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Webinar: Analyzing data with missing values
using multiple imputation

Overview

Duration: 1 hour
Where: Join us from anywhere!
Cost: Free—but registrations are limited

Missing values are common in many fields. If analyses do not properly account for missing values, the resulting estimates may be biased. Multiple imputation provides a flexible approach to missing data that results in valid statistical inference.

Join Meghan Cain, Senior Statistician, as she introduces concepts fundamental to missing data and multiple imputation. This webinar will demonstrate how to use Stata to create multiply imputed datasets and perform analyses that properly account for missing-data uncertainty.

How to join

The webinar is free, but you must register to attend. Registrations are limited so register soon.

We will send you an email prior to the start of the course with instructions on how to access the webinar.

Presenter: Meghan Cain

Meghan Cain portrait

Meghan Cain is a Senior Statistician at StataCorp. She earned her PhD in quantitative psychology from the University of Notre Dame, where her research focused on structural equation modeling, multilevel modeling, and Bayesian statistics. At Stata, she develops and presents trainings on these and other topics. She also conducts webinars, works with developers to produce Stata documentation, and contributes to Stata blogs.


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