Introduction to Data Linkage and Analysing Linked Data - Online (join a waiting list)

Description:

This short course is designed to give participants a practical introduction to data linkage and is aimed at both analysts intending to link data themselves and researchers who want to understand more about the linkage process and its implications for analysis of linked data—particularly the implications of linkage error. 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). 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. 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 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 (no coding required; see below)

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

The course is aimed at analysts and 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 implementing data linkage or 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.

Preparatory Reading - Recommended (not required):

• 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. https://www.gov.uk/government/publications/joined-up-data-in-government-the-future-of-data-linking-methods/quality-assessment-in-data-linkage
• 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. https://doi.org/10.1093/ije/dyz203
• 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

Programme (09:45-16:45)

Day 1: Introduction to Data Linkage

• 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: Introduction to Analysing Linked Data

• Cleaning and preparing linked administrative data for analysis
• Linkage error and bias
• Linkage quality assessment
• Handling linkage error in analysis
• Reporting studies of linked data

 

Cost:

The fee per teaching day is: • £30 per day for students registered at UK/EU University. • £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff and staff at UK/EU registered charity organisations and recognised UK/EU research institutions. • £100 per day for all other participants 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.

Website and registration:

Register for this course

Region:

South East

Keywords:

Quality in Quantitative Research, Data linkage, Data Matching , Use of Administrative Sources , Longitudinal Research

Related publications and presentations from our eprints archive:

Quality in Quantitative Research
Data linkage

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