Cause and effect

Interpreting an empirical relationship

Earlier, we referred to the negative relationship between schooling of women and their fertility. Discovery of a relationship, however, does not explain why the variables are related. Providing an explanation for an association requires additional understanding of how one variable, taken as the dependent variable, interacts with not only the designated independent variable, but also with other variables related to both the dependent and independent variables. Thus, any time you discover a relationship between two variables you are forced to think about why the relationship exists. As shown in Figure 3.3, there are three main ways to interpret a relationship between two variables.

First, you can say that the two variables, as shown by A and B in example (1), are related and that is all. The arrow pointing both ways between A and B shows this kind of relationship. Sometimes it is impossible to say which variable influences the other. We only know that the two variables are related. Without more information, we cannot say which of the variable is the cause of changes in the other. Even so, such limited findings may be useful to other researchers.

In conducting research, however, our goal is to offer the strongest explanation we can about any relationship we find. If possible, we would like to say what we think may be the causes of variation in some variable. This leads us to think in terms of cause and effect; how do variations in one variable show up as observable differences in another variable?   Three decisions are required to establish one variable as the cause of changes in another variable. These are:

• Deciding on the direction of influence: which variable influences the other;
• Establishing a strong relationship between the presumed cause and its effect; and
• Eliminating other possible causal factors

Establishing the direction of influence

Efforts to establish a cause and effect or causal relationship are generally based on a theoretically derived association between two or more variables. To illustrate this process, we return to our hypothesis for the possible relationship between the preference for a nuclear family orientation of wives and their participation in family decision making. We want to test whether their nuclear family orientation could be the cause of differences in participation of wives in financial decision making. Our first step is to decide on the possible direction of a possible causal relationship. Which variable affects the other? Which is to be considered as the independent and which as the dependent variable?

Establishing a possible direction of a causal relationship is based on a simple rule:

The variable thought to be the independent variable must precede the dependent variable in time.

Let's apply this criterion to the hypothesized relationship between nuclear family orientation and their participation in family financial decisions.    Which variable generally precedes the other?   For virtually all women in developing countries, regardless of their culture, develop views regarding family orientation occurs before marriage. Thus, it is reasonable to designate family orientation as the independent variable for any explanation of the relationship between it and later participation in family matters. On this basis, participation in family financial matters would be treated as the dependent variable.

This was an easy decision. In other cases, you may have to review the research literature on the variables you are analyzing to see which should be treated as the independent or the dependent variable in a relationship.