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Course structure

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.

Course 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 sessions.

Please note that is recommended you bring a laptop for this course.

Event Outline

Day 1 will focus on the methods and practicalities of data linkage (including deterministic and probabilistic approaches) using worked examples. Day 2 will focus more on analysis of linked data, including concepts of linkage error, how to assess linkage quality and how to account for the resulting bias and uncertainty in analysis of linked data. Examples will be drawn predominantly from health data, but the concepts will apply to many other areas. This course includes a mixture of lectures and practical sessions that will enable participants to put theory into practice.  

The course will start with registration and coffee at 9:30AM with formal teaching starting at 9:45AM and finishing at 16:45PM.

Day 1

• Course overview
• Deterministic linkage algorithms
• Linkage error
• Probabilistic linkage theory and practical demonstration
• Practical considerations (including variable selection, handling missing data and managing processing requirements)
• Overview of advanced topics including privacy preservation, string comparators and linkage of multiple files.

Day 2

• Recap: Common myths and misconceptions about probabilistic linkage
• Linkage error bias
• Linkage quality assessment
• Handling linkage error in analysis
• Reporting studies of linked data
• Software demonstration: Splink – open-source toolkit for probabilistic record linkage and deduplication at scale.

Learning Outcomes

By the end of the course participants will:

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

Course Fees

The price per teaching day is:

• £35 per day for students registered at University
• £75 per day for staff at academic institutions, Research Councils researchers, public sector staff and staff at registered charity organisations and recognised UK/EU research institutions
• £250 per day for all other participants.

Fees do not include travel and accommodation costs.

In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. No refunds are available after this date.

If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of its cancellation of a course, including but not limited to any travel or accommodation costs.

The University of Southampton’s Online Store T&Cs also continue to apply.

Preparatory Optional Reading

Doidge JC, Christen P and Harron K (2020). Quality assessment in data linkage. In: Joined up data in government: the future of data linking methods.

Harron K, Doidge JC & Goldstein H (2020) Assessing data linkage quality in cohort studies, Annals of Human Biology, 47:2, 218-226, DOI: 10.1080/03014460.2020.1742379

Harron KL, Doidge JC, Knight HE, et al. A guide to evaluating linkage quality for the analysis of linked data. Int J Epidemiol. 2017;46(5):1699–1710. doi:10.1093/ije/dyx177

Doidge JC, Harron K (2019). Reflections of modern methods: Linkage error bias. International Journal of Epidemiology. 48(6):2050-60.

Sayers A, Ben-Shlomo Y, Blom AW, Steele F. Probabilistic record linkage. Int J Epidemiol. 2016;45(3):954–964. doi:10.1093/ije/dyv322

Doidge JC, Harron K. Demystifying probabilistic linkage: Common myths and misconceptions. Int J Popul Data Sci. 2018;3(1):410. doi:10.23889/ijpds.v3i1.410

Course team

Course information last modified: 11 Sep 2024, 13:46