By incorporating DIF into the scoring model we can correct for bias in the scores that would result from differential measurement across study and as a function of covariates. For instance, suppose that a harmonized item was constructed from two items measuring similar content but with slightly different item stems. If endorsement rates for these items differed in part due to wording differences in the item stems, and not just due to underlying differences in depression, then we would expect to detect study DIF for this item. Failing to account for the study DIF would lead to artificially elevated depression scores for participants receiving the easier-to-endorse prompt. Generating depression scores from a model that incorporates DIF removes such potential sources of bias, ensuring that the scores are commensurate across studies (or other subpopulations).
