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http://cjb.sagepub.com/content/35/12/1459The online version of this article can be found at:

DOI: 10.1177/0093854808320922

2008 35: 1459 originally published online 10 September 2008Criminal Justice and BehaviorGlenn D. Walters

Testing and Validating a Two-Dimensional ModelSelf-Report Measures of Psychopathy, Antisocial Personality, and Criminal Lifestyle :

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SELF-REPORT MEASURES OF PSYCHOPATHY,

ANTISOCIAL PERSONALITY, AND

CRIMINAL LIFESTYLETesting and Validating a Two-Dimensional Model

GLENN D. WALTERSFederal Correctional Institution, Schuylkill, Pennsylvania

This article reports results from five studies. Exploratory factor analysis was used to select indicators from the Psychological

Inventory of Criminal Thinking Styles, Levenson Self-Report Psychopathy scales, and Personality AssessmentInventoryAntisocial Features Scale. The 10 indicators were subjected to confirmatory factor analysis, the results of which

show that the two-dimensional model (proactive, reactive) achieves significantly better fit than a general one-factor model

and a two-factor social learning model (criminal thinking, antisocial behavior) with 521 medium-security and 116 maximum-

security inmates. The construct validity of the two-dimensional model is confirmed in a path analysis pairing (a) proactive

scales with positive outcome expectancies for crime and (b) reactive scales with hostile attribution biases. Implications for a

unified theory of aggression and criminality are discussed.

Keywords: Personality Assessment Inventory; Levenson Self-Report Psychopathy; Psychological Inventory of Criminal

Thinking Styles; proactive; reactive

Whereas research and practice in forensic psychology have grown at an unprecedentedrate, theory has failed to keep pace with new developments in the field. Forensicresearchers and practitioners who are looking for theoretical inspiration and guidance must

consequently find both in theories from related disciplines or in general psychological prin-

ciples that overlook the intricacies of forensic psychology research and practice. Theory is

barely mentioned in three recently published textbooks on forensic psychology (Bartol &

Bartol, 2004; Goldstein, 2007; Weiner & Hess, 2006), and the two families of theory that

receive the most attention in these bookspersonality models (Cleckley, 1976; Hare, 1996)

and behavioral models (Andrews & Bonta, 1998; Robins, 1966)are seriously flawed as

general explanations of crime and forensic psychology (see Walters, 2004). From physics

to psychology, it is well known that to remain viable, a field must be grounded in substan-

tive theory. Psychopathy, antisocial personality, and criminal lifestyle are three constructs that

have been offered as possible psychological explanations for criminal behavior. The simi-

larities between these three constructs are striking and so suggest that they share structural

1459

CRIMINAL JUSTICE AND BEHAVIOR, Vol. 35 No. 12, December 2008 1459-1483

DOI: 10.1177/0093854808320922

2008 International Association for Correctional and Forensic Psychology

AUTHORS NOTE:I would like to thank Matthew Geyer, Charles Schlauch, and Patti Walters for their assis-

tance in collecting and entering data for this project. The assertions and opinions contained herein are my pri-

vate views and should not be construed as being official or as reflecting the views of the Federal Bureau of

Prisons or the U.S. Department of Justice. Address all correspondence to Glenn D. Walters, Psychology

Services, FCI-Schuylkill, PO Box 700, Minersville, PA 17954-0700; e-mail: [email protected].

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and content features. If research could demonstrate that these three crime-related constructs

lie along the same dimension or dimensions, then perhaps we would have the beginnings of

a substantive theory of forensic psychology to guide research and practice in the field.

A TWO-DIMENSIONAL MODEL OF CRIMINALITY

A critical first step in developing a theoretical model is to determine the underlying, or

latent, structure of the construct on which the model is based, and one way to do so is with

taxometric analysis. The taxometric method (Meehl, 1995; Ruscio, Haslam, & Ruscio,

2006) allows researchers to gauge whether the latent structure of a construct is categorical

(taxonic) or continuous (dimensional). An early taxometric study on the Psychopathy

ChecklistRevised (PCL-R; Hare, 2003; for a reference to the abbreviations used in this

article, see appendix) showed signs of taxonic structure (Harris, Rice, & Quinsey, 1994),but more recently conducted and more methodologically sound studies have produced results

more congruent with a dimensional interpretation of the latent structure of psychopathy as

measured by the PCL-R/PCL:SV (Edens, Marcus, Lilienfeld, & Poythress, 2006; Guay,

Ruscio, Knight, & Hare, 2007; Walters, Duncan, & Mitchell-Perez, 2007; Walters, Gray,

et al., 2007), the Psychopathic Personality Inventory (Lilienfeld & Andrews, 1996; Marcus,

John, & Edens, 2004), and the Levenson Self-Report Psychopathy scales (LSRP;

Levenson, Kiehl, & Fitzpatrick, 1995; Walters, Brinkley, Magaletta, & Diamond, in press).

Dimensional results have also been obtained when the taxometric method has been applied

to measures of antisocial personality (Marcus, Lilienfeld, Edens, & Poythress, 2006; Walters,

Diamond, Magaletta, Geyer, & Duncan, 2007) and criminal lifestyle (Walters, 2007a;

Walters & McCoy, 2007).

Once research has established that crime-related constructs such as psychopathy, antiso-

cial personality, and criminal lifestyle have a dimensional, rather than taxonic, latent struc-

ture, the next step is to determine the content of these underlying dimensions. Research in

developmental psychology may be of benefit in identifying both the number and the nature

of dimensions shared by psychopathy, antisocial personality, and criminal lifestyle. In a

principal components analysis regarding teacher ratings of student aggression, Dodge and

Coie (1987) uncovered two factors, which they labeledproactive aggression and reactive

aggression. The two-dimensional model of childhood aggression was replicated in a seriesof confirmatory factor analyses (Poulin & Boivin, 2000), and scales that were designed to

measure proactive and reactive aggression in children have a moderately high and relatively

narrow range of intercorrelationspecfically, .77 to .83 (Day, Bream, & Pal, 1992; Dodge

& Coie, 1987; Hubbard et al., 2002; Poulin & Boivin, 2000; Price & Dodge, 1989). Despite

a high degree of intercorrelation between factors and a belief on the part of some investi-

gators that the proactivereactive breakdown has outlived its usefulness (Bushman &

Anderson, 2001), the factors each exhibit a differential pattern of correlation with outside

criteria: proactive aggression with positive outcome expectancies for aggression and reactive

aggression with hostile attribution biases (Crick & Dodge, 1996). This same countervailing

relationship has been observed in incarcerated juvenile delinquents (Smithmyer, Hubbard,

& Simons, 2000) and adult prison inmates (Walters, 2007b).

The two-dimensional model advanced in this article holds that proactive and reactive

criminality are psychological functions (motives) with developmental roots in proactive and

1460 CRIMINAL JUSTICE AND BEHAVIOR



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reactive childhood aggression. Although these two functions derive from different theoreti-

cal perspectivesproactive criminality/aggression from socialcognitive learning theory

(Bandura, 1986) and reactive criminality/aggression from frustrationaggression response

theory (Berkowitz, 1993)they share a great deal in common and thus overlap extensively.

Amygdala and orbital frontal cortex dysfunction have been implicated in both proactive and

reactive aggression, although the nature of the deficit and the actual brain pathways involved

in each pattern appear to differ (Blair, 2004). In addition, proactive and reactive aggression

are correlated with peer rejection and delinquency; however, reactive aggression is associ-

ated with emotional dysregulation and poor social adjustment, and proactive aggression is

associated with better psychological and social adjustment (Card & Little, 2006). The two-

dimensional model proposes that the motives that drive childhood aggression extend into

adult criminality and exist in the form of two overlapping dimensions: proactive aggression/

criminality and reactive aggression/criminality. These two dimensions, despite a moderate to

high degree of intercorrelation, demonstrate semidistinct patterns of association. Proactiveaggression/criminality correlates with positive outcome and efficacy expectancies for

aggression/crime, and reactive aggression/criminality correlates with poor socialemotional

adjustment and hostile attribution biases for aggression/crime.

Indicators from self-report measures of criminal lifestyle, antisocial personality, and psy-

chopathy can be organized in ways other than the proactivereactive breakdown proposed

by the two-dimensional model of criminality. One alternate conceptualization is to assign all

the criminal thinking indicators to one factor and all the antisocial behavior indicators to a

second factor. Social learning theory has been used to explain aggression (Bandura, 1973)

and criminality (Akers & Jensen, 2006). Five core assumptions underpin social learning

theory: Learning is a social process; learning is an internal process; behavior is directed

toward particular goals; behavior eventually becomes self-regulated; and reinforcement and

punishment have direct (behavioral) and indirect (cognitive) effects (Bandura, 1986). As such,

social learning theory provides a bridge, or transition, between behavioral learning theories

and cognitive learning theories (Ormrod, 1999). Because social learning theory focuses on

the cognitive and behavioral aspects of learning, it is more apt to divide indicators from self-

report measures of criminal lifestyle, antisocial personality, and psychopathy along cogni-

tive and behavioral lines than along proactive and reactive lines, despite serving as the

conceptual foundation for proactive aggression/criminality. Consequently, one alternate

model against which the two-dimensional model (proactive, reactive) is compared is a sociallearning alternative composed of cognitive and behavioral factors.

SELECTING INDICATORS

The Psychological Inventory of Criminal Thinking Styles (PICTS; Walters, 1995) is a

well-researched self-report measure of criminal thinking. Previous studies have shown that

the PICTS can be partitioned into two general factors (see Walters, 2005a) that Egan,

McMurran, Richardson, and Blair (2000) label wilful criminality and lack of thoughtfulness.

The five scales that Egan et al. assigned to the wilful criminality factor (Mollification [Mo],

Entitlement [En], Power Orientation [Po], Sentimentality [Sn], and Superoptimism [So])

appear to reflect proactive criminal thinking, whereas the three scales that they assigned to

the lack of thoughtfulness factor (Cutoff [Co], Cognitive Indolence [Ci], Discontinuity [Ds])

Walters / FACTOR AND PATH ANALYSIS OF SELF-REPORT MEASURES 1461



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apparently reflect reactive criminal thinking. Results from several exploratory and confir-

matory factor analyses, however, suggest that Sn may belong to a third factor, commonly

referred to as denial of harm (Walters, 1995, 2002, 2005a). One question posed by this arti-

cle is whether or not to include the Sn in the present analyses given its uncertain status with

respect to the proactivereactive dimensions and whether it loads as well onto a general

criminal thinking factor as do the other seven PICTS scales.

The LSRP scales (Levenson et al., 1995) were created to assess psychopathy in nonin-

carcerated populations, but they have also been used in incarcerated populations (Brinkley,

Schmitt, Smith, & Newman, 2001). In an effort to create self-report indices comparable to

Factor 1 (callous and remorseless use of others) and Factor 2 (chronic antisocial lifestyle)

of the PCL-R, Levenson et al. (1995) divided the 26 LSRP items into primary and secondary

psychopathy scales, with the primary scale serving as a proxy for Factor 1 and the secondary

scale as a proxy for Factor 2. Whereas the LSRP Secondary Psychopathy Scale (LSRP-SP)

is a reasonably good index of the antisocial behavior tapped by Factor 2 of the PCL-R,questions have been raised about the construct validity of the LSRP Primary Psychopathy

Scale (LSRP-PP; Lilienfeld & Fowler, 2006). Chief among these concerns is the absence

of a meaningful relationship between (a) the LSRP-PP and low trait anxiety (Levenson et al.,

1995; McHoskey, Worzel, & Szyarto, 1998) and (b) the scales tendency to correlate higher

with Factor 2 of the Psychopathic Personality Inventory than with Factor 1 (Lilienfeld,

Skeem, & Poythress, 2004; Wilson, Frick, & Clements, 1999). In addition, nearly one third

of the LSRP-PP items are reverse scored, which may introduce error into the responses of

individuals with lower reading skills and less motivation than that of the normative college

sample. Consequently, another goal of this article is to determine whether the LSRP-PP

should be included in subsequent analyses.

The Antisocial Features Scale (ANT) of the Personality Assessment Inventory (PAI;

Morey, 2007) is composed of 24 items designed to assess the behavioral and personality

characteristics of antisocial personality and psychopathy. There are three ANT subscales:

Antisocial Behaviors (ANT-A), Egocentricity (ANT-E), and Stimulus Seeking (ANT-S).

According to Morey (2007), elevated ANT-E scores reflect inflated self-importance, cal-

lousness, low social anxiety, and the desire to satisfy personal goals and impulses at the

expense of others. This description suggests that ANT-E measures proactive, as opposed to

reactive, criminality. By contrast, ANT-S seems to align more closely with reactive crimi-

nality, as indicated by high levels of recklessness, impulsivity, and novelty seeking in thosewho score high on this subscale (Morey, 2007). To the extent that ANT-A is a catalogue of

a persons level of prior antisocial activity, it could reflect either proactive or reactive crim-

inality. The question posed by Study 2 is whether ANT-A, like the LSRP-PP, loads suffi-

ciently well onto an antisocial behavior factor to be retained in this study.

HYPOTHESES

Five hypotheses were tested in this article, one for each study:

Hypothesis 1: Sn will be the lowest loading indicator on a general criminal thinking factor in anexploratory factor analysis of the eight PICTS scales, thus justifying its removal from subse-quent analyses.




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Hypothesis 2: The LSRP-PP and the ANT-A will be the two lowest loading indicators on a gen-eral antisocial behavior factor in an exploratory factor analysis of the five LSRP/ANT indica-tors, thus justifying their removal from subsequent analyses.

Hypothesis 3: Grouping the PICTS, LSRP, and ANT indicators into proactive and reactive factors

(two-dimensional model) will provide a significantly better fit for data collected on a largesample of medium-security federal prisoners than will loading all the indicators onto a singlefactor (general model) or grouping the indicators into criminal thinking and antisocial behaviorfactors (social learning model).

Hypothesis 4: Results from Study 3 (Hypothesis 3) will be replicated in a smaller sample of maximum-security federal prisoners.

Hypothesis 5: The construct validity of the two-dimensional model will be supported in a pathanalysis of correlations between the proactive and reactive dimensions and the measures ofpositive outcome expectancies for crime and hostile attribution biases.

STUDY 1

METHOD

Participants. Participants for this first study included 625 male federal prisoners who

completed the PICTS during a routine intake procedure at a medium-security federal prison.

These 625 PICTS protocols had never been included in any previous factor analyses of the

PICTS (Walters, 1995, 2002, 2005a). The mean age of the participants was 35.11 years

(SD = 9.04), and the average educational level was 11.33 years (SD = 1.94). Ethnically,over half the sample was Black (58.9%, n = 368), with Whites making up 24.5% (n = 153);

Hispanics, 15.5% (n = 97); and Asian/Native Americans, 1.1% (n = 7). The majority of par-ticipants listed their marital status as single (65.1%, n = 407), followed by married (22.4%,n = 140), divorced/separated (11.2%, n = 70), and widowed (1.3%, n = 8). The modalinstant offense in this sample was drugs (43.8%, n = 274), followed by illegal weapons(17.1%, n = 107), robbery (14.4%, n = 90), miscellaneous offenses (11.2%, n = 70), violence(9.1%, n = 57), and property crimes (4.3%, n = 27).

Measure. The PICTS is a self-report inventory with 80 items rated on a 4-point Likert-type

scale: Strongly agree responses earn a respondent 4 points; agree, 3 points; uncertain,

2 points; and disagree, 1 pointexcept for the DefensivenessRevised Scale, which is

reverse scored (strongly agree = 1, disagree = 4). Aside from the two 8-item validity scales(ConfusionRevised [Cf-r] and DefensivenessRevised), the PICTS generates scores for

eight nonoverlapping 8-item thinking-style scales (Mo, Co, En, Po, Sn, So, Ci, and Ds), four

10-item factor scales (Problem Avoidance, Infrequency, Self-Assertion/Deception, and

Denial of Harm), two content scales (Current and Historical), two composite scales (Proactive

Criminal Thinking and Reactive Criminal Thinking), and one general score (General Criminal

Thinking). The present investigation focuses on the eight thinking-style scales, all of which

have been found to possess adequate reliability (r= .73.93 after 2 weeks; r= .47.86 after1012 weeks), internal consistency ( = .54.79) and validity (unweighted mean correlations

of .12.20 with institutional adjustment/recidivism; Walters, 2002, 2006).

Procedure. The PICTS is routinely administered to inmates within 2 weeks of their arrival at

the institution where this study took place. Of the 687 inmates who arrived at the institution




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during an 18-month period, 2 refused to be tested; 21 could not read well enough to complete

the PICTS; 24 left more than 10 PICTS items blank; 6 produced extreme scores on the

ConfusionRevised Scale (T-score > 100); and 9 selected the uncertain option for all 80PICTS items. Eliminating these 62 individuals from the study resulted in a final sample of

625 participants. Informed consent was not required because administration of the PICTS was

a routine clinical procedure. Nonetheless, institutional review board approval was obtained

for the use of these data in research. A principal-axis factor analysis of a single general factor

(criminal thinking) was conducted using the eight PICTS scales as indicators.

RESULTS

A single factor was extracted from a principal-axis factor analysis of the eight PICTS

scales using 625 male inmates of a medium-security federal prison. The first factor accounted

for 63.12% of the total variance in the eight thinking styles (eigenvalue = 5.05), and the sec-ond factor accounted for less than 10% of the variance in the thinking-style scales (eigenvalue =0.79). Factor loadings on the first factor (general criminal thinking) were as follows: Mo = .753,Co = .789, En = .795, Po = .799, Sn= .628, So = .765, Ci = .823, and Ds = .724.

DISCUSSION

As predicted, the Sn scale was the weakest loading indicator in an exploratory factor

analysis of the eight PICTS scales. The Sn scale has not loaded particularly well onto the

proactive or reactive factor in previous factor analytic research (Walters, 1995, 2005a), and

in the present study it was the weakest correlating indicator when the eight PICTS scales wereloaded onto a general criminal thinking factor. As such, it was dropped for the confirmatory

factor analyses in Studies 3 and 4 and the construct validity analyses in Study 5.

STUDY 2

METHOD

Participants. Participants included 1,702 federal prisoners (n = 1,221 males, n = 481

females) who completed the LSRP and PAI as part of a national mental health prevalencestudy conducted in 14 federal correctional institutions (Diamond & Magaletta, 2006). The

average age of each participant was 34.29 years (SD = 9.59) with 11.17 years of education(SD = 2.54). The ethnic breakdown was as follows: White, 32.0% (n = 544); Black, 40.1%(n = 683); Hispanic, 26.8% (n = 456); Asian/Native American, 1.1% (n = 19). Over a thirdof the sample (37.7%, n = 600) had never been married, with 40.2% (n = 639) describingtheir current marital status as married or common law, 20.7% (n = 329) as divorced or sep-arated, and 1.5% (n = 24) as widowed. The majority of participants came from low-securityinstitutions (55.6%, n = 947), with 25.4% (n = 432) emanating from medium-security insti-tutions and 19.0% (n = 323) from high-security institutions.

Measures. The LSRP is a 26-item self-report inventory designed to assess psychopathy in

nonincarcerated populations. The first 16 items measure primary psychopathy (i.e., LSRP-PP;

affective and interpersonal features), and the last 10 items measure secondary psychopathy




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(i.e., LSRP-SP; chronic antisocial lifestyle). Each LSRP item is rated on a 4-point Likert-type

scale (disagree strongly, disagree somewhat, agree somewhat, agree strongly), with seven of

the items being reversed scored to control for various response style or test-taking sets, such

as social desirability. Reliability (LSRP-PP: = .82; LSRP-SP: = .63) and validity (corre-lations with the PCL-R and passive avoidance errors) have been found to be satisfactory

(Brinkley et al., 2001; Epstein, Poythress, & Brandon, 2006), although concerns have been

raised about the construct validity of the LSRP-PP (Lilienfeld & Fowler, 2006).

The PAI is a 344-item self-report measure in which each item is rated on a 4-point scale

(1 = very true, 2 = mainly true, 3 = slightly true, 4 =false). In this study, only the three 8-itemnonoverlapping subscales of the ANT were employed: ANT-A, ANT-E, ANT-S. Internal

consistency (as measured by the alpha coefficient []) and testretest reliability (as mea-sured after 2428 days [r]) are satisfactory for the ANT-A ( = .73.80, r= .80.86), ANT-E( = .63, r= .70.79), and ANT-S ( = .69.77, r= .78.84), and there is evidence for both

the reliability and the validity of the ANT in correctional and forensic samples (Edens &Ruiz, 2005).

Procedure. The LSRP and PAI were normally administered during a single testing ses-

sion, although one test was occasionally administered several days after the rest of the test

battery. Spanish versions of each test were available for Spanish-speaking inmates who

could not read English. There were 225 inmates from the national mental health prevalence

study who completed both the LSRP and PAI but were not included in the final sample of

1,702 participants because they achieved T-scores of 80 or higher on the PAI Inconsistency

Scale, 80 or higher on the PICTS Infrequency Scale, or 92 or higher on the PAI Negative

Impression Scale. Informed consent was obtained from the inmates who participated in theoriginal national mental health prevalence study, and institutional review board approval

was sought and obtained for the use of these data in research. A principal-axis factor analy-

sis of a single general factor (antisocial behavior) was conducted using the two LSRP scales

and three ANT subscales.

RESULTS

A single factor was extracted from the five LSRP/ANT indicators in a sample of 1,702

male and female inmates from 14 federal facilities. The first factor accounted for 50.66%

of the total variance in the eight thinking styles (eigenvalue = 2.53), and the second factoraccounted for 17.64% of the variance in the PICTS scales (eigenvalue = 0.88). Factor load-ings on the first factor (general antisocial behavior) were as follows: LSRP-PP = .554,LSRP-SP = .574, ANT-A = .562, ANT-E = .681, and ANT-S = .721.

DISCUSSION

The hypothesis for Study 2 was that the LSRP-PP and the ANT-A would be the two

weakest loading indicators on a general antisocial behavior factor when the five indicators

from the LSRP and ANT were subjected to an exploratory factor analysis in a large groupof federal prisoners. The results support this hypothesis and suggest that the LSRP-PP and

the ANT-A could be removed from subsequent analyses because their status as proactive or

reactive measures is uncertain and they do not load particularly well onto the antisocial

behavior factor of the social learning model.




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STUDY 3

METHOD

Participants. A group of 521 male inmates from a medium-security federal prisonlocated in the northeastern United States served as participants in this study. Each partici-

pant produced a complete and valid PICTS (no more than 10 unanswered items and a

ConfusionRevised Scale T-score of 100 or less), LSRP (no more than 2 unanswered

items), and ANT (no more than 2 unanswered items), all of which were administered rou-

tinely within 2 weeks of an inmates arrival at the institution. Validity indices for the PAI

were unavailable because the 24 ANT items were administered separately as a single instru-

ment rather than imbedded in the larger 344-item PAI. The average age of inmates in this

sample was 34.64 years (SD = 9.97), and the mean educational level was 11.42 years (SD =1.46). The ethnic breakdown was as follows: Black, 68.1% (n = 355); White, 16.5% (n =86); Hispanic, 13.4% (n = 70); and Asian/Native American, 2.0% (n = 10). Marital statuswas as follows: single, 74.9% (n = 390); married, 16.1% (n = 84); divorced, 8.3% (n = 43);and widowed, 0.8% (n = 4). The modal confining offense was drugs (45.3%, n = 236), fol-lowed by miscellaneous offenses, such as firearms and fraud (32.8%, n = 171), robbery(13.1%, n = 68), violence (6.0%, n = 31), and property crimes (2.9%, n = 15). The presentsample was independent of previous samples used to test the taxometric structure of the

PICTS (Walters, 2007a; Walters & McCoy, 2007), LSRP (Walters et al., in press), and ANT

(Walters, Diamond, et al., 2007).

Measures. Seven of the eight 8-item PICTS scales (Mo, Co, En, Po, So, Ci, Ds) served asindicators in this study. Research indicates that PICTS scales possess adequate to good reli-

ability (r = .73.93 after 2 weeks; r = 47.86 after 1012 weeks), internal consistency( = .54.79), unidimensionality (precision of = .01.03),1 and validity (unweighted meancorrelations of .12.20 with institutional adjustment/recidivism; Walters, 2006). The 10-itemLSRP-SP was the eighth indicator employed in this study. Internal consistency ( = .67, pre-cision of = .02) is adequate, and validity is reasonable (Brinkley et al., 2001; McHoskeyet al., 1998) for the LSRP-SP. Two of three ANT subscales, ANT-E and ANT-S, also served

as indicators in this study. In this study, the 24 ANT items were administered as a 24-item

inventory rather than as part of the full PAI. Unidimensionality, as measured by the precision

of the alpha coefficient, was satisfactory in the present sample of participants: ANT-E (.02)and ANT-S (.03).

Procedure. The PICTS, LSRP, and ANT were administered in random order to all partic-

ipants during a single testing session, although in 10% to 15% of cases, one of the measures

was completed several days after the other measures. Only inmates who could read English

were included in the investigation. Overall, 4 inmates refused to be tested, and 74 inmates par-

ticipated in the testing but were excluded from the final sample because of reading, language,

and education difficulties (less than 6 years of formal schooling; n = 38), random responding

(n = 7), missing data (n = 26), or achievement of a T-score of greater than 100 on the PICTSConfusionRevised Scale (n = 3). PICTS protocols with no more than 10 missing items andLSRP and ANT protocols with no more than 2 missing items were included in this investi-

gation. Valid protocols with missing items were prorated by (a) calculating an average item




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score for the items that were completed and (b) adding this number (or 2 times this number,

in the case of 2 missing items) to the raw total for the scale.

Informed consent was not required, because testing was a routine clinical procedure at

the institution where this study took place. Nonetheless, institutional review board approval

was obtained from the Bureau of Prisons for the use of these data in research. Following

approval, data were fit to three models: The one-factor model (M1) loaded all 10 indicators

onto a single latent factor; the two-dimensional (proactivereactive) model (M2) loaded

Mo, En, Po, So, and ANT-E onto a proactive latent factor and Co, Ci, Ds, LSRP-SP, and

ANT-S onto a reactive latent factor; the social learning model (MSL) loaded Mo, Co, En,

Po, So, Ci, and Ds onto a criminal thinking latent factor and LSRP-SP, ANT-E, and ANT-S

onto an antisocial behavior latent factor. Each model was estimated with maximum likeli-

hood, and metrics were set at 1.00 for the first pathway between a latent factor and an

observed variable (indicator) and between each error term and observed variable. All analy-

ses were conducted with a structural equation modeling (SEM) program (Amos 4.0;Arbuckle & Wothke, 1999).

Maximum likelihood estimation (MLE) is the most common method for estimating the

coefficients in SEM analysis, although several assumptions must be met before this estima-

tion approach can be used. First, the sample size must be adequate. As such, sample size was

determined to be adequate, using a power analysis (MacCallum, Browne, & Sugawara,

1996). Second, MLE assumes multivariate normality because highly nonnormal data can

lead to inflated-model chi-square values and downwardly biased parameter standard errors

(Bentler & Chou, 1987). Third, the sample covariance matrix that MLE attempts to repro-

duce assumes linearity (Klein, 2005). Univariate and multivariate normality, as well as

homoscedasticity (homogeneity of covariance matrices), were tested to determine whether

data should be transformed, and 90% confidence intervals (CIs) for the standardized regres-

sion coefficients were calculated with 2,000 bootstrapped trials. Finally, MLE assumes that

the models have been validly specified. Critical ratios (CRs) for the unstandardized regres-

sion coefficients and standardized residual covariances were consequently computed. CR

values were calculated by dividing the regression or factor estimate by the standard error of

the estimate (with scores above 1.96 denoting a significant effect at the .05 level). Fit statis-

tics employed in the present investigation included the model chi-square, the comparative fit

index (CFI; Bentler, 1990), the root mean square error of approximation (RMSEA; Browne

& Cudeck, 1993), and the Akaike information criterion (AIC; Akaike, 1987).

RESULTS

Normality and homoscedasticity. Table 1 lists the means, standard deviations, ranges,

skew, and kurtosis of the 10 indicator variables. All such indicators showed signs of signif-

icant univariate skew (CR > 1.96), and Mardias (1974) coefficient of multivariate kurtosisrevealed the presence of significant multivariate kurtosis (CR = 14.68). Heteroscedasticitywas assessed by constructing a multiple linear regression in which Mo was arbitrarily

selected as the outcome variable and the other 9 indicators served as predictor variables.

With a procedure described by Pryce (2005), the unstandardized residuals of the multipleregression were saved, and Levenes (1960) Test for the Equality of Variances was run on

each indicator, divided at the median into a high-scoring group and a low-scoring group. A




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significant Levene Ftest was interpreted as a sign of heteroscedasticity, based on the pres-

ence of significantly different group variances. Heteroscedasticity was observed in all 10

indicators. Hence, nonnormality and heteroscedasticity were characteristic of the indicators

used in this study. According to research, under conditions of nonnormality and heteroscedas-

ticity, ranking methods and transformations are useful (Zwick, 1986), and a percentile-

ranking transformation may be a particularly powerful and reliable method for transforming

nonnormal and heteroscedastic data (Zimmerman & Zumbo, 2005).

Three data transformations were examined in an effort to determine which did the best

job of normalizing the distribution and homogenizing the sample covariances. A base-e log-

arithmic transformation of the data produced significant univariate skew on 10 indicators, 1

heteroscedastic indicator, and a significant coefficient of multivariate kurtosis (CR = 3.96).A square root transformation, however, produced 8 skewed indicators, 8 heteroscedastic

indicators, and a significant coefficient of multivariate kurtosis (CR = 5.24). The percentile-rank transformation was the only data transformation procedure to yield no skewed indica-

tors, no heteroscedastic findings, and a nonsignificant coefficient of multivariate kurtosis

(CR = 1.60). The platykurtotic distribution produced by the percentile-rank transformations(negative kurtosis; see Table 1) is a consequence of the rectangular nature of the percentile

distribution, although this feature of the percentile-rank transformation does not impede its

ability to serve as an effective proxy for statistical analysis (Zimmerman & Zumbo, 2005).

Accordingly, percentile-rank transformations were employed in this study.

Regression weights and residual covariances. The first column of Table 2 reproduces the

unstandardized regression weights for the coefficient pathways in a confirmatory factor

analysis of percentile-rank transformation indicators organized into the two-dimensional

model, followed by the standard error of estimate, the CR, the standardized coefficient, and

the 90% bootstrapped CI of the standardized coefficient. All CRs in the two-dimensional

model were significant at the .001 level; likewise, all CRs in the one-factor and social learn-

ing models were significant at the .001 level.


TABLE 1: Descriptive Statistics for the 10 Indicators in Study 3

Raw Scores Percentile Ranks

Indicator Range M SD Skewa Kurtosisb Skewa Kurtosisb

PICTS

Mo 8-26 13.01 4.12 0.82 0.18 0.02 1.23Co 8-31 13.55 5.12 0.87 0.11 0.04 1.27En 8-29 13.25 3.84 0.86 0.57 0.01 1.20Po 8-29 12.49 4.08 1.15 1.13 0.03 1.24So 8-28 15.00 4.11 0.71 0.14 0.02 1.21Ci 8-32 16.02 4.76 0.34 0.35 0.00 1.20Ds 8-32 15.44 5.02 0.58 0.04 0.00 1.20

LSRP-SP 10-37 21.14 5.12 0.26 0.34 0.00 1.20ANT-E 0-22 4.01 3.88 1.33 1.93 0.03 1.24ANT-S 0-22 6.28 3.94 0.86 0.60 0.01 1.20

Note. N= 521. See appendix for all abbreviations used in the article.a. Standard error of skew was .11.b. Standard error of kurtosis was .21.



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Standardized residual covariances measure the difference between the sample covari-

ance matrix and the implied covariance matrix, divided by the sample covariance standard

deviation. Discrepancies were defined by a CR significant at the .05 level (CR > 1.96).There were two significant discrepancies in the two-dimensional model (between ANT-E

and ANT-S and between So and LSRP-SP), five significant discrepancies in the one-factor

model, and two significant discrepancies in the social learning model.

Goodness-of-fit indices. Results produced by the goodness-of-fit indices are listed in Table 3.

Whereas the two-dimensional model displayed adequate fit on the CFI and RMSEA, the

one-factor and social learning models displayed borderline to poor fit. Direct comparisons

between models using the AIC statistic (final two columns of Table 3) reveal a highly signif-

icant difference in relative fit between the individual models. An AIC difference (AIC) ofless than 2 is considered nonsignificant, whereas differences of 2-4, 4-7, 7-10, and more than

10 provide weak, definite, strong, and very strong evidence, respectivelythat is, the model

with the lower AIC value is superior to the model with the higher AIC value (Burnham &Anderson, 2002). Differences of 140.52 and 89.65 are therefore highly significant and so

indicate very strong evidence that the two-dimensional model provides a significantly better

fit for the data than either the one-factor model or the social learning model.


TABLE 2: Regression Weights Obtained With the Two-Dimensional Model: Study 3

Regression Path Estimate SE CR (90% CI)

Mo Proactivea 1.000 .75 (.71.79)

En Proactive 1.012 0.059 17.12***

.76 (.72.80)Po Proactive 1.040 0.059 17.66*** .78 (.75.82)

So Proactive 0.987 0.059 16.67*** .74 (.70.78)

ANT-E Proactive 0.811 0.060 13.56*** .61 (.56.66)

Co Reactivea 1.000 .86 (.83.88)

Ci Reactive 0.959 0.042 22.61*** .82 (.79.85)

Ds Reactive 0.935 0.043 21.76*** .80 (.76.83)

LSRP-SP Reactive 0.795 0.046 17.24*** .68 (.63.72)

ANT-S Reactive 0.665 0.048 13.74*** .57 (.51.62)

Proactive Reactive 448.57 38.57 11.63*** .84 (.80.88)

Note. Estimate = unstandardized regression coefficient; (90% CI) = standardized coefficient and 90th-percentilebiased corrected confidence interval of the standardized coefficient (B= 2,000).

a. Set to 1; no CR possible.***p< .001.

TABLE 3: Goodness-of-Fit Statistics: Study 3

Model 2(N =521) df CFI RMSEA (90% CI) AIC AIC wAIC

M1 312.72*** 35 .90 .124 (.111.136) 352.72 140.52 .0000000

M2 170.20*** 34 .95 .088 (.075.101) 212.20 0.00 .9999999

MSL 259.85*** 34 .92 .113 (.100.126) 301.85 89.65 .0000000

Note. Model = type of model based on number and configuration of factors; M1

= one-factor model (generaldimension); M2 = two-dimensional model (proactive and reactive); MSL = social learning model (criminal thinking,antisocial behavior); AIC = difference between AIC values obtained by the different models, with the lowest valueset at 0; wAIC = Akaike weight.***p< .001.



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DISCUSSION

The results of this third study furnish preliminary support for the hypothesis that two

correlated dimensionsproactive and reactive criminalityunderpin popular crime-related

constructs, such as psychopathy, antisocial personality, and criminal lifestyle. Results areconsistent in showing (a) fair to modest absolute fit for the two-dimensional model and

highly significant (p < .001) paths between each indicator and its assigned factor (proactiveor reactive) and (b) modest to poor absolute fit for the two alternate models (one-factor

model, social learning model) with which the two-dimensional model was compared. When

direct nonnested comparisons were made (AIC), the two-dimensional model proved supe-rior to both alternate models. Overall, the results of this study support the hypothesis that

self-report measures of psychopathy, antisocial personality, and lifestyle criminality share

two general dimensions. Whether these two dimensions reflect proactive and reactive crim-

inality, however, requires further investigation, in terms of cross-validating these preliminary

findings and testing the construct validity of the 10 indicators as measures of proactive and

reactive criminality.

STUDY 4

METHOD

Participants. Participants were 116 maximum-security male inmates who were adminis-

tered the PICTS, LSRP, and ANT as part of the standard intake procedure for a unit-based

psychology program held in a U.S. federal penitentiary in the mid-Atlantic region of the

United States. As in Study 3, informed consent was not sought, given the clinical nature of

the data collection procedures, although institutional review board approval was obtained

for the use of these data in research. The average participant in this study was 35.03 years

of age (SD = 8.49) and had accumulated 11.22 years of education (SD = 1.44). Ethnically,62.9% (n = 73) of the participants were Black; 30.2% (n = 35), White; 5.2% (n = 6),Hispanic; and 1.7% (n = 2), Asian/Native American. Over three quarters of the sample char-acterized their marital status as single (n = 94, 81.0%), with the remainder of the samplebeing composed of married (n = 16, 13.8%) and divorced (n = 6, 5.2%) participants. The

modal confining offense was robbery (27.6%, n = 32), followed by violent crimes (26.7%,n = 31), drugs (22.4%, n = 26), firearms (18.1%, n = 21), property crimes (3.4%, n = 4),and miscellaneous offenses (1.7%, n = 2).

Measures. The 10 indicators from the PICTS, LSRP, and ANT of the previous study

were employed as indicators in the present investigation, although there was one notewor-

thy administrative difference between the two studies. Whereas the ANT was administered

as a stand-alone procedure in Study 3, it was completed as part of the full PAI in the present

study. Consequently, scores on the PAI Inconsistency Scale, the PICTS Infrequency Scale,

and the PAI Negative Impression Scale were available to assess protocol validity. The

following were screened out of the sample: PAI protocols with more than 20 omitted items,a PAI Inconsistency Scale T-score of 80 or more, a PICTS Infrequency Scale T-score of 80

or more, a PAI Negative Impression Scale T-score of 92 or more, PICTS protocols with

more than 10 omitted items, or a ConfusionRevised Scale T-score of more than 100. These

criteria resulted in the elimination of two protocols from the present study.




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Procedure. The procedure for the present investigation was identical to that utilized in

the first study, except that the sample was composed of male inmates from a maximum-

security penitentiary, instead of male inmates from a medium-security federal correctional

institution.

RESULTS

Normality check. Table 4 lists the means, standard deviations, ranges, skew, and kurtosis

of the 10 indicator variables. Eight indicators (Mo, Co, En, Po, So, Ds, ANT-E, ANT-S)

show signs of significant univariate skew (CR > 1.96); Mardias (1974) coefficient of mul-tivariate kurtosis reveals significant multivariate kurtosis (CR = 4.41); and two indicators(Ds, LSRP-SP) dichotomized at the median display significant heteroscedasticity (p < .05).Percentile-rank transformations, however, display no signs of univariate skew, multivariate

kurtosis (CR = 1.56), or heteroscedasticity. Percentile-rank transformations were employedaccordingly in this study.

Regression weights and residual covariances. Table 5 lists the unstandardized regression

estimates, standard errors, and CRs for the regression and covariance paths of the 10 indi-

cators. All CRs in the one-factor, two-dimensional, and social learning models were significant

at the .001 level. The standardized regression coefficients for each path in the two-dimensional

model and the 90% bootstrapped CI are also reported. There was one significant standard-

ized residual covariance in the two-dimensional model (between ANT-E and ANT-S), one

significant standardized residual covariance in the one-factor model, and no significant

standardized residual covariances in the social learning model.

Goodness-of-fit indices. When goodness-of-fit indices were applied to the three models,

there was evidence of modest fit for the two-dimensional model and generally poor fit for

the one-factor and social learning models (see Table 6). Direct comparisons (AIC) betweenthe two-dimensional model and the two competing models indicate strong and very strong


TABLE 4: Descriptive Statistics for the 10 Indicators in Study 4

Raw Scores Percentile Ranks

Indicator Range M SD Skewa Kurtosisb Skewa Kurtosisb

PICTS

Mo 824 13.34 3.87 0.54 0.44 0.02 1.22

Co 829 15.34 5.33 0.26 0.86 0.02 1.24

En 825 14.95 4.12 0.16 0.82 0.00 1.20

Po 824 13.09 3.89 0.56 0.56 0.02 1.22

So 826 16.53 4.30 0.40 0.33 0.00 1.19

Ci 832 17.91 4.83 0.01 0.36 0.00 1.21

Ds 830 16.82 5.23 0.44 0.73 0.00 1.20

LSRP-SP 1037 20.84 5.20 0.35 0.26 0.00 1.20

ANT-E 019 3.94 3.50 1.50 3.17 0.02 1.22

ANT-S 017 5.93 3.92 0.89 0.01 0.02 1.20

Note. N= 116.a. Standard error of skew was .22.b. Standard error of kurtosis was .45.



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evidence that the two-dimensional model provides a significantly better fit for the data than

that of the social learning and one-factor models, respectively.

DISCUSSION

The modest shrinkage in absolute goodness-of-fit from Study 3 to Study 4 may have

more to do with reduced power than weak theory. The reason is that the sample in Study 4had much less power to accept models with close fit (0.36) and reject models with not-close

fit (0.22) than that of the sample in Study 3, where the power to accept models with close

fit and reject models with not-close fit both exceeded 0.90. Taken as a whole, these find-

ings confirm the hypothesis that a two-dimensional model with proactive and reactive latent

dimensions may have value in explaining criminality as assessed by offender self-report.

Like that of Study 3, the absolute fit of the two-dimensional model to the data in this study

was less impressive than the relative fit of the two-dimensional model, in comparison to the

one-factor and social learning models. Even though the present study successfully cross-

validated the relationships observed in Study 3 using a small group of penitentiary inmates,

it still did not answer one very important question: namely, how can we be sure that the two

latent factors in the two-dimensional model actually represent proactive and reactive crim-

inality? To answer this question, a fifth study was conducted to test the construct validity

of the proactive and reactive dimensions of the two-dimensional model.


TABLE 5: Regression Weights Obtained With the Two-Dimensional Model: Study 4


Mo Proactivea 1.000 .76 (.62.85)

En Proactive 0.945 0.129 7.30***

.71 (.60.80)Po Proactive 0.929 0.129 7.20*** .70 (.61.78)

So Proactive 0.856 0.130 6.60*** .64 (.50.75)

ANT-E Proactive 0.505 0.131 3.85*** .38 (.21.55)

Co Reactivea 1.000 .83 (.73.90)

Ci Reactive 0.945 0.100 9.45*** .79 (.69.86)

Ds Reactive 1.017 0.098 10.38*** .85 (.76.91)

LSRP-SP Reactive 0.541 0.113 4.80*** .45 (.29.58)

ANT-S Reactive 0.527 0.113 4.68*** .44 (.27.57)

Proactive Reactive 448.99 82.95 5.41*** .86 (.76.94)


a. Set to 1; no CR possible.***p< .001.


Model 2(N =236) df CFI RMSEA (90% CI) AIC AIC wAIC

M1 87.76*** 35 .89 .114 (.085.145) 127.76 13.42 .0018332

M2 72.34*** 34 .92 .099 (.067.131) 114.34 0.00 .9860960

MSL 81.04*** 34 .90 .110 (.079.141) 123.04 8.70 .0127206

Note. Model = type of model based on number and configuration of factors; M1 = one-factor model (general

dimension); M2 = two-dimensional model (proactive and reactive); MSL = social learning model (criminal thinking,antisocial behavior); AIC = difference between AIC values obtained by the different models, with the lowest valueset at 0; wAIC = Akaike weight.***p< .001.



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STUDY 5

METHOD

Participants. Participants for the fifth study were male inmates who completed testing dur-ing the second half of the year, when data were being collected for the third study and when

a change in policy added two new measures to the standard intake test battery: the Outcome

Expectancies for Crime Inventory (OEC; Walters, 2003) and the Hostile Attribution Bias

(HAB) measure. There were 356 inmates who were processed into the institution during this

6-month period. Of this number, 3 refused to be tested; 20 could not read English; 8 experi-

enced significant reading problems or had fewer than 6 years of education; 3 produced invalid

PICTS protocols (2 left more than 10 items unanswered and 1 had a T-score of more than 100

on the ConfusionRevised Scale); 2 left more than 2 items blank on the LSRP; 3 left more

than 2 items blank on the ANT scale; 23 left more than 2 items blank on the OEC; and 3 left

1 or more items blank on the HAB. This resulted in a final sample of 291 male inmates, with

a mean age of 33.56 years (SD = 9.26) and mean educational level of 11.48 years (SD = 1.38).The ethnic breakdown for the sample was as follows: Black, 68.0%; White, 15.5%; Hispanic,

13.7%; and Asian/Native American, 2.7%. Over three quarters of the sample (77.0%) listed

their marital status as single, with another 15.1%, 6.9%, and 1.0% stating that they were mar-

ried, divorced, and widowed, respectively. Nearly half the sample was serving time for a drug

offense (45.7%), with 13.4% serving time for robbery, 3.4% for violence, 3.1% for a property

crime, and 34.4% for a miscellaneous offense.

Measures. In addition to the PICTS, LSRP, and ANT, the OEC and HAB were adminis-tered. The OEC lists 16 potential outcomes for crime, divided into 12 anticipated positive

outcomes (acceptance, approval, control, excitement, freedom, love, power, prestige, purpose,

respect, security, status) and four anticipated negative outcomes (death, jail/prison, loss of

family, loss of job). Respondents are each instructed to rate a crime they have committed

(i.e., the confining offense or some other criminal act) on the basis of the outcomes they

would currently anticipate receivingthat is, if they were living in the community and

committed the crime right now.

A 7-point rating scale is used with the OEC, reflecting degree of belief in which the out-

come will occur across similar situations (1 = never, 7 = always). The sum of the ratings

from the 12 positive outcome expectancy items constitutes the OEC-POS score (range =12-84). Internal consistency for the OEC-POS was strong in the present sample ( = .90),and previous research indicates that scores on the OEC-POS fall precipitously as a conse-

quence of an inmates involvement in a therapeutic intervention designed to reduce positive

outcome expectancies for crime (Walters, 2003).

The HAB consists of three vignettes similar to situations used in previous studies on hos-

tile attribution biases in children (Dodge, Price, Bachorowski, & Newman, 1990) but which

take place in an adult correctional facility. The first vignette asks respondents to interpret

the intentions of an inmate who bumps into them as he passes the respondent in the com-

missary line. Using a 5-point Likert-type scale, the respondent signifies the degree to whichhe believes the bump was intentional (1 = definitely unintentional, 5 = definitely inten-tional). The second and third vignettes involve being struck in the back with a basketball

and being reprimanded by a lieutenant (supervisory staff member) for having ones shirt




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untucked (when other inmates with untucked shirts are seemingly ignored). These two

vignettes were also rated using the 5-point Likert-type scale previously described. The total

score for the three vignettes (HAB-TOT) served as an outcome measure in this study

(range = 315), with higher scores indicating greater hostile attributional bias. Internalconsistency in the present sample was modest when using Cronbachs alpha coefficient

( = 54) but moderate when interitem correlations were calculated (r= .27.31). Initialvalidation of the HAB shows that although it correlates with a putative measure of reactive

criminal thinking (Co), it fails to correlate with a putative measure of proactive criminal

thinking (En; Walters, 2007b).

Procedure. Percentile-rank converted scores on the PICTS, LSRP, ANT, OEC-POS, and

HAB-TOT were fit to two models: a theory-congruent model and a theory-incongruent

model. The theory-congruent model made Mo, En, Po, So, and ANT-E correlates of OEC-

POS and Co, Ci, Ds, LSRP-SP, and ANT-S correlates of HAB-TOT. The theory-incongruentmodel made Mo, En, Po, So, and ANT-E correlates of HAB-TOT and Co, Ci, Ds, LSRP-SP,

and ANT-S correlates of OEC-POS. The prediction was that the theory-congruent model

would demonstrate significantly better fit than that of the theory-incongruent model. MLE

was used to estimate the coefficients in an SEM recursive path analysis of proactive and reac-

tive predictors and outcome expectancy and hostile attribution bias outcomes, as computed

by Amos 4.0. Covariance curves were drawn between each of the predictor variables given

the intercorrelated nature of proactive and reactive criminal thinking and behavior.

Unstandardized regression coefficients and the standardized residual covariances of the

comparison between the sample and the implied covariance matrices were computed as CRs

in which the regression estimate was divided by the standard error of the estimate (withscores above 1.96 denoting a significant effect at the .05 level). Four principal fit indices

were also calculated: the model chi-square, the CFI, the RMSEA, and the AIC. Whereas the

CFI and RMSEA were used to assess the absolute fit of the theory-congruent and theory-

incongruent models, the AIC was used to assess the relative fit of the two models.

RESULTS

Regression weights. Table 7 lists the unstandardized and standardized regression weights

for the pathways between the predictor variables (five proactive and five reactive) and the two

outcome measures in the theory-congruent model. Table 8 lists the unstandardized and stan-

dardized regression weights for pathways between the predictor variables and the two out-

come measures in the theory-incongruent model. The results indicate five significant CRs in

the theory-congruent model, four of which were in the predicted direction and one of which

was in the opposite direction: So OEC-POS, ANT-E OEC-POS, Co HAB-TOT, Ds HAB-TOT (a negative relationship, contrary to predictions), and ANT-S HAB-TOT.There were no significant pathways in the theory-incongruent model.

Goodness-of-fit indices. Table 9 lists the goodness-of-fit results for the two models. The

chi-square was significant for the theory-incongruent model but not for the theory-congruentmodel, denoting better fit for the latter. Both models achieved CFI values in the good-fit

range, but whereas the RMSEA value for the theory-incongruent model indicated modest

fit, the RMSEA value for the theory-congruent model displayed good fit. Moreover, the




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upper limit of the 90% RMSEA CI fell in the poor-fit range for the theory-incongruentmodel and in the good-fit range for the theory-congruent model. There were three discrep-

ancies (CR > 1.96,p < .05) in the theory-incongruent model (So OEC-POS, ANT-E OEC-POS, ANT-S HAB-TOT) but no discrepancies in the theory-congruent model.


TABLE 7: Regression Weights for the Theory-Congruent Model: Study 5


Mo OEC-POS 0.015 0.073 0.20 .02 (.10, .14)

En OEC-POS 0.058 0.080 0.73 .06 (.08, .19)Po OEC-POS 0.072 0.074 0.97 .07 (.05, .21)

So OEC-POS 0.162 0.076 2.13* .16 (.03, .30)

ANT-E OEC-POS 0.190 0.064 2.97** .19 (.08, .30)

Co HAB-TOT 0.248 0.085 2.92** .25 (.13, .39)

Ci HAB-TOT 0.012 0.079 0.16 .01 (.11, .13)

Ds HAB-TOT 0.163 0.082 1.98* .16 (.29, .02)

LSRP-SP HAB-TOT 0.078 0.074 0.97 .08 (.04, .19)

ANT-S HAB-TOT 0.171 0.064 2.67** .17 (.06, .27)

Note. Estimate = unstandardized regression coefficient; (90% CI) = standardized coefficient and 90th-percentilebiased corrected confidence interval of the standardized coefficient (B= 2,000).*p< .05. **p< .01.

TABLE 8: Regression Weights for the Theory-Incongruent Model: Study 5


Mo HAB-TOT 0.126 0.075 1.68 .13 (.00.25)

En HAB-TOT 0.076 0.083 0.92 .08 (.06.21)

Po HAB-TOT 0.056 0.077 0.73 .06 (.07.19)

So HAB-TOT 0.015 0.079 0.19 .02 (.12.15)

ANT-E HAB-TOT 0.057 0.066 0.86 .06 (.06.17)

Co OEC-POS 0.095 0.086 1.10 .09 (.05.24)

Ci OEC-POS 0.106 0.080 1.33 .11 (.03.23)Ds OEC-POS 0.046 0.083 0.55 .05 (.19.10)

LSRP-SP OEC-POS 0.125 0.072 1.73 .12 (.00.24)

ANT-S OEC-POS 0.114 0.065 1.75 .11 (.02.22)



Model 2(N =291) df CFI RMSEA (90% CI) SRC AIC AIC wAIC

M2-TC 8.06 11 1.00 .000 (.000.047) 0 142.06 0.00 .9999997

M2-TI 38.26*** 11 0.98 .092 (.062.125) 3 172.26 30.20 .0000003

Note. M2 = two-dimensional model in which Mo, En, Po, So, and ANT-E make up the proactive dimension and Co,Ci, Ds, ANT-S, and LSRP-SP make up the reactive dimension; TC = theory congruent (i.e., proactive scales predictOEC-POS and reactive scales predict HAB-TOT); TI = theory incongruent (i.e., proactive scales predict HAB-TOTand reactive scales predict OEC-POS); SRC = number of significant discrepancies in the standardized residualcovariances (p< .05, CR > 1.96); AIC = Akaike Information Criterion; AIC = difference between AIC valuesobtained by the TC and TI models; wAIC = Akaike weight.***p< .001.



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Although the standardized pathway coefficients, chi-square, RMSEA, and standardized

residual covariance results imply that the theory-congruent model fit the data better than

the theory-incongruent model, none of these procedures permit direct model comparison.

To compare the models directly, the difference between the model AIC values must be calcu-

lated. The difference between AIC values obtained in the present investigation (AIC = 30.20)furnishes very strong evidence (AIC > 10) that the theory-congruent model fit the databetter than the theory-incongruent model.

DISCUSSION

The results of Study 5 furnish support for the construct validity of a two-dimensional

model of crime-related cognition and behavior. A path analysis of 10 predictor variables from

the PICTS, LSRP, and ANT, classified as proactive or reactive and regressed onto self-

reported positive outcome expectancies for crime and hostile attribution biases, reveal that

theory-congruent pairings (proactive with outcome expectancies and reactive with hostile

attribution biases) achieve significantly better fit than that of theory-incongruent pairings

(proactive with hostile attribution biases and reactive with outcome expectancies). Consistent

with prior research on childhood aggression (Crick & Dodge, 1996), juvenile delinquency

(Smithmyer et al., 2000), and adult criminality (Walters, 2007b), proactive criminal thinking

and behavior are differentially associated with positive outcome expectancies for crime,

whereas reactive criminal thinking and behavior are differentially associated with hostile

attribution biases. These findings furnish preliminary support for the construct validity of the

proactivereactive breakdown proposed by the two-dimensional model.

GENERAL DISCUSSION

The studies described in this article were inspired by research indicating that crime-

related constructs such as psychopathy, antisocial personality, and criminal lifestyle have a

dimensional, rather than taxonic, latent structure (Edens et al., 2006; Marcus et al., 2006;

Walters, 2007a). It was reasoned that if all three constructs are dimensional, then perhaps

they also share one or more dimensions. To test this possibility, a two-dimensional model

with proactive and reactive latent factors was constructed and compared to a one-factor

model in which all three constructs were loaded onto a single latent factor and a social learn-

ing model composed of two latent factors: criminal thinking and antisocial behavior. The

results of an SEM analysis of self-report data evaluating all three constructs (psychopathy,

antisocial personality, criminal lifestyle) in a reasonably sized sample of medium-security

male prison inmates and a small cross-validation sample of maximum-security male prison

inmates show modest to adequate fit for the two-dimensional model from an analysis of

regression weights, residual covariances, and goodness-of-fit indices. The analysis also

revealed that the two-dimensional model is superior to the one-factor and social learning

models in direct comparisons using the AIC relative fit measure. The present results conse-

quently indicate that a two-dimensional model comprising proactive and reactive latent fac-tors may underpin crime-related constructs like psychopathy, antisocial personality, and

criminal lifestyle while casting doubt on alternate one-factor and social learning models.




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A principal implication of this study is that it provides support for a unified theory of

antisocial behavior, from childhood aggression to adult criminality. Besides the fact that

childhood aggression and adult criminality are dimensional rather than taxonic (Dodge,

Lochman, Harnish, Bates, & Pettit, 1997; Walters, 2007a), there are at least three other

points on which the two constructs converge. First, confirmatory factor analyses indicate

that two dimensionswhat Dodge and colleagues (Crick & Dodge, 1996; Dodge &

Coie, 1987; Dodge et al., 1997) and Walters (2007b) refer to asproactive and reactive

do a better job of accounting for childhood aggression (Poulin & Boivin, 2000) and adult

criminality (Walters, 2007b) than that of a one-factor model and a social learning model

with cognitive and behavioral factors. Second, these two dimensions are highly corre-

lated. The rating scales that have been used to classify children as proactive or reactive

have been found to correlate with each other (.41 to .90), although the majority of corre-

lations cluster between .77 and .83 (Day et al., 1992; Dodge & Coie, 1987; Hubbard et al.,

2002; Price & Dodge, 1989; Poulin & Boivin, 2000). Intercorrelations between thePICTS proactive and reactive composite scales, however, range from .60 to .72 (Walters,

2006; Walters & Mandell, 2007). Some might argue that this level of intercorrelation

makes the dimensions redundant. However, in the present study, as well as in previous

studies on aggressive children (Crick & Dodge, 1996), juvenile delinquents (Smithmyer

et al., 2000), and adult prisoners (Walters, 2007b), the two dimensions have correlated

differentially with positive outcome expectancies for aggression/crime and hostile attri-

bution biases in ways that are consistent with theory and congruent with the construct

validity of the individual measures.

Card and Little (2006) assert that correlations between proactive and reactive aggression

are largely a consequence of the restricted range of the methods used (i.e., rating scales in

childhood aggression research and self-report measures in the present study). Whereas there

is a strong likelihood that shared method variance is partially responsible for the height of

the correlation between proactive and reactive aggression/criminality, I would contend that

it does not fully account for this correlation. Grounded in similar neurobiological, psycho-

logical, and sociological processes, proactive and reactive aggression/criminality are struc-

turally, functionally, and developmentally related. The two-dimensional model being

advocated in this article can be considered one level of a larger theory in which aggression

and criminality are hierarchically organized, with a general tendency to aggress against oth-

ers and violate the rules of society at the top of the hierarchy. Below this would be theproactive and reactive functions of aggression/criminality and, below that, the specific attri-

butions, expectancies, goals, values, and thinking styles that facilitate aggression and crim-

inality. Results from the current investigation indicate that the general tendency toward

aggression/criminality does not adequately explain the overlap among psychopathy, anti-

social personality, and criminal lifestyle and that we need to look to lower levels in the hier-

archy (proactive, reactive) to gain a better understanding of the relationships between these

three crime-related constructs.

The current results also have implications for clinical practice. Correctional assessment

and classification should consider both proactive and reactive criminality when evaluating

inmates. Because reactive criminality leads to more overt and obvious forms of acting-out

behavior, it is more likely to be disruptive to the orderly running of a correctional institution.

Yet whereas reactive criminality did a better job of predicting the total number of disciplinary




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reports received by prisoners in one study (Walters, 2005b), proactive criminality did a

better job of predicting prison-based aggressive behavior in another study (Walters &

Mandell, 2007). Consequently, when evaluating an inmate for programming or classifica-

tion purposes, both forms of criminality should be taken into account. In classifying

inmates using the proactivereactive model, it will be important to avoid using a categori-

cal scheme in which inmates are classified as proactive or reactive, because research indi-

cates that proactive and reactive criminality, like proactive and reactive childhood

aggression, are highly correlated dimensions. Accordingly, most individuals will be high or

low on both dimensions. Another practical implication of the present findings is that proac-

tive and reactive criminality may require different forms of intervention. Whereas reactive

criminality appears to respond to behaviorally oriented skill development techniques and

programs (Walters, 2008), proactive criminality will probably require more cognitively ori-

ented interventions in which outcome expectancies for crime and criminal goals are tar-

geted. Many correctional programs address skill deficits by providing inmates with angerand stress management training (Young, Dembo, & Henderson, 2007), but few programs

address the criminogenic features of proactive criminal thinking.

Although the proactivereactive breakdown that defines the two-dimensional model may

seem to parallel the Factor 1Factor 2 structural breakdown of the PCL-R, the similarities

are more apparent than real. Both components of the two-dimensional model share more in

common with Factor 2, the behavioral or chronic antisocial lifestyle component of psy-

chopathy, than with Factor 1, the personality or callous and remorseless use-of-others com-

ponent of psychopathy. Moreover, the one original indicator from the present investigation

that seems to share the most in common with Factor 1 of the PCL-R (the LSRP-PP) failed

to load sufficiently onto the general antisocial behavior factor of the social learning model

and demonstrate unambiguous allegiance to either the proactive or reactive factors of the

two-dimensional model to justify including it in this series of five studies. In fact, proactive

and reactive scales from both the PICTS (Walters, 2002; Walters & Mandell, 2007) and the

ANT (Salekin, Rogers, Ustad, & Sewell, 1998; Walters, Duncan, & Geyer, 2003) consis-

tently correlate better with Factor 2 of the PCL-R/PCL:SV than Factor 1 of the PCL-R/

PCL:SV. Therefore, despite superficial similarities between the proactive and reactive fac-

tors of the two-dimensional model and Factors 1 and 2 of the PCL-R, the two models

should not be confused.

A potential limitation of the current set of studies is that they were conducted on maleinmates from medium- and maximum-security federal prisons who were administered a

series of self-report questionnaires. A reasonable question at this juncture is whether these

results generalize to female offenders and to both male and female nonoffenders. The issue

of whether indicators from the PICTS, LSRP, and ANT apply to nonoffenders is neither

moot nor trivial. All three measures have been used in nonoffender samples, including the

PICTS (see Walters & McCoy, 2007). The LSRP, in fact, was developed for the express

purpose of creating a self-report measure that could be used with nonincarcerated partici-

pants (Levenson et al., 1995). If it can be shown that the present findings generalize to

nonoffender populations, then perhaps it would indicate that the proactivereactive differ-

entiation has value in describing behaviors and motivations beyond childhood aggression

and adult criminality. It will also be important to know whether the current findings gener-

alize to non-self-report measures of psychopathy, antisocial personality, and criminal




22/26

lifestyle. Conducting a confirmatory factor analysis of individuals administered the PCL-R

as a measure of psychopathy, the Structured Clinical Interview for DSM-IV Axis II

Personality Disorders (First, Gibbon, Spitzer, Williams, & Benjamin, 1997) as a measure

of antisocial personality, and the Lifestyle Criminality Screening Form (Walters, White, &

Denney, 1991) as a measure of criminal lifestyle may shed additional light on the validity

of the proactivereactive model, although these measures may be incapable of generating

a sufficient number of proactive indicators to conduct the analyses. In short, further

research is required to determine the generalizability of the present findings.

Diminished power is a limitation shared by Studies 4 and 5. Study 4 had a sample size

of 118, and Study 5 had to be computed with 11 degrees of freedom, because covariance

curves were drawn between each predictor to account for the high degree of intercorrela-

tion between predictors in the two-dimensional model. A study with 291 participants and

11 degrees of freedom has modest power to accept close-fitting models ( 0.41) and reject

not-close-fitting models ( 0.30; MacCallum et al., 1996). However, the theory-congruentand theory-incongruent models had equivalent power, and the theory-congruent model

achieved significantly better fit than that of the theory-incongruent model in direct com-

parisons between the two models. Accounting for all the covariances between the predictor

variables may have artificially elevated certain fit measures such as the CFI, but once again,

the theory-congruent and theory-incongruent models shared this same advantage, and the

theory-congruent model was clearly the better-fitting model.

Study 5 was also limited by the fact that the outcome measures were exclusively self-

report. Retrospective behavioral measures, such as prior arrests for proactive crimes (rob-

bery, burglary) and reactive crimes (assault, domestic violence), were unavailable for a

large portion of the participants in this study, and prospective behavioral measures, such as

proactive and reactive disciplinary reports, have proved unreliable and thus require more

information about the offense than what is normally available.

The development, appraisal, and refinement of theoretical models form the essence of

science. A two-dimensional model of criminality believed to underlie such popular crime-

related concepts as psychopathy, antisocial personality, and criminal lifestyle received

moderate support in the present investigation. Alternate models and explanationssuch as

a general factor model and a social learning model composed of cognitive and behavioral

factorswere evaluated and found to be lacking. Furthermore, results obtained with male

inmates of a medium-security federal prison were cross-validated on a group of maleinmates of a maximum-security federal prison, with modest shrinkage in model fit. The

theoretical value of the two-dimensional model in clarifying the connection between

childhood aggression and adult criminality, however, requires longitudinal research. Such

studies, if they are not already being done, will take decades to complete. In the meantime,

researchers can contribute to the development and evaluation of theoretical paradigms

such as the two-dimensional model by expanding the focus to other offender and nonof-

fender populations, by using validated behavioral outcome measures, and by creating

alternative explanatory models against which the proactivereactive model can be evalu-

ated. There is no reason why psychopathy, antisocial personality, and criminal lifestyle

cannot individually contribute to our understanding of criminal behavior now that it has

been shown that proactive and reactive criminality may account for a certain portion of the

variance shared by each.




23/26

Appendix: Abbreviations

AIC: Akaike information criterion

ANT: Antisocial Features Scale

ANT-A: Antisocial Features ScaleAntisocial Behaviors subscaleANT-E: Antisocial Features ScaleEgocentricity subscale

ANT-S: Antisocial Features ScaleStimulus Seeking subscale

CFI: comparative fit index

CI: confidence intervals

CR: critical ratio

HAB: Hostile Attribution Bias

HAB-TOT: Hostile Attribution Biastotal score from three vignettes

LSRP: Levenson Self-Report Psychopathy Scale

LSRP-PP: Levenson Self-Report Psychopathy ScalePrimary Psychopathy

LSRP-SP: Levenson Self-Report Psychopathy ScaleSecondary PsychopathyMLE: maximum likelihood estimation

OEC: Outcome Expectancies for Crime Inventory

OEC-POS: Outcome Expectancies for Crime Inventorypositive outcome expectancy items

PAI: Personality Assessment Inventory

PCL-R: Psychopathy ChecklistRevised

PCL:SV: Psychopathy Checklist: Screening Version

PICTS: Psychological Inventory of Criminal Thinking Styles (eight scales)

Mo: Mollification Scale

Co: Cutoff Scale

En: Entitlement ScalePo: Power Orientation Scale

Sn: Sentimentality Scale

So: Superoptimism Scale

Ci: Cognitive Indolence Scale

Ds: Discontinuity Scale

RMSEA: root mean square error of approximation

NOTE

1. Precision of alpha is calculated as the standard error of item intercorrelations (Cortina, 1993), with higher standard

errors suggesting a greater likelihood of multidimensionality. The precision of alpha values for the rationally derivedPsychological Inventory of Criminal Thinking Styles scales are comparable to the precision of alpha values obtained for the

factorially derived Psychopathy ChecklistRevised factor (.02) and facet scores (.02.05, N = 409; Walters, Duncan, &

Mitchell-Perez, 2007).

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