This workshop will cover the main types of weighting, to correct for bias in sample data. These types of weights are designed to compensate for different selection probabilities, to correct for non-response, and to post-stratify data to match the demographic distributions found in census data or other criterion distributions. We will also discuss the loss in precision (the increase in the size of confidence intervals) that may result from weighting.
Prior knowledge: This workshop presupposes some basic knowledge of sampling. Attend if you are interested in learning about the process of weighting sample data to correct for bias.
Technology requirement: None.