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Simple mediation model. The independent variable causes the mediator variable; the mediator variable causes the dependent variable. In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator ...
To quantify the effect of a moderating variable in multiple regression analyses, regressing random variable Y on X, an additional term is added to the model. This term is the interaction between X and the proposed moderating variable. [1] Thus, for a response Y and two variables x 1 and moderating variable x 2,:
Moderated mediation, also known as conditional indirect effects, [2] occurs when the treatment effect of an independent variable A on an outcome variable C via a mediator variable B differs depending on levels of a moderator variable D. Specifically, either the effect of A on B, and/or the effect of B on C depends on the level of D.
This total amount of variance in the dependent variable that is accounted for by the independent variable can then be broken down into areas c and d. Area c is the variance that the independent variable and the dependent variable have in common with the mediator, and this is the indirect effect.
A mediator variable is used to explain the correlation between two variables. A moderator variable affects the direction or strength of the correlation between two variables. A spurious relationship is a relationship in which the relation between two variables can be explained by a third variable.
A suppressor variable is a variable that increases the predictive validity of another variable when included in a regression equation. [1] Suppression can occur when a single causal variable is related to an outcome variable through two separate mediator variables, and when one of those mediated effects is positive and one is negative.
Graphical model: Whereas a mediator is a factor in the causal chain (top), a confounder is a spurious factor incorrectly implying causation (bottom). In statistics, a spurious relationship or spurious correlation [1] [2] is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third ...
The term e i is known as the "error" and contains the variability of the dependent variable not explained by the independent variable. [citation needed] With multiple independent variables, the model is y i = a + bx i,1 + bx i,2 + ... + bx i,n + e i, where n is the number of independent variables. [citation needed]