Nonsampling Errors in Surveys

2012 ◽  
Keyword(s):  
Nonsampling Errors

scholarly journals Measuring and Communicating the Uncertainty in Official Economic Statistics

2021 ◽  
Vol 37 (2) ◽  
pp. 289-316
Author(s):  
Gian Luigi Mazzi ◽  
James Mitchell ◽  
Florabela Carausu
Keyword(s):  
Big Data ◽  
Historical Perspective ◽  
Data Uncertainty ◽  
Data Sets ◽  
Official Statistics ◽  
Economic Statistics ◽  
Nonsampling Errors

Abstract Official economic statistics are uncertain even if not always interpreted or treated as such. From a historical perspective, this article reviews different categorisations of data uncertainty, specifically the traditional typology that distinguishes sampling from nonsampling errors and a newer typology of Manski (2015). Throughout, the importance of measuring and communicating these uncertainties is emphasised, as hard as it can prove to measure some sources of data uncertainty, especially those relevant to administrative and big data sets. Accordingly, this article both seeks to encourage further work into the measurement and communication of data uncertainty in general and to introduce the Comunikos (COMmunicating UNcertainty In Key Official Statistics) project at Eurostat. Comunikos is designed to evaluate alternative ways of measuring and communicating data uncertainty specifically in contexts relevant to official economic statistics.

scholarly journals Accuracy of Mixed-Source Statistics as Affected by Classification Errors

2016 ◽  
Vol 32 (3) ◽  
pp. 619-642 ◽  
Author(s):  
Arnout van Delden ◽  
Sander Scholtus ◽  
Joep Burger
Keyword(s):  
Expert Knowledge ◽  
Quality Criteria ◽  
Practical Method ◽  
Target Population ◽  
Classification Error ◽  
Combining Data ◽  
Classification Errors ◽  
Micro Level ◽  
Error Probabilities ◽  
Nonsampling Errors

Abstract Publications in official statistics are increasingly based on a combination of sources. Although combining data sources may result in nearly complete coverage of the target population, the outcomes are not error free. Estimating the effect of nonsampling errors on the accuracy of mixed-source statistics is crucial for decision making, but it is not straightforward. Here we simulate the effect of classification errors on the accuracy of turnover-level estimates in car-trade industries. We combine an audit sample, the dynamics in the business register, and expert knowledge to estimate a transition matrix of classification-error probabilities. Bias and variance of the turnover estimates caused by classification errors are estimated by a bootstrap resampling approach. In addition, we study the extent to which manual selective editing at micro level can improve the accuracy. Our analyses reveal which industries do not meet preset quality criteria. Surprisingly, more selective editing can result in less accurate estimates for specific industries, and a fixed allocation of editing effort over industries is more effective than an allocation in proportion with the accuracy and population size of each industry. We discuss how to develop a practical method that can be implemented in production to estimate the accuracy of register-based estimates.

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