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Introduction
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References
Basu, K. K. (2015). The Leader's Role in Managing Change:
Five Cases of Technology-Enabled Business Transformation.
Global Business & Organizational Excellence, 34(3), 28-42.
doi:10.1002/joe.21602.
Connelly, B., Dalton, T., Murphy, D., Rosales, D., Sudlow, D.,
& Havelka, D. (2016). Too Much of a Good Thing: User
Leadership at TPAC. Information Systems Education Journal,
14(2), 34-42.
Rouse, M. (2018). Changed Block Tracking. Retrieved from
Techtarget Network:
https://searchvmware.techtarget.com/definition/Changed-Block-
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5

Assessing Similarities and Differences in Self-Control
between Police Officers and Offenders
Ryan C. Meldrum1 & Christopher M. Donner2 & Shawna
Cleary3 &
Andy Hochstetler4 & Matt DeLisi4
Received: 2 August 2019 /Accepted: 21 October 2019 /
Published online: 2 December 2019
# Southern Criminal Justice Association 2019
Abstract
Research provides consistent evidence that non-offenders have
greater self-control than
offenders. While such differences exist across a range of
samples, the ability of
measures of self-control to discriminate between different
groups merits additional
attention. We advance research on this topic by comparing the
self-control of police
officers to offenders. Results indicate police officers score
higher than offenders do on
global self-control. Results also indicate that, when analyzing
differences across the six
dimensions of self-control conceptualized by Gottfredson and
Hirschi (1990), police
officers consistently score lower in impulsivity, self-
centeredness, and anger than
offenders. At the same time, police officers have a greater
preference for physical
activities than offenders do, and the risk-seeking and simple
tasks dimensions are
inconsistently associated with being a police officer relative to
an offender across the

different models estimated. Discussion centers on the
implications of these findings for
theory and for the screening of potential police recruits.
Keywords Self-control . Police officers . Prisoners . Grasmick
et al. (1993) Scale
American Journal of Criminal Justice (2020) 45:167–189
https://doi.org/10.1007/s12103-019-09505-4
* Ryan C. Meldrum
[email protected]
Christopher M. Donner
[email protected]
Shawna Cleary
[email protected]
Andy Hochstetler
[email protected]
Matt DeLisi
[email protected]
Extended author information available on the last page of the
article
http://crossmark.crossref.org/dialog/?doi=10.1007/s12103-019-
09505-4&domain=pdf
mailto:[email protected]
Introduction
Self-control is a core individual-level construct that has
profound implications for behavior
transcending multiple contexts across the life course
(Gottfredson & Hirschi, 2019; Hay &
Meldrum, 2015; Moffitt, Poulton, & Caspi, 2013; Pratt, 2016).
Toward the right tail of the

self-control distribution, reflecting individuals with higher self-
control, there are numerous
behavioral benefits. Persons with greater self-control are, on
average, better students, have
greater work performance, have higher incomes and accumulate
more wealth, and experi-
ence generally low psychopathology evidenced by fewer
psychiatric symptoms, less use of
alcohol, and abstention from drugs and risky behaviors. Those
with greater self-control also
enjoy more cohesive, agreeable relationships, have higher self-
esteem and self-efficacy, and
experience heightened wellbeing and happiness (e.g.,
Baumeister & Alquist, 2009; DeLisi,
2013; Krueger, Caspi, Moffitt, White, & Stouthamer-Loeber,
1996; Moffitt et al., 2011;
Tangney, Baumeister, & Boone, 2004). To illustrate, in a recent
study using decades of data
from a prospective birth cohort, Caspi et al. (2016) found that
persons characterized by high
self-control left little to no adverse societal footprint in terms
of their involvement in social
problems, social burden, and crime.
Toward the left tail of the self-control distribution, reflecting
individuals with lower
self-control, there are numerous behavioral liabilities.
Gottfredson and Hirschi’s (1990)
theoretical construct nicely instantiates low self-control with its
presentation of a person
who is impulsive, risk seeking, self-centered, easily angered,
prefers simple tasks, and
action-oriented. In sharp contrast to their peers with higher self-
control, those with low
self-control impose a disproportionate and substantial societal
burden in terms of their

involvement in unhealthy behaviors and attendant medical costs,
accidents, substance
use, and dysfunctional behaviors (Gottfredson & Hirschi, 2019;
Caspi et al., 2016;
DeLisi, 2011; Hay & Meldrum, 2015; Moffitt et al., 2011).
Moreover, low self-control
is associated with the full spectrum of criminal, externalizing,
and antisocial behaviors
evidenced by multiple meta-analytic reviews (de Ridder,
Lensvelt-Mulders,
Finkenauer, Stok, & Baumeister, 2012; Pratt & Cullen, 2000;
Vazsonyi, Mikuška, &
Kelley, 2017). As Vazsonyi et al. (2017, p. 59) recently stated,
“self-control theory has
established itself as one of the most influential pieces of
theoretical scholarship during
the past century, as it continues to stand up to a plethora of
rigorous empirical tests.”
Against this backdrop of the established importance of self-
control and evidence
supporting the core argument of Gottfredson and Hirschi’s
(1990) general theory of
crime, the current study contributes to the self-control literature
by comparing self-
control levels of offenders to non-offenders (e.g., Turner &
Piquero, 2002). Though this
topic has received considerable attention in the literature, to
date no studies have
evaluated such differences when juxtaposing the self-control
levels of police officers
and offenders, and we believe such a study is worthy of
empirical investigation. As we
will discuss, there are several reasons to suspect that police
officers would, on average,
have substantively higher levels of global self-control than

offenders, though there is
also reason to suspect exceptions may exist for certain
dimensions of self-control
emphasized by Gottfredson and Hirschi (1990). Consequently,
this study will contrib-
ute to the existing literature on the generality and
dimensionality of self-control, while
also providing important implications for police policy and
practice.
In the following sections, we first provide a brief overview of
self-control theory and
its arguments concerning differences in self-control between
offenders and non-
168 American Journal of Criminal Justice (2020) 45:167–189
offenders. Next, we draw attention to the policing literature,
noting the traits that police
agencies desire among officers and the manner in which these
traits overlap with
Gottfredson and Hirschi’s (1990) conceptualization of self-
control. In the process, we
also review research investigating how self-control relates to
officer behavior. After
outlining the goals of the current study and stating our
hypotheses, we present an
empirical analysis that compares the self-control of offenders
against police officers.
Theory and Prior Research
Self-Control Theory

In an effort to provide a general theory of crime, Gottfredson
and Hirschi (1990)
proposed low self-control is “… the individual-level cause of
crime” (p. 232, original
emphasis). Their theory assumes that people make rational
decisions and that crime
does not require any special motivation; it is simply an
expression of one’s natural
predisposition to pursue pleasure and avoid pain (Gottfredson &
Hirschi, 1990). The
authors further contend that those who lack self-control are
more likely to pursue the
immediate pleasure of criminal behavior when presented with an
opportunity to do so.
In conceptualizing self-control, Gottfredson and Hirschi (1990)
define it as “the differen-
tial tendency of people to avoid criminal acts whatever the
circumstances in which they find
themselves” (p. 87). Individuals with low self-control tend to
engage in crime and behaviors
analogous to crime because they lack the capacity to consider
the long-term consequences of
their behavior (see also Gottfredson & Hirschi, 2019). They go
on to posit that crime and its
analogous acts are immediately gratifying, simple, and exciting,
and they presume that
people involved in these types of behaviors will exhibit similar
characteristics. Specifically,
they argue that individuals lacking self-control (1) have a here-
and-now orientation, so that
they seek immediate gratification; (2) prefer easy and simple
endeavors and tend to dislike
activities that require diligence, tenacity, and persistence; (3)
engage in risky and exciting,
rather than cautious and cognitive, behaviors; (4) are quick-

tempered; (5) are attracted to
endeavors that entail little skill or planning; and (6) are unkind,
insensitive, and self-centered.
Gottfredson and Hirschi (1990) further assert that, “There is
considerable tendency for these
traits to come together in the same people, …it seems
reasonable to consider them as
comprising a stable construct useful in the explanation of
crime” (pp. 90–91).
Gottfredson and Hirschi’s (1990) theoretical premise advances
the hypothesis that
offenders should have lower self-control relative to non-
offenders (pp. 130–131). Prior
research has consistently supported this assertion, and these
self-control differences are
across a range of different samples (e.g., Beaver, DeLisi, Mears,
& Stewart, 2009; Carroll
et al., 2006; Turner & Piquero, 2002; Winfree Jr, Taylor, He, &
Esbensen, 2006).1 For
example, Turner and Piquero (2002) compared self-control
levels of 393 offenders and 120
1 In previous research, the determination of differentiating
‘offenders’ from ‘non-offenders’ has been based, for the
most part, on the participant’s own self-reported involvement in
crime and delinquency. For example, Turner and
Piquero (2002), using NLSY data of adolescents, categorized
‘offenders’ as those who self-reported engaging in at
least one of 14 delinquency items within the preceding 3 years.
Similarly, Winfree Jr et al. (2006), using adolescent
self-report data from a national evaluation of the GREAT
program, classified ‘offenders’ as those who self-reported
engaging in at least one of 17 delinquency items within the
preceding year.

American Journal of Criminal Justice (2020) 45:167–189 169
non-offenders over seven waves of data collection. Across the
first four waves, using a
behavioral measure of self-control, they found significant mean-
differences in self-control in
three of the four waves. In each of these, non-offenders had
statistically lower means, which
indicated higher self-control. Across the last three waves, using
an attitudinal measure of
self-control, they found significant mean-differences in self-
control in all three waves.
Again, non-offenders had statistically lower means, which
indicated higher self-control.
In a similar manner, Winfree Jr et al. (2006) examined self-
control differences
among a sample of 2921 offenders and 1650 non-offenders. To
measure self-control,
they utilized the four impulsivity and four risk seeking items
from the Grasmick et al.
(1993) scale to create an impulsivity scale, a risk seeking scale,
and an eight-item global
self-control scale. Their results demonstrated significant mean-
differences across all
three self-control measures, with non-offenders consistently
yielding higher self-con-
trol. Moreover, results from a multivariate regression model
indicated that being in the
offender group was significantly related to higher impulsivity,
higher risk seeking, and
lower global self-control. While such findings are illuminating,
the generality of self-
control can be further demonstrated by comparing offenders not

simply to a general
population sample of non-offenders but to a sample of
individuals that should be (but
not always are) high in self-control: police officers.
Policing and Self-Control
Police officers interact with the public on a daily basis, and, as
law enforcers and
peacekeepers, they have an obligation to “serve and protect.”
Whether they are
attempting to diffuse a domestic violence situation, conducting
a traffic stop, rendering
first aid at an accident scene, assisting a disabled motorist, or
maintaining order at a
civil protest, they are entrusted by society to behave with
steadfast professionalism and
integrity. The nature of the profession, including regular
encounters with rude, defiant,
and sometimes violent individuals, does not make this
commitment easy. Further, with
the job comes a tremendous amount of authority and discretion
(e.g., Bittner, 1970;
Brooks, 1993; Skogan & Frydl, 2004; Reiss, 1971; Walker,
1993). Officers have the
legally prescribed power to deprive a citizen of his/her freedom
of movement, and they
can use legally appropriate physical force to do so. Within this
context, officers have to
‘wear many hats,’ and the job frequently places them in
stressful situations where quick
decisions need to be made. Moreover, they, particularly patrol
officers, often perform
job duties outside of direct supervision.
Given the uniqueness of the policing profession, it is easy to

understand why there
are certain personality traits/characteristics that police officers
are expected to
possess—and that agencies try to identify in their applicants
through the hiring
processes. In line with Gottfredson and Hirschi’s (1990)
conceptualization of self-
control, both scholarly and professional sources emphasize that
officers should be
thoughtful and deliberate (rather than impulsive), courteous and
caring (rather than
self-centered), and slow to anger (rather than having a volatile
temper) (e.g., Capps,
2014; Morison, 2017; Ohio Law Enforcement Foundation,
2001).
Police departments across the United States are in general
agreement that self-
control—and/or its underlying elements—is a desirable
characteristic of police officers.
For example, Larry Capps, a former assistant chief of the
Missouri City (TX) Police
Department, identified having a controlled temper as a key trait
that police officers
should possess (Capps, 2014). He suggests that a controlled
temper involves self-
control (or self-discipline), and that it requires an abundance of
competence, confi-
dence, and emotional maturity. This is particularly important
when officers encounter
citizens who have lost their tempers, as trying to resolve a

volatile situation becomes
exponentially more problematic if officers respond by losing
their own temper.
Other examples also illustrate the centrality of different
elements of self-control in
the policing profession. According to California’s Commission
on Peace Officer
Standards and Training (2014), there are certain behavioral
traits that departments
should evaluate when selecting/hiring applicants for law
enforcement positions.
Among these are impulse/anger control, even temper, stress
tolerance and recovery,
thoroughness, attention to detail, situational/problem analysis,
and decision-making/
judgment. Similarly, the Ohio Law Enforcement Foundation
(2001) identified self-
control and discipline as key characteristics that departments
should consider during
their hiring process. In their own recruiting efforts, the
Bainbridge Island (WA) Police
Department (Bainbridge Island Police Department, 2012)
recognized being analytical,
having a calming demeanor, having compassion and empathy,
being detail-oriented,
being emotionally resilient, having frustration tolerance, being
non-impulsive, being
patient, and having self-control as key characteristics that are
sought in their applicants.
More recently, a forum of approximately 50 law enforcement
practitioners from
around the country convened to discuss challenges and
strategies for twenty-first
century law enforcement hiring practices. Recruiters selected

the practitioners for this
forum, in large part, because their agencies had implemented
innovative hiring pro-
grams that have shown promise in their communities and that
may be useful models for
other jurisdictions (Morison, 2017). The forum identified seven
key traits of the “21st
century police officer.” Among these were empathy, self-
control, and problem-solving
skills. Moreover, community residents advocate for these same
qualities. According to
research from Whetstone, Reed, and Turner (2006), community
members expect a high
degree of competency from police officers, and their findings
revealed that community
members expect officers to possess—among other qualities—
self-discipline, patience,
and attention to detail.
In essence, self-control and several of its underlying dimensions
articulated by
Gottfredson and Hirschi (1990) are key traits that police
administrators (and the
community) look for in their recruits/officers. To corroborate
this assertion, policing
scholars have identified aspects of self-control in several
studies as predictors of
“successful” officers. For example, research from Hargrave and
Berner (1984) found
that police supervisors in California generally agreed effective
officers were, among
other things, emotionally controlled. Similarly, Hogue, Black,
and Sigler’s (1994)
research of Alabama police officers identified several preferred
characteristics, such

as emotional stability, patience, and being slow to anger. Using
the NEO Personality
Inventory (NEO-PI), Detrick and Chibnall (2006) found that the
best entry-level
officers (as rated by Field Training Officers) were low in
neuroticism and high in
conscientiousness, the latter being a concept that correlates
highly with self-control
(e.g., De Vries & Van Gelder, 2013; Jones, 2017). Looking
deeper into the subscales,
however, revealed more nuanced results. The best officers were
low in the angry-
hostility subscale, but were on par with their “average officer”
counterparts on the
impulsiveness subscale (both subscales under neuroticism). The
best officers also rated
higher on the self-discipline subscale, but were on par with
their counterparts on the
deliberation subscale (both subscales under conscientiousness).
Interestingly, the best
officers rated higher in extraversion and had higher scores on
the excitement seeking
subscale. Overall, the sample of training officers concluded that
the best officers were
emotionally controlled, slow to anger, highly conscientious, and
disciplined.
Related research also links low self-control and related
constructs to negative police
behavior. For example, Hiatt and Hargrave (1988) demonstrated
officers that departments

disciplined for misconduct scored significantly higher on the
Minnesota Multiphasic Per-
sonality Inventory (MMPI) hypomania scale, indicating that
these officers had higher levels
of disinhibition and lack of restraint. Additionally, Hargrave
and Hiatt (1989) found that
problem officers had significantly lower scores on the self-
control subscale of the California
Personality Inventory (CPI). Likewise, Girodo (1991) found that
high extraversion, high
neuroticism, and disinhibition were significant NEO-PI
predictors of on-the-job misconduct
among a sample of federal undercover drug agents. Sarchione,
Cuttler, Muchinsky, and
Nelson-Gray’s (1998) research further identified that officers
who had been formally
disciplined for misconduct scored significantly lower on three
subscales of the CPI (respon-
sibility, socialization, and self-control). While not directly
assessing the effects of self-control
on police misconduct, Pogarsky and Piquero (2004) used the
impulsivity items from the
Grasmick et al. (1993) scale to assess whether impulsivity
mediated the relationship between
deterrence and police misconduct, finding that impulsivity had a
direct effect on misconduct.
Recent findings also reveal that low self-control predicts
officers’ citizen complaints (behav-
ioral self-control measure; Donner & Jennings, 2014) and
officers’self-reported engagement
in misconduct (Grasmick et al., 1993 measure; Donner, Fridell,
& Jennings, 2016).
The Current Study
Past research comparing self-control levels of offenders to non-

offenders finds non-
offenders possess greater self-control. Likewise, the policing
literature consistently
identifies self-control—and several of its elements—as traits
that police officers should
embody. Given the unique position that police officers occupy
and the legally pre-
scribed authority, discretion, and tools (e.g., firearms) that
accompany the profession,
high self-control appears to be a natural prerequisite. Taken
together, these observations
lead to the conclusion that police officers, on average, should
possess significantly
higher levels of self-control than offenders.
To our knowledge, no study has made this direct comparison.
While it might seem
obvious to expect that police officers would have more self-
control than offenders
would, we view this gap in the literature as something worthy of
empirical investigation
for several reasons. First, comparing the self-control of police
officers to offenders
offers a unique test of the ability of measures of self-control to
discriminate between
individuals who should, according to Gottfredson and Hirschi
(1990, pp. 130-131),
occupy opposing ends of the self-control distribution. Second, if
minimal differences in
self-control between police officers and offenders are observed,
this would potentially
raise important concerns about existing screening procedures
used in the recruitment
processes of potential officers. Third, being able to glean
further insight into the self-
control of police officers is of great importance, particularly at

a time of increased
public scrutiny of officer behavior and concerns over officer
misbehavior.
Accordingly, the current study compares the self-control levels
of a sample of police
officers to offenders by combining multiple existing datasets,
each of which includes the
Grasmick et al. (1993) self-control scale. Based on theory and
prior research (e.g.,
Gottfredson & Hirschi, 1990; Turner & Piquero, 2002; Winfree
Jr et al., 2006), the primary
hypothesis tested is that police officers will score, on average,
substantively higher on global
self-control relative to offenders. In addition, our review of the
policing literature consistently
identifies that police officers should be low in impulsivity, slow
to anger, considerate (i.e.,
low in self-centeredness), and able to navigate a complex and
stressful job (i.e., low in
preference for simple tasks). Yet, in considering the other two
dimensions of self-control
(risk seeking, physically oriented) emphasized by Gottfredson
and Hirschi (1990), the police
recruitment literature seemingly places less emphasis on these
two aspects. This may
partially reflect the fact that the nature of police work involves
an acceptance of risk (e.g.,
Herbert, 1998; Maskaly & Donner, 2015; Skolnick & Fyfe,
1993; Van Maanen, 1975) and
an expectation of physicality (e.g., Anderson, Plecas, & Segger,

2001; Bissett, Bissett, &
Snell, 2012; Hunter, Bamman, Wetzstein, & Hilyer, 1999;
Shephard & Bonneau, 2003).
Police officers must sometimes run towards danger: they pursue
fleeing suspects, rescue
citizens from burning cars and buildings, and use hand-to-hand
combat to disarm suspects
and intervene in fights. Further, officers must be able to react
instantly to whatever crisis is at
hand—this requires a certain level of physical fitness. In fact,
evaluations of police recruits
include physical fitness, and officers must increase physical
fitness through training and,
while in police academies, they learn strategies for dealing with
risks inherent to police work
(e.g., Bureau of Justice Statistics, 2016).
Given these realities, it is possible—and perhaps even likely—
that differences in levels of
risk seeking and being physically oriented between police
officers and offenders could be
minimal, even as significant differences are observed for global
self-control and its other
four dimensions. Thus, our secondary hypothesis is that, when
comparing self-control levels
of police officers to offenders at the dimension-level, we expect
offenders will score higher
than police officers will in impulsivity, simple tasks, self-
centeredness, and anger, but that
there will be minimal or perhaps no differences in scores
between officers and offenders for
the risk-seeking and physical-oriented dimensions.
Method
Participants and Procedure

To examine similarities and differences in the self-control
levels of police officers and
offenders, we combined four different datasets. Two of these
datasets provide
information on offenders, while the other two provide
information on police officers.
Below, we briefly describe these four different data sources.2
Readers interested in
2 Authors of the current study played a principal role in the
design and collection of the data for each of the four data
sources. With regard to the selection of these four specific data
sources, they were included in the current study
because they each contained the Grasmick et al. (1993) self-
control scale. To our knowledge no other data sources
outside of the two we utilize in the current study exist that
include data on the self-control levels of police officers for
each of the six dimensions included in the Grasmick et al.
(1993) scale. Similarly, very few datasets on prisoners exist
that include the Grasmick et al. (1993) scale other than the two
data sources used in the current study (e.g., Mitchell &
MacKenzie, 2006). Existing relationships among the authors of
the current study facilitated the utilization of the two
offender datasets and two police officer datasets.
more detailed information concerning the methodologies
employed to produce each of
the four datasets are referred to existing studies cited in the
below descriptions.
To create our sample of offenders, we first made use of survey

data collected in 2001
from male prison parolees located at four work-release facilities
located in a Midwest-
ern state.3 All of the participants had been released from a state
prison within the prior
six months and were serving conditional parole sentences. To
collect the survey data,
brochures were first distributed at all four work-release
facilities letting potential
participants know researchers were administering questionnaires
in small groups. It
was made clear to all individuals that participation was
voluntary, confidential, and that
they had the right to refuse to answer any of the questions on
the survey. Of the 480
parolees who were invited, 208 participated, yielding a
participation rate of 43%.
Research staff were present when surveys were administered in
small groups (from
September through December 2001) in order to answer
questions and provide clarifi-
cation about items on the survey. Compensation in the amount
of $30 was provided to
participants. Of the parolees who participated in the original
study, 29% were incar-
cerated for violent crimes (murder, rape, assault, robbery), 22%
were incarcerated for
drug crimes (possession and selling), and the remaining 49%
were incarcerated for a
variety of other offense types (burglary, motor vehicle theft,
fraud, etc.). For additional
information about this data source, see DeLisi, Hochstetler, &
Murphy (2003).
Next, we utilized survey data collected in 2000–2001 from 295
male prison inmates

located at two prison facilities (one medium security and the
other a facility that housed
both medium and maximum-security inmates) in Oklahoma.
Three separate random
samples were drawn at the time of the original study: (1)
inmates convicted of sex
offenses participating in a sex offender treatment program, (2)
inmates convicted of a
sex offense not participating in a sex offender treatment
program, and (3) inmates with
no record of having committed a sex offense. After random
selection, potential
participants were informed by memoranda they were chosen to
participate in a study
about the social, economic, and criminal history backgrounds of
inmates.4 All individ-
uals were provided a cover letter attached to a survey
questionnaire outlining informed
consent, and it was made clear that participation was voluntary
and that no compen-
sation was being provided for participation. The overall
participation rate across the
three inmate groups was 40%. Of the 295 participants, 68%
were incarcerated for a sex-
related offense (top three by frequency: rape, lewd molestation,
sodomy) and the
remaining 32% were incarcerated for crimes other than sex
offenses (top three by
frequency: 1st degree murder, armed robbery, felony drug
possession). For additional
information about this data source, see Cleary (2014). After
combining the information
for two offender data sources, verifying the presence of
common indicators of self-
control and demographic characteristics (described below), and
removing cases with

missing data, complete data on each of the items used in the
current study was available
for 457 of the 503 male prison inmates and parolees.
To create our sample of police officers, we first made use of
survey data collected
via an online platform—Qualtrics—in 2012 from a
geographically diverse, multi-
3 Following the IRB protocols of the original study, the name of
the state is blinded.
4 All memoranda were generated by the individual prisons,
which in addition took on the responsibility for
scheduling data collection within each prison (the principal
researcher and assistants were present for all data
collection). Questionnaires were self-administered in the
visitation rooms of the two prison facilities.
agency sample of 101 first-line police supervisors in the United
States who were partici-
pating in the National Police Research Platform. The three
organizations consisted of one
large police department in the West, one large police
department in the Midwest, and one
statewide training academy in the South that trains police
employees from multiple depart-
ments within the same state. Following IRB approval and
securing agency cooperation,
initial exposure to the larger research project was provided by
research team members during
the first week of new supervisory training. Subsequently, email
solicitations were sent to the

subset of supervisor subjects who had at least 0.5 years of
experience in the role of first-line
supervisor. At the time of survey solicitation, the respondents
had been participating with the
Platform project between 0.5 and 3.5 years. Of the 475
individuals who were contacted, 101
police supervisors fully completed the survey instrument, which
represents a participation
rate of 21%.5 This data source serves as the basis for several
published studies (Donner,
Fridell, & Jennings, 2016).
Next, we used survey data collected via an online platform—
Opinio—in December of
2018 and January of 2019 from non-supervisory police officers
(e.g., patrol, detectives) from
a medium-sized police department located in a Midwestern
state. After obtaining IRB
approval for the larger study and securing cooperation from the
police department’s
administration, a member of the research team recruited
participants during seven roll call
briefings over the period of four days. Emails were then sent to
249 non-supervisory officers
with an invitation to voluntarily complete an online survey. A
$10 donation was offered to a
police officer memorial fund for every completed survey. Of the
249 officers invited to
participate, 113 completed the online survey, yielding a
participation rate of 45%.
Before proceeding to the descriptions of the measures for the
current investigation, we
should specify that because the sample of prison inmates and
parolees consisted entirely of
males, the decision was made to limit the analysis of police

officers to males as well. After
the removal of female police officers from the data, verification
of the presence of common
indicators of self-control and demographic characteristics for
the combined sample of male
police officers (which had to match the indicators for the
sample of offenders), and removal
of cases with missing data, complete information on each of the
survey items used in the
current investigation was available for 174 (81%) of the 214
police officers. Overall, we
analyzed data on a sample of 631 offenders and officers.
Measures
Self-Control Each of the four data sources contained the self-
control items developed
by Grasmick et al. (1993). The Grasmick scale, which taps into
the six dimensions of
low self-control as outlined by Gottfredson and Hirschi (1990),
is a widely used
measure of low self-control within the criminological literature
(e.g., DeLisi,
Hochstetler, & Murphy, 2003; Pratt & Cullen, 2000; Vazsonyi
et al., 2017). Of the
24 items originally appearing as part of this scale, 22 were
common across each of the
four data sources. Of the two items that were not included
across each data source, one
pertained to the impulsivity dimension (Item #1: “I often act on
the spur of the moment
without stopping to think”), and the second pertained to the
self-centeredness dimen-
sion (Item #3: “If something I do upsets people, it’s their
problem, not mine”). In

5 Low-to-moderate response rates are not uncommon in policing
research, especially given the online
methodology and the sensitive nature of some of the survey
items (e.g., Bishopp & Boots, 2014; Gould, 2000).
addition, while each of the items measuring self-control among
police officers was
based on a four-category response set ranging from “strongly
disagree” (= 1) to
“strongly agree” (= 4), only one of the two data sources for the
sample of offenders
was based on this four-category response set. The items from
the other data source for
offenders included a fifth “neutral” option in the middle of the
response scale, making
the maximum value equal to 5 (“strongly agree”).
To account for the difference in response options across data
sources, we first standard-
ized responses to each of the 22 individual items. We then
reverse-coded each item so that
higher values indicate greater self-control. Following this, we
created a 22-item measure of
global self-control by averaging together all of the items (α =
0.89). In addition, to test our
secondary hypothesis, we created four-item averages for four of
the six dimensions of self-
control (simple-tasks [α = 0.82], risk-seeking [α = 0.79],
physical activities [α = 0.72],
anger [α = 0.85]) and three-item averages for the two
dimensions of self-control where an
item was dropped because it was not common across all four

data sources (impulsivity [α =
0.73], self-centeredness [α = 0.76]). Each of the seven multi-
item measures (i.e., global self-
control and the six subscales) were then standardized so that
each measure had a mean of
zero and a standard deviation of one. Descriptive statistics for
the global measure of self-
control, the six measures representing each of the dimensions of
self-control, and each of the
other measures described below are reported in Table 1.
Police Officers and Offenders Twenty-eight percent of the
sample is comprised of
police officers, while 72% is comprised of offenders. For the
analysis, we created a
dichotomized variable labeled police officer to distinguish
officers from offenders in the
data; police officers were assigned a value of 1 and offenders
were assigned a value of
0. Thus, the key distinction we focus on in our analyses
(outlined below) is whether this
dichotomy is associated with differences, both substantively and
statistically speaking,
in global self-control and in each of the six dimensions.
Demographics For the portion of the analyses that involve
multivariate modeling, we
include three covariates capturing the age, race, and education
level of each participant;
recall sex is a constant in the sample as all participants are
male. Age is measured in whole
years; the youngest participant in the sample is 18 and the
oldest participant is 76. The mean
age for the police officers in the sample (μ = 39.3) is similar to
the mean age for the offenders
(μ = 38.2) based on a t-value of 1.29 (p = 0.20). Race is

measured dichotomously with the
variable labeled White (= 1; non-White = 0). More than three-
quarters of the police officers
in the sample (82.2%) are White, while slightly less than two-
thirds of the offenders (64.6%)
are White (t = 4.35, p < 001). Education is also measured
dichotomously with the variable
labeled as More Than High School (= 1; high-school
degree/GED or less = 0); participants
who reported taking at least some college-level classes were
coded as 1. As would be
expected, nearly all of the police officers in the sample report at
least some education beyond
a high-school degree (94.3%), while only a little more than one-
third of offenders report any
education beyond high-school (36.5%); the difference between
the two samples based on a
t-test is statistically significant at p < .001.6
6 Both race and education were originally measured as
categorical variables across each of the four datasets.
Yet, the coding scheme differed between the datasets. Thus, in
order to create uniform measures for race and
education when combining the four datasets, the dichotomous
measurement approach was employed.
Analytic Plan
The analyses unfolded in two stages. In the first stage, we
conducted a series of t-tests
to examine mean differences in levels of self-control between
police officers and

offenders.7 Specifically, t-tests were conducted for the global
measure of self-control
as well as the six individual dimensions of self-control. The
results of these t-tests are
accompanied by histograms, which overlay the global self-
control (and its individual
dimensions) distribution of scores for the police officers on top
of the distribution of
scores for the offenders. Together, the t-tests and the overlaid
histograms provide an
initial test of our hypotheses and offer insight into similarities
and differences between
police officers and offenders with regard to self-control.
In the second stage, we estimated a series of OLS regression
models to examine the
extent to which being a police officer, relative to an offender, is
associated with greater
self-control when accounting for age, race, and education. In
these models, the self-
control measures are modeled as the dependent variables, the
dichotomous variable
police officer is modeled at the independent variable, and age,
race, and education level
7 Prior to this, we examined whether mean differences in global
self-control existed between (1) the two
separate samples of police officers and (2) the two separate
samples of offenders. A t-test for mean differences
in global self-control between the two samples of police officers
indicated the sample of police supervisors
scored slightly lower (μ = 0.27) than the non-supervisory
sample of officers (μ = 0.54) based on a t-value of
−2.80 (p < .01). Likewise, a t-test for mean differences in
global self-control between the two samples of
offenders indicated the sample of offenders from Oklahoma

scored lower in self-control (μ = −0.38) than the
other sample of offenders (μ = 0.13) based on a t-value of −5.21
(p < .001). Given the exploratory nature of
this study, we elected to pool together the two officer samples
and the two offender samples for the analysis.
Because both the officer and offenders are drawn from larger
populations of each, we have little reason to
believe that any one of the four samples included in our
analyses represents an extreme outlier. The fact that
the visual distribution of scores for global self-control
(presented in the results section) provides little evidence
of a bimodal distribution for both the officer and offender
sample reinforces this belief.
Table 1 Descriptive Statistics (N = 631)
Variables % Mean SD Min. Max. Skew Kurtosis
Global Self-Controla – 0.00 1.00 −3.58 3.22 −0.17 3.03
Impulsivitya – 0.00 1.00 −2.85 2.44 −0.36 2.62
Simple Tasksa – 0.00 1.00 −3.07 2.56 −0.42 2.95
Risk-Seekinga – 0.00 1.00 −2.70 2.97 −0.01 2.83
Physical Activitiesa – 0.00 1.00 −2.27 3.59 0.09 3.10
Self-Centerednessa – 0.00 1.00 −3.13 2.45 −0.37 2.67
Angera – 0.00 1.00 −2.51 2.42 −0.30 2.66
Police Officerb 27.6% – – 0 1
Age – 38.48 10.01 18 76
Whitec 69.4% – – 0 1
More Than High Schoold 52.5% – – 0 1
SD = standard deviation; a higher scores indicate greater self-
control; b reference is offender; c reference is
non-White; d reference is high school diploma/GED or less

are modeled as covariates. To be clear, these OLS models were
not estimated in order to
claim that being a police officer, relative to an offender, causes
someone to be higher or
lower in self-control. Rather, these models identify the strength
of the association
between officer/offender group membership and self-control
when holding constant
age, race, and education level. We focus on the standardized
effect of the police officer
variable in each model to identify the relative magnitude of the
differences in self-
control between police officers and offenders and to further
assess our hypothesis that
larger differences will be observed for certain dimensions of
self-control (i.e., impul-
sivity, simple tasks, self-centeredness, anger) than other
dimensions (i.e., risk-seeking,
physical activities). Following the presentation of these models,
we present the results
of a supplementary analysis.
Results
Bivariate Analyses
Figure 1 displays the results of the t-tests and the overlaid
histograms for police officer
and offender self-control (recall higher values indicate greater
self-control).8 The top
portion of Fig. 1 displays the results for the global measure of

self-control. What is
clear is that the distribution of scores for police officer self-
control generally falls on the
right side of the range of values, whereas the distribution of
scores for offender self-
control is more centered along the range of values. This visual
difference between the
distributions of scores is reinforced by the difference in the
mean values between the
two groups: police officers have a mean value of 0.40, offenders
have a mean value of
−0.15, and this difference is statistically significant based on a
t-value of 6.42 (p < .001;
Cohen’s d = 0.57). Thus, we find preliminary evidence in
support of our first hypothesis
that police officers score, on average, higher on global self-
control relative to offenders.
The two additional rows of histograms in Fig. 1 provide the
results as they pertain to
the six dimensions of (low) self-control reflected in the
Grasmick et al. (1993) scale.
What is evident from these six histograms, and the
accompanying t-test information, is
that comparing global self-control between police officers and
offenders masks the fact
that, for particular dimensions of self-control, average
differences between the two
groups are much larger than for other dimensions. In partial
support of our secondary
hypothesis, there are small to moderate differences between
police officers and of-
fenders for the dimensions of impulsivity (Cohen’s d = 0.58),
simple tasks (Cohen’s
d = 0.37), self-centeredness (Cohen’s d = 0.60), and anger
(Cohen’s d = 0.74). For each

of these four dimensions, the t-values are large and statistically
significant, and the
absolute difference in means between the two groups is a value
of at least 0.35.
When focusing on the two dimensions of self-control we
hypothesized to be less
likely to differentiate police officers from offenders, we find
little support. First, for the
8 As a further consideration, it should be pointed out that the
visual overlay of the histograms only represents
the relative distribution of scores for the two samples (i.e.,
offenders and officers). It does not take into account
the fact that the distribution of scores for the offenders is based
on a larger sample size (N = 457) than that of
the officers (N = 174). This should be kept in mind when
considering what the distribution of scores would
look like for the combined sample of offenders and officers that
is examined in the subsequent multivariate
regression models.
risk-seeking dimension, the mean value for police officers is
0.26, which can be
compared to the mean value for offenders of −0.10. The t-value
of 4.01 (p < .001)
and the Cohen’s d value of 0.36 make evident that police
officers are less prone to seek
out risks than offenders. Second, for the physical activities
dimension, the mean value
for officers is −0.17, which is lower than the mean value for
offenders of 0.06 (t-

value = −2.62, p < .01, Cohen’s d = 0.23), indicating that the
police officers have a
slightly stronger preference for physical activities relative to
the offenders.
Multivariate Analyses
We next turned our attention to the OLS regression models.
Table 2 displays the results
predicting the global measure of self-control (Model 1) and
each of the six dimensions
of self-control (Model 2 through Model 7). Beginning with
Model 1, police officers
score 0.38 points higher (p < .001) on global self-control
relative to offenders when
holding constant age, race, and education level. Stated in terms
of standardized effects,
police officers score 0.17 standard deviations higher on global
self-control relative to
offenders. Thus, Model 1 provides additional support for our
first hypothesis. In
addition, Model 1 indicates that individuals with anything more
than a high-school
education score higher on global self-control (β = 0.16, p <
.001).
Model 2 through Model 7 provide estimates when each of the
six separate dimen-
sions of self-control is modeled as a dependent variable in place
of the global measure
of self-control. Overall, the pattern of results is in many ways
similar to the pattern that
emerged from the t-tests and histograms. First, the largest
differences in self-control
Fig. 1 Overlaid histograms: Offender and officer self-control (N

= 631)
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between police officers and offenders are observed for (low)
anger (β = 0.26, Model 7),
(low) self-centeredness (β = 0.25, Model 6), (low) impulsivity
(β = 0.20, Model 2), and
(low) risk seeking (β = 0.16, Model 4), where police officers
score higher than
offenders do on each of those four dimensions. Second, Model 5
indicates police
officers have a lower preference for mental over physical
activities than offenders (β =
−0.18, Model 7). Unlike the results of the bivariate analyses,
however, Model 3
indicates there is no statistical difference between police
officers and offenders in the
preference for simple tasks9 (β = 0.03, p = 0.51).10
Supplementary Analyses
As a means to further consider exactly which dimensions of
self-control are more
strongly associated with being a police officer relative to an
offender, we estimated a
logistic regression model in which each of the six self-control
subscales and the

demographic variables were included as predictors of being a
police officer (relative
to being an offender).11 The results of this model are presented
in Table 3. As shown,
when considering each of the six dimensions of self-control
simultaneously, being
lower in impulsivity (OR = 1.79), lower in self-centeredness
(OR = 1.65), and lower in
anger (OR = 1.58) is positively associated with the likelihood of
being a police officer
(relative to an offender). Conversely, being lower in simple
tasks and lower in a
preference for physical activities is negatively associated with
the likelihood of being
a police officer (simple tasks OR = 0.60; physical activities OR
= 0.54). There is no
statistically significant association between risk seeking and the
likelihood of being a
police officer. Lastly, being White (OR = 2.72) and having more
than a high school
education (OR = 34.03) is positively associated with the
likelihood of being a police
officer as opposed to an offender. Overall, the results of this
supplementary model are
consistent with the results of the OLS models for four of the six
self-control subscales
(impulsivity, physical activities, self-centeredness, and anger)
but inconsistent with
regard to the simple tasks and risk-seeking subscales.
Discussion
In this study, we compared the self-control of a sample of
current and former prisoners
to a sample of police officers in order to advance research on
the ability of measures of

9 Education appears to be mediating the effect, as when
education is removed from the model, the standard-
ized effect of being a police officer (β = 0.16) is statistically
significant (p < .001).
10 At the suggestion of an anonymous reviewer, we also
examined interactive effects between race and status
(Officer vs. Offender) for each of the seven models presented in
Table 2. After applying a bonferroni
correction for multiple testing (.05/7), only in the model for not
preferring physical activities (i.e. preferring
mental activities) did the interaction between race and status
achieve statistical significance. Specifically, the
interaction indicated that while police officers in general are
more likely to prefer physical over mental
activities than offenders, this association is particularly evident
among non-white participants. We also
examined potential interactive effects between education and
status (Officer vs. Offender) for each of the
models, finding no evidence of a moderating effect.
11 We first estimated the model using OLS regression to obtain
variance inflation factors (VIFs) and tolerance
statistics for each of the predictors. There was no evidence of
problematic multicollinearity among the
predictors; all VIFs were below 2.0 (max VIF = 1.87) and all
tolerance statistics were above 0.40 (min = 0.53).
self-control to differentiate offenders from non-offenders. In
support of our primary
hypothesis, police officers scored higher on a global measure of
self-control than
offenders. This finding, which was consistent throughout both

bivariate and multivar-
iate analyses, would come as no surprise to Gottfredson and
Hirschi (1990; 2019), as
well as authors of policing literature who maintain that police
officers should have high
levels of self-control (Hargrave & Berner, 1984; Hogue et al.,
1994; Detrick &
Chibnall, 2006). Yet, it is also worth pointing out that we
observed a considerable
degree of overlap in the distribution of scores for police officers
and offenders on global
self-control; there were many instances where police officers
score substantively lower
in self-control than offenders (and where offenders score
substantively higher in self-
control than police officers). Thus, it is likely that a number of
other factors differentiate
police officers from offenders in addition to self-control.
Our secondary hypothesis was that police officers and offenders
would differ on four
of the dimensions of self-control (impulsivity, simple tasks,
self-centeredness, and
anger), but exhibit few or perhaps no differences on the
physical activity and risk-
seeking dimensions. Through bivariate analyses, we observed
differences between
police officers and offenders for the impulsivity, simple tasks,
self-centeredness, and
anger dimensions, as hypothesized. Yet, contrary to
expectations, officers scored lower
in risk seeking and higher in preferences for physical activities
than offenders. Within
the OLS models, substantively similar patterns were observed
for five of the six
dimensions of self-control, with the notable exception being

that officers were no more
or less likely to prefer simple tasks relative to offenders.
At the same time, a slightly different pattern of results emerged
from the supple-
mentary logistic regression model, which offered somewhat
greater support for our
secondary hypothesis. Specifically, being low in impulsivity,
low in self-centeredness,
and low in anger was associated with the likelihood of being an
officer as opposed to an
offender, while risk seeking did not differentiate police officers
from offenders. While
these results are consistent with what we hypothesized, we did
not anticipate that
Table 3 Logistic regression of being a police officer relative to
an offender on self-control subscales and
demographics (N = 631)
Variables b SE OR
Age −.01 .01 .99
White 1.00** .29 2.72
More Than H. S. 3.53*** .38 34.03
Impulsivity .58** .17 1.79
Simple Tasks −.51** .16 .60
Risk Seeking .07 .15 1.08
Physical Activities −.62*** .13 .54
Self-Centeredness .50** .17 1.65
Anger .46** .17 1.58

Nagelkerke R2 0.54
Higher values on each of the self-control subscale scores
indicate greater self-control; *p < .05, **p < .01,
***p < .001 (two-tailed)
having a lower preference for simple tasks or a lower preference
for physical activities
would be negatively associated with the likelihood of being a
police officer, which is
what emerged from the logistic regression model. Overall,
across the different bivariate
and multivariate analyses, support for our secondary hypothesis
was found with regard
to the impulsivity, self-centeredness, and anger dimensions, but
less so with regard to
the simple tasks, risk-seeking, and physical activities
dimensions.
Theoretical and Policy Implications
Gottfredson and Hirschi (1990) note that offenders tend to lack
restraint; on the other
hand, police officers must show restraint in the face of verbal
and/or physical attacks by
suspects, victims and citizens alike. Resisting the impulse to
strike or retort back surely
indicates high self-control, perhaps illustrating why police
officers in our study scored
particularly low on tendency toward anger, impulsivity and self-
centeredness. As

previously discussed, policing is an inherently risky and
physical occupation; further,
although the work of police officers is often portrayed as
simple—they go out and catch
the “bad guys”—it involves much more. Policing involves
report writing, testifying in
court, and interacting with citizens individually and in groups;
these are not simple
tasks. Our findings that police officers scored substantively
higher than offenders on a
global measure of self-control, (low) impulsivity, (low) self-
centeredness, and (low)
anger supports Gottfredson and Hirschi’s (1990) overall concept
of self-control. More-
over, the findings are consistent with past research explicitly
comparing the self-control
of offenders to non-offenders (Turner & Piquero, 2002; Winfree
Jr et al., 2006).
The results from this study also have the potential to inform
police policy and practice.
Given the nature of the policing profession (e.g., authority,
discretion, limited supervision), it
is imperative that departments employ officers with desirable
characteristics, and the
professional and scholarly literature reviewed earlier advocates
for police administrators to
identify applicants with traits consistent with high self-control.
Accordingly, many agencies
attempt to do so through a battery of testing hurdles during the
hiring process. Much of this
process, however, involves “selection by elimination” (see e.g.,
Metchik, 1999). Applicants
who are determined to be unsuitable (i.e. those low in self-
control and other undesirable
characteristics) are “screened out” of the hiring process.

Historically, the hiring process to “screen out” undesirable
candidates has included
scenario-based questions in written tests and oral panel
interviews, background inves-
tigations, and psychological assessments (e.g., Arrigo &
Claussen, 2003; Cochrane,
Tett, & Vandercreek, 2003; Kane & White, 2012; Palmiotto,
2001). Though these
hurdles are commonplace across agencies in the United States,
it may be more useful
for police administrators to “select in” desirable candidates (see
e.g., Sanders, 2003;
White, 2008). This is because screening out “undesirable”
candidates may not auto-
matically result in an applicant pool of “desirable” candidates;
it may, in fact, result in a
candidate pool of both “desirable” and “neutral” candidates.
Given that prior literature
has identified high self-control as a desirable characteristic
(e.g., Hogue et al., 1994;
Detrick & Chibnall, 2006), police administrators and
practitioners could, in part, begin
to design their hiring process around identifying applicants who
score particularly high
on the construct. If administrators truly wish to hire “desirable”
candidates, they would
be wise to more carefully—and intentionally—“select in” those
candidates with desir-
able qualities.
The difference in approaches may seem subtle, but “screen out”

strategies often
involve ever-changing lists of disqualifying criteria recognized
after the fact, while
“select in” strategies rarely need altering and are viewed as
stable indicators of
decision-making predispositions. Here, police administrators,
background investiga-
tors, and oral board interviewers might seek to identify
applicants who possess both
attitudinal (e.g., on the Grasmick et al., 1993 scale) and other
behavioral indicators of
high self-control as opposed to simply hiring applicants who
have no identifiable
evidence of disqualifying characteristics. Further, given that
low self-control is a known
predictor of police misconduct (e.g., Donner, Maskaly, &
Thompson, 2018; Pogarsky
& Piquero, 2004) and intentions to use force more quickly
(Staller et al., 2019), it seems
even more prudent that agencies hire individuals who are high
in self-control.
Limitations and Directions for Future Research
Though this study provides a unique test of Gottfredson and
Hirschi’s (1990) claims
concerning differences in self-control between offenders and
non-offenders, it is not
without limitations. A first limitation concerns the sample.
Specifically, the police
officers and offenders included in this study were not randomly
drawn, and the sample
analyzed was the result of an amalgamation of data collected
over a period covering
roughly 18 years (offenders contributing data in the years 2000–
2001 and officers

contributing data as recent as the start of 2019); participation
rates were also below 50%
across each of the four data sources. Thus, while the sample
analyzed in the current
study is particularly unique, the pattern of findings might have
been different had this
study been based on a contemporaneous random sample of
offenders and police
officers that are more representative of the respective
populations. As no such data
currently exists, we relied on what was available. Future efforts
should be directed at
making comparisons that are more generalizable between the
self-control levels of
offenders and officers to assess the validity of the patterns of
findings revealed herein.
Related to this issue is that fact that, analytically speaking, we
treated offenders and
police officers as two homogenous samples. There may be
importance differences
among offenders (e.g., white-collar vs. sex offenders) and
officers (supervisors vs. foot
patrol) with regard to self-control that should be considered in
future research.12
Second, we measured self-control with attitudinal items from
the Grasmick et al.
(1993) self-control scale. Though this strategy has been widely
used and validated in
previous research (see e.g., Pratt & Cullen, 2000; Vazsonyi et
al., 2017), future
researchers could utilize measurements that are more in line
with Hirschi and
Gottfredson’s (1993) preference for behavioral measures or
more theoretically consis-
tent with Hirschi’s (2004) reconceptualization of self-control.

Third, the use of a
prisoner sample to represent offenders has an inherent selection
bias, as these individ-
uals were caught, convicted, and imprisoned. The use of such a
sample fails to account
for the many offenders who are not caught or who are caught
and sentenced to
punishments less severe than prison. This limitation, of course,
applies to any study
12 We would like to thank an anonymous reviewer for raising
this issue. The exploratory nature of our study,
combined with the relatively small sample size, lead us to
examine average differences between all offender types and
all police officer types. However, as we comment, future
research based on large random samples of offenders and
officers could compare the self-control levels of different types
of offenders to different types of officers.
that makes use of a prison sample, of which there have been
many in the criminal
justice literature (e.g., Ireland, 2011; Mitchell & MacKenzie,
2006; Reisig & Mesko,
2009; Smith, 2015). That said, future researchers should
consider samples that com-
prise a wider spectrum of the “offender” pool.
Fourth, due to data limitations, the present study does not take
into account the
importance of organizational factors and police culture on
officer behavior. This
influence may be exercised directly (through policies and

supervision) or indirectly
(through values and culture). According to Skogan and Frydl
(2004), “Police behavior
is affected by broad forces, including features of the
organizations that hire, train, and
supervise police, as well as the environment in which they
work” (p. 155). In fact, prior
studies of organizational explanations of police behavior speak
to the influence of
recruitment and selection (e.g., Sechrest & Burns, 1992), police
leadership (e.g.,
Goldstein, 1975), organizational response to police deviance
(e.g., Sherman, 1978),
and police culture and socialization (e.g., Herbert, 1998). Future
research should
attempt to replicate our results, while accounting for the
importance of organization
and culture.
Fifth, the framework of our study—comparing the self-control
levels of offenders to
non-offenders (i.e. police officers)—makes the implicit
assumption that the officers in
our sample are not themselves “offenders.” Though police
departments make every
effort to hire “non-offenders” (or, subsequently fire officers
who engage in post-hiring
misconduct), it is possible that our sample of officers contained
individuals who have
committed undetected law violations, which could affect the
results.13 It is also
important to note that the police code of silence, as part of the
larger police culture,
may contribute to the dark figure of police misconduct in which
police officers violate
criminal laws but are not reported or caught (e.g., Ivkovic,

2005). Thus, future
researchers studying this topic should therefore attempt to
identify—and only
include—officers who have no discernable criminal/misconduct
history. Lastly, while
we compared the self-control levels of a sample of offenders
with a sample of police
officers, an additional issue worthy of future investigation
would be to assess where the
average level of self-control of non-offenders who are not
members of the policing
profession falls relative to offenders and police officers.
Conclusion
While prior research consistently provides evidence that non-
offenders have greater
self-control than offenders, no prior study has made a direct
comparison of offenders to
police officers. We viewed this gap in the literature as a topic
worthy of empirical
examination and believe that this study contributes to both the
self-control and policing
literatures. Through a combination of unique officer and
offender datasets, our findings
demonstrate that police officers, in fact, do score significantly
higher than offenders on
a global measure of self-control. Additionally, when analyzing
differences between
police officers and offenders across the six dimensions of self-
control, we consistently
found that officers are lower in impulsivity, self-centeredness,
and anger, but that they
13 This possibility is perhaps supported by the observation that
a handful of police officers in the sample

scored below the offender mean on global self-control (see Fig.
1).
are slightly higher with regard to preferring physical to mental
activities. Overall, these
findings offer support for the generality of self-control theory.
Moreover, they yield
important practical implications for police administrators who
have a significant
interest in hiring desirable candidates into the policing
profession.
Acknowledgements The authors would like to express their
appreciation to the anonymous reviewers for
their comments and suggestions on an earlier draft of this
manuscript.
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Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps
and institutional affiliations.
Ryan C. Meldrum is an associate professor in the Department of
Criminology and Criminal Justice at Florida
International University. His researchinterests include testing
and refining theories of delinquency and crime,
prosecutorial discretion, and adolescent development. His
recent research has appeared in journals such as
Justice Quarterly, Criminal Justice & Behavior, Intelligence,
Sleep Health, and Developmental Psychology.
Christopher M. Donner is an Assistant Professor in the
Department of Criminal Justice & Criminology at
Loyola University Chicago, and he received his Ph.D. in
Criminology from the University of South Florida.
His current research focuses on police issues, with a particular
emphasis on police integrity and misconduct.
His recent publications have appeared in a variety of outlets,
including the Journal of Criminal Justice,
Policing and Society, and Deviant Behavior.
Shawna Cleary is a Professor in the School of Criminal Justice
at the University of Central Oklahoma, where
she is also the School’s Graduate Advisor and Director of Field

Studies and Internships. She has served as a
member of the Attorney General of Oklahoma’s Domestic
Violence and Sexual Assault Advisory Council
since 2006. Dr. Cleary is the author of Sex Offenders and Self-
Control: Explaining Sexual Violence.
Andy Hochstetler is Professor in the Department of Sociology at
Iowa State University. His recent research
has appeared in outlets such as the American Journal of Public
Health, Justice Quarterly, and Rural Sociology
among others.
Matt DeLisi is College of Liberal Arts and Sciences Dean’s
Professor, Coordinator of Criminal Justice
Studies, Professor in the Department of Sociology,and Faculty
Affiliate of the Center for the Study of Violence
at Iowa State University. Dr. DeLisi is the only scientist in the
world who is Fellow of both the Academy of
Criminal Justice Sciences and the Association for Psychological
Science.
Affiliations
Ryan C. Meldrum1 & Christopher M. Donner2 & Shawna
Cleary3 & Andy
Hochstetler4 & Matt DeLisi4
1 Department of Criminology and Criminal Justice, Florida
International University, Miami,
FL 33199, USA
2 Department of Criminal Justice and Criminology, Loyola
University Chicago, Chicago, IL 60660, USA
3 School of Criminal Justice, University of Central Oklahoma,
Edmond, OK 73034, USA

4 Department of Sociology, Iowa State University, Ames, IA
50011, USA
Reproduced with permission of copyright owner.
Further reproduction prohibited without permission.
Assessing Similarities and Differences in Self-Control
�between Police Officers and
OffendersAbstractIntroductionTheory and Prior ResearchSelf-
Control TheoryPolicing and Self-ControlThe Current
StudyMethodParticipants and ProcedureMeasuresAnalytic
PlanResultsBivariate AnalysesMultivariate
AnalysesSupplementary AnalysesDiscussionTheoretical and
Policy ImplicationsLimitations and Directions for Future
ResearchConclusionReferences

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