In elaboration analysis, this controls for the extraneous variable.

Simultaneously controls for the effects of several independent variables.

Refer to the random processes in a statistical model.

In this elaboration outcome, an antecedent variable creates a spurious association between X and Y.

Displays the causal links between all variables in a complex model and provides estimates of the direct and indirect effects of one variable on another.

The association between two variables when no other variable is controlled.

Shows the effect of X on Y while controlling for all other independent variables in a multiple regression analysis.

In this elaboration outcome, the control variable is intervening and there is no association in either partial table.

Produced by omitting important variables from a statistical model.

Multivariate analysis involving three-variable contingency tables.

This elaboration outcome increases confidence that the original relationship is nonspurious.

Two or more independent variables in a multiple regression are highly correlated with one another.