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Visualization strategies for regression estimates with randomization inference

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  • Marshall A. Taylor

    (New Mexico State University)

Abstract

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 sig- nificance 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 vi- sualizations 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 us- ing a nonprobability sample of activists and participants at a 1962 anticommunism school. Copyright 2020 by StataCorp LP.

Suggested Citation

  • Marshall A. Taylor, 2020. "Visualization strategies for regression estimates with randomization inference," Stata Journal, StataCorp LP, vol. 20(2), pages 309-335, June.
    • Handle: RePEc:tsj:stataj:v:20:y:2019:i:2:p:309-335
    DOI: 10.1177/1536867X20930999
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