O3: Qualitative Data Analysis: Coding

This online course offers a structured and practice-oriented introduction to qualitative data analysis, designed for novice coders and for those who want to strengthen their coding skills and analytical confidence. You will learn how to engage analytically with qualitative data, develop and apply different types of codes (inductive, deductive, abductive, and interpretative), and move from coding to meaningful patterns and insights.

Throughout the course, you work hands on with a shared sample dataset, gaining experience in organizing data, developing codes, and building higher level analytical structures such as categories, themes, and pattern codes. Practical exercises are supported by QDA software training using NVivo, with the option to follow along in ATLAS.ti or MAXQDA. No prior experience with QDA software is required.

By the end of the course, you will feel confident in your ability to conduct qualitative coding and analysis in a systematic and methodologically informed way. The final day introduces major qualitative coding strategies, including thematic analysis, qualitative content analysis, grounded theory coding, and abductive coding, helping you situate your skills within broader qualitative methods.

For those who wish to continue developing these skills, the course provides a strong foundation for the in person all day workshop Qualitative Data Analysis: Methods Workshop, where these approaches are explored in greater depth through extended practice and guided methodological reflection.

This online course offers a structured, supportive, and practice-oriented introduction to qualitative data analysis, with a clear emphasis on coding as the core analytical practice. It is designed for novice coders who are beginning to work with qualitative data, as well as for those who already have some exposure to qualitative research and want to strengthen their coding skills and analytical confidence.

The course treats qualitative data analysis as an iterative and reflective process rather than a purely technical task. Across five days, you work with a shared empirical dataset and gradually develop your analysis through guided engagement, hands-on practice, and independent reflection. The course is designed to make the logic of qualitative analysis visible and approachable, helping you understand not only how to code, but why analytical decisions matter.

Day 1 Understanding Qualitative Data Analysis and Engaging with Data

The course begins by situating qualitative data analysis within broader research processes. You are introduced to different types of qualitative inquiry, common data sources, and key considerations around sampling and research design. Particular attention is given to the transition from data collection to analysis: how to engage analytically with qualitative material, how to move from reading to coding, and what counts as a meaningful coding unit in relation to the research question, theoretical focus, and analytical goals.

These early sessions help you develop a clear conceptual foundation for qualitative analysis and avoid common pitfalls when beginning data work, such as premature coding or misalignment between research questions and analytical strategies.

Day 2 Initial Coding and First Cycle Analysis

The second day focuses on initial coding as first cycle analysis. You learn how codes function as analytical tools that help identify features of the data that are relevant to your research questions. Rather than treating coding as a purely technical task, the course emphasizes coding as an interpretive and analytical process.

Through hands-on work with interview material, you practice applying and revising codes and reflect on how different coding decisions lead to different analytical insights. Attention is given to the relationship between data-driven and theory-informed coding, supporting you in developing confidence in making and explaining your coding choices.

Day 3 From Codes to Patterns and Analytical Pathways

On the third day, the course moves beyond individual codes toward pattern development and higher-level analysis. Through structured hands-on work, you learn how to group and compare codes in order to identify emerging patterns and analytical directions.

This day also introduces different analytical pathways that guide qualitative analysis. Sessions on pattern coding and theme development demonstrate how codes can be combined, compared, and refined to generate meaning and insight. You learn how inductive and deductive approaches can be used separately or in combination, and how analytical choices influence the types of claims that can be made from qualitative data.

Day 4 Ensuring Analytical Quality and Presenting Coherent Findings

The fourth day focuses on refining your analysis and strengthening analytical coherence. You learn how provisional coding frameworks and codebooks support consistency, transparency, and reflexivity, while remaining open to revision as your analysis deepens. Through guided hands-on work, you revisit and refine your coding and emerging patterns.

Building on earlier discussions of analytical pathways, the course returns to questions of analytical quality and credibility. You reflect on how interpretations are developed, checked, and justified, and how meaning is generated through systematic comparison and reflexive engagement with your data.

This day also introduces practical ways of making sense of findings and communicating qualitative analysis clearly. You work with examples of data displays and presentation strategies that help make analytical arguments visible and accessible to others.

Day 5 Choosing Coding Strategies within Qualitative Methods

The final day is devoted to deepening your understanding of how coding practices are embedded within broader qualitative methodological approaches. After several days of hands-on analytical work, this session invites you to step back and reflect on how different ways of coding imply different ways of thinking about data, theory, and explanation.

The course concludes with a structured and comparative introduction to four key qualitative coding strategies: thematic analysis, qualitative content analysis, grounded theory coding, and abductive coding. You learn what distinguishes these approaches in terms of their analytical goals, their relationship to theory, and the kinds of research questions they are designed to address. Rather than focusing on procedural detail, the emphasis is on understanding the logic of each strategy and the implications this has for coding, interpretation, and claims-making.

Drawing on examples from qualitative research practice, the session helps you develop the ability to make informed methodological choices. You are encouraged to consider how your research question, data characteristics, and theoretical orientation should guide the selection of a coding strategy. Particular attention is given to understanding these approaches as complementary rather than hierarchical, and to recognizing that methodological flexibility is a key strength in qualitative research.

Course Rhythm and Learning Experience

The course takes place over five consecutive days in an online format. Each day includes two live interactive sessions in the morning with a short break in between, from 9:00 to 10:30 and from 11:00 to 12:30. These live sessions form the shared core of the course and combine interactive lectures, guided discussion, and hands-on analytical work.

Throughout the week, you work with a shared sample dataset prepared by the instructor. Using the same data across all days allows you to focus fully on developing analytical skills and to experience how qualitative analysis unfolds step by step, from initial engagement to interpretation and presentation. This shared empirical focus makes the course accessible to novice coders while providing enough depth for more experienced participants.

From Monday to Thursday, you are also invited to complete short daily assignments in the afternoon using the shared dataset. These assignments are optional and designed as learning opportunities rather than requirements. You can approach them flexibly, choosing how deeply you want to engage depending on your interests, experience, and available time. Completing them supports the full learning experience, while selective engagement still allows you to benefit meaningfully from the course.

At the same time, the course encourages you to continuously relate what you are learning to your own research. Through guided discussion and opportunities for individual consultation, you can reflect on how the analytical choices, coding strategies, and methods introduced in the course connect to your own project, research questions, and data.

The course is supported by shared materials and light preparatory reading that help you get the most out of the live sessions, without requiring extensive advance preparation.

Practical exercises are integrated throughout the course using NVivo software, though you may also follow along using ATLAS.ti or MAXQDA. No prior experience with QDA software is required. Step by step, you learn how software can support data organization, coding, codebook development, and pattern exploration, while the course consistently emphasizes that analytical judgment, interpretation, and methodological coherence remain your responsibility as a researcher.

Learning Outcomes and Progression

By the end of the course, you will have a solid foundation in qualitative coding and data analysis, practical experience using QDA software, and the confidence to conduct qualitative analysis in a systematic and methodologically informed way. You will be able to apply and refine codes, develop patterns, and make transparent analytical decisions grounded in your research questions.

You will also gain a clear conceptual overview of major qualitative coding strategies and a better sense of how your coding skills can be developed further. The course provides a strong foundation for continued methodological training, including the in-person full-day workshop Qualitative Data Analysis: Methods Workshop, where these approaches are explored in greater depth through extended practice and guided methodological reflection.

This description is subject to change at the discretion of the Instructor

2 Credits

For completion of all work before and during the course, as outlined by the Instructor, and 90% participation and attendance of the course.