Nonresponse bias when estimating victimization rates: A nonresponse analysis using latent class analysis

The study expands empirical knowledge on nonresponse bias when estimating victimization rates by using latent class analysis (LCA). Based on information about proxy-nonrespondents (hard-to-reach respondents and soft refusals), the study identifies subgroup(s) of persons who are systematically underr...

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Bibliographic Details
Main Author: Leitgöb-Guzy, Nathalie 1982- (Author)
Format: Electronic Article
Language:English
Published: 2022
In: International review of victimology
Year: 2022, Volume: 28, Issue: 1, Pages: 109-133
Online Access: Volltext (lizenzpflichtig)
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Keywords:
Proxy-nonrespondents
Latent Class Analysis
Victimization rates
Nonresponse analysis
nonresponse bias
Victimization Surveys

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520 |a The study expands empirical knowledge on nonresponse bias when estimating victimization rates by using latent class analysis (LCA). Based on information about proxy-nonrespondents (hard-to-reach respondents and soft refusals), the study identifies subgroup(s) of persons who are systematically underrepresented by refusal and unreachability and determines whether an over- or underestimation of different offense-specific crime rates (prevalence and incidence rates) is to be expected. Therefore, a broad review of the current state of research is carried out, followed by a nonresponse analysis of a large-scale victimization survey conducted in Germany (n = 35,503). The paper illustrates that a variety of factors must be considered when analyzing nonresponse in victimization surveys and that the current state of research does not allow definitive conclusions about the amount and direction of nonresponse bias. The following analysis shows that LCA constitutes an excellent approach to determine nonresponse bias in surveys. In each sample, one class of person was identified that is systematically underrepresented, both by refusal and unreachability. Here, victimization rates of violent crime tend to be significantly higher, indicating an underestimation of crime rates. 
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