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
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 selfreported 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 selfprotective 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 selfprotective 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
5
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 selfprotection 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
6
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
9
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
10
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, timestable 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 “hotheaded,” 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.
13
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 selfcontrol, 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
15
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,
16
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
17
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
18
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.
19
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 fourpoint 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 interitem 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
20
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
21
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.
22
Table 1: Descriptive Statistics
Variable
Dependent variable
Victim injury
Offender characteristics
Race
Age
Married
Height (inches) before centering
Height (inches) centered
Weight (pounds)
Stress
Violent arrests
Abuse
Self-control
Income (see below)
Weapons
Gun
Other weapon
No weapon
Control variables
Multiple victims
Multiple offenders
Black victim
Victim age vulnerability
Knew victim
Female victim
Public incident
Daytime incident
Victim resisted
Bystanders
Offender income
None
$1-$5,000
$5,000-$9,999
$10,000-$14,999
$15,000-$24,000
$25,000-$34,999
$35,000-$49,999
missing
Min
Max
Mean
S.D.
% missing
0
1
0.267
0.443
0
0
17
0
63
-7.436
130
0
0
1
1.46
1
45
1
77
6.564
265
4
15
50
3.38
0.246
25.323
0.092
70.436
0.0
172.856
0.763
1.800
14.379
2.392
0.432
6.863
0.290
2.863
2.862
26.572
0.887
2.267
12.324
0.437
0
0
0
0
0
0
4.62
0
0
0.51
0
0
0
1
1
1
0.446
0.108
0.446
0.498
0.311
0.498
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
0.230
0.697
0.201
0.073
0.486
0.254
0.544
0.390
0.267
.200
0.422
0.461
0.402
0.260
0.501
0.436
0.499
0.490
.443
.401
2.05
0
8.21
8.21
5.13
3.08
0
0
0
0
%
47.18
9.23
10.77
11.28
8.72
4.1
4.1
4.62
23
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 withinperson 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
24
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
25
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
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
1.022†
0.83
Offender characteristics
Abuse
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
26
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
27
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
28
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
Height
No weapon
Model 1
Model 2
Model 3
Model 4
.861†
.858*
1.055
0.936
0.84
0.553
.381†
.602**
.711*
Height weapon interaction
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
29
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
Predicted Probability of Injury
0.6
0.5
0.4
0.3
no weapon
0.2
weapon
0.1
0
Short
Avg
Offender Height
30
Tall
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.
31
Table 4: Logistic Regression of Interaction between Race and Weapons (N=195)
Odds Ratios
Black
Model
1
Model 2
Model 3
Model 4
0.468
0.457
0.198*
0.065**
0.823
0.594
0.331**
6.547*
9.678*
No weapon
Race weapon interaction
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
0.35
0.3
0.25
0.2
Black
0.15
Non-black
0.1
0.05
0
no weapon
weapon
32
DISCUSSION
This study, contrary to expectations, finds that few offender characteristics predict victim
injury during robbery incidents. Specifically, offender violent arrest history, level of selfcontrol, 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.
33
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
34
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
35
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.
36
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
37
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 withinperson 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.
38
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43
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
44
APPENDIX B
CORRELATION MATRIX
Correlation matrix of all variables:
victimin
victimin
AfrAmer
c_height
weight
totvio
selfcon
age
income
marry
stress
abuse
vblack
vage
vnonstrange
vfemale
public
day
bystand
multvics
multoffen
commercial
vresist
noweap
gunweap
othweap
stress
abuse
vblack
vage
vnonstrange
vfemale
public
day
bystand
multvics
multoffen
commercial
vresist
noweap
gunweap
othweap
multvics
multoffen
commercial
vresist
noweap
gunweap
othweap
AfrAmer c_height
weight
totvio
selfcon
age
income
marry
1.0000
0.0275
-0.0264
0.1844
-0.0003
0.1266
0.0714
-0.0025
-0.0002
-0.0055
0.0527
-0.0710
-0.2461
0.1362
0.0559
0.0119
-0.0576
-0.0154
-0.0571
0.0317
0.0478
-0.1272
1.0000
0.0334
-0.0000
-0.0767
0.1027
-0.0439
-0.0249
-0.0204
-0.1015
-0.0320
-0.1160
0.0817
-0.1123
0.0515
-0.0927
0.0879
-0.0264
0.0950
-0.0061
-0.0024
0.0137
1.0000
-0.0367
0.0574
0.1509
0.0029
-0.0540
-0.1536
0.0037
-0.0650
-0.0390
-0.0400
-0.1258
-0.0597
-0.1768
-0.0200
-0.1439
-0.0215
-0.0275
0.0330
-0.0087
1.0000
0.1832
0.1527
-0.0290
-0.0903
-0.1156
-0.0723
0.0166
0.0725
-0.0689
0.1230
-0.0633
-0.1073
-0.2143
0.0761
-0.0605
0.0509
-0.1046
0.0858
1.0000
0.2586
0.1093
-0.1724
-0.0474
-0.0534
0.0946
-0.0543
-0.0757
-0.0371
-0.0168
-0.0464
0.0190
0.0130
0.0791
-0.0594
0.0222
0.0597
1.0000
0.1626
0.0429
-0.0008
-0.0793
-0.0514
0.0541
-0.0043
0.0843
-0.0929
-0.0664
-0.0308
-0.0117
-0.0334
0.0506
-0.0259
-0.0396
vage vnonst~e
vfemale
public
day
bystand
1.0000
-0.0229
-0.1999
-0.1244
-0.0479
0.1241
0.0722
-0.1415
0.1107
1.0000
0.0018
-0.0602
-0.0190
0.1053
0.0915
-0.1254
0.0541
1.0000
-0.0914
-0.1838
-0.0226
0.0585
0.0516
-0.0695
-0.0450
-0.1334
-0.0296
0.1063
0.1040
0.0237
0.1510
-0.1252
-0.1103
0.0417
-0.0085
0.0548
0.1094
-0.2855
0.3632
0.0004
-0.1410
0.2250
1.0000
-0.1369
0.1249
0.2662
-0.2327
-0.1353
-0.1336
0.0754
-0.2155
-0.0224
0.3978
0.0113
-0.0112
-0.0495
0.0595
-0.0415
0.0347
0.1375
-0.0276
-0.0966
0.0465
-0.1257
0.2178
-0.1471
1.0000
0.4394
-0.0656
-0.0435
0.2188
0.0329
0.2125
0.2282
-0.0081
-0.1188
-0.0141
-0.1086
0.1699
0.0296
0.0798
0.1276
-0.0780
-0.2426
0.1361
-0.1302
-0.0155
0.0432
-0.0443
stress
abuse
vblack
1.0000
0.2951
-0.1231
0.0847
0.0783
0.1237
-0.0902
0.0882
0.0816
0.0721
0.0583
0.0413
0.0289
0.0200
-0.0200
-0.0001
1.0000
0.0493
0.0972
-0.0274
-0.0176
-0.0083
0.0605
0.0386
0.1757
0.1316
-0.0952
0.2167
0.0402
-0.0060
-0.0547
1.0000
0.0199
0.1016
0.0172
-0.1130
-0.0639
-0.0364
0.3126
0.1049
-0.2013
0.0618
-0.0907
0.2018
-0.1776
1.0000
-0.0543
0.2222
0.0057
-0.0482
0.0781
0.4516
-0.0597
-0.0436
0.1365
-0.0390
0.0160
0.0368
1.0000
-0.1954
-0.5566
0.1878
0.0545
0.0663
0.0905
-0.4061
0.0406
-0.0952
0.0459
0.0791
1.0000
0.0646
-0.1155
0.1619
0.2765
-0.0971
0.1761
-0.0772
0.0727
-0.0958
0.0367
1.0000
-0.0990
-0.0808
-0.0441
-0.0564
0.4842
0.0034
-0.0189
0.0366
-0.0283
multvics multof~n commer~l
vresist
noweap
gunweap
othweap
1.0000
0.0091
-0.0601
0.0816
1.0000
-0.8049
-0.3141
1.0000
-0.3106
1.0000
1.0000
0.0295
-0.0025
0.0465
-0.1775
0.2178
-0.0640
1.0000
-0.1028
0.1432
-0.0294
0.0359
-0.0104
1.0000
-0.1806
-0.0778
0.1657
-0.1406
45