The Third Variable Problem
THE THIRD-VARIABLE PROBLEM
Correlational data are frequently misinterpreted, especially when presented by newspaper reporters, talk-show hosts, or television newscasters. The Most common problem in interpreting correlations is Third-Variable Problem. A correlation simply indicates that there is a weak, moderate, or strong relationship (either positive or negative), or no relationship, between two variables. When interpreting a correlation, it is also important to remember that although the correlation between the variables may be very strong, it may also be that the relationship is the result of some third variable that influences both of the measured variables. The third-variable problem results when a correlation between two variables is dependent on another (third) variable. A good example of the third-variable problem is a well-cited study conducted by social scientists and physicians in Taiwan. The researchers attempted to identify the variables that best predicted the use of birth control—a question of interest to the researchers because of overpopulation problems in Taiwan. They collected data on various behavioral and environmental variables and found that the variable most strongly correlated with contraceptive use was the number of electrical appliances (yes, electric take this correlation at face value, it means that individuals with more electrical appliances tend to use contraceptives more, whereas those with fewer electrical appliances tend to use contraceptives less.
It should be obvious to you that this is not a causal relationship (buying electrical appliances does not cause individuals to use birth control, nor does using birth control cause individuals to buy electrical appliances). Thus, we probably do not have to worry about people assuming either causality or directionality when interpreting this correlation. The problem here is that of a third variable. In other words, the relationship between electrical appliances and contraceptive
Correlational data are frequently misinterpreted, especially when presented by newspaper reporters, talk-show hosts, or television newscasters. The Most common problem in interpreting correlations is Third-Variable Problem. A correlation simply indicates that there is a weak, moderate, or strong relationship (either positive or negative), or no relationship, between two variables. When interpreting a correlation, it is also important to remember that although the correlation between the variables may be very strong, it may also be that the relationship is the result of some third variable that influences both of the measured variables. The third-variable problem results when a correlation between two variables is dependent on another (third) variable. A good example of the third-variable problem is a well-cited study conducted by social scientists and physicians in Taiwan. The researchers attempted to identify the variables that best predicted the use of birth control—a question of interest to the researchers because of overpopulation problems in Taiwan. They collected data on various behavioral and environmental variables and found that the variable most strongly correlated with contraceptive use was the number of electrical appliances (yes, electric take this correlation at face value, it means that individuals with more electrical appliances tend to use contraceptives more, whereas those with fewer electrical appliances tend to use contraceptives less.
It should be obvious to you that this is not a causal relationship (buying electrical appliances does not cause individuals to use birth control, nor does using birth control cause individuals to buy electrical appliances). Thus, we probably do not have to worry about people assuming either causality or directionality when interpreting this correlation. The problem here is that of a third variable. In other words, the relationship between electrical appliances and contraceptive