Average crime clearance rates have remained remarkably stable in the United States since the 1980s, despite many advances in investigative technologies or fluctuations in crime. Taking these average trends at face value, some have suggested that this stability indicates that police departments can do little to alter their clearance rates. However, in this study, we find that the average trends mask substantial long-term variation in crime clearance among police agencies. Using group-based trajectory modeling, we test whether large U.S. police departments have reported uniquely different long-term clearance rate trends from 1981 to 2013 and what organizational factors might contribute to different trends. As we discuss, this method has attractive qualities that provide for a more rigorous analysis compared with past comparative work. Our results show diverse levels and patterns of clearance both within individual crime types and across multiple crime types that appear to covary with organizational factors. We explain how finite mixture modeling can advance both quantitative and qualitative research by identifying departmental differences in performance for further study.

中文翻译：

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警察机构之间的犯罪清除变率

自1980年代以来，尽管侦查技术取得了许多进步或犯罪有起伏，但平均犯罪清除率仍在美国保持了稳定。考虑到这些平均趋势的面值，有人认为这种稳定性表明警察部门几乎无济于事改变他们的通关率。但是，在这项研究中，我们发现平均趋势掩盖了警察机构之间犯罪清除的长期长期差异。使用基于群体的轨迹模型，我们测试了美国大型警察部门在1981年至2013年之间是否报告了独特的长期清除率趋势，以及哪些组织因素可能导致了不同趋势。正如我们所讨论的那样，与过去的比较工作相比，该方法具有吸引人的品质，可提供更严格的分析。我们的结果表明，在个人犯罪类型内以及在与组织因素共同作用的多种犯罪类型中，清除的级别和方式不同。我们通过确定部门绩效差异来进一步研究，说明有限混合模型如何促进定量和定性研究。