Semi-Automated Rasch Analysis using In-plus-out-of-questionnaire Log-likelihood release_xgoj2uxbtbaqfpcddzbynwme3e

Abstract

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, labor-intensive task, requiring human expertise and bares the possibility results in different but equally suited instruments. 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 datasets, we confirm that optimization of IPOQ-LL leads to the desired behavior in the case of multi-dimensional and inhomogeneous surveys. On three publicly available real-world datasets, our method leads to instruments that have, for all practical purposes, similar clinimetrical properties as those obtained by Rasch analysis experts through a manual procedure.
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