Skip navigation
Please use this identifier to cite or link to this item:
Title: Visual Inference and Graphical Representation in Regression Discontinuity Designs
Authors: Korting, Christina
Lieberman, Carl
Matsudaira, Jordan
Pei, Zhuan
Shen, Yi
Keywords: Regression Discontinuity Design
Regression Kink Design
Graphical Methods
Visual Inference
Expert Prediction
JEL Code: C10
Issue Date: Feb-2020
Series/Report no.: 638
Abstract: Despite the widespread use of graphs in empirical research, little is known about readers’ ability to process the statistical information they are meant to convey (“visual inference”). In this paper, we evaluate several key aspects of visual inference in regression discontinuity (RD) designs by measuring how well readers can identify discontinuities in graphs. First, we assess the effects of graphical representation methods on visual inference, using randomized experiments crowdsourcing discontinuity classifications with graphs produced from data generating processes calibrated on datasets from 11 published papers. Second, we evaluate visual inference by both experts and non-experts and study experts’ ability to predict our experimental results. We find that experts perform comparably to non-experts and partly anticipate the effects of graphical methods. Third, we compare experts’ visual inference to commonly used econometric procedures in RD designs and observe that it achieves similar or lower type I error rates. Fourth, we conduct an eyetracking study to further understand RD visual inference, but it does not reveal gaze patterns that robustly predict successful inference. We also evaluate visual inference in the closely related regression kink design.
Appears in Collections:IRS Working Papers

Files in This Item:
File Description SizeFormat 
638.pdf4.44 MBAdobe PDFView/Download

Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.