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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 types of coding exist. Let’s start with the two main approaches,  deductive  and  inductive   coding.

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 (for free)

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

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28 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

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Coding in qualitative research

C oding is a central part of qualitative data analysis, yet I often find that doctoral students particularly struggle with knowing how to code their qualitative data. In today’s post, I want to share some foundational information for coding to provide a sense of the role of coding as a central function of qualitative data analysis.

In qualitative research, a researcher begins to understand and make sense of the data through coding. Thus, coding plays a critical role in the data analysis process (Miles, Huberman, & Saldana, 2014).

A code is an identified or highlighted section of text, frequently a word or short quotation, that helps illustrate the topic of the study. Saldana (2015) defines a code as “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” (p. 3).

Coding breaks down the data to the smallest unit, or idea, that can stand alone. Coding data “leads you from the data to the idea, and from the idea to all the data pertaining to that idea” (Richards & Morse, 2007, p. 137). Coding effectively indexes the data, and serves as a tool to help researchers build connections between different pieces of data.

Coding breaks down data into easy-to-digest pieces (the codes themselves), which can then be organized and reorganized into patterns and ideas that answer the research questions (Bernard, Wutich, & Ryan, 2016; Grbich, 2007).

Codes may be used only one time, or perhaps they are used numerous times through the course of data analysis; other times, you may assign one or more codes to a statement from an interview, for example, to identify the significance of the passage of data (Miles et al., 2014).

Continuous refining and adjusting of the coding scheme is inherent to coding and data analysis. This refinement occurs through the expansion of ideas, the analysis of additional data, and the search for themes and patterns. While coding may seem like a precursor to actual data analysis, in reality coding represents a crucial step in the analytic process.

As a novice qualitative researcher, students might wonder what data should be coded and how the data should be identified. Richards and Morse (2007) joke, “If it moves, code it” (p. 146).

First, you should code all of the data including all transcripts, documents, observations, notes, memos, visual evidence, and anything else gathered during data collection. Second, you should code what participants are doing or did in the past, including activities, strategies, and assumptions (Emerson, Fretz, & Shaw, 1995).

Ideas that directly relate to the literature, framework, and research questions should be coded, in addition to any ideas that seem potentially important or related to the overall goals of the study.

Also, coding ideas that were expected at the beginning of the dissertation as well as those that were unexpected can prove useful (Creswell, 2007). The number of codes that students should have after multiple rounds of data analysis varies, but basic guidelines can help determine an approximate figure.

For example, Lichtman (2006) suggests that education related qualitative research studies should have between 80 and 100 codes that then get distilled into five to seven major categories or themes.

Undoubtedly, while some students end up with more or fewer codes and major themes, aiming for 80-100 emerging themes is a useful target at the start of data analysis.

Saldana (2015), in defining approaches to qualitative coding, identifies two types: “lumping” and “splitting.”

“Lumpers” begin analysis with a single, overarching code for an entire paragraph or passage of text, essentially “lumping” the data together to fit more data into fewer, broader codes.

In contrast, “splitters” initially break a given passage into component parts using six or eight more specific codes rather than one, “splitting” the passage up.

We often find that doctoral students commonly fall into one of these two approaches, but we suggest that using a bit of both approaches is likely a productive avenue.

After a document or two has been coded, take a step back and think about how you are engaging in the process of coding. Determine if you have adopted the lumper or the splitter approach, and then work purposefully to incorporate the other approach in subsequent coding efforts.

By bridging the divide between lumping and splitting, you will have a more comprehensive set of codes that will better enable the next round of data analysis.

The most common types of codes are identified by their ancient Greek descriptors: etic  and emic .

Etic codes come from the perspective of the researcher and the framework, literature, and research questions of the study.

In contrast, emic coding focuses on the participant’s perspective and are not always bound to the aims or goals of the study.

Obviously, coding of both types provides valuable insights.

Either approach can be a good place to start with data analysis; we recommend novice qualitative researchers and doctoral students begin with etic coding before moving on to emic.

The fact that etic codes are derived from the research study provides more structure for initial coding than the comparatively unstructured and open-ended emic approach.

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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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Qualitative Data Analysis: Coding

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

Coding Qualitative Data

Planning your coding strategy.

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

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

What will you code? 

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

Where will your codes come from? 

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

How will you apply your codes to your data? 

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

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

What will be coded? 

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

How will you explain the coding process? 

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

How will you report the results of your coding process? 

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

How will you code collaboratively?

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

Coding Features in QDA Software Programs

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

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

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

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

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

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

Useful Resources on Coding

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

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

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

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Coding Qualitative Data: How to Code Qualitative Research

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

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

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

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

Keep reading to learn:

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

What is coding in qualitative research?

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

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

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

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

What is qualitative data analysis?

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

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

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

Methods of qualitative data analysis

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

Automatic coding software

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

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

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

Why is it important to code qualitative data?

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

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

Automated vs. Manual coding of qualitative data

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

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

Thematic analysis software is autonomous, which means…

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

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

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

What is thematic coding?

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

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

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

How to manually code qualitative data

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

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

Let’s break it down a little further…

Deductive coding vs. inductive coding

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

What is Deductive Coding?

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

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

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

What is Inductive Coding?

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

Here’s how inductive coding works:

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

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

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

Categorize your codes with coding frames

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

Flat Coding Frame

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

Hierarchical Coding Frame

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

In this example:

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

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

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

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

Tips for coding qualitative data

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

Use a codebook to keep track of your codes

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

Make sure to track:

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

How to create high-quality codes - 4 tips

1. cover as many survey responses as possible..

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

2. Avoid commonalities.

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

3. Capture the positive and the negative.

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

4. Reduce data — to a point.

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

Group responses based on themes, not wording

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

• Looked like a dump
• Could eat off the floor

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

Make accuracy a priority

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

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

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

Conclusion: 6 main takeaways for coding qualitative data

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

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

Some more detail in our downloadable guide

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

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

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

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

Happy coding!

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Qualitative Research Journal

ISSN : 1443-9883

Article publication date: 8 May 2019

Issue publication date: 4 June 2019

Qualitative research has gained in importance in the social sciences. General knowledge about qualitative data analysis, how to code qualitative data and decisions concerning related research design in the analytical process are all important for novice researchers. The purpose of this paper is to offer researchers who are new to qualitative research a thorough yet practical introduction to the vocabulary and craft of coding.

Design/methodology/approach

Having pooled, their experience in coding qualitative material and teaching students how to code, in this paper, the authors synthesize the extensive literature on coding in the form of a hands-on review.

The aim of this paper is to provide a thorough yet practical presentation of the vocabulary and craft of coding. The authors, thus, discuss the central choices that have to be made before, during and after coding, providing support for novices in practicing careful and enlightening coding work, and joining in the debate on practices and quality in qualitative research.

Originality/value

While much material on coding exists, it tends to be either too comprehensive or too superficial to be practically useful for the novice researcher. This paper, thus, focusses on the central decisions that need to be made when engaging in qualitative data coding in order to help researchers new to qualitative research engage in thorough coding in order to enhance the quality of their analyses and findings, as well as improve quantitative researchers’ understanding of qualitative coding.

• Transparency
• Qualitative data
• Qualitative data analysis

Skjott Linneberg, M. and Korsgaard, S. (2019), "Coding qualitative data: a synthesis guiding the novice", Qualitative Research Journal , Vol. 19 No. 3, pp. 259-270. https://doi.org/10.1108/QRJ-12-2018-0012

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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Chapter 18. Data Analysis and Coding

Introduction.

Piled before you lie hundreds of pages of fieldnotes you have taken, observations you’ve made while volunteering at city hall. You also have transcripts of interviews you have conducted with the mayor and city council members. What do you do with all this data? How can you use it to answer your original research question (e.g., “How do political polarization and party membership affect local politics?”)? Before you can make sense of your data, you will have to organize and simplify it in a way that allows you to access it more deeply and thoroughly. We call this process coding . [1] Coding is the iterative process of assigning meaning to the data you have collected in order to both simplify and identify patterns. This chapter introduces you to the process of qualitative data analysis and the basic concept of coding, while the following chapter (chapter 19) will take you further into the various kinds of codes and how to use them effectively.

To those who have not yet conducted a qualitative study, the sheer amount of collected data will be a surprise. Qualitative data can be absolutely overwhelming—it may mean hundreds if not thousands of pages of interview transcripts, or fieldnotes, or retrieved documents. How do you make sense of it? Students often want very clear guidelines here, and although I try to accommodate them as much as possible, in the end, analyzing qualitative data is a bit more of an art than a science: “The process of bringing order, structure, and interpretation to a mass of collected data is messy, ambiguous, time-consuming, creative, and fascinating. It does not proceed in a linear fashion: it is not neat. At times, the researcher may feel like an eccentric and tormented artist; not to worry, this is normal” ( Marshall and Rossman 2016:214 ).

To complicate matters further, each approach (e.g., Grounded Theory, deep ethnography, phenomenology) has its own language and bag of tricks (techniques) when it comes to analysis. Grounded Theory, for example, uses in vivo coding to generate new theoretical insights that emerge from a rigorous but open approach to data analysis. Ethnographers, in contrast, are more focused on creating a rich description of the practices, behaviors, and beliefs that operate in a particular field. They are less interested in generating theory and more interested in getting the picture right, valuing verisimilitude in the presentation. And then there are some researchers who seek to account for the qualitative data using almost quantitative methods of analysis, perhaps counting and comparing the uses of certain narrative frames in media accounts of a phenomenon. Qualitative content analysis (QCA) often includes elements of counting (see chapter 17). For these researchers, having very clear hypotheses and clearly defined “variables” before beginning analysis is standard practice, whereas the same would be expressly forbidden by those researchers, like grounded theorists, taking a more emergent approach.

All that said, there are some helpful techniques to get you started, and these will be presented in this and the following chapter. As you become more of an expert yourself, you may want to read more deeply about the tradition that speaks to your research. But know that there are many excellent qualitative researchers that use what works for any given study, who take what they can from each tradition. Most of us find this permissible (but watch out for the methodological purists that exist among us).

Qualitative Data Analysis as a Long Process!

Although most of this and the following chapter will focus on coding, it is important to understand that coding is just one (very important) aspect of the long data-analysis process. We can consider seven phases of data analysis, each of which is important for moving your voluminous data into “findings” that can be reported to others. The first phase involves data organization. This might mean creating a special password-protected Dropbox folder for storing your digital files. It might mean acquiring computer-assisted qualitative data-analysis software ( CAQDAS ) and uploading all transcripts, fieldnotes, and digital files to its storage repository for eventual coding and analysis. Finding a helpful way to store your material can take a lot of time, and you need to be smart about this from the very beginning. Losing data because of poor filing systems or mislabeling is something you want to avoid. You will also want to ensure that you have procedures in place to protect the confidentiality of your interviewees and informants. Filing signed consent forms (with names) separately from transcripts and linking them through an ID number or other code that only you have access to (and store safely) are important.

Once you have all of your material safely and conveniently stored, you will need to immerse yourself in the data. The second phase consists of reading and rereading or viewing and reviewing all of your data. As you do this, you can begin to identify themes or patterns in the data, perhaps writing short memos to yourself about what you are seeing. You are not committing to anything in this third phase but rather keeping your eyes and mind open to what you see. In an actual study, you may very well still be “in the field” or collecting interviews as you do this, and what you see might push you toward either concluding your data collection or expanding so that you can follow a particular group or factor that is emerging as important. For example, you may have interviewed twelve international college students about how they are adjusting to life in the US but realized as you read your transcripts that important gender differences may exist and you have only interviewed two women (and ten men). So you go back out and make sure you have enough female respondents to check your impression that gender matters here. The seven phases do not proceed entirely linearly! It is best to think of them as recursive; conceptually, there is a path to follow, but it meanders and flows.

Coding is the activity of the fourth phase . The second part of this chapter and all of chapter 19 will focus on coding in greater detail. For now, know that coding is the primary tool for analyzing qualitative data and that its purpose is to both simplify and highlight the important elements buried in mounds of data. Coding is a rigorous and systematic process of identifying meaning, patterns, and relationships. It is a more formal extension of what you, as a conscious human being, are trained to do every day when confronting new material and experiences. The “trick” or skill is to learn how to take what you do naturally and semiconsciously in your mind and put it down on paper so it can be documented and verified and tested and refined.

At the conclusion of the coding phase, your material will be searchable, intelligible, and ready for deeper analysis. You can begin to offer interpretations based on all the work you have done so far. This fifth phase might require you to write analytic memos, beginning with short (perhaps a paragraph or two) interpretations of various aspects of the data. You might then attempt stitching together both reflective and analytical memos into longer (up to five pages) general interpretations or theories about the relationships, activities, patterns you have noted as salient.

As you do this, you may be rereading the data, or parts of the data, and reviewing your codes. It’s possible you get to this phase and decide you need to go back to the beginning. Maybe your entire research question or focus has shifted based on what you are now thinking is important. Again, the process is recursive , not linear. The sixth phase requires you to check the interpretations you have generated. Are you really seeing this relationship, or are you ignoring something important you forgot to code? As we don’t have statistical tests to check the validity of our findings as quantitative researchers do, we need to incorporate self-checks on our interpretations. Ask yourself what evidence would exist to counter your interpretation and then actively look for that evidence. Later on, if someone asks you how you know you are correct in believing your interpretation, you will be able to explain what you did to verify this. Guard yourself against accusations of “ cherry-picking ,” selecting only the data that supports your preexisting notion or expectation about what you will find. [2]

The seventh and final phase involves writing up the results of the study. Qualitative results can be written in a variety of ways for various audiences (see chapter 20). Due to the particularities of qualitative research, findings do not exist independently of their being written down. This is different for quantitative research or experimental research, where completed analyses can somewhat speak for themselves. A box of collected qualitative data remains a box of collected qualitative data without its written interpretation. Qualitative research is often evaluated on the strength of its presentation. Some traditions of qualitative inquiry, such as deep ethnography, depend on written thick descriptions, without which the research is wholly incomplete, even nonexistent. All of that practice journaling and writing memos (reflective and analytical) help develop writing skills integral to the presentation of the findings.

Remember that these are seven conceptual phases that operate in roughly this order but with a lot of meandering and recursivity throughout the process. This is very different from quantitative data analysis, which is conducted fairly linearly and processually (first you state a falsifiable research question with hypotheses, then you collect your data or acquire your data set, then you analyze the data, etc.). Things are a bit messier when conducting qualitative research. Embrace the chaos and confusion, and sort your way through the maze. Budget a lot of time for this process. Your research question might change in the middle of data collection. Don’t worry about that. The key to being nimble and flexible in qualitative research is to start thinking and continue thinking about your data, even as it is being collected. All seven phases can be started before all the data has been gathered. Data collection does not always precede data analysis. In some ways, “qualitative data collection is qualitative data analysis.… By integrating data collection and data analysis, instead of breaking them up into two distinct steps, we both enrich our insights and stave off anxiety. We all know the anxiety that builds when we put something off—the longer we put it off, the more anxious we get. If we treat data collection as this mass of work we must do before we can get started on the even bigger mass of work that is analysis, we set ourselves up for massive anxiety” ( Rubin 2021:182–183 ; emphasis added).

The Coding Stage

A code is “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” ( Saldaña 2014:5 ). Codes can be applied to particular sections of or entire transcripts, documents, or even videos. For example, one might code a video taken of a preschooler trying to solve a puzzle as “puzzle,” or one could take the transcript of that video and highlight particular sections or portions as “arranging puzzle pieces” (a descriptive code) or “frustration” (a summative emotion-based code). If the preschooler happily shouts out, “I see it!” you can denote the code “I see it!” (this is an example of an in vivo, participant-created code). As one can see from even this short example, there are many different kinds of codes and many different strategies and techniques for coding, more of which will be discussed in detail in chapter 19. The point to remember is that coding is a rigorous systematic process—to some extent, you are always coding whenever you look at a person or try to make sense of a situation or event, but you rarely do this consciously. Coding is the process of naming what you are seeing and how you are simplifying the data so that you can make sense of it in a way that is consistent with your study and in a way that others can understand and follow and replicate. Another way of saying this is that a code is “a researcher-generated interpretation that symbolizes or translates data” ( Vogt et al. 2014:13 ).

As with qualitative data analysis generally, coding is often done recursively, meaning that you do not merely take one pass through the data to create your codes. Saldaña ( 2014 ) differentiates first-cycle coding from second-cycle coding. The goal of first-cycle coding is to “tag” or identify what emerges as important codes. Note that I said emerges—you don’t always know from the beginning what will be an important aspect of the study or not, so the coding process is really the place for you to begin making the kinds of notes necessary for future analyses. In second-cycle coding, you will want to be much more focused—no longer gathering wholly new codes but synthesizing what you have into metacodes.

You might also conceive of the coding process in four parts (figure 18.1). First, identify a representative or diverse sample set of interview transcripts (or fieldnotes or other documents). This is the group you are going to use to get a sense of what might be emerging. In my own study of career obstacles to success among first-generation and working-class persons in sociology, I might select one interview from each career stage: a graduate student, a junior faculty member, a senior faculty member.

Second, code everything (“ open coding ”). See what emerges, and don’t limit yourself in any way. You will end up with a ton of codes, many more than you will end up with, but this is an excellent way to not foreclose an interesting finding too early in the analysis. Note the importance of starting with a sample of your collected data, because otherwise, open coding all your data is, frankly, impossible and counterproductive. You will just get stuck in the weeds.

Third, pare down your coding list. Where you may have begun with fifty (or more!) codes, you probably want no more than twenty remaining. Go back through the weeds and pull out everything that does not have the potential to bloom into a nicely shaped garden. Note that you should do this before tackling all of your data . Sometimes, however, you might need to rethink the sample you chose. Let’s say that the graduate student interview brought up some interesting gender issues that were pertinent to female-identifying sociologists, but both the junior and the senior faculty members identified as male. In that case, I might read through and open code at least one other interview transcript, perhaps a female-identifying senior faculty member, before paring down my list of codes.

This is also the time to create a codebook if you are using one, a master guide to the codes you are using, including examples (see Sample Codebooks 1 and 2 ). A codebook is simply a document that lists and describes the codes you are using. It is easy to forget what you meant the first time you penciled a coded notation next to a passage, so the codebook allows you to be clear and consistent with the use of your codes. There is not one correct way to create a codebook, but generally speaking, the codebook should include (1) the code (either name or identification number or both), (2) a description of what the code signifies and when and where it should be applied, and (3) an example of the code to help clarify (2). Listing all the codes down somewhere also allows you to organize and reorganize them, which can be part of the analytical process. It is possible that your twenty remaining codes can be neatly organized into five to seven master “themes.” Codebooks can and should develop as you recursively read through and code your collected material. [3]

Fourth, using the pared-down list of codes (or codebook), read through and code all the data. I know many qualitative researchers who work without a codebook, but it is still a good practice, especially for beginners. At the very least, read through your list of codes before you begin this “ closed coding ” step so that you can minimize the chance of missing a passage or section that needs to be coded. The final step is…to do it all again. Or, at least, do closed coding (step four) again. All of this takes a great deal of time, and you should plan accordingly.

Researcher Note

People often say that qualitative research takes a lot of time. Some say this because qualitative researchers often collect their own data. This part can be time consuming, but to me, it’s the analytical process that takes the most time. I usually read every transcript twice before starting to code, then it usually takes me six rounds of coding until I’m satisfied I’ve thoroughly coded everything. Even after the coding, it usually takes me a year to figure out how to put the analysis together into a coherent argument and to figure out what language to use. Just deciding what name to use for a particular group or idea can take months. Understanding this going in can be helpful so that you know to be patient with yourself.

—Jessi Streib, author of The Power of the Past and Privilege Lost 

Note that there is no magic in any of this, nor is there any single “right” way to code or any “correct” codes. What you see in the data will be prompted by your position as a researcher and your scholarly interests. Where the above codes on a preschooler solving a puzzle emerged from my own interest in puzzle solving, another researcher might focus on something wholly different. A scholar of linguistics, for example, may focus instead on the verbalizations made by the child during the discovery process, perhaps even noting particular vocalizations (incidence of grrrs and gritting of the teeth, for example). Your recording of the codes you used is the important part, as it allows other researchers to assess the reliability and validity of your analyses based on those codes. Chapter 19 will provide more details about the kinds of codes you might develop.

Saldaña ( 2014 ) lists seven “necessary personal attributes” for successful coding. To paraphrase, they are the following:

• Having (or practicing) good organizational skills
• Perseverance
• The ability and willingness to deal with ambiguity
• Flexibility
• Creativity, broadly understood, which includes “the ability to think visually, to think symbolically, to think in metaphors, and to think of as many ways as possible to approach a problem” (20)
• Commitment to being rigorously ethical
• Having an extensive vocabulary [4]

Writing Analytic Memos during/after Coding

Coding the data you have collected is only one aspect of analyzing it. Too many beginners have coded their data and then wondered what to do next. Coding is meant to help organize your data so that you can see it more clearly, but it is not itself an analysis. Thinking about the data, reviewing the coded data, and bringing in the previous literature (here is where you use your literature review and theory) to help make sense of what you have collected are all important aspects of data analysis. Analytic memos are notes you write to yourself about the data. They can be short (a single page or even a paragraph) or long (several pages). These memos can themselves be the subject of subsequent analytic memoing as part of the recursive process that is qualitative data analysis.

Short analytic memos are written about impressions you have about the data, what is emerging, and what might be of interest later on. You can write a short memo about a particular code, for example, and why this code seems important and where it might connect to previous literature. For example, I might write a paragraph about a “cultural capital” code that I use whenever a working-class sociologist says anything about “not fitting in” with their peers (e.g., not having the right accent or hairstyle or private school background). I could then write a little bit about Bourdieu, who originated the notion of cultural capital, and try to make some connections between his definition and how I am applying it here. I can also use the memo to raise questions or doubts I have about what I am seeing (e.g., Maybe the type of school belongs somewhere else? Is this really the right code?). Later on, I can incorporate some of this writing into the theory section of my final paper or article. Here are some types of things that might form the basis of a short memo: something you want to remember, something you noticed that was new or different, a reaction you had, a suspicion or hunch that you are developing, a pattern you are noticing, any inferences you are starting to draw. Rubin ( 2021 ) advises, “Always include some quotation or excerpt from your dataset…that set you off on this idea. It’s happened to me so many times—I’ll have a really strong reaction to a piece of data, write down some insight without the original quotation or context, and then [later] have no idea what I was talking about and have no way of recreating my insight because I can’t remember what piece of data made me think this way” ( 203 ).

All CAQDAS programs include spaces for writing, generating, and storing memos. You can link a memo to a particular transcript, for example. But you can just as easily keep a notebook at hand in which you write notes to yourself, if you prefer the more tactile approach. Drawing pictures that illustrate themes and patterns you are beginning to see also works. The point is to write early and write often, as these memos are the building blocks of your eventual final product (chapter 20).

In the next chapter (chapter 19), we will go a little deeper into codes and how to use them to identify patterns and themes in your data. This chapter has given you an idea of the process of data analysis, but there is much yet to learn about the elements of that process!

Qualitative Data-Analysis Samples

The following three passages are examples of how qualitative researchers describe their data-analysis practices. The first, by Harvey, is a useful example of how data analysis can shift the original research questions. The second example, by Thai, shows multiple stages of coding and how these stages build upward to conceptual themes and theorization. The third example, by Lamont, shows a masterful use of a variety of techniques to generate theory.

Example 1: “Look Someone in the Eye” by Peter Francis Harvey ( 2022 )

I entered the field intending to study gender socialization. However, through the iterative process of writing fieldnotes, rereading them, conducting further research, and writing extensive analytic memos, my focus shifted. Abductive analysis encourages the search for unexpected findings in light of existing literature. In my early data collection, fieldnotes, and memoing, classed comportment was unmistakably prominent in both schools. I was surprised by how pervasive this bodily socialization proved to be and further surprised by the discrepancies between the two schools.…I returned to the literature to compare my empirical findings.…To further clarify patterns within my data and to aid the search for disconfirming evidence, I constructed data matrices (Miles, Huberman, and Saldaña 2013). While rereading my fieldnotes, I used ATLAS.ti to code and recode key sections (Miles et al. 2013), punctuating this process with additional analytic memos. ( 2022:1420 )

Example 2:” Policing and Symbolic Control” by Mai Thai ( 2022 )

Conventional to qualitative research, my analyses iterated between theory development and testing. Analytical memos were written throughout the data collection, and my analyses using MAXQDA software helped me develop, confirm, and challenge specific themes.…My early coding scheme which included descriptive codes (e.g., uniform inspection, college trips) and verbatim codes of the common terms used by field site participants (e.g., “never quit,” “ghetto”) led me to conceptualize valorization. Later analyses developed into thematic codes (e.g., good citizens, criminality) and process codes (e.g., valorization, criminalization), which helped refine my arguments. ( 2022:1191–1192 )

Example 3: The Dignity of Working Men by Michèle Lamont ( 2000 )

To analyze the interviews, I summarized them in a 13-page document including socio-demographic information as well as information on the boundary work of the interviewees. To facilitate comparisons, I noted some of the respondents’ answers on grids and summarized these on matrix displays using techniques suggested by Miles and Huberman for standardizing and processing qualitative data. Interviews were also analyzed one by one, with a focus on the criteria that each respondent mobilized for the evaluation of status. Moreover, I located each interviewee on several five-point scales pertaining to the most significant dimensions they used to evaluate status. I also compared individual interviewees with respondents who were similar to and different from them, both within and across samples. Finally, I classified all the transcripts thematically to perform a systematic analysis of all the important themes that appear in the interviews, approaching the latter as data against which theoretical questions can be explored. ( 2000:256–257 )

Sample Codebook 1

This is an abridged version of the codebook used to analyze qualitative responses to a question about how class affects careers in sociology. Note the use of numbers to organize the flow, supplemented by highlighting techniques (e.g., bolding) and subcoding numbers.

01. CAPS: Any reference to “capitals” in the response, even if the specific words are not used

01.1: cultural capital 01.2: social capital 01.3: economic capital

(can be mixed: “0.12”= both cultural and asocial capital; “0.23”= both social and economic)

01. CAPS: a reference to “capitals” in which the specific words are used [ bold : thus, 01.23 means that both social capital and economic capital were mentioned specifically

02. DEBT: discussion of debt

02.1: mentions personal issues around debt 02.2: discusses debt but in the abstract only (e.g., “people with debt have to worry”)

03. FirstP: how the response is positioned

03.1: neutral or abstract response 03.2: discusses self (“I”) 03.3: discusses others (“they”)

Sample Coded Passage:

* Question: What other codes jump out to you here? Shouldn’t there be a code for feelings of loneliness or alienation? What about an emotions code ?

Sample Codebook 2

This is an example that uses "word" categories only, with descriptions and examples for each code

Further Readings

Elliott, Victoria. 2018. “Thinking about the Coding Process in Qualitative Analysis.” Qualitative Report 23(11):2850–2861. Address common questions those new to coding ask, including the use of “counting” and how to shore up reliability.

Friese, Susanne. 2019. Qualitative Data Analysis with ATLAS.ti. 3rd ed. A good guide to ATLAS.ti, arguably the most used CAQDAS program. Organized around a series of “skills training” to get you up to speed.

Jackson, Kristi, and Pat Bazeley. 2019. Qualitative Data Analysis with NVIVO . 3rd ed. Thousand Oaks, CA: SAGE. If you want to use the CAQDAS program NVivo, this is a good affordable guide to doing so. Includes copious examples, figures, and graphic displays.

LeCompte, Margaret D. 2000. “Analyzing Qualitative Data.” Theory into Practice 39(3):146–154. A very practical and readable guide to the entire coding process, with particular applicability to educational program evaluation/policy analysis.

Miles, Matthew B., and A. Michael Huberman. 1994. Qualitative Data Analysis: An Expanded Sourcebook . 2nd ed. Thousand Oaks, CA: SAGE. A classic reference on coding. May now be superseded by Miles, Huberman, and Saldaña (2019).

Miles, Matthew B., A. Michael Huberman, and Johnny Saldaña. 2019. Qualitative Data Analysis: A Methods Sourcebook . 4th ed. Thousand Oaks, CA; SAGE. A practical methods sourcebook for all qualitative researchers at all levels using visual displays and examples. Highly recommended.

Saldaña, Johnny. 2014. The Coding Manual for Qualitative Researchers . 2nd ed. Thousand Oaks, CA: SAGE. The most complete and comprehensive compendium of coding techniques out there. Essential reference.

Silver, Christina. 2014. Using Software in Qualitative Research: A Step-by-Step Guide. 2nd ed. Thousand Oaks, CA; SAGE. If you are unsure which CAQDAS program you are interested in using or want to compare the features and usages of each, this guidebook is quite helpful.

Vogt, W. Paul, Elaine R. Vogt, Diane C. Gardner, and Lynne M. Haeffele2014. Selecting the Right Analyses for Your Data: Quantitative, Qualitative, and Mixed Methods . New York: The Guilford Press. User-friendly reference guide to all forms of analysis; may be particularly helpful for those engaged in mixed-methods research.

• When you have collected content (historical, media, archival) that interests you because of its communicative aspect, content analysis (chapter 17) is appropriate. Whereas content analysis is both a research method and a tool of analysis, coding is a tool of analysis that can be used for all kinds of data to address any number of questions. Content analysis itself includes coding. ↵
• Scientific research, whether quantitative or qualitative, demands we keep an open mind as we conduct our research, that we are “neutral” regarding what is actually there to find. Students who are trained in non-research-based disciplines such as the arts or philosophy or who are (admirably) focused on pursuing social justice can too easily fall into the trap of thinking their job is to “demonstrate” something through the data. That is not the job of a researcher. The job of a researcher is to present (and interpret) findings—things “out there” (even if inside other people’s hearts and minds). One helpful suggestion: when formulating your research question, if you already know the answer (or think you do), scrap that research. Ask a question to which you do not yet know the answer. ↵
• Codebooks are particularly useful for collaborative research so that codes are applied and interpreted similarly. If you are working with a team of researchers, you will want to take extra care that your codebooks remain in synch and that any refinements or developments are shared with fellow coders. You will also want to conduct an “intercoder reliability” check, testing whether the codes you have developed are clearly identifiable so that multiple coders are using them similarly. Messy, unclear codes that can be interpreted differently by different coders will make it much more difficult to identify patterns across the data. ↵
• Note that this is important for creating/denoting new codes. The vocabulary does not need to be in English or any particular language. You can use whatever words or phrases capture what it is you are seeing in the data. ↵

A first-cycle coding process in which gerunds are used to identify conceptual actions, often for the purpose of tracing change and development over time.  Widely used in the Grounded Theory approach.

A first-cycle coding process in which terms or phrases used by the participants become the code applied to a particular passage.  It is also known as “verbatim coding,” “indigenous coding,” “natural coding,” “emic coding,” and “inductive coding,” depending on the tradition of inquiry of the researcher.  It is common in Grounded Theory approaches and has even given its name to one of the primary CAQDAS programs (“NVivo”).

Computer-assisted qualitative data-analysis software.  These are software packages that can serve as a repository for qualitative data and that enable coding, memoing, and other tools of data analysis.  See chapter 17 for particular recommendations.

The purposeful selection of some data to prove a preexisting expectation or desired point of the researcher where other data exists that would contradict the interpretation offered.  Note that it is not cherry picking to select a quote that typifies the main finding of a study, although it would be cherry picking to select a quote that is atypical of a body of interviews and then present it as if it is typical.

A preliminary stage of coding in which the researcher notes particular aspects of interest in the data set and begins creating codes.  Later stages of coding refine these preliminary codes.  Note: in Grounded Theory , open coding has a more specific meaning and is often called initial coding : data are broken down into substantive codes in a line-by-line manner, and incidents are compared with one another for similarities and differences until the core category is found.  See also closed coding .

A set of codes, definitions, and examples used as a guide to help analyze interview data.  Codebooks are particularly helpful and necessary when research analysis is shared among members of a research team, as codebooks allow for standardization of shared meanings and code attributions.

The final stages of coding after the refinement of codes has created a complete list or codebook in which all the data is coded using this refined list or codebook.  Compare to open coding .

A first-cycle coding process in which emotions and emotionally salient passages are tagged.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Eight ways to get a grip on intercoder reliability using qualitative-based measures

Huit façons de maîtriser la fidélité intercodeur en utilisant des mesures qualitatives, nicholas cofie.

1 Faculty of Health Sciences, Queen's University, Ontario, Canada

Heather Braund

Nancy dalgarno.

The use of quantitative intercoder reliability measures in the analysis of qualitative research data has often generated acrimonious debates among researchers who view quantitative and qualitative research methodologies as incompatible due to their unique ontological and epistemological traditions. While these measures are invaluable in many contexts, critics point out that the use of such measures in qualitative analysis represents an attempt to import standards derived for positivist research. Guided by extant research and our experience in qualitative research, we argue that it is possible to develop a qualitative-based measure of intercoder reliability that is compatible with the interpretivist epistemological paradigm of qualitative research. We present eight qualitative research process-based guidelines for evaluating and reporting intercoder reliability in qualitative research and anticipate that these recommendations will particularly guide beginning researchers in the coding and analysis processes of qualitative data analysis.

L’utilisation de mesures quantitatives de la fidélité intercodeur dans l’analyse de données de recherche qualitative a souvent suscité des débats acrimonieux parmi les chercheurs qui considèrent qu’en raison de leurs traditions ontologiques et épistémologiques différentes, les méthodologies de recherche quantitative et qualitative sont incompatibles. Bien que ces mesures soient précieuses dans de nombreux contextes, les critiques soulignent que leur utilisation dans l’analyse qualitative constitue une tentative d’importer des normes dérivées de la recherche positiviste. Nous nous appuyons sur les recherches existantes et sur notre expérience en recherche qualitative pour soutenir qu’il est possible de développer une mesure qualitative de la fidélité intercodeur qui soit compatible avec le paradigme épistémologique interprétativiste de la recherche qualitative. Nous proposons huit recommandations, fondées sur des lignes directrices en recherche qualitative pour évaluer et rapporter la fidélité intercodeur en recherche qualitative. Nous espérons qu’elles seront particulièrement utiles pour guider les chercheurs débutants dans les processus de codage et d’analyse des données qualitatives.

Introduction

The use of quantitative intercoder reliability (ICR) measures, such as the kappa statistic, weighted kappa statistic, and binomial intraclass correlation coefficients (ICC), in the analysis of qualitative research data has often generated acrimonious debates among researchers who view quantitative and qualitative research methodologies as incompatible due to their unique ontological and epistemological traditions. 1 - 3 Braun and Clarke, 1 for example, assert that reliability is not an appropriate criterion for judging qualitative work and that quantitative measures of ICR are epistemologically problematic. ICR has been defined as a numerical measure of the agreement between different coders regarding how the same data should be coded. 3 ICR can help provide confidence that systematic efforts were made to ensure the final qualitative data analytic framework is a credible and accurate representation of the data. 3

ICR measures are used to assess the rigor and transparency of the coding frame and its application to the data. 4 - 7 A high ICR may be used to assure the research team and audience that the coding frame is sufficiently well specified to allow for its communicability across persons. 5 , 8 , 9 Performing an ICR assessment also ensures that multiple researchers can understand and contribute to the analytic process and provides confidence that the analysis transcends the imagination of a single individual. ICR assessment further ensures that the patterns in the latent content is fairly robust to the degree that if readers were to code the same qualitative text, they would make the same judgments or produce the same results. 10 ICR fosters reflexivity and can serve as a badge of trustworthiness 3 to the extent that some journal editors and reviewers now request or require a measure of ICR before agreeing to publish qualitative studies. 11 Taken together, ICR might improve the systematicity, communicability, and transparency of the coding process and promote reflexivity and dialogue within research teams. 3 Other critics note, however, that the use of ICR in qualitative analysis represents an attempt to import standards derived for positivist research 12 , 13 and that its use could mask the fact that a rigorous, in-depth qualitative analysis was not undertaken.

A major pitfall surrounding the use of quantitative ICR measures in qualitative research is that such use may create the incorrect assumption that somehow quantitative ICR measures do not essentially contradict the interpretative agenda of qualitative research 1 , 14 - 16 which requires the researcher to see the research field as composed of multiple perspectival realities that are intrinsically constituted by an individual’s social context and personal history. 17 As O’Conner and Joffe 3 note, the role of the qualitative researcher is not to reveal universal objective facts but to apply their theoretical expertise to interpret and communicate the diversity of perspectives on a given topic. Despite this inherent pitfall, some qualitative researchers often resort to quantitative based ICR measures or use their own methods that may not be well grounded in the literature. Also, in the absence of clear or adequate guidelines, some authors hesitate to engage in ICR assessments. We present eight process-based guidelines on ways to get a grip on intercoder reliability using qualitative-based measures. This paper is intended for use by researchers across the continuum and is particularly valuable for beginning researchers.

An alternative measure of ICR

We argue that it is possible to develop a robust measure of ICR that is unique and compatible with the interpretivist epistemological paradigm of qualitative research. This paradigm is premised on relativist ontology and subjectivist epistemology and assumes that reality as we know it is constructed intersubjectively through the meanings and understandings developed socially and experientially and that we cannot separate ourselves from what we know. 18 This measure need not be statistical or quantitative. It can be descriptive and must be able to qualitatively characterize the extent to which independent coders agree or disagree on codes produced from interview, focus group, visual, and textual data. This approach must emphasize the need to achieve consistency between coders rather than mere quantification of the extent of agreement between coders and encourages reflexivity and authenticity throughout the qualitive analysis process. This alternative view of ensuring consistency is echoed by many qualitative researchers who argue that coding and identification of themes by independent researchers could be followed by a group discussion of overlaps and divergences 19 without necessarily quantifying the degree of consensus achieved between the coders. 3 In the rest of the commentary, we present and discuss a set of guidelines for evaluating and reporting ICR in qualitative data analysis based on prior research and the authors’ own experiences in the application of qualitative and quantitative research methods. 3 , 20 - 22 These guidelines are intended to be used in conjunction with other guidelines including those described elsewhere in the lietrature. 23 We have several years of diverse experience in mixed research methodology including coding and analyzing interviews, focus groups, and textual data, as well as narrative responses from survey data.

Ways to get a grip on evaluating and reporting ICR

Guided by extant research and our experience in qualitative research, we recommend eight ways to get a grip on evaluating and reporting ICR in qualitative research with the goal of achieving consistency in the coding process. These are summarized in Table 1 .

Ways to get a grip on Intercoder Reliability

* The code names do not have to be identical, but the meaning of the codes must be the same .

**In inductive and abductive analyses, coding can be an iterative process; therefore, new codes may be added to the codebook until code saturation is reached .

• We suggest that at least two researchers must code the data, except in situations where the goal of the coding is to assess the extent of intracoder (within a single coder) reliability, wherein emphasis is placed on the extent of consistency with respect to how the same person codes data at multiple time points. 20 - 21 As Conner and Joffe 3 describe, if the same person returns to the data at another time, it is possible to assess the extent of consistency in the coding process, thereby promoting researcher reflexivity. 5
• To ensure transparency and minimize bias, we recommend that at least one of the coders in the research team must be external to or removed from the data collection process in such a way that this external coder may view and code the data from a fresh perspective.
• We recommend that at least one of the coders have expertise and previous experience with coding qualitative data to ensure that the coding and development of themes are done in a rigorous and robust manner, thereby increasing the consensus among coders.
• Steps must be taken to ensure that use of novice coders (together with experienced coders) does not produce unreasonable discrepancies in coding and development of themes.
• We also suggest that if a project includes multiple participant groups, a minimum of two researchers should code transcripts from each participant group. 3 , 20
• We highly recommend that the coders use the same framework for analysis to ensure that basic concepts or themes developed within the analysis are consistent with the theoretical framework guiding the research.
• Accordingly, we suggest that coders should focus on shared meaning of codes through a dialogue and consensus processes. However, where discrepancies in codes and themes emerge, we recommend that another coder with expertise in qualitative methods is consulted to resolve such observed discrepancies.
• We recommend that the resulting codebook (based on consensus reached from selected transcripts) should be used to code the remaining transcripts. In inductive and abductive analyses, coding can be an iterative process; therefore, we suggest that new codes may be added to the codebook until a reasonable code saturation is reached. 24 The researchers could therefore schedule regular team meetings to discuss and achieve consensus on the newly added codes. We recommend that researchers should try to use as many criteria as often as possible to increase the rigor, trustworthiness, authenticity, and meaningfulness of qualitative research. However, if the researcher is unable to use all criteria, they should reflect and justify why they were unable to apply all the criteria.

We note that while there are valid reasons for incorporating quantitative-based measures of ICR into qualitative research, it is possible to develop a qualitative-based measure of ICR that is unique and compatible with the interpretivist epistemological paradigm of qualitative research. Drawing on prior research and research experience, we note further that this alternative measure does not need to be statistical in nature, however it must be able to characterize the extent to which independent researchers agree or disagree on codes produced from qualitative data and encourage reflexivity and authenticity throughout the qualitive analysis process. We anticipate that the recommendations presented here will guide researchers across the continuum, particularly beginning researchers in assessing the degree to which quality of process in ICR was met for qualitative data analysis.

Acknowledgement

The authors acknowledge Amber Hastings-Truelove, PhD, Britney Lester, M.Ed, Shannon Hill, MA, Eleftherios Soleas, PhD for their review and feedback.

Conflicts of Interest

The Authors declare no conflicts of interest.

Varieties of Qualitative Research Methods pp 73–78 Cite as

Coding Qualitative Data

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With the advent and proliferation of analysis software (e.g., Nvivo, Atlas.ti), coding data has become much easier in terms of application. Where autocoding algorithms do much to assist and enlighten a researcher in analysis, coding qualitative data remains an act that must largely be undertaken by a human in order to fully address the research question(s) (Kaufmann, A. A., Barcomb, A., & Riehle, D. (2020). Supporting interview analysis with autocoding. HICSS. https://www.semanticscholar.org/paper/Supporting-Interview-Analysis-with-Autocoding-Kaufmann-Barcomb/b6e045859b5ce94e1eb144a9545b26c5e9fa6f32 ). Even seasoned qualitative researchers can find the process of coding their datum corpus to be arduous at times. For novice researchers, the task can quickly become baffling and overwhelming.

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Further Readings

Analyzing Qualitative Data: Nvivo 12 Pro for Windows (2 hours). https://www.youtube.com/watch?v=CKPS4LF9G8A

How to Analyze Interview Transcripts. (2 minutes). https://www.rev.com/blog/analyze-interview-transcripts-in-qualitative-research

How to Know You Are Coding Correctly (4 minutes). https://www.youtube.com/watch?v=iL7Ww5kpnIM

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Rogers, M. (2023). Coding Qualitative Data. In: Okoko, J.M., Tunison, S., Walker, K.D. (eds) Varieties of Qualitative Research Methods. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-031-04394-9_12

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UC reduces the gender gap in IT

Progress made in efforts to reach younger students, make field more welcoming.

Corin Manning was only 14 years old when she discovered what she wanted to do for the rest of her life.

She and a friend signed up for a summer camp, IndeedWeCode, which teaches coding or computer programming to young Black girls in Cleveland, Ohio. Manning , now a senior at the University of Cincinnati, fell in love with coding and web development that summer and now is on the verge of starting her career in Cleveland with the industrial automation company Rockwell Automation.

Manning's experience is similar to initiatives from UC's School of Information Technology, such as its Early IT Program and Early IT Summer Camp , as it focuses on reducing the gender gap in information technology.

“It's super important to make sure you get to people early,” Manning said. “I can take it all the way back to elementary school. My mom had me in a bunch of different STEM camps since I was young. It was already kind of embedded in me. I knew I wanted to do STEM, I just didn't know what path, and the IndeedWeCode camp solidified it.”

Emma Rader, left, explains the building management system her team created while Taylor O'Black, right, looks on at the 2023 IT Expo. The 2024 IT Expo will be from 8:45 a.m. to 2 p.m. April 9 in the Campus Rec Center. Photo/Greg Humbert

UC makes strides

When Bekah Michael , an associate professor-educator in the School of Information Technology, part of the university’s College of Education, Criminal Justice, and Human Services, and the executive staff director for the Ohio Cyber Range Institute , began her studies in IT, she was often the only woman in her classes.

She loved her classmates and never felt discriminated against by her classmates because of her gender, but it was obvious that she was different.

“I noticed that there weren’t very many women,” she said. “It took me a while but [I] realized the field was not diverse at all, which was so crazy because it felt so accepting to me.

“This is such an exceptional field,” she said. “You can make so much money, there was so much flexibility, and women just weren’t getting in. There are many reasons why this was the case, and we continue to study it and work to remove barriers and build a more inclusive community. This is why I came back to UC to study and teach after being in industry for many years.”

Narrowing gender gap

Women studying IT still find themselves underrepresented in their classes, but the gap is narrowing at UC.

• In fall 2023, there were 679 women in UC’s School of Information Technology (28% of the total student body).
• In fall 2023, 41% of the school’s graduate students were women (512).
• The number of first-time first-year women enrolled in the SoIT grew by 161% from fall 2019 to fall 2023.
• Enrollment of women in the cybersecurity Bachelor of Science Degree program grew by 31% from fall 2022 to fall 2023.
• The number of women on the school’s IT faculty increased by 60% from fall 2019 to fall 2023.

“It’s an amazing thing for our students that women are represented in our department,” Michael said. “Other underrepresented groups are represented in our department and school. That’s a part of what we need to support our students. Our students need to see all of us at the front of the classroom so they don’t feel alone.

“I talk to people at national conferences all the time about our makeup, and they are just blown away. That is not their experience.”

Bekah Michael, executive staff director of the Ohio Cyber Range Institute, explains how members of the Ohio Cyber Reserve are being trained at the University of Cincinnati's 1819 Innovation Hub. Photo/Joseph Fuqua

Building a positive culture

As Jen Fritz , an associate professor-educator in the school sees it, there's nothing about IT that prevents women from entering the field other than the perception that it's a male-dominated industry.

“There’s nothing in this field that a woman can’t do,” Fritz said. “In IT, it’s just as easy for women to be competent in the field as men.

“It’s not the field that’s keeping women out. It’s how comfortable they are in the environment.”

When Fritz began her studies in information systems in the 1990s after retiring from the Marine Corps, it wasn't uncommon to see nude women appear on screensavers in computer labs. While that was accepted then, the technology sector has become more welcoming to women in the years since, Fritz said.

Still, there's progress to be made.

Breanna King, a senior from Cincinnati, encountered people who questioned her knowledge and didn't give her credit for her ideas during a work experience. That led her to believe the hostility she faced was a result of her gender.

“The negative experiences that I've had with it have caused me to stray from the ability to have those experiences, to not really share my opinion as much and caused me to second-guess myself a lot,” King said. “It doesn't happen every day. It doesn't happen more often than not. It's just when they do occur, it kind of sticks with you a little bit more than the positive ones sadly.”

While that negative environment sullied her experience, King did find a better culture during a co-op with the Department of Justice (DOJ).

With the DOJ, King felt supported as both a woman and a person with disabilities who uses a wheelchair, cane or leg braces for her mobility issues. She also found a culture of people who wanted to learn and find solutions to the problems they faced.

“It's nice being able to get that experience on the ground,” said King, who has accepted a data analyst job with the DOJ. “I don't think I would have been able to understand that aspect of it outside of a co-op.”

Wenhan Jia shows the Bearcat a video game her team created at the 2023 IT Expo. The 2024 IT Expo will be from 8:45 a.m. to 2 p.m. April 9 in the Campus Rec Center. Photo/Greg Humbert

Overcoming negative perceptions

To overcome the perceptions of IT that sometimes make women feel unwelcome, faculty and students at UC have created support systems.

Organizations including Women in CyberSecurity cater to female students. The clubs provide a safe place for students to share their successes and failures, practice technical and nontechnical skills and build their professional networks.

Other programs and organizations including Cyber@UC, Bearcat Coders and IT Proud also provide spaces where students can support each other, and they receive support from faculty, too.

“They're very supportive. You can go to them at any time, even if you're not their student that semester. It feels nice to have them there,” said Manning, who works as a student success coach for the IT program to show fellow students the same support she's received at UC.

Not only do they want to help their fellow students at UC, the organizations also have reached out to younger students as well.

“I've been able to talk to girls who are in high school and even girls who are in middle school who are interested in this field already. When we went to the library, there were two girls there who were in Girl Scouts together. One of my opening questions was, ‘Has anyone heard of steganography?’” King said, referencing the practice of concealing a file, message, image or video within another file, message, image or video. “And two little girls raised their hands, they were no older than maybe fifth or sixth grade, and they were able to tell me all about it. I think the resources have helped that gap a lot.”

Aanshi Patel meets with a guest at the 2023 IT Expo. The 2024 IT Expo will be from 8:45 a.m. to 2 p.m. April 9 in the Campus Rec Center. Photo/Greg Humbert

Highlighting the opportunities

To reduce the gender gap in technology fields, it's important to show women and girls that opportunities exist, professor Fritz said.

“We can’t just go out and say, ‘IT is for anyone. Let’s see how many women or how many minorities we can get into the field,’” she said. “That doesn’t solve our problem. That doesn’t make it more equitably distributed. What we need is word of mouth and personal experience: ‘My sister did this, now I’m doing it.’ And we’ve seen that.”

To increase that personal experience, UC has created programs such as the Early IT Program and Early IT Summer Camp.

The Early IT Summer Camp is a free program that allows high school students to explore numerous career opportunities available through information technology including in cybersecurity, software development, game development and simulation and data and cloud technologies.

The Early IT Program allows high school students to complete the first year of their bachelor’s degrees at more than 50 high schools. With training from UC, high school teachers are enabled to teach the college courses at their schools.

Corin Manning won the Student Trailblazer Award at the 2024 Onyx & Ruby Gala Feb. 17. Photo/provided

All students who complete their first year of Early IT classes in high school with a C or above average are automatically admitted to the UC information technology program.

“At UC, IT is becoming a lot more accessible because we have things like Early IT,” Nikki Holden , an instructor-educator in the school, said. “We’re getting more people in IT who wouldn’t have gravitated toward IT 10, 15 years ago.”

Students, including King and Manning, also have taken advantage of the IT Accelerated Program that allows students to complete bachelor’s and master’s degrees simultaneously in five years. Manning is earning a bachelor’s degree in IT and a Master of Business Administration Degree while King is earning a bachelor’s degree in cybersecurity and master’s degree in IT.

Through the efforts to reach younger students, UC faculty hope their efforts will make more underrepresented students feel comfortable in IT and that students from all backgrounds will become interested in the field.

“Students need to have someone who looks like them teaching. Regardless of what the class is or the level, they need to have someone they associate with, so having women teaching makes women feel more comfortable in the classroom,” Fritz said. “But when I teach, I don’t look at a person as male or female, minority or anything else. I look at them as students. I try to treat them all equally.”

Featured image at top: University of Cincinnati information technology students hold up sweatshirts. Photo/Margot Harknett

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Whether you’re a first-generation student or from a family of Bearcats, UC is proud to support you at every step along your journey. We want to make sure you succeed — and feel right at home.

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Revolutionize Your Research: Unleash the Power of Qualitative Coding Software

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

Developed by and for researchers – since 1989

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

Sally S. Cohen – NYU Rory Meyers College of Nursing

Qualitative Coding is Faster and Smarter with MAXQDA

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

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

Qualitative coding made easy

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

Tools tailor made for coding inductively

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

Organize your code system

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

Further ways of coding qualitative data

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

Creative coding

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

Visualize your qualitative coding and data

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

AI Assist: Qualitative coding software meets AI

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

Free tutorials and guides on qualitative coding software

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

Free MAXQDA Trial for Windows and Mac

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

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

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

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

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

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

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

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

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

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

Some common qualitative coding methods include:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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