Yes, the Qualitative Comparative Analysis (QCA) methodology can be applied to a single case study of a company. QCA is a research method that allows for the analysis of complex causal relationships within a small number of cases. It is particularly useful when studying cases where there are multiple variables and diverse outcomes.

Dear Frank,

as long as you have at least two cases that differ on at least one exogenous variable and one endogenous variable, you can use QCA (Thiem 2014, 2022). As long as you have this minimal degree of variation on your eight cases, you are fine with QCA.

One additional note on the distinction between cases / sub-cases: if you have eight projects as your cases in QCA, the company from which these are drawn is part of what John Mackie (1965, following Andersson (1938)) has famously called the "causal field", that is, the constant setting in which some causal mechanism plays out. That mechanism may change once you leave that specific field. In other words, once you start to look at projects across companies, the variable "company" may itself become part of the causal mechanism explaining a certain project outcome (for example, because of differing organizational cultures, etc.).

Best wishes

Alrik

References

• Anderson, John. 1938. "The Problem of Causality." Australasian Journal of Psychology and Philosophy 16 (2):127-42.
• Mackie, John L. 1965. "Causes and Conditions." American Philosophical Quarterly 2 (4):245-64.
• Thiem, Alrik. 2014. "Navigating the Complexities of Qualitative Comparative Analysis: Case Numbers, Necessity Relations, and Model Ambiguities." Evaluation Review 38 (6):487-513.
• Thiem, Alrik. 2022. "Qualitative Comparative Analysis (QCA)." In Handbook of Research Methods in International Relations, ed. R. J. Huddleston, T. Jamieson and P. James. Cheltenham: Edward Elgar, pp.607-628. Open access at: https://www.elgaronline.com/view/book/9781839101014/book-part-9781839101014-44.xml

Hello Frank Lee Zhang,

QCA is about comparing cases, where in-depth knowledge is required about each and every case included in the analysis. In qualitative research, in-depth knowledge is acquired through case studies.

One can use QCA to study countries, in which case the countries become "cases". Or one can study differences between regions in a particular country, in which situation the country stops being a "case". The regions now become the cases being studied.

It really does not matter if you are studying a single company. If your cases are the company's projects then those projects are your cases. The company is not a "case study", it is just the universe of your research.

The short answer is yes, you can use QCA to study 8 different projects, with the appropriate care about the number of explanatory conditions, since there should be a proper balance in the so called "case-to-factor ratios" to avoid the problem of overdetermination.

You can always compare your one actual case with counterfactual cases. You can manipulate your explanatory conditions and investigate if the outcome would still occur. (Keep in mind that cases do not vary, so you work with explanatory and outcome conditions, not with variables. Variables is what you have in correlational methods.) See Goertz (2017) Multimethod Research, Causal Mechanisms, and Case Studies: An integrated approach, and Mahoney and Barrenechea (2019) The logic of counterfactual analysis in case-study explanation.

Dear Frank

If you have a limited number of cases, you need to analyze a partially defined Boolean function, which is a multitude of functions and not a single one. Crama et al. (1988) considered a Boolean function that is only partially defined, and whose minimal set of supporting variables is not unique. Their mathematically sound solution encompasses potentially several irredundant disjunctive forms (IDFs) for each of the possible minimal sets of supporting variables. Though the QCA researchers were aware of (and frequently cited) this work, they practically ignored it, obviously since it does not supply the strong result of a unique theory they aspired to have.

Usually, only a single explanatory model is obtained by QCA, a highly desirable (albeit occasionally unjustified) feature. Such a model is obtained at the expense of any competing solutions, which are eliminated on the grounds that they are dominated by the model selected. Thiem et al. (2020) hold that the too strong results of QCA ‘have often neither been the upshot of the collection of high-quality data material nor the skillful use of pertinent theories but simply an algorithmic corollary of the uncritical import of the Quine-McCluskey (QMC) algorithm from electrical engineering.’

Recently, Rushdi and Badawi (2016) have looked at QCA from the point of view of computer engineering. They were hopeful that “the QCA community would not mind ‘their’ intervention as outsiders, would welcome ‘their’ comments, observations, and questions, and would tolerate ‘their’ objections and criticism as a sort of constructive and fruitful intellectual debate.” To the best of my knowledge, these questions went ignored and unanswered. Of course, I cannot rule out the possibility that these questions are irrelevant and meaningless. Please, consult some of the references below.

Best Regards

Ali

References

Crama, Y., Hammer, P. L., & Ibaraki, T. (1988). Cause-effect relationships and partially defined Boolean functions. Annals of Operations Research, 16(1), 299-325.

Rushdi, A. M. A., & Badawi, R. M. S. (2019). Computer engineers look at qualitative comparative analysis. International Journal of Mathematical, Engineering and Management Sciences, 4(4), 851-860.

Thiem A, Mkrtchyan L, Haesebrouck T, Sanchez D. (2020). Algorithmic bias in social research: A meta-analysis. PloS one. 2020 Jun 8;15(6):e0233625.

Rushdi, A. M. A. (2018). Utilization of Karnaugh maps in multi-value qualitative comparative analysis. International Journal of Mathematical, Engineering and Management Sciences (IJMEMS), 3(1), 28-46.

Rushdi, R. A., & Rushdi, A. M. (2018). Karnaugh-map utility in medical studies: The case of Fetal Malnutrition. International Journal of Mathematical, Engineering and Management Sciences, 3(3), 220-244.

Rushdi, A. M., & Badawi, R. S. (2021). Using eight-variable Karnaugh maps to unravel hidden technicalities in Qualitative Comparative Analysis. Asian Journal of Education and Social Studies, 17(2), 26-42.