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MS Psychology
3.2: Critically evaluate psychological research.
3.4: Apply ethical standards to evaluate psychological science and practice.
4.1: Demonstrate effective writing for scientific purposes.
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 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 walters@kutztown.edu 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).
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 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
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 [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 Results
Descriptive statistics for the two independent variables and Variable-level analyses
Table 2 summarizes the variable-level results attained by P With the exception of the association between higher P the practical goal of using the P and R scales to predict Person-level analyses
The outcome profiles of individuals achieving elevated The person-level results provide support for both the Table 1. Descriptive statistics for the 10 variables included in this Variable n M SD Range
Prior convictions 3,039 4.28 2.63 0–30 n No. (%) No. (%)
Substance misuse (yes–no) 3,039 2,055 (67.6%) 984 (32.4%) Note: Variable¼ postdicted or predicted dependent variable or one of the 226 WALTERS Discussion
As anticipated, the PICTS proactive and reactive scales were levels of institutional infractions. These results are largely The relationship between institutional misconduct and Table 3. Mean scores and double-entry intraclass correlations for participants High Pa High Rb High P & Rc Nonelevatedd
Group means Disciplinary reports rate 0.159 �0.013 0.132 �0.033 Note. Group means¼ z scores; High P¼ participants with proactive (P) T scores 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 Dichotomous outcomes b [95% CI] OR z p b [95% CI] OR z p Frequency count outcomes b [95% CI] exp(b) z p b [95% CI] exp(b) z p 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 DIVERGENT PATTERNS OF CORRELATION 227 reactive criminal thinking, although in less structured situa- Proactive and reactive aggression and criminal thinking
It should be noted that the results reported here place pro- It would be a mistake to conclude on the basis of these and expressive homicide are more different than they are Theoretical and practical implications
There are both theoretical and practical implications to these Limitations
In closing, it is important to consider several study limita- 228 WALTERS sentences. A second potential limitation of this study is that Conclusion
In this study, findings from variable- and person-level analy- References
Adjorlolo, S., & Chan, H. C. (2017). The nature of instrumentality and Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (1996). Bushman, B. J., & Anderson, C. A. (2001). Is it time to pull the plug Card, N. A., & Little, T. D. (2006). Proactive and reactive aggression in Carroll, A., McCarthy, M., Houghton, S., O’Connor, E. S., & Zadow, C. Center for Human Resource Research. (2009). NLSY79 user’s guide. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psycho- Dodge, K. A., & Coie, J. D. (1987). Social-information processing fac- Folk, J. B., Disabato, D. J., Daylor, J. M., Tangney, J. P., Barboza, S., Furr, R. M. (2010). The double-entry intraclass correlation as an index Hamlyn, B., Maxwell, C., Hales, J., & Tait, C. (2003). The 2003 Crime Hare, R. D. (2003). The Hare Psychopathy Checklist–Revised Manual Huizinga, D., & Jakob-Chien, C. (1998). The contemporaneous co- Koolen, S., Poorthuis, A., & van Aken, M. A. G. (2012). Cognitive dis- Martinelli, A., Ackermann, K., Bernhard, A., Freitag, C. M., & McCrae, R. R. (2008). A note on some measures of profile agreement. Miethe, T. D., & Drass, K. A. (1999). Exploring the social context of Muth�en, B., & Muth�en, L. (1998–2017). Mplus user’s guide (8th ed.). Polman, H., Orobio de Castro, B., Koops, W., van Boxtel, H. W., & Poulin, F., & Boivin, M. (2000). Reactive and proactive aggression: Rieffe, C., Broekhof, E., Kouwenberg, M., Faber, J., Tsutsui, M. M., & Salfati, C. G., & Bateman, A. L. (2005). Serial homicide: An investiga- Smeets, K. C., Oostermeijer, S., Lappenschaar, M., Cohn, M., van der Smith, G. T. (2005). On construct validity: Issues of method and meas- Swogger, M. T., Walsh, Z., Maisto, S. A., & Conner, K. R. (2014). Urben, S., Habersaat, S., Pihet, S., Suter, M., Ridder, J., & St�ephan, P. Walters, G. D. (1995). The Psychological Inventory of Criminal Walters, G. D. (2005). Proactive and reactive aggression: A lifestyle Walters, G. D. (2007). Measuring proactive and reactive criminal think- DIVERGENT PATTERNS OF CORRELATION 229 Walters, G. D. (2012). Crime in a psychological context: From career Walters, G. D. (2013). The Psychological Inventory of Criminal Walters, G. D. (2016). Mediating the distal crime-drug relationship Walters, G. D., & Di Fazio, R. (2016). Psychopathy and the criminal Walters, G. D., & Elliott, W. N. (1999). Predicting release and discip- Walters, G. D., & Geyer, M. D. (2005). Construct validity of the Walters, G. D., Hagman, B. T., & Cohn, A. M. (2011). Toward a hier- theory and confirmatory factor analysis. Psychological Assessment, Walters, G. D., & Lowenkamp, C. T. (2016). Predicting recidivism with Walters, G. D., & Mandell, W. (2007). Incremental validity of the Walters, G. D., White, T. W., & Denney, D. (1991). The Lifestyle Walters, G. D., & Yurvati, E. (2017). Testing the construct validity of Weinberger, D. A., & Schwartz, G. E. (1990). Distress and restraint as Westen, D., & Rosenthal, R. (2005). Improving construct validity: 230 WALTERS Copyright of Journal of Personality Assessment is the property of Taylor & Francis Ltd and Outline placeholder This study Method Participants Measures Data collection Data analysis Results Variable-level analyses Person-level analyses Discussion Proactive and reactive aggression and criminal thinking Theoretical and practical implications Limitations Conclusion References 5
Typing Template for APA Papers: A Sample of Proper Formatting for APA Style
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References
American Nurses Association. (n.d.). American Psychological Association. (2020). Center for Substance Abuse Treatment. (2014). Copeland, T., Henderson, B., Mayer, B., & Nicholson, S. (2013). Three different paths for tabletop gaming in school libraries. Holland, R. A., & Forrest, B. K. (2017).
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
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 .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.
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).
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
dichotomous and count risk and outcome measures.
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.
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 inmates
who elevated only the reactive scale were more similar to
inmates who elevated both scales.
investigation.
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
Mental illness (yes–no) 3,039 476 (15.7%) 2,563 (84.3%)
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.
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
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.
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
with elevated proactive profiles, elevated reactive profiles, elevated proactive
and reactive profiles, and nonelevated profiles.
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
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
�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 participants with P T scores <60 and R T scores
<60. LCSF¼ Lifestyle Criminality Screening Form; PCL–R¼ Psychopathy
Checklist–Revised.
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
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
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
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.
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.
active and reactive criminal thinking squarely within the
broader context of research on proactive and reactive
aggression. Although a 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.
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
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.
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 (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.
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
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.
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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