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Bayesian Peer Calibration with Application to Alcohol Use.

Miles Q Ott1, Joseph W Hogan2, Krista J Gile3

  • 1Augsburg College, Minneapolis, U.S.A.

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Summary
This summary is machine-generated.

This study introduces a new Bayesian model to analyze college student alcohol consumption using both self and peer reports. The model helps understand drinking behavior and reporting accuracy within social networks.

Keywords:
Alcohol usebayesian comparative calibrationmultiple sourcespeer-reportsself-reports

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Area of Science:

  • Social Sciences
  • Psychology
  • Public Health

Background:

  • Self-reported data can be limited in accuracy for behavioral measures.
  • Peer reports offer supplementary information but also have limitations.
  • Understanding alcohol consumption in college social networks is crucial.

Purpose of the Study:

  • To develop a novel Bayesian comparative calibration model for analyzing alcohol consumption data.
  • To integrate self-reported and peer-reported data to improve accuracy.
  • To investigate discrepancies in peer reporting within a college network.

Main Methods:

  • Developed a Bayesian comparative calibration model for continuous, count, and binary outcomes.
  • Utilized covariate information to model the joint distribution of self and peer reports.
  • Applied the model to data from college students in a freshman dormitory.

Main Results:

  • The model provides insights into alcohol consumption at both population and individual levels.
  • Identified and quantified discrepancies in individual peer reports.
  • Characterized the effects of covariates on drinking behavior and reporting discrepancies.

Conclusions:

  • Combining self and peer reports with a novel Bayesian model enhances understanding of alcohol consumption.
  • The model effectively estimates peer-reporting discrepancies in network surveys.
  • Covariates influence both drinking behavior and the accuracy of peer reports in college settings.