University of Michigan - The Total Data Quality Framework 

Welcome to the Specialization and Course 1!

Introduction to Course 1: The Total Data Quality Framework

What Are Designed Data?

Example: Developing an Online Survey with SurveyMonkey

What are Gathered Data?

Example: Scraping Data from the Web

Hybrid Data: Designed and Gathered

The Total Data Quality Framework

Interview: Perspectives on the Meaning of Total Data Quality

Course Syllabus

Meet your Instructors

Course Pre-Survey

Interview Guest Biographies

Measurement and Representation Concepts

Defining Validity

Threats to Validity for Designed Data

Cognitive Interviewing (Think Aloud)

Try It Out: Using The Survey Quality Predictor Application

Threats to Validity for Gathered Data

Defining Data Origin

Data Origin Threats for Designed Data

Data Origin Threats for Gathered Data

Defining Data Processing

Data Processing Threats for Designed Data

Case Study: Between-Coder Variance

Data Processing Threats for Gathered Data

Case Study: Author Name Ambiguity in Bibliographic Data

Interview Guest Biography

Case Study: The Google Flu Trends Example

Case Study: Suchman and Jordan, and Interviewer Effects

Case Study: COVID-19 Tracking in the U.S.

Case Study Guest Contributor Biographies

Understanding Validity

Understanding Data Origin

Understanding Data Processing

Defining Data Access

Defining Target Populations

Part 1 of 2: Data Access Threats for Gathered Data

Part 2 of 2: Data Access Threats for Gathered Data

Case Study: Random Samples from Twitter APIs May Not Be Random

Data Access Threats for Designed Data

Case Study: Evaluating Sampling Frames/Commercial Data

Data Source Definition

Data Source Threats for Designed Data

Data Source Threats for Gathered Data

Case Study: How Content and User Characteristics Can Impact Quality of Gathered Data

Case Study: Who is Missing in Twitter User Data?

Defining Data Missingness

Data Missingness Threats for Designed Data

Imputing Missing Values Demo, Before and After Estimates

Data Missingness Threats for Gathered Data

Gathering Twitter Data Using APIs (code and step-by-step instructions)

Articles for the Case Study (Random Samples from Twitter APIs May Not Be Random)

Understanding Data Access

Understanding Data Missingness

Why is Data Analysis Part of Total Data Quality?

Threats to the Quality of Data Analysis for Designed Data

Demo: Alternative Approaches to Analyzing Survey Data

Threats Concerning Data Analysis for Gathered Data

Case Study: Algorithm Bias in Gathered Data

Case Study: Analytic Error in NCSES Surveys

Optional Tutorial: Using the Free R Software

Course Conclusion

References for The Total Data Quality Framework

Course Post-Survey

Data Analysis Threats