Correlation in Psychology

 

Correlation. The relationship between two variables. When two variables vary in a specific way with each other, they are said to covary. The covariation can be described in a graph of the relationship or in a summary statistic known as a correlation coefficient. There are a few common correlation coefficients.

Correlation coefficient. A statistic that summarizes a correlation between two variables. Correlations range from -1.0 to +1.0. Positive correlation values represent relationships such that as one variable increases, so does the other. Negative correlation values represent relationships that are inverse. In an inverse relationship, one value increases as the other value decreases. A common coefficient is the Pearson Product Moment Correlation Coefficient reported using a lower case, italicized letter r. See also correlation.

Third Variable Problem in Psychology

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Third Variable Problem. The possibility that a third unknown variable might be the cause of the correlation between two variables, which means we cannot conclude a cause-effect relationship exists just because two variables are correlated.

One very important point about correlational studies is the fact that a strong relationship between any two measured quantities does not mean that one causes the other. We know the strength of the relationship and its direction (positive or negative) but we cannot assume which one causes the other. We may hypothesize that the one event or behavior that comes first causes the event or behavior that comes afterward but that is only true if there are no other events or behaviors that could cause changes in both of the strongly correlated events or behaviors. This problem of understanding cause-effect relationships in correlational studies is known as the third variable problem, which acknowledges the fact that we cannot be sure if an unidentified variable better account for the relationship.

Church attendance may be correlated with good health but that does not mean if you attend church you will have good health. Without conducting an experiment and controlling relevant variables, we cannot assume the relationship is cause-effect. For example, church attendance and good health might be positively correlated because those church members who stay home do so because they are too ill to attend church.

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