Visual Inference and Graphical Representation in Regression Discontinuity Designs
Quarterly Journal of Economics · 2023 · Christina Korting、Carl Lieberman、Jordan Matsudaira、Zhuan Pei、Yi Shen
中文摘要
尽管图形在实证研究中被广泛使用，但关于读者处理其旨在传达的统计信息（“视觉推断”）的能力，人们知之甚少。我们在断点回归（RD）设计的背景下研究视觉推断，方法是基于11篇来自顶尖经济学期刊的已发表论文校准的数据生成过程生成图形，测度读者识别图形中不连续性的准确度。首先，我们使用随机实验评估不同图形表示方法对视觉推断的影响。我们发现，组宽和拟合线对参与者是否正确感知不连续性的存在与否影响最大。我们的实验结果使我们可以向实践者提供有证据支持的建议，并建议使用无拟合线的小组宽作为构建RD图形的起点。其次，我们将使用我们偏好方法构建的图形上的视觉推断与广泛使用的计量推断程序进行比较。我们发现，视觉推断实现了相似或更低的第一类错误（假阳性）率，并对计量推断形成补充。
Abstract
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 in 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 effects on whether participants correctly perceive the presence or absence of a discontinuity. Our experimental results allow us to make evidence-based recommendations to practitioners, and we suggest using small bins with no fit lines as a starting point to construct RD graphs. Second, we compare visual inference on graphs constructed using our preferred method with widely used econometric inference procedures. We find that visual inference achieves similar or lower type I error (false positive) rates and complements econometric inference.