Large-scale assessments (LSAs) use Mislevy’s “plausible value” (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the properties of methods used in current practice for dealing with missing data in background variables in educational LSAs, which rely on the missing indicator method (MIM), with other methods based on multiple imputation. In this context, we present a fully conditional specification (FCS) approach that allows for a joint treatment of PVs and missing data. Using theoretical arguments and two simulation studies, we illustrate under what conditions the MIM provides biased or unbiased estimates of population parameters and provide evidence that methods such as FCS can provide an effective alternative to the MIM. We discuss the strengths and weaknesses of the approaches and outline potential consequences for operational practice in educational LSAs. An illustration is provided using data from the PISA 2015 study.

大规模评估（LSA）使用Mislevy的“合理价值”（PV）方法，将学生的熟练程度与背景调查表中管理的非认知变量联系起来。此方法要求完全观察背景变量，很少满足此要求。在本文中，我们评估并比较了当前实践中用于处理教育LSA中背景变量中缺失数据的方法的属性，这些方法依赖于缺失指标方法（MIM）和其他基于多重插补的方法。在这种情况下，我们提出了一种完全条件规范（FCS）方法，可以对PV和丢失的数据进行联合处理。使用理论论证和两次模拟研究，我们说明了MIM在什么条件下会提供总体参数的有偏估计或无偏估计，并提供证据表明FCS等方法可以提供MIM的有效替代方法。我们讨论了这种方法的优缺点，并概述了教育LSA中操作实践的潜在后果。使用PISA 2015研究的数据提供了一个插图。