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Elevate your qualitative research with cutting-edge Qualitative Coding Software

MAXQDA is your go-to solution for qualitative coding, setting the standard as the top choice among Qualitative Coding Software. This powerful software is meticulously designed to accommodate a diverse array of data formats, including text, audio, and video, while offering an extensive toolkit tailored specifically for qualitative coding endeavors. Whether your research demands data categorization, thematic visualization, mixed-methods analysis, or quantitative content examination, MAXQDA empowers you to seamlessly uncover the profound insights crucial for your qualitative research.

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Revolutionize Your Research: Unleash the Power of Qualitative Coding Software

Qualitative coding software is an essential companion for researchers and analysts seeking to delve deeper into their qualitative data. MAXQDA’s user-friendly interface and versatile feature set make it the ideal tool for those embarking on qualitative coding journeys. Its capabilities span across various data types, ensuring you have the tools required to effectively organize, analyze, and interpret your qualitative data.

Developed by and for researchers – since 1989

Having used several qualitative data analysis software programs, there is no doubt in my mind that MAXQDA has advantages over all the others. In addition to its remarkable analytical features for harnessing data, MAXQDA’s stellar customer service, online tutorials, and global learning community make it a user friendly and top-notch product.

Sally S. Cohen – NYU Rory Meyers College of Nursing

Qualitative Coding is Faster and Smarter with MAXQDA

MAXQDA makes qualitative coding faster and easier than ever before. Code and analyze all kinds of data – from texts to images and audio/video files, websites, tweets, focus group discussions, survey responses, and much more. MAXQDA is at once powerful and easy-to-use, innovative and user-friendly, as well as the only leading qualitative coding software that is 100% identical on Windows and Mac.

As your all-in-one Qualitative Coding Software, MAXQDA can be used to manage your entire research project. Easily import a wide range of data types such as texts, interviews, focus groups, PDFs, web pages, spreadsheets, articles, e-books, bibliographic data, videos, audio files, and even social media data. Organize your data in groups, link relevant quotes to each other, make use of MAXQDA’s wide range of coding possibilities for all kind of data and for coding inductively as well as deductively. Your project file stays flexible and you can expand and refine your category system as you go to suit your research.

Qualitative coding made easy

Coding qualitative data lies at the heart of many qualitative data analysis methods. That’s why MAXQDA offers many possibilities for coding qualitative data. Simply drag and drop codes from the code system to the highlighted text segment or use highlighters to mark important passages, if you don’t have a name for your category yet. Of course, you can apply your codes and highlighters to many more data types, such as audio and video clips, or social media data. In addition, MAXQDA permits many further ways of coding qualitative data. For example, you can assign symbols and emojis to your data segments.

Tools tailor made for coding inductively

Besides theory-driven qualitative data analysis, MAXQDA as an all-in-one qualitative coding software strives to empower researchers that rely on data-driven approaches for coding qualitative data inductively. Use the in-vivo coding tool to select and highlight meaningful terms in a text and automatically add them as codes in your code system while coding the text segment with the code, or use MAXQDA’s handy paraphrase mode to summarize the material in your own words and inductively form new categories. In addition, a segment can also be assigned to a new (free) code which enables researchers to employ a Grounded Theory approach.

Organize your code system

When coding your qualitative data, you can easily get lost. But with MAXQDA as your qualitative coding software, you will never lose track of the bigger picture. Create codes with just one click and apply them to your data quickly via drag & drop. Organize your code system to up to 10 levels and use colors to directly distinguish categories. If you want to code your data in more than one perspective, code sets are the way to go. Your project file stays flexible and you can expand and refine your category system as you go to suit your research.

Further ways of coding qualitative data

MAXQDA offers many more functionalities to facilitate the coding of your data. That’s why researchers all around the world use MAXQDA as their qualitative coding software. Select and highlight meaningful terms in a text and automatically add them as codes in your code system, code your material using self-defined keyboard shortcuts, code a text passage via color coding, or use hundreds of symbols and emoticons to code important text segments. Search for keywords in your text and let MAXQDA automatically code them or recode coded segments directly from the retrieved segments window. With the unique Smart Coding tool reviewing and customizing your categorization system never has been this easy.

Creative coding

Coding qualitative data can be overwhelming, but with MAXQDA as your qualitative coding software, you have an easy-to-use solution. In case you created many codes which in hindsight vary greatly in their scope and level of abstraction, MAXQDA is there to help. Creative coding effectively supports the creative process of generating, sorting, and organizing your codes to create a logical structure for your code system. The graphic surface of MAXMaps – MAXQDA’s tool for creating concept maps – is the ideal place to move codes, form meaningful groups and insert parent codes. Of course, MAXQDA automatically transfers changes made in Creative Coding Mode to your Code System.

Visualize your qualitative coding and data

As an all-in-one Qualitative Coding Software, MAXQDA offers a variety of visual tools that are tailor-made for qualitative research. Create stunning visualizations to analyze your material. Of course, you can export your visualizations in various formats to enrich your final report. Visualize the progression of themes with the Codeline, use the Word Cloud to explore key terms and the central themes, or make use of the graphical representation possibilities of MAXMaps, which in particular permit the creation of concept maps. Thanks to the interactive connection between your visualizations with your MAXQDA data, you’ll never lose sight of the big picture.

AI Assist: Qualitative coding software meets AI

AI Assist – your virtual research assistant – supports your qualitative coding with various tools. AI Assist simplifies your work by automatically analyzing and summarizing elements of your research project and by generating suggestions for subcodes. No matter which AI tool you use – you can customize your results to suit your needs.

Free tutorials and guides on qualitative coding software

MAXQDA offers a variety of free learning resources for qualitative coding, making it easy for both beginners and advanced users to learn how to use the software. From free video tutorials and webinars to step-by-step guides and sample projects, these resources provide a wealth of information to help you understand the features and functionality of MAXQDA as qualitative coding software. For beginners, the software’s user-friendly interface and comprehensive help center make it easy to get started with your data analysis, while advanced users will appreciate the detailed guides and tutorials that cover more complex features and techniques. Whether you’re just starting out or are an experienced researcher, MAXQDA’s free learning resources will help you get the most out of your qualitative coding software.

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Get your maxqda license, compare the features of maxqda and maxqda analytics pro, faq: qualitative coding software.

When it comes to qualitative coding software, MAXQDA stands out as a top choice for researchers. MAXQDA is a comprehensive qualitative data analysis tool that offers a wide range of features designed to streamline the coding process and assist researchers in making sense of their qualitative data.

MAXQDA’s user-friendly interface and robust set of tools make it a reliable and powerful option for qualitative coding tasks, making it a popular choice among researchers.

One highly recommended software tool for coding qualitative data is MAXQDA. MAXQDA provides researchers with a set of tools for analyzing and interpreting their qualitative data, making it an excellent choice for qualitative coding tasks.

MAXQDA offers a range of features, including text analysis and data visualization, making it a comprehensive solution for qualitative data analysis.

Coding qualitative data involves systematically categorizing and labeling segments of your data to identify themes, patterns, and trends. MAXQDA simplifies this process by providing an intuitive interface and tools specifically designed for qualitative coding tasks.

To code qualitative data with MAXQDA, you typically follow these steps:

• Import your qualitative data into MAXQDA, such as interview transcripts, survey responses, or text documents.
• Read through your data to gain a deep understanding of the content.
• Identify keywords, phrases, or themes relevant to your research objectives.
• Create codes in MAXQDA to represent these keywords, phrases, or themes.
• Apply the created codes to specific segments of your data by highlighting or selecting the relevant text.

MAXQDA’s flexibility and organization features make it an excellent choice for coding qualitative data efficiently and effectively.

Qualitative coding methods are techniques used to analyze and categorize qualitative data. These methods help researchers make sense of the data and identify key themes, patterns, and insights. MAXQDA supports various qualitative coding methods, making it a versatile tool for researchers.

Some common qualitative coding methods include:

• Thematic Coding: This involves identifying and categorizing recurring themes or topics in the data.
• Content Analysis: Researchers analyze the content of the data to understand its meaning and context.
• Grounded Theory: A systematic approach to developing theories based on the data itself.
• Framework Analysis: A method for structuring and analyzing large amounts of qualitative data.
• Constant Comparative Analysis: Comparing new data with existing data to refine codes and categories.

MAXQDA’s tools and features are designed to support these coding methods, allowing researchers to choose the approach that best suits their research goals.

Qualitative coding is the process of systematically analyzing and categorizing qualitative data to identify patterns, themes, and insights. It involves assigning codes or labels to specific segments of qualitative data, such as interview transcripts, survey responses, or text documents. These codes help researchers organize and make sense of the data, facilitating data interpretation and the extraction of meaningful information.

MAXQDA is a valuable tool for qualitative coding as it provides researchers with the means to create, apply, and manage codes efficiently, allowing for a more structured and rigorous analysis of qualitative data.

For Mac users looking for qualitative coding software, MAXQDA is an excellent choice. MAXQDA offers a Mac version of its software that is fully compatible with macOS, providing Mac users with a seamless qualitative data analysis experience.

With MAXQDA for Mac, researchers can take advantage of all the features and capabilities that make MAXQDA a top choice in qualitative coding software. Whether you’re conducting research on a Mac computer or prefer the Mac environment, MAXQDA is a reliable and efficient solution.

For students venturing into qualitative research, MAXQDA is an ideal qualitative coding software choice. MAXQDA offers a user-friendly interface and a range of resources designed to support students in their research journey. It provides academic licenses at affordable prices, making it accessible to students on a budget.

MAXQDA’s intuitive design and comprehensive features empower students to code, analyze, and interpret qualitative data effectively. It also offers educational resources and tutorials to help students get started with qualitative research and coding.

Qualitative coding software, such as MAXQDA, offers a range of key features that are essential for effective qualitative data analysis. Some of the key features of qualitative coding software include:

• Code Management: The ability to create, organize, and manage codes for data segmentation.
• Data Import: The capability to import various types of qualitative data, including text, audio, and video files.
• Annotation Tools: Tools for adding comments, annotations, and notes to the data for context and analysis.
• Data Visualization: Graphs, charts, and visual aids to represent and explore data patterns.
• Search and Retrieval: Efficient search functions to locate specific data segments or codes within large datasets.
• Collaboration Tools: Features for collaborative coding and analysis with team members.
• Reporting and Export: The ability to generate reports, export data, and share findings with others.

MAXQDA excels in offering these features and more, making it a comprehensive solution for qualitative coding and analysis.

Qualitative coding software, like MAXQDA, plays a crucial role in assisting researchers with qualitative data interpretation. Here’s how:

1. Structure and Organization: Coding software helps researchers organize their qualitative data into manageable segments by assigning codes and categories. This structured approach facilitates easier data interpretation by breaking down complex information into meaningful units.

2. Pattern Recognition: By coding and categorizing data, researchers can quickly identify patterns, trends, and recurring themes. MAXQDA’s tools allow for easy visualization of these patterns, aiding in data interpretation.

3. Cross-Referencing: Qualitative coding software allows researchers to cross-reference data segments, codes, and categories. This cross-referencing helps in exploring relationships and connections within the data, leading to deeper insights.

4. Collaboration: Collaborative coding and analysis tools in software like MAXQDA enable researchers to work together, share interpretations, and refine their understanding of the data collectively.

In summary, qualitative coding software streamlines the process of data interpretation by providing tools and features that enhance the researcher’s ability to uncover meaningful insights from qualitative data.

Yes, qualitative coding software, including MAXQDA, is suitable for both beginners and experienced researchers. MAXQDA is known for its user-friendly interface, making it accessible to those who are new to qualitative research and coding.

For beginners, MAXQDA provides educational resources and tutorials to help them get started with qualitative data analysis. It offers a gentle learning curve, allowing novice researchers to quickly grasp the essentials of coding and analysis.

Experienced researchers benefit from MAXQDA’s advanced features and capabilities. It offers a robust set of tools for in-depth analysis, data visualization, and complex coding tasks. Researchers with extensive experience can leverage these features to enhance the rigor and depth of their qualitative research.

In essence, MAXQDA caters to researchers at all levels, making it a versatile choice for qualitative coding.

Qualitative coding can be done without software, but it can be a more time-consuming and labor-intensive process. When coding without software, researchers typically rely on manual methods such as highlighting, underlining, or physically tagging segments of printed text.

However, using qualitative coding software like MAXQDA offers several advantages. It streamlines the coding process, provides tools for efficient organization and retrieval of coded data, and offers features like data visualization and collaboration. These benefits can significantly enhance the quality and efficiency of qualitative coding.

While it’s possible to code qualitatively without software, utilizing a dedicated tool like MAXQDA can save researchers time and effort and lead to more rigorous and comprehensive data analysis.

FEEDING YOUR QUALITATIVE NEEDS

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Export Results

After you've done your work highlighting materials in Taguette, you can export in a variety of ways -- your whole project, codebook, all your highlighted quotes (or ones for a specific tag!), and highlighted documents. It's a good practice to keep an archival copy of your work!

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Coding Qualitative Data: How to Code Qualitative Research

Authored by Alyona Medelyan, PhD – Natural Language Processing & Machine Learning

How many hours have you spent sitting in front of Excel spreadsheets trying to find new insights from customer feedback?

You know that asking open-ended survey questions gives you more actionable insights than asking your customers for just a numerical Net Promoter Score (NPS) . But when you ask open-ended, free-text questions, you end up with hundreds (or even thousands) of free-text responses.

How can you turn all of that text into quantifiable, applicable information about your customers’ needs and expectations? By coding qualitative data.

Keep reading to learn:

• What coding qualitative data means (and why it’s important)
• Different methods of coding qualitative data
• How to manually code qualitative data to find significant themes in your data

What is coding in qualitative research?

Coding is the process of labeling and organizing your qualitative data to identify different themes and the relationships between them.

When coding customer feedback , you assign labels to words or phrases that represent important (and recurring) themes in each response. These labels can be words, phrases, or numbers; we recommend using words or short phrases, since they’re easier to remember, skim, and organize.

Coding qualitative research to find common themes and concepts is part of thematic analysis . Thematic analysis extracts themes from text by analyzing the word and sentence structure.

Within the context of customer feedback, it's important to understand the many different types of qualitative feedback a business can collect, such as open-ended surveys, social media comments, reviews & more.

What is qualitative data analysis?

Qualitative data analysis is the process of examining and interpreting qualitative data to understand what it represents.

Qualitative data is defined as any non-numerical and unstructured data; when looking at customer feedback, qualitative data usually refers to any verbatim or text-based feedback such as reviews, open-ended responses in surveys , complaints, chat messages, customer interviews, case notes or social media posts

For example, NPS metric can be strictly quantitative, but when you ask customers why they gave you a rating a score, you will need qualitative data analysis methods in place to understand the comments that customers leave alongside numerical responses.

Methods of qualitative data analysis

• Content analysis: This refers to the categorization, tagging and thematic analysis of qualitative data. This can include combining the results of the analysis with behavioural data for deeper insights.
• Narrative analysis: Some qualitative data, such as interviews or field notes may contain a story. For example, the process of choosing a product, using it, evaluating its quality and decision to buy or not buy this product next time. Narrative analysis helps understand the underlying events and their effect on the overall outcome.
• Discourse analysis: This refers to analysis of what people say in social and cultural context. It’s particularly useful when your focus is on building or strengthening a brand.
• Framework analysis: When performing qualitative data analysis, it is useful to have a framework. A code frame (a hierarchical set of themes used in coding qualitative data) is an example of such framework.
• Grounded theory: This method of analysis starts by formulating a theory around a single data case. Therefore the theory is “grounded’ in actual data. Then additional cases can be examined to see if they are relevant and can add to the original theory.

Automatic coding software

Advances in natural language processing & machine learning have made it possible to automate the analysis of qualitative data, in particular content and framework analysis

While manual human analysis is still popular due to its perceived high accuracy, automating the analysis is quickly becoming the preferred choice. Unlike manual analysis, which is prone to bias and doesn’t scale to the amount of qualitative data that is generated today, automating analysis is not only more consistent and therefore can be more accurate, but can also save a ton of time, and therefore money.

The most commonly used software for automated coding of qualitative data is text analytics software such as Thematic .

Why is it important to code qualitative data?

Coding qualitative data makes it easier to interpret customer feedback. Assigning codes to words and phrases in each response helps capture what the response is about which, in turn, helps you better analyze and summarize the results of the entire survey.

Researchers use coding and other qualitative data analysis processes to help them make data-driven decisions based on customer feedback. When you use coding to analyze your customer feedback, you can quantify the common themes in customer language. This makes it easier to accurately interpret and analyze customer satisfaction.

Automated vs. Manual coding of qualitative data

Methods of coding qualitative data fall into two categories: automated coding and manual coding.

You can automate the coding of your qualitative data with thematic analysis software . Thematic analysis and qualitative data analysis software use machine learning, artificial intelligence (AI) , and natural language processing (NLP) to code your qualitative data and break text up into themes.

Thematic analysis software is autonomous, which means…

• You don’t need to set up themes or categories in advance.
• You don’t need to train the algorithm — it learns on its own.
• You can easily capture the “unknown unknowns” to identify themes you may not have spotted on your own.

…all of which will save you time (and lots of unnecessary headaches) when analyzing your customer feedback.

Businesses are also seeing the benefit of using thematic analysis softwares that have the capacity to act as a single data source, helping to break down data silos, unifying data across an organization. This is now being referred to as Unified Data Analytics.

What is thematic coding?

Thematic coding, also called thematic analysis, is a type of qualitative data analysis that finds themes in text by analyzing the meaning of words and sentence structure.

When you use thematic coding to analyze customer feedback for example, you can learn which themes are most frequent in feedback. This helps you understand what drives customer satisfaction in an accurate, actionable way.

To learn more about how thematic analysis software helps you automate the data coding process, check out this article .

How to manually code qualitative data

For the rest of this post, we’ll focus on manual coding. Different researchers have different processes, but manual coding usually looks something like this:

• Choose whether you’ll use deductive or inductive coding.
• Read through your data to get a sense of what it looks like. Assign your first set of codes.
• Go through your data line-by-line to code as much as possible. Your codes should become more detailed at this step.
• Categorize your codes and figure out how they fit into your coding frame.
• Identify which themes come up the most — and act on them.

Let’s break it down a little further…

Deductive coding vs. inductive coding

Before you start qualitative data coding, you need to decide which codes you’ll use.

What is Deductive Coding?

Deductive coding means you start with a predefined set of codes, then assign those codes to the new qualitative data. These codes might come from previous research, or you might already know what themes you’re interested in analyzing. Deductive coding is also called concept-driven coding.

For example, let’s say you’re conducting a survey on customer experience . You want to understand the problems that arise from long call wait times, so you choose to make “wait time” one of your codes before you start looking at the data.

The deductive approach can save time and help guarantee that your areas of interest are coded. But you also need to be careful of bias; when you start with predefined codes, you have a bias as to what the answers will be. Make sure you don’t miss other important themes by focusing too hard on proving your own hypothesis.  

What is Inductive Coding?

Inductive coding , also called open coding, starts from scratch and creates codes based on the qualitative data itself. You don’t have a set codebook; all codes arise directly from the survey responses.

Here’s how inductive coding works:

• Break your qualitative dataset into smaller samples.
• Read a sample of the data.
• Create codes that will cover the sample.
• Reread the sample and apply the codes.
• Read a new sample of data, applying the codes you created for the first sample.
• Note where codes don’t match or where you need additional codes.
• Create new codes based on the second sample.
• Go back and recode all responses again.
• Repeat from step 5 until you’ve coded all of your data.

If you add a new code, split an existing code into two, or change the description of a code, make sure to review how this change will affect the coding of all responses. Otherwise, the same responses at different points in the survey could end up with different codes.

Sounds like a lot of work, right? Inductive coding is an iterative process, which means it takes longer and is more thorough than deductive coding. But it also gives you a more complete, unbiased look at the themes throughout your data.

Categorize your codes with coding frames

Once you create your codes, you need to put them into a coding frame. A coding frame represents the organizational structure of the themes in your research. There are two types of coding frames: flat and hierarchical.

Flat Coding Frame

A flat coding frame assigns the same level of specificity and importance to each code. While this might feel like an easier and faster method for manual coding, it can be difficult to organize and navigate the themes and concepts as you create more and more codes. It also makes it hard to figure out which themes are most important, which can slow down decision making.

Hierarchical Coding Frame

Hierarchical frames help you organize codes based on how they relate to one another. For example, you can organize the codes based on your customers’ feelings on a certain topic:

In this example:

• The top-level code describes the topic (customer service)
• The mid-level code specifies whether the sentiment is positive or negative
• The third level details the attribute or specific theme associated with the topic

Hierarchical framing supports a larger code frame and lets you organize codes based on organizational structure. It also allows for different levels of granularity in your coding.

Whether your code frames are hierarchical or flat, your code frames should be flexible. Manually analyzing survey data takes a lot of time and effort; make sure you can use your results in different contexts.

For example, if your survey asks customers about customer service, you might only use codes that capture answers about customer service. Then you realize that the same survey responses have a lot of comments about your company’s products. To learn more about what people say about your products, you may have to code all of the responses from scratch! A flexible coding frame covers different topics and insights, which lets you reuse the results later on.

Tips for coding qualitative data

Now that you know the basics of coding your qualitative data, here are some tips on making the most of your qualitative research.

Use a codebook to keep track of your codes

As you code more and more data, it can be hard to remember all of your codes off the top of your head. Tracking your codes in a codebook helps keep you organized throughout the data analysis process. Your codebook can be as simple as an Excel spreadsheet or word processor document. As you code new data, add new codes to your codebook and reorganize categories and themes as needed.

Make sure to track:

• The label used for each code
• A description of the concept or theme the code refers to
• Who originally coded it
• The date that it was originally coded or updated
• Any notes on how the code relates to other codes in your analysis

How to create high-quality codes - 4 tips

1. cover as many survey responses as possible..

The code should be generic enough to apply to multiple comments, but specific enough to be useful in your analysis. For example, “Product” is a broad code that will cover a variety of responses — but it’s also pretty vague. What about the product? On the other hand, “Product stops working after using it for 3 hours” is very specific and probably won’t apply to many responses. “Poor product quality” or “short product lifespan” might be a happy medium.

2. Avoid commonalities.

Having similar codes is okay as long as they serve different purposes. “Customer service” and “Product” are different enough from one another, while “Customer service” and “Customer support” may have subtle differences but should likely be combined into one code.

3. Capture the positive and the negative.

Try to create codes that contrast with each other to track both the positive and negative elements of a topic separately. For example, “Useful product features” and “Unnecessary product features” would be two different codes to capture two different themes.

4. Reduce data — to a point.

Let’s look at the two extremes: There are as many codes as there are responses, or each code applies to every single response. In both cases, the coding exercise is pointless; you don’t learn anything new about your data or your customers. To make your analysis as useful as possible, try to find a balance between having too many and too few codes.

Group responses based on themes, not wording

Make sure to group responses with the same themes under the same code, even if they don’t use the same exact wording. For example, a code such as “cleanliness” could cover responses including words and phrases like:

• Looked like a dump
• Could eat off the floor

Having only a few codes and hierarchical framing makes it easier to group different words and phrases under one code. If you have too many codes, especially in a flat frame, your results can become ambiguous and themes can overlap. Manual coding also requires the coder to remember or be able to find all of the relevant codes; the more codes you have, the harder it is to find the ones you need, no matter how organized your codebook is.

Make accuracy a priority

Manually coding qualitative data means that the coder’s cognitive biases can influence the coding process. For each study, make sure you have coding guidelines and training in place to keep coding reliable, consistent, and accurate .

One thing to watch out for is definitional drift, which occurs when the data at the beginning of the data set is coded differently than the material coded later. Check for definitional drift across the entire dataset and keep notes with descriptions of how the codes vary across the results.

If you have multiple coders working on one team, have them check one another’s coding to help eliminate cognitive biases.

Conclusion: 6 main takeaways for coding qualitative data

Here are 6 final takeaways for manually coding your qualitative data:

• Coding is the process of labeling and organizing your qualitative data to identify themes. After you code your qualitative data, you can analyze it just like numerical data.
• Inductive coding (without a predefined code frame) is more difficult, but less prone to bias, than deductive coding.
• Code frames can be flat (easier and faster to use) or hierarchical (more powerful and organized).
• Your code frames need to be flexible enough that you can make the most of your results and use them in different contexts.
• When creating codes, make sure they cover several responses, contrast one another, and strike a balance between too much and too little information.
• Consistent coding = accuracy. Establish coding procedures and guidelines and keep an eye out for definitional drift in your qualitative data analysis.

Some more detail in our downloadable guide

If you’ve made it this far, you’ll likely be interested in our free guide: Best practises for analyzing open-ended questions.

The guide includes some of the topics covered in this article, and goes into some more niche details.

If your company is looking to automate your qualitative coding process, try Thematic !

If you're looking to trial multiple solutions, check out our free buyer's guide . It covers what to look for when trialing different feedback analytics solutions to ensure you get the depth of insights you need.

Happy coding!

CEO and Co-Founder

Alyona has a PhD in NLP and Machine Learning. Her peer-reviewed articles have been cited by over 2600 academics. Her love of writing comes from years of PhD research.

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Coding qualitative data for valuable insights

Qualitative researchers, at one point or another, will inevitably find themselves involved in coding their data. The coding process can be arduous and time-consuming, so it's essential to understand how coding contributes to the understanding of knowledge in qualitative research .

Qualitative research tends to work with unstructured data that requires some systematic organization to facilitate insights relevant to your research inquiry. Suppose you need to determine the most critical aspects for deciding what hotel to stay in when you go on vacation. The decision process that goes into choosing the "best" hotel can be located in various and separate places (e.g., travel websites, blogs, personal conversations) and scattered among pieces of information that may not be relevant to you. In qualitative research, one of the goals prior to data analysis is to identify what information is important, find that information, and sort that information in a way that makes it easy for you to come to a decision.

Qualitative coding is almost always a necessary part of the qualitative data analysis process . Coding provides a way to make the meaning of the data clear to you and to your research audience.

What is a code?

A code in the context of qualitative data analysis is a summary of a larger segment of text. Imagine applying a couple of sticky notes to a collection of recipes, marking each section with short labels like "ingredients," "directions," and "advice." Afterward, someone can page through those recipes and easily locate the section they are looking for, thanks to those sticky notes.

Now, suppose you have different colors of sticky notes, where each color denotes a particular cuisine (e.g., Italian, Chinese, vegetarian). Now, with two ways to organize the data in front of you, you can look at all of the ingredient sections of all the recipes belonging to a cuisine to get a sense of the items that are commonly used for such recipes.

As illustrated in this example, one reason someone might apply sticky notes to a recipe is to help the reader save time in getting the desired information from that text, which is essentially the goal of qualitative coding. Coding allows a reader to get the information they are looking for to facilitate the analysis process. Moreover, this process of categorizing the different pieces of data helps researchers see what is going on in their data and identify emerging dimensions and patterns.

The use of codes also has a purpose beyond simply establishing a convenient means to draw meaning from the data . When presenting qualitative research to an audience, researchers could rely on a narrative summary of the data, but such narratives might be too lengthy to grasp or difficult to convey to others.

As a result, researchers in all fields tend to rely on data visualizations to illustrate their data analysis . Naturally, suppose such visualizations rely on tables and figures like bar charts and diagrams to convey meaning. In that case, researchers need to find ways to "count" the data along established data points, which is a role that coding can fulfill. While a strictly numerical understanding of qualitative research may overlook the finer aspects of social phenomena, researchers ultimately benefit from an analysis of the frequency of codes, combinations of codes, and patterns of codes that can contribute to theory generation. In addition, codes can be visualized in numerous ways to present qualitative insights. From flow charts to semantic networks, codes provide researchers with almost limitless possibilities in choosing how to present their rich qualitative data to different audiences.

Applying codes

To engage in coding, a researcher looks at the data line-by-line and develops a codebook by identifying data segments that can be represented by words or short phrases.

In the example above, a set of three paragraphs is represented by one code displayed in green in the right margin. Without codes, the researcher might have to re-read all of the text to remind themselves what the data is about. Indeed, any researcher who examines the codebook of a project can glean a sense of the data and analysis.

Analyzing codes

Think of a simple example to illustrate the importance of analyzing codes. Suppose you are analyzing survey responses for people's preferences for shopping in brick-and-mortar stores and shopping online. In that case, you might think about marking each survey response as either "prefers shopping in-person" or "prefers shopping online." Once you have applied the relevant codes to each survey response, you can compare the frequencies of both codes to determine where the population as a whole stands on the subject.

Among other things, codes can be analyzed by their frequency or their connection to other codes (or co-occurrence with other codes). In the example above, you may also decide to code the data for the reasons that inform people's shopping habits, applying labels such as "convenience," "value," and "service." Then, the analysis process is simply a matter of determining how often each reason co-occurs with preferences for in-person shopping and online shopping by analyzing the codes applied to the data.

As a result, qualitative coding transforms raw data into a form that facilitates the generation of deeper insights through empirical analysis.

That said, coding is a time-consuming, albeit necessary, task in qualitative research and one that researchers have developed into an array of established methods that are worth briefly looking at.

Years of development of qualitative research methods have yielded multiple methods for assigning codes to data. While all qualitative coding approaches essentially seek to summarize large amounts of information succinctly, there are various approaches you can apply to your coding process.

Inductive coding

Probably the most basic form of coding is to look at the data and reduce it to its salient points of information through coding. Any inductive approach to research involves generating knowledge from the ground up. Inductive coding, as a result, looks to generate insights from the qualitative data itself.

Inductive coding benefits researchers who need to look at the data primarily for its inherent meaning rather than for how external frameworks of knowledge might look at it. Inductive coding can also provide a new perspective that established theory has yet to consider, which would make a theory-driven approach inappropriate.

Deductive coding

A deductive approach to coding is also useful in qualitative research . In contrast with inductive coding, a deductive coding approach applies an existing research framework or previous research study to new data. This means that the researcher applies a set of predefined codes based on established research to the new data.

Researchers can benefit from using both approaches in tandem if their research questions call for a synthesized analysis . Returning to the example of a cookbook, a person may mark the different sections of each recipe because they have prior knowledge about what a typical recipe might look like. On the other hand, if they come across a non-typical recipe (e.g., a recipe that may not have an ingredients section), they might need to create new codes to identify parts of the recipe that seem unusual or novel.

Employing both inductive coding and deductive coding , as a result, can help you achieve a more holistic analysis of your data by building on existing knowledge of a phenomenon while generating new knowledge about the less familiar aspects.

Thematic analysis coding

Whether you decide to apply an inductive coding or deductive coding approach to qualitative data, the coding should also be relevant to your research inquiry in order to be useful and avoid a cumbersome amount of coding that might defeat the purpose of summarizing your data. Let's look at a series of more specific approaches to qualitative coding to get a wider sense of how coding has been applied to qualitative research.

The goal of a thematic analysis arising from coding, as the name suggests, is to identify themes revolving around a particular concept or phenomenon. While concepts in the natural sciences, such as temperature and atomic weight, can be measured with numerical data, concepts in the social sciences often escape easy numerical analysis. Rather than reduce the beauty of a work of art or proficiency in a foreign language down to a number, thematic analysis coding looks to describe these phenomena by various aspects that can be grouped together within common themes.

Looking at the recipe again, we can describe a typical recipe by the sections that appear the most often. The same is true for describing a sport (e.g., rules, strategies, equipment) or a car (e.g., type, price, fuel efficiency, safety rating). While later analysis might be able to numerically measure these themes if they are particular enough, the role of coding along the lines of themes provides a good starting point for recognizing and analyzing relevant concepts.

Process coding

Processes are phenomena that are characterized by action. Think about the act of driving a car rather than describing the car itself. In this case, process coding can be thought of as an extension of thematic coding, except that the major aspects of a process can also be identified by sequences and patterns, on the assumption that some actions may follow other actions. After all, drivers typically turn the key in the ignition before releasing the parking brake or shifting to drive. Capturing the specific phases and sequences is a key objective in process coding.

Structural coding

The "structure" of a recipe in a cookbook is different from that of an essay or a newspaper article. Also, think about how an interview for research might be structured differently from an interview for a TV news program. Researchers can employ structural coding to organize the data according to its distinct structural elements, such as specific elements, the ordering of information, or the purpose behind different structures. This kind of analysis could help, for instance, to achieve a greater understanding of how cultures shape a particular piece of writing or social practice.

Longitudinal coding

Studies that observe people or practices over time do so to capture and understand changes in dynamic environments. The role of longitudinal coding is to also code for relevant contextual or temporal aspects. These can then be analyzed together with other codes to assess how frequencies and patterns change from one observation or interview to the next. This will help researchers empirically illustrate differences or changes over time.

Whatever your approach, code your data with ATLAS.ti

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Qualitative data analysis software should effectively facilitate qualitative coding. Researchers can choose between manual coding and automated coding , where tools can be employed to suggest and apply codes to save time. ATLAS.ti is ideal for both approaches to suit researchers of all needs and backgrounds.

Manual coding

At the core of any qualitative data analysis software is the interface that allows researchers the freedom of assigning codes to qualitative data . ATLAS.ti's interface for viewing data makes it easy to highlight data segments and apply new codes or existing codes quickly and efficiently.

In vivo coding

Interpreting qualitative data to create codes is often a part of the coding process. This can mean that the names of codes may differ from the actual text of the data itself.

However, the best names for codes sometimes come from the textual data itself, as opposed to some interpretation of the text. As a result, there may be a particular word or short phrase that stands out to you in your data set, compelling you to incorporate that word or phrase into your qualitative codes. Think about how social media has slang or acronyms like "YOLO" or "YMMV" which condense a lot of meaning or convey something of importance in the context of the research. Rather than obscuring participants’ meanings or experiences within another layer of interpretation, researchers can build meaningful and rich insights by using participants’ own words to create in vivo codes .

In vivo coding is a handy feature in ATLAS.ti for when you come across a key term or phrase that you want to create a code out of. Simply highlight the desired text and click on "Code in Vivo" to create a new code instantly.

Code Manager

One of the biggest challenges of coding qualitative data is keeping track of dozens or even hundreds of codes, because a lack of organization may hinder researchers in the main objective of succinctly summarizing qualitative data.

Once you have developed and applied a set of codes to your project data, you can open the Code Manager to gain a bird's eye view of all of your codes so you can develop and reorganize them, into hierarchies, groups, or however you prefer. Your list of codes can also be exported to share with others or use in other qualitative or quantitative analysis software .

Use ATLAS.ti for efficient and insightful coding

Intuitive tools to help you code and analyze your data, available starting with a free trial.

Traditionally, qualitative researchers would perform this coding on their data manually by hand, which involves carefully reading each piece of data and attaching codes. For qualitative researchers using pen and paper, they can use highlighters or bookmark flags to mark the key points in their data for later reference. Qualitative researchers also have powerful qualitative data analysis software they can rely on to facilitate all aspects of the coding process.

Although researchers can use qualitative data analysis software to engage in manual coding, there is also now a range of software tools that can even automate the coding process . A number of automated coding tools in ATLAS.ti such as AI Coding, Sentiment Analysis, and Opinion Mining use machine learning and natural language processing to apply useful codes for later analysis. Moreover, other tools in ATLAS.ti rely on pattern recognition to facilitate the creation of descriptive codes throughout your project.

One of the most exciting implications of recent advances in artificial intelligence is its potential for facilitating the research process, especially in qualitative research. The use of machine learning to understand the salient points in data can be especially useful to researchers in all fields.

AI Coding , available in both the Desktop platforms and Web version of ATLAS.ti, performs comprehensive descriptive coding on your qualitative data . It processes data through OpenAI's language models to suggest and apply codes to your project in a fraction of the time that it would take to do manually.

Sentiment Analysis

Participants may often express sentiments that are positive or negative in nature. If you are interested in analyzing the feelings expressed in your data, you can analyze these sentiments . To conduct automated coding for these sentiments, ATLAS.ti employs machine learning to process your data quickly and suggest codes to be applied to relevant data segments.

Opinion Mining

If you want to understand both what participants talked about and how they felt about it, you can conduct Opinion Mining. This tool synthesizes key phrases in your textual data according to whether they are being talked about in a positive or negative manner. The codes generated from Opinion Mining can provide a useful illustration of how language in interviews, focus groups, and surveys is used when discussing certain topics or phenomena.

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A guide to coding qualitative research data

Last updated

12 February 2023

Reviewed by

Each time you ask open-ended and free-text questions, you'll end up with numerous free-text responses. When your qualitative data piles up, how do you sift through it to determine what customers value? And how do you turn all the gathered texts into quantifiable and actionable information related to your user's expectations and needs?

Qualitative data can offer significant insights into respondents’ attitudes and behavior. But to distill large volumes of text / conversational data into clear and insightful results can be daunting. One way to resolve this is through qualitative research coding.

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• What is coding in qualitative research?

This is the system of classifying and arranging qualitative data . Coding in qualitative research involves separating a phrase or word and tagging it with a code. The code describes a data group and separates the information into defined categories or themes. Using this system, researchers can find and sort related content.

They can also combine categorized data with other coded data sets for analysis, or analyze it separately. The primary goal of coding qualitative data is to change data into a consistent format in support of research and reporting.

A code can be a phrase or a word that depicts an idea or recurring theme in the data. The code’s label must be intuitive and encapsulate the essence of the researcher's observations or participants' responses. You can generate these codes using two approaches to coding qualitative data: manual coding and automated coding.

• Why is it important to code qualitative data?

By coding qualitative data, it's easier to identify consistency and scale within a set of individual responses. Assigning codes to phrases and words within feedback helps capture what the feedback entails. That way, you can better analyze and   understand the outcome of the entire survey.

Researchers use coding and other qualitative data analysis procedures to make data-driven decisions according to customer responses. Coding in customer feedback will help you assess natural themes in the customers’ language. With this, it's easy to interpret and analyze customer satisfaction .

• How do inductive and deductive approaches to qualitative coding work?

Before you start qualitative research coding, you must decide whether you're starting with some predefined code frames, within which the data will be sorted (deductive approach). Or, you may plan to develop and evolve the codes while reviewing the qualitative data generated by the research (inductive approach). A combination of both approaches is also possible.

In most instances, a combined approach will be best. For example, researchers will have some predefined codes/themes they expect to find in the data, but will allow for a degree of discovery in the data where new themes and codes come to light.

Inductive coding

This is an exploratory method in which new data codes and themes are generated by the review of qualitative data. It initiates and generates code according to the source of the data itself. It's ideal for investigative research, in which you devise a new idea, theory, or concept. 

Inductive coding is otherwise called open coding. There's no predefined code-frame within inductive coding, as all codes are generated by reviewing the raw qualitative data.

If you're adding a new code, changing a code descriptor, or dividing an existing code in half, ensure you review the wider code frame to determine whether this alteration will impact other feedback codes.  Failure to do this may lead to similar responses at various points in the qualitative data,  generating different codes while containing similar themes or insights.

Inductive coding is more thorough and takes longer than deductive coding, but offers a more unbiased and comprehensive overview of the themes within your data.

Deductive coding

This is a hierarchical approach to coding. In this method, you develop a codebook using your initial code frames. These frames may depend on an ongoing research theory or questions. Go over the data once again and filter data to different codes. 

After generating your qualitative data, your codes must be a match for the code frame you began with. Program evaluation research could use this coding approach.

Inductive and deductive approaches

Research studies usually blend both inductive and deductive coding approaches. For instance, you may use a deductive approach for your initial set of code sets, and later use an inductive approach to generate fresh codes and recalibrate them while you review and analyze your data.

• What are the practical steps for coding qualitative data?

You can code qualitative data in the following ways:

1. Conduct your first-round pass at coding qualitative data

You need to review your data and assign codes to different pieces in this step. You don't have to generate the right codes since you will iterate and evolve them ahead of the second-round coding review.

Let's look at examples of the coding methods you may use in this step.

Open coding : This involves the distilling down of qualitative data into separate, distinct coded elements.

Descriptive coding : In this method, you create a description that encapsulates the data section’s content. Your code name must be a noun or a term that describes what the qualitative data relates to.

Values coding : This technique categorizes qualitative data that relates to the participant's attitudes, beliefs, and values.

Simultaneous coding : You can apply several codes to a single piece of qualitative data using this approach.

Structural coding : In this method, you can classify different parts of your qualitative data based on a predetermined design to perform additional analysis within the design.

In Vivo coding : Use this as the initial code to represent specific phrases or single words generated via a qualitative interview (i.e., specifically what the respondent said).

Process coding : A process of coding which captures action within data.  Usually, this will be in the form of gerunds ending in “ing” (e.g., running, searching, reviewing).

2. Arrange your qualitative codes into groups and subcodes

You can start organizing codes into groups once you've completed your initial round of qualitative data coding. There are several ways to arrange these groups. 

You can put together codes related to one another or address the same subjects or broad concepts, under each category. Continue working with these groups and rearranging the codes until you develop a framework that aligns with your analysis.

3. Conduct more rounds of qualitative coding

Conduct more iterations of qualitative data coding to review the codes and groups you've already established. You can change the names and codes, combine codes, and re-group the work you've already done during this phase. 

In contrast, the initial attempt at data coding may have been hasty and haphazard. But these coding rounds focus on re-analyzing, identifying patterns, and drawing closer to creating concepts and ideas.

Below are a few techniques for qualitative data coding that are often applied in second-round coding.

Pattern coding : To describe a pattern, you join snippets of data, similarly classified under a single umbrella code.

Thematic analysis coding : When examining qualitative data, this method helps to identify patterns or themes.

Selective coding/focused coding : You can generate finished code sets and groups using your first pass of coding.

Theoretical coding : By classifying and arranging codes, theoretical coding allows you to create a theoretical framework's hypothesis. You develop a theory using the codes and groups that have been generated from the qualitative data.

Content analysis coding : This starts with an existing theory or framework and uses qualitative data to either support or expand upon it.

Axial coding : Axial coding allows you to link different codes or groups together. You're looking for connections and linkages between the information you discovered in earlier coding iterations.

Longitudinal coding : In this method, by organizing and systematizing your existing qualitative codes and categories, it is possible to monitor and measure them over time.

Elaborative coding : This involves applying a hypothesis from past research and examining how your present codes and groups relate to it.

4. Integrate codes and groups into your concluding narrative

When you finish going through several rounds of qualitative data coding and applying different forms of coding, use the generated codes and groups to build your final conclusions. The final result of your study could be a collection of findings, theory, or a description, depending on the goal of your study.

Start outlining your hypothesis , observations , and story while citing the codes and groups that served as its foundation. Create your final study results by structuring this data.

• What are the two methods of coding qualitative data?

You can carry out data coding in two ways: automatic and manual. Manual coding involves reading over each comment and manually assigning labels. You'll need to decide if you're using inductive or deductive coding.

Automatic qualitative data analysis uses a branch of computer science known as Natural Language Processing to transform text-based data into a format that computers can comprehend and assess. It's a cutting-edge area of artificial intelligence and machine learning that has the potential to alter how research and insight is designed and delivered.

Although automatic coding is faster than human coding, manual coding still has an edge due to human oversight and limitations in terms of computer power and analysis.

• What are the advantages of qualitative research coding?

Here are the benefits of qualitative research coding:

Boosts validity : gives your data structure and organization to be more certain the conclusions you are drawing from it are valid

Reduces bias : minimizes interpretation biases by forcing the researcher to undertake a systematic review and analysis of the data 

Represents participants well : ensures your analysis reflects the views and beliefs of your participant pool and prevents you from overrepresenting the views of any individual or group

Fosters transparency : allows for a logical and systematic assessment of your study by other academics

• What are the challenges of qualitative research coding?

It would be best to consider theoretical and practical limitations while analyzing and interpreting data. Here are the challenges of qualitative research coding:

Labor-intensive: While you can use software for large-scale text management and recording, data analysis is often verified or completed manually.

Lack of reliability: Qualitative research is often criticized due to a lack of transparency and standardization in the coding and analysis process, being subject to a collection of researcher bias. 

Limited generalizability : Detailed information on specific contexts is often gathered using small samples. Drawing generalizable findings is challenging even with well-constructed analysis processes as data may need to be more widely gathered to be genuinely representative of attitudes and beliefs within larger populations.

Subjectivity : It is challenging to reproduce qualitative research due to researcher bias in data analysis and interpretation. When analyzing data, the researchers make personal value judgments about what is relevant and what is not. Thus, different people may interpret the same data differently.

• What are the tips for coding qualitative data?

Here are some suggestions for optimizing the value of your qualitative research now that you are familiar with the fundamentals of coding qualitative data.

Keep track of your codes using a codebook or code frame

It can be challenging to recall all your codes offhand as you code more and more data. Keeping track of your codes in a codebook or code frame will keep you organized as you analyze the data. An Excel spreadsheet or word processing document might be your codebook's basic format.

Ensure you track:

The label applied to each code and the time it was first coded or modified

An explanation of the idea or subject matter that the code relates to

Who the original coder is

Any notes on the relationship between the code and other codes in your analysis

Add new codes to your codebook as you code new data, and rearrange categories and themes as necessary.

• How do you create high-quality codes?

Here are four useful tips to help you create high-quality codes.

1. Cover as many survey responses as possible

The code should be generic enough to aid your analysis while remaining general enough to apply to various comments. For instance, "product" is a general code that can apply to many replies but is also ambiguous. 

Also, the specific statement, "product stops working after using it for 3 hours" is unlikely to apply to many answers. A good compromise might be "poor product quality" or "short product lifespan."

2. Avoid similarities

Having similar codes is acceptable only if they serve different objectives. While "product" and "customer service" differ from each other, "customer support" and "customer service" can be unified into a single code.

3. Take note of the positive and the negative

Establish contrasting codes to track an issue's negative and positive aspects separately. For instance, two codes to identify distinct themes would be "excellent customer service" and "poor customer service."

4. Minimize data—to a point

Try to balance having too many and too few codes in your analysis to make it as useful as possible.

What is the best way to code qualitative data?

Depending on the goal of your research, the procedure of coding qualitative data can vary. But generally, it entails: 

Reading through your data

Assigning codes to selected passages

Carrying out several rounds of coding

Grouping codes into themes

Developing interpretations that result in your final research conclusions 

You can begin by first coding snippets of text or data to summarize or characterize them and then add your interpretative perspective in the second round of coding.

A few techniques are more or less acceptable depending on your study’s goal; there is no right or incorrect way to code a data set.

What is an example of a code in qualitative research?

A code is, at its most basic level, a label specifying how you should read a text. The phrase, "Pigeons assaulted me and took my meal," is an illustration. You can use pigeons as a code word.

Is there coding in qualitative research?

An essential component of qualitative data analysis is coding. Coding aims to give structure to free-form data so one can systematically study it.

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The Sheridan Libraries

• Qualitative Data Analysis Software (nVivo, Atlas.TI, and more)
• Sheridan Libraries

Qualitative Data Analysis Software (QDAS) overview

Choosing qda software, core qdas functions.

• Other QDAS Software
• Qualitative Data Sources

For direct assistance

Contact us , JHU Data Services   for assistance with access to nVivo and ATLAS.ti at the Data Services offices on A level, JHU Eisenhower Library.

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Qualitative research has benefited from a range of software tools facilitating most qualitative methodological techniques, particularly those involving multimedia digital data. These guides focus on two major QDAS products, nVivo and ATLAS.ti.  Both programs can be found on the workstations at the Data Services computer lab on A-level, Eisenhower Library, and nVivo is available through JHU's SAFE Desktop . This guide also lists other QDA software and linked resources.

Many university libraries have produced comprehensive guides on nVivo, ATLAS.ti, and other QDA software, to which we will provide links with our gratitude

Schmider, Christian. n.d. What Qualitative Data Analysis Software Can and Can’t Do for You – an Intro Video . MERIT Library at the School of Education: School of Education, University of Wisconsin-Madison. Accessed January 7, 2020. https://www.youtube.com/watch?v=tLKfaCiHVic .

• Supported Methods
• Decision Factors
• Compare QDA Software

Qualitative Data Analysis (QDA) Software supports a variety of qualitative techniques and methodologies

Qualitative techniques supported by  QDAS

• Coding and Classifying
• Writing: analysis, description, memos
• Relating: finding and annotating connections, relationships, patterns
• Audio/Visual analysis: marking, clipping, transcribing, annotating
• Text mining: computer-aided discovery in large amounts of unstructured text
• Visualization: diagramming, relationship and network patterns, quantitative summary 

QDAS  supported methodologies

• Ethnography
• Case studies
• Grounded theory/ phenomenology
• Discourse/narrative analysis
• Sociolinguistic analysis
• Collaborative qualitative research
• Text analysis & text mining

Overview of qualitative methods from ATLAS.ti:  https://atlasti.com/qualitative-research-methods/

Decision factors for your research

• Methods to feature facilitation (in disciplinary context): How many features directly support your methodology?
• Interface for collection, analysis, reports: Do features accommodate most phases of your research workflow?
• Visualization and outputs: Does it produce and successfully export needed visualization without extensive modification?
• Cost and access to software: Is it worth the investment cost as well as in learning to use it? Look for education discounts.
• Software Comparisons: Commercial & Free. (George Mason University) Lists of flagship software, free software, and tools for converting codebooks among QDA software.
• QDA Software Comparison Chart (NYU Libraries) Comparison chart of QDA software from NYU Library's LibGuide
• Top 14 Qualitative Data Analysis Software Guide with descriptive summaries of the main QDA software, several with business focus.
• Dueling CAQDAS using ATLAS.ti and NVivo Webinar comparing features and use of ATLAS.ti and NVIvo for qualitative data analysis. Includes live demos.

Basic functions common to most QDA programs, and to NVivo and ATLAS.ti in particular:

• Application of a maintained set of terms and short phrases linked to segments of text or audio/video that can be queried and gathered for comparative analysis. 
• Longer narrative notes attached to text or a/v segments, or to codes
• Quick access to codes and segments that can be brought together in panel views for comparison, advanced Boolean search options, and flexible interlinking of segments, codes, and annotation
• Most QDAS facilitates transcribing audio and video, ideally maintaining the links between transcript and A/V segments. 
• Gathering codes, segments, and annotations facilitates pattern discovery and further description of relationships. Some QDAS support social network analysis techniques and visualization
• A range of reports using queries and filters to assemble data and annotations facilitates analysis and writing results.
• ​ Typically includes code tables, social network graphs, and annotated A/V clips.
• Shared access to data & analysis, facilitating comments and discussion, and tracking contributor actions and changes.
• Next: NVivo >>
• Last Updated: Jan 30, 2024 5:15 PM
• URL: https://guides.library.jhu.edu/QDAS

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Qualitative Data Analysis: Coding

• Atlas.ti web
• R for text analysis
• Microsoft Excel & spreadsheets
• Other options
• Planning Qual Data Analysis
• Free Tools for QDA
• QDA with NVivo
• QDA with Atlas.ti
• QDA with MAXQDA
• PKM for QDA
• QDA with Quirkos
• Working Collaboratively
• Qualitative Methods Texts
• Transcription
• Data organization
• Example Publications

Coding Qualitative Data

Planning your coding strategy.

Coding is a qualitative data analysis strategy in which some aspect of the data is assigned a descriptive label that allows the researcher to identify related content across the data. How you decide to code - or whether to code- your data should be driven by your methodology. But there are rarely step-by-step descriptions, and you'll have to make many decisions about how to code for your own project.

Some questions to consider as you decide how to code your data:

What will you code? 

What aspects of your data will you code? If you are not coding all of your available data, how will you decide which elements need to be coded? If you have recordings interviews or focus groups, or other types of multimedia data, will you create transcripts to analyze and code? Or will you code the media itself (see Farley, Duppong & Aitken, 2020 on direct coding of audio recordings rather than transcripts). 

Where will your codes come from? 

Depending on your methodology, your coding scheme may come from previous research and be applied to your data (deductive). Or you my try to develop codes entirely from the data, ignoring as much as possible, previous knowledge of the topic under study, to develop a scheme grounded in your data (inductive). In practice, however, many practices will fall between these two approaches. 

How will you apply your codes to your data? 

You may decide to use software to code your qualitative data, to re-purpose other software tools (e.g. Word or spreadsheet software) or work primarily with physical versions of your data. Qualitative software is not strictly necessary, though it does offer some advantages, like: 

• Codes can be easily re-labeled, merged, or split. You can also choose to apply multiple coding schemes to the same data, which means you can explore multiple ways of understanding the same data. Your analysis, then, is not limited by how often you are able to work with physical data, such as paper transcripts. 
• Most software programs for QDA include the ability to export and import coding schemes. This means you can create a re-use a coding scheme from a previous study, or that was developed in outside of the software, without having to manually create each code. 
• Some software for QDA includes the ability to directly code image, video, and audio files. This may mean saving time over creating transcripts. Or, your coding may be enhanced by access to the richness of mediated content, compared to transcripts.
• Using QDA software may also allow you the ability to use auto-coding functions. You may be able to automatically code all of the statements by speaker in a focus group transcript, for example, or identify and code all of the paragraphs that include a specific phrase. 

What will be coded? 

Will you deploy a line-by-line coding approach, with smaller codes eventually condensed into larger categories or concepts? Or will you start with codes applied to larger segments of the text, perhaps later reviewing the examples to explore and re-code for differences between the segments? 

How will you explain the coding process? 

• Regardless of how you approach coding, the process should be clearly communicated when you report your research, though this is not always the case (Deterding & Waters, 2021).
• Carefully consider the use of phrases like "themes emerged." This phrasing implies that the themes lay passively in the data, waiting for the researcher to pluck them out. This description leaves little room for describing how the researcher "saw" the themes and decided which were relevant to the study. Ryan and Bernard (2003) offer a terrific guide to ways that you might identify themes in the data, using both your own observations as well as manipulations of the data. 

How will you report the results of your coding process? 

How you report your coding process should align with the methodology you've chosen. Your methodology may call for careful and consistent application of a coding scheme, with reports of inter-rater reliability and counts of how often a code appears within the data. Or you may use the codes to help develop a rich description of an experience, without needing to indicate precisely how often the code was applied. 

How will you code collaboratively?

If you are working with another researcher or a team, your coding process requires careful planning and implementation. You will likely need to have regular conversations about your process, particularly if your goal is to develop and consistently apply a coding scheme across your data. 

Coding Features in QDA Software Programs

• Atlas.ti (Mac)
• Atlas.ti (Windows)
• NVivo (Windows)
• NVivo (Mac)
• Coding data See how to create and manage codes and apply codes to segments of the data (known as quotations in Atlas.ti).

• Search and Code Using the search and code feature lets you locate and automatically code data through text search, regular expressions, Named Entity Recognition, and Sentiment Analysis.
• Focus Group Coding Properly prepared focus group documents can be automatically coded by speaker.
• Inter-Coder Agreement Coded text, audio, and video documents can be tested for inter-coder agreement. ICA is not available for images or PDF documents.
• Quotation Reader Once you've coded data, you can view just the data that has been assigned that code.

• Find Redundant Codings (Mac) This tool identifies "overlapping or embedded" quotations that have the same code, that are the result of manual coding or errors when merging project files.
• Coding Data in Atlas.ti (Windows) Demonstrates how to create new codes, manage codes and applying codes to segments of the data (known as quotations in Atlas.ti)
• Search and Code in Atlas.ti (Windows) You can use a text search, regular expressions, Named Entity Recognition, and Sentiment Analysis to identify and automatically code data in Atlas.ti.
• Focus Group Coding in Atlas.ti (Windows) Properly prepared focus group transcripts can be automatically coded by speaker.
• Inter-coder Agreement in Atlas.ti (Windows) Coded text, audio, and video documents can be tested for inter-coder agreement. ICA is not available for images or PDF documents.
• Quotation Reader in Atlas.ti (Windows) Once you've coded data, you can view and export the quotations that have been assigned that code.
• Find Redundant Codings in Atlas.ti (Windows) This tool identifies "overlapping or embedded" quotations that have the same code, that are the result of manual coding or errors when merging project files.
• Coding in NVivo (Windows) This page includes an overview of the coding features in NVivo.
• Automatic Coding in Documents in NVivo (Windows) You can use paragraph formatting styles or speaker names to automatically format documents.
• Coding Comparison Query in NVivo (Windows) You can use the coding comparison feature to compare how different users have coded data in NVivo.
• Review the References in a Node in NVivo (Windows) References are the term that NVivo uses for coded segments of the data. This shows you how to view references related to a code (or any node)
• Text Search Queries in NVivo (Windows) Text queries let you search for specific text in your data. The results of your query can be saved as a node (a form of auto coding).
• Coding Query in NVivo (Windows) Use a coding query to display references from your data for a single code or multiples of codes.
• Code Files and Manage Codes in NVivo (Mac) This page offers an overview of coding features in NVivo. Note that NVivo uses the concept of a node to refer to any structure around which you organize your data. Codes are a type of node, but you may see these terms used interchangeably.
• Automatic Coding in Datasets in NVivo (Mac) A dataset in NVivo is data that is in rows and columns, as in a spreadsheet. If a column is set to be codable, you can also automatically code the data. This approach could be used for coding open-ended survey data.
• Text Search Query in NVivo (Mac) Use the text search query to identify relevant text in your data and automatically code references by saving as a node.
• Review the References in a Node in NVivo (Mac) NVivo uses the term references to refer to data that has been assigned to a code or any node. You can use the reference view to see the data linked to a specific node or combination of nodes.
• Coding Comparison Query in NVivo (Mac) Use the coding comparison query to calculate a measure of inter-rater reliability when you've worked with multiple coders.

The MAXQDA interface is the same across Mac and Windows devices. 

• The "Code System" in MAXQDA This section of the manual shows how to create and manage codes in MAXQDA's code system.
• How to Code with MAXQDA

• Display Coded Segments in the Document Browser Once you've coded a document within MAXQDA, you can choose which of those codings will appear on the document, as well as choose whether or not the text is highlighted in the color linked to the code.
• Creative Coding in MAXQDA Use the creative coding feature to explore the relationships between codes in your system. If you develop a new structure to you codes that you like, you can apply the changes to your overall code scheme.
• Text Search in MAXQDA Use a Text Search to identify data that matches your search terms and automatically code the results. You can choose whether to code only the matching results, the sentence the results are in, or the paragraph the results appear in.
• Segment Retrieval in MAXQDA Data that has been coded is considered a segment. Segment retrieval is how you display the segments that match a code or combination of codes. You can use the activation feature to show only the segments from a document group, or that match a document variable.
• Intercorder Agreement in MAXQDA MAXQDA includes the ability to compare coding between two coders on a single project.
• Create Tags in Taguette Taguette uses the term tag to refer to codes. You can create single tags as well as a tag hierarchy using punctuation marks.
• Highlighting in Taguette Select text with a document (a highlight) and apply tags to code data in Taguette.

Useful Resources on Coding

Deterding, N. M., & Waters, M. C. (2021). Flexible coding of in-depth interviews: A twenty-first-century approach. Sociological Methods & Research , 50 (2), 708–739. https://doi.org/10.1177/0049124118799377

Farley, J., Duppong Hurley, K., & Aitken, A. A. (2020). Monitoring implementation in program evaluation with direct audio coding. Evaluation and Program Planning , 83 , 101854. https://doi.org/10.1016/j.evalprogplan.2020.101854

Ryan, G. W., & Bernard, H. R. (2003). Techniques to identify themes. Field Methods , 15 (1), 85–109. https://doi.org/10.1177/1525822X02239569. 

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• Last Updated: Mar 17, 2024 6:44 PM
• URL: https://guides.library.illinois.edu/qualitative

Lightweight package to conduct qualitative coding.

To test with some sample data:

Click “Select project folder” and “my_qcoder_project.” There are two ways to add codes. To use an existing code, highlight the text to be coded, select the code, click “Add selected code” and then “Save changes.” Text to be assigned a new (or existing) code should be surrounded by (QCODER) (/QCODER) tags. The closing tag is followed immediately by the code enclosed in curly brackets and prefixed with a # for example {#samplecode}

Installation

To install the latest development version, run

Please note that this is not a release-ready version and should be considered experimental and subject to changes. Still, we encourage you to install and send us feedback on our issue tracker.

The motivation stems from the need for a free, open source option for analyzing textual qualitative data. Textual qualitative data refers to text from interview transcripts, observation notes, memos, jottings and primary source/archival documents. A detailed discussion of the motivation and other software can be found in our motivation document .

Using QCoder

QCoder is designed to be easy to use and to require minimal knowledge of computer systems and code. Like all software, including other applications for QDA there will be a learning period, but as we develop Qcoder our goal will be to keep the interface simple and steadily improve it. Currently we have a very minimal prototype.

Once you have installed QCoder, load it with the library command.

This readme file is going to use sample data to illustrate basic QCoder functionality. We will be using the simplest approach which is to use the QCoder defaults for file names and folders. If you follow those same patterns and conventions with your data you can use QCoder in the same way. A full vignette will explain how to use non standard names and file locations.

To begin we will create a QCoder project with sample data. (To create an empty project leave out the sample option.)

This will create one main folder and four subfolders. Unless you specified otherwise it will be in your current working directory (you can find this with the getwd() command at the console). If you have a specific location where you want to put the folder change your working directory.

These will hold the documents to be coded, information about the codes, unit information and the r data frames that will be the core of the analysis. For this example the folder and file structures for the sample data will look similar to this.

In our example we’ve already placed our documents into the “documents” folder. At this point we only have tested support for txt files. If you have documents in other formats you can use “Save As” to convert to txt. If you have doc, docx, html, pdf, rtf or some other formats these can be processed if you install the textreadr package. For many users this will simply require

However for other users, particularly those on linux systems, additional steps are required. Please follow the installation instructions for pdftools .

QCoder has the option to import a list of predefined codes from a CSV file (if you have this in a spreadsheet you can “Save As” csv). This file should have exactly 3 columns with headings:

• code_id (A unique number for each code)
• code (One word description, can use underscores or hyphens)
• code.description (Longer description of the code, must be enclosed in quotation marks.)

To use project defaults, this file should be called codes.csv . Here are the contents of the sample data csv file that comes with QCoder.

You are not restricted to using the listed codes in the csv file, but this file allows you to produce a detailed codebook including descriptions. (Creating a user interface for adding new codes is high priority item on the project road map.)

Units represent the unit of analysis data are about. Often this is individual people, but it may also be organizations, events or locations. Units may be associated with multiple documents. In the sample data a minimum units file is used, but additional columns can be used to assign attribute data.

The default file name is units.csv; if stored in a spreadsheet this can be created by using “Save As” csv.

(Treatment of units is a work in progress and subject to change.)

A second file (and data frame once imported) connects units to documents. Our framework allows each unit to be associated with multiple documents and each document with multiple units. (Note that the sample data is designed to allow you to add more unit-document links and hence does not link each unit to a document.)

Importing the data

To import this data into Qcode user the import_project_data() function.

Now it’s time to start coding.

Coding uses a “Shiny App” to provide a user interface to the data. To launch the app use the function qcode() .

Which will launch this application. If your current working directory is not the location of your project, use the use_wd = FALSE option. However, on Windows this will not work unless you have set a HOME or R_USER.

Once you have selected your project there will be a drop down menu on the “Add codes to text” tab to allow you to pick a specific document to code. This will pull a document into the editor.

Select your project folder.

Once you have a project, use the drop down menu to select a particular document to code. This will open in an editor. When done coding (instructions below), click Save changes.

Switching to the “Codes” tab a list of codes from the codes file is displayed.

Our sample data already has some coding done, and the code-text data is displayed on the “Coded data” tab.

Coding the data

To add codes to the documents uses a tagging system. Text to be assigned a code should be surrounded by (QCODER) (/QCODER) tags. The closing tag is followed immediately by the code enclosed in curly brackets and prefixed with a # for example {#samplecode}

(QCODE)This is the text that is being assigned a code.(/QCODE){#instructions}

One pair of {} can contain multiple codes, each with at # and separated by commas.

Alternatively, to use an existing code, highlight the text to be coded, select the code or codes, click “Add selected code.”

When you have finished coding a document press the “Save changes” button.

Cautions and known issues

Each time you save, Qcoder makes a backup copy of your documents data frame. This is for safety and reproducability. This can end up with a lot of files if you save often. You may want to periodically delete some backups to save storage space. An important goal is to move to using git for this purpose.

Currently when you create a new code while coding, this code will be displayed on the Coded data tab, but not on the Codes or Summary tabs. You must go to the first tab of the the qcode application to update those displays. This is a high priority development item.

QCoder can be used right now for coding. However, we are not yet ready for release.

Our immediate goal is to create a somewhat more advanced minimum viable product. Please see the issue tracker for a list of short-term and longer-term goals. These goals include interoperability with other QDA packages.

The most important thing is to have more people try qcoder and give us feedback! We do not want to release and then discover that our testing has missed problems that are obvious to our intended user base.

Contributors

• Elin Waring
• Dan Sholler
• Jenny Draper
• Beth Duckles

• University Libraries
• Research Guides
• Topic Guides
• Qualitative Data and Analysis Tools
• Coding and Analysis Software

Qualitative Data and Analysis Tools: Coding and Analysis Software

• Recording and Transcription
• Qualitative Training and Consultation

What's a CAQDAS?

CAQDAS (pronounced like cactus) is a common acronym for Coding and Qualitative Data Analysis Software. It describes a range of tools that can be used to apply codes or tags to unstructured data (like text, audio, video, or images) and summarize or analyze the data using some combinations of the data itself and the applied codes.

Some CAQDAS is primarily or exclusively focused on manual coding (highlight a relevant section and select a tag) while others also include options to automatically apply tags based on words, phrases, or machine learning analysis. CAQDAS also vary in the range of mixed methods analysis tools and visualization for codes and text (such as code overlaps), with some also able to integrate data from surveys, citation managers, and other sources in analysis.

This page provides descriptions and links to some of the most common free and commercial CAQDAS tools below. If you are trying to decide on a tool, we recommend reading the box titled "Which CAQDAS is right for me?" first.

Does the Library or Virginia Tech provide access to qualitative software?

As a rule, no. Neither the University Libraries nor general campus computing labs provided licensed access options for paid CAQDAS, including NVivo, Dedoose, atlas.ti, MaxQDA, QDAMiner, or others. The only exception is that short-term access is available by arrangement for research use of atlas.ti in the Media Production Suite on the fourth floor of Newman Library (send inquiries to [email protected]).

However, there are free options that are suitable for some users, and most paid CAQDAS provides free trials of 7-30 days for new users to test out the software. More information on licensing is below in the "Which CAQDAS is right for me?" section.

How can I learn more about this software?

The University Libraries offers workshops on NVivo, Dedoose, Atlas.ti and Taguette, as well as qualitative coding principles and collaborative coding in spreadsheets. Resources for self-learning can be found on the Qualitative Training and Consultation tab.

Which CAQDAS is right for me?

If you're new to qualitative data analysis, choosing a tool can be intimidating, particularly since the most widely used tools are commercial and not widely available in campus labs.

The questions below and the chart below can help you narrow your options for tools. Note that most paid tools have free trials available, and introductory workshops and consultation assistance are available for many through Data Services (see "Qualitative Training and Consultation" tab).

Simple projects can sometimes also be accomplished using tools already available to you, such as spreadsheets like Excel or Google Sheets, although they're not included in the chart because they don't share the same basic structure of CAQDAS. Additional tools not in the chart can be found in the sections below with brief descriptions and links.

What kind of sources can I analyze?

All major CAQDAS allows for analyzing text-based documents (Word, plain text, web page text, etc.) and most provide support for PDF files as well, though some do not support images in PDFs or older PDFs without text tagging. Some tools also support coding areas of images or directly coding time segments of audio or video (without first transcribing). Additionally, some tools allow importing spreadsheets, survey data, citation manager bibliographies, or other data types for use in mixed methods analysis (see "Analysis" section in chart).

Some tools also have options to import data directly from specific social media sites (X/Twitter, Facebook, YouTube, etc.), sometimes with special features. These are not listed in the chart because permissions and protocols change frequently and tend to break this functionality. If you are interested in working with qualitative data from a specific social media platform, please reach out to a  consultant  for assistance.

In most cases, coding and analysis are simplest if multimedia sources are first converted to text before importing to a project (see "Recording and Transcription" or "Qualitative Training and Consultation" tab), unless the visual or audio structure itself is being analyzed, beyond just the surface language.

How can I organize and apply codes?

All tools allow for manual selection and coding or tagging of the surface text of documents and (when the document types are supported) time segments of audio/visual sources and regions of images. Some tools also allow automatic coding of selected words or phrases when they appear in documents, often with tables or word clouds available to help find relevant phrases. Additionally, certain packages provide for automatic coding of text based on formatting or speaker names or even automatic identification of topics, sentiment (positive or negative), and named entities (people, places, groups, etc.) using machine learning or large language models. Both the level of necessary pre-processing and quality of these autocoding features vary widely, but they can be valuable tools to supplement manual coding or work across larger collections of sources.

Can I perform mixed methods analysis (and what kinds)?

All CAQDAS provide some basic functions for analysis, like the ability to count and view the subset of sources or text sections that have a specific tag applied to them. Most also provide for summaries of word counts in the text of one or more documents. However, support for various types of mixed methods analysis is one of the most significant differences between CAQDAS packages, so it is worth checking carefully 

What options are available to collaborate with other users?

Not all CAQDAS are equally good for projects with multiple coders, whether users are divvying up documents or coding each document multiple times. It is always possible to have different users of the same machine (or a cloud backup of the file with a service like OneDrive or Google Drive) open the same file and do work sequentially, but there are also two models that allow for multiple users to work on projects at the same time. Some use a synchronous cloud-based model, where all users can work on the same project simultaneously and changes are reflected in real time. Others have options to create multiple copies of a project that users can work on separately before merging them together at a later time.

Additionally, some packages with collaboration support also include extra features. Some allow for training and testing coders on a standard dataset to ensure pre-defined codes are applied consistently across users. Others also provide the ability to calculate measures of inter-rater reliability or coder agreement to measure how similar the final application of codes was across users. In all cases with collaboration, there are ways to choose a single final code from those applied by multiple users.

It's also worth pointing out that in choosing a CAQDAS package, it is worth consulting with likely collaborators about what (if any) packages they already use or have access to. What interoperability there is for qualitative data is generally limited to moving projects between packages, and currently only works well for the most common aspects of projects, such as textual data sources and code application, but not for unique or complex features such as coding multimedia or mixed methods analysis output. Therefore, it is strongly recommended to agree on a package with your entire research team early in the planning or data collection phases.

What does the software cost?

There are 3 basic licensing models for CAQDAS. Some tools are free and open source, although they tend to have a limited number of options. The remaining tools may be available as subscriptions (monthly or annual, typically with free upgrades), one-time purchases (major upgrades may require an additional fee), or both. Some tools have cloud-based data backup or syncing, either included in all licenses or as a paid add-on. All paid software is licensed by user, although some allow paying based on the number of users at one time, rather than the number of total users.

What if I want to mix and match features?

Each CAQDAS package stores data in its own proprietary project format, with limited or no interchangeability between software. Recently, however, major software companies agreed to support a qualitative interchange format called REFI-QDA (or sometimes QDPX) that allows for moving some elements of projects between tools by exporting text and XML-based files in a special format that the other tools can interpret.

This might allow, for example, coding documents using advanced auto-coding features in one package before exporting to analyze in a different package. However, advance pilot testing is important if you plan this kind of workflow, as even when data exported from one package can be imported to another, there may be losses or changes to structure because of differences in how the packages represent information internally. We recommend reaching out to a qualitative research  consultant for help if you think you may need features that are not available in a single tool.

Is CAQDAS accessible for the visually impaired?

All major CAQDAS include at least some accessibility features, most commonly screen reader compatibility and keyboard shortcuts, but vary widely in the range of available options. Atlas.ti also provides public information on WCAG 2.0 compliance. For more assistance with research software accessibility, please contact Accessible Technologies .

CAQDAS Package Features

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• Last Updated: Dec 12, 2023 2:28 PM

 Your Data, Expertly Coded

Get your data meticulously hand-coded by PhD-qualified research specialists, who understand exactly what markers want.

How It Works

Getting your qualitative data carefully coded is as easy as 1-2-3 .

What Exactly You Get

You’ll receive 3 core deliverables when you work with us:

We can code data for various types of qualitative analyses, including (but not limited to) content analysis, thematic analysis, narrative analysis and discourse analysis.

The Grad Coach Difference

Three things make Grad Coach the obvious choice for qualitative coding:

100% Manual Coding

Your data will be manually coded by PhD-qualified, seasoned qualitative researchers, that have a deep understanding of English language usage. No AI or automation here.

Context-Sensitive Coding

All your data will be coded against the backdrop of your research aims and objectives, as well as your intended analysis method, ensuring you have a highly relevant coding structure.

Text & Audio-Based Coding

If you’re working with audio recordings and haven’t yet transcribed them, our team of seasoned transcribers will carefully transcribe your data, completely manually.

Fast-track your coding, today

Get a quick, no-obligation quote to have your data coded by experts.

Or book a free, no-obligation consultation with a friendly Coding Specialist.

Frequently Asked Questions

Below are some of the most popular coding-related questions we get asked.

Qualitative Coding

Do you code manually or with software.

To ensure the highest quality of coding, we code all content completely manually (in other words, it's done by humans).

Coding is handled by our experienced, highly-qualified team of qualitative research specialists. All our coders have extensive academic experience, are native English speakers (from the US, UK and SA) and have worked on numerous research projects.

We do not use any automation or software-based coding tools, as these tools can never be as accurate and effective as human-based coding. Quality is our priority.

Can I see a sample/example of your coding?

Yes, certainly. You can download a sample coding project here .

What format do you provide the coded content in?

We code all content in Word , using the comments feature to label the respective words and phrases.  We then export all coded content into an Excel spreadsheet for easy navigation, filtering and sorting. You can view a sample of this here .

Can your coding be imported into NVivo, ATLAS.ti, MaxQDA, etc.?

The summary Excel spreadsheet that we provide ( see example here ) can be imported into most qualitative analysis software packages. However, you should check the import capabilities of your chosen software beforehand, to ensure compatibility.

My interviews aren't transcribed yet. Can you code these?

We will need transcribed versions of your interviews. If you need us to transcribe, we do offer a transcription service in addition to coding. We will quote you separately for this service if needed.

What is the process if I work with you?

The typical engagement process is as follows:

First, we'll have an initial discussion (over email or Google Meet ) to understand your project and specific requirements. Once we have these details, we'll provide you with a firm quote and timeline.

2 - Project kickoff

You'll send us your data (e.g., interview transcripts), along with the details regarding your research aims and objectives, research questions and methodology, so that we can assess the best possible approach to coding your data.

3 - Approval and execution

We'll review all the information and propose a coding structure/approach. Once you've agreed to this, we'll get to work coding and send you the completed project as per the agreed timeline.

How long will it take to get my data coded?

This depends on a few factors, including the size and complexity of your dataset, as well as our capacity at your time of enquiring. We have completed coding projects in as little as 24 hours , but a typical project requires at least a few days .

Feel free to request a quotation, at which point we'll also confirm our availability/timelines.

How much does coding cost?

Our fee is based on the quantity and length of the interview transcripts (or any other text-based data set).

For a rough indication of typical costs, please visit the pricing page . For a firm quotation, please email us or book a free initial consultation .

What format do you require the data to be in?

We code in Microsoft Word , so please send us your data in this format (i.e., DOCX). If your documents are in another format, we can convert them to Word format, but this will impact the turnaround time.

Can you code my interviews one by one, as I complete them?

We can, but we don't recommend it. We recommend that you wait until you have your complete data set before starting with the coding process. Coding is an iterative process, and so we need to review the entire data set (e.g., all interviews) to ensure a comprehensive coding structure.

Should I include my interview questions in my transcripts?

Yes, we need these in order to understand the context of each response.

Can you assist with the qualitative analysis as well?

We can assist you in undertaking your analysis on a coaching basis , but this is separate from the coding service. If you would like guidance through the analysis phase, please book an initial consultation with one of our friendly coaches to discuss how we can help you.

Please keep in mind that the analysis itself needs to be your own work. We can coach you through the process step by step and provide detailed feedback regarding your writing, but we cannot write up your analysis for you, as that would constitute academic misconduct.

I still have questions…

No problem. Feel free to email us or book an initial consultation to discuss.

Still have a question? No problem – feel free to  email us  or  book a consultation .

What our clients say

David's depth of knowledge in research methodology was truly impressive. He demonstrated a profound understanding of the nuances and complexities of my research area, offering insights that I hadn't even considered. His ability to synthesize information, identify key research gaps, and suggest research topics was truly inspiring. I felt like I had a true expert by my side, guiding me through the complexities of the proposal. 

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I had been struggling with the first 3 chapters of my dissertation for over a year. I finally decided to give GradCoach a try and it made a huge difference. Alexandra provided helpful suggestions along with edits that transformed my paper. My advisor was very impressed.

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Working with Kerryn has been brilliant. She has guided me through that pesky academic language that makes us all scratch our heads. I can't recommend Grad Coach highly enough; they are very professional, humble, and fun to work with. If like me, you know your subject matter but you're getting lost in the academic language, look no further, give them a go.

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So helpful! Amy assisted me with an outline for my literature review and with organizing the results for my MBA applied research project. Having a road map helped enormously and saved a lot of time. Definitely worth it.

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Everything about my experience was great, from Dr. Shaeffer’s expertise, to her patience and flexibility. I reached out to GradCoach after receiving a 78 on a midterm paper. Not only did I get a 100 on my final paper in the same class, but I haven’t received a mark less than A+ since. I recommend GradCoach for everyone who needs help with academic research.

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Not convinced? Read more reviews and testimonials here .

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Qualitative Coding Boot Camp: An Intensive Training and Overview for Clinicians, Educators, and Administrators

Ellen childs.

1 Research Scientist, Health, Law, Policy and Management Department, Boston University School of Public Health

Lindsay B. Demers

2 Assistant Professor, Education Evaluation Core, Department of Medicine, Boston University School of Medicine

3 Director, Education Evaluation Core, Department of Medicine, Boston University School of Medicine

Associated Data

B. Facilitators' Guide.docx

C. Pre- and Postevaluation.docx

All appendices are peer reviewed as integral parts of the Original Publication.

Introduction

Qualitative coding is a tool for analyzing data involving strings of meaningful words. While many schools and universities have staff who can assist faculty with quantitative data analysis, qualitative data analysis is interpretive and requires both content-specific knowledge and research methodology tools. In this qualitative coding boot camp, we introduce clinician-educators, staff, and administrators to a general overview of qualitative coding and analysis.

We designed and implemented an in-person training to help researchers who had limited exposure to qualitative research gain a general orientation to it. We provided an overview of qualitative data collection and qualitative coding and developed focused research questions related to sample interviews for participants to use in working together to develop a codebook. We concluded by discussing the iterative process of coding, how to work from codes to themes for a manuscript, and how to present and disseminate results.

To examine participants' learning during the boot camp session, we used a series of nonparametric sign tests to compare pre- and postsession responses on our evaluation form. The results of these tests showed significant growth in participant comfort with undertaking qualitative analysis.

Qualitative coding is an important skill for clinicians and their research teams to have, as it can help them to understand the experiences of those around them through an empirical lens. With this 2-hour training, we were able to increase participants' comfort level with the set of skills required to analyze qualitative data rigorously.

Educational Objectives

By the end of this activity, learners will be able to:

• 1. Recognize, identify, and list the different approaches to qualitative data collection.
• 2. Identify codes and create a codebook based on their research questions.
• 3. Synthesize codes into overarching themes.
• 4. Move from data to a written results section.

Qualitative research methods are used regularly within studies of medical education and medical interventions to understand the experience and perspectives of individuals. 1 However, training in analyzing qualitative data, such as interview or focus group transcripts, is not as pervasive as training for quantitative data, and there are often fewer institutional resources in place to provide support for analyzing qualitative data.

The presenters were two PhD researchers who work with many clinician-educators having limited experience conducting research. Together, we identified the need for a general primer to introduce qualitative data coding to clinician-educators. Specifically, through individual conversations with clinician-educators, as well as through a needs assessment conducted by coauthor Lindsay B. Demers, it became apparent that the process of coding was particularly daunting to novice qualitative researchers. In response, we designed this training to provide an intensive 2-hour session focused on learning the theory and skills behind qualitative coding, with time for participants to code inductively and begin to develop a qualitative codebook.

It is worth noting that at the institution at which this training was developed and run, there are many emerging resources for clinician-educators in regard to research support. These resources range from informal, one-on-one consultations with our institution's Education Evaluation Core to formal health sciences education courses on evaluation, assessment, and research methods. While our training provides a limited exposure to qualitative coding and familiarization with terms, it is not adequate for moving from novice qualitative researcher to expert qualitative researcher. Moreover, we do not expect researchers to feel comfortable independently designing and conducting a qualitative study at the training's conclusion. Rather, our goal is just to increase comfort with basic terms and some of the key components of qualitative coding. For this reason, we believe the course would work best for researchers who want a refresher on qualitative coding or who are trying to understand a bit more about the kinds of data that qualitative approaches yield. Furthermore, we acknowledge that qualitative coding is just one component of the broader process of qualitative data collection and analysis, but coding is an important part of the process that is time intensive and conducive to a 2-hour boot camp.

Existing materials in MedEdPORTAL offer some information about coding or the uses of qualitative research, but our training provides practical experience and technological training on current tools for qualitative coding. Previous MedEdPORTAL resources 2 , 3 have described mixed-methods research design workshops, discussing the theory behind what sorts of questions quantitative and qualitative research can answer and how mixed methods can strengthen research projects. Both those resources focus more on the process of determining one's research method, not on data analysis. Harris 4 has provided a general overview for analyzing qualitative data, describing generalities of the uses for qualitative data and how to code for themes. Our training adds to the content presented in Harris's workshop and provides an in-depth coding activity with which to experience inductive coding and codebook development, as well as a general overview of the computer technology available to assist with qualitative coding.

The training was run through the Department of Medicine's Education Evaluation Core, directed by coauthor Lindsay B. Demers. The Education Evaluation Core runs a variety of trainings for faculty, trainees, and administrators concerning research methods and data analysis on campus. We advertised the boot camp to medical educators, research staff, and other administrators who had conducted or expected to conduct qualitative research and wanted a general introduction to qualitative analysis and coding techniques. The training was run once in the morning and once in the afternoon to increase the likelihood that interested parties could fit it in around their clinic schedules. Approximately 6 weeks before the training, a calendar invitation was sent to all the education researcher teams in the department through Microsoft Outlook, and attendees signed up simply by accepting the Outlook invitation. The training was held in a conference room in a main instructional building that was familiar to all attendees.

As noted above, we offered the training to all clinician- and scientist-educators as well as their research support staff and administrators. We did not limit this training to faculty alone because we recognized that research and administration staff often undertook analysis of qualitative data, be it for quality improvement activities or for scholarly research. We asked that interested participants sign up in advance so that we could limit the number of attendees to 12–15 per session. Although the session could be conducted with a larger group if necessary, we wanted to engage in discussion with all groups and to answer any particular questions they had about their projects.

Attendees were not required to do any reading in advance, nor were they required to have any specific training or baseline knowledge. Ideally, attendees should have had exposure to the theory behind qualitative research given that this presentation focused primarily on the coding aspect of qualitative data analysis. Because we expected that not everyone would have a complete knowledge of qualitative research, we did include a brief overview of the overall theory. When implementing this resource at other institutions, facilitators should be experienced with qualitative analysis so they are able to effectively answer questions that go beyond the scope of what is presented in the PowerPoint slide deck ( Appendix A ).

The session was approximately 2 hours long. The time spent on each activity is provided in the attached facilitators' guide ( Appendix B ), and the discussion points highlighted during each slide are provided in the slide deck. Tables in the conference room were set up in a large square that was open at the back so participants could all see the presentation and talk with their groupmates across the table. As presenters, we began and ended the session by administering an evaluation ( Appendix C ). When participants broke into small groups, we distributed three different research questions to orient the participants' reading of the example interviews. Participants independently read the interviews and started developing potential inductive codes, and then worked together in their small groups to begin the process of developing the codebook. After small groups had the opportunity to code the texts individually and have a group discussion, they reconnected as a larger group, and we led a discussion on how the process had gone and what participants had learned from the experience. We went on to describe some strategies to continue analysis and framing for a manuscript. We discussed the benefit of multiple coders for increasing validity and reliability. We also raised issues related to presenting qualitative data (e.g., quotes from interview transcripts) in medical journals with low word counts. We ended the discussion by introducing some of the benefits of qualitative data-analysis software, giving a brief preview of the utility of the software for organizing files.

Example Data Set

We designed the training to be adaptable to any qualitative data set the facilitators could access. With the identified qualitative data set in mind, facilitators had to develop qualitative research questions that could be answered with the data. We opted to break our ∼15 participants into small groups of four to five, and so we identified three research questions, one for each group. Within the example data set, facilitators needed to identify two to three interview transcripts that contained information relevant to the research questions. We found that two interviews approximately six pages in length were at the upper limit of what people felt comfortable reading and analyzing in the 40 minutes dedicated to individual and small-group work.

In our implementation of this workshop, we used the example data set that comes with the NVIVO software (Version 11, 2015, QSR International) and focuses on the economy and ecology of a small fishing town in North Carolina. We intentionally chose a topic outside of medicine so that clinicians, scientists, researchers, and administrators would all come to the table with relatively similar levels of knowledge. In a less diverse group of participants, a topic closely related to areas of specialty would likely be equally as effective. If training facilitators do not have access to publicly available qualitative interviews, most qualitative data-analysis software programs have sample texts for use. Facilitators could also consider using other publicly available transcripts (e.g., congressional hearings or news interviews).

To successfully implement this workshop, facilitators must have the following materials:

• • Hard copies of the slide deck to hand out to participants.
• • A computer that has the PowerPoint slides and NVIVO.
• • A projector and screen on which to project the slides and NVIVO tutorial.
• • Hard copies of the interviews and research questions for participants.
• • Optional but suggested: colorful markers or pens for participants to use while coding the interviews.
• • A published qualitative study to give participants so they have an example of what a rigorous write-up should look like. We used one by Childs, Laws, Drainoni, et al. 5

The pre- and postevaluation forms we used were identical. The items used a 5-point response scale to assess comfort with a range of qualitative analysis tasks aligned with our learning objectives. To link pre- and postsession responses anonymously, we asked respondents to create a unique identifier based on their mother's maiden name, their birth month, and the street where they were raised. To protect participants' anonymity, we did not ask them to identify their role in the department on the evaluation form. However, if a larger group is involved, facilitators may wish to ask this question of attendees.

Although the evaluation form did not contain any formative questions regarding participant satisfaction with the workshop and/or ideas for improvements, we solicited this feedback informally from attendees after their participation. Based on the feedback we received, the only change we made after the first implementation was to provide a hard copy of the slides in addition to the other handouts we distributed.

To ensure anonymity of respondents when gathering evaluation data, we did not ask them to report their role within the department. However, each session was approximately 75% faculty and 25% administrators/research support staff. In total, 17 learners participated in the training across two sessions.

As described above, our evaluation of the training's effectiveness consisted of a pre- and postevaluation form. In both questionnaires, respondents were asked to indicate their comfort with a variety of qualitative research tasks on a 5-point scale (1 = Very Uncomfortable , 5 = Very Comfortable ). In the postsurvey, respondents were also asked to list two things they had learned from attending the training. Pre- and postevaluation data were linked using an anonymous, unique identifier created by respondents.

To assess pre- to postsession change, we used a series of nonparametric sign tests. For ease of interpretation, we present pre- and postsession means alongside the corresponding sign-test results for each qualitative research task about which we surveyed participants ( Table ). Although we saw statistically significant gains in each area of interest, there were two instances in which scores decreased from pre- to postsession. We hypothesize that this was a result of people assuming a higher level of comfort prior to learning the complexities of qualitative coding.

Qualitative research is an important methodological tool for medical professionals. Regular, rigorous training on qualitative data analysis can assist clinicians in analyzing and disseminating their research. Effective qualitative coding requires training in best practices regarding how to approach the coding process, tips on how to evolve codes into broader themes, and suggestions for how to organize themes in manuscripts.

Based on our evaluation data from the implementation of this workshop, which were closely aligned with the training learning objectives, we are confident that the workshop is effective at achieving its goals. While we do not expect participants to be experts at qualitative research by the end of the program, we believe that through exposure to some fundamental concepts related to qualitative research and by working through the process of preliminarily coding qualitative data, participants will gain comfort with the concepts and skills related to qualitative research. With regard to generalizability, because we implemented the workshop across a wide range of medical education personnel (from clinicians to administrators), the results of our evaluation are likely to be replicated with a diverse group of learners, especially as the training requires no prerequisite research knowledge. The biggest challenge we encountered in running the boot camp concerned scheduling. Clinicians and their staff have especially busy schedules. Taking this into account, we sent out invitations approximately 2 months in advance to ensure that as many interested learners could attend as possible. We also recommend running the session a few times and on different days to better accommodate a diverse group of medical education personnel.

The limitations of our training program are grounded in the nature of it being a boot camp. Some participants recommended that we expand the boot camp into a longer 3-hour session or into two 2-hour sessions. While our evaluations indicate that individuals gained comfort about the qualitative coding process, we acknowledge that additional training beyond this workshop is necessary for a novice researcher to rigorously design and conduct a qualitative research project. In addition to regular workshops, our institution's Education Evaluation Core is able to offer ongoing support for researchers throughout the process and can provide follow-up to the materials provided in this session. There is also a master's program in health sciences education at our school through which researchers can receive in-depth exposure to qualitative methods. Because no 2-hour training can cover the entirety of qualitative research, we recommend that participants have at least some familiarity with qualitative research generally, with a goal of enriching their understanding of how qualitative coding works and what sorts of data they should expect when undertaking a qualitative study.

In the future, we plan to continue developing and refining trainings and resources for clinician-educators to research and evaluate their ongoing work. Developing additional training related to other data-collection, analysis, and dissemination work is a continuing goal.

A. Qualitative Coding Boot Camp.pptx

Disclosures

None to report.

Funding/Support

Dr. Childs reports grants from the American Heart Association/National Institutes of Health outside the submitted work.

Ethical Approval

The Boston University Medical Campus Institutional Review Board approved this study.