Causal Case Study: Explanatory Theories

Conceptual Overview and Discussion

Scientific explanations involve making causal statements, such as “a lightning strike ignited the fire,” or “bacteria caused the infection.” In social sciences, explanations involve volitional actors—human beings—and therefore simple mechanical causality as in the above examples does not apply. To explain, for example, why the crime rate in certain neighborhoods has increased, or why a particular business firm has managed to outperform its competition, a whole network of causally connected factors needs to be identified. By focusing on telling a story—a temporal sequence of events in their context—case studies can accomplish this better than most other research methods (which are based on analyzing variances and cannot uncover causal direction).

Exploratory and descriptive case studies tell a story (what happened and how), but they do not pinpoint causality (why it happened) beyond identifying the chronology of events. For example, an exploratory case study may reveal that a patient had a parent with heart disease, was sedentary and consumed a high-fat diet, then had a heart attack. A descriptive case study might tell a story about a business firm with declining sales and profits, unmotivated employees, and lagging investment in research and development. While exploratory and descriptive case studies are important, they do not provide explanations or causal connections. Causal case studies do that, through extended research design and data analysis.

Causal case studies start with description. No explanation can be reached before the phenomenon of interest, whether a particular heart attack or superior business performance, is described; that is, we have a descriptive understanding of how the phenomenon manifests itself and what sequence of events has preceded it. But a temporal sequence of events is not a sufficient indication of cause-effect relationships; to uncover those, the case study researcher must take the analysis further. This entails looking for patterns, or themes, in the data. For example, maybe in neighborhoods with increasing crime rates, families with stable incomes have started moving elsewhere in search of more spacious accommodations, followed by local businesses. Or maybe businesses that outperform their competition have uniquely differentiated their products.

Application

Describing the case story and identifying and analyzing patterns within or across cases and iteratively comparing them with the data will allow researchers to uncover the causal networks at play in the focal case(s). They will be able to explain that businesses with superior performance are in tune with the needs of different customer groups, have chosen to serve one or a few of them, and then acquired the necessary resources and aligned all their activities to serve their customers exceptionally well, at a price customers are willing to pay. Customers reward them with profits, and they attract more customers with similar needs. Competitors are unable to copy what these superior performers are doing. This kind of causal explanation of a particular case is not yet an explanatory theory.

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