The best protection against accepting a spurious relationship is to subject any relationship you find to additional analysis. Think about additional variables that might cause a change in either or both of the variables in your relationship. Try using these additional variables to see whether the initial relationship still  stands or disappears when you take into account the effects of additional variables. The chapters in Part 4 on analyzing data show how to do this.

In our example, we could see whether the empirical relationship between nuclear family orientation and participation in family financial decisions remained after testing for the influence of other variables. Example (3) in Figure 3.3 illustrates the possible effects of variables C, D, and E on the initial relationship between variable A and B. To illustrate, we could test whether the relationship was still observed when we tested for it among younger wives as compared with older wives, as represented by "C" in Example (3). This would rule out long term changes in norms affecting relations between wives and husbands. We might do other tests as well; such as seeing whether the relationship was still observed within low and high status families, as represented by "D; or among wives with different levels of schooling, shown as "E" in Example (3). If the relationship persisted after making these more refined tests, we would have a stronger basis for asserting that family orientation, indeed, causes wives to be more assertive in family financial decisions.

Perhaps you now see how difficult it is to claim you have discovered a causal relationship. We can never collect data on all the variables that might affect some dependent variable we are studying. Therefore, we can never know all the relationships that may exist between a dependent variable and other variables. Recognizing this fact, researchers are extremely cautious in making cause and effect statements.

Three Web sites provide clear, short explanations of relationships and the requirements for establishing a causal relationship. Types of Relationships, distinguishes between a correlational relationship and a causal relationship. A correlational relationship is like the one described in Example 1 in Figure 3.3. Without any additional information, this example simply shows the interdependence between two variables. With a correlational relationship, as pointed out in Types of Relationships, all one can say is that the two variables are related and nothing more. The relationship doesn't tell us whether one variable causes changes in another. The other two sites, Causal Relationships, and Establishing Cause & Effect, present brief, clear explanations of the three criteria that must be met before you can say you have evidence for a causal relationship. To repeat, these are timing, a correlation between the presumed causal variable and the dependent variable, and the elimination of all other possible causes.