Analysis of Complex Sample Data

Spring 2019



SURV 625 - Applied Sampling and at least two graduate level statistical methods course covering topics including linear regression and logistic regression.


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. The course will utilize exercises on real survey data to illustrate the methods addressed in class. Students will learn the use of computer software that takes account of complex sample design in estimation.