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Correlation Attenuation Due to Measurement Error: A New Approach Using the Bootstrap Procedure

机译：由于测量误差引起的相关衰减：一种使用引导程序的新方法

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Issues with correlation attenuation due to measurement error are well documented. More than a century ago, Spearman proposed a correction for attenuation. However, this correction has seen very little use since it can potentially inflate the true correlation beyond one. In addition, very little confidence interval (CI) research has been done for correction for attenuation. In the present study, the authors propose a bootstrap procedure for estimating the deattenuated correlation and corresponding CIs. The authors use Monte Carlo simulations to generate data under certain conditions and assess the performance of the bootstrapped deattenuated correlation. The authors investigate for bias and 95% CI coverage. Results indicate that the bootstrap deattenuated correlation provided adequate percentile CI coverage in all but three conditions. The bias-corrected and accelerated CI, however, provided adequate coverage under all simulation conditions.
机译：由于测量误差而引起的相关衰减问题已得到充分记录。一个多世纪以前，斯皮尔曼提出了一种衰减校正方法。但是，这种校正几乎没有用处，因为它可能使真实的相关性膨胀到一个以上。另外，很少进行置信区间（CI）研究来校正衰减。在本研究中，作者提出了一种引导程序，用于估计已衰减的相关性和相应的CI。作者使用蒙特卡洛模拟在特定条件下生成数据，并评估自举衰减相关的性能。作者调查偏差和95％CI覆盖率。结果表明，自举衰减相关性在除三个条件外的所有条件下均提供了足够的百分位数CI覆盖范围。但是，经过偏置校正和加速的CI在所有模拟条件下均提供了足够的覆盖范围。

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