This introductory course on the analysis of data from complex sample designs covers the development and handling of selection and other compensatory weights; methods for handling missing data; the effect of stratification and clustering on estimation and inference; alternative variance estimation procedures; methods for incorporating weights, stratification, clustering, and imputed values in estimation and inference procedures for complex sample survey data; and generalized design effects and variance functions.
Analysis of Complex Sample Survey Data
Instructor(s): Yan Li
3 credits (Sampling & Estimation course)
Prerequisites: SurvMeth 625 - Applied Sampling and at least two graduate level statistical methods course covering topics including linear regression and logistic regression.