Development and Validation of the Antisocial Traits Scale in the General Population

Abstract

Antisocial traits have severe outcomes for the perpetrator, victim, and society. Developing an assessment tool for antisocial traits that is theoretically grounded, has strong psychometric properties, and can be administered in the general population is very important for the identification and treatment of the problems associated with antisocial traits. The initial item pool was generated as the result of an extensive literature review based on criteria of Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5). Items were examined through exploratory factor analysis in a sample of 2,051 residents of Tehran. The structural validity was tested on a separate 2,049 people. The final version explains 58.4% of the total variation of the construct and included 20 questions loaded on six factors. The resultant Antisocial Traits Scale (ASTS-20) has the potential to be a useful measure for early detection of at-risk people who should be targeted by preventive interventions aimed at reducing the likelihood of criminality or recidivism.

Keywords

antisocial antisocial traits questionnaire validation study

The terms “antisocial behavior” and “antisocial traits” are used interchangeably to denote the beliefs, attitudes, behaviors, and personality traits that are characteristic of individuals who have a tendency to take advantage of or harm others and violate the law, behaviors that in turn lead to negative interpersonal and societal outcomes (Shackelford & Weekes-Shackelford, 2018). Antisocial traits are important psychosocial constructs characterized by a notable lack of emotional attachment to others, repeated violations of laws and regulations, and a tendency toward deceitfulness and impulsive behaviors (Curtis, 2016). The presence of antisocial traits is necessary, but not sufficient for a diagnosis of antisocial personality disorder (ASPD; Livesley & Larstone, 2018). Both antisocial traits and a diagnosis of ASPD can be distinguished from psychopathy, with leading antisocial behavior expert Robert Hare suggesting that “most psychopaths meet the criteria for ASPD, but most individuals with ASPD are not psychopaths” (Hare, 1996). In Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association [APA], 1994) and Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; APA, 2013), ASPD has been defined with a hybrid approach that includes both personality- and behavior-based metrics (APA, 2013).

The ability to identify and understand antisocial traits is important for psychosocial health professionals who work in forensic systems for two key reasons. First and foremost, these traits are associated with multiple negative outcomes, such as crime, recidivism, and aggressive behaviors (Brazil et al., 2016; Olver et al., 2013). In fact, antisocial traits are the strongest predictor of a wide range of problems, including crime (Eddy & Reid, 2002). Second, in prison settings, individuals with antisocial traits disproportionately contribute to discord and rule violations (Gendreau et al., 1997).

The relationship between antisocial traits and criminality is very strong. Brown (2004) in a thorough review of antisocial behavior and crime showed a bidirectional relationship between the two constructs: Sometimes antisocial behavior precedes criminality and sometimes it increases following criminal behavior. This relationship is so strong that, in some countries, such as United Kingdom and Australia, law enforcement agencies, in addition to their traditional role of crime control, are legally obliged to identify and curb antisocial behaviors (McCarthy, 2014). However, antisocial traits are not exclusively found among persons convicted of a crime; rather such traits are present in the general population to varying degrees and sometimes the configuration of such tendencies transcends the diagnostic threshold of a variety of clinical diagnoses, namely, ASPD. Various genetic, familial, social, and individual-level risk factors for antisocial behaviors have been identified in the literature (Moffitt, 2005).

Some evidence indicates that the global incidence of antisocial behaviors is increasing, which has led to increasing concerns in this area (Livesley & Larstone, 2018). However, the lack of clear consensus on how to define and measure these traits significantly limits our understanding of the prevalence of antisocial traits (Armitage, 2002). Current estimates suggest that the prevalence of ASPD in the general population of the United States is somewhere between 0.2% and 3.6% (Grant et al., 2005; Torgersen et al., 2001). Iran currently has a prison population of approximately 240,000, with incarcerated individuals living in overcrowded prisons with more than 150% occupancy level (Walmsley, 2018). Nevertheless, there is little information and research about the status of antisocial traits in the general population of Iran (Aghababaei et al., 2014). This might be due to the fact that there is lack of appropriate short-form instruments to be used in the general population for researchers in this field. Recently, some short-form instruments have been released that are exclusively for specific groups, such as students (Chegeni & Atari, 2016). Thus, the need for accurate and easy-to-administer measures of antisocial traits is clear.

Indeed, the appropriate conceptualization and measurement of antisocial traits is of high importance. Several definitions of antisocial traits have been made by various organizations (e.g., APA) and theorists such as Farrington (Farrington & Jeremy, 2003) and Hare (Hare et al., 1991). The lack of consensus regarding the definition of antisocial traits has made it difficult to measure or measure consistently. To achieve an appropriate definition of this construct, a look into the various theoretical approaches in this area can be beneficial.

As discussed previously, there is a substantial overlap between criminal behavior and antisocial behavior (i.e., behaviors that are indicative of the presence of antisocial traits), but both concepts are not equal. In other words, not everything that is illegal is antisocial in nature, and not everything that is antisocial in nature is illegal. Criminologists define criminal behaviors as offenses against the law (Millie, 2008)—delinquency for juvenile offending and crime for adults. This definition suggests its obvious relationship with antisocial traits and behaviors. Although psychosocial health specialists (psychologists, psychiatrists, and social workers) and criminologists define and assess antisocial behaviors with different approaches focused on specific aspects of the behaviors themselves, the common factor in these different definitions is disregarding the rights of others and violating the rules (Frick, 2012). Mental health clinicians conceptualize these traits as indicative of a mental disorder. Therefore, severe harm to others, committing different subtypes of antisocial acts, and persistence for a certain period of time are among the main features of this definition (Loeber et al., 1998).

The conceptualization of antisocial traits as related to mental disorder focuses on beliefs and attitudes of the individuals who show this tendency to harm others and violate the law (Rydhmer, 2005), and various tools have been designed for the measurement of the traits in this framework. Such measures include the aggression subscales of the Multidimensional Personality Questionnaire (MPQ) and the California Psychological Inventory (CPI), and a specific subscale of the Minnesota Multiphasic Personality Inventory (MMPI; i.e., psychopathy). Additional measurement tools that were developed in this area were designed by Harpur and colleagues (1988; i.e., Psychopathy Checklist [PCL]), its revised version (PCL-R; Hare, 2003), and its screening version (PCL-SV; Hart et al., 1995). However, these tools are not feasible to use outside of specialized clinical and forensic settings because its observer-rated nature makes its broader use expensive and time consuming (Williams et al., 2007). In addition, it requires a review of criminal records which are not available in nonforensic people (Uzieblo et al., 2010). Therefore, they tailored versions such as Self-Report Psychopathy Scale (SRP), SRP-II, and SRP-III (Neumann et al., 2012), all of which are based on Hare’s conceptualization of psychopathy (which, it should be noted, is quite different from the DSM-5 conceptualization). In addition, the large number of items makes administration of the measure quite lengthy, which is an obstacle for broad dissemination.

To our knowledge, there is a dearth of valid and reliable tools for measuring antisocial traits in the general population that utilize a sound conceptual framework and are feasible for broad administration. In addition, many existing tools that measure antisocial traits lack psychometric properties and very few are available in the Persian language. In an interventional study on incarcerated individuals by Khademi and Sief (2011), a checklist of 15 questions was used that was created through the determination of face validity by five experts (Khademi & Sief, 2011). Some other measures have an arbitrary conceptual framework (Hare et al., 1991); some focus on unique groups such as incarcerated individuals, limiting their generalizability to the general population (Mobarake, 2015); and other measures are only validated for special age groups, such as the Youth Psychopathic Trait Inventory (Andershed et al., 2007) and the Add Health Self-Report Delinquency (AHSRD) which is a measure of violent and nonviolent delinquency among at-risk youth (Pechorro et al., 2019). Also, some scale includes additional, irrelevant traits. Dark triad and tetrad have been addressed as “socially aversive” characteristics in the general population (Book et al., 2016).

One of the most popular measurement tools for antisocial traits is the Hare Psychopathy Checklist, the initial version of which has been reassessed (Hare, 2003). Despite the widespread use of this tool, several limitations on the use of this tool are noteworthy. First of all, it is designed to measure psychopathy, which covers only the most severe form of antisocial behavior. Second, the tool is highly sensitive to and dependent on the administrator, the type of interview, and scorings. Therefore, some researchers have criticized the validity of this tool (Dahle, 2006; Martens, 2008).

The accurate conceptualization and assessment of antisocial traits is essential for effective planning for preventive and therapeutic interventions and can reduce the costs and consequences of this problem (Romeo et al., 2006). Identifying the individuals with antisocial traits is advantageous in the control of social deviations, at least in two ways. First, their potential for social deviance can be moderated by certain interventions. Second, the likelihood of committing such behavior can be diminished by reducing other risk factors. This in turn will minimize societal burden of the social deviation.

In light of this, it is important to develop measures of antisocial traits that (a) are theoretically grounded, (b) reflect the way antisocial traits manifest in the general population (not just individual in criminal and mental health settings), and (c) are sufficiently brief so that they do not create a burden for respondents and researchers. Furthermore, the vast differences in the cultural context in different societies suggest that different measurement tools may be needed in different countries (Neumann et al., 2013).

A deep understanding of antisocial traits, identifying their various aspects, and having proper research tools can help researchers carry out a better investigation on this problem. The aim of this study is the development and psychometric validation of the Antisocial Traits Scale (ASTS-20), a self-report scale of a Persian-language scale designed to measure antisocial traits in the general, as well as criminal and clinical, populations. Specifically, the construct validity and factor structure of the instrument will be evaluated.

Method

This study is part of the project “Surveillance System for Social Deviances and Their Determinants (SSSDD)” in Tehran, Iran. SSSDD was launched in 2016 by the Social Welfare Organization of Iran with the purpose of controlling and preventing the most important social deviances of the country, such as substance abuse and domestic violence. In addition to these social deviations, some of their determinants, such as antisocial traits, are assessed. This is highlighting the need to construct a specific tool for measuring antisocial traits.

Participants

Because of the need for both exploratory and confirmatory factor analyses to validate a construct, we conducted exploratory factor analysis (EFA) on a sample of individuals aged between 18 and 60 living in Tehran province (N = 2,051) and then the confirmatory factor analysis (CFA) was conducted in a separate sample (n = 2,049).

Sampling Procedures

In a multistage sampling, three cities in Tehran province, including Tehran itself and two other cities of the province, were selected and then these three cities and three rural areas around each of them formed the sampling framework. Each selected city was divided into five large geographical sections (north, east, south, west, and center). In each section, three districts were selected randomly and finally, according to quotas based on age and gender, a proportional-to-population sample (PPS) was recruited. Those who met the inclusion criteria were invited to participate in the study after briefing about the study and their right to withdraw or not answering items they preferred not to. The total process of invitation and informed consent was documented with their signature.

Research Design

The implementation of this study was carried out in two stages: (a) the development of the questionnaire and (b) the evaluation of its psychometric properties. In the first stage, the conceptual framework of the study was based on DSM-5 as the most valid definition of the concept. Relevant facets of Personality Inventory for DSM-5 (PID-5), that is, Deceitfulness, Manipulativeness, Impulsivity, Hostility, Risk-Taking, Irresponsibility, Callousness, Restricted Affectivity, Grandiosity, and Distractibility, were consulted to form our item pool. Three to five questions were chosen from each selected facet. Due to the fact that most ASPD conceptualizations emphasize impulsivity, the Barratt Impulsivity Scale (Patton et al., 1995) was also added. In addition, five questions about rule-breaking were added to our item pool due to its relevance to antisocial traits. Finally, the items of the PCL (Hare, 2003) that were not covered in our collection were added.

The face and content validity of the questionnaire were evaluated by asking the opinion of experts (i.e., psychiatrists, psychologists, sociologists, and social workers who are or were working in this academic field and/or providing service to individuals with substance use problems, individuals engaged in sex work, imprisoned individuals, and other socially deviant people) as well as performing a pilot study. The qualitative analysis of the questionnaire items was done by two main researchers of this study (H.R. and F.A.) with backgrounds in questionnaire development, clinical work, and research in the field of social deviations until consensus is achieved.

After the analysis of the initial item pool, minor changes were applied to the questionnaire, and the final form of the questionnaire was approved with 46 items. A five-category Likert-type scale (1 = strongly disagree to 5 = fully agree) was used for responses. The ordering of questions was randomly determined and, finally, the first version of the questionnaire was designed as a written self-administered questionnaire which was filled out by the study participants.

Cronbach’s alpha coefficient was used to evaluate the internal consistency of the questionnaire. For EFA, a principal component analysis (PCA) with direct oblimin rotation was performed using the SPSS software. To test how well the extracted model of the questionnaire was fit, a CFA with the maximum likelihood (ML) method was performed on the variance–covariance matrix using the Stata software (version 12). The model fit was evaluated using the goodness-of-fit indices such as the root mean square error of approximation (RMSEA) < 0.08 and comparative fit index (CFI), Tucker–Lewis index (TLI) > 0.95, and standardized root mean squared residual (SRMR) < 0.08 (Browne & Cudeck, 1993). Prior to the interview, all participants signed a written informed consent form regarding the study objectives, confidentiality, and the right to withdraw from participation in the study.

Results

Of the total sample (n = 4,100), about half (50.3%) were female, 49.3% were married, 13.4% were currently unemployed, and 26.0% had attained at least an undergraduate level of education. The mean age was 34.91 (SD = 12.28). Face validity was demonstrated by consensus among experts in the field. Almost all participants indicated that there is no problem in clarity and they can easily read and understand items. The content validity ratio (CVR) for the total scale was 0.89 and the content validity index (CVA) for all items was greater than 0.62 which indicates that the items are acceptable and necessary for measuring the construct.

The result of Kaiser–Meyer–Olkin (KMO) test for sampling adequacy was 0.85, indicating a sufficient sample size for factor analysis. Bartlett’s test of sphericity rejected null hypothesis, indicating the relevance of factor analysis for these data, χ²(190) = 7,876.20, p < .001. As shown in the scree plot, questions were loaded on six factors (see Figure 1), which explained 58.36% of the total variance of the structure. The eigenvalues and loadings of the factors are shown in Table 2.

Figure 1:

Scree Plot of Eigenvalues From PCA

The six underlying factors extracted from EFA were named as Maleficence, Lack of Planning, Risk-Taking, Distractibility, Impulsive Decision-Making, and Law-Breaking, respectively. The first factor (Maleficence) consists of six questions that point to a person’s tendency to harass and deceive others, explaining 24.1% of the total variance. Questions about behaviors such as exploiting, harassing, and deceiving others were among the questions loaded on this component. The second factor (Lack of Planning) included three questions that were related to lack of planning behaviors, as well as lack of planning for the future and job security, and explained 8.8% of the variance of the entire construct. The third factor (Risk-Taking) included issues such as the act of engaging in dangerous activities, enjoying them, and not considering their harmful consequences, and explained 8.0% of the variance. The fourth factor (Distractibility) involved two items regarded the tendency to become distracted while engaged in various activities and explained 6.2% of variance. The fifth factor (Impulsive Decision-Making) included three questions about quick and unpredictable decision-making, which explained 5.9% of variance. Finally, the sixth factor (Law-Breaking) consisted of three questions and explained 5.4% of variance including issues related to the belief in the success of people who did not comply with the law, as well as violating the law (Table 1). Details regarding these factors and the factor loading associated with them are presented in Table 2. The scale demonstrated adequate internal consistency (Cronbach’s alpha coefficient = .79). The results of this test for each subscale of the questionnaire are presented in Table 3.

Table 1:

ASTS Factors Extracted

Component	Eigenvalue	Variance (%)	Cumulative variance (%)
1	4.82	24.13	24.13
2	1.75	8.77	32.90
3	1.60	7.99	40.90
4	1.23	6.17	47.07
5	1.17	5.86	52.93
6	1.07	5.37	58.30

Note. ASTS = Antisocial Traits Scale.

Table 2:

ASTS Factor Loadings by Item

Items	Components
Items	Maleficence	Lack of Planning	Distractibility	Risk-Taking	Impulsive Decision-Making	Law-Breaking
Abuse of others to achieve desires	.80
Enjoy disgracing subordinates	.77
Careless of the rights of others	.66
Deception	.65
Careless to others’ concerns	.65
Telling false stories	.63
I am thinking of keeping my job		.71
I think carefully about different issues		.70
I am thinking of the future		.70
Taking risky actions			.81
Enjoy risky actins			.77
Careless to the harmful consequences of risky acts			.65
Distractibility				.81
Losing conversation				.78
Impulsive actions					.79
Impulsive/rapid decision-making					.78
Act at the moment					.71
Violators are more successful						.84
If I don’t comply with the law, doing things is easier						.83
I commit fraud to progress faster						.57

Note. ASTS = Antisocial Traits Scale.

Table 3:

Descriptive Statistics for ASTS Subscales

Component	No. of items	M (SD)	Range of possible scores	α
Maleficence	6	11.09 (4.57)	6–30	.83
Lack of Planning	3	5.96 (2.08)	3–15	.73
Risk-Taking	3	7.26 (2.94)	3–15	.75
Distractibility	2	5.30 (1.99)	2–10	.81
Impulsive Decision-Making	3	6.27 (2.01)	3–15	.79
Law-Breaking	3	7.52 (2.96)	3–15	.80

Note. ASTS = Antisocial Traits Scale.

The final sample size for the CFA was 2,049; missing data were imputed by the hot-deck imputation method. The SRMR and the RMSEA values of 0.03 and 0.04, respectively, suggest that the latent and the measurement models are acceptable. However, the CFI and the TLI values of 0.94 and 0.92, respectively, are just below accepted parameters. Overall, these fit indices indicate that the model is acceptable. Standardized parameter estimates are provided in Figure 2.

Figure 2:

Confirmatory Factor Analysis (CFA) Results for the Antisocial Traits Scale

The age of the respondents was negatively correlated significantly with the ASTS total (r = −.16, p < .001), Maleficence (r = −.10, p < .001), Risk-Taking (r = −.21, p < .001), Distractibility (r = −.07, p < .001), and Law-Breaking (r = −.15, p < .001) scores. However, Lack of Planning (r = .017; p = .26) and Impulsive Decision-Making (r = −.03; p = .057) subscales did not show a statistically significant correlation with age. There were statistically significant differences in the mean of ASTS total (p < .001), Maleficence (p < .001), Risk-Taking (p < .001), Distractibility (p = .023), Lack of Planning (p < .001), and Law-Breaking (p < .001) scores between females and males. The means in male were significantly higher in all six domains (Table 4).

Table 4:

Mean of ASTS and Its Subscales by Gender

	Male	Female	p value
ASTS and Its Subscales	M (SD)	M (SD)	p value
Total ASTS	44.31 (10.77)	42.16 (10.66)	<.001
Maleficence	11.62 (4.54)	10.54 (4.52)	<.001
Lack of Planning	6.08 (2.14)	5.85 (2.02)	<.001
Risk-Taking	7.53 (2.87)	6.98 (2.98)	<.001
Distractibility	5.22 (1.95)	5.37 (2.03)	.013
Impulsive Decision-Making	6.23 (1.96)	6.32 (2.05)	.150
Law-Breaking	7.83 (3.08)	7.22 (2.86)	<.001

Note. ASTS = Antisocial Traits Scale.

Discussion

ASTS-20 is a 20-item self-report scale that was developed for the assessment of antisocial traits in the general adult population in Iran. The results of this study suggest that it is composed of six factors reflecting Maleficence, Distractibility, Law-Breaking, Risk-Taking, Lack of Planning, and Impulsive Decision-Making. The STS-20 demonstrated strong psychometric properties, suggesting that it is a reliable and valid questionnaire for assessing antisocial traits in the general population.

The mean scores of ASTS-20 in males were significantly higher than those of females in all six domains. This result is in line with earlier studies that showed that the female scores in antisocial behavior are consistently lower than the male ones (Andershed et al., 2002; Torgersen et al., 2001). In addition, scores on the ASTS-20 were negatively correlated with age, such that antisocial traits were less common in older individuals.

For its construct validity, we used EFA and CFA which is the best method of validation when criterion validity is not researched. However, among the six factors of ASTS-20, the factor Risk-Taking Behavior comprised just two items which explains about 8% of the total variance of the construct. Although there is not a consensus regarding the least number of items which should be retained in any factor, most writers prescribe a factor with two items is possible (Marsh et al., 1998; Raubenheimer, 2004). Therefore, we saved this factor because (a) the concept of risk-taking behavior is important in almost any conceptualization of antisocial behavior, especially in DSM-5 which is the conceptual basis of our instrument and (b) the high value of Cronbach’s alpha.

What psychologists and psychiatrists consider “antisocial behavior” is more or less the same as the construct of “social deviance” used by sociologists and social workers. Social deviance is defined as problems stemming from disregarding social norms. This includes violence and other illegal activities, such as sex work and illicit substance use (Rarani et al., 2013). The cause of social deviations is considered to be multifactorial, like any other social problem. For instance, in case of growing inequality, social deviation increases (Rowlingson, 2011). Nevertheless, among these individuals, only those who have a predisposing personality trait show deviant behavior. Therefore, antisocial trait is an umbrella term, the extreme and pathologic form of which is the ASPD. The severe case of ASPD, in turn, seems to be the same as psychopathy in Hare’s conceptualization (Hare, 2003).

To the best of our knowledge, this scale is one of the first studies to develop and validate an assessment of antisocial traits in the general population in Iran. The development of such a measure is particularly important given that existing instruments assessing antisocial traits cannot be used in Iran conveniently. For example, Hare’s PCL which is the “gold standard” measure of antisocial traits, has two limitations in this regard: It is developed only for persons convicted of a crime, not the general population, and its administration needs an external rating of an individual’s criminal records. Other existing scales are only valid for specific age groups or lack a clear theoretical background, whereas the ASTS-20 can be used with all adults and stem directly from the DSM-5 conceptualization of ASPD.

There are several limitations in this study that need to be addressed further. First, social desirability bias may be particularly relevant with this scale (especially when administered in self-report format) given the sensitive nature of antisocial traits kind (Holden & Passey, 2010). Second, the criterion validity, which is the strongest type of validity, has not been studied yet. Third, test–retest reliability was not considered in the design of the study which should be paid attention in future studies. In future studies, it is necessary to determine the criterion validity and cutoff point for the questionnaire, using a complete psychiatric examination and interview with the family members, friends, and coworkers of the participants as the golden standard of such traits.

Despite these limitations, the ASTS-20 has several strengths, including its theoretical grounding, short length, strong psychometric properties, easy implementation, and applicability to the general population. Should further research confirm the validity of the measure and efforts to disseminate the measure be successful, the ASTS-20 has the potential to be an important tool for researchers, policy-makers, and clinicians in the field of crime and justice as it can potentially help identify individuals with antisocial traits, which is essential for any intervention to prevent crime and reduce the risk of recidivism.

Conclusion

Antisocial traits are a notable risk factor for criminal activity, which has immense social and economic costs. The ASTS-20 is a reliable and valid short-form questionnaire which can be used by psychosocial health professionals who work in forensic systems to identify and treat high-risk antisocial incarcerated individuals, and prevent multiple negative outcomes such as crime, recidivism, and aggressive behaviors. The ASTS-20 could be particularly useful in helping the screening and outcome evaluation efforts of programs designed to prevent crime and reduce the risk of recidivism.

Footnotes

ORCID iD

Fardin Alipour

Hassan Rafiey is a psychiatrist and associated professor of Social Welfare Management Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. His research interests include social problem prevention with an emphasis on addiction and validity and reliability of the measurement instrument which is used in psychosocial research.

Fardin Alipour has a PhD in social work and is an assistance professor in the Department of Social Work, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. His main research interests include social planning, research methodology, and psychosocial interventions after disasters.

Richard LeBeau is a postdoctoral fellow in the Anxiety and Depression Research Center (ADRC). His research interest is in understanding the role of social rejection in the onset and maintenance of anxiety and mood disorders, particularly within marginalized population.

Yahya Salimi has a PhD in epidemiology at the Kermanshah University of Medical Sciences.

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