Understanding and Managing Missing Data.pdf
This document provides a clear and practical guide to understanding missing data mechanisms, including Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR). Through real-world scenarios and examples, it explains how different types of missingness impact data analysis and decision-making. It also outlines common strategies for handling missing data, including deletion techniques and imputation methods such as mean imputation, regression, and stochastic modeling.
Designed for researchers, analysts, and students working with real-world datasets, this guide helps ensure statistical validity, reduce bias, and improve the overall quality of analysis in fields like public health, behavioral science, social research, and machine learning.
Funding
Self-funded research conducted as part of graduate coursework at Pace University.
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- Intelligent robotics
- Artificial intelligence not elsewhere classified
- Natural language processing
- Planning and decision making
- Satisfiability and optimisation
- Data engineering and data science
- Data management and data science not elsewhere classified
- Data mining and knowledge discovery
- Data models, storage and indexing
- Neural networks
- Machine learning not elsewhere classified
