1 March 2024 1143 words, 5 min. read

6 Qualitative Data Coding Techniques

By Pierre-Nicolas Schwab PhD in marketing, director of IntoTheMinds
In this article, you'll find a comparison of 6 coding techniques for qualitative data (interviews, focus groups). Each technique's advantages and disadvantages are presented so you can make the best choice.

Data from qualitative research should be more often analyzed. Our market research agency is one of the last to practice qualitative interview coding, and 99% of our customers need to learn what it is. In today’s article, I explain the 6 possible coding approaches and compare their respective advantages and disadvantages.

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Approach Advantages Disadvantages
Inductive coding Allows you to discover new themes and patterns in the data.
Flexible and adaptable to current information
Useful for exploring unknown data.
Time-consuming and labor-intensive
It can be subjective and subject to bias
Difficult to replicate and compare
Deductive coding Efficient and faster than inductive coding
More objective and dependable
Easier to replicate and compare results.
Important themes or patterns may go undetected.
Less flexible and adaptable to the discovery of current information
Requires pre-existing coding scheme
Thematic analysis Identifies recurring themes throughout the data
Useful for identifying broad patterns in data.
It can be time-consuming to code copious quantities of data
It may not capture certain nuances in the data
Anchored theory Develops theories based on data
Useful for exploring new and emerging phenomena.
It can be time-consuming and complex to code
It can be not easy to generalize results to other contexts
Narrative analysis Focuses on the stories and experiences shared in the data
Useful for understanding participants’ lived experiences
It can be time-consuming to code copious quantities of data
It may not be generalizable to other populations
Discourse analysis Examines how language is used in data
Useful for understanding how power and ideology are reproduced in the data.
It can be time-consuming and complex to code
It can be not easy to interpret the results

Inductive coding

Inductive coding is a method of data analysis in which themes and categories emerge directly from the data without a predefined framework. This approach is guided by the observations conducted during the research, enabling open exploration of the data.


  • Discovery of New Themes and Patternsinductive coding identifies unexpected themes and patterns in the data. This approach allows the data to speak for itself, unconstrained by preconceived categories or theories. This encourages the discovery of new insights.
  • Flexibility and adaptability: it offers unrivaled flexibility. Researchers can adjust the analytical framework in response to the data they analyze. This adaptability is crucial in exploratory research, where initial hypotheses may evolve.
  • Exploration of Unknown Data: inductive coding is particularly useful when the researcher is unfamiliar with the data they are analyzing. It forces analysts to keep an open mind and deeply understand the subject.


  • Tedious: the open-ended nature of inductive coding can make the process lengthy and laborious. It requires careful examination of the data and constant refinement of categories.
  • Subjectivity and bias: since this approach relies heavily on the researcher’s interpretation, subjectivity and bias are risks.
  • Difficulties of Replication and Comparison: since coding schemes are tailor-made, they are difficult to reuse. Replication of research or Comparison of results is therefore complicated.

Deductive coding

Deductive coding applies a predefined coding scheme to the data. Simply put, you define a coding guide in advance and apply it to each interview. This method is based on existing theories or hypotheses that guide the analysis. This is the type of coding we apply most often at IntoTheMinds. We used the interview guide and literature review to develop the coding grid.


  • Efficiency: deductive coding is more efficient than its inductive counterpart, as it applies a predefined coding scheme to the data. This structured approach speeds up the analysis process.
  • Objectivity and reliability: deductive coding minimizes the influence of researcher bias, using established codes, improving the objectivity and reliability of results.
  • Replicability: the standardized nature of deductive coding simplifies the replication of research and Comparison of results across different studies.


  • Missing themes: this approach can overlook important themes or patterns not anticipated in the initial coding scheme, potentially omitting significant insights.
  • Flexibilitydeductive coding is less adaptable to current information that doesn’t fit into predefined categories. This limits its usefulness in exploratory research.
  • Need for a pre-existing coding scheme: it requires a well-developed coding scheme before data analysis can begin. This is only sometimes possible, for example, when literature research is impossible (the subject is too new).

Thematic Analysis

Thematic analysis is a flexible method of qualitative analysis that identifies, analyzes, and reports on themes within the data. It is not limited to the structure of the data and allows a great deal of freedom in interpreting the data.


  • Identify recurring themes: thematic analysis is ideal for identifying and analyzing recurring themes across a data set.
  • Trend detection: effective for discerning patterns and trends.


  • Time requiredcoding large data sets can be extremely time-consuming. You can counter this problem by using generative AI.
  • Overlooking nuances: while thematic analysis is good at identifying broad themes, it can overlook the subtleties and nuances of individual data elements.

Grounded theory

Grounded theory is a methodological approach that aims to build theories from data analysis. It is particularly useful for exploring little-known or emerging phenomena. It was developed by Glaser and Strauss in 1967.


  • Theory development: grounded theory has no equivalent for developing theories directly from data. It is, therefore, the most appropriate coding for understanding complex phenomena.
  • Exploring new phenomena: this approach is particularly well suited to exploring new or emerging phenomena.


  • Complexity and time: the iterative coding, categorizing, and theory development process can be complex and time-consuming.
  • Difficulty of generalization: the results of grounded theory research can be difficult to generalize to other contexts.

Narrative Analysis

Narrative analysis focuses on the stories and experiences shared in the data. It explores how individuals make sense of their experiences through stories.


  • Focus on stories and experiences: narrative analysis focuses on the stories and experiences shared within the data. It, therefore, provides an understanding of participants’ different potential.
  • Understanding of lived experiences: it provides insights into the lived experiences of individuals.


  • Challenges of application to large data sets: as with other approaches, analyzing large corpora is laborious.
  • Generalization problemsgeneralization is complicated as insights are specific to individuals.

Discourse analysis

Discourse analysis examines the use of language in data. It focuses on how language shapes and is shaped by social and cultural contexts. In qualitative interviews, one aspect of discourse analysis is to look at metaphors and analyze how they reveal who we are. This is the approach taken by Dr. Zaltman’s method.


  • Examination of language use: discourse analysis enables us to understand how discourse shapes and is shaped by social contexts.
  • Insights into ideology: discourse allows us to analyze how power and ideology are present in the data.


  • Analytical complexityspecific expertise is required. This is the prerogative of linguists rather than sociologists.
  • Interpretation: results can be difficult to interpret. The nuances of language and context require detailed, sometimes subjective, analysis.

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