Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models

Administrative data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect. We introduce the “generalized multitrait-multimethod” (GMTMM) model, which can be seen as a general framework for evaluating the quality of administrative and survey data simultaneously. This framework allows both survey and administrative data to contain random and systematic measurement errors. Moreover, it accommodates common features of administrative data such as discreteness, nonlinearity, and nonnormality, improving similar existing models. The use of the GMTMM model is demonstrated by application to linked survey-administrative data from the German Federal Employment Agency on income from of employment, and a simulation study evaluates the estimates obtained and their robustness to model misspecification. Supplementary materials for this article are available online.

行政数据（administrative data）在统计学领域的重要性日益凸显，但与其他类型数据一样，可能存在测量误差。为避免此类误差破坏科学研究分析的有效性，对行政数据中的测量误差程度进行估算至关重要。然而目前，大多数评估此类误差的方法要么需要成本高昂的审计工作，要么需要与假定为完美的调查数据进行比对。我们提出了“广义多特质-多方法（generalized multitrait-multimethod，GMTMM）”模型，可将其视为同时评估行政数据与调查数据质量的通用框架。该框架允许调查数据与行政数据同时包含随机与系统测量误差。此外，它还适配行政数据的常见特征，如离散性、非线性与非正态性，对现有同类模型进行了改进。我们通过德国联邦就业局提供的就业收入关联调查-行政数据实例，演示了GMTMM模型的应用，并通过模拟研究评估了所得估计值及其对模型误设的稳健性。本文补充材料可在线获取。