Qualitative Research Designs

Lecturer: Michael Gibbert

Modality: In presence

Week 2: 19-23 August 2024

Workshop Contents and Objectives

At its core, a research design is a basic strategy that guides the research process, from data collection, analysis, interpretation and discussion of results. But it is much more than simply a descriptive chronological sequence: research design is about engaging in difficult trade-off decisions that will inevitably present themselves in any empirical research. Quantitative research enjoys fairly standardised reporting conventions, with clear guidance regarding transparent, reliable, and ultimately rigorous data collection and analysis procedures (e.g., Appelbaum et al., 2018). Research designs in qualitative work are much less codified. Part of the problem is that the two use different criteria for assessing rigour and that these criteria have been studied separately for decades, with only limited cross-fertilisation (e.g., Aguinis et al., 2019, Gibbert et al., 2008; Gioia et al., 2013). And yet, rigour matters, especially in qualitative research as a predictor of article citation impact (Hoorani et al., 2019).

As such, the basic premise of this course is a conundrum: one of the weaknesses of qualitative research is that it tends to be ‘messy’, which is why we need a clear research design. At the same time, the strength of qualitative methods is that it is ‘flexible’, i.e. it allows for a variety of designs and even accommodates changes in the research design along the way in iterative cycles of data collection and analysis (Denzin and Lincoln, 2005; Yin, 1994; Glaser and Strauss, 1967; Gerring, 2007).

The neuralgic problem of qualitative research, therefore, are its designs. As such, a certain versatility is needed when it comes to designing qualitative research along the way, i.e. before and even during the entire research process, from empirical data collection and analysis all the way to write-up and responding to reviewer comments. I like the label research ‘design’ (from the Latin designare, to ‘mark out, point out; devise; choose, designate, appoint) as it essentially implies a creative act. I, therefore, prefer to see the ‘design’ of research-design more like a verb than a noun, and nowhere would the need for active, creative (re-) designing of research be more appropriate than in qualitative methods.

This versatility can stem only from a minimum level of sophistication in using individual designs both in isolation and in combination. And this sophistication is based on a deep understanding of the strengths and weaknesses of individual designs in terms of quality criteria from nomothetic and idiographic approaches. This course, therefore, starts off with an introduction to, and application of, individual research designs, be they exploratory or explanatory (days one through four), with a strong emphasis on their strengths and weaknesses in view of answering a given research question. The final day is then dedicated to balancing strengths and weaknesses across designs by combining different designs, and we use two generic pathways for illustration, an inductive strategy and a deductive strategy.

Pedagogy and Scope

The class is divided into two main parts. In the first part, we introduce the relevant tool for the day. In the second part, we apply the tool both to existing material (published articles) as well as to your own research project.

Participants will not be required to do the ‘theory’ readings in preparation for each class. Instead, we will explore them on a rolling basis in the sessions. We will practically work in small groups with specific methodological approaches during class, always with an eye on their own (PhD- or other research-) projects. The emphasis in the group work is learning from the best (and sometimes, apparently ‘worst’) practices published in reputable journals.

N.B. that this course is specifically on research design, including the implications for data collection (e.g. via interviews, archival material, participant or direct observation, ethnography) and analysis (be it with or without the help of software such as Atlas.ti, NVivo, be it a thick description, co-variational, process-based, longitudinal or cross-sectional). At the same time, it does not engage in the actual practice of individual data collection or analysis strategies, for which there are separate courses at the Summer School.

Detailed lecture plan (daily schedule)

Day 1.
The research design as a function of the research question: a main division is between explanatory and exploratory approaches. The first day puts specific emphasis on exploratory designs, which use one case/unit of observation only to explore the phenomenon in depth via thick descriptions (Geertz, 1973), ethnographies, or revelatory cases (Gerring, 2007; Yin, 1994).

Day 2.
The main weaknesses of exploratory approaches are allegedly their limited generalizability and certainly their compromised internal validity (e.g. Gibbert et al., 2008; Eisenhardt, 1989). That is why we devote the second day to explanatory designs with a focus either on literal replication or theoretical replication across units of observation/cases that contain no sub-units. The main takeaways here are the increased explanatory power in terms of internal (for theoretical replication) and external validity of the findings, albeit at the cost of the number of variables that can be accommodated, i.e. at the cost of exploration.

Day 3.
The third day focuses on a variant of the exploratory design of day one, i.e. a design where within an overall unit of observation, we study several (at least two) sub-units (e.g. Yin, 1994, Gerring, 2007). This affords greater purchase on the empirical phenomenon by automatically ‘controlling’ the context within which the two sub-units are studied and combines many of the strengths of exploratory and explanatory designs.

Day 4.
The sub-units can be either cross-sectional (e.g. different teams in the same organization) or longitudinal (e.g. organizational change, c.f. Hoorani et al., 2020), and as such, day four starts by putting the spotlight on longitudinal designs and their desirable characteristics. Notably, it is currently unclear in the methods literature what a ‘longitudinal’ design entails, and as such, we spend some time discussing the different meanings of the term in relation to structuring the research design, i.e. in either a process-oriented approach and a variance-oriented approach (Blatter and Haverland, 2012, Langley, 1999, Langley et al., 2013; Cloutier and Ravasi, 2021). Day four also tackles the combination of cross-sectional and longitudinal designs, which combine the characteristics (and, therefore, challenges) of both.

Day 5.
The final day is dedicated to sophisticated combinations of the research designs from the first four days. We structure this in terms of an ‘inductive’ strategy and a ‘deductive’ strategy (e.g. Gibbert et al., 2020; Cornelissen, 2017), and detail which designs, in which sequence (chronological or otherwise) can be employed. The eye on the final day is, in particular, on the write-up (and review process) phase and how different combinations of individual designs can be crafted, such as to emphasize the theoretical contribution of the study.

Recommended readings or preliminary material

• Aguinis H, Solarino AM. 2019. Transparency and replicability in qualitative research: The case of interviews with elite informants. Strategic Management Journal in press.
• Appelbaum M, Cooper H, Kline RB, Mayo-Wilson E, Nezu AM, Rao SM. 2018. Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report. American Psychologist 73(1): 3-25.
• Blatter, J., & Haverland, M. (2012). Designing case studies: Explanatory approaches in small-N research. Palgrave Macmillan.
• Cloutier, C., & Ravasi, D. (2021). Using tables to enhance trustworthiness in qualitative research. Strategic Organization, 19(1), 113-133.
• Cornelissen, J. (2017). Editor’s comments: Developing propositions, a process model, or a typology? Addressing the challenges of writing theory without a boilerplate. Academy of Management Review, 42(1), 1-9.
• Daft RL, Lewin AY. 2008. Rigor and relevance in organization studies: Idea migration and academic journal evolution. Organization Science 19(1): 177-183.
• Denzin NK, Lincoln YS. 2005. The Sage Handbook of Qualitative Research. Sage: London, UK.
• Geertz, C. 1973. The interpretation of cultures. Basic books.
• Gibbert M, Ruigrok W. 2010. The ‘‘what’’ and ‘‘how’’ of case study rigor: Three strategies based on published work. Organizational Research Methods 13(4): 710-737.
• Gibbert M, Ruigrok W, Wicki B. 2008. What passes as a rigorous case study? Strategic Management Journal 29(13): 1465-1474.
• Gibbert, M., Nair, L., Hoorani, B. 2020. Building theory from outliers. Organizational Research Methods 24(1), 172-181.
• Gioia DA, Corley KG, Hamilton AL. 2013. Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods 16(1): 15-31.
• Glaser BG, Strauss AL. 1967. The discovery of grounded theory: Strategies for qualitative research. Aldine: Chicago.
• Hoorani, B. H., Nair, L. B., & Gibbert, M. (2019). Designing for impact: The effect of rigor and case study design on citations of qualitative case studies in management. Scientometrics, 121(1), 285-306.
• Langley, A. (1999). Strategies for theorizing from process data. Academy of Management review, 24(4), 691-710.
• Langley, A. N. N., Smallman, C., Tsoukas, H., & Van de Ven, A. H. (2013). Process studies of change in organization and management: Unveiling temporality, activity, and flow. Academy of management journal, 56(1), 1-13.
• Yin RK. 1994. Case study research: Design and methods. Sage: Thousand Oaks, CA.