ANOVA/MANOVA are for categorical predictors. With continuous predictors you need to use a regression model.* Consider whether you need to evaluate interactions among the predictors to test your hypothesis correctly (something that is pretty much built into a multi-way ANOVA). For example: might the association of Magical Thinking with the outcomes depend on the level of Paranoid Ideation?
What you then have is best analyzed as a multivariate (multiple outcome) regression model. The regression coefficient estimates will be the same as for separate regressions for each outcome, but handling the outcomes properly together allows for evaluation of correlations among outcomes and corresponding adjustments of the error estimates for the coefficients.
Issues specific to multivariate regressions are explained in this R Journal article and these notes.
*ANOVA/MANOVA are essentially just regression models with categorical predictors that also include their interactions. There is no fundamental distinction of them from regression, and the same software accomplishes both (perhaps with different displays of results).