Comparing correction methods to reduce misclassification bias

Multivariate analysis of psychological dat

Multi-source statistics:Basic situations and methods

Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems

Editing and Estimation of Measurement Errors in Administrative and Survey Data

Bakker, B.F.M. [Promotor]Ganzeboom, H.B.G. [Promotor]Elzinga, C.H. [Copromotor

Comparing correction methods to reduce misclassification bias

Multivariate analysis of psychological dat

Methods for estimating the quality of multisource statistics

With the increasing availability of data, official business statistics are more often based on multiple data sources. Evaluating accuracy, i.e. bias and variance, of output based on multiple sources has therefore become an important topic. Estimating the accuracy is important to inform users about data quality, and it can be a trigger to adjust processing steps when accuracy drops below an acceptable level. An inventory of methods to estimate output accuracy of multisource statistics has been made in the European project KOMUSO. The bias and variance of multisource statistics are affected by errors on the representation side (units and populations) and by errors on the measurement side. Additionally, when combining sources at microlevel, unit-level linkage errors may occur. We will introduce recently developed methods to estimate bias and variance of outputs as affected by representation error, linkage error, and measurement error, illustrated by examples for business statistics.</p