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:
This activity is designed to help introductory statistics students understand how survey data (stratified samples) are collected and how weights are needed to create population estimates.
Data provided by the Inter-university Consortium for Political and Social Research
This activity is designed to help students understand how to create population estimates with weighted data. An online app allows students to visualize how estimates vary based upon appropriate use of weights. Additional information on the dataset may be found at: Here
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.
Data provided by the Inter-university Consortium for Political and Social Research. Additional information on this specific dataset may be found here
Data provided by the National Center for Health Statistics.
Data provided by the National Center for Health Statistics and is available within the MOSAIC Package in R.
Contact Pam Fellers or Shonda Kuiper for R Markdown files.