Abductive Coding in Qualitative Analysis
Abductive coding is a flexible qualitative data analysis technique that allows researchers to develop new theories about their data based on existing theories and concepts.
You can think of abductive coding as a “hybrid” of inductive and deductive analysis. It lets researchers use existing theories and concepts (like in deductive analysis) while finding new insights and perspectives directly from the data (like in inductive analysis).
Researchers use abductive coding to help understand complicated ideas and develop possible explanations for what they observe. It is a way to explore data with an open mind and generate new ideas to better understand a research topic.
This article explores the principles and application of abductive coding in qualitative analysis.
What Is Abductive Coding?
Abductive coding has been offered as a way to address the debate between inductive and deductive analysis in qualitative research (Vila-Henninger, 2022). Instead of siloing inductive from deductive coding, abductive coding combines the best of both approaches.
Researchers generate abductive codes in a codebook based on unexpected findings. The abductive codes help form new theories grounded in (but not restricted by) what Salganik (2018) calls “ready-made data.” i.e., theories from pre-existing research and literature.
How Abductive Coding Works
Researchers start abductive coding from a deductive codebook. As they analyze the data, they create new data-driven inductive codes for cases that don't fit existing theories (based on what is already theoretically established or what is expected based on existing theories). Abduction helps them build a codebook and develop new theories based on anomalies in those theories.[1]
In short, abductive coding combines what we already know with new observations to understand topics better and form more complete theories. It challenges the traditional dichotomy between induction and deduction by offering a blended approach to theory-building.
Inductive Coding vs. Deductive Coding vs. Abductive Coding
Inductive coding involves finding patterns and themes in the data, and letting theories, observations, and meanings emerge from the "bottom-up." Deductive coding starts with predefined concepts or theories and applies a "top-down" analysis to the data.
Abductive coding goes beyond inductive observations or following established deductive rules. Instead, it combines observation-based and rule-based thinking, providing a nuanced and comprehensive approach to theory-building.
Instead of simply categorizing or describing data, abductive coding generates hypotheses that challenge assumptions, identifies possible gaps in data, and gives researchers the creative freedom to explore new ideas in a novel way (Deterding and Waters, 2018).
Inductive coding: Derives codes from the data, focusing on observations.
Deductive coding: Applies predetermined codes to the data based on existing theories.
Abductive coding: Makes inferences about the data based on existing theories and concepts while allowing for new abductive codes based on anomalies and surprises.
| Coding Method | Approach | Iterative Process | Anomalies | Theory-Building |
|---|---|---|---|---|
| Inductive Coding | Data-driven exploration and theory development from observations | Builds theory alongside the data | Not emphasized | Theory emerges from patterns and themes in the data |
| Deductive Coding | Theory-driven analysis and application of predefined concepts | Applies existing theories or concepts | Not emphasized | Theory applied to analyze the data |
| Abductive Coding | Simultaneous use of inductive and deductive coding | Combines deductive framework with inductive code development | Used to develop inductive codes based on unexpected data | Comprehensive theory-building incorporating both approaches |
By integrating inductive and deductive concepts and embracing reflexivity through tools like analytical memos, abductive coding helps researchers gain richer insights from their analysis.
How To Do Abductive Coding
Here is a step-by-step guide that blends Timmermans and Tavory's (Tavory and Timmermans 2014) stages of abduction with Vila-Henniger’s (2022) approach:
0: Key Points To Keep In Mind
When does abductive coding begin?
Abductive coding begins when your research begins. It is a way of thinking about qualitative data analysis that allows you to use both your existing knowledge and the data to generate hypotheses. The ultimate goal is to remain flexible and open to new ideas
Where does abductive coding shift from deductive to inductive coding?
The transition occurs when you identify new codes and relationships in the data that aren’t based on your existing knowledge or on your data. This is where the inductive aspect of abductive coding comes in, between steps 3 and 4 below.
Can you use abductive coding in collaborative team research?
Yes! For abductive coding in groups, you hold meetings to align on coding decisions and improve intercoder reliability. The meetings serve a similar purpose to reflexivity with memos, helping address biases, by offering a way to reach a consensus as a team.
1: Familiarize Yourself with the Data
Immerse yourself in the data. You want to deeply understand the research context, themes, and patterns. This familiarity will help you identify potential gaps or anomalies in the data that may require further exploration and help form your research question and hypothesis later on.
2: Create an Abductive Codebook
Start by selecting a few initial transcripts. The data corpus will often be a compilation of data sets so try to pull “test” samples from different sets to get reliable results without coding the full corpus.
When doing group coding, select the initial transcripts as a team or let an experienced researcher choose. Divide the transcripts among researchers to create individual codebooks that are combined later on through split or consensus coding (agreeing on the codes).
2.a. Create a deductive codebook based on existing theor(ies) to help answer your research questions.
The deductive codebook allows you to try and address the research question based on observed data from the existing work. The purpose is to generate codes grounded in data.
The deductive codebook should use existing theories and concepts as a foundation but be broad enough to allow new insights to emerge later. This establishes boundaries to keep the analysis focused but is flexible enough to add inductive codes.
2.b. Create inductive codes based on data with a group.
Before continuing, take a step back from your data and ask yourself questions like:
What do I need to understand about this data?
What are the taken-for-granted assumptions that I am making?
Does enough supporting data back the preexisting theory?
Am I reinforcing that theory or refuting it?
You start theory-building by coding the same samples as in Step 2.a. You want to revisit that data with fresh eyes. This can help identify new insights you may have missed the first time and lead to new theoretical hypotheses.
Like open coding in grounded theory, your inductive coding breaks down your qualitative data into smaller parts and assigns codes to label them.
If you are group coding, it is helpful for the team to meet throughout this process to reach consensus on codes, improve intercoder reliability, and update the codebook.
When the researcher(s) feel there are no more inductive codes to add, you have finalized your abductive codebook.
2.c. Code the full corpus using the abductive codebook.
You now code the entire data corpus using the abductive codebook. You should also recode any data coded in the previous sub-steps.
Overall, Step 2 emphasizes active engagement with the data, promoting fresh perspectives and the discovery of unexpected findings to create an abductive codebook.
3: Filter Data to Find Excerpts Related to Your Research Topic
In step 3, you will use the codes derived from step 2 to find more specific excerpts that relate directly to your research topic.
Vila-Henniger refers to data filtering as "code equations," which are ways to look at the cross sections between multiple codes to find more specific excerpts. Code equations look at what excerpts appear across two codes. Or they identify excerpts that appear in one code but not another.
Let’s say you have a research question about how people are motivated to make an impact. From step 2, you may have two separate codes: “Climate Change” and “Congress.” Each has their own collection of excerpts. Code equations let you look at excerpts coded by both “Climate Change” and “Congress” to learn more about that research question.
Vila-Henniger refers to this process as “data reduction.” It means that each time you do one of these “code equations” you reduce the number of excerpts that come out of the equation, thus making these excerpts more specific to the research questions.
💡 You can use CAQDAS like Delve to streamline your code equations. Delve has a simple interface for filtering your excerpts by codes. These filters make it easier to discover relationships involving multiple codes from different data sets.
By utilizing these filters, researchers can “operationalize phenomena” that span across multiple codes and explore how these codes relate to each other within the data.
4: In-Depth Abductive Qualitative Analysis
The last step is where you conduct in-depth abductive qualitative analysis. It consists of two sub-steps: inductive coding of your remaining excerpts and manual qualitative analysis.
4.a: Inductive coding of reduced cases
Now that you’ve found all excerpts that fit your filters in Step 3, you code those excerpts inductively.
You analyze the remaining excerpts manually to develop a second inductive coding scheme. This scheme is specific to the verified excerpts and helps you better understand the phenomenon by capturing details you haven’t documented yet.
You may also return to Step 3 to revise the code equation in a way that extends your theory further. Abductive coding is an iterative process, and you may need to go back and forth between the two steps until you are satisfied with your analysis.
4.b: In-depth qualitative analysis
The final step is to conduct an in-depth qualitative analysis of the data.
During this step, you closely examine the data, coding patterns, and any emergent themes. You also reflect on your interpretations and consider alternative explanations.
This iterative process will help you understand the phenomenon, confirm or refute your hypothesis, and offer more nuanced final results.
If you work with a team, you should hold meetings to finalize the results, discuss how to write them, and then publish them.
⚠️ Suppose you find that the code equation from step 2, or the inductive codes from step 3.a, are insufficient. You should return to those steps to refine the analysis to provide a more complete response to the initial research question.
More Tips for Abductive Coding
Be open to surprises. Abductive coding aims to generate new insights, so you need to be open to the possibility that an existing theoretical framework is incorrect.
Use a variety of data sources. This will help you to get a more comprehensive understanding of the phenomenon you are studying.
Be iterative. Abductive coding is an iterative process. Go back and forth between your data and theoretical framework, continuously questioning the integrity of your findings.
Be creative. Abductive coding requires creativity and curiosity, so don't be afraid to think outside the box. Similarly, be confident in your intuition as that guides the process.
Benefits & Limitations of Abductive Coding
Abductive coding is a helpful way to generate new insights and theories. While it provides many benefits, it is also important to be aware of the limitations of this method before using it.
Benefits:
Can generate new insights and theories.
Can be used to test and refine existing theories.
Goes beyond surface-level descriptions.
Encourages reflexivity by encouraging the reflection of biases.
Provides a deeper, more nuanced understanding of data.
Can be used to integrate inductive and deductive reasoning.
Can be used to explore complex data sets.
Limitations:
Can be time-consuming and demanding.
Requires a high level of creativity and intuition.
Lack of consensus on how to conduct abductive coding (Carleheden, 2016).
Can be difficult to replicate due to its reliance on interpretation.
Can be challenging to integrate with other qualitative research methods.
Incorporating CAQDAS in Abductive Coding
Technology like CAQDAS (computer-assisted qualitative data analysis software) can support and streamline the abductive coding process, reducing the time constraints it adds to your work.
Here are some of the features that can help you with abductive coding:
Ability to easily filter snippets to help with code equations. Use Delve’s simple filtering capabilities to see cross-sections between codes. Use AND / OR filters to see how different snippets appear across cross-sections of codes.
Global search: Functions like global search allows you to quickly and easily find specific patterns or themes across your entire dataset. This can save you a lot of time when actively searching for recurring patterns or anomalies in your data.
Memos: Memos are an essential part of abductive coding. They allow you to record your thoughts, reflections, and interpretations as you analyze your data. Tools like Delve’s memo function are a great way to engage your data reflexively and develop new insights.
Data visualization: Tabulating data streamlines the process of finding relationships between different codes and themes. It’s a way to better understand your data by identifying patterns you might not have noticed through manual coding.
Here are some additional benefits Delve for abductive coding:
Delve is easy to use and learn.
Delve is a top-rated CAQDAS software.
Delve is affordable and student and educator friendly.
Delve is compatible with a variety of data formats.
Delve is cloud-based and includes auto-save so your work is never lost.
Delve is constantly being updated with new features and functionality.
Wrapping Up
Abductive coding combines deductive and inductive coding elements to go beyond surface-level descriptions, uncovering deeper meanings in qualitative data and generating new insights.
By incorporating technology like Delve, researchers can enhance the efficiency and effectiveness of abductive coding.
Qualitative analysis doesn't have to be overwhelming
Take Delve's free online course to learn how to find themes and patterns in your qualitative data. Get started here.
References
Vila-Henninger, L., Dupuy, C., Van Ingelgom, V., Caprioli, M., Teuber, F., Pennetreau, D., Bussi, M., & Le Gall, C. (2022). Abductive Coding: Theory Building and Qualitative (Re)Analysis. Sociological Methods & Research. https://doi.org/10.1177/00491241211067508
Timmermans Stefan, Tavory Iddo. 2012. Theory Construction in Qualitative Research From Grounded Theory to Abductive Analysis. Sociological Theory 30(3):167-86.
Alvesson, Mats and Dan Kärreman. Qualitative Research and Theory Development: Mystery as Method. London: SAGE Publications Ltd, 2011. Sage Research Methods, 10 Jul 2023, doi: https://doi.org/10.4135/9781446287859.
Deterding, Nicole M. and Mary Waters. Flexible Coding of In-depth Interviews: A Twenty-first-century Approach. Sociological Methods & Research 50 (2018): 708 - 739.
Cite This Article
Delve, Ho, L., & Limpaecher, A. (2023c, August 07). Abductive Coding in Qualitative Analysis https://delvetool.com/blog/abductive-coding