SURV747: Practical Tools for Sampling Part I

Data Analysis, Data Curation/Storage

This course and the textbook give students the necessary tools to design and select single- and multi-stage survey samples in the real world. We will cover topics on calculating a sample size for a specified level of precision or within the confines of the survey budget, identifying and creating strata, allocating the sample to the strata given a set of constraints or requirements for detectable differences between group estimates, estimating variance components, and determining what sample sizes to use at different stages in a multi-stage sample.

We will use specialized software for the calculations mentioned. This course will emphasize R but some examples in SAS and Stata are also discussed. Sample size calculations can be done using the R PracTools package written by the instructors or with Microsoft Excel; SAS procedures and Microsoft Excel are used for the mathematical programming (Unit 4). Survey weights can be computed with the R survey package for many designs and estimators—a topic covered in Part II of the Practical Tools series.

R is downloaded for free from . Students may also find a helpful interface to execute program code. R packages for this class include, for example, PracTools (developed for the textbook), survey, and sampling. Three videos on the R survey package and five videos on PracTools are posted on For those new to R, there are 48 MarinStatsLectures available at

There will be small-scale homework problems each week for students to gain practice using all methods covered in the course. The emphasis will be on using the methods to solve practical problems; we review theory as needed for a clear understanding of the underlying assumptions. All are encouraged to discuss their own survey design challenges and solutions during our weekly online meetings.

Course objectives: 

By the end of the course, students will understand:

  • Sample size calculations using estimation targets based on coefficient of variation, margin of error, and power requirements.
  • Mathematical programming to determine sample sizes needed to achieve estimation goals for a series of subgroups and analysis variables.
  • Resources for designing area probability simples.
  • Methods of sample allocation for multistage samples.

Grading will based on

  • 8 Homeworks (60% of grade)
  • A take-home final exam (20% of grade)
  • 6 Quizzes (10% of grade)
  • Class Participation (10% of grade) in discussion during the weekly online meetings and posting questions to the weekly forum (deadline: 24 hours before class) demonstrating understanding of the required readings and video lectures

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

Some experience with the R statistical computing software is helpful.

Course Dates


Fall Semester (September – December)