Data Missingness Patterns in Homicide Datasets: An Applied Test on a Primary Data Set.

Autor:	Neuilly MA; Department of Criminal Justice and Criminology, Washington State University, Pullman, Washington., Hsieh ML; Department of Political Science, University of Wisconsin-Eau Claire, Eau Claire, Wisconsin hsiehm@uwec.edu., Kigerl A; Washington State Institute for Criminal Justice, Washington State University, Pullman, Washington., Hamilton ZK; Department of Criminal Justice & Criminology, Washington State University, Spokane, Washington.
Jazyk:	angličtina
Zdroj:	Violence and victims [Violence Vict] 2020 Aug 01; Vol. 35 (4), pp. 589-614.
DOI:	10.1891/VV-D-17-00189
Abstrakt:	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. (© Copyright 2020 Springer Publishing Company, LLC.)
Databáze:	MEDLINE
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