Module 10: Missing Data

Dates - Venue - Description - Target audience - Exam
Course prerequisites - Teacher - Course material - Fees - Enrol


3 full days: Monday May 29, Tuesday May 30 and Wednesday May 31, 2017, from 9 am to 12.15 pm and from 1.15 pm to 4.30 pm.


Faculty of Science , Site Sterre, Krijgslaan 281, Building S9, Ghent


Missing data (i.e. data that were intended to be collected, but were not) form an important problem in many statistical data analyses, for the following two reasons.

First, many statistical software packages include by default only the subjects without missing data in the analysis. They thus make inefficient use of the observed data by discarding information from subjects whose data was only partially missing.

Second, in many cases, subjects without missing data form a selective subgroup. Statistical results obtained for that group may not generalise to the intended study population.

The goal of this course is to develop an understanding of the fundamental problems caused by missing data. We will see how overly simplistic methods of correction for missing data (such as single imputation and last-value-carried-forward) may fail, and provide methods for valid analysis in the presence of missing data under more general conditions including likelihood-based model estimation, weighting and multiple imputation.

We will emphasize the distinction between data missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR) and illustrate their implications in standard analyses.

In addition, considerable attention will be given to the relative advantages and limitations of the different missing data approaches.

Target audience

This course targets researchers who need to analyse incomplete data sets and are seeking practical tools to handle missing data in their own analyses.


Participants can, if they wish, take part in an exam. Upon succeeding in this test a certificate from Ghent University will be issued to participants with a university degree at the bachelor level or an equivalent degree.

To qualify for reimbursement from the UGent Doctoral Schools one must attend all classes and pass the exam. Additional conditions and procedure.

Course prerequisites

Participants are expected to be familiar with basic statistical data analysis and linear regression analysis.


Foto van Ineke van GrembergheDr. Ineke van Gremberghe is post-doctoral fellow at Ghent University. She obtained a master degree in Biotechnology, a PhD in Biology and a master degree in Statistical Data Analysis at Ghent University. She works as FLAMES coordinator and statistical consultant for Stat-Gent Crescendo. She has experience in statistical data analysis of different types of data (data visualisation, linear mixed models, causal mediation analysis, multivariate methods) and in R programming.

Course material

Copies of lecture notes.


A different price applies, depending on your main type of employment.

EmploymentModule 10Exam
Industry/Private sector1 600 30
Non-profit, government, university outside AUGent2 270 30
(Doctoral)student outside AUGent2 210 30

1 If three or more employees from the same company enrol simultaneously for this course a reduction of 10% on the module price is taken into account.

2 AUGent staff and AUGent doctoral students who pay through use of an SAP internal order/invoice can participate at these special rates.

Enrol for this course