Chapter 27 Week 5 - Formative test

A formative test helps you assess your progress in the course, and helps you address any blind spots in your understanding of the material. If you get a question wrong, you will receive a hint on how to improve your understanding of the material.

Complete the formative test ideally after you’ve seen the lecture, but before the lecture meeting in which we can discuss any topics that need more attention.

Question 1: A model is called “fully mediated” when the direct effect between the independent and dependent variable is non-significant.

TRUE

FALSE

Question 2: In comparing nested models in SEM, one chooses the model with the largest p-value for the Chi-square test of model fit.

TRUE

FALSE

Question 3: A confounder is a special case of a mediator.

FALSE

TRUE

Question 4: Match the name to the definition: 1) total effect, 2) direct effect, 3) indirect effect.

1) The partial bivariate regression coefficient between X and Y
2) The sum of the direct effect and any indirect effects linking X and Y
3) The product of any regression coefficients between variables mediating the link between X and Y

1) The sum of the direct effect and any indirect effects linking X and Y
2) The product of any regression coefficients between variables mediating the link between X and Y
3) The partial bivariate regression coefficient between X and Y

1) The sum of the direct effect and any indirect effects linking X and Y
2) The partial bivariate regression coefficient between X and Y
3) The product of any regression coefficients between variables mediating the link between X and Y

Question 5: In order to for mediation to exist, there must also exist a total effect between X and Y.

TRUE

FALSE

Question 6: When are model A and B nested?

If one model is obtained by constraining some parameters of the other model

If both model use the same dependent variables

If both models have an equal number of DF

If both models use exactly the same covariance matrix

Question 7: Bootstrapping…

…empirically derives the sampling distribution by randomly shuffling the data many times, and calculating the bootstrapped parameters in each sample.

…better estimates the population value of parameters by randomly sampling from the data many times, and calculating the bootstrapped parameters in each sample.

…better estimates the population value of parameters by randomly sampling from the data many times, and calculating the bootstrapped parameters in each sample.

…empirically derives the sampling distribution by randomly sampling from the data many times, and calculating the bootstrapped parameters in each sample.