Qualitative coding is a core process in qualitative research – bridging the gap between raw, non-numerical data and meaningful insights. Whether you’re delving into interviews, focus groups, or open-ended survey responses, coding qualitative data turns complicated narratives into easily identifiable themes and patterns, enabling researchers to draw conclusions from their complex data.
In this article, we’ll cover the basics, essentials steps, and best practices for coding qualitative data, and then dive into the stages of how to code qualitative data with Dr. Philip Adu, Founded and Methodology Expert at the Center for Research Methods Consulting, LLC.
What is Qualitative Coding?
Coding in qualitative data is the process of labeling your data to help identify key themes and connections between the data. Codes can be descriptive (e.g., indicating that a text is about a topic) or can be analytical (e.g., expressing why an issue matters).
Coding qualitative data is a common challenge for many researchers as it can be a time-consuming process. Qualitative research often involves large, text-heavy data sets from data types such as open-ended interview questions and survey transcripts. Later in this article, we’ll discuss how using qualitative data analysis software for qualitative coding can be a solution for this challenge.
Types of Qualitative Data
Qualitative data can appear in many forms which require different coding approaches. Some of the most common types of qualitative data are:
- Interviews
- Focus groups
- Open-ended survey responses
- Observational notes
- Text documents
- Case studies
Before you can begin to code, you must understand your data type and select the appropriate coding method to allow for a thorough analysis.
Benefits of Coding Qualitative Data
By systematically coding your data, you can analyze large qualitative datasets for patterns, themes, and sentiment – helping you extract deeper insights and improve your research. To further break down the benefits of coding, the following are clear advantages that enhance the research process:
- Organization: Sorting data into manageable categories allows you to quickly review and identify reoccurring patterns and themes.
- Rigor and consistency: Creating a coding system adds consistency in your data interpretation and analysis, crucial for rigorous research.
- Improved communication: Well-coded qualitative data allows you to clearly communicate and defend your findings.
- Data reduction: By simplifying and reducing your data to its core elements, the analysis process becomes easier and more efficient.
Inductive vs. Deductive Coding in Qualitative Data
When coding qualitative data, researchers often make a choice between two coding approaches: inductive or deductive. Both methods have their own place in qualitative research and provide different strengths, depending on the context of your research.
Inductive Coding
This approach involves coding from the bottom-up, letting the codes emerge directly from your data. Inductive coding is especially helpful when researching new or less well-researched topics.
With inductive coding, researchers tend to dive headfirst into the data and create preliminary codes from their initial readings. These are then adjusted and refined as their coding and research progresses. This flexible coding is ideal for exploratory research and is often used in grounded theory, ethnography, and phenomenological studies.
Deductive Coding
Deductive coding is the opposite – a top-down approach where coding is guided by pre-existing frameworks or theories. Researchers often use this coding method when exploring specific theoretical constructs or testing hypotheses.
This systematic method of coding provides consistency and ensures you stay aligned with your research goals. Deductive coding is often applied in content analysis, case studies, and comparative research.
By understanding the differences between inductive and deductive coding, you can decide on the right method for your research that will provide the most valuable insights.
How to Verify the Validity and Reliability of Your Codes
Ensuring your qualitative coding is accurate and valid is critical. Below are a few steps to help you verify your coding:
- Intercoder reliability: Compare the coding of the same dataset coded by multiple researchers – looking for high agreement among the codes.
- Codebook refinement: Create a detailed guide with examples and clear definitions for each code in your research. Be sure to update your codebook as you receive feedback and work through the coding process.
- Pilot testing: Test a small portion of your data to help determine any ambiguity or issue with your coding scheme before moving on to your full dataset.
- Software tools: Leveraging qualitative data analysis tools like NVivo can help you manage and organize your coding system – making it easy to maintain consistency in coding.
Learn more about how to ensure coding reliability in our article, Managing a Team when Coding in Qualitative Research.
In this next section, hear from Dr. Philip Adu to learn about the stages of coding qualitative data.
How Does Qualitative Coding Work?
Think about analyzing interview transcripts with open-ended questions: capturing significant information from the data and putting them into ‘containers’, called codes (in NVivo).
When coding, it is important to label each code created, providing a brief description about each of the codes, and documenting your reflections.
You then sort the codes into ‘big containers’ called parent codes based on their content similarities and unique relationships.
Lastly, you use the parent codes (i.e. themes) to address the research question(s). Here's an illustration of the high-level process:
Imagine doing all these using a user friendly qualitative analysis software such as NVivo. It contains features which help you to work on multiple data, run queries, code significant parts of data, add descriptions and memos (reflections) to the codes generated, create illustrations to better display your findings, and brainstorm ideas using the ‘Mind Maps’ function in NVivo.
>> Watch On-Demand Webinar: NVivo: Thematic Analysis Using NVivo
Three Ways to Ensure Credibility in Your Qualitative Coding Process
The coder’s findings (such as themes, models, and/or theories) don’t only represent the data but reflect his/her subjective intent and thought process, background, and experiences.
To ensure credibility, the coder needs to be transparent in the coding process. There are three main qualitative data analysis stages that NVivo could be effectively used for to maintain transparency, attain consistency of the labels or nodes created, and reach meaningful findings with visual representations.
1. Pre-coding stage (getting to know your data)
I always start the coding process with a review of all the data, to familiarize myself.
The‘Query’ command (in NVivo) for example, is a great tool to use so as to know the kind of words participants use and how often they are used.
The ‘Word Frequency’ result could be displayed as a ‘Word Cloud’ with varied word fonts depicting the number of times the words are utilized:
In addition, I sometimes want to know how a specific word or phrase is used in a participants’ responses. So, I use the ‘Text Search’ command resulting in the creation of a ‘Word Tree’.
The ‘Word Tree’ shows words or phrases used before and after the searched word. This helps in knowing the context in which it was used:
2. Coding stage (assigning labels to the nodes)
When coding, the consistency of codes or nodes generated is crucial. It helps you to easily see the relationships between nodes and sometimes figure out underlying ideas and meanings among them.
So how do you maintain consistency of nodes?
One strategy is to select specific coding method(s) consistent with the research question to create labels for the nodes. In NVivo, video and transcript data can be labeled with “coding stripes” which indicate which section of a data is assigned a code (Saldana, 2013).
NVivo’s autocoding feature can speed up your coding process by using AI to identify text passages that match your initial coding of the unstructured data.
3. Post-Coding stage (presenting your findings)
The main purpose of conducting a qualitative analysis is to generate themes to address the research questions (Adu, 2013).
After coming up with themes, the next stage is to present the findings.
How the results are presented to your audience could impact the credibility of the results. To ensure credibility, a researcher should present each theme with its respective meaning and evidence from the data. Adding a visual representation of the themes, their relationships, and related ideas helps the audience to better understand the findings (Saldana, 2013).
The NVivo ‘Explore’ function is an excellent tool to create project maps, concept maps, charts, and cluster trees just to mention a few.
>> Watch On-Demand Webinar: Explore and Visualize Your Data Using NVivo to Tell the Story
Qualitative Coding with NVivo
In conclusion, data analysis is a very intensive process, with qualitative researchers carefully and systematically reducing the data to themes to address the research question. To attain credible findings, the coder should extensively document the process and clearly explain how they arrived at the findings.
NVivo is a great qualitative analysis software which could be used to code data, document the data analysis process, and present a visual presentation of the results so as to increase credibility and save significant amounts of time.
References
Adu, P. (2013, November 22).
Qualitative analysis coding and categorizing.
Saldana, J. (2013).
The coding manual for qualitative researchers. London: Sage
Philip Adu
Dr. Philip Adu is the Founder and Methodology Expert at the Center for Research Methods Consulting, LLC. You can access some of his webinars at the ‘Methodology Related Presentations – TCSPP’ YouTube Channel. He completed his Doctoral degree in Education with a concentration in Learning, Instructional Design and Technology from West Virginia University (WVU). Dr. Adu recently authored a book titled, “A Step-by-Step Guide to Qualitative Data Coding” (available on routledge.com or amazon.com).
