Rasch analysis is a procedure to develop and validate instruments that aim to measure persons' traits. However, manual Rasch analysis is a complex and time-consuming task, even more so when the possibility of differential item functioning (DIF) is taken into consideration. Furthermore, manual Rasch analysis by construction relies on a modeller's subjective choices. As an alternative approach, we introduce a semi-automated procedure that is based on the optimization of a new criterion, called

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... plus-out-of-questionnaire log likelihood with differential item functioning (IPOQLL-DIF) which extends our previous criterion. We illustrate our procedure on artificially generated data as well as on several real-world datasets containing potential DIF items. On these real-world datasets, our procedure found instruments with similar clinimetric properties as those suggested by experts through manual analyses.