The Total Data Quality Framework
Master the essentials of data quality evaluation and management using the comprehensive Total Data Quality Framework.
Master the essentials of data quality evaluation and management using the comprehensive Total Data Quality Framework.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Total Data Quality Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.5
(29 ratings)
2,632 already enrolled
Instructors:
James Wagner
English
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What you'll learn
Differentiate between designed and gathered data types
Apply the Total Data Quality Framework dimensions
Evaluate data validity and processing methods
Assess data access and source quality
Manage missing data challenges
Implement data quality assessment strategies
Skills you'll gain
This course includes:
8.2 Hours PreRecorded video
7 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This foundational course introduces students to the Total Data Quality Framework, covering both designed and gathered data evaluation. The curriculum explores measurement dimensions including validity, data origin, and processing, as well as representation dimensions such as data access, sources, and missingness. Through case studies and practical examples, students learn to identify potential threats to data quality and implement effective quality assessment strategies across different data types and collection methods.
Introduction, Different Types of Data and the Total Data Quality Framework
Module 1 · 2 Hours to complete
Measurement Dimensions of Total Data Quality: Validity, Data Origin, and Data Processing
Module 2 · 4 Hours to complete
Representation Dimensions of Total Data Quality: Data Access, Data Source, and Data Missingness
Module 3 · 3 Hours to complete
Data Analysis as an Important Aspect of TDQ
Module 4 · 1 Hours to complete
Fee Structure
Instructors
Research Leader in Survey Methodology
Brady T. West serves as a Research Associate Professor in the Survey Methodology Program at the University of Michigan’s Survey Research Center, part of the Institute for Social Research. He earned his PhD in Survey Methodology from Michigan in 2011, following an MA in Applied Statistics in 2002 and a BS in Statistics with Highest Honors in 2001, both from the same institution. His research focuses on the implications of measurement error in auxiliary variables and survey paradata for survey estimation, along with survey nonresponse, interviewer effects, and multilevel regression models for clustered and longitudinal data. West is the lead author of Linear Mixed Models: A Practical Guide using Statistical Software, Second Edition (2014, Chapman Hall/CRC Press) and co-author of Applied Survey Data Analysis (2017, Chapman Hill) with Steven Heeringa and Pat Berglund. Residing in Dexter, MI, he enjoys family life with his wife, Laura, their children, Carter and Everleigh, and their American Cocker Spaniel, Bailey.
James Wagner
4.5 rating
33 Reviews
3,747 Students
3 Courses
Research Professor
James Wagner, Ph.D., is a Research Professor at the University of Michigan's Survey Research Center (UM-SRC). His expertise centers on survey methodology, particularly addressing nonresponse issues during data collection. Dr. Wagner has advanced the field with his work on responsive and adaptive survey designs, which aim to enhance data quality by mitigating nonresponse biases. He has also contributed to statistical decision rules that guide these methodologies.His scholarly work spans a range of prestigious journals, such as Public Opinion Quarterly, Statistics in Medicine, and Journal of the Royal Statistical Society. Dr. Wagner co-authored the book Adaptive Survey Design (2017), which provides an in-depth exploration of innovative survey methodologies.In addition to his research, Dr. Wagner has over 20 years of practical experience in sample design, having worked on diverse and complex sampling projects. He teaches courses in statistics, sampling, and methods to address nonresponse as part of the Michigan Program in Survey and Data Science and the Joint Program in Survey Methodology. His expertise has been sought after by several federal statistical agencies, where he has served as a consultant on strategies to improve data quality.
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4.5 course rating
29 ratings
Frequently asked questions
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