- Thread starter noetsi
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I am not sure I am sophisticated enough to do a simulation which would have to be in R because we don't have PROC IML. I am a long way from knowing the R code (I use it for basic stuff, I mean basic coding not basic stats).

I have to run a federally mandated model, that is with 41 mandated variables

MLM is not going to run with 41 control variables I am pretty sure, but I can try.

In a fixed model you have:

Y = X*beta+ eps

But in a mixed model:

Y = X*beta + Z*v + eps

If you just add a few variables (the Z*v part) I guess that it would be possible to estimate that. Agree?

loss′=7.8–9.4hours–.08effort+.39hours∗effort

How do you interpret the .39 slope for the interaction term hours*effort. What is it actually telling you. The impact of hours goes up with each unit of increased effort (by .39)?

This is about simple effects. It is often recommended when you have interaction.

In the equation from above they test the simple effect of hours at level 2 and effort at level 30. They get a value of about 10 for the simple effect. Does that mean the value of the DV goes up by 10 when you move from 2 hours one unit? I don't really understand what simple effect slopes show.

Unlike regular regression I don't think simple effects show the impact of a one unit change

The set of regression coefficients are multiplied by their corresponding values, and the resulting products are summed to form the linear combination. As an example, we will show how to estimate the predicted loss for a subject who averages 2 hours of exercise per week at an effort level of 30.