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

Department of Justice to prepare the following resource:

Document Title: The Impact of Forensic Evidence on Arrest 

and Prosecution

Author(s): David A. Schroeder, Ph.D., Kristin ElinkSchuurman-Laura, M.S.

Document Number: 250721

Date Received: August 2017

Award Number: 2011-DN-BX-0003

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.
i

The Impact of Forensic Evidence on Arrest and Prosecution

National Institute of Justice

#2011-2822

Principle Investigator

David A. Schroeder, Ph.D.1

Senior Research Associate

Kristin Elink-Schuurman-Laura, M.S.

University of New Haven

Henry C. Lee College of Criminal Justice and Forensic Sciences

The authors also wish to acknowledge Deputy Director of the Quality Assurance Unit of the 

Connecticut Superior Court Operations Division, Joseph Greelish; Former Director of the 

Connecticut Forensic Science Laboratory, Dr. Ken Zercie; current Director of the Connecticut 

Forensic Science Laboratory, Dr. Guy Valaro; Lecturer for the Henry C. Lee College of Criminal 

Justice & Forensic Sciences, Peter Massey; and the Connecticut State Police and all the 

contributing Chiefs of Police across the State of Connecticut, for their assistance in collecting all 

of the necessary data for this research. 

This project was supported by Award Number 2011-DN-BX-0003 awarded by the National 

Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The points of view in 

this document are those of the author and do not necessarily represent the official position or 

policies of the U.S. Department of Justice.

1. Contact information:

300 Boston Post Rd, West Haven, CT 06516

Tel: 203-931-2959

Email: dschroeder@newhaven.edu
ii

List of Tables

Table 1.1: Crime rates and crime lab submissions (2006-2009)

Table 1.2: Most commonly collected evidence by offense

Table 1.3: Forensic evidence stages by offense

Table 2.1: Sample size by crime type and phase

Table 2.2: Connecticut and national crime rates and case clearance

Table 9.1: Usefulness of FAPE

Table 9.2: Using FAPE

Table 9.3: Prosecutors and FAPE

Table A.1: Logistic Regression Predictors

Table A.2: OLS sentence length predictors

Table B.1: Descriptive characteristics of assault incidents

Table B.2: Descriptive characteristics of burglary incidents

Table B.3: Descriptive characteristics of robbery incidents

Table B.4: Descriptive characteristics of rape incidents

Table B.5: Descriptive characteristics of homicide incidents

Table C.1: Evidence type-assault

Table C.2: Evidence type-burglary

Table C.3: Evidence type-robbery

Table C.4: Evidence type-rape

Table C.5: Evidence type-homicide

Table E.1: Arrest

Table F.1: Assault convictions

Table F.2: Burglary convictions

Table F.3: Robbery convictions

Table F.4: Rape convictions

Table F.5: Homicide convictions

Table G.1: Assault sentencing

Table G.2: Burglary sentencing

Table G.3: Robbery sentencing

Table G.4: Rape sentencing

Table G.5: Homicide sentencing

Table H.1: Timeliness of evidence

Table H.2: Arrest and timeliness

Table 3: Convictions and timeliness
iii

List of Figures

Figure 9.1: Mode rating of usefulness for solving cases

Figure 9.2: Percentages of solved cases by evidence type

Figure D.1: Assault case flow

Figure D.2: Burglary case flow

Figure D.3: Robbery case flow

Figure D.4: Rape case flow

Figure D.5: Homicide case flow
iv

Contents

List of Tables .................................................................................................................................. ii

List of Figures................................................................................................................................iii

Introduction..................................................................................................................................... 1

Methodology................................................................................................................................... 7

Crime Rate .................................................................................................................................. 8

Forensic Services ........................................................................................................................ 9

Analytical Strategy.................................................................................................................... 10

Assault........................................................................................................................................... 12

Physical Evidence Submitted, Examined & Reported.............................................................. 12

Tracking Cases through the Justice System.............................................................................. 12

Predictors of Criminal Justice Outcomes.................................................................................. 13

Arrest..................................................................................................................................... 13

Conviction............................................................................................................................. 13

Plea/Trial............................................................................................................................... 14

Sentencing............................................................................................................................. 14

Hard to Solve Cases.................................................................................................................. 14

Time and Evidence Types......................................................................................................... 14

Burglary ........................................................................................................................................ 16

Physical Evidence Submitted, Examined, and Reported .......................................................... 16
v

Tracking Cases through the Justice System.............................................................................. 16

Predictors of Criminal Justice Outcomes.................................................................................. 17

Arrest..................................................................................................................................... 17

Plea/Trial............................................................................................................................... 17

Sentencing............................................................................................................................. 17

Hard to Solve Cases (stranger and no witnesses) ..................................................................... 18

Time and Evidence Type .......................................................................................................... 18

Robbery......................................................................................................................................... 19

Physical Evidence Examined, Submitted & Reported.............................................................. 19

Tracking Cases Through the Justice System ............................................................................ 19

Predictors of Criminal Justice Outcomes.................................................................................. 19

Arrest..................................................................................................................................... 19

Conviction............................................................................................................................. 19

Plea/Trial............................................................................................................................... 20

Sentencing............................................................................................................................. 20

Hard to Solve Cases.................................................................................................................. 20

Time and Evidence Types......................................................................................................... 20

Rape .............................................................................................................................................. 22

Physical Evidence Submitted, Examined & Reported.............................................................. 22

Tracking Cases Through the Justice System ............................................................................ 22
vi

Predictors of Criminal Justice Outcomes.................................................................................. 22

Arrest..................................................................................................................................... 22

Conviction............................................................................................................................. 23

Plea/Trial............................................................................................................................... 23

Sentencing............................................................................................................................. 23

Hard to Solve Cases.................................................................................................................. 23

Time and Evidence Types......................................................................................................... 23

Homicide....................................................................................................................................... 25

Physical Evidence Submitted, Examined, & Reported............................................................. 25

Tracking Cases Through the Justice Process............................................................................ 25

Predictors of Criminal Justice Outcomes.................................................................................. 26

Arrest..................................................................................................................................... 26

Conviction............................................................................................................................. 26

Plea/Trial............................................................................................................................... 26

Sentencing............................................................................................................................. 26

Hard to Solve Cases (stranger and/or no witnesses)................................................................. 27

Time and Evidence Types......................................................................................................... 27

Discussion..................................................................................................................................... 29

The Impact of Forensic Evidence on Arrest and Prosecution Surveys......................................... 31

Methodology............................................................................................................................. 31
vii

Demographics ........................................................................................................................... 32

Investigations............................................................................................................................ 32

FAPE timeliness........................................................................................................................ 33

Prosecutors and FAPE .............................................................................................................. 34

FAPE usefulness....................................................................................................................... 35

Assault................................................................................................................................... 36

Burglary ................................................................................................................................ 37

Robbery................................................................................................................................. 37

Rape ...................................................................................................................................... 37

Homicide............................................................................................................................... 37

Discussion................................................................................................................................. 38

Limitations.................................................................................................................................... 39

References..................................................................................................................................... 40

Appendix A: Variables and Analytical Models............................................................................ 46

Appendix B: Case Descriptive Characteristics............................................................................. 62

Appendix C: Evidence Submitted, Examined, and Reported ....................................................... 72

Appendix D: Case Flow Charts .................................................................................................... 72

Appendix E: Arrest Analysis ........................................................................................................ 75

Appendix F: Conviction Analysis................................................................................................. 78

Appendix G: Sentencing Analysis................................................................................................ 83
viii

Appendix H: Time and Evidence Type Analysis ......................................................................... 88
1

Introduction

The National Institute of Justice (NIJ) grant solicitation, Social Science Research on 

Forensic Science (NIJ-2011-2822), was heavily reliant on the NIJ study, The Role and Impact of 

Forensic Evidence in the Criminal Justice Process (Peterson, Sommers, Baskin, and Johnson, 

2010). The objectives of Peterson et al., (2010) were to 1) “Estimate the percentage of crime 

scenes from which one or more types of forensic evidence is collected;” 2) “Describe and catalog 

the kinds of forensic evidence collected at crime scenes;” 3) “Track the use and attrition of 

forensic evidence in the criminal justice system from crime scenes through laboratory analysis, 

and then through subsequent criminal justice processes;” 4) “Identify which forms of forensic 

evidence contribute most frequently (relative to their availability at a crime scene) to successful 

case outcomes” (Peterson et al. 2010, Pg. 11-12). Peterson, et al. (2010) analyzed crime lab, 

investigative, and prosecutorial case file information of crimes that fit into one of five crime 

categories: assault; burglary; homicide; rape; and robbery. Peterson et al. (2010) concluded their 

analysis by making ten recommendations regarding future research on the utility of forensic 

evidence. The current study, The Impact of Forensic Evidence on Arrest and Prosecution, 

addressed several of these recommendations via a methodology that informs the four research 

objectives listed above.

The first Peterson et al. (2010) recommendation is generally related to simple replication 

- refining and performing a similar analysis in another jurisdiction. This study addressed this 

recommendation by mimicking the methodology used by Peterson et al. (2010). Variables, crime 

categories, sample size, and analytical models were all borrowed from Peterson et al. (2010). 

Another recommendation of the Peterson et al. (2010) makes clear the need for a more 

detailed assessment of how the mere existence of available forensic evidence affects the arrest 

and prosecution of offenders. A major finding of Peterson et al. (2010) stated most evidence goes 

unexamined, but its presence in cases was associated with arrest and movement of cases through 

the justice process. As forensic evidence moves through the criminal justice system, less and less 

evidence makes it to the next stage. This movement resembles a funnel or an inverted pyramid 

(Peterson, 1974). Added studies are needed to review how unexamined forensic and tangible 

evidence teams with other conventional investigative procedures to lead to arrests (page 9). The 

same phenomenon has also been discovered in another recent analysis of the use of one type of 

forensic evidence, namely DNA. Schroeder (2007) discovered that among his sample of 

homicide cases from the City of New York (1996 – 2003) the group of cases that had the highest 

clearance rate was the group in which evidence for DNA analysis was collected from the scene 

but never analyzed. 

There are three general areas of explanation for why unexamined evidence is associated 

with higher clearance rates. First, the collection of forensic evidence from crime scenes has 

become so commonplace that its collection is not contingent upon the needs of the investigation, 

but simply a matter of protocol. Therefore, when cases present non-forensic evidence (e.g. 

suspect interrogations, witness statements and identifications) sufficient to advance the case 

through the system the byproduct is a great deal of collected, but unanalyzed forensic evidence. 

Second, the analysis of forensic evidence is so time-consuming as to influence its utility. 
2

Detectives may simply be relying on more traditional methods (e.g. suspect interrogations, 

witness statements and identifications) as these methods can produce the desired result more 

directly. Third, that there is some latent interactive effect between the existence of forensic 

evidence and other non-forensic forms of evidence (again, most likely suspect interrogations, 

witness statements and identifications) which fosters a greater ability to make an arrest than 

those non-forensic forms of evidence do by themselves. Given the infrequency of the analysis of 

forensic evidence (Strom & Hickman, 2010; Peterson et al., 2010; McEwen, 2011) all three of 

these explanations would seem likely. However, which of these occurrences is happening most 

frequently, or even more often, is currently unknown given the existing literature. Moreover, 

there is no existing research that specifically examines how the threat (real or perceived) of 

analyzing collected forensic evidence may have affected the use of more traditional forms of 

evidence (e.g. suspect interrogations, witness statements and identifications). In addition, 

Peterson et al. (2010) recommended exploring how forensic evidence effects prosecutorial

perceptions and decisions. Therefore, to improve upon Peterson et al. (2010), regarding these 

recommendations the current research includes a qualitative survey was issued to investigators 

from the relevant investigating agencies. The focus of this survey is to ascertain whether 

investigators’ perception match the reality of what evidence aids in case clearance as well as 

their views on whether the presence of forensic evidence influences prosecutor decision-making.

Peterson et al. (2010) suggested an exploration of the prioritization of forensic evidence 

analysis as a means of reducing backlog and in making the analysis process more efficient to the 

needs of the existing criminal justice system. To address this, a three-step process was used. 

First, forensic evidence was analyzed in relation to arrest and conviction rates for a significant 

number of cases within each offense categories. Second, once certain forms of forensic evidence 

have been associated with higher arrest and conviction rates for certain categories of offense, an 

analysis of other associated variables were used to rule out any intervening variable which may 

account for these correlations. Third, a predictive statement of probable efficacy can then be 

calculated and assigned to any incoming evidence given the type of evidence and the type of 

offense being investigated. The findings gleaned from the results of these analyses can inform 

investigators and crime labs in prioritizing the analysis of any piece of forensic evidence. This 

process also serves to address Peterson et al.’s (2010) urgings to better understand the 

relationship between DNA evidence and different types of crime (property and personal). 

Traditionally DNA has been seen as efficacious in the investigation of violent crime (i.e. 

homicide and sexual assaults) but several studies have indicated that DNA is a very useful tool in 

the investigations of burglary and other offenses (Bond, 2007a, 2007b; Roman et al., 2009; 

Gabriel, Boland & Holt 2010). By paying close attention to what impact DNA evidence has on 

burglaries and robberies, this study addresses both recommendations. 

From the above summarization of Peterson et al.’s (2010) recommendations, it would 

seem clear that for a follow-up study to advance the research already completed the study should 

be performed in an area with a population of case files similar in category and number of crimes, 

which is serviced by a centralized crime lab. Further, that access is available to analyze a 

significant number of case files in these categories of crime over a longer period of time, at both 

the investigative and the prosecutorial level. Finally, that a qualitative component be employed 

that can directly record the perceptions of investigators as to the use and effect of forensic 

evidence on the movement of cases through the criminal justice system.
3

To address these areas of focus mentioned above, this study tracked the submission, 

analysis and dissemination of forensic evidence for a significant number of cases in the State of 

Connecticut. To achieve this, relationships were established to access crime lab, investigative, 

and case outcome (court file) information. However, since Peterson et al. (2010) examined cases 

from five areas or cities (Los Angeles County, Indianapolis, Evansville, Fort Wayne and South 

Bend) within two states (California and Indiana, respectively), designed to represent, city, county 

and state crime lab services, a replication of this same methodology was utilized in a more 

homogeneous environment (a single crime lab covering a wide area with myriad offenses) to 

provide a productive and rich comparisons between the two studies. The time frame from which 

cases are sampled was expanded to a three-year period (2006-2009) rather than the one or twoyear period (2003 – 2004) used by Peterson et al. (2010). This sample of cases was 

representative of the five crime categories examined by Peterson et al. (2010): assault; burglary; 

homicide; rape; and robbery.

This study’s methodology is similar to Peterson et al. (2010), however it differed in two 

important ways. First, in examining case file information, the coding of the existence of a 

witness includes a dichotomous indication as to whether the witness named a suspect, and also 

record the presence of any suspect statement. Second, the current study includes a qualitative 

second phase in which surveys regarding detective’s opinions of the utility of FAPE were 

administered to detectives for comparison to the results of the quantitative analysis.

It should be noted that while every effort was made to obtain investigative information 

for the entire sample of 2801 cases, not all the police departments from the original sample 

agreed to participate. Most non-participating police departments were, by themselves 

inconsequential due to the small number of cases in their possession from the original sample. 

The sole exception to this was one large police department, representing 153 cases that could not 

participate due to logistical issues. Investigative and courtroom information from nonparticipating police departments were not included in this study. It should also be noted that, 

even at participating police departments, it was not always possible to gather information for 

every case in the sample due to legal or logistic issues. 

The Connecticut State Forensic Science Laboratory (The Crime Lab), located in Meriden 

Connecticut, is a full-service forensic laboratory. The Crime Lab staff’s criminalistics divisions 

are in the areas of forensic biology, DNA database entry and matching (CODIS), mitochondrial 

DNA, nuclear DNA, trace evidence, and arson/chemistry. The Crime Lab also staffs 

identification divisions in the areas of imprints/impressions, latent prints, questioned documents, 

and firearms/tool mark identification. The Crime Lab also has dedicated laboratories for forensic 

science, toxicology and controlled substances, computer crime and electronic evidence, and 

crime scene reconstruction. The Crime Lab is accredited by The American Society of Crime 

Laboratory Directors/Laboratory Accreditation Board (ASCLD/LAB) and annually receives 

approximately 150,000 requests for analysis (18,000 of which are specifically for DNA analyses, 

and 4,000 for fingerprint analyses/comparison). The Crime Lab also has access/involvement 

with the CODIS (DNA), AFIS (fingerprints) and IBIS/NIBIN (firearms) databases. The Crime 

Lab services approximately ninety percent of the requests for forensic analyses by law 

enforcement within the State of Connecticut.
4

Table 1.2: Most commonly collected evidence by offense 

Study Biological 

(%) 

Firearms/Weapons 

(%) 

Fingerprints 

(%) 

Peterson et. Al, 

(2010)

Homicide 38.3 83.0 28.5

Assault 4.0 22.4 1.2

Rape 53.5 1.8 4.3

Robbery 1.0 5.5 9.3

Burglary 1.0 0.4 16.5

Current Study*

Homicide 89.7 54.3 35.2

Assault 30.8 70.0 14.2

Rape 94.2 0.6 2.9

Robbery 47.3 23.3 44.4

Burglary 32.3 1.7 69.4

*Used submission rates as opposed to collected rates.

Most major crime investigations involved processing a crime scene (Peterson et al., 1984) 

but evidence collection and examination rates varies by crime type (Peterson, Ryan, Houlden, & 

Mihajlovic, 1987; Peterson et al. 2010; McEwen, 2011). This study found homicides and rapes 

tend to have higher evidence collection rates while property crimes have lower rates, which 

agrees with Peterson et al.’s (2010) findings. Biological evidence, fingerprints, and firearms are 

the most common evidence types collected (Peterson et al., 1984; Peterson et al., 1987; Peterson 

et al., 2010; McEwen, 2011), which agrees with this study.

Table 1.1: Crime rates and crime lab submissions (2006-2009) 

Crime type Crimes reported in 

Connecticut 

Submitted to 

Connecticut crime lab 

Assault 22,315 1,201 (5.3%)

Burglary 61,506 3,442 (5.5%)

Robbery 16,830 1,075 (6.3%)

Rape 2,752 1,829 (66.4%)

Homicide 478 438 (91.6)
5

Table 1.3: Forensic evidence stages by offense 

Study Collection (%) Submission 

(%) 

Examination 

(%) 

Report 

Generated 

(%) 

Homicide

Peterson et 

al (2010)

97.0 88.5 81.0 --

Current 

study

91.6 98.6 85.2 82.9

Assault

Peterson et 

al (2010)

30.3 11.9 9.2 --

Current 

study

5.3 99.0 82.7 82.7

Robbery

Peterson et 

al (2010)

24.8 10.9 9.9 --

Current 

study

6.3 98.9 86.3 86.2

Rape

Peterson et 

al (2010)

63.8 32.2 18.6 --

Current 

study

66.4 97.7 84.5 84.5

Burglary

Peterson et 

al (2010)

19.6 13.0 9.2 --

Current study 5.5 99.3 84.8 84.9

Offender identification and case clearance are achieved via suspect confession; witness 

testimony; and physical evidence (Rossmo, 2009; Edmond & Vuille, 2014, Klockars & 

Mastrofski, 1991; Simon, 2006). However, early studies show the majority of case clearance was 

the result of information provided by the public or victim/witnesses and few cases were 

significantly aided by forensic evidence (Greenwood et al., 1975; Eck, 1983; Coupe & Griffiths, 

1996). Peterson et al (2010) found that while forensic evidence aided in clearance for some crime 

types, often non-forensic types of evidence were strong predictors of clearance. 

Peterson et al. (2010) stated that the majority of arrests occurred before evidence could be 

examined. This agrees with other studies that found suspects were often identified before the 

results of forensic reports were available to police (Peterson et al., 1984; Schroeder, 2007; King, 

Wells, Katz, Maguire, & Frank., 2013; McEwen, 2011), which makes the actual influence of 

forensic evidence questionable.

Peterson et al. (2010) found forensic evidence was not a predictor for arrest for homicide 

cases. Rather, witness reports to police as well as the relationship between victim and offender 

that increased the likelihood of arrest instead of physical evidence. This agrees with several 

homicide studies that showed non-forensic variables have a significant impact on case clearance, 
6

including police procedure (Wellford & Cronin, 1999, 2000; Keel, Jarvis & Murihead, 2009) and 

community involvement (Riedel & Rinehart, 1996). However, there is some support that forensic 

evidence does influence case outcome for homicide (Wellford & Cronin, 2000; Keel et al., 

2009).

Studies show forensic evidence was a strong predictor for whether a case will reach court 

(Briody, 2002, 2004; Roman et al., 2009; Peterson et al., 2013). However, there is conflicting 

studies on whether forensic evidence aids in convictions. Early studies showed forensic evidence 

had little impact on conviction rates (Lassers, 1968; Peterson et al., 1987). Peterson et al. (2010)

found physical evidence increased the chance burglary, assault, homicide, rape cases reached 

court but not for robbery. However, physical evidence was not a predictor for conviction.

The relationship between forensic evidence and sentencing is also muddy, with some 

studies showing physical evidence results in longer sentences (Briody, 2004; McEwen, 2011) 

while other studies show forensic evidence predicts shorter sentences (Briody, 2004). Peterson et 

al’s (2010) results showed physical evidence was a predictor for longer sentences only in assault

cases.

The literature suggests that forensic evidence has no impact on whether a defendant 

pleads guilty or whether plea deals are offered (Briody, 2002, 2004; Rosett & Cressey, 1976), 

however, Peterson et al. (2010) found physical evidence increased the likelihood a case would be 

resolved though trial for assault whereas burglary cases were more likely to end in a plea deal if 

physical evidence was present. In addition, plea negotiated cases tended to have less examined 

evidence than trial cases for violent crimes. 
7

Methodology

A random stratified sample of assaults, burglaries, robberies, and rapes were pulled from 

the Connecticut State Forensic Science Laboratory as well as all homicide cases. To be eligible 

for sampling, evidence collected in connection with a criminal investigation must have been 

submitted to the Connecticut State Forensic Science Laboratory between 2006 and 2009. The 

methodology for data collection closely adheres to the methods used by Peterson et al. (2010) to 

allow for comparison between this study and its predecessor. Peterson’s sample is based on

calculating the rate of each crime by dividing the number of cases for a particular crime by the 

total number of cases for all crime types. The results of these calculations are shown in Table 2.1

and were used as a guide to determine the sample size for this study. Attempts were made to 

oversample due to an expected reduction of viable cases.

Table 2.1: Sample size by crime type and phase 

Crime type CT 

Averages 

2006-2009 

Threshold 

Percentage 

Number 

of 

cases 

needed 

Number 

of cases: 

crime lab 

phase 

Number of 

cases: 

detective 

phase 

Number 

of cases: 

court 

phase 

Assault 5,579 6.13% 342 393 335 313

Burglary 15,1377 6.18% 950 1011 749 702

Rape 688 55.56% 382 310 211 184

Robbery 4,208 18.48% 776 636 461 435

Homicide 120 85.52% 102x2 438 284 238

Unclassified -- -- -- 13 -- --

Totals 2654 2801 2040 1872

The coding process had three phases: gathering variables on what forensic evidence was 

analyzed and the results of those tests gathered from the Connecticut State Forensic Science 

Laboratory (crime lab phase); investigative and case clearance status as well as victim/suspect 

demographics collected at participating Connecticut police department (detective phase); and 

sentencing outcomes gathered from Connecticut court documents (court phase). Coding for the 

crime lab phase and the detective phase involved sending graduate research assistants to either 

the Connecticut State Forensic Science Laboratory or participating Connecticut police 

departments to gather information from official records. There were 60 participating police 

departments of various sizes across Connecticut. Collecting information from the court phase

involved requesting the docket numbers connected to the sample of cases and then accessed the 

online database for the Connecticut court system (www.jud.ct.gov). Court phase data was 

collected up until August 25, 2016 and was not collected for any police department that had less 

than five cases represented in the sample. As the coding process progressed, it became obvious 

some cases were inappropriate and were removed from the sample. These cases include those 

that were determined to be misclassified (e.g. an assault that was really a traffic stop); couldn’t 

be found at the police department; had restricted access due to privacy laws (e.g. the suspect was 

a minor); only had an Office of the Chief Medical Examiner (OCME) case numbers; belonged to 

any agency that wasn’t the Connecticut State Police or local police department; the crime 

occurred before November 2005; or were determined to be unfounded by the police department. 

Appropriate cases for the detective phase analysis only include those with investigative 

information from participating police departments. Court phase eligible cases include cases 
8

where conviction status was available. It should be noted it was not always possible to gather 

investigative or courtroom information for every case in the sample for every participating police 

department due to legal or logistical issues. Only cases that were from participating police 

departments and were available to researchers were included in this study for the investigative 

phase analyses and the court phase analyses. 

Crime Rate

Connecticut’s crime rates have been consistently lower than national averages. From

2006 to 2009, the crime rates were lower than the national average for all crime types examined. 

Connecticut’s clearance rates have habitually been higher than the national average, including

during the study period. Connecticut’s crime rates before and after the study period did not 

significantly differ for any crime type (see Table 2.2).
9

Table 2.2: Connecticut and national crime rates and case clearance 

Year Offense CT Rate 

(per 

100,000) 

CT 

Clearance 

(%) 

National 

Rate (per 

100,000) 

National 

Clearance 

(%) 

2005 

Murder 3.2 70.5 5.6 62.1

Aggravated 

Assault

144.9 63.5 291.1 55.2

Robbery 118.0 26.4 140.7 25.4

Burglary 445.2 14.8 726.7 12.7

Rape 21.6 39.9 31.7 41.3

2006 

Murder 3.9 60.0 5.8 60.7

Aggravated 

Assault

148.8 62.9 292.0 54.0

Robbery 127.3 25.2 150.0 25.2

Burglary 443.6 14.8 733.1 12.6

Rape 20.4 41.3 31.6 40.9

2007 

Murder 3.1 48.1 5.7 61.2

Aggravated 

Assault

154.5 62.0 287.2 54.1

Robbery 123.6 27.0 148.3 25.9

Burglary 447.8 14.7 726.1 12.4

Rape 20.1 42.0 30.6 40.0

2008 

Murder 3.7 64.1 5.4 63.6

Aggravated 

Assault

168.7 65.2 277.5 54.9

Robbery 115.6 28.3 145.9 26.8

Burglary 431.9 15.8 733.0 12.5

Rape 19.6 36.4 29.8 40.4

2009 

Murder 3.0 71.0 5.0 66.6

Aggravated 

Assault

164.3 65.1 264.7 56.8

Robbery 113.5 28.1 133.1 28.2

Burglary 430.6 14.8 717.7 12.5

Rape 18.5 36.9 29.1 41.2

2010 

Murder 3.7 61.4 4.8 64.8

Aggravated 

Assault

162.1 66.2 252.3 56.4

Robbery 99.4 31.3 119.1 28.2

Burglary 424.1 14.8 699.6 12.4

Rape 16.8 30.9 27.5 40.3

Sources: Department of Public Safety, 2016; Federal Bureau of Investigation, 2016.

Forensic Services

The Connecticut Forensic Science Laboratory handles the majority of the forensic needs 

of Connecticut’s police force. This forensic laboratory has several divisions to handle a variety 
10

of evidence types, including firearm/toolmark evidence, trace evidence, biological evidence, 

electronic evidence, imprints, and fingerprint evidence (Department of Emergency Services and 

Public Protection, 2012). A 2010 survey (Hayes, 2010) compared the Connecticut Forensic 

Science Laboratory to other U.S. forensic labs and found that Connecticut’s turnaround time for

sex assault kits, homicides with a suspect, DNA testing for property crimes, latent print 

processing; and imprint processing, crime scene reconstruction, firearm/toolmark analysis, and 

forensic photography processing were slower than the national average. Connecticut’s 

turnaround times for DNA cases without a suspect, rushed DNA tests, DNA tests with a suspect, 

arson debris analysis, gunshot residue tests, and AFIS submission and reporting were on par with 

most of the other forensic labs. Connecticut had the fastest turnaround time for NIBIN 

submissions and reporting in the entire country. 

The Connecticut Forensic Science Laboratory has had an increasing DNA backlog since 

2005 (Hartford Courant, 2011). By 2011, Connecticut DNA backlog became the largest in the 

U.S. (Matthews, 2014). To help mitigate this backlog, Connecticut officials have requested law 

enforcement agencies limit the number of DNA evidence submissions to the Connecticut 

Forensic Science Laboratory (New Haven Register, 2012). These efforts combined with 

additional funding has resulting in reducing the DNA backlog (Flynn, 2015).

Analytical Strategy 

This study seeks to ascertain the impact forensic evidence has on three different 

outcomes: 1) arrest, 2) conviction, and 3) sentence length. Since the first two outcomes are 

binary in nature (yes, no), a logistical regression will be used for each crime type. Sentence 

length will be analyzed using OLS regression. While sample sizes allow us to analyze these 

outcomes for each crime type, rape has too few cases to allow for a multivariate OLS regression 

for sentence length. See Appendix A for a list of variables. 

This study did attempt to mimic the analyses done by Peterson et al. (2010), however it 

became necessary to make substitutions for several variables. Peterson et al. (2010) had a

‘friend/acquaintance’ variable that did not match up exactly with this study’s coding of 

suspect/victim relationships. Instead, the variable ‘friend’ was used. Peterson et al. (2010) used 

‘crime scene evidence’ as a variable, however since the random selection for this study of cases 

from a crime lab, every case had crime scene evidence and a substitution variable was not 

available. Peterson et al. (2010) had ‘arrest within 10 minutes’, however, only days between 

when the crime occurred and arrest was coded for this study. ‘One day arrest’ was substituted for 

this variable. Peterson et al. (2010) had a variable named ‘direct arrest’, which is defined as a 

suspect admission, suspect arrested in another case, suspect surrender, police saw the crime, 

recovery of property related to the crime, or traffic stop lead to an arrest. This study will 

substitute a variable that accounts for whether a suspect was named or described by a witness 

and whether a suspect made a statement to the police. Since Peterson et al.’s (2010) data came 

from five study sites from two different states, variables Indianapolis and Los Angeles were used 

to represent the two states to analyze the difference between study sites. Because this study 

collected data from a single state, similar variables will not be used. Peterson et al. (2010) also 

includes a correctional factor to account for a lack of randomization in certain outcome stages 
11

due to sampling directly from the police department rather than from the crime lab itself. Since 

this study randomly selected data from the crime lab, a correction factor is not needed.

Further, this study analyzed whether disaggregating evidence into separate categories and 

controlling for whether an evidence report was available at least three days before case clearance 

or verdict changed the relationship between forensic evidence and case outcomes. However, 

there were too few cases to utilize a logistical regression so a chi-square test was used. 
12

Assault

Of the original 393 assault cases randomly sampled from the State Lab, 335 cases 

(85.2%) with investigative information available to the researchers. It should be noted that some 

records were not available due to legal factors or due to logistical issues (i.e. the records were 

stored in places that were not easily accessible). Victims were typically male (76.1%) as were the 

suspects (74.6%). Most victims and suspects were Black (49.0% and 41.8% respectively) and 

both victims and suspects tended to be between the ages of 20-29 (36.1% and 19.7% 

respectively. Most cases (47.2%) involved people who were strangers and most victims (77.0%) 

required medical treatment. The majority (69.0%) of the cases had at least one witness who gave 

a statement to the police and, in most cases (65.4%), either named the suspect or gave a 

description of the suspect. Assaults were reported to the police on an average of 13 days and the 

average arrest occurred about 97 days after the crime occurred. Compared to Peterson et al.’s 

(2010) data (see Appendix B for a comparison of descriptive statistics between this study and 

Peterson et al, 2010), this study’s assault cases were reported to the police quicker but the cases 

took longer to clear.

Physical Evidence Submitted, Examined & Reported

Of the 393 assault cases, all but four had at least one piece of evidence submitted for 

examination. Weapons/firearms evidence (70.0%) was by far the most common evidence type 

submitted, with biological evidence (30.8%) a distant second. The most common 

weapons/firearm evidence was NIBIN related evidence, such bullets and cartridges found at the 

scene as well as confiscated firearms. Fingerprint evidence was only submitted in 14.2% of the 

cases and all other evidence types were submitted in less than 6% of assaults. Peterson et al. 

(2010) and McEwen (2011) agreed weapons evidence is the most common evidence type for 

assault cases. However, Peterson et al. (2010) found fingerprint evidence to be more prevalent in 

assaults than biological evidence. 

While 99.0% of assault cases had evidence submitted for examination, 82.7% of the 

evidence was examined and generated a report based on the findings. Weapons/firearms 

evidence had the highest percentage of examined and reported physical evidence. Not 

surprisingly, if evidence was examined, a report was generated for that evidence for all cases. 

Peterson et al. (2010) found a much lower rate (9.2%) of evidence examination.

Tracking Cases through the Justice System

The movement of assault cases through the criminal justice system is shown in Figure 

D.1. Cases with at least one evidence report generated did not significantly differ from cases 

without evidence reports when it came to arrests (χ = 1.884, p = 0.170) or convictions (χ = 3.463, 

p = 0.063). While Peterson et al. (2010) found assault cases with physical evidence was more 

likely to end in arrest and conviction, they did not control for whether the evidence was analyzed

or when that analysis was reported back to detectives.
13

Predictors of Criminal Justice Outcomes

Arrest

The strongest predictor of arrest was the relationship between the offender and the victim. 

Incidences involving friends were over 10 times more likely to end in arrest and incidences that 

occurred between intimate partners or family were about 8 times more likely to end in arrest. 

How quickly the crime was reported to police also increased the odds of arrest but only 

marginally. Unlike Peterson et al.’s (2010) findings (see Appendix E for a comparison of 

predictors between this study and Peterson et al, 2010), lab examined evidence were not 

significant predictors of arrest.

Like Peterson et al. (2010), several extralegal factors were found to be significant 

predictors of arrest. Arrests were more likely if the suspect was Black or Latino. While Peterson

et al. (2010) found victim sex and race as well as offender sex to be significantly related to arrest, 

this study did not support this. 

Peterson et al. (2010) found arrest was significantly more likely in Los Angeles than 

Indianapolis. While the data comes from a single crime laboratory, police departments were 

categorized by the number of full-time sworn officers. However, police department size did not 

have a significant relationship with arrest. 

There may be a simple explanation why Peterson et al. (2010) found physical evidence to 

be significant predictors of arrest while the results for this study did not. Several studies have 

noted a difference in the utilization of forensic evidence from city to city (McEwen, 2011; King 

et al., 2013; Roman et al., 2009). These differences include how much evidence is collected for a 

specific crime type as well as what evidence type crime scene investigators focus on for that 

particular crime type. For example, Peterson et al. (2010) showed low rates of examination for 

biological evidence in assault cases while this study shows biological evidence examination was 

the second only to weapon/firearm evidence. 

Conviction

Of the 331 assault cases with court data available to the researchers, 26.8% resulted in a 

conviction. This study’s findings differ from Peterson et al. (2010) in several ways. While 

Peterson et al. (2010) found witness reports and victim treatments to have a significant 

relationship with conviction, this study did not. Lab examined evidence doubled the likelihood of 

conviction, as did whether an arrest occurred on the day of the crime (see Appendix F for a 

comparison of predictors between this study and Peterson et al, 2010). Witness reports and 

victim/suspect relationships were not significant predictors of conviction. While this study shows

lab examined evidence to be a significant predictor of conviction, the Nagelkerke’s R-square of 

0.038 shows that a large percentage of variation within this study’s data is still unexplained. 
14

Plea/Trial

This study’s findings were similar to Peterson et al. (2010) in that very few cases were 

adjudicated through trial. About 81% of convicted cases involved a guilty plea and only four 

cases were adjudicated. While Peterson et al. (2010) found physical evidence increased the 

likelihood of case resolution through trial, there was not enough cases were physical evidence 

was not examined for assault cases to conduct a similar analysis.

Sentencing

The average sentence length for convicted offenders in assault cases were approximately 

3035 days. Considering this average sentence length of about 8 years, most cases in this study 

must have been violent assaults. This is supported by the low number (3.3%) of misdemeanor 

assault convictions in the sample. This study’s data had significantly longer average sentence 

lengths for assaults than Peterson et al.’s (2010) cases (see Appendix G for a comparison of 

predictors between this study and Peterson et al, 2010). Compared to the national average of 41 

months (Rosenmerkel, Durose, & Farole, 2009), this study saw longer average sentence lengths 

for assault cases. Of the 82 cases with sentencing information, the majority (79.3%) of offenders 

served their time out at a state prison, with only 6.1% serving in a county jail. Only 10 (12.2%) 

received probation and 2 received either conditional or unconditional releases without a 

prescribed probation period.

While these results agree that witness reports have a significant relationship with 

sentencing, this study found it decreased sentence length while Peterson et al. (2010) found it 

increases sentence length. Victim report and intimate or friend victim/suspect relationship 

decreases sentence length. The number of prior convictions, whether the victim is a young adult 

or whether the victim is a Latino increases sentence length. Unlike Peterson et al. (2010), this 

study found physical evidence to have no significant relationship with sentencing. 

Hard to Solve Cases

While Peterson et al. (2010) had 198 cases that had no witnesses or known relationship 

between the victim and offender, only 42 of this study’s cases matched that description. All 42 

cases had evidence submitted for examination, 85.7% had the evidence examined, and 81.1% of 

the cases had a report generated based on the results of the examination. Of the 42 cases, only 12 

(28.6%) ended in an arrest and 7 (18.9%) resulted in a conviction. About 71% of the hard to 

solve cases had forensic evidence examined. Cases that were not hard to solve, unsurprisingly, 

had higher rates of arrest (54.3%) and conviction (81.1%) than the hard to solve cases. 

Time and Evidence Types

For evidence to be able to influence an outcome, such as arrest or conviction, the 

evidence must have been examined and a report generated stating the results of the examination 

before the outcome occurs. While it may reinforce the officer’s decision to arrest the suspect, it 

did not influence the initial decision to arrest. While Peterson et al. (2010) does mention most 
15

arrests take place before physical evidence can be examined for the case, he did not take time 

factors into consideration when conducting his inferential analyses. 

Table H.1 demonstrates how frequent evidence is submitted for examination before case 

clearance and conviction. Only about 36% of all evidence is submitted before a case is cleared 

and only about 24% of cases had evidence reports available before a case was cleared. Peterson

et al. (2010) noted that only 4.3% of his sample of assault cases had evidence examined before 

an arrest was made. This means that a clear majority of assault cases are cleared without the aid 

of physical evidence.

A larger portion of the sample had reports available before the offender was convicted 

but it is clear not every case goes to court with physical evidence reports present. The 5% of 

cases without evidence prior to a conviction could be due to several factors, including issues 

transporting or storing the evidence and backlogs at the forensic laboratory. 

For the most part, Peterson et al (2010) did not separate his analysis based on evidence 

type. While Peterson et al. (2010) did occasionally run additional analysis to see which evidence 

type helped the most, he did not consistently report how different evidence types impacted case 

outcomes and instead aggregated all evidence types into one variable. While this method does 

give a more generalized understanding of how physical evidence can influence outcomes, 

separating the evidence types has a practical purpose. Resource allocation is an ever-present 

issue when it comes to collecting and analyzing physical evidence (Greenwood, et al., 1975; 

Steadman, 2000, 2002; Office of Justice Programs, 2011; Parker & Peterson, 1972). Knowing 

which evidence type has the best chance of increasing the odds of arrest and conviction would 

allow for a more informed way of deciding which types of cases to send crime scene 

investigators to and what kind of evidence they should focus on. 

Cases with examined biological and fingerprint evidence were more likely to end in

arrest than cases without these evidence types. Weapons/firearm evidence decreased the 

likelihood of arrest. Trace evidence did not significantly influence the odds of arrest for assault 

cases. For all evidence types, there were too few cases with evidence reports generated before a 

case was cleared to analyze. 

Examined biological evidence was more likely to end in a conviction than cases without 

this evidence type. However, when factoring in whether the report was generated before a verdict 

was reached, there were too few cases to analyze. Weapons/firearm and fingerprint evidence had 

no impact on conviction. 

All evidence types had too few cases that had timely reports, which demonstrates the 

scarcity of case outcomes directly influenced by what the forensic evidence tells the investigator 

or courts. This suggests that whatever impact physical evidence has on a case does not directly 

come from the information any one evidence examination can provide to an investigation.
16

Burglary

Burglaries, by far, made up most the entire sample, with 36.1% of the randomly selected 

cases falling into this category. The original sample was comprised of 1011 burglary cases; 

however, investigative information was only available to the researchers for 749 of those cases 

(74.1%). Most victims and offenders were male (57.4% and 46.2% respectively) and White 

(66.8% and 24.2%). Unsurprisingly, the majority (75.9%) of the victims did not know the 

offenders. Victims and suspects were typically (75.6% and 18.0%) 30 years of age or older.

A suspect was only named or described in 23.9% of the cases and most (86.4%) 

burglaries were reported to the police by the victims of the crime. Burglary had the lowest arrest 

rate of 27.6% and 10.7% of those arrests occurred the same day the crime was reported to police. 

On average, an arrest took place 357 days from the incident. Again, the data shows quicker 

report times but longer average times between incidents to case clearance compared to Peterson

et al.’s (2010) data (see Appendix B for a comparison of descriptive statistics between this study 

and Peterson et al, 2010). Only 11.9% of burglaries ended in a conviction, which is the lowest 

conviction rate in the sample. However, this low clearance rate is not surprising considering the 

national average of burglary clearance rates are often one of the lowest (Federal Bureau of 

Investigation, 2016).

Physical Evidence Submitted, Examined, and Reported

Burglary cases had low evidence collection rates, which agrees with earlier studies 

(Peterson et al., 2010; McEwen, 2011). About 84.8% of the evidence submitted for analyses was 

examined. Fingerprints was the most common evidence type submitted for burglaries, followed 

by biological evidence. Of the fingerprint evidence submitted, 67.9% was examined and a report 

was generated for all but 2 cases. Biological evidence was examined in 32.3% of the cases, 

however, only 18.1% of the cases had that evidence examined. See Table C.2 for the evidence 

categories submitted and examined. Only 166 (16.4%) cases had physical evidence that linked 

the tested suspect to the crime. Tangible evidence was present in 48.3% of the cases. 

Tracking Cases through the Justice System

Police investigation data as well as courtroom documents were consulted to better 

understand how physical evidence impacts the outcomes of these burglary cases. Figure D.2

shows the progression of burglary cases as they move through the criminal justice system. The 

original sample included 1011 cases. Arrest information was obtained for 207 of those cases and 

courtroom outcomes were collected for 89 cases. The presence of examined evidence or 

evidence reports did not impact the likelihood of arrest (χ= 0.673, p = 0.412 and χ= 1.071, p = 

0.301) as well as convictions (χ= 0.407, p = 0.524 and χ= 0.493, p = 0.483). While Peterson et al. 

(2010) and McEwen (2011) found crime scene evidence to be significantly related to arrest and 

conviction rates, they failed to account for whether the evidence was examined. Peterson et al.

(2010) does mention that less than half of the burglary sample had evidence examined, he did not 

control for it in his analyses. 
17

Predictors of Criminal Justice Outcomes

Arrest

The results of this study’s findings differed significantly from Peterson et al.’s (2010). 

Other studies contradict this study’s findings, as some show forensic evidence to increase suspect 

identification (Roman et al., 2009; Bond, 2007b; Gabriel et al, 2010) as well as arrest for 

property crimes (Roman et al, 2009; McEwen, 2011; Gabriel et al, 2010). Not only was lab 

examined evidence not a significant predictor, but neither was any other variable. Peterson et al.

(2010) found that whether the witness reported the crime and an offender that was a family 

member or had an intimate relationship with the victim were significantly related to arrest (see 

Appendix E for a comparison of arrest predictors between this study and Peterson et al, 2010). 

Suspect sex as well as ethnicity was not found to be a significant predictor from this study’s 

results but Peterson et al (2010) found that Black female suspects were more likely to be arrested 

than White offenders. It should be noted that while Peterson et al.’s (2010) Nagelkerke’s Rsquare is higher than ours, both show little variation in the data is explained by the model. 

Conviction

Only 89 (12.6%) out of the 749 burglary cases ended in a conviction. Both Peterson et al.

(2010) and this study show examined evidence has no significant relationship with conviction. 

While Peterson et al. (2010) found a quick arrest to be a predictor of conviction, this study found 

none of the variables to be predictors (see Appendix F for a comparison of conviction predictors 

between this study and Peterson et al, 2010).

Plea/Trial

About 96% of convictions were the result of a guilty plea. Again, this study agrees with 

Peterson et al.’s (2010) findings. The low rate of convictions by trial makes it difficult to analyze 

how physical evidence influences the outcomes of plea deals.

Sentencing

Peterson et al.’s (2010) sample had longer average sentence for burglary than this study

(see Appendix G for a comparison of sentencing predictors between this study and Peterson et al, 

2010). Both studies had longer sentences than the national average sentence length for 

burglaries, which is 41 months (Rosenmerkel et al. 2009). Both Peterson et al (2010) and these 

results show having a public defender has a significant relationship with sentence length. 

However, while Peterson et al (2010) found the presence of a public defender decreases sentence 

length while this study found the opposite. Presence of a public defender was the only significant 

variable while Peterson et al (2010) found plea bargain and young adult victims were related to 

sentence length.
18

Hard to Solve Cases (stranger and no witnesses)

Unlike Peterson et al.’s (2010) data, this study had fewer hard to solve burglary cases. 

Peterson et al’s (2010) burglary sample was mostly (84.6%) hard to solve cases while only 

42.8% of burglary cases were categorized as such in the current data. About 88.5% of the hard to 

solve cases had lab examined evidence. Peterson et al.’s (2010) hard to solve burglaries had a 

3.8% arrest rate and a 4.0% conviction rate. This study found 63.1% of the hard to solve 

burglaries ended in arrest and 26.4% ended in conviction.

Time and Evidence Type

Interestingly, unlike other crime types, burglary cases typically had evidence reports 

generated before outcomes (arrests and convictions). Unfortunately, the timeliness of these 

reports does not seem to increase the likelihood of arrest or conviction. Peterson et al. (2010)

found that 28% of burglary cases had evidence examined prior to an arrest while 62.5% of this 

study’s burglary cases had evidence reports generated before case clearance.

Biological evidence did increase the likelihood of arrest and conviction. This agrees with 

previous studies on DNA evidence and property crimes (Roman et al, 2009; Gabriel et al, 2010).

Fingerprint evidence decreased the likelihood of arrest and conviction. Unlike the other crime 

types, burglary cases suffered from having too few cases where evidence was not examined

before arrest or conviction to analyze. 
19

Robbery

A total of 636 robberies were randomly selected and 461 (72.4%) cases had investigative 

information available to the researchers. About half the victims were male, 30 or older, and 

White. Offenders were predominately male, 30 or older, and Black. The majority (84.2%) of 

offenders were strangers. Only 11.7% of victims required medical treatment. Most (40.3%) had 

two or more witnesses. On average, robberies were reported to the police on the same day and 

the mean time from crime to arrest was 1564 days. Peterson et al. (2010) saw a longer time 

between when the crime occurred and when it was reported to the police but a shorter time for 

case clearance than this study (see Appendix B for a comparison of descriptive statistics between 

this study and Peterson et al, 2010). 

Physical Evidence Examined, Submitted & Reported

Physical evidence was submitted in 98.7% of robbery cases and had a report generated in 

85.2% of the cases. The most common evidence type was biological (47.3%) and fingerprint 

(44.4%) evidence. Weapons/firearm evidence was submitted in 23.3% of the cases. Peterson et 

al. (2010) saw similar trends but had higher rates of weapons/firearms submissions. McEwen 

(2011), on the other hand, found fingerprints to be the most common evidence type for his 

Colorado study site.

Tracking Cases Through the Justice System

About half of robberies ended in an arrest, with 46.2% being suspended or still open. 

However, only 30.6% of robberies ended in a conviction. Most (95.7%) of convictions ended in 

a guilty plea. There was no difference between cases with and without examined evidence for 

either arrest (χ = 0.179, p = .673) or conviction (χ = 1.409, p = .235). 

Predictors of Criminal Justice Outcomes

Arrest

Peterson et al. (2010) found crime scene evidence was a significant predictor of arrest, as 

was witness reports and friend/acquaintance relationship between victim and offender (see 

Appendix E for a comparison of arrest predictors between this study and Peterson et al, 2010). 

The current data indicate that victim reporting was the only predictor of arrest. The Nagelkerke’s

R-square of this study’s model is smaller than Peterson et al.’s (2010). However, both Peterson et 

al.’s (2010) and this study’s analyses point to a high level of unexplained variance. 

Conviction

About 32% of the robbery cases ended in a conviction. Peterson et al. (2010) found 

victim reports, intimate/family relations between victim and offender, and whether the victim 

received medical treatment were positive predictors of conviction (see Appendix F for a 

comparison of conviction predictors between this study and Peterson et al, 2010). This study’s 

findings, on the other hand, show arrest variables are the only predictors of conviction. Both 
20

studies agree physical evidence does not have a significant relationship with conviction. 

However, the Nagelkerke’s R-square of this study’s model shows a high level of unexplained 

variance.

Plea/Trial

Most (97.1%) of robberies had plea bargains. Only 4 cases did not end in a guilty plea 

and all 4 cases had examined evidence. Most (90.4%) cases that had a plea deal had examined 

evidence. However, 13 robberies ended in a guilty plea without the presence of examined 

evidence. 

Sentencing

The average sentence was 114.48 months, with a median of 119.99 months, which is 

about 27 months more than the national average (Rosenmerkel et al. 2009). Cases with guilty 

pleas had an average of 112.30 month sentences while trial cases had an average of 188.99 

months. This study’s data had longer average sentences than Peterson et al.’s (2010) data. Both 

studies show lab examined evidence is not a predictor of sentence length. Peterson et al. (2010)

found plea bargains reduced sentence length while victim age increased it (see Appendix G for a 

comparison of sentencing predictors between this study and Peterson et al, 2010). This study also 

had several predictors of sentencing, including the number of prior convictions which negatively 

impacts sentence length. Victim reports and having a Black female for a victim significantly 

increases sentence length. 

Hard to Solve Cases

Peterson et al. (2010) had 694 (64%) robbery cases that were hard to solve (strangers 

with no witnesses) but this study only had 114 (24.8%) cases that met that criteria. The arrest (χ 

= 2.596, p =.107) and conviction (χ = 0.411, p = .522) rates between hard to solve cases and all 

other cases was not significantly different. Most (83.3%) hard to solve cases had examined 

evidence. 

Time and Evidence Types

While Peterson et al. (2010) admits that only 21 robbery cases had evidence examined 

before an arrest occurred, he did not control for timeliness in his analyses. Table H.1

demonstrates how frequent evidence is submitted and examined before case clearance and 

conviction. While less than half of cases had evidence reports generated before case clearance, 

most (89.3%) cases had reports available before conviction. 

When controlling for timeliness, evidence has a negative relationship with arrest. The 

presence of examined weapons/firearm evidence is correlated with a higher rate of arrest. 

However, there is not enough information to analyze how the relationship changes when 

controlling for timeliness. 
21

Overall, forensic evidence does not significantly impact conviction rates. 

Weapons/firearm evidence alone does increase the likelihood of conviction. However, there is 

not enough information to analyze how the relationship changes when controlling for timeliness.
22

Rape

Of the 310 randomly selected rape cases, 211 (68.0%) had investigative variables 

available to the researchers. The majority (92.9%) of victims were female and most (88.6%) 

suspects were male. Victims tended to be white (57.8%) and in their 20s. Offenders tended to 

either be White (30.3%) or Black (31.3%) and 30 or older (35.5%). Most (37.0%) victims were 

friends with their attacker. Victims received medical treatment in 88.2% of the cases. The 

majority (75.8%) of cases were reported by the victim and most (66.8%) cases did not have 

witnesses. 

Only 29.4% of cases ended in arrest and 15.2% ended in a conviction. Few (5.2%) cases 

had an arrest within a day of the incident. On average, it took 9.19 days for a rape to be reported 

to the police and 199.53 days for an arrest to occur. Unlike other crimes, Peterson et al. (2010)

had both a shorter average time between crime and when it was reported as well as a shorter time 

between when a crime occurred and arrest (see Appendix B for a comparison of descriptive 

statistics between this study and Peterson et al, 2010). 

Physical Evidence Submitted, Examined & Reported

Physical evidence was submitted in 97.7% of rape cases and examined in 84.5% of cases. 

Biological evidence was by far the most common evidence type submitted and examined. Both 

Peterson et al. (2010) and McEwen (2011) had high rates of biological evidence in rape cases.

Sex assault kits made up the bulk of biological evidence submitted. Trace was the second most 

common evidence type; however, it was present in only 5.2% of cases. 

Tracking Cases Through the Justice System

Figure D.4 shows how rape cases moved through the criminal justice system. It’s clear 

the majority of rape cases remain open or suspended. Conviction rates for rapes are also very 

low, with only half of arrest ending in a guilty sentence. Cases with examined evidence (χ = 

3.410, p = .065) were not more or less likely to end in arrest. There were too few cases that 

ended in conviction that did not have examined evidence to analyze the impact of the evidence. 

This could suggest prosecutors hesitate to try a rape case without forensic evidence or juries are 

reluctant to convict without physical evidence. 

Predictors of Criminal Justice Outcomes

Arrest

Like Peterson et al. (2010), these findings show victim/offender relationships to be a 

predictor of arrest (see Appendix E for a comparison of arrest predictors between this study and 

Peterson et al, 2010). While Peterson et al (2010) found examined evidence increased the 

chances of arrest, this study suggests it is not significantly related to arrest. Both studies agree 

that witness reporting to police has an impact on arrest, however, this study’s findings suggest it 

increases the chances of arrest while Peterson et al (2010) found it decreased chances. 
23

Conviction

About 17% of rape cases ended in a conviction. Intimate or family relations between 

victim and offender were almost six times more likely to end in a conviction. Surprisingly, the 

chances of conviction go down if the victim received medical treatment. Peterson et al. (2010), 

on the other hand, found medical treatment increases the likelihood of conviction. Both studies 

found physical evidence has no impact on conviction outcomes (see Appendix F for a 

comparison of conviction predictors between this study and Peterson et al, 2010). The strongest 

predictor of conviction was whether a witness reported the crime to police. Peterson et al. (2010) 

found forensic evidence to be a predictor of conviction while this study did not. However, other 

studies show forensic evidence aids in convictions for rape cases (Briody, 2002; Gabriel et al, 

2010). Peterson et al.’s (2010) model had an impressive Nagelkerke’s R-square of .844 while 

this study’s model had a significantly lower R-square of .331.

Plea/Trial

Of the 32 cases with trial information to the researchers, only one case did not end with a 

guilty plea. The one trial case and 30 guilty plea cases had examined physical evidence. One case 

that ended in a guilty plea had no examined physical evidence. While this study did not have 

enough cases to analyze the impact of forensic evidence on plea deals, Briody (2002) found 

DNA evidence had no significant relationship with guilty pleas.

Sentencing

The average sentence length was about 77 months, which is about 83 months shorter than 

the average sentences reported by Peterson et al. (2010) and 61 months shorter than the national 

average (Rosenmerkel et al, 2009). Only 27 rape cases had sentencing information available. 

Because of this sample size, it is not possible to conduct a regression that has all the variables

tested by Peterson et al (2010). A simple regression was used to determine whether examined 

evidence is a predictor of sentence length and found it had no relationship with sentence length. 

These findings agree with Peterson et al’s (2010) results (see Appendix G for a comparison of 

sentencing predictors between this study and Peterson et al, 2010). However, both McEwen 

(2011) and Briody (2002) found forensic evidence had a relationship with sentence length. 

McEwen (2011) found probative physical evidence increased sentence length while Briody 

(2002) found DNA evidence decreased sentence length.

Hard to Solve Cases

Of the 211 rape cases, 18.5% were ‘hard to solve’ (no witnesses and strangers). Only 6 of 

the ‘hard to solve’ cases ended in arrest, however 31 (79.48%) of those cases had examined 

evidence. ‘Hard to solve’ cases were three times (χ = 6.184, p = .013) less likely to end in arrest. 

Time and Evidence Types

While Peterson et al. (2010) mentions only 1.6% of the rape cases he sampled had 

evidence examined prior to an arrest, he did not control for timeliness in his statistical analyses. 
24

Table H.1 demonstrates how frequently evidence is submitted and examined before an 

outcome. While most evidence categories have reports generated before conviction, the table 

shows less than half of biological evidence, which is the most common evidence type submitted 

for rape cases, had a report generated before an arrest occurred. 

Teasing out the impact different evidence categories have on arrest and conviction 

outcomes can tell us how useful an evidence type is for case clearance and may help police 

departments better allocate their limited resources. 

Overall, evidence did not have an impact on arrest. More telling, the most common 

evidence type collected, biological evidence, did not have a significant relationship with arrest. 

There were not enough cases with other evidence types to analyze.

There were not enough cases without examined evidence to analyze its impact on 

conviction. In fact, only one case that lacked examined evidence ended in conviction. Biological 

evidence did not have a significant relationship with conviction. When it came to timeliness, 

there were too few cases where evidence was not examined before conviction to test. 
25

Homicide

Out of the cohort of 438 homicide cases with evidence submitted to the crime lab, 284 

(64.8%) constituted the total number of cases for which investigative information could be 

gathered from participating police departments. Some cases were unavailable to researchers at 

participating police departments for both legal and logistical reasons. Both victims and offenders 

tended to be males and Black. Victims almost evenly fell into the 20-29 years old category 

(41.2%) and the 30 or older category (40.5%) while offenders were mostly 30 years or older 

(30.3%). Most homicide victims had a previous relationship with the offender (43.3%), in most 

cases (21.1%) they were friends. The majority (61.3%) of homicide victims received medical 

treatment. Most homicides (56.7%) had at least one witness and a description or name of the 

offender was provided in 58.5% of the cases. On average, it took 1.18 days for a homicide to be 

reported to police and case clearance occurred, as an average, about 177 days from the incident. 

Peterson et al. (2010) saw quicker report times and shorter case clearance times than this study’s 

sample (see Appendix B for a comparison of descriptive statistics between this study and 

Peterson et al, 2010). 

Physical Evidence Submitted, Examined, & Reported

Of the 438 homicide cases, 432 had evidence submitted for examination, which took 

place in 85.2% of the cases. A report detailing the results of those examinations were generated 

in 363 of the cases. Biological evidence was, by far, the most common evidence submitted, 

examined, and reported. Firearms/weapons evidence was the second most common, followed by 

fingerprint evidence. Only 35.4% of homicides had evidence that linked the suspect to the crime 

but 77.6% of cases had tangible evidence. Peterson et al.’s (2010) homicide cases had a high 

collection rate as well. Peterson et al. (2010) had more fingerprint evidence collected than 

biological evidence for homicide cases while McEwen (2011) had higher rates of biological 

evidence.

Tracking Cases Through the Justice Process

Figure D.5 shows how homicide cases moved through the criminal justice system. 

Evidence was examined in all but 45 homicides. Homicide, by far, had the highest arrest rate, 

with only 20.1% of the cases categorized as either suspended or open. A greater percentage of 

homicides that ended in arrest resulted in a conviction. Of the 163 homicide arrests, only 57 

cases did not end in a conviction. Considering the consequences of a guilty verdict, it is not 

surprising that homicide cases had the highest percentage of ‘not guilty’ pleas.

Unfortunately, there are not enough homicide cases that did not end in arrest to 

adequately analyze in a bivariate test. However, there is enough cases to explore the impact 

examined evidence has on conviction rates, which was shown to be insignificant (χ= 0.659, p = 

0.417). 
26

Predictors of Criminal Justice Outcomes

Arrest

Peterson et al. (2010) found that forensic evidence was not a predictor of arrest but 

pointed out that may be due to a lack of homicide cases without physical evidence. Other studies 

showed the collection of certain evidence types (Wellford & Cronin, 2000; Keel et al., 2009) or 

evidence that was the result of close physical contact between offender and victim (Addington, 

2006; Roberts, 2007; Alderden & Lavery, 2007; Puckett & Lundman, 2003; Litwin, 2004)

increased arrest rates. However, other studies suggest DNA evidence has no impact on case 

clearance (Schroeder, 2007; Schroeder & White, 2009). While Peterson et al. (2010) found 

witness report was a significant predictor of arrest, this study shows otherwise (see Appendix E 

for a comparison of arrest predictors between this study and Peterson et al, 2010). This study

shows the presence of lab examined evidence was a strong predictor of arrest. However, when 

the regression was re-run to control for whether an evidence report was generated before the case 

was cleared, examined physical evidence was not a significant predictor of arrest. 

Like Peterson et al. (2010), this study found victim gender as well as victim and offender 

race to be predictors of arrest. Cases with White male victims and Black suspects (both male and 

female) were more likely to end in arrest.

Conviction

Of the 238 homicide cases with courtroom information available, 106 (44.5%) ended in a 

conviction. Peterson et al. (2010) found victim/offender relationships was a predictor of 

conviction but not forensic evidence (see Appendix F for a comparison of conviction predictors 

between this study and Peterson et al, 2010). This study also found forensic evidence had no 

significant relationship with conviction, however, none of the other variables were significant 

including victim/offender relationships. While both this study and Peterson et al (2010) found 

physical evidence had no impact on conviction for homicide cases, Briody (2004) found DNA 

and fingerprint evidence increased conviction rates. 

Plea/Trial

Of the 106 homicide convictions, 74 (69.8%) were adjudicated via a guilty plea. The 

majority (95.9%) of the cases that had a guilty plea had examined physical evidence. Most plea 

cases had examined biological (57 cases), weapons/firearms (43 cases), and fingerprint evidence. 

(38 cases). Only three cases that ended in a guilty plea did not have any lab examined evidence. 

Of the 28 trial cases, 27 had lab examined evidence. Biological (22 cases), weapons/firearm (21 

cases), and fingerprint (15 cases) evidence. Both Peterson et al. (2010) and Briody (2004) found 

physical evidence had no impact on whether a defendant would enter a guilty plea. 

Sentencing

The average sentence was about 10688 days. Unlike all other crime types, there are

almost identical sentence lengths in both studies. Both studies had longer sentences than the 
27

national average of 244 months (Rosenmerkel et al, 2009). Both studies also agree plea bargains 

are strong predictors of sentence length and decrease the number of incarcerated months. Neither 

this study or Peterson et al (2010) showed forensic evidence to be predictors of sentencing (see 

Appendix G for a comparison of sentencing predictors between this study and Peterson et al, 

2010). Briody (2004), on the other hand, found DNA to have a negative impact on sentence 

length. 

Hard to Solve Cases (stranger and/or no witnesses)

Only four homicide cases had no witnesses and occurred between strangers. All four 

cases had examined physical evidence; two of those cases ended in arrest and one ended in a 

conviction. It is difficult to compare these findings to Peterson et al. (2010) since the previous 

study had a large enough sample size (N = 35) to determine whether evidence may have 

increased the likelihood of arrest and conviction. Peterson et al. (2010) found the rates of arrest 

and conviction for hard to solve cases with and without lab examined evidence did not differ 

significantly. 

Time and Evidence Types

While Peterson et al. (2010) acknowledged only 12% of his homicide sample had 

evidence examined prior to arrest, he did not control for this time variable in his analyses. As 

mentioned before, this study showed examined evidence was correlated with arrest. However, 

when the timeliness of the examination was included in the analysis, examined evidence did not 

have a relationship with arrest. 

The frequencies of evidence that was submitted and had an evidence report generated 

before a case was cleared or before a verdict was reached are shown in Table H.1. On average an 

evidence report was generated about 73 days after a case was cleared. Often, most homicide 

cases were cleared before evidence could be submitted for examination. Weapons/firearm 

evidence was the most likely to have a report generated before an arrest.

Peterson et al. (2010) often did not report whether separating evidence into categories 

changed the results of his analyses. Analyzing the differences between evidence categories 

provides practical information on what crime types benefit from the examination of what types 

of evidence and may aid in resource allocation decisions. 

Biological, fingerprint, electronic/print, and trace examined evidence had a significant 

relationship with arrest. Weapons/firearm evidence decreased the likelihood of arrest. However, 

there were too few cases with evidence reports generated before a case was cleared to analyze. 

Biological and fingerprint evidence increased the likelihood of conviction. 

Weapons/firearm, electronic/print, and trace evidence did not have a significant relationship with 

conviction. Unfortunately, the high rate of convictions coupled with the high rate of examined 

evidence for homicide cases makes it impossible to analyze how the timeliness of this evidence 

impacts conviction rates. 
28

There were not enough cases with pattern or ‘other’ evidence to analyze the impact 

examined evidence of these categories have on arrest or conviction.
29

Discussion

Robbery had the highest rate of evidence examined, followed closely by homicide. 

Biological evidence was the most commonly submitted evidence type for homicide, rape, and 

robbery. Assaults mostly had firearms/weapons evidence submitted while burglary’s most 

common evidence was fingerprints. 

Demographics between the two studies were roughly similar, with Black and White 

ethnicities being dominant for both suspects and victims for most crime types. Peterson et al 

(2010), however, had higher rates of Latino victims and suspects. Most crimes were committed 

by males and most victims, except for rape, were also males.

While Peterson et al (2010) often saw longer times between when a crime occurred and 

when it was reported to the police, the crimes in his sample were often cleared more quickly. 

Peterson et al (2010) had longer sentences for burglaries and rapes while this study had longer 

sentences for assaults and robberies. Interestingly, homicide sentences were roughly identical 

between the two studies and both studies saw longer homicide sentences than the national 

average (Rosenmerkel et al, 2009). Rape had few cases with sentencing information (N = 27) but 

this is most likely due to the low (10.3%) conviction rate.

Peterson et al. (2010) found physical evidence variables to be predictors of arrest for 

several crimes. However, this study show homicide is the only crime in which evidence has a 

significant relationship with arrest. Both studies, for the most part, agree physical evidence does 

not impact conviction outcomes. The only exception is assault, which this study found to be 

positively influenced by forensic evidence. Both studies agree physical evidence is not a 

predictor of sentence length. It seems investigative factors and victim/offender relationships tend 

to influence case outcomes more than physical evidence. 

A bivariate analysis of evidence types and case clearance showed biological evidence 

increased the likelihood of arrest for all crime types (assault cases, biological evidence more than 

triples the likelihood of arrest in assault cases) with the exceptions of robbery and rape. The 

interaction between biological evidence and rape could be due to several factors. Most (82.9%) 

of rape cases had examined biological evidence and a lack of cases without biological evidence 

may be responsible. Most (62.2%) rape victims knew their assailants making the investigation 

one of establishing consent rather than identifying an offender. As for conviction, bivariate 

analyses showed biological evidence increased conviction rates for assaults, burglaries, and 

homicides.

Bivariate analyses also found fingerprint evidence aided in case clearance for homicide 

and assault but reduced the likelihood for arrest in burglary cases. Fingerprint evidence increased 

the likelihood for conviction in homicide cases but decreases the likelihood for burglary. 

Weapons/firearm evidence aided case clearance for robberies but decreased clearance rates for 

homicide and assaults. Weapons/firearms evidence increased the likelihood of conviction only 

for robberies. Homicide cases were aided by several evidence types beyond biological or 

fingerprint evidence. Homicide clearance increased when electronic/print or trace evidence was 

examined.
30

While most cases had evidence reports available before a verdict was reached, few cases 

had evidence reports available at least three days before an arrest was made. Burglary (62.8%) 

had the highest rate of evidence, it had one of the lowest (20.5%) clearance rates. Peterson et al 

(2010) also admitted that little evidence was examined before an arrest occurred. Some would 

suggest this lack of timeliness is the reason physical evidence does not have stronger 

relationships with arrest. However, if this was the case, when controlling for timeliness, there 

should have been positive relationships between evidence and arrest. Instead, this study saw 

either no relationship or a negative relationship between these two variables.

Nagelkerke’s R-square or R-square is used to measure the amount of unexplained 

variance in regression models. The higher the number, the better the model is at explaining the 

outcomes seen in the data and thus the stronger model has a higher model. Peterson et al. (2010)

had higher Nagelkerke’s R-squares in his arrest models for burglary, robbery, and rape while this 

study’s models were better for assault and homicide. Peterson et al.’s (2010) conviction model 

was better for all crimes. For OLS models on sentencing, this study’s models were better for 

assault and robbery while Peterson et al.’s (2010) were better for burglary and rape. Homicide 

sentencing models were roughly the same. While exploring the differences between models, 

especially why this study’s arrest models were better for violent crimes, this study looked to the 

crime rates between the study sites but found Peterson et al.’s (2010) sites had both higher 

violent crime rates as well as higher property crime rates (Federal Bureau of Investigation, 

2016). 
31

The Impact of Forensic Evidence on Arrest and Prosecution Surveys

Little research exists analyzing how detectives perceive forensic evidence. This lack of 

research is startling considering investigators are responsible for identifying and constructing a 

case against the offender by interpreting both forensic and non-forensic evidence. Because most

forensic analysis requests are submitted by detectives (Steadman, 2000), it behooves us to 

understand what forensic evidence they deem useful. 

An early study by Greenwood, Chaiken, Petersilia, and Prusoff (1975) suggested 

forensics and investigative efforts in general played a very small part in case clearance. Most 

cases were closed due to catching a criminal in the act, information provided by the public, and 

clerical work such as looking up a license plate. A property crime study (Eck, 1983) found 

detectives relied mostly on victim statements, however fingerprint evidence was a strong 

predictor of case clearance. 

More recent literature shows that forensic evidence is, at least on the surface, highly 

valued. A survey of Michigan law enforcement agencies found most the participants believed 

that knowledge of forensic science was desirable for new recruits (Lambert et al., 2003). This 

view persisted regardless of the geological setting, size of the agency, or whether it was a 

sheriff’s department or local agency (Lambert et al., 2007). While analyzing the use of the 

National Integrated Ballistic Information Network (NIBIN), interviews with detectives showed 

they viewed the forensic resource as a powerful investigative tool (King, Wells, Katz, Maguire, 

& Frank, 2013). While forensic knowledge seems to be prized in the law enforcement 

community, forensic evidence is mainly used to either corroborate witness or suspect statements 

or used for leverage when attempting to obtain a confession rather than identify the offender 

(Horvath and Messig, 1996). The limited use of forensic evidence has been blamed on a lack of 

training for detectives in what forensic evidence can accomplish (Horvath and Messig, 1996; 

Ludwig & Fraser, 2013). 

Methodology 

To ascertain how law enforcement personnel perceive forensic evidence, a survey was 

administered to detectives within the State of Connecticut. Out of a solicited 300 surveys, 

attempted within 47 separate law enforcement agencies, only 153 surveys were successfully 

returned from 27 police departments, representing a 51% response rate. Paper surveys were used 

and the targeted population for these surveys were any detectives currently employed in a 

Connecticut law enforcement agency from which data was collected in the quantitative phase of 

this research. Confidentiality was guaranteed for participants. The survey included questions 

created to gather information from five specific areas of interest: demographics, case specific 

evidence, general experiences concerning physical evidence, timeliness of forensic evidence, and 

how prosecutors utilize physical evidence. In addition to these five areas of interest, participants 

were invited to comment on what they felt would make forensic evidence a more powerful tool 

in case clearance and conviction.

Demographic questions included the size of the participant’s law enforcement agency; 

level of education and investigative experiences; and gender. Crime specific experience with 

forensic evidence was measured by asking participants how they would rate the usefulness of 
32

both forensic (DNA, fingerprint, other forms of forensic evidence) and non-forensic evidence 

(suspect and witness statements) when investigating burglaries, rapes, robberies, homicides, and 

gun-related crimes. General experiences with forensic evidence was assessed by asking the 

participant to rate statements that evaluate how useful forensic evidence is in solving cases and 

whether the lack of forensic evidence has a profound negative effect on case clearance. Data 

regarding detectives’ thoughts on the current timeliness of forensic evidence reports and whether 

investigations would be improved by quicker reports was also solicited. The final area of interest, 

concerning prosecutors’ usage of forensic evidence, assessed detectives’ opinions on how the 

presence or absence of forensic evidence impacts prosecutorial decisions.

The actual usefulness of evidence type was ranked by conducting a chi-square analysis

for each evidence type in each crime type to determine whether there was a statistically 

significant relationship between the evidence type and case clearance. Evidence types were then 

ranked based on the percentage of cleared cases with each evidence type. For example, 34.7% of 

cleared assault cases had biological evidence compared to the 16.0% of cleared assault cases 

with fingerprint evidence. 

Demographics

Respondents were mostly from large (49.0%) and medium (48.4%) agencies, with only 

2% from small agencies. Most (80.4%) of the respondents were males and had at least a 

Bachelor’s degree (51.0%). About half of the respondents were detectives between 2006 to 2009. 

The average career length as a detective was about 6 years (SD = 4.93 years), with the longest 

career spanning 23 years. 

Investigations

Respondents were asked to rate whether they agree on how the forensic analysis of 

physical evidence (FAPE) helps in investigations. The rating was between 1 to 5, with a rating of 

1 indicates total agreement, 3 denotes a neutral stance and a 5 indicates total disagreement. On 

average, respondents were neutral on whether property or violent crimes would not have been 

cleared without the presence of FAPE. However, most agreed that they experienced at least one 

case where arrest would not have been possible without FAPE and have worked on cases that 

would have not been cleared without FAPE. On average, the use of databases was also viewed in 

a positive light. 
33

Table 9.1: Usefulness of FAPE

Question Mean 

(SD)

The majority of property crime cases that I’ve investigated that were cleared 

would never have been cleared without FAPE. (N = 149)

3.01

(1.01)

The majority of violent crime cases that I’ve investigated that were cleared 

would never have been cleared without FAPE. (N = 150)

3.11

(0.96)

I have worked on at least one investigation that ended in an arrest solely 

because of FAPE. (N = 150)

2.02

(1.33)

The majority of the time, when DNA profiles or fingerprints are entered into 

databases, hits are made that provide information that lead directly to case 

clearance. (N = 151)

2.72

(1.04)

I have worked on many cases that would never have been solved without 

FAPE. (N = 149)

2.74 

(1.02)

I have worked on many cases that were never solved in spite of FAPE. (N = 

150)

2.85 

(0.99)

The most common effect FAPE has on the investigations I’ve conducted is to 

tell me something I already knew. (N = 153)

3.12

(0.91)

FAPE timeliness

As with investigative experiences, respondents were asked to rate statements on the 

timeliness of FAPE. The rating was between 1 to 5, 1 indicates total agreement and a 5 indicates 

total disagreement. Most respondents agreed that the usefulness of forensic evidence would 

increase if the turnaround time from submission to analysis was shorter. While most 

investigators would rather wait until they have the results of evidence analysis before making an 

arrest, respondents were almost neutral when asked whether they did indeed wait on results. 

Investigators were mostly neutral when asked whether the results of an analysis conflicted with 

other investigative information. 
34

Table 9.2: Using FAPE

Question Mean 

(SD)

I feel that if FAPE were conducted more quickly, it would be of greater benefit 

in making arrests. (N = 153)

2.03

(1.06)

In most investigations I would rather wait until I have seen the results of some 

FAPE before making investigative decisions. (N = 150)

2.57

(0.97)

In most investigations I do in fact wait until I have seen the results of FAPE 

before making investigative decisions. (N = 150)

2.91

(0.97)

In most investigations that use FAPE, the results of the analyses conflict with 

some other investigative information discovered before the results of the 

analysis were known. (N = 148)

3.41

(0.79)

Prosecutors and FAPE

Investigators were asked to rate whether they agree with statements concerning their 

experience with prosecutors and FAPE. The rating was between 1 to 5, 1 indicates total 

agreement and a 5 indicates total disagreement. Most respondents were neutral when asked 

whether prosecutors were reluctant to charge cases without forensic evidence. However, most 

slightly agreed that prosecutors may feel trying a case or negotiating a plea deal without FAPE is 

difficult. On average, respondents felt they could make arrests in both property and violent cases 

without confirmation from the prosecutor. 
35

Table 9.3: Prosecutors and FAPE

Question Mean 

(SD)

Most prosecutors, when dealing with property crime are reluctant to prosecute 

without FAPE. (N = 143)

3.05

(0.92)

Most prosecutors, when dealing with violent crime are reluctant to prosecute 

without FAPE. (N = 144)

3.05

(0.93)

Most prosecutors believe that juries will not convict suspects unless some 

FAPE is part of the prosecution’s case. (N = 142)

2.75

(0.91)

Most prosecutors, believe that they will have a very difficult time negotiating 

appropriate plea agreements with suspects unless some FAPE is part of the 

prosecution’s case. (N = 142)

2.75

(0.86)

I feel comfortable/I am allowed to determine probable cause and make arrests 

in property crime cases without conferring with the prosecutor. (N = 144)

2.13

(1.02)

I feel comfortable/I am allowed to determine probable cause and make arrests 

in violent crime cases without conferring with the prosecutor. (N = 147)

2.39

(1.06)

FAPE usefulness

Respondents were asked to rate the usefulness of various forms of evidence and forensic 

analysis of physical evidence (FAPE) for case clearance for several crime types. DNA has a high 

rating throughout all crime types. Fingerprints have a relatively high rating except for assault or 

firearm crimes. Witness statements were consistently rated higher than suspect statements and, in 

several crimes, were rated as high as DNA evidence. Other forms of FAPE had low ratings 

except for firearm evidence, in which it tied for second alongside fingerprint evidence. 
36

Note: the rating for fingerprints in rape cases was equally split between 2 and 1. 

The above chart shows the percentage of cleared cases with each evidence type. The bars 

with the bars with a black outline indicate the evidence type has a statistically significant 

relationship with arrest.

0

1

2

3

4

5

6

Assault Burglary Robbery Rape Homicide Firearms

Figure 9.1: Mode rating of usefulness for 

solving cases

Suspect statement Witness statement DNA Fingerprints Other FAPE

0

10

20

30

40

50

60

70

80

90

Assault Burglary Robbery Rape Homicide

Figure 9.2: Percentages of solved cases by 

evidence type

DNA Fingerprints Other FAPE
37

Assault

Investigators rated DNA as the most useful forensic evidence category for assault cases. 

However, cleared assault cases had higher percentage of other forms of FAPE than both DNA 

and fingerprint evidence. Judging by percentages of evidence in solved cases, while fingerprint 

evidence was rated the second most useful form of forensic evidence, it was the least prevalent in 

cleared assault cases. Bivariate analyses, on the other hand, showed DNA evidence (χ = 22.93, p 

= .000) and fingerprint evidence (χ = 8.13, p = .004) increased the likelihood of arrest but other 

forms of FAPE did not (χ = 3.30, p = .069). However, fingerprints had a higher percentage of 

cleared cases than DNA for assaults. In other words, the bivariate analysis show that while 

investigators were correct in ranking other forms of FAPE as least useful, they were mistaken 

when they rated DNA evidence as more useful than fingerprint evidence. 

Burglary

DNA and fingerprint evidence were tied as the most useful rated form of FAPE by 

investigators, however, the percentage of cleared cases with fingerprints far outstripped cleared 

cases with DNA evidence. The low value investigators placed on other forms of FAPE as well as 

the high value placed on fingerprints, seem to be justified. The chi-square tests show DNA (χ = 

16.18, p = .000), fingerprint (χ = 15.0, p = .000), and other forms of FAPE (χ = 7.73, p = .005) 

increased the chances of an arrest. Figure 9.2 shows fingerprints were, in reality, the most useful.

Robbery

As with the other crime types, DNA was rated the most useful evidence type for robbery 

cases. However, judging by the percentages of evidence types in cleared cases, fingerprints and 

other forms of FAPE outperformed DNA. Bivariate analyses show DNA (χ = 0.63, p = .427), 

and fingerprint evidence (χ = 3.06, p = .080) have no relationship with arrest. However, other 

forms of FAPE (χ = 14.63, p = .000) increase the likelihood of arrest. In short, while DNA and 

fingerprints are rated as very useful by investigators, the data shows that other forms of FAPE, 

which was rated least useful, is the only evidence type that helps case clearance. 

Rape

Investigators’ perception on the usefulness of evidence for rape matches the trends seen

in cleared crimes. However, chi-square tests show DNA (χ = 0.82, p = .364). had no significant 

relationship with arrest. Unfortunately, there were not enough cases with either fingerprint 

evidence or other forms of FAPE to analyze. 

Homicide

DNA was rated the most useful for homicide cases and was present in a high number of 

cleared cases. However, other forms of FAPE, which were deemed least useful, was just as 

prevalent in solved cases. DNA (χ = 6.97, p = .008) and fingerprint (χ = 9.79, p = .002) evidence 

had positive relationships with arrest. Other forms of FAPE (χ = 0.88, p = .348), on the other 

hand, did not. So, while other forms of FAPE were just as common in cleared homicide cases as 

DNA evidence, other forms of FAPE did not aid in arrest. Overall, investigators were accurate in

rating evidence for homicide cases, despite the frequency of other forms of FAPE in cleared 

cases. 
38

Discussion

DNA was viewed as one of the most useful forms of FAPE. However, DNA only lived 

up to expectations in homicides. DNA did not have a significant relationship with arrest for 

robbery or rape cases. Conversely, other FAPE was rated the least useful in all crimes but 

firearm crimes but had strong showings in burglaries and robberies. However, other forms of 

FAPE was only significant these two crime types. Overall, it seems investigators’ perception of 

FAPE does not match the reality. DNA has been grossly overestimated for all but two crime 

types while other forms of FAPE, such as trace evidence or other class-level evidence, has been 

unduly devalued for property crimes. 

Investigators do not have accurate views on what evidence helps the most for different 

crime types. Investigators were most accurate evaluating evidence for violent crimes such as 

homicide and assault cases. The perceived usefulness of fingerprints in burglaries was correct, 

however investigators were overconfident in DNA for this crime type. Investigators were correct 

in their rating of DNA evidence for homicide and assault cases but overestimated fingerprint 

evidence for robberies while devaluing other forms of FAPE for the same crime type. 
39

Limitations

Although this study was well designed and yielded important findings on the efficacy of 

forensic evidence, several limitations must be acknowledged. While other similar studies have 

used multiple sites, these studies suffer from issues concerning differences in laws, in police 

culture, and prosecutorial policies when attempting to measure the efficacy of forensic evidence 

(Baskin & Sommers, 2010, 2011, 2012; Sommers & Baskin, 2011; Roman et al., 2009; King et 

al., 2013). A single state-wide study site, with a single crime lab, does not suffer from these 

limitations. However it has become obvious that crime labs vary in what evidence is collected 

and examined (Peterson et al., 2010; McEwen, 2011) so the generalizability of this single statewide study could be a concern. 

While the study period was selected to ensure a large number of the selected cases would 

have judicial adjudication, changes in procedure, laws, and technological advances that have 

occurred since 2009 are not represented in this data. The biggest changes concerned DNA 

evidence as the procedures and laws in Connecticut for this evidence type has changed since the 

end of the study period. In particular, a new law has allowed the collection of DNA samples 

from arrestees as long as the suspect is charged with a subset of felonies. Prior to this law, DNA 

was only collected if the offender was convicted of a felony (Prince, 2011; National Institute of 

Justice, 2012). To address the growing backlog of DNA evidence, law enforcement agencies 

have started to limit DNA submissions to the Connecticut State Forensic Laboratory (New 

Haven Register, 2012). These efforts, aided by federal funding, have paid off as Connecticut’s 

DNA backlog has been significantly reduced (Flynn, 2015). 

The cases selected for this study originated from the Connecticut State Crime Laboratory, 

which means all the cases reviewed included at least one piece of forensic evidence and cases 

without evidence submitted to this crime lab were excluded based on this selection process. It is 

unknown how many cases contained evidence but were never submitted to this particular lab or 

cases were evidence was submitted to another crime lab. 

While Peterson et al. (2010) included whether a case was referred to the prosecutor and 

whether the prosecutor decided to charge the defendant, this study does not include information 

on these decision steps. Due to data gathering complications, including privacy laws, this study 

could only determine whether or not a defendant was arrested and whether they were convicted 

of the crime. If a suspect was arrested or convicted, the data cannot tell us the specific reason 

(e.g.-how each piece of evidence impacted the decision to arrest or convict; whether a prosecutor 

decided a case wasn’t strong enough to bring to court, etc.). Unfortunately, many of these 

questions cannot be answered without interviewing the detectives and prosecutors that handled 

each case, which would be a very difficult task.

While this study addressed many of Peterson et al.’s (2010) recommendations, there were 

two recommendations we could not address. The first is improving the crime laboratory 

information and management systems (LIMS). This study did not analyze crime lab management 

or procedures. Connected with that recommendation was the recommendation to perform a cost 

analysis of forensic evidence. While both recommendations are commendable and should be 

explored, they were beyond the scope of this study. 
40

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46

Appendix A: Variables and Analytical Models

Offense Type of offense (based on crime lab report)

Location: 3

Variable type: Numeric (width: 24; decimal: 0)

Value Label Unweighted 

frequency

%

1 Assault 663 10.7

2 Burglary 1387 22.4

3 Robbery 1208 19.5

4 Rape 524 8.5

5 Homicide 2398 38.7

Missing 19 0.3

Based upon 6199 valid cases out of 6617 total cases

• Mean: 3.49

• Median: 3.00

• Mode: 5

• Minimum: 1

• Maximum: 5

• Standard Deviation: 1.454

TypeEvCol Type of evidence collected

Location: 66

Variable type: String (width: 60; decimal: 0)

Based upon 6199 valid cases out of 6617 total cases

ForCSLoc Forensic crime scene location

Location: 67

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

1 Interior 2927 47.2

2 Exterior 1550 25.0

Missing 1722 27.8

Based upon 6199 valid cases out of 6617 total cases

• Mean: 1.35

• Median: 1.00

• Mode: 1

• Minimum: 1

• Maximum: 2

• Standard Deviation: 0.476

Submit Submitted to crime lab?

Location: 68

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 46 0.7

1 Yes 5723 92.3
47

Missing 430 6.9

Based upon 6199 valid cases out of 6617 total cases

• Mean: 0.99

• Median: 1.00

• Mode: 1

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.089

Sub_Date Submitted date

Location: 69

Variable type: Date (width: 8; decimal: 0)

Based upon 6199 valid cases out of 6617 total cases

Examine Evidence examined?

Location: 70

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 533 8.6

1 Yes 4931 79.5

Missing 735 11.9

Based upon 6199 valid cases out of 6617 total cases

• Mean: 0.90

• Median: 1.00

• Mode: 1

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.297

Exam_Date Examined date

Location: 71

Variable type: Date (width: 8; decimal: 0)

Based upon 6199 valid cases out of 6617 total cases

ReportGen Report generated

Location: 72

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 429 6.9

1 Yes 4859 78.4

Missing 911 14.7

Based upon 6199 valid cases out of 6617 total cases

• Mean: 0.92

• Median: 1.00

• Mode: 1

• Minimum: 0

• Maximum: 1
48

• Standard Deviation: 0.273

ReportGen_Date Report generated date

Location: 73

Variable type: Date (width: 8; decimal: 0)

Based upon 6199 valid cases out of 6617 total cases

IdEvid Identification of Evidence

Location: 75

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

1 Inconclusive 139 2.2

2 Negative 789 12.7

3 Positive 3908 663.0

Missing 1363 22.0

Based upon 6199 valid cases out of 6617 total cases

• Mean: 2.78

• Median: 3.00

• Mode: 3

• Minimum: 1

• Maximum: 3

• Standard Deviation: 0.479

IndivEvid Individualization of evidence

Location: 76

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

1 Inconclusive 719 11.6

2 Individual 2465 39.8

3 Class 1294 20.9

Missing 1721 27.8

Based upon 6199 valid cases out of 6617 total cases

• Mean: 2.13

• Median: 2.00

• Mode: 2

• Minimum: 1

• Maximum: 3

• Standard Deviation: 0.658

LinkSuspCrime Link suspect to crime

Location: 79

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 3868 62.4

1 Yes 881 14.2

Missing 1450 23.4
49

Based upon 6199 valid cases out of 6617 total cases

• Mean: 0.19

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.389

CrimOff Type of offense (based on crime police report)

Location: 82

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

1 Assault 543 11.9

2 Burglary 1014 22.2

3 Robbery 885 19.4

4 Rape 379 8.3

5 Homicide 1747 38.2

6* Inappropriate -- --

*These cases not represented in the descriptive statistics for this 

variable

Based upon 4568 valid cases out of 6617 total cases

• Mean: 3.39

• Median: 3.00

• Mode: 5

• Minimum: 1

• Maximum: 5

• Standard Deviation: 1.470 

DayCrimeToReprt Total number of days between crime and report

Location: 88

Variable type: Numeric (width: 8; decimal: 0)

Based upon 4403 valid cases out of 6617 total cases

• Mean: 0.84

• Median: 0.00

• Mode: 0

• Minimum: 0 

• Maximum: 474

• Standard Deviation: 12.984

DayCrimeToArr Total number of days between crime and arrest

Location: 89

Variable type: Numeric (width: 8; decimal: 0)

Based upon 2710 valid cases out of 6617 total cases

• Mean: 222.52

• Median: 33.00

• Mode: 0

• Minimum: 0 
50

• Maximum: 2845

• Standard Deviation: 422.016

DayReprtToArr Total number of days between reported to police and arrest

Location: 90

Variable type: Numeric (width: 8; decimal: 0)

Based upon 2685 valid cases out of 6617 total cases

• Mean: 221.32

• Median: 32.00

• Mode: 0

• Minimum: 0 

• Maximum: 2842

• Standard Deviation: 422.317

VicSex Victim sex

Location: 91

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

1 Female 1644 36.0

2 Male 2791 61.1

Missing 133 2.9

Based upon 4568 valid cases out of 6617 total cases

• Mean: 1.63

• Median: 2.00

• Mode: 2

• Minimum: 1 

• Maximum: 2

• Standard Deviation: 0.483

VicAge Victim age

Location: 92

Variable type: Numeric (width: 8; decimal: 0)

Based upon 4280 valid cases out of 6617 total cases

• Mean: 34.30

• Median: 30.00

• Mode: 21

• Minimum: 0 

• Maximum: 92

• Standard Deviation: 15.638

VicRace_W Victim race-white

Location: 93

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 2100 46.0

1 Yes 1997 43.7

Missing 471 10.3
51

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.49

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.500

VicRace_B Victim race-black

Location: 94

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 2764 60.5

1 Yes 1333 29.2

Missing 471 10.3

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.33

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.469

VicRace_L Victim race-latino

Location: 95

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 3485 76.3

1 Yes 612 13.4

Missing 471 10.3

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.15

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.357

VicRace_A Victim race-asian

Location: 96

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 4021 88.0

1 Yes 76 1.7

Missing 471 10.3
52

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.02

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.357

VicRace_O Victim race-other

Location: 97

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 4018 88.0

1 Yes 79 1.7

Missing 471 10.3

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.02

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.138

SuspSex Suspect sex

Location: 98

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

1 Female 130 2.8

2 Male 3567 78.1

Missing 871 19.1

Based upon 4568 valid cases out of 6617 total cases

• Mean: 1.96

• Median: 2.00

• Mode: 2

• Minimum: 1 

• Maximum: 2

• Standard Deviation: 0.184

SuspAge Suspect age

Location: 99

Variable type: Numeric (width: 8; decimal: 0)

Based upon 1651 valid cases out of 6617 total cases

• Mean: 30.26

• Median: 27.00

• Mode: 21

• Minimum: 13 
53

• Maximum: 84

• Standard Deviation: 11.794

SuspRace_W Suspect race-white

Location: 100

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 2185 47.8

1 Yes 1127 24.7

Missing 1256 27.5

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.34

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.474

SuspRace_B Suspect race-black

Location: 101

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 1683 36.8

1 Yes 1629 35.7

Missing 1256 27.5

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.49

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.500

SuspRace_L Suspect race-latino

Location: 102

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 2796 61.2

1 Yes 516 11.3

Missing 1256 27.5

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.16

• Median: 0.00

• Mode: 0

• Minimum: 0
54

• Maximum: 1

• Standard Deviation: 0.363

SuspRace_A Suspect race-asian

Location: 103

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 3300 72.2

1 Yes 12 0.3

Missing 1256 27.5

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.00

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.060

SuspRace_O Suspect race-other

Location: 104

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 3284 71.9

1 Yes 28 0.6

Missing 1256 27.5

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.01

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.092

NumWit Total number of witnesses

Location: 105

Variable type: Numeric (width: 8; decimal: 0)

Based upon 3946 valid cases out of 6617 total cases

• Mean: 1.70

• Median: 1.00

• Mode: 0

• Minimum: 0

• Maximum: 30 

• Standard Deviation: 2.671

VicReprt Victim report to police

Location: 106

Variable type: Numeric (width: 8; decimal: 0)
55

Value Label Unweighted 

frequency

%

0 No 1971 43.1

1 Yes 2333 51.1

Missing 264 5.8

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.54

• Median: 1.00

• Mode: 1

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.498

WitReprt Witness report to police

Location: 107

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 1831 40.1

1 Yes 2349 51.4

Missing 388 8.5

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.56

• Median: 1.00

• Mode: 1

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.496

WitNameSusp Witness name/provide suspect

Location: 108

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 1395 30.5

1 Yes 2676 58.6

Missing 497 10.9

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.66

• Median: 1.00

• Mode: 1

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.475

VicSuspRel_S Victim relationship to suspect-stranger

Location: 109

Variable type: Numeric (width: 8; decimal: 0)
56

Value Label Unweighted 

frequency

%

0 No 1366 29.9

1 Yes 1985 43.5

Missing 1217 26.6

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.59

• Median: 1.00

• Mode: 1

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.491

VicSuspRel_IP Victim relationship to suspect-intimate partner

Location: 110

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 3014 66.0

1 Yes 337 7.4

Missing 1217 26.6

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.10

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.301

VicSuspRel_Fam Victim relationship to suspect-family

Location: 111

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 3213 70.3

1 Yes 138 3.0

Missing 3351 26.6

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.04

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.199

VicSuspRel_Fnd Victim relationship to suspect-friend

Location: 112

Variable type: Numeric (width: 8; decimal: 0)
57

Value Label Unweighted 

frequency

%

0 No 2659 58.2

1 Yes 692 15.1

Missing 1217 26.6

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.21

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.405

VicSuspRel_W Victim relationship to suspect-work

Location: 113

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 3152 69.0

1 Yes 199 4.4

Missing 1217 26.6

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.06

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.236

VicMedTx Victim received medical treatment

Location: 114

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 2420 53.0

1 Yes 1883 41.2

Missing 265 5.8

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.44

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.496

SuspStmnt Suspect gave statement

Location: 115

Variable type: Numeric (width: 8; decimal: 0)
58

Value Label Unweighted 

frequency

%

0 No 2191 48.0

1 Yes 1763 38.6

Missing 614 13.4

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.45

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.497

SuspArr Suspect arrested

Location: 116

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 1715 37.5

1 Yes 2470 54.1

Missing 383 8.4

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.59

• Median: 1.00

• Mode: 1

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.492

DefCon Defendant convicted?

Location: 119

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 2677 58.6

1 Yes 1459 31.9

Missing 432 9.5

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.35

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 1

• Standard Deviation: 0.478

NumPrevArrCon Number of previous arrests that end in convictions

Location: 122

Variable type: Numeric (width: 8; decimal: 0)
59

Based upon 1417 valid cases out of 6617 total cases

• Mean: 0.74

• Median: 0.00

• Mode: 0

• Minimum: 0

• Maximum: 16

• Standard Deviation: 1.386

Plea Plea bargain

Location: 138

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

0 No 242 5.3

1 Yes 1128 24.7

2 Nolo 

contendere

39 0.9

Missing 3159 69.2

Based upon 4568 valid cases out of 6617 total cases

• Mean: 0.86

• Median: 1.00

• Mode: 1

• Minimum: 0

• Maximum: 2

• Standard Deviation: 0.423

SentLeng Sentence length (Days)

Location: 140

Variable type: Numeric (width: 8; decimal: 0)

Based upon 1443 valid cases out of 6617 total cases

• Mean: 7588.77

• Median: 5475.00

• Mode: 7300

• Minimum: 0

• Maximum: 21900

• Standard Deviation: 6698.637

DefenseType Defense attorney type

Location: 145

Variable type: Numeric (width: 8; decimal: 0)

Value Label Unweighted 

frequency

%

1 Private counsel 1371 30.0

2 Public defender 27 0.6

Missing 3170 69.4

Based upon 4568 valid cases out of 6617 total cases

• Mean: 1.02

• Median: 1.00
60

• Mode: 1

• Minimum: 1

• Maximum: 2

• Standard Deviation: 0.138
61

Predictors for the Selection Models:

Table A.1: Logistic Regression Predictors

Arrest Conviction 

Current Study Peterson et al. 

(2010) 

Current Study Peterson et al. 

(2010) 

Witness reports Witness reports Witness reports Witness reports

Victim reports Victim reports Victim reports Victim reports

Intimate/family Intimate/family Intimate/family Intimate/family

Friend Friend/acquaintance Friend Friend/acquaintance

Examined 

evidence

Crime scene 

evidence

Examined 

evidence

Crime scene 

evidence

Time incident to 

report

Time incident to 

report

Victim received 

medical treatment

Victim received 

medical treatment

Victim male Victim male One day arrest Arrested within 10 

minutes

Suspect male Suspect male Suspect 

statement/suspect 

named or 

described

Direct arrest

Victim teen Victim teen -- LA 

Victim young 

adult

Victim young adult -- Indy

Victim Black Victim Black -- Correction factor

Victim Latino Victim Latino

Suspect Black Suspect Black

Suspect Latino Suspect Latino

Table A.2: OLS sentence length predictors

Current Study Peterson et al. (2010) 

Witness reports Witness reports

Victim reports Victim reports

Intimate/family Intimate/family

Friend Friend/acquaintance

Examined evidence Crime scene evidence

Victim received 

medical treatment

Victim received medical 

treatment

One day arrest Arrested within 10 minutes

Suspect 

statement/suspect 

named or described

Direct arrest

-- LA 

-- Indy

Gender*race 

interaction

Gender*race interaction

-- Correction factor
62

Appendix B: Case Descriptive Characteristics

Table B.1: Descriptive characteristics of assault incidents

 Current study Peterson et al. (2010) 

N 393 859

Victim: 

% male 76.1 69.0

% <20 21.2 25.0

% 20-29 36.1 33.3

% 30+ 34.9 41.7

White 23.6 36.7

Black 49.0 50.3

Latino 15.2 12.3

Asian 0.3 1.5

Other 1.5 0.1

Suspect: 

% male 74.6 86.4

% <20 11.0 43.9

% 20-29 19.7 29.1

% 30+ 19.4 26.0

White 14.3 29.9

Black 41.8 55.7

Latino 13.4 13.5

Asian 0.9 0.8

Other 0.0 0.1

Victim/Suspect 

Relationship: 

% intimate/family 8.4 37.1

%friend/acquaintance -- 24.9

% friend 17.0 --

%work 5.4 --

%stranger 47.2 38.0

%victim received medical 

treatment 

77.0 52.4

# of Witnesses:

%0 21.5 33.0

%1 29.3 43.5

%2+ 39.7 23.5

% witness report to police 62.4 33.3
63

% victim report to police 71.0 79.7

% arrests 44.5 49.4

% convictions 25.1 20.5

% arrested within 10 

minutes 

-- 29.9

% arrested within a day 23.0 --

Time from incident to 

police report (mean days) 

0.13 1.37

Time from incident to 

arrest (mean days) 

96.62 21.57

 

Time from reported to 

arrest (mean days) 

85.01 --
64

Table B.2: Descriptive characteristics of burglary incidents

 Current study Peterson et al. (2010) 

N 749 1263

Victim: 

% male 57.4 51.7

% <20 1.7 7.4

% 20-29 13.8 26.0

% 30+ 75.6 66.6

White 66.8 54.0

Black 7.9 26.9

Latino 7.5 13.8

Asian 2.1 4.3

Other 1.9 1.0

Suspect: 

% male 46.2 85.0

% <20 18.0 24.2

% 20-29 13.8 39.3

% 30+ 75.6 36.5

White 24.2 41.4

Black 8.9 43.9

Latino 6.5 13.3

Asian 0.1 1.4

Other 0.1

Victim/Suspect Relationship: 

% intimate/family 2.1 7.5

%friend/acquaintance -- 11.2

% friend 4.8 --

%work 4.0 --

%stranger 34.4 81.3

%victim received medical 

treatment 

0.4 0.6

# of Witnesses:

%0 72.4 95.3

%1 13.9 3.5

%2+ 6.7 1.2

% witness report to police 15.9 4.7

% victim report to police 86.4 7.5
65

% arrests 27.6 8.2

% convictions 11.9 3.2

% arrested within 10 minutes -- 29.9

% arrested within a day 3.9 --

Time from incident to police 

report (mean days) 

1.25 3.65

Time from incident to arrest 

(mean days) 

363.96 35.62

 

Time from reported to arrest 

(mean days) 

360.32 --
66

Table B.3: Descriptive characteristics of robbery incidents

 Current study Peterson et al. (2010) 

N 461 1081

Victim: 

% male 52.5 66.6

% <20 8.5 16.4

% 20-29 31.7 26.5

% 30+ 50.8 57.1

White 46.0 37.4

Black 17.8 20.9

Latino 12.8 32.2

Asian 2.8 6.5

Other 4.8 2.8

Suspect: 

% male 92.8 93.1

% <20 10.2 28.3

% 20-29 19.5 48.8

% 30+ 26.2 22.9

White 25.8 12.1

Black 51.8 60.1

Latino 12.1 26.6

Asian 0.2 1.0

Other 0.0 0.2

Victim/Suspect Relationship: 

% intimate/family 1.3 6.0

%friend/acquaintance -- 19.5

% friend 3.9 --

%work 0.9 --

%stranger 84.2 74.5

%victim received medical 

treatment 

11.7 10.0

# of Witnesses:

%0 29.7 51.9

%1 21.3 29.6

%2+ 40.3 18.5

% witness report to police 34.3 30.4

% victim report to police 85.0 45.8
67

% arrests 51.2 22.6

% convictions 30.6 12.6

% arrested within 10 minutes -- 9.4

% arrested within a day 15.4 --

Time from incident to police 

report (mean days) 

0.02 2.38

Time from incident to arrest 

(mean days) 

1564.00 56.16

 

Time from reported to arrest 

(mean days) 

1564.00 --
68

Table B.4: Descriptive characteristics of rape incidents

 Current study Peterson et al. (2010) 

N 211 602

Victim: 

% male 5.7 0

% <20 28.9 48.1

% 20-29 34.1 26.0

% 30+ 29.4 25.9

White 57.8 53.9

Black 18.0 28.6

Latino 9.5 16.3

Asian 0.0 1.2

Other 1.4 --

Suspect: 

% male 88.6 100

% <20 10.9 16.7

% 20-29 19.0 41.0

% 30+ 35.5 42.3

White 30.3 32.9

Black 31.3 45.9

Latino 14.2 20.3

Asian 0.0 0.9

Other 0.5 --

Victim/Suspect Relationship: 

% intimate/family 20.9 36.2

%friend/acquaintance -- 42.7

% friend 37.0 --

%work 4.3 --

%stranger 25.1 21.1

%victim received medical 

treatment 

88.2 68.3

# of Witnesses:

%0 66.8 78.3

%1 11.4 11.5

%2+ 9.0 10.2

% witness report to police 25.1 11.3

% victim report to police 75.8 66.3
69

% arrests 29.4 45.0

% convictions 15.2 11.1

% arrested within 10 minutes -- 10.6

% arrested within a day 5.2 --

Time from incident to police 

report (mean days) 

9.19 7.56

Time from incident to arrest 

(mean days) 

199.53 53.08

 

Time from reported to arrest 

(mean days) 

198.29 --
70

Table B.5: Descriptive characteristics of homicide incidents

 Current study Peterson et al. (2010) 

N 284 400

Victim: 

% male 80.3 85.5

% <20 15.1 25.1

% 20-29 41.2 36.8

% 30+ 40.5 38.1

White 23.9 14.1

Black 49.3 49.9

Latino 21.5 32.1

Asian 0.4 2.9

Other 0.4 1.0

Suspect: 

% male 77.5 94.8

% <20 12.3 20.8

% 20-29 30.3 47.7

% 30+ 25.7 31.5

White 16.9 15.4

Black 38.7 54.2

Latino 15.1 28.8

Asian 0.0 1.6

Other 1.1 0.0

Victim/Suspect Relationship: 

% intimate/family 18.7 19.4

%friend/acquaintance -- 25.4

% friend 21.1 --

%work 3.5 --

%stranger 23.2 55.2

%victim received medical 

treatment 

61.3 62.8

# of Witnesses:

%0 18.3 24.0

%1 18.3 62.2

%2+ 38.4 13.8

% witness report to police 74.6 67.0

% victim report to police 8.1 15.8
71

% arrests 58.1 55.5

% convictions 37.3 34.5

% arrested within 10 minutes -- 14.8

% arrested within a day 14.8 --

Time from incident to police 

report (mean days) 

1.18 0.47

Time from incident to arrest 

(mean days) 

174.02 35.56

 

Time from reported to arrest 

(mean days) 

173.89 --
72

Appendix C: Evidence Submitted, Examined, and Reported

Table C.1: Evidence type-assault

Evidence type Total Submitted Examined Report 

generated 

 n % n % n % n % 

Total 393 -- 389 99.0% 325 82.7% 325 82.7%

Biological 124 31.6% 121 30.8% 102 26.0% 102 26.0%

Pattern Evidence 3 0.8% 2 0.5% 2 0.5% 2 0.5%

Firearms/Weapons 112 28.5% 275 70.0% 221 56.2% 221 56.2%

Fingerprints 58 14.8% 56 14.2% 53 13.5% 53 13.5%

Electronic/printed data 21 5.3% 20 5.1% 12 3.1% 12 3.1%

Trace 17 4.3% 17 4.3% 17 4.3% 17 4.3%

Other 2 0.5% 2 0.5% 2 0.5% 2 0.5%

Table C.2: Evidence type-burglary

Evidence type Total Submitted Examined Report 

generated 

 n % n % n % n % 

Total 1011 -- 1004 99.3% 857 84.8% 852 84.9%

Biological 339 33.5% 327 32.3% 183 18.1% 179 17.7%

Pattern Evidence 20 2.0% 19 1.9% 18 1.5% 15 1.5%

Firearms/Weapons 18 1.8% 17 1.7% 16 1.6% 16 1.6%

Fingerprints 708 70.0% 702 69.4% 686 67.9% 684 67.7%

Electronic/printed data 42 20.8% 38 3.8% 33 3.3% 32 3.2%

Trace 28 2.8% 26 2.6% 25 2.5% 25 2.5%

Other 0 0.0% 0 0.0% 0 0.0% 0 0.0%
73

Table C.3: Evidence type-robbery

Evidence type Total Submitted Examined Report 

generated 

 n % N % n % n % 

Total 636 -- 629 98.9% 549 86.3% 548 86.2%

Biological 316 49.7% 301 47.3% 220 34.6% 220 34.6%

 

Pattern Evidence 46 1.6% 38 1.4% 38 1.4% 38 1.4%

Firearms/Weapons 665 23.7% 652 23.3% 587 21.0% 586 20.9%

Fingerprints 1262 54.1% 1243 44.4% 1211 43.2% 1209 43.2%

Electronic/printed 

data 

274 9.8% 229 8.2% 158 5.6% 154 5.5%

Trace 210 7.5% 185 6.6% 176 6.3% 174 6.2%

Other 31 1.1% 18 0.6% 17 0.6% 17 0.6%

Table C.4: Evidence type-rape

Evidence type Total Submitted Examined Report 

generated 

 n % n % n % n % 

Total 310 -- 303 97.7% 262 84.5% 262 84.5%

Biological 300 96.8% 292 94.2% 257 82.9% 257 82.9%

Pattern Evidence 0 0.0% 0 0.0% 0 0.0% 0 0.0%

 

Firearms/Weapons 2 0.6% 2 0.6% 2 0.6% 2 0.6%

Fingerprints 11 3.5% 9 2.9% 9 2.9% 9 2.9%

Electronic/printed data 10 3.2% 10 3.2% 3 1.0% 2 0.6%

Trace 19 6.1% 16 5.2% 16 5.2% 16 5.2%

Other 0 0.0% 0 0.0% 0 0.0% 0 0.0%
74

Table C.5: Evidence type-homicide

Evidence type Total Submitted Examined Report 

generated 

 n % n % n % n % 

Total 438 -- 432 98.6% 373 85.2% 363 82.9%

Biological 421 96.1% 393 89.7% 287 65.5% 261 59.6%

Pattern Evidence 14 3.2% 13 3.0% 13 3.0% 13 3.0%

Firearms/Weapons 241 55.0% 238 54.3% 237 54.1% 236 53.9%

 

Fingerprints 157 35.8% 154 35.2% 150 34.2% 150 34.2%

Electronic/printed data 95 21.7% 67 15.3% 36 8.2% 35 8.0%

Trace 89 20.3% 72 16.4% 67 15.3% 65 14.8%

Other 29 6.6% 16 3.7% 15 3.4% 15 3.4%
75

Appendix E: Arrest Analysis

Table E.1: Arrest

Current study Peterson et al. 

(2010) 

Estimate Odds 

Ratio 

Estimate Odds 

Ratio 

Assault 

Witness reports to 

police

0.92

(.537) 2.53 .224(.165) 1.25

Victim reports to police -1.59

(.603) 0.20** 1.06(.218) 2.87***

Intimate/family 2.17

(.973) 8.73* .642(.199) 1.90**

Friend/Acquaintance

-- --

.272

(.215) 1.31

Friend 2.42

(.604) 11.25*** -- --

Crime scene evidence

-- --

1.24

(.178) 3.45***

Lab examined 

evidence

.21

(.762) 1.23 1.27(.281) 3.57***

Los Angeles

-- --

2.14

(.271) 8.51***

Indianapolis

-- --

.625

(.186) 1.87**

Nagelkerke’s R-square .535 .271

Burglary 

Witness reports to 

police

-.47

(.547) .624 1.64(.329) 5.16***

Victim reports to police -1.03

(.694) .357 .265(.326) 1.30

Intimate/family -1.37

(.756) .253 1.23(.390) 3.42**

Friend/Acquaintance

-- --

.575

(.348) 1.78

Friend -.027

(.470) .974 -- --

Crime scene evidence

-- --

1.23

(.333) 3.41***

Lab examined 

evidence -.258 .465 .602(.465) 1.83

Los Angeles

-- --

1.06

(.450) 2.88*

Indianapolis

-- --

-.515

(.411) .598

Nagelkerke’s R-square 0.100 0.275
76

Current study Peterson et al. 

(2010) 

Robbery Estimate Odds 

Ratio 

Estimate Odds 

Ratio 

Witness reports to 

police

.06

(.297) 1.65 .452(.227) 1.57*

Victim reports to police-1.70

(.673) .182** .748(.573) 2.11

Intimate/family

-- --

.607

(.587) 1.84

Friend/Acquaintance

-- --

1.20

(.312) 3.32***

Friend 1.24

(.722 3.45 -- --

Crime scene evidence

-- --

1.88

(.176) 6.54***

Lab examined 

evidence

.164

(.383) 1.17 1.33(.221) 3.77

Los Angeles

-- --

1.42

(.621) 4.11*

Indianapolis

-- --

-.009

(.244) .991

Nagelkerke’sR-square .159 .231

Rape 

Witness reports to 

police

1.350

(.683) 3.85

*

-.991

(.495) .371*

Victim reports to police .475

(.743) 1.60 2.70(.441) 14.80***

Intimate/family 1.945

(.508) 6.99*** 1.91(.337) 6.70***

Friend/Acquaintance

-- --

1.18

(.309) 3.26***

Friend .695

(.443) 2.00 -- --

Crime scene evidence

-- --

.920

(.227) 2.51***

Lab examined 

evidence

1.127

(.718) 3.08 .491(.256) 1.63*

Los Angeles

-- --

3.46

(.483) 31.69***

Indianapolis

-- --

3.10

(.317) 22.12***

Nagelkerke’sR-square .188 .433
77

Current study Peterson et al. 

(2010)

Homicide Estimate Odds 

Ratio

Estimate Odds 

Ratio

Victim reports to police .547

(1.22) 1.72 -.222(.315) .801

Intimate/family -2.43

(1.08) .088* 1.054(.404) 2.87**

Friend/Acquaintance

-- --

1.83

(.425) 6.25***

Friend .002

(1.43) 1.00 -- --

Crime scene evidence

-- --

.377

(.693 1.46

Lab examined 

evidence

1.97

(.872) 7.20* .553(.314) 1.74

Los Angeles

-- --

-.564

(.345) .569

Indianapolis

-- --

-.052

(.420) .949

Nagelkerke’s R-square .323 .277

*p<.05, **p<.01, *** p=.000

Note: Standard errors are in parentheses. 
78

Appendix F: Conviction Analysis

Table F.1: Assault convictions

 Current study Peterson et al. 

(2010) 

 Estimate Odds 

Ratio 

Estimate Odds 

Ratio 

Witness reports to 

police

.324

(.207)

1.383 -.721

(.351)

.486*

Victim reports to police -.002

(.221)

.998 -1.41

(.896)

.244

Intimate/family -.379

(.246)

.685 -.754

(.400)

.471

Friend/Acquaintance -- -- -.505

(.489)

.603

Friend .043

(.208)

1.044 -- --

Crime scene evidence -- -- -.120

(.380)

.887

Lab examined 

evidence

.697

(.263)

2.00** .699

(.531)

2.01

Victim medical 

treatment

.012

(.174)

1.01 3.25

(1.07)

25.68***

Arrest within 10 

minutes of crime

incident

-- -- -.012

(.353)

.988

Arrest on the same day 

as crime incident

.415

(.194)

1.51* -- --

Direct arrest -- -- .701

(.638)

2.02

Suspect gave a 

statement or witnesses 

available

.082

(.170)

1.08 -- --

Los Angeles -- -- -.588

(.969)

.555

Indianapolis -- -- -2.51

(.834)

.082**

Correction factor -- -- -.404

(.524)

.677

Nagelkerke’s R-square .038 .287

*p<.05, **p<.01, *** p=.000

Note: Standard errors are in parentheses. 
79

Table F.2: Burglary convictions

 Current study Peterson et al. 

(2010) 

Estimate Odds 

Ratio 

Estimate Odds 

Ratio 

Witness reports to 

police

-1.00

(.697)

.367 -.346

(1.23)

.708

Victim reports to police .298

(.762)

1.34 -- --

Intimate/family -1.57

(1.10)

.207 -1.11

(1.42)

.330

Friend/Acquaintance -- -- -.969

(1.01)

.379

Friend .243

(.535)

1.27 -- --

Crime scene evidence -- -- 1.06

(1.46

2.87

Lab examined 

evidence

.718

(.570)

2.05 .139

(1.15)

1.15

Arrest within 10 

minutes of crime 

incident

-- -- 2.29

(1.19)

9.83*

Arrest on the same day 

as crime incident

-.063

(.576)

.939 -- --

Los Angeles -- -- -- --

Indianapolis -- -- -- --

Correction factor -- -- .553

(1.08)

1.74

Nagelkerke’s R-square .098 .399

*p<.05, **p<.01, *** p=.000

Note: Standard errors are in parentheses
80

Table F.3: Robbery convictions

 Current study Peterson et al. 

(2010) 

 Estimate Odds 

Ratio 

Estimate Odds 

Ratio 

Witness reports to 

police

-.204

(.352)

.816 -.004

(1.00)

.996

Victim reports to police -.065

(.499)

.937 2.26

(1.08)

9.62*

Intimate/family -- -- 37.37

(.638)

1.70***

Friend/Acquaintance -- -- .215

(1.06)

1.24

Friend .286

(.674)

1.33 -- --

Crime scene evidence -- -- -1.74

(2.90)

.175

Lab examined 

evidence

.542

(.445)

1.75 -.316

(.939)

.729

Victim medical 

treatment

.675

(.501)

1.96 37.54

(.409)

2.00***

Arrest within 10 

minutes of crime 

incident

-- -- -1.07

(.626)

.343

Arrest on the same day 

as crime incident

.778

(.355)

2.17* -- --

Direct arrest -- -- 1.55

(.991)

4.72

Suspect gave a 

statement or witnesses 

available

1.852

(.736)

6.37* -- --

Los Angeles -- -- -- --

Indianapolis -- -- -- --

Correction factor -- -- .670

(1.15)

11.14*

Nagelkerke’s R-square .086 .245

*p<.05, **p<.01, *** p=.000

Note: Standard errors are in parentheses. 
81

Table F.4: Rape convictions

 Current study Peterson et al. 

(2010) 

 Estimate Odds 

Ratio 

Estimate Odds 

Ratio 

Witness reports to 

police

1.929

(.971)

6.88* .204

(1.05)

1.23

Victim reports to police -.310

(.998)

.734 3.02

(1.29)

20.41*

Intimate/family 1.780

(.867)

5.93* -1.69

(.940)

.184

Friend/Acquaintance -- -- -4.39

(2.09)

.012*

Friend -.468

(.810)

.626 -- --

Crime scene evidence -- -- 1.91

(1.17)

6.75

Lab examined 

evidence

3.029

(1.847)

20.68 .955

(1.01)

2.60

Victim medical 

treatment

-2.408

(1.036)

.090* 1.73

(.809)

5.64*

Arrest within 10 

minutes of crime 

incident

-- -- .128

(1.37)

1.14

Arrest on the same day 

as crime incident

.642

(.900)

1.90 -- --

Direct arrest -- -- 3.23

(1.20)

25.31**

Los Angeles -- -- -.311

(.948)

.733

Indianapolis -- -- -.221

(1.23)

.801

Correction factor -- -- 3.56

(.993)

35.22***

Nagelkerke’s R-square .331 .844

*p<.05, **p<.01, *** p=.000

Note: Standard errors are in parentheses. 
82

Table F.5: Homicide convictions

 Current study Peterson et al. 

(2010) 

 Estimate Odds 

Ratio 

Estimate Odds 

Ratio 

Witness reports to 

police

.206

(.539)

1.22 -1.45

(1.07)

.235

Victim reports to police .417

(.688)

1.51 .558

(1.05)

1.80

Intimate/family -.504

(.570)

.604 -2.14

(1.11)

.118*

Friend/Acquaintance -- -- -2.17

(1.02)

.115*

Friend .013

(.448)

1.013 -- --

Crime scene evidence -- -- .644

(1.56)

1.90

Lab examined 

evidence

1.424

(.914)

4.15 .172

(.753)

1.19

Victim medical 

treatment

.376

(.463)

1.45 -.301

(.559)

.740

Arrest within 10 

minutes of crime 

incident

-- -- -2.05

(.915)

.128*

Arrest on the same day 

as crime incident

.122

(.526)

1.12 -- --

Direct arrest -- -- .741

(.613)

2.10

Suspect gave a 

statement or was 

named/described by a 

witness

.709

(.837)

2.03 -- --

Los Angeles -- -- 1.65

(.889)

5.22

Indianapolis -- -- -.581

(.634)

.560

Correction factor -- -- -4.01

(1.78)

.018*

Nagelkerke’s R-square .097 .182

*p<.05, **p<.01, *** p=.000

Note: Standard errors are in parentheses. 
83

Appendix G: Sentencing Analysis

Table G.1: Assault sentencing

Current study Peterson et, al. (2010)

B S.E Sig. B S.E Sig.

Witness reports -73.16 25.98 .009 21.22 9.90 .036

Victim reports -94.70 23.34 .000 14.39 17.54 .415

Intimate -72.51 27.64 .014 6.89 11.77 .560

Acquaintance -- -- -- 14.98 12.26 .226

Friends -81.09 21.01 .001 -- -- --

Suspect arrested 

within 10 minutes

-- -- -- -8.17 9.85 .410

Suspect arrested 

within a day

40.55 20.90 .063 -- -- --

Victim medical 

treatment

-- -- -- 19.12 12.77 .139

Public defender -38.30 47.26 .425 4.11 7.40 .581

Plea bargain -65.07 38.01 .098 -4.69 15.09 .757

# prior arrests -- -- -- .291 .555 .602

# prior 

convictions

32.84 11.68 .009 -.280 1.10 .800

Lab examined 

evidence

-- -- -- 51.99 11.38 .000

Indianapolis -- -- -- 7.06 12.70 .580

Victim teen 30.38 30.38 .326 -9.21 12.70 .471

Victim young 

adult

53.21 20.34 .014 15.11 9.95 .134

Victim black 

male

-- -- -- -2.63 14.58 .857

Victim black 

female

47.31 35.17 .190 9.98 13.70 .469

Victim Latina -7.43 33.64 .827 -- -- --

Victim Latino 73.57 26.59 .010 -- -- --

Suspect black 

male

11.47 28.68 .693 -1.07 11.14 .924

Suspect Latino 60.69 37.42 .116 230.33 44.00 .000

Suspect black 

female

-- -- -- 17.16 18.51 .357

R

2

.854 .573

Mean sentence 

(month)

99.78 29.67

Mean sentence 

(days)

3035.05 902.46

Median sentence 

(months)

66.00 12.00

Median sentence 

(days)

2007.50 365.00
84

Table G.2: Burglary sentencing

Current study Peterson et, al. (2010)

B S.E Sig. B S.E Sig.

Witness reports 23.56 52.64 .658 -47.15 25.42 .161

Victim reports 17.87 39.32 .653 -17.96 38.45 .672

Intimate -50.20 36.69 .182 -14.64 34.43 .699

Acquaintance -- -- -- 42.66 45.98 .199

Friends -9.93 18.43 .594 -- -- --

Suspect arrested 

within 10 minutes

-- -- -- 46.06 31.26 .237

Suspect arrested 

within a day

23.33 21.01 .277 -- -- --

Public defender 53.55 26.71 .055 -54.87 17.33 .051

Plea bargain -- -- -- -672.94 38.34 .000

# prior arrests -- -- -- -1.89 4.22 .685

# prior 

convictions

-2.13 2.84 .461 19.18 7.70 .088

Lab examined 

evidence

-28.21 29.05 .340 -68.67 28.69 .096

Los Angeles -- -- -- 82.42 31.68 .080

Victim teen -6.65 28.15 .815 8.88 39.66 .837

Victim young 

adult

3.76 19.48 .848 76.12 21.86 .040

Victim black 

male

13.00 19.85 .518 -51.91 25.67 .136

Victim black 

female

-53.00 50.56 .304 -20.22 22.50 .435

Victim Latino -5.90 29.00 .840 -- -- --

Suspect black 

male

-14.56 15.53 .357 -60.53 31.48 .150

Suspect Latino -7.61 18.04 .676 -- -- --

R2.566 .995

Mean sentence 

(month)

45.08 56.56

Mean sentence 

(days)

1371.26 1720.37

Median sentence 

(months)

36.00 34.00

Median sentence 

(days)

1095.00 1034.17
85

Table G.3: Robbery sentencing

Current study Peterson et, al. (2010)

B S.E Sig. B S.E Sig.

Witness reports -6.155 25.23 .808 24.56 17.01 .152

Victim reports 26.42 17.74 .045 58.35 84.95 .494

Intimate -- -- -- 14.06 59.94 .815

Acquaintance -- -- -- -5.11 24.93 .838

Friends -20.14 29.69 .500 -- -- --

Suspect arrested 

within 10 minutes

-- -- -- -10.00 16.00 .534

Suspect arrested 

within a day

5.85 15.99 .715 -- -- --

Victim medical 

treatment

38.66 45.12 .114 .988 26.36 .970

Public defender -57.53 49.10 .245 34.10 25.41 .183

Plea bargain -- -- -- -74.45 24.72 .003

# prior arrests -- -- -- 1.02 1.18 .392

# prior convictions -14.56 5.06 .005 -.821 1.51 .587

Lab examined 

evidence

22.28 24.17 .360 34.31 19.68 .085

Los Angeles -- -- -- -72.20 93.23 .441

Indianapolis -- -- -- 6.36 28.81 .826

Victim teen 12.87 24.42 .600 -8.64 24.32 .723

Victim young adult -22.26 18.43 .231 36.15 17.78 .045

Victim black male -22.60 22.38 .316 -40.72 26.02 .121

Victim black female 63.22 27.49 .024 -18.56 33.84 .585

Victim Latina -7.63 29.38 .796 40.58 37.97 .288

Victim Latino -39.11 26.91 .150 10.99 24.36 .653

Suspect black male 4.81 18.45 .795 -.884 20.56 .966

Suspect Latino -6.03 28.89 .835 10.36 25.89 .690

Suspect black female -- -- -- 74.28 44.04 .095

Suspect Latina -- -- -- -146.55 230.05 .526

R2.529 .334

Mean sentence 

(month)

114.48 75.19

Mean sentence (days) 3482.20 2287.03

Median sentence 

(months)

119.99 60.00

Median sentence 

(days)

3650.00 1825.00
86

Table G.4: Rape sentencing

Current study Peterson et, al. (2010)

B S.E Sig. B S.E Sig.

Witness reports -- -- -- 104.73 90.92 .264

Victim reports -- -- -- 21.93 74.89 .773

Intimate -- -- -- -185.5 115.4 .125

Acquaintance -- -- -- -119.3 116.85 .321

Suspect arrested 

within 10 minutes

-- -- -- 8.15 63.53 .899

Victim medical 

treatment

-- -- -- -28.96 79.62 .720

Public defender -- -- -- -163.1 80.18 .057

Plea bargain -- -- -- -378.0 118.0 .005

# prior arrests -- -- -- -10.12 6.25 .123

# prior 

convictions

-- -- -- 8.20 9.63 .406

Lab examined 

evidence

45.12 88.73 .509 59.67 52.17 .268

Los Angeles -- -- -- 10.45 113.17 .927

Victim teen -- -- -- -131.9 76.55 .102

Victim young 

adult

-- -- -- 85.55 82.37 .313

Victim black 

female

-- -- -- 65.17 93.77 .496

Victim Latina -- -- -- -270.3 114.3 .029

Suspect black 

male

-- -- -- -115.2 103.3 .279

Suspect Latino -- -- -- 44.32 91.59 .634

R2.111 .801

Mean sentence 

(month)

76.72 160.40

Mean sentence 

(days)

2333.84 4878.83

Median sentence 

(months)

60.00 60.00

Median sentence 

(days)

1825.00 1825.00
87

Table G.5: Homicide sentencing

Current study Peterson et, al. (2010)

B S.E Sig. B S.E Sig.

Witness reports -22.17 79.41 .781 -105.10 100.40 .296

Victim reports 5.42 71.86 .940 28.07 129.47 .829

Intimate 30.54 67.43 .652 261.90 136.28 .058

Acquaintance -- -- -- 68.86 88.87 .440

Friends -58.38 53.59 .281 -- -- --

Suspect arrested 

within 10 minutes

-- -- -- -100.56 95.60 .300

Suspect arrested 

within a day

2.70 62.22 .965 -- -- --

Victim medical 

treatment

-127.15 64.48 .054 -84.87 82.50 .306

Public defender -- -- -- -30.88 79.97 .700

Plea bargain -215.22 55.87 .000 -326.89 78.07 .000

# prior arrests -- -- -- -1.36 7.37 .853

# prior convictions 2.77 33.36 .934 9.49 11.42 .408

Lab examined 

evidence

-128.38 139.40 .361 207.87 117.63 .080

Link suspect to crime -- -- -- 225.35 94.66 .019

Los Angeles -- -- -- -435.88 118.17 .000

Indianapolis -- -- -- -262.78 109.62 .018

Victim teen 2.37 64.24 .971 51.09 98.51 .605

Victim young adult 81.08 60.28 .184 204.55 92.36 .029

Victim black male -6.13 71.21 .932 -91.89 125.97 .467

Victim Latino -34.55 70.45 .626 241.66 126.98 .060

Victim black female 43.64 103.49 .675 149.53 164.21 .365

Victim Latina -- -- -- -148.83 246.73 .548

Suspect black male 52.20 67.16 .440 150.74 119.25 .209

Suspect Latino 24.85 79.16 .755 -86.60 128.98 .503

Suspect black female -232.75 124.50 .067 -106.17 252.51 .675

Suspect Latina -- -- -- -146.55 230.05 .526

R2.488 .428

Mean sentence 

(month)

351.39 423.59

Mean sentence (days) 10688.34 12884.21

Median sentence 

(months)

299.99 300.00

Median sentence 

(days)

9125.00 9125.01
88

Appendix H: Time and Evidence Type Analysis

Table H.1: Timeliness of evidence

Evidence type Cases with 

submitted 

evidence at 

least 3 days 

before 

clearance (%) 

Cases with 

reports 

generated at 

least 3 days 

before 

clearance (%) 

Cases with 

submitted 

evidence at 

least 3 days 

before 

verdict (%) 

Cases with 

reports 

generated at 

least 3 days 

before verdict 

(%) 

Assault

All 57 (35.4) 29 (21.2) 110 (97.3) 78 (86.7)

Biological 21 (31.3) 12 (21.1) 44 (93.6) 31 (83.8)

Pattern 1 (100.0) 1 (100.0) 1 (100.0) 1 (100.0)

Weapons/firearms 37 (37.8) 17 (20.7) 68 (100.0) 45 (88.2)

Fingerprint 10 (35.7) 7 (25.9) 21 (95.5) 16 (88.9)

Electronic/document 5 (41.7) 3 (50.0) 8 (100.0) 5 (100.0)

Trace 3 (42.9) 3 (42.9) 6 (100.0) 6 (100.0)

Other -- -- -- --

Burglary

All 208 (77.0) 135 (62.8) 222 (98.2) 150 (94.9)

Biological 89 (78.8) 48 (76.2) 95 (96.9) 50 (96.2)

Pattern 3 (30.0) 0 (0.0) 7 (100.0) 5 (100.0)

Weapons/firearms 4 (44.4) 3 (37.5) 6 (85.7) 5 (83.3)

Fingerprint 133 (79.6) 94 (59.5) 141 (100.0) 107 (95.5)

Electronic/document 8 (61.5) 7 (63.6) 9 (100.0) 8 (100.0)

Trace 4 (57.1) 3 (50.0) 5 (100.0) 4 (100.0)

Other -- -- -- --

Robbery

All 119 (46.3) 77 (35.2) 199 (96.1) 150 (89.3)

Biological 57 (46.7) 18 (20.7) 91 (91.0) 43 (70.5)

Pattern 3 (30.0) 3 (60.0) 4 (80.0) 4 (80.0)

Weapons/firearms 16 (23.9) 12 (18.8) 54 (98.2) 48 (92.3)

Fingerprint 68 (54.0) 43 (36.1) 106 (99.1) 80 (92.0)

Electronic/document 22 (48.9) 16 (50.0) 31 (96.9) 24 (92.3)

Trace 14 (56.0) 10 (40.0) 22 (100.0) 19 (95.0)

Other -- -- -- --

Rape

All 81 (71.1) 43 (42.6) 93 (100.0) 59 (95.2)

Biological 78 (72.9) 42 (42.9) 90 (100.0) 58 (95.1)

Pattern -- -- -- --

Weapons/firearms 0 (0.0) 0 (0.0) 1 (100.0) 1 (100.0)

Fingerprint 7 (100.0) 5 (71.4) 7 (100.0) 6 (100.0)

Electronic/document 2 (33.3) -- 3 (100.0) --

Trace 4 (66.7) 3 (50.0) 6 (100.0) 4 (80.0)

Other -- -- -- --
89

Evidence type Cases with 

submitted 

evidence at 

least 3 days 

before 

clearance (%) 

Cases with 

reports 

generated at 

least 3 days 

before 

clearance (%) 

Cases with 

submitted 

evidence at 

least 3 days 

before 

verdict (%) 

Cases with 

reports 

generated at 

least 3 days 

before verdict 

(%) 

Homicide

All 98 (57.0) 71 (44.1) 142 (99.3) 130 (99.2)

Biological 81 (52.9) 38 (30.6) 122 (100.0) 93 (97.9)

Pattern 2 (33.3) 2 (33.3) 5 (100.0) 5 (100.0)

Weapons/firearms 68 (63.6) 57 (53.3) 93 (98.9) 88 (98.9)

Fingerprint 29 (35.4) 20 (24.7) 64 (100.0) 63 (100.0)

Electronic/document 11 (30.6) 4 (18.2) 22 (100.0) 15 (100.0)

Trace 16 (42.1) 9 (25.7) 30 (100.0) 26 (100.0)

Other 3 (25.0) 2 (16.7) 9 (100.0) 8 (88.9)
90

Table H.2: Arrest and timeliness

Examined evidence 

type

Odds 

ratio

Cramer’s V Report generated 

before arrest

Odds 

ratio

Cramer’s 

V

Assault

All 1.41 .057 All -- --

Biological 3.73* .265 Biological -- --

Pattern -- -- Pattern -- --

Weapons/firearms 0.61* .119 Weapons/firearms -- --

Fingerprints 2.87** .158 Fingerprints -- --

Electronic/document -- -- Electronic/document -- --

Trace 1.18 .016 Trace -- --

Other -- -- Other -- --

Burglary

All 0.87 .020 All 0.76 .054

Biological 2.19*** .150 Biological -- --

Pattern -- -- Pattern -- --

Weapons/firearms -- -- Weapons/firearms -- --

Fingerprints 0.52*** .145 Fingerprints 0.70 .079

Electronic/document 2.06 .060 Electronic/document -- --

Trace 0.65 .028 Trace -- --

Other -- -- Other -- --

Robbery

All 1.12 .020 All 0.18*** .247

Biological 0.86 .036 Biological -- --

Pattern -- -- Pattern -- --

Weapons/firearms 2.66*** .186 Weapons/firearms -- --

Fingerprints 1.38 .080 Fingerprints -- --

Electronic/document 1.67 .076 Electronic/document -- --

Trace 1.47 .050 Trace -- --

Other -- -- Other -- --

Rape

All 2.46 .133 All 0.93 .017

Biological 1.45 .064 Biological 0.95 .012

Pattern -- -- Pattern -- --

Weapons/firearms -- -- Weapons/firearms -- --

Fingerprints -- -- Fingerprints -- --

Electronic/document -- -- Electronic/document -- --

Trace -- -- Trace -- --

Other -- -- Other -- --

*p<.05, **p<.01, ***p = .000
91

Examined evidence 

type

Odds 

ratio

Cramer’s V Report generated 

before arrest

Odds 

ratio

Cramer’s 

V

Homicide

All All

Biological 2.24** .172 Biological -- --

Pattern -- -- Pattern -- --

Weapons/firearms 0.48* .148 Weapons/firearms -- --

Fingerprints 2.63** .204 Fingerprints -- --

Electronic/document 3.36* .132 Electronic/document -- --

Trace 2.54* .142 Trace -- --

Other -- -- Other -- --

*p<.05, **p<.01, ***p = .000

Table 3: Convictions and timeliness

Examined evidence 

type

Odds 

ratio

Cramer’s 

V

Report generated 

before arrest

Odds 

ratio

Cramer’s 

V

Assault

All 1.90 .087 All -- --

Biological 2.04* .142 Biological -- --

Pattern -- -- Pattern -- --

Weapons/firearms 0.85 .035 Weapons/firearms -- --

Fingerprints 1.63 .075 Fingerprints -- --

Electronic/document -- -- Electronic/document -- --

Trace -- -- Trace -- --

Other -- -- Other -- --

Burglary 

All 1.26 .024 All -- --

Biological 2.39*** .134 Biological -- --

Pattern -- -- Pattern -- --

Weapons/firearms -- -- Weapons/firearms -- --

Fingerprints 0.57** .091 Fingerprints -- --

Electronic/document -- -- Electronic/document -- --

Trace -- -- Trace -- --

Other -- -- Other -- --

Robbery

All 1.50 .059 All -- --

Biological 1.12 .027 Biological -- --

Pattern -- -- Pattern -- --

Weapons/firearms 3.24*** .233 Weapons/firearms -- --

Fingerprints 1.05 .013 Fingerprints -- --

Electronic/document 1.25 .028 Electronic/document -- --

Trace 1.55 .056 Trace -- --

Other -- -- Other -- --

*p<.05, **p<.01, ***p = .000
92

Examined evidence 

type

Odds 

ratio

Cramer’s 

V

Report generated 

before arrest

Odds 

ratio

Cramer’s 

V

Rape

All -- -- All -- --

Biological 2.08 .086 Biological -- --

Pattern -- -- Pattern -- --

Weapons/firearms -- -- Weapons/firearms -- --

Fingerprints -- -- Fingerprints -- --

Electronic/document -- -- Electronic/document -- --

Trace -- -- Trace -- --

Other -- -- Other -- --

Homicide

All 2.55 .103 All -- --

Biological 2.27** .182 Biological -- --

Pattern -- -- Pattern -- --

Weapons/firearms 0.59 .119 Weapons/firearms -- --

Fingerprints 2.48** .220 Fingerprints -- --

Electronic/document 1.39 .051 Electronic/document -- --

Trace 1.74 .107 Trace -- --

Other 2.07 .082 Other -- --

*p<.05, **p<.01, ***p = .000