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