Date(s) - 09/15/2017
10:00 am - 12:00 pm
Category No Categories
This is the first part of the two-part workshop. Please register for the second part as well
Much of the data that social scientists analyze features some type of missing data. Students may choose to skip a survey question, government records may be lost, or patients may drop out of treatment. Missing data analysis, in particular multiple imputation techniques, allow researchers to account for this partial loss of information and reduce biases. This workshop is designed to help participants conduct multiple imputation analyses using STATA. By the end of this workshop, you should be able to:
- Describe the potential effects of missing data on subsequent analyses
- Evaluate the degree and pattern of missing data within a data set
- Estimate a basic multiple imputation model for cross-sectional and longitudinal data
To get the most out of this workshop, participants should be familiar with both multiple regression analysis and STATA. Follow the pre-requisites guide to know the requirements.
Instructor: Kailas Venkitasubramanian
Registrations are closed for this event.