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.

中文翻译：

---

使用问卷内外对数似然的半自动 Rasch 分析

Rasch 分析是一种流行的统计工具，用于开发和验证旨在衡量人类表现、态度和感知的工具。尽管有各种软件包可用，但基于 Rasch 分析构建一个好的仪器仍然被认为是一项复杂的劳动密集型任务，在此过程中需要人类的专业知识和相当主观的判断。在本文中，我们提出了一种基于第一原理的 Rasch 分析半自动化方法，该方法减少了对人工输入的需求。为此，我们引入了一个新的标准，称为问卷内加外对数似然（IPOQ-LL）。在人工数据集上，我们确认 IPOQ-LL 的优化在多维和非均匀调查的情况下会导致所需的行为。在三个公开可用的真实世界数据集上，