Coefficient plots are a popular tool for visualizing regression estimates. The appeal of these plots is that they visualize confidence intervals around the estimates and generally center the plot around zero, meaning that any estimate that crosses zero is statistically nonsignificant at least at the alpha level around which the confidence intervals are constructed. For models with statistical significance levels determined via randomization models of inference and for which there is no standard error or confidence intervals for the estimate itself, these plots appear less useful. In this article, I illustrate a variant of the coefficient plot for regression models with p-values constructed using permutation tests. These visualizations plot each estimate’s p-value and its associated confidence interval in relation to a specified alpha level. These plots can help the analyst interpret and report the statistical and substantive significances of their models. I illustrate using a nonprobability sample of activists and participants at a 1962 anticommunism school.

系数图是用于可视化回归估计的流行工具。这些图的吸引力在于，它们使估计值周围的置信区间可视化，并且通常使图的中心位于零附近，这意味着，越过零的任何估计至少在构建置信区间所围绕的alpha级别上在统计上都不重要。对于具有通过推理的随机模型确定的统计显着性水平的模型，并且对于其估计值本身没有标准误差或置信区间的模型，这些图似乎不太有用。在本文中，我说明了使用置换检验构造的p值回归模型的系数图的变体。这些可视化绘制了每个估计值的p值及其与指定alpha级别相关的置信区间。这些图可以帮助分析师解释和报告其模型的统计和实质意义。我举例说明了在1962年的反共主义学校使用活动家和参与者的非概率样本。