Abstract
Social survey data sets of large numbers of individual respondents' opinions are generally viewed as supporting reliable inferences of relationships between the prevalence of noise-induced annoyance and noise exposure levels. The current analyses identify conditions under which noise dose distributions and acoustic measurement uncertainty lead to appreciable mis-estimation of the slopes of empirical dose-response relationships with respect to those of true slopes in exposure ranges of interest. These findings were revealed by Monte Carlo methods for creating simulated data sets with varying exposure ranges and degrees of dose uncertainty. These simulated data sets support quantitative comparisons of dose-response relationships between empirical outcomes and known (assumed) relationships. The effect of noise dose uncertainty is appreciable for dose uncertainties with standard deviations greater than about 2 decibels. Limited dose ranges as well as haystack-shaped (non-uniform) dose distributions magnify the biasing effect of dose uncertainty on the slopes of observed relationships. Narrow exposure ranges can also create a false asymptotic behavior in the relationship. These phenomena are well documented in the non-acoustic literature.
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