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Updated: Dec 12, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Data Missingness Patterns in Homicide Datasets: An Applied Test on a Primary Data Set.

Melanie-Angela Neuilly1, Ming-Li Hsieh2, Alex Kigerl3

  • 1Department of Criminal Justice and Criminology, Washington State University, Pullman, Washington.

Violence and Victims
|August 14, 2020
PubMed
Summary
This summary is machine-generated.

Homicide data missingness is often assumed to be random. However, this study reveals that homicide data is actually Missing Not At Random, challenging standard analytical methods.

Keywords:
clearancehomicidesmissing datamissingness assumptionsnonignorable

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Area of Science:

  • Criminology
  • Statistics
  • Data Science

Background:

  • Conventional research on homicide data assumes a Missing At Random (MAR) pattern.
  • This assumption is challenged by the unmodeled relationship between missing data and case clearance.
  • Homicide clearance data often lacks variables necessary for satisfactory modeling under MAR.

Purpose of the Study:

  • To examine the missing data mechanisms in homicide datasets.
  • To compare the performance of different statistical methods in handling missing data.
  • To determine if homicide data exhibits a Missing Not At Random (MNAR) pattern.

Main Methods:

  • Utilized primary data collected in New Jersey.
  • Compared Listwise Deletion, Multiple Imputation, Propensity Score Matching, and Log-Multiplicative Association Models.
  • Analyzed missing data patterns in relation to homicide clearance.

Main Results:

  • Findings indicate that homicide datasets exhibit a Missing Not At Random (MNAR) pattern.
  • Standard multiple imputation strategies may threaten the validity of results for ignorable patterns.
  • The relationship between missing data and clearance is a key factor suggesting non-ignorability.

Conclusions:

  • Homicide data missingness is best characterized as Missing Not At Random (MNAR).
  • The assumption of MAR in homicide data analysis is likely invalid.
  • Researchers should reconsider imputation strategies for homicide datasets to ensure valid results.