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

Abstract

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.