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Visualization Strategies for Regression Estimates with Randomization Inference

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  • Taylor, Marshall A.

    (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 non-significant at at least 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 paper, 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 both the statistical and substantive significance of their models. Illustrations are provided using a nonprobability sample of activists and participants at a 1962 anti-Communism school.

Suggested Citation

  • Taylor, Marshall A., 2019. "Visualization Strategies for Regression Estimates with Randomization Inference," SocArXiv bsd7g, Center for Open Science.
    • Handle: RePEc:osf:socarx:bsd7g
    DOI: 10.31219/osf.io/bsd7g
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    References listed on IDEAS

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    1. Peter M. Aronow, 2012. "A General Method for Detecting Interference Between Units in Randomized Experiments," Sociological Methods & Research, , vol. 41(1), pages 3-16, February.
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