Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth-death processes
arXiv:1405.4265 · doi:10.1214/15-AOAS809
Abstract
Surveys often ask respondents to report nonnegative counts, but respondents may misremember or round to a nearby multiple of 5 or 10. This phenomenon is called heaping, and the error inherent in heaped self-reported numbers can bias estimation. Heaped data may be collected cross-sectionally or longitudinally and there may be covariates that complicate the inferential task. Heaping is a well-known issue in many survey settings, and inference for heaped data is an important statistical problem. We propose a novel reporting distribution whose underlying parameters are readily interpretable as rates of misremembering and rounding. The process accommodates a variety of heaping grids and allows for quasi-heaping to values nearly but not equal to heaping multiples. We present a Bayesian hierarchical model for longitudinal samples with covariates to infer both the unobserved true distribution of counts and the parameters that control the heaping process. Finally, we apply our methods to longitudinal self-reported counts of sex partners in a study of high-risk behavior in HIV-positive youth.
Published at http://dx.doi.org/10.1214/15-AOAS809 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
References in corpus (4)
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- Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth-death processes
- Truth and memory: Linking instantaneous and retrospective self-reported cigarette consumption
- Using a Birth-Death Process to Account for Reporting Errors in Longitudinal Self-reported Counts of Behavior