SURVMETH 742 - Inference From Complex Samples
Section: 001
Term: WN 2018
Subject: Survey Methodology (SURVMETH)
Department: SRC-PSM Graduate Program
With permission of department.
Advisory Prerequisites:
BIOSTAT 602/STAT 511, SURVMETH 612 and 617.
May not be repeated for credit.
Primary Instructor:

This course covers the theoretical and empirical properties of various variance estimation strategies (e.g., Taylor series approximation, replicated methods, and bootstrap methods for complex sample designs) and how to incorporate those methods into inference for complex sample survey data. Variance estimation procedures are applied to descriptive estimators and to analysis techniques such as regression, analysis of variance, and analysis of categorical data. Generalized variances and design effects are presented. Methods of model-based inference for complex sample survey are also examined, and the results contrasted to the design-based type of inference used as the standard in the course. The course will use real survey data to illustrate the methods discussed in class. Students will learn the use of computer software that takes account of the sample design in estimation. Students will carry out a research and analysis project, using techniques and skills learned during the course. A paper describing the student's research will be submitted at the end of the course, and each student will give a short presentation of his/her findings.

SURVMETH 742 - Inference From Complex Samples
Schedule Listing
001 (SEM)
19Graduate Standing
Tu 1:00PM - 2:30PM
Note: Department consent required. Please contact the Program in Survey Methodology (
NOTE: Data maintained by department in Wolverine Access. If no textbooks are listed below, check with the department.

ISBN: 9780470284308
Complex surveys : a guide to analysis using R, Author: Thomas Lumley., Publisher: John Wiley 2010
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