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Monday 29 July – Friday 2 August
14:00–17:30 (ending slightly earlier on Friday)
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:
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.
Marie-Hélène teaches qualitative research methods at the Open University of Catalonia (UOC) and is a freelance methodologist in qualitative data analysis. She was educated in Quebec, Beirut and Oxford where she read social work. A clinician by training, she worked as a mental health officer in humanitarian missions for MSF, MDM and UNWRA in psychosocial aid programs for survivors of war trauma in East Africa and the Middle East. Her clinical work led her to research the harm that INGOs can do in the name of doing good when imposing Western paradigms in culturally and politically different contexts.
Marie-Hélène is an NVivo Certified Platinum Trainer and is a member of the NVivo Core Trainer Team who teaches the NVivo online courses. She is a sought-after methodologist who has taught qualitative data analysis in more than sixty universities and research centres worldwide, in countries including Qatar and Iran. Since 2009, Marie-Hélène has taught the introductory and advanced courses in qualitative data analysis at the ECPR Methods School and teaches similar courses at the IPSA-NUS Summer School in Singapore. Her methodological interests range from advances in qualitative data analysis, qualitative evidence synthesis, decolonising epistemology and participatory methodologies. Read more about Marie-Hélène.
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
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
At the end of this course, you will be able to
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.
Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least two hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
Please check your course format before registering.
Live classes will be held daily for two hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.
In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.
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:
Please bring your own laptop with software installed. NVivo hardware requirements as per QSR International
Windows version
|
Minimum |
Recommended |
Processor |
1.2 GHz single-core processor (32-bit) 1.4 GHz single-core processor (64-bit) |
2.0 GHz dual-core processor or faster |
Memory |
2 GB RAM or more |
4 GB RAM or more |
Display |
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
|
Minimum |
Recommended |
Processor |
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 |
Memory |
2 GB of RAM (as defined by the Mac OS X Mavericks minimum requirements) |
4 GB RAM |
Display |
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 |
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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.
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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.
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
Winter School
Advanced Qualitative Data Analysis