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Visual Inference and Graphical Representation in Regression Discontinuity Designs

Christina Korting, Carl Lieberman, Jordan Matsudaira, Zhuan Pei and Yi Shen

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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"). We study visual inference within the context of regression discontinuity (RD) designs by measuring how accurately readers identify discontinuities in graphs produced from data generating processes calibrated on 11 published papers from leading economics journals. First, we assess the effects of different graphical representation methods on visual inference using randomized experiments. We find that bin widths and fit lines have the largest impacts on whether participants correctly perceive the presence or absence of a discontinuity. Incorporating the experimental results into two decision theoretical criteria adapted from the recent economics literature, we find that using small bins with no fit lines to construct RD graphs performs well and recommend it as a starting point to practitioners. Second, we compare visual inference with widely used econometric inference procedures. We find that visual inference achieves similar or lower type I error rates and complements econometric inference.

Date: 2021-12
New Economics Papers: this item is included in nep-ecm and nep-exp
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http://arxiv.org/pdf/2112.03096 Latest version (application/pdf)

Related works:
Working Paper: Visual Inference and Graphical Representation in Regression Discontinuity Designs (2021) Downloads
Working Paper: Visual Inference and Graphical Representation in Regression Discontinuity Designs (2020) Downloads
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