Why does impression management positively influence interview ratings? The mediating role of competence and warmth

Though interviews assess job applicants' skills and abilities, they can be influenced by extraneous factors, including impression management (IM) tactics. Interviewees’ self‐promotion and ingratiation IM tactics predict higher interview ratings; however, researchers have yet to determine why these tactics work. We assessed whether two fundamental dimensions of social perception, competence and warmth, mediate the relationship between IM tactics and interview ratings. We hypothesized that interviewee competence mediates the relationship between self‐promotion and interview ratings, and interviewee warmth mediates the relationship between ingratiation and interview ratings. Using real employment interviews, we found that competence mediates the relationship between self‐promotion and interview ratings, but warmth did not mediate the relationship between ingratiation and interview ratings in the way we expected.     

A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

The Savage–Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage–Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys–Zellner–Siow prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage–Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, non-linear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage–Dickey density ratio.