Measurement How to test a sufficient or a necessary condition with a case study

of scores with the pattern predicted by the hypothesis. The test result is either a confirmation or a rejection of the hypothesis. The rules for this decision should be very precise and their application should be rigorous. These rules should aim at avoiding type 1 error confirming the hypothesis in an instance in which it actually should not have been confirmed and, therefore, allow for the possibility that type 2 error rejecting the hypothesis in an instance in which it actually should not have been rejected may occur. In operational terms, this means that rules must be formulated in such a way that it cannot be easily con- cluded that there actually is a presenceabsence of A or B. Data analysis in case study research is qualitative. Qualitative analysis is called “pattern matching”. Pattern matching is comparing two or more patterns by visual inspection in order to determine whether pat- terns match i.e. that they are the same or do not match i.e. that they differ. Pattern matching in theory-testing is comparing an observed pattern with an expected pattern. It is a non-statistical test of the cor- rectness of the hypothesis. For testing a necessary or sufficient condition the test itself is straightforward. The expectation is that A or B is present or absent. If the observations indicate that the predicted condition or effect is indeed present or absent, then the hypothesis can be confirmed. If the observations indicate that this is not true, the hypothesis must be rejected.

5.1.8 Implications for the theory

In any theory-testing research, both the confirmation and the rejection of a hypothesis can be artefacts produced by research errors, even if the procedures have been conducted correctly. Assuming that the study was conducted adequately, a confirmation of the hypothesis shows that the proposition is true in one case namely the one that was studied and this might be taken as an indi- cation of the likelihood that the proposition is also supported in other cases. It can, however, not be concluded that the proposition is correct for all cases in the domain to which the theory is assumed to apply. Only after many failures to reject the proposition in different “least likely” instances, can we begin to accept the “generalizability” of the proposition. Assuming that a study was conducted adequately, a rejection of the hypothesis can mean a that there is something wrong with the prop- osition i.e. that A is not a sufficient condition for B or that it is not a