Introduction to Linking Data
Date:
15/04/2015
Organised by:
University of Manchester
Presenter:
Natalie Shlomo
Level:
Entry (no or almost no prior knowledge)
Contact:
Short courses administrator; cmist-courses@manchester.ac.uk
Description:
Outline
The one day course will introduce basic concepts of data linkage, provide background information on data linkage applications and different data sources as well as aspects of preparing datasets for data linkage. By the end of the day, participants should have an understanding of what is involved when merging datasets.
The course will provide the basis for those interested in more advanced topics of data linkage and the actual implementation of probabilistic data linkage which will be covered on the following two days.
Objective
- To provide background information and basic concepts of data linkage.
Prerequisites
No prerequisites are required to undertake this course.
Recommended reading
- Gill, L. (2001) Methods for Automatic Record Matching and Linkage and their use in National Statistics, The National Statistics Methodology Series, ONS
- Herzog, T. N., Scheuren, F. J. and Winkler, W. E. (2007) Data Quality and Record Linkage Techniques. New York: Springer. ISBN 978-0-387-69502-0
- Mason, C.A. and Shihfen, T. (2008) Data Linkage Using Probabilistic Decision Rules: A Primer, Birth Defects Research (Part A): Clinical and Molecular Teratology. 82, 812-821.
- Winkler, W. E. (1995) Matching and Record Linkage, in B.G. Cox et al. (ed) Business Survey Methods. New York: J. Wiley, 355-384.
Cost:
£195 (£140 for those from educational and charitable institutions)
Website and registration:
http://www.cmist.manchester.ac.uk/study/courses/short/introductory/intro-to-linking-data/
Region:
North West
Keywords:
Qualitative Data Handling and Data Analysis, Quantitative Data Handling and Data Analysis, Mixed Methods Data Handling and Data Analysis
Related publications and presentations:
Qualitative Data Handling and Data Analysis
Quantitative Data Handling and Data Analysis
Mixed Methods Data Handling and Data Analysis