Managing Missing Data: Weighting, Calibration, and Imputation

Incomplete datasets and nonresponse bias can severely compromise the validity of your statistical analysis. Understanding how to systematically address missing values is essential for producing accurate, reliable insights. This written course guides you through the foundational concepts and mathematical adjustments needed to correct for missing data. You will transition from simply ignoring empty cells to confidently applying modern weighting, calibration, and imputation strategies to restore dataset integrity. What you'll learn: - Understand the fundamental mechanisms of missingness, including Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). - Apply nonresponse adjustment techniques using estimated response propensities. - Implement calibration methods such as poststratification, raking, and general regression estimation to align sample data with known population totals. - Compare and execute various imputation techniques to substitute missing values with statistically sound estimates. - Evaluate missing data patterns programmatically using modern data preparation workflows. The course begins with core definitions of missing data types before moving step-by-step through weighting adjustments, calibration math, and imputation models. You will read detailed explanations and review clear code and formula examples designed to build your practical toolkit. This text-based course is designed for beginner data analysts, researchers, and junior statisticians. No prior experience with complex survey adjustment is required, though a basic familiarity with introductory statistics is helpful. Start mastering the art of data restoration and ensure your statistical analyses are robust and unbiased.