R-Squared Change - Multivariate Multiple Regression

Hello everyone,

I've been asked to conduct a multivariate multiple regression with multiple DVs (3 measures of a dependence construct) predicted by a set of IVs (4 variables of a tolerance construct). However, what I'm broadly interested in is whether these tolerance variables are predictive of the set of DVs after adjusting for a set of covariates (age and sex), so I'm essentially looking for an R-squared change value for the two models predicting the set of DVs (How well or how much better does model 2 fit or predict the set of DVs after adjusting for age and sex?). Another question would be to see if the set of IVs fit or predict the same set of DVs after adjusting for age, sex, anxiety, and depression. So I'm looking for a similar type of R-squared change between model 1 (age, sex, anxiety, depression) and model 2 (adding in the 4 tolerance constructs). In terms of SPSS this would be analogous to a hierarchical or step-wise multiple regression where you put all of your control variables into the first block and then add in your variables of interest (the tolerance variables) into the second block and examining the R-square change between the first and second model. Also all variables are continuous with the exception of sex.

Is anyone familiar with how to do this in either SPSS, SAS, Mplus, or Stata? I'm most familiar with SPSS and somewhat with Mplus and SAS and will venture into using Stata if I have a general understanding of the commands on how to run it. Any information or help would be greatly appreciated!

Thank you so much!