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

• 成果类型: Article
• 署名作者: Oberski, D. L.; Kirchner, A.; Eckman, S.; Kreuter, F.
• 署名单位: Utrecht University; Research Triangle Institute; University of Nebraska System; University of Nebraska Lincoln; University of Mannheim; University System of Maryland; University of Maryland College Park
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1302338
• 发表日期: 2017
• 页码: 1477-1489
• 关键词: Measurement error; imputation; Identifiability; validation; validity; earnings

摘要

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.