Design Strategies for Maximizing Total Data Quality
Learn essential techniques for ensuring data quality in collection and analysis. Master TDQ framework for both designed and organic data.
Learn essential techniques for ensuring data quality in collection and analysis. Master TDQ framework for both designed and organic data.
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.
Instructors:
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What you'll learn
Design effective strategies for maximizing total data quality
Identify and measure critical aspects of data generation processes
Implement quality control measures for both designed and organic data
Develop solutions for common data quality challenges
Optimize data collection and processing workflows
Skills you'll gain
This course includes:
4.9 Hours PreRecorded video
7 quizzes, 1 peer review
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores strategies for maximizing total data quality (TDQ) throughout the data collection and analysis process. Students learn essential tools and techniques for evaluating and improving data quality across all stages of the TDQ framework. The curriculum covers critical aspects including data validity, processing quality, source quality, and minimizing data missingness. Through practical examples and case studies, learners develop skills in both designed and organic data gathering methods, with a focus on implementing quality control measures before conducting statistical analysis.
Introduction and Maximizing Validity and Data Origin Quality
Module 1 · 2 Hours to complete
Maximizing Processing and Data Access Quality
Module 2 · 2 Hours to complete
Maximizing Data Source Quality and Minimizing Data Missingness
Module 3 · 2 Hours to complete
Maximizing the Quality of Data Analysis
Module 4 · 2 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.
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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