SURV746: Practical Tools for Sampling and Weighting: Part 1

Credit(s)/ECTS: 
2/4
Core/Elective: 
Elective

This course is a statistical methods class appropriate for second year Master’s students and PhD students. The course will be a combination of hands-on applications and general review of the theory behind different approaches to sampling and weighting.

Topics covered include:

  •     Sample size calculations using estimation targets based on relative standard error, margin of error, and power requirements;
  •     Use of mathematical programming to determine sample sizes needed to achieve estimation goals for a series of subgroups and analysis variables;
  •     Resources for designing area probability samples;
  •     Methods of sample allocation for multistage samples;
  •     Steps in weighting, including computation of base weights, nonresponse adjustments, and uses of auxiliary data;
  •     Nonresponse adjustment alternatives, including weighting cell adjustments, formation of cells using regression trees, and propensity score adjustments;
  •     Weighting via poststratification, raking, general regression estimation, and other types of calibration

Examples of the types of projects which will be completed by students are:

  • Design a stratified single-stage sample that achieves specified goals for precision of a series of domain estimates while meeting a budget constraint. 
  • Power requirements for detecting domain differences will also be considered. Anticipated rates of nonresponse and ineligible units will be accounted for. 
  • Multicriteria, mathematical programming optimization methods will be used to determine the allocation. 
  • Design an area probability sample in which students will use an existing sample of primary units and determine a plan for sampling segments and persons within segments.  Rates will be determined to achieve target sample sizes for different demographic groups.

 

There will also be small-scale homework problems so that students get some practice using all methods covered in the class. The emphasis will be on using the methods to solve practical problems with theory being reviewed so that it is clear what the assumptions are for different techniques. Throughout the course there will be a heavy emphasis on how to program the techniques using R, Excel, and SAS.

Grading: 
  • Homework 55%
  • Project 15%
  • Final Exam 20%
  • Class participation and quizzes 10%
Prerequisites: 

Sampling theory (e.g., SURV440) and Applied sampling (e.g., SURV626)

Some experience in the use of statistical software package is helpful.

Course syllabus: 

Course Dates

2018

Spring Term (March – May)