Approach to Statistical Analyses

Linear trajectories for changes in HR, RSA and PEP during diagnostic interviewing were assessed on a person by person basis via ordinary least squares regression. A regression model was conducted for each individual such that time-varying HR, RSA and PEP values were regressed on time (epoch number). The standardized regression coefficients were then extracted and aggregated into a standard nomothetic data set in order to assess individual differences in slopes per study hypotheses.

To evaluate the effects of diagnosis on baseline HR, RSA and PEP, a multivariate analysis of variance (MANOVA) was conducted for differences in HR, RSA and PEP during the introductory module of the ADIS-5, while controlling for sex and age.

To investigate the effect of clinical diagnoses on physiologic stress responses to the diagnostic interview, three multiple regression models were conducted for HR, RSA and PEP, respectively, with GAD, MDD and SAD as predictors. We covaried age, sex and baseline levels of the dependent variable as control variables. To examine the moderating role of suppression in GAD, MDD and SAD, for each model we tested the moderating role of suppression via an interaction term between diagnosis and the ERQ suppression subscale.

To examine the role of worry on physiological functioning in GAD, MDD and SAD, three multiple regression models were conducted for HR, RSA and PEP respectively, with worry as a predictor. Worry was defined as PSWQ score. We covaried age, sex and baseline levels of the dependent variable as control variables. Again, to examine the role of emotional suppression on worry, for each model we tested the moderating role of suppression via an interaction term between diagnosis and the ERQ suppression subscale. All statistical analyses were performed in R 2.1 (R Core Team, 2015).