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Author(s): Coid, Jeremy W; Yang, Min; Ullrich, Simone; Roberts, Amanda D.L;
Hare, Robert D.
Article title: Prevalence and correlates of psychopathic traits in the household
population of Great Britain
Year of publication: 2009
Citation: Coid, J. W. et al. (2009) ‘Prevalence and correlates of psychopathic traits
in the household population of Great Britain.’ International Journal of Law and
Psychiatry, 32 (2) 65–73.
Link to published version: http://dx.doi.org/10.1016/j.ijlp.2009.01.002
DOI: 10.1016/j.ijlp.2009.01.002
Prevalence and Correlates of Psychopathic Traits in the Household Population 1
Abstract Word Count: 210
Article Word Count: 6,110
Prevalence and Correlates of Psychopathic Traits in the Household Population of
Great Britain
Jeremy Coid a, Min Yang a, Simone Ullrich a, Amanda Roberts a, Robert D. Hare b
a Forensic Psychiatry Research Unit, St. Bartholomew’s Hospital, London, United Kingdom
b Department of Psychology, University of British Columbia, Vancouver, Canada
Running Title
Prevalence and Correlates of Psychopathic Traits in the Household Population
Correspondence
Prof Jeremy Coid, MD, Forensic Psychiatry Research Unit, St. Bartholomew’s Hospital, William
Harvey House, 61 Bartholomew Close, London, EC1A 7BE, Ph: +44 (0)20 7601 8138, Fax: +44
(0)20 7601 7969 (j.w.coid@qmul.ac.uk)
Funding support
Department of Health, England and Wales.
Prevalence and Correlates of Psychopathic Traits in the Household Population
2
ABSTRACT
There are no previous surveys of psychopathy and psychopathic traits in representative
general population samples using standardized instruments. This study aimed to measure
prevalence and correlates of psychopathic traits, based on a two-phase survey using the
Psychopathy Checklist: Screening Version (PCL: SV) in 638 individuals, 16-74 years, in
households in England, Wales and Scotland. The weighted prevalence of psychopathy was
0.6% (95% CI: 0.2-1.6) at a cut score of 13, similar to the noncriminal/nonpsychiatric sample
described in the manual of the PCL: SV. Psychopathy scores correlated with: younger age,
male gender; suicide attempts, violent behaviour, imprisonment and homelessness; drug
dependence; histrionic, borderline and adult antisocial personality disorders; panic and
obsessive-compulsive disorders. This survey demonstrated that, as measured by the PCL: SV,
psychopathy is rare, affecting less than 1% of the household population, although it is
prevalent among prisoners, homeless persons, and psychiatric admissions. There is a half-
normal distribution of psychopathic traits in the general population, with the majority having
no traits, a significant proportion with non-zero values, and a severe subgroup of persons with
multiple associated social and behavioral problems. This distribution has implications for
research into the etiology of psychopathy and its implications for society.
Declaration of Interest: R. Hare receives royalties from sales of the PCL: SV.
Prevalence and Correlates of Psychopathic Traits in the Household Population 3
1. Introduction
Psychopathy can be differentiated from other personality disorders on the basis of
characteristic interpersonal, affective, and behavioral symptoms. The classic clinical features
were described by Cleckley (1941) who asserted that these personalities are not only found in
prisons but in the community, giving examples of apparently successful individuals of higher
social status whose façade of normality could extend into superficial material and social
success. More recently, several commentators (Babiak & Hare, 2006; Hall & Benning, 2006;
Lykken, 1995) have argued that some traits of the interpersonal and affective domains of
psychopathy might be of advantage to achieve professional success in certain areas.
However, while anecdotal examples abound, the concept of the “successful psychopath” has
been subjected to little systematic investigation. Ullrich, Farrington, and Coid (2008) found
that the domains of psychopathy are unrelated to success in a community sample of men, but
only a few of the men had high psychopathy scores. Nonetheless, De Olivier-Souza, Ignácio,
Moll, and Hare (2008) suggested that even among community members with high
psychopathy scores their “success” often is ephemeral and defined without recognition of its
negative impact on others.
The international standard for the assessment of psychopathy, the Psychopathy Checklist-
Revised (PCL-R; Hare, 1991, 2003), was developed with offender populations, whereas its
derivative, the Psychopathy Checklist: Screening Version (PCL: SV; Hart, Cox, & Hare,
1995), was developed and validated for use with non-forensic samples. The two instruments
are highly correlated and measure the same construct (Cooke, Michie, Hart & Hare, 1999;
Guy & Douglas, 2006). Although not included in the ICD or DSM classifications, analyses of
traits associated with personality disorders reveal a dimension remarkably similar to the
personality features that constitute psychopathy (Blackburn & Coid, 1998; Ullrich &
Prevalence and Correlates of Psychopathic Traits in the Household Population 4
Marneros, 2004; 2007). This underlying factor consisted of impulsive, dissocial, paranoid,
histrionic and borderline dimensions in terms of the ICD classification and antisocial,
paranoid, histrionic, narcissistic, borderline, and passive-aggressive traits according to DSM.
The reliability and validity of the PCL-R and PCL: SV for the measurement of
psychopathy are established (Acheson, 2005; Hare & Neumann, 2008), together with their
predictive validity for future violent and criminal behaviour (Douglas, Strand, Belfrage,
Fransson, & Levender, 2006; Hemphill, 2007; Leistico, Salekin, DeCoster & Rogers, 2008).
Recent developments in factor analysis have indicated the importance of different
components of psychopathy. Although previously considered a higher-order construct
underpinned by two correlated factors (Hare, 1991), subsequent confirmatory factor analysis
has described a hierarchical three-factor model (Cooke & Michie, 2001) and more recently a
four-factor model for both the PCL-R (Hare, 2003; Neumann, Hare, & Newman, 2007;
Neumann, Vitacco, Hare & Wupperman, 2005) and the PCL: SV (Vitacco, Neumann &
Jackson, 2005). This model (Table 1) permits finer descriptive analysis of individuals
encountered in clinical practice and allows empirical study of subcomponents of
psychopathy, including the possibility that these have different etiologies. Although there is
consensus on the necessity of differentiating the traditional factors of psychopathy, and
researchers agree on the interpersonal, affective and impulsive/lifestyle components,
divergent opinions result in debate over antisocial behaviors and whether they constitute an
integral facet of psychopathy (Hare & Neumann, 2006) or are merely a negative outcome of
the core psychopathic personality traits (Cooke, Michie, Hart, & Clark, 2004).
Research on correlates and etiology of psychopathy has focused heavily on male
prisoners and psychiatric patients in high security settings. Little is known of the
epidemiology of psychopathy based on representative samples from the general population.
Such studies are rare and have to overcome various obstacles, particularly due to the low base
Prevalence and Correlates of Psychopathic Traits in the Household Population 5
rates of psychopathic traits (Hall & Benning, 2006). Nevertheless, clinicians and researchers
have known that psychopaths exist in the general population and have recognized the
importance of studying psychopathic traits in non-institutionalized studies (Kirkman, 2002).
Studying psychopathy in non-forensic samples can rule out the effects of incarceration and
recurrent institutionalization on dependent measures (Lilienfeld, 1994). Recent studies have
demonstrated that psychopathic traits are continuously distributed among forensic, clinical
and community samples, and that individuals with levels of psychopathy comparable with
those in correctional and forensic psychiatric populations can be found in the general
population (DeMatteo, Heilbonn, & Marczyk, 2006; De Oliveira-Souza et al., 2008;
Neumann & Hare, 2008). These studies indicate that high levels of psychopathy in
community samples have much the same predictive value, with respect to antisocial and
criminal behavior, as they do in forensic populations.
The aim of this study was to estimate the prevalence and correlates of psychopathy, as
measured by the PCL: SV, in the general population of Great Britain, using a two-phase
survey of a large representative household sample of adults, aged 16-74, conducted in 2000.
Both the PCL-R (Guay, Ruscio, Knight, & Hare, 2007) and the PCL: SV (Walters, Gray,
Jackson, Sewell, Rogers, Taylor, et al., 2007) measure a dimensional construct, and our
primary analyses therefore involved correlations between the PCL: SV (and its factors) and
its correlates, including demographic characteristics, verbal intelligence, DSM-IV Axis-II
personality disorder traits, ICD-10 clinical syndromes, and social and behavioral problems. In
addition, however, the PCL-R and PCL: SV scores can be used to provide convenient
threshold or cut-scores for psychopathy, thereby allowing estimates to be made of the
prevalence of the disorder in our sample.
Prevalence and Correlates of Psychopathic Traits in the Household Population 6
2. Method
2.1. Sample
The sample included 638 subjects participating in the second of a two-phase survey of
Psychiatric Morbidity among Adults aged 16-74 years living in Private Households in
England, Wales and Scotland (Singleton, Bumpstead, O’Brien, Lee, & Meltzer, 2001). The
Small Users Postcode Address File (PAF) was used as the sampling frame and the Kish Grid
Method (Kish, 1965) to systematically select one person in each eligible household. A total of
8,886 (69.5%) selected adults who agreed to complete a first phase computer-assisted
interview by the UK Office of National Statistics were asked whether they would be willing
to be contacted, if selected, to take part in the second phase of the survey.
The phase II sample was drawn on the basis of two self-report diagnostic instruments
described below. Eligible subjects included (1) all satisfying one or more sift criteria for
psychotic disorder; (2) half who sifted positive for antisocial and borderline DSM-IV
personality disorder, with no evidence of psychotic disorder; (3) 1 in 14 who screened
positive for other personality disorders with no evidence of psychotic disorder, and (4) 1 in
14 subjects who showed no evidence of either psychosis or personality disorder. The sample
was sifted in this manner with the intention of identifying uncommon psychiatric disorders in
the second phase and with a view to constructing weighting variables to estimate their
prevalence. Of 1,036 subjects selected for the second stage, 638 (61.6%) agreed to participate
and were interviewed.
The second phase attrition rate (38.4%) was mostly due to refusals. Compared to
respondents, they were more likely to be non-White (8.5% vs. 2.9%, p=.001), with no
educational qualifications (39.7% vs. 31.0%, p=.004), less likely to have a university degree
Prevalence and Correlates of Psychopathic Traits in the Household Population 7
(9.7% vs. 16.0%, p=.004), of lower social class (31.3% vs. 22.2%, p=.000), and more likely
living in rented accommodation (43.1% vs. 33.9%, p=.003). Other background factors,
including age, gender, legal marital status, employment status and family type, were similar
between participants and non-respondents. The weighting procedure for the sample was
designed to take these attrition factors into account.
Over half of all participants (56.7%) were female, with ages ranging from 16 to 74 years
(mean=45.4, SD=15.6), with no statistical significant difference between men and women.
Only a small proportion of the sample (2.8%) was non-white. Nearly half were married or
cohabiting, just over a quarter were single, and one in seven was divorced. More than two-
thirds had formal educational qualifications and over half were in paid employment, either
full or part time.
2.2. Diagnostic measures
Probable cases of personality disorder were identified in Phase I using the screening
questionnaire of the Structured Clinical Interview for Axis-II disorder (SCID II; First,
Gibbon, Spitzer, & Williams, 1997). Subjects entered "yes" or "no" responses to 116
questions on laptop computer. Axis-II disorder categories were created by applying
algorithms developed in a previous survey of prisoners (Singleton, Meltzer, Gatward, Coid,
& Deasy, 1998). Subjects screening positive for psychotic disorder (Bebbington & Nayani,
1994) responded positively to one of: auditory hallucinations; having received a diagnosis of
psychosis or psychotic symptoms; receipt of anti-psychotic medication; or having had an in-
patient stay in a mental hospital or ward. Fulfillment of any of these criteria and diagnoses
from the SCID-II screen determined selection for a second phase interview in which
schizophrenia or other non-affective psychotic disorder was assessed using the SCAN
Prevalence and Correlates of Psychopathic Traits in the Household Population 8
(Schedules for Clinical Assessment in Neuropsychiatry; Wing, Babor, Brugha, Burke,
Cooper, Giel, et al., 1990; World Health Organisation Division of Mental Health, 1999) and
personality disorder using the SCID-II interview (First et al., 1997).
Additional ICD-10 clinical syndromes were measured using self-report instruments in
Phase I. Affective and anxiety disorders in the week preceding interview were assessed using
the revised version of the Clinical Interview Schedule (CIS-R; Lewis & Pelosi, 1990). Data
are presented on the prevalence of six ICD-10 syndromes: mixed anxiety and depressive
disorder, generalized anxiety disorder, depressive episode, all phobias, obsessive-compulsive
disorder, and panic disorder. Alcohol misuse was measured using the Alcohol Use Disorders
Identification Test (AUDIT; Babor, de la Fuente, Saunders, & Grant, 1992), and alcohol
dependence using the Severity of Alcohol Dependence Questionnaire (SAD-Q; Stockwell,
Murphy, & Hodgson, 1983). Questions designed to measure drug use were included for a
series of different substances for any of five questions to measure drug use and dependence
over the past year (Singleton, Lee & Meltzer, 2002).
The National Adult Reading Test (Nelson & Willison, 1991; NART), a measure of
premorbid verbal intelligence, was also applied in the first phase of the study, together with
questions on healthcare service use, criminal justice involvement, and other social and
behavioral problems over the lifetime.
Psychopathy was measured using the PCL: SV, a 12-item rating scale derived from the
PCL-R. Items are rated on a 3-point scale (0 = does not apply, 1 = applies to a certain extent,
2 = applies) and summed to yield total scores ranging from 0 to 24. This represents a
dimensional measure of the degree to which a given individual matches the prototypical
psychopath. The recommended (standard) procedure is to score the PCL: SV by integrating
interview and collateral information.
Prevalence and Correlates of Psychopathic Traits in the Household Population 9
Although the PCL: SV measures a dimensional construct, researchers have adopted a score of
18 or greater as a convenient cut score for “probable psychopathy,” and scores between 13
and 17 as an indication of “possible psychopathy.” These cut scores were used in the
MacArthur Violence Risk Assessment Study, which included samples of civil psychiatric
patients and a comparison sample from the community (Steadman, Silver, Monahan,
Applebaum, Robbins, Mulvey, et al., 2000). As in the present study, PCL: SV assessments in
the MacArthur study were based on clinical inferences obtained primarily from interview
data. Research with the PCL-R indicates that scores from interview-based assessments are
lower than from those that include adequate collateral information (Alterman, Cacciola, &
Rutherford, 1993; Hare, 2003). We therefore conducted analyses using cut scores of both 13
and 11 for possible psychopathy.
Interviewers in phase II of the survey were psychology graduates trained by the principal
investigator (J.C.) by reviewing the research background to psychopathy, the PCL: SV
assessment procedure, and in scoring using a large group format and involving the viewing of
videotapes of assessment interviews to establish norms for scoring individual PCL: SV items.
They were supervised throughout the fieldwork period by a trained field manager to provide
quality assurance and standardization. Alpha coefficients of total, male, and female PCL: SV
scores were within the acceptable range (Total=0.83, male=0.83, female=0.79) suggesting
good internal consistency. Inter-item correlations (M=0.27, SD=0.13, Md=0.25), which
should range between .25 and .50, also indicated satisfactory homogeneity.
Prevalence and Correlates of Psychopathic Traits in the Household Population 10
2.3. Statistical analysis
Weights were calculated to compensate for differential sampling probabilities and non-
response and have been previously reported (Singleton et al., 2002). Weighted analyses were
performed on all statistical procedures throughout the study. For PCL: SV total and four
factor scores, descriptive statistics for demographic characteristics and social adversity
measures were calculated using SPSS (v12). Spearman’s nonparametric correlation
coefficients were calculated for inter-item correlations and Cronbach’s alpha coefficient for
overall consistency among the 12 items of PCL: SV. Partial correlation analysis was
performed among the four factors of psychopathy, controlling for gender. Weighted Poisson
regression analysis was applied to investigate associations between PCL: SV total scores and
demographic characteristics of respondents, Axis II disorders, intelligence, and social
problems experienced by respondents, adjusting for confounders or comorbid disorders
applicable to each variable of interest. As the four PCL: SV factors (Table 1) are strongly
correlated with each other, the analysis took this into account when investigating associations
between variables of interest and factor scores. We used weighted multivariate Poisson
regression analysis which treats the four factor scores as repeated measures within each
respondent. The full variance-covariance structure of the four factor scores was then captured
by the model. The effect of each variable on the PCL: SV scores was tested by the standard
Z-score statistic. All regression analyses were performed in MLwiN (V2.0). Extra Poisson
variation was allowed in the Poisson regression model to reflect excessive variation of PCL:
SV scores due to extreme values.
Prevalence and Correlates of Psychopathic Traits in the Household Population 11
3. Results
3.1. Prevalence and score distribution
Unweighted data included 11 (1.8%) subjects who scored 11 or more on the PCL: SV, 4
(0.6%) scoring 13 or more, with only 1 subject above the recommended cut-off for probable
psychopathy of 18, who scored 20. The weighted prevalence of “possible” psychopathy,
using a suggested cut score of 11 or more points in this population was 2.3% (95% CI: 1.2-
3.8); 3.7% (95% CI: 1.8-6.6) in men and 0.9% (95% CI: 0.2-2.8) in women. The prevalence
of possible psychopathy using a cut score of 13 or more was 0.6%; 1.3% in men (95% CI:
0.3-3.4) and 0% in the females.
The weighted distributions of PCL: SV scores among men and women in the population
are demonstrated in Figure 1. The figure suggests a quasi-continuous (or half-normal)
distribution of psychopathic traits, accounted for by a sub-group of the population, with the
majority (70.8%) of persons demonstrating no psychopathic traits. The prevalence at every
level of psychopathy measured using the PCL: SV was higher in men than women, with an
overall gender ratio of 4:1. Mean total PCL: SV scores were 1.52 (SD=0.16) for men and
0.54 (SD=0.08) for women.
3.2. Demography and intelligence
Partial correlation analyses among the four factors, controlling for age, demonstrated that
all were significantly intercorrelated (Table 2). The strongest correlation was between the
Factor 4 (Antisocial) and Factor 3 (Lifestyle).
Prevalence and Correlates of Psychopathic Traits in the Household Population 12
Lower mean PCL: SV total scores among persons aged 55-74 years, accounted for by
lower Factor 2, 3 and 4, but not Factor 1 (Interpersonal) scores are demonstrated in Table 3.
Mean total and all factor scores were significantly lower among women. Non-White
participants demonstrated higher total, Interpersonal and Lifestyle scores. Marital and
employment status did not correlate with psychopathy scores, except that persons who were
economically inactive had lower Interpersonal and higher Lifestyle scores. There was no
correlation between psychopathy scores and social class. However, persons who rented rather
than owned their home had higher PCL: SV scores, accounted for by higher Affective,
Lifestyle, and Antisocial, but not Interpersonal scores.
Regression analyses of premorbid intelligence coefficients (NART scores) and PCL: SV
scores (after controlling for age and factor intercorrelations) demonstrated negative
associations in the combined sample of men and women with total scores ((standard error)=
-0.029(0.008), p=0.002), Lifestyle scores ((standard error)=-0.040(0.012), p=0.008) and
Antisocial scores ((standard error)=-0.044(0.010), p<0.0001), but with no significant
correlations with Interpersonal and Affective scores.
3.3. Axis II Personality disorders
Correlations between dimensional scores of individual DSM-IV personality disorders and
weighted PCL: SV scores are presented in Table 4. Total PCL: SV scores in the combined
male and female sample were significantly correlated with borderline, histrionic, and adult
antisocial scores. Interpersonal factor scores were positively correlated with narcissistic
scores. Affective factor scores were positively correlated with schizoid and adult antisocial
scores, but negatively with obsessive-compulsive and avoidant scores. Lifestyle factor scores
were significantly correlated with histrionic, borderline, and adult antisocial scores.
Prevalence and Correlates of Psychopathic Traits in the Household Population 13
Antisocial factor scores correlated positively with conduct disorder and adult antisocial
scores.
3.4. Co-morbid clinical syndromes
Associations between weighted PCL: SV scores and ICD-10 clinical syndromes indicate
that there were no associations between mixed anxiety and depressive disorder, generalized
anxiety disorder, or depressive episodes and the psychopathy scores (Table 5). Total PCL:
SV scores were higher for participants with obsessive-compulsive and panic disorder.
Phobias were only related to the Affective factor. Obsessive-compulsive disorder was
associated with higher scores on the Interpersonal, Affective, and Lifestyle factors. Panic
disorder was associated with higher scores on the Interpersonal, Lifestyle, and Antisocial
factors.
There were no significant associations between total and individual factor scores for
participants diagnosed with schizophrenia or other non-affective psychoses.
There were significantly higher total PCL: SV scores for participants with a history of
cannabis use in the past year, lifetime heroin and amphetamine use, and dependence on any
drug (Table 6). There were no associations between alcohol dependence or hazardous
drinking and psychopathy scores. Differential associations with factor scores indicated that
cannabis use and dependence on any drug were significantly associated with the Lifestyle and
Antisocial factors but not with the Interpersonal or Affective factors. Heroin use was
associated with the Interpersonal, Affective, and Antisocial factors but not with the Lifestyle
factor. Amphetamine use was correlated only with the Interpersonal and Antisocial factors,
whereas cocaine use was correlated only with the Interpersonal factor.
Prevalence and Correlates of Psychopathic Traits in the Household Population 14
3.5. Social and behavioral problems
Associations between self-reported lifetime behavioral and social problems and PCL: SV
scores indicate that total scores were associated with reporting criminal convictions,
imprisonment, assaulting another person in the past 5 years, experiencing violence in the
family home, being homeless, psychiatric admission, and parasuicide, but not with
experiencing sexual abuse or financial crises over the lifetime (Table 7). Interpersonal factor
scores were not associated with any social or behavioral problems except financial crisis.
However, the Affective factor was independently associated with criminal convictions,
imprisonment, violence towards others, experiencing violence in the family home, and
homelessness. Lifestyle factor scores were independently associated with criminal
convictions, imprisonment, experiencing violence (but not behaving violently towards
others), homelessness, psychiatric hospital admission and attempted suicide. Antisocial factor
scores were independently associated with reporting criminal convictions, imprisonment,
violence towards others, experiencing violence, homelessness, psychiatric hospitalization,
and attempted suicide.
4. Discussion
4.1. Prevalence of self-disclosed psychopathy in the British household population
To our knowledge, this survey is the first to measure psychopathy in a representative
general population sample using a standardized instrument. Estimates of the prevalence of
psychopathy and psychopathic traits depend on the measurement tool and the thresholds
used. With the PCL: SV a score of 13 is used for “possible” psychopathy. Using this cut
Prevalence and Correlates of Psychopathic Traits in the Household Population 15
score, 0.6% of the sample of men and women met the threshold. Recalculation using a cut
score of 11 yielded a prevalence of 2.3%; 3.7% in men and 0.9% in women. Most previous
studies have used university students or self-report measures of psychopathy (Forth, Brown,
Hart, & Hare, 1996; Levenson, Kiehl, & Fitzpatrick, 1995; Lynam, Whiteside, & Jones,
1999; Salekin, Trobst, & Krioukova, 2001). A more direct comparison is with PCL: SV data
from the community sample (n=519) in the MacArthur Violence Risk Study (Neumann &
Hare, 2008) selected to match socioeconomic characteristics of a sample of civil psychiatric
patients. The prevalence of possible psychopathy was 1.7% (2.0 % men and 1.6% women) at
a cut score of 13, and 5.2% (7.6% men and 3.7% women) at a cut score of 11. These values
probably are higher than in the present study because they were derived from a selected part
of a particular urban population rather than from a representative sample of the general
population. The MacArthur comparison group would be expected to have higher mean PCL:
SV scores because it included a narrower age range of 18-40 years and was selected from
urban census tracts in which the experimental group of patients in the follow-up study resided
after discharge. Many were described as “disproportionately impoverished,” with “higher
crime rates than the city as a whole” (Steadman, Mulvey, Monahan, Robbins, Applebaum,
Grisso, et al., 1998). In contrast, our participants were drawn from a wide age range,
including a representative number of elderly and young persons, selected from rural and
semi-rural as well as urban areas, and the data were weighted. In this context, the distribution
of scores in the US urban community sample, which resembles our own and contrasts with
that observed in correctional and forensic psychiatric hospital samples (Hare, 2003), is of
considerable interest. It provides some robust support for conceptualizing the distribution of
psychopathy in the general population as continuous with, but different from, that observed
among selected, offender samples.
Prevalence and Correlates of Psychopathic Traits in the Household Population 16
The present data are broadly consistent with other estimates of around 0.75 to 1% for
psychopathy in the general population, based on extrapolations of the ratio of psychopathy to
antisocial personality disorder (ASPD) in prisons to the general population (approximately 3
or 4:1) (Blair, Mitchell & Blair, 2005; Hare, 2003). The prevalence of ASPD among men
aged 16-74 years in this population was lower than North American surveys at 1% (Coid,
Yang, Tyrer, Roberts, & Ullrich, 2006). All men scoring 13 or more on the PCL: SV received
a diagnosis of ASPD.
Our findings also show that psychopathic features will be elevated among homeless
persons and, to a lesser extent, persons admitted to psychiatric hospitals. The association with
homelessness was not surprising given the fact that some criteria of the PCL: SV such as
impulsiveness, irresponsibility and criminal behaviors increase the risk of becoming “of no
fixed abode.” Since the strongest inter-relations were found for the more behavior-related
factors of psychopathy, this suggests some contamination of outcome and predictor variables.
However, previous research on ASPD has demonstrated that conduct disorder preceded the
onset of homelessness and that the prevalence of ASPD was not significantly affected by
discounting ASPD symptoms thought to be confounded with homelessness (North, Smith, &
Spitznagel, 1993). The relationship between psychopathic traits and admission to a
psychiatric hospital is not unexpected in view of recent research on ASPD in the general
population, where a significantly higher proportion reported a lifetime psychiatric admission
when compared to the rest of the population (Ullrich & Coid, accepted).
Prevalence and Correlates of Psychopathic Traits in the Household Population 17
4.2. Distribution of psychopathic traits in the general population
Our findings confirm that the majority of the general population has very few
psychopathic personality traits or associated behaviours, particularly when compared with
individuals in correctional or forensic psychiatric institutions. The majority of the adult
household population demonstrated no or only very few psychopathic traits, but with a small
subgroup scoring up to a maximum of 20 points. The PCL: SV total scores were distributed
continuously with one mode at a score of zero and the shape of a half-normal distribution.
From an epidemiological point of view, the distribution of symptoms or traits has strong
etiological implications. Psychopathy is likely to be multiply determined (Lilienfeld, 1998).
Therefore, a true dichotomous distribution indicating one single cause would not have been
expected. A simultaneous joint exposure to various risk factors (complete co-participation of
causes) would have resulted in a bimodal distribution of the psychopathy scores. A
continuous normal distribution, on the other hand, is thought to be determined by the effects
of multiple, moderate risk factors, similar in magnitude, which act additively as well as
independently (van Os & Verdoux, 2003). However, the half-normal shape of psychopathy
scores in the general household population of Britain points in the direction of various risk
factors with different potency which contribute independently, but with a certain degree of
co-participation.
4.3. Demography and intelligence
In the present study, PCL: SV total and factor scores were generally lower among
persons 55 or older than among younger persons, consistent with cross-sectional analyses of
male prison PCL-R data. These data indicate that total PCL-R scores decline little as a
Prevalence and Correlates of Psychopathic Traits in the Household Population 18
function of age (at least until about age 55 or 60), but that some decreases occur in the
Lifestyle and Antisocial factors (Hare, 2003; Ullrich, Paelecke, Kahle, & Marneros, 2003).
Similar age-related declines have been observed in the prevalence of ASPD (Regier, Boyd,
Burke, Rae, Myers, Kramer, et al., 1988) and in criminal behaviour in general (Farrington,
1986), suggesting that certain behaviour-related traits related to psychopathy decrease in
severity with age. Although there is evidence that the criminal activities of psychopathic
offenders may decline in frequency with age (Harpur & Hare, 1994), this may be an artifact
related to being in prison for longer periods than other offenders as they age (Porter, Birt, &
Boer, 2001).
Reported prevalences of psychopathy are lower among females than males in forensic
samples (Douglas et al., 2005; Hare, 2003; Verona & Vitale, 2006) but with similar factor
structure and correlates (Hare, 2003; Neumann et al, 2007). Selection and differences among
study settings are likely to have influenced these findings. It has also been argued that it may
be more difficult to measure certain items in women (Salekin et al., 2001) or that some
psychopathy-related behaviors manifest differently in females (Hare, 2003). In this
community survey, a sex ratio of 4:1 suggested either that there is gender bias in
identification or manifestation of certain items, or that there are true sex differences in the
distribution of psychopathic traits in the general population, as suggested by Figure 1.
Psychopathic traits were found at all socioeconomic levels but were associated with lack
of social success. Although studies of male offenders have demonstrated associations
between low social class, educational failure and antisocial lifestyle, psychopathic prisoners
are no more likely to come from lower social class backgrounds than other offenders (Hare,
2003). Previous evidence indicates that PCL-R measures are not unduly influenced by
ethnicity, based on studies of White and African-American subjects (Cooke, Kosson, &
Michie, 2001; Skeem, Edens, Camp, & Colwell, 2004) although there may be cultural
Prevalence and Correlates of Psychopathic Traits in the Household Population 19
differences in expression of psychopathic traits. We could not examine ethnic or cultural
factors due to the small number of participants and heterogeneous nature of the non-White
category.
Recent studies have tested Cleckley’s (1941) hypothesis of the intellectual abilities of the
psychopath using the contemporary three/four factor models. Across all studies, a specific
pattern of association has been established. The Interpersonal factor was positively associated
with verbal IQ and an intellectual measure reflecting creativity, practicality, and analytic
thinking (Salekin, Neumann, Leistico, & Zalot, 2004; Vitacco et al, 2005). The Affective
factor was negatively associated with verbal IQ (Salekin et al., 2004) as were the
Lifestyle/antisocial components of psychopathy (Vitacco et al., 2005). In accordance with
these findings, our study demonstrated an inverse relationship between verbal intelligence
and psychopathic traits at the population level, explained by Lifestyle and Antisocial factors.
However, the negative association with the Affective factor was not confirmed by our study.
Moreover, our findings do not support the hypothesis of an association between the
interpersonal domain of psychopathy and greater cognitive abilities. It has to be considered,
however, that our measure of intelligence was more simplistic than in other studies and that
the lack of association might reflect measurement error.
4.4. Axis II personality disorder
Following adjustments, there were significant associations between PCL: SV and Axis II
disorders, similar to those previously demonstrated in forensic samples (Cooke et al., 1999;
Ullrich & Marneros, 2007). These studies have indicated that PCL-R and PCL: SV total
scores correlate most strongly with antisocial, borderline, histrionic, and narcissistic scores.
Specific correlates between narcissistic traits and the interpersonal factor confirm similarities
Prevalence and Correlates of Psychopathic Traits in the Household Population 20
between narcissistic personality features and interpersonal items of superficial charm,
grandiosity, and deceitfulness. The emotional dysfunction reflected in the Affective factor
may explain its correlates with detachment from social relationships and restricted emotional
expression of schizoid personality disorder, together with negative correlates with social
anxiety, measured by avoidant traits, and conscientiousness, rigidity and perfectionism,
measured by obsessive-compulsive traits.
The Lifestyle factor was strongly correlated both with excessive emotionality and
attention-seeking of histrionic, and instability of self-image and affect, and impulsivity of
borderline personality disorder. Axis II adult antisocial scores were highly correlated with the
Affective, Lifestyle, and Antisocial factors, suggesting that emotional dysfunction,
impulsivity and irresponsibility can contribute to an antisocial lifestyle in adulthood.
However, the lack of an association between conduct disorder and total PCL: SV scores was
unexpected. Conduct disorder is a necessary precursor of ASPD, and the association with the
Antisocial factor was therefore expected. The most likely explanation for lack of associations
with total and other factor scores is twofold: firstly, adjustments in the regression analysis,
secondly, the nature of the sample. Conduct disorder in this household population was less
prevalent than among prisoners using the same research diagnostic instruments (Singleton et
al 1998). Although childhood conduct disorder was not uncommon among the household
population, the prevalence of ASPD was low (Coid et al., 2006). More cases had a good
prognosis and did not result in adverse adult outcomes such as ASPD, in marked contrast to
prisoners.
Prevalence and Correlates of Psychopathic Traits in the Household Population 21
4.5. Psychotic, affective and anxiety disorders
According to Cleckley’s original observations, psychopathic individuals do not show
either psychoneurotic or psychotic symptoms (Cleckley, 1941). In this study, persons with
schizophrenia, depressive episodes, mixed anxiety and depressive disorder, generalized
anxiety disorder, and phobias did not have raised psychopathy scores, corresponding to
offender samples which have not demonstrated associations with affective disorders (Cooke
et al., 1999; Hare, 2003). In offender samples, measures of anxiety tend to have low, negative
correlations with the PCL-R. It has been argued that anxiety in psychopathy is related
primarily to an antisocial lifestyle and its consequences (Schmitt & Newman, 1999), but
more recently that it is independent of psychopathy (Coid, Yang, Ullrich, Roberts, Moran,
Bebbington, et al., accepted; Hare, 2003). The association with obsessive-compulsive
disorder (OCD) was unexpected. This suggests that at lower levels of psychopathic traits
there may be associations with OCD and panic disorder at the general population level, but
among populations with higher trait levels, and individuals with psychopathy, these
associations are absent.
4.6. Substance misuse, social and behavioral problems
Previous studies have shown that psychopathy is related to alcohol misuse (Hare, 2003).
In this study, psychopathic traits were not associated with alcohol use or hazardous drinking,
although the latter was very prevalent in this household population. Psychopathic individuals
in forensic samples are likely to have more severe problems due to alcohol misuse, but this
survey indicated weak associations with psychopathic traits at the population level.
Prevalence and Correlates of Psychopathic Traits in the Household Population 22
All categories of illicit drug misuse except cocaine were associated with higher PCL: SV
scores, but with differing patterns of association between individual factors. Being dependent
on any drug, use of cannabis in the past year, and lifetime amphetamine and heroin use were
associated with the Antisocial factor, consistent with findings with offenders (Hare, 2003).
However, specific associations between heroin, cocaine, and amphetamine use and the
Interpersonal factor at the population level were of interest, indicating the possibility of
psychopathic personality traits influencing preference for certain substances.
As expected, strong associations were obtained between raised PCL: SV scores and a
range of self-reported social and behavioral problems, in particular violence and criminality,
but also self-harm and psychiatric hospitalization. Also, as expected, these problems were
associated most strongly with the Antisocial factor. However, independent associations with
the Lifestyle component confirmed the importance of impulsivity and instability in
contributing to these adverse outcomes. The findings also confirmed independent
contributions of the Affective factor to criminal behaviour and violence (Vitacco et al., 2005),
the latter possibly relating to fearlessness and lack of aversion to engaging in fighting. No
associations were found with the Interpersonal factor except for financial crises, raising the
intriguing possibility that this component contributed to financial manipulation,
mismanagement and failure in those with higher disposable incomes. It has been argued that
certain individuals in the general population can be highly problematic, particularly in work
settings, and manifest interpersonal and affective features of psychopathy, but without
exhibiting the antisocial acts or lifestyle of criminal psychopaths (Babiak & Hare, 2006; Hall
& Benning, 2006).
No associations were found between psychopathy and sexual victimization over the
lifetime (it was not possible to identify age of victimization). There have been reports of early
sexual and physical abuse among psychopathic offenders (Farrington, 2006) but the effects
Prevalence and Correlates of Psychopathic Traits in the Household Population 23
are small and inconsistent, and any observed associations are not necessarily causal in nature
(DiLalla & Gottesman, 1991). No information on circumstances of admission to a psychiatric
hospital was available to explain associations with the Lifestyle and Antisocial factors. These
were most likely related to drug misuse, self-harm, and adjustment disorders secondary to
situational crises.
4.7. Four-factor model
There has been lack of agreement over whether psychopathy is better described by a three- or
four-factor model (Cooke & Michie, 2001; Cooke et al., 2004; Hare, 2003; Hare & Neumann,
2006; Neumann et al., 2005; Skeem, Mulvey, &Grisso, 2003; Vitacco et al., 2005). Our
findings indicate that it would be inappropriate to dispense with the fourth (Antisocial) factor.
Debates over factor structure based on confirmatory factor analysis of psychopathy items are
not supported by correlates with external measures in an epidemiologically representative
sample. Separation of the original PCL-R Factor 1 into interpersonal and affective factors has
proven useful in the clinical description of psychopathy. The Interpersonal factor, with
features similar to narcissistic personality disorder, may represent a higher functioning
component of psychopathy and is unlikely to result from cognitive dysfunction. At lower
levels of the trait within the population, it may convey certain advantages in social
functioning. The Affective factor, characterised by callousness, lack of empathy and sense of
responsibility, comprises essential components of the psychopathy construct. Although these
items do not correlate with verbal intelligence in a community sample, further research may
reveal cognitive processes involved in this particular form of emotional dysfunction,
increasing the risk for goal-directed, instrumental aggression (Blair et al., 2005).
Prevalence and Correlates of Psychopathic Traits in the Household Population 24
The closely inter-correlated nature of the Lifestyle and Antisocial factors indicates that
dispensing with the latter may be difficult to achieve, especially in clinical assessment.
Although the Lifestyle factor demonstrated independent associations with several measures,
few distinguished it from the Antisocial factor, except measures of Axis II personality
disorder.
4.8. Methodological limitations
This study examined correlates with psychopathic traits at the population level and not
with psychopathy as categorically defined. The sampling frame for the survey was designed
to estimate the prevalence of uncommon conditions using self-report instruments in the first
phase to screen for participants who received research diagnostic interviews in the second
(Singleton et al., 2001). However, our estimates are limited by the rarity of individuals with
psychopathic features, and by the fact that no women exceeded the commonly-used PCL: SV
cut score of 13 for psychopathy. This may have been due to reluctance of psychopathic
individuals to participate. Furthermore, subsequent attrition in both phases of the survey may
not have been compensated for by weighting. In addition, prisoners and homeless persons
were not included in this survey. Heavy reliance on interview data could have led to an
underestimate of psychopathy scores.
It can be argued that the skewed distributions of the factors of psychopathy towards zero
(particularly the interpersonal component) had a negative impact on the statistical power of
some analyses. However, Poisson regression analyses considering overdispersion is the
method of choice to address this problem. Nevertheless, it cannot be ruled out that some
nonsignificant findings were due to the lack of statistical power.
Prevalence and Correlates of Psychopathic Traits in the Household Population 25
Diagnostic categories of affective and anxiety disorders and substance use were derived
from self-report measures in the first phase of the survey and were not measured in the
second. This may have resulted in false positives. In the second phase, lack of collateral
information available to interviewers may have biased PCL: SV scores. This may be a serious
problem when measuring a condition characterized by conning, pathological lying, and
intense impression management. Subsequent research may be able to obtain more extensive
collateral information than was available to us. In spite of these limitations, it is encouraging
that correlates of the PCL: SV and its components were consistent with the literature on
psychopathy in forensic populations.
4.9. Conclusion
Psychopathy is a rare condition in the general population. In this study only a very small
minority of individuals met common criteria for psychopathy or demonstrated elevated levels
of psychopathic traits. These findings are in accordance with previous research. Furthermore,
psychopathic traits were associated with multiple social and behavioral problems and a
substantial co-morbidity with mental disorders on both Axis I and II of the DSM
classification. The results of our study indicate that elevated psychopathic traits in non-
incarcerated and non-psychiatric individuals are a disabling condition with various negative
outcomes similar to those found in forensic and psychiatric samples.
Prevalence and Correlates of Psychopathic Traits in the Household Population 26
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Prevalence and Correlates of Psychopathic Traits in the Household Population
34
Table 1
The PCL: SV four-factor model of psychopathy
Label Items
Factor 1 Interpersonal Superficial
Grandiose
Deceitful
Factor 2 Affective Lacks remorse
Lacks empathy
Doesn’t accept responsibility
Factor 3 Lifestyle Impulsive
Lacks goals
Irresponsible
Factor 4 Antisocial Poor behavioral controls
Adolescent antisocial behaviour
Adult antisocial behaviour
Note. Copyright 1995 R.D. Hare and Multi-Health Systems, 3770 Victoria Park Avenue,
Toronto, Ontario, M2H 3M6. All rights reserved. Reprinted by permission. Note that the item
titles cannot be scored without reference to the formal criteria contained in the PCL: SV
Manual. The Interpersonal and Affective Factors comprise Part 1 described in the PCL: SV
Manual. The Lifestyle and Antisocial factors comprise Part 2 described in the PCL: SV
Manual.
Prevalence and Correlates of Psychopathic Traits in the Household Population 35
Table 2
PCL: SV inter-factor correlation coefficients (Log transformed)
Pearson’s simple correlation Partial correlation
(adjusted for gender and other factors)
Interpersonal Affective Lifestyle Interpersonal Affective Lifestyle
Affective 0.32*** 0.26***
Lifestyle 0.25*** 0.44*** 0.18*** 0.38***
Antisocial 0.38*** 0.50*** 0.54*** 0.32*** 0.45*** 0.49***
Note. ***p<0.0001 (two tailed)
Prevalence and Correlates of Psychopathic Traits in the Household Population
36
Table 3
Sociodemographic and socioeconomic characteristics of the total sample – weighted data (n=620)
Respondents Total Interpersonal Affective Lifestyle Antisocial
Demographic
characteristic
Category group
N (%) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
16-34 211 (34.0) 1.22 (2.49) 0.20 (0.61) 0.24 (0.81) 0.29 (0.68) 0.49 (1.13)
35-54 268 (43.2) 1.13 (2.44) 0.19 (0.54) 0.20 (0.69) 0.29 (0.81) 0.46 (1.17)
Age group
55-74 141 (22.8) 0.46 (1.25)** 0.12 (0.42) 0.08 (0.38)* 0.09 (0.41)* 0.17 (0.63)**
Male 301 (48.5) 1.52 (2.80) 0.27 (0.70) 0.31 (0.84) 0.32 (0.83) 0.61 (1.32)
Gender
Female 319 (51.5) 0.54
(1.45)***
0.09
(0.30)***
0.07
(0.46)***
0.17
(0.52)**
0.21
(0.68)***
White 601 (96.9) 0.97 (2.22) 0.17 (0.53) 0.18 (0.68) 0.23 (0.67) 0.39 (1.05)
Ethnic origin
Non-White 19 (3.1) 2.28 (3.09)* 0.51 (0.75)* 0.34 (0.73) 0.70
(1.07)** 0.73 (1.45)
Married/cohabiting/widowed 354 (57.1) 0.83 (1.93) 0.12 (0.39) 0.15 (0.52) 0.20 (0.66) 0.36 (1.02)
Separated/divorced 82 (5.9) 1.40 (2.86) 0.27 (0.66) 0.23 (0.80) 0.33 (0.85) 0.58 (1.26)
Legal
marital status
Single 184 (29.7) 1.22 (2.54) 0.24 (0.69) 0.26 (0.87) 0.30 (0.68) 0.43 (1.05)
Prevalence and Correlates of Psychopathic Traits in the Household Population 37
Table 3 continued
Sociodemographic and socioeconomic characteristics of the total sample – weighted data (n=620)
Demographic characteristic Category group Respondents Total Interpersonal Affective Lifestyle Antisocial
N (%) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
Working 393 (63.4) 0.88 (1.95) 0.22 (0.60) 0.16 (0.63) 0.18 (0.52) 0.33 (0.88)
Unemployed 21 (3.3) 3.08 (4.34) 0.27 (0.52) 0.82 (1.48) 0.67 (0.99) 1.31 (2.07)
Employment status
Economically inactive 206 (33.3) 1.05 (2.43) 0.09 (0.37)* 0.18 (0.63) 0.32 (0.90)* 0.45 (1.18)
I 32 (5.3) 0.87 (2.23) 0.38 (0.59) 0.27 (0.84) 0.08 (0.57) 0.13 (0.86)
II 160 (27.1) 0.80 (1.63) 0.19 (0.52) 0.17 (0.63) 0.13 (0.39) 0.30 (0.78)
IIINM 154 (26.1) 0.47 (1.14) 0.08 (0.33)* 0.04 (0.22) 0.18 (0.51) 0.17 (0.57)
IIIM 112 (19.0) 1.54 (2.96) 0.21 (0.58) 0.31 (0.88) 0.37 (0.91) 0.65 (1.44)
IV 94 (16.0) 1.36 (2.93) 0.19 (0.74) 0.21 (0.76) 0.39 (0.93) 0.57 (1.35)
Social class
V 38 (6.5) 1.56 (2.71) 0.22 (0.63) 0.25 (0.65) 0.38 (0.89) 0.71 (1.25)
Owned outright 115 (18.5) 0.38 (1.22) 0.13 (0.41) 0.06 (0.33) 0.07 (0.30) 0.12 (0.56)
Owned with mortgage 302 (48.7) 0.64 (1.43) 0.14 (0.44) 0.11 (0.49) 0.12 (0.41) 0.27 (0.77)
Housing tenure
Rented 203 (32.7) 1.92 (3.25)** 0.26 (0.71) 0.37 (0.98)* 0.52 (1.02)** 0.77 (1.48)**
Note. For comparing the mean scores between the category levels within each variable, other demographic variables are adjusted for. *p0.05, **p0.01,
***p0.001
Prevalence and Correlates of Psychopathic Traits in the Household Population
38
Table 4
Associations between dimensional scores of Axis II personality disorder criteria and
PCL: SV scores
Axis II disorder Total Factor 1
I
nterpersonal
Factor 2
Affective
Factor 3
Lifestyle
Factor 4
Antisocial
Avoidant -1.26 -1.14 -2.69** -1.02 -1.78
Dependent -0.15 -0.52 0.72 1.90 0.07
Obsessive-compulsive -0.79 0.19 -2.28* -0.76 1.56
Paranoid 0.46 -1.34 0.95 -0.01 0.25
Schizotypal 0.51 0.93 1.19 1.65 0.44
Schizoid 1.71 -1.50 3.56** 1.83 0.54
Histrionic 2.50* 0.77 1.29 3.21** 0.24
Narcissistic 1.44 5.95*** 1.63 -0.49 0.18
Borderline 2.73** 1.06 0.73 3.13** 1.95
Conduct disorder 0.33 0.70 -1.34 -1.90 2.25*
Adult antisocial 5.48*** 0.73 5.53*** 2.60** 5.52***
Note. Adjustments: Gender, age, ethnicity, drug dependence, alcohol dependence, affective/anxiety
disorder, psychosis, intercorrelations of four factors, and comorbid PD scores (Factor 4 was not
controlled for conduct disorder and adult antisocial for Factor 1, Factor 2, Factor 3). The association is
presented by z-score as the partial regression coefficient over its standard error. *p0.05, **p0.01,
***p0.001
Prevalence and Correlates of Psychopathic Traits in the Household Population 39
Table 5
Association between ICD-10 categories of Axis I affective/anxiety disorder and PCL: SV
scores
Total Factor 1
Interpersonal
Factor 2
Affective
Factor 3
Lifestyle
Factor 4
Antisocial
Present/
absent
Respondent
s
N(%) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
No 520 (84.0) 0.93 (2.05) 0.16 (0.49) 0.16 (0.59) 0.24 (0.70) 0.36 (0.98) Mixed anxiety/
depressive
disorder
Yes 99 (16.0) 1.49 (3.19) 0.26 (0.76) 0.32 (1.04) 0.26 (0.68) 0.64 (1.40)
No 556 (89.9) 0.87 (2.06) 0.18 (0.55) 0.15 (0.62) 0.20 (0.59) 0.34 (0.93) Generalized
anxiety disorder Yes 63 (10.2) 2.31 (3.42) 0.16 (0.47) 0.51 (1.05) 0.63 (1.25) 1.01 (1.78)
No 579 (93.5) 0.99 (2.20) 0.17 (0.53) 0.18 (0.65) 0.24 (0.68) 0.40 (1.05) Depressive
episode Yes 40 (6.5) 1.46 (3.11) 0.22 (0.64) 0.36 (1.02) 0.38 (0.95) 0.50 (1.26)
No 583 (94.3) 0.92 (2.10) 0.17 (0.53) 0.16 (0.65) 0.21 (0.61) 0.37 (0.99) All phobias
Yes 35 (5.7) 2.73 (3.86) 0.25 (0.65) 0.62 (1.06)* 0.90 (1.43) 0.96 (1.81)
No 602 (97.3) 0.45 (2.13) 0.17 (0.52) 0.17 (0.63) 0.23 (0.66) 0.39 (1.03) Obsessive-
compulsive
disorder
Yes 17 (2.7) 3.67
(4.67)***
0.57
(0.92)**
1.00 (1.58)* 1.03 (1.33)* 1.07 (1.79)
No 610 (98.7) 0.99 (2.22) 0.17 (0.54) 0.19 (0.69) 0.23 (0.65) 0.38 (1.03) Panic disorder
Yes 8 (1.3) 3.08 (4.56)** 0.36 (0.77)* 0.0 (0.0) 1.23 (2.20)** 1.49 (2.54)**
Note. Adjustments: Age, sex, ethnicity, employment, any PD, psychosis, drug dependence, alcohol dependence,
intercorrelations of four factors. * P0.05, **p0.01, ***p0.001
Prevalence and Correlates of Psychopathic Traits in the Household Population 40
Table 6
Association between substance misuse and PCL: SV scores
Total Score Factor 1
Interpersonal
Factor 2
Affective
Factor 3
Lifestyle
Factor 4
Antisocial
Present/
absent
Respondent
s
N (%) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
No 542 (87.6) 0.78 (1.92) 0.16 (0.52) 0.15 (0.58) 0.19 (0.60) 0.28 (0.85) Cannabis use –
past year (1-3,6) Yes 77 (12.4) 2.74 (3.54)** 0.31 (0.64) 0.51 (1.13) 0.66 (1.09)* 1.27 (1.78)**
No 610 (98.7) 0.94 (2.07) 0.15 (0.46) 0.17 (0.63) 0.24 (0.68) 0.38 (1.01) Heroin use – ever
(1-3,6) Yes 8 (1.3) 7.13
(6.38)***
1.99
(1.89)***
1.67
(2.04)**
0.84 (1.57) 2.63
(2.28)***
No 576 (93.2) 0.86 (1.97) 0.14 (0.45) 0.15 (0.59) 0.22 (0.67) 0.35 (0.95) Cocaine use –
ever (1-3, 6) Yes 42 (6.8) 3.18 (4.28) 0.63 (1.13)** 0.75 (1.36) 0.58 (0.99) 1.23 (1.90)
No 546 (88.3) 0.83 (1.87) 0.15 (0.46) 0.14 (0.56) 0.22 (0.65) 0.32 (0.90) Amphetamine use
– ever (1-3,6) Yes 72 (11.7) 2.49 (3.95)* 0.39 (0.93)* 0.56 (1.21) 0.46 (0.95) 1.07 (1.74)**
No 577 (93.4) 0.84 (2.00) 0.17 (0.53) 0.16 (0.63) 0.20 (0.62) 0.32 (0.90) Drug dependence
– any (1-3, 5) Yes 41 (6.6) 3.47 (3.90)* 0.31 (0.62) 0.59 (1.10) 0.91 (1.22)* 1.65 (2.01)**
No 553 (89.6) 0.89 (2.10) 0.16 (0.51) 0.16 (0.64) 0.21 (0.64) 0.36 (1.00) Alcohol
dependence (1-3,
4)
Yes 64 (10.4) 2.15 (3.22) 0.36 (0.70) 0.42 (0.97) 0.59 (0.99) 0.77 (1.46)
No 429 (69.5) 0.82 (2.01) 0.16 (0.52) 0.13 (0.54) 0.20 (0.64) 0.34 (1.01) Hazardous
drinking (1-3, 7) Yes 188 (30.5) 1.49 (2.73) 0.23 (0.57) 0.33 (0.92) 0.37 (0.80) 0.57 (1.17)
Note. Adjustments: 1 = age, sex, ethnicity, employment, 2 = any personality disorder, affective/anxiety disorder,
3 = psychosis, 4 = any drug dependence, 5 = alcohol dependence, 6 = hazardous drinking, 7 = any drug use,
inter-correlations of four factors, *p0.05, **p0.01, ***p0.001
Prevalence and Correlates of Psychopathic Traits in the Household Population 41
Table 7
Association between social / behavioral problems and PCL: SV scores
Total Score Factor 1
Interpersonal
Factor 2
Affective
Factor 3
Lifestyle
Factor 4
Antisocial
Prese
nt/abs
ent
Responden
ts
N (%) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
No 525 (85.0) 0.63 (1.51) 0.14 (0.44) 0.12 (0.53) 0.18 (0.52) 0.20 (0.63) Any
convictions Yes 93 (15.0) 3.19 (3.99)*** 0.37 (0.90) 0.60 (1.14)** 0.64 (1.23)** 1.58 (1.92)***
No 593 (96.0) 0.80 (1.82) 0.17 (0.52) 0.16 (0.62) 0.19 (0.55) 0.29 (0.80) Prison sentence
Yes 25 (4.0) 6.06 (4.76)*** 0.36 (0.87) 0.93 (1.35)** 1.58 (1.78)*** 3.19 (2.17)***
No 520 (84.1) 0.70 (1.66) 0.15 (0.45) 0.10 (0.43) 0.20 (0.59) 0.25 (0.81) Violence in
past 5 years Yes 98 (15.9) 2.73 (3.83)*** 0.33 (0.86) 0.69 (1.30)*** 0.48 (1.07) 1.23 (1.70)***
No 534 (86.4) 0.88 (2.15) 0.18 (0.54) 0.16 (0.62) 0.20 (0.63) 0.35 (1.01) Victim
violence in
family home
Yes 84 (13.6) 1.89 (2.79)** 0.18 (0.53) 0.38 (0.99)* 0.56 (0.97)** 0.77 (1.30)**
No 558 (90.3) 0.93 (2.11) 0.17 (0.54) 0.17 (0.62) 0.22 (0.66) 0.36 (1.00) Sexual abuse
Yes 60 (9.7) 1.83 (3.32) 0.21 (0.50) 0.35 (1.10) 0.47 (0.97) 0.80 (1.47)
No 564 (91.3) 0.82 (1.89) 0.17 (0.53) 0.15 (0.60) 0.19 (0. 58) 0.31 (0.88) Homelessness
Yes 54 (8.7) 3.07 (4.16)*** 0.26 (0.66) 0.57 (1.20)* 0.86 (1.28)** 1.38 (1.99)***
No 512 (82.8) 0.83 (1.87) 0.14 (0.49) 0.15 (0.56) 0.20 (0.58) 0.34 (0.91) Financial crisis
Yes 106 (17.2) 1.94 (3.50) 0.33 (0.69)* 0.41 (1.08) 0.48 (1.08) 0.73 (1.57)
No 490 (79.2) 0.85 (2.05) 0.17 (0.53) 0.17 (0.65) 0.19 (0.55) 0.32 (0.91) Psychiatric
hospital
admission
Yes 129 (20.8) 1.66 (2.90)** 0.20 (0.57) 0.27 (0.79) 0.46 (1.06)** 0.73 (1.47)**
No 516 (83.4) 0.83 (1.91) 0.16 (0.47) 0.16 (0.60) 0.19 (0.61) 0.32 (0.90) Attempted
suicide Yes 103 (16.6) 1.99 (3.42)* 0.29 (0.80) 0.34 (1.00) 0.52 (0.99)* 0.86 (1.59)*
Note. Adjustments = age, sex, ethnicity, employment, alcohol dependence, drug dependence, psychosis,
affective/anxiety. *p0.05, **p0.01, ***p0.001
Prevalence and Correlates of Psychopathic Traits in the Household Population
42
Figure 1. Distribution of Weighted PCL: SV Total Scores
0
10
20
30
40
50
60
70
80
0 1-2 3-4 5-6 7-8 9-10 11-12 13-14 15
% Participants
Female Sample
Male Sample
Overall Sample
