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What is qualitative data analysis?
Qualitative analysis 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 qualitative analysis in particular; rather, it explores the common denominators all methods share and provides tools and food for thought to implement them in practice.
Why is this course relevant?
Historically, qualitative analysis 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, qualitative analysis has been associated with a ‘black box’ problem: we are told about what data and how they were collected and what results these yielded, but nothing in between. There is also a misconception that qualitative analysis merely involves the identification of themes in the 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 synthesizing these in a storyline, model, or schema - is most frequently than not absent in published qualitative studies. Conversely, the widespread myths that the ‘method will emerge’ from the data, or that researcher’s tacit knowledge will lead him/her to ‘make sense’ of the data, account for opaque reporting since no audit trail accompany the analytic process.
What contribution does this course make?
This course teaches the key concepts to plan, conduct and report qualitative analysis in a transparent, traceable and auditable way in one’s research. Participants will learn (1) key design issues to take into account when planning the phase of analysis 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 and (5) best and worse practices when presenting qualitative findings in doctoral theses and journal articles. The different stages of qualitative analysis will be demonstrated with NVivo software so participants can put into practice the concepts seen and become cognizant of the advantages and pitfalls of using QDA software. To that end, participants are invited to use NVivo 12 Pro for Windows or NVivo 12 for Mac or other QDA software they are familiar with. See below the section Software and Hardware Requirements.
The course learning objectives are:
- To demonstrate the influence that methodology has on the choice of data analysis
- To describe the different approaches to code qualitative data
- To distinguish the strategies to transform qualitative data into findings
- To situate the competing philosophies regarding validity in qualitative research
- To present qualitative results evocatively and effectively
At the end of this course, participants will be able:
- To develop an analytical plan of their study
- To choose the right approach to code their data
- To identify what strategy to use to transform data into findings
- To reflect on their positionality regarding validity in qualitative research
- To present qualitative findings convincingly and transparently
Day 1 – Foundations of QDA. The course opens 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. Our attention then turns to some popular approaches to QDA; in turn, the aim, sampling requirements and analytical procedures of qualitative content analysis, thematic analysis, cross-case analysis, grounded theory and analytic induction are presented. The second half of the class is a workshop where participants set-up an NVivo project, import and classify their data in preparation for day 2.
Day 2 – Coding qualitative data. The concepts and approaches for coding qualitative data are introduced on day 2. 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 of key importance. The key concepts that shape the coding process - meaning and coding units, codes, codebook and coding schemes - are discussed 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 with the use of visualisations to map the coding process and the emerging relationships between codes. A coding workshop with NVivo follows where participants code their own data and reflect on the process.
Day 3 – Transforming data into findings. Transforming data into findings starts with the question: “I have a mountain of information here. Which bits go together (Miles & Huberman, 1994: 256)”. Coding is a first step in the transformation process as it reduces the amount of data in codes and organises these in thematic families. However, coding alone is no analysis. Qualitative analysis involves that the researcher detects the structure the lies behind the data and explains how the structure connects people, places, processes or practices in one coherent storyline. To do so, a range of strategies for transforming data are proposed and are followed with a discussion on the logic of scientific reasoning: in turn, examples of inductive, deductive, abductive and retroductive analyses are presented. The nuts and bolts of retrieval procedures that operate on data concurrence, overlap, sequence, proximity, precedence and exclusion in NVivo are introduced. 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 worse practices when presenting qualitative findings in journal articles and theses? And what to think of the ubiquitous catchy phrase “themes were identified in the data” when themes were part of the interview guide? We explore these questions so participants gain a critical understanding about what 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 on the reporting of analysis, results and interpretation in a qualitative report. The second part of the class is a workshop during which participants assess the quality of the reporting of qualitative analysis in a qualitative study.
Day 5 – Validating qualitative results. Validity of qualitative research is a debated issue amongst qualitative scholars as 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 has proposed and what the naturalistic paradigm, and other complementary strategies, have put forward since 1980s. Participants then reflect on their own epistemological positionality, first individually and then in small group discussions. We look at different techniques to confirm the trustworthiness of qualitative results, amongst 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. The class concludes with the Master class where teams present examples of qualitative analysis of a research project.