Hello,
I am looking at predictor variables (scores on partner perception measures) on a criterion variable (relationship satisfaction), in order to see how scores on multiple measures predict the participants overall relationship satisfaction. I believe that a multiple regression is the best statistical method to do this ( but I'm open to suggestions). The challenge I'm facing is that I'm looking at multiple populations, (differing age ranges, gender, races). In order to compare these groups across the regressions, do I need to run a multiple regression for each group that I am examining? If so, should I use a bonferroni to correct for the many analyses I am running? Is there a better test I should be using that will help me examine how multiple predictors predict an outcome for multiple groups? Thanks for any help you can provide!
I am looking at predictor variables (scores on partner perception measures) on a criterion variable (relationship satisfaction), in order to see how scores on multiple measures predict the participants overall relationship satisfaction. I believe that a multiple regression is the best statistical method to do this ( but I'm open to suggestions). The challenge I'm facing is that I'm looking at multiple populations, (differing age ranges, gender, races). In order to compare these groups across the regressions, do I need to run a multiple regression for each group that I am examining? If so, should I use a bonferroni to correct for the many analyses I am running? Is there a better test I should be using that will help me examine how multiple predictors predict an outcome for multiple groups? Thanks for any help you can provide!