Visualising Multilevel Regression and Poststratification: Alternatives to the Current Practice

TL;DR

This paper reviews current visualization practices for Multilevel Regression and Poststratification (MRP), highlighting limitations like lack of uncertainty display, and proposes improved graphical strategies to better communicate estimates and model effects.

Contribution

It systematically examines MRP visualization practices, applies MRP to the 2016 US election data, and proposes new visualization strategies that incorporate uncertainty and bias-variance considerations.

Findings

Uncertainty is rarely displayed in current MRP visualizations.

Choropleth maps are most common but may mislead without uncertainty info.

Enhanced visualizations can better illustrate model effects and trade-offs.

Abstract

Surveys provide important evidence for policymaking, decision-making, and understanding of society. However, conducting the large surveys required to provide subpopulation level estimates is expensive and time-consuming. Multilevel Regression and Poststratification (MRP) is a promising method to provide reliable estimates for subpopulations from surveys without the amount of data needed for reliable direct estimates. Graphical displays have been widely used to communicate and diagnose MRP estimates. However, there have been few studies on how visualisation should be performed in this field. Accordingly, this study examines the current practice of MRP visualisation using a systematic literature review. This study also applies MRP to estimate the Trump vote share in the U.S. 2016 presidential election using the Cooperative Congressional Election Study (CCES) data to illustrate the implication of current visualisation practices and explore alternatives for improvement. We find that uncertainty is not often displayed in the current practice, despite its importance for survey inference. The choropleth map is the most frequently used to display MRP estimates even though it only shows point estimates and could hinder the information conveyed. Using various graphical representations, we show that visualisation with uncertainty can illustrate the effect of different model specifications on the estimation result. In addition, this study also proposes a visualisation strategy to also take the bias-variance trade-off into account when evaluating MRP models.