Topics of statistical theory for register‐based statistics and data integration

TLDR

Integrated statistical production from surveys, censuses, and administrative registers is increasingly common, offering reduced response burden, cost efficiency, and richer spatial‑demographic and longitudinal insights, yet it introduces new challenges beyond traditional survey sampling and data integration. This article aims to develop conceptual statistical theory for integrating survey and census data to broaden their scope and enhance quality. The authors propose a two‑phase life‑cycle model for integrated microdata that maps potential error sources and outlines quality‑assessment concepts beyond the assumption of error‑free data. A shared understanding of these issues is expected to facilitate coordinated research and development efforts.

Abstract

Official statistics production based on a combination of data sources, including sample survey, census and administrative registers, is becoming more and more common. Reduction of response burden, gains of production cost efficiency as well as potentials for detailed spatial‐demographic and longitudinal statistics are some of the major advantages associated with the use of integrated statistical data. Data integration has always been an essential feature associated with the use of administrative register data. But survey and census data should also be integrated, so as to widen their scope and improve the quality. There are many new and difficult challenges here that are beyond the traditional topics of survey sampling and data integration. In this article we consider statistical theory for data integration on a conceptual level. In particular, we present a two‐phase life‐cycle model for integrated statistical microdata, which provides a framework for the various potential error sources, and outline some concepts and topics for quality assessment beyond the ideal of error‐free data. A shared understanding of these issues will hopefully help us to collocate and coordinate efforts in future research and development.