Data presentation Data analysis

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 necessary condition for B, or b that something is wrong with the domain that was specified in the theory i.e. A may be a sufficient or a necessary condition for B in other instances of the domain. The researcher must try to explain the result of the test on the basis of other information about the case. This information may help to develop an improved version of the original proposition or of the spec- ification of the boundaries of its domain. If the hypothesis is rejected in the first test, then the researcher can interpret the rejection as meaning that the proposition is not correct. Such a conclusion cannot be drawn lightly, presuming that the explo- ration at the beginning of the research was conducted seriously and that, thus, the proposition that was formulated and tested was based on sound practical and theoretical insights. However, if it is decided that the proposition should be changed, then the reformulated proposi- tion needs to be tested in new theory-testing research.

5.1.9 Replication strategy

Any rejection or confirmation of a hypothesis needs to be replicated in further tests. If the hypothesis was tested for the first time, we recom- mend a strategy of replication in which the same proposition is tested again in similar cases. If the hypothesis is confirmed in such replica- tions, then it can be concluded that the proposition is supported for at least a part of the domain. Before continuing with further replications in less similar cases, in order to determine whether the proposition holds also in other parts of the domain, we recommend with necessary conditions first to conduct a test for trivialness. A necessary condition is trivial if there is no variation in either the dependent or the independent concept, or in both. An example is a proposition that states that globalization is a necessary condition for the success of off-shoring projects, which is trivial because globaliza- tion is present for all off-shoring projects, both unsuccessful and suc- cessful ones. A simple way of testing for trivialness consists of selecting a case in a different manner from that used in earlier tests. If initial tests were conducted in cases that were selected on the basis of the pres- ence of the dependent concept, a next case should be selected on the basis of the absence of the independent concept or the reverse. In our example, it would immediately become clear that no off-shoring projects without globalization could be found. After having found initial support for the proposition and, in case of necessary conditions, having found that it is not trivial, we recommend a