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Qualitative Data Analysis: Concepts and Approaches PM

Course Dates and Times

Monday 29 July – Friday 2 August 

14:00–17:30 (ending slightly earlier on Friday)


Marie-Hélène Paré

Are you planning to conduct interviews or focus groups for your data collection, or perhaps do participant observation during events or meetings? Or will you be collecting policy papers, press articles, or Internet data from blogs, Facebook or Twitter?

If you do any of the above, you will soon or later have to face the pile of data you collected and analyse them.

Will you know how?

This course gives you strategic understanding and applied skills in planning, conducting, and reporting the process of qualitative data analysis (QDA) in one’s research. It teaches the foundational concepts underlying the process of qualitative analysis and explores, in sequence:

  1. the influence that epistemology and research design has on choosing an analytical method
  2. approaches to coding qualitative data, that is, deciding the right coding unit, efficiently managing a coding scheme, developing meaningful categories and memo-ing the coding process judiciously
  3. strategies to transform qualitative data into findings, and the nuts and bolts of retrieval procedures
  4. debates surrounding the quality of qualitative research and the techniques to confirm the trustworthiness of qualitative results
  5. best and worst practices when presenting qualitative findings including crafting visual displays that tell the story of the findings effectively and appraising the quality of reporting of qualitative analysis in published studies.

The course blends lectures and applied exercises and daily assignments after class. If you are conducting qualitative research as part of your PhD or postdoc, or for a government or research centre, you will particularly benefit from it.

ECTS Credits for this course and, below, tasks for additional credits.

2 credits Do the readings and take an active part in the course.

2 additional credits As above, plus complete the four daily assignments and an academic essay. Daily assignments involve writing a 700–900-word paper answering questions about the concepts covered each day in class. They must be produced outside class hours and emailed to the Instructor at the end of each day. The academic essay is a longer paper (4,000 words) demonstrating the knowledge and skills gained over the course of the week, and their application in your research. You have until 10 August 2019 to submit the essay.

Instructor Bio

Marie-Hélène is a highly regarded methodologist who has NVivo Certified Platinum Trainer status. She has shared her expertise in qualitative data analysis with over 60 universities and research centres around the world, including Qatar and Iran. Since 2009, Marie-Hélène has been teaching introductory and advanced courses in qualitative data analysis at the ECPR Methods School. Her areas of methodological interest include qualitative evidence synthesis, decolonising epistemology, and participatory methodologies. Marie-Hélène is dedicated to advancing the field of qualitative data analysis and sharing her knowledge with others.


What is qualitative data analysis (QDA)?

QDA is the search for patterns in textual, visual or artefact data to uncover associations, identify relationships and propose explanations about the studied phenomenon.

The process of analysis involves several steps, often described in sequence although much intertwined in practice, known as getting familiar with the material, coding the data, identifying patterns and generating results and presenting findings.

A range of methods of qualitative analysis exists (thematic analysis, grounded theory, content analysis, etc.) all of which come with their epistemological standpoint, sampling requirements, coding procedures, techniques to generate findings and quality criteria.

This course does not focus on a method of QDA in particular; rather, it explores the common denominators all methods share and provides tools and food for thought to implement them.

Why is this course relevant?

Historically, QDA has been criticised for being opaque and subjective given that it is sometimes difficult to see how researchers went from hundreds of interview pages to a handful of conclusions since no discussion is provided about what the researchers actually did when they analysed their data. For this reason, QDA has a ‘black box’ association problem: we are told about the data, how they were collected and what results they yielded, but nothing in between. There is also a misconception that QDA merely involves the identification of themes in data and the reporting of themes in the form of quotes, often lots of them.

The analytical task researchers should be concerned about – that is, the examination of commonalities and differences across units of analysis, discovering patterns and relationships across the data, and synthesising these in a storyline, model, or schema – is usually absent in published qualitative studies. Conversely, the widespread myths that the ‘method will emerge’ from the data, or that the researcher’s tacit knowledge will lead him/her to ‘make sense’ of the data, account for opaque reporting since no audit trail accompanies the analytic process.

What will I learn on this course?

How to plan, conduct and report QDA in a transparent, traceable and auditable way in your research. You will learn

  1. key design issues to consider when planning the analysis phase in qualitative study
  2. different approaches to code qualitative data
  3. strategies to transform qualitative data into findings
  4. debates about the quality of qualitative research and techniques to confirm the trustworthiness of qualitative findings
  5. best and worst practices when presenting qualitative findings in doctoral theses and journal articles.

The different stages of QDA will be demonstrated with NVivo software so you can put concepts into practice and learn the advantages and pitfalls of QDA software. To that end, please use NVivo 12 Pro for Windows, NVivo 12 for Mac or other QDA software you are familiar with. See Software and Hardware Requirements.

Course objectives

  1. To demonstrate the influence that methodology has on the choice of data analysis.
  2. To describe the different approaches to coding qualitative data.
  3. To distinguish the strategies for transforming qualitative data into findings.
  4. To situate the competing philosophies regarding validity in qualitative research.
  5. To present qualitative results evocatively and effectively.

At the end of this course, you will be able to

  1. develop an analytical plan of your study
  2. choose the right approach to code your data
  3. identify what strategy to use to transform data into findings
  4. reflect on your position regarding validity in qualitative research
  5. present qualitative findings convincingly and transparently.

Day 1 – Foundations of QDA
We open with a lecture on the foundations of qualitative analysis with definitions, historical problems and challenges ahead. The qualitative analysis cycle is introduced as a heuristic device to understand that qualitative analysis, often if not always, occurs iteratively between the phases of coding data, generating meaning, confirming findings and presenting results. We situate the phase of qualitative analysis within the research design and consider the influence that research questions, study purpose and type of data collected have on the choice of data analysis method. We then look at some popular approaches to QDA; I present in turn the aim, sampling requirements and analytical procedures of qualitative content analysis, thematic analysis, cross-case analysis, grounded theory and analytic induction. During the second half of the class you will set up an NVivo project and import and classify data in preparation for Day 2. 

Day 2 – Concepts and approaches for coding qualitative data
In qualitative research, coding is defined as the process by which data are segmented in coding units and assigned a code that represents a concept, construct or idea. Coding is a core task in qualitative analysis, so knowing how to code one’s data meaningfully and efficiently is key.

We discuss the key concepts that shape the coding process – meaning and coding units, codes, codebook and coding schemes – alongside situations where descriptive, interpretive, and pattern codes can be used to capture distinct levels of abstraction in the data. We then examine the specificities of theme- versus design-based coding schemes and review their respective strengths and requirements. We conclude by using visualisations to map the coding process and the emerging relationships between codes. Finally, in a coding workshop with NVivo, you will code your own data and reflect on the process.

Day 3 – Transforming data into findings
‘I have a mountain of information here. Which bits go together?’ (Miles & Huberman, 1994: 256). Coding is a first step in the transformation process because it reduces the amount of data in codes and organises them in thematic families. However, coding alone is no analysis.

Qualitative analysis involves the researcher detecting the structure that lies behind the data and explaining how the structure connects people, places, processes or practices in one coherent storyline. To do so, we propose a range of strategies for transforming data, and follow with a discussion on the logic of scientific reasoning: in turn, I present examples of inductive, deductive, abductive and retroductive analyses. I then introduce the nuts and bolts of retrieval procedures that operate on data concurrence, overlap, sequence, proximity, precedence and exclusion in NVivo.

We conclude with a discussion on the ladder of abstraction in qualitative analysis.

Day 4 – Presenting qualitative findings
Beyond using quotes to illustrate participant views, in what ways can qualitative findings be presented? What are the best and worst practices when presenting qualitative findings in journal articles and theses? And what about the ubiquitous phrase ‘themes were identified in the data’, when themes were part of the interview guide?

We explore these questions so you gain a critical understanding of how visual display(s) can tell the story of their findings effectively and evocatively. To that end, we learn that models are best to illustrate conceptual integration, matrices are good for cross-tabs, tables work for typology, and diagrams suit the depiction of structure.

We move on with a discussion and examples of the reporting of analysis, results and interpretation in a qualitative report.

The second part of the class is a workshop during which you assess the the reporting of qualitative analysis in a qualitative study.

Day 5 – Validating qualitative results
Validity of qualitative research is a debated issue among qualitative scholars because it brings together divergent perceptions and antagonistic practices, depending on whether you espouse empiricism, critical realism, constructionism or subjectivism as your anchored epistemology.

We review the current debates on the quality of qualitative research, examining what the conventional scientific paradigm, the naturalistic paradigm, and other complementary strategies, have put forward since the 1980s.

I then ask you to reflect on your own epistemological positionality, individually, then in small-group discussions. We look at different techniques to confirm the trustworthiness of qualitative results, among which we discuss member checking, researcher effects, triangulation and using deviant cases, and comment on their appropriateness depending on the research context and the nature of data collected.

We conclude with a masterclass in which teams present examples of qualitative analysis of a research project.

Basic knowledge of qualitative research required.

This is a bring-your-laptop course where NVivo software is used for demonstration only. No knowledge of NVivo is required.

If you want to learn how to analyse qualitative data with NVivo, take Introduction to NVivo for Qualitative Data Analysis.


Software Requirements

We will use NVivo software for the workshop sessions.

Please install NVivo 12 Pro for Windows or NVivo 12 Mac (or other QDA software you are familiar with) on your laptop prior to the course.

Demonstrations, however, as well as troubleshooting, will only be done for NVivo.

Download the 14-day free NVivo trial for Windows or Mac

It is your responsibility to ensure that NVivo works well on your laptop before the course because no IT support will be provided during or outside teaching hours by the Instructor, teaching assistant, ECPR staff, or CEU IT services.

Once you have NVivo installed, verify that it works properly by following the instructions below:

  1. Launch NVivo.
  2. On the Start screen (Windows version), in the New section, click Sample Project. On the Welcome to NVivo for Mac screen (Mac version), click 'Create a copy of the sample project'.
  3. NVivo opens a copy of the sample project which is stored in your default project location.
  4. If you can’t open the sample project, contact QSR international by submitting a support request form online (see section Contact Us Online at the bottom of the page).

Hardware Requirements

Please bring your own laptop with software installed. NVivo hardware requirements as per QSR International

Windows version





1.2 GHz single-core processor (32-bit) 1.4 GHz single-core processor (64-bit)

2.0 GHz dual-core processor or faster


2 GB RAM or more

4 GB RAM or more


1024 x 768 screen resolution

1680 x 1050 screen resolution or higher

Operating system

Microsoft Windows 7

Microsoft Windows 7 or later

Hard disk

Approximately 5 GB of available hard-disk space

Approximately 8 GB of available hard-disk space


Mac version





Mac computer with an Intel Core 2 Duo, Core i3, Core i5, Core i7, or Xeon processor

Mac computer with an Intel Core i5, Core i7, or Xeon processor


2 GB of RAM (as defined by the Mac OS X Mavericks minimum requirements)



1280 x 800 screen resolution

1440 x 900 screen resolution or higher

Operating system

Mac OS X 10.9 (Mavericks) or later

Mac OS X 10.9 (Mavericks) or later

Hard disk

Approximately 2 GB of available hard-disk space

Approximately 4 GB SSD of available hard-disk space


Bazeley, P. (2013). Qualitative Data Analysis: Practical strategies. London: Sage.

Bernard, H. R., & Ryan, G. W. (2010). Analyzing Qualitative Data: Systemic Approaches. Thousand Oaks: Sage.

Boeije, H. R. (2010). Analysis in Qualitative Research. London: Sage.

Boyatzis, R. E. (1998). Transforming Qualitative Data: Thematic Analysis and Code Development. Thousand Oaks: Sage.

Coffey, A., & Atkinson, P. (1996). Making Sense of Qualitative Data: Complementary Research Strategies Thousand Oaks: Sage.

Dey, I. (1993). Qualitative Data Analysis: A User-Friendly Guide for Social Scientists. London: Routledge.

Flick, U. (Ed.). (2014). The Sage Handbook of Qualitative Data Analysis. London: Sage.

Gibson, W. J., & Brown, A. (2009). Working with Qualitative Data. London: Sage.

Grbich, C. (2013). Qualitative Data Analysis: An Introduction (2nd ed.). London: Sage.

Harding, J. (2013). Qualitative Data Analysis: From start to Finish. London: Sage.

Kawulich, B. B. (2004). Data analysis techniques in qualitative research. Journal of Research in Education, 14(1), 96-113.

LeCompte, M. (2000). Analyzing Qualitative Data. Theory Into Practice, 39(3), 146-154.

Leech, N. L., & Onwuegbuzie, A. J. (2007). An Array of Qualitative Data Analysis Tools: A Call for Data Analysis Triangulation. School Psychology Quarterly, 22(4), 557-584.

Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis (2nd ed.). Thousand Oaks: Sage.

Richards, L. (1998). Closeness to Data: The Changing Goals of Qualitative Data Handling. Qualitative Health Research, 8(3), 319-328.

Ritchie, J., Lewis, J., Nicholls, C. M. N., & Ormston, R. (Eds.). (2014). Qualitative Research Practice: A Guide for Social Science Students and Researchers: Sage.

Ryan, G. W., & Bernard, H. R. (2003). Techniques to Identify Themes. Field Methods, 15(1), 85-109.

Sandelowski, M. (1995). Qualitative Analysis: What It Is and How to Begin. Research in Nursing & Health 18(4), 371 -375.

Saldaña, J. (2009). The Coding Manual for Qualitative Researchers. London: Sage.

Spradley, J. P. (1979). The Ethnographic Interview. Fort Worth: Holt, Rinehart and Winston.

Strauss, A. L. (1987). Qualitative Analysis for Social Scientists. New York: Cambridge University Press.

Thomas, D. R. (2006). A General Inductive Approach for Analyzing Qualitative Evaluation Data. American Journal of Evaluation, 27(2), 237-246.

Tesch, R. (1990). Qualitative Research: Analysis Types and Software Tools. New York: Falmer Press.

Recommended Courses to Cover Before this One

Summer School

Research Designs

Introduction to Interpretive Research Designs 

Expert Interviews for Qualitative Data Generation

Analysing Discourse – Analysing Politics

Introduction to NVivo for Qualitative Data Analysis

Winter School

Introduction to NVivo for Qualitative Data Analysis

Recommended Courses to Cover After this One

Winter School

Advanced Qualitative Data Analysis