Data Science Intermediate

Multiple imputation of missing data
Paola Zaninotto, Tra Pham and Tim Morris

This course is for anyone needing to address the issue of missing information in their quantitative data. It covers the most important principles of missing data analysis and how to effectively address the issues using multiple imputation.

The aim is to develop skills in conducting multiple imputation analysis for cross-sectional data

By the end of this course you will be able to:

  • Identify different mechanisms of missing data
  • Use a multiple imputation method for dealing with missing data in cross-sectional studies
  • Specify perform and select models

There are three pre-recorded lectures, and two computer practical tasks in R and Stata with solutions.

An understanding of regression models, quantitative data structures and types of variables.