Mathematical characterization of dose uncertainty effects on functions summarizing findings of community noise attitudinal surveys.

Previous Monte Carlo simulations have quantified the extent to which dose (sound level) uncertainty in community noise dose-response surveys can bias the shape of inferred dose-response functions. The present work extends the prior findings to create a mathematical model of the biasing effect. The exact effect on any particular data set depends on additional attributes (situational variables) beyond dose uncertainty itself. Several variables and their interaction effects are accounted for in the model. The model produced identical results to the prior Monte Carlo simulations and thereby demonstrated the same slope reduction effect. This model was further exercised to demonstrate the nature and extent of situational variable interaction effects related to the range of doses employed and their distribution across the range. One manifestation was a false asymptotic behavior in the observed dose-response relationship. The mathematical model provides a means to not only predict dose uncertainty effects but also to serve as a foundation for correcting for such effects in regression analyses of transportation noise dose-response relationships.

An examination of dose uncertainty and dose distribution effects on community noise attitudinal survey outcomes.

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.