Semi‐automated Rasch analysis using in‐plus‐out‐of‐questionnaire log likelihood

Rasch analysis is a popular statistical tool for developing and validating instruments that aim to measure human performance, attitudes and perceptions. Despite the availability of various software packages, constructing a good instrument based on Rasch analysis is still considered to be a complex, labour‐intensive task, requiring human expertise and rather subjective judgements along the way. In this paper we propose a semi‐automated method for Rasch analysis based on first principles that reduces the need for human input. To this end, we introduce a novel criterion, called in‐plus‐out‐of‐questionnaire log likelihood (IPOQ‐LL). On artificial data sets, we confirm that optimization of IPOQ‐LL leads to the desired behaviour in the case of multi‐dimensional and inhomogeneous surveys. On three publicly available real‐world data sets, our method leads to instruments that are, for all practical purposes, indistinguishable from those obtained by Rasch analysis experts through a manual procedure.