Course Syllabus

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CLASS MEETINGS

            1:10-4:00 PM, Wednesdays, January 4 - April 26

            Room 1208 Lefrak, University of Maryland; Room G300 ISR-Perry, University of Michigan; T10, US Census Bureau, Suitland, Maryland

 

INSTRUCTOR

            Brady T. West, Room 4118 ISR (Michigan) & Room 1218 Lefrak (Maryland)

734/223-9793 (cell); 734/647-4615 (office);

Email: bwest@umich.edu

Office hours by appointment

 

ASSISTANTS

         Administrative / Technical: Jodi Holbrook, Room 4012 ISR, University of Michigan

734/764-6595; hjodi@umich.edu

 Grading: Edmundo Roberto Melipillan, Room 4132 ISR, University of Michigan

734/764-4369; robmeli@umich.edu

 

COURSE CONTENT, GOALS, & EXPECTED COMPETENCIES

Applied Sampling is an applied statistical methods course concerned almost exclusively with the design of data collection. Little of the analysis of collected data will be discussed, but rather the course will concentrate on problems of applying sampling methods to human populations.

The course is presented at a moderately advanced statistical level. While mathematical aspects are not covered, statistical notation and some algebraic derivations are. A thorough understanding of statistical notation and principles will be needed.

The goal of the course is to develop sampling skills and an understanding of the principles and properties of sampling techniques used in scientific research. Students will learn and be tested on the following competencies:

  1. The meaning and application of variance in populations and sampling distributions, and understand the difference between element and sampling variance.
  2. The methods and properties of sampling techniques including simple random, stratified random, cluster, systematic, multistage, probability proportionate to size, and stratified multistage sampling.
  3. How sampling variance is estimated for the mean, proportions, and totals (aggregates) for each of the sampling techniques examined in the course.
  4. How to estimate and use in practice sampling variance for complex sample surveys, including balanced and jackknife repeated replication and the Taylor series approximation.
  5. The differences between sampling and non-sampling errors.
  6. How nonresponse can affect survey estimates and what techniques can be used to reduce nonresponse and compensate for nonresponse bias.

Course Summary:

Date Details Due