Handling Missing Data
Practical Imputation Methods for Real-World Analysis
Most analysts treat missing data as a problem to eliminate rather than understand. You delete rows, lose information, and unknowingly introduce bias into your results. This book shows you why that approach fails and what actually works. Missing data isn't just a statistical nuisance—it's a signal about your data's structure. This guide walks you through the complete imputation landscape: from diagnosing why data is missing, to selecting the right method for your situation, to validating that your approach produces trustworthy results. You'll learn when simple methods fail catastrophically, how multiple imputation builds uncertainty into your estimates, and why auxiliary variables matter more than you think. Each technique is explained with real examples, practical decision trees, and implementation guidance across major statistical software platforms. Whether you're analyzing survey responses, clinical trials, or observational studies, this book provides the frameworks and tools to handle incomplete data with confidence and rigor.
Est. Length
207 pages (10 chapters)
Language
English
Publication date
Apr 2, 2026
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