The Pennsylvania State University

The Graduate School

College of the Liberal Arts

THE EFFECTS OF OFFENDER CHARACTERISTICS ON INJURY DURING

ROBBERY

A Thesis in

Crime, Law, and Justice

by

Adele M. Costigan

© 2012 Adele M. Costigan

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Arts

August 2012

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The thesis of Adele M. Costigan was reviewed and approved* by the following:

Julie Horney

Professor of Crime, Law, and Justice

Thesis Advisor

Richard Felson

Professor of Crime, Law, and Justice and Sociology

Eric Silver

Professor of Sociology and Crime, Law, and Justice

John Iceland

Professor of Sociology and Demography

Department Head

*Signatures are on file in the Graduate School.

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ABSTRACT

Victims of robbery deal not only with sometimes devastating property loss, but they also

face the threat of serious injury or death. The very real danger of injury during robbery makes it

important to explore the interaction between offenders and victims to get a better understanding

of the factors that influence injury. Previous research on injury during robbery has focused on

situational factors and largely ignored offender characteristics that might make injury more

likely. The current study explores seeks to address this question by examining data on self-

reported robbery incidents among incarcerated Nebraska inmates and assessing relevant offender

characteristics that impact victim injury outcomes. Using logistic regression, it is found that

incidents involving black offenders and taller offenders are less likely to result in injury to the

victim, even after controlling for important situational factors. Furthermore, interactions are

found between offender height and offender weapon use and offender race and weapon use. The

findings support the idea that offenders are less likely to injure when they feel that they have a

credible threat. Other offender characteristics are not found to significantly affect the likelihood

of victim injury, but the importance of victim resistance is highlighted.

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TABLE OF CONTENTS

List of Tables……………………………………………………………………………………...v

List of Figures…………………………………………………………………………………….vi

Introduction………………………………………………………………………………………..1

Literature Review………………………………………………………………………………….4

Resistance…………………………………………………………………………………4

Weapon Effects……………………………………………………………………………7

Other Factors………………………………………………………………………………8

The Current Study………………………………………………………………………………..10

Data and Methods………………………………………………………………………………..16

Sample……………………………………………………………………………………16

Variables…………………………………………………………………………………18

Analysis…………………………………………………………………………………..24

Results……………………………………………………………………………………………24

Discussion......................................................................................................................................33

References………………………………………………………………………………………..39

Appendix A: Self-Control Scale…………………………………………………………………44

Appendix B: Correlation Matrix…………………………………………………………………45

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LIST OF TABLES

Table 1: Descriptive Statistics…………………………………………………………………...23

Table 2: Logistic Regression of Predictors of Injury…………………………………………….26

Table 3: Logistic Regression of Interaction between Height and Weapons……………………..29

Table 4: Logistic Regression of Interaction between Race and Weapons……………………….32

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LIST OF FIGURES

Figure 1: Predicted Probability of Injury for Height and Weapon Interaction…………………..30

Figure 2: Predicted Probability of Injury for Race and Weapon Interaction…………………….32

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INTRODUCTION

Robbery is both a crime against person and property, which makes it a unique type of

serious offense to study. Not only does robbery often lead to a loss of property, but it can also

involve violence, injury, or even death. While the distress of property loss should not be

understated, the issue of injury is particularly important, as bodily harm represents a very real

and serious threat for past and potential future victims of robbery. For this reason, a great deal of

criminological interest has developed in the subject of victim injury during robbery. In

particular, a large amount of research deals with understanding the link between victim

resistance and victim injury. Researchers often ask the question of whether or not taking self-

protective behaviors during a robbery incident will be beneficial or detrimental to victims trying

to avoid injury.

In addition, robbery is a crime that involves a complex interaction between offender,

victim, and contextual factors that will determine the outcome of the incident. Offenders and

victims anticipate and interpret each other’s appearances and actions when forming their own

decisions about how to behave. While thinking about robbery as a situationally-based and

interactional crime, this project seeks to establish a better understanding of why robbery

sometimes ends in injury to the victim.

One matter that is largely missing from criminologists’ understanding of robbery injury is

how offender characteristics might affect robbery outcomes. In any robbery incident, situational

factors as well as offender and victim characteristics will interact to influence whether or not a

victim sustains an injury. The question of whether offender traits influence the likelihood of

injury is thus quite significant when studying robbery. The possibility that offender

characteristics affect victim injury deserves more attention and investigation, and this study aims

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to explore this issue. Patterns may exist to indicate that certain types of robbers are more likely

to injure across all situations than others, and this information should not be overlooked.

The current study approaches the issue of robbery injury from a rational choice

perspective. Rational choice theory of crime specifies that crime results from the rational

choices made by individuals who seek to maximize benefits and minimize costs. According to

Cornish and Clarke (1986), this choice occurs in two stages. In the first stage, an actor makes

the decision to either engage in crime to meet his or her needs or refrain from crime. This choice

reflects previous learning experiences and background factors. Second, the actor must choose

whether or not to commit a particular act. This decision is influenced by the immediate situation

and the consideration of costs and benefits. Rational choice theory according to Cornish and

Clarke (1986) asserts that offenders make decisions based on reason. Thus, criminals are more

or less rational and seek to maximize pleasure and minimize pain through their criminal activity.

This perspective on offending behavior as a rational choice has been explored by previous

researchers in the area of robbery (Feeney, 1986; Morrison and O’Donnell, 1996). Rational

choice theory is discussed with regard to victim resistance as an explanation for why resistance

could decrease crime completion and eventually decrease crime rates (Feeney, 1986; Kleck,

1988; Kleck and DeLone, 1993). In particular, gun possession on the part of potential victims is

suggested to deter potential offenders from robbery.

Wright and Decker (1997) suggest that when victims resist, armed robbers use force to

bring victims back into compliance, and that this often results in injury to the victim. Based on

this finding, injury appears to result from the rational choice made by the offender; injury is

instrumental in completing the robbery and maximizing the gains for the robber. Zimring (1977)

questions the choice process, however, positing that the truly rational robber should desist and

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simply find another victim upon encountering resistance. Since the data do not indicate that this

happens regularly, the author questions whether it is appropriate to consider robbery as a rational

choice that is exclusively instrumental. One perspective that offers possible insight into the issue

is put forward by Shover and Honaker (1992). Based on an ethnographic study of property

offender decision making, the researchers are able to describe the decision-making process that

occurs for offenders. They argue that offenders make rational decisions, but that these decisions

are bounded by the contexts in which they are made. That is, offenders are oriented toward

rewards and costs but make decisions based on the limited information available to them when

choosing a course of action. For this reason, potential offenders may discount or ignore the risks

of committing crime. Thus, robbers may make rational choices when committing a robbery and

deciding whether to injure victims or not, but this rationality may be bounded.

Based on the idea of bounded rationality, this study explores whether different kinds of

offenders are more likely to injure than others. This conceptual framework assumes that victim

injury during robbery results from a choice made by the offender, and that this choice is made

based on a calculation of rewards and costs. While Shover and Honaker (1992) find that lifestyle

characteristics affect the decision to commit property offenses, this study expects to find offender

characteristics that affect the decision to injure victims. It does not argue that certain offenders

are more rational than others, but that robbers will differ in their choice to injure based on their

particular circumstances, including both background characteristics and current situation.

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LITERATURE REVIEW

Resistance

Much research has also looked at whether victim resistance influences victim injury

outcomes. Results from these studies have been mixed, but this research provides insight into

the issues that need more attention and further investigation. Some studies conclude that victim

resistance increases the likelihood of injury (Feeney and Weir, 1975; Wright and Decker, 1997;

Zimring and Zuehl, 1986). On the other hand, studies have produced results that suggest that

resistance is associated with a lower likelihood of injury (Hart and Miethe, 2009; Kleck, 1988;

Kleck and DeLone, 1993; Kleck and Sayles, 1990; Tark and Kleck, 2004). It should be noted

that some of these studies look at other crimes besides robbery. Kleck (1988) and Tark and

Kleck (2004) study several types of violent crime, and Kleck and Sayles (1990) study rape, but

their results imply that resistance during robbery should also work to reduce injury. Still other

studies examining victim resistance and injury find no significant relationship between self-

protective behavior and victim injury (Cook, 1986; Guerette and Santana 2010;; Ziegenhagen

and Brosnan, 1985). While Cook (1986) does find evidence of victim resistance being correlated

with an increased likelihood of injury, he asserts that no strong conclusions can be drawn from

his analysis because of the lack of information about time-ordering in the robberies being

examined. Overall, it appears that the more recent research and research done with greater

statistical sophistication tends to indicate that resistance decreases the chance of injury or has no

significant effect on injury. However, the issue is far from resolved.

While findings from studies of victim resistance and injury have been mixed, Tark and

Kleck (2004) attribute the confusion to the mistake of ignoring sequence. The authors argue that

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most victim injuries occur not as a result of resistance, but as a result of offender attack at the

outset of the incident. Attacks that injure victims may then provoke resistance on the part of

victims as a way of counteracting the physical attack. On the other hand, strong physical attacks

might injure a victim and make him or her unable to resist. Thus, the time-order within the

robbery or assault must be considered when investigating victim injury; only injuries occurring

after self-protective behavior should be considered a result of the resistance. Cook (1986), Kleck

(1988), and Guerette and Santana (2010) also discuss the importance of taking sequence into

account when looking at victim resistance and injury. When time-order is factored into the

analysis, studies generally find that resistance either decreases or has no effect on victim injury

(Tark and Kleck, 2004). Guerette and Santana (2010) account for the time order of victim

resistance to victim injury by including a dichotomous variable coded as 1 if the victim specified

that the injury took place during or after they resisted. The authors found none of the victim

resistance actions to be statistically significant in predicting injury. Kleck and DeLone (1993)

take a different approach in addressing time-order. They treat victim resistance as the dependent

variable and victim injury as one of its possible causes. In a logistic regression, they find a

positive association between injury and resistance and interpret this finding as illustrating that

when victims are attacked and injured, they are more likely to resist. For this reason, the authors

assert that victim resistance is more likely a response to injury rather than the other way around.

Tark and Kleck (2004) are able to analyze the effects of self-protective behavior on

victim injury while taking sequence into account for incidents of sexual assault, assault, robbery,

and confrontational burglaries. Because the NCVS data used in this study included information

about whether the injury occurred before or after victim resistance, the authors consider the

effects of resistance first for cases only where the injury occurred after resistance, and then for

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all cases regardless of sequence. The results indicate that once sequence of events is taken into

account, nearly all forms of victim resistance are associated with lower rates of post-resistance

injury compared to non-resistance. It is important to remember that this analysis contains

incidents of various types of crime, however, as it is possible that resistance works differently in

different situations.

Evidence has been presented for both harmful and beneficial effects of victim resistance

with regard to injury, but research has also investigated the issue of resistance and property loss.

As a second important crime outcome during robbery, crime completion has been found by

several studies to be reduced when victims resist. In a study of both rape and robbery incidents,

Guerette and Santana (2010) find that greater levels of victim resistance increase the effort

needed by offenders and thereby decrease crime completion rates. Kleck and DeLone (1993)

also tackle the issue of victim resistance in robbery. The authors find that self-protective

behaviors, and armed resistance in particular, reduce the probability of a robbery being

completed (i.e. property being taken). Similarly, Tark and Kleck (2004) find that resistance by

victims reduces the likelihood of property loss compared to nonresistance. All three of these

studies utilize crime incidents reported in the National Crime Victimization Survey (NCVS) data

and conclude that victim resistance has a negative effect on crime completion.

Another issue within the literature on victim resistance involves the type of self-

protection that victims employ. Some studies look at not only victim resistance, but whether or

not the resistance was forceful or non-forceful. For example, Cook (1986) examines the effects

of forceful resistance like pushing, hitting, or using a weapon versus non-forceful resistance like

screaming, calling for help, arguing with the offender, or trying to run away. In their study,

Kleck and DeLone (1993) include a total of eight different forms of victim resistance and find

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varying effects across the different types. They estimate the effects of resistance with a gun,

resistance with a knife, resistance with another weapon, resistance using weaponless physical

force, resistance by threatening or arguing with the offender, resistance by trying to get help or

scare the offender away, resistance by evasive action without force, and any other form of

resistance. The findings suggest that resistance with a gun reduces the likelihood of injury, while

unarmed physical force and trying to get help or scare the offender increase it. For this reason,

they argue that studies of victim resistance should address specific forms of self-protective

behavior rather than lump all types into one category. Other studies appear to confirm that the

effects of resistance differ across kinds of protective behavior (Tark and Kleck, 2004; Feeney

and Weir, 1975; Kleck, 1988).

Weapon effects

Previous research relevant to the current study also examines weapon use on the part of

offenders. In a qualitative analysis based on accounts by robbery victims, Luckenbill (1982)

found that victims were likely to comply to robbers’ demands when they believed the offenders

had lethal resources. Kleck and DeLone (1993) use NCVS data to investigate the effects of

offender weapon use in robbery incidents. The authors find that robbers with handguns are

significantly more likely to complete their robberies and get away with property, but are less

likely to injure. Ziegenhagen and Brosnan (1985) also find some support for this finding. Their

analysis indicates that offenders with guns exercise greater control and are able to complete their

crimes more often.

In the area of injury, research has on robbery and other crimes has generally found that

offender weapon use decreases the likelihood that the offender will physically attack the victim

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(Feeney and Weir, 1975; Kleck and McElrath, 1991; Wells and Horney,2002; Ziegenhagen and

Brosnan, 1985). These results suggest that robber gun possession may render the use of force

unnecessary when carrying out a robbery, thus making injury less likely. Indeed, Kleck and

DeLone (1993) show that offender firearm possession in robbery reduces the likelihood of victim

injury. Similarly, Kleck and McElrath (1991) find that deadly weapons, including firearms,

reduce the probability of injury during assaults. Cook (1987) also finds that injury is overall less

likely when a gun is used by the offender.

Other factors

Previous research has also identified a host of other factors that might affect victim injury

outcomes during robbery. Most studies include these factors as control variables, but do not

focus on or come to a consensus about their effects. Victim characteristics have included victim

age (Cook, 1987; Guerette and Santana, 2010; Kleck and DeLone, 1993; Kleck and McElrath,

1991; Kleck and Sayles, 1990; Luckenbill, 1982; Normandeau, 1968; Tark and Kleck, 2004;

Ziegenhagen and Brosnan, 1985), victim race (Cook, 1987; Guerette and Santana, 2010; Kleck

and DeLone, 1993; Kleck and McElrath, 1991; Kleck and Sayles, 1991; Normandeau, 1968;

Tark and Kleck, 2004; Ziegenhagen and Brosnan, 1985; Zimring and Zuehl, 1986), victim sex

(Guerette and Santana, 2010; Kleck and DeLone, 1993; Kleck and McElrath, 1991; Kleck and

Sayles, 1990; Luckenbill, 1982; Normandeau, 1968; Tark and Kleck, 2004; Ziegenhagen and

Brosnan, 1985; Zimring and Zuehl, 1986).

In a study of assaults, Felson and Painter-Davis (forthcoming) suggest that adversary

effects help predict lethal outcomes. That is, offenders take into account the perceived threat of

their adversaries, and thus are more likely to use weapons when facing young black men.

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Offenders are also more likely to kill young black opponents. By this logic, victim

characteristics that influence perceived threat to the robber should also affect injury during

robbery.

Additionally, several important situational factors are controlled for or investigated by

previous research, including location of the incident (Feeney and Weir, 1975; Guerette and

Santana, 2010; Hart and Miethe, 2009; Kleck, 1988; Kleck and DeLone, 1993; Kleck and

McElrath, 1991; Normandeau, 1968; Tark and Kleck, 2004; Zimring and Zuehl, 1986), time of

the incident (Guerette and Santana, 2010; Hart and Miethe, 2009; Kleck and DeLone, 1993;

Kleck and McElrath, 1991; Kleck and Sayles, 1990; Tark and Kleck, 2004), victim and offender

relationship (Cook, 1987; Guerette and Santana, 2010; Luckenbill, 1977; Normandeau, 1968;

Sampson and Lauritsen, 1994; Tark and Kleck, 2004; Zimring and Zuehl, 1986), number of

offenders (Kleck and DeLone, 1993; Kleck and McElrath, 1991; Luckenbill, 1982; Normandeau,

1968; Tark and Kleck, 2004; Ziegenhagen and Brosnan, 1985; Zimring and Zuehl, 1986),

offender alcohol and drug use (Guerette and Santana, 2010; Hart and Miethe, 2009; Sampson

and Lauritsen, 1994; Tark and Kleck, 2004; Wells and Horney, 2002), and presence of

bystanders (Guerette and Santana, 2010; Luckenbill, 1977; Sampson and Lauritsen, 1994; Tark

and Kleck, 2004).

An important limitation exists within the literature on victim injury. Almost all of the

quantitative analyses done on robbery and violent crimes utilize the NCVS data (Cook, 1986;

Guerette and Santana, 2010; Hart and Miethe, 2009; Kleck and DeLone, 1993; Tark and Kleck,

2004; Kleck and McElrath,1991; Kleck and Sayles, 1990) or police records (Feeney and Wier,

1975; Luckenbill, 1982; Normandeau, 1968; Zimring and Zuehl, 1986). Incidents found in

police records are likely not representative of all robbery events, since many crimes are never

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reported to the police. NCVS samples are likely more useful and representative in many regards

than official police records and are important in the study of robbery injury. Yet NCVS data

have important constraints. Because the data are based on interviews with crime victims, the

NCVS excludes incidents in which the victim is killed. In addition, victims of nonfatal gunshot

wounds are greatly underrepresented in the NCVS (Cook, 1986). The NCVS also only captures

information from victims who were willing to talk about their victimization. Additionally, while

a great deal of information can be extracted about the victims, the NCVS possesses little

information about the offender in crime instances. Although sex, age, race, and relationship to

victim are reported, the NCVS reports little else about the offender. Also, victims must

frequently guess at this information if their attacker is not known to them. Offender

characteristics and their effects on outcome represent a major knowledge gap in the literature on

victim injury.

THE CURRENT STUDY

The current study seeks to advance knowledge regarding the outcome of robbery by

focusing primarily on offender characteristics as predictors of injury. Little research has focused

on offender characteristics that influence victim injury during a robbery. Several studies control

for offender age, race, and sex, but very few other characteristics are considered. This gap in

the literature is likely due to the fact that many studies utilize the NCVS, which contains very

little offender information. In their review of individual correlates of violent offending,

Sampson and Lauritsen (1994) conclude that sex, age, race, social class, family processes, family

structure, and physical abuse as a child are all important characteristics that distinguish violent

offenders. In particular, they identify being male, young, black, of a low social class, and having

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been physically abused as risk factors for violent offending. However, Sampson and Lauritsen

(1994) are discussing characteristics that distinguish violent from non-violent individuals, which

may be less applicable when examining the behaviors of robbers, all of whom are already

committing a violent offense. The question remains as to whether these same characteristics

influence the level of violence that offenders exhibit while committing robbery. I assume,

however, that these factors should be considered relevant to predicting the level of violence that

robbers are willing to use.

In the current study, I consider two kinds of offender characteristics that are believed to

affect the amount of violence used by the robber and the possibility of victim injury. First, time-

stable characteristics are those traits that remain constant in these data and include age, race,

height, weight, violent arrest history, self-control, and childhood abuse. Age, height, and weight

are characteristics that change over time, but they are only measured once in the current study.

Second, time-varying offender characteristics that speak to current life circumstances include

income, stress, and marital status. These are characteristics that can vary from month to month.

Based on previous research, I examine age in the current study and expect it to influence

the likelihood of victim injury. Specifically, I hypothesize that younger offenders will be more

likely to injure their victims because young offenders are more impulsive, are more “hot-

headed,” and will be less cognizant of the potential costs associated with hurting a victim. Using

the rationality perspective, offender age can be seen as a factor that might make injury-inducing

violence more attractive given the life circumstances of the offender.

The offender characteristics of height, weight, and race are all variables that can be

conceptualized as sources of power during the interactive robbery process. Offender sources of

power might work to reduce victim injury in two ways. First, offenders who are perceived as

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more threatening may provoke less resistance on the part of the victim, which in turn could lead

to reduced likelihood of injury. Second, offenders with credible threats might feel less

compelled to injure victims from the outset. An important aspect to any robbery is the offender’s

perception of whether his threat is credible. From a rational choice perspective, a robber with a

strong and credible threat should feel more confident about gaining compliance from a victim

and completing the robbery successfully. Without a credible threat, however, an offender may

believe that injury is necessary to intimidate the victim to gain a credible threat and communicate

that they “mean business.” As Tedeschi and Felson (1994) argue, weak actors may need to use

physical preemptive force in order to prove the credibility of their threat. Only with a credible

threat can offenders increase the likelihood of compliance. Therefore, injury should be a

strategic choice used more often by offenders who lack a credible threat to begin with. Since

offender size can be conceptualized as a source of power or perceived threat, much like the use

of a gun during a robbery interaction between offender and victim, this research seeks to

understand if offender size has an impact on the likelihood of a victim being injured, and I

hypothesize that taller offenders and heavier offenders will be less likely to injure their victims.

Additionally, offender race is expected to work in a similar manner. As discussed by

Felson and Painter-Davis (forthcoming), racial stereotypes mean that blacks are generally more

feared than other racial groups. They discuss how research has shown that black strangers

provoke more fear than white strangers (St. John and Heald-Moore, 1995), people are more

likely to believe that a target has a gun if the target is black (Correll, Park, Judd, and

Wiitenbrink, 2002), and people associate threat and crime more with blacks than whites

(Duncan, 1976). Therefore, black offenders may either consciously or subconsciously recognize

their race to be a credible threat, in that others tend to fear them, and act accordingly. I predict

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that black offenders will be less likely to injure than non-black offenders, because blacks might

understand their race as a credible threat and will not feel the need to injure victims for

intimidation. When looking at robbery killings reported by the Detroit Police Department,

Zimring (1977) finds that a disproportionate percentage of robbery killings are committed by

black offenders, further suggesting the importance of including race as an important factor in any

injury analysis.

However, there is reason to believe that the relationships of size and race with injury will

be affected by offender weapon use, as having a weapon will be a more important source of

power and threat. The idea of credible threat is the key element in explaining the effects of size,

race, and weapon use. Large size, perceived dangerousness based on racial stereotypes, and

weapon use are all sources of credible threat that may come into play during a robbery. Previous

research finds weapons, and particularly guns, to be significant predictors of injury. Because

weapons give offenders a particularly strong source of power and a credible threat, differences

between offenders of different sizes and races should be reduced. In other words, short offenders

and non-black offenders, who otherwise may seem less threatening, will not need to take violent

measures when they have a weapon. In this way, weapon use should act as an equalizer between

different offenders, making all of them threatening. Therefore, this study predicts that offender

size and race will only have important effects when offenders are not using weapons.

I also expect to find a relationship between offender self-control and injury. Self-control,

as described by Gottfredson and Hirschi (1990), relates to an individual’s ability to think ahead

about costs and benefits and act accordingly in the present moment. This characteristic is

comprised of six elements: future orientation (or lack thereof), self-centeredness, anger/temper,

lack of diligence, preference for physical as opposed to mental tasks, and risk preference.

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Gottfredson and Hirschi argue that individuals with low self-control are more likely to engage in

risky behaviors including crime than those with higher self-control. Using this concept of self-

control, it is hypothesized that offenders with lower self-control will be more likely to injure

their victims than offenders with greater levels of self-control, since low self-control should work

to change the cost-benefit comparison of impulsive acts of violence. In the heat of the moment,

offenders with low self-control should be less likely to consider all possible costs and potential

negative consequences.

Violent criminal history is another offender characteristic I will explore in relation to

victim injury. I hypothesize that offenders with a greater number of violent arrests will be more

likely to injure their victims during robbery than offenders with fewer violent arrests. In this

case, violent history is meant to capture the offender’s inclination toward violence. Offenders

with greater experience and acceptance of violence will likely view it as a more acceptable and

appealing option than those offenders with less extensive violent histories. In a similar vein,

offenders who have experienced childhood abuse are hypothesized to be more likely to injure

victims than offenders who have not been abused. Sampson and Lauritsen (1994) assert that

abuse is correlated with violence. Felson and Lane (2009) also examine childhood abuse and

adult crime and find that, as a social learning perspective might predict, adult offenders model

the kinds of behaviors that they are exposed to as children. Keeping this in mind, I argue that

offenders who have been exposed to violence through childhood abuse will be more likely to see

it as acceptable because of social learning processes and thus will make different choices. . If

violence is more accepted, using violence that could lead to injury should have more perceived

benefits and fewer costs for abused offenders than offenders without an abusive past.

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I also examine three time-varying life circumstances as offender characteristics in this

study. Current income, current marital status, and current stress are all offender characteristics

that are believed to influence the decision-making of offenders. Because previous research has

found marriage to decrease criminal activity (Sampson, Laub, and Wimer, 2006), I hypothesize

that being married at the time of the robbery will make injury less likely, since offenders will

have more to lose by inflicting harm on others that could result in severe consequences. It is also

expected that offenders who have greater levels of stress in their lives at the time of the robbery

will be more likely to injure victims than offenders with less stress. In a review of research done

on family violence, Gelles (1980) concludes that family violence has often been found to be

linked to stress and stressful events. Stress can potentially inhibit individuals’ rational choice

decision-making ability (Broadbent, 1971). Lastly, income is also explored for effects on victim

injury. Like stress and marriage, income is a current life circumstance that can affect the

perceived costs and benefits of actions. Lower income generally means a greater need for

money, which should influence the relative benefits and costs of various actions during robbery.

If violence is perceived as necessary to gain immediate compliance and complete the robbery,

offenders with lower income may resort to injury-inducing violence more readily than offenders

with higher incomes.

In the current study I seek to examine victim injuries using data reported by offenders

(Horney, 2001) and thus enhance knowledge in the subject area. In this way, the study can add

to the literature by including instances of robbery that may not have been reported to the police

and instances that may not have been reported by victims. More importantly, the data include a

wealth of information on the offenders. The study is significant in that it affords particular

16

attention to how offender characteristics affect victim injury outcomes, an issue that has been

largely ignored.

DATA AND METHODS

Sample

Data from the Second Nebraska Inmate Study (Horney, 2001) will be used in the

proposed study. The data were obtained by randomly sampling two-thirds of newly admitted

inmates to Nebraska’s Diagnostic and Evaluation Unit over a one year period. Inmates selected

were convicted of felonies and sentenced to serve at least one year in the correctional facility.

The original sample consisted of 919 male inmates, but 14% were transferred out of the unit

before they could be contacted. Of the 854 inmates who were contacted, 87% agreed to

participate and completed interviews, while 9% refused to participate, 4% lacked the language

skills to allow participation, and <1% were transferred to another institution before the interview

could be completed. The final sample size was reduced to 717 respondents because of 23

completed interviews that were lost from a computer hard drive.

The data were gathered through interviews using life event calendars that lasted between

one and five hours. The life event calendar method, which is also sometimes referred to as the

event history calendar, has been found in previous research to be a useful, valid, and reliable

technique for retrospective interviewing (Belli, Shay, and Stafford, 2001; Caspi and Amell,

1994; Caspi, Moffitt, Thornton, Freedmand, Amell, Harrington, Smijers, and Silva, 1996;

Freedman, Thornton, Camburn, Alwin, and Young-DeMarco, 1988; Roberts and Horney, 2010;

Wells and Horney, 2002). For a review of life event calendar data and the quality of the method,

17

see Roberts and Horney (2010). Through face-to-face interviews with trained doctoral students,

the inmates were asked about the 36 months prior to the arrests that resulted in their

incarceration. The life event calendars were computerized, but interviewers read the questions

aloud to the inmates and subsequently recorded the answers on laptops. In addition to the

computerized calendars, paper calendars were provided as visual aids for structuring recall, as is

discussed by Caspi et al. (1996). If a respondent did not understand a question, the interviewer

would reword the question, and probes were used if needed. When asked about robberies,

respondents reported whether they had committed any robberies during the entire 36 month

period. If they answered in the affirmative, respondents were then asked about the number of

robberies committed during each month. For every reported robbery, interviewers used a

structured interview to obtain details about the robberies. In addition, respondents were also

asked to describe the robbery event in their own words, providing a short narrative for each

incident, which was recorded by the interviewers. Inmates were paid $5.00 for their

participation.

One section of the interview focused specifically on robbery. Respondents were asked to

use the life event calendar method to report up to ten incidents of robbery that they committed

during this 36 month period. When asked to report up to ten robberies, a total of 101 out of the

717 inmates (about 14%) reported committing any robberies and produced a total of 332 robbery

incidents. Series incidents, or cases in which the offender reported a robbery that was one of a

series of very similar robberies, were removed from the sample. Since offenders often reported

several robberies that could not be distinguished, all but the most recent incident were removed,

as is commonly done with the NCVS data set (Lynch, 2006). The most recent incident was

included in the data but was coded as being one in a series. The total robbery sample size was

18

reduced to 196. One additional case had to be omitted because of missing information. Thus,

the final sample includes 195 robbery incidents from a total of 101 inmates, which corresponds

to an average of 1.93 robberies per inmate among those inmates who reported any robberies

during the 36 month period. Dichotomous variables are used to indicate whether robberies had

multiple offenders, had multiple victims, were one of a series, and were commercial rather than

personal.

Variables

Dependent Variable

The dependent variable for the proposed study is victim injury. This variable was

measured by asking respondents the question “Did you cause any injuries to the victim(s)?” for

each robbery. Respondents could answer either “yes” or “no”. In the data, answers of yes are

coded as 1, while those answering in the negative are coded as 0.

Independent Variables

This research focuses on several independent variables of interest, all of which are

offender characteristics that might influence victim injury. Many offender characteristics are

explored: race, age, income, marital status, height, weight, stress, violent history, abuse as a

child, and self-control.

Race is represented in the model by a dummy variable for whether respondents report

being black or not, with all other race categories, including white, Native American, Hispanic,

and other, included in the omitted non-black category. Exploratory work revealed that these race

groups were appropriate, as Native American, Hispanic, and other respondents were similar to

whites but differed more from blacks on several key variables. For age, respondents simply

19

reported their birth day and year, which is used to calculate age at the time of interview. While,

this age may be off by up to three years from the time of the robbery incident, it is unlikely that

the difference of a few months or years will significantly impact results. The variable of income

was measured by asking respondents about their income during the last month and whether that

amount had changed over the course of the 36-month period. If their income had changed,

inmates were then asked about how much income they received in other months. The question

asked “What was your total personal income from legal wages before taxes?” which means that

all illegal income was excluded. Respondents were placed into nine categories of income,

ranging from “less than $5,000” to “$100,000 or more.” The variable of marital status is simply

a binary variable that shows whether or not the inmate reported being married during the month

of the robbery in the life events calendar section of the interview. Respondents were asked if

they were married during the last month of the calendar period, and whether this status changed

at all during the period. If it had changed, respondents could indicate in which month or months

they changed marital status. Height and weight are both continuous variables that give inmates

height in inches and weight in pounds1. The variable for height is centered around the group

mean. This is done because the variable is measured in inches, and any interaction models may

become problematic with the lowest value being so much higher than zero. Criminal violent

history is measured by total number of arrests for violent offenses.

To measure childhood abuse, and following the example of the Conflict Tactics Scale,

respondents were asked how often their parents or primary caregivers took any of the following

actions: “throw something at you that could hurt,” “threaten you with a knife or gun,” “twist your

arm or hair,” “insult or swear at you,” “push or shove you,” “use a knife or gun on you,” “punch

1 I also tried combining height and weight into one size or BMI variable. During preliminary analyses, however, it

became clear that height and weight had different effects, and combining them did not make sense.

20

or hit you with something that could hurt,” “shout or yell at you,” “choke you,” “slam you up

against a wall,” “beat you up,” “grab you,” “slap or spank you,” “burn or scald you on purpose,”

“threaten to hit or throw something at you,” or “kick you.” For each of these items, respondents

could answer “never,” “once or twice,” “sometimes,” “frequently,” or “most of the time.” These

answers were given scores of 0 through 4. The answers from these 16 items are added into one

abuse variable, with scores ranging from 0 to 64.

The variable of self-control is measured with the 24 question item scale developed by

Grasmick, Tittle, Bursik, and Arneklev (1993) scale of self-control. Respondents used a four-

point likert scale with “Strongly disagree,” “Disagree somewhat,” “Agree somewhat,” or

“Strongly agree” to respond to the 24 questions (which are listed in Appendix A). Answers to

the self-control questions are combined into a scale by taking the mean of all twenty-four items.

Other studies have reported on the reliability and validity of the Grasmick et al. self-control scale

(Longshore, Turner, and Stein, 1996; Piquero and Rosay, 1998). Preliminary analyses with the

current data produced a Cronbach’s alpha coefficient of .848, which suggests a fairly high inter-

item reliability.

Stress was measured through a series of questions that asked respondents if they had been

under severe stress during any of the months in the calendar period for any of the following

reasons: financial, work, death of a significant person, serious illness or injury to respondent or

someone close to the respondent, domestic, or other. Inmates could respond with “yes” or “no.”

If inmates responded in the affirmative, they were then asked in which months they experienced

the stress. The variable used in the analysis adds the number of reported stressors together for

the month in which the robbery was committed. Thus, values range from 0 to 6, with 0

21

indicating that the respondent reported no severe stress during that month and 6 indicating that

the respondent reported 6 severe stressors.

Control Variables

The study also includes multiple control variables that are meant to control for the

situational aspects of robbery that have been discussed in the literature. Binary variables were

originally included to indicate whether a robbery had multiple victims, whether a robbery had

multiple offenders, and whether a robbery was committed against a business rather than a person.

A binary variable was included for whether or not the reported victim was black, as is done in

past studies on victim injury (Kleck and Delone, 1993; Kleck and McElrath, 1991; Kleck and

Sayles, 1990; Tark and Kleck, 2004). Similarly, a dummy variable was used to indicate whether

or not the victim was younger than 15 years old or older than 35 years old. Previous research has

measured victim age in this way, because both young and old victims are generally more

vulnerable (Kleck and Delone, 1993; Tark and Kleck, 2004). A value of 1 is assigned to any

cases in which all victims were younger than 15 or older than 35, and a value of 0 is given to all

other cases where the victim or victims were reported to be between 15 and 35 years old.

Another binary control variable was originally included to account for the victim-offender

relationship. Respondents were asked, “What was your relationship to the victim?” The answer

categories were “stranger”, “knew by sight”, “acquaintance”, “friend”, “coworker”, and “family

member.” Values of 1 are given to cases in which any of the victims were known to the

offender. A binary variable is also used to indicate whether or not any of the victims were

female, with a value of 1 indicating that at least one of the victims was female. Two additional

variables control for the time and place of the robbery incident. A binary variable is included to

22

indicate if the robbery occurred in a public place, and another variable indicates if the robbery

occurred during the day. Victim resistance is also utilized in the model as a control; a binary

variable is coded as 1 for those robberies where inmates reported that the victim resisted in any

way. Additionally, a binary variable will indicate whether or not any bystanders were present at

the robbery incident.

A set of dummy variables are included to control for offender weapon use: if the

respondent used a gun of any kind, if the respondent used any other weapon, and if the offender

used no weapon. These dummy variables were created using respondents’ responses to the

question “Did you personally use a weapon to commit the robbery?” The wording of the

question thus means that only weapons used by the respondent are counted, which means that

other weapons used by co-offenders may not be accounted for. If the question was answered in

the affirmative, respondents could then select any of a number of weapons that they used.

Respondents who answered negatively were coded as using no weapon. If respondents indicated

using a handgun, automatic handgun, shotgun, hunting rifle, or automatic rifle, they were coded

as using a gun. If respondents indicated using a knife, bottle/glass, other sharp object, or other

blunt object they were coded as using some other type of weapon. There were a few cases where

respondents indicated using both a gun and another kind of weapon. These were coded as

having a gun, since the presence of a gun is likely more salient than some other type of weapon

during a robbery situation. Table 1 displays descriptive data for all of the variables.

23

Table 1: Descriptive Statistics

Variable Min Max Mean S.D. % missing

Dependent variable Victim injury 0 1 0.267 0.443 0

Offender characteristics

Race 0 1 0.246 0.432 0

Age 17 45 25.323 6.863 0

Married 0 1 0.092 0.290 0

Height (inches) before centering 63 77 70.436 2.863 0

Height (inches) centered -7.436 6.564 0.0 2.862 0

Weight (pounds) 130 265 172.856 26.572 0

Stress 0 4 0.763 0.887 4.62

Violent arrests 0 15 1.800 2.267 0

Abuse 1 50 14.379 12.324 0

Self-control 1.46 3.38 2.392 0.437 0.51

Income (see below)

Weapons

Gun 0 1 0.446 0.498 0

Other weapon 0 1 0.108 0.311 0

No weapon 0 1 0.446 0.498 0

Control variables

Multiple victims 0 1 0.230 0.422 2.05

Multiple offenders 0 1 0.697 0.461 0

Black victim 0 1 0.201 0.402 8.21

Victim age vulnerability 0 1 0.073 0.260 8.21

Knew victim 0 1 0.486 0.501 5.13

Female victim 0 1 0.254 0.436 3.08

Public incident 0 1 0.544 0.499 0

Daytime incident 0 1 0.390 0.490 0

Victim resisted 0 1 0.267 .443 0

Bystanders 0 1 .200 .401 0

Offender income %

None 47.18

$1-$5,000 9.23

$5,000-$9,999 10.77

$10,000-$14,999 11.28

$15,000-$24,000 8.72

$25,000-$34,999 4.1

$35,000-$49,999 4.1

missing 4.62

24

Analysis

In order to understand how the above variables affect victim injury outcomes, the study

utilizes binary logistic regression for analysis of the data. All analyses are conducted using the

Stata statistical software program. Logistic regression is appropriate since the dependent variable

is binary. Because some respondents report multiple incidents, the study must take the within-

person clustering effects into account. Specifically, Stata allows users to account for design

effects by assigning primary sampling units, weights, and strata. For this study, the primary

sampling unit will be set to the offender identification number when running the regression

model.

Although most of the variables of interest have no missing data, for the few cases of

missing data, mean substitution is used. The amount of missing data is presented above in Table

1 with the descriptive information. Since the amount of missing data for any one variable is not

a substantial amount, mean substitution provides a good way of maximizing the information that

can be used and allowing the inclusion of all cases in the analysis.

RESULTS

All of control variables were included in preliminary analyses to discover their possible

correlations with the independent and dependent variables. Only a few of the control variables

discussed here were related to the independent and dependent variables in ways that affected the

analysis. For this reason, the variables for commercial robbery and for victim resistance are

included in the final models, as they have statistically significant effects on victim injury. The

effects for gun use were not statistically significant, but the effects for other weapon use were

either borderline significant or significant in all analyses. Since weapon use is theoretically an

25

important situational factor and the effects of other weapons were often statistically significant or

very close to significant, the dummy variables for weapon use were also included in the final

models. Finally, demographic characteristics of the victim, specifically age, race, and gender,

are kept in the analysis despite showing no statistically significant effects. The other control

variables for multiple offenders, multiple victims, victim-offender relationship, time, location,

and presence of bystanders are not included since they were not found to have significant effects

on victim injury. Appendix B displays a complete correlation matrix with all of the variables.

Results from logistic regression analyses of victim injury are presented in Table 2.

Model 1 includes offender characteristics alone and shows that, with one exception, these

offender characteristics are not related to the likelihood of victim injury. Contrary to what was

expected, violent history, self-control, weight, income, marital status, and stress did not impact

the likelihood of injury. These effects were not statistically significant, and in addition, the odds

ratios show that the effects are generally small in size. In Model 2, the relevant controls are

added to the equation, and those offender characteristics remain non-significant. In other words,

this study finds that offenders with varying levels of violent history, self-control, weight, income,

marital status, and stress did not differ significantly in their patterns of injuring robbery victims.

A few offender characteristics, however, are found to affect victim injury and produce

some interesting results. In Model 1, race has a statistically significant effect on victim injury,

controlling for the other offender characteristics in the model. Black offenders are found to be

67.8% less likely to injure victims during robbery. Offender height and history of childhood

abuse are both found to have borderline statistically significant results, meaning that the

significance level falls between .05 and .1. Since the sample being used is relatively small, these

results are discussed. The odds ratio in Model 1 of Table 2 shows that taller offenders are less

26

likely to injure their victims, as each additional inch of height above the mean makes injury

15.9% less likely. On the other hand, childhood abuse is associated with a slightly greater

likelihood of victim injury. For each additional form of abuse that respondents reported, victim

injury is 2.2% more likely. This is a relatively small effect size, but one that is borderline

statistically significant.

Table 2: Logistic Regression of Predictors of Injury (N=195)

Odds Ratios

Model 1 Model 2

Offender characteristics

Black .322* .113*

Violent arrests 1.079 1.093

Self-control 1.329 1.706

Height 0.841† 0.780†

Weight 1.007 1.02

Age 0.986 0.945

Income 1.006 0.935

Married 0.461 0.599

Stress 0.821 0.83

Abuse 1.022† 1.008

Weapons

Gun

1.524

Other

6.992**

Controls

Black victim

3.352*

Victim age vulnerability

1.073

Female victim

0.505

Resistance

5.239**

Commercial 0.059** † 0.10 <P<0.05 *0.01<P<0.05 ** P<0.01

In Model 2 of Table 2, the relevant controls are added to the model. The effect of race

remains statistically significant and actually increases in magnitude when controlling for weapon

27

use, victim demographics, resistance, and whether the robbery was commercial or not. After

controlling for these variables, black offenders are 88.7% less likely to injure their robbery

victims than their white, Native American, Hispanic, and other race counterparts. Height also

remains borderline significant with a negative relationship to injury. In this model, each

additional inch of height above the mean makes injury 22% less likely, which represents a small

increase in the size of the effect. The effect of abuse, on the other hand, almost completely

disappears and becomes statistically non-significant. Thus, it appears that the effect of childhood

abuse does not actually have a significant, independent impact on the likelihood that an offender

will injure during a robbery interaction.

As discussed previously, very few of the control variables taken from previous research

were found to be associated with victim injury, and for this reason, only demographic variables

as well as weapon use, commercial robbery, and victim resistance were included in the final

models. Prior research has found that a robber’s gun use decreases the likelihood of injury

during a robbery when compared to the use of a less lethal weapon and when compared to no

weapon use. The analyses presented here do not replicate this finding; the use of a gun by the

robber seems to have no statistically significant effects on victim injury. The odds ratio for gun

use indicates that guns are associated with more injury than robberies with no weapons, but as

stated before, this effect is not statistically significant. The use of another type of weapon, on the

other hand, does appear to have an impact on victim injury. This study finds that when the

offender uses another type of weapon versus no weapon, injury is almost 600% more likely to

occur, controlling for offender characteristics and the other control variables. This effect is

highly significant. It is interesting that gun use and other weapon use compared to no weapon

28

work in the same direction of making injury more likely, but the effect for guns is smaller and

not significant, whereas the effect for other weapons is quite large and statistically significant.

In addition, while victim age and gender do not significantly predict injury, victim race

has a statistically significant effect. Robberies with a black victim increase the likelihood of

injury by 235.2% compared to robberies with a victim of any other race. Victim resistance and

whether a robbery was commercial or not have highly statistically significant and large effects on

victim injury. Incidents in which respondents reported victim resistance of some kind are

423.9% more likely to result in injury than those incidents that did not involve resistance,

controlling for offender characteristics, weapon use, victim demographics, and commercial

robbery status. Commercial versus person robbery also makes a difference in the chance of

victim injury. Specifically, injury is 94.1% less likely in commercial robbery cases than in

personal robbery cases, controlling for offender characteristics, victim resistance, weapon use,

and victim demographics. These results are substantively important and confirm the use of

resistance and commercial status as vital control variables when examining offender

characteristics.

It is possible that weapons exert such a strong situational influence that they dilute some

of the offender characteristic effects. Weapon use, like race and size, gives offenders a source of

credible threat that should work to reduce injury. These offender characteristics might be

influential only in cases where no weapon is present. Because this study expects that the

offender characteristics related to credible threat will be moderated by weapon use, possible

interactions of weapon use with height, weight, and race were tested. Both height and race were

found to interact with weapon use, and the results are displayed in Table 3 and Table 4. In these

29

models, the variable for weapon use is simply a dichotomous variable with no weapon coded 1

and any kind of a weapon considered the alternative or omitted category.

Table 3 shows the effects of height, weapon use, and the interaction between the two.

Models 1 through 3 show the effects of height, having no weapon, and then the interaction term.

In Model 4, victim and offender demographic variables and the controls for resistance and

commercial robbery are added. The results indicate that there is a statistically significant

interaction between the two variables, even after controlling for victim and offender

demographic information, resistance, and commercial robbery status.

Table 3: Logistic Regression of Interaction between Height and Weapons (N=195)

Odds Ratios

Model 1 Model 2 Model 3 Model 4

Height .861† .858* 1.055 0.936

No weapon

0.84 0.553 .381†

Height weapon interaction

.602** .711*

Black

.184**

Age

0.975

Black victim

2.167

Victim age vulnerability

1.018

Female victim

0.548

Resistance

3.770**

Commercial .051** † 0.10 <P<0.05 *0.01<P<0.05 ** P<0.01

In order to ease the interpretation of the results, predicted probabilities are calculated and

presented graphically in Figure 1. This graph shows the predicted probability of injury for short,

average, and tall offenders both with and without a weapon. For the purpose of calculating

predicted probabilities, a short offender is assigned a height two standard deviations below the

30

mean, a tall offender is assigned a height two standard deviations above the mean, and an

average offender is assigned a height at the mean. In inches, this translates into a height of about

5’4’’ for short offenders, about 5’10’’ for average offenders, and about 6’4’’ for tall offenders.

The graph shows the effect of height on injury when a weapon is present and when it is not. As

indicated previously by regression, short offenders have the highest probability of injuring

regardless of weapon use, followed by average and then tall offenders. However, the effect of

height looks quite different for robberies committed with weapons compared to those committed

without them. When a weapon is used, there is a relatively small and fairly constant decrease in

the likelihood of injury as height increases. When no weapon is used, stark differences arise in

the likelihood of injury between short offenders and average or tall offenders. In fact, an

offender who is two standard deviations above the mean for height has a probability of close to

0, whereas an offender who is two standard deviations below the mean height has a probability

of about .5 for injury. On the other hand, when a weapon is involved, the probability of injury is

more similar among the three types of offenders.

Figure 1: Predicted Probability of Injury for Height and Weapon Interaction

0

0.1

0.2

0.3

0.4

0.5

0.6

Short Avg Tall

Pre

dic

ted

Pro

bab

ility

of

Inju

ry

Offender Height

no weapon

weapon

31

The interaction between race and weapons is explored in Table 4. The additive effects of

just the black dichotomous variable and the no weapons variable are presented in Model 1 and

Model 2, and the interaction term is added in Model 3. This interaction proves to be statistically

significant, and remains significant after adding the other variables in Model 4. Again, the

results are presented graphically in Figure2, which displays the predicted probability of injuring

a victim for black and non-black offenders both with and without a weapon. Non-black

offenders are more likely to injure victims than black offenders regardless of presence of a

weapon. However, when weapons are present, the disparity between blacks and non-blacks

becomes greater; the presence of a weapon makes the likelihood of injury much less likely for

blacks, whereas the effect is not so strong when there is no weapon. This interaction works in

the opposite direction as might be expected and as the previously presented interaction with

height. Weapons were expected to work as an equalizer, giving non-black offenders a credible

threat, and thus reducing the difference between blacks and non-blacks, but this proved not to be

the case in these data.

32

Table 4: Logistic Regression of Interaction between Race and Weapons (N=195)

Odds Ratios

Model

1 Model 2 Model 3 Model 4

Black 0.468 0.457 0.198* 0.065**

No weapon

0.823 0.594 0.331**

Race weapon interaction

6.547* 9.678*

Height

.803*

Age

0.965

Black victim

2.024

Victim age vulnerability

0.886

Female victim

0.571

Resistance

4.039**

Commercial .038** † 0.10 <P<0.05 *0.01<P<0.05 ** P<0.01

Figure 2: Predicted Probability of Injury for Race and Weapon Interaction

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

no weapon weapon

Black

Non-black

33

DISCUSSION

This study, contrary to expectations, finds that few offender characteristics predict victim

injury during robbery incidents. Specifically, offender violent arrest history, level of self-

control, weight, age, marital status, income, current stressors, and history of childhood abuse are

not found to significantly impact the likelihood that an offender will injure his victim. This

finding is important, however, in understanding the dynamics of robbery. Based on these data

and analyses, it appears that offenders with varying levels of these variables are not more likely

than others to injure across all kinds of situations. All robbers must make decisions regarding

the amount of force that they use during an incident. The findings of this study suggest that the

rationale used to make these decisions is not bound by the offender characteristics explored here.

That is, varying levels of violent history, self-control, weight, age, marital status, income, stress,

and abuse do not work to alter the perceived costs and benefits associated with injuring victims.

Instead, different kinds of offenders appear to make decisions in similar ways, at least for the

variables measured here.

Although this finding runs counter to the hypotheses presented earlier, it should perhaps

not be entirely surprising that many of the offender characteristics were found to have minimal

effects on victim injury. The data used for this study come from interviews with serious

offenders who have been sentenced to prison terms. This means that all of the offenders sampled

are men who have committed some sort of serious crime, which may or may not have been

robbery, and have been caught and sentenced to prison. These robbery incidents may differ

substantively from the kinds of robberies represented in the NCVS. Therefore, future research

might continue to focus on offender characteristics, but use alternate sources of data or different

characteristics that might distinguish robbers from one another.

34

Two offender characteristics, on the other hand, did prove to affect the likelihood of

victim injury during robbery incidents. Both offender race and height influence injury, and both

of them are interpreted as situational aspects of offender perceived credible threat. The results

from this study indicate that taller robbers are less likely to injure victims, regardless of the other

situational factors. This effect was anticipated, because larger offenders will be perceived as

more threatening and dangerous to victims. This finding is interpreted to mean that with the

credible threat of size, taller offenders realize their perceived dangerousness and do not feel the

need to injure victims simply to intimidate. Shorter offenders, on the other hand, are more likely

to injure, regardless of other aspects of the interaction, like victim resistance, victim

characteristics, and robber weapon use. Short offenders may feel that injury is the best way to

prove that they are serious and dangerous, since they may appear less threatening, in order to

complete the robbery.

It is interesting that offender weight did not influence injury in the same way as height.

Weight has no real effects on the likelihood of injuring. While it is somewhat surprising that

height and weight, both of which are measures of size, do not have similar effects, this finding

indicates that offenders think about height differently from weight. While they might believe

being tall to be a power advantage, they do not make the same reasoning regarding weight. One

possible explanation is that many heavy offenders view their weight not as a source of threat, but

as a symbol of being slow or out of shape. This could work to curb the advantages of being

heavy.

The interaction between weapon use and height is also interesting and in the expected

direction. That is, the use of a weapon by the offender has an equalizing effect on the influence

of height. When no weapons are present, the difference between shorter and taller offenders in

35

likelihood to injure victims is quite large. Given the previous discussion of credible threat, this

makes sense. When the offender does have a weapon, the difference between offenders of

varying heights is substantially reduced. The interaction between height and weapon use makes

sense from the perspective of offender rationality. When a robber has a weapon, he goes into the

interaction with a credible threat. A weapon presents the offender with a tool for threatening

victims and maintaining control of the situation. For this reason, a short robber with a weapon

does not need to intimidate or prove anything by injuring, as a weapon presents a more potent

credible threat than height.

Black offenders are found to injure less than offenders that are white, Native American,

Hispanic, or of any other race. This finding is in accordance with the expected effects. Because

of racial stereotypes that pervade our culture, blacks are perceived to be more dangerous and

frightening. If victims are more fearful of black robbers than robbers of other races, black

offenders have a source of power that makes them more threatening. Based on the results of this

study, it appears that black offenders recognize the credible threat that they have, and thus are

less likely to injure victims for intimidation. This again speaks to the rational decision-making

process based on the available information that offenders use. Since black offenders are

perceived as more threatening, they realize that they possess a credible threat and there is less

benefit to injuring victims. The interaction effect that is found between offender race and

weapon use, however, runs contrary to what this study expected to find. It was expected that the

presence of a weapon would act as an equalizer. The interaction found here, on the other hand,

suggests that the disparity between black and non-black offenders grows wider when the robber

is using a weapon. The difference between blacks and non-blacks in likelihood to injure is most

pronounced when there is a weapon present. This finding is puzzling given the hypotheses

36

regarding perceived threat and the other findings of the study. Perhaps blacks and non-blacks

differ in level of experience, level of comfort, or intentions to injure when carrying a weapon.

Additional research might delve farther into the issue of offender race and weapon use as they

are related to robbery interactions and victim injury.

Previous research in the area of victim injury has consistently found gun and weapon

effects, with guns and deadly weapons making injury less probable. This study did not replicate

these weapon effects. In the additive model, gun use was found to increase the likelihood of

injury, but this effect is not statistically significant. The use of any other weapon was also found

to greatly increase the likelihood of injury, and this finding is statistically significant. That is,

robbers are almost 600% more likely to injure when using another weapon compared to no

weapon. Although weapons are found to increase the chance of injury overall, the interaction

models illustrate how weapon effects depend on height and race of the offender. While this

study cannot adequately address why these weapon effects differ from previous findings, a few

possible explanations can be offered. The findings may reflect differences in intent to injure that

cannot be controlled in this study. If, in these data most offenders carrying weapons are doing so

because they wish to injure, weapons could have a positive effect on injury. Also, the difference

in weapon effects may be a result of the data being analyzed in this study versus previous

research. As noted earlier, much of the literature utilizes NCVS data, while this study analyzes

data from incarcerated individuals. It is not improbable that the types of robberies represented in

these two data sets differ on certain elements, and that this difference might account for the

findings regarding weapons. This might also explain why many of the situational factors

discussed in previous research, such as time and place or the incident, number of offenders, and

victim-offender relationship, do not significantly influence victim injury in the current study.

37

Three of the control variables did display statistically significant effects on likelihood of

victim injury, and although this is not the emphasis of the study, these variables deserve some

discussion. First of all, victim race exhibits some influence on the likelihood of injury, as

victims who are black are more likely to be injured than those who are other races. This finding

echoes the findings made by Felson and Painter-Davis (forthcoming) regarding adversary effects.

Because robbery is an interaction between offender and victim, offenders make decisions about

how much force to use based on the available information. Since racial stereotypes depict blacks

as more dangerous and threatening, offenders may feel that greater force and intimidation is

necessary when robbing a black victim. Second, commercial robberies are found to be less

likely to result in injury than personal robberies, regardless of victim characteristics, offender

characteristics, weapon use, and victim resistance. This likely has to do with the rationality that

robbers use when making decisions. When robbing a commercial business, the costs of injuring

a victim may appear more salient compared to the benefits since commercial robberies may seem

more risky to begin with.

Perhaps most interesting is the consistently strong and statistically significant effect of

victim resistance on victim injury. As discussed, previous research has been mixed regarding the

direction and strength of the effect of resistance on injury. While this study cannot truly address

the issue because of a lack of time-order information, the results here seem to suggest that victim

resistance is associated with a greater likelihood of injury. This study cannot control for whether

the injury came before or after resistance. But it does control for multiple offender, victim, and

situational characteristics that might affect victim resistance or offender initial attack. For

example, offenders may be more likely to injure from the outset if they are short, which then

might cause the victim to resist. By controlling for such characteristics, this study can tentatively

38

approach the effects of resistance. Victim resistance is found to play a strong and important role

on the likelihood of injury, suggesting that victims who attempt to fight back in some way have a

higher probability of being injured.

Obviously, limitations exist in this study. The sample only represents a group of

offenders who have been caught and sanctioned for some sort of criminal offense, which means

that the data is not necessarily representative of all offenders or robbery incidents. However,

research based on this data can be seen as complementary to research done utilizing NCVS data,

as it offers different strengths and the ability to approach the issue of injury during robbery using

offender rather than victim testimonials. Also, only a handful of offender characteristics that

could potentially affect injury outcomes are explored here. Future research might focus on a

wider range of characteristics or replicate this study with more data. Since some respondents

reported multiple robbery incidents, the use of a hierarchical linear model to investigate within-

person effects could be interesting and informative. Unfortunately, there was not a sufficient

amount of variation in these data to perform this kind of analysis, but future research might do

so.

In conclusion, many of the offender characteristics explored in this research are found to

have no significant impact on victim injury outcomes during robbery incidents. Two factors

relating to offender perceived threat, race and height, are found to influence the likelihood of

victim injury. When offenders feel that they have a credible threat, they decide that injuring

victims is less necessary and provides fewer benefits and more costs. As weapons provide a very

strong threat and source of power, the effects of race and height are moderated by offender

weapon use.

39

REFERENCES

Belli, Robert F., William L. Shay, and Frank P. Stafford. 2001. “Event History Calendars and

Question List Surveys: A Direct Comparison of Interviewing Methods.” Public Opinion

Quarterly 65: 45-74.

Broadbent, Donald E. 1971. Decision and Stress. London: Academic Press.

Caspi, Avshalom and James W. Amell. 1994. “The Reliability of Life History Calendar Data.”

DPPP Technical Report, Vol. 94-101. Madison, WI: University of Wisconsin.

Caspi, Avshalom, Terrie E. Moffitt, Arland Thornton, Deborah Freedman, James W. Amell,

Honalee Harrington, Judith Smijers, and Phil A. Silva. 1996. “The Life History

Calendar: A Research and Clinical Assessment Method for Collecting Retrospective

Event-History Data.” International Journal of Methods in Psychiatric Research 6: 101-

114.

Cook, Philip J. 1986. “The Relationship between Victim Resistance and Injury in

Noncommerical Robbery.” The Journal of Legal Studies 15(2): 405-416.

Cook, Philip J. 1987. “Robbery Violence.” The Journal of Criminal Law and Criminology

78(2): 357-376.

Cornish, Derek and Ronald Clarke. 1986. The Reasoning Criminal: Rational Choice

Perspectives on Offending. New York, NY:Springer-Verlag.

Correll, Joshua, Bernadette Park, Charles M. Judd, and Bernd Wiitenbrink. 2002. “The Police

Officer’s Dilemma: Using Ethnicity to Disambiguate Potentially Threatening

Individuals.” Journal of Personality and Social Psychology 83: 1314-1329.

40

Duncan, Birt L. 1976. Differential Social Perceptions and Attribution of Intergroup Violence:

Testing the Lower Limits of Stereotyping of Blacks. Journal of Personality and Social

Psychology 34: 590-598.

Feeney, Floyd. 1986. “Robbers as Decision Makers.” In: D. Cornish and R. Clarke (eds.) The

Reasoning Criminal: Rational Choice Perspectives on Offending. New York,

NY:Springer-Verlag.

Feeney, F., and A. Weir. 1975. "The Prevention and Control of Robbery." Criminology

13(1):102–105.

Felson, Richard B. and Lane, K. J. 2009. “Social Learning, Sexual and Physical Abuse, and

Adult Crime.” Aggressive Behavior 35: 489–501.

Felson, Richard B. and Noah Painter-Davis. “Race, Weaponry, and Lethal Outcomes in

Assaults.” Forthcoming in Social Science Research.

Freedman, Deborah, Arland Thornton, Donald Camburn, Duane Alwin, and Linda Young-

DeMarco. 1988. “The Life History Calendar: A Technique for Collecting Retrospective

Data.” Sociological Methodology 18: 37-68.

Gelles, Richard J. 1980. “Violence in the Family: A Review of Research in the Seventies.”

Journal of Marriage and Family 42: 873-885.

Gottfredson, Michael R., and Travis Hirschi. 1990. A General Theory of Crime. Stanford:

Stanford University Press.

Grasmick, Harold G., Charles R. Tittle, Robert J. Bursik Jr., and Bruce J. Arneklev. 1993.

“Testing the Core Empirical Implications of Gottfredson and Hirschi’s General Theory of

Crime.” Journal of Research in Crime and Delinquency 30:5-29.

41

Guerette, Rob T. and Shannon A. Santana. 2010. “Explaining Victim Self-Protective Behavior

Effects on Crime Incident Outcomes: A Test of Opportunity Theory.” Crime &

Delinquency 56(2): 198-226.

Hart, Timothy C. and Terance D. Miethe. 2009. “Self-Defensive Gun Use by Crime Victims: A

Conjunctive Analysis of Its Situational Contexts.” Journal of Contemporary Criminal

Justice 25(1): 6-19.

Hochstetler, Andy. 2001. “Opportunities and Decisions: Interactional Dynamics in Robbery and

Burglary Groups.” Criminology 39(3): 737-762.

Horney, J. (2001). Criminal events and criminal careers. In R. Meier, L. Kennedy, & V. Sacco

(Eds.), The process and structure of crime: Criminal events and crime analysis. New

Brunswick, NJ: Transaction.

Jacobs, Bruce A. 2010. “Serendipity in Robbery Target Selection.” British Journal of

Criminology 50: 514-529.

Kleck, Gary. 1988. “Crime Control through the Private Use of Armed Force.” Social Problems

35(1): 1-21.

Kleck, Gary and Miriam A. DeLone. 1993. “Victim Resistance and Offender Weapon Effects

in Robbery.” Journal of Quantitative Criminology 9(1): 55-81.

Kleck, Gary and Karen McElrath. 1991. “The Effects of Weaponry on Human Violence.”

Social Forces 69(3): 669-692.

Kleck, Gary and Susan Sayles. 1990. “Rape and Resistance.” Social Problems 37(2): 149-162.

Longshore, Douglas, Susan Turner, and Judith A. Stein. 1996. “Self-control in a Criminal

Sample: An Examination of Construct Validity.” Criminology 34:209-228.

42

Luckenbill, David F. 1977. “Criminal Homicide as a Situated Transaction.” Social Problems

25(2): 176-186.

Luckenbill, David F. 1982. “Compliance Under Threat of Severe Punishment.” Social Forces

60(3): 811-825.

Lynch, James P. 2006. “Problems and Promise of Victimization Surveys for Cross‐National

Research.” Crime and Justice 34: 229-287.

Morrison, Shona A. and Ian O'Donnell. 1996. “An Analysis of the Decision Making Practices

of Armed Robbers.” The Politics and Practice of Situational Crime Prevention, Crime

Prevention Studies 5 NewYork: Criminal Justice Press.

Normandeau, Andre. 1968. “Patterns in Robbery.” Criminology 6:2-16.

Piquero, Alex R. and Andre B. Rosay. 1998. “The Reliability and Validity of Grasmick, et al.’s

Self-control Scale: A Comment on Longshore, et al.” Criminology 36:157-173.

Roberts, Jennifer, Edward P. Mulvey, Julie Horney, John Lewis, and Michael L. Arter. 2005.

“A Test of Two Methods of Recall for Violent Events.” Journal of Quantitative

Criminology 21(2): 175-193.

Roberts, Jennifer and Julie Horney. 2010. “The Life Event Calendar Method in Criminological

Research.” In A. Piquero & D. Weisburd (Eds.), Handbook of Quantitative Criminology.

New York: Springer.

Sampson, Robert J., John H. Laub, and Christopher Wimer. 2006. “Does Marriage Reduce

Crime? A Counterfactual Approach to Within-Individual Causal Effects.” Criminology

44: 465-508.

Sampson, Robert J. and Janet L. Lauritsen. 1994. “Violent Victimization and Offending:

Individual-, Situational-, and Community-Level Risk Factors.” In: Albert J. Reiss, Jr. and

43

Jeffery A. Roth (eds.) Understanding and Preventing Violence Volume 3. National

Academy Press.

Shover, Neal and David Honaker. 1992. “The Socially Bounded Decision Making of Persistent

Property Offenders.” The Howard Journal 31(4): 276-293.

St. John, Craig and Tamara Heald Moore. 1995. “Fear of Black Strangers.” Social Science

Research 24: 262-280.

Tedeschi, James T. and Richard B. Felson. 1994. Violence, Aggression, and Coercive Actions.

Washington, DC: American Psychological Association.

Tark, Jongyeon and Gary Kleck. 2004. “Resisting Crime: The Effects of Victim Action on the

Outcomes of Crime.” Criminology 42(4): 861-909.

Wells, William and Julie Horney. 2002. “Weapon Effects and Individual Intent to do Harm:

Influences on the Escalation of Violence.” Criminology 40(2): 265-295.

Wright, Richard T. and Scott H. Decker. 1997. “Creating the Illusion of Impending Death:

Armed Robbers in Action.” The HFG Review 2(1): 10-19.

Wright, Richard T., Fiona Brookman, and Trevor Bennett. 2006. “The Foreground Dynamics of

Street Robbery in Britain.” British Journal of Criminology 46: 1-15.

Ziegenhagen, Eduard A. and Dolores Brosnan. 1985. “Victim Responses to Robbery and Crime

Control Policy.” Criminology 23(4): 675-695.

Zimring, Franklin E. 1977. “Determinants of the Death Rate from Robbery: A Detroit Time

Study.” The Journal of Legal Studies 6: 317-332.

Zimring, Franklin E. and James Zuehl. 1986. “Victim Injury and Death in Urban

Robbery: A Chicago Study.” Journal of Legal Studies 15: 1-40

44

APPENDIX A

SELF-CONTROL SCALE

24 items asked of respondents on Grasmick et al. self-control scale:

1. I devote time and effort preparing for the future

2. I act on the spur of the moment without stopping to think

3. I do things that bring me pleasure here and now, even at the cost of some distant goal

4. I base my decisions on what will happen to me in the short run rather than in the long run

5. I try to avoid projects that I know will be difficult

6. When things get complicated, I quit or withdraw

7. I do things in life which are easiest and bring me the most pleasure

8. I avoid difficult tasks that stretch my abilities to the limit

9. I test myself by doing things that are a little risky

10. I take risks just for the fun of it

11. I find it exciting to do things for which I might get in trouble

12. Excitement and adventure are more important to me than security

13. If I have a choice, I will do something physical rather than something mental

14. I feel better when I am on the move than when I am sitting and thinking

15. I’d rather get out and do things than read and think about ideas

16. Compared to other people my age, I have a greater need for physical activity

17. I look out for myself first, even if it means making things difficult for other people

18. I’m not very sympathetic to other people when they’re having problems

19. I don’t care if the things I do upset people

20. I will try to get things I want even when I know it’s causing problems for other people

21. I lose my temper easily

22. When I’m angry at people I feel more like hurting them than talking to them about why I’m

angry

23. When I’m really angry, other people better stay away from me

24. When I have a serious disagreement with someone, it’s usually hard for me to talk calmly

about it without getting upset

45

APPENDIX B

CORRELATION MATRIX

Correlation matrix of all variables:

othweap -0.0640 -0.0104 -0.1406 0.0816 -0.3141 -0.3106 1.0000

gunweap 0.2178 0.0359 0.1657 -0.0601 -0.8049 1.0000

noweap -0.1775 -0.0294 -0.0778 0.0091 1.0000

vresist 0.0465 0.1432 -0.1806 1.0000

commercial -0.0025 -0.1028 1.0000

multoffen 0.0295 1.0000

multvics 1.0000

multvics multof~n commer~l vresist noweap gunweap othweap

othweap -0.0001 -0.0547 -0.1776 0.0368 0.0791 0.0367 -0.0283 0.1107 0.0541

gunweap -0.0200 -0.0060 0.2018 0.0160 0.0459 -0.0958 0.0366 -0.1415 -0.1254

noweap 0.0200 0.0402 -0.0907 -0.0390 -0.0952 0.0727 -0.0189 0.0722 0.0915

vresist 0.0289 0.2167 0.0618 0.1365 0.0406 -0.0772 0.0034 0.1241 0.1053

commercial 0.0413 -0.0952 -0.2013 -0.0436 -0.4061 0.1761 0.4842 -0.0479 -0.0190

multoffen 0.0583 0.1316 0.1049 -0.0597 0.0905 -0.0971 -0.0564 -0.1244 -0.0602

multvics 0.0721 0.1757 0.3126 0.4516 0.0663 0.2765 -0.0441 -0.1999 0.0018

bystand 0.0816 0.0386 -0.0364 0.0781 0.0545 0.1619 -0.0808 -0.0229 1.0000

day 0.0882 0.0605 -0.0639 -0.0482 0.1878 -0.1155 -0.0990 1.0000

public -0.0902 -0.0083 -0.1130 0.0057 -0.5566 0.0646 1.0000

vfemale 0.1237 -0.0176 0.0172 0.2222 -0.1954 1.0000

vnonstrange 0.0783 -0.0274 0.1016 -0.0543 1.0000

vage 0.0847 0.0972 0.0199 1.0000

vblack -0.1231 0.0493 1.0000

abuse 0.2951 1.0000

stress 1.0000

stress abuse vblack vage vnonst~e vfemale public day bystand

othweap 0.2250 -0.1471 -0.0443 -0.1272 0.0137 -0.0087 0.0858 0.0597 -0.0396

gunweap -0.1410 0.2178 0.0432 0.0478 -0.0024 0.0330 -0.1046 0.0222 -0.0259

noweap 0.0004 -0.1257 -0.0155 0.0317 -0.0061 -0.0275 0.0509 -0.0594 0.0506

vresist 0.3632 0.0465 -0.1302 -0.0571 0.0950 -0.0215 -0.0605 0.0791 -0.0334

commercial -0.2855 -0.0966 0.1361 -0.0154 -0.0264 -0.1439 0.0761 0.0130 -0.0117

multoffen 0.1094 -0.0276 -0.2426 -0.0576 0.0879 -0.0200 -0.2143 0.0190 -0.0308

multvics 0.0548 0.1375 -0.0780 0.0119 -0.0927 -0.1768 -0.1073 -0.0464 -0.0664

bystand -0.0085 0.0347 0.1276 0.0559 0.0515 -0.0597 -0.0633 -0.0168 -0.0929

day 0.0417 -0.0415 0.0798 0.1362 -0.1123 -0.1258 0.1230 -0.0371 0.0843

public -0.1103 0.0595 0.0296 -0.2461 0.0817 -0.0400 -0.0689 -0.0757 -0.0043

vfemale -0.1252 -0.0495 0.1699 -0.0710 -0.1160 -0.0390 0.0725 -0.0543 0.0541

vnonstrange 0.1510 -0.0112 -0.1086 0.0527 -0.0320 -0.0650 0.0166 0.0946 -0.0514

vage 0.0237 0.0113 -0.0141 -0.0055 -0.1015 0.0037 -0.0723 -0.0534 -0.0793

vblack 0.1040 0.3978 -0.1188 -0.0002 -0.0204 -0.1536 -0.1156 -0.0474 -0.0008

abuse 0.1063 -0.0224 -0.0081 -0.0025 -0.0249 -0.0540 -0.0903 -0.1724 0.0429

stress -0.0296 -0.2155 0.2282 0.0714 -0.0439 0.0029 -0.0290 0.1093 0.1626

marry -0.1334 0.0754 0.2125 0.1266 0.1027 0.1509 0.1527 0.2586 1.0000

income -0.0450 -0.1336 0.0329 -0.0003 -0.0767 0.0574 0.1832 1.0000

age -0.0695 -0.1353 0.2188 0.1844 -0.0000 -0.0367 1.0000

selfcon 0.0516 -0.2327 -0.0435 -0.0264 0.0334 1.0000

totvio 0.0585 0.2662 -0.0656 0.0275 1.0000

weight -0.0226 0.1249 0.4394 1.0000

c_height -0.1838 -0.1369 1.0000

AfrAmer -0.0914 1.0000

victimin 1.0000

victimin AfrAmer c_height weight totvio selfcon age income marry