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Definition of qualitative analysis
Qualitative analysis is the search for patterns in textual, visual, or artefact data and the explanation for why these patterns exist in the first place. While statistics rely on the use of probability theory to estimate population inferences, qualitative analysis uses social theory to decontextualise the raw data in segments and recontextualise them in codes, so second level constructs may be generated, relationships uncovered, and hypotheses formulated. Qualitative analysis may be conducted inductively by identifying conceptual categories directly in the data, or deductively by applying predefined theoretical notions onto the material. A mixed approach to analysis - when both induction and deduction are used in different points in time in the analytical process - is increasingly popular since it makes use of the researcher’s theoretical sensitivity and allows space for meaning making and concept formation.
Criticisms about qualitative analysis
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, the phase of qualitative analysis has been associated with a ‘black box’ problem: we are told what data 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. The analytical tasks 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 researchers’ tacit knowledge will lead them to ‘make sense’ of the data account for opaque reporting since no audit trail accompanied the analytic process.
Contribution of this course
This course introduces the key concepts to plan, conduct, and report qualitative analysis in a transparent, traceable, and auditable way. Participants will learn the best approach to code and analyse their data based on their research questions, study purpose, type of data collected, and the expected outcome of their study. The stages of qualitative analysis will be demonstrated with NVivo so participants can learn how theory is put into practice as well as the advantages and potential pitfalls of using qualitative software for data analysis. Participants are invited to use the NVivo free trial for the course or other qualitative software they are familiar with for the workshop sessions and daily assignments. Software demonstrations by the instructor and teaching assistant will only be done using NVivo, however.
The learning objectives are:
- To review the historical problems associated with QDA
- To illustrate the relationship between epistemology, methodology, and method of QDA
- To try out different approaches to code qualitative data
- To apply different strategies to seek patterns and uncover relationships
- To craft graphic displays that effectively present qualitative findings
At the end of this course, participants will be able to:
- Recognise the ‘black box’ problem of QDA in their field
- Design a QDA plan congruent with their research design
- Choose the right approach to code a qualitative dataset
- Apply different techniques to seek patterns in the data
- Communicate qualitative findings effectively using displays
Day 1 – Foundations of QDA. The course opens with a lecture on the foundations of qualitative data analysis with definitions, history, 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 data coding, seeking patterns, and results display. We then situate the phase of qualitative analysis within the research design and look at the influence that the research questions, the study purpose, and the type of data collected have on the choice of a method to analyse qualitative data. Our attention then turns to a number of popular approaches to QDA. In turn, the aim, specificities, and sampling requirements of thematic analysis, qualitative content analysis, grounded theory, cross-case analysis, and analytic induction are presented. The second half of the class is a workshop where participants set-up a NVivo project, import, organise, and classify data in preparation for day 2.
Day 2 – Data coding. The concepts and approaches to code qualitative data are introduced on day 2. In qualitative research, coding is the process by which data are segmented in coding units and assigned a code that captures what the data are about. 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, such as meaning and coding units, codes, codebook and coding scheme, are discussed alongside situations where descriptive, interpretive, and patterns codes are used to capture different levels of abstraction in the data. We then examine the properties of theme- versus design-based coding schemes and review their respective strengths and requirements. We conclude with the use of graphic displays to map coding networks and emerging relationships. A coding workshop with NVivo follows where participants put into practice the above concepts, and code text, multimedia, and social media data.
Day 3 – Seeking patterns. Day 3 presents the most common strategies to seek patterns and uncover relationships across the data, as the analytic process moves towards the generation of results and the formulation of conclusions. In qualitative analysis, seeking patterns may occur inductively while coding the data, deductively using retrieval techniques that tease out hypotheses from theory, or abductively in the search of an explanation that accounts for all cases observed. With this introduction, we move on and examine what a pattern is, how to recognize one in the data, and the role patterns play in weaving together cases, contexts, and attributes around a study conclusions. A hands-on session follows where patterns based on data overlap, proximity, sequence, nesting, and exclusion are sought and examined in NVivo. The class concludes with a discussion about the levels of abstraction in qualitative analysis and, most specifically, the level of abstraction that common qualitative designs (qualitative content analysis, thematic analysis, cross-case analysis, analytic induction and grounded theory) set to achieve.
Day 4 – Reporting findings. Day 4 covers the best and worse practices when reporting qualitative analysis and communicating research findings. We open the class with a discussion about the widespread problem in qualitative analysis which is the practice by which researchers present raw data only in the form of quotes, and often in excessive number, as study results. We clarify the difference between data, analysis, and findings, learn to differentiate coding devices from analytic tools, and offer suggestions about which information of the analysis process must be presented in which sections of research papers. The use of graphic displays to present qualitative findings is then introduced; in turn, we assess how models are best to illustrate conceptual integration, matrices are appropriate for cross-tabulated information, tables are suitable to present typology, and diagrams work well to depict structure. The second half of the class is a workshop on the visualization displays available in NVivo, where other features to export data and findings from a research project are also tried out.
Day 5 – Validating findings and master class. We start Day 5 with a discussion about the different techniques available to qualitative researchers to validate one’s findings. We examine these strategies and comment on their appropriateness depending on the research context and the nature of the data collection. We pursue with a review of the debates surrounding the assessment of the quality of qualitative research and, more particularly, the phase of qualitative analysis. The second part of the class is dedicated to the master class where teams present in small groups the process and outcome of analysing a qualitative dataset of their own or from the sample data.
Assignment for ECTS credits
Participants can earn 2 ECTS credits by actively participating in class discussions and works. Two supplementary credits may be earned upon the production of daily assignments and an academic essay.