Dealing With Missing Data
Learn advanced techniques for handling missing data in surveys, including weighting methods, imputation strategies, and statistical software implementation.
Learn advanced techniques for handling missing data in surveys, including weighting methods, imputation strategies, and statistical software implementation.
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 Survey Data Collection and Analytics 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.
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English
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What you'll learn
Master weighting techniques for sample surveys
Learn methods for adjusting nonresponse in survey data
Develop skills in using statistical software for data imputation
Understand calibration techniques using external data
Implement various imputation methods for missing items
Skills you'll gain
This course includes:
3.4 Hours PreRecorded video
21 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course focuses on handling missing data in survey research, covering essential techniques for weighting sample surveys and implementing imputation methods. Students learn methods for adjusting nonresponse, using external data for calibration, and applying various imputation techniques. The course emphasizes practical implementation using statistical software packages including R, Stata, and SAS, providing hands-on experience with real-world survey data challenges.
General Steps in Weighting
Module 1 · 5 Hours to complete
Specific Steps
Module 2 · 4 Hours to complete
Implementing the Steps
Module 3 · 3 Hours to complete
Imputing for Missing Items
Module 4 · 4 Hours to complete
Summary of Course 5
Module 5 · 12 Minutes to complete
Fee Structure
Instructor
A Leader in Survey Methodology
Dr. Richard L. Valliant is a Research Professor at the University of Michigan and the Joint Program for Survey Methodology at the University of Maryland, bringing over 40 years of expertise in survey sampling, estimation theory, and statistical computing. Previously, he served as an Associate Director at Westat and worked as a mathematical statistician with the Bureau of Labor Statistics. Dr. Valliant possesses extensive applied experience in survey estimation and sample design across various establishment and household surveys. He is a Fellow of the American Statistical Association and has held editorial positions for several statistical journals, contributing significantly to the field of statistics.
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Frequently asked questions
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