2015/08/22 by Daniel Leonard Oberski, Daniel L. Oberski, Oberski, Daniel Leonard +6
Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference #stat.AP
paper · pdf · doi:10.48550/arxiv.1508.05502
arxiv created 2015/08/22 · openalex publication_date 2015/08/22 · arxiv updated 2015/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Administrative register 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 register 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-register data from the German Federal Employment Agency on income from and duration of employment, and a simulation study evaluates the estimates obtained. KEY WORDS: Measurement error, Latent Variable Models, Official statistics, Register data, Reliability