This course provides an introduction to the theory and application of Multiple Imputation (MI) (Rubin 1987) which has become a very popular way for handling missing data, because it allows for correct statistical inference in the presence of missing data. With the advent of MI algorithms implemented in statistical standard software (SAS, Stata, SPSS), the method has become more accessible to data analysts. Since we consider it essential for potential users of MI techniques to understand its underlying principles, we will introduce some statistical theory on analysis of incomplete data at the beginning of this course. For didactic purposes, some na%C3%AFve ways of handling missing data are introduced as well, in order to convey what constitutes a %E2%80%98good%E2%80%99 imputation method.The statistics software R has become a lingua franca for statistical analysis and is predominantly used for examples and demonstrations in this course, but the purpose of the course is not to teach doing MI using R, and while we recommend basic R skills for this course, it can be attended without prior knowledge in R. We hope that by the end of the course people are able to use MI algorithms in general, and to apply Rubin%E2%80%99s combining rules for estimators, as well as to explain to readers of their work how and why they used the method.

Course leader
Prof. Dr. Susanne Rssler, Dr. Florian Meinfelder

Target group
Participants will find the course useful if they:- are survey methodologists working with incomplete data;- are researchers who want to learn more about the analysis of incomplete data in general;- are already aware of MI and its benefits, but feel insecure about the available parameter settings in MI algorithms implemented in their preferred statistical software.Prerequisites:- profound understanding of sampling theory;- an advanced understanding of the (generalized) linear model;- familiarity with statistical distributions;- basic knowledge of the Bayesian paradigm;- basic knowledge of matrix algebra;- solid skills in either R, SPSS or Stata (recommended for exercises).Knowledge of R can be acquired in the course "Introduction to Data Analysis Using R" by Steinhauer/Wrbach in the previous week.

Course aim
By the end of the course participants will:- be familiar with the theoretical implications of the MI framework and will be aware of the explicit and implicit assumptions (e.g. will be able to explain within an article why MAR was assumed, etc.);- know when to use MI (and when not);- be aware how to specify a "good" imputation model and how to use diagnostics;- be familiar with the availability of the various MI algorithms;- be able to not only replicate situations akin to the case studies covered in the course, but also know how to handle incomplete data in general.

Credits info
4 ECTS - Certificate of attendance issued upon completion.Optional bookings:- 2 ECTS points via the University of Mannheim for regular attendance and satisfactory work on daily assignments (EUR 20).- 4 ECTS points via the University of Mannheim for regular attendance and satisfactory work on daily assignments and for submitting a paper/report of about 5000 words to the lecturer(s) up to 4 weeks after the end of the summer school (EUR 50).

Fee info
EUR 250: Student/PhD student rate. EUR 350: Academic/non-profit rate.Early bird discount: EUR 50 for applicants who book and pay by April 30.The rates include the tuition fee, course materials, the academic program, access to library and IT facilities, coffee/tea, and a number of social activities.



Scholarships
10 DAAD scholarships are available via the Center for Doctoral Studies in Social and Behavioral Sciences (CDSS) at the University of Mannheim.

GESIS-Leibniz Institute for the Social Sciences
Address: Knowledge Transfer, Unter Sachsenhausen 6-8 Cologne
Postal code: 50667
City: Cologne
Country: Germany
Website: http://www.gesis.org/summerschool
E-mail: summerschool@gesis.org
Phone: +49-221-476940