Items do not always function equally in different groups (e.g. across genders, languages and cultures). Consequently, where patient-reported outcomes are used and different groups are compared, data should be checked for differential item functioning (DIF). An item that functions with the same constant magnitude of difference across a construct measured possesses uniform DIP. In contrast, non-uniform DBF is characterised by an uneven difference in item function across the latent variable measured. The aims of this paper are to report on the methodological aspects of DIF using Rasch analysis and to demonstrate how the mean scores in a scale can be adjusted due to uniform DIF. Different examples of DIF are reported including examples of differences between the mean scores before and after adjusting for an identified uniform DIF. In conclusion, the difference between subpopulations and, therefore, other outcomes such as economic impact could be under or overestimated if one or more items in a dimension possess DIF.