An introduction to missing data

[3.5 hours] - [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.

An introduction to missing data [3.5 hours] - [starts on 18-03-2025 à 09:00] - [English] - [Louvain-la-Neuve]

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
• Overview of common but sub-optimal solutions and their possible consequences
• Exploratory analysis regarding missing data
• A brief introduction 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).

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

Methods and method families discussed
Key statistical tools
   Key statistical tools
Data processing
   Data set cleaning and processing for statistical analysis