Survey research methods: A guide for creating post-stratification weights to correct for sample bias

Abstract

<br>Nonrepresentative data pose one of the greatest validity threats in survey research. Samples that are underrepresented and/or overrepresented based on demographic subgroups can introduce bias that distorts both the accuracy and the inferences made about the results. This article discusses the concept of poststratification weighting, a <i>post hoc</i> statistical procedure used to correct for sampling bias in survey research studies. Procedural steps for calculating poststratification weights are presented, and an example involving a simulated cohort of students in a medical school is provided for demonstration purposes. SPSS statistical software coding is presented to help researchers get started with their own calculations of poststratification weights.<br>