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Multivariate analysis involving three-variable contingency tables.
In elaboration analysis, this controls for the extraneous variable.
Produced by omitting important variables from a statistical model.
Two or more independent variables in a multiple regression are highly correlated with one another.
Refer to the random processes in a statistical model.
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.
In this elaboration outcome, an antecedent variable creates a spurious association between X and Y.
In this elaboration outcome, the control variable is intervening and there is no association in either partial table.
Simultaneously controls for the effects of several independent variables.
Shows the effect of X on Y while controlling for all other independent variables in a multiple regression analysis.
This elaboration outcome increases confidence that the original relationship is nonspurious.