Survey Weighting and Data Linkage Analytics in R

Modern data analysis often requires working with complex survey samples and merging disparate datasets. To draw accurate conclusions, you must understand how to apply weight adjustments and link records securely and ethically. This text-based course guides you from the fundamental concepts of survey design to executing practical statistical matching in R. By completing this course, you will gain the skills needed to prepare raw, complex data, apply weightings for representative modeling, and combine datasets without compromising data integrity. You will learn how to handle real-world data limitations and ensure your statistical conclusions are both valid and mathematically sound. What you'll learn: - Understand the core principles of survey sampling, weighting, and representative data. - Calculate descriptive statistics and run weighted linear and logistic regressions in R. - Apply record linkage and statistical matching techniques to merge distinct datasets. - Implement modern R workflows using specialized packages designed for complex survey analysis. - Address ethical considerations, data privacy, and modern anonymization standards when combining data. The course begins with foundational definitions of survey weights and sampling designs before moving into step-by-step R syntax for weighted modeling. You will then progress to data integration techniques, exploring how to match records across databases safely and systematically. This course is designed for beginner data analysts, social scientists, and researchers who want to work with real-world survey data. No advanced statistical background is required, making this the perfect starting point for learning complex data integration. Start learning how to unlock deeper insights from complex, combined datasets today.