To understand different types of missing data. To be able to make a valid statistical analysis in various missing data situations with the R software, including advanced cases.

Content

It is quite common that we do not get all the information we want for  our statistical analysis. For example, in medical research a person can  refuse to provide certain information they feel sensitive, such as  weight, substance abuse, sexual orientation etc. Particularly, missing  data in longitudinal studies is more the rule than an exception. Missing  data in statistical analysis causes all sorts of problems. For example,  the desired statistical method cannot be directly applied; loss of  information or the results obtained can be biased if the analysis is not  done properly accomplished. The course introduces various missing data  mechanisms and their effects on statistical analysis. In addition, it  presents and evaluates some of the commonly used methods for statistical  analysis with missing data. Also advanced methods are presented, as well as special methods for the analysis of  longitudinal data including likelihood-based methods and multiple imputation.

Modes of Study

Course work, exam.

This course is an advanced study version of MTTA2 Statistical Analysis with Missing Data, and the student can only complete one of the two versions.