Criminal Behavior Assignment | Custom Essay Services

Understanding Persons With Mental Illness Who Are and Are Not
Criminal Justice Involved: A Comparison of Criminal Thinking and
Psychiatric Symptoms
Nicole R. Gross and Robert D. Morgan
Texas Tech University
Research has begun to elucidate that persons with mental illness become involved in the criminal justice
system as a result of criminality and not merely because of their mental illness. This study aims to clarify
the similarities and differences in criminal thinking and psychiatric symptomatology between persons
with mental illness who are and are not criminal justice involved. Male and female (n 94) participants
admitted to an acute psychiatric facility completed measures to assess criminal thinking (i.e., Psychological
Inventory of Criminal Thinking Styles and Criminal Sentiments Scale–Modified) and psychiatric
symptomatology (Millon Clinical Multiaxial Inventory–Third Edition). In addition to the inpatient
sample, 94 incarcerated persons with mental illness from a previously conducted study were selected
based on their match with the current sample on several key demographic and psychiatric variables. The
results of this study indicated that hospitalized persons with mental illness with a history of criminal
justice involvement evidenced similar thinking styles to persons with mental illness who were incarcerated.
Persons with mental illness without criminal justice involvement evidenced fewer thinking styles
supportive of a criminal lifestyle than the incarcerated sample. Furthermore, the persons with mental
illness sample with no history of criminal justice involvement showed significantly lower levels of
psychopathology shown to be risk factors for criminal justice involvement (e.g., antisocial personality,
drug dependence, alcohol dependence). These findings have implications for offender-type classification,
development of targeted treatment interventions, and program placement.
Keywords: criminal thinking, offender, criminal justice involvement, mental illness
Persons with mental illness (PMI) are 3 times more likely to be
incarcerated than admitted to a psychiatric facility (Abramsky &
Fellner, 2003; Torrey, Kennard, Eslinger, Lamb, & Pavle, 2010).
Consequently, correctional institutions have become the largest
providers of mental health treatment in the United States (Abramsky
& Fellner, 2003). Notably, 14.5% of male and 31% of female
offenders in jails have a serious mental illness (i.e., schizophrenia
spectrum disorder; schizoaffective disorder; schizophreniform disorder;
brief psychotic disorder; delusional disorder; psychotic disorder
not otherwise specified [NOS]; bipolar disorder I, II, and NOS; major
depressive disorder; and depressive disorder NOS; Steadman, Osher,
Robbins, Case, & Samuels, 2009). PMI are disproportionally represented
in correctional institutions because less than 6% of the general
population is estimated to suffer from a severe mental illness (American
Psychiatric Association, 2000; Kessler, Chiu, Demler,&Walters,
2005). It appears that PMI are involved in and affected across criminal
justice (CJ) and mental healthcare systems; however, it remains unclear
how PMI involved in the mental healthcare system compare to
PMI involved in the CJ system.
When compared to offenders without mental illness, PMI who are
placed in community supervision (i.e., probation and parole) after
being released from a correctional facility are significantly more likely
to recidivate (continued criminal behavior resulting in arrest and
reincarceration; Messina, Burdon, Hagopian, & Prendergast, 2006).
Likewise, it is estimated that 37–53% of PMI released from mental
health facilities psychiatrically recidivate (decompensate and are consequently
readmitted to a mental health facility) within 1 year of being
discharged (Hillman, 2001; Segal & Burgess, 2006). Commonalities
such as high criminal recidivism and psychiatric hospitalization rates
between PMI who are and are not CJ involved may indicate common
risk factors such as criminal thinking, poverty, homelessness, and
unemployment (Draine, Salzer, Culhane, & Hadley, 2002; Mgustshini,
2010) between the two groups. Such results would identify a
neglected treatment area for PMI regardless of setting (CJ or mental
health) that, if addressed, may improve treatment outcomes (e.g.,
symptom reduction, reduced criminal recidivism, and psychiatric hospitalizations).
Investigating the role of mental illness in the provocation
and exacerbation of criminal behavior is thus warranted. PMI
who are CJ involved may have unique mental health needs and
criminal risk factors when it comes to offending behavior. Additionally,
similarities in criminal thought patterns may affect psychological
functioning and mental health recovery (e.g., symptom management,
rehospitalization) of PMI who are not CJ involved.
Although it may seem plausible that PMI enter the CJ system as
a result of their mental health symptoms, it has been suggested that
some PMI have comorbid criminal dispositions that result in their
This article was published Online First October 29, 2012.
Nicole R. Gross and Robert D. Morgan, Department of Psychology,
Texas Tech University.
Correspondence concerning this article should be addressed to Robert D.
Morgan, Department of Psychology, Texas Tech University, Box 42051,
Lubbock, TX 79409-2051. E-mail:
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Law and Human Behavior © 2012 American Psychological Association
2013, Vol. 37, No. 3, 175–186 0147-7307/13/$12.00 DOI: 10.1037/lhb0000013
CJ involvement (Hiday, 1999). Elbogen and Johnson (2009) found
that severe mental illness did not predict future violence if not
paired with historical, clinical, and dispositional contextual factors
(e.g., past violence, unemployment, low socioeconomic status,
victimization; Draine et al., 2002). Although violence does not
necessarily result in CJ involvement, the aforementioned contextual
factors that are related to the prediction of violence are also
commonly associated with crime in general. Furthermore, Draine
and colleagues (2002) noted that the relationship between mental
illness and crime is weak, and they suggest that poverty, lack of
education, unemployment, and limited prosocial relationships
likely serve as moderating variables in this relationship. Mental
illness appears to predispose individuals to reside in environments
that foster criminal behavior (Draine et al., 2002; Fisher, Silver, &
Wolff, 2006). For example, offenders and PMI often live in lowincome
areas, are single, have limited social and family support,
and have a history of unemployment (Fisher et al., 2006). Homelessness
and a family history of incarceration have been found to
be more prevalent among PMI who are CJ involved than offenders
without mental illness (Ditton, 1999). Additionally, PMI who are
CJ involved were more likely to be unemployed before their arrest
when compared with offenders without mental illness (Ditton,
1999), and male PMI with substance abuse disorders were twice as
likely to have a criminal record as those without a substance abuse
disorder. This is likely due to the role of substance abuse as a
prominent risk factor for criminal behavior (Andrews & Bonta,
2006). Among a sample of hospitalized veterans, substance abuse
accounted for most of the variance in the risk of incarceration
despite the presence or absence of mental illness (Erickson, Rosenheck,
Trestman, Ford, & Desai, 2008). Additionally, the link
between mental illness and violent behavior was weak if the
individual did not have comorbid substance use issues (Elbogen &
Johnson, 2009; Swartz et al., 1998).
PMI appear to experience multiple risk factors that strongly
influence CJ involvement. With regards to the number and intensity
of risk factors experienced by PMI, Girard and Wormith
(2004) found that PMI evidenced higher scores on the Levels of
Service Inventory/Case Management Inventory (LSI/CMI; a commonly
used measure of risk assessment for predicting criminal
recidivism) than persons without mental illness, suggesting that
PMI experience more criminal risk factors than individuals without
mental illness. Many of these criminal risk factors measured by
the LSI (e.g., education, employment, housing, substance abuse)
correspond to poverty, joblessness, and other factors that Draine
and colleagues (2002) identified as factors that predispose PMI to
engage in criminal behavior. The criminal risk factors measured by
the LSI/CMI are positively correlated with criminal recidivism. In
a study examining PMI perceived risk for psychiatric rehospitalization,
individuals endorsed risk factors such as unemployment,
lack of education, lack of housing, and economic difficulties
(Mgustshini, 2010) that correspond to those used in the prediction
of criminal behavior. Thus, it is reasonable to suggest that criminal
risk factors may also play a role in the frequency of psychiatric
hospitalizations. These predisposing environmental factors are important
in terms of treatment and recidivism (criminal and psychiatric)
given that they increase the potential for the presence of
criminal attitudes and thought patterns in PMI, even if they are not
currently engaging in criminal behavior.
Carr and colleagues (2009) examined criminal thinking styles in
a sample of civil psychiatric patients and compared the results to
findings from a previously published study using offenders without
mental illness. Results indicated that the civil psychiatric
patients scored significantly higher on five of eight criminal thinking
style scales. Morgan and colleagues (2010) examined criminal
thinking and psychiatric symptomatology in a sample of 416
incarcerated PMI. It was found that incarcerated PMI exhibited
criminal thinking patterns similar to those of incarcerated offenders
without mental illness. Additionally, the psychiatric symptomatology
of the incarcerated PMI was similar to that of inpatient
psychiatric samples. These comparisons were made with prevalence
rates known from other published samples of offenders
without mental illness (criminal thinking comparison) and nonoffender
psychiatric patients (psychiatric symptomatology comparison).
Furthermore, a recent study conducted with 4,204 incarcerated
male and female participants found results consistent with
Carr and colleagues (2009) and Morgan et al. (2010) such that
incarcerated PMI evidenced similar criminal thinking styles when
compared with those without mental illness (Wolff, Morgan, Shi,
Fisher, & Huening, 2011). Additionally, those with severe mental
illness (i.e., schizophrenia or bipolar disorder) displayed higher
levels of criminal thinking than those without mental illness and
those with less severe mental illness (i.e., depression, posttraumatic
stress disorder, and anxiety; Wolff et al., 2011). Although
these three studies provided valuable information for understanding
PMI that are and are not CJ involved, significant research
design and methodological problems limited conclusive interpretations.
Specifically, Carr and colleagues (2009) and Morgan and
colleagues (2010) lacked a direct comparison group, and Wolff et
al. (2011) was limited in that there was no mental health sample
that was not CJ involved as a control. The proposed study aims to
extend these prior studies by examining criminal thinking with
PMI who are and are not CJ involved and provide a direct comparison
group for Morgan et al. (2010).
Results from Morgan et al. (2010) suggested that PMI who are
CJ involved may have different psychiatric needs and features
when compared with PMI who are not CJ involved. Furthermore,
general criminal thinking has been shown to partially mediate the
relationship between mental illness and institutional violence for
incarcerated PMI (Walters, 2011), suggesting that the behavior of
PMI who act violently should be considered a product of more
than mental illness. It appears that PMI who are CJ involved are
not merely criminals because of their mental illness but are “criminals
who happen to be mentally ill” (Morgan et al., 2010). This
assertion has important implications for PMI who are CJ involved,
but the applicability of these results to PMI who are not CJ
involved has yet to be empirically examined. Thus, the exploration
of criminal thinking in PMI is warranted. If criminal thought
patterns are found within PMI, or a subset of PMI, who are not CJ
involved, what effects do such dispositions have on other aspects
of functioning (e.g., mental health functioning)? To expand on
Morgan et al. (2010), criminal thinking and psychiatric symptomatology
data from PMI admitted to a short-term psychiatric facility
were gathered to examine potential differences between this group
and incarcerated PMI. Because mental illness and criminal propensities
are conceptualized as comorbid disorders (Morgan et al.,
2010; Wolff et al., 2011), the presentation and effects of such
comorbidity may manifest differently in PMI who are not CJ
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
involved. Additionally, the data collected from PMI who are not
CJ involved will allow for the examination of criminal thinking as
a risk factor in predicting the number of psychiatric hospitalizations
and relapse.
The purpose of this study was to further examine differences
and similarities in psychiatric symptoms and criminal thinking
between PMI who are incarcerated and psychiatric inpatients. The
aim of the proposed study was to identify distinguishing features
of PMI who are and are not CJ involved. It was hypothesized that
all three groups would evidence similar overall levels of psychiatric
symptomatology and produce similar symptom profiles. Such
results would coincide with the results from Morgan et al. (2010).
It was hypothesized that PMI admitted to the short-term psychiatric
hospital without a history of CJ involvement would evidence
less criminal thinking, as measured by the Psychological Inventory
of Criminal Thinking Styles (PICTS) and the Criminal Sentiments
Scale–Modified (CSS-M), than the PMI who were incarcerated or
admitted to the short-term psychiatric facility with a history of CJ
involvement. Lastly, it was expected that criminal thinking would
be positively correlated with psychiatric hospitalizations such that
those evidencing higher levels of criminal thinking would also
have a greater number of lifetime psychiatric hospitalizations.
The following method section is a replication of that found in
Morgan et al. (2010). The procedure and all measures used,
with the exception of a modified demographic form, are the same
as those used in Morgan et al. (2010). This replication was necessary
to allow for a direct comparison between the PMI sample
admitted to a short-term psychiatric hospital in this study with the
incarcerated PMI sample in Morgan et al. (2010).
Participants consisted of 94 short-term psychiatric male (n 53,
56.4%) and female (n 41, 43.6%) patients from an acute
psychiatric hospital located in West Texas. Participants were at
least 18 years old (M 38.55, SD 11.35) and were admitted to
the facility for at least 5 days (M 8.24, SD 7.02). We
incorporated a 5-day minimum for the length of time admitted to
the facility to increase the likelihood that the participant would be
experiencing chronic and enduring psychiatric problems and not
transient, crisis-type issues. Over half of the sample (n 51,
54.3%) had been convicted of a crime in the past; for that reason,
the inpatient sample was split into two groups (i.e., with and
without past CJ involvement) for the completion of data analysis.
Approximately 80% (n 76) of the inpatients had been admitted
to a psychiatric facility before their admission at the time of
Additionally, the Structured Clinical Interview for DSM–IV
Axis I Disorders (SCID-I) was administered by a doctoral-level
counseling psychology student (who received supervision and
training from a licensed psychologist) to approximately 20% of the
sample to assess the reliability of the participant’s diagnosis as
reported by the institutional file. It appears that the institutional file
provided a reliable diagnosis for the participants because there was
a 77.8% agreement rate between the diagnosis found in the institutional
file and the diagnosis determined by the administration of
the SCID-I. On the basis of the diagnoses recorded for each
participant from institutional records, the inpatient sample had a
primary Axis I diagnosis of bipolar disorders (n 37, 39.4%)
followed by major depressive disorder (n 24, 25.5%), schizophrenia
(n 11, 11.7%), schizoaffective disorder (n 9, 9.6%),
other mood disorders (e.g., drug-induced, NOS; n 8, 8.5%),
adjustment disorder (n 2, 2.1%), psychosis NOS (n 1. 1.1%),
acute stress disorder (n 1. 1.1%), and Asperger’s syndrome (n
1. 1.1%).
Participants were also drawn from those who participated in the
Morgan et al. (2010) study. This sample was composed of 94
incarcerated male (n 53, 56.4%) and female (n 41, 43.6%)
adults with mental illness. Participants were selected based on their
match with participants in our sample on sex, Axis I diagnosis,
age, ethnicity, years of formal education, and relationship status
when possible. Analyses revealed no significant differences among
significant demographic characteristics of Axis I diagnosis, age,
race, and relationship status. However, the two groups differed
significantly with regards to the number of years of formal education
completed, t 2.81, p .005, with the inpatient mental
health sample having completed more years of formal education
(M 12.46, SD 2.13) than the incarcerated mental health
sample (M 11.52, SD 2.44).
Demographic characteristics of both groups along with the
results of the between-group analyses of the demographics of each
sample are shown in Table 1.
A written informed consent form was used to inform potential
participants of the purpose of the study, the potential risks, confidentiality,
and their rights as human subjects. A self-report demographic
form was used to gather information regarding age, ethnicity,
relationship status, time hospitalized, CJ involvement,
psychiatric history (e.g., treatment, number of hospitalizations),
mental health diagnoses, employment status, and public assistance
The PICTS (Walters, 1995), a self-report measure composed of
80 items and designed to assess thought patterns associated with
criminal behavior (Walters, 2006), was used. For example, the
PICTS contains items such as “The more I got away with crime the
more I thought there was no way the police or authorities would
ever catch up with me”; “The way I look at it, I’ve paid my dues
and am therefore justified in taking what I want”; and “I have
justified selling drugs, burglarizing homes, or robbing banks by
telling myself that if I didn’t do it someone else would.” Responses
to the items on the PICTS are provided using a 4-point Likert scale
(1 disagree, 4 strongly agree; Walters, 2006). The PICTS
produces two content scales (i.e., Current Criminal Thinking and
Historical Criminal Thinking), two composite scales (i.e., Proactive
Criminal Thinking and Reactive Criminal Thinking), eight
thinking style scales (i.e., Mollification, Cutoff, Entitlement,
Power Orientation, Sentimentality, Superoptimism, Cognitive Indolence,
and Discontinuity), and five Factor and Special Scales
(i.e., Problem Avoidance, Interpersonal Hostility, Self-Assertion,
Denial of Harm, and Fear of Change; Walters, 2006). There are no
cutoff scores distinguishing the presence or absence of each of the
eight criminal thinking scales, but guidelines are provided for
interpreting the criminal thinking style T-scores as low (40),
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
average (40, 60), high (60, 70), and very high (70;
Walters, 2006). The PICTS has acceptable validity when compared
with other means of assessing criminality (i.e., criminal history such
as arrests, diversity of offenses, age of first offense, and psychopathy)
(Walters, 2006; Walters & Schlauch, 2008). The PICTS has demonstrated
moderate to high levels of internal consistency and test-retest
reliability in offender samples. The internal consistency for the subscales
ranged from .54 to .88 for male and female offenders (Walters,
1995; Walters, Elliott, & Miscoll, 1998). Test-retest reliability was
.68–.85 after 2 weeks and .57–.72 after 12 weeks (Walters, 1995;
Walters et al., 1998). The PICTS has not been normed on a clinical
sample. The internal consistency for the PICTS in this study was high,
yielding a Cronbach’s of .95.
The CSS-M (Simourd, 1997), a self-report measure composed
of 41 items, designed to assess “attitudes, values, and beliefs
related to criminal behavior” (Wormith & Andrews, 1984), was
used. The CSS-M measures the content of criminal thoughts
whereas the PICTS measures the process of criminal thinking
(Simourd & Olver, 2002). For example, the CSS-M contains items
such as “The police are as crooked as the people they arrest,”
“Pretty well all laws deserve our respect,” and “You cannot get
justice in court.” Responses to the items on the CSS-M are provided
using a 3-point Likert-type scale (Simourd, 1997; Simourd
& Olver, 2002). The CSS-M yields a total score and five subscale
scores (attitude toward the law [Law], attitude toward the court
[Court], attitude toward the police [Police], tolerance for law
violations [TLV], and identification with criminal others [ICO];
Simourd, 1997; Simourd & Olver, 2002; Simourd & van de Ven,
1999). The Law, Court, and Police subscales are then combined to
form the Law-Court-Police (LCP) subscale that assesses the level
of respect an individual has for the criminal and legal system
(Simourd & Olver, 2002). Additionally, the TLV subscale assesses
the degree to which an individual justifies their criminal behavior,
and the ICO subscale assesses how the individual perceives the
criminal behavior of others (Simourd & Oliver, 2002). Scores
greater than 19 indicate clinical significance whereas scores of 30
or higher are considered “high” (Simourd, 1997). The CSS-M has
not been normed on a clinical sample; however, it has been shown
to be a valid and reliable measure with offender populations
(Andrews, Wormith, & Kiessling, 1985; Roy & Wormith, 1985;
Wormith & Andrews, 1984). Internal consistency ranged from .73
to .91 (Simourd, 1997; Simourd & Olver, 2002). Additionally,
when compared with other criminal risk assessment measures (i.e.,
Hare Psychopathy Checklist–Revised and Level of Service
Inventory–Revised), the convergent validity ranged from .25 to .37
(Simourd, 1997). The internal consistency for the CSS-M in this
study was high, yielding a Cronbach’s of .88.
The Millon Clinical Multiaxial Inventory–Third Edition
(MCMI-III; Millon, 1994) was used. MCMI-III is a self-report
measure composed of 175 true/false items, providing an integrated
understanding of a respondent’s personality and clinical syndromes
(Millon, 1994). The MCMI-III yields 14 Personality Disorder
Scales that coincide with Diagnostic and Statistical Manual–
4th Edition (DSM–IV; American Psychiatric Association, 1994)
Axis II disorders, and there are 10 Clinical Syndrome Scales that
coincide with DSM–IV Axis I disorders (Millon, 1994). A Correction
Scale detects careless or random responding, and the Modifying
Indices and the Validity Index assess validity and response
style (Millon, 1994). MCMI-III is strongly correlated with the
MCMI-II, with correlations ranging from .59 to .88 (Millon, Davis,
& Millon, 1997). The MCMI-III has been designed for use with
clinical populations and has been normed on a clinical population.
Table 1
Between-Sample Comparison of Demographic Results
Psychiatric inpatient
Morgan et al., (2010)
n % n % 2 p
Ethnicity 10.16 0.07
Caucasian 54 58.1 51 54.3
Hispanic 20 21.5 13 13.9
African American 8 8.6 22 23.4
Asian 1 1.1 0 0.0
American Indian 1 1.1 0 0.0
Other 9 9.7 8 8.5
Diagnosis 0.57 0.90
Schizophrenia/Other Psychotic Disorder 21 22.3 20 21.3
Bipolar I and II 38 40.4 38 40.4
MDD/Other Mood Disorder 29 30.9 32 34.0
Other Mental Health Disorder 6 6.4 4 4.3
Relationship Status 8.15 0.15
Single 34 42.5 43 45.7
Partnered/Common Law 5 6.3 4 4.3
Divorced 21 26.3 23 24.5
Separated 8 10.0 9 9.6
Married 7 8.8 15 15.9
Widowed 5 6.3 0 0.0
M SD M SD t p
Age 38.55 11.35 36.83 10.61 1.08 0.28
Education (Years) 12.46 2.13 11.52 2.443 2.81 0.005
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Validity of the MCMI-III with relation to the diagnostic determination
from clinicians was low to moderate, and the correlations
ranged from .07 to .37 (Millon, 1994). Internal consistency reliability
ranged from .66 to .90 whereas test–retest reliability ranged
from .82 to .86 at 5–14 days from the original test date (Millon,
1994). The internal consistency for the MCMI-II in this study was
high, yielding a Cronbach’s of .92.
SCID-I (First, Spitzer, Gibbon, & Williams, 1997), a semistructured
interview used to assist in diagnosing DSM–IV Axis I disorders
that is composed of six modules that assess mood episodes,
mood disorders, psychotic symptoms, psychotic disorders, substance
abuse disorders, and anxiety and other disorders (First et al.,
1997), was used in this study. During administration, various
questions regarding mental health symptoms are posed to the
examinee, and on the basis of their response the examiner determines
the presence or absence of the symptom (First et al., 1997).
The positive and negative ratings of symptoms within a diagnostic
category are combined to determine if DSM–IV diagnostic criteria
for the disorder have been satisfied (First et al., 1997). In terms of
Kappa ratings, interrater reliability for the SCID-I ranges from .57
to 1.0, and test–retest reliability over a 7- to 10-day period ranges
from .35 to .78 (Zanarini, et al., 2000). SCID-I has been developed
as a means of improving the diagnostic accuracy of clinicians;
thus, most comparisons with unstructured interviews or the best
estimate diagnosis procedure have demonstrated superior validity
for the SCID-I (Basco et al., 2000).
Individuals admitted to the psychiatric hospital were identified
and recruited for participation in this study using a bed locator
sheet (an updated record that identified the names, admission
dates, cautionary ratings, and room assignments for all current
inpatients). Consumers were considered eligible for participation if
they had been admitted to the facility for a minimum of 5 days,
were able to communicate in English, were not admitted to the
facility after being adjudicated not competent to stand trial and
not restorable, were not receiving competency restoration services,
and were at least 18 years of age. Participants were
selected in the order of their presentation on the bed locator
sheets such that the first available consumer (e.g., met inclusion
criteria, had not already participated or refused participation)
was contacted by a research assistant for recruitment into the
study presented here. Research assistants approached the identified
consumers either in the day room of the facility or in their
assigned rooms and asked them to meet with a research assistant
regarding participation.
During this meeting, consumers were verbally informed about
the purpose of the study, the tasks they would be asked to complete,
and of their rights as a research participant (i.e., confidentiality,
right to withdraw) should they decide to participate. Additionally,
risks and benefits of participation were also verbally
explained. Consumers willing to participate were provided with a
written informed consent form to read, and then they were given an
opportunity to ask any questions they had about participation. For
participants who indicated reading difficulty or an inability to read,
the consent form was read aloud by a research assistant. To ensure
the participant’s ability to give consent, the research assistant
asked all potential participants to provide a summary, in their own
words, regarding the information presented in the consent form.
Once all questions were answered and the research assistant determined
that the individual had provided informed consent, the
patient was asked to sign one copy of the informed consent form.
The research assistant collected the signed consent form and
provided the patient with an unsigned copy of the consent form for
their records.
After consent was provided, the participant was provided a
packet containing the demographic form, CSS-M, PICTS, and
MCMI-III. They were instructed to complete the measures in the
order in which they were provided to them in the packet. The
demographic form was always presented first, the CSS-M and
PICTS were counterbalanced to control for any sequencing effects
and presented after the demographic form, and the MCMI-III was
presented last. The MCMI-III was the last measure completed by
the participants in an attempt to reduce attrition rates due to the
length of the measure. A research assistant remained with participants
while the measures were completed to enable participants to
ask any questions or report any concerns that arose during the
completion of the measures. For those participants who indicated
difficulty reading and had the consent form read aloud to them, a
research assistant also read the measures to those participants (n
12, 12.8%) and allowed them to record their responses on the
research materials. Additionally, some participants requested assistance
with reading because of fatigue or difficulty with the
measure, and for those participants portions of the testing were
read aloud to them (n 6, 6.4%).
In addition to the packet of measures provided to the participant,
the SCID-I was administered by a research assistant to approximately
20% of the sample. Administration of the SCID-I was
predetermined using a random numbers table to select the packet/
participant numbers that would include the additional interview.
All attempts were made to ensure that data were collected from
participants during one uninterrupted session; however, this was
not feasible across all participants. Interruptions ranged from short
breaks (e.g., medication administration, restroom breaks, visitation,
cigarette breaks) to breaks that required testing to be completed
the next day (e.g., participant fatigue). Research assistants
maintained a research log that documented the reason for, and the
length of, all breaks/interruptions. A total of 10 participants
(10.6%) completed the measures across two separate testing sessions
on different days, 34 participants (36.2%) took one short
break during testing, 20 participants (21.3%) took two to four
breaks during testing, and 40 participants (42.6%) completed the
measures without interruption. Research assistants recorded additional
information regarding the start and completion times for
testing, the date of testing, and any other notable or relevant
information (e.g., problematic behavior, questions or problems
with measures/questions).
After all measures were completed, a review of each participant’s
facility file was completed by a research assistant to gather
additional information and to corroborate the self-report information
provided by the participant. Information regarding current
DSM-IV–Text Revision (DSM-IV-TR) (American Psychiatric Association,
2000) mental health diagnoses (Axis I through Axis IV
and Global Assessment of Functioning scores), admission date,
prescribed medications and dosages, history of psychiatric hospitalizations,
and other mental health treatment received was extracted
from the

Advanced Topics in Psychology: Psychology of Criminal Behavior (GPSN 493)
Prof. Yannie ten Broeke
What is a Critical Literature Review (a.k.a., a Critical Review of the Literature or Literature Review)?
The final research paper for this course is a Critical Literature Review (a.k.a., a Critical Literature Review). A
Critical Literature Review (CLR) is a summary and critical analysis of an area of research that addresses a
specific topic, answers a specific research question, and/or supports a specific point-of-view or argument.
ò The CLR for this class must describe and examine the relationship between a specific risk factor (“causal”
variable) for antisocial behavior (we have and will discuss many in class) and aggressive, violent, and/or
interpersonal criminal behavior as an “outcome” variable.
ò The CLR must address and explain HOW and WHY the relationship between the risk factor and outcome
behavior exists from multiple perspectives, including developmental, biological, and individual differences.
ò All opinions and assertions in this CLR MUST be supported by data from reliable sources (research
published in scholarly journals), and that data MUST be correctly cited in the text of the CLR and
accounted for in the “References” section.
ò A minimum of FOUR scholarly journal articles about research studies must be used in this CLR, at least
one article must examine the topic from a development perspective, one must examine the topic from a
biological perspective, one must examine how individual differences can help to explain the relationship
between the “causal” and “outcome” variables, and the fourth article can be from anyone of these or a
different perspective, such as social learning theory or other environmental factors.
ò Please feel free to use as many articles as you feel necessary, but they all must be from scholarly journals,
and they all must be accounted for in the “References” section. Textbooks, Wikipedia, and non-scholarly
on-line articles are not appropriate sources for college-level research papers.
ò LENGTH: 3000 words (approximately 8 pages, including title page and references)
ò APA-STYLE: Proper APA-formatting (including font, font size, margins, etc.), title and reference pages,
in-text citations, and references must be used throughout the paper.
ò Papers must be college-level—well-organized, thoughtful, and free of grammatical and spelling errors.
This paper must be intelligible, meaning that a reasonably intelligent person can understand what is being
o Please make ample use of the tutorial and writing resources available to you through the college,
online, and on our Canvas Page “PAPER WRITING RESOURCES & HELP” to assist completion of
this CLR.
ò DUE: MAY 20
Please submit through Canvas, it is not necessary to submit a hardcopy to class.

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