Mediation and Moderation Analysis Quiz

What is the purpose of mediation analysis?

• To understand the mechanisms through which an exposure or treatment affects an outcome.

• To prevent any analysis of the relationship between exposure and outcome

• To complicate the understanding of the relationship between exposure and outcome

• To ignore the relationship between exposure and outcome

Common assumption test in simple mediation analysis:

• The outcome is not affected by the mediator

• The predictor is not affected by the mediator

• The same as regression analysis

In simple mediation analysis, what are the three paths that are typically examined?

• Initial effect, secondary effect, tertiary effect

• Primary effect, direct effect, indirect effect

• Primary effect, secondary effect, tertiary effect

• Total effect, direct effect, and indirect effect

What are the key steps involved in conducting a mediation analysis?

• Specifying the predictive model, estimating the total effect, estimating the direct effect, estimating the indirect effect, and assessing the significance of the indirect effect.

• Skipping the analysis, estimating the total cause, estimating the direct effect, estimating the indirect effect, and assessing the significance of the indirect effect.

• Guessing the outcome, estimating the total effect, estimating the direct effect, estimating the indirect effect, and assessing the significance of the direct effect.

• Using random data, estimating the total effect, estimating the direct cause, estimating the indirect effect, and assessing the significance of the total effect.

How is the indirect effect calculated in mediation analysis?

• By dividing the coefficient of the mediator by the coefficient of the independent variable

• By multiplying the coefficient of the mediator (m to y) by the coefficient of the independent variable (x to m) in the regression model.

• By adding the coefficient of the mediator to the coefficient of the independent variable

• By taking the square root of the coefficient of the mediator

*/**/***=statistically significant (p<0.05).

The figure beside shows the mediation relationship that is

• partial

• full

• not significant

What is the purpose of moderation analysis?

• To examine the relationship between two variables and a third variable that affects the strength or direction of the original relationship.

• To prevent any analysis of the relationship between exposure and outcome

• To complicate the understanding of the relationship between exposure and outcome

• To ignore the relationship between exposure and outcome

What are the key steps involved in conducting a moderation analysis?

• Specifying the predictive model, estimating the total effect, estimating the direct effect, estimating the interaction effect, and assessing the significance of the interaction effect.

• Skipping the analysis, estimating the total cause, estimating the direct effect, estimating the interaction effect, and assessing the significance of the interaction effect.

• Guessing the outcome, estimating the total effect, estimating the direct effect, estimating the interaction effect, and assessing the significance of the direct effect.

• Using random data, estimating the total effect, estimating the direct cause, estimating the interaction effect, and assessing the significance of the total effect.

What are the common statistical methods used for testing mediation and moderation effects?

• Factor analysis and Discriminant analysis

• Regression analysis and bootstrapping

• T-test and ANOVA

• Mean, Median, Mode

What is the difference between moderation and mediation analysis?

• Mediation analysis examines the relationship between two variables and a third variable that affects the strength or direction of the original relationship.

• Moderation analysis examines the relationship between two variables without considering any third variable.

• Mediation analysis examines the relationship between three variables without considering any interaction effects.

• Moderation analysis examines the relationship between two variables and a third variable that affects the strength or direction of the original relationship.