University of Maryland - Dealing With Missing Data
- Offered byCoursera
Dealing With Missing Data at Coursera Overview
Duration | 18 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Dealing With Missing Data at Coursera Highlights
- Earn a certificate from the university of Maryland College Park upon completion of course.
- Flexible deadlines according to your schedule.
Dealing With Missing Data at Coursera Course details
- 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®.
Dealing With Missing Data at Coursera Curriculum
General Steps in Weighting
Introduction
Quantities to Estimate
Goals of Estimation
Statistical Interpretation of Estimates
Coverage Problems
Improving Precision
Effects of Weighting on SEs
Class notes + additional reading
Class notes
Class Notes
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Class Notes
Class Notes
Introductory quiz on weights
Quantities
Goals
Interpretation
Coverage
Improving precision
Effects on SEs
Specific Steps
Overview
Base Weights
Nonresponse Adjustments
Response Propensities
Tree algorithms
Calibration
Class Notes
Class Notes
Class Notes
Class Notes
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Class Notes
Overview
Base weights
Nonresponse
Trees
Calibration
Implementing the Steps
Software
Base Weights
More on Base Weights
Nonresponse Adjustments
Examples of Calibration
Software for Poststratification
Class Notes
Class Notes + Software
Class Notes
Class Notes + Software for propensity classes
Class Notes + Software for calibration
Software
Quiz on base weights
Quiz on nonresponse adjustments
Quiz on calibration and poststratification
Imputing for Missing Items
Reasons for Imputation
Means and hotdeck
Regression Imputation
Effect on Variances
mice R package
mice example
Class Notes
Class Notes
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Class Notes + mice R package
Reasons for imputing
Means and hot deck
Regression imputation
Effects on variances
Imputation software
Summary
Class Notes
Dealing With Missing Data at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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