  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
