The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource: 

Document Title: Cases Associated with Violence in the 

National Missing and Unidentified Persons 

System (NamUs): The Examination of 

Circumstances & Characteristics Project

Author(s): Steven P. Hafner, Sc.D., B.J. Spamer, 

M.F.S., Bruce Budowle, Ph.D.

Document Number: 301932

Date Received: August 2021 

Award Number: 2016-MU-BX-K007

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice.
Cases Associated with Violence in the 

National Missing and 

Unidentified Persons System (NamUs)

The Examination of Circumstances & Characteristics Project

Submitted December 2020

University of North Texas Health Science Center

3500 Camp Bowie Blvd

Fort Worth, Texas 76107-2699

Authors

Steven P. Hafner, ScD, Research Scientist1

B.J. Spamer, MFS, Executive Director, NamUs Operations2

Bruce Budowle, PhD, Professor and Executive Director, University of North Texas 

Center for Human Identification3

This project was supported by Award No. 2016-MU-BX-K007, awarded by the National 

Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, 

findings, and conclusions or recommendations expressed in this document are those of the 

authors and do not necessarily reflect those of the Department of Justice. 

 

1 Steven.Hafner@unthsc.edu

2 BJ.Spamer@unthsc.edu

3 Bruce.Budowle@unthsc.edu

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Table of Contents

Executive Summary...........................................................................................1 

I. Introduction...................................................................................................3 

II. Missing Persons.............................................................................................7 

 Background............................................................................................7 

 Methods..................................................................................................8 

 Results................................................................................................14 

Recommendations....................................................................................31 

 Conclusion...........................................................................................33 

III. Unidentified Persons....................................................................................34 

 Background..........................................................................................34 

 Methods................................................................................................35 

 Results................................................................................................41 

 Recommendations....................................................................................52 

 Conclusion...........................................................................................54 

IV. Special Section: Missing and Unidentified Indigenous Persons..................................55 

 Background..........................................................................................55 

 Methods................................................................................................56 

 Results: MP..........................................................................................56 

 Results: UP..........................................................................................72 

 Recommendations....................................................................................72 

 Conclusion...........................................................................................72 

V. General Conclusion.......................................................................................73 

VI. References................................................................................................75 

Appendix A - Technical Note................................................................................78

Appendix B - Final Classification Keywords.............................................................90 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
1

NamUs Examination of Circumstances and Characteristics Study

Executive Summary

This report details the approach, findings, and recommendations of a systems improvement and 

expansion project for a national public safety program – the National Missing and Unidentified 

Persons System (NamUs). Based on analyses of data from the NamUs database, 

recommendations are made that could be implemented to improve response to and resolution of 

cases within the database in which violence is a contributing factor. 

The National Missing & Unidentified Persons System (NamUs)

NamUs is an asset of the U.S. Department of Justice and serves as a centralized database and 

resource center for cases of missing, unidentified, and unclaimed persons. NamUs offers a 

technological component – the database located at www.NamUs.gov – that can be used by law 

enforcement, medical examiners/coroners, allied professionals, and the public to manage, track, 

and resolve cases. Additionally, NamUs offers or provides funding support for various services, 

such as forensic services (DNA, fingerprints, forensic odontology), analytical services, and 

victim services to assist in the resolution of cases and to help family, friends, and other persons 

affiliated with a case in coping with stress faced by such persons in the wake of a missing, 

unidentified, and/or unclaimed persons event. 

Violence Against Persons in NamUs

This report includes six sections, a technical appendix, and an appendix of classification 

keywords. Part I provides a general introduction to the NamUs program, an overview of 

violence and victimization in the United States, and a description of the nexus between NamUs 

cases and violence and victimization. 

 Part II of this report focuses on missing persons (MP) cases within the database. Data 

from 34,507 resolved (archived) and unresolved (published) cases were used to complete a 

qualitative text mining case classification process to classify cases as violent or non-violent using 

keywords indicative of violence (e.g., fight, hit). Then, wider case information was used to 

complete a descriptive analysis comparing violent and non-violent cases across various case 

characteristics. This analysis was stratified by gender of the case (male, female). Key findings 

and corollary recommendations are presented with nine recommendations arising from five key 

findings outlined within this section. 

Part III of this report focuses on unidentified persons (UP) cases within the database. 

Data from 15,841 resolved (archived) and unresolved (published) cases were used to classify 

cases as violent or non-violent based both on manner of death (MOD) and qualitative text 

mining. All cases with a MOD as homicide listed were classified as violent, as well as additional 

cases uncovered through the text mining using keywords indicative of case violence. Then, 

broader case information was used to descriptively analyze the violent versus non-violent cases 

by decedent gender to identify any differences between violent and non-violent cases based on 

select case characteristics. Key results and associated recommendations are presented with nine 

recommendations arising from five key findings. 

Part IV of this report is a dedicated special section to violence against American Indian 

and Alaska Native (AI/AN) women and men within the database. Similar procedures as used in 

parts II and III of the report were used to complete this section. Overall, there were only 792 

AI/AN MP cases and 159 UP cases. Although MP results are presented, most results involve at 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
2

NamUs Examination of Circumstances and Characteristics Study

least one cell with a count of less than ten persons. Results for UP cases are not presented due to 

the lack of robust numbers of cases on which to make comparisons and conclusions. Thus, no 

recommendations are made for systems improvement and expansion within this section. Should 

more AI/AN cases populate the database, a more extensive analysis with attendant 

recommendations may be completed.

 Finally, Part V of this report provides a general conclusion to the work presented. 

Additionally, important limitations to the report overall are outlined for consideration. Part VI 

presents references used throughout the report. The appendices to this report provide greater 

context and information on the design and implementation of this project. Appendix A presents 

a technical note that provides more detail as to the classification and analysis processes for both 

MP and UP cases. Appendix B provides the keyword classification dictionaries used for the 

classification section of the respective sections.

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
3

NamUs Examination of Circumstances and Characteristics Study

I. Introduction

The National Missing & Unidentified Persons System

The National Missing and Unidentified Persons System (NamUs) is a U.S. Department of Justice 

asset funded and administered by the National Institute of Justice (NIJ). NIJ is one of two 

science agencies housed within the Office of Justice Programs. NamUs is managed through a 

cooperative agreement with the University of North Texas (UNT) Health Science Center’s UNT 

Center for Human Identification (UNTCHI). NamUs brings together law enforcement, medical 

examiners, coroners, forensic experts, families, and the public to help resolve missing, 

unidentified, and unclaimed person cases throughout the nation – what has been called “the 

nation’s silent mass disaster” (Ritter, 2007). The NamUs database provides criminal justice users 

a secure, online system to store, share, and compare sensitive case information, and public access

allows family members and other public stakeholders a mechanism to report cases and 

participate in the search for potential leads. As of October 25, 2020, the NamUs database 

contained 19,501 published missing person (MP) cases, 13,486 published unidentified person 

(UP) cases, and 10,011 cases of unclaimed decedents who have been identified by name, but 

whose next of kin have not been located for death notification. A published case is a case that 

has been entered by the public, law enforcement, a medicolegal authority, or some other 

affiliated personnel and that has been vetted as a legitimate case that can be made publicly 

available on the NamUs website.

Case Entry and Search

Missing person cases are entered into NamUs by both public and professional (i.e., law 

enforcement) users, while UP cases are entered only by medical examiners/coroners (ME/Cs), or 

their designees. Before being published for public viewing and searching, MP cases entered into 

NamUs must be reported to law enforcement and that investigating agency must validate and 

give NamUs permission to publish the case. Although there is an unclaimed persons database 

that is part of NamUs, it was not used for this project. 

All active cases that have been vetted (e.g., have an active MP report) and published can 

be searched at www.NamUs.gov by any registered user or non-registered visitor. Information 

displayed to the public is limited, as there are data fields that are restricted from public view if 

deemed sensitive to a case investigation or to a MP. The public search component of NamUs is a 

defining feature of the database that allows for stakeholders from multiple fields to assist in the 

resolution of cases. 

NamUs Services

As a national resource center for MP and UP cases, NamUs provides forensic services to assist 

with case resolutions, including forensic odontology and fingerprint examination, and leverages 

DNA analyses and anthropological services performed by subject matter experts within the 

UNTCHI forensic laboratories. A team of Regional Program Specialists (RPSs) serves as a force 

multiplier for criminal justice agencies across the country, providing investigative support and 

guidance to assist with case investigations. RPSs also directly support families of missing 

persons by connecting families to investigating agencies to file missing person reports, 

facilitating reference sample collections for DNA analyses, and participating in missing person 

events across the country to intake new cases and augment existing NamUs case files. Finally, 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
4

NamUs Examination of Circumstances and Characteristics Study

the NamUs Victim Services Division addresses the needs of family, friends, and others impacted 

by the death or disappearance of a loved one by facilitating trauma-informed therapies, a peer 

support network, reunifications, and referrals for other services. 

NamUs Impact

By connecting people, information, forensic science, and technology, NamUs supports families 

and provides medical examiners, coroners, and criminal justice professionals with access to the 

investigative and scientific tools needed to find missing persons, help identify decedents and 

victims of crime, assist in resolving criminal cases, and reduce violent crime and human 

trafficking. 

A Nexus with Violence and Victimization

Violence and Victimization

Globally, violence is a persistent phenomenon that affects both women, men, and childrenas seen 

in both public health and criminal justice data. According to the 2010 National Intimate Partner 

and Sexual Violence Survey (NISVS), 9.4% of women and 2.2% of men had been raped by an 

intimate partner in their lifetime while 16.9% of women and 8.0% of men have experienced 

sexual violence other than rape by an intimate partner. 24.3% of women and 13.8% of men have

experienced severe physical violence by an intimate partner, 10.7% of women and 2.1% of men 

have been stalked by an intimate partner, and 48.4% of women and 48.8% of men have 

experienced psychological aggression by an intimate partner (Breiding, Chen, & Black, 2014). 

Using 2011 data from NISVS, Breiding and colleagues (2014) found that 19.3% of women and 

1.7% of men have been raped, 43.9% of women and 23.4% of men have experienced other forms 

of sexual violence, 15.2% of women and 5.7% of men have experienced stalking, and 22.3% of 

women and 14.0% of men have experienced severe physical violence by an intimate partner in 

their lifetime. Finally, and most recently, the 2015 NISVS found that 21.3% of women and 2.6% 

of men experienced completed or attempted forced penetration in their lifetime, and 16.0% of 

females and 5.8% of men experienced stalking at some point in their lifetime. Regarding 

intimate partner violence,18.3% of women and 8.2% of men experienced contact sexual violence 

by an intimate partner, 30.6% of women and 31.0% of men experienced physical violence by an 

intimate partner, 10.4% of women and 2.2% of men experienced stalking by an intimate partner, 

and 36.4% of women and 34.2% of men experienced psychological aggression by an intimate 

partner in their lifetime (Smith et al., 2018) 

Additionally, according to the Centers for Disease Control and Prevention’s (CDC) Webbased Injury Statistics Query and Reporting System (WISQARS), in 2018, homicide was the 

fourth leading cause of death for females ages one to four and five to nine, the fifth leading cause 

of death for females ages 10-14 and 25-34, the third leading cause of death for females 15-24, 

and the eighth leading cause of death for females 35-44. For males, homicide was the second 

leading cause of death for ages one to four, the fourth leading cause of death for ages five to nine 

and 10-14, the third leading cause of death for ages 15-24 and 25-34, the fifth leading cause of 

death for ages 35-44, and the ninth leading cause of death for ages 45-54 (National Center for 

Injury Prevention and Control, 2020). Furthermore, for women, as many as 38% of murders of 

women are committed by an intimate partner globally (World Health Organization, 2016). In 

cases where the victim’s relationship to the offender was known, 93% of murdered women were 

killed by a male they knew, with 63% of those victims being the wife or intimate partner of the 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
5

NamUs Examination of Circumstances and Characteristics Study

perpetrator (Violence Policy Center, 2016). Additionally, according to the Federal Bureau of 

Investigation’s (FBI) National Incident-based Reporting System, males were far more likely to 

be perpetrators of violent crime (79%) and more likely to be victims of violent crime (51%) 

(Crime Data Explorer, 2019). Similarly, the FBI’s Supplemental Homicide Report for 2018 

shows 12,495 males were victims of homicide compared to 3,686 females, and that the gender of 

the oldest offender was more often male (n = 12,034) than female (n = 1,711) (Puzzanchera, 

Chamberlin, & Kang, 2020).

Capturing Violence in NamUs Cases

For MP cases, NamUs does include built-in functionality for case entrants to indicate if some 

type of nefarious activity was related to the case. This functionality is operationalized through 

the inclusion of a “foul play” indicator that may be checked by the case entrant, NamUs staff, or 

investigative personnel (if linked as a Case Contributor) if it is believed that violence or coercion 

may have played a role in the missing persons case. For UP cases, foul play would be 

represented by including a MOD as “homicide” by the case entrant. It is important to note, 

however, that there may be wide variation in the use of the “foul play” indicator depending on if 

a case is an ongoing investigation, depending on the particular case entrant, who may always or 

never check the “foul play” box, or for other reasons related to case investigation. 

As of October 25, 2020, of the unidentified decedent cases reported to NamUs, 2,691 of 

the 18,018 active and archived cases were reported to be victims of homicide. Another 7,116 of 

all published and archived cases were reported to have an “undetermined” MOD at the time of 

entry into NamUs; it is possible that many of these cases may contain circumstances or cause of 

death information which suggest a homicidal MOD is probable. Additionally, of the 42,432 

active and archived MP cases in NamUs, 3,106 indicated that foul play was involved, and 776 

indicated that foul play was possible.

Phase 1: Examination of Circumstances and Characteristics (ECC) Project

Purpose of the Project

There is a clear overlap with the serious problem of violence with a portion of the cases in 

NamUs. Although NamUs currently focuses on all MP and UP cases, there are good reasons to 

address the relationship and impact of violence within the subject populations. First, given its 

current mission and functionalities, NamUs potentially could expand its role to provide services, 

information, and guidance to men and women and their families who have been subjected to 

violence. Additionally, information gleaned from this project could be used by personnel and 

agencies to assist in the investigation and resolution of cases involving violence by providing key 

context and information specific to violent cases that could support an investigation. 

To improve and expand the NamUs program, including its services, information, and 

guidance to persons of all ages and their families, a clear understanding of case characteristics 

specific to missing and unidentified cases is critical. Through comparison with cases involving 

either men or women, such characteristics may be identified and may be used to create targeted 

outreach and programmatic activities for women and men, including their families. Given that 

NamUs includes information on both women and men, it is uniquely situated as a data source to 

provide the level and type of comparisons necessary for comprehensive and in-depth 

investigation that will yield results that potentially can be put into practice. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
6

NamUs Examination of Circumstances and Characteristics Study

Goals of the Project

Although there is a nexus between cases in NamUs and the issues of violence and victimization,

there has been no comprehensive assessment 1) to determine the extent to which cases in the 

NamUs system may involve violence or how such violence may differ by gender, 2) to ascertain 

case characteristics (e.g., perpetrator identified, victim-perpetrator relationship, etc.) for both MP 

and UP cases, 3) to determine criminal justice involvement (e.g., arrest, prosecution) of 

identified perpetrators, and 4) to make recommendations for system improvement, enhanced data 

collection, and specific services for MP and UP cases involving violence. Because of the 

outreach that NamUs has, there is a need to assess the current role the program serves with 

violence against persons, and more importantly, develop and enhance NamUs to better serve the 

victims of violence against persons. 

Specific Aims

To achieve its overarching goals, this phase of the ECC project has two specific aims:

1. To classify MP and UP cases as violent or non-violent through the development and 

implementation of a qualitative, automated case classification method. 

2. To determine if differences, including gender-specific differences, between violent and 

non-violent cases exist across case characteristics.

Project Approach and Outcomes

To achieve the aims of this project, a novel strategy using text mining and case classification was

used. These methods were used for MP and UP cases separately and did not include unclaimed 

persons. In the first phase of this project, the project sought to ascertain the extent to which 

violence was present in active and archived cases within the NamUs system, including the 

category of violence and how violent cases may differ across characteristics of the case (e.g., 

demographics, etc.). This effort was undertaken in order to provide a more comprehensive 

picture of the extent of violence, types of violence, unique needs that may impact victims or 

perpetrators using violence, and how they may impact system and service improvements\. 

Phase 2 of this project will ascertain criminal justice involvement within violent cases, as 

well as make recommendations for systems improvement. Given the aims and methods of this 

phase of the project are related to systems improvement and expansion, it was deemed Not 

Human Subjects Research by the North Texas Regional Institutional Review Board.

In fulfilling these aims, UNTCHI will be able to make recommendations for systems 

improvement and expansion to include enhanced and more relevant data collection and services 

that could benefit the NamUs program as it intersects with case violence. Improvements may 

include new or expanded data fields for more comprehensive data collection and 

recommendations to the various service divisions (e.g., victim services) to improve service 

provision to violent cases, which may differ from the requirements of non-violent cases. 

Overall, this project will benefit the NamUs program, its users, and the MP and UP who populate

the system through enhanced understanding of how violence plays a role in case data collection, 

management, and resolution. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
7

NamUs Examination of Circumstances and Characteristics Study

II. Missing Persons

Background 

Each year, over 600,000 persons are reported missing in the United States (FBI, 2020). As of 

October 25, 2020, there were 19,501 active MP cases within the NamUs database. Fortunately, 

most MPs are recovered alive in a short period of time, a fact evidenced by MP statistics from 

the FBI’s National Crime Information Center for 2019. These statistics show that there were 

roughly 609,000 MP entries into the system with 87,438 cases active as of December 31, 2019. 

Over the course of the year, 607,104 cases were purged from the system due to the MP being 

located, the MP voluntarily returning home, or the record being found invalid (FBI, 2020). Thus, 

long-term (i.e., one year or longer) MP events occur infrequently as a proportion of the total 

number of MP cases filed each year; however, it remains unclear as to the extent to which 

violence contributes to case duration and clearance. 

While many cases represent voluntary disappearances, misunderstandings regarding 

whereabouts, miscommunications, or some other benign factor that has led a person or people to 

believe another person is missing involuntarily, some cases do involve violence victimization or 

perpetration. Bonny and colleagues (YEAR) classified MP events into more formal categories

reflecting these types of events. In their classification, MPs are considered missing due to 

dysfunction (e.g., mental health issue), to escape (e.g., leave due to violent relationship), or are 

unintentionally missing (e.g., missing under the influence of a third party). In an Australian 

study, Henderson and colleagues (2000) outlined four possible reasons for a MP event: 

independence or rebellion (e.g., conflict over parental authority), safety concerns (e.g., 

abduction, accident), unintentional (e.g., miscommunication), and escaping adverse consequence 

(e.g., leaving a violent environment). In each typology, a specific category for cases that may 

involve violence experienced or perpetrated by the MP is given, indicating that such cases are 

prevalent and contribute to the overall burden of MP cases. 

Violence and Missing Persons

Although it is not entirely clear how many MP cases involve foul play, as seen in the typologies 

of missing persons, there are instances in which foul play or violence may be involved. As of 

October 25, 2020, 2,483 of the 19,501 active NamUs MP cases (12.7%) indicated that foul play 

may have been a factor in the person’s disappearance. However, the true extent to which 

violence or nefarious circumstances contribute to MP cases as a whole is little understood. 

Additionally, the types of violence that may contribute to MP cases is understudied. While it 

may be believed that physical violence is most common in such instances, other forms of 

violence, such as psychological aggression (e.g., arguments), may also contribute to the 

disappearances in particular cases of voluntary MP events. 

The current project sought to better understand the burden of violence in MP cases within 

a national database. To do so, it relied on case file information from active and archived cases 

within a database. Violence was defined broadly to include both physical and non-physical 

aggression that may result in physical, psychological, emotional, or other harm to an individual. 

In completing this study, a broader, more nuanced picture as to the extent of violence within 

cases within the database was elicited, as well as a better understanding as to how violence may 

be related to other characteristics of a case.

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
8

NamUs Examination of Circumstances and Characteristics Study

Methods 

This section describes briefly the methods used to complete case classification and case analysis. 

A more detailed treatment of methods, including more nuanced information on dictionary 

building, misspellings, coding, and reporting is provided in Appendix A – Technical Note. 

Project Data

Data for this project came from the MP sections of the NamUs database. Data include all 

published and archived cases as of the pull date of August 29, 2019. Data were pulled by the 

Senior Business Intelligence Developer and delivered to the Project Director using the secure, 

password-protected sharing service OneDrive. Data were delivered as a single Microsoft Excel 

file with three separate sheets. These sheets included Case Information that provided 

demographic and case details on the missing person; Circumstances that provided text entries for 

general circumstance information available for public view, Agency Notes available only for 

professional users, and Circumstances of Resolution that provided information on the outcome of 

a resolved case; and Agency Information that provided details as to the entering and contributing 

parties on the case. These three sheets were saved as separate Microsoft Excel files. Data were 

maintained on an encrypted, password-protected, UNTHSC-provided computer. 

Case Classification

Initial Classification Dictionary

To classify cases as violent or non-violent, a classification dictionary was created in WordStat 

8.0.20 (Provalis Research; Montreal, Quebec, Canada) that included keywords relevant to MP

cases. See Appendix B – Final Classification Keywords for a list of keywords used to classify 

cases as violent. This dictionary was developed collaboratively by the project team in order to 

ensure the best coverage and most exhaustive list of potential keywords that were relevant to the 

classification of cases as violent or non-violent. The dictionary for this project was also built 

using several sources, including keywords identified by project staff, through thesauri both 

online and within the software, and from various external sources, including the CDC’s National 

Intimate Partner and Sexual Violence Survey, the Conflict Tactics Scale-2 (Straus et al., 1996), 

and CDC’s Youth Risk Factor Behavioral Surveillance System and Behavioral Risk Factor 

Surveillance System. In total, 100 words were initially identified and included in the dictionary 

under six categories: general violence (40 words), psychological aggression (13 words), physical 

violence (30 words), coercive control (7 words), sexual violence (4 words), and stalking (6 

words). Amendments to the initial dictionary were based on classification outcomes. The final 

dictionary shown in Appendix B includes 124 words that were identified and included in the 

final dictionary under six categories: general violence (46 words), psychological aggression (17 

words), physical violence (40 words), coercive control (10 words), sexual violence (5 words), 

and stalking (6 words). 

Classification Runs

In total, there were three separate classification runs for MP cases. The order of steps and 

overall approach to these runs varied slightly and were based on lessons learned from the 

previous runs. 

General Classification Procedures. MP case classification involved three runs of the 

data following the same general procedures and was carried out using WordStat. More detailed 

information on each classification run can be found in Appendix A. First, data were delivered 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
9

NamUs Examination of Circumstances and Characteristics Study

and assembled to include all relevant circumstantial text fields – Circumstances Text, Agency 

Notes, and Circumstances of Resolution Text. Then, data were cleaned by removing any cases 

that did not have any information in any of the circumstance fields, that only indicated some 

form of “unknown,” or that contained only numbers or symbols. Cleaned data were then 

imported into WordStat for classification. Several classifications were tested, including with and 

without an exclusion list based on lessons learned from previous runs of the data and included 

keywords that created false positives, which did not indicate true violence. In Run 2, a 

misspellings check was added to the procedures. This check was completed using WordStat’s 

built-in functionality and provided a list of potential misspellings. Each potential misspelling was 

checked using a “keyword-in-context” feature to ensure the misspelling truly represented a 

misspelled word, a proper noun, or some otherwise correct spelling for a different word. The 

final run of the data included use of the exclusions list previously created, as well as corrected 

misspelled words. 

Following the final runs, data were exported from WordStat and imported into Stata. A 

random sample of 10% was drawn for second coding by a human coder (Hafner). Percent 

agreement was calculated between WordStat and the human coder. Following the calculation of 

percent agreement, a disagreements check was performed to understand discrepancies in coding 

between WordStat and the human coder. Through this process, and through review of the 

dictionary by co-investigator (Spamer), it was determined that there were additional words that 

needed to be added to the dictionary. As such, it was determined that second and third runs of 

the data would be necessary. In the third run of the data, computer-human agreement was found 

to be 88%. This level of agreement was found to be close to high, which Schweta, Bajpai, and 

Chaturvedi (2015) define as a percent agreement of 90% or above. 

In the third run, tertiary coding was completed by a second human coder (co-investigator 

Spamer) using a subsample of 20% of the 10% of cases originally sampled for second coding. 

Percent agreement was then calculated between the two human coders and was found to be 93%. 

This level of agreement is considered to be high (Schweta, Bajpai, & Chaturvedi, 2015). 

Final Violence Coding. Following tertiary coding, given the high levels of agreement 

between WordStat and the human coder, and the high level of agreement between the two human 

coders, a final coding of all cases classified as violent by WordStat was performed. In doing so, 

only cases classified as violent by WordStat were examined to ensure they truly represented 

violence and that the classification as violent was not spurious based on keywords being used in 

a different context (e.g., DNA “hit”). Overall, 7,659 individual cases classified as violent were 

manually read with 3,493 cases being retained as truly violent cases. Later, 86 cases ruled as 

homicides by ME/Cs but not classified as violent during the classification process and based on 

circumstantial information were manually added to the violent case count, bringing the total 

number of violent cases to 3,579.

Case Analysis 

Following completion of case classification, the data were prepared for analysis such that 

meaningful comparisons could be made between violent and non-violent cases for both female 

and male cases.

Final Dataset Creation 

To arrive at a final analytic dataset, all non-relevant data fields pertaining to the final coding 

moving forward were dropped. This action created final coded data that were imported into 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
10

NamUs Examination of Circumstances and Characteristics Study

Stata. In Stata, further data fields were removed that were not pertinent for the case analysis

(e.g., CircumstancesText), leaving a set of numerical indicator variables denoting the presence of 

violence. Given that cases could have more than one line per case of circumstantial information 

(e.g., Agency Notes), these numerical values had to be collapsed and summed to arrive at one 

line per case in the final dataset. Following this collapse, indicator variables were created and 

dichotomized, and data were checked for consistency. No immediate errors were uncovered. 

The resulting dataset was then merged on Case ID with the Case Information dataset saved 

initially when the data were received. This final dataset contained 34,410 cases; however, one 

case did not have any associated Case ID. This case was dropped resulting in 34,509 cases. 

Further, given that this analysis intended to look at results by gender, cases with no gender

indicated (n = 1) or with “unsure” gender (n = 1) were also dropped. These steps resulted in a 

final analytic dataset containing 34,507 unique cases. 

Variables

Variables used in the analysis stage of this project included violence variables, variables for all 

cases, variables for resolved cases only, and variables for unresolved cases only. Each set of 

variables is described below. 

Violence Variables. Violence variables (Table 1) included one indicator variable 

denoting if violence of any type was present within the case and six different types of violence. 

Table 1. Violence Variables

Variable Definition Coding N

Finalviolence Indicates whether or not any type of violence was 

present in a case

0 = No

1 = Yes

30,928

3,579

Total: 34,507

GV

(GENERAL_VIOLENCE)

Includes forms of violence that could not definitively 

be classified within other categories, such as physical 

or verbal violence. This type of violence includes 

terms such as “anger,” “altercation,” “dispute,” and 

“aggression”

0 = No

1 = Yes

32,840

1,667

Total: 34,507

PV

(PHYSICAL_VIOLENCE)

Includes forms of violence that cause bodily injury 

through person-to-person contact or through the use of 

a weapon. This type of violence includes keywords 

such as “hit,” “slap,” “injure,” “stab,” and “shoot”

0 = No

1 = Yes

32,045

2,462

Total 34,507

SV

(SEXUAL_VIOLENCE)

Includes violence that encompasses forced or coerced 

unwanted sexual activity. This type of violence 

includes terms such as “rape,” “molest,” and “sexual 

assault”

0 = No

1 = Yes

34,474

33

Total: 34,507

ST

(STALKING)

Includes violence that encompasses threatening, 

harassing, and unwanted behaviors that causes a 

person to have concerns or fear for safety. This type 

of violence includes terms such as “follow,” “spy,” 

and “unwanted communication” 

0 = No

1 = Yes

34,345

162

Total: 34,507

CC

(COERCIVE_CONTROL)

Includes violence that controls or threatens an 

individual’s actions, behaviors, or thoughts. This type 

0 = No

1 = Yes

34,252

255

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
11

NamUs Examination of Circumstances and Characteristics Study

of violence includes keywords “force,” “manipulate,” 

and “trafficking”

Total: 34,507

PA

(PSYCHOLOGICAL_AGGRES

SION)

Includes violence aimed at harming a person’s mental 

and emotional wellbeing, and includes terms such as 

“name call,” “humiliate,” “argue,” and “verbal 

disagreement”

0 = No

1 = Yes

34,018

489

Total: 34,507

All Case Variables. All case variables (Table 2) include information related to case 

demographics and general case characteristics. 

Table 2. All Case Variables

Variable Definition Coding N

Age_of_Case_groups2 Indicates period of time between date 

of last contact and present day

0 = <90 days

1 = 3-12 mo.

2 = 1-2 years

3 = 2-5 years

4 = 5-10 years

5 = 10-20 years

6 = 20+ years

7 = Unknown

339

1,715

2,535

6,941

9,590

6,180

7,194

13

Total: 34,507

StateName2 State where person in case went 

missing from (not necessarily state 

where missing person was found)

1 = Alabama

2 = Alaska

3 = Arizona

4 = Arkansas

5 = California

6 = Colorado

7 = Connecticut

8 = Delaware

9 = DC

10 = Florida

11 = Georgia

12 = Guam

13 = Hawaii

14 = Idaho

15 = Illinois

16 = Indiana

17 = Iowa

18 = Kansas

19 = Kentucky

20 = Louisiana

21 = Maine

22 = Maryland

23 = Massachusetts

24 = Michigan

25 = Minnesota

26 = Mississippi

27 = Missouri

28 = Montana

29 = Nebraska

30 = Nevada

31 = New Hampshire

453

456

1,568

529

4,710

1,370

802

93

107

2,650

712

3 

182

304

846

404

183

220

504

492

175

383

378

1,719

322

199

737

139

217

495

63

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
12

NamUs Examination of Circumstances and Characteristics Study

32 = New Jersey

33 = New Mexico

34 = New York

35 = North Carolina

36 = North Dakota

37 = Northern Mariana Islands

38 = Ohio

39 = Oklahoma

40 = Oregon

41 = Pennsylvania

42 = Puerto Rico

43 = Rhode Island

44 = South Carolina

45 = South Dakota

46 = Tennessee

47 = Texas

48 = Utah

49 = Vermont

50 = Virgin Islands

51 = Virginia

52 = Washington

53 = West Virginia

54 = Wisconsin

55 = Wyoming

761

372

1,204

676

86

2 

827

809

929

835

39

41

406

76

887

2,380

281

71

22

550

1,249

178

315

96

Total: 34,507

FoulPlay2 Marker used to indicate if nefarious, 

violent, or otherwise potentially 

criminal activity led to the 

disappearance of a missing person

0 = No

1 = Possibly

2 = Uncertain

3 = Yes

3,932

309

6,589

2,544

Total: 13,374

SexName2 Indicates biological gender of 

missing person as defined by 

reporting party

0 = Female

1 = Male

15,119

19,388

Total: 34,507

RaceEthnicity2 Indicates race/ethnicity of missing 

person as defined by reporting party

0 = White / Caucasian

1 = American Indian / Alaska 

Native

2 = Asian 

3 = Black / African American 

4 = Hawaiian / Pacific Islander

5 = Hispanic / Latino

6 = Other

7 = Uncertain

8 = 2+ Races

20,349

713

764

6,013

20

3,478

364

117

2,685

Total: 34,503

Age_Missing_Group2 Indicates age of missing person at 

time of missing event as defined by 

reporting party

0 = <18

1 = 18-20

2 = 21-30

3 = 31-40

4 = 41-50

5 = 50+

6 = Unknown

9,314

2,020

7,157

5,519

4,165

5,425

907

Total: 34,507

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
13

NamUs Examination of Circumstances and Characteristics Study

Resolved Cases Variables. Variables for resolved cases are shown in Table 3 and 

include information related to the case resolution information. 

Table 3. Resolved Case Variables (N = 18,051) 

Variable Definition Coding N

CaseResolutionStatus2 Indicates if the final disposition of resolved cases 

was with a deceased or alive person

0 = Deceased

1 = Alive

4,563

13,473

Total: 18,036

NamUsAssisted Indicator defined by professional user at case closeout to denote if NamUs was useful or an important 

factor in case resolution

0 = No

1 = Yes

15,593

2,357

Total: 17,950

MannerOfDeath2 Indicates manner of death for resolved cases in 

which the missing person was found deceased

1 = Accident

2 = Homicide

3 = Natural

4 = Pending

5 = Suicide

6 = Undetermined

55

124

25

113

59

232

Total: 608

Unresolved Cases Variables. For unresolved cases, variables included one variable

related to the presence or absence of DNA samples uploaded and completed as shown in Table 4. 

Table 4. Unresolved Case Variables (N = 16,456) 

Variable Definition Coding N

DNA_Indicator2 Indicates if at least one DNA sample 

analysis has been completed

0 = No

1 = Yes

7,502

8,954

Total: 16,456

Case Analysis 

First, an indicator variable was created to show if the case was resolved or unresolved. This 

variable was created using the lifecycle status of the case – whether the case was archived 

(resolved) or published (unresolved) at the time the data were drawn. Subsequent analysis of the 

data for variables for resolved and unresolved cases was completed using only those variables 

within each respective classification. Then, univariate descriptive statistics for each variable 

were completed followed by univariate statistics stratified by MP gender. Finally, gender

stratified (male, female) bivariate statistics comparing final case violence (yes, no) across levels 

of each variable were run in order to assess differences in each variable by the presence or 

absence of case violence for each case. All cases that had information on the variable of interest 

were included in the analysis. As such, the resulting tables to do not display that same number of 

observations. All analyses were carried out in Stata 14.2 (StataCorp.; College Station, TX). 

Results 

Overall, the data analysis included 34,507 cases. Demographically, 15,119 (43.8%) were 

female, and 19,388 (56.2%) were male. White/Caucasian individuals made up the majority of 

cases by race/ethnicity representing 59.0% of all cases. The majority of cases were individuals 

under the age of 18 (27.0%). Tables 1-32 represent gender stratified results for victimization, as 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
14

NamUs Examination of Circumstances and Characteristics Study

well as gender stratified bivariate comparisons between final case violence and case 

characteristics by case resolution status. Results marked with an asterisk (*) include at least one 

cell count of fewer than 10 cases.

Overall Violence (N =34,507) 

The percentage of females that experienced any form of violence was 12.7% (n = 1,916), while it 

was 8.6% (n = 1,663) for males (Figures 1 and 2). Thus, a total of 3,579 persons experienced 

violence, or 10.4% of all cases. 

12.7%

87.3%

Figure 1. Percent of Cases Involving Any Violence, Females 

(N = 15,119)

Violent Non-Violent

8.6%

91.4%

Figure2. Percent of Cases Involving Any Violence, Males 

(N = 19,388)

Violent Non-Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
15

NamUs Examination of Circumstances and Characteristics Study

General Violence (N = 34,507) 

As with overall violence, a higher proportion of females (6.1%, n = 919) than males (3.9%, n = 

748) experienced general violence (Figures 3 and 40. 

6.1%

93.9%

Figure 3. Percent of Cases Involving General Violence, 

Females (N = 15,119)

General Violence No General Violence

3.9%

96.1%

Figure 4. Percent of Cases Involving General Violence, 

Males (N = 19,388)

General Violence No General Violence

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
16

NamUs Examination of Circumstances and Characteristics Study

Physical Violence (N = 34,507) 

Among females, 8.4% (1,275) experienced physical violence, while 6.1% (n = 1,187) of males 

experienced physical violence (Figures 5 and 6). 

Sexual Violence* (N = 34,507) 

8.4%

91.6%

Figure 5. Percent of Cases Involving Physical Violence, 

Females (N = 15,119)

Physical Violence No Physical Violence

6.1%

93.9%

Figure 6. Percent of Cases Involving Physical Violence, 

Males (N = 19,388)

Physical Violence No Physical Violence

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
17

NamUs Examination of Circumstances and Characteristics Study

There were 0.2% (n = 27) females who experienced sexual violence (Figure 7), while 0.03% (n = 

6) of males experienced sexual violence (Figure 8).

Stalking Behavior (N = 34,507) 

Among females, 0.6% (n = 95) experienced some type of stalking behavior (Figure 9), while 

0.4% (n = 67) of males experienced stalking behavior (Figure 10).

0.2%

99.8%

Figure 7. Percent of Cases Involving Sexual Violence, 

Females (N = 15,119)

Sexual Violence No Sexual Violence

0.03%

99.97%

Figure 8. Percent of Cases Involving Sexual Violence, Males 

(N = 19,388)

Sexual Violence No

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
18

NamUs Examination of Circumstances and Characteristics Study

Coercive Control (N = 34,507) 

Among females, 1.4% (n = 204) experienced coercive control (Figure 11), while 0.3% (n = 51) 

of males did as well (Figure 12). 

0.6%

99.4%

Figure 9. Percent of Cases Involving Stalking Behavior, 

Females (N = 15,119)

Stalking Behavior No Stalking Behavior

0.4%

99.6%

Figure 10. Percent of Cases Involving Stalking Behavior, 

Males (N = 19,388)

Stalking Behavior No Stalking Behavior

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
19

NamUs Examination of Circumstances and Characteristics Study

Psychological Aggression (N = 34,507) 

For females, 1.8% (n = 269) experienced psychological aggression (Figure 1). For males, the 

proportion was lower at 1.1% (n = 220) (Figure 14). 

1.4%

98.6%

Figure 11. Percent of Cases Involving Coercive Control, 

Females (N = 15,119)

Coercive Control No Coercive Control

0.3%

99.7%

Figure 12. Percent of Cases Involving Coercive Control, 

Males (N = 19,388)

Coercive Control No Coercive Control

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
20

NamUs Examination of Circumstances and Characteristics Study

Age of Case (Groups) (N = 34,507)* 

Among female non-violent cases, the highest proportion of cases were 5-10 years old (29.8%, n 

= 3,936) while the highest proportion of violent cases were 20+ years old (41.2%, n = 790) 

(Figure 15). This trend was mirrored among male violent cases where 33.8% (n = 562) were 20+ 

years old, while among male non-violent cases, the highest proportion of cases were observed 

among 5-10 year old cases (28.1%, n = 4,971) (Figure 16). 

1.8%

98.2%

Figure13. Percent of Cases Involving Psychological 

Aggression, Females (N = 15,119)

Psychological Aggression No Psychological Aggression

1.1%

98.9%

Figure 14. Percent of Cases Involving Psychological 

Aggression, Males (N = 19,388)

Psychological Aggression No Psychological Aggression

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
21

NamUs Examination of Circumstances and Characteristics Study

State Name (N = 34,507) 

Figures 17 and 18 show the highest percentage of violent cases in absolute terms of case 

numbers (i.e., do not represent rates of violence), including the non-violent case percentage for 

comparison, by state. Among both females (Figure 17) and males (Figure 18) California had the 

highest percentage of all violent cases with 14.7% (n = 281) of all violent cases among females 

0.9

4.9

7.2

20.8

29.8

16.8

19.7

0.5 0.1

2.8

5

13.1

17.3

20.1

41.2

0

0

5

10

15

20

25

30

35

40

45

<90 Days 3-12 Months 1-2 Years 2-5 Years 5-10 Years 10-20 Years 20+ Years Unknown

Percentage

Case Age by Age Group

Figure 15. Comparison of Case Violence Across Age of Case 

Groups, Females (N = 15,119)

Non-Violent Violent

1.1

5.4

7.9

21.2

28.1

18 18.3

0.03 0.9

3.4

5.2

12.1

21.1

23.5

33.8

0.1

0

5

10

15

20

25

30

35

40

<90 Days 3-12 Months 1-2 Years 2-5 Years 5-10 Years 10-20 Years 20+ Years Unknown

Percentage

Case Age by Age Group

Figuree 16. Comparison of Case Violence Across Age of 

Case Groups, Males (N = 19,388)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
22

NamUs Examination of Circumstances and Characteristics Study

and 13.8% (n = 230) of all violent cases among males. These results may be due simply to the 

fact that the states represented enter more data than other states into the NamUs database.

Foul Play (N = 13,374) 

Among female non-violent cases, 50.9% (n = 2,306) indicated uncertainty if foul play was likely 

(Figure 19). However, among female violent cases, 65.3% (n = 637) indicated likely foul play. 

A high proportion of cases not classified as violent did indeed have the foul play indicator 

checked (16.7%, n = 758). aAmong male non-violent cases, 54.8% (n = 3,850) indicated 

13.5

8.7

6.4

5.4

3.3

4.2

3.2 2.5

3.5

14.7

8.8

6.9

5.9

4.3 4.1 3.7 3.3 2.9 2.7

0

2

4

6

8

10

12

14

16

Figure 17. Top States by Percent of All Violent Cases with 

Non-Violent Case Comparison, Females (N = 15,119)

% of All Non-Violent Cases % of All Violent Cases

13.6

6.8

5.4

7.4

4.6

3.8 3.7 3.6

2.2 2.6

13.8

7.2 7

6.2

5.2

4.3 4.2

3.3 2.7 2.7

0

2

4

6

8

10

12

14

16

Figure 18. Top States by Percent of All Violent Cases with 

Non-Violent Case Comparison, Males (N = 19,388)

% of All Non-Violent Cases % of All Violent Cases

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
23

NamUs Examination of Circumstances and Characteristics Study

uncertainty as to the presence of foul play (Figure 20). Among male violent cases, 60.3% (n = 

509) indicated likely foul play. Again, a high proportion of cases that were not classified as 

being violent had the foul play indicator checked (9.1%, n = 640). 

Population/Ethnicity (N = 34,503)* 

Among females, White/Caucasian persons accounted for 55.0% (n = 7,263) of non-violent and 

63.5% (n = 1,216) of violent cases (Figure 21). Black/African American females accounted for 

29.8

2.6

50.9

16.7

11.1

1.8

21.8

65.3

0

10

20

30

40

50

60

70

No Possibly Uncertain Yes

Percentage

Foul Play Indicators

Figure 19. Comparison of Case Violence Across Foul Play Indicators, 

Females (N = 5,505)

Non-Violent Violent

33.8

2.3

54.8

9.1

12.1

1.5

26.1

60.3

0

10

20

30

40

50

60

70

No Possibly Uncertain Yes

Percentage

Foul Play Indicators

Figure 20. Comparison of Case Violence Across Foul Play Indicators, 

Males (N = 7,869)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
24

NamUs Examination of Circumstances and Characteristics Study

20.8% of non-violent and 14.2% of violent cases, Hispanic / Latino females accounted for 11.0% 

of non-violent and 9.3% of violent cases, and American Indian/Alaska Native females accounted 

for 2.0% of non-violent and 1.7% of violent cases. Among males, White/Caucasian persons 

accounted for 61.4% (n = 10,885) of non-violent cases and 59.2% (n = 985) of violent cases 

(Figure 22). These results are to be expected as the majority of persons in the NamUs database 

are White/Caucasian. Black/African American males accounted for 15.5% of non-violent and 

15.0% of violent cases, Hispanic / Latino males accounted for 9.5% of non-violent and 10.3% of 

violent cases, and American Indian/Alaska Native males accounted for 2.1% of non-violent and 

2.2% of violent cases. 

55

2 2.3

20.8

0.1

11

1 0.3

7.6

63.5

1.7 2.4

14.2

0

9.3

1 0.1

7.8

0

10

20

30

40

50

60

70

White /

Caucasian

American

Indian /

Alaska

Native

Asian Black /

African

American

Hawaiian /

Pacific

Islander

Hispanic /

Latino

Other Uncertain 2+ Races

Percentage

Population / Ethnic Group

Figure 21. Comparison of Case Violence Across Population / 

Ethnic Groups, Females (N = 15,118)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
25

NamUs Examination of Circumstances and Characteristics Study

Age Missing (Group) (N = 34,507) 

 Among females, the highest percentage of cases were the under 18 age group for both nonviolent (39.4%, n = 5,207) and violent (31.4%, n = 602) categories (Figure 23). Among males, 

the highest proportion of non-violent cases was the 21-30 age group (22.3%, n = 3,948), and the 

highest proportion of violent cases was the under 18 age group (27.3%, n = 454) (Figure 24) 

61.4

2.1 2.2

15.5

0.1

9.5

1 0.4

7.8

59.2

2.2 1.9

15

0

10.3

1.9 0.1

9.4

0

10

20

30

40

50

60

70

White /

Caucasian

American

Indian /

Alaska

Native

Asian Black /

African

American

Hawaiian /

Pacific

Islander

Hispanic /

Latino

Other Uncertain 2+ Races

Percentage

Racial / Ethnic Group

Figure 22. Comparison of Case Violence Across Racial / 

Ethnic Groups, Males (N = 19,385)

Non-Violent Violent

39.4

6

17.8

13.6

9.5 11

2.7

31.4

8.2

25.6

16.3

9.7

7.1

1.8

0

5

10

15

20

25

30

35

40

45

<18 18-20 21-30 31-40 41-50 50+ Unknown

Percentage

Age Group

Figure 23. Comparison of Case Violence Across Age Groups 

at Time of Missing Person Event, Females (N = 15,119)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
26

NamUs Examination of Circumstances and Characteristics Study

Resolved Cases (N = 18,051) 

Case Resolution Status (N = 18,036) 

Among females, 85.1% (n = 6,624) of non-violent cases and 64.5% (n = 465) of violent cases 

had a resolution where the individual was found alive (Figure 25). Among males, 67.7% (n = 

6,031) of non-violent cases and 55.8% (n = 353) had a resolution where the missing person was 

found alive (Figure 26). 

17.2

5.5

22.3

17.6

14.3

20.5

2.7

27.3

6.5

22.4

17.3

11.5

13.1

1.9

0

5

10

15

20

25

30

<18 18-20 21-30 31-40 41-50 50+ Unknown

Percentage

Age Group

Figure 24. Comparison of Case Violence Across Age Groups 

at Time of Missing Person Event, Males (N = 19,388)

Non-Violent Violent

14.9

85.1

35.5

64.5

0

10

20

30

40

50

60

70

80

90

Deceased Alive

Percentage

Case Resolution Status

Figure 25. Comparison of Case Violence by Case Resolution 

Status, Females (N = 8,500)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
27

NamUs Examination of Circumstances and Characteristics Study

NamUs Assisted (N = 17,950) 

Among females, 9.5% (n = 738) of non-violent cases and 15.8% (n = 113) of violent cases 

indicated NamUs assistance (Figure 27). For males, 15.8% (n = 1,402) of non-violent cases and 

16.6% (n = 104) of violent cases indicated NamUs assistance (Figure 28). 

32.3

67.7

44.2

55.8

0

10

20

30

40

50

60

70

80

Deceased Alive

Percentage

Case Resolution Status

Figure 26. Comparison of Case Violence by Case Resolution 

Status, Males (N = 9,536)

Non-Violent Violent

90.5

9.5

84.2

15.8

0

10

20

30

40

50

60

70

80

90

100

No Yes

Percentage

NamUs Assistance

Figure 27. Comparison of Case Violence by NamUs 

Assistance, Females (N = 8,466)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
28

NamUs Examination of Circumstances and Characteristics Study

Manner of Death (N = 608)* 

Among female cases classified as non-violent, the highest proportion of cases had undetermined 

as the MOD (42.4%, n = 42). Among female violent cases, 87.2% (n = 68) of cases were ruled as 

homicides, representing the highest proportion of MOD (Figure 29). Among male non-violent 

cases, the highest proportion for MOD was undetermined (48.3%, n = 173). For male violent 

cases, the highest proportion of MOD was homicide (76.7%, n = 56) (Figure 30).

84.2

15.8

83.4

16.6

0

10

20

30

40

50

60

70

80

90

No Yes

Percentage

NamUs Assistance

Figure 28. Comparison of Case Violence by NamUs 

Assistance, Males (N = 9,484)

Non-Violent Violent

13.1

0 3

34.3

7.1

42.4

0

87.2

1.3 1.3 1.3

9

0

10

20

30

40

50

60

70

80

90

100

Accident Homicide Natural Pending Suicide Undetermined

Percentage

Manner of Death

Figure 29. Comparison of Case Violence Across Manners of 

Death, Females (N = 177)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
29

NamUs Examination of Circumstances and Characteristics Study

Unresolved Cases (N = 16,456) 

DNA Availability (N = 16,456) 

Among female non-violent cases, 59.2% (n = 3,207) of cases had DNA samples available, while 

65.5% (n = 781) of violent cases also had such information available (Figure 31). Among males, 

49.8% (n = 4,392) of non-violent cases and 55.7% (n = 574) of violent cases had DNA samples 

available for analysis (Figure 32). 

11.7

0

5.9

20.7

13.4

48.3

0

76.7

0

5.5 4.1

13.7

0

10

20

30

40

50

60

70

80

90

Accident Homicide Natural Pending Suicide Undetermined

Percentage

Manner of Death

Figure 30. Comparison of Case Violence Across Manners of 

Death, Males (N = 431)

Non-Violent Violent

40.8

59.2

34.5

65.5

0

10

20

30

40

50

60

70

No Yes

Percentage

DNA Availability

Figure 31. Comparison of Case Violence by DNA Availability, 

Females (N = 6,612)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
30

NamUs Examination of Circumstances and Characteristics Study

50.2 49.8

44.3

55.7

0

10

20

30

40

50

60

No Yes

Percentage

DNA Availability

Figure 32. Comparison of Case Violence by DNA Availability, 

Males (N = 9,844)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
31

NamUs Examination of Circumstances and Characteristics Study

Recommendations 

The following recommendations are made based on the results from the case classification and 

analysis. The recommendations serve to improve the designation of violent cases, as well as in 

the investigation and resolution of cases involving violence based on the unique features that 

were uncovered\. Additionally, these recommendations provide detailed suggestions for systems 

improvement and expansion for future research, evaluation, and analysis efforts. Overall, these 

recommendations could expand the NamUs mission by bringing together improved information 

gathering, technology enhancements, and human services to better resolve cases involving 

violence.

Figures 1 and 2 show that a higher proportion of female cases (12.7%) than male cases (8.6%) 

involved any type of violence.

• Recommendation 1: Implement discrete violence fields similar to that of the “foul play” 

field that can be checked by the case entrant to identify the case as possibly involving 

violence. Such fields may refer to specific types of violence identified in the text mining 

component of this project such as “fight,” “trafficking,” or other common keywords. 

• Recommendation 2: The Victim Services Division should conduct a discovery phase to 

determine what victim services are appropriate for MP victims of violence or for family 

and friends of UP cases involving violence in order to more fully be able to address the 

needs of such persons. 

Figures 15 and 16 show that the majority of violent cases for both females and males fell within 

the 20+ year age case group (41.2% female, 33.8% male)

• Recommendation 3: For long-term cold cases (20+ years), additional investigation or 

inquiry into the potential presence of violence should be undertaken by the assigned RPS 

or another member of the NamUs team.

Figures 19 and 20 show that there are cases which were classified by us as violent, but which 

were not indicated as having foul play (11.1% of female violent cases, 12.1% of male violent 

cases). Alternatively, there were cases in which the case was not classified as violent, but had 

the foul play indicator checked (16.7% of female non-violent cases, 9.1% of male non-violent 

cases). Further, Figures 19 and 20 show that a large proportion of non-violent cases do not have 

the indicator checked for foul play (16.7% females, 9.1% males). Additionally, overall, 21,133 

cases (61% of all cases) did not provide any information for this field. 

• Recommendation 4: For cases in which there is circumstantial text evidence of foul 

play, but the foul play indicator was not checked, or for cases in which there was no 

circumstantial text evidence of foul play, but the foul play indicator was checked, the 

assigned RPS should make efforts to obtain additional information from the case entrant 

prior to publishing the case. 

• Recommendation 5: Given the high proportion of cases that did not have any foul play 

indicator checked, make this field mandatory to capture more comprehensive information 

on the potential burden of violence across all cases. 

• Recommendation 6: If a case is marked as positive for foul play, then require the 

specific type of violence to also be checked referenced in Recommendation 1, and vice 

versa.

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
32

NamUs Examination of Circumstances and Characteristics Study

Figures 27 and 28 demonstrate a relatively low percentage of cases being assisted by NamUs for 

both males and females regardless of violent case status (non-violent: 9.5% female, 15.8% male; 

violent: 15.8% female, 16.6% male). More information is needed to determine if these data 

accurately reflect the value of NamUs or are an underrepresentation of the impact of NamUs.

• Recommendation 7: Require users at case close-out to indicate if NamUs assisted with 

case resolution. This prompt should capture the various ways in which NamUs may have 

helped in a case including through biometric services, analytical services, RPS assistance, 

among other areas in which NamUs serves to assist in case resolution. 

Figures 29 and 30 demonstrate a large proportion of non-violent cases with either undetermined 

or pending MOD (76.7% combined females, 69.0% combined males). 

• Recommendation 8: It is likely that some of these cases do involve violence or foul 

play. As such, a prompt or reminder message should be sent to the case entrant to ensure 

that the case file is fully filled out, including the foul play indicator and any associated 

circumstantial text.

• Recommendation 9: Have RPSs perform additional review of cases at close-out to have 

better information to analyze by ensuring all the fields are completed. In reviewing 

MOD)\ information, it was noted that a large proportion of cases did not have final MOD 

information. Thus, NamUs staff should pay special attention to quality assurance before 

archival to have more accurate and complete data to develop best practices.

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
33

NamUs Examination of Circumstances and Characteristics Study

Conclusion 

Overall, this study of MP case violence and associated characteristics provides a more detailed 

and nuanced insight into the impact and experiences of violence among MP within the NamUs 

database. The findings also help to detail concrete and distinct recommendations for systems 

expansion to better capture case violence and to better serve persons impacted by violent cases 

by identifying unique demographic and investigative characteristics of such cases that may aid in 

case investigation and resolution.. The key findings and results of the project also point to areas 

for further investigation where gaps in knowledge remain due to limitations of the NamUs data. 

However, in all, this report provides critical insights into case violence and the need for NamUs 

systems improvement that will lead to better services and system functionality for violent cases 

within the system and handled by staff and NamUs service components. 

Limitations

The MP case classification and analysis had a number of limitations. First, NamUs is an 

operational database that is not intended to be a complete record of all investigative reports and

notes. As such, some information necessary to carry out our analysis is lacking. Similarly, as an 

operational database, there is no standardized case entry as there might be with data collection 

through a typical survey data collection effort. Thus, there is variation in the type and amount of 

information provided for each case depending on several factors, including the integrity of any 

ongoing investigations, which may directly impact indicators of violence within NamUs. 

Second, it is possible that there were deficiencies in our dictionary. All relevant 

keywords may not have been included, and some inflected forms may have been omitted. As 

such, violence and victimization may have been under-classified within WordStsat.

 Further, keywords were placed under broad violence types. It is possible that individual 

keywords were placed under inappropriate violence types. The classification of keywords into 

types was at the subjective interpretation of the primary coder in consultation with the secondary 

coder. However, it is still possible that alternative keyword classifications under different 

violence types might have affected the violence type results.

 Additionally, secondary coding took place on only 10% of all cases, and tertiary coding 

occurred on only 20% of that 10% sample. These levels of second and tertiary coding may not 

have been high enough to truly assess and verify the dictionary in question. As such, 

deficiencies in the dictionary, whether the inclusion of inappropriate keywords or the exclusion 

of necessary words, may not have been adequately picked up using these coding sample 

thresholds. 

Finally, final acceptance of results relied upon the inter-rater agreement between both the 

human coder and WordStat, as well as between two human coders. The level at which results 

were accepted may have been lower than was warranted to accept the results as true and valid. A 

higher threshold for inter-rater agreement may have impacted the results likely resulting in lower 

numbers of violent cases. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
34

NamUs Examination of Circumstances and Characteristics Study

III. Unidentified Persons

Background 

The issue of unidentified decedents presents a challenge ME/C and law enforcement (LE) 

agencies across the United States (Ritter, 2007). It is estimated that over 4,400 unidentified 

decedents are recovered each year with 1,000 of those decedents remaining unidentified after one 

year (Hickman, Hughes, Strom & Ropero-Miller, 2007). In the same survey, the authors found 

that there were over 13,400 unidentified persons within ME/C offices. Hughes (2007) used the 

CDC’s National Death Index (NDI) and found that between 1980 and 2004, there were 10,300 

UPs within the index at some point during those years; however, it should be noted that the NDI 

is neither a national database for UP nor do states uniformly report on death certificates if a 

person’s identity is unknown. As of October 28, 2020, there were 13,485 active UP cases within 

the NamUs database. Most of these NamUs UP cases are not known to be the result of foul play 

and thus tend to represent natural, accidental, suicidal, or undetermined MOD. Overall, the 

burden of UP cases on the ME/C system and to LE agencies is significant due, in part, to the 

volume of cases, as well as the lack of resources, both human and forensic, needed to attain 

positive identification (Ritter, 2007).

Violence and Unidentified Persons

Although the majority of NamUs UP cases involve non-violent MOD, a portion of UP cases do 

entail homicide as the MOD. As of October 28, 2020, 2,077 (15.4%) of active NamUs UP cases 

had been ruled homicides by ME/Cs. However, that number may be higher given that 5,724 

cases were listed as having an undetermined MOD, which may include cases of homicide. In 

examining UPs within the National Crime Information Center, Hughes (2007) found that 27% of 

all UP cases had been ruled homicides. Hughes also found that the majority of deaths had an 

undetermined cause of death (49%), which is slightly higher than in NamUs (42.4%) as of 

October 28, 2020. It is likely that some of these undetermined cases were homicides, but not 

ruled as such due to the condition of the remains, the amount of remains recovered, or unknown 

circumstantial information. Regardless, the number of UP homicides is likely underestimated

within NamUs.

Understanding UP Violence in NamUs

The current project sought to better understand the burden of violence in UP cases within a 

national database. To do so, it relied on case file information from active and archived cases 

within the database. Violence was defined in two ways. First, any case ruled as a homicide by a 

ME/C was coded as violent. Second, text mining of undetermined and pending manners of 

death, as well as cases for which no MOD was provided, was conducted to determine if physical 

aggression resulted in deadly harm to an individual. In completing this work, a broader, more 

nuanced picture as to the extent of violence associated with cases within the database was 

elicited, as well as a better understanding as to how violence may be related to other 

characteristics of a case.

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
35

NamUs Examination of Circumstances and Characteristics Study

Methods 

Similar to the MP section, this section describes the methods used to complete case classification 

and analysis. A more detailed discussion of the methods, including more nuanced information 

on dictionary building, misspellings, coding, and reporting are provided in Appendix A – 

Technical Note. 

Project Data

Data for this project came from the unidentified section of the NamUs database. Data include all 

published (active) and archived (resolved) cases as of the pull date of August 29,2019. Data 

were pulled by the Senior Business Intelligence Developer and delivered to the project team

using the secure, password-protected sharing service OneDrive. Data were maintained on an 

encrypted, password protected, UNTHSC-provided computer. 

Data were delivered as a single Microsoft Excel file with three separate sheets and 

downloaded via OneDrive. Sheet one included Case Information that provided demographic and 

case details on the UP; sheet two included Circumstances that provided in text entries spaces for 

general circumstance information available for public view, Agency Notes available only for 

professional users, and Circumstances of Resolution that provided information as to the outcome 

of a resolved case, and cause of death information; and sheet three included Agency Information 

with detailed information on the case owner and any contributors. These three sheets were saved 

as separate Microsoft Excel files. 

Case Classification 

Initial Classification

Initially, the MOD provided by the ME/C was used to classify cases as violent or non-violent. 

All cases indicated as homicides were classified as violent while all other cases excluding cases 

where MOD determination was “pending” were classified as non-violent. For the study 

purposes, suicide was considered a non-violent MOD, as were accidental and natural MOD. 

Initially, text mining classification of cases as violent or non-violent was similar to that of 

the MP cases and was used for cases indicated as having an undetermined MOD or for which no 

MOD was provided. In the final run of classification of the data, cases with a pending MOD

were also mined using similar procedures as the procedures used for undetermined and not 

provided MOD cases. As with MP cases, WordStat 8.0.20 (Provalis Research; Montreal, 

Canada) was used for the classification of cases as violent or non-violent.

Classification Dictionary

Similar to the MP dictionary, to classify cases as violent or non-violent, a classification 

dictionary was created in WordStat that included keywords relevant to UP cases. This dictionary 

was initially based on the MP dictionary but was further developed and modified collaboratively 

by the project team to ensure the best coverage and most exhaustive list of potential keywords 

that were relevant to the classification of UP cases as violent or non-violent. In total, 37 words 

were identified and included in the initial dictionary. Keywords were not categorized into 

specific types of violence as with the MP dictionary; rather, each keyword was a standalone 

indicator of case violence. Amendments to the initial dictionary were based on the iterative

classification outcomes. The final classification dictionary included 40 keywords and can be 

found in Appendix B. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
36

NamUs Examination of Circumstances and Characteristics Study

Classification Runs

In total, there were three classification runs for UP cases. The order of steps and overall 

approach to the runs varied only slightly and was based on lessons learned from previous runs.

General Classification Approach. Runs 1 and 2 involved cases with an “undetermined” 

MOD or with a MOD that was not provided. Run 3 included only cases in which the MOD was 

“pending.” The same general process was followed for each classification run. First, data were 

delivered and, unless cleaned data were already available, data were cleaned, resulting in the 

removal of cases that did not have any information in the circumstance fields, that only indicated 

some form of “unknown,” or that contained only numbers or symbols. Cleaned data were then 

imported into WordStat for classification. 

A misspellings check was also conducted that was completed using WordStat’s built-in 

functionality. Each potential misspelling was checked using a “keyword-in-context” feature to 

ensure the misspelling truly represented a misspelled word, a proper noun, or some other 

otherwise correct spelling for a different word. The final run of the data included corrected 

misspelled words. Overall, this final classification resulted in cases being coded as violent or 

non-violent by WordStat for analysis.

Following each run, data were imported into Stata, and a random sample of 10% was 

drawn for second coding. Percent agreement was calculated between WordStat and the human 

coder. Following the calculation of percent agreement, a disagreements check was performed to 

understand discrepancies in coding between WordStat and the human coder. For Run 2, percent 

agreement was calculated to be 88.2%. For Run 3 with just “pending” MOD case, percent 

agreement was 82%. All “pending” disagreements were instances in which the computer found 

a keyword and coded a case as violent, but in which the human coder did not find the case to 

indicate potential or actual violence. As such, these cases were removed during the final 

violence coding procedures described below. 

When appropriate, tertiary coding was conducted by a second human coder to check the 

coding of the first human coder. To do so, a subsample of 20% of the 10% sample drawn for 

second coding was drawn at random in Stata. Tertiary coding also resulted in a percent 

agreement between human coders resulting in 97.8% agreement for Run 2 and 95.2% agreement 

for Run 3 with “pending” cases only. 

Final Violence Coding. Following secondary and tertiary coding, and upon findings of 

adequate percent agreement for both secondary and tertiary coding, a manual coding of all cases 

indicated as violent by WordStat was conducted to verify the violent nature of the case. These 

final codings represent final case violence for the purposes of further analysis. During this 

process, in Run 2, 96 cases were found to be valid cases of violence. In Run 3, an additional 14 

cases were found to be valid cases of violence. These 110 cases were then manually re-coded 

within the data to be violent. 

Case Analysis 

Following completion of case classification, the data were prepared for analysis such that 

meaningful comparisons could be made between violent and non-violent cases for both female 

and male cases.

Final Dataset Creation 

To arrive at a final analytic dataset, all cases in which violence was present based on 

classification procedures were changed to indicate such violence by switching the mOD

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
37

NamUs Examination of Circumstances and Characteristics Study

classification to “homicide.” In all, there were 16,857 cases available for analysis, of which 

2,655 indicated violence. For analysis, which was stratified by gender, cases with no gender 

provided (n = 7) or unsure gender (n = 1,009) were dropped resulting in a final case count of 

15,841. 

Variables

Variables used in the analysis stage of this project included violence variables, case 

characteristics, demographic characteristics, and biometric characteristics. Each set of variables 

is described below. 

All Case Variables. All case variables included demographic characteristics and general 

case characteristics as displayed in Table 1.

Table 1. All Case Variables (N = 15,841) 

Variable Definition Code N

Final Violence Indicate whether or not a case did or is 

suspected of involving foul play or violence

0 = No

1 = Yes

13,186

2,655

Total: 15,841

SexName2 Indicates initial biological gender determination 

as defined by reporting party

0 = Female

1 = Male

3,458

12,383

Total: 15,841

RaceEthnicity2 Indicates initial race/ethnicity determination as 

defined by reporting party

0 = White / 

Caucasian

1 = American Indian 

/ Alaska Native

2 = Asian 

3 = Black / African 

American 

4 = Hispanic / 

Latino

5 = Other

6 = Uncertain

7 = 2+ Races

6,239

102

265

2,555

1,877

178

2,666

1,928

Total: 15,810

Age Group Estimated age group of decedent based on 

median age of decedent

0 = <18

1 = 18-20

2 = 21+

645

330

12,677

Total: 13,652

StateName2 State where person in case was found deceased 1 = Alabama

2 = Alaska

3 = Arizona

4 = Arkansas

5 = California

6 = Colorado

7 = Connecticut

8 = Delaware

9 = DC

10 = Florida

11 = Georgia

78

54

2,161

130

2,833

115

85

42

51

1,162

285

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
38

NamUs Examination of Circumstances and Characteristics Study

12 = Guam

13 = Hawaii

14 = Idaho

15 = Illinois

16 = Indiana

17 = Iowa

18 = Kansas

19 = Kentucky

20 = Louisiana

21 = Maine

22 = Maryland

23 = Massachusetts

24 = Michigan 

25 = Minnesota

26 = Mississippi

27 = Missouri

28 = Montana

29 = Nebraska

30 = Nevada

31 = New 

Hampshire

32 = New Jersey 

33 = New Mexico

34 = New York

35 = North Carolina

36 = North Dakota

37 = Ohio

38 = Oklahoma

39 = Oregon

40 = Pennsylvania

41 = Puerto Rico

42 = Rhode Island

43 = South Carolina

44 = South Dakota

45 = Tennessee

46 = Texas

47 = Utah

48 = Vermont

49 = Virgin Islands

50 = Virginia

51 = Washington

52 = West Virginia

53 = Wisconsin 

54 = Wyoming

2

64

23

249

78

19

30

113

172

21

367

240

475

58

60

144

21

8 

325

8 

436

161

1,589

157

3 

171

243

218

363

235

21

74

7 

179

1,926

50

22

3 

198

169

46

75

17

Total: 15,840

ConditionOfRemainsName2 Indicates extent to which remains had a 

recognizable face 

0 = Not 

recognizable

1 = Recognizable 

face

11,633

3.891

Total: 15,524

Age of Case Groups2 Indicates period of time between when 

decedent’s body was found and present day

0 = 0-1Years

2 = 1-5 years

3 = 5-10 years

4 = 10-20 years

234

2,265

2,404

3,641

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
39

NamUs Examination of Circumstances and Characteristics Study

5 = 20+ years

6 = Unknown

7,296

1 

Total: 15,841

Resolved Case Variables. Resolved case variables include characteristics related to the 

case investigation or to outcomes of the case as shown in Table 2. 

Table 2. Resolved Case Variables (N = 3,605) 

Variable Definition Code N

NamUsAssisted Indicator defined by professional user at 

case close-out to denote if NamUs was 

useful or an important factor in case 

resolution

0 = No

1 = Yes

615

1,556

Total: 2,171

MannerOfDeathName3 Indicates manner of death as determined by 

a medical examiner or coroner

1 = Accident

2 = Homicide

3 = Natural

4 = Pending

5 = Suicide

6 = Undetermined

438

566

239

469

114

1,043

Total: 2,869

MethodOfId_Anthropology For resolved cases, indicates if 

anthropology was a method used to make 

positive identification

0 = No

1 = Yes

3,320

273

Total: 3,593

MethodOfId_CircumstantialInforma For resolved cases, indicates if 

circumstantial information was a method 

used to make positive identification

0 = No

1 = Yes

3,184

409

Total: 3,593

MethodOfId_Dental For resolved cases, indicates if dental 

comparison was a method used to make 

positive identification

0 = No

1 = Yes

3,268

325

Total: 3,593

MethodOfId_Fingerprints For resolved cases, indicates if fingerprint 

comparison was a method used to make 

positive identification

0 = No

1 = Yes

2,650

943

Total: 3,593

MethodOfId_MtDNA For resolved cases, indicates if 

mitochondrial DNA was a method used to 

make positive identification

0 = No

1 = Yes

2,444

1,149

Total: 3,593

MethodOfId_NucDNA For resolved cases, indicates if nuclear 

DNA was a method used to make positive 

identification

0 = No

1 = Yes

2,049

1,544

Total: 3,593

MethodOfId_Other For resolved cases, indicates if other means 

were a method used to make positive 

identification

0 = No

1 = Yes

3,572

21

Total: 3,593

MethodOfId_Radiograph For resolved cases, indicates if radiographs 

were a method used to make positive 

identification

0 = No

1 = Yes

3,522

71

Total: 3,593

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
40

NamUs Examination of Circumstances and Characteristics Study

MethodOfId_VisualIdentification For resolved cases, indicates if visual 

identification was a method used to make 

positive identification

0 = No

1 = Yes

3,223

370

Total: 3,593

Unresolved Case Variables. Unresolved case variables included a single biometric 

characteristic indicating if DNA had been uploaded and the analysis completed as shown in 

Table 3.

Table 3. Unresolved Case Variables (N = 12,236) 

Variable Definition Code N

DNA_Indicator2 Indicates if at least one DNA sample 

analysis has been completed

0 = No

1 = Yes

5,965

6,271

Total: 12,236

Analytic Approach 

Similar to the MP analysis, analysis of the final cleaned case data proceeded in various steps. 

First, univariate descriptive statistics for each variable were completed as reported in the variable 

tables. Then, univariate statistics stratified by missing person gender were run for all violence 

variables. Finally, gender stratified (male, female) bivariate statistics comparing final case 

violence (yes, no) across levels of each remaining variable were run in order to assess differences 

in each variable by the presence or absence of case violence for each of the genders. 

For each variable, all available cases with information on the variable were included for 

analysis. As such, many variables represent different sample sizes and samples. Further, 

analyses were separated into variables appropriate for all cases, variables for resolved cases only, 

and variables for unresolved cases. Resolved and unresolved status was determined by the 

lifecycle status of the case as either archived (resolved) or published (unresolved) as of the date 

of the data pull. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
41

NamUs Examination of Circumstances and Characteristics Study

Results 

Overall, there were 15,841 valid cases for analysis. Demographically, 3,458 (21.8%) cases were 

female, and 12,383 (78.2%) cases were male. Figures 33-52 show results for the comparisons 

made in the analysis. An asterisk (*) indicates that at least one cell count was less than 10 cases.

All Cases (N = 15,841)

Final Violence (N = 15, 841) 

Among females, 27.2%, (n = 941) experienced a violent death (Figure 33). The proportion of 

males who experienced a violent death was lower than that for females (13.8%, n = 1,714) 

(Figure 34).

72.8%

27.2%

Figure 33. Percent of Violent and Non-Violent Deaths 

Among Females (N = 3,458)

No Yes

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
42

NamUs Examination of Circumstances and Characteristics Study

State (N = 15,840) 

Figures 35 and 36 display the states with the highest percentage of violent cases with their nonviolent case percentages presented for comparison. These numbers represent absolute 

percentages, and do not represent rates. As such, the percentages displayed may represent 

participation in NamUs as opposed to actual higher percentages per capita among the states 

shown. Among both females (Figure 35) and males (Figure 36), California is the top state 

displaying the most violent cases with the state representing 20.0% (n = 188) of all violent cases 

among females and 19.5% (n = 335) of all violent cases among males.

86.2%

13.8%

Figure 34. Percent of Violent and Non-Violent Deaths 

Among Males (N = 12,383)

No Yes

16.1

6.2

13.3

6.8

4.2 3.1 3

9.9

1.7 2.5

20

10.3 9.8 8.6

5.3

3.5 3.1 2.8 2.3 2.1

0

5

10

15

20

25

Figure 35. Top States by Percent of All Violent Cases with 

Non-Violent Case Comparison, Females (N = 3,458)

% of All Non-Violent Cases % of All Violent Cases

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
43

NamUs Examination of Circumstances and Characteristics Study

Population/Ethnicity (N = 15,810)* 

Among female non-violent cases, 38.2% (n = 960) were White/Caucasian individuals, while 

52.1% (n = 490) of female violent cases were among the same demographic (Figure 37). Black / 

African Americans made up 21.0% of non-violent and 23.5% of violent cases, Hispanic / Latino 

females made up 8.6% of non-violent and 2.4% of violent cases, and American Indian / Alaska 

Native females made up 0.9% of non-violent cases and 0.4% of violent cases. Among male nonviolent cases, 37.9% (n = 4,030) of cases were among White/Caucasian individuals while among 

male violent cases, 44.3% (n = 759) were among White/Caucasian individuals (Figure 38). 

Black / African Americans made up 14.1% of non-violent and 17,7% of violent cases, Hispanic / 

Latino males made up 14.2% of non-violent and 7.6% of violent cases, and American Indian / 

Alaska Native males made up 0.7% of non-violent cases and 0.4% of violent cases. These 

results are not surprising given that White/Caucasian individuals make up the majority of all 

cases.

17.9

11.8

6.9

10.9

16.6

2.4 1.5

2.6 2.3 1.7

19.5

13.8

10 9.9

6.9

3.7 2.8 2.7 2.6 2.3

0

5

10

15

20

25

California Texas Florida New York Arizona Michigan Georgia New

Jersey

Maryland Puerto

Rico

Figure 36. Top States by Percent of All Violent Cases with 

Non-Violent Case Comparison, Males (N = 12,382)

% of All Non-Violent Cases % of All Violent Cases

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
44

NamUs Examination of Circumstances and Characteristics Study

Age Group (N = 13,652) 

Among females, 87.9% (n = 1,816) of non-violent cases and 78.0% (n = 653) of violent cases 

fell into the 21 and over age category (Figure 39). Similarly, among males, 95.6% (n = 8,790) of 

non-violent cases and 91.2% (n = 1,418) of violent cases fell in the 21 and over age group 

(Figure 40). 

38.2

0.9 2

21

8.6

0.6

21

7.9

52.1

0.4 1.2

23.5

2.4 1

13.2

6.3

0

10

20

30

40

50

60

White /

Caucasian

American

Indian /

Alaska

Native

Asian Black /

African

American

Hispanic /

Latino

Other Uncertain 2+ Races

Percentage

Population / Ethnicity

Figure 37. Comparison of Violent Cases Across Population / 

Ethnic Groups, Females (N = 3,456)

Non-Violent Violent

37.9

0.7 1.6

14.1 14.2

1.3

17.1

13.2

44.3

0.4 1.8

17.7

7.6

0.9

11.5

15.8

0

5

10

15

20

25

30

35

40

45

50

White /

Caucasian

American

Indian /

Alaska

Native

Asian Black /

African

American

Hispanic /

Latino

Other Uncertain 2+ Races

Percentage

Population / Ethnicity

Figure 38. Comparison of Violent Cases Across Population / 

Ethnic Groups, Males (N = 12,345)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
45

NamUs Examination of Circumstances and Characteristics Study

Condition of Remains (N = 15,524) 

Among females, 79.4% (n = 1,966) of non-violent cases and 77.2% (n = 712) of violent cases 

included non-recognizable remains (Figure 41). Similarly, among males, 74.4% (n = 7,793) of 

non-violent cases and 70.2% (n = 1,162) of violent cases involved remains that were not 

recognizable (Figure 42).

9

3.2

87.9

15.5

6.5

78

0

10

20

30

40

50

60

70

80

90

100

<18 18-20 21+

Percentage

Median Age Group

Figure 39. Comparison of Violent Cases Across Median Age 

Groups, Females (N = 2,903)

Non-Violent Violent

2.6 1.8

95.6

5.6 3.2

91.2

0

20

40

60

80

100

120

<18 18-20 21+

Percentage

Median Age Group

Figure 40. Comparison of Violent Cases Across Median Age 

Groups, Males (N = 10, 749)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
46

NamUs Examination of Circumstances and Characteristics Study

Age of Case (N = 15,841)* 

Among female non-violent cases, 43.2% (n = 1,088) fell into the 20+ year group as did the 

majority of violent cases (72.7%, n = 684) (Figure 43). Similarly, among male non-violent 

cases, 41.0% (n = 4,369) fell into the 20+ year age group while 67.4% (n = 1,155) of male 

violent cases also fell into this same age group (Figure 44). 

79.4

20.6

77.2

22.8

0

10

20

30

40

50

60

70

80

90

Not Recognizable Recognizable

Percentage

Condition of Remains

Figure 41. Comparison of Case Violence Across Condition of 

Remains, Females (N = 3,397)

Non-Violent Violent

74.4

25.6

70.2

29.8

0

10

20

30

40

50

60

70

80

Not Recognizable Recognizable

Percentage

Condition of Remains

Figure 42. Comparison of Case Violence Across Condition of 

Remains, Males (N = 12,127)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
47

NamUs Examination of Circumstances and Characteristics Study

Resolved Cases (N = 3,605) 

NamUs Assisted (N = 2,171) 

Among females, 73.2% (n = 300) of non-violent cases and 75.7% (n = 112) of violent cases 

indicated NamUs assistance (Figure 45). Likewise, 72.2% (n = 1,027) of male non-violent cases 

and 61.6% (n = 117) of male violent cases also reported NamUs assistance.

1.6

13.9

17.5

23.8

43.2

0.4

4.8 6.7

15.4

72.7

0

10

20

30

40

50

60

70

80

0-1 Year 1-5 Years 5-10 Years 10-20 Years 20+ Years

Percentage

Age of Case Groups

Figure 43. Comparison of Case Violence By Age of Case 

Groups, Females (N = 3,458)

Non-Violent Violent

1.7

16.7 16.6

24.1

41

0.4 0.01

5.5 7.5

19.3

67.4

0

0

10

20

30

40

50

60

70

80

0-1 Year 1-5 Years 5-10 Years 10-20 Years 20+ Years Unknown

Percentage

Age of Case Groups

Figure 44. Comparison of Case Violence By Age of Case 

Groups, Males (N = 12,383)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
48

NamUs Examination of Circumstances and Characteristics Study

Manner of Death (N = 2,869)* 

Overall, all violent cases had a MOD of homicide. Among non-violent cases, the majority of 

MOD across both genders and overall was undetermined with 56.4% (n = 269) of females

(Figure 47) and 42.4% (n = 774) of males (Figure 48) having an undetermined MOD.

26.8

73.2

24.3

75.7

0

10

20

30

40

50

60

70

80

No Yes

Percentage

NamUs Assisted

Figure 45. Comparison of Case Violence by NamUs 

Assistance in Case Resolution, Females (N = 558)

Non-Violent Violent

27.8

72.2

38.4

61.6

0

10

20

30

40

50

60

70

80

No Yes

Percentage

NamUs Assisted

Figure 46. Comparison of Case Violence by NamUs 

Assistance in Case Resolution, Males (N = 1,613) 

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
49

NamUs Examination of Circumstances and Characteristics Study

Combined Method of ID (N = 3,593)* 

Among females, the highest proportion of method of identification was based on short tandem 

repeat (STR) typing for both non-violent (46.5%, n = 311) and violent (51.9%, n = 125) cases 

(Figure 49). This trend was also observed among males with 41.9% (n = 987) of non-violent 

cases and 37.2% (n = 121) of violent cases (Figure 50). However, among females, only 13.6% 

of non-violent cases and 12.5% of violent cases were resolved using fingerprints while among 

males, 30% of non-violent cases and 35.1% of violent cases were resolved using fingerprints. 

13

0

5.5

21.6

3.6

56.4

0

100

0 0 0 0

0

20

40

60

80

100

120

Accident Homicide Natural Pending Suicide Undetermined

Percentage

Manner of Death

Figure 47. Comparison of Case Violence by Manner of 

Death, Females (N = 718)

Non-Violent Violent

20.6

0

11.7

20

5.3

42.4

0

100

0 0 0 0

0

20

40

60

80

100

120

Accident Homicide Natural Pending Suicide Undetermined

Figure 48. Comparison of Case Violence by Manner of 

Death, Males (N = 2,151)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
50

NamUs Examination of Circumstances and Characteristics Study

Unresolved Cases (N = 12,236) 

DNA Availability (N = 12,236) 

Overall, as shown in Table 17, most cases had at least one DNA reference sample uploaded and 

tested. Among females, 55.9% (n = 1,031) of non-violent cases and 64.6% (n = 452) of violent 

cases had DNA testing information for at least one sample uploaded (Figure 51). Among males, 

7.3 9.6 13 13.6

41.6

46.5

1.5 1.5

7.6 4.2

10 12.5 12.5

49.8 51.9

0 1.7 5.8

0

10

20

30

40

50

60

Percentage

Method of Identification

Figure 49. Comparison of Case Violence Across Methods of 

Identification, Females (N = 910)

Non-Violent Violent

8.3

12.4

7.7

30 28.2

41.9

0.4 2.2

11.5

5.5 8.6 8

35.1

26.2

37.2

0.3 1.5

9.8

0

5

10

15

20

25

30

35

40

45

Percentage

Method of Identification

Figure 50. Comparison of Case Violence Across Methods of 

Identification, Males (N = 2,683)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
51

NamUs Examination of Circumstances and Characteristics Study

49.3% (n = 4,091) of non-violent cases had DNA typing uploaded while 50.2% (n = 697) of 

violent cases had uploaded DNA typing results (Figure 52). 

44.1

55.9

35.4

64.6

0

10

20

30

40

50

60

70

No Yes

Percentage

DNA Sample Available

Figure 51. Comparison of Case Violence by DNA Sample 

Test Availability, Females (N = 2,544)

Non-Violent Violent

50.7 49.8 49.3 50.2

0

10

20

30

40

50

60

No Yes

Percentage

DNA Sample Availability

Figure 52. Comparison of Case Violence by DNA Sample 

Test Availability, Males (N = 9,692)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
52

NamUs Examination of Circumstances and Characteristics Study

Recommendations 

Based on the case classification and analysis, the following recommendations are made. The 

recommendations are put forth in hopes of improving the identification of cases involving 

violence, as well as enhancing the investigation and resolution of such cases based on the unique 

characteristics of violent cases that were uncovered. Further, these recommendations serve to 

enhance data collection, which will serve to improve future research, evaluation, and analysis 

using NamUs case data. In all, these recommendations further the NamUs mission by bridging 

improvements in data and information gathering, enhancements in database technology, and 

NamUs’ human services to better resolve cases involving violence. 

As shown in Figures 41 and 42, the vast majority of both violent and non-violent cases for both 

males and females had unrecognizable remains.

• Recommendation 1: Given the high levels of unrecognizable remains, ensure that 

biometric characteristics, such as DNA, fingerprints, and dental records, are obtained to 

assist in the case resolution.

• Recommendation 2: For cases with unrecognizable remains, NamUs should develop 

forensic art resources to create composite sketches or enhance post-mortem images to 

assist with public identification of such cases. 

• Recommendation 3: Implement a quality assurance process for resolved cases to verify 

that the initial entries for population/ethnicity, age, gender, etc. are indeed correct in the 

case file prior to case closeout. 

As shown in Figures 43 and 44, the age of the case for the majority of both violent and nonviolent cases for both males and females was 20 years or more.

• Recommendation 4: Such cases are more likely to be remains that were buried, and as a 

major barrier to exhumation and subsequent identification is the financial barrier to law 

enforcement, NamUs should provide funding to exhume such remains for further 

identification purposes.

As shown in Figures 47 and 48, the majority of both male (42.4%) and female (56.4%) nonviolent cases had an undetermined MOD. 

• Recommendation 5: Implement a quality assurance process for resolved cases to verify 

MOD, when possible, and to ensure the case file is complete and up-to-date with 

information uncovered through the resolution. 

• Recommendation 6: For cases in which the ME/C was unable to enter MOD into 

NamUs due to confidentiality or due to ongoing investigation, provide the opportunity for 

such personnel to enter such information and make it visible only to NamUs personnel 

and the associated criminal justice agency.

• Recommendation 7: Partnerships should be explored between NamUs and the agencies

that hold the death certificates to cross-check NamUs MOD against official death 

certificates held by outside agencies.

As shown in Figures 49 and 50, a high proportion of both male and female violent and nonviolent cases were resolved using some type of DNA testing; however, the proportion of male 

violent and non-violent cases that were resolved using fingerprints was over twice the proportion 

of violent and non-violent female cases.

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
53

NamUs Examination of Circumstances and Characteristics Study

• Recommendation 8: Beginning with unidentified males, specific effort should be made 

to obtain fingerprint records for comparison to fingerprints systems, as NamUs 

fingerprint service is more likely to get a hit on male cases because they are more 

represented in fingerprint databases.

As shown in Figures 51 and 52, a higher proportion of unresolved, active female violent cases 

have a DNA sample available (64.6%) compared to male violent cases (50.2%). 

• Recommendation 9: Encourage RPSs to work with case owners for all violent cases in 

order to obtain DNA samples. 

. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
54

NamUs Examination of Circumstances and Characteristics Study

Conclusion 

This investigation into UP case violence and the case characteristics associated with such 

violence within the NamUs database offers insights into the differences that exist between 

violent and non-violent cases. In doing so, the results enable the making of recommendations for 

systems improvement and expansion to better understand case violence, which will enhance the 

ability for NamUs to respond to and assist with UP cases involving violence by working with 

law enforcement to obtain DNA samples (mitochondrial and nuclear), fingerprints, or to assist in 

exhumations that may be financially infeasible for law enforcement agencies, for example. 

Further, the findings of this project highlight areas for future research and evaluation to fill 

critical gaps in knowledge that could not be answered through this study due to limitations of the 

project. Overall, however, the results presented in this report provide more detailed information 

that provide direction for the improvement and expansion of the NamUs system, including the 

database and the various service divisions that make-up the NamUs program, such as the 

inclusion and mandating of various data fields to better capture violence in NamUs, and 

increased efforts to obtain biometric samples for identification of cases. 

Limitations

The UP classification and analysis had several limitations. As with the MP classification, 

operational data were used for systems improvement purposes. The data were never intended to 

be used for such purposes. They represent crowd sourced data as opposed to data collected in a 

systematic manner such as data collected using survey data collection techniques. As such, they 

may be deficient for a systems improvement study. 

 Second, the classification dictionary may have been deficient. Coverage of all relevant 

keywords may not have been achieved, including the coverage of all possible inflected forms of 

each keyword. Due to these likely deficiencies, violent cases may have been under-classified.

 Further, only 20% of cases were used for secondary coding of the computer 

classifications, and only 10% of the 20% of second coded cases were used for tertiary coding. 

These levels may not have been adequate to accurately assess and verify the dictionary’s 

validity. Thus, all deficiencies within the dictionary may not have been ascertained using these 

numbers of cases.

 Additionally, inter-rater agreement was calculated as percent agreement between the 

computer and a human coder, and subsequently, between two human coders. While the percent 

agreements were near or above 90%, this threshold may not have been high enough to accept the 

results in their totality as completely valid. As such, the final classifications of all cases may not 

have been acceptable or appropriate due to misclassifications accepted as true and valid based on 

the inter-rater agreement thresholds.

 Finally, a large proportion of cases had remains that were not identifiable, which likely 

impacted the accuracy and precision of case file information, particularly demographic 

information. In particular, cases in which the entire body was not recovered or in which there 

was severe decomposition may have been more greatly affected by this issue. In such cases, 

characteristics such as population/ethnicity, gender, age, time since death, or other characteristics 

may have been difficult if not impossible to estimate. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
55

NamUs Examination of Circumstances and Characteristics Study

IV. Special Section: Missing and Unidentified 

Indigenous Persons

Background 

The serious issue of missing and murdered indigenous persons (MMIP), particularly women and 

girls, has gained national attention through the grassroots advocacy and work of tribes, tribal 

organizations, and tribal members (for purposes of this report, “indigenous persons” refers to 

American Indian and Alaska Native [AI/AN] persons). This advocacy has resulted in increased 

efforts to continue raising awareness of and enhance response to the issues as exemplified by the 

establishment of The Presidential Task Force on Missing and Murdered American Indians and 

Alaska Natives known as Operation Lady Justice (OLJ, n.d.). The purpose of this Task Force is 

to engage in consultation with tribes and villages; to work to enhance response to missing or 

murdered AI/AN persons through the development of model protocols for law enforcement 

response, and data sharing and database use; to establish multi-disciplinary cold case review 

teams; and to design an educational outreach and awareness campaign (OLJ, n.d.). The 

establishment of this Task Force, including its mandates, exemplifies the broad scope and 

pressing nature of these issues. 

Violence among AI/AN Persons

As reported by Rosay (2016), AI/AN persons, both women and men, face high levels of violence 

victimization. Overall, in a nationally-representative sample of self-identified AI/AN women 

and men, 84.3% of women and 81.6% of men had ever experienced violence in their lifetime. 

These rates are higher than for non-Hispanic Whites. AI/AN women experienced 1.2 times and 

AI/AN men experienced 1.3 times the rates of victimization as non-Hispanic Whites. More 

specifically, 66.4% of women and 73.0% of men have experienced psychological aggression by 

an intimate partner, 55.5% of women and 43.2% of men have experienced physical violence by 

an intimate partner, 56.1% of women and 27.5% of men have experienced sexual violence, and 

48.8% of women and 18.5% of men have experienced stalking. 

Data Issues with MMIP

Tribal advocates have stressed that there are little data to fully understand the true scope and 

nature of MMIP (Sovereign Bodies Institute [SBI], 2019), particularly in urban areas (Urban 

Indian Health Institute [UIHI], 2018). While it is unclear exactly how many MMIP there may 

be, advocates maintain that most databases do not fully capture the true extent of these issues due 

to underreporting, reports not taken by law enforcement, and misclassification of indigenous 

persons as non-indigenous, among other reasons. (SBI, 2020; UIHI, 2018). This lack of data 

completeness and quality has resulted in efforts to improve data collection, including efforts at 

the policy level. For example, the federal Savanna’s Act mandates improved coordination of, 

access to, and reporting of statistics on MMIP across federal, state, and tribal jurisdictions. 

Further, it mandates clearer protocols on how such data collection may be improved. The Act 

includes NamUs as a party to the legislation as one database that should be engaged in data 

collection processes and process improvements (Congress.gov, 2020 Oct 10).

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
56

NamUs Examination of Circumstances and Characteristics Study

Various entities or organizations are making attempts to improve data collection and 

quality regarding MMIP. Among non-governmental organizations, SBI has worked to create a 

database tailored specifically to the data issues of indigenous persons and communities that may 

not be collected by databases for the general population (SBI, 2019). UIHI has also engaged in 

data collection from law enforcement agencies in urban population centers to better describe the 

issues among urban AI/AN persons (UIHI, 2018). Overall, efforts from community and nongovernmental organizations have been at the forefront of discussion regarding the MMIP issues. 

Among governmental systems, NamUs has actively worked with the Bureau of Indian 

Affairs at the U.S. Department of the Interior to define and implement data fields specifically for 

AI/AN persons. These data fields include tribal affiliation/enrollment, primary residence on 

tribal land, and missing from or found on tribal land, among others. Additionally, NamUs has 

worked to increase its outreach to tribal communities through trainings, webinars, and monthly 

publication of tribal-specific data informational sheets. 

Methods 

Case Classification 

Case classification procedures to determine if a case was violent or non-violent have been 

described above and followed the same procedures outlined in the MP and UP methods sections, 

respectively.

Case Analysis 

Variables

Variables used for this special section included the same variables as used in the overall MP and 

UP sections of the report with the notable exception of population/ethnicity. 

Population/ethnicity is not included as all cases involve persons identified as AI/AN alone or in 

combination with another population/ethnicity. Further, initial attempts were made to use the 

tribal-specific variables described above; however, for both female and male cases, these 

variables suffered from high levels of cell counts under 10 cases. As such, it was deemed 

inappropriate to use these variables, as the results may have been misleading or overreaching 

what could actually be concluded by data from so few cases. 

Analytic Approach 

Cases were analyzed using similar methods used in the MP and UP overall analyses. 

Results: MP

Overall, there were 792 valid cases for classification and analysis. Females made up 42.8% (N = 

339) and males encompassed 57.2% (N = 453) of all cases. Figures 53-80 display results for MP 

cases, including gender stratified findings for types of violence and gender stratified bivariate 

results comparing case characteristics by presence of case violence across case resolution status. 

An asterisk (*) next to a variable indicates that there was at least one cell for which the case 

count was less than 10. 

All Cases

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
57

NamUs Examination of Circumstances and Characteristics Study

Overall Violence (N = 792) 

Among females, 10.9% (n = 37) experienced some form of violence (Figure 53), while 8.6% (n 

= 39) of males experienced some form of violence (Figure 54). 

General Violence (N = 792) 

89.1%

10.9%

Figure 53. Overall Case Violence, Females (N = 339)

No Yes

91.4%

8.6%

Figure 54. Overall Case Violence, Males (N = 453)

No Yes

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
58

NamUs Examination of Circumstances and Characteristics Study

As with overall violence, a higher proportion of females (5.0%, n = 17) (Figure 55) than males 

(4.6%, n = 21) (Figure 56) experienced general violence.

95.0%

5.0%

Figure 55. General Violence, Females (N = 339)

No Yes

95.4%

4.6%

Figure 56. General Violence, Males (N= 453)

No Yes

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
59

NamUs Examination of Circumstances and Characteristics Study

Physical Violence (N = 792) 

Among females , 7.7% (26) experienced physical violence (Figure 57), while 6.4% (n = 29) of 

males experienced physical violence (Figure 58). 

92.3%

7.7%

Figure 57. Physical Violence, Females (N = 339)

No Yes

93.6%

6.4%

Figure58. Physical Violence, Males (N = 453)

No Yes

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
60

NamUs Examination of Circumstances and Characteristics Study

Sexual Violence (N = 792)* 

No cases for females or for males involved sexual violence.

Stalking Behavior (N = 792)* 

For females, 0.6% (n = 2) experienced some type of stalking behavior (Figure 59), while 0.2% (n 

= 1) of males experienced stalking behavior (Figure 60). 

99.4%

0.6%

Figure 59. Stalking Behavior, Females (N = 339)

No Yes

99.8%

0.2%

Figure 60. Stalking Behavior, Males (N = 453)

No Yes

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
61

NamUs Examination of Circumstances and Characteristics Study

Coercive Control (N = 792)* 

Among females, 1.2% (n = 4) experienced this type of violence (Figure 61), while 0.2% (n = 1) 

of males did as well (Figure 62). 

98.8%

1.2%

Figure 61. Coercive Control, Females (N = 339)

No Yes

99.8%

0.2%

Figure 62. Coercive Control, Males (N = 453)

No Yes

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
62

NamUs Examination of Circumstances and Characteristics Study

Psychological Aggression (N = 792)* 

Overall, 1.8% (n = 6) of females (Figure 63) and 1.8% (n = 8) of males (Figure 64) experienced 

psychological aggression. 

98.2%

1.8%

Figure 63. Psychological Aggression, Females (N = 339)

No Yes

98.2%

1.8%

Figure 64. Psychological Aggression, Males (N = 453)

No Yes

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
63

NamUs Examination of Circumstances and Characteristics Study

Age of Case (Groups) (N = 792)* 

Among female non-violent cases, the highest proportion of cases were 5-10 years old (23.8%, n 

= 72) while the highest proportion of violent cases were 20+ years old (43.2%, n = 16) (Figure 

65). This trend was mirrored among male non-violent cases where the highest proportion of 

cases were observed among 5-10 year old cases (22.2%, n = 92) while the highest proportion of 

violent cases were 20+ years old (41.0%,n = 16) (Figure 66). 

State Name (N = 792)* 

1.3

8.6 10.3

22.5 23.8

14.6

18.9

2.7

5.4

10.8 10.8

16.2

10.8

43.2

0

5

10

15

20

25

30

35

40

45

50

<90 days 3-12 months 1-2 years 2-5 years 5-10 years 10-20 years 20+ years

Percentage

Age of Case Groups

Figure 65. Comparison of Case Violence Across Age of Case 

Groups, Females (N = 339)

Non-Violent Violent

1.7

9.4 11.1

20.5 22.2

15.9

19.1

2.6 2.6

5.1

15.4

20.5

12.8

41

0

5

10

15

20

25

30

35

40

45

<90 days 3-12 months 1-2 years 2-5 years 5-10 years 10-20 years 20+ years

Percentage

Age of Case Groups

Figure 66. Comparison of Case Violence Across Age of Case 

Groups, Males (N = 453)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
64

NamUs Examination of Circumstances and Characteristics Study

Figures 67 and 68 show the states representing the highest proportion of violent casesin NamUs 

with their non-violent case percentages shown for comparison. Among females, Washington 

state had the highest proportion of violent cases, representing 18.9% (n = 7) of all violent 

cases(Figure 67). Among males, Arizona had the highest proportion of violent cases, 

representing 18.0% (n = 7) of all violent cases (Figure 68). The numbers presented represent 

absolute numbers of case violence, and do not represent rates of violence within the states; as 

such, the numbers may reflect higher usage of the NamUs system as opposed to actual higher 

levels of violent cases within these states.

13.6 13.9

6

9.6

3

9.3

0 0

18.9

16.2

10.8 10.8

8.1

5.4 5.4

2.7

0

2

4

6

8

10

12

14

16

18

20

Washington Oklahoma Alaska Arizona Montana California Illinois CO, FL, IA,

ME, MI, MO,

NM, ND, SD

Figure 67. Top States by Percent of All Violent Cases with 

Non-Violent Case Comparison, Females (N = 339)

% of Total Non-Violent Cases % of Total Violent Cases

11.4

5.8

10.9

16.4

1.5

3.1 2.4 2.2

3.9

0

18

15.4

10.3

7.7 7.7 7.7 7.7

5.1 5.1

2.6

0

2

4

6

8

10

12

14

16

18

20

Figure 68. Top States by Percent of All Violent Cases with 

Non-Violent Case Comparison, Males (N = 453)

% of Total Non-Violent Cases % of Total Violent Cases

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
65

NamUs Examination of Circumstances and Characteristics Study

Foul Play (N = 410)* 

Among female non-violent cases, 61.5% (n = 83) of cases indicated uncertainty if foul play was 

likely (Figure 69). However, among female violent cases, 44.4% (n = 12) of cases indicated 

likely foul play. A high proportion of cases not classified as violent did indeed have the foul play 

indicator checked (19.3%, n = 26). Among male non-violent cases, 66.4% (n = 146) indicated 

uncertainty as to the presence of foul play (Figure 70). Among male violent cases, 53.6% (n = 

15) indicated likely foul play. Again, as with female results, a high proportion of cases that were 

not classified in the classification process as being violent had that foul play indicator checked 

(11.4%, n = 25). 

17.8

1.5

61.5

19.3

3.7

14.8

37

44.4

0

10

20

30

40

50

60

70

No Possibly Uncertain Yes

Percentage

Foul Play Present

Figure 69. Comparison of Case Violence by Presence of Foul 

Play, Females (N = 162)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
66

NamUs Examination of Circumstances and Characteristics Study

Age Missing (Group) (N = 792)* 

Among females, the highest percentage of cases were for the under 18 age group for both nonviolent (34.8%, n = 105) and violent (27.0%, n = 10) categories (Figure 71). Among males, the 

highest proportion of non-violent cases was for the 21-30 age group (26.8%, n = 111), and the 

highest proportion of violent cases was for the same age group (30.8%, n = 12) (Figure 72). 

19.1

3.2

66.4

11.4

3.6

0

42.9

53.6

0

10

20

30

40

50

60

70

No Possibly Uncertain Yes

Percentage

Foul Play Present

Figure 70. Comparison of Case Violence by Presence of Foul 

Play, Males (N = 278)

Non-Violent Violent

34.8

6.6

17.6 15.9

10.6

8.6

6

27

13.5

24.3

10.8

16.2

5.4

2.7

0

5

10

15

20

25

30

35

40

<18 18-20 21-30 31-40 41-50 50+ Unknown

Percentage

Age Group

Figure 71. Comparison of Case Violence by Age Group at 

Missing Person Event, Females (N = 339)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
67

NamUs Examination of Circumstances and Characteristics Study

Resolved Cases (N = 391) 

Case Resolution Status (N = 391)* 

Among females, 21.1% of non-violent cases and 25.0% of violent cases had a resolution where 

the individual was found deceased (Figure 73). Among males, 44.0% (n = 84) of non-violent 

cases and 61.5% (n = 8) of violent cases had a resolution where the missing person was found 

deceased (Figure 74). 

13.3

5.1

26.8

15.9 15.7

18.4

4.8

10.3 10.3

30.8

25.6

20.5

2.6

0

0

5

10

15

20

25

30

35

<18 18-20 21-30 31-40 41-50 50+ Unknown

Percentage

Age Group

Figure 72. Comparison of Case Violence by Age Group at 

Missing Person Event, Males (N = 453)

Non-Violent Violent

21.1

78.9

25

75

0

10

20

30

40

50

60

70

80

90

Deceased Alive

Percentage

Resolution Status

Figure 73. Comparison of Case Violence by Case Resolution 

Status, Females (N = 187)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
68

NamUs Examination of Circumstances and Characteristics Study

NamUs Assisted (N = 390)* 

Among females, 8.6% (n = 15) of non-violent cases and 8.3% (n = 1) of violent cases indicated 

NamUs assistance (Figure 75). For males, 16.3% (n = 31) of non-violent cases and 38.5% (n = 

5) of violent cases indicated NamUs assistance (Figure 76). 

44

56

61.5

38.5

0

10

20

30

40

50

60

70

Deceased Alive

Percentage

Resolution Status

Figure 74. Comparison of Case Violence by Case Resolution 

Status, Males (N = 204)

Non-Violent Violent

91.4

8.6

91.7

8.3

0

10

20

30

40

50

60

70

80

90

100

No Yes

Percentage

NamUs Assisted

Figure 75. Comparison of Case Violence by NamUs 

Assistance in Case Resolution, Females (N = 187)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
69

NamUs Examination of Circumstances and Characteristics Study

Manner of Death (N = 29)* 

Among females classified as non-violent, the highest proportion of cases had undetermined as 

the MOD (77.8%, n = 7). Among female violent cases, 100.0% (n = 1) of cases were ruled as 

homicides (Figure 77). Among male non-violent cases, the highest proportion for manner of 

death was undetermined (75.0%, n = 12) for non-violent cases. For male violent cases, the most 

represented MOD was homicide (100.0%, n = 3) (Figure 78). 

83.7

16.3

61.5

38.5

0

10

20

30

40

50

60

70

80

90

No Yes

Percentage

NamUs Assisted

Figure 76. Comparison of Case Violence by NamUs 

Assistance in Case Resolution, Males (N = 203)

Non-Violent Violent

11.1

0 0

11.1

0

77.8

0

100

0 0 0 0

0

20

40

60

80

100

120

Accident Homicide Natural Pending Suicide Undetermined

Percentage

Manner of Death

Figure 77. Comparison of Case Violence Across Manners of 

Death, Females (N = 10)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
70

NamUs Examination of Circumstances and Characteristics Study

Unresolved Cases (N = 401) 

DNA Availability (N = 401)* 

Among female non-violent cases, 60.6% (n = 77) had DNA available while 64.0% (n = 16) of 

violent cases also had DNA available (Figure 79). Among males, 51.1% (n = 114) of nonviolent cases and 50.0% (n = 13) of violent cases had DNA available for analysis (Figure 80). 

6.3

0 0

12.5

6.3

75

0

100

0 0 0 0

0

20

40

60

80

100

120

Accident Homicide Natural Pending Suicide Undetermined

Percentage

Manner of Death

Figure 78. Comparison of Case Violence by Manner of 

Death, Males (N = 19)

Non-Violent Violent

39.4

60.6

36

64

0

10

20

30

40

50

60

70

No Yes

Percentage

DNA Availability

Figure 79. Comparison of Case Violence by DNA Availability, 

Females (N = 152)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
71

NamUs Examination of Circumstances and Characteristics Study

48.9 51.1 50 50

0

10

20

30

40

50

60

No Yes

Percentage

DNA Availability

Figure 80. Comparison of Case Violence by DNA Availability, 

Males (N = 249)

Non-Violent Violent

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
72

NamUs Examination of Circumstances and Characteristics Study

Results: UP 

Results for AI/AN UP case are not shown due to low cell counts. In most of the results, there is 

at least one cell with a cell count of less than 10. Given these low numbers, the AI/AN results 

were not included. 

Recommendations 

No recommendations are made for the AI/AN special section due to low numbers in the 

analyses. It was determined by the project team that providing recommendations for changes to 

the entirety of a national system based on results that often involved tables with cell counts less 

than 10 would not be appropriate. Once more cases have been entered into the database and the 

analysis can be re-run with higher case numbers, recommendations may be possible and 

appropriate. 

Conclusion 

Overall, results for MP AI/AN cases are presented. Results for AI/AN UP cases were not 

included due to low cell counts. Low cell counts also were observed with the MP cases. As 

such, no recommendations were made for systems improvement and expansion, as it was felt that 

it would be inappropriate to make broad generalizations for systems change based on several 

variables that had low cell count. However, the results for MP cases do provide preliminary 

insights into trends, which can be more fully described once more case data are captured within 

the system for MP and UP AI/AN cases.

Limitations

This tribal special section was limited in various ways. As with the general population analyses, 

the analysis presented in this section made use of operational data not designed specifically for 

the purposes of systems improvement or expansion inquiry. As such, the variables, including 

their codings, may not be the most appropriate data for the purposes of this report. 

Second, the data fields used suffered from low numbers of cases with valid information, 

which limits the conclusions that can be drawn from any analysis involving these variables. As 

such, no recommendations for systems improvement and expansion are provided. Once more 

cases populate the NamUs database, it may be possible to make more general conclusions for 

systems improvement and expansion. 

Finally, the cases used in these analyses represent self-identified AI/AN persons and not 

necessarily AI/AN persons enrolled or affiliated with a state- or federally-recognized tribe. As 

such, no inferences should be made regarding criminal jurisdiction. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
73

NamUs Examination of Circumstances and Characteristics Study

V. General Conclusion

The work presented in this report led to noteworthy observations and conclusions that were 

useful in the development of potential hypotheses and recommendations for system improvement 

and expansion. Through this work, thoughtful and needed improvements or expansions to the 

system can be made informed by the relevant data. In all, this project provided valuable insights 

that may enable NamUs to better serve its constituent users, to more efficiently and effectively 

work to resolve cases, and, more broadly, to address the issues of missing and unidentified 

persons through prevention and intervention facilitated by better and more complete data 

collection, biometric sample collection, and service provision to persons impacted by such cases. 

Summary of Recommendations 

Overall, the work completed in this report resulted in several recommendations for NamUs 

improvement and expansion. In all, both the MP and UP sections resulted in five key findings 

with nine associated recommendations for a total of 18 recommendations for potential 

consideration for systems improvement and expansion. The implementation of these 

recommendations will depend on technological capabilities and human resources training to 

ensure that as many of the recommendations as possible are put in place. Some recommendations 

may require more data before considering possible implementation. Additionally, tracking and 

evaluation elements should be put in place to assess the impact of these recommendations and 

changes that occur that may have on case investigation, tracking, and resolution. 

General Limitations

This project has several limitations. First, it uses data from a source that is not intended for the 

project purposes. The NamUs database is first and foremost an operational database used for the 

management, tracking, and resolution of cases. As such, data fields and responses are not 

optimized for the systems improvement purposes. 

 Second, the NamUs system relies on crowd sourced data; that is, NamUs relies on outside 

parties to enter data into the database and does not itself as a program actively solicit cases. 

Further, participation in the system is largely voluntary, as is the provision of data for many of 

the data fields. Thus, there is no single, comprehensive, or standardized data entry across all 

users. Without these elements, cases as data points may vary substantially in terms of data 

provided and the completeness of that data. 

 Third, NamUs launched NamUs 2.0 in May 2018. Since that date, continual updates 

have been made to NamUs. These dynamic updates may change the types of data that are

collected, how it is collected, and how they are reported. For example, the tribal data fields were 

added in December 2018, and therefore, they do not represent complete data for all tribal cases 

that occurred before that date.

Next Steps

Project Phase 2

Following completion of case classification, a second phase of the current project may be 

considered to ascertain final case disposition for cases classified as violent. In this second phase, 

follow-up surveys may be conducted both electronically through e-mail and over the phone 

through a call to the investigating agency. This survey may include questions about the 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
74

NamUs Examination of Circumstances and Characteristics Study

perpetrator, victim-perpetrator relationship, and any criminal justice involvement due to the case, 

such as arrest or prosecution. The results of this phase may allow for recommendations to 

improve system data collection, as well as provide information on the characteristics of 

perpetrators and violent cases within NamUs that may be used to enhance case resolution, for 

example, by helping to build profiles of both victims and offenders that may assist in identifying 

potentially violent cases and in apprehending any associated violent offenders. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
75

NamUs Examination of Circumstances and Characteristics Study

VI. References

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framework be generated using behavioral themes? Journal of Investigative Psychology 

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partner violence victimization—National Intimate Partner and Sexual Violence Survey, 

United States, 2011. Morbidity and Mortality Weekly Report, 63(8), 1-18. 

Breiding, M.J., Chen, J., & Black, M.C., (2014). Intimate Partner Violence in the United States – 

2010. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease 

Control and Prevention, National Center for Injury Prevention and Control. Accessed 26 

Oct 2020 at https://www.cdc.gov/violenceprevention/pdf/cdc_nisvs_ipv_report_2013 

_v17_single_a.pdf. 

Crime Data Explorer. (2019). All Violent Crime Offender vs. Victim demographics. Accessed 17 

July 2019 at https://crime-data-explorer.fr.cloud.gov/explorer/national/unitedstates/crime/2007/2017. 

Congress.gov. (2020, Oct. 10). Savanna’s Act. Washington, D.C.: 116th U.S. Congress. 

Accessed 5 Nov 2020 at https://www.congress.gov/116/plaws/publ165/PLAW116publ165.pdf. 

Federal Bureau of Investigations. (2020). 2019 NCIC Missing and Unidentified Persons

Statistics. Washington, DC: U.S. Department of Justice, Federal Bureau of Investigations. 

Accessed 25 Oct 2020 at https://www.fbi.gov/file-repository/2019-ncic-missing-personand-unidentified-person-statistics.pdf/view. 

Henderson, M., Henderson, P., & Kiernan, C. (2000). Missing persons: Incidences, issues and 

impacts. Australian Institute of Criminology: Trends and Issues in Crime and Criminal 

Justice, 144, 1-6 

Hickman, M.J., Hughes, K.A., Strom, K.J., & Ropero-Miller, J.D. (2007). Medical Examiners 

and Coroners’ Offices, 2004. Washington DC: U.S. Department of Justice, Office of 

Justice Programs, Bureau of Justice Statistics. Accessed 17 Jul 2020 at 

https://www.bjs.gov/content/pub/pdf/meco04.pdf. 

Hughes, K.A., (2007). Unidentified Human Remains in the United States, 1980-2004. 

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Justice Statistics. Accessed 17 Jul 2020 at 

https://www.bjs.gov/content/pub/pdf/uhrus04.pdf. 

National Center for Injury Prevention and Control. (2020). Leading Causes of Death Reports, 

1981 - 2018. Atlanta, GA: U.S. Department of Health and Human Services, Centers for 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
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NamUs Examination of Circumstances and Characteristics Study

Disease Control and Prevention, National Center for Injury Control and Prevention. 

Accessed 26 Oct 2020 at https://webappa.cdc.gov/sasweb/ncipc/ leadcause.html. 

Operation Lady Justice. (n.d.). The Presidential Task Force on Missing and Murdered American 

Indians and Alaska Natives. Accessed 5 Nov 2020 at 

https://operationladyjustice.usdoj.gov/. 

Puzzanchera, C., Chamberlin, G., and Kang, W. (2020). "Easy Access to the FBI's 

Supplementary Homicide Reports: 1980-2016." Accessed 5 Nov 2020 at 

https://www.ojjdp.gov/ojstatbb/ezashr/

Ritter, N. (2007). Missing persons and unidentified remains: The nation’s silent mass disaster. 

NIJ Journal, 256, 2-7. 

Rosay, A.B. (2016). Violence Against American Indian and Alaska Native Women and Men: 

2010 findings from the National Intimate Partner and Sexual Violence Survey. 

Washington, DC: U.S. Department of Justice, Office of Justice Programs, National 

Institute of Justice. Accessed 4 Aug 2020 at 

https://www.ncjrs.gov/pdffiles1/nij/249736.pdf. 

Shweta, Bajpai, R.C., & Chaturvedi, H.K. (2015). Evaluation of inter-rater agreement and interrater reliability for observational data: An overview of concepts and methods. Journal of 

the Indian Academy of Applied Psychology, 41(3), 21-27. 

Smith, S.G., Zhang, X., Basile, KC., Merrick, MT., Wang, J., Kresnow, M., & Chen, J. (2018). 

The National Intimate and Sexual Violence Survey (NISVS): 2015 Data Brief – Updated 

Release. Atlanta, GA: U.S. Department of Health and Human Services, Centers for 

Disease Control and Prevention, National Center for Injury Control and Prevention. 

Accessed 26 Oct 2020 at https://www.cdc.gov/violenceprevention/pdf/2015databrief508.pdf. 

Sovereign Bodies Institute. (n.d.). About the data. Accessed 5 Nov 2020 at 

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Sovereign Bodies Institute. (2020). To' Kee Skuy' Soo Ney-Wo-Chek': I will see you again in a 

good way. Progress Report. Accessed 5 Nov 2020 at https://2a840442-f49a-45b0-b1a1-

7531a7cd3d30.filesusr.com/ugd/6b33f7_c7031acf738f4f05a0bd46bf96486e58.pdf. 

Straus, M.A., Hamby, S.L., Boney-McCoy, S., & Sugarman, D.B. (1996). The revised Conflict 

Tactics Scales (CTS2): Development and preliminary psychometric data. Journal of 

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Urban Indian Health Institute. (2018). Missing and murdered indigenous women and girls.

Seattle, WA: Seattle Indian Health Board, Urban Indian Health Institute. Accessed 5 Nov 

2020 at file:///C:/Users/sph0085/Downloads/UIHI-Missing-and-Murdered-IndigenousWomen-and-Girls-Report-20191009.pdf. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
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NamUs Examination of Circumstances and Characteristics Study

Violence Policy Center. (2016). When men murder women: An analysis of 2014 homicide data.

Accessed 23 Oct 2018 at http://www.vpc.org/studies/wmmw2016.pdf. 

World Health Organization. (2016 Nov). Violence against women: Intimate partner and sexual 

violence against women. Accessed 23 Oct 2018 at 

http://www.who.int/mediacentre/factsheets/fs239/en/

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
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Appendix A

Technical Note

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
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NamUs Examination of Circumstances and Characteristics Study

Purpose 

The purpose of this technical note is to provide more nuanced and in-depth information into the 

methods underlying the results and recommendations of the ECC project. Information detailed 

within is a more comprehensive accounting for the technical steps taken as part of this report. 

Case Classification

MP Case Classification 

Run 1 

Data delivered for Run 1 included 34,866 cases. Following delivery of data for Run 1, data were 

cleaned by removing any cases that did not have any information in either of the circumstance 

fields, that indicated some form of “unknown,” or that contained only numbers or symbols. This 

cleaning resulted in the removal of 537 cases leaving a total case count of 34,329 unique cases. 

After cleaning, data were imported into WordStat for analysis using the previously-built 

classification dictionary described in the respective MP methods section. This classification 

resulted in 5,054 cases being coded as violent. 

Percent Agreement. Next, data were imported into Stata in order to draw a random 

sample of 10% of cases (N=3,433) for second coding by a human coder. Second coding resulted 

in 87% agreement between WordStat and the human coder. Overall, of the 449 disagreements 

between WordStat and the human coder, 325 cases (72.4%) were instances in which WordStat 

coded the case as violent, but the human coder did not. Alternatively, 124 cases (27.6%) of 

cases were instances in which WordStat coded the case as non-violent, but the human coder 

coded the case as violent. 

Disagreements and Dictionary Changes. After second coding, a disagreements check 

was performed in order to evaluate the relevance and functionality of keywords that led to 

discrepant coding between WordStat and the human coder. Following the disagreements check, 

appropriate amendments to the dictionary were made, including the addition of new keywords or 

inflected forms including, but not limited to: argue, abduct, homicide, exploitation, 

disagreement, killer, kidnapping, and danger. Some words were re-classified to more 

appropriate categories of violence, including foul play, riot, and brawl from general violence to 

physical violence. Further, the term “trafficking” was specified to be “gender trafficking” as it 

was felt that not all forms of trafficking could be adequately captured within the current 

dictionary and that a broader conversation on trafficking as an issue of interest to stakeholders 

was warranted before finalizing any further trafficking keywords. Finally, an exclusion list for 

keywords and phrases that were consistently resulting in false positives was created including 

but not limited to words and phrases such as: “substance abuse,” “follow-up,” “Air Force,” “selfharm,” and “bipolar disorder” among others. A full listing of excluded words and phrases is 

shown in Table 1. 

Table 1. Exclusion List 

Keyword Excluded Phrase

Abuse Abuse alcohol

Abuse drugs

Abuse substances

Abuses alcohol

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
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NamUs Examination of Circumstances and Characteristics Study

Abuses drugs

Abuses substances

Alcohol abuse

Alcohol abuser

Substance abuse

Substance abuser

Drug abuse

Drug abuser

Harm Self-harm

Follow Follow up

Follow-up

Followed-up

Followed up

Disorder Removed from dictionary

Data Discrepancies. Through the disagreement checking process, two data 

inconsistencies were discovered. First, 18 cases were identified in which some of the lines 

related to the cases were not present. There were likely several more cases with this issue. This 

issue made disagreement checking more difficult, as often the score provided by the computer 

was not substantiated by the single line of text but was validated upon checking the case within 

the original data. While the exact cause of this issue is unknown, it is likely that it occurred 

during the exportation process from either WordStat or Stata after the random sample of cases 

was selected. It is possible that not all lines were selected and exported as part of the random 

sample. Alternatively, it is possible that the process through which multiple-line cases were 

collapsed and summed to check for agreement with the human coded data was faulty. This issue 

was not discovered initially, as there was no reason to believe cases would not be exported in 

whole by CaseID. Although there were data inconsistencies, it is not believed that the intercoder agreement was affected, as the exact same information was coded by both the computer 

and the human. In the second run of the data, particular attention was paid to any import and 

export processes to ensure that no cases were lost in part or in full. Additionally, it was

attempted to use Stata as little as possible and to work within the spreadsheets manually 

whenever possible. 

Run Termination. Following these steps, however, we eventually terminated this 

particular run due to missing Circumstances of Resolution data for text analysis. These data had 

not been requested by the project team initially but were later determined by to be important for 

case classification. As such, no tertiary coding was conducted as part of this run. 

Run 2

Data delivered for the second classification included again 34,866 cases and also included 

Circumstances of Resolution in addition to other circumstance fields. These data were merged 

with general Circumstances and Agency Notes fields to create the full set of data fields that 

would be classified. Data were cleaned using the same process as in Run 1. This cleaning 

resulted in the removal of 356 cases in their totality and one case with a missing Case ID 

resulting in a final sample of 34,509 unique cases for classification. Cleaned data were then 

imported into WordStat for classification. Several classifications were tested, including with and 

without an exclusion list created based on lessons learned from Run 1 of the data. 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
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Misspellings Check. In Run 2, we also added a misspellings check to the procedures. 

This check was completed using WordStat’s built-in functionality. The misspellings check was 

conducted using the lowest level of confidence (i.e., moderate) of a potential misspelling for 

words in the classification dictionary to ensure the broadest set of potential misspelled words was 

captured and assessed. Each potential misspelling was checked using a “keyword-in-context” 

feature to ensure the misspelling truly represented a misspelled word, a proper noun, or some 

otherwise correct spelling for a different word. Overall, 134 potentially misspelled words were 

identified by WordStat. Upon checking the keyword-in-context, it was determined that 40 

instances represented valid misspellings and were added within the classification dictionary. The 

other instances were generally proper nouns, names, or parts of internet addresses and were not 

added to the classification dictionary. 

Percent Agreement. The final run of the data included use of the exclusions list 

previously created, as well as corrected misspelled words. Overall, this classification resulted in 

7,059 cases being coded as violent by WordStat. This number increased by nearly 2,000 cases 

from Run 1, likely due to the addition of keywords (e.g., beat, argue, homicide, prostitution), as 

well as the inclusion of Circumstances of Resolution as a text field for analysis. Following the 

final Run 2, data were imported into Stata, and a random sample of 10% (N=3,451) was drawn 

for second coding. Percent agreement was calculated and was found to be 89%. Of the 383 

disagreements, 343 (89.6%) were found to be instances in which WordStat classified a case a 

violent, but the human coder did not. Alternatively, 40 (10.4%) cases were instances in which 

the human coder classified the case as violent, but WordStat did not. 

Disagreements and Dictionary Changes. Following the calculation of percent 

agreement, a disagreements check was performed to understand discrepancies in coding between 

WordStat and the human coder. Through this process, no additional keywords were uncovered 

to be added to the dictionary. However, through review of the dictionary by co-investigator 

Spamer, it was determined that there were additional words and their inflected forms that needed 

to be added to the dictionary. Additional keywords added included: pimp, prostituting, sexual 

assault, suspect, confession, offender, gunshot, strangle, asphyxiate, ligature, knifepoint, bury, 

smother, manslaughter, guilty, and sentence. As such, it was determined that a third run of the 

data would be necessary. 

Tertiary Coding. No tertiary coding was conducted as part of this run of the data. 

Run 3

Baseline data were imported from Run 2, as the cleaning procedure was already complete for the 

full dataset that included Circumstances of Resolution. New keywords were received from coinvestigator Spamer following Run 2 were integrated into the dictionary. Overall, 7,659 (22.2%) 

of cases had at least one keyword indicating violence. This number increased by 600 cases from 

Run 2, likely due to the addition of keywords by co-investigator Spamer.

Misspellings Check. Then, in WordStat, a misspellings check was conducted in the same 

manner as before, and all identified misspellings that represented truly misspelled words were 

categorized into the WordStat dictionary. Overall, 108 potential misspellings were identified by 

WordStat. Of those potential misspellings, nine were valid and were added to the classification 

dictionary. The non-valid misspellings represented proper nouns, names, and locations, among 

other categories.

Percent Agreement. Following classification in WordStat, a random sample of 10% 

(N=3,451) for second coding was drawn in Stata. Following WordStat coding, percent 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
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agreement between WordStat and the human coder was calculated and found to be 88%. 

Overall, WordStat and the human coder disagreed on 401 cases. Of these disagreements, 389 

(97%) represented cases where WordStat classified the case as violent, but the human coder did 

not. Alternatively, 12 (3.0%) cases were classified as violent by the human coder, but as nonviolent by WordStat. 

Tertiary Coding. Given the high level of agreement between WordStat and the human 

coder, tertiary coding was completed by a second human coder (co-investigator Spamer) using a 

subsample of 20% of the 10% of cases originally sampled for second coding (N=690). The 

agreement between the two human coders was 93%. Overall, the human coders disagreed on 48 

cases. Six cases were coded violent by Spamer, but as non-violent by Hafner, accounting for 

12.5% of all disagreements. Alternatively, 42 cases were coded as violent by the Hafner, but as 

non-violent Spamer, accounting for 87.5% of disagreements. 

 

Final Violence Coding

Following tertiary coding, given the high levels of agreement between WordStat and the human 

coder, as well as between the two human coders, a final coding of all cases classified as violent 

by WordStat was performed. In doing so, only cases classified as violent by WordStat were 

examined to ensure they truly represented violence and that the classification as violent was not 

spurious based on keywords being used in a different context (e.g., DNA “hit”). Overall, there 

were 7,659 cases from Run 3 coded as violent by WordStat, but only 3,493 were retained as 

actually indicating suspected or confirmed violence by the human coder. Later, 86 cases with a 

manner of death of homicide that were not classified in the case classification as violent were 

added as violent cases resulting in the final case count of 3,579 violent cases. 

UP Case Classification 

Run 1

Run 1 consisted only of cases in which the manner of death was undetermined or for which no 

manner of death was provided. Data delivered for this first run included 16,857 cases overall 

with 6,6617 undetermined cases and 2,581 cases where the manner of death was unknown, 

totaling 9,198 cases for classification. Following delivery of data, the data were cleaned by 

removing any cases that did not have any information in either of the circumstance fields, that 

indicated some form of “unknown,” or that contained only numbers or symbols. This cleaning 

resulted in the removal of 103 cases leaving a total case count of 9,095 unique cases. After this 

initial cleaning, data were imported into WordStat for analysis using the previously-built 

classification dictionary described above. 

Misspellings. As with MP cases, we used WordStat’s built-in misspelling functionality to 

check for potentially misspelled yet valid keywords. Overall, 11 potential misspellings were 

identified using the lowest level of confidence within the WordStat functionality. In the end, 

nine of these misspellings were found to be valid and were included in the classification 

dictionary. 

Percent Agreement. Next, data were imported into Stata in order to draw a random 

sample of 10% of cases for second coding by a human coder. During the second coding, a case 

that should have been cleaned out was discovered and was removed leaving an overall sample of 

9,094 cases. However, after discovery of the invalid case described, the removal of it resulted in 

a second coding sample of 909 valid cases. Second coding resulted in 87.2% agreement between 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
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WordStat and the human coder. Of the 116 total disagreement cases, 102 (87.9%) were cases in 

which WordStat classified the case as violent, but the human coder did not. Fourteen cases 

(12.1%) were instances in which the human coder classified the case as violent, but WordStat did 

not. Overall, following this misspelling check and additional cleaning, the classification resulted 

in 1,244 cases being coded as violent. 

Disagreements Check. A disagreements check was performed in order to evaluate the 

relevance and functionality of keywords that led to discrepant coding between WordStat and the 

human coder. Following the disagreements check, it was determined that all words were 

relevant to the analysis and that even though some words (e.g, trauma) might result in several 

false positives that such particular keywords were too central to the violent case classification to 

be removed. 

Tertiary Coding. After second coding, tertiary coding was conducted by a second 

human coder. For this coding, a subsample of 20% of the 10% used for second coding was 

drawn (n = 182) and was delivered to the tertiary coder. However, during the tertiary coding 

process, truncated circumstances were discovered by the second human coder. 

Run Termination. Given the truncated circumstances uncovered, we eventually aborted 

this first run due to the truncated circumstance data. As such, the data used for this run were no 

longer considered valid and were discarded. 

Run 2

Data delivered for the second classification included the same number of cases as Run 1 (N = 

9,198). Initial cleaning resulted in the removal of 113 cases and a final sample of 9,085 unique 

cases for classification. Cleaned data were then imported into WordStat for classification. 

However, during the second coding process described below, two additional cases were found 

that were cleaned out from the data resulting in 115 removals and a unique sample of 9,083 

cases. 

Misspellings Check. We conducted a misspellings check that was completed using 

WordStat’s built-in functionality, and each potential misspelling was checked using a “keywordin-context” feature to ensure the misspelling truly represented a misspelled word, a proper noun, 

or some other otherwise correct spelling for a different word. Overall, 18 potential misspelled 

words were found, and 11 words were found to be valid misspellings of a keyword. These 11 

misspellings were included in the classification dictionary.

Percent Agreement. Following the final Run 2, which included corrected misspelled 

words, this classification run resulted in 1,256 cases being coded as violent by WordStat. Data 

were then imported into Stata, and a random sample of 10% of all cases (n = 908) was drawn for 

second coding. Initial percent agreement was calculated and was found to be 87.2%. 

Disagreements and Dictionary Changes. Following the calculation of percent 

agreement, a disagreements check was performed to understand discrepancies in coding between 

WordStat and the human coder. Through this process, a discrepancy was discussed among 

coders for the word “fetus.” It was determined that just because that decedent was a fetus, infant, 

baby, or similar, that it could not be assumed that nefarious actions had led to its death. Thus, 

changes were made to classifications of 13 cases in which fetus or a similar keyword caused a 

violent classification to a non-violent classification. This change resulted in a new percent 

agreement between WordStat and the human coder of 88.2%. Of the 107 disagreements, 94 

(87.9%) were cases in which the WordStat classified a case as violent but the human coder did 

not. These types of disagreements occur when a keyword is located in the text being analyzed, 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

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but in context, the keyword is not indicating violence in the intended way. The other 13 (12.1%) 

disagreements were cases in which the human coder coded the case as violent, but there was no 

keyword found by WordStat in the text. 

No changes to the classification dictionary were made based off of the disagreements 

from Run 2. 

Tertiary Coding. For tertiary coding, a subsample of 20% of the 10% sample drawn for 

second coding was drawn at random in Stata (n = 182). Tertiary coding resulted in a percent 

agreement of 97.8% between human coders. 

Final Violence Coding. Following secondary and tertiary coding, a manual coding of all 

cases indicated as violent by WordStat was conducted to verify the violent nature of the case. In 

all, 1,256 cases were re-evaluated. Resulting from this process were 96 valid cases of case 

violence that were retained in later analyses as violent cases, having met the threshold to be 

deemed as truly violent cases. This threshold typically involved explicit statements that the case 

was possibly, probably, or definitely a homicide. Inferences of violence (e.g., shallow grave) did 

not meet the threshold for violent classification. 

Run 3

Following discussion with co-investigator Spamer, it was determined that “pending” manner of 

death cases should also be classified as violent or non-violent. As such, full data representing 

16,857 cases, of which 1,064 were pending cases, were imported for classification. Data 

cleaning resulted in one case being removed for a new total case count of 1,063 pending cases. 

Misspellings Check. Then, in WordStat, a misspellings check was conducted in the same 

manner as before, and all identified misspellings that represented truly misspelled words were 

categorized into the WordStat dictionary. Overall, only two possible misspellings were 

identified, and neither was found to be valid. 

Percent Agreement. Overall, WordStat classification resulted in 163 cases in which a 

violent keyword was present. Following classification in WordStat, a random sample of 10% (N 

= 106) for second coding was drawn in Stata. Following WordStat coding, percent agreement 

between WordStat and the human coder was calculated and found to be 82%. 

Disagreements and Dictionary Changes. Overall, there were no consistently apparent 

keywords leading to misclassifications. “Trauma” and “shallow grave,” did appear a few times, 

as did “gunshot,” “burned,” and “foul play,” but all keywords were too central to the violent 

classification dictionary to result in any changes to the dictionary.

Tertiary Coding. Given the level of agreement between WordStat and the human coder, 

tertiary coding was completed by a second human coder (co-investigator Spamer) using a 

subsample of 20% of the 10% of cases originally sampled for second coding (n = 21). The 

agreement between the two human coders was 95.2%. 

Final Violence Coding. Following tertiary coding, a final coding of all cases classified 

as violent by WordStat was performed. Only cases classified as violent by WordStat (n = 163) 

were examined to ensure they truly represented violence and that the classification as violent was 

not spurious based on keywords being used in a different context (e.g., DNA “hit”). Of the 163 

cases coded as violent by WordStat, 14 cases were retained as actually indicating suspected or 

confirmed violence by the human coder. The threshold for retention typically involved explicit 

statements that the case was possibly, probably, or definitely a homicide. Assumptions of 

violence (e.g., shallow grave) did not meet the threshold for violent classification. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
85

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Final UP Violence Coding

Overall, there were 16,857 cases under consideration for the UP analysis. 6,574 cases were 

undetermined manner of death, 2,509 cases did not have a manner of death provided, and 1,064 

cases had a pending manner of death at the time the data were pulled. From text mining 

classification, 110 total cases were found to indicate confirmed or suspected violence (96 cases 

from undetermined or no manner of death, 14 cases from pending manner of death). 

Case Re-Classification. During the final coding process, it was noticed that the inflected 

forms for “abduct” and “suspicious” were not completely outlined. Missing were the inflected 

forms “abduction” and “suspicion.” Overall, 4 cases of “suspicion” and 47 cases of “abduction” 

were discovered. The classification of the associated cases was thus changed from non-violent 

to violent. In total, the original and re-classifications resulted in 2,655 cases being coded as 

violent either through a homicide determination or through the case classification process. 

MP Case Analysis 

Analytical Variables 

Table 1 outlines the analytical variables used as part of case analysis, including their original 

coding as delivered by NamUs and the steps taken to code them in their final form. 

Violence Variables

Variable Original Coding Re-code Notes

Finalviolence Yes

No

0 = No

1 = Yes

Copied “finalhumancode”

Dichotomized from “finalhumancode” copy

GV

(GENERAL_VIOLENCE)

0

1 

2 

3 

4 

5 

6 

7 

8 

9 

10

11

12

16

18

39

0 = No

1 = Yes

Copied “General_Violence” to create “GV

” 

Replaced “GV” = 0 if finalviolence=0

Dichotomized from “GV”

PV

(PHYSICAL_VIOLENCE)

0

1 

2 

3 

4 

5 

6 

7 

8 

9 

10

11

12

13

14

16

17

23

0 = No

1 = Yes

Copied “Physical_Violence” to create “PV”

Replaced “PV” = 0 if finalviolence=0

Dichotomized from “PV”

SV

(SEXUAL_VIOLENCE)

0

1 

2 

3

0 = No

1 = Yes

Copied “Sexual_Violence” to create “SV”

Replaced “SV” = 0 if finalviolence=0

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
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4

6

Dichotomized from “SV”

ST

(STALKING)

0

1 

2 

3 

4 

5 

6 

8 

9 

10

0 = No

1 = Yes

Copied “Stalking” to create “ST”

Replaced “ST” = 0 if finalviolence=0

Dichotomized from “ST”

CC

(COERCIVE_CONTROL)

0

1 

2 

3 

4 

5 

6 

8 

10

23

0 = No

1 = Yes

Copied “Coercive_Control” to create “CC”

Replaced “CC” = 0 if finalviolence=0

Dichotomized from “CC”

PA

(PSYCHOLOGICAL_AGGRESSION)

0

1 

2 

3 

4 

5 

6

0 = No

1 = Yes

Copied “Psychological_Aggression” to 

create “PA”

Replaced “PA” = 0 if finalviolence=0

Dichotomized from “PA”

All Case Characteristics

Variable Original Coding Re-code Notes

Age_of_Case_groups2 <90 days

3-12 mo.

1-2 years

2-5 years

5-10 years

10-20 years

20+ years

Unknown

0 = <90 days

1 = 3-12 mo.

2 = 1-2 years

3 = 2-5 years

4 = 5-10 years

5 = 10-20 years

6 = 20+ years

7 = Unknown

Encode of “Age_of_Case_groups”

Re-labeled due to re-ordering of categories

StateName2 Alabama

Alaska

Arizona

Arkansas

California

Colorado

Connecticut

Delaware

DC

Florida

Georgia

Guam

Hawaii

Idaho

Illinois

Indiana

Iowa

Kansas

Kentucky

Louisiana

Maine

Maryland

Massachusetts

Michigan

Minnesota

Mississippi

Missouri

Montana

Nebraska

Nevada

1 = Alabama

2 = Alaska

3 = Arizona

4 = Arkansas

5 = California

6 = Colorado

7 = Connecticut

8 = Delaware

9 = DC

10 = Florida

11 = Georgia

12 = Guam

13 = Hawaii

14 = Idaho

15 = Illinois

16 = Indiana

17 = Iowa

18 = Kansas

19 = Kentucky

20 = Louisiana

21 = Maine

22 = Maryland

23 = Massachusetts

24 = Michigan

25 = Minnesota

26 = Mississippi

27 = Missouri

28 = Montana

29 = Nebraska

30 = Nevada

Encode of “StateMissing”

Re-labeled due to re-ordering of categories

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

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New Hampshire

New Jersey

New Mexico

New York

North Carolina

North Dakota

Northern Mariana 

Islands

Ohio

Oklahoma

Oregon

Pennsylvania

Puerto Rico

Rhode Island

South Carolina

South Dakota

Tennessee

Texas

Utah

Vermont

Virgin Islands

Virginia

Washington

West Virginia

Wisconsin

Wyoming

31 = New Hampshire

32 = New Jersey

33 = New Mexico

34 = New York

35 = North Carolina

36 = North Dakota

37 = Northern Mariana 

Islands 

38 = Ohio

39 = Oklahoma

40 = Oregon

41 = Pennsylvania

42 = Puerto Rico

43 = Rhode Island

44 = South Carolina

45 = South Dakota

46 = Tennessee

47 = Texas

48 = Utah

49 = Vermont

50 = Virgin Islands

51 = Virginia

52 = Washington

53 = West Virginia

54 = Wisconsin

55 = Wyoming

FoulPlay2 No

Possibly

Uncertain

Yes

0 = No

1 = Possibly

2 = Uncertain

3 = Yes

Encode of “FoulPlay”

SexName2 Female

Male

Other

0 = Female

1 = Male

2 = Other

Encode of “SexName”

Re-labeled due to re-ordering of categories

RaceEthnicity2 [Multiple Categories] 0 = White / Caucasian

1 = American Indian / 

Alaska Native

2 = Asian 

3 = Black / African 

American 

4 = Hawaiian / Pacific 

Islander

5 = Hispanic / Latino

6 = Other

7 = Uncertain

8 = 2+ Races

Encode of “RaceEthnicity”

Re-labeled due to re-ordering of categories

Original coding of individuals races checked 

through “codebook” of individual categories:

1=AI/AN

8=Asian

17=Black/AA

28=Hawaiian/PI

31=Hispanic/Latino

35=Other

38=Uncertain

40=White/Caucasian

Age_Missing_Group2 18-20

21-30

31-40

41-50

50+

<18

Unknown

0 = <18

1 = 18-20

2 = 21-30

3 = 31-40

4 = 41-50

5 = 50+

6 = Unknown

Encode of “Age_Missing_Group”

Resolved Case Characteristics

Variable Original Coding Re-code Notes

MannerOfDeath2 Accident

Homicide

Natural

Pending

Suicide

Undetermined

1 = Accident

2 = Homicide

3 = Natural

4 = Pending

5 = Suicide 

6 = Undetermined

Encode of “MannerOfDeath”

CaseResolutionStatus2 Alive

Deceased

0 = Deceased

1 = Alive

Encode of “CaseResolutionStatus”

Re-labeled due to re-ordering of categories

NamUsAssisted 0

1

0 = No

1 = Yes

No “encode” necessary as numeric already

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Chosen over NamUsAssisted_YN, because 

that variable put all missing data as Yes

Resolved Case Characteristics

Variable Original Coding Re-code Notes

DNA_Indicator2 N

Y 

0 = No

1 = Yes

Encode of “DNA_Indicator”

Re-labeled due to re-ordering of categories

Missing Cases

During the analysis of cases, it was noticed that across several variables, some of which were 

mandatory fields, there was consistently four cases missing. After consulting the data scientist 

who provided the data, it was uncovered that there had been a mistake in assembling the dataset 

such that four specific cases were listed with shifted rows, which created the four case deficit. 

The data scientist provided corrected data for these cases, and these cases were then manually recoded to reflect the true answers for the cases within the full dataset.

Case Re-Classifications

Initially, resolved cases were not classified as violent or non-violent based on their manner of 

death. However, after consultation with co-investigators, it was determined that cases with a 

listed manner of death of “homicide” should be forced into the analysis as violent cases even 

when circumstantial information mined did not indicate any foul play or violence. In doing so, 

86 cases that were identified as having “homicide” as their manner of death, but which lacked 

circumstantial information indicating violence, were moved to be violent cases for the remainder 

of the classification. 

UP Case Analysis

Analytical Variables 

Table 1 outlines the analytical variables used as part of case analysis, including their original 

coding as delivered by NamUs and the steps taken to code them in their final form.

All Case Characteristics

Variable Original Coding Re-code Notes

finalviolence Variable created by research 

team

0 = No

1 = Yes

Based on case classification, as 

well as medical examiner/coroner 

rulings of “homicide” manner of 

death

StateName2 [Multiple Values] None Encode of “StateName”

ConditionOfRemainsName2 Not Recognizable (8

categories) 

Recognizable

0 = Not recognizable

1 = recognizable face

Encode of “ConditionOfRemains”

Re-labeled due to new categories

Age of Case Groups 0-1 Years

1-5 years

5-10 years

10-20 years

20+ years

Unknown

1 = 0-1Years

2 = 1-5 years

3 = 5-10 years

4 = 10-20 years

5 = 20+ years

6 = Unknown

Encode of “Age_of_Case_groups”

No re-label – already in correct 

order

SexName2 Female

Male

Unsure

0 = Female

1 = Male

2 = Unsure

Encode of “SexName”

Re-labeled due to change of 

categories

RaceEthnicity2 [Multiple Categories] 0 = White / Caucasian

1 = American Indian / 

Alaska Native

2 = Asian 

3 = Black / African 

American 

49 unique values

Encode of “RaceEthnicity”

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
89

NamUs Examination of Circumstances and Characteristics Study

4 = Hawaiian / Pacific 

Islander

5 = Hispanic / Latino

6 = Other

7 = Uncertain

8 = 2+ Races

AgeGroup Based on median age of case 0 = <18

1 = 18-20

2 = 21+

Used data only from cases where 

both minimum and maximum age 

were provided to calculate a 

median age

Resolved Case Characteristics

Variable Original Coding Re-code Notes

NamUsAssisted 0 

1 

0 = No

1 = Yes

Already numeric, no encode 

allowed

MannerOfDeathName3 Accident

Homicide

Natural

Pending

Suicide

Undetermined

1 = Accident

2 = Homicide

3 = Natural

4 = Pending

5 = Suicide

6 = Undetermined

Encode of 

“MannerOfDeathName2”

MethodOfId_Anthropology 0

1 

0 = No

1 = Yes

Already numeric, no encode

Added labels

MethodOfId_CircumstantialInforma 0 

1 

0 = No

1 = Yes

Already numeric, no encode

Added labels

MethodOfId_Dental 0 

1 

0 = No

1 = Yes

Already numeric, no encode

Added labels

MethodOfId_Fingerprints 0

1 

0 = No

1 = Yes

Already numeric, no encode

Added labels

MethodOfId_MtDNA 0

1 

0 = No

1 = Yes

Already numeric, no encode

Added labels

MethodOfId_NucDNA 0 

1

0 = No

1 = Yes

Already numeric, no encode

Added labels

MethodOfId_Other 0 

1

0 = No

1 = Yes

Already numeric, no encode

Added labels

MethodOfId_Radiograph 0

1 

0 = No

1 = Yes

Already numeric, no encode

Added labels

MethodOfId_VisualIdentification 0

1 

0 = No

1 = Yes

Already numeric, no encode

Added labels

Unresolved Case Characteristics

Variable Original Coding Re-code Notes

DNA_Indicator2 No

Yes

0 = No

1 = Yes

Encode of “DNA_Indicator”

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
90

NamUs Examination of Circumstances and Characteristics Study

Appendix B

Final Classification Keywords

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
91

NamUs Examination of Circumstances and Characteristics Study

Final MP Classification Keywords

General Violence (46)

• Violence

• Aggression

• Attack

• Assault

• Threat

• Abuse 

• Rampage

• Conflict 

• Dispute

• Mistreat

• Maltreat

• Battle

• Skirmish

• Kerfuffle

• Ruckus 

• Fracas

• Fury

• Hubbub 

• Hostile

• Brouhaha 

• Tirade

• Brutal

• Row

• Disorder

• Cruel

• Scuttle

• Discriminate

• Anger

• Torture

• Neglect

• Endanger 

• Bully

• General Violence

• Fight

• Suspicious 

• Confront 

• Altercation

• Danger

• Offender

• Confess 

• Bury

• Grave

• Guilty

• Sentence

• Gang

• Suspect 

Psychological Aggression

(17) 

• Yell

• Shout 

• Scream

• Insult 

• Name Call

• Humiliate

• Made Fun Of 

• Cyberbully 

• Cursed At 

• Psychological 

Aggression

• Verbal Fight

• Verbal Assault

• Intimidate

• Verbal Disagreement

• Argue

• Disagreement

• Verbal Altercation

Physical Violence (40) 

• Hit

• Slap

• Push

• Shove 

• Punch 

• Kick

• Stab

• Shoot 

• Bite

• Burn

• Pull

• Destroy

• Pull Hair

• Slam

• Choke 

• Suffocate

• Weapon

• Twisted Arm

• Grab

• Knock Down 

• Kill

• Murder

• Abduct 

• Kidnap

• Physical Violence

• Physical Fight

• Physical Assault

• Rumble 

• Harm

• Injure 

• Homicide 

• Beat

• Brawl

• Riot

• Foul Play

• Physical 

Confrontation 

• Strangle

• Asphyxiate 

• Smother 

• Manslaughter 

Coercive Control (10)

• Force

• Coerce

• Manipulate 

• Isolated

• Coercive Control 

• Control 

• Sex Traffick

• Exploit 

• Pimp

• Human Trafficking 

Sexual Violence (5) 

• Rape

• Penetrate

• Molest

• Sexual Violence 

• Sexual Assault

Stalking (6)

• Stalk

• Follow 

• Spy

• Watch

• Broke In

• Unwanted Communication

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
92

NamUs Examination of Circumstances and Characteristics Study

Final UP Classification Keywords

• Sexual Assault

• Rape

• Suspect

• Offender

• Confess 

• Bury

• Shot 

• Strangle

• Asphyxiate

• Ligature

• Knife

• Stab

• Grave

• Smother 

• Manslaughter

• Guilty

• Sentence

• Burn

• Trauma

• Blunt Force 

• Bludgeon 

• Torture

• Suspicious 

• Foul Play 

• Dismember

• Choke 

• Suffocate

• Murder 

• Homicide

• Beat

• Attack

• Assault

• Hit

• Punch 

• Fight

• Gun

• Kill

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.