9.00  11.00  Lecture:
Introduction
The principles of statistical inference. The missing data: Why are they missing and why is it a problem? An outline of the multiple imputation strategy. 
11.00 12.00  Exercise:
Exercise 1
Generate missing data! 
12.00  13.00  Lunch break 
13.00  14.30  Lecture:
Missing values
Missing values  mechanisms and concepts 
14.30 15.30  Exercise:
Exercise 2
Understand the misssing data structure 
15.30 16.00  A first look at two case studies
We look at two of your projects. What are the missing data mechanisms? 
9.00  11.00  Lecture:
Missing data patterns and imputations
Missing values patterns. What is imputation? Dofile: MisLect3.do 
11.00  12.00  Exercise:
Exercise 3
Simple imputation of missing data 
12.00  13.00  Lunch break 
13.00  15.00  Lecture:
Missing data and multiple imputation in Stata 12
How to define and work with data sets with missing observations. How to impute missing data. How to obtain parameter estimates based on data with multiple imputed values. Dofile: MisLect4.do 
15.00  16.00  The two case studies
Could multiple imputations solve the problems? 
9.00  12.00  Exercises:
Exercise 4: Analysis of The Drug Study using the method of multiple imputation Exercise 5: Analysis of ESS using the method of multiple imputation Dofile for the first part Exercise 6: Analysis of Birth Weight data using the method of multiple imputation 
12.00  13.00  Lunch break 
13.00  14.00  Exercises continued 
14.00  15.30  Lecture:
Sensitivity analysis and presentation
Presentation and discussion of results. Sensitivity analysis and other loose ends. 
15.30  16.00  Course evaluation
