Francis M, Selvin S, Spear R, Rappaport S
School of Public Health, University of California, Berkeley 94720.
Am Ind Hyg Assoc J. 1989 Jan;50(1):37-43. doi: 10.1080/15298668991374282.
Daily 8-hr time-weighted average (TWA) measurements may not be independent since production rates, maintenance schedules, work practices, and ventilation can result in trends where consecutive values are correlated (autocorrelation). A sampling program which involves collection of measurements on consecutive days, therefore, can result in biased estimates of the mean and variance of the exposure distribution if a high degree of autocorrelation exists. Three simulated data sets were examined to assess the effects of autocorrelation on the estimation of exposure distributions. Results indicated that about 30% of estimated mean values from a highly-autocorrelated series were outside the 95% confidence interval observed for an uncorrelated series. Three data sets obtained from actual workplaces were found to have relatively little autocorrelation. This suggests that for workplaces such as those analyzed here, a random sampling program may not be necessary, and sequential sampling may produce accurate estimates of the parameters of the exposure distribution.
每日8小时时间加权平均（TWA）测量值可能并非相互独立，因为生产率、维护计划、工作方式和通风情况可能导致连续测量值之间呈现相关趋势（自相关性）。因此，如果存在高度自相关性，一个涉及连续多日采集测量值的采样计划可能会导致对暴露分布均值和方差的估计出现偏差。研究了三个模拟数据集，以评估自相关性对暴露分布估计的影响。结果表明，高度自相关序列中约30%的估计均值超出了非相关序列的95%置信区间。从实际工作场所获得的三个数据集显示自相关性相对较低。这表明对于此处分析的这类工作场所，可能无需随机采样计划，顺序采样可能会得出暴露分布参数的准确估计值。
