Assessing Data Quality and Disclosure Risk in Numeric Data (join a waiting list)
|NCRM, University of Southampton|
Louise Corti, Sharon Bolton, Cristina Magder, Anca Vlad and Myles Offord
St Clements Building, London School of Econonics, St Clements Lane, London
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In this hands-on day course you will learn about the principles of, and tools for, assessing data quality and reviewing disclosure risk in numeric data sources. Data assessment is extremely useful whether it is for wishing to create a high quality data for publishing, thereby supporting the transparency and replication agenda (e.g. to meet funder or journal policy), or simply to check unknown data that has been accessed for reuse. The requirements of the GDPR when processing and de-identifying data benefit from quick examination, using tools where possible.
The course will introduce the key elements of data quality and disclosure risk, including: file checks, data and metadata checks, and direct and indirect identifiers. The day makes use of two tools to undertake review. The first is QAMyData that automatically assesses elements of quality, such as missingness, duplication, outliers and direct identifiers. A user can specify and set thresholds in the QAMYData tool, to indicate what one is prepared to accept (i.e. no missing data or data must be fully labelled). Issues are identified in both a summary and detailed report. The second tool is R sdcMicro, a practical tool for checking disclosure risk through examining combinations of key variables.
Practical demonstrations and hands-on exercises will be used throughout. The course will be held in a lab where the software will be mounted. However, these software are easily downloaded to a laptop and be quickly used after the workshop and can be integrated into data cleaning and processing pipelines for data creators, users, reviewers and publishers.
The course covers:
By the end of the course participants will:
Academics, lecturers, researchers and data publishers from all sectors who are interested in the practical elements of assessing numeric data for quality and disclosure risk.
Some knowledge about the creation and QA of survey or numeric data are expected, as is familiarity with some kind of statistics software tools e.g. SPSS, STATA or R.
Intermediate (some prior knowledge)
The fee per teaching day is:
Website and registration
Qualitative Data Handling and Data Analysis, Disclosure review , Privacy Impact Assessment , sdcMicro , Open source tools , Transparency , Replication , , , , Data Publishing
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