Weighted Data

When a researcher is interested in examining distinct subgroups within a population, it is often best to use a stratified random sample to better represent the entire population. A stratified random sample involves dividing the population of interest into several smaller groups, called "strata" and then taking a simple random sample from each of these smaller groups. This method is commonly used when we want to guarantee a large enough sample from each subgroup. When this type of sampling method is used, it is important to use weights to take the relative size of each subgroup into account. This "Weighted Data" site introduces basic techniques used in estimating and testing population parameters using weights. Note that these labs can be used at various levels:

  1. Introductory courses with no statistics background: Should it Pass? And Political Preferences1
  2. Courses that require some statistics background: Political Preferences2, CAM and NHANES(Health)
  3. Advanced course supplements: Mathematical Details of the Rao-Scott Method and Types of Weights, Subsetting, Strata and Clustering


Introductory Activities: Calculating population estimates with weighted data


Intermediate Activities: Hypothesis testing with categorical weighted data

These activities describes the use of hypothesis tests with weighted data. Online apps allows students to visualize how estimates vary based upon appropriate use of weights.


Advanced Supplements


Contact Pam Fellers or Shonda Kuiper for R Markdown files.


Thanks to Dr. Pam Fellers as well as 2015 Grinnell MAP students Karin Yndestad and Ruby Barnard-Mayers for creating these course materials and online apps.