guidance on COVID-19 prevention and treatment during the period assessed or contradicting the existing state of scientific evidence for any topics not covered by the CDC (eTable in Supplement 1 ). We conservatively classified inaccurate information as misinformation rather than disinformation because the intent of the propagator cannot be objectively assessed. The University of Massachusetts Institutional Review Board determined that this study did not meet criteria for human participant research. This study followed the Standards for Reporting Qualitative Research ( SRQR ) reporting guidelines.

First, we conducted structured searches of social media platforms and general web searches in late spring of 2022 to identify media containing COVID-19 misinformation attributed to US-based physicians, defined as using doctor of medicine (MD) or doctor of osteopathic medicine (DO) after their name and being licensed to practice medicine in the US at some time or never licensed but working in the US. The start date was selected in relation to the availability of the COVID-19 vaccines. Search terms included the following: “COVID,” “vaccine,” “doctor” or “physician,” “ineffective,” “pharmaceutical,” “medication,” “ivermectin,” “hydroxychloroquine,” and “purchase.” Search terms were refined based on initial searches to include “COVID misinformation,” “doctor” or “physician,” and/or “conspiracy theory.” Conspiracy theories were defined as communicating skepticism of all information that does not fit the theory, overinterpreting evidence that fits the theory, and/or evidence of internal inconsistency. 17 The platforms searched were selected based on the volume of news articles, popularity, and searchability (Instagram, Twitter, YouTube, Facebook, Parler, TikTok, The New York Times , National Public Radio) 18 ; if the findings on one platform indicated that another platform could have additional new data, it was added to the search list. Due to the large volume and repetitiveness of Tweets, Twitter searches focused initially on America’s Frontline Doctors’ Twitter profile because of the volume of COVID-19 misinformation in its Tweets, 19 its large following, and the potential for physicians propagating misinformation to follow the page. Followers of the America’s Frontline Doctors’ page with an MD or DO in their header were traced on Twitter and other platforms as well. General internet searches using Google’s search engine were conducted to identify misinformation attributed to physicians in third party platforms, such as local news articles.

The following information was collected from each source: physician’s name, medical specialty, the state(s) in which they were currently or had been licensed, whether their license to practice was active, had lapsed, or been revoked based on state medical board site searches, when the misinformation was posted (if available), from what source it was found, and the number of followers the physician had (if the source was a social media platform). Misinformation was classified into the following categories: medication, vaccine, mask/distancing, and other unsubstantiated or false claims. After the initial searches were completed, the physicians’ names were searched on the social media platforms and through general online searches to identify misinformation they posted that may have been missed in the initial searches and extended through December 2022.

Descriptive statistics were used to quantify the types of misinformation, the frequency in which they appeared, the platforms on which they were found, and characteristics of the physicians identified (eg, specialty and state[s] in which the physician was licensed). We calculated the total, median, and IQR for the number of followers on platforms with the highest volume of users (Twitter, Facebook, YouTube, Instagram) using Stata software, version 17 (StataCorp).

We performed directed qualitative content analysis 20 of the misinformation using a validated rapid qualitative analysis approach. 21 The analytic team (S.S. and M.D.) populated a templated summary table with misinformation text extracted from each media platform. The team divided the physician list and generated a summary of the misinformation associated with each of the physicians. In the second step of this analytic process, each team member individually identified pertinent and common themes, subthemes, and supporting quotes for each. After this was done individually, the team met to discuss their findings and combine the findings into a final list of themes and subthemes. Considerations regarding reflexivity included that S.G. is a public health professor and physician, and M.D. and S.S. are aspiring physicians, which may have increased sensitivity to potential harms.

A total of 52 US physicians were identified as having communicated COVID-19 misinformation in the period assessed. All but 2 were or had been licensed to practice medicine in the US; the others were researchers. The 50 physicians who currently were or had been licensed represented 28 distinct medical specialties (3 of 50 had 2 different specialties; primary care was the most common overall [18 (36.0%)]) and they were licensed or working in 29 states across the US ( Figure and Table 1 ). Forty-four of the 50 physicians (88.0%) held an active license in at least 1 state; 3 (6.0%) did not have an active license, 4 (8.0%) had had a license suspended or revoked, and 1 (2.0%) had active licenses in 2 states and revoked/suspended licenses in 2 other states. Nearly one-third (16 of 52) were affiliated with groups with a history of propagating medical misinformation, such as America’s Frontline Doctors. Specific types of misinformation included the following: (1) vaccines were unsafe and/or ineffective, (2) masks and/or social distancing did not decrease risk for contracting COVID-19, (3) medications for prevention or treatment were effective despite not having completed clinical trials or having been FDA approved, and (4) other (eg, conspiracy theories).

Most of the 52 physicians (40 [76.9%]) who posted misinformation did so in more than 1 of the 4 categories identified. Vaccine misinformation was posted by the majority (42 [80.8%]), followed by other misinformation (28 [53.8%]; eg, government and public health officials deliberately falsified COVID-19 statistics) and medication misinformation (27 [51.9%]).

Of these 52 physicians, 20 (38.5%) posted COVID-19 misinformation on 5 or more different social media platforms and 40 (76.9%) appeared on 5 or more third-party online platforms such as news outlets. Twitter was the most used platform, with 37 of the 52 physicians (71.2%) posting misinformation and a median of 67 400 followers (IQR, 12 900-204 000). Additional details of physicians’ reach by platforms and followers are in Table 2 and Table 3 .

Major themes identified included the following: (1) claiming vaccines were unsafe and/or ineffective, (2) promoting unapproved medications for prevention or treatment, (3) disputing mask-wearing effectiveness, and (4) other misinformation, including unsubstantiated claims, eg, virus origin, government lies, and other conspiracy theories. Supportive quotes are listed in Table 4 .

The most common theme identified was physicians discouraging the public from receiving COVID-19 vaccines. Promoting fear and distrust of the vaccine and reliance on “natural” immunity were common subthemes.

Some of the misinformation propagated by physicians claimed that COVID-19 vaccines were ineffective at preventing COVID-19 spread. A common approach included circulating counts of positive case rates by vaccination status, claiming that most positive cases were among vaccinated individuals. This claim is technically true but misleading, as many more people are vaccinated, and the proportion of unvaccinated people who are infected is much higher. 22 Some stated that the significant increase in case rates after the initial vaccine rollout was evidence for ineffectiveness.

Assertions that COVID-19 vaccines were harmful was not supported by scientific evidence at the time. Unfounded claims included that the vaccines caused infertility, irreparable damage to one’s immune system, increased risk of developing a chronic illness for children, and a higher risk of cancer and death. Claims that myocarditis was common in children who received the vaccine and that the risks of myocarditis outweighed the risk of vaccination were also unfounded. 23 Several physicians redistributed news articles with stories of individuals suddenly or mysteriously dying from the vaccine, despite evidence from the CDC confirming that deaths caused by a COVID vaccine were extremely rare (9 deaths for over 600 million doses administered in the US as of January 2023) and could be attributed only to the Johnson and Johnson COVID-19 vaccine, which was used much less frequently than other manufacturers’ vaccines in many countries. 24

Many of the identified physicians promoted the use of treatments that had not been tested or FDA approved for use in relation to COVID-19. The 2 most prominent medications promoted were ivermectin and hydroxychloroquine, which have been found to not be effective at treating COVID-19 infections in randomized clinical trials. 25 , 26

Anecdotal personal experiences of successfully treating patients with untested medications were commonly used to support claims about safety and effectiveness, such as patients’ conditions were not improving before receiving the untested medication, but the patient recovered after starting the treatment.

Many physicians posted links or screenshots to articles claiming that ivermectin decreased mortality and hospitalization and increased time to recovery and viral clearance. Although some of the articles appeared to be peer-reviewed, none were in high-quality peer-reviewed biomedical journals, and the FDA had not approved the use of these medications for treating COVID-19. At least 1 of the cited articles has been retracted due to misinterpretation of the data. 27

Many of the physicians propagating misinformation about masking effectiveness portrayed masks in a negative light. Claims centered on ineffectiveness, harm, or both.

Most of the misinformation propagated about wearing protective masks asserted that studies conducted before the pandemic definitively concluded that masks do not prevent the spread of respiratory viral infections. Additionally, data showing rising cases in areas enforcing mask mandates were interpreted to mean that the mandates did nothing to slow the spread of infection.

Allegations of consequences of mask wearing included medical and social or developmental effects, all of which were unfounded. 28 Alleged medical consequences included claims that wearing a face mask restricts one’s oxygen, increases the amount of carbon dioxide being inhaled, and causes mask wearers to inhale bacteria that gets trapped. Many physicians focused on negative consequences related to children and mask mandates in schools, claiming that masks interfered with social development despite lack of evidence and that requiring children to wear masks was a form of child abuse.

This misinformation category included conspiracy theories related to domestic and foreign governments and pharmaceutical companies. Theories related to the government included the following: (1) the COVID-19 pandemic was planned by government officials—the “plandemic”; (2) government and public health officials withheld key information regarding COVID-19 from the public, such as hydroxychloroquine effectiveness, falsified statistics to make the virus appear more severe, and censored information that challenged government messaging; (3) the virus originated in a laboratory in China, which contradicted scientific evidence at the time; and (4) the virus was part of a National Institutes of Health–funded study, was leaked, and that the leak was covered up by government and public health officials. Theories related to pharmaceutical companies included that they played a role in discouraging the use of ivermectin and hydroxychloroquine because these medications were inexpensive and easily accessible, and pharmaceutical companies benefited from the promotion of more novel and expensive treatments.

This study was the first, to our knowledge, to identify the types of COVID-19 misinformation propagated by US physicians on social media and the platforms they used, as well as characterize the physicians who spread the misinformation. The content of misinformation physicians spread was similar to the misinformation spread by others; this study contributes new information about the range of specialties and regions of the country the physicians represented. The widely varying number of followers on social media for each physician suggested that the impact of any individual physician’s social media postings also may vary.

Some of the physicians identified belonged to organizations that have been propagating medical misinformation for decades, 10 but these organizations became more vocal and visible in the context of the pandemic’s public health crisis, political divisiveness, and social isolation. Understanding the motivation for misinformation propagation is beyond the scope of this study, but it has become an increasingly profitable industry within and outside of medicine. For example, America’s Frontline Doctors implemented a telemedicine service that charged $90 per consult, primarily to prescribe hydroxychloroquine and ivermectin for COVID-19 to patients across the country, profiting at least $15 million from the endeavor. 29 Twitter’s elimination of safeguards against misinformation 30 and the absence of federal laws regulating medical misinformation on social media platforms suggest that misinformation about COVID-19 and other medical misinformation is likely to persist and may increase. Deregulation of COVID-19 misinformation on social media platforms may have far-reaching implications because consumers may struggle to evaluate the accuracy of the assertions made. 31

National physicians’ organizations, such as the American Medical Association, have called for disciplinary action for physicians propagating COVID-19 misinformation, 32 but stopping physicians from propagating COVID-19 misinformation outside of the patient encounter may be challenging. 33 Although professional speech may be regulated by courts 34 and the FDA has been called on to address medical misinformation, 16 few physicians appear to have faced disciplinary action. Factors such as licensing boards’ lack of resources available to dedicate toward monitoring the internet 35 and state government officials’ challenges to medical boards’ authority to discipline physicians propagating misinformation 36 may limit action.

Scientific evidence depends on a body of accumulated research to inform practice and guidelines and the evidence depends on the best quality research available at any given time. A recent Cochrane Review has been misinterpreted to have definitively shown that wearing masks does not reduce transmission of respiratory viruses and has been used to support assertions that masks definitively “do not work.” 37 Although the Federal Bureau of Investigation and Department of Energy presented a theory to Congress that the COVID-19 virus was the result of a laboratory leak, 38 scientific evidence and a more recent report from the Office of the Director of National Intelligence demonstrate lack of evidence for a laboratory leak and favor a zoonotic origin of the virus. 39 , 40 These recent challenges to prior understandings illuminate the importance of transparency and reproducibility of the process by which conclusions are drawn.

This study had some limitations. We conducted the study in the spring of 2022, after many major social media platforms had begun to establish policies to combat the propagation of COVID-19 misinformation, which means that the current study may underrepresent the extent of misinformation present before these policies were put in place. On some platforms (eg, Twitter), we were unable to analyze all posts by individuals due to the high volume of Tweets and degree of repetition. This study focused on online platforms whose content was readily accessible to the public; different approaches to identifying misinformation and searches of less used platforms might identify other physicians and include other topics. Misinformation disseminated in other ways, such as during clinical care, was not captured. Vaccines had been approved at the start of the period studied, but accessibility may have varied in the early days of the initial rollout. Finally, the state of scientific evidence for COVID-19 guidelines has evolved rapidly over the course of the pandemic, and this study represents a cross-section of time. The current evidence base for preventive and treatment practices, such as duration of vaccine effectiveness, may differ from the evidence base during the study time frame.

Results of this mixed-methods study of the propagation of COVID-19 misinformation by US physicians on social media suggest that physician-propagated misinformation has reached many people during the pandemic and that physicians from a range of specialties and geographic regions have contributed to the “infodemic.” High-quality, ethical health care depends on inviolable trust between health care professionals, their patients, and society. Understanding the degree to which the misinformation about vaccines, medications, masks, and conspiracy theories spread by physicians on social media influences behaviors that put patients at risk for preventable harm, such as illness or death, will help to guide actions to regulate content or discipline physicians who participate in misinformation propagation related to COVID-19 or other conditions. A coordinated response by federal and state governments and the profession that takes free speech carefully into account is needed.

Accepted for Publication: July 6, 2023.

Published: August 15, 2023. doi:10.1001/jamanetworkopen.2023.28928

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Sule S et al. JAMA Network Open .

Correction: This article was corrected on October 25, 2023, to fix dates in several of the quotes in Table 4 due to coding errors and to correct minor wording inaccuracies in several of the quotes. In addition, the date range of the initial social media searches was clarified in the Methods.

Corresponding Author: Sarah L. Goff, MD, PhD, Department of Health Promotion and Policy, School of Public Health and Health Sciences, University of Massachusetts, 715 N Pleasant St, Amherst, MA 01002 ( [email protected] ).

Author Contributions: Dr Goff had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Mss Sule and DaCosta are considered co–first authors.

Concept and design: Sule, Gilson, Goff.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Sule, DaCosta.

Statistical analysis: Sule, DaCosta, Gilson.

Administrative, technical, or material support: DaCosta, DeCou, Gilson, Goff.

Supervision: DaCosta, Goff.

Conflict of Interest Disclosures: Dr Wallace reported contributing to this work while she was a student at University of Massachusetts Amherst, before and outside of her official capacity as a government employee. No other disclosures were reported.

Funding/Support: The study was funded via internal support by the University of Massachusetts (Dr Goff).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed here are those of the authors and do not represent the official policy or position of the US Department of Veteran Affairs or the US government.

Data Sharing Statement: See Supplement 2 .

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ChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development

• Conference paper
• First Online: 02 July 2024
• Cite this conference paper

• Amanda Barany   ORCID: orcid.org/0000-0003-2239-2271 12 ,
• Nidhi Nasiar   ORCID: orcid.org/0009-0006-7063-5433 12 ,
• Chelsea Porter   ORCID: orcid.org/0009-0005-0246-8509 12 ,
• Andres Felipe Zambrano   ORCID: orcid.org/0000-0003-0692-1209 12 ,
• Alexandra L. Andres   ORCID: orcid.org/0000-0001-7509-1574 12 ,
• Dara Bright 13 ,
• Mamta Shah   ORCID: orcid.org/0000-0002-4932-2831 12 , 14 ,
• Xiner Liu 12 ,
• Sabrina Gao 12 ,
• Jiayi Zhang   ORCID: orcid.org/0000-0002-7334-4256 12 ,
• Shruti Mehta 12 ,
• Jaeyoon Choi 15 ,
• Camille Giordano 12 &
• Ryan S. Baker   ORCID: orcid.org/0000-0002-3051-3232 12  

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14830))

Included in the following conference series:

• International Conference on Artificial Intelligence in Education

In qualitative data analysis, codebooks offer a systematic framework for establishing shared interpretations of themes and patterns. While the utility of codebooks is well-established in educational research, the manual process of developing and refining codes that emerge bottom-up from data presents a challenge in terms of time, effort, and potential for human error. This paper explores the potentially transformative role that could be played by Large Language Models (LLMs), specifically ChatGPT (GPT-4), in addressing these challenges by automating aspects of the codebook development process. We compare four approaches to codebook development – a fully manual approach, a fully automated approach, and two approaches that leverage ChatGPT within specific steps of the codebook development process. We do so in the context of studying transcripts from math tutoring lessons. The resultant four codebooks were evaluated in terms of whether the codes could reliably be applied to data by human coders, in terms of the human-rated quality of codes and codebooks, and whether different approaches yielded similar or overlapping codes. The results show that approaches that automate early stages of codebook development take less time to complete overall. Hybrid approaches (whether GPT participates early or late in the process) produce codebooks that can be applied more reliably and were rated as better quality by humans. Hybrid approaches and a fully human approach produce similar codebooks; the fully automated approach was an outlier. Findings indicate that ChatGPT can be valuable for improving qualitative codebooks for use in AIED research, but human participation is still essential.

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This work was supported by funding from the Learning Engineering Virtual Institute (LEVI) Engagement Hub. All opinions expressed are those of the authors.

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Barany, A. et al. (2024). ChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. AIED 2024. Lecture Notes in Computer Science(), vol 14830. Springer, Cham. https://doi.org/10.1007/978-3-031-64299-9_10

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