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Inferences About a Quantile Shift Measure of Effect Size When There Is a Covariate

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  • Rand R. Wilcox

Abstract

When comparing two independent groups, a possible appeal of the quantile shift measure of effect size is that its magnitude takes into account situations where one or both distributions are skewed. Extant results indicate that a percentile bootstrap method performs reasonably well given the goal of making inferences about this measure of effect size. The goal here is to suggest a method for making inferences about this measure of effect size when there is a covariate. The method is illustrated with data dealing with the wellbeing of older adults.

Suggested Citation

  • Rand R. Wilcox, 2022. "Inferences About a Quantile Shift Measure of Effect Size When There Is a Covariate," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(2), pages 1-52, March.
    • Handle: RePEc:ibn:ijspjl:v:11:y:2022:i:2:p:52
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    References listed on IDEAS

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    1. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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