NCRM Training: Assessing Data Quality and Disclosure Risk in Numeric Data

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:

• Short presentations on the principles and practicalities of assessing data quality and undertaking disclosure review in numeric data;
• Practical demonstrations and hands-on exercises for assessing data quality and undertaking disclosure review in numeric data;
• A short session surgery on installing the tools for onward use.

By the end of the course participants will:

• Appreciate the principles and practicalities involved in assessing data quality and undertaking disclosure review in numeric data;
• Gain hand-on experience with using two piece of software: QAMyData, for assessing data and metadata quality, and R sdcMicro for disclosure review in numeric data;
• Know how to install QAMyData and R sdcMicro on their own computers.

Target Audience

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.

Pre-requisites

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.