JSM 2005 - Toronto

Abstract #303450

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 393
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract - #303450
Title: Bayesian Models To Adjust for Response Bias in Survey Data: An Example in Estimating Rape and Domestic Violence from the NCVS
Author(s): Qingzhao Yu*+ and Elizabeth A. Stasny
Companies: The Ohio State University and The Ohio State University
Address: 1958 Neil Avenue, Columbus, OH, 43210,
Keywords: EB Algorithm ; Incompletely Classified Data ; Panel Survey ; Survey Mode
Abstract:

It is difficult to estimate accurately the rates of rape and domestic violence due to the sensitive nature of the crimes. There is evidence that biases in estimating crime rates from survey data may arise because some women respondents are "gagged" in reporting crimes by the use of a telephone rather than a personal interview and by the presence of a spouse during the interview. On the other hand, as some data on these crimes are collected through repeated surveys, it would be more efficient in data analysis if we could make use of information from previous data. In this paper, we propose a pseudo-Bayesian model to adjust estimates of rape and domestic violence rates to account for the response bias due to the "gag" factors, using the previous data as prior information. The strength of the Bayesian model is its ability to combine information from long observational records in a sensible way. A Bayesian EM algorithm is applied for computation, and our approach is illustrated using the yearly crime data from the National Crime Victimization Survey.

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