SURV748: Step by Step in Survey Weighting

Data Analysis, Data Curation/Storage

This course and the textbook give students the necessary tools to calculate analysis weights for various survey designs in a real-world setting. We will cover topics on calculating base weights for single- and multistage designs, adjusting weights for unknown study eligibility and nonresponse using a few techniques, and aligning survey estimates with known population values through weight calibration.

We will use specialized software for the procedures mentioned. This course will emphasize R but some examples in SAS and Stata are also discussed. R is downloaded for free from Students may also find a helpful interface to execute program code. For those new to R, there are many MarinStatsLectures available at .com/playlist?list=PLqzoL9-eJTNBDdKgJgJzaQcY6OXmsXAHU

There will be 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 weighting challenges and solutions during our weekly online meetings.

Course objectives: 

By the end of the course, students will understand:

  • Role of survey weights in population inference.
  • 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 classification algorithms, and propensity score adjustments.
  • Weighting via poststratification, raking, general regression estimation, and other types of calibration.
  • Assessing if weights are not needed.

Grading will based on

  • 4 Homework assignments (60% of grade)
  • A take-home final exam (30% 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), Sampling I (e.g., SURV626), or Practical Tools (Part II) for Sampling.

Some experience with variance estimation (e.g., SURV742), statistical analysis using survey data, and the R statistical computing software will be  helpful.

Course Dates


Fall Semester (September – December)


Fall Semester (September – December)