Administrative data obtained from government registers provide a wealth of potential for the social sciences. Administrative registers may contain considerable measurement errors, including definition, reporting, timing, processing, editing, linkage, and coverage errors. This chapter explores how to estimate the extent of measurement errors in both administrative register data and survey answers. It demonstrates one approach to doing so: latent variable modeling. Modeling these mode effects allows for the identification of both random classification errors and method effects without the need for multiple “traits” (true values). It could therefore be termed a “single-trait-multimethod” (STMM) approach. The chapter describes the data on neighborhood of residence obtained from a survey and an important Dutch official administrative register, and then details the latent class model built to estimate classification error rates in these measures. The costs of full-scale audits compared with latent variable modeling are gigantic, meaning that a small inaccuracy still leaves latent variable modeling an attractive alternative.