Course Summary
Learn how to handle missing data in your analysis and make informed decisions even in the absence of complete information with this course.Key Learning Points
- Understand the types and mechanisms of missing data.
- Learn how to handle missing data using different imputation methods.
- Discover the importance of missing data analysis in research and decision making.
Job Positions & Salaries of people who have taken this course might have
- USA: $71,000
- India: ₹5,00,000
- Spain: €33,000
- USA: $71,000
- India: ₹5,00,000
- Spain: €33,000
- USA: $60,000
- India: ₹4,00,000
- Spain: €28,000
- USA: $71,000
- India: ₹5,00,000
- Spain: €33,000
- USA: $60,000
- India: ₹4,00,000
- Spain: €28,000
- USA: $86,000
- India: ₹6,00,000
- Spain: €40,000
Related Topics for further study
Learning Outcomes
- Understand the importance of handling missing data in research and decision making.
- Gain knowledge on different methods of handling missing data.
- Learn how to use R and other software for missing data analysis.
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and data analysis.
- Understanding of R programming language.
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
Similar Courses
- Data Cleaning and Preprocessing
- Data Analysis with Python
Related Education Paths
Notable People in This Field
- Andrew Gelman
- Hadley Wickham
Related Books
Description
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®.
Outline
- 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
- Class Notes
- Class Notes
- 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
- Class Notes
- 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
- Class Notes
- Class Notes
- Class Notes + mice R package
- Reasons for imputing
- Means and hot deck
- Regression imputation
- Effects on variances
- Imputation software
- Summary of Course 5
- Summary
- Class Notes
Summary of User Reviews
Learn about missing data on Coursera. Find out what others are saying about this course, including the overall rating. Users rave about the course's practical approach to handling missing data. However, some users have reported issues with the pace and technical difficulty of the course.Key Aspect Users Liked About This Course
Users love the practical approach to handling missing data in this course.Pros from User Reviews
- The course includes a number of practical exercises and case studies
- The instructors are knowledgeable and helpful
- The course covers a variety of techniques for handling missing data
Cons from User Reviews
- Some users found the course to be too technical or difficult to follow
- The pace of the course may be too slow for some users
- The course may not be appropriate for beginners with no background in statistics
Richard Valliant, Ph.D.
3.8 Raiting
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