Interpretation hierarchical regression 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:


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*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?
 A: Interpretation of hierarchical regression


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*Model Summary Box:
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.


*Now look at change statistics
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)


*The ANOVA table:
It indicates that the models as a whole are significant or not.
Hope it helps! 
