SEX, LIES AND SELF-REPORTED COUNTS: BAYESIAN MIXTURE MODELS FOR HEAPING IN LONGITUDINAL COUNT DATA VIA BIRTH-DEATH PROCESSES.

ABSTRACT: Surveys often ask respondents to report non-negative 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 hierarch

SUBMITTER: Crawford FW 

PROVIDER: S-EPMC4617556 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature