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To provide a comprehensive overview of the total survey error framework and methods for measuring data quality.

March 21, 2026Open Access

An Introduction to Quantifying Data Quality Slide deck

Key Points

To provide a comprehensive overview of the total survey error framework and methods for measuring data quality.
Reviewed the total survey error framework
Explained how to quantify measurement and representation errors
Provided examples from survey and digital behavioral data
Discussed R packages and tools for bias assessment
Guided on selecting appropriate tools for data analysis.
Clarified the components of measurement and representation error
Illustrated tools available for assessing data quality
Emphasized the importance of choosing the right tool to minimize biases

Abstract

This presentation provides a revision of the total survey error (TSE) framework. It explains how to quantify both the measurement and representation error, including examples from survey and digital behavioral data. It also gives an overview of R packages and tools to assess biases and guidance on how to choose the most suitable tool. This presentation is the first part of a set of presentations on the topic of "Indicators & Metrics of Data Quality". Presentation available after free registration: https://elearning.gesis.org/course/section.php?id=1210

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