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

effect is also present in that case. If a case is selected in which the effect is absent, the hypothesis is that the condition is also absent in that case. If the proposition specifies a necessary condition and a case is selected in which the effect is present, the hypothesis is that the condition is also present in that case. If a case in which the condition is absent is selected, the hypothesis is that the effect is also absent in that case.

5.1.5 Measurement

In order to compare the prediction expressed in the hypothesis with the facts of the case, these facts must first be measured. Measurement is a process in which a score or scores are generated for analysis. Measurement consists of a data collection, and b coding. Measure- ment issues are discussed in Appendix 1 “Measurement”. As mentioned above, a complication regarding the case selection in this specific type of theory-testing case study is that the value of one of the concepts must be known before case selection. Otherwise it is not possible to identify and select this specific case in the first place. Hence, the principles of measurement as discussed in Appendix 1 also apply to the procedures of case selection.

5.1.6 Data presentation

For testing a sufficient condition it must first be shown that the condition A was present or effect B was absent in the case, so that the case can be accepted for the test. Next, the observed score of effect B or the score of condition A must be present. For testing a necessary condition it must first be shown that the effect B was present or the condition A was absent in the case, so that the case can be accepted for the test. Next, the observed score of condition A or the score of effect B must be present. In a serial or parallel sin- gle case study, the data must be presented for each case separately.

5.1.7 Data analysis

Data analysis is the interpretation of scores obtained in a study in order to generate the outcome of the study. After having measured the actual score of either effect B or condition A, data analysis consists of testing the hypothesis. Hypothesis-testing is comparing the observed pattern 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