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.