The real magic happens when we combine these variables into separate models for CIOs, managers, and staff. When we do this, some factors that seem to be associated with salary at the bivariate level are no longer significant predictors after controlling for the others. And the effect of some variables that may have been weakly or unassociated may only pop out when considered in the multivariable environment. On the basis of precedent, we began by creating three separate models, one for each of the organizational-level categories: CIOs, managers, and staff. Our general approach to selecting an appropriate multiple regression model was to first build a full model containing all potential predictors of salary and then reduce that model to obtain more precise estimates of the relationships between salary and the significant predictors (table 1).1 In the following sections, we unpack each of the respective models, providing a more detailed explanation and analysis of the multivariable models for CIOs, managers, and staff.