Handling Missing Data and Survey Weighting in Data Analysis

Missing data is an inevitable challenge in real-world datasets, particularly in survey research. Leaving gaps unaddressed or handling them incorrectly can severely bias your results and lead to flawed business or scientific conclusions. This text-based course guides you through the foundational concepts of data missingness, equipping you with the practical strategies needed to clean, impute, and weight your data for robust analysis. By the end of this course, you will be able to confidently diagnose missingness patterns and apply mathematically sound corrections to restore the integrity of your datasets. What you'll learn: - Understand the core mechanisms of missing data, including Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). - Apply modern single and multiple imputation techniques to fill in data gaps responsibly. - Configure and calculate survey weights to adjust for non-response and underrepresented demographics. - Analyze how missingness impacts statistical power and the validity of your analytical models. - Practice diagnostic workflows using step-by-step written tutorials to detect patterns of missingness. We begin with essential terminology and the conceptual frameworks of data quality before moving into practical methodologies for imputation and weighting. You will read through clear, conceptual explanations and practical examples designed to build your skills progressively. This course is designed for beginner data analysts, social science researchers, and students who want to transition from clean textbook data to messy, real-world datasets. No advanced statistical background is required to get started. Start reading today to master the critical skills of data cleaning and survey weighting.