Abstract
Many statistical offices have been moving towards an increased use of administrative data sources for statistical purposes, both as a substitute and as a complement to survey data. Moreover, the emergence of big data constitutes a further increase in available sources. As a result, statistical output in official statistics is increasingly based on complex combinations of sources. The quality of such statistics depends on the quality of the primary sources and on the ways they are combined.
This paper analyses the appropriateness of the current set of output quality measures for multiple source statistics, it explains the need for improvement and outlines directions for further work. The usual approach for measuring the quality of the statistical output is to assess quality through the measurement of the input and process quality. The paper argues that in multisource production environment this approach is not sufficient. It advocates measuring quality on the basis of the output itself - without analysing the details of the inputs and the production process - and proposes directions for further development.