Ignoring these three statements, we followed the statistical recommendation of Dr. Speranza et al. Unfortunately, we do not have dimensional information on depression other than the depression subscale of the SCL-90-R. Given the high internal consistency of the SCL-90-R, each subscale, e.g., depression, entered as a confounder will show major statistical effects on the dependent variable, which is also an SCL-90-R subscale in our model. Thus, when we entered depression, the three Toronto Alexithymia Scale factors, the Temperament and Character Inventory, age, and gender as independent variables and the SCL-90-R global severity index (without items assessing depression) as an dependent variable into a hierarchical linear regression model, SCL-90-R depression resulted in R2=0.785 and difficulties identifying feelings added an additional R2chg=0.030 (Fchg=41.3, p<0.001) to the variance. However, it is important to consider that the correlation (Pearson) between SCL-90-R global severity index (without depression) and the depression subscale was r=0.89! Thus, the adjustment of a statistical model for, e.g., depression is more reasonable if the confounder variable is not as highly correlated with the dependent variable. This was done by Luminet et al. (2001) in predicting posttreatment scores for alexithymia with pretreatment scores of alexithymia with adjustment for scores for depression or by Grabe et al. (1) when they predicted alexithymia with Temperament and Character Inventory dimensions with adjustment for the SCL-90-R global severity index.