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Activity Number: 294 - SPEED: Statistics in Social Sciences and Survey Research Part 2
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 11:15 AM
Sponsor: Survey Research Methods Section
Abstract #323804
Title: WITHDRAWN Demographic Edit and Imputation for Multilevel Data from the FBI’s National Incident Based Reporting System
Author(s): Philip Lee and Amang Sukasih and Dan Liao and Marcus Berzofsky and Alexia Cooper
Companies: RTI and RTI and RTI and RTI International and RTI
Keywords: Missing Data; Administrative Data; Crime Statistics; Hot Deck; Predictive Mean Matching
Abstract:

The National Incident-Based Reporting System (NIBRS) is used by law enforcement agencies for collecting and reporting a variety of data on each crime incident known to the police. With over 8,000 agencies reporting millions of crimes each year, this is a big administrative data set with over 40 tables. The various tables can be joined together to produce very granular and specific estimates, such as victim, offender, arrestee, and incident characteristics. However, there are missing values or reported “unknown” values as responses to some data items. Assuming missing at random, in this presentation we treat missing data through editing and imputation procedures including a hot deck imputation approach. In particular, we employ the Multivariate Imputation by Chained Equations (MICE), provided in the R “MICE” package, to impute missing demographic variables such as age, gender, and race. We will present the computational process and its modeling challenges, when dealing with a big data set with a multilevel data structure. The proposed methodology can be tailored for other administrative data or survey data collected from establishments.


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