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Introduction to Data Linkage and Analysing Linked Data – Oxford

10th May 2018 @ 9:30 am - 11th May 2018 @ 4:45 pm

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This is a training and capacity building event co-organised by the Administrative Data Research Centre England (ADRC_E) and the Consumer Data Research Centre (CDRC).

This short course is designed to give participants a practical introduction to data linkage and is aimed at researchers either intending to use data linkage themselves or those who want to understand more about the process so that they can analyse linked data.

Day 1 (Introduction to Data Linkage) will cover examples of the uses of data linkage, data preparation, and methods for linkage (including deterministic and probabilistic approaches and privacy-preserving linkage).

Day 2 (Introduction to Analysing Linked Data) will cover processing of linked data, concepts of linkage error and bias, and handling linkage error in analysis. The main focus of this course will be health data, although the concepts will apply to many other areas.

Participants can choose to join for Day 1 and Day 2, or each day separately.

This course includes a mixture of lectures and practical sessions that will enable participants to put theory into practice.


Katie Harron is a Senior Lecturer in Quantitative Methods at UCL GOS Institute of Child Health and the Administrative Data Research Centre for England. Katie is a statistician with research interests in using linkage of administrative data and electronic health records for health research. Her current research involves establishing how nationally representative, population-level administrative data can be used to identify and measure determinants of variation in service use and educational outcomes for children born preterm by linking information on maternal, household and social environments. Katie completed her PhD in Statistics at University College London. She is a co-editor of a Wiley commissioned book “Methodological developments in data linkage” with Prof Harvey Goldstein and Prof Chris Dibben.

James Doidge is a Senior Research Associate at UCL GOS Institute of Child Health and the Administrative Data Research Centre for England. James’s interests in research methods and evidence-based public policy have led to experience in a wide range of research fields, including child health and development, child protection and the social determinants of health, nutrition, education and crime. His current role focuses on driving forward the utilisation of large linked administrative datasets for research purposes, through development of relevant statistical methods and exemplary studies that demonstrate the potential value of these data.

Target Audience

The course is aimed at researchers who need to gain an understanding of data linkage techniques and of how to analyse linked data. The course provides an introduction to data linkage theory and methods for those who might be using linked data in their own work. Participants may be academic researchers in the social and health sciences or may work in government, survey agencies, official statistics, for charities or the private sector.

The course does not assume any prior knowledge of data linkage. Some experience of using Excel or other software will be useful for the practical session.

Course Contents

The course covers:

  • Overview of data linkage (data linkage systems, benefits of data linkage, types of projects)
  • Overview of linkage methods (deterministic and probabilistic, privacy-preserving)
  • The linkage process (data preparation, blocking, classification)
  • Classifying linkage designs
  • Evaluating linkage quality and bias (types of error, analysis of linked data)
  • Reporting analysis of linked data
  • Practical sessions

Learning outcomes

By the end of the course participants will:

  • Understand the background and theory of data linkage methods
  • Perform deterministic and probabilistic linkage
  • Evaluate the success of data linkage
  • Appropriately report analysis based on linked data


Day 1: Introduction to Data Linkage

  • Overview
  • Deterministic linkage
  • Linkage error
  • Advanced linkage methods including probabilistic linkage
  • Practical session
  • Multiple files and emerging methods


Day 2: Introduction to Analysing Linked Data

  • Data processing
  • Merging and restructuring data
  • Linkage error and bias
  • Classifying linkage designs
  • Handling linkage error in analysis
  • Reporting of linkage studies

Please email for more information.

Computer Software and Computer workshops

This event includes computer workshops. Participants will need to bring their own laptops with a Windows operating system with Excel, and LinkPlus software (freely available from

Please note LinkPlus is not compatible with Macs.

Course fee (per day)

£30 – UK registered students
£60 – staff at UK academic institutions and research centres, UK-registered charity and voluntary organisations, staff in public sector and government
£220 – all other participants including staff from commercial organisations

Register here

All fees include event materials, lunch, morning and afternoon tea. They do not include travel and accommodation costs.

Participants can choose to join for Day 1 and Day 2, or each day separately.

Numbers on the course are limited and allocated on a first come, first served basis.




10th May 2018 @ 9:30 am
11th May 2018 @ 4:45 pm
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University of Oxford, Park End Street
Oxford, OX1 1HP. United Kingdom
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