In experiments, the researcher manipulates selected independent variables and measures the effect of these manipulations on the dependent variables. In the survey and observation methods, the information is usually obtained under normal or near normal conditions and the researcher intervenes only to gather data, not to alter the environment. Causality means that a relationship exists between two or more events. The first event can be seen as a cause of the second if the occurrence of the first increases the likelihood of the occurrence of the second. The objective is to provide evidence that suggests a high probability of one event leading to another. It does not have to be demonstrated conclusively.

Three types of evidence are used to demonstrate the existence of a causal relationship:

1 Concomitant variation, which reflects the extent to which a cause (x) and effect (y) occur together or vary together in some predictable way.

2 Time order of occurrence of the causal factor (x), which must either precede or take place simultaneously with the effect (y). This is a logical requirement for a causal relationship, but there are situations where things become confusing because the two factors seem to affect one another.

3 Absence of other causal factors, which is the most difficult type of evidence to demonstrate. It states that no other factors could have caused a similar change in the dependent variable (y). This demands extensive testing to eliminate all other possible explanations for the change in x. It accounts for the importance of selecting an appropriate experimental design.

As well as these three types of evidence, if a causal relationship is to be useful to management the relationship should be operative in a variety of environments. It should also be a relationship that is stable enough to make future actions by executives worthwhile.

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