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Dealing With Missing Data
  • Level Foundation
  • Duration 18 hours
  • Course by University of Maryland, College Park
  • Offered by

About

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®.

Modules

Introduction

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Introductory quiz on weights

1 Videos

  • Introduction

1 Readings

  • Class notes + additional reading

Quantities to Estimate

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quantities

1 Videos

  • Quantities to Estimate

1 Readings

  • Class notes

Goals of Estimation

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Goals

1 Videos

  • Goals of Estimation

1 Readings

  • Class Notes

Statistical Interpretation of Estimates

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Interpretation

1 Videos

  • Statistical Interpretation of Estimates

1 Readings

  • Class Notes

Coverage Problems

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Coverage

1 Videos

  • Coverage Problems

1 Readings

  • Class Notes

Improving Precision

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Improving precision

1 Videos

  • Improving Precision

1 Readings

  • Class Notes

Effects of Weighting on Standard Errors

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Effects on SEs

1 Videos

  • Effects of Weighting on SEs

1 Readings

  • Class Notes

Overview

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Overview

1 Videos

  • Overview

1 Readings

  • Class Notes

Base Weights

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Base weights

1 Videos

  • Base Weights

1 Readings

  • Class Notes

Nonresponse Adjustments

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Nonresponse

1 Videos

  • Nonresponse Adjustments

1 Readings

  • Class Notes

Response Propensities

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Response Propensities

1 Readings

  • Class Notes

Tree algorithms

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Trees

1 Videos

  • Tree algorithms

1 Readings

  • Class Notes

Calibration

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Calibration

1 Videos

  • Calibration

1 Readings

  • Class Notes

Software

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Software

1 Videos

  • Software

1 Readings

  • Class Notes

Base Weights

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Base Weights

1 Readings

  • Class Notes + Software

More on Base Weights

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz on base weights

1 Videos

  • More on Base Weights

1 Readings

  • Class Notes

Nonresponse Adjustments

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz on nonresponse adjustments

1 Videos

  • Nonresponse Adjustments

1 Readings

  • Class Notes + Software for propensity classes

Calibration

    • 2 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz on calibration and poststratification

2 Videos

  • Examples of Calibration
  • Software for Poststratification

1 Readings

  • Class Notes + Software for calibration

Reasons for Imputation

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Reasons for imputing

1 Videos

  • Reasons for Imputation

1 Readings

  • Class Notes

Means and hotdeck

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Means and hot deck

1 Videos

  • Means and hotdeck

1 Readings

  • Class Notes

Regression

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Regression imputation

1 Videos

  • Regression Imputation

1 Readings

  • Class Notes

Effect on Variances of Imputing for Missing Values

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Effects on variances

1 Videos

  • Effect on Variances

1 Readings

  • Class Notes

Software for Imputation

    • 2 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Imputation software

2 Videos

  • mice R package
  • mice example

1 Readings

  • Class Notes + mice R package

Summary of Course 5

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Summary

1 Readings

  • Class Notes

Auto Summary

"Dealing With Missing Data" is a foundational course in Big Data and Analytics offered by Coursera. Led by expert instructors, it explores techniques for handling missing data in sample surveys, including nonresponse adjustments, calibration, and imputation methods. The course covers statistical software like R®, Stata®, and SAS®, and is designed for learners seeking to enhance their data analysis skills. With a duration of 1080 minutes, subscription options include Starter and Professional plans, making it accessible for both beginners and professionals aiming to deepen their knowledge.

Richard Valliant, Ph.D.
Instructor

Richard Valliant, Ph.D.