Bootstrapping Confidence Intervals for Robust Measures of Association.Report as inadecuate




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A Monte Carlo simulation study was conducted to determine the bootstrap correction formula yielding the most accurate confidence intervals for robust measures of association. Confidence intervals were generated via the percentile, adjusted, BC, and BC(a) bootstrap procedures and applied to the Winsorized, percentage bend, and Pearson correlation coefficients. Type I error, bias, efficiency, and interval length were compared across correlational and bootstrap methods. Results reveal the superior resiliency of the robust measures over the Pearson r, though neither robust correlation outperformed the other. Unexpectedly, the four bootstrap techniques achieved roughly equivalent outcomes. Based on these results, it appears that the more complex bootstrapping procedures may not be worth the additional computational expenditures. (Contains 6 tables, 2 figures, and 76 references.) (Author/SLD)

Descriptors: Correlation, Monte Carlo Methods, Robustness (Statistics), Simulation











Author: King, Jason E.

Source: https://eric.ed.gov/?q=a&ft=on&ff1=dtySince_1992&pg=6697&id=ED464944







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