Combining and Analyzing Complex Data
Master survey data analysis techniques including statistical modeling, record linkage, and ethical considerations in data combination.
Master survey data analysis techniques including statistical modeling, record linkage, and ethical considerations in data combination.
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.
4.1
(58 ratings)
8,635 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Apply survey weights to estimate descriptive statistics
Implement statistical models with complex survey data
Master record linkage techniques for combining datasets
Understand ethical implications of data combination
Use R software for advanced survey data analysis
Skills you'll gain
This course includes:
2.95 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on advanced techniques for analyzing and combining complex survey data. Students learn to use survey weights for estimating descriptive statistics and model parameters, implement record linkage methods, and address ethical considerations in data combination. The curriculum covers practical applications using R programming, including linear and logistic regression analysis with survey data. Special emphasis is placed on handling missing data, statistical matching, and obtaining proper consent for data linkage.
Basic Estimation
Module 1 · 2 Hours to complete
Models
Module 2 · 2 Hours to complete
Record Linkage
Module 3 · 3 Hours to complete
Ethics
Module 4 · 1 Hours 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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4.1 course rating
58 ratings
Frequently asked questions
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