Timeline for Interaction and Mediation in linear regression
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 19, 2017 at 5:49 | comment | added | Sheridan Grant | The coefficients are technically "unbiased" assuming that the model is correct (which it never really is :P ). But right, you can't interpret each IV independently: it makes no sense to say "holding IV1 and IV2 constant, a unit change in IV3 is associated with a change in DV of..." Audience depending, you could maybe say, "When IV1 is equal to $x_1$, a unit change in IV2 is associated with a change of $y$ in DV (incorporating both the IV2 and IV3 coefficient), and when IV1 is equal to $x_2$, the relationship changes to __ because __ (IV3 is different now that IV1 has changed)." Does that help? | |
| Jun 19, 2017 at 5:03 | comment | added | Shay | There are theoretical reasons to believe that the ratio between inside and outside activity (IV3) should be relevant as well in predicting the DV. My doubts are about the interpretation of the results - since IV3 is highly correlated with IV1,2 it seems very "biased" to take the full model results as is. How should I account for possible mediation/multi-linearity? (in the full model IV3, the ratio, get a Tolerance < 0.4) Thanks! | |
| Jun 19, 2017 at 4:56 | comment | added | Shay | There are theoretical reasons to believe that the ratio between inside and outside activity (IV3) should be relevant as well in predicting the DV. However, | |
| Jun 19, 2017 at 4:34 | history | answered | Sheridan Grant | CC BY-SA 3.0 |