Adjunct Research Professor
The Total Data Quality Framework
What You'll Learn
- Identify the essential differences between designed and gathered data.
- Summarize the key dimensions of the Total Data Quality (TDQ) Framework.
- Describe why data analysis defines an important dimension of the Total Data Quality framework.
- Define the three measurement dimensions of the Total Data Quality framework.
4 Modules
12 Hours
3 hrs per module (approx.)
Rating
About The Total Data Quality Framework
By the end of this first course in the Total Data Quality specialization, learners will be able to:
1. Identify the essential differences between designed and gathered data and summarize the key dimensions of the Total Data Quality (TDQ) Framework;
2. Define the three measurement dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data;
3. Define the three representation dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data; and
4. Describe why data analysis defines an important dimension of the Total Data Quality framework, and summarize potential threats to the overall quality of an analysis plan for designed and/or gathered data.
This specialization as a whole aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality.
This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
Skills You'll Gain
- Data Analysis
- Data Classification
- Data Quality
- Data Quality Assessment
- Data Validation
- Data Wrangling
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to The Total Data Quality Framework, a focused course on understanding and applying the principles of total data quality (TDQ). You will explore both measurement and representation dimensions of data, including validity, origin, processing, access, source, and missingness. This course also highlights the importance of data analysis as a critical aspect of TDQ. Engage with practical quizzes and assignments to build actionable skills in managing high-quality data.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Introduction, Different Types of Data, and the Total Data Quality Framework
- Video: Welcome to the Specialization and Course 1!
- Reading: Course Syllabus
- Reading: Meet your Instructors
- Reading: Course Pre-Survey
- Video: Introduction to Course 1: The Total Data Quality Framework
- Video: What Are Designed Data?
- Video: Example: Developing an Online Survey with SurveyMonkey
- Video: What are Gathered Data?
- Reading: File for use in next example
- Video: Example: Scraping Data from the Web
- Video: Hybrid Data: Designed and Gathered
- Video: The Total Data Quality Framework
- Video: Interview: Perspectives on the Meaning of Total Data Quality
- Reading: Interview Guest Biographies
- Graded: Measurement and Representation Concepts
Module 2: Measurement Dimensions of Total Data Quality: Validity, Data Origin, and Data Processing
- Video: Defining Validity
- Video: Threats to Validity for Designed Data
- Video: Cognitive Interviewing (Think Aloud)
- Reading: Interview Guest Biography
- Video: Try It Out: Using The Survey Quality Predictor Application
- Video: Threats to Validity for Gathered Data
- Reading: Case Study: The Google Flu Trends Example
- Video: Defining Data Origin
- Video: Data Origin Threats for Designed Data
- Reading: Case Study: Suchman and Jordan, and Interviewer Effects
- Video: Data Origin Threats for Gathered Data
- Reading: Case Study: COVID-19 Tracking in the U.S.
- Video: Defining Data Processing
- Video: Data Processing Threats for Designed Data
- Video: Case Study: Between-Coder Variance
- Reading: Case Study Guest Contributor Biographies
- Video: Data Processing Threats for Gathered Data
- Video: Case Study: Author Name Ambiguity in Bibliographic Data
- Graded: Understanding Validity
- Graded: Understanding Data Origin
- Graded: Understanding Data Processing
Module 3: Representation Dimensions of Total Data Quality: Data Access, Data Source, and Data Missingness
- Video: Defining Data Access
- Video: Defining Target Populations
- Video: Part 1 of 2: Data Access Threats for Gathered Data
- Video: Part 2 of 2: Data Access Threats for Gathered Data
- Reading: Gathering Twitter Data Using APIs (code and step-by-step instructions)
- Reading: Articles for the Case Study (Random Samples from Twitter APIs May Not Be Random)
- Video: Case Study: Random Samples from Twitter APIs May Not Be Random
- Video: Data Access Threats for Designed Data
- Reading: Files for use in the following example
- Video: Case Study: Evaluating Sampling Frames/Commercial Data
- Video: Data Source Definition
- Video: Data Source Threats for Designed Data
- Video: Data Source Threats for Gathered Data
- Video: Case Study: How Content and User Characteristics Can Impact Quality of Gathered Data
- Video: Case Study: Who is Missing in Twitter User Data?
- Video: Defining Data Missingness
- Video: Data Missingness Threats for Designed Data
- Video: Imputing Missing Values Demo, Before and After Estimates
- Video: Data Missingness Threats for Gathered Data
- Graded: Understanding Data Access
- Graded: Understanding Data Missingness
Module 4: Data Analysis as an Important Aspect of TDQ
- Video: Why is Data Analysis Part of Total Data Quality?
- Video: Threats to the Quality of Data Analysis for Designed Data
- Reading: Case Study: Analytic Error in NCSES Surveys
- Reading: Optional Tutorial: Using the Free R Software
- Reading: Files for the next Demo
- Video: Demo: Alternative Approaches to Analyzing Survey Data
- Video: Threats Concerning Data Analysis for Gathered Data
- Video: Case Study: Algorithm Bias in Gathered Data
- Reading: Course Conclusion
- Reading: References for The Total Data Quality Framework
- Graded: Data Analysis Threats
Grading Policy
Your final grade will be based on 7 graded quizzes. Each quiz contributes between 14–16% of the final grade. You must score at least 80% on each quiz to pass.
Research Assistant Professor
Research Professor
Research Associate Professor
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No prior experience required
