The University of Southampton
University of Southampton Institutional Repository

Methods for combining administrative data to estimate population counts

Methods for combining administrative data to estimate population counts
Methods for combining administrative data to estimate population counts
Governments require information about population counts and characteristics in order to make plans, develop policies and provide public services. The main source of this information is the traditional population censuses. However, they are costly, and the information collected by the decennial censuses goes out-of-date easily. For this reason, this thesis has two main aims: to develop methodologies to combine administrative data sources to estimate population counts in the absence of both a traditional census, and to produce uncertainty estimates for the estimated population counts.

Although, the methodologies are illustrated using administrative data sources from England and Wales, they can easily be applied to other countries' administrative data sources. The most comprehensive administrative sources in England and Wales are the NHS Patient Register and the Customer Information System. However, it is known that both of these sources exceed the census estimates. Therefore, it is crucial to use another source to adjust the bias to estimate population counts using these administrative sources.

Three substantial chapters assessing methodologies to combine administrative sources with the auxiliary information are presented. The first of these chapters presents a basis methodology, log-linear models with offsets, which is extended in the following chapters. The second chapter extends these models by using individually linked administrative sources. The third chapter improves on the basis models to produce measures of uncertainty.

This thesis evaluates different log-linear models in terms of their capacity for producing accurate population counts for age group, sex and local authority groups both within the classical and the Bayesian framework. On the other hand, it also presents a detailed perspective to understand which population groups tend to be missed by the administrative data in England and Wales, and how much they can be improved just by combining them with the specific association structures obtained from auxiliary data sources.
Yildiz, Dilek
71798192-b121-4cd0-9025-7ad5131ac6d5
Yildiz, Dilek
71798192-b121-4cd0-9025-7ad5131ac6d5
Smith, Peter
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Van Der Heijden, Peter
85157917-3b33-4683-81be-713f987fd612

(2016) Methods for combining administrative data to estimate population counts. University of Southampton, Faculty of Social, Human and Mathematical Sciences, Doctoral Thesis, 156pp.

Record type: Thesis (Doctoral)

Abstract

Governments require information about population counts and characteristics in order to make plans, develop policies and provide public services. The main source of this information is the traditional population censuses. However, they are costly, and the information collected by the decennial censuses goes out-of-date easily. For this reason, this thesis has two main aims: to develop methodologies to combine administrative data sources to estimate population counts in the absence of both a traditional census, and to produce uncertainty estimates for the estimated population counts.

Although, the methodologies are illustrated using administrative data sources from England and Wales, they can easily be applied to other countries' administrative data sources. The most comprehensive administrative sources in England and Wales are the NHS Patient Register and the Customer Information System. However, it is known that both of these sources exceed the census estimates. Therefore, it is crucial to use another source to adjust the bias to estimate population counts using these administrative sources.

Three substantial chapters assessing methodologies to combine administrative sources with the auxiliary information are presented. The first of these chapters presents a basis methodology, log-linear models with offsets, which is extended in the following chapters. The second chapter extends these models by using individually linked administrative sources. The third chapter improves on the basis models to produce measures of uncertainty.

This thesis evaluates different log-linear models in terms of their capacity for producing accurate population counts for age group, sex and local authority groups both within the classical and the Bayesian framework. On the other hand, it also presents a detailed perspective to understand which population groups tend to be missed by the administrative data in England and Wales, and how much they can be improved just by combining them with the specific association structures obtained from auxiliary data sources.

PDF
D.Yildiz_Thesis_not_signed.pdf - Other
Restricted to Repository staff only

More information

Published date: April 2016
Organisations: University of Southampton, Social Statistics & Demography

Identifiers

Local EPrints ID: 397608
URI: http://eprints.soton.ac.uk/id/eprint/397608
PURE UUID: 4a6df3a4-f848-431e-afd8-689073d5bfda
ORCID for Peter Smith: ORCID iD orcid.org/0000-0003-4423-5410
ORCID for Peter Van Der Heijden: ORCID iD orcid.org/0000-0002-3345-096X

Catalogue record

Date deposited: 15 Jul 2016 09:28
Last modified: 24 May 2019 00:31

Export record

Contributors

Author: Dilek Yildiz
Thesis advisor: Peter Smith ORCID iD
Thesis advisor: Peter Van Der Heijden ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×