I'm running hierarchical regression to determine whether or not a number of independent variables are able to explain a dependent variable. I have following models:
- Model 1: only controlvariables
- Model 2: Model 1 + independent variables
- Model 3: Model 2 + interaction term
When interpreting the significance of the different terms, should I only consider the last model or should I consider the model where I entered the terms. For instance, to investigate which controlvariables are significantly significant to the dependent variable, should I look at Model 1, and then, for the independent variables, consider Model 2? Or should I just look at the last model for the significance of the different terms?
Best Answer
Interpretation of hierarchical regression
Read 3rd column named 'R square' for all your models and interpret like this. Check the R Square in the Model Summary box. Variables entered in Block 1 (control variable) explained X (depends on your output) % of the variance in DV.
After Block 2 variables (IDV's) has been included , the model as a whole explained Y (depends on your output) % of variance in DV.
Adding Block 3 variable (interaction term), the model as a whole explained Z (depends on your output) % of variance in DV.
The column labelled R Square Change shows how much change in R square (explained variation) as compare to previous model. For example, for model 1, it is same as X, for model 2, it is same as (Y - X) and so on.
To infer if this change is statistically significant or not, you need to look at the last column (Sig. F Change)
It indicates that the models as a whole are significant or not.
Hope it helps!