Qualitative Data Analysis: From Theory to Practice   

Prof. Paré's course was very well-structured and pedagogically extremely well-taught. She clearly has long experience in teaching qualitative data analysis, and does it exceptionally well. — ECPR Summer School student

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Consider registering for Case Study Analysis, Data Visualization, Network Analysis I & II, or other related courses instead.

This course provides participants with a theoretical understanding and the applied, practical skills needed for planning, conducting, and reporting the findings of their own qualitative data analyses (QDA). Participants learn to design an analytical plan for their research, explain the epistemology and methodology behind the decisions that shape the QDA process, choose appropriate approaches to coding their data, and apply a wide range of qualitative techniques to find patterns and uncover relationships in their data. By the end of the course, participants will also be able to report their findings in line with best QDA practices. It also introduces participants to the main differences between qualitative content analysis, thematic analysis, cross-case analysis, analytic induction, and grounded theory and teaches them the advantages and potential pitfalls of using software for computer-assisted qualitative data analysis (CAQDAS).

This course is a stand-alone course. However, it can also be combined with other one-week courses, such as Experimental Methods, Interview and Focus Group Research, or Quantitative Text Analysis I.


This one-week, 20-hour course runs Monday-Friday, 9:00 am-1:00 pm, June 26-30, 2017.


Marie-Hélène Paré (picture), Open University of Catalonia

Detailed Description

This course provides the theoretical understanding and applied skills for planning, conducting, and reporting QDA research. It teaches participants how to design the analytical plan of their study, the influence of epistemology and methodology on the decisions shaping their QDA, appropriate approaches to coding data, qualitative techniques that allow them to identify patterns and uncover relationships in their data, and best practices for effectively and transparently reporting qualitative findings. It also discusses the main differences between qualitative content analysis, thematic analysis, cross-case analysis, analytic induction, and grounded theory.

The first day of the course starts out with a brief overview of the main problems that qualitative data analysis has historically been known for as well as the contemporary practices that explain why large numbers of qualitative studies still fail to present their analyses in a transparent manner – beyond the 'here are some themes that were identified in the data' catch phrase. Participants learn how to situate the qualitative analysis stage within a larger research design and to understand the relationship and interconnectedness between ontology, epistemology, methodology, and the wide range of QDA methods available to social scientists. Participants have the opportunity to develop the analytic plan of their own, independent study.

Day 2 focuses on the concepts and approaches to coding qualitative data. On paper and using the CAQDAS software NVivo, participants study and learn to apply the concepts of meaning units, coding units, codes, codebooks, and coding schemes. The use of descriptive, interpretive, and pattern codes is introduced to capture different levels of abstraction of the data. The common problems one encounters when coding qualitative data – such as the coding trap, code duplication, and 'what should I do after coding?' – are exemplified using several case studies, before we use graphical approaches to tease out conceptual associations, display emerging relationships, or falsify hypotheses.

The most common strategies to seek patterns and uncover relationships between people, places, and processes using induction, deduction, or abduction are covered on day 3. We first look at the definition of patterns, what they mean in the context of qualitative analysis, and the role they play in helping researchers uncover the underlying structure of the data, see its configuration, and weave large chunks of data in propositions, explanations, or hypotheses. Using NVivo, we learn how to use the syntax command to efficiently search for patterns based on data overlap, proximity, sequence, nesting, and exclusion before linking the concept of patterns to study outcome, looking at the ladder of abstraction in qualitative analysis.

On the fourth day, we cover the best and worst practices for reporting qualitative analyses and communicating qualitative research findings. We take a quick look at the most widespread problems in reporting qualitative findings, such as researchers presenting raw data in the form of excessive quotes as the primary results of their study. Clarifying the difference between data, analysis, findings, and conclusions, we use case examples to explore which information of the analytical process must be presented, how it needs to presented, and where in research reports or manuscripts it needs to be presented. Participants learn to use evocative graphic displays to present qualitative findings with the help of models, matrices, tables, and diagrams in NVivo.

The final day covers strategies to ascertain the trustworthiness of a qualitative study and techniques to validate findings. Depending on a researcher's epistemological stance, ascertaining trustworthiness can be done using the canons of quantitative research or widely used, alternative criteria. We examine these criteria as well as a range of techniques to validate findings, such as member checking, triangulation, ruling out rival explanations, and checking for researcher effects. This is followed by an introduction to the aim and some of the details five widely popular social science QDA methods: qualitative content analysis, thematic analysis, cross-case analysis, grounded theory and analytic induction. During the last part of the class, individual participants or teams can present their own (computer-assisted) qualitative data analyses.

The courses teaches participants how to put theory into practice using the NVivo software, which also makes them cognizant of the advantages and potential pitfalls of using software for CAQDAS. However, this course does not cover the full range of NVivo functionalities.


There are no formal prerequisites for this course. Prior knowledge of qualitative data analysis or NVivo is not required. However, a basic background in qualitative research would be beneficial.


Participants are expected to bring a WiFi-enabled laptop computer and should download the 14-day free trial version of NVivo 11 Pro for Windows in preparation for this course. As the restrictions of the free trial version, participants should only installed it after June 17, 2017. Mac owners should install the Pro edition for Windows using Boot Camp, Parallels, or VMware Fusion, as the Mac version lacks important functions and is inadequate for this course. Consult the QSR International website for compatibility options and system requirements. Installation support can be provided by the Methods School.

Core Readings

Boyatzis, Richard E. 1998. Transforming Qualitative Data: Thematic Analysis and Code Development. Thousand Oaks, CA: Sage Publications.

Bazeley, Patricia. 2013. Qualitative Data Analysis: Practical Strategies. Thousand Oaks, CA: Sage Publications.

Bazeley, Patricia., and Kristi Jackson. 2013. Qualitative Data Analysis with NVivo. Thousand Oaks, CA: Sage Publications.

Blaikie, Norman W. H. 2010. Designing Social Research. 2nd edition. Cambridge: Polity Press.

Corbin, Juliet M., and Anselm L. Strauss. 1990. Grounded Theory Research: Procedures, Canons, and Evaluative Criteria. Qualitative Sociology 13: 3-21.

Huberman, A. Michael, and Matthew B. Miles. 1994. Data Management and Analysis Methods. In: Norman. K. Denzin, and Yvonna S. Lincoln, eds. Handbook of Qualitative Research. Thousand Oaks, CA: Sage Publications.

Tesch, Renata. 1990. Qualitative Research: Analysis Types and Software Tools. New York, NY: Routledge.

Schreier, Margrit. 2014. Qualitative Content Analysis. In: Uwe Flick, ed. The Sage Handbook of Qualitative Data Analysis. Thousand Oaks, CA: Sage Publications.

Suggested Readings

Bernard, H. Russell., Amber Y. Wutrich, and Gery W. Ryan. 2017. Analyzing Qualitative Data: Systemic Approaches. 2nd edition. Thousand Oaks, CA: Sage Publications.

Coffey, Amanda J., and Paul A. Atkinson. 1996. Making Sense of Qualitative Data. Thousand Oaks, CA: Sage Publications.

Gibson, William J., and Andrew Brown. 2009. Working with Qualitative Data. Thousand Oaks, CA: Sage Publications.

Grbich, Carol. 2013. Qualitative Data Analysis: An Introduction. 2nd edition. Thousand Oaks, CA: Sage Publications.

Huberman, A. Michael, and Matthew B. Miles. 1994. Qualitative Data Analysis: An Expanded Sourcebook. 2nd edition. Thousand Oaks, CA: Sage Publications.

Ritchie, Jane, Jane Lewis, Carol McNaughton-Nicholls, and Rachel Ormston, eds. 2014. Qualitative Research Practice: A Guide for Social Science Students and Researchers. Thousand Oaks, CA: Sage Publications.

Saldaña, Johnny. 2009. The Coding Manual for Qualitative Researchers. Thousand Oaks, CA: Sage Publications.

Schreier, Margrit. 2012. Qualitative Content Analysis in Practice. Thousand Oaks, CA: Sage Publications.

Strauss, Anselm L., and Juliet Corbin. 1998. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. 2nd edition. Thousand Oaks, CA: Sage Publications.

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