Exposure uncertainty effects on regression parameter values

Abstract

Exposure uncertainty in community noise exposure-response investigations can bias the results of regression analyses, especially when the range of sound levels is limited, the standard deviation of sound level uncertainty is more than a decibel or two and the distribution of sound levels across the sample population deviates significantly from a uniform one. The bias is manifested in the solved values of the regression coefficients, leading to incorrect representations of both the slope and intercept. Methods are available to reduce the magnitude of such bias and this paper examines the regression calibration approach. A basic overview of regression calibration is provided as well as its implementation in a logistic regression analysis. The effectiveness and limitations of the procedure are discussed, as are the requisite additional field measurements necessary to quantify situational parameters required in the calibration process. While coefficient bias can be mitigated, coefficient uncertainty that might otherwise be due only to uncertainty in the predicted variable (fraction annoyed) increases with increasing exposure uncertainty. Such uncertainty impacts the ability to observe differences between sample populations in both scalar and categorical variables.