Missing Data Methods

Course Description:

This intensive summer school course on Missing Data Methods is designed for researchers, data analysts, and statisticians who encounter missing data in their work. Missing data can significantly impact the validity of research findings and statistical analyses. This course will provide a comprehensive overview of the concepts, techniques, and best practices for handling missing data. Participants will learn about different types of missing data, the assumptions underlying various methods, and how to implement these methods using the R statistical software. The course will include a mix of lectures, hands-on exercises, and case studies to guarantee a practical understanding of the topics covered.

Prerequisites:

Basic knowledge of statistics and familiarity with the R statistical software

Learning Outcomes:

By the end of this course, participants will be able to:

Identify different types of missing data and understand their implications.
Apply appropriate methods for handling missing data in different contexts.
Utilize statistical software to implement missing data techniques.
Evaluate the impact of missing data methods on the results of their analyses.
Develop strategies to prevent and address missing data in research designs.

Course Timetable

Day 1: Introduction and Basic Concepts

Welcome and Course Introduction
Overview of Missing Data
Types of Missing Data

Mechanisms of Missing Data
- MCAR, MAR, MNAR
Introduction to Simple Imputation Methods
- Mean, Hot Deck, Conditional Mean and Predictive mean Imputation
- Pros and Cons
- Hands-on Session
- Implementing Simple Imputation in R

Day 2: Advanced Imputation Methods

Multiple Imputation
Concepts and Implementation
Maximum Likelihood Methods
EM Algorithm
Model-based Methods
Regression Imputation
Stochastic Regression Imputation
Doubly Robust Methods
Hands-on Session
Implementing Advanced Imputation in R

Day 3: Case Studies and Best Practices

Practical Issues in Missing Data
Handling MNAR Data
Sensitivity Analysis
Case Studies

Real-world Applications

Prevention and Planning

- Study Design Strategies
- Data Collection Techniques
- Final Hands-on Session and Wrap-up
- Implementing a Complete Workflow

Q&A and Course Feedback

Cost

400 euro.

Payment details:

Names: Adiaba Consulting Group

IBAN BE56 3632 3839 2088, BIC: INGBNL2A

(Please add following reference: your_last_name/MissingData/2024

Participants are encouraged to bring their own laptops with pre-installed statistical software (R, Python, or SAS) for the hands-on session

Registration:

Google Sites

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