Comparative Study of Complex Survey Estimation Software in ONS

Andy Fallows
Megan Pope
Jonathan Digby-North
Gary Brown
Daniel Lewis
Office for National Statistics, UK


Many official statistics across the UK Government Statistical Service (GSS) are produced using data collected from sample surveys. These survey data are used to estimate population statistics through weighting and calibration techniques. For surveys with complex or unusual sample designs, the weighting can be fairly complicated. Even in more simple cases, appropriate software is required to implement survey weighting and estimation.
As with other stages of the survey process, it is preferable to use a standard, generic calibration tool wherever possible. Standard tools allow for efficient use of resources and assist with the harmonisation of methods. In the case of calibration, the Office for National Statistics (ONS) has experience of using the Statistics Canada Generalized Estimation System (GES) across a range of business and social surveys.
GES is a SAS-based system and so is only available in conjunction with an appropriate SAS licence. Given recent initiatives and encouragement to investigate open source solutions across government, it is appropriate to determine whether there are any open source calibration tools available that can provide the same service as GES. This study compares the use of GES with the calibration tool ‘R evolved Generalized software for sampling estimates and errors in surveys’ (ReGenesees) available in R, an open source statistical programming language which is beginning to be used in many statistical offices.
ReGenesees is a free R package which has been developed by the Italian statistics office (Istat) and includes functionality to calibrate survey estimates using similar techniques to GES.
This report describes analysis of the performance of ReGenesees in comparison to GES to calibrate a representative selection of ONS surveys. Section 1.1 provides a brief introduction to the current use of SAS and R in ONS. Section 2 describes GES and ReGenesees in more detail. Sections 3.1 and 3.2 consider methods for analysing and comparing the performance of the two tools using case studies from business and social surveys respectively. The analyses cover a range of issues including use with large datasets and complex calibration problems. Section 3.3 describes more general comparisons between the uses of each tool. The report finishes with a conclusion and recommendations. Annex A provides a glossary of key terms used in this report.
Key words: R, GES, ReGenesees, Calibration

[Full Text]

Romanian Statistical Review 3/2015