NEW - An introduction to missing data and multiple imputation

[2 half days] - [starts on 02-03-2027 at 13:30] - [English] - []

Are there missing values in the dataset that you want to analyze?

Many databases contain missing values for certain variables of interest. Ignoring them during analysis (by including only rows with no missing values) can, in many cases, lead to biased results. Methods exist to take account of the presence of missing values in the analyses of interest and thus correct the results obtained.

Training aims
The aim of this training course is to understand the possible consequences of ignoring the presence of missing values in an analysis or of using too simple solutions (such as mean imputation) and to discover some better solutions that are available in the common statistical software (such as R and SPSS).

Prerequisites
This training course require some basic knowledge of descriptive statistics and linear regression.

Content

• General information on missing data: mechanisms and patterns
• Exploratory analysis regarding missing data
• Overview of common but sub-optimal solutions and their possible consequences
• An introduction (with an example in R) to two common solutions that can be used with a wide range of statistical analyses (such as linear regression and structural equation models): Full Information Maximum Likelihood (FIML) and Multiple Imputation (MI).

Location
: To be specified -

Attendance
The training is open to all but requires prior registration.

Time slots for training
02-03-2027 from 13:30 to 16:30
04-03-2027 from 09:30 to 12:30

Rate
UCLouvain member, UCLouvain student, Researcher : Training available free of charge but registration in advance is required.
Administrative fees will be applied in case of late cancellation.
Invoice paid by a university, UCLouvain clinic member, SHS Namur member : 175 euros
Company, Non-UCLouvain member : 270 euros

Financial support
This training course is recognized by the IABE, enabling participants to earn CPD points.
(Note that this is true for all SMCS courses.)

For more information (open)