5 Typing Template for APA Papers: A Sample of Proper Formatting for APA Style Student A. Sample College Name, Grand Canyon University Course Number: Course Title Instructor’s Name Running head: ASSIGNMENT TITLE HERE 1 Assignment Due Date Typing Template for APA Papers: A Sample of Proper Formatting for APA Style This is an electronic template for papers written according to the style of the American Psychological Association (APA, 2020) as outlined in the seventh edition of the Publication Manual of the American Psychological Association . The purpose of the template is to help students set the margins and spacing. Margins are set at 1 inch for top, bottom, left, and right. The text is left-justified only; that means the left margin is straight, but the right margin is ragged. Each paragraph is indented 0.5 inch. It is best to use the tab key to indent, or set a first-line indent in the paragraph settings. The line spacing is double throughout the paper, even on the reference page. One space is used after punctuation at the end of sentences. The font style used in this template is Times New Roman and the font size is 12 point. This font and size is required for GCU papers. The Section Heading The heading above would be used if you want to have your paper divided into sections based on content. This is a Level 1 heading, and it is centered and bolded, and the initial word and each word of four or more letters is capitalized. The heading should be a short descriptor of the section. Note that not all papers will have headings or subheadings in them. Papers for beginning undergraduate courses (100 or 200 level) will generally not need headings beyond Level 1. The paper title serves as the heading for the first paragraph of the paper, so “Introduction” is not used as a heading. Subsection Heading The subheading above would be used if there are several sections within the topic labeled in a first level heading. This is a Level 2 heading, and it is flush left and bolded, and the initial word and each word of four or more letters is capitalized. Subsection Heading APA dictates that you should avoid having only one subsection heading and subsection within a section. In other words, use at least two subheadings under a main heading, or do not use any at all. Headings are used in order, so a paper must use Level 1 before using Level 2. Do not adjust spacing to change where on the page a heading falls, even if it would be the last line on a page. The Title Page When you are ready to write, and after having read these instructions completely, you can delete these directions and start typing. The formatting should stay the same. You will also need to change the items on the title page. Fill in your own title, name, course, college, instructor, and date. List the college to which the course belongs, such as College of Theology, College of Business, or College of Humanities and Social Sciences. GCU uses three letters and numbers with a hyphen for cours.

1. 5
Typing Template for APA Papers: A Sample of Proper Formatting for APA Style
Student A. Sample
College Name, Grand Canyon University
Course Number: Course Title
Instructor’s Name
Running head: ASSIGNMENT TITLE HERE
1
Assignment Due Date
Typing Template for APA Papers: A Sample of Proper Formatting for APA Style
This is an electronic template for papers written according to the style of the American
Psychological Association (APA, 2020) as outlined in the seventh edition of the Publication
Manual of the American Psychological Association . The purpose of the template is to help
students set the margins and spacing. Margins are set at 1 inch for top, bottom, left, and right.
The text is left-justified only; that means the left margin is straight, but the right margin is
ragged. Each paragraph is indented 0.5 inch. It is best to use the tab key to indent, or set a first-
line indent in the paragraph settings. The line spacing is double throughout the paper, even on the
reference page. One space is used after punctuation at the end of sentences. The font style used
in this template is Times New Roman and the font size is 12 point. This font and size is required
for GCU papers.
The Section Heading
The heading above would be used if you want to have your paper divided into sections based on
content. This is a Level 1 heading, and it is centered and bolded, and the initial word and each
word of four or more letters is capitalized. The heading should be a short descriptor of the
section. Note that not all papers will have headings or subheadings in them. Papers for beginning
undergraduate courses (100 or 200 level) will generally not need headings beyond Level 1. The
paper title serves as the heading for the first paragraph of the paper, so “Introduction― is
not used as a heading.
Subsection Heading
The subheading above would be used if there are several sections within the topic labeled in a
first level heading. This is a Level 2 heading, and it is flush left and bolded, and the initial word
and each word of four or more letters is capitalized.

2. Subsection Heading
APA dictates that you should avoid having only one subsection heading and subsection within a
section. In other words, use at least two subheadings under a main heading, or do not use any at
all. Headings are used in order, so a paper must use Level 1 before using Level 2. Do not adjust
spacing to change where on the page a heading falls, even if it would be the last line on a page.
The Title Page
When you are ready to write, and after having read these instructions completely, you can delete
these directions and start typing. The formatting should stay the same. You will also need to
change the items on the title page. Fill in your own title, name, course, college, instructor, and
date. List the college to which the course belongs, such as College of Theology, College of
Business, or College of Humanities and Social Sciences. GCU uses three letters and numbers
with a hyphen for course numbers, such as CWV-101 or UNV-104. The date should be written
as Month Day, Year. Spell out the month name.
Formatting References and Citations
APA Style includes rules for citing resources. The Publication Manual (APA, 2020) also
discusses the desired tone of writing, grammar, punctuation, formatting for numbers, and a
variety of other important topics. Although APA Style rules are used in this template, the
purpose of the template is only to demonstrate spacing and the general parts of the paper. GCU
has prepared an APA Style Guide available in the Student Success Center and on the GCU
Library’s Citing Sources in APA guide (https://libguides.gcu.edu/APA) for help in correctly
formatting according to APA Style.
The reference list should appear at the end of a paper. It provides the information necessary for a
reader to locate and retrieve any source you cite in the body of the paper. Each source you cite in
the paper must appear in your reference list; likewise, each entry in the reference list must be
cited in your text. A sample reference page is included below. This page includes examples of
how to format different reference types. The first reference is to a webpage without a clear date,
which is common with organizational websites (American Nurses Association, n.d.). Next is the
Publication Manual referred to throughout this template (APA, 2020). Notice that the manual
reference includes the DOI number, even though this is a print book, as the DOI was listed on
book, and does not include a publisher name since the publisher is also the author. A journal
article reference will also often include a DOI, and as this article has four authors, only the first
would appear in the in-text citation (Copeland et al., 2013). Government publications like the
Treatment Improvement Protocol series documents from the Center for Substance Abuse
Treatment (2014) are another common source found online. A book without a DOI is the last
example (Holland & Forrest, 2017).
References
American Nurses Association. (n.d.). Scope of practice . https://www.nursingworld.org/practice-
policy/scope-of-practice/

3. American Psychological Association. (2020). Publication manual of the American Psychological
Association (7th ed.). https://doi.org/10.1037/0000165-000
Center for Substance Abuse Treatment. (2014). Improving cultural competence (HHS
Publication No. 14-4849). U.S. Department of Health and Human Services, Substance Abuse
and Mental Health Services Administration. https://www.ncbi.nlm.nih.gov/books/NBK248428/
Copeland, T., Henderson, B., Mayer, B., & Nicholson, S. (2013). Three different paths for
tabletop gaming in school libraries. Library Trends, 61 (4), 825–835.
https://doi.org/10.1353/lib.2013.0018
Holland, R. A., & Forrest, B. K. (2017). Good arguments: Making your case in writing and
public speaking . Baker Academic.
Cortes - Conqueror of Mexico (45:20) Links to an external site.
Hernan Cortez: Conqueror of the Aztecs Links to an external site. - LiveScience.com
Hernan Cortez biography Links to an external site. - Biography.com
Aztec capital falls to Cortes Links to an external site. - History.com
Francisco Pizarro traps Incan emperor Atahualpa
Pizarro executes last Inca emperor - History.com
Read and learn about Cortez and the Aztecs and Pizarro and the Inca. Both had some advantages
and circumstances that helped them conquer Native people. Write a short account of the
conquering of the Aztecs by Cortez including who, what, when, where and why it happened. Be
sure to explain why it was so easy for Cortez and his men to conquer the mighty Aztec Empire.
There are at least ten reasons. Be sure to discuss at least half of those. Please explain each of
your examples. This is a college level assignment and I do expect college level writing. There
should be an introduction, content and conclusion. Write as if your reader knows nothing about
the topic.
No Grammatical or Spelling Errors
NO QUOTES
Double Space

4. 300 word minimum
Remember to write in your own words
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Assessing the Proactive and Reactive Dimensions of Criminal Thought Process: Divergent
Patterns of Correlation With Variable- and Person-Level Measures of Criminal Risk and Future
Outcome
Glenn D. Walters
Department of Criminal Justice, Kutztown University
ABSTRACT The goal of this study was to determine whether measures of proactive and reactive
criminal thinking display divergent patterns of correlation with outside criteria. A sample of
3,039 male medium-security federal prisoners who completed the Psychological Inventory of
Criminal Thinking Styles (PICTS) served as participants in this study. Despite being highly
correlated (r¼ .75), the PICTS proactive and reactive scales displayed divergent patterns of
correlation with the eight risk/outcome measures. As predicted, the proactive scale corresponded
with lower crim- inal risk, older age of first conviction, and decreased odds of prior substance
misuse and mental illness, whereas the reactive scale corresponded with higher criminal risk,
earlier age of first con- viction, greater odds of prior substance misuse and mental illness, and
more evidence of subse- quent arrest. Contrary to predictions, the proactive scale was associated
with increased rather than decreased commission of disciplinary infractions in prison. When
participants with elevated proactive scores were compared to participants with elevated reactive
scores on the eight risk/out- come variables, the results revealed that the two profiles were
moderately negatively correlated. Thus, although proactive criminal thinking is associated with
below-average criminal risk and below-average future negative outcomes, reactive criminal
thinking does just the opposite.
ARTICLE HISTORY Received 11 March 2018 Revised 11 July 2018
The proactive and reactive dimensions of criminal thought process (i.e., how rather than what an
offender thinks) in Walters’s (2012) two-dimensional model of adult criminal thinking has
its foundation in prior research on proactive and reactive childhood aggression. Like proactive

5. and react- ive childhood aggression (Dodge & Coie, 1987; Poulin & Boivin, 2000), proactive
and reactive criminal thinking over- lap extensively with one another yet appear to represent dis-
tinct concepts or processes (Walters, Hagman, & Cohn, 2011; Walters & Yurvati, 2017). In other
words, although correlating .50 or higher with each other, proactive and reactive aggression
(Martinelli, Ackermann, Bernhard, Freitag, & Schwenck, 2018) and proactive and reactive crim-
inal thinking (Walters, 2007) consistently display divergent patterns of association with various
outside criteria, such as hostile attribution biases. A developmental progression is therefore
proposed in which the instrumentality of proactive aggression gives rise to the planned and
calculated features of antisocial cognition, referred to as proactive criminal thinking, and the
impulsivity of reactive aggression gives rise to the reckless and emotional features of antisocial
cog- nition, referred to as reactive criminal thinking (Walters, 2005). Taken as a whole, the two
dimensions of criminal thought process explain the complex nature of crime and
the paradox of highly correlated scales that form divergent associations with the same external
criteria.
Just as proactive and reactive childhood aggression have different external correlates (Koolen,
Poorthuis, & van Aken, 2012; Swogger, Walsh, Maisto, & Conner, 2014; Urben et al., 2018), so,
too, do proactive and reactive criminal thinking correlate differentially with the same external
crite- ria. Research has fairly consistently demonstrated that react- ive criminal thinking
correlates better with indexes of criminal risk, as represented by scores on the Lifestyle Criminal
Screening Form (Walters, 1995; Walters & Elliott, 1999) and the second factor of the
Psychopathy Checklist (Walters & Di Fazio, 2016), than does proactive criminal thinking. There
is also evidence that whereas reactive crim- inal thinking mediates the past crime—future drug
use rela- tionship, proactive criminal thinking does not (Walters, 2016). When it comes to
predicting recidivism, proactive and reactive criminal thinking appear to correlate similarly with
subsequent offending (see Walters, 2012), but the effect size of the reactive scale typically
exceeds the effect size of the proactive scale when both scales are included as predic- tors in the
same regression equation (Walters & Lowenkamp, 2016). Finally, although reactive criminal
thinking tends to outperform proactive criminal thinking in
CONTACT Glenn D. Walters [email protected] Department of Criminal Justice, 361 Old
Main, Kutztown University, Kutztown, PA 19530-0730. � 2018 Taylor & Francis Group, LLC
JOURNAL OF PERSONALITY ASSESSMENT 2020, VOL. 102, NO. 2, 223–230
https://doi.org/10.1080/00223891.2018.1508469
http://crossmark.crossref.org/dialog/?doi=10.1080/00223891.2018.1508469&domain=pdf
http://orcid.org/0000-0002-7219-1542
https://doi.org/10.1080/00223891.2018.1508469
http://www.tandfonline.com
predicting institutional adjustment (Folk et al., 2016; Walters & Geyer, 2005), the opposite effect
has also been found (Walters & Mandell, 2007).

6. Does the fact that measures of proactive and reactive aggression and criminal thinking overlap
extensively mean that these scales are assessing the same construct, are redun- dant to one
another, or do not warrant separate treatment and interpretation? Some might argue that it
depends on the level of association between the two variables, yet two varia- bles can correlate
extensively and still not be measuring the same construct (Cronbach & Meehl, 1955). Hence, a
high correlation between two scores on a psychometric instrument should be considered a
necessary but not sufficient condition for concluding that the two scores are measuring the same
construct. Before it can be concluded that two scales are measuring the same construct, similar
patterns of convergent and discriminant correlation should be observed between scores on these
two scales (Smith, 2005; Westen & Rosenthal, 2005). Hence, if two scales correlate similarly
with the same set of external criteria then it is more likely they are measur- ing the same
construct, but if the scales achieve dissimilar patterns of correlation with the same set of external
criteria then it is more likely that they are measuring different con- structs. The purpose of this
investigation was to determine whether a criminal thinking measure designed to assess pro-
active and reactive criminal thought process exhibits divergent patterns of correlation with
external criteria despite a high degree of intercorrelation.
The Psychological Inventory of Criminal Thinking Styles (PICTS; Walters, 1995) is designed to
assess criminal thought process by providing scores on scales of proactive and react- ive criminal
thinking. As previously stated, proactive criminal thinking represents the planned, calculated,
and emotionless features of the criminal thought process, whereas reactive criminal thinking
encompasses the impulsive, irrational, and emotional aspects of the criminal thought process.
Walters and Yurvati (2017) examined the construct validity of the proactive and reactive scales
of the PICTS by correlating them with three putative measures of proactive criminal thought or
cognitive insensitivity (Moral Disengagement: Bandura et al., 1996; Offending, Crime, and
Justice Neutralization scale: Hamlyn et al., 2003; Denver Youth Survey [DYS] Neutralization
scale: Huizinga & Jakob-Chien, 1998) and three putative measures of reactive criminal thought
or cognitive impulsivity (Weinberger Adjustment Inventory–Impulse Control: Weinberger &
Schwartz, 1990; National Longitudinal Survey of Youth–Child Risk-Taking scale: Center for
Human Resource Research, 2009; DYS Impulsivity scale: Huizinga & Jakob-Chien, 1998).
Zero-order correlations and regression coefficients revealed that the PICTS proactive scale
corresponded significantly better with three putative proactive measures than with three putative
reactive measures, whereas the PICTS reactive scale corre- sponded significantly better with
three putative reactive meas- ures than with three putative proactive measures.
Because proactive criminal thinking encompasses the planned and calculated aspects of
antisocial cognition and
reactive criminal thinking subsumes the impulsive and irre- sponsible aspects, a reasonable
assumption is that reactive criminal thinking will be more closely tied to criminal risk factors and
the negative consequences of a criminal lifestyle than proactive criminal thinking. In other
words, the impul- sive and reckless nature of reactive criminal thinking makes it far more likely
that the individual will engage in less suc- cessful patterns of criminality and be at greater risk
for detection by law enforcement than the duplicity that evolves from proactive criminal
thinking. This is discussed in the childhood aggression literature, where the aggressive actions of
children who score higher on measures of reactive aggres- sion have a greater likelihood of

7. coming to the attention of parents and school officials than the aggressive actions of children
who score higher on measures of proactive aggres- sive (Card & Little, 2006; Rieffe et al., 2016).
Although dif- ferences between proactive and reactive aggression have been consistently found
at the variable level, the research is mixed when it comes to comparisons made at the person
level (Carroll, McCarthy, Houghton, O’Connor, & Zadow, 2018; Smeets et al., 2017).
Accordingly, this study examined differences in proactive and reactive criminal thinking at both
the variable and person levels.
This study
The purpose of this investigation was to determine whether reactive criminal thinking, because
of its impetuous and irresponsible nature, is more closely tied to criminal history risk than
proactive criminal thinking, despite a moderate to high degree of intercorrelation between the
two forms of criminal thought process. In the previously mentioned Walters and Yurvati (2017)
study, proactive and reactive latent factors achieved divergent patterns of correlation with
alternate measures of proactive and reactive criminal think- ing despite correlating .65 with each
other. In the present study, a large group of incarcerated felons who had been administered the
PICTS within several weeks of entering a medium-security federal prison were evaluated for
criminal risk and future behavioral problems using both variable-level and person-level data. It
was predicted that proactive and reactive criminal thinking would display divergent patterns of
correlation at both the variable and person levels.
The research questions that drove this study were both conceptual and practical. Conceptually,
this study was designed to determine whether proactive criminal thinking is less apt to be
associated with criminal risk and poor out- comes than reactive criminal thinking, presumably
because it is less subject to detection by law enforcement, just as proactive aggression is less
subject to detection by parents and school officials than reactive aggression (Card & Little, 2006;
Rieffe et al., 2016). Practically, this study was designed to determine whether administering
measures of both pro- active and reactive criminal thinking is worthwhile, given an extensive
degree of overlap between the two scales. It was hypothesized that historical measures of
criminal risk (e.g., prior convictions, substance misuse) and prospective
224 WALTERS
measures of negative outcome (i.e., institutional misconduct and recidivism) would correlate
positively with (variable- level analysis) and be above average (person-level analysis) on the
reactive scale and correlate negatively with and be below average on the proactive scale.
Method
Participants
The sample for this study consisted of 3,039 male inmates who completed the PICTS as part of a
routine intake evaluation for inmates entering a medium-security federal prison sometime
between March 2003 and August 2010. This number represents over 95% of all inmates admitted
to this medium-security insti- tution during the time period in which data were collected. The

8. average age of participants at the time of evaluation was 35.0 years (SD¼ 9.87) and the racial
and ethnic breakdown was 63.0% African American, 18.4% Hispanic, 17.2% White, 0.8%
Asian, and 0.6% Native American.
Measures
The PICTS is an 80-item self-report measure designed to assess eight criminal thinking patterns
or styles: mollifica- tion, cutoff, entitlement, power orientation, sentimentality, superoptimism,
cognitive indolence, and discontinuity (Walters, 1995). Seven of the eight PICTS thinking style
scales have been found to load onto one of two higher order factors referred to as proactive
(mollification, entitlement, power orientation, and superoptimism) and reactive (cutoff, cognitive
indolence, and discontinuity) criminal thinking. Whereas proactive criminal thinking reflects the
planned, calculated, and callous or unemotional features of antisocial cognition, reactive criminal
thinking reflects the impulsive, irresponsible, and emotional features. The internal consist- ency,
stability, and predictive and construct validity of the PICTS dimensional scales (proactive and
reactive) have received support in several studies conducted over the last several years (Walters,
2012).
Eight variables served as dependent variables in this study. Four of the variables were criminal
history or criminal risk indicators: number of prior convictions, age at first conviction (in years),
total score based on retrievable items from the Lifestyle Criminality Screening Form (LCSF;
Walters, White, & Denney, 1991), and Facet 4 (Antisocial) of the Psychopathy
Checklist–Revised (PCL–R; Hare, 2003). The PCL–R items were scored exclusively from
file data (presentence investiga- tion report [PSI]) and were restricted to Facet 4 because these
were the only items addressed with regularity in the PSI. Fifty randomly selected cases were
independently rated on Facet 4 of the PCL–R by a second rater. These ratings were then com-
pared to the original ratings using a two-way mixed effects model (absolute agreement, average
measures). The results revealed that the raters achieved an above-average level of interrater
agreement on the Facet 4 measure (intraclass correl- ation coefficient [ICC]¼ .84).
The last four dependent variables were prior substance misuse (yes–no), prior mental illness
(yes–no), number of disciplinary reports received for institutional infractions dur- ing a 1- to
76-month (M¼ 30.03) period of incarceration, and number of subsequent arrests experienced
during a 1- to 76-month (M¼ 25.33) follow-up. The regression analyses performed on the
disciplinary reports and subsequent arrests outcome measures included time at risk in prison and
time at risk in the community, respectively, as covariates, along with age and race. For the
profile comparison portion of the study, number of disciplinary reports received was divided by
number of months (time at risk) in federal prison to cre- ate a rate of disciplinary infractions
indicator, and subse- quent arrests were divided by number of months (time at risk) in the
community to create a rate of subsequent arrests indicator.
Data collection
Descriptive statistics were computed for the two independ- ent variables (proactive and reactive
criminal thinking) and eight dependent variables (prior convictions, age at first con- viction,
LCSF total score, Facet 4 of PCL–R, prior substance misuse, prior mental illness

9. [schizophrenia, bipolar disorder, major depression], disciplinary infractions, and subsequent
arrests) included in this study. Data for the independent variable came from the PICTS and data
for the dependent variables came from a review of electronic files maintained by the Federal
Bureau of Prisons (presentence investigation report, disciplinary files) or other federal law
enforcement agencies (FBI National Crime Information Center). Data were complete for all
measures except for subsequent arrests. This was because only 1,435 members of the study
cohort had been released from custody at the time the arrest outcome data were being collected.
The use of these data for research purposes was approved by the Federal Bureau of Prisons and
Kutztown University institutional review boards.
Data analysis
Data were analyzed at both the variable and person levels. Eight regressions were performed at
the variable level, one for each dependent variable in this study. The three continu- ous
dependent variables (age at first conviction, LCSF total score, and PCL–R Facet 4 score) were
assessed with stand- ard regression and a maximum likelihood (ML) estimator. The two
dichotomous dependent variables (substance misuse and mental illness) were assessed with
binomial logistic regression analysis and the three count-dependent variables (prior convictions,
disciplinary reports, and subsequent arrests) were assessed with negative binomial regression. In
the latter two regressions, a maximum likelihood with robust standard errors (MLR) estimator
was employed. Age (in years) and race (White¼ 1, non-White¼ 2) were included as
covariates in all eight regressions, whereas time spent in prison served as a third covariate in the
regression
DIVERGENT PATTERNS OF CORRELATION 225
equation predicting disciplinary reports and time at risk in the community was added to the
regression equation pre- dicting subsequent arrests. All analyses were performed with Mplus 8.1
(Muth�en & Muth�en, 1998–2017).
The second step of the data analysis entailed assigning individual cases to four patterns using
clinical guidelines provided in the PICTS manual (Walters, 2013) and then performing several
person-level analyses. The four patterns used in this study were an elevated proactive pattern
(P�T score of 60, R<T score of 60), an elevated reactive pattern (P<T score of 60, R�T
score of 60), an elevated proactive and reactive pattern (P�T score of 60 and R�T score of
60), and an unelevated pattern (P<T score of 60, R<T score of 60). The outcome measures were
first standardized (z scores) and then the mean scores for each outcome were calculated. These
mean score profiles were then compared across the four groups of patterns using the double-
entry ICC (McCrae, 2008). This was done to determine the degree to which the four groups
differed from one another on the eight outcome measures.
Results
Descriptive statistics for the two independent variables and eight dependent variables used in this
study are summarized in Table 1. An intercorrelational matrix of the eight depend- ent variables
revealed a modest degree of association between variables (M¼ .18, SD¼ .18, range¼

10. .02�.57), with the highest correlations (.42�.57) occurring between the four criminal history
indicators (prior convictions, age at first conviction, LCSF total score, PCL–R Facet 4 score).
The two independent variables (PICTS proactive and react- ive scales) correlated at r¼ .75.
Variable-level analyses
Table 2 summarizes the variable-level results attained by P and R in each of the eight regression
analyses. P was associ- ated with reduced odds of achieving four outcomes (LCSF total score,
PCL–R Factor 4 score, substance misuse, and mental illness), increased odds of achieving two
outcomes (age at first conviction and disciplinary reports), and non- significant results on two
outcomes (prior convictions and subsequent arrests). R was associated with increased odds of
achieving six outcomes (LCSF total score, PCL–R Facet 4 score, substance misuse, mental
illness, prior convictions, and subsequent arrests), reduced odds of achieving one out- come (age
at first conviction), and nonsignificant results for one outcome (disciplinary reports).
With the exception of the association between higher P and increased odds of disciplinary
infractions, these results are fully congruent with the research hypothesis tested in this study.
Whereas the standardized regression coefficients were small to modest, the odds ratios obtained
from the binomial logistic regression and negative binomial regression analyses were even
smaller. These latter results consequently provide meaningful support for the conceptual goal of
this study but are not particularly informative when it comes to
the practical goal of using the P and R scales to predict dichotomous and count risk and outcome
measures.
Person-level analyses
The outcome profiles of individuals achieving elevated scores (T� 60) on the proactive scale,
the reactive scale, the proactive and reactive scales, and neither scale are presented in Table 3 as
person-level analyses. Assessing strength of relationship with the double-entry ICC, it was noted
that the proactive and reactive patterns achieved a moderately strong inverse correlation with one
another. Although the reactive pattern achieved a strong positive correlation with the dual
elevation pattern, in which both P and R were ele- vated, the proactive pattern correlated
positively, although only weakly, with the unelevated pattern.
The person-level results provide support for both the conceptual and practical objectives of this
study. A correl- ation of –.56 between the risk/outcome patterns for inmates who elevated the
proactive scale alone and risk/outcome pat- terns for inmates who elevated both the proactive
and react- ive scales compared to a correlation of .69 between the risk/ outcome patterns for
inmates who elevated the reactive scale alone and risk/outcome patterns for inmates who
elevated both scales is striking. Coupled with the fact that the risk/ outcome patterns for the
proactive group correlated minim- ally yet positively with the risk/outcome patterns for the
unelevated group and the risk/outcome patterns for the reactive group correlated negatively with
the risk/outcome patterns for the unelevated group, this suggests that inmates who elevated only
the proactive scale were more similar to inmates who did not elevate either scale, whereas

11. inmates who elevated only the reactive scale were more similar to inmates who elevated both
scales.
Table 1. Descriptive statistics for the 10 variables included in this investigation.
Variable n M SD Range
Prior convictions 3,039 4.28 2.63 0–30 Age at first conviction 3,039 20.74 5.93 7–62 LCSF
total score 3,039 4.57 1.88 0–10 PCL–R Facet 4 score 3,039 3.15 1.92 0–10 Disciplinary
reports 3,039 1.44 2.63 0–41 Subsequent arrests 1,435 1.21 1.62 0–14 Proactive dimension
3,039 52.48 13.66 32–128 Reactive dimension 3,039 43.06 13.35 24–96
n No. (%) No. (%)
Substance misuse (yes–no) 3,039 2,055 (67.6%) 984 (32.4%) Mental illness (yes–no) 3,039
476 (15.7%) 2,563 (84.3%)
Note: Variable¼ postdicted or predicted dependent variable or one of the independent
variables; prior convictions¼ prior criminal convictions; age at first conviction¼ age at time
of first conviction; LCSF total score¼ total score from the Lifestyle Criminality Screening
Form; PCL–R Facet 4 score¼ Facet 4 (antisocial) score from the Psychopathy
Checklist–Revised; disciplinary reports¼ number of disciplinary reports received in prison
con- trolling for time at risk; subsequent arrests¼ number of subsequent arrests following
release from prison after controlling for time at risk; proactive dimension¼ Psychological
Inventory of Criminal Thinking Styles (PICTS) Proactive (P) scale score; reactive dimension¼
PICTS Reactive (R) scale score; substance misuse¼ history of prior substance misuse versus
no history of prior substance misuse; mental illness¼ history of mental illness versus no history
of mental illness; n¼ participants with nonmissing data.
226 WALTERS
Discussion
As anticipated, the PICTS proactive and reactive scales were highly correlated, with the strength
of correlation suggesting that the two scales shared more than half their variance in common.
Despite extensive overlap, the scales displayed divergent patterns of association with six
measures of crim- inal risk and two measures of future criminal outcome using both variable-
and person-level analyses. In nearly every case the reactive scale was associated with higher
levels of criminal risk and negative outcome, whereas the proactive scale was associated with
lower levels of criminal risk and negative outcome. Hence, the reactive scale was associated with
an above-average number of prior convictions, an ear- lier age of first conviction, higher LCSF
and PCL–R risk scores, more evidence of prior substance misuse and mental illness, and a
greater likelihood of subsequent arrest, whereas the proactive scale was associated with a below-
average number of prior convictions, a later age of first conviction, lower LCSF and PCL–R
risk scores, less evidence of prior substance misuse and mental illness, and significantly higher

12. levels of institutional infractions. These results are largely consistent with the notion that reactive
criminal thinking, by virtue of its impulsive and irresponsible nature, is more likely to be
associated with higher criminal risk and a greater proportion of future crime-related problems.
These results corroborate prior findings from the Walters and Yurvati (2017) study, which also
used the PICTS to assess criminal thought process, and suggest that proactive and reactive
criminal thinking are distinct constructs, despite their overlap. The one inconsistent finding (i.e.,
above-aver- age institutional misconduct in relationship to proactive criminal thinking) warrants
further discussion.
The relationship between institutional misconduct and proactive criminal thinking reminds us
that proactive crim- inal thinking is not simply a less discriminating version of reactive criminal
thinking. It was hypothesized that pro- active criminal thinking would correlate negatively with
criminal risk and show better outcomes than reactive crim- inal thinking because it is not saddled
with the impulsivity and low self-control that afflict reactive criminal thinking. It is for this
reason that individuals with profiles in which only proactive criminal thinking is elevated might
be less subject to detection by law enforcement than individuals with pro- files in which only
reactive criminal thinking is elevated. Why, then, was proactive criminal thinking associated
with a higher rate of institutional misconduct than reactive crim- inal thinking? Although prior
research indicates that pro- active criminal thinking is associated with lower levels of
institutional misconduct relative to reactive criminal think- ing (Folk et al., 2016; Walters &
Geyer, 2005), there is at least one other study that agrees with the results reported here (Walters
& Mandell, 2007). It is possible that the struc- ture provided by prison diminishes the role of
reactive crim- inal thinking in the behaviors that lead to prison misconduct, such that proactive
criminal thinking is just as likely to be associated with the violation of prison rules as reactive
criminal thinking, if not more so, because in such a highly structured environment stealth and
subterfuge are less likely to provide protection. That institutional miscon- duct correlated with
proactive criminal thinking might mean that proactive criminal thinking is just as problematic as
Table 3. Mean scores and double-entry intraclass correlations for participants with elevated
proactive profiles, elevated reactive profiles, elevated proactive and reactive profiles, and
nonelevated profiles.
High Pa High Rb High P & Rc Nonelevatedd
Group means Prior convictions �0.153 0.200 0.198 �0.041 Age at first conviction 0.039
�0.201 �0.104 0.036 Substance misuse �0.013 0.183 0.117 �0.038 Mental illness
�0.028 0.375 0.169 �0.067 LCSF total score �0.096 0.324 0.209 �0.062 PCL–R
Facet 4 score �0.190 0.202 0.108 �0.024
Disciplinary reports rate 0.159 �0.013 0.132 �0.033 Subsequent arrests rate �0.050
�0.017 0.102 �0.011 Double-entry intraclass correlations High P �.55 �.56 .19 High R
.69 �.44 High P & R �.77
Note. Group means¼ z scores; High P¼ participants with proactive (P) T scores �60 and
reactive (R) T scores <60; High R¼ participants with P T scores <60 and R T scores �60;
High P & R¼ participants with P T scores �60 and R T scores �60; nonelevated¼

13. participants with P T scores <60 and R T scores <60. LCSF¼ Lifestyle Criminality Screening
Form; PCL–R¼ Psychopathy Checklist–Revised.
an¼ 191. bn¼ 256. cn¼ 353. dn¼ 2,239.
Table 2. Regression results for the proactive and reactive dimension scores.
Variable Proactive dimension Reactive dimension
Continuous outcomes b [95% CI] b z p b [95% CI] b z p Age at first conviction 0.037 [0.016,
0.058] 0.084 3.41 <.001 �0.063 [�0.085, �0.042] �0.143 �5.84 <.001 LCSF total
score �0.017 [�0.025, �0.010] �0.125 �4.55 <.001 0.034 [0.026, 0.041] 0.240 8.86
<.001 PCL–R Facet 4 score �0.018 [�0.026, �0.011] �0.131 �4.76 <.001 0.028
[0.020, 0.035] 0.191 7.00 <.001
Dichotomous outcomes b [95% CI] OR z p b [95% CI] OR z p Substance misuse �0.011
[�0.020, �0.002] 0.971 �2.47 .014 0.023 [0.013, 0.032] 1.023 4.80 <.001 Mental illness
�0.017 [�0.028, �0.005] 0.984 �2.92 .003 0.035 [0.024, 0.046] 1.036 6.28 <.001
Frequency count outcomes b [95% CI] exp(b) z p b [95% CI] exp(b) z p Prior convictions
�0.002 [�0.005, 0.000] 0.998 �1.92 .054 0.007 [0.005, 0.010] 1.007 6.45 <.001
Disciplinary reports 0.008 [0.002, 0.015] 1.008 2.49 .013 0.001 [�0.006, 0.008] 1.001 0.31
.756 Subsequent arrests 0.001 [�0.006, 0.007] 1.001 0.16 .871 0.008 [0.001, 0.015] 1.008 2.22
.027
Note: Age (in years) and race (1¼White, 2¼ non-White) were included in each of the eight
regressions as covariates; in addition, time at risk in federal prison served as a covariate in the
disciplinary reports regression and time at risk in the community served as a covariate in the
subsequent arrests regression. Variable¼ postdicted or predicted dependent variable;
continuous outcomes were subjected to least squares multiple regression, dichotomous outcomes
were subjected to binomial logistic regression analysis, and frequency count outcomes were
subjected to negative binomial regression; b [95% CI]¼ unstandardized coefficient with the
95% confidence interval, b¼ standardized coefficient in least squares regression; OR¼
logistic regression odds ratio; exp(b)¼ incidence rate ratio; z¼Wald Z-test, p¼ significance
level of the Wald Z-test.
DIVERGENT PATTERNS OF CORRELATION 227
reactive criminal thinking, although in less structured situa- tions someone with a proactive
PICTS profile might have a better chance of avoiding detection by law enforcement than if they
were in a more structured situation. This possibility requires further study.
Proactive and reactive aggression and criminal thinking
It should be noted that the results reported here place pro- active and reactive criminal thinking
squarely within the broader context of research on proactive and reactive aggression. Although a

14. fairly extensive body of research exists in support of the argument that proactive and reactive
aggression represent distinct processes despite being highly correlated (Polman, Orobio de
Castro, Koops, van Boxtel, & Merk, 2007), some researchers have questioned the mean-
ingfulness of the proactive—reactive distinction in aggressive behavior (Bushman & Anderson,
2001). One reason for the skepticism is the degree of overlap and lack of orthogonality between
the two constructs. Because much of the research on proactive and reactive aggression has been
conducted at the variable level, researchers have started studying the pro- active—reactive
question with both variable-level and per- son-level data. Adopting this approach, Smeets et al.
(2017) observed variable-level differences between proactive and reactive aggression but failed
to find consistent support for person-level differences. Carroll et al. (2018), by comparison,
found meaningful distinctions between proactive and react- ive aggression at both the variable
and person levels. This study is more in line with the Carroll et al. (2018) results in identifying
meaningful differences between proactive and reactive criminal thinking at both the variable
(regression analyses) and person (elevation patterns) levels despite a high degree of
intercorrelation. In fact, the person-level find- ings were even stronger than the variable-level
results in this study. This suggests that proactive and reactive criminal thinking, although not
identical to proactive and reactive aggression, can be understood and studied within the larger
context of the proactive—reactive aggression literature.
It would be a mistake to conclude on the basis of these results that proactive criminal thinking is
less dangerous or problematic than reactive criminal thinking. In many ways, proactive criminal
thinking might be more dangerous and more problematic than reactive criminal thinking. The
fact that proactive criminal thinking is less likely to lead to immediate negative consequences
than reactive criminal thinking—in other words, that criminal behavior inspired by proactive
criminal thinking has a greater likelihood of going undetected, at least initially—does not make
it innocuous. We need only consider the instrumental/proactive—expres- sive/reactive
breakdown of homicide motives to find a paral- lel in another area of criminology to illustrate
this point. In an early study on instrumental—expressive motives for homicide, Miethe and
Drass (1999) discovered that 36% of the situational factors they examined were unique to instru-
mental homicides, 30% were unique to expressive homicides, and 34% were common to both
forms of homicide. Similar to proactive and reactive criminal thinking, instrumental
and expressive homicide are more different than they are similar, despite the fact many
homicides are driven by a combination of instrumental and expressive motives (Adjorlolo &
Chan, 2017). Just because instrumental homi- cides are more difficult to solve and are more
likely to go unsolved than expressive homicides makes them no less worthy of law enforcement
attention (Salfati & Bateman, 2005). The same could be said for proactive and reactive criminal
thinking, where the risk and outcome effects might be stronger for reactive criminal thinking but
where the degree of support for a criminal lifestyle is equal across these two dimensions of
criminal thought process.
Theoretical and practical implications
There are both theoretical and practical implications to these results. One theoretical implication
is that despite their high intercorrelation (.75 in this study), the proactive and reactive scales of
the PICTS appear to be measuring different con- structs. Results from the Walters and Yurvati

15. (2017) study revealed that the PICTS proactive and reactive scales were assessing latent
constructs with features that reflected the pro- active (planned, calculated, and callous) and
reactive (impul- sive, irresponsible, and emotional) dimensions of criminal thought process,
respectively. According to the results reported here, scores on the PICTS proactive and reactive
scales corre- lated differentially with criminal risk and outcome. With one notable exception, the
proactive scale correlated negatively with several criminal risk measures, whereas the reactive
scale corre- lated positively with these same measures and subsequent arrests. When the mean
profiles of risk and outcome measures were compared for PICTS with elevated proactive
criminal thinking and elevated reactive criminal thinking, the outcome was a moderately strong
inverse double-entry ICC. A practical implication that can be drawn from these results is that the
PICTS proactive and reactive scales potentially provide infor- mation useful in evaluating and
managing prison inmates. Individuals with elevations on either scale are at risk for future
problems, although the problems will differ depending on the relative elevation of each scale.
Interventions differ depending on whether reactive (e.g., problem solving and cognitive skills
training) or proactive (e.g., moral education and cognitive restructuring) criminal thinking is
elevated, so a comprehen- sive evaluation will be of assistance in establishing the appro- priate
treatment for whichever pattern is present.
Limitations
In closing, it is important to consider several study limita- tions. First, the sample consisted of
male inmates housed in a single medium-security federal correctional institution. As such, the
generalizability of these results to female prisoners, nonincarcerated offenders, state and jail
inmates, and felons housed in low- or high-security facilities cannot be assumed. The
generalizability of the recidivism findings is also an issue because inmates serving longer
sentences were less likely to have been released from confinement and included in the recidivism
analyses than inmates serving shorter
228 WALTERS
sentences. A second potential limitation of this study is that all eight dependent variables came
from official records, a procedure that could have limited the scope and depth of analysis. A
deeper analysis could have produced richer infor- mation through inmate self-report and the
inclusion of dependent variables that assess offender attitudes (criminal thought content),
expectancies, and attributions. Third, the PICTS was administered at a single point in time (i.e.,
intake). PICTS administered at a later date, after the inmate had become more accustomed to
incarceration, or at mul- tiple times to assess changes in antisocial cognition might have painted
a more accurate or representative picture of the inmate’s criminal thought process. Fourth,
the procedure used to assess similarity between outcome profiles—the dou- ble-entry ICC—is
one of the more popular approaches to determining the extent to which the scatter, elevation, and
shape of the different outcome profiles corresponded with one another. It has been argued that
the double-entry ICC’s superiority to alternative procedures has not been demon- strated, but
neither is there evidence that it is inferior to these other procedures (Furr, 2010). Sixth, the effect
sizes for the dichotomous and count outcomes were very small, although it should be noted that
in each case these were regression coefficients that controlled for both age and race.

16. Conclusion
In this study, findings from variable- and person-level analy- ses confirmed that the constructs of
proactive and reactive criminal thinking, despite extensive overlap, are distinct, separate, and
meaningful entities and that scales based on these constructs could have practical utility in
assessing offender risk and predicting future outcome.
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230 WALTERS
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 Abstract
o Outline placeholder
 This study
o Method
 Participants
 Measures
 Data collection
 Data analysis
o Results
 Variable-level analyses
 Person-level analyses
o Discussion
 Proactive and reactive aggression and criminal thinking

20.  Theoretical and practical implications
 Limitations
o Conclusion
o References
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