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Administrative register data are increasingly important in statistics, but,like other types of data, may contain measurement errors. To prevent sucherrors from invalidating analyses of scientific interest, it is thereforeessential to estimate the extent of measurement errors in administrative data.Currently, however, most approaches to evaluate such errors involve eitherprohibitively expensive audits or comparison with a survey that is assumedperfect.  We introduce the "generalized multitrait-multimethod" (GMTMM) model, whichcan be seen as a general framework for evaluating the quality of administrativeand survey data simultaneously. This framework allows both survey and registerto contain random and systematic measurement errors. Moreover, it accommodatescommon features of administrative data such as discreteness, nonlinearity, andnonnormality, improving similar existing models. The use of the GMTMM model isdemonstrated by application to linked survey-register data from the GermanFederal Employment Agency on income from and duration of employment, and asimulation study evaluates the estimates obtained.  KEY WORDS: Measurement error, Latent Variable Models, Official statistics,Register data, Reliability

Evaluating the quality of survey and administrative data with generalized multitrait-multimethod models