Missing Data Imputation : A Simple, Practical Guide to Multiple Imputation Using R
|Starts:||10:00 20 Mar 2013|
|Ends:||12:00 20 Mar 2013|
|What is it:||Seminar|
|Organiser:||School of Arts, Languages and Cultures|
|Who is it for:||Current University students, University staff|
Datasets with missing values are ubiquitous in the social sciences. Although it is common to just ignore these missing data points and use 'techniques' such as list-wise deletion that eliminate entire observations, this is not a particularly good strategy for analysis as it results in the loss of valuable information at best and severe selection bias at worst. The simple removal of cases that include missing data is, however, not the only option available to analysts as individual data points may be replaced using a range of different techniques; for example, replacement by random values (within certain parameters), mean values, values predicted from regression models, or values imputed using a dedicated imputation procedure.
This session provides an introduction to data imputation and demonstrates multiple imputation using the R library Amelia, a simple to use and powerful package for multiple imputation. Although this session uses the R and Rcmdr packages, the content is designed be of interest to a general audience.
Booking form: http://www.methods.manchester.ac.uk/events/2013-03-20/booking.shtml
Travel and Contact Information
Coupland Building 3