Mediation (statistics) - Other Third Variables

Other Third Variables

(1) Confounding:

Another model that is often tested is one in which competing variables in the model are alternative potential mediators or an unmeasured cause of the dependent variable. An additional variable in a causal model may obscure or confound the relationship between the independent and dependent variables. Potential confounders are variables that may have a causal impact on both the independent variable and dependent variable. They include common sources of measurement error (as discussed above) as well as other influences shared by both the independent and dependent variables.

In experimental studies, there is a special concern about aspects of the experimental manipulation or setting that may account for study effects, rather than the motivating theoretical factor. Any of these problems may produce spurious relationships between the independent and dependent variables as measured. Ignoring a confounding variable may bias empirical estimates of the causal effect of the independent variable.

(2) Suppression:

Suppression variables increase the predictive validity of another variable by its inclusion into a regression equation. For example, higher intelligence scores (X) cause a decrease in errors made at work on an assembly line (Y). However an increase in intelligence (X) could cause an increase in errors made on an assembly line (Y) as it may also relate to an increase in boredom while at work (Z) thereby introducing an element of carelessness resulting in a higher percentage of errors made on the job. Such a suppressor variable will lead to an increase in magnitude of the relationship between two variables.

In general, the omission of suppressors or confounders will lead to either an underestimation or an overestimating of the effect of X on Y, thereby either reducing or artificially inflating the magnitude of a relationship between two variables.

(3) Moderators:

Other important third variables are moderators. Moderators are variables that can make the relationship between two variables either stronger or weaker. Such variables further characterize interactions in regression by affecting the direction and/or strength of the relationship between X and Y. A moderating relationship can be thought of as an interaction. It occurs when the relationship between variables A and B depends on the level of C. See moderation for further discussion.