Item Nonresponse and Imputation

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Item Nonresponse and Imputation - Course Details

Delve into the course contents and find out about the faculty members.

Single Course Price:

400.00 EUR (tax exempt)



Prof. Jörg Drechsler (Institute for Employment Research)

Video lecture:

Prof. Jörg Drechsler (Institute for Employment Research)


Course Dates

To see all courses in the upcoming term click here.


This course is part of the Mannheim Data Science Certificate: Item Nonresponse & Multiple Imputation. Book this course or the entire certificate here!

In order to book the course with alumni conditions, please get in touch with Manon Pfeifer directly.

Course Description

Short Course Description
Missing data are a common problem which can lead to biased results if the “missingness” is not taken into account at the analysis stage. Imputation is often suggested as a strategy to deal with item nonresponse allowing the analyst to use standard complete data methods after the imputation. However, several misconceptions about the aims and goals (isn't imputation making up data?) of imputation make some users skeptical about the approach. In this course we will illustrate why thinking about the missing data is important and clarify which goals a useful imputation method should try to achieve (and which not).

Students should be familiar with generalized linear models and basic probability theory. The statistical software R will be used for illustrations and for (some of) the homework assignments. Thus, basic knowledge of R is required to be able to complete the assignments.

Course Objectives
By the end of the course, students will…

  • understand why the default way of dealing with missing data as implemented in most statistical software is often problematic.
  • realize that it is better not to account for the missingness instead of applying simplistic imputation methods such as mean imputation or last-observation carried forward.
  • know what is meant by a missing data mechanism and understand the implication of the different mechanisms.
  • be familiar with the principle ideas and concepts of multiple imputation.

Course Composition
This is a 2 ECTS course, which runs for 4 weeks. The content of the course is broken down into 4 units:

  1. Introduction & Missing Data Mechanisms
  2. Default Strategies of (Not) Dealing with Missing Data and Their Implications
  3. Common Misconceptions Regarding Imputation & Basic Imputation Methods
  4. Stratified Sampling II, Systematic selection

Learning and Teaching Methods
In this course, you are responsible for watching video recorded lectures and reading the required literature for each unit and then “attending” mandatory weekly one-hour online meetings where students have the chance to discuss the materials from a unit with the instructor. In addition, students are encouraged to post questions about the materials covered in the videos and readings of the week in the forum before the meetings. Just like in an on-site course, homework will be assigned and graded and there will be a final exam at the end of the course.

Grading will be based on:

  • Homework assignments (worth 50% total)
  • Quizzes (worth 15% total)
  • Participation in discussion during the weekly online meetings, submission of questions via e-mail demonstrating understanding of the required readings and video lectures, and positive contributions on Piazza, see below (worth 10%)
  • A final open-book online exam (worth 25%)


ZFU Certification and Online Dispute Resolution

ZFU Certification

The Mannheim Master of Applied Data Science & Measurement program is certified according to the regulations of the ZFU (Staatliche Zentralstelle für Fernunterricht).


Online Dispute Resolution

Online dispute resolution according to Art. 14 Sect. 1 ODR-VO: The European Commission provides a platform for online dispute resolution (ODR). You can find more information under


Located in the heart of the German and European economy, Mannheim Business School (MBS), the umbrella organization for management education at the University of Mannheim, is considered to be one of the leading institutions of its kind in Germany and is continuously ranked as Germany’s #1.