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This study uses Bayesian models to analyze sonic boom annoyance, accounting for repeated participant responses. The models provide similar population dose-response curves but differ in specific annoyance calculations.

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Area of Science:

  • Acoustics
  • Statistical Modeling
  • Human Response to Noise

Background:

  • Community noise surveys often collect single observations per participant.
  • Analyzing repeated observations within individuals requires specialized statistical approaches.
  • Understanding human annoyance from sonic booms is crucial for aircraft design and community impact assessment.

Purpose of the Study:

  • To apply two Bayesian statistical models for analyzing sonic boom exposure and human annoyance.
  • To account for within-subject correlation in panel sample data using multilevel models.
  • To develop a method for calculating population-level summary dose-response curves.

Main Methods:

  • Utilized two Bayesian statistical models: a multilevel logistic regression and a multilevel ordinal regression.
  • Employed data from a NASA pilot study featuring a panel sample with multiple observations per participant.
  • Implemented multilevel (hierarchical/mixed-effects) models to address within-subject correlation.

Main Results:

  • Both Bayesian models successfully analyzed sonic boom exposure and human annoyance data.
  • The multilevel models effectively handled the panel sample structure.
  • Summary dose-response curves derived from both models were visually similar.
  • Discrepancies were observed in noise dose estimates at a fixed percentage of highly annoyed individuals.

Conclusions:

  • Bayesian multilevel models are suitable for analyzing sonic boom annoyance data from panel surveys.
  • The choice of model can influence specific quantitative estimates of noise dose and annoyance.
  • Further research may refine these models for more accurate sonic boom impact assessments.