Dealing With Missing Data
By Coursera
on Coursera
· Data Science
About This Course
This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®.
Instructor
Richard Valliant
Frequently Asked Questions
How much does Dealing With Missing Data cost?
Visit the Dealing With Missing Data course page for current pricing and available discounts.
Who teaches Dealing With Missing Data?
Dealing With Missing Data is taught by Richard Valliant, Ph.D., University of Maryland, College Park.
What skill level is Dealing With Missing Data for?
This course is designed for all levels learners.