Visual Inference and Graphical Representation in Regression Discontinuity Designs
Zhuan Pei and
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Christina Korting: Cornell University
Jordan Matsudaira: Columbia University
Yi Shen: University of Waterloo
Working Papers from Princeton University, Department of Economics, Industrial Relations Section.
Despite the widespread use of graphs in empirical research, little is known about readersâ€™ ability to process the statistical information they are meant to convey (â€œvisual inferenceâ€ ). 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 expertsâ€™ 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 expertsâ€™ 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.
Keywords: Regression Discontinuity Design; Regression Kink Design; Graphical Methods; VisualInference; Eyetracking; Expert Prediction (search for similar items in EconPapers)
JEL-codes: C10 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:pri:indrel:638
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