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The term "causation" refers to the idea that a change in one variable will result in another change. In this case, the definition of causation is expanded to include the idea that a precondition can influence a variable of interest. For example, one can imagine that a person's gender influences credit card usage. This means that gender could be seen as having a causal relationship with credit card usage, even though it is impossible to change a person's gender to observe whether credit card usage would change. The term "influence" is sometimes used instead of "cause" if it is more appropriate, but the logic of the analysis remains the same. If two variables are causally linked, then assuming they will be associated is reasonable. If an association provides evidence of causation, then the lack of association suggests that causation is not present. Therefore, an association between attitude and behaviour is evidence of a causal relationship: Attitude --> Behaviour (A. Aaker et al., 2013).
Researchers need to remember the vast array of causal relationships when attempting to establish causality in their studies. This requires implementing various methods and analysis techniques of varying complexity.
Both experimental and non-experimental quantitative studies can observe a comprehensive set of causal relationships. Such relationships may be direct, mediated, or mutual, with some being more complex than others. These complexities can range from simple linear regression to structural equation models (SEMs).
In Figure 7, nine types of causal relationships are depicted:
1. A direct linear causal relationship is one in which Y is a function only of A.
2. A mediated causal relationship is in which the influence of A on Y is mediated by B.
3. A direct causal relationship is one in which it is possible to estimate the total effect (direct and indirect) of A on Y.
4. Direct mutual linear causal relationship in which the influence of A on Y is reciprocal.
5. Indirect mutual linear causal relationship in which Y reciprocally influences the influence of A on Y (mediated by B).
6. Multi-mediated causal relationship (domino) in which A generates a sequential unfolding of effects over time on Y.
7. Moderate direct causal relationship in which the influence of A on Y is contingent on the conditions of C.
8. Causal relationship in which A (exogenous variable) initiates a complex structure (path) of influences (direct and mediated) on Y.
9. Apparent or spurious correlation refers to a statistical association between two variables that do not have a causal link. This type of correlation can arise due to mere chance or the influence of a third variable. It is important to be aware of spurious correlations to avoid incorrect conclusions or erroneous predictions based on statistical data.
