Inductive coding is a qualitative data analysis method used to develop themes from a text such as an interview transcript.
It’s the most common data analysis method used by my research students.
This blog post will give you everything you need to write your own research methods section on inductive coding.
In your data analysis section in your methodology chapter, you will want to:
- Define inductive coding,
- Highlight its benefits and limitations (explaining how the benefits outweigh the limitations), then
- Give a clear step-by-step overview of exactly how you will code your data.
Let’s get started.
A Note:While all the information you need to write your methods section in your dissertation is below, I’d strongly encourage you to engage with two very important texts: Braun and Clarke’s (2006) Using thematic analysis in psychology (access it here) and Attride-Stirling’s (2001) Thematic Networks (access it here).Inductive Coding Definition and Key Features
Inductive coding is a research method for generating themes from textual datasets, such as transcribed semi-structured interviews or transcriptions of speeches and audio.
It is also known as “open coding” or “data-driven coding”.
Below are some key features of inductive coding, each of which you should note in your methods section.
1. It is a Qualitative Approach
It is a qualitative method, meaning its focus is on generating themes that are contextualized, interpretive, and engaged with socially-mediated meanings (i.e. pragmatics and semantics, both of which are worth exploring to familiarize yourself with how to read texts-in-context).
Note that as a qualitative method of content analysis, its strength is in its ability to develop deep and nuanced understandings of texts. It doesn’t focus on counting occurrences of words or phrases. Rather, it attempts to generate overarching themes from texts.
2. Themes Emerge During the Coding Process
There are two ways you could code the text. You could either:
- Apply a pre-existing theoretical framework and pre-existing themes to the text before coding the data. This is good for testing hypotheses, or
- Approach the data with an open mind, with the goal of having themes organically emerge during the reading.
Inductive coding uses the second approach: our goal is to let themes emerge during our close reading of the text. If you want to test pre-set themes against the dataset, you might want to read my article on deductive coding.
This open-minded approach, where themes emerge during, rather than before, we read the data, means inductive coding is called a grounded approach (Charmaz, 2014).
There are several advantages to letting the themes emerge during the data coding process. These include:
- Researchers can uncover themes that they may not have previously considered.
- Researchers’ initial biases are (supposedly) not baked into the themes that are generated (Thomas, 2006).
- It can lead to a more nuanced and open-minded understanding of the studied phenomena.
A Critical Note: While the goal of inductive coding is to approach the data in an biased way, and to allow the ‘data to do the talking’, this in my opinion is rather naive. The researcher may be able to be open and flexible, but the biases, life history, subjectivity, and interests of the researcher will inevitably affect how the data is coded. See my critique at the end of the article, and how to address this criticism in your methods discussion.3. It is an Iterative Process
Inductive coding is an iterative process, which means that it involves repeatedly reading the data and refining the codes over each re-reading of the text.
This means that, as we become more familiar and immersed in the text, we should be able to refine our insights and find deeper and deeper layers to the text each time we read it (Elo & Kyngäs, 2008).
An Analogy: Have you ever re-read your favorite book three or four times, and found that each time you read the book, you get something new out of it? A new feeling, insight, or appreciation of the narrative? The same happens when we iteratively re-read our texts during the inductive coding process.Inductive vs Deductive Coding
Inductive and deductive coding are two primary methods used in qualitative research to analyze and interpret data.
In your methods section, it’s worth explaining why you chose an inductive approach over a deductive approach.
Here’s a definition of each:
- Inductive Coding: Codes and themes are derived directly from the data (i.e. while you read the transcripts), not from pre-conceived theories or hypotheses.
- Deductive Coding: Codes and themes are pre-determined based on existing theories, hypotheses, or research questions. Reading the data is about testing the theories (e.g. seeing how strong your pre-conceived themes and hypotheses are) rather than generating themes and theories from the text.
Inductive and deductive coding are in many ways opposites.
Inductive coding starts with specific observations made while reading the text, then develops broader patterns or themes from the dataset. Eventually, you’ll be able to develop a theory or overarching argument that answers the research question.
The advantage of inductive coding is that it allows for new and unexpected themes to emerge from the data, potentially leading to novel insights. However, it lacks the clear structure of a deductive coding method. Researchers (especially new researchers) can feel lost and confused during the theme generation process.
Conversely, deductive coding begins with a theory or hypothesis, then seeks to test this against the observed data. For example, if you already have four themes that you really want to test in the dataset, you might use deductive coding to see if the themes match your hypothesis that you’ve already generated prior to reading the data.
Deductive coding allows researchers to directly address specific research questions or hypotheses. I will often use it if I’ve done a literature review and concluded the literature review with a focused interest on addressing a ‘gap in the literature’ or testing someone themes that I found emerged from the academic literature on the topic.
The deductive approach to coding can be more straightforward and quicker than inductive coding as it involves coding data to pre-determined categories. However, a significant downside (and, to me, the reason inductive coding is superior) is that deductive coding may not capture all the nuances in the data and often fails to reveal unexpected or novel themes not accounted for in the initial hypothesis, nor previous literature on the topic.
Below is a comparison table that outlines the differences:
| Inductive Coding | Deductive Coding | |
|---|---|---|
| Definition | Codes and themes are derived directly from the data | Codes and themes are pre-determined based on existing theories, hypotheses, or research questions |
| Approach | Broadening: Begins with specific observations, leading to broader patterns or themes | Narrowing: Begins with a theory or hypothesis, and tests this against observed data |
| Pros | Allows for new and unexpected themes to emerge from the data | More straightforward and quicker than inductive coding as it involves coding data to pre-determined categories |
| Cons | Time-consuming and requires significant expertise | May not capture all the nuances in the data; unexpected or novel themes may be overlooked |
Inductive Coding Step by Step Guide
At this point, you’re probably wondering exactly how to go about reading your data and developing themes (aka ‘coding’).
Inductive coding aims to be flexible and let themes emerge as we work. Nevertheless, you will still need some guidelines for approaching the dataset so you know what to do first, second, third, etc.
For this, I lean heavily on Braun and Clarke’s (2006) Using thematic analysis in psychology (access it here) and Attride-Stirling’s (2001) Thematic Networks (access it here). The following step-by-step guide is based on and adapted from their important work.
Step 1: Read the Data
The first step in inductive coding is immersing yourself in the data by reading and rereading your transcripts multiple times (Elo & Kyngäs, 2008). Literally read the transcripts.
At this point, you may wish to take notes on a notebook or post-it on your initial thoughts or impressions. These general notes will likely be messy – they are your initial thoughts. Don’t overthink it or try to impose too much order at this point. Simply take rough notes. Your goal is to start to engage with the transcripts and ‘get to know them’.
Example
Imagine you are working with interview transcripts from first-year teachers who you interviewed about their transitions from college to teaching. During the initial reading of the data, you might take some rough notes on the experiences, emotions, and events detailed by the teachers.
Step 2: Create Codes
In the second read-through, you should have some general idea about ideas that will recur throughout the text.
You will read a comment and be able to remember that this comment comes up again later in the transcript. In other words, you can read the transcripts with greater contextual knowledge.
At this point, directly label the transcripts with codes, or base notes on ideas you have noticed have come up more than once in the dataset (see example below).
These observations might not be combined into coherent themes yet, but they represent potential areas for more in-depth analysis, which will be brought together into a coherent theme in the next step.
Example
When re-reading your transcript on the new teachers’ interviews, start to take notes in the margins on topics that seem to jump out because they occur several times. For example, beside one paragraph you might write ‘talks about anxiety’ or ‘feels like she lacks support’. These are your basic codes.
Step 3: Develop Basic Themes
In your third reading, your goal is to combine your codes into what Attride-Stirling (2001) call ‘basic themes’. Identify codes that seem to point to a similar theme, or recurring idea, that you see throughout your dataset.
These basic themes are the building blocks of your emerging hypothesis or argument in your dissertation. They might be used to create paragraphs in your ‘discussion’ section.
Note that this is the longest step as it is an iterative process that involves moving back and forth between the data, codes, and emerging themes, continuously refining and defining them until you have a set of themes that accurately represent the data (Elo & Kyngäs, 2008).
Example
You might find that you wrote ‘talks about anxiety’ on the margin of one transcript, ‘feels worried’ on another, and ‘is losing sleep’ on a third. Here, it’s your job as the researcher to be able to recognize that these codes all cohere around a basic theme: “Anxiety”. Beside each of these comments, tag them as being a part of the theme ‘Anxiety’. I use # to write themes, so beside each of those three margin comments, I would write #Anxiety
Step 4: Develop Organizational Themes
Now you have basic themes, you’ll want to see if you can combine themes that point to a consistent higher-level idea emergent from the data. Attride-Stirling calls these ‘organizational themes’. I use organizational themes as the basis for chapters in my discussion and analysis.
Example
Returning to our transcript of new teachers, I might have found the following themes: #Anxiety, #Stress, #Joy, #Exhaustion, #Learning-From-Mistakes, #Building-Resources, #Behavior-Management-Improvement, #New-Skills. Here, I feel I can develop two organizational themes:
- “New Teachers are Feeling Overwhelmed” (combining #Anxiety, #Stress, and #Exhaustion).
- “New Teachers are Experiencing a Rapid Learning Curve” (combining #Learning-From-Mistakes, #Building-Resources, and #Behavior-Management-Improvement, #New-Skills)
Step 5: Develop a Global Theme
Lastly, you are going to want to develop a global theme, which is the core argument of your dissertation. It is the one-sentence elevator pitch about your dissertation which draws together all of your findings into one final theme, stated as an argument. See the example below.
Example
I need to find an overarching argument that combines my two organizational themes “New teachers are feeling overwhelmed” and “New teachers are experiencing a rapid learning curve.” I decide upon the global theme: “New teachers need extra support to manage overwhelm during their rapid learning curve.”
From Inductive Coding to Dissertation Discussion
Allow the inductive coding process to shape the organizational structure of the discussion sections of your dissertation.
Generally, I encourage students to shape their dissertation in the following way:
- The global theme becomes the thesis statement, phrased as an assertion about the dataset.
- The organizational themes, which describe underpin global theme, become the discussion chapters, phrased again as assertions about the dataset.
- The basic themes become sub-sections in the discussion chapters. Each basic theme could be 500-800 words long and include key quotes directly from the dataset, as well as detailed analysis of the quotes.
For the above sample study on the experiences of new teachers, the discussion section would end up looking like this:
Title: “The Experiences and Challenges of Transitioning to Teaching”
Thesis Statement: “New teachers can experience overwhelm from a rapid learning curve and require support to manage their professional transition.” (Global Theme)
- Discussion Chapter 1: The new teachers are experiencing a rapid learning curve (Organizational Theme)
- Sub-Section 1.1: How new teachers learn from their mistakes (Basic Theme)
- Sub-Section 1.2: New teachers expend time and energy building a resource library from scratch (Basic Theme)
- Sub-Section 1.3: The new teachers are struggling with behavior management in the classroom (Basic Theme)
- Sub-Section 1.4: The new ew teachers are developing a vast array of new skills in the first year (Basic Theme)
- Discussion Chapter 2: The new teachers are experiencing extreme overwhelm (Organizational Theme)
- Sub-Section 2.1: The new teachers report high levels of anxiety (Basic Theme)
- Sub-Section 2.2: The new teachers report high levels of stress (Basic Theme)
- Sub-Section 2.3: The new teachers report high levels of exhaustion (Basic Theme)
A Note:When writing your discussion section, include many quotes directly from the dataset, which you can find by tracing back your coding process. After each quote, discuss and analyze it in detail.Strengths and Limitations of an Inductive Coding Approach
Strengths
- Discovery of New Themes: One of the key strengths of an inductive coding approach is the ability to uncover novel themes or patterns that may not have been previously considered (Thomas, 2006). Because inductive coding does not require a pre-existing theoretical framework, researchers are often able to identify unexpected trends and insights in their data.
- Grounded in Data: Inductive coding is inherently grounded in the data itself, offering an authentic, detailed, and nuanced understanding of the studied phenomena. This ensures that findings are directly linked to the perspectives and experiences of the participants (Braun & Clarke, 2006).
- Flexible and Adaptable: This approach is flexible and can adapt to the needs of various research designs. It does not require hypotheses or pre-determined categories, making it suitable for exploratory studies or those with less known about the topic (Charmaz, 2014).
Limitations
- Time-consuming: The inductive coding process can be quite time-intensive, as it involves close, detailed analysis of the data and requires multiple iterations to refine codes and themes (Elo & Kyngäs, 2008).
- Requires Skill: To perform inductive coding effectively, researchers must have a solid understanding of the process and a certain level of qualitative analysis skill. Without it, there’s a risk of missing significant nuances in the data or misinterpreting participant meanings (Saldana, 2015). New researchers should study pragmatics, semiotics, or semantics to develop skills in coding data.
- Potential for Researcher Bias: While the approach is grounded in the data, the interpretative nature of inductive coding means that researcher bias can potentially influence the coding and theme development process. Researchers must be conscious of this and take steps to limit the influence of their preconceptions on the analysis (Charmaz, 2014). To minimize this bias, you could ask peers to also code the data separately from you, and compare and contrast your themes.
Conclusion
Inductive coding is my preferred approach to coding data for a qualitative dissertation based on textual dataset, such as transcriptions of semi-structured interviews or content analyses of purely textual datasets. With more complex datasets, such as multimodal texts, you might need to use an approach such as a discourse analysis, multimodal analysis or media analysis method. If your goal is to directly test a set of pre-defined themes, you’ll want to go with a deductive coding approach.
Be sure to clearly explain why you chose inductive coding in your methods section, including by explaining its weaknesses and strengths, before stating why on balance you think the strengths outweigh the weaknesses. Furthermore, you will need to explain your step-by-step process of generating codes, basic themes, organizational themes, and a global theme, so your readers have a clear understanding of how you went about analyzing the data.
References
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Charmaz, K. (2014). Constructing grounded theory. New York: Sage.
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115.
Saldana, J. (2015). The coding manual for qualitative researchers. New York: Sage.
Thomas, D. R. (2006). A general inductive approach for analyzing qualitative evaluation data. American Journal of Evaluation, 27(2), 237-246.
Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]
Thank you for this summary! Appreciated