is qualitative research inductive

Inductive, Deductive & Abductive Coding

Qualitative Coding Approaches Explained Simply (+ Examples)

By: Derek Jansen (MBA) | Expert Reviewer: Dr. Eunice Rautenbach | April 2024

Qualitative coding is a topic that often leaves students feeling a little confused – but it doesn’t have to be! In this post, we’ll unpack and explore the three overarching approaches to qualitative coding – inductive , deductive and hybrid – so that you can choose the best option for your project. 

Overview: Coding Approaches

• What exactly is “ qualitative coding “?
• Inductive coding
• Deductive coding
• Hybrid (abductive) coding
• Qualitative coding software
• Key takeaways

What (exactly) is “qualitative coding”?

Simply put, qualitative coding is the process of categorising and labelling textual data to lay the foundation for identifying themes, patterns, and ultimately, insights. In other words, it’s the first step toward qualitative data analysis .

In practical terms, coding involves meticulously reading through a dataset – for example, interview transcripts, field notes, or documents – and assigning ‘codes’ to various excerpts from the text. These codes can be words, phrases, or short little summaries that capture the essence of each data segment. That probably sounds a bit fluffy and conceptual, so let’s look at a practical example .

Imagine you have an interview transcript where a participant discusses their experience with a specific online learning platform. A segment of the transcript might read: “I found online classes challenging because I struggled with time management and staying motivated.” When it comes to coding this transcript, you might assign codes like “Time Management Challenges” and “Motivation Issues” to this specific passage. In other words, you’d be labelling and categorising snippets of text as you work your way through the transcript.

Now, the exact pieces of text you decide to label and which specific codes you use will depend on the coding structure that you adopt , as well as your research aims and research questions . We explain different coding structures and options in a separate post , so, for now, the key takeaway is that coding is about categorising and labeling data.

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The “Big 3” coding approaches

Now that we’ve defined what we mean by qualitative coding, we can start to explore the three overarching approaches to coding – that is, inductive , deductive and hybrid (also called abductive) coding. Let’s unpack each of these.

Inductive Coding

In simple terms, the inductive approach involves developing codes based on the data itself , as opposed to approaching the dataset with a pre-determined set of codes based on existing theory.

In practical terms, this means that you, as the researcher, will start the coding process with no preconceived codes or categories . Instead, you’ll read through each passage of text and allow the codes to emerge organically from the data, based on the patterns that you see .

In short, the inductive approach is bottom-up and iterative. This makes it ideal for exploratory research, especially when there is limited existing theory and understanding of a specific phenomenon. For example, if you were undertaking a study exploring how virtual reality affects the emotional well-being of elderly patients with limited mobility, you might consider using the deductive approach.

Deductive Coding

In contrast to inductive coding, the deductive approach uses an existing theory or theoretical framework as a basis for a pre-defined set of codes . This set of codes is developed in advance and is typically contained within something called a codebook .

In practical terms, deductive coding means that you’ll approach the data with a set of predefined codes and simply apply these codes to the data as you identify relevant passages or words . Importantly, with this approach, you don’t develop any new codes while coding – even if you see patterns in the data that aren’t represented by the existing code set.

This approach probably sounds a little rigid (and it is), but this top-down approach is useful when your research aims are more confirmatory in nature. In other words, the deductive approach can work well when your research aims involve testing a theory, rather than exploring an phenomena. For example, if you were undertaking a dissertation where you’re assessing the relevance of a specific motivation theory to a unique context, you might consider using the deductive approach.

Hybrid (Abductive) Coding

Last but certainly not least, let’s look at hybrid coding, which is sometimes also referred to as abductive coding.

As the name suggests, hybrid coding combines the inductive and deductive approaches in an attempt to get the best of both worlds. With this approach, you might start with some predefined codes and then proceed to develop additional codes, based on the patterns you observe along the way. Naturally, the hybrid approach to coding offers a good deal of flexibility . This makes it particularly effective for studies that incorporate both exploratory and confirmatory research aims .

How to choose the right coding approach

As you can see, the right coding approach – inductive, deductive or hybrid – will depend largely on the nature of your research aims and research questions . If your aims are primarily exploratory and there’s not a large body of existing research regarding your topic, an inductive coding approach typically makes sense.

Conversely, if your research aims involve confirming or even contradicting an existing theory, a deductive approach would likely be better suited. So, as with all methodological choices, your coding approach needs to be informed, first and foremost, by your research aims.

A quick about qualitative coding software…

It’s worth quickly mentioning that there are various software options available to assist with the coding process. Popular options include NVivo, Delve, Atlas T.I. and MAXQDA . Now, while these tools can certainly assist in terms of managing the coding process, it’s important to understand that they are not essential , at least not for small datasets – which is commonly the case for student projects.

For the vast majority of projects, you can code your dataset using a simple word processor such as Microsoft Word or Google Docs. In fact, at Grad Coach, we code datasets for student projects every day using nothing more than Word and Excel. Taking a low-tech approach also helps you absorb and digest the data more deeply, as you naturally spend more time reading through it.

Long story short, while there are software options available, don’t feel obligated to use them , unless your university specifically requires you to do so. On the flip side, be sure to check if y our university has any restrictions in terms of what software you can use, especially anything AI-powered. You don’t want to run into a case of academic misconduct just because you used the wrong software!

Key Takeaways

We’ve covered quite a bit of ground here, so let’s do a quick recap.

• Qualitative coding is the process of categorising and labelling textual data to lay the foundation your qualitative analysis.
• There are three overarching approaches to coding – inductive, deductive and hybrid.
• Your choice of approach needs to be informed by your research aims and research questions.

If you need a hand coding your qualitative data, be sure to check out private coaching , as well as our “done-for-you” qualitative coding service .

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This post is an extract from our bestselling short course, Qualitative Research Bootcamp . If you want to work smart, you don't want to miss this .

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Qualitative Research Journal

ISSN : 1443-9883

Article publication date: 31 October 2018

Issue publication date: 15 November 2018

The purpose of this paper is to explain the rationale for choosing the qualitative approach to research human resources practices, namely, recruitment and selection, training and development, performance management, rewards management, employee communication and participation, diversity management and work and life balance using deductive and inductive approaches to analyse data. The paper adopts an emic perspective that favours the study of transfer of human resource management practices from the point of view of employees and host country managers in subsidiaries of western multinational enterprises in Ghana.

Design/methodology/approach

Despite the numerous examples of qualitative methods of data generation, little is known particularly to the novice researcher about how to analyse qualitative data. This paper develops a model to explain in a systematic manner how to methodically analyse qualitative data using both deductive and inductive approaches.

The deductive and inductive approaches provide a comprehensive approach in analysing qualitative data. The process involves immersing oneself in the data reading and digesting in order to make sense of the whole set of data and to understand what is going on.

Originality/value

This paper fills a serious gap in qualitative data analysis which is deemed complex and challenging with limited attention in the methodological literature particularly in a developing country context, Ghana.

• Qualitative
• Emic interviews documents

Azungah, T. (2018), "Qualitative research: deductive and inductive approaches to data analysis", Qualitative Research Journal , Vol. 18 No. 4, pp. 383-400. https://doi.org/10.1108/QRJ-D-18-00035

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Answers to the most commonly asked questions here

Inductive Approach (Inductive Reasoning)

Inductive approach, also known in inductive reasoning, starts with the observations and theories are proposed towards the end of the research process as a result of observations [1] .  Inductive research “involves the search for pattern from observation and the development of explanations – theories – for those patterns through series of hypotheses” [2] . No theories or hypotheses would apply in inductive studies at the beginning of the research and the researcher is free in terms of altering the direction for the study after the research process had commenced.

It is important to stress that inductive approach does not imply disregarding theories when formulating research questions and objectives. This approach aims to generate meanings from the data set collected in order to identify patterns and relationships to build a theory; however, inductive approach does not prevent the researcher from using existing theory to formulate the research question to be explored. [3] Inductive reasoning is based on learning from experience. Patterns, resemblances and regularities in experience (premises) are observed in order to reach conclusions (or to generate theory).

Application of Inductive Approach (Inductive Reasoning) in Business Research

Inductive reasoning begins with detailed observations of the world, which moves towards more abstract generalisations and ideas [4] . When following an inductive approach, beginning with a topic, a researcher tends to develop empirical generalisations and identify preliminary relationships as he progresses through his research. No hypotheses can be found at the initial stages of the research and the researcher is not sure about the type and nature of the research findings until the study is completed.

As it is illustrated in figure below, “inductive reasoning is often referred to as a “bottom-up” approach to knowing, in which the researcher uses observations to build an abstraction or to describe a picture of the phenomenon that is being studied” [5]

Here is an example:

My nephew borrowed $100 last June but he did not pay back until September as he had promised (PREMISE). Then he assured me that he will pay back until Christmas but he didn’t (PREMISE). He also failed in to keep his promise to pay back in March (PREMISE). I reckon I have to face the facts. My nephew is never going to pay me back (CONCLUSION).

Generally, the application of inductive approach is associated with qualitative methods of data collection and data analysis, whereas deductive approach is perceived to be related to quantitative methods . The following table illustrates such a classification from a broad perspective:

However, the statement above is not absolute, and in some instances inductive approach can be adopted to conduct a quantitative research as well. The following table illustrates patterns of data analysis according to type of research and research approach .

When writing a dissertation in business studies it is compulsory to specify the approach of are adopting. It is good to include a table comparing inductive and deductive approaches similar to one below [6] and discuss the impacts of your choice of inductive approach on selection of primary data collection methods and research process.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research approaches. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research design ,  methods of data collection ,  data analysis  and  sampling  are explained in this e-book in simple words.

John Dudovskiy

[1] Goddard, W. & Melville, S. (2004) “Research Methodology: An Introduction” 2nd edition, Blackwell Publishing

[2] Bernard, H.R. (2011) “Research Methods in Anthropology” 5 th edition, AltaMira Press, p.7

[3] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th  edition, Pearson Education Limited

[4] Neuman, W.L. (2003) “Social Research Methods: Qualitative and Quantitative Approaches” Allyn and Bacon

[5] Lodico, M.G., Spaulding, D.T &Voegtle, K.H. (2010) “Methods in Educational Research: From Theory to Practice” John Wiley & Sons, p.10

[6] Source: Alexandiris, K.T. (2006) “Exploring Complex Dynamics in Multi Agent-Based Intelligent Systems” Pro Quest

A qualitative simulation checking approach of programmed grounded theory and its application in workers’ involvement: extending Corbin and Struss’ grounded theory checking mechanism

• Published: 29 April 2024

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• Haoran Wang 1 ,
• Bin Hu 1 &
• Yanting Duan 1  

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The validity and reliability of a grounded theory research based on interpretivism involves four dimensions: credibility, transferability, dependability, and confirmability. In order to enhance the credibility of a qualitative study, the findings of the grounded theory need to be checked for consistency with reality. Traditional checking approaches lack universal applicability and are difficult for researchers to implement. This paper proposes a qualitative simulation checking approach for programmed grounded theory research, which can enhance the validity and reliability of programmed grounded theory research in terms of credibility, transferability, and dependability dimensions. This approach is not only a more generally applicable checking approach, but also provides a virtual experiment platform for qualitative research. To overcome the deficiencies caused by following a single approach, a checking framework that integrates programmed grounded theory, qualitative simulation checking, and member checking was proposed. The methodology of this paper is validated by applying to a case of sanitation workers’ involvement in the Internet of Things environment.

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Acknowledgements

The authors would like to thank the participants in the study discussed in this paper as well as the managers of the sanitation work in Shenzhen, China.

This work was supported by the [National Natural Science Foundation of China] (grant number [72371110], [71971093], [72132001]) and the [Fundamental Research Funds for the Central Universities] (grant number [2023WKZDJC007]).

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by HW, BH and YD. The first draft of the manuscript was written by HW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Wang, H., Hu, B. & Duan, Y. A qualitative simulation checking approach of programmed grounded theory and its application in workers’ involvement: extending Corbin and Struss’ grounded theory checking mechanism. Qual Quant (2024). https://doi.org/10.1007/s11135-024-01864-3

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DOI : https://doi.org/10.1007/s11135-024-01864-3

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