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Data Missingness Patterns in Homicide Datasets: An Applied Test on a Primary Data Set.
Violence and Victims ( IF 1.317 ) Pub Date : 2020-08-01 , DOI: 10.1891/vv-d-17-00189
Melanie-Angela Neuilly 1 , Ming-Li Hsieh 2 , Alex Kigerl 3 , Zachary K Hamilton 4
Affiliation  

Research on homicide missing data conventionally posits a Missing At Random pattern despite the relationship between missing data and clearance. The latter, however, cannot be satisfactorily modeled using variables traditionally available in homicide datasets. For this reason, it has been argued that missingness in homicide data follows a Nonignorable pattern instead. Hence, the use of multiple imputation strategies as recommended in the field for ignorable patterns would thus pose a threat to the validity of results obtained in such a way. This study examines missing data mechanisms by using a set of primary data collected in New Jersey. After comparing Listwise Deletion, Multiple Imputation, Propensity Score Matching, and Log-Multiplicative Association Models, our findings underscore that data in homicide datasets are indeed Missing Not At Random.

中文翻译:

凶杀数据集中的数据缺失模式:对主要数据集的应用测试。

尽管存在遗失数据与清除率之间的关系,但对凶杀案遗失数据的研究通常会假定“随机遗失”模式。但是,使用凶杀数据集中传统上可用的变量无法令人满意地对后者进行建模。因此,有人认为凶杀数据中的缺失遵循了不可忽略的模式。因此,如本领域所推荐的针对可忽略模式的多种插补策略的使用将对以这种方式获得的结果的有效性构成威胁。本研究通过使用新泽西州收集的一组主要数据来检验缺失的数据机制。在比较按列表删除,多重插补,倾向得分匹配和对数乘法关联模型后,
更新日期:2020-08-01
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