Termine & Aktivitäten

Mo. 22.08.2022

Missing data are a pervasive problem in the social sciences. Data for a given unit may be missing entirely, for example, because a sampled respondent refused to participate in a survey (survey nonresponse). Alternatively, information may be missing only for a subset of variables (item nonresponse), for example, because a respondent refused to answer some of the questions in a survey.

The goals of the course are to introduce participants to the basic concepts and statistical foundations of missing data analysis and MI, and to enable them to use MI in their own work. The course puts heavy emphasis on the practical application of MI and on the complex decisions and challenges that researchers are facing in its course. The focus is on MI using iterated chained equations (aka “fully conditional specification”) and its implementation in the software package Stata. Participants should have a good working knowledge of Stata to follow the applied parts of the course and to successfully master the exercises. Participants who are not familiar with Stata may still benefit from the course but will likely find the exercises quite challenging.