qualitative research open coding

What is Open Coding in Qualitative Research?

Introduction

What is meant by open coding, uses for open coding, method of open coding, when should i use open coding, axial coding, and selective coding.

Qualitative coding aims to bring order to unstructured data . Whether it's a blog post, an interview transcript , or notes from a set of striking observations , qualitative researchers rely on codes in order to identify relationships between and provide an explanation for social phenomena. That said, there are countless approaches to coding; the best approach depends on your research inquiry.

Among approaches to coding data in qualitative research , open coding is perhaps one of the least prescriptive approaches. When employing open coding, the researcher codes the data based on what they see in the data and how they interpret what is going on. This is a good first step in the data analysis process if your research project is focused on the development of new themes, concepts, or theories. If that is the case for your study, let's look a little more closely at the open coding process.

Open coding is an integral part of the qualitative analysis process where exploratory inquiries are involved. Qualitative data almost always undergoes some sort of coding process to help researchers conduct analysis on and draw meaning from the data. When qualitative data is coded, it greatly facilitates analysis and the proposal of new theories and concepts based on the produced codes. Open coding is one of many strategies used to code data. While other approaches may be deductive in nature (i.e., reliant on existing theories), open coding adopts an inductive approach , requiring researchers to examine the data with as few preconceived notions as possible. In a sense, the researcher remains "open" to the various possibilities of meaning in the data so that the representations of meaning contained in a research project's codes are as close to the data as possible.

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Open coding is often applied to the analytic process in grounded theory methodology , thematic analysis , and any other inductive approach to qualitative research . Essentially, any research project involving an exploratory research question (i.e., an inquiry that develops or proposes a new theory rather than tests existing theory) will often employ open coding as a part of its research methods .

A code is a word or short phrase that describes something that is going on in the data -- you can also think of codes as tags that you attach to segments of data. Open codes are created when the researcher examines qualitative data (such as text, images, videos , etc.), selects a relevant segment of data, and attaches a code (or codes) that capture the meaning or the aspects that are relevant to the research question within that data segment. As mentioned previously, the researcher should look at the data with as open a mind as possible, avoiding the temptation of applying existing theories to the coding process. This approach allows for new theory developed directly from the data.

Open coding is only one strategy that can be complemented by other methods of coding. In a grounded theory approach , open, axial , and selective coding make up the steps of a larger process that aims to contribute new theory or propose new concepts. The researcher conducts open coding to apply short and descriptive phrases to larger segments of data. Think of these codes as keywords used to condense and organize the data for subsequent stages of analysis .

At the axial coding stage, the researcher draws connections between discrete parts of their study represented by open codes. Axial coding consists of grouping codes and placing them in a new category that embodies a broader meaning. As a result, if the codes produced in open coding are discrete elements of meaning, axial codes thus represent broader categories that group those open codes together. It is up to the researcher to determine how open codes should be categorized and how those categories place discrete codes within the broader context of their study.

Finally, selective coding narrows the analysis process to a single core category or categories related to the codes in your project. At this stage, the researcher acknowledges that certain categories are more consequential to the research question and warrant a more central role in the emerging theory. Ultimately, selective coding connects the discrete meaning represented in the codes and categories with the broader theory development in an exploratory study.

Keep in mind that the grounded theory approach is an iterative process. As categories and themes develop, the researcher develops an understanding of the collected data that can be employed in subsequent iterations of data collection and coding. At this stage of the analysis process, the researcher continues to avoid applying existing theories to the data but, instead, uses their understanding of the codes and categories they produce to engage in a more nuanced iteration of open coding as they continue to develop a coherent theory.

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Coding Qualitative Data: How to Code Qualitative Research

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

Thematic analysis.

This refers to the uncovering of themes, by analyzing the patterns and relationships in a set of qualitative data. A theme emerges or is built when related findings appear to be meaningful and there are multiple occurences. Thematic analysis can be used by anyone to transform and organize open-ended responses, online reviews and other qualitative data into significant themes.

Content analysis:

This refers to the categorization, tagging and thematic analysis of qualitative data. Essentially content analysis is a quantification of themes, by counting the occurrence of concepts, topics or themes. Content analysis can involve combining the categories in qualitative data with quantitative data, such as behavioral data or demographic data, for deeper insights.

Narrative analysis:

Some qualitative data, such as interviews or field notes may contain a story on how someone experienced something. For example, the process of choosing a product, using it, evaluating its quality and decision to buy or not buy this product next time. The goal of narrative analysis is to turn the individual narratives into data that can be coded. This is then analyzed to understand how events or experiences had an impact on the people involved.

Discourse analysis:

This refers to analysis of what people say in social and cultural context. The goal of discourse analysis is to understand user or customer behavior by uncovering their beliefs, interests and agendas. These are reflected in the way they express their opinions, preferences and experiences. It’s particularly useful when your focus is on building or strengthening a brand , by examining how they use metaphors and rhetorical devices.

Framework analysis:

When performing qualitative data analysis, it is useful to have a framework to organize the buckets of meaning. A taxonomy or code frame (a hierarchical set of themes used in coding qualitative data) is an example of the result. Don't fall into the trap of starting with a framework to make it faster to organize your data.  You should look at how themes relate to each other by analyzing the data and consistently check that you can validate that themes are related to each other .

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.

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.

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 .

Automated vs. Manual coding of qualitative data

Methods of coding qualitative data fall into three categories: automated coding and manual coding, and a blend of the two.

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 software. The capacity to aggregate data sources into a single source of analysis helps to break down data silos, unifying the analysis and insights across departments . This is now being referred to as Omni channel analysis or Unified Data Analytics .

Use Thematic Analysis Software

Try Thematic today to discover why leading companies rely on the platform to automate the coding of qualitative customer feedback at scale. Whether you have tons of customer reviews, support chat or open-ended survey responses, Thematic brings every valuable insight to the surface, while saving you thousands of hours.

Advances in natural language processing & machine learning have made it possible to automate the analysis of qualitative data, in particular content and framework analysis.  The most commonly used software for automated coding of qualitative data is text analytics software such as Thematic .

While manual human analysis is still popular due to its perceived high accuracy, automating most of 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.

Our Theme Editor tool ensures you take a reflexive approach, an important step in thematic analysis. The drag-and-drop tool makes it easy to refine, validate, and rename themes as you get more data. By guiding the AI, you can ensure your results are always precise, easy to understand and perfectly aligned with your objectives.

Thematic is the best software to automate code qualitative feedback at scale.

Don't just take it from us. Here's what some of our customers have to say:

I'm a fan of Thematic's ability to save time and create heroes. It does an excellent job using a single view to break down the verbatims into themes displayed by volume, sentiment and impact on our beacon metric, often but not exclusively NPS.

It does a superlative job using GenAI in summarizing a theme or sub-theme down to a single paragraph making it clear what folks are trying to say. Peter K, Snr Research Manager.

Thematic is a very intuitive tool to use. It boasts a robust level of granularity, allowing the user to see the general breadth of verbatim themes, dig into the sub-themes, and further into the sentiment of the open text itself. Artem C, Sr Manager of Research. LinkedIn.

AI-powered software to transform qualitative data at scale through a thematic and content analysis.

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. A major advantage is that it gives you a more complete, unbiased look at the themes throughout your data.

Combining inductive and deductive coding

In practice, most researchers use a blend of inductive and deductive approaches to coding.

For example, with Thematic, the AI inductively comes up with themes, while also framing the analysis so that it reflects how business decisions are made . At the end of the analysis, researchers use the Theme Editor to iterate or refine themes. Then, in the next wave of analysis, as new data comes in, the AI starts deductively with the theme taxonomy.

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 manually 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 words

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 practices 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!

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

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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Qualitative Data Coding 101

How to code qualitative data, the smart way (with examples).

By: Jenna Crosley (PhD) | Reviewed by:Dr Eunice Rautenbach | December 2020

As we’ve discussed previously , qualitative research makes use of non-numerical data – for example, words, phrases or even images and video. To analyse this kind of data, the first dragon you’ll need to slay is  qualitative data coding  (or just “coding” if you want to sound cool). But what exactly is coding and how do you do it? 

Overview: Qualitative Data Coding

In this post, we’ll explain qualitative data coding in simple terms. Specifically, we’ll dig into:

• What exactly qualitative data coding is
• What different types of coding exist
• How to code qualitative data (the process)
• Moving from coding to qualitative analysis
• Tips and tricks for quality data coding

What is qualitative data coding?

Let’s start by understanding what a code is. At the simplest level,  a code is a label that describes the content  of a piece of text. For example, in the sentence:

“Pigeons attacked me and stole my sandwich.”

You could use “pigeons” as a code. This code simply describes that the sentence involves pigeons.

So, building onto this,  qualitative data coding is the process of creating and assigning codes to categorise data extracts.   You’ll then use these codes later down the road to derive themes and patterns for your qualitative analysis (for example, thematic analysis ). Coding and analysis can take place simultaneously, but it’s important to note that coding does not necessarily involve identifying themes (depending on which textbook you’re reading, of course). Instead, it generally refers to the process of  labelling and grouping similar types of data  to make generating themes and analysing the data more manageable. 

Makes sense? Great. But why should you bother with coding at all? Why not just look for themes from the outset? Well, coding is a way of making sure your  data is valid . In other words, it helps ensure that your  analysis is undertaken systematically  and that other researchers can review it (in the world of research, we call this transparency). In other words, good coding is the foundation of high-quality analysis.

What are the different types of coding?

Now that we’ve got a plain-language definition of coding on the table, the next step is to understand what overarching types of coding exist – in other words, coding approaches . Let’s start with the two main approaches, inductive and deductive .

With deductive coding, you, as the researcher, begin with a set of  pre-established codes  and apply them to your data set (for example, a set of interview transcripts). Inductive coding on the other hand, works in reverse, as you create the set of codes based on the data itself – in other words, the codes emerge from the data. Let’s take a closer look at both.

Deductive coding 101

With deductive coding, we make use of pre-established codes, which are developed before you interact with the present data. This usually involves drawing up a set of  codes based on a research question or previous research . You could also use a code set from the codebook of a previous study.

For example, if you were studying the eating habits of college students, you might have a research question along the lines of 

“What foods do college students eat the most?”

As a result of this research question, you might develop a code set that includes codes such as “sushi”, “pizza”, and “burgers”.  

Deductive coding allows you to approach your analysis with a very tightly focused lens and quickly identify relevant data . Of course, the downside is that you could miss out on some very valuable insights as a result of this tight, predetermined focus. 

Inductive coding 101 

But what about inductive coding? As we touched on earlier, this type of coding involves jumping right into the data and then developing the codes  based on what you find  within the data. 

For example, if you were to analyse a set of open-ended interviews , you wouldn’t necessarily know which direction the conversation would flow. If a conversation begins with a discussion of cats, it may go on to include other animals too, and so you’d add these codes as you progress with your analysis. Simply put, with inductive coding, you “go with the flow” of the data.

Inductive coding is great when you’re researching something that isn’t yet well understood because the coding derived from the data helps you explore the subject. Therefore, this type of coding is usually used when researchers want to investigate new ideas or concepts , or when they want to create new theories. 

A little bit of both… hybrid coding approaches

If you’ve got a set of codes you’ve derived from a research topic, literature review or a previous study (i.e. a deductive approach), but you still don’t have a rich enough set to capture the depth of your qualitative data, you can  combine deductive and inductive  methods – this is called a  hybrid  coding approach. 

To adopt a hybrid approach, you’ll begin your analysis with a set of a priori codes (deductive) and then add new codes (inductive) as you work your way through the data. Essentially, the hybrid coding approach provides the best of both worlds, which is why it’s pretty common to see this in research.

Need a helping hand?

How to code qualitative data

Now that we’ve looked at the main approaches to coding, the next question you’re probably asking is “how do I actually do it?”. Let’s take a look at the  coding process , step by step.

Both inductive and deductive methods of coding typically occur in two stages:  initial coding  and  line by line coding . 

In the initial coding stage, the objective is to get a general overview of the data by reading through and understanding it. If you’re using an inductive approach, this is also where you’ll develop an initial set of codes. Then, in the second stage (line by line coding), you’ll delve deeper into the data and (re)organise it according to (potentially new) codes. 

Step 1 – Initial coding

The first step of the coding process is to identify  the essence  of the text and code it accordingly. While there are various qualitative analysis software packages available, you can just as easily code textual data using Microsoft Word’s “comments” feature. 

Let’s take a look at a practical example of coding. Assume you had the following interview data from two interviewees:

What pets do you have?

I have an alpaca and three dogs.

Only one alpaca? They can die of loneliness if they don’t have a friend.

I didn’t know that! I’ll just have to get five more. 

I have twenty-three bunnies. I initially only had two, I’m not sure what happened. 

In the initial stage of coding, you could assign the code of “pets” or “animals”. These are just initial,  fairly broad codes  that you can (and will) develop and refine later. In the initial stage, broad, rough codes are fine – they’re just a starting point which you will build onto in the second stage. 

How to decide which codes to use

But how exactly do you decide what codes to use when there are many ways to read and interpret any given sentence? Well, there are a few different approaches you can adopt. The  main approaches  to initial coding include:

• In vivo coding 

Process coding

• Open coding

Descriptive coding

Structural coding.

• Value coding

Let’s take a look at each of these:

In vivo coding

When you use in vivo coding , you make use of a  participants’ own words , rather than your interpretation of the data. In other words, you use direct quotes from participants as your codes. By doing this, you’ll avoid trying to infer meaning, rather staying as close to the original phrases and words as possible. 

In vivo coding is particularly useful when your data are derived from participants who speak different languages or come from different cultures. In these cases, it’s often difficult to accurately infer meaning due to linguistic or cultural differences. 

For example, English speakers typically view the future as in front of them and the past as behind them. However, this isn’t the same in all cultures. Speakers of Aymara view the past as in front of them and the future as behind them. Why? Because the future is unknown, so it must be out of sight (or behind us). They know what happened in the past, so their perspective is that it’s positioned in front of them, where they can “see” it. 

In a scenario like this one, it’s not possible to derive the reason for viewing the past as in front and the future as behind without knowing the Aymara culture’s perception of time. Therefore, in vivo coding is particularly useful, as it avoids interpretation errors.

Next up, there’s process coding , which makes use of  action-based codes . Action-based codes are codes that indicate a movement or procedure. These actions are often indicated by gerunds (words ending in “-ing”) – for example, running, jumping or singing.

Process coding is useful as it allows you to code parts of data that aren’t necessarily spoken, but that are still imperative to understanding the meaning of the texts. 

An example here would be if a participant were to say something like, “I have no idea where she is”. A sentence like this can be interpreted in many different ways depending on the context and movements of the participant. The participant could shrug their shoulders, which would indicate that they genuinely don’t know where the girl is; however, they could also wink, showing that they do actually know where the girl is. 

Simply put, process coding is useful as it allows you to, in a concise manner, identify the main occurrences in a set of data and provide a dynamic account of events. For example, you may have action codes such as, “describing a panda”, “singing a song about bananas”, or “arguing with a relative”.

Descriptive coding aims to summarise extracts by using a  single word or noun  that encapsulates the general idea of the data. These words will typically describe the data in a highly condensed manner, which allows the researcher to quickly refer to the content. 

Descriptive coding is very useful when dealing with data that appear in forms other than traditional text – i.e. video clips, sound recordings or images. For example, a descriptive code could be “food” when coding a video clip that involves a group of people discussing what they ate throughout the day, or “cooking” when coding an image showing the steps of a recipe. 

Structural coding involves labelling and describing  specific structural attributes  of the data. Generally, it includes coding according to answers to the questions of “ who ”, “ what ”, “ where ”, and “ how ”, rather than the actual topics expressed in the data. This type of coding is useful when you want to access segments of data quickly, and it can help tremendously when you’re dealing with large data sets. 

For example, if you were coding a collection of theses or dissertations (which would be quite a large data set), structural coding could be useful as you could code according to different sections within each of these documents – i.e. according to the standard  dissertation structure . What-centric labels such as “hypothesis”, “literature review”, and “methodology” would help you to efficiently refer to sections and navigate without having to work through sections of data all over again. 

Structural coding is also useful for data from open-ended surveys. This data may initially be difficult to code as they lack the set structure of other forms of data (such as an interview with a strict set of questions to be answered). In this case, it would useful to code sections of data that answer certain questions such as “who?”, “what?”, “where?” and “how?”.

Let’s take a look at a practical example. If we were to send out a survey asking people about their dogs, we may end up with a (highly condensed) response such as the following: 

Bella is my best friend. When I’m at home I like to sit on the floor with her and roll her ball across the carpet for her to fetch and bring back to me. I love my dog.

In this set, we could code  Bella  as “who”,  dog  as “what”,  home  and  floor  as “where”, and  roll her ball  as “how”. 

Values coding

Finally, values coding involves coding that relates to the  participant’s worldviews . Typically, this type of coding focuses on excerpts that reflect the values, attitudes, and beliefs of the participants. Values coding is therefore very useful for research exploring cultural values and intrapersonal and experiences and actions.   

To recap, the aim of initial coding is to understand and  familiarise yourself with your data , to  develop an initial code set  (if you’re taking an inductive approach) and to take the first shot at  coding your data . The coding approaches above allow you to arrange your data so that it’s easier to navigate during the next stage, line by line coding (we’ll get to this soon). 

While these approaches can all be used individually, it’s important to remember that it’s possible, and potentially beneficial, to  combine them . For example, when conducting initial coding with interviews, you could begin by using structural coding to indicate who speaks when. Then, as a next step, you could apply descriptive coding so that you can navigate to, and between, conversation topics easily. 

Step 2 – Line by line coding

Once you’ve got an overall idea of our data, are comfortable navigating it and have applied some initial codes, you can move on to line by line coding. Line by line coding is pretty much exactly what it sounds like – reviewing your data, line by line,  digging deeper  and assigning additional codes to each line. 

With line-by-line coding, the objective is to pay close attention to your data to  add detail  to your codes. For example, if you have a discussion of beverages and you previously just coded this as “beverages”, you could now go deeper and code more specifically, such as “coffee”, “tea”, and “orange juice”. The aim here is to scratch below the surface. This is the time to get detailed and specific so as to capture as much richness from the data as possible. 

In the line-by-line coding process, it’s useful to  code everything  in your data, even if you don’t think you’re going to use it (you may just end up needing it!). As you go through this process, your coding will become more thorough and detailed, and you’ll have a much better understanding of your data as a result of this, which will be incredibly valuable in the analysis phase.

Moving from coding to analysis

Once you’ve completed your initial coding and line by line coding, the next step is to  start your analysis . Of course, the coding process itself will get you in “analysis mode” and you’ll probably already have some insights and ideas as a result of it, so you should always keep notes of your thoughts as you work through the coding.  

When it comes to qualitative data analysis, there are  many different types of analyses  (we discuss some of the  most popular ones here ) and the type of analysis you adopt will depend heavily on your research aims, objectives and questions . Therefore, we’re not going to go down that rabbit hole here, but we’ll cover the important first steps that build the bridge from qualitative data coding to qualitative analysis.

When starting to think about your analysis, it’s useful to  ask yourself  the following questions to get the wheels turning:

• What actions are shown in the data? 
• What are the aims of these interactions and excerpts? What are the participants potentially trying to achieve?
• How do participants interpret what is happening, and how do they speak about it? What does their language reveal?
• What are the assumptions made by the participants? 
• What are the participants doing? What is going on? 
• Why do I want to learn about this? What am I trying to find out? 
• Why did I include this particular excerpt? What does it represent and how?

Code categorisation

Categorisation is simply the process of reviewing everything you’ve coded and then  creating code categories  that can be used to guide your future analysis. In other words, it’s about creating categories for your code set. Let’s take a look at a practical example.

If you were discussing different types of animals, your initial codes may be “dogs”, “llamas”, and “lions”. In the process of categorisation, you could label (categorise) these three animals as “mammals”, whereas you could categorise “flies”, “crickets”, and “beetles” as “insects”. By creating these code categories, you will be making your data more organised, as well as enriching it so that you can see new connections between different groups of codes. 

Theme identification

From the coding and categorisation processes, you’ll naturally start noticing themes. Therefore, the logical next step is to  identify and clearly articulate the themes  in your data set. When you determine themes, you’ll take what you’ve learned from the coding and categorisation and group it all together to develop themes. This is the part of the coding process where you’ll try to draw meaning from your data, and start to  produce a narrative . The nature of this narrative depends on your research aims and objectives, as well as your research questions (sounds familiar?) and the  qualitative data analysis method  you’ve chosen, so keep these factors front of mind as you scan for themes. 

Tips & tricks for quality coding

Before we wrap up, let’s quickly look at some general advice, tips and suggestions to ensure your qualitative data coding is top-notch.

• Before you begin coding,  plan out the steps  you will take and the coding approach and technique(s) you will follow to avoid inconsistencies. 
• When adopting deductive coding, it’s useful to  use a codebook  from the start of the coding process. This will keep your work organised and will ensure that you don’t forget any of your codes. 
• Whether you’re adopting an inductive or deductive approach,  keep track of the meanings  of your codes and remember to revisit these as you go along.
• Avoid using synonyms  for codes that are similar, if not the same. This will allow you to have a more uniform and accurate coded dataset and will also help you to not get overwhelmed by your data.
• While coding, make sure that you  remind yourself of your aims  and coding method. This will help you to  avoid  directional drift , which happens when coding is not kept consistent. 
• If you are working in a team, make sure that everyone has  been trained and understands  how codes need to be assigned. 

Psst... there’s more!

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

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31 Comments

I appreciated the valuable information provided to accomplish the various stages of the inductive and inductive coding process. However, I would have been extremely satisfied to be appraised of the SPECIFIC STEPS to follow for: 1. Deductive coding related to the phenomenon and its features to generate the codes, categories, and themes. 2. Inductive coding related to using (a) Initial (b) Axial, and (c) Thematic procedures using transcribe data from the research questions

Thank you so much for this. Very clear and simplified discussion about qualitative data coding.

This is what I want and the way I wanted it. Thank you very much.

All of the information’s are valuable and helpful. Thank for you giving helpful information’s. Can do some article about alternative methods for continue researches during the pandemics. It is more beneficial for those struggling to continue their researchers.

Thank you for your information on coding qualitative data, this is a very important point to be known, really thank you very much.

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hello, great article! well written and easy to understand. Can you provide some of the sources in this article used for further reading purposes?

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Wonderful one thank you so much.

Hello, I am doing qualitative research, please assist with example of coding format.

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Being a complete novice to the field of qualitative data analysis, your indepth analysis of the process of thematic analysis has given me better insight. Thank you so much.

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Thank you so much for your precise and very helpful information about coding in qualitative data.

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I still don’t understand the coding and categorizing of qualitative research, please give an example on my research base on the state of government education infrastructure environment in PNG

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Just at the right time when I needed to distinguish between inductive and

deductive data analysis of my Focus group discussion results very helpful

Very useful across disciplines and at all levels. Thanks…

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Chapter 10: Qualitative Data Collection & Analysis Methods

10.6 Qualitative Coding, Analysis, and Write-up: The How to Guide

This section provides an abbreviated set of steps and directions for coding, analyzing, and writing up qualitative data, taking an inductive approach. The following material is adapted from Research Rundowns, retrieved from https://researchrundowns.com/qual/qualitative-coding-analysis/ .

Step1: Open coding

At this first level of coding, the researcher is looking for distinct concepts and categories in the data, which will form the basic units of the analysis. In other words, the researcher is breaking down the data into first level concepts, or master headings, and second-level categories, or subheadings.

Researchers often use highlighters to distinguish concepts and categories. For example, if interviewees consistently talk about teaching methods, each time an interviewee mentions teaching methods, or something related to a teaching method, the researcher uses the same colour highlight. Teaching methods would become a concept, and other things related (types, etc.) would become categories – all highlighted in the same colour. It is valuable to use different coloured highlights to distinguish each broad concept and category. At the end of this stage, the transcripts contain many different colours of highlighted text. The next step is to transfer these into a brief outline, with main headings for concepts and subheadings for categories.

Step 2: Axial (focused) coding

In open coding, the researcher is focused primarily on the text from the interviews to define concepts and categories. In axial coding, the researcher is using the concepts and categories developed in the open coding process, while re-reading the text from the interviews. This step is undertaken to confirm that the concepts and categories accurately represent interview responses.

In axial coding, the researcher explores how the concepts and categories are related. To examine the latter, you might ask: What conditions caused or influenced concepts and categories? What is/was the social/political context? What are the associated effects or consequences? For example, let us suppose that one of the concepts is Adaptive Teaching , and two of the categories are tutoring and group projects . The researcher would then ask: What conditions caused or influenced tutoring and group projects to occur? From the interview transcripts, it is apparent that participants linked this condition (being able to offer tutoring and group projects) with being enabled by a supportive principle. Consequently, an axial code might be a phrase like our principal encourages different teaching methods . This discusses the context of the concept and/or categories and suggests that the researcher may need a new category labeled “supportive environment.” Axial coding is merely a more directed approach to looking at the data, to help make sure that the researcher has identified all important aspects.

Step 3: Build a data table

Table 10.4 illustrates how to transfer the final concepts and categories into a data table. This is a very effective way to organize results and/or discussion in a research paper. While this appears to be a quick process, it requires a lot of time to do it well.

Table 10.4 Major categories and associated concept

Step 4: Analysis & write-up

Not only is Table 10.4 an effective way to organize the analysis, it is also a good approach for assisting with the data analysis write-up. The first step in the analysis process is to discuss the various categories and describe the associated concepts. As part of this process, the researcher will describe the themes created in the axial coding process (the second step).

There are a variety of ways to present the data in the write-up, including: 1) telling a story; 2) using a metaphor; 3) comparing and contrasting; 4) examining relations among concepts/variables; and 5) counting. Please note that counting should not be a stand-alone qualitative data analysis process to use when writing up the results, because it cannot convey the richness of the data that has been collected. One can certainly use counting for stating the number of participants, or how many participants spoke about a specific theme or category; however, the researcher must present a much deeper level of analysis by drawing out the words of the participants, including the use of direct quotes from the participants´ interviews to demonstrate the validity of the various themes.

Here are some links to demonstrations on other methods for coding qualitative data:

• https://www.youtube.com/watch?reload=9&v=phXssQBCDls
• https://www.youtube.com/watch?v=lYzhgMZii3o
• http://qualisresearch.com/DownLoads/qda.pdf

When writing up the analysis, it is best to “identify” participants through a number, alphabetical letter, or pseudonym in the write-up (e.g. Participant #3 stated …). This demonstrates that you drawing data from all of the participants.  Think of it this way, if you were doing quantitative analysis on data from 400 participants, you would present the data for all 400 participants, assuming they all answered a specific question.  You will often see in a table of quantitative results (n=400), indicating that 400 people answered the question.  This is the researcher’s way of confirming, to the reader, how many participants answered a particular research question.  Assigning participant numbers, letters, or pseudonyms serves the same purpose in qualitative analysis.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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28 Coding and Analysis Strategies

Johnny Saldaña, School of Theatre and Film, Arizona State University

• Published: 04 August 2014
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This chapter provides an overview of selected qualitative data analytic strategies with a particular focus on codes and coding. Preparatory strategies for a qualitative research study and data management are first outlined. Six coding methods are then profiled using comparable interview data: process coding, in vivo coding, descriptive coding, values coding, dramaturgical coding, and versus coding. Strategies for constructing themes and assertions from the data follow. Analytic memo writing is woven throughout the preceding as a method for generating additional analytic insight. Next, display and arts-based strategies are provided, followed by recommended qualitative data analytic software programs and a discussion on verifying the researcher’s analytic findings.

Coding and Analysis Strategies

Anthropologist Clifford Geertz (1983) charmingly mused, “Life is just a bowl of strategies” (p. 25). Strategy , as I use it here, refers to a carefully considered plan or method to achieve a particular goal. The goal in this case is to develop a write-up of your analytic work with the qualitative data you have been given and collected as part of a study. The plans and methods you might employ to achieve that goal are what this article profiles.

Some may perceive strategy as an inappropriate if not colonizing word, suggesting formulaic or regimented approaches to inquiry. I assure you that that is not my intent. My use of strategy is actually dramaturgical in nature: strategies are actions that characters in plays take to overcome obstacles to achieve their objectives. Actors portraying these characters rely on action verbs to generate belief within themselves and to motivate them as they interpret the lines and move appropriately on stage. So what I offer is a qualitative researcher’s array of actions from which to draw to overcome the obstacles to thinking to achieve an analysis of your data. But unlike the pre-scripted text of a play in which the obstacles, strategies, and outcomes have been predetermined by the playwright, your work must be improvisational—acting, reacting, and interacting with data on a moment-by-moment basis to determine what obstacles stand in your way, and thus what strategies you should take to reach your goals.

Another intriguing quote to keep in mind comes from research methodologist Robert E. Stake (1995) who posits, “Good research is not about good methods as much as it is about good thinking” (p. 19). In other words, strategies can take you only so far. You can have a box full of tools, but if you do not know how to use them well or use them creatively, the collection seems rather purposeless. One of the best ways we learn is by doing . So pick up one or more of these strategies (in the form of verbs) and take analytic action with your data. Also keep in mind that these are discussed in the order in which they may typically occur, although humans think cyclically, iteratively, and reverberatively, and each particular research project has its own unique contexts and needs. So be prepared for your mind to jump purposefully and/or idiosyncratically from one strategy to another throughout the study.

QDA (Qualitative Data Analysis) Strategy: To Foresee

To foresee in QDA is to reflect beforehand on what forms of data you will most likely need and collect, which thus informs what types of data analytic strategies you anticipate using.

Analysis, in a way, begins even before you collect data. As you design your research study in your mind and on a word processor page, one strategy is to consider what types of data you may need to help inform and answer your central and related research questions. Interview transcripts, participant observation field notes, documents, artifacts, photographs, video recordings, and so on are not only forms of data but foundations for how you may plan to analyze them. A participant interview, for example, suggests that you will transcribe all or relevant portions of the recording, and use both the transcription and the recording itself as sources for data analysis. Any analytic memos (discussed later) or journal entries you make about your impressions of the interview also become data to analyze. Even the computing software you plan to employ will be relevant to data analysis as it may help or hinder your efforts.

As your research design formulates, compose one to two paragraphs that outline how your QDA may proceed. This will necessitate that you have some background knowledge of the vast array of methods available to you. Thus surveying the literature is vital preparatory work.

QDA Strategy: To Survey

To survey in QDA is to look for and consider the applicability of the QDA literature in your field that may provide useful guidance for your forthcoming data analytic work.

General sources in QDA will provide a good starting point for acquainting you with the data analytic strategies available for the variety of genres in qualitative inquiry (e.g., ethnography, phenomenology, case study, arts-based research, mixed methods). One of the most accessible is Graham R. Gibbs’ (2007)   Analysing Qualitative Data , and one of the most richly detailed is Frederick J. Wertz et al.'s (2011)   Five Ways of Doing Qualitative Analysis . The author’s core texts for this article came from The Coding Manual for Qualitative Researchers ( Saldaña, 2009 , 2013 ) and Fundamentals of Qualitative Research ( Saldaña, 2011 ).

If your study’s methodology or approach is grounded theory, for example, then a survey of methods works by such authors as Barney G. Glaser, Anselm L. Strauss, Juliet Corbin and, in particular, the prolific Kathy Charmaz (2006) may be expected. But there has been a recent outpouring of additional book publications in grounded theory by Birks & Mills (2011) , Bryant & Charmaz (2007) , Stern & Porr (2011) , plus the legacy of thousands of articles and chapters across many disciplines that have addressed grounded theory in their studies.

Particular fields such as education, psychology, social work, health care, and others also have their own QDA methods literature in the form of texts and journals, plus international conferences and workshops for members of the profession. Most important is to have had some university coursework and/or mentorship in qualitative research to suitably prepare you for the intricacies of QDA. Also acknowledge that the emergent nature of qualitative inquiry may require you to adopt different analytic strategies from what you originally planned.

QDA Strategy: To Collect

To collect in QDA is to receive the data given to you by participants and those data you actively gather to inform your study.

QDA is concurrent with data collection and management. As interviews are transcribed, field notes are fleshed out, and documents are filed, the researcher uses the opportunity to carefully read the corpus and make preliminary notations directly on the data documents by highlighting, bolding, italicizing, or noting in some way any particularly interesting or salient portions. As these data are initially reviewed, the researcher also composes supplemental analytic memos that include first impressions, reminders for follow-up, preliminary connections, and other thinking matters about the phenomena at work.

Some of the most common fieldwork tools you might use to collect data are notepads, pens and pencils, file folders for documents, a laptop or desktop with word processing software (Microsoft Word and Excel are most useful) and internet access, a digital camera, and a voice recorder. Some fieldworkers may even employ a digital video camera to record social action, as long as participant permissions have been secured. But everything originates from the researcher himself or herself. Your senses are immersed in the cultural milieu you study, taking in and holding on to relevant details or “significant trivia,” as I call them. You become a human camera, zooming out to capture the broad landscape of your field site one day, then zooming in on a particularly interesting individual or phenomenon the next. Your analysis is only as good as the data you collect.

Fieldwork can be an overwhelming experience because so many details of social life are happening in front of you. Take a holistic approach to your entree, but as you become more familiar with the setting and participants, actively focus on things that relate to your research topic and questions. Of course, keep yourself open to the intriguing, surprising, and disturbing ( Sunstein & Chiseri-Strater, 2012 , p. 115), for these facets enrich your study by making you aware of the unexpected.

QDA Strategy: To Feel

To feel in QDA is to gain deep emotional insight into the social worlds you study and what it means to be human.

Virtually everything we do has an accompanying emotion(s), and feelings are both reactions and stimuli for action. Others’ emotions clue you to their motives, attitudes, values, beliefs, worldviews, identities, and other subjective perceptions and interpretations. Acknowledge that emotional detachment is not possible in field research. Attunement to the emotional experiences of your participants plus sympathetic and empathetic responses to the actions around you are necessary in qualitative endeavors. Your own emotional responses during fieldwork are also data because they document the tacit and visceral. It is important during such analytic reflection to assess why your emotional reactions were as they were. But it is equally important not to let emotions alone steer the course of your study. A proper balance must be found between feelings and facts.

QDA Strategy: To Organize

To organize in QDA is to maintain an orderly repository of data for easy access and analysis.

Even in the smallest of qualitative studies, a large amount of data will be collected across time. Prepare both a hard drive and hard copy folders for digital data and paperwork, and back up all materials for security from loss. I recommend that each data “chunk” (e.g., one interview transcript, one document, one day’s worth of field notes) get its own file, with subfolders specifying the data forms and research study logistics (e.g., interviews, field notes, documents, Institutional Review Board correspondence, calendar).

For small-scale qualitative studies, I have found it quite useful to maintain one large master file with all participant and field site data copied and combined with the literature review and accompanying researcher analytic memos. This master file is used to cut and paste related passages together, deleting what seems unnecessary as the study proceeds, and eventually transforming the document into the final report itself. Cosmetic devices such as font style, font size, rich text (italicizing, bolding, underlining, etc.), and color can help you distinguish between different data forms and highlight significant passages. For example, descriptive, narrative passages of field notes are logged in regular font. “Quotations, things spoken by participants, are logged in bold font.”   Observer’s comments, such as the researcher’s subjective impressions or analytic jottings, are set in italics.

QDA Strategy: To Jot

To jot in QDA is to write occasional, brief notes about your thinking or reminders for follow up.

A jot is a phrase or brief sentence that will literally fit on a standard size “sticky note.” As data are brought and documented together, take some initial time to review their contents and to jot some notes about preliminary patterns, participant quotes that seem quite vivid, anomalies in the data, and so forth.

As you work on a project, keep something to write with or to voice record with you at all times to capture your fleeting thoughts. You will most likely find yourself thinking about your research when you're not working exclusively on the project, and a “mental jot” may occur to you as you ruminate on logistical or analytic matters. Get the thought documented in some way for later retrieval and elaboration as an analytic memo.

QDA Strategy: To Prioritize

To prioritize in QDA is to determine which data are most significant in your corpus and which tasks are most necessary.

During fieldwork, massive amounts of data in various forms may be collected, and your mind can get easily overwhelmed from the magnitude of the quantity, its richness, and its management. Decisions will need to be made about the most pertinent of them because they help answer your research questions or emerge as salient pieces of evidence. As a sweeping generalization, approximately one half to two thirds of what you collect may become unnecessary as you proceed toward the more formal stages of QDA.

To prioritize in QDA is to also determine what matters most in your assembly of codes, categories, themes, assertions, and concepts. Return back to your research purpose and questions to keep you framed for what the focus should be.

QDA Strategy: To Analyze

To analyze in QDA is to observe and discern patterns within data and to construct meanings that seem to capture their essences and essentials.

Just as there are a variety of genres, elements, and styles of qualitative research, so too are there a variety of methods available for QDA. Analytic choices are most often based on what methods will harmonize with your genre selection and conceptual framework, what will generate the most sufficient answers to your research questions, and what will best represent and present the project’s findings.

Analysis can range from the factual to the conceptual to the interpretive. Analysis can also range from a straightforward descriptive account to an emergently constructed grounded theory to an evocatively composed short story. A qualitative research project’s outcomes may range from rigorously achieved, insightful answers to open-ended, evocative questions; from rich descriptive detail to a bullet-pointed list of themes; and from third-person, objective reportage to first-person, emotion-laden poetry. Just as there are multiple destinations in qualitative research, there are multiple pathways and journeys along the way.

Analysis is accelerated as you take cognitive ownership of your data. By reading and rereading the corpus, you gain intimate familiarity with its contents and begin to notice significant details as well as make new insights about their meanings. Patterns, categories, and their interrelationships become more evident the more you know the subtleties of the database.

Since qualitative research’s design, fieldwork, and data collection are most often provisional, emergent, and evolutionary processes, you reflect on and analyze the data as you gather them and proceed through the project. If preplanned methods are not working, you change them to secure the data you need. There is generally a post-fieldwork period when continued reflection and more systematic data analysis occur, concurrent with or followed by additional data collection, if needed, and the more formal write-up of the study, which is in itself an analytic act. Through field note writing, interview transcribing, analytic memo writing, and other documentation processes, you gain cognitive ownership of your data; and the intuitive, tacit, synthesizing capabilities of your brain begin sensing patterns, making connections, and seeing the bigger picture. The purpose and outcome of data analysis is to reveal to others through fresh insights what we have observed and discovered about the human condition. And fortunately, there are heuristics for reorganizing and reflecting on your qualitative data to help you achieve that goal.

QDA Strategy: To Pattern

To pattern in QDA is to detect similarities within and regularities among the data you have collected.

The natural world is filled with patterns because we, as humans, have constructed them as such. Stars in the night sky are not just a random assembly; our ancestors pieced them together to form constellations like the Big Dipper. A collection of flowers growing wild in a field has a pattern, as does an individual flower’s patterns of leaves and petals. Look at the physical objects humans have created and notice how pattern oriented we are in our construction, organization, and decoration. Look around you in your environment and notice how many patterns are evident on your clothing, in a room, and on most objects themselves. Even our sometimes mundane daily and long-term human actions are reproduced patterns in the form of roles, relationships, rules, routines, and rituals.

This human propensity for pattern making follows us into QDA. From the vast array of interview transcripts, field notes, documents, and other forms of data, there is this instinctive, hardwired need to bring order to the collection—not just to reorganize it but to look for and construct patterns out of it. The discernment of patterns is one of the first steps in the data analytic process, and the methods described next are recommended ways to construct them.

QDA Strategy: To Code

To code in QDA is to assign a truncated, symbolic meaning to each datum for purposes of qualitative analysis.

Coding is a heuristic—a method of discovery—to the meanings of individual sections of data. These codes function as a way of patterning, classifying, and later reorganizing them into emergent categories for further analysis. Different types of codes exist for different types of research genres and qualitative data analytic approaches, but this article will focus on only a few selected methods. First, a definition of a code:

A code in qualitative data analysis is most often a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data. The data can consist of interview transcripts, participant observation fieldnotes, journals, documents, literature, artifacts, photographs, video, websites, e-mail correspondence, and so on. The portion of data to be coded can... range in magnitude from a single word to a full sentence to an entire page of text to a stream of moving images.... Just as a title represents and captures a book or film or poem’s primary content and essence, so does a code represent and capture a datum’s primary content and essence. [ Saldaña, 2009 , p. 3]

One helpful pre-coding task is to divide long selections of field note or interview transcript data into shorter stanzas . Stanza division “chunks” the corpus into more manageable paragraph-like units for coding assignments and analysis. The transcript sample that follows illustrates one possible way of inserting line breaks in-between self-standing passages of interview text for easier readability.

Process Coding

As a first coding example, the following interview excerpt about an employed, single, lower-middle-class adult male’s spending habits during the difficult economic times in the U.S. during 2008–2012 is coded in the right-hand margin in capital letters. The superscript numbers match the datum unit with its corresponding code. This particular method is called process coding, which uses gerunds (“-ing” words) exclusively to represent action suggested by the data. Processes can consist of observable human actions (e.g., BUYING BARGAINS), mental processes (e.g., THINKING TWICE), and more conceptual ideas (e.g., APPRECIATING WHAT YOU’VE GOT). Notice that the interviewer’s (I) portions are not coded, just the participant’s (P). A code is applied each time the subtopic of the interview shifts—even within a stanza—and the same codes can (and should) be used more than once if the subtopics are similar. The central research question driving this qualitative study is, “In what ways are middle-class Americans influenced and affected by the current [2008–2012] economic recession?”

Different researchers analyzing this same piece of data may develop completely different codes, depending on their lenses and filters. The previous codes are only one person’s interpretation of what is happening in the data, not the definitive list. The process codes have transformed the raw data units into new representations for analysis. A listing of them applied to this interview transcript, in the order they appear, reads:

BUYING BARGAINS

QUESTIONING A PURCHASE

THINKING TWICE

STOCKING UP

REFUSING SACRIFICE

PRIORITIZING

FINDING ALTERNATIVES

LIVING CHEAPLY

NOTICING CHANGES

STAYING INFORMED

MAINTAINING HEALTH

PICKING UP THE TAB

APPRECIATING WHAT YOU’VE GOT

Coding the data is the first step in this particular approach to QDA, and categorization is just one of the next possible steps.

QDA Strategy: To Categorize

To categorize in QDA is to cluster similar or comparable codes into groups for pattern construction and further analysis.

Humans categorize things in innumerable ways. Think of an average apartment or house’s layout. The rooms of a dwelling have been constructed or categorized by their builders and occupants according to function. A kitchen is designated as an area to store and prepare food and the cooking and dining materials such as pots, pans, and utensils. A bedroom is designated for sleeping, a closet for clothing storage, a bathroom for bodily functions and hygiene, and so on. Each room is like a category in which related and relevant patterns of human action occur. Of course, there are exceptions now and then, such as eating breakfast in bed rather than in a dining area or living in a small studio apartment in which most possessions are contained within one large room (but nonetheless are most often organized and clustered into subcategories according to function and optimal use of space).

The point here is that the patterns of social action we designate into particular categories during QDA are not perfectly bounded. Category construction is our best attempt to cluster the most seemingly alike things into the most seemingly appropriate groups. Categorizing is reorganizing and reordering the vast array of data from a study because it is from these smaller, larger, and meaning-rich units that we can better grasp the particular features of each one and the categories’ possible interrelationships with one another.

One analytic strategy with a list of codes is to classify them into similar clusters. Obviously, the same codes share the same category, but it is also possible that a single code can merit its own group if you feel it is unique enough. After the codes have been classified, a category label is applied to each grouping. Sometimes a code can also double as a category name if you feel it best summarizes the totality of the cluster. Like coding, categorizing is an interpretive act, for there can be different ways of separating and collecting codes that seem to belong together. The cut-and-paste functions of a word processor are most useful for exploring which codes share something in common.

Below is my categorization of the fifteen codes generated from the interview transcript presented earlier. Like the gerunds for process codes, the categories have also been labeled as “-ing” words to connote action. And there was no particular reason why fifteen codes resulted in three categories—there could have been less or even more, but this is how the array came together after my reflections on which codes seemed to belong together. The category labels are ways of answering “why” they belong together. For at-a-glance differentiation, I place codes in CAPITAL LETTERS and categories in upper and lower case Bold Font :

Category 1: Thinking Strategically

Category 2: Spending Strategically

Category 3: Living Strategically

APPRECIATING WHAT YOU'VE GOT

Notice that the three category labels share a common word: “strategically.” Where did this word come from? It came from analytic reflection on the original data, the codes, and the process of categorizing the codes and generating their category labels. It was the analyst’s choice based on the interpretation of what primary action was happening. Your categories generated from your coded data do not need to share a common word or phrase, but I find that this technique, when appropriate, helps build a sense of unity to the initial analytic scheme.

The three categories— Thinking Strategically , Spending Strategically , and Living Strategically —are then reflected upon for how they might interact and interplay. This is where the next major facet of data analysis, analytic memos, enters the scheme. But a necessary section on the basic principles of interrelationship and analytic reasoning must precede that discussion.

QDA Strategy: To Interrelate

To interrelate in QDA is to propose connections within, between, and among the constituent elements of analyzed data.

One task of QDA is to explore the ways our patterns and categories interact and interplay. I use these terms to suggest the qualitative equivalent of statistical correlation, but interaction and interplay are much more than a simple relationship. They imply interrelationship . Interaction refers to reverberative connections—for example, how one or more categories might influence and affect the others, how categories operate concurrently, or whether there is some kind of “domino” effect to them. Interplay refers to the structural and processual nature of categories—for example, whether some type of sequential order, hierarchy, or taxonomy exists; whether any overlaps occur; whether there is superordinate and subordinate arrangement; and what types of organizational frameworks or networks might exist among them. The positivist construct of “cause and effect” becomes influences and affects in QDA.

There can even be patterns of patterns and categories of categories if your mind thinks conceptually and abstractly enough. Our minds can intricately connect multiple phenomena but only if the data and their analyses support the constructions. We can speculate about interaction and interplay all we want, but it is only through a more systematic investigation of the data—in other words, good thinking—that we can plausibly establish any possible interrelationships.

QDA Strategy: To Reason

To reason in QDA is to think in ways that lead to causal probabilities, summative findings, and evaluative conclusions.

Unlike quantitative research, with its statistical formulas and established hypothesis-testing protocols, qualitative research has no standardized methods of data analysis. Rest assured, there are recommended guidelines from the field’s scholars and a legacy of analytic strategies from which to draw. But the primary heuristics (or methods of discovery) you apply during a study are deductive , inductive , abductive , and retroductive reasoning. Deduction is what we generally draw and conclude from established facts and evidence. Induction is what we experientially explore and infer to be transferable from the particular to the general, based on an examination of the evidence and an accumulation of knowledge. Abduction is surmising from the evidence that which is most likely, those explanatory hunches based on clues. “Whereas deductive inferences are certain (so long as their premises are true) and inductive inferences are probable, abductive inferences are merely plausible” ( Shank, 2008 , p. 1). Retroduction is historic reconstruction, working backwards to figure out how the current conditions came to exist.

It is not always necessary to know the names of these four ways of reasoning as you proceed through analysis. In fact, you will more than likely reverberate quickly from one to another depending on the task at hand. But what is important to remember about reasoning is:

to base your conclusions primarily on the participants’ experiences, not just your own

not to take the obvious for granted, as sometimes the expected won't always happen. Your hunches can be quite right and, at other times, quite wrong

to examine the evidence carefully and make reasonable inferences

to logically yet imaginatively think about what is going on and how it all comes together.

Futurists and inventors propose three questions when they think about creating new visions for the world: What is possible (induction)? What is plausible (abduction)? What is preferable (deduction)? These same three questions might be posed as you proceed through QDA and particularly through analytic memo writing, which is retroductive reflection on your analytic work thus far.

QDA Strategy: To Memo

To memo in QDA is to reflect in writing on the nuances, inferences, meanings, and transfer of coded and categorized data plus your analytic processes.

Like field note writing, perspectives vary among practitioners as to the methods for documenting the researcher’s analytic insights and subjective experiences. Some advise that such reflections should be included in field notes as relevant to the data. Others advise that a separate researcher’s journal should be maintained for recording these impressions. And still others advise that these thoughts be documented as separate analytic memos. I prescribe the latter as a method because it is generated by and directly connected to the data themselves.

An analytic memo is a “think piece” of reflexive free writing, a narrative that sets in words your interpretations of the data. Coding and categorizing are heuristics to detect some of the possible patterns and interrelationships at work within the corpus, and an analytic memo further articulates your deductive, inductive, abductive, and retroductive thinking processes on what things may mean. Though the metaphor is a bit flawed and limiting, think of codes and their consequent categories as separate jigsaw puzzle pieces, and their integration into an analytic memo as the trial assembly of the complete picture.

What follows is an example of an analytic memo based on the earlier process coded and categorized interview transcript. It is not intended as the final write-up for a publication but as an open-ended reflection on the phenomena and processes suggested by the data and their analysis thus far. As the study proceeds, however, initial and substantive analytic memos can be revisited and revised for eventual integration into the final report. Note how the memo is dated and given a title for future and further categorization, how participant quotes are occasionally included for evidentiary support, and how the category names are bolded and the codes kept in capital letters to show how they integrate or weave into the thinking:

March 18, 2012 EMERGENT CATEGORIES: A STRATEGIC AMALGAM There’s a popular saying now: “Smart is the new rich.” This participant is Thinking Strategically about his spending through such tactics as THINKING TWICE and QUESTIONING A PURCHASE before he decides to invest in a product. There’s a heightened awareness of both immediate trends and forthcoming economic bad news that positively affects his Spending Strategically . However, he seems unaware that there are even more ways of LIVING CHEAPLY by FINDING ALTERNATIVES. He dines at all-you-can-eat restaurants as a way of STOCKING UP on meals, but doesn’t state that he could bring lunch from home to work, possibly saving even more money. One of his “bad habits” is cigarettes, which he refuses to give up; but he doesn’t seem to realize that by quitting smoking he could save even more money, not to mention possible health care costs. He balks at the idea of paying $1.50 for a soft drink, but doesn’t mind paying $6.00–$7.00 for a pack of cigarettes. Penny-wise and pound-foolish. Addictions skew priorities. Living Strategically , for this participant during “scary times,” appears to be a combination of PRIORITIZING those things which cannot be helped, such as pet care and personal dental care; REFUSING SACRIFICE for maintaining personal creature-comforts; and FINDING ALTERNATIVES to high costs and excessive spending. Living Strategically is an amalgam of thinking and action-oriented strategies.

There are several recommended topics for analytic memo writing throughout the qualitative study. Memos are opportunities to reflect on and write about:

how you personally relate to the participants and/or the phenomenon

your study’s research questions

your code choices and their operational definitions

the emergent patterns, categories, themes, assertions, and concepts

the possible networks (links, connections, overlaps, flows) among the codes, patterns, categories, themes, assertions, and concepts

an emergent or related existent theory

any problems with the study

any personal or ethical dilemmas with the study

future directions for the study

the analytic memos generated thus far [labeled “metamemos”]

the final report for the study [adapted from Saldaña, 2013 , p. 49]

Since writing is analysis, analytic memos expand on the inferential meanings of the truncated codes and categories as a transitional stage into a more coherent narrative with hopefully rich social insight.

QDA Strategy: To Code—A Different Way

The first example of coding illustrated process coding, a way of exploring general social action among humans. But sometimes a researcher works with an individual case study whose language is unique, or with someone the researcher wishes to honor by maintaining the authenticity of his or her speech in the analysis. These reasons suggest that a more participant-centered form of coding may be more appropriate.

In Vivo Coding

A second frequently applied method of coding is called in vivo coding. The root meaning of “in vivo” is “in that which is alive” and refers to a code based on the actual language used by the participant ( Strauss, 1987 ). What words or phrases in the data record you select as codes are those that seem to stand out as significant or summative of what is being said.

Using the same transcript of the male participant living in difficult economic times, in vivo codes are listed in the right-hand column. I recommend that in vivo codes be placed in quotation marks as a way of designating that the code is extracted directly from the data record. Note that instead of fifteen codes generated from process coding, the total number of in vivo codes is thirty. This is not to suggest that there should be specific numbers or ranges of codes used for particular methods. In vivo codes, though, tend to be applied more frequently to data. Again, the interviewer’s questions and prompts are not coded, just the participant's responses:

The thirty in vivo codes are then extracted from the transcript and listed in the order they appear to prepare them for analytic action and reflection:

“SKYROCKETED”

“TWO-FOR-ONE”

“THE LITTLE THINGS”

“THINK TWICE”

“ALL-YOU-CAN-EAT”

“CHEAP AND FILLING”

“BAD HABITS”

“DON'T REALLY NEED”

“LIVED KIND OF CHEAP”

“NOT A BIG SPENDER”

“HAVEN'T CHANGED MY HABITS”

“NOT PUTTING AS MUCH INTO SAVINGS”

“SPENDING MORE”

“ANOTHER DING IN MY WALLET”

“HIGH MAINTENANCE”

“COUPLE OF THOUSAND”

“INSURANCE IS JUST WORTHLESS”

“PICK UP THE TAB”

“IT ALL ADDS UP”

“NOT AS BAD OFF”

“SCARY TIMES”

Even though no systematic reorganization or categorization has been conducted with the codes thus far, an analytic memo of first impressions can still be composed:

March 19, 2012 CODE CHOICES: THE EVERYDAY LANGUAGE OF ECONOMICS After eyeballing the in vivo codes list, I noticed that variants of “CHEAP” appear most often. I recall a running joke between me and a friend of mine when we were shopping for sales. We’d say, “We're not ‘cheap,’ we're frugal .” There’s no formal economic or business language is this transcript—no terms such as “recession” or “downsizing”—just the everyday language of one person trying to cope during “SCARY TIMES” with “ANOTHER DING IN MY WALLET.” The participant notes that he’s always “LIVED KIND OF CHEAP” and is “NOT A BIG SPENDER” and, due to his employment, “NOT AS BAD OFF” as others in the country. Yet even with his middle class status, he’s still feeling the monetary pinch, dining at inexpensive “ALL-YOU-CAN-EAT” restaurants and worried about the rising price of peanut butter, observing that he’s “NOT PUTTING AS MUCH INTO SAVINGS” as he used to. Of all the codes, “ANOTHER DING IN MY WALLET” stands out to me, particularly because on the audio recording he sounded bitter and frustrated. It seems that he’s so concerned about “THE LITTLE THINGS” because of high veterinary and dental charges. The only way to cope with a “COUPLE OF THOUSAND” dollars worth of medical expenses is to find ways of trimming the excess in everyday facets of living: “IT ALL ADDS UP.”

Like process coding, in vivo codes could be clustered into similar categories, but another simple data analytic strategy is also possible.

QDA Strategy: To Outline

To outline in QDA is to hierarchically, processually, and/or temporally assemble such things as codes, categories, themes, assertions, and concepts into a coherent, text-based display.

Traditional outlining formats and content provide not only templates for writing a report but templates for analytic organization. This principle can be found in several CAQDAS (Computer Assisted Qualitative Data Analysis Software) programs through their use of such functions as “hierarchies,” “trees,” and “nodes,” for example. Basic outlining is simply a way of arranging primary, secondary, and sub-secondary items into a patterned display. For example, an organized listing of things in a home might consist of:

Large appliances

Refrigerator

Stove-top oven

Microwave oven

Small appliances

Coffee maker

Dining room

In QDA, outlining may include descriptive nouns or topics but, depending on the study, it may also involve processes or phenomena in extended passages, such as in vivo codes or themes.

The complexity of what we learn in the field can be overwhelming, and outlining is a way of organizing and ordering that complexity so that it does not become complicated. The cut-and-paste and tab functions of a word processor page enable you to arrange and rearrange the salient items from your preliminary coded analytic work into a more streamlined flow. By no means do I suggest that the intricate messiness of life can always be organized into neatly formatted arrangements, but outlining is an analytic act that stimulates deep reflection on both the interconnectedness and interrelationships of what we study. As an example, here are the thirty in vivo codes generated from the initial transcript analysis, arranged in such a way as to construct five major categories:

“DON’T REALLY NEED”

“HAVEN’T CHANGED MY HABITS”

Now that the codes have been rearranged into an outline format, an analytic memo is composed to expand on the rationale and constructed meanings in progress:

March 19, 2012 NETWORKS: EMERGENT CATEGORIES The five major categories I constructed from the in vivo codes are: “SCARY TIMES,” “PRIORTY,” “ANOTHER DING IN MY WALLET,” “THE LITTLE THINGS,” and “LIVED KIND OF CHEAP.” One of the things that hit me today was that the reason he may be pinching pennies on smaller purchases is that he cannot control the larger ones he has to deal with. Perhaps the only way we can cope with or seem to have some sense of agency over major expenses is to cut back on the smaller ones that we can control. $1,000 for a dental bill? Skip lunch for a few days a week. Insulin medication to buy for a pet? Don’t buy a soft drink from a vending machine. Using this reasoning, let me try to interrelate and weave the categories together as they relate to this particular participant: During these scary economic times, he prioritizes his spending because there seems to be just one ding after another to his wallet. A general lifestyle of living cheaply and keeping an eye out for how to save money on the little things compensates for those major expenses beyond his control.

QDA Strategy: To Code—In Even More Ways

The process and in vivo coding examples thus far have demonstrated only two specific methods of thirty-two documented approaches ( Saldaña, 2013 ). Which one(s) you choose for your analysis depends on such factors as your conceptual framework, the genre of qualitative research for your project, the types of data you collect, and so on. The following sections present a few other approaches available for coding qualitative data that you may find useful as starting points.

Descriptive Coding

Descriptive codes are primarily nouns that simply summarize the topic of a datum. This coding approach is particularly useful when you have different types of data gathered for one study, such as interview transcripts, field notes, documents, and visual materials such as photographs. Descriptive codes not only help categorize but also index the data corpus’ basic contents for further analytic work. An example of an interview portion coded descriptively, taken from the participant living in tough economic times, follows to illustrate how the same data can be coded in multiple ways:

For initial analysis, descriptive codes are clustered into similar categories to detect such patterns as frequency (i.e., categories with the largest number of codes), interrelationship (i.e., categories that seem to connect in some way), and initial work for grounded theory development.

Values Coding

Values coding identifies the values, attitudes, and beliefs of a participant, as shared by the individual and/or interpreted by the analyst. This coding method infers the “heart and mind” of an individual or group’s worldview as to what is important, perceived as true, maintained as opinion, and felt strongly. The three constructs are coded separately but are part of a complex interconnected system.

Briefly, a value (V) is what we attribute as important, be it a person, thing, or idea. An attitude (A) is the evaluative way we think and feel about ourselves, others, things, or ideas. A belief (B) is what we think and feel as true or necessary, formed from our “personal knowledge, experiences, opinions, prejudices, morals, and other interpretive perceptions of the social world” ( Saldaña, 2009 , pp. 89–90). Values coding explores intrapersonal, interpersonal, and cultural constructs or ethos . It is an admittedly slippery task to code this way, for it is sometimes difficult to discern what is a value, attitude, or belief because they are intricately interrelated. But the depth you can potentially obtain is rich. An example of values coding follows:

For analysis, categorize the codes for each of the three different constructs together (i.e., all values in one group, attitudes in a second group, and beliefs in a third group). Analytic memo writing about the patterns and possible interrelationships may reveal a more detailed and intricate worldview of the participant.

Dramaturgical Coding

Dramaturgical coding perceives life as performance and its participants as characters in a social drama. Codes are assigned to the data (i.e., a “play script”) that analyze the characters in action, reaction, and interaction. Dramaturgical coding of participants examines their objectives (OBJ) or wants, needs, and motives; the conflicts (CON) or obstacles they face as they try to achieve their objectives; the tactics (TAC) or strategies they employ to reach their objectives; their attitudes (ATT) toward others and their given circumstances; the particular emotions (EMO) they experience throughout; and their subtexts (SUB) or underlying and unspoken thoughts. The following is an example of dramaturgically coded data:

Not included in this particular interview excerpt are the emotions the participant may have experienced or talked about. His later line, “that’s another ding in my wallet,” would have been coded EMO: BITTER. A reader may not have inferred that specific emotion from seeing the line in print. But the interviewer, present during the event and listening carefully to the audio recording during transcription, noted that feeling in his tone of voice.

For analysis, group similar codes together (e.g., all objectives in one group, all conflicts in another group, all tactics in a third group), or string together chains of how participants deal with their circumstances to overcome their obstacles through tactics (e.g., OBJ: SAVING MEAL MONEY > TAC: SKIPPING MEALS). Explore how the individuals or groups manage problem solving in their daily lives. Dramaturgical coding is particularly useful as preliminary work for narrative inquiry story development or arts-based research representations such as performance ethnography.

Versus Coding

Versus coding identifies the conflicts, struggles, and power issues observed in social action, reaction, and interaction as an X VS. Y code, such as: MEN VS. WOMEN, CONSERVATIVES VS. LIBERALS, FAITH VS. LOGIC, and so on. Conflicts are rarely this dichotomous. They are typically nuanced and much more complex. But humans tend to perceive these struggles with an US VS. THEM mindset. The codes can range from the observable to the conceptual and can be applied to data that show humans in tension with others, themselves, or ideologies.

What follows are examples of versus codes applied to the case study participant’s descriptions of his major medical expenses:

As an initial analytic tactic, group the versus codes into one of three categories: the Stakeholders , their Perceptions and/or Actions , and the Issues at stake. Examine how the three interrelate and identify the central ideological conflict at work as an X vs. Y category. Analytic memos and the final write-up can detail the nuances of the issues.

Remember that what has been profiled in this section is a broad brushstroke description of just a few basic coding processes, several of which can be compatibly “mixed and matched” within a single analysis (see Saldaña’s [2013]   The Coding Manual for Qualitative Researchers for a complete discussion). Certainly with additional data, more in-depth analysis can occur, but coding is only one approach to extracting and constructing preliminary meanings from the data corpus. What now follows are additional methods for qualitative analysis.

QDA Strategy: To Theme

To theme in QDA is to construct summative, phenomenological meanings from data through extended passages of text.

Unlike codes, which are most often single words or short phrases that symbolically represent a datum, themes are extended phrases or sentences that summarize the manifest (apparent) and latent (underlying) meanings of data ( Auerbach & Silverstein, 2003 ; Boyatzis, 1998 ). Themes, intended to represent the essences and essentials of humans’ lived experiences, can also be categorized or listed in superordinate and subordinate outline formats as an analytic tactic.

Below is the interview transcript example used in the coding sections above. (Hopefully you are not too fatigued at this point with the transcript, but it’s important to know how inquiry with the same data set can be approached in several different ways.) During the investigation of the ways middle-class Americans are influenced and affected by the current (2008–2012) economic recession, the researcher noticed that participants’ stories exhibited facets of what he labeled “economic intelligence” or EI (based on the formerly developed theories of Howard Gardner’s multiple intelligences and Daniel Goleman’s emotional intelligence). Notice how themeing interprets what is happening through the use of two distinct phrases—ECONOMIC INTELLIGENCE IS (i.e., manifest or apparent meanings) and ECONOMIC INTELLIGENCE MEANS (i.e., latent or underlying meanings):

Unlike the fifteen process codes and thirty in vivo codes in the previous examples, there are now fourteen themes to work with. In the order they appear, they are:

EI IS TAKING ADVANTAGE OF UNEXPECTED OPPORTUNITY

EI MEANS THINKING BEFORE YOU ACT

EI IS BUYING CHEAP

EI MEANS SACRIFICE

EI IS SAVING A FEW DOLLARS NOW AND THEN

EI MEANS KNOWING YOUR FLAWS

EI IS SETTING PRIORITIES

EI IS FINDING CHEAPER FORMS OF ENTERTAINMENT

EI MEANS LIVING AN INEXPENSIVE LIFESTYLE

EI IS NOTICING PERSONAL AND NATIONAL ECONOMIC TRENDS

EI MEANS YOU CANNOT CONTROL EVERYTHING

EI IS TAKING CARE OF ONE’S OWN HEALTH

EI MEANS KNOWING YOUR LUCK

There are several ways to categorize the themes as preparation for analytic memo writing. The first is to arrange them in outline format with superordinate and subordinate levels, based on how the themes seem to take organizational shape and structure. Simply cutting and pasting the themes in multiple arrangements on a word processor page eventually develops a sense of order to them. For example:

A second approach is to categorize the themes into similar clusters and to develop different category labels or theoretical constructs . A theoretical construct is an abstraction that transforms the central phenomenon’s themes into broader applications but can still use “is” and “means” as prompts to capture the bigger picture at work:

Theoretical Construct 1: EI Means Knowing the Unfortunate Present

Supporting Themes:

Theoretical Construct 2: EI is Cultivating a Small Fortune

Theoretical Construct 3: EI Means a Fortunate Future

What follows is an analytic memo generated from the cut-and-paste arrangement of themes into an outline and into theoretical constructs:

March 19, 2012 EMERGENT THEMES: FORTUNE/FORTUNATELY/UNFORTUNATELY I first reorganized the themes by listing them in two groups: “is” and “means.” The “is” statements seemed to contain positive actions and constructive strategies for economic intelligence. The “means” statements held primarily a sense of caution and restriction with a touch of negativity thrown in. The first outline with two major themes, LIVING AN INEXPENSIVE LIFESTYLE and YOU CANNOT CONTROL EVERYTHING also had this same tone. This reminded me of the old children’s picture book, Fortunately/Unfortunately , and the themes of “fortune” as a motif for the three theoretical constructs came to mind. Knowing the Unfortunate Present means knowing what’s (most) important and what’s (mostly) uncontrollable in one’s personal economic life. Cultivating a Small Fortune consists of those small money-saving actions that, over time, become part of one's lifestyle. A Fortunate Future consists of heightened awareness of trends and opportunities at micro and macro levels, with the understanding that health matters can idiosyncratically affect one’s fortune. These three constructs comprise this particular individual’s EI—economic intelligence.

Again, keep in mind that the examples above for coding and themeing were from one small interview transcript excerpt. The number of codes and their categorization would obviously increase, given a longer interview and/or multiple interviews to analyze. But the same basic principles apply: codes and themes relegated into patterned and categorized forms are heuristics—stimuli for good thinking through the analytic memo-writing process on how everything plausibly interrelates. Methodologists vary in the number of recommended final categories that result from analysis, ranging anywhere from three to seven, with traditional grounded theorists prescribing one central or core category from coded work.

QDA Strategy: To Assert

To assert in QDA is to put forward statements that summarize particular fieldwork and analytic observations that the researcher believes credibly represent and transcend the experiences.

Educational anthropologist Frederick Erickson (1986) wrote a significant and influential chapter on qualitative methods that outlined heuristics for assertion development . Assertions are declarative statements of summative synthesis, supported by confirming evidence from the data, and revised when disconfirming evidence or discrepant cases require modification of the assertions. These summative statements are generated from an interpretive review of the data corpus and then supported and illustrated through narrative vignettes—reconstructed stories from field notes, interview transcripts, or other data sources that provide a vivid profile as part of the evidentiary warrant.

Coding or themeing data can certainly precede assertion development as a way of gaining intimate familiarity with the data, but Erickson’s methods are a more admittedly intuitive yet systematic heuristic for analysis. Erickson promotes analytic induction and exploration of and inferences about the data, based on an examination of the evidence and an accumulation of knowledge. The goal is not to look for “proof” to support the assertions but plausibility of inference-laden observations about the local and particular social world under investigation.

Assertion development is the writing of general statements, plus subordinate yet related ones called subassertions , and a major statement called a key assertion that represents the totality of the data. One also looks for key linkages between them, meaning that the key assertion links to its related assertions, which then link to their respective subassertions. Subassertions can include particulars about any discrepant related cases or specify components of their parent assertions.

Excerpts from the interview transcript of our case study will be used to illustrate assertion development at work. By now, you should be quite familiar with the contents, so I will proceed directly to the analytic example. First, there is a series of thematically related statements the participant makes:

“Buy one package of chicken, get the second one free. Now that was a bargain. And I got some.”

“With Sweet Tomatoes I get those coupons for a few bucks off for lunch, so that really helps.”

“I don’t go to movies anymore. I rent DVDs from Netflix or Redbox or watch movies online—so much cheaper than paying over ten or twelve bucks for a movie ticket.”

Assertions can be categorized into low-level and high-level inferences . Low-level inferences address and summarize “what is happening” within the particulars of the case or field site—the “micro.” High-level inferences extend beyond the particulars to speculate on “what it means” in the more general social scheme of things—the “meso” or “macro.” A reasonable low-level assertion about the three statements above collectively might read: The participant finds several small ways to save money during a difficult economic period . A high-level inference that transcends the case to the macro level might read: Selected businesses provide alternatives and opportunities to buy products and services at reduced rates during a recession to maintain consumer spending.

Assertions are instantiated (i.e., supported) by concrete instances of action or participant testimony, whose patterns lead to more general description outside the specific field site. The author’s interpretive commentary can be interspersed throughout the report, but the assertions should be supported with the evidentiary warrant . A few assertions and subassertions based on the case interview transcript might read (and notice how high-level assertions serve as the paragraphs’ topic sentences):

Selected businesses provide alternatives and opportunities to buy products and services at reduced rates during a recession to maintain consumer spending. Restaurants, for example, need to find ways during difficult economic periods when potential customers may be opting to eat inexpensively at home rather than spending more money by dining out. Special offers can motivate cash-strapped clientele to patronize restaurants more frequently. An adult male dealing with such major expenses as underinsured dental care offers: “With Sweet Tomatoes I get those coupons for a few bucks off for lunch, so that really helps.” The film and video industries also seem to be suffering from a double-whammy during the current recession: less consumer spending on higher-priced entertainment, resulting in a reduced rate of movie theatre attendance (currently 39 percent of the American population, according to CNN); coupled with a media technology and business revolution that provides consumers less costly alternatives through video rentals and internet viewing: “I don’t go to movies anymore. I rent DVDs from Netflix or Redbox or watch movies online—so much cheaper than paying over ten or twelve bucks for a movie ticket.”

“Particularizability”—the search for specific and unique dimensions of action at a site and/or the specific and unique perspectives of an individual participant—is not intended to filter out trivial excess but to magnify the salient characteristics of local meaning. Although generalizable knowledge serves little purpose in qualitative inquiry since each naturalistic setting will contain its own unique set of social and cultural conditions, there will be some aspects of social action that are plausibly universal or “generic” across settings and perhaps even across time. To work toward this, Erickson advocates that the interpretive researcher look for “concrete universals” by studying actions at a particular site in detail, then comparing those to other sites that have also been studied in detail. The exhibit or display of these generalizable features is to provide a synoptic representation, or a view of the whole. What the researcher attempts to uncover is what is both particular and general at the site of interest, preferably from the perspective of the participants. It is from the detailed analysis of actions at a specific site that these universals can be concretely discerned, rather than abstractly constructed as in grounded theory.

In sum, assertion development is a qualitative data analytic strategy that relies on the researcher’s intense review of interview transcripts, field notes, documents, and other data to inductively formulate composite statements that credibly summarize and interpret participant actions and meanings, and their possible representation of and transfer into broader social contexts and issues.

QDA Strategy: To Display

To display in QDA is to visually present the processes and dynamics of human or conceptual action represented in the data.

Qualitative researchers use not only language but illustrations to both analyze and display the phenomena and processes at work in the data. Tables, charts, matrices, flow diagrams, and other models help both you and your readers cognitively and conceptually grasp the essence and essentials of your findings. As you have seen thus far, even simple outlining of codes, categories, and themes is one visual tactic for organizing the scope of the data. Rich text, font, and format features such as italicizing, bolding, capitalizing, indenting, and bullet pointing provide simple emphasis to selected words and phrases within the longer narrative.

“Think display” was a phrase coined by methodologists Miles and Huberman (1994) to encourage the researcher to think visually as data were collected and analyzed. The magnitude of text can be essentialized into graphics for “at-a-glance” review. Bins in various shapes and lines of various thicknesses, along with arrows suggesting pathways and direction, render the study as a portrait of action. Bins can include the names of codes, categories, concepts, processes, key participants, and/or groups.

As a simple example, Figure 28.1 illustrates the three categories’ interrelationship derived from process coding. It displays what could be the apex of this interaction, LIVING STRATEGICALLY, and its connections to THINKING STRATEGICALLY, which influences and affects SPENDING STRATEGICALLY.

Figure 28.2 represents a slightly more complex (if not playful) model, based on the five major in vivo codes/categories generated from analysis. The graphic is used as a way of initially exploring the interrelationship and flow from one category to another. The use of different font styles, font sizes, and line and arrow thicknesses are intended to suggest the visual qualities of the participant’s language and his dilemmas—a way of heightening in vivo coding even further.

Accompanying graphics are not always necessary for a qualitative report. They can be very helpful for the researcher during the analytic stage as a heuristic for exploring how major ideas interrelate, but illustrations are generally included in published work when they will help supplement and clarify complex processes for readers. Photographs of the field setting or the participants (and only with their written permission) also provide evidentiary reality to the write-up and help your readers get a sense of being there.

QDA Strategy: To Narrate

To narrate in QDA is to create an evocative literary representation and presentation of the data in the form of creative nonfiction.

All research reports are stories of one kind or another. But there is yet another approach to QDA that intentionally documents the research experience as story, in its traditional literary sense. Narrative inquiry plots and story lines the participant’s experiences into what might be initially perceived as a fictional short story or novel. But the story is carefully crafted and creatively written to provide readers with an almost omniscient perspective about the participants’ worldview. The transformation of the corpus from database to creative nonfiction ranges from systematic transcript analysis to open ended literary composition. The narrative, though, should be solidly grounded in and emerge from the data as a plausible rendering of social life.

A simple illustration of category interrelationship.

An illustration with rich text and artistic features.

The following is a narrative vignette based on interview transcript selections from the participant living through tough economic times:

Jack stood in front of the soft drink vending machine at work and looked almost worriedly at the selections. With both hands in his pants pockets, his fingers jingled the few coins he had inside them as he contemplated whether he could afford the purchase. One dollar and fifty cents for a twenty-ounce bottle of Diet Coke. One dollar and fifty cents. “I can practically get a two-liter bottle for that same price at the grocery store,” he thought. Then Jack remembered the upcoming dental surgery he needed—that would cost one thousand dollars—and the bottle of insulin and syringes he needed to buy for his diabetic, “high maintenance” cat—about one hundred and twenty dollars. He sighed, took his hands out of his pockets, and walked away from the vending machine. He was skipping lunch that day anyway so he could stock up on dinner later at the cheap-but-filling-all-you-can-eat Chinese buffet. He could get his Diet Coke there.

Narrative inquiry representations, like literature, vary in tone, style, and point of view. The common goal, however, is to create an evocative portrait of participants through the aesthetic power of literary form. A story does not always have to have a moral explicitly stated by its author. The reader reflects on personal meanings derived from the piece and how the specific tale relates to one’s self and the social world.

QDA Strategy: To Poeticize

To poeticize in QDA is to create an evocative literary representation and presentation of the data in the form of poetry.

One form for analyzing or documenting analytic findings is to strategically truncate interview transcripts, field notes, and other pertinent data into poetic structures. Like coding, poetic constructions capture the essence and essentials of data in a creative, evocative way. The elegance of the format attests to the power of carefully chosen language to represent and convey complex human experience.

In vivo codes (codes based on the actual words used by participants themselves) can provide imagery, symbols, and metaphors for rich category, theme, concept, and assertion development, plus evocative content for arts-based interpretations of the data. Poetic inquiry takes note of what words and phrases seem to stand out from the data corpus as rich material for reinterpretation. Using some of the participant’s own language from the interview transcript illustrated above, a poetic reconstruction or “found poetry” might read:

Scary Times Scary times... spending more (another ding in my wallet) a couple of thousand (another ding in my wallet) insurance is just worthless (another ding in my wallet) pick up the tab (another ding in my wallet) not putting as much into savings (another ding in my wallet) It all adds up. Think twice: don't really need skip Think twice, think cheap: coupons bargains two-for-one free Think twice, think cheaper: stock up all-you-can-eat (cheap—and filling) It all adds up.

Anna Deavere Smith, a verbatim theatre performer, attests that people speak in forms of “organic poetry” in everyday life. Thus in vivo codes can provide core material for poetic representation and presentation of lived experiences, potentially transforming the routine and mundane into the epic. Some researchers also find the genre of poetry to be the most effective way to compose original work that reflects their own fieldwork experiences and autoethnographic stories.

QDA Strategy: To Compute

To compute in QDA is to employ specialized software programs for qualitative data management and analysis.

CAQDAS is an acronym for Computer Assisted Qualitative Data Analysis Software. There are diverse opinions among practitioners in the field about the utility of such specialized programs for qualitative data management and analysis. The software, unlike statistical computation, does not actually analyze data for you at higher conceptual levels. CAQDAS software packages serve primarily as a repository for your data (both textual and visual) that enable you to code them, and they can perform such functions as calculate the number of times a particular word or phrase appears in the data corpus (a particularly useful function for content analysis) and can display selected facets after coding, such as possible interrelationships. Certainly, basic word-processing software such as Microsoft Word, Excel, and Access provide utilities that can store and, with some pre-formatting and strategic entry, organize qualitative data to enable the researcher’s analytic review. The following internet addresses are listed to help in exploriong these CAQDAS packages and obtaining demonstration/trial software and tutorials:

AnSWR: www.cdc.gov/hiv/topics/surveillance/resources/software/answr

ATLAS.ti: www.atlasti.com

Coding Analysis Toolkit (CAT): cat.ucsur.pitt.edu/

Dedoose: www.dedoose.com

HyperRESEARCH: www.researchware.com

MAXQDA: www.maxqda.com

NVivo: www.qsrinternational.com

QDA Miner: www.provalisresearch.com

Qualrus: www.qualrus.com

Transana (for audio and video data materials): www.transana.org

Weft QDA: www.pressure.to/qda/

Some qualitative researchers attest that the software is indispensable for qualitative data management, especially for large-scale studies. Others feel that the learning curve of CAQDAS is too lengthy to be of pragmatic value, especially for small-scale studies. From my own experience, if you have an aptitude for picking up quickly on the scripts of software programs, explore one or more of the packages listed. If you are a novice to qualitative research, though, I recommend working manually or “by hand” for your first project so you can focus exclusively on the data and not on the software.

QDA Strategy: To Verify

To verify in QDA is to administer an audit of “quality control” to your analysis.

After your data analysis and the development of key findings, you may be thinking to yourself, “Did I get it right?” “Did I learn anything new?” Reliability and validity are terms and constructs of the positivist quantitative paradigm that refer to the replicability and accuracy of measures. But in the qualitative paradigm, other constructs are more appropriate.

Credibility and trustworthiness ( Lincoln & Guba, 1985 ) are two factors to consider when collecting and analyzing the data and presenting your findings. In our qualitative research projects, we need to present a convincing story to our audiences that we “got it right” methodologically. In other words, the amount of time we spent in the field, the number of participants we interviewed, the analytic methods we used, the thinking processes evident to reach our conclusions, and so on should be “just right” to persuade the reader that we have conducted our jobs soundly. But remember that we can never conclusively “prove” something; we can only, at best, convincingly suggest. Research is an act of persuasion.

Credibility in a qualitative research report can be established through several ways. First, citing the key writers of related works in your literature review is a must. Seasoned researchers will sometimes assess whether a novice has “done her homework” by reviewing the bibliography or references. You need not list everything that seminal writers have published about a topic, but their names should appear at least once as evidence that you know the field’s key figures and their work.

Credibility can also be established by specifying the particular data analytic methods you employed (e.g., “Interview transcripts were taken through two cycles of process coding, resulting in five primary categories”), through corroboration of data analysis with the participants themselves (e.g., “I asked my participants to read and respond to a draft of this report for their confirmation of accuracy and recommendations for revision”) or through your description of how data and findings were substantiated (e.g., “Data sources included interview transcripts, participant observation field notes, and participant response journals to gather multiple perspectives about the phenomenon”).

Creativity scholar Sir Ken Robinson is attributed with offering this cautionary advice about making a convincing argument: “Without data, you’re just another person with an opinion.” Thus researchers can also support their findings with relevant, specific evidence by quoting participants directly and/or including field note excerpts from the data corpus. These serve both as illustrative examples for readers and to present more credible testimony of what happened in the field.

Trustworthiness , or providing credibility to the writing, is when we inform the reader of our research processes. Some make the case by stating the duration of fieldwork (e.g., “Seventy-five clock hours were spent in the field”; “The study extended over a twenty-month period”). Others put forth the amounts of data they gathered (e.g., “Twenty-seven individuals were interviewed”; “My field notes totaled approximately 250 pages”). Sometimes trustworthiness is established when we are up front or confessional with the analytic or ethical dilemmas we encountered (e.g., “It was difficult to watch the participant’s teaching effectiveness erode during fieldwork”; “Analysis was stalled until I recoded the entire data corpus with a new perspective.”).

The bottom line is that credibility and trustworthiness are matters of researcher honesty and integrity . Anyone can write that he worked ethically, rigorously, and reflexively, but only the writer will ever know the truth. There is no shame if something goes wrong with your research. In fact, it is more than likely the rule, not the exception. Work and write transparently to achieve credibility and trustworthiness with your readers.

The length of this article does not enable me to expand on other qualitative data analytic strategies, such as to conceptualize, abstract, theorize, and write. Yet there are even more subtle thinking strategies to employ throughout the research enterprise, such as to synthesize, problematize, persevere, imagine, and create. Each researcher has his or her own ways of working, and deep reflection (another strategy) on your own methodology and methods as a qualitative inquirer throughout fieldwork and writing provides you with metacognitive awareness of data analytic processes and possibilities.

Data analysis is one of the most elusive processes in qualitative research, perhaps because it is a backstage, behind-the-scenes, in-your-head enterprise. It is not that there are no models to follow. It is just that each project is contextual and case specific. The unique data you collect from your unique research design must be approached with your unique analytic signature. It truly is a learning-by-doing process, so accept that and leave yourself open to discovery and insight as you carefully scrutinize the data corpus for patterns, categories, themes, concepts, assertions, and possibly new theories through strategic analysis.

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Neag School of Education

Educational Research Basics by Del Siegle

Open, in vivo, axial, and selective coding.

Open, axial, and selective coding are three distinct processes used in qualitative research, particularly in the field of grounded theory. They involve the systematic analysis and categorization of data to identify patterns, themes, and relationships.

Open Coding:

Open coding is the initial stage of qualitative data analysis. It is a method where data are initially broken down and analyzed to identify concepts, categories, or themes. It involves generating initial codes that capture the main ideas or concepts found in the data. Open coding allows for exploration and discovery, as the researcher remains open to emerging patterns and concepts without predetermined categories. The researcher reads and re-reads the data, line by line or segment by segment, to identify significant concepts, actions, and meanings. Open coding helps in developing a comprehensive understanding of the data by identifying a wide range of ideas and perspectives.

In Vivo Coding:

In vivo coding is a specific technique sometimes used during the open coding phase. It involves using participants’ exact words or phrases as codes to capture their lived experiences and perspectives. In vivo codes are verbatim representations of participants’ language, preserving the authenticity and richness of their expressions. Therefore, in vivo coding is a technique employed within the broader open coding process. It is a way to create codes based on participants’ exact words, contributing to the development of categories and themes during the open coding phase of qualitative analysis.

Axial Coding:

Axial coding is the next step in the qualitative data analysis process. It involves a more focused and systematic examination of the data to identify relationships between categories and subcategories identified during the open coding phase. Axial coding aims to establish connections and linkages between concepts, exploring how they relate to each other and contribute to the overall phenomenon under study. This process involves reorganizing and re-categorizing the codes based on their relationships, often using visual tools such as diagrams or matrices to visualize the connections. Axial coding helps to identify key themes, subthemes, and the underlying structure or framework that emerges from the data.

Selective Coding:

In grounded theory, the researcher is attempting to develop a theory or explanation that accounts for the observed phenomena. Selective coding is an important step in this process and follows open coding and axial coding. It involves further refining and organizing the data to identify a core category or central theme that captures the essence of the research. The goal is to develop a comprehensive understanding of the data and to create a theory or explanation that accounts for the observed phenomena.

Therefore , open coding is the initial phase where data are broken down to identify concepts and generate codes. Sometimes in vivo coding is employed within open coding to capture participants’ exact words and expressions to preserve authenticity. Axial coding follows open coding and focuses on finding relationships between categories and subcategories. Finally, selective coding aims to develop a theory that explains the topic of study by refining and organizing data into a core category or central theme. When conducting a grounded theory study, the goal is to achieve a comprehensive understanding of the data and create an explanatory theory, and open, axial, and selective coding are often used to achieve this.

Open coding

In qualitative research, analysis often focus on quotations , segments of the material which present an insight or information. A code is a simplification of the idea or insight. Open coding refers to coding the material without a pre-defined code list. Instead, potential codes emerge during the analysis process. Thus, it is an inductive approach, where the aim is to let the data speak for itself.

There are many genres of open coding: thematic analysis , grounded theory , frame analysis , among others. Each involve their own terminology and conventions how to analyse and present methods, sometimes specific to disciplinary practices. However, the methods often involve stages such as:

• familiarizing with the material and conducting open coding
• discussing with colleagues about the codes and clarifying their meanings
• conducting additional coding after discussion
• summarizing open codes by organizing and grouping them
• reporting findings

How you conduct open coding depends on your specific field of study and its established practices, as well as the methodological choices. Computer-assisted qualitative data analysis software can support wide range of approaches for this work.

Thematic analysis

Thematic analysis ( Broun & Clarke, 2006 ) seeks to find "something important about the data in relation to the research question, and represents some level of patterned response or meaning within the data set." This is fairlys loose: thematic analysis tells something about the material through coding it (which for Broun & Clarke can be inductive or deductive; i.e., open or closed). Furthermore, what is a theme can also vary: themes may be more semantic -- i.e., focusing on what and how things are described -- or more latent aspects -- i.e., interpreting what is told. Thematic analysis focus on phases:

• working to transcribe the data and make initial observations
• coding the data systematically
• seek to combine codes into (potential) themes
• reviewing (potential) themes by examining codes, corresponding quotes and even whole material
• define and name the themes from the materials

Grounded theory

Grounded theory (Glaser & Strauss; Glaser; Strauss & Corbin) aims to develop theoretical insights from the material. It is based on examining the material and conducting coding of the material in stages:

• open coding to examine and familiarize with the materials
• axial coding to examine, group and merge open codes to categories and concepts
• selective coding to scope the research project to chosen concepts and examining the phenomena from these lenses.

The goal is to increase the abstraction level of the analysis during these stages: whereas open codes are grounded to the data, axial codes, concepts, and categories are more abstract. Therefore, the aim of the process is to develop a new theory based on the data.

An important aspect in theory-development is making memos about thoughts and observations throughout the data analysis process. These seek to elaborate observations on the meaning of codes and their relationships. One goal with these is the ability to trace back how codes, quotations, concepts etc. were developed and what is their intended meaning.

Grounded theory is a well-adapted methodological framework, however it has various different ways to approach and use it within a specific research process ( Birks et al., 2013 , Ralph et al., 2015 ).

What is a good code?

This depends on your research interests. There are two common pitfalls: either not coding data sufficiently or then conducting too fine-grained analysis, where the coding does not summarise data. While it is impossible to provide a universal guideline for this, I would be scared if a sentence had several overlapping codes. I would also be scared if most paragraphs had no codes.

What about validity?

Open coding is always based on interpretations. Depending on how approach your research problem, different approaches may be helpful to be incorporated into the analysis process, such as being reflexive about the analysis process, seeking to find evidence to disconfirm observations or involving other researchers in the process (for more, see Creswell & Miller, 2000 ).

Example papers

Sometimes it is easier to understand how the methods are used by examining papers showing how it has been used. The papers have been chosen so that the teaching team has been involved in analysing and writing them and we are happy to discuss any details in these and show how computers were used in write-up of this process.

• Grön, K., & Nelimarkka, M. (2020). Party Politics, Values and the Design of Social Media Services. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1–29.

Computational tools for open coding approach

• Atlas.TI web and desktop - recommend tool
• Atlas.TI 22 for desktop only

Additional reading

• Silver & Lewins: Using Software in Qualitative Research: A Step-by-Step Guide

• Open access
• Published: 09 May 2024

Examining the feasibility of assisted index case testing for HIV case-finding: a qualitative analysis of barriers and facilitators to implementation in Malawi

• Caroline J. Meek 1 , 2 ,
• Tiwonge E. Mbeya Munkhondya 3 ,
• Mtisunge Mphande 4 ,
• Tapiwa A. Tembo 4 ,
• Mike Chitani 4 ,
• Milenka Jean-Baptiste 2 ,
• Dhrutika Vansia 4 ,
• Caroline Kumbuyo 4 ,
• Jiayu Wang 2 ,
• Katherine R. Simon 4 ,
• Sarah E. Rutstein 5 ,
• Clare Barrington 2 ,
• Maria H. Kim 4 ,
• Vivian F. Go 2 &
• Nora E. Rosenberg 2  

BMC Health Services Research volume  24 , Article number:  606 ( 2024 ) Cite this article

Metrics details

Assisted index case testing (ICT), in which health care workers take an active role in referring at-risk contacts of people living with HIV for HIV testing services, has been widely recognized as an evidence-based intervention with high potential to increase status awareness in people living with HIV. While the available evidence from eastern and southern Africa suggests that assisted ICT can be an effective, efficient, cost-effective, acceptable, and low-risk strategy to implement in the region, it reveals that feasibility barriers to implementation exist. This study aims to inform the design of implementation strategies to mitigate these feasibility barriers by examining “assisting” health care workers’ experiences of how barriers manifest throughout the assisted ICT process, as well as their perceptions of potential opportunities to facilitate feasibility.

In-depth interviews were conducted with 26 lay health care workers delivering assisted ICT in Malawian health facilities. Interviews explored health care workers’ experiences counseling index clients and tracing these clients’ contacts, aiming to inform development of a blended learning implementation package. Transcripts were inductively analyzed using Dedoose coding software to identify and describe key factors influencing feasibility of assisted ICT. Analysis included multiple rounds of coding and iteration with the data collection team.

Participants reported a variety of barriers to feasibility of assisted index case testing implementation, including sensitivities around discussing ICT with clients, privacy concerns, limited time for assisted index case testing amid high workloads, poor quality contact information, and logistical obstacles to tracing. Participants also reported several health care worker characteristics that facilitate feasibility (knowledge, interpersonal skills, non-stigmatizing attitudes and behaviors, and a sense of purpose), as well as identified process improvements with the potential to mitigate barriers.

Conclusions

Maximizing assisted ICT’s potential to increase status awareness in people living with HIV requires equipping health care workers with effective training and support to address and overcome the many feasibility barriers that they face in implementation. Findings demonstrate the need for, as well as inform the development of, implementation strategies to mitigate barriers and promote facilitators to feasibility of assisted ICT.

Trial registration

NCT05343390. Date of registration: April 25, 2022.

Peer Review reports

Introduction

To streamline progress towards its goal of ending AIDS as a public health threat by 2030, the Joint United Nations Programme on HIV/AIDS (UNAIDS) launched a set of HIV testing and treatment targets [ 1 ]. Adopted by United Nations member states in June 2021, the targets call for 95% of all people living with HIV (PLHIV) to know their HIV status, 95% of all PLHIV to be accessing sustained antiretroviral therapy (ART), and 95% of all people receiving ART to achieve viral suppression by 2025 [ 2 ]. Eastern and southern Africa has seen promising regional progress towards these targets in recent years, and the region is approaching the first target related to status awareness in PLHIV- in 2022, 92% of PLHIV in the region were aware of their status [ 3 ]. However, several countries in the region lag behind [ 4 ], and as 2025 approaches, it is critical to scale up adoption of evidence-based interventions to sustain and accelerate progress.

Index case testing (ICT), which targets provision of HIV testing services (HTS) for sexual partners, biological children, and other contacts of known PLHIV (“index clients”), is a widely recognized evidence-based intervention used to identify PLHIV by streamlining testing efforts to populations most at risk [ 5 , 6 , 7 ]. Traditional approaches to ICT rely on passive referral, in which index clients invite their contacts for testing [ 5 ]. However, the World Health Organization (WHO) and the President’s Emergency Plan for HIV/AIDS Relief (PEPFAR) have both recommended assisted approaches to ICT [ 6 , 8 , 9 , 10 ], in which health care workers (HCWs) take an active role in referral of at-risk contacts for testing, due to evidence of improved effectiveness in identifying PLHIV compared to passive approaches [ 10 , 11 , 12 , 13 , 14 ]. As a result, there have been several efforts to scale assisted ICT throughout eastern and southern Africa in recent years [ 15 , 16 , 17 , 18 , 19 , 20 ]. In addition to evidence indicating that assisted ICT can be effective in increasing HIV testing and case-finding [ 16 , 17 , 21 , 22 , 23 , 24 ], implementation evidence [ 25 ] from the region suggests that assisted ICT can be an efficient [ 14 ], acceptable [ 5 , 13 , 15 , 18 , 20 , 21 , 26 ], cost-effective [ 27 ], and low-risk [ 21 , 22 , 24 , 28 , 29 ] strategy to promote PLHIV status awareness. However, the few studies that focus on feasibility, or the extent to which HCWs can successfully carry out assisted ICT [ 25 ], suggest that barriers exist to feasibility of effective implementation [ 18 , 19 , 20 , 30 , 31 , 32 ]. Developing informed implementation strategies to mitigate these barriers requires more detailed examination of how these barriers manifest throughout the assisted ICT process, as well as of potential opportunities to facilitate feasibility, from the perspective of the HCWs who are doing the “assisting”.

This qualitative analysis addresses this need for further detail by exploring “assisting” HCWs’ perspectives of factors that influence the feasibility of assisted ICT, with a unique focus on informing development of effective implementation strategies to best support assisted ICT delivery in the context of an implementation science trial in Malawi.

This study was conducted in the Machinga and Balaka districts of Malawi. Malawi is a country in southeastern Africa in which 7.1% of the population lives with HIV and 94% of PLHIV know their status [ 4 ]. Machinga and Balaka are two relatively densely populated districts in the southern region of Malawi [ 33 ] with HIV prevalence rates similar to the national average [ 34 ]. We selected Machinga and Balaka because they are prototypical of districts in Malawi implementing Ministry of Health programs with support from an implementing partner.

Malawi has a long-established passive ICT program, and in 2019 the country also adopted an assisted component, known as voluntary assisted partner notification, as part of its national HIV testing policy [ 32 ]. In Malawi, ICT is conducted through the following four methods, voluntarily selected by the index client: 1) passive referral, in which HCWs encourage the index client to refer partners for voluntary HTS, 2) contract referral, in which HCWs establish an informal ‘contract’ with index clients that agrees upon a date that the HCW can contact the contact clients if they have not yet presented for HTS; 3) provider referral, in which HCWs contact and offer voluntary HTS to contact clients; and 3) dual referral, in which HCWs accompany and provide support to index clients in disclosing their status and offering HTS to their partners [ 8 ]. 

While Malawi has one of the lowest rates of qualified clinical HCWs globally (< 5 clinicians per 100,000 people) [ 35 ], the country has a strong track record of shifting HTS tasks to lay HCWs, who have been informally trained to perform certain health care delivery functions but do not have a formal professional/para-professional certification or tertiary education degree, in order to mitigate this limited medical workforce capacity [ 32 , 36 ]. In Malawi, lay HCW roles include HIV Diagnostic Assistants (who are primarily responsible for HIV testing and counseling, including index case counseling) and community health workers (who are responsible for a wider variety of tasks, including index case counseling and contact tracing) [ 32 ]. Non-governmental organization implementing partners, such as the Tingathe Program, play a critical role in harnessing Malawian lay HCW capacity to rapidly and efficiently scale up HTS, including assisted ICT [ 32 , 37 , 38 , 39 ].

Study design

Data for this analysis were collected as part of formative research for a two-arm cluster randomized control trial examining a blended learning implementation package as a strategy for building HCW capacity in assisted ICT [ 40 ]. Earlier work [ 32 ] established the theoretical basis for testing the blended learning implementation package, which combines individual asynchronous modules with synchronous small-group interactive sessions to enhance training and foster continuous quality improvement. The formative research presented in this paper aimed to further explore factors influencing feasibility of the assisted ICT from the perspective of HCWs in order to inform development of the blended learning implementation package.

Prior to the start of the trial (October-December 2021), the research team conducted 26 in-depth interviews (IDIs) with lay HCWs at 14 of the 34 facilities included in the parent trial. We purposively selected different types of facilities (hospitals, health centers, and dispensaries) in both districts and from both randomization arms, as this served as a qualitative baseline for a randomized trial. Within these facilities, we worked with facility supervisors to purposively select HCWs who were actively engaged in Malawi’s ICT program from the larger sample of HCWs eligible for the parent trial (had to be at least 18 years old, employed full-time at one of the health facilities included in the parent trial, and involved in counseling index clients and/or tracing their contacts). The parent trial enrolled 306 HCWs, who were primarily staff hired by Tingathe Program to support facilities implementing Malawi’s national HIV program.

Data collection

IDIs were conducted by three trained Malawian interviewers in a private setting using a semi-structured guide. IDIs were conducted over the phone when possible ( n  = 18) or in-person at sites with limited phone service ( n  = 8). The semi-structured guide was developed for this study through a series of rigorous, iterative discussions among the research team (Additional file 1 ). The questions used for this analysis were a subset of a larger interview. The interview guide questions for this analysis explored HCWs’ experiences with assisted ICT, including barriers and facilitators to implementation. Probing separately about the processes of counseling index clients and tracing their contacts, interviewers asked questions such as “What is the first thing that comes to mind when you think of counseling index clients/tracing contacts?”, “What aspects do you [like/not like] about…?” and “What do your colleagues say about…?”. When appropriate, interviewers probed further about how specific factors mentioned by the participant facilitate or impede the ICT implementation experience.

The IDIs lasted from 60–90 min and were conducted in Chichewa, a local language in Malawi. Eleven audio recordings were transcribed verbatim in Chichewa before being translated into English and 15 recordings were directly translated and transcribed into English. Interviewers summarized each IDI after it was completed, and these summaries were discussed with the research team routinely.

Data analysis

The research team first reviewed all of the interview summaries individually and then met multiple times to discuss initial observations, refining the research question and scope of analysis. A US-based analyst (CJM) with training in qualitative analysis used an inductive approach to develop a codebook, deriving broad codes from the implementation factors mentioned by participants throughout their interviews. Along with focused examination of the transcripts, she consulted team members who had conducted the IDIs with questions or clarifications. CJM regularly met with Malawian team members (TEMM, MM, TAT) who possess the contextual expertise necessary to verify and enhance meaning. She used the Dedoose (2019) web application to engage in multiple rounds of coding, starting with codes representing broad implementation factors and then further refining the codebook as needed to capture the nuanced manifestations of these barriers and facilitators. Throughout codebook development and refinement, the analyst engaged in memoing to track first impressions, thought processes, and coding decisions. The analyst presented the codebook and multiple rounds of draft results to the research team. All transcripts and applied codes were also reviewed in detail by additional team members (MJB, DV). Additional refinements to the codebook and results interpretations were iteratively made based on team feedback.

Ethical clearance

Ethical clearance was provided by UNC’s IRB, Malawi’s National Health Sciences Research Committee, and the Baylor College of Medicine IRB. Written informed consent was obtained from all participants in the main study and interviewers confirmed verbal consent before starting the IDIs.

Participant characteristics are described in Table  1 below.

Factors influencing feasibility of assisted ICT: barriers and facilitators

Participants described a variety of barriers and facilitators to feasibility of assisted ICT, manifesting across the index client counseling and contact client tracing phases of the implementation process. Identified barriers included sensitivities around discussing ICT with clients, privacy concerns, limited time for ICT amid high workloads, poor quality contact information, and logistical obstacles to tracing. In addition to these barriers, participants also described several HCW characteristics that facilitated feasibility: ICT knowledge, interpersonal skills, positive attitudes towards clients, and sense of purpose. Barriers and facilitators are mapped to the ICT process in Fig.  1 and described in greater detail in further sections.

Conceptual diagram mapping feasibility barriers and facilitators to the ICT process

Feasibility barriers

Sensitivities around discussing ict with clients.

Participants described ICT as a highly sensitive topic to approach with clients. Many expressed a feeling of uncertainty around how open index clients will be to sharing information about their contacts, as well as how contacts will react when approached for HTS. When asked about difficult aspects of counseling index clients, many HCWs mentioned clients’ hesitance or declination to participate in assisted ICT and share their contacts. Further, several HCWs mentioned that some index clients would provide false contact information. These index client behaviors were often attributed to confidentiality concerns, fear of unwanted status disclosure, and fear of the resulting implications of status disclosure: “They behave that way because they think you will be telling other people about their status…they also think that since you know it means their life is done, you will be looking at them differently .” Populations commonly identified as particularly likely to hesitate, refuse, or provide false information included youth (described as “ shy ” “ thinking they know a lot ” and “ difficult to reveal their contacts ”) and newly diagnosed clients (“it may be hard for them to accept [their HIV diagnosis]” ). One participant suggested that efforts to pair index clients with same-sex HCWs could make them more comfortable to discuss their contacts.

When asked about the first things that come to mind when starting to trace contacts, many participants discussed wondering how they will be received by the contact and preparing themselves to approach the contact. When conducting provider or contract referral, HCWs described a variety of challenging reactions that can occur when they approach a contact for HTS- including delay or refusal of testing, excessive questioning about the identity of the index client who referred them for testing, and even anger or aggression. Particularly mentioned in the context of male clients, these kinds of reactions can lead to stress and uncertain next steps for HCWs: “I was very tensed up. I was wondering to myself what was going to happen…he was talking with anger.”

Participants also noted the unique sensitivities inherent in conducting dual referral and interacting with sexual partners of index clients, explaining that HIV disclosure can create acute conflict in couples due to perceived blame and assumptions of infidelity. They recounted these scenarios as particularly difficult to navigate, with high stakes that require high-quality counseling skills: “sometimes if you do not have good counseling the marriage happens to get to an end.” . Some participants discussed concern about index client risk of intimate partner violence (IPV) upon partner disclosure: “they think that if they go home and [disclose their HIV status], the marriage will end right there, or for some getting to a point of [being] beaten.”

Privacy concerns

Participants also reported that clients highly value privacy, which can be difficult to secure throughout the ICT process. In the facility, while participants largely indicated that counseling index clients was much more successful when conducted in a private area, many reported limited availability of private counseling space. One participant described this challenge: “ if I’m counseling an index client and people keep coming into the room…this compromises the whole thing because the client becomes uncomfortable in the end.” Some HCWs mentioned working around this issue through use of screens, “do-not-disturb” signs, outdoor spots, and tents.

Participants also noted maintaining privacy as a challenge when tracing contact clients in the field, as they sometimes find clients in a situation that is not conducive to private conversations. One participant described: “ we get to the house and find that there are 4, 5 people with our [contact client]…it doesn’t go well…That is a mission gone wrong. ” Participants also noted that HCWs are also often easily recognizable in the community due to their bikes and cars, which exacerbates the risk of compromising privacy. To address privacy challenges in the community, participants reported strategies to increase discretion, including dressing to blend in with the community, preparing an alternate reason to be looking for the client, and offering HTS to multiple people or households to avoid singling out one person.

Limited time for ICT amid high workloads

Some participants indicated that strained staffing capacity leads HCWs to have to perform multiple roles, expressing challenges in balancing their ICT work with their other tasks. As one participant described, “Sometimes it is found that you are assigned a task here at the hospital to screen anyone who comes for blood testing, but you are also supposed to follow up [with] the contacts the same day- so it becomes a problem…you fail to follow up [with] the contacts.” Some also described being the only, or one of few staff responsible for ICT: “You’re doing this work alone, so you can see that it is a big task to do it single-handedly.” The need to counsel each index client individually, as a result of confidentiality concerns, further increases workload for the limited staff assigned to this work. Further, HCWs often described contact tracing in the field as time-consuming and physically taxing, which leaves them less time and energy for counseling. Many HCWs noted the need to hire more staff dedicated to ICT work.

High workloads also resulted in shorter appointments and less time to counsel index clients, which participants reported limits the opportunity for rapport that facilitates openness or probes for detailed information about sexual partners. Participants emphasized the importance of having enough time to meaningfully engage with index clients: “For counseling you cannot have a limit to say, ‘I will talk to him for 5 min only.’ …That is not counseling then. You are supposed to stay up until…you feel that this [person] is fulfilled.” . In addition, high workload can reduce the capacity of HCWs to deliver quality counseling: “So you find that as you go along with the counseling, you can do better with the first three clients but the rest, you are tired and you do short cuts.”

High workloads also lead to longer queues, which may deter clients from coming into the clinic or cause them to leave before receiving services: “Sometimes because of shortage of staff, it happens that you have been assigned a certain task that you were supposed to do but at the same time there are clients who were supposed to be counseled. As a result, because you spent more time on the other task as a result you lose out some of the clients because you find that they have gone.” In response to long queues, several participants described ‘fast-tracking’ contact clients who come in for HTS in effort to maximize case-finding by prioritizing those who have been identified as at risk of HIV.

Poor quality contact information

Participants repeatedly discussed the importance of eliciting accurate information about a person’s sexual partners, including where, when, and how to best contact them. As one participant said, “ Once the index has given us the wrong information then everything cannot work, it becomes wrong…if he gives us full information [with] the right details then everything becomes successful and happens without a problem. ” Adequate information is a critical component of the ICT process, and incorrect or incomplete information delays or prevents communication with contact clients.

Inadequate information, which can include incorrect or incomplete names, phone numbers, physical addresses, and contextual details, can arise from a variety of scenarios. Most participants mentioned index clients providing incorrect information as a concern. This occurred either intentionally to avoid disclosure or unintentionally if information was not known. Poor quality contact information also results from insufficient probing and poor documentation, which is often exacerbated by aforementioned HCW time and energy constraints. In one participant’s words, “The person who has enlisted the contact…is the key person who can make sure that our tracing is made easy.” Participants noted the pivotal role of the original HCW who first interacts with the index client in not only eliciting correct locator information but also eliciting detailed contextual information. For example, details about a contact client’s profession are helpful to trace the client at a time when they will likely be at home. Other helpful information included nicknames, HIV testing history, and notes about confidentiality concerns.

Logistical obstacles to tracing

Some contact clients are reached by phone whereas others must be physically traced in the community. Some participants reported difficulty with tracing via phone, frequently citing network problems and lack of sufficient airtime allocated by the facility. Participants also reported that some clients were unreachable by phone, necessitating physical tracing. Physically tracing a contact client requires a larger investment of resources than phone tracing, especially when the client lives at a far distance from the clinic. Participants frequently discussed having to travel far distances to reach contact clients, an issue some saw as exacerbated by people who travel to clinics at far distances due to privacy concerns.

While most participants reported walking or biking to reach contact clients in the community, some mentioned using a motorcycle or Tingathe vehicle. However, access to vehicles is often limited and these transportation methods require additional expenses for fuel. Walking or biking was also reported to expose HCWs to inclement weather, including hot or rainy seasons, and potential safety risks such as violence.

Participants reported that traveling far distances can be physically taxing and time-consuming, sometimes rendering them too tired or busy to attend to other tasks. Frequent travel influenced HCW morale, particularly when a tracing effort did not result in successfully recruiting a contact client. Participants frequently described this perception of wasted time and energy as “ painful ”, with the level of distress often portrayed as increasing with the distance travelled. As one HCW said, “You [can] find out that he gave a false address. That is painful because it means you have done nothing for the person, you travelled for nothing.”

HCWs described multiple approaches used to strategically allocate limited resources for long distances. These approaches included waiting to physically trace until there are multiple clients in a particular area, reserving vehicle use for longer trips, and coordinating across HCWs to map out contact client locations. HCWs also mentioned provision of rain gear and sun protection to mitigate uncomfortable travel. Another approach involved allocating contact tracing to HCWs based in the same communities as the contact clients.

Feasibility facilitators

Hcw knowledge about ict.

Participants reported that HCWs with a thorough understanding of ICT’s rationale and purpose can facilitate client openness. Clients were more likely to engage with HCWs about assisted ICT if they understood the benefits to themselves and their loved ones. One HCW stated, “If the person understands why we need the information, they will give us accurate information.”

Participants also discussed the value of deep HCW familiarity with ICT procedures and processes, particularly regarding screening clients for IPV and choosing referral method. One participant described the importance of clearly explaining various referral methods to clients: “So…people come and choose the method they like…when you explain things clearly it is like the index client is free to choose a method which the contact can use for testing”. Thorough knowledge of available referral methods allows HCWs to actively engage with index clients to discuss strategies to refer contacts in a way that fits their unique confidentiality needs, which was framed as particularly important when IPV is identified as a concern. Multiple participants suggested the use of flipcharts or videos, saying these would save limited HCW time and energy, fill information gaps, and provide clients with a visual aid to supplement the counseling. Others suggested recurring opportunities for training, to continuously “refresh” their ICT knowledge in order to facilitate implementation.

HCW interpersonal skills

In addition, HCWs’ ability to navigate sensitive conversations about HIV was noted as a key facilitator of successful implementation. Interpersonal skills were mentioned as mitigating the role’s day-to-day uncertainty by preparing HCWs to engage with clients, especially newly diagnosed clients: “ I need to counsel them skillfully so that they understand what I mean regardless that they have just tested positive for HIV.”

When discussing strategies to build HCW skills in counseling index clients and tracing contact clients, participants suggested establishing regular opportunities to discuss challenges and share approaches to address these challenges: “ I think that there should be much effort on the [HCWs] doing [ICT]. For example, what do I mean, they should be having a meeting with the facility people to ask what challenges are you facing and how can we end them?”. Another participant further elaborated, saying “We should be able to share experiences with our [colleagues] so that we can all learn from one another. And also, there are other people who are really brilliant at their job. Those people ought to come visit us and see how we are doing. That is very motivating.”

HCW non-stigmatizing attitudes and behaviors

Participants also highlighted the role of empathy and non-judgement in building trust with clients: “ Put yourself in that other person’s shoes. In so doing, the counseling session goes well. Understanding that person, that what is happening to them can also happen to you. ”. Participants viewed trust-building as critical to facilitating client comfort and openness: “if they trust you enough, they will give you the right information.” Further, participants associated HCW assurance of confidentiality with promoting trust and greater information sharing: “ Also assuring them on the issue of confidentiality because confidentiality is a paramount. If there will not be confidentiality then the clients will not reveal.”

HCW sense of purpose

Lastly, several participants reported that a sense of purpose and desire to help people motivated them to overcome the challenges of delivering assisted ICT. One participant said, “ Some of these jobs are a ministry. Counseling is not easy. You just need to tell yourself that you are there to help that person. ” Many seemed to take comfort in the knowledge that their labors, however taxing, would ultimately allow people to know their status, take control of their health, and prevent the spread of HIV. Participants framed the sense of fulfillment from successful ICT implementation as a mitigating factor amidst challenges: “ If [the contact client] has accepted it then I feel that mostly I have achieved the aim of being in the health field…that is why it is appealing to me ”.

Participants described a variety of barriers to assisted ICT implementation, including sensitivities around discussing ICT with clients, privacy concerns, limited time for ICT amid high workloads, poor quality contact information, and logistical obstacles to tracing. These barriers manifested across each step of the process of counseling index clients and tracing contacts. However, participants also identified HCW characteristics and process improvements that can mitigate these barriers.

Further, participants’ descriptions of the assisted ICT process revealed the intimately interconnected nature of factors that influence feasibility of assisted ICT. Sensitivities around HIV, privacy limitations, time constraints, and HCW characteristics all contribute to the extent to which counseling index clients elicits adequate information to facilitate contact tracing. Information quality has implications for HCW capacity, as inadequate information can lead to wasted resources, including HCW time and energy, on contact tracing. The opportunity cost of wasted efforts, which increases as the distance from which the contact client lives from the clinic increases, depletes HCW morale. The resulting acceleration of burnout, which is already fueled by busy workloads and the inherent uncertainty of day-to-day ICT work, further impairs HCW capacity to effectively engage in quality counseling that elicits adequate information from index clients. This interconnectedness suggests that efforts to mitigate barriers at any step of the assisted ICT process may have the potential to ripple across the whole process.

Participants’ descriptions of client confidentiality and privacy concerns, as well as fear of consequences of disclosure, align with previous studies that emphasize stigma as a key barrier to assisted ICT [ 15 , 18 , 19 , 20 , 30 , 31 ] and the overall HIV testing and treatment cascade [ 41 ]. Our findings suggest that anticipated stigma, or the fear of discrimination upon disclosure [ 42 ], drives several key barriers to feasibility of assisted ICT implementation. Previous studies also highlight the key role of HCWs in mitigating barriers related to anticipated stigma; noting the key role of HCW ICT knowledge, interpersonal skills, and non-stigmatizing attitudes/behaviors in securing informed consent from clients for ICT, tailoring the referral strategy to minimize risk to client confidentiality and safety, building trust and rapport with the client, and eliciting accurate contact information from index clients to facilitate contact tracing [ 18 , 19 , 20 , 30 ].

Our findings also reflect previous evidence of logistical challenges related to limited time, space, and resources that can present barriers to feasibility for HCWs [ 18 , 19 , 20 , 30 , 31 ]. Participants in the current study described these logistical challenges as perpetuating HCW burnout, making it harder for them to engage in effective counseling. Cumulative evidence of barriers across different settings (further validated by this study) suggests that assisted ICT implementation may pose greater burden on HCWs than previously thought [ 7 ]. However, our findings also suggest that strategic investment in targeted implementation strategies has the potential to help overcome these feasibility barriers.

In our own work, these findings affirmed the rationale for and informed the development of the blended learning implementation package tested in our trial [ 40 , 43 ]. Findings indicated the need for evidence-based training and support to promote HCW capacity to foster facilitating characteristics. Participants discussed the value of "refresher" opportunities in building knowledge, as well as the value of learning from other’s experiences. The blended learning implementation package balances both needs by providing time for HCWs to master ICT knowledge and skills with a combination of asynchronous, digitally delivered content (which allows for continuous review as a "refresher") and in-person sessions (which allow for sharing, practicing, and feedback). Our findings also highlight the value of flexible referral methods that align with the client’s needs, so our training content includes a detailed description of each referral method process. Further, our training content emphasizes client-centered, non-judgmental counseling as our findings add to cumulative evidence of stigma as a key barrier to assisted ICT implementation [ 41 ].

In addition, participants frequently mentioned informal workarounds currently in use to mitigate barriers or offered up ideas for potential solutions to try. Our blended learning implementation package streamlines these problem-solving processes by offering monthly continuous quality improvement sessions at each facility in our enhanced arm. These sessions allow for structured time to discuss identified barriers, share ideas to mitigate barriers, and develop solutions for sustained process improvement tailored to their specific setting. Initial focus areas for continuous quality improvement discussions include use of space, staffing, allocation of airtime and vehicles, and documentation, which were identified as barriers to feasibility in the current study.

Our study provides a uniquely in-depth examination of HCWs’ experiences implementing assisted ICT, exploring how barriers can manifest and interact with each other at each step of the process to hinder successful implementation. Further, our study has a highly actionable focus on informing development of implementation strategies to support HCWs implementing assisted ICT. Our study also has limitations. Firstly, while our sole focus on HCWs allowed for deeper exploration of assisted ICT from the perspective of those actually implementing it on the ground, this meant that our analysis did not include perspectives of index or contact clients. In addition, we did not conduct sub-group analyses as interpretation of results would be limited by our small sample size.

Assisted ICT has been widely recognized as an evidence-based intervention with high promise to increase PLHIV status awareness [ 5 , 6 , 7 , 10 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 , 26 , 27 , 28 , 29 ], which is important as countries in eastern and southern Africa strive to reach global UNAIDS targets. Study findings support cumulative evidence that HCWs face a variety of feasibility barriers to assisted ICT implementation in the region; further, the study’s uniquely in-depth focus on the experiences of those doing the “assisting” enhances understanding of how these barriers manifest and informs the development of implementation strategies to mitigate these barriers. Maximizing assisted ICT’s potential to increase HIV testing requires equipping HCWs with effective training and support to address and overcome the many feasibility barriers they face in implementation. Findings demonstrate the need for, as well as inform the development of, implementation strategies to mitigate barriers and promote facilitators to feasibility of assisted ICT.

Availability of data and materials

Qualitative data on which this analysis is based, as well as data collection materials and codebooks, are available from the last author upon reasonable request. The interview guide is included as an additional file.

Abbreviations

Acquired Immunodeficiency Syndrome

Antiretroviral Therapy

Health Care Worker

Human Immunodeficiency Virus

HIV Testing Services

Index Case Testing

In-Depth Interview

Intimate Partner Violence

Institutional Review Board

President’s Emergency Plan for HIV/AIDS Relief

People Living With HIV

Joint United Nations Programme on HIV/AIDS

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Acknowledgements

We are grateful to the Malawian health care workers who shared their experiences through in-depth interviews, as well as to the study team members in Malawi and the United States for their contributions.

Research reported in this publication was funded by the National Institutes of Health (R01 MH124526) with support from the University of North Carolina at Chapel Hill Center for AIDS Research (P30 AI50410) and the Fogarty International Center of the National Institutes of Health (D43 TW010060 and R01 MH115793-04). The funders had no role in trial design, data collection and analysis, decision to publish or preparation of the manuscript.

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Caroline J. Meek

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Caroline J. Meek, Milenka Jean-Baptiste, Jiayu Wang, Clare Barrington, Vivian F. Go & Nora E. Rosenberg

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Tiwonge E. Mbeya Munkhondya

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Mtisunge Mphande, Tapiwa A. Tembo, Mike Chitani, Dhrutika Vansia, Caroline Kumbuyo, Katherine R. Simon & Maria H. Kim

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TAT, KRS, SER, MHK, VFG, and NER contributed to overall study conceptualization, with CJM, CB, and NER leading conceptualization of the analysis presented in this study. Material preparation and data collection were performed by TEMM, MM, TAT, MC, and CK. Analysis was led by CJM with support from MJB and DV. The first draft of the manuscript was written by CJM with consultation from NER, TEMM, MM, TAT, MJB, and DV. JW provided quantitative analysis support for participant characteristics. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Meek, C.J., Munkhondya, T.E.M., Mphande, M. et al. Examining the feasibility of assisted index case testing for HIV case-finding: a qualitative analysis of barriers and facilitators to implementation in Malawi. BMC Health Serv Res 24 , 606 (2024). https://doi.org/10.1186/s12913-024-10988-z

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