Explore the evolution from the Total Survey Error Framework to an Error Framework for Digital Traces of Humans in this comprehensive tutorial presented at FAT*2020. Delve into the challenges of adapting survey methodologies to digital data analysis, covering topics such as survey guidelines, digital traces, and the intersection of computer science and social science. Learn about key concepts including target populations, signals, entities, measurement, representation, and sampling. Examine the rationale behind using the Survey Error Framework and investigate its limitations when applied to digital traces. Gain insights into natural experiments, coverage error, entity selection error, data preprocessing, and analysis techniques. Enhance your understanding of how to effectively work with and interpret digital traces of human behavior in research and data science applications.
From the Total Survey Error Framework to an Error Framework for Digital Traces of Humans
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Introduction
Survey Guidelines
Typical Sources
Digital Traces
Computer Science and Social Science
Common Vocabulary
Construct
Target Population
Signals
Entities
What is it
Measurement and Representation
Sampling
Why the Survey Error Framework
Challenges
Measurement Representation
Natural Experiments
Coverage Error
Entity Selection Error
Data Preprocessing
Data Analysis
Taught by
ACM FAccT Conference
