Modeling Misclassification in Spousal Violence Reporting: Evidence from Bayesian Quantile Regression

2026-05-14

AI Summary

This study addresses misclassification in sensitive binary outcomes—such as self-reported intimate partner violence—arising from underreporting and other reporting errors. It proposes the first Bayesian quantile regression framework that explicitly incorporates a misclassification mechanism by introducing a latent true response variable to model both false-negative and false-positive errors. A novel Markov chain Monte Carlo (MCMC) algorithm is developed for estimation, enabling unbiased inference on covariate effects across the entire conditional distribution. Simulation studies and empirical analysis demonstrate that the proposed method substantially outperforms conventional models that ignore misclassification. When applied to intimate partner violence data, the approach reveals pervasive underreporting and shows that correcting for misclassification can materially alter substantive conclusions, thereby highlighting its practical relevance and methodological innovation.

Abstract

Quantile regression extends regression analysis beyond the conditional mean, providing a richer characterization of covariate effects across the outcome distribution. For sensitive binary outcomes, however, misclassification due to underreporting can substantially bias inference. We propose a Bayesian quantile regression framework for misclassified binary outcomes that introduces a latent true response and explicitly models false negative and false positive reporting errors. Estimation is performed through a novel Markov chain Monte Carlo (MCMC) algorithm. Simulation studies under varying prior specifications and misclassification rates demonstrate improved performance over models that ignore misclassification. We apply the method to self-reported spousal violence data, examining associations with employment status and household wealth while adjusting for socio-demographic factors. The results indicate that underreporting exceeds overreporting across quantiles and that accounting for misclassification can change substantive conclusions.

Problem

• misclassification
• spousal violence
• quantile regression
• underreporting
• binary outcomes

Innovation

• Bayesian quantile regression
• misclassification
• latent true response
• MCMC algorithm
• sensitive binary outcomes