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

TL;DR

The paper introduces the GMTMM model, a comprehensive framework for assessing measurement errors in administrative and survey data, accounting for complex data features and improving error estimation accuracy.

Contribution

It presents the generalized multitrait-multimethod model that simultaneously evaluates measurement errors in survey and administrative data, accommodating nonlinearity, discreteness, and nonnormality.

Findings

Successful application to German employment data

Simulation study confirms model effectiveness

Improved error estimation over existing methods

Abstract

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