Hi there

These two alternatives allow you to study quite different things.

The sequence of 9 bivariate regression equations allows you to look at the simple relationship between each of the predictor variables and severity of schizophrenia systems. It's a lot like just looking at 9 different correlations.

The multiple regression allows you to look at:

- How accurately you can predict severity of schizophrenia symptoms
when using all 9 predictors simultaneously- The relationship between each of the individual predictor variables and severity of schizophrenia symptoms
while holding all the other predictor variables constant.

So they're each giving you quite different information, and it really depends on which is the better match to your actual research questions and/or hypotheses.

As above - it's because the analyses are looking at different things (the relationship between a predictor and the DV, vs the relationship between a predictor and the DV while holding all other predictors constant).When I run independent bivariate regressions, 3 of the predictor variables come up significant. When I run the multiple regression with all 9 of the predictors in one model, none of the predictors have a significant effect. I'm also, unsure why there is this discrepancy.