Should you then check the assumptions of the robust regression with the same code as the squared regression? (like Shapiro-test, but then with the robust regression fit) How do you check for assumptions then? Or you just don't check them and use bootstrap method?
Here, I use robust regression mostly for checking whether outliers are a problem in the dataset. If the robust results match the normal regression results then that should not be the case. Normality (Shapiro-Wilk) should not be a problem in robust regression since outliers produce most problems with nonnormality and outliers are downweighted in robust regression.
A moderated mediation consists of two regression models (prediction of the mediator and prediction of the dv). You could run these two regressions in R and get VIFs.
Vielen, vielen Dank. Videos mit solchen genauen Informationen fehlen oft. Hat mir sehr geholfen!
Should you then check the assumptions of the robust regression with the same code as the squared regression? (like Shapiro-test, but then with the robust regression fit) How do you check for assumptions then? Or you just don't check them and use bootstrap method?
Here, I use robust regression mostly for checking whether outliers are a problem in the dataset. If the robust results match the normal regression results then that should not be the case. Normality (Shapiro-Wilk) should not be a problem in robust regression since outliers produce most problems with nonnormality and outliers are downweighted in robust regression.
Please you video on robust linear regression. i have dataset like to explore and seem have hint deadlock.
Here is the tutorial about robust regression:
ua-cam.com/video/qte9ASvgElI/v-deo.html
How can I check no multicollinearity for PROCESS moderated mediation
A moderated mediation consists of two regression models (prediction of the mediator and prediction of the dv). You could run these two regressions in R and get VIFs.