Advanced Qualitative Data Analysis

Who is this course for?
This course is designed for participants who wish to acquire methodological expertise in qualitative data analysis generally and, more specifically, widen their understanding and applied skills in conducting QCA, TA, CCA and GT in NVivo. The course will benefit participants who plan to conduct one of the above methods in their PhD or postdoctoral research, and to those wanting to generally broaden their area of methodological expertise in qualitative research. The course responds well to participants who have collected their data and want to apply the methods’ coding and analytic procedures on their dataset as well as those who don’t have data yet. 

Contribution of this course
Among the methods available to analyse qualitative data qualitatively, the four methods on which this course is based have been widely used across the social sciences. Their procedures to carry out analysis are straightforward, which makes the analytic journey transparent, traceable, and auditable. Each method is also unique in its own right, in that each one suits a particular type of research question; responds to specific objectives; requires a distinct sampling strategy; implements specific coding and analytical procedures; and generates concrete findings.

The course also sheds light on some of the malpractices and misrepresentations from which the four methods suffer in the qualitative literature, because of the lack of standardised training in qualitative analysis and researchers’ obscured reporting. To this end, the course’s daily assignments ask participants to appraise the quality of reporting of published studies that use the four methods.

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

1. Describe the aim, objectives, and expected outcomes of QCA, TA, CCA and GT
2. Demonstrate how each method suits a given research design
3. Implement each method’s coding and analytic procedures in NVivo
4. Generate graphic displays that match each method’s findings
5. Appraise the quality of reporting of studies that used the four methods
6. Propose designs where methods integration is feasible.

Day 1, Qualitative content analysis (Schreier, 2012) 
Qualitative content analysis is particularly suited to studies that aim to explore and describe the manifest and latent meaning of categories in text, multimedia, pictures, and social media data. In the first part of the class, we review the methodological tenets that distinguish the quantitative from the qualitative approach to content analysis and look at sampling requirements, coding units vs unit of analysis, and the building of a coding frame where categories are organised. This leads us to conduct the initial phase of data coding and conduct a preliminary reliability check to assess the categories' adequacy to capture meaning in the data. In the second part of the day, we move to NVivo, where we aggregate categories in sets and cross-tabulate them in search of coding co-occurrence. We display the results in models where we use both qualitative and quantitative indicators to show the coding occurrence across categories.

Day 2, Thematic analysis (Boyatzis, 1998) 
Thematic analysis is an indisputably popular method used by qualitative researchers in the social sciences. However, when looking at the different approaches to thematic analysis, Boyatzis' approach is one of the very few that has formalised its procedures in a series of clearly-defined stages known as seeing and encoding themes, codes development, and scoring / clustering of themes. We begin the day by looking at the concepts of pattern recognition and labelling consistency which are fundamental in Boyatzis’ understanding of how a theme is first seen, recognised, and then consistently ascribed the same meaning by the researcher. In the second part of the day, we move to NVivo where we cross-tabulate codes in matrices to find out where coding across themes overlaps. Instances of coding co-occurrence are examined and conceptual associations are formalised in relationship nodes, NVivo's unique feature to put forward propositions, and formulate / falsify hypotheses.

Day 3, Cross-case analysis (Miles & Huberman, 1994) 
Among the different schools of case study research, the strategies proposed by Miles and Huberman for within- and cross-case analysis have had a tremendous impact in the way qualitative researchers examine similarities and differences across cases, make generalisable claims and promote theoretical elaboration. The first part of this day centres on the first stage of cross-case analysis, that is, a description of what is going on in each case and explanations about why the phenomenon occurs the way it does. We then move on to identify the overall pattern that gives explanation to the overall phenomenon and we formulate propositions about what could happen if similar circumstances were met elsewhere. In the second half of the day, we reproduce these stages in NVivo using matrix queries, memos, 'see also' links, relationship nodes and the model.

Day 4, Grounded theory (Strauss & Corbin, 1998) 
Grounded theory is often claimed to be the method of choice for many qualitative researchers when conducting qualitative analysis. However, under scrutiny, only a scarce amount of studies actually implement the tenets proposed by the different schools of GT. We start the day by looking at the malpractice of labelling a study 'a grounded theory' to legitimise one’s work while implementing none of the methodology's tenets, and the negative impact that this malpractice has had on the GT representation in academia. In NVivo we examine the association between open coding and theoretical sampling in the generation of categories until saturation is reached. In the second part of the day, the categories of axial coding are applied onto the data and patterns of relationships between categories are identified. We conclude with the phase of selective coding, where a core category is identified and theoretical hypotheses formalised using relationship nodes.

Day 5, Integration & quality appraisal
We address the possibilities for methods integration and propose some tools to assess the quality of qualitative analysis. The day starts with a comparative overview of the similarities and differences of the four methods along the epistemological spectrum. This overview leads us to assess how different stances regarding knowledge creation inevitably influence the type of research questions asked, the type of analytic devices each method uses, and the level of abstraction reached in the results they generate. We then look at how, in the analytic process, some of the methods’ features – i.e. approaches to code generation, sampling strategy, means to validate findings – may be combined in a single study, only when it is methodologically justified. In the second half of the day, we review some appraisal tools proposed in the qualitative literature to assess the quality of qualitative analysis.

Teaching & data
Teaching methods include lectures, guided exercises with NVivo, and group work. All four methods will be taught using sample data provided by the instructor. If you have your own data, you are welcome to use them during the guided exercises. If you want to develop your appraisal skills, complete the daily assignments, which consist of assessing the quality of reporting of published studies that used the four methods, outside of class hours.

Methodological requirements

This course requires understanding of the philosophy underlying critical realist epistemology and some of its associated methods to analyse qualitative data.

I expect you to have experience in analysing qualitative data, including coding and managing a coding scheme; seeking patterns across themes and cases; formalising associations in propositions or falsifying hypotheses against empirical material; representing findings in graphic displays and recording the analytic process in memos. If you have done all the above, you are ready to take this course.

Merely identifying themes in qualitative data and reporting these using quotes is not analysis, and critically falls below the requirements of this course. If you don’t meet the above requirements, I recommend you enrol on an introductory course in qualitative analysis or thoroughly read the prerequisite texts below.

Foundations of QDA

Seeking Patterns Across Data

Software requirements

You must be an advanced NVivo user to follow the course, meaning that you can teach the basic and advanced functions of NVivo to a colleague. You must know how to independently create nodes, relationship nodes, classifications and sets; set up a framework matrix; run text, coding and matrix queries; use 'see also' links and annotations; and generate maps to depict data and findings. The short course WA111 Introduction to NVivo for Qualitative Data Analysis provides introductory knowledge to NVivo, but not enough to follow this course satisfactorily.

This is a bring-your-laptop course where we use NVivo 12. Download the NVivo 14-day free trial for Windows and Mac. You must ensure that NVivo works well on your machine regardless of the OS, because no technical assistance will be provided at the Winter School. Find more on installation instructions in the Software and Hardware section below.