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.

在使用潜在类别分析 (LCA) 估计受害率时，该研究扩展了关于无反应偏差的经验知识。根据有关代理未答复者（难以接触的答复者和软拒绝）的信息，该研究确定了因拒绝和无法到达而系统性不足的人群，并确定是否高估或低估了不同的特定犯罪率（流行率和发病率）是可以预期的。因此，对当前研究状态进行了广泛审查，然后对在德国进行的大规模受害调查（n = 35,503）进行了无反应分析。该论文说明，在受害调查中分析不答复时必须考虑多种因素，并且目前的研究状态不允许对不答复偏差的数量和方向做出明确的结论。以下分析表明，LCA 是确定调查中的无响应偏差的绝佳方法。在每个样本中，确定了一类人被系统地代表性不足，包括拒绝和无法访问。在这里，暴力犯罪的受害率往往要高得多，表明对犯罪率的低估。确定了一类人的代表性不足，包括拒绝和无法访问。在这里，暴力犯罪的受害率往往要高得多，表明对犯罪率的低估。确定了一类人的代表性不足，包括拒绝和无法访问。在这里，暴力犯罪的受害率往往要高得多，表明对犯罪率的低估。