Qual­it­at­ive Meth­ods and Reas­on­ing

Course content

As befits its focus on data analytics, the program as a whole generally emphasizes the quantitative methods that data analytics is typically taken to imply and posits that informed decision making often and ideally means reliance on the kind of ‘big’ and ‘hard’ data frequently associated with it. Naturally, the program also emphasizes the limitation of quantitative methods, both epistemically and practically: there are certain things that quantitative methods will never do, certain things they will never do well or without substantive risks, errors and implications, and certain things that they can only do at great cost. These are topics of other courses in the program. They do, however, leave open the question how of such things might nonetheless be made ‘knowable’ and managerial decisions about them be made on an empirically informed basis.

Those things – the things that cannot be made knowable with quantitative methods, or can be made knowable only poorly or with great cost and chance of error – are the stuff of qualitative methods and small sample reasoning. And that is what this course is about.

The course is organized around three main themes: qualitative data collection, qualitative analysis, and processes of qualitative reasoning in managerial practice.

The course will examine four types of qualitative data collection methods: interviews, participant observations, focus groups, and archival research. All four represent low-cost ways of generating insights into business processes that in keys ways complement what can be achieved with what we typically refer to as ‘data analytics’. All four are also methods that students are expected to have received rudimentary introductions to in prior coursework at the bachelor level, and so we are teaching a progressed version of all four. Specifically, we are exploring ‘analytical interviews’ and ‘auto-ethnography’ and thinking about the affordances of focus groups and archival research for understanding social interaction and decision-making processes, respectively.

Common to all of these forms of data is a set of challenges: how do you analyze data, how does analysis become interpretation, how do you make the “leap” from interpretation to conceptualization, and how do you – across these processes – relate to complex data in which you yourself are complicit? Qualitative data analysis begins from an inductively oriented, systematic, in-depth engagement with the data, which might be structured by particular methodological protocols, and often happens concurrently with data collection. Interpretation, especially when dealing with small samples, involves acknowledging complexity and ambiguity, developing multiple interesting explanations on the basis of data, and subsequently interrogating and selecting between those explanations. Moving from interpretation to conceptualization involves connecting one’s interpretations to theory, and recursively bringing theory to bear in refining and evaluating interpretations. Relating to data requires reflexivity about one’s own assumptions, one’s role in the creation and interpretation of data, and the persistent challenges that complexity, ambiguity and uncertainty might represent to understanding managerial problems.

Throughout the course, we discuss how the methods of collecting and interpreting qualitative data pertain to managerial practice. Organizational research on managerial work provides unequivocal evidence that managers are reasoning over qualitative data all the time as part of their managerial practice, but that much of that reasoning is unsystematic and opportunistic and, consequently, error-prone. The same research on managerial work, however, also suggests that managerial work is rich in un(der)exploited opportunities for qualitative data collection and analysis. We demonstrate how the rigorous application of qualitative methods and reasoning can exploit these opportunities, show how that exploitation can be integrated into a managerial practice, and explore the consequence of doing so for organizations. Specifically, we examine the relevance of qualitative methods to a theory-based understanding of managerial action and the origination of novel theories, to learning from rare and ambiguous events, to understanding the processes that precede outcomes and the unintended consequences that they generate, and to organizational reflexivity and deceleration more broadly.  

Course Format

The course treats its methods in turn, building on students’ prior knowledge about qualitative methods like field observations and interviews and extending them into the more sophisticated practice of systematic qualitative inference with the complications that (i) decision situations can be beset by knowledge problems and (ii) that observers may be complicit to the problems they are studying. This progression is reflected in each of the empirical methods, but also in the course’s attention to analysis and reasoning. Across all applications, the informed management decision requires not just qualitative data but also that this qualitative data be analyzed, interpreted and conceptualized with a mindfulness of the biases, distortions and self-deceptive tendencies that are endemic to efforts to study one’s own practice. To bring about this mindfulness, the course is going to focus on both the methodological tools that might allow students to engage with small-sample data and on the organizational circumstances that might provide room and opportunity for this kind of data to be usefully applied.

Because qualitative research is a craft, our emphasis throughout the course is on doing qualitative research, as opposed to talking and theorizing about it as an abstract phenomenon. At the start of the course, students will be asked to undertake a qualitative research project on a theme and organization of their own choosing, and as the course progresses they will be introduced to progressively more refined tools for doing so. The assignment the central activity of the course and the teaching activities are intended to support that activity.

The teaching activities take three forms: (podcasted) lectures, (demonstration-oriented) exercise classes, and group supervision. For each topic covered, students will be given access to a podcasted lecture that introduces and discusses the topic. Each lecture will be followed by an exercise class oriented towards demonstration: demonstrating how particular methods are applied, how particular styles of analyses work, etc. Following the exercise classes, students are encouraged to try out the methods and techniques covered in relation to their own projects, effectively doing the assignment as the course transpires. The group supervision sessions are intended as occasion to reflect on the practice, challenges and opportunities, and relevance of applying the course’ techniques and methods in students’ particular context and assignments.