I have a question about what explaining unique variance means in regression models and outputs.
I often read in research papers that "We found that outcomes E and F
could not simply be explained by individual differences C and D
. Each predictor, predictors A and B
was uniquely related to one measure of outcomes E and F
above and beyond individual differences C and D
. ... Taken together, outcomes E and F
may be shaped by individual differences C and D
, but cannot be explained fully by those constructs."
Does this mean a multiple regression model was computed with A
, B
, C
and D
as predictors and E
and F
as outcomes? And if A
and B
explain unique variance, does that mean the correlation coefficients for those two predictors are significant? Essentially, what does it mean that predictors A and B
was uniquely related to one measure of outcomes E and F
above and beyond individual differences C and D
?
Here's the DOI to the paper: http://dx.doi.org/10.1037/emo0000927 and the public link. You can look at the first paragraph on page 10 under the "Discussion" section of Study 21 and the "Perceived benefits of IER Interactions" for analysis strategy under Study 1.
Sorry if the question is too abstract. I don't have access to the data nor the regression models. Happy to elaborate more, and I appreciate any input on this!