Measuring Total Data Quality
Learn comprehensive methods for evaluating and measuring data quality across designed and gathered datasets.
Learn comprehensive methods for evaluating and measuring data quality across designed and gathered datasets.
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
Learn metrics for evaluating Total Data Quality
Create comprehensive quality concept maps
Apply quality metrics to real datasets
Understand data validity and processing quality
Assess data source quality and missingness
Skills you'll gain
This course includes:
5.7 Hours PreRecorded video
7 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course covers the measurement and evaluation of Total Data Quality (TDQ). Students learn various metrics for assessing data quality at different stages, from data origin to analysis. The curriculum includes hands-on experience with real data and code examples in R, covering validity assessment, processing quality, data access, source quality, and missingness analysis. Through practical examples and case studies, students learn to create quality concept maps and understand trade-offs between different quality aspects.
Introduction and Measuring Validity and Data Origin Quality
Module 1 · 3 Hours to complete
Measuring Processing and Data Access Quality
Module 2 · 1 Hours to complete
Measuring Data Source Quality and Data Missingness
Module 3 · 2 Hours to complete
Measuring the Quality of Data Analysis
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.
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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