Commonly used methods for measuring output quality of multisource statistics

Abstract

Estimation of output quality based on sample surveys is well established. It accounts for the effects of sampling and non-response errors on the accuracy of an estimator. When administrative data are used or combinations of administrative data with survey data, more error types need to be taken into account. Moreover, estimators in multisource statistics can be based on different ways of combining data sources. That partly affects the methodology that is needed to estimate output quality. This paper presents results of the ESSnet project Quality of Multisource Statistics that studied methods to estimate output quality. We distinguish three main groups of methods: scoring methods, (re)sampling methods and methods based on parametric modeling. Each of those is split into methods that can be used for both single and multisource statistics and methods that can be applied to multisource statistics only. We end the paper by discussing some of the main challenges for the near future. We argue that estimating output quality for multisource statistics is still more an art than a technique.