Introduction, Different Types of Data and the Total Data Quality Framework-Welcome!
Welcome to the Specialization and Course 1!
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Course Syllabus
Meet your Instructors
Course Pre-Survey
Introduction, Different Types of Data and the Total Data Quality Framework-Introducing Different Types of Data
Introduction to Course 1: The Total Data Quality Framework
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What Are Designed Data?
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Example: Developing an Online Survey with SurveyMonkey
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What are Gathered Data?
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File for use in next example
Example: Scraping Data from the Web
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Hybrid Data: Designed and Gathered
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Introduction, Different Types of Data and the Total Data Quality Framework-Introducing The Data Quality Framework
The Total Data Quality Framework
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Interview: Perspectives on the Meaning of Total Data Quality
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Interview Guest Biographies
Measurement Dimensions of Total Data Quality: Validity, Data Origin, and Data Processing-Validity
Defining Validity
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Threats to Validity for Designed Data
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Cognitive Interviewing (Think Aloud)
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Interview Guest Biography
Try It Out: Using The Survey Quality Predictor Application
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Threats to Validity for Gathered Data
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Case Study: The Google Flu Trends Example
Measurement Dimensions of Total Data Quality: Validity, Data Origin, and Data Processing-Data Origin
Defining Data Origin
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Data Origin Threats for Designed Data
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Case Study: Suchman and Jordan, and Interviewer Effects
Data Origin Threats for Gathered Data
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Case Study: COVID-19 Tracking in the U.S.
Measurement Dimensions of Total Data Quality: Validity, Data Origin, and Data Processing-Data Processing
Defining Data Processing
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Data Processing Threats for Designed Data
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Case Study: Between-Coder Variance
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Case Study Guest Contributor Biographies
Data Processing Threats for Gathered Data
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Case Study: Author Name Ambiguity in Bibliographic Data
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Representation Dimensions of Total Data Quality: Data Access, Data Source, and Data Missingness-Data Access
Defining Data Access
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Defining Target Populations
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Part 1 of 2: Data Access Threats for Gathered Data
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Part 2 of 2: Data Access Threats for Gathered Data
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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)
Case Study: Random Samples from Twitter APIs May Not Be Random
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Data Access Threats for Designed Data
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Files for use in the following example
Case Study: Evaluating Sampling Frames/Commercial Data
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Representation Dimensions of Total Data Quality: Data Access, Data Source, and Data Missingness-Data Source
Data Source Definition
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Data Source Threats for Designed Data
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Data Source Threats for Gathered Data
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Case Study: How Content and User Characteristics Can Impact Quality of Gathered Data
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Case Study: Who is Missing in Twitter User Data?
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Representation Dimensions of Total Data Quality: Data Access, Data Source, and Data Missingness-Data Missingness
Defining Data Missingness
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Data Missingness Threats for Designed Data
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Imputing Missing Values Demo, Before and After Estimates
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Data Missingness Threats for Gathered Data
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Data Analysis as an Important Aspect of TDQ-Data Analysis as an Important Aspect of Total Data Quality
Why is Data Analysis Part of Total Data Quality?
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Threats to the Quality of Data Analysis for Designed Data
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Case Study: Analytic Error in NCSES Surveys
Optional Tutorial: Using the Free R Software
Files for the next Demo
Demo: Alternative Approaches to Analyzing Survey Data
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Threats Concerning Data Analysis for Gathered Data
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Case Study: Algorithm Bias in Gathered Data
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Data Analysis as an Important Aspect of TDQ-Course Conclusion
Course Conclusion
References for The Total Data Quality Framework
Course Post-Survey