Getting the Most Out of Surveys: Multilevel Regression and Poststratification

Abstract

AbstractGood causal inference requires good measurement; even the most thoughtfully designed research can be derailed by noisy data. Because policy scholars are often interested in public opinion as a key dependent or independent variable, paying careful attention to the sources of measurement error from surveys is an essential step toward detecting causation. This chapter introduces multilevel regression and poststratification (MRP), a method for adjusting public opinion estimates to account for observed imbalances between the survey sample and population of interest. It covers the history of MRP, recent advances, an example analysis with code, and concludes with a discussion of best practices and limitations of the approach.