The Barratt Impulsiveness Scale-11 in Community and Incarcerated Samples: Motor and Nonplanning Impulsivity Effects on Criminal Recidivism

Abstract

Although the Barratt Impulsiveness Scale Version 11 (BIS-11) is one of the most widely used instruments to assess impulsivity, its factor structure remains controversial. Several authors have suggested that cultural factors may have an impact on its factor structure. It is also necessary to study the measurement invariance of the scale in different populations, especially in the case of incarcerated individuals, given their high level of impulsivity. This study had two objectives: (a) to evaluate the factor structure of the BIS-11 and its measurement invariance across community and incarcerated samples and (b) to evaluate the effect of impulsiveness on criminal recidivism. The results revealed a two-factor structure: Motor and Nonplanning Impulsivity. This solution was invariant across groups. In addition, increased Motor Impulsivity was related to higher criminal recidivism through an increase in aggression. These results corroborate the importance of implementing interventions to reduce impulsivity as a means of preventing recidivism.

Keywords

Barratt Impulsiveness Scale Version 11 factor structure measurement invariance community offenders

Impulsivity is a multidimensional construct associated with a “predisposition toward rapid, unplanned reactions to internal or external stimuli without regard to the negative consequences of these reactions to the impulsive individual or to others” (Moeller et al., 2001, p. 1784). In recent years, interest in the construct of impulsivity has grown significantly due to its importance in occupational and educational settings, and its impact on numerous psychiatric disorders (Steinberg et al., 2013). It has been suggested that impulsiveness has a substantial effect on different behaviors and psychological processes (e.g., Franken et al., 2008) and is also thought to underlie multiple psychological disorders (e.g., substance abuse, pathological gambling; American Psychiatric Association, 2000).

Impulsivity, Aggression, and Incarcerated Samples

According to Lynam and Miller (2004), impulsivity is one of the most widely studied constructs in forensic psychiatry. Patton et al. (1995) suggest that it is related to the ability to observe and conform to social norms in society. A number of studies have found that individual differences in impulsivity are related to risky social behaviors (e.g., aggression; Houston et al., 2003) and criminal offenses (Leverso et al., 2015). According to Griffin et al. (2018), impulsivity is associated with criminality through engagement in problematic behaviors (e.g., substance abuse, gambling, aggression, and violence). In addition, Lynam and Miller (2004) report that the level of impulsivity is a reliable indicator in distinguishing between incarcerated and nonincarcerated individuals, and a significant precursor of delinquent acts. Cuomo et al. (2008) also suggest that impulsivity plays a role in violent behaviors and in reiterated offenses, which may lead to multiple incarcerations. The association between impulsivity and aggression is also widely corroborated (e.g., Bousardt et al., 2015). Gordon and Egan (2011) suggest that when emotional arousal increases, an individual’s inability to control their impulses may trigger aggressive behaviors.

Barratt Impulsiveness Scale Version 11 (BIS-11)

According to Reise et al. (2013), the BIS-11 (Patton et al., 1995) is the gold-standard self-report instrument to assess impulsivity and has been widely used for research purposes (Stanford et al., 2009). The original English version of BIS-11 has been translated into at least 11 other languages (e.g., Chinese, Dutch, Spanish, French; see Stanford et al., 2009, for a review). The BIS-11 comprises 30 items derived from the original 34-item BIS-10 (Barratt, 1985). Patton et al. (1995) found that its factor structure is formed by six first-order factors (Attention, Motor, Self-Control, Cognitive Complexity, Perseverance, and Cognitive Instability) and three second-order factors. The first second-order factor is called “Attentional Impulsiveness” (formed by Attention and Cognitive Instability factors) and is defined as the lack of focus on an ongoing task. The second factor is “Motor Impulsiveness” (formed by Motor and Perseverance factors) and refers to action without inhibition of prepotent or ongoing responses. Finally, the third factor is “Nonplanning Impulsiveness” (formed by Self-Control and Cognitive Complexity factors) and is defined as orientation toward the present rather than to the future.

Since the study by Patton et al. (1995), various studies have assessed the factor structure of the BIS-11 in different samples (for a review, see Vasconcelos et al., 2012). Someya et al. (2001) and Yang et al. (2007) replicated the structure proposed by Patton et al. (1995) in a sample of Japanese and Chinese community samples, respectively. Many studies, however, have been unable to replicate this structure and the findings have varied considerably depending on nationality or type of sample (community, forensic, etc.). Additional studies have also identified three factors (Coutlee et al., 2014, and Spinella, 2007, with English community populations; Fossati et al., 2001, with Italian university students; Güleç et al., 2008, with Turkish adults; Hartmann et al., 2011, with German adolescents; Li & Chen, 2007, with Chinese adolescents), but with a different organization of items from that of Patton et al. (1995). However, other studies in English-speaking community populations have found a two-factor structure: Cognitive and Behavioral Impulsiveness (Morean et al., 2014; Reise et al., 2013), although the items comprising the two factors vary. Vasconcelos et al. (2015) found a two-structure factor (Nonplanning and Inhibitory Control) in a Brazilian population. With English-speaking adults, Steinberg et al. (2013) found a one-factor structure for the scale formed by eight items. Other studies, however, have been unable to find a factor structure with a good fit (Preuss et al., 2008, in German population; Zhou et al., 2006, in Chinese population).

In incarcerated samples, the studies by Haden and Shiva (2008, 2009), conducted with English-speaking forensic psychiatric samples, found that the BIS-11 presented a two-factor structure: Motor and Nonplanning, each with 12 items. This structure was replicated in a study with a sample of incarcerated Peruvian women, using the Spanish version of the BIS-11, although the items differed between the two studies (Loyola, 2011). In U.K. adult individuals charged with a crime, Ireland and Archer (2008) found a three-factor structure which, however, did not coincide with the original structure proposed by Patton et al. (1995). In U.S. incarcerated individuals, Ruiz et al. (2010) were unable to confirm the structural models previously described in the literature. In Portuguese juvenile individuals convicted of a crime, the works by Pechorro reported a first-order factor structure composed of six factors, but failed to replicate the three second-order factors originally proposed (Pechorro et al., 2015, 2017).

In short, the findings to date on the scale’s factor structure are inconclusive, and although there is evidence to support a multifactor structure, the components found in different studies and samples are inconsistent. Nonetheless, according to Vasconcelos et al. (2012), the Nonplanning Impulsiveness factor is identified more frequently than the Motor and Attentional dimensions. Specifically, Attentional Impulsiveness seems to be the most unstable factor (Fossati et al., 2001; Hartmann et al., 2011).

As regards its reliability, the review conducted by Stanford et al. (2009) found an internal consistency of .83 for the total score of BIS-11, and Cronbach’s α ranging between .59 and .74 for the second-order factors. In this study, the test–retest reliability at 1 month was .83 for the total score and ranged from .61 to .72 for the second-order factors. The systematic review by Vasconcelos et al. (2012) found that Cronbach’s alpha reported in different studies ranged from .69 to .80. They also found moderate to large magnitude of test–retest reliability, indicating that the scale tends to produce similar score patterns at least 1 month after the first evaluation. These results suggest that the items have a satisfactory homogeneous quality.

With respect to its validity, different measures have been used to evaluate its concurrent validity. Among the most significant, we can highlight aggressive behaviors (Smith et al., 2006), antisocial personality disorder (Dolan & Fullam, 2004), neuropsychological measures (Spinella, 2007), psychopathic traits (Pechorro et al., 2017), and similar self-report impulsivity measures (Stanford et al., 2009). The BIS-11 exhibits positive associations with all these variables. In relation to its discriminant validity, different studies have used symptoms of social anxiety as a criterion (e.g., Pechorro et al., 2015). Individuals with social anxiety are characterized by being behaviorally inhibited (Crozier & Alden, 2001), so impulsivity is not expected to show association with these symptoms.

The Present Study

This study has two main aims. The first is to assess the factor structure and psychometric properties of the BIS-11 in a Spanish community and incarcerated populations. The second is to determine the extent to which the BIS-11 subscales explain the variables of recidivism and number of incarcerations in persons convicted of a crime, controlling for the possible mediator effect of aggression in this association.

Concerning the first objective, as mentioned above, there is still no consensus on the most appropriate factor structure of the scale. This compromises the interpretability of study results and, consequently, the better understanding of the impulsivity construct. Most previous studies have evaluated aspects of reliability, validity, and factor structure, but none have assessed the measurement invariance of the scale, with the exception of the study by Morean et al. (2014), which confirmed scalar measurement invariance across several English-speaking adult subgroups (e.g., sex, age, race). Assessing measurement invariance is important because it ensures that the evaluated construct is the same across groups, enabling interpretable cross-group comparisons (Vandenberg & Lance, 2000). Although the BIS-11 is commonly used to compare impulsivity ratings, without scalar measurement invariance, the presence of cross-group differences cannot be interpreted, as such differences might be the result of measurement artifacts. For example, impulsivity is generally high in incarcerated individuals compared with controls (Barratt et al., 1997) but, without studies to ensure that the measurement invariance criteria are met, we cannot establish that the differences in impulsivity found between both samples actually exist. Although several studies have assessed the factor structure of the BIS-11 in justice-involved samples (e.g., Haden & Shiva, 2008), studies that include both community and incarcerated samples are scarce (Pechorro et al., 2017; Steinberg et al., 2013) and, to our knowledge, no studies have assessed its measurement invariance across groups.

In addition, factorial structure differences could be influenced by cultural differences (Ruiz et al., 2010). To our knowledge, no studies have assessed the factor structure of the BIS-11 in Spanish community and incarcerated populations. We only found two studies that had used the adolescent version of BIS-11 (Fossati et al., 2002) in young Spanish participants: (a) in children aged 8 to 12 years, Cosi et al. (2008) found a three-factor structure with slight differences compared with the original factor structure (Patton et al., 1995), and (b) in adolescents aged between 12 and 14 years, Martínez-Loredo et al. (2015) replicated the two-factor structure found by Fossati et al. (2002).

According to this review, our first aim was to evaluate the replicability of previously reported BIS-11 factor models (for a review of tested models, see Reise et al., 2013) using confirmatory and exploratory factor analysis (EFA) in a Spanish community sample. The best model was tested in an incarcerated sample and then measurement invariance assessed. Finally, we evaluated its internal consistency and concurrent validity.

With regard to the second aim, as we have reported, the relationship between impulsivity and aggressiveness has been widely corroborated (Bousardt et al., 2015). Studies have also suggested that impulsivity is related to criminality and to recidivism and number of incarcerations (Cuomo et al., 2008). Moreover, various authors have reported that aggression rates are higher in persons convicted of a crime (Swogger et al., 2010). Our aim was to assess the real impact of impulsivity on both recidivism and number of incarcerations. To our knowledge, studies that have attempted to analyze this relationship have mainly focused on examining whether associations exist between these variables (using correlations) or on assessing the predictive power of impulsivity on recidivism and incarcerations (using regression analysis). Given the relationship between aggression and impulsivity, and between aggression and recidivism/incarcerations, our study aimed to assess not only, as in previous studies, the direct effects of the BIS-11 subscales on recidivism and incarcerations, but also the role of aggression as mediator in this relationship.

Method

Participants

This study consists of two samples: a community and an incarcerated population. The community participants were recruited among students from the University of Castilla–La Mancha (Spain) and individuals related to these students (e.g., friends, family), using snowball sampling. This sample was formed by 1,142 participants (ages 18–74 years). The incarcerated sample was recruited from a penitentiary center in Castilla–La Mancha (Spain), where the medical team made a random choice of the possible participants using blind coded number lists. This sample comprised 229 male participants (ages 21–74 years). Table 1 shows the demographic characteristics of both samples.

Table 1:

Sample Characteristics

Characteristic	Community sample			Incarcerated sample(n = 229)
Characteristic	Total(n = 1,142)	EFA subsample(n = 571)	CFA subsample(n = 571)	Incarcerated sample(n = 229)
Age, mean (SD), years	25.66 (11.16)	25.32 (10.71)	26.00 (11.59)	40.46 (10.25)
Gender (male)	558 (48.9%)	276 (48.3%)	282 (49.4%)	229 (100%)
Education level
Elementary	73 (6.4%)	40 (7.0%)	33 (5.8%)	117 (51.1%)
Secondary	478 (41.9%)	221 (38.7%)	257 (45.0%)	64 (27.9%)
University	559 (48.9%)	292 (51.1%)	267 (46.7%)	41 (17.9%)
Others	32 (2.8%)	18 (3.2%)	14 (2.5%)	7 (3.1%)

Note. EFA = exploratory factor analysis; CFA = confirmatory factor analysis.

The inclusion criteria for both samples were as follows: (a) being able to read and write, (b) being able to speak Spanish with ease, and (c) giving informed consent to participate in the study. In addition, participants in the convicted group had to be serving a sentence for violent and/or nonviolent crimes. The exclusion criteria were as follows: (a) for the incarcerated sample, having committed crimes while under the effect of substances; including individuals who were under the effect of substances while committing the crime would make it difficult to determine whether the crime was related to substance use (which implies disinhibited behavior) or to factors of impulsivity; and (b) for both samples, presence of neurological damage. In the community sample, presence of neurological damage was self-reported by the participants. In the incarcerated sample, this information was obtained from the prison’s medical service.

Procedure

In the incarcerated sample, it was necessary to obtain authorization from the Secretariat General for Penitentiary Institutions of the Spanish Ministry of the Interior. Approval and collaboration had first to be granted by the prison’s director of medical services. Once the project’s viability had been analyzed, the director of medical services applied to the prison administration for authorization. After that, permission and authorization were requested from the Sub-Directorate General for the Coordination of Prison Health Services, who granted their authorization through the figure of the Sub-Secretary for International Relations and Regional Coordination. Once the authorization had been obtained, data collection was conducted in an office belonging to the Nursing Department at the Ocaña I Penitentiary Center in the province of Toledo (Spain). The questionnaires were administered individually by an expert psychologist. Previously, individuals were informed of the aims and characteristics of the study, the questionnaires to be completed and that they would not be granted prison privileges in exchange for their participation. They were also informed that their data were protected under Organic Law 15/1999 of December 13 on Protection of Personal Data. After giving their signed informed consent, data collection began. The response rate was 45.43%.

In the community sample, participants were recruited by means of advertisements placed in university buildings and on websites created for students and the general population. In addition, a chain-sampling system was established with university students being asked to invite their relatives, friends, and acquaintances to participate. Those interested in participating were informed about the study by two psychologists and their informed consent was obtained. In both samples, the data were collected individually in one 30- to 45-min session. This study was approved by the Clinical Research Ethics Committee of the Health Service Area of Toledo (Spain) and the Secretariat General for Penitentiary Institutions of the Spanish Ministry of the Interior.

Measures

In the community sample, data on sociodemographic characteristics were collected via self-report questionnaire. This included information about gender, age, and educational status. For the incarcerated sample, this questionnaire was administered by an interviewer with experience in the penitentiary field. Additional information about recidivism and number of incarcerations was obtained from the administrative files of the penitentiary center using a double-blind identification system.

BIS-11

This is a 30-item self-report measure of trait impulsivity (Patton et al., 1995). Participants rated how often they think or act as described in each item on a 4-point Likert-type scale, from 1 (“rarely”) to 4 (“almost always”). According to Stanford et al. (2009), internal consistency of the second-order factors ranges between .59 and .74, and between .27 and .72 in the first-order factors. Cronbach’s alpha for the total score of the BIS-11 was .83. This study used the Spanish adaptation of the BIS-11 (Oquendo et al., 2001).

Buss–Perry Aggression Questionnaire (BPAQ)

The Spanish version of the scale was used in this study (Buss & Perry, 1992; Rodriguez et al., 2002). This 29-item self-report questionnaire is composed of four subscales: Physical Aggression, Verbal Aggression, Anger, and Hostility. A total aggression score is obtained by summing the subscales. The items are scored on a 5-point scale, ranging from 1 (“extremely uncharacteristic of me”) to 5 (“extremely characteristic of me”). Reliability coefficients for the BPAQ (Cronbach’s alpha) range from .72 to .89 (Buss & Perry, 1992; Rodriguez et al., 2002). Cronbach’s alphas obtained in this work are .78, .65, .71, and .76 for Physical Aggression, Verbal Aggression, Anger, and Hostility, respectively.

Impulsive/Premeditated Aggression Scales (IPAS)

This 30-item questionnaire focuses on an individual’s aggressive acts during the last 6 months (Stanford et al., 2003). The items are scored on a 5-point Likert-type scale ranging from 1 (“strongly agree”) to 5 (“strongly disagree”). Factors measured in this scale are Premeditated Aggression (e.g., “Some of the acts were an attempt at revenge”) and Impulsive Aggression (e.g., “I feel I lost control of my temper during the acts”). The scale was translated into Spanish for the present study. Translation was carried out from English to Spanish first and then from Spanish to English to test matching. This process was carried out by two independent, professionally qualified translators with extensive experience in translation and edition. Cronbach’s alpha values in this study were .67 and .80 for the Impulsive and Premeditated Aggression scales, respectively.

Data Analysis

We investigated alternative factor structures for the BIS-11 by means of EFA and thereafter confirming the models in confirmatory factor analysis (CFA) using two split subsamples of the total community sample. The 1,142 participants were randomly assigned without replacement to two subsample groups. Sample 1 (n = 571) was used to identify models in EFA and Sample 2 (n = 571) was used for CFA analysis. The two subsamples were statistically equivalent in sex distribution, age, and education level (all ps > .188; see Table 1). The SPSS Version 20 was used for the EFA.

To validate the five factorial models of the BIS-11 (Reise et al., 2013) and our version based on EFA analysis, we performed CFA using Mplus Version 7.4 (Muthén & Muthén, 1998–2015). The best BIS-11 factor model obtained in the CFA in the community sample was also validated in an incarcerated sample. A method robust to non-normality was used to estimate all model parameters (maximum likelihood parameter estimates with standard errors and a mean-adjusted chi-square test statistic; MLM). For identification of the CFA models, item variances were estimated freely and the model was standardized by fixing factor variances at one. Model fit was assessed by the root mean square approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual (SRMR). According to Byrne (2001), RMSEA values lower or equal to 0.08 represent a reasonable fit. CFI values greater or equal to 0.90 are indicative of an acceptable fit (Bentler, 1992). For SRMR, Byrne (2001) suggests a cut-off point of ≤0.08. The chi-square test is usually not a reliable measure of model fit with large sample sizes (Bentler & Bonett, 1980). We also performed analyses of measurement invariance across both community and incarcerated groups using multigroup analysis. Configural, metric, and scalar invariance were tested. CFA nested models are typically compared using a chi-square difference test. Nevertheless, this test has been shown to be sensitive to sample size (Meade & Lautenschlager, 2004). As our samples differed in number of participants, the criteria proposed by Cheung and Rensvold (2002) were used in this study: invariance is accepted if there is a change of less than 0.01 in the CFI values of the models and if the RMSEA values fall within one another’s confidence intervals (CIs).

We also examined the internal consistency of the BIS-11 in both groups. Concurrent validity was assessed by correlating the BIS-11 subscale scores with the IPAS (Stanford et al., 2003) subscales in both samples. Finally, structural equation modeling was run to test the direct effects of BIS-11 subscales and an aggression latent variable (formed by the subscales of the BPAQ; Buss & Perry, 1992) on recidivism and number of incarcerations in the incarcerated sample. In addition, we also tested the indirect effects of BIS-11 subscales on recidivism and number of incarcerations through the latent variable of aggression.

Results

EFA

In Sample 1, EFA was estimated using maximum likelihood, and a subsequent oblimin rotation and Kaiser normalization with 25 iterations were used to determine the factor structure. Factors were retained on the basis of the scree plot elbow and total variance explained. The scree plot elbow suggested a two-factor solution, which explained 26.23% of the variance. The results obtained in the Kaiser–Meyer–Olkin sampling adequacy test (KMO = .80) and Bartlett’s sphericity test, χ²(435) = 3,826.17, p < .001, were adequate. Variables were considered to load onto a factor if the variable had a loading >±.30. Items with loadings less than .30 in their respective factors (Items 9 “concentrate easily,” 10 “save regularly,” 23 “think about only one thing,” 27 “interested in present,” and 29 “like puzzles”) were excluded and a new EFA with the remaining 25 items was performed. The new EFA showed adequate KMO sampling adequacy test (KMO = .80) and Bartlett’s sphericity test, χ²(300) = 3,216.99, p < .001, results. The total explained variance of this solution was 30.00%. Table 2 shows the item loadings for the two factors. Items of the first factor were mainly consistent with a priori definition of Attentional and Motor higher-order factors. Items on the second factor were consistent with a priori definition of Nonplanning higher-order factor. The correlations between Factors 1 and 2 were −.09. The 25 items included in the last EFA were retained for the CFA study.

Table 2:

Exploratory Factor Analysis Results (Sample 1): Factor Loadings for the Two Retained Factors

Item no.	Abbreviated item content	Factor 1	Factor 2
17	Act on impulse	.66
2	Do things without thinking	.61
14	Say things without thinking	.61
6	Have racing thoughts	.55
28	Am restless at the theater	.55
16	Change jobs	.54
26	Have extraneous thoughts	.49
3	Make up my mind quickly	.48
5	Don’t pay attention	.45
19	Act on spur of the moment	.45
11	Squirm at plays or lectures	.44
21	Change residences	.44
25	Spend more than earn	.44
22	Buy things on impulse	.42
4	Am happy-go-lucky	.39
18	Bored solving problems	.37
24	Change hobbies	.37
8	Am self-controlled	−.36
13	Plan for job security		.71
30	Am future oriented		.69
1	Plan tasks carefully		.61
20	Am a steady thinker		.58
12	Am a careful thinker		.54
15	Like to think about problems		.51
7	Plan trips ahead of time		.49

CFA

We performed CFA in Sample 2 to assess the five models of the BIS-11 tested in Reise et al. (2013) and the modified version based in our EFA results. As regards the factor models analyzed in Reise et al. (2013), the fit statistics of the unidimensional model did not show a good fit to the observed data, χ²(406) = 1,175.66, p < .001; RMSEA = 0.06, RMSEA 95% CI = [0.05, 0.06]; CFI = 0.14; SRMR = 0.18). To improve the model, items with loadings lower than .30 were excluded (Items 4, 5, 7, 9, 10, 12, 13, 15, 20, 22, 23, 24, 26, 27, 28, 29, and 30). In addition, correlated residuals suggested by modification indices were included if there was a substantive or empirical rationale for allowing these residual terms to covary. Thus, residuals of Items 1 and 2 were correlated. The new unidimensional version with 13 items also failed to show a good fit, χ²(65) = 213.04, p < .001; RMSEA = 0.06, RMSEA 95% CI = [0.05, 0.07]; CFI = 0.37; SRMR = 0.30. The CFA results of the other BIS-11 models were unacceptable as they resulted in a nonpositive defined covariance matrix.

In relation to the model based on our EFA results (25 items), the fit statistics failed to show a good fit, χ²(276) = 441.24, p < .001; RMSEA = 0.04, RMSEA 95% CI = [0.03, 0.05]; CFI = 0.76; SRMR = 0.06. Items with loadings lower than .30 were excluded (Items 4, 5, 6, 8, 16, 22, 24, 26, and 28 for Factor 1 and Items 7, 12, 15, and 20 for Factor 2), and suggested by modification indices, correlated residuals of Items 17 and 2 were included. The new 12-item EFA version still failed to show a good fit, χ²(54) = 110.20, p < .001; RMSEA = 0.04, RMSEA 95% CI = [0.03, 0.05]; CFI = 0.88; SRMR = 0.06. An examination of this model showed that some items’ loadings were lower than .30 (Items 11, 18, 19, and 21 for Factor 1), so they were also excluded. In the new 8-item EFA version, some correlated residuals suggested by modification indices were also included: Items 17 and 2 and Items 17 and 3. This 8-item version showed a good fit to the observed data, χ²(19) = 36.50, p = .001; RMSEA = 0.04, RMSEA 95% CI = [0.02, 0.06]; CFI = 0.92; SRMR = 0.07. Table 3 shows the standardized factor loadings for this version. All indicators in the CFA had significant loadings (all ps < .001). Correlation between both factors was −.11¹ (p = .015). The first and second factors comprise mainly items of Motor Impulsiveness and Nonplanning factors, respectively.

Table 3:

Standardized Factor Loadings of the 8-Item Version of the BIS-11 Scale

Items (abbreviated content)	Community sample 2(n = 571)			Incarcerated sample(n = 229)
Items (abbreviated content)	Loading	R ²	Item-total correlation	Loading	R ²	Item-total correlation
Factor 1: Motor Impulsivity
2. Do things without thinking	.75	.56	.56	.76	.58	.55
3. Make up my mind quickly	.49	.24	.38	.56	.32	.44
14. Say things without thinking	.54	.29	.53	.57	.33	.52
17. Act on impulse	.79	.62	.56	.91	.83	.52
25. Spend more than earn	.37	.14	.29	.35	.12	.35
Cronbach’s α	.70			.71
Factor 2: Nonplanning Impulsivity
1. Plan tasks carefully	.42	.18	.38	.39	.15	.34
13. Plan for job security	.99	.98	.63	.95	.90	.64
30. Am future oriented	.57	.32	.55	.62	.38	.50
Cronbach’s α	.70			.67

Note. Cronbach’s alpha and item-total correlations in the community group were obtained with the total community sample (n = 1142).

The 8-item EFA version also showed a good fit to the observed data in the incarcerated sample, χ²(19) = 39.60, p = .004; RMSEA = 0.07, RMSEA 95% CI = [0.04, 0.09]; CFI = 0.94; SRMR = 0.06. In this sample, correlation between factors was −.11 (p = .167). All factor loadings were also statistically significant (all ps < .001; see Table 3). Cronbach’s alpha for both Motor and Nonplanning Impulsivity subscales and item-total correlations are shown in Table 3 for both samples. According to these results, both factors of the 8-item EFA version showed an acceptable internal consistency. The model power value calculated for the RMSEA statistic (MacCallum et al., 1996) for the community sample with current sample size (n = 571) and with the number of variables introduced in the model (df = 19) was 0.99. In the incarcerated sample, the model power value was 0.87 (n = 229 and df = 19).

Measurement Invariance

The configural measurement invariance assessment of the 8-item EFA version showed satisfactory goodness-of-fit indices, χ²(46) = 119.48, p < .001; RMSEA = 0.063, RMSEA 95% CI = [0.05, 0.08]; CFI = 0.940; SRMR = 0.06, suggesting that both factors were measured by the same items in both groups. The fit statistics for the metric, χ²(42) = 106.61, p < .001; RMSEA = 0.062, RMSEA 95% CI = [0.05, 0.08]; CFI = 0.947; SRMR = 0.06, and scalar models, χ²(38) = 108.73, p < .001; RMSEA = 0.068, RMSEA 95% CI = [0.05, 0.08]; CFI = 0.942; SRMR = 0.05, were also acceptable. Following the criteria proposed by Cheung and Rensvold (2002), there were no differences between the metric and the configural models (metric invariance) and between the metric and scalar models (scalar invariance). These results suggest that the factor loadings (metric) and indicator means (scalar) of the items are equivalent across groups.

Concurrent Validity

To assess the concurrent validity, correlations were performed between the 8-item version of the BIS-11 and the IPAS subscales. In both samples, the Motor Impulsivity subscale shows positive significant correlations with the Impulsive (r_xy = .34, and r_xy = .36, all ps < .001, in community and incarcerated groups, respectively) and Premeditated IPAS subscales (r_xy = .26, p < .001, and r_xy = .18, p = .008, in community and incarcerated groups, respectively). The Nonplanning Impulsivity subscale shows a positive association with the Premeditated Aggression subscale (r_xy = .06, p = .036) and no correlation with the Impulsive Aggression subscale (r_xy = .05, p = .093) in the community sample. In the incarcerated sample, no significant associations were found between the Nonplanning Impulsivity and the IPAS subscales (r_xy = −.02, p = .792, and r_xy = .04, p = .519, for Impulsive and Premeditated Aggression, respectively). A negative correlation was also found between both BIS-11 subscales in the community sample (r_xy = −.15, p < .001), whereas there was no significant association between subscales in the incarcerated sample (r_xy = −.11, p = .092).

Structural Equation Modeling

First, a structural model with direct associations between recidivism and (a) BIS-11 subscales and (b) an aggression latent variable formed by the BPAQ subscales was tested in the incarcerated sample. We also tested the indirect effects of BIS-11 on recidivism through the aggression variable. The goodness-of-fit indices for this model were adequate, χ²(12) = 21.42, p = .045; χ²/df = 1.79; CFI = .97; RMSEA = .06. The standardized parameter estimates for the direct and indirect effects are presented in Figure 1A. The results showed that the aggression latent variable was the best predictor of recidivism. In addition, a significant indirect effect of the Motor Impulsivity BIS-11 subscale on recidivism through the aggression latent variable was found (p = .005).

Figure 1:

Models of the relation between Motor and Nonplanning Impulsivity subscales and aggression and dependent variables: (a) recidivism and (b) number of incarcerations.

Finally, an analog second model was tested with the number of incarcerations as dependent variable. This model also resulted in a well-fitting model, χ²(12) = 16.44, p = .175; χ²/df = 1.37; CFI = .99; RMSEA = .04. Figure 1B shows their standardized parameter estimates. The results showed that the aggression variable was the best predictor of the number of incarcerations. In addition, the Nonplanning Impulsivity BIS-11 subscale was also marginally significant (p = .059). Finally, the Motor Impulsivity BIS-11 subscale showed a significant indirect effect on number of incarcerations through the aggression latent variable (p = .005).

Discussion

This study had two main aims: (a) to assess the factor structure of the BIS-11 and its psychometric properties in Spanish community and incarcerated populations and (b) to evaluate the effects of the BIS-11 subscales on the variables of recidivism/incarcerations and the role of aggression as a mediator in this relationship. Regarding the first objective, our study shows that the best factorial model is a two-factor solution: (a) “Motor Impulsivity” and (b) “Nonplanning Impulsivity.” This result is consistent with other studies that reported a two-factor structure as the best solution. Specifically, our findings are similar to those obtained in samples whose language is Latin based (Fossati et al., 2002, and Martínez-Loredo et al., 2015, with Italian and Spanish adolescent samples, respectively; Vasconcelos et al., 2015, in Brazilian university students). Although there are differences in the number of items used, the items associated with each factor in our results also appear in those studies. However, few items coincide with the results of the studies conducted in English-speaking community samples (Morean et al., 2014; Reise et al., 2013). This result suggests that cultural differences between populations may impact on the factor structure of the BIS-11. In forensic populations, however, these cultural differences appear to be less significant. Our factor structure coincides with that described in the study by Loyola (2011), conducted with female individuals convicted of a crime in Peru, and with the works by Haden and Shiva (2008, 2009), conducted in English-speaking forensic populations. Although these latter authors propose 12-item factors, our distribution coincides with their solution. These findings appear to suggest that the construct of impulsivity is more stable and less affected by cultural differences in prison population compared with community samples.

At a theoretical level, our results do not coincide with the constructs originally proposed by Barratt (1985). In fact, our findings suggest the absence of an Attentional factor. Accordingly, several studies have failed to corroborate the existence of an Attentional Impulsiveness factor (Vasconcelos et al., 2012, for a review). For example, the study by Swann et al. (2002) found significant correlations between Nonplanning and Motor factors and a rapid response style, and between the Nonplanning factor and the inability to wait for a reward. Nevertheless, no associations were found between the Attentional factor and these two types of impulsivity.

It is also worth noting that our BIS-11 factor structure is consistent with some previous studies, despite the reduction in the number of items derived from our results. Accordingly, several studies have reported a substantial reduction in the number of items after analyzing its factor structure. For example, the analyses conducted by Morean et al. (2014) and Steinberg et al. (2013) reduced the scale to just 8 items, whereas the studies by Coutlee et al. (2014) and Kahn et al. (2019) reduced the scale to a total of 13 and 12 items, respectively. In this sense, Steinberg et al. (2013) suggest that the more items included, the more likely it is that one of them departs from the central construct being measured. Using updated statistical techniques (e.g., Item Response Theory, measurement invariance) helps to improve scales by identifying the true underlying constructs, while also reducing the length of the instrument, selecting the most appropriate items without losing information. In this sense, our multigroup analysis results showed that our 8-item EFA version of the BIS-11 was invariant across the community and prison groups, suggesting that their scores represented genuine differences in motor and nonplanning impulsivity between both groups. This result is relevant because it shows that our version of BIS-11 is truly measuring the same constructs in both samples, making it possible to carry out multigroup comparisons.

Regarding its reliability, the Cronbach’s alpha results showed that the internal consistencies of our two factors were acceptable. Cronbach’s alpha of the BIS-11 total score was not calculated for two reasons: (a) the unidimensional model of the BIS-11 was not supported by the CFA, suggesting the multidimensionality of the items, and (b) the correlation between the two factors were low in both samples, indicating they measure different constructs of impulsivity. In relation to the concurrent validity, our results suggest different associations between Motor and Nonplanning Impulsive factors and Impulsive and Premeditated Aggression scales. In both samples, we found that the Motor factor showed a positive association with both subscales of the IPAS questionnaire. This result is consistent with studies that suggest impulsivity is typically associated with aggressive behaviors (Smith et al., 2006). Nevertheless, no associations were found between the Nonplanning factor and IPAS questionnaires in either sample (the correlation between Nonplanning factor and Premeditated Aggression, although significant, must be considered low). Accordingly, several studies have also failed to find associations between a Nonplanning Impulsive factor and antisocial behaviors (e.g., Ruiz et al., 2010). According to Arce and Santisteban (2006), Motor Impulsivity is associated with an inability to inhibit a response that has already been started, and Nonplanning Impulsivity is related to an inability to predict the consequences of one’s behavior. Thus, Nonplanning Impulsivity is responsible for making a decision to initiate an aggressive act, and Motor Impulsivity is responsible for the inability to inhibit immediate aggressive behavior. This suggests that although Nonplanning Impulsivity is what triggers an aggressive act, the presence of Motor Impulsivity is required for this behavior to actually be realized (as it is responsible for such behavior not being checked in time). This could explain why we found no association between the Nonplanning factor and the IPAS subscales.

The second aim of the study was to evaluate the effects of the BIS-11 subscales on the variables of recidivism/incarcerations and the role of aggression as a mediator in this relationship. Our results found that higher levels of aggression were directly associated with recidivism. In addition, although we found no direct relation between impulsivity and recidivism, the Motor Impulsivity factor did show an indirect effect on recidivism through the aggression variable. In relation to number of incarcerations, the findings were similar: aggression directly predicts number of incarcerations and Motor Impulsivity has an indirect effect on engagement in aggressive behaviors. In other words, increased Motor Impulsivity is related to higher levels of recidivism and number of incarcerations through an increment in aggressive behaviors. The association between aggression and criminality has been extensively demonstrated and our findings corroborate this premise (e.g., Redondo et al., 2019). In addition, our results also coincide with studies that have found impulsivity is related to criminality due to its involvement in the development of violent behaviors: impulsivity increases the likelihood of engaging in aggressive acts, which, in turn, increases the likelihood of recidivism and incarcerations (Griffin et al., 2018). In fact, Nelson and Trainor (2007) suggest that variables such as impulsivity, aggression, violence, psychopathy, and substance abuse have a common biological background, and that interventions for these variables could be more effective if they focused directly on the impulsivity trait (Moeller et al., 2001). Accordingly, Berman et al. (2012) state that an approach that targets impulsive behavior specifically, particularly for individuals with a history of violent acts, might be more effective in reducing violent criminal recidivism than separate interventions for related factors (i.e., substance abuse). According to these authors, an intervention targeting impulsivity can help individuals to identify, accept, and manage their personal distress, and this can lead to a better control of their impulsive feelings (i.e., lack of reflection). Accordingly, several studies have begun to focus on impulsivity as a means of reducing aggressive behaviors and, consequently, preventing recidivism in forensic populations (e.g., Fielenbach et al., 2017; Howells et al., 2010).

However, our findings show the limited ability of the Nonplanning Impulsivity factor to predict aggressive behaviors and recidivism/incarcerations. As previously mentioned, Nonplanning Impulsivity influences the decision to initiate an aggressive act, but the presence of Motor Impulsivity is needed for this act to be realized. This might explain the lack of association between variables. Nonetheless, despite its adequate reliability indices, we cannot discard the possibility that the functioning of the Nonplanning factor, with just three items, is not sufficiently sensitive to detect such effects, as other studies have found associations between Nonplanning impulsivity and aggressive behaviors in forensic populations (e.g., Gordon & Egan, 2011).

The present work presents certain limitations. First, the sampling procedure used was not random, which means the representativeness of the sample is not ensured, which, additionally, could cause bias in the findings (e.g., the age differences between the community and prison samples). Finally, although the power estimated for the prison population is adequate, the sample size is modest for the types of analyses conducted.

Conclusion

Despite the BIS-11 being one of the most widely used instruments to assess impulsivity, its factor structure continues to be the subject of controversy. According to Steinberg et al. (2013), there may exist variations in the nature of impulsivity across different populations. For this reason, it is necessary to evaluate the constructs that this questionnaire measures across different cultures and samples (Vasconcelos et al., 2012). Our findings indicate that BIS-11 measures two factors, Motor and Nonplanning Impulsivity, and that this factor structure is adequate in Spanish community and incarcerated populations. Hence, this version of the BIS-11 is appropriate for reliable research on impulsiveness in these samples. Nonetheless, it would be recommended to conduct further studies in larger incarcerated samples and in other types of Spanish populations to confirm this structure and obtain a broader assessment of the functioning of the Nonplanning factor in relation to variables such as aggression and recidivism. Finally, our results suggest that Motor Impulsivity enhances recidivism in forensic samples through its association with an increase in aggressive behaviors. This finding underscores the importance of implementing interventions designed to improve the control of impulsivity in incarcerated populations as an indirect means to reduce recidivism.

Footnotes

ORCID iDs

Laura Ros

Jorge J. Ricarte

Notes

Laura Ros is an associate professor of psychology at University of Castilla–La Mancha, Spain. Her research interests include psychometrics and applied psychological assessment in forensic, health, and clinical settings.

Carmen Zabala is an associate professor in the Department of Nursing, Physiotherapy and Occupational Therapy at the University of Castilla–La Mancha, Spain. Her research focuses on cognitive and emotional variables related to mental health and aggressive behavior, with a particular interest on mental health in prison and recidivism.

Dulce Romero-Ayuso is an associate professor in the Department of Physiotherapy and a researcher at CYMCIC at the University of Granada, Spain. Her research focuses on cognitive neuroscience, especially in the study of executive functions and sensory processing in different mental and neurodevelopmental disorders.

Verónica Jimeno is an associate professor in the Department of Psychology and member of the Criminology Research Center at the University of Castilla–La Mancha, Spain. Her research focuses on victimology of childhood and adolescence, with a particular interest on the effects of victimization processes on the affective, social, and cognitive development of minors and adolescents.

Jorge J. Ricarte is an associate professor in the Department of Psychology at the University of Castilla–La Mancha, Spain. His research focuses on cognitive variables related to mental health and aggressive behavior, with a particular interest on the role of autobiographical memories processing in developmental violence.

References

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed.).

Arce

Santisteban

(2006). Impulsivity: A review. Psicothema, 18, 213–220.

Barratt

E. S.

(1985). Impulsiveness subtraits: Arousal and information processing. In Spence

J. T.

Izard

C. E.

(Eds.), Motivation, emotion and personality (pp. 137–146). Elsevier Science.

Barratt

E. S.

Stanford

Felthous

Kent

(1997). The effects of phenytoin on impulsive and premeditated aggression: A controlled study. Journal of Clinical Psychopharmacology, 17, 341–349. https://doi.org/10.1097/00004714-199710000-00002

Bentler

P. M.

(1992). On the fit of models to covariances and methodology to the bulletin. Psychological Bulletin, 112, 400–404. https://doi.org/10.1037/0033-2909.112.3.400

Bentler

P. M.

Bonett

(1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. https://doi.org/10.1037/0033-2909.88.3.588

Berman

A. H.

Farzanfar

Kristiansson

Carlbring

Friedman

R. H

. (2012). Design and development of a Telephone-Linked Care (TLC) system to reduce impulsivity among violent forensic outpatients and probationers. Journal of Medical Systems, 36, 1031–1042. https://doi.org/10.1007/s10916-010-9565-1

Bousardt

A. M. C.

Hoogendoorn

A. W.

Noorthoorn

E. O.

Hummelen

J. W.

Nijman

H. L. I.

(2015). Predicting inpatient aggression by self-reported impulsivity in forensic psychiatric patients. Criminal Behaviour and Mental Health, 26, 161–173. https://doi.org/10.1002/cbm.1955

Buss

A. H.

Perry

M. P.

(1992). The Aggression Questionnaire. Journal of Personality and Social Psychology, 63, 452–459. https://doi.org/10.1037/0022-3514.63.3.452

10.

Byrne

B. M.

(2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Lawrence Erlbaum.

11.

Cheung

G. W.

Rensvold

R. B.

(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. https://doi.org/10.1207/S15328007SEM0902_5

12.

Cosi

Vigil-Colet

Canals

Lorenzo-Seva

(2008). Psychometric properties of the Spanish Adaptation of the Barratt Impulsiveness Scale-11-A for children. Psychological Reports, 103, 336–346. https://doi.org/10.2466/pr0.103.2.336-346

13.

Coutlee

C. G.

Politzer

C. S.

Hoyle

R. H.

Huettel

S. A.

(2014). An Abbreviated Impulsiveness Scale constructed through confirmatory factor analysis of the Barratt Impulsiveness Scale version 11. Archives of Scientific Psychology, 2, 1–12. https://doi.org/10.1037/arc0000005

14.

Crozier

W. R.

Alden

L. E.

(2001). International handbook of social anxiety: Concepts, research and interventions relating to the self and shyness. Wiley.

15.

Cuomo

Sarchiapone

Di Giannantonio

Mancini

Roy

(2008). Aggression, impulsivity, personality traits, and childhood trauma of prisoners with substance abuse and addiction. The American Journal of Drug and Alcohol Abuse, 34, 339–345. https://doi.org/10.1080/00952990802010884

16.

Dolan

Fullam

(2004). Behavioural and psychometric measures of impulsivity in a personality disordered population. Journal of Forensic Psychiatry & Psychology, 15, 426–450. https://doi.org/10.1080/14789940410001721048

17.

Fielenbach

Donkers

F. C.

Spreen

Bogaerts

(2017). Neurofeedback as a treatment for impulsivity in a forensic psychiatric population with substance use disorder: Study protocol of a randomized controlled trial combined with an N-of-1 clinical trial. JMIR Research Protocols, 6, Article e13. https://doi.org/10.2196/resprot.6907

18.

Fossati

Barratt

E. S.

Acquarini

Di Ceglie

(2002). Psychometric properties of an adolescent version of the Barratt Impulsiveness Scale-11 for a sample of Italian high school students. Perceptual and Motor Skills, 95, 621–635. https://doi.org/10.2466/pms.2002.95.2.621

19.

Fossati

Di Ceglie

Acquarini

Barratt

E. S.

(2001). Psychometric properties of an Italian version of the Barratt Impulsiveness Scale-11 (BIS-11) in nonclinical subjects. Journal of Clinical Psychology, 57, 815–828. https://doi.org/10.1002/jclp.1051

20.

Franken

I. H.

van Strien

J. W.

Nijs

Muris

(2008). Impulsivity is associated with behavioral decision-making deficits. Psychiatry Research, 158, 155–163. https://doi.org/10.1016/j.psychres.2007.06.002

21.

Gordon

Egan

(2011). What self-report impulsivity measure best postdicts criminal convictions and prison breaches of discipline? Psychology, Crime & Law, 17, 305–318. https://doi.org/10.1080/10683160903203946

22.

Griffin

S. A.

Lynam

D. R.

Samuel

D. B.

(2018). Dimensional conceptualizations of impulsivity. Personality Disorders: Theory, Research, and Treatment, 9, 333–345. https://doi.org/10.1037/per0000253

23.

Güleç

Tamam

Güleç

M. Y.

Turhan

Karakusl

Zengin

Stanford

M. S.

(2008). Psychometric properties of the Turkish version of the Barratt Impulsiveness Scale. Bulletin of Clinical Psychopharmacology, 18, 251–258.

24.

Haden

S. C.

Shiva

(2008). Trait impulsivity in a forensic inpatient sample: An evaluation of the Barratt Impulsiveness Scale. Behavioral Sciences and the Law, 27, 675–690. https://doi.org/10.1002/bsl.820

25.

Haden

S. C.

Shiva

(2009). A comparison of factor structures of the Barratt Impulsiveness Scale in a mentally ill forensic inpatient sample. International Journal of Forensic Mental Health, 8, 198–207. https://doi.org/10.1080/14999010903358920

26.

Hartmann

A. S.

Rief

Hilbert

(2011). Psychometric properties of the German version of the Barratt Impulsiveness Scale, version 11 (BIS-11) for adolescents. Perceptual and Motor Skills, 112, 353–368. https://doi.org/10.2466/08.09.10.PMS.112.2.353-368

27.

Houston

R. J.

Stanford

M. S.

Villemarette-Pittman

N. R.

Conklin

S. M.

Helfritz

L. E.

(2003). Neurobiological correlates and clinical implications of aggressive subtypes. Journal of Forensic Neuropsychology, 3, 67–87. https://doi.org/10.1300/J151v03n04_05

28.

Howells

Tennant

Day

Elmer

(2010). Mindfulness in forensic mental health: Does it have a role? Mindfulness, 1, 4–9. https://doi.org/10.1007/s12671-009-0001-x

29.

Ireland

J. L.

Archer

(2008). Impulsivity among adult prisoners: A confirmatory factor analysis study of the Barratt Impulsivity Scale. Personality and Individual Differences, 45, 286–292. https://doi.org/10.1016/j.paid.2008.04.012

30.

Kahn

J. P.

Cohen

R. F.

Eatin

Aubin

Bellivier

Belzeaux

Bougerol

Courtet

Dubertret

Gard

Henry

Olié

Polosan

Passerieux

Leboyer

., & FondaMental Advanced Centers of Expertise in Bipolar Disorders Collaborators. (2019). Reconsideration of the factorial structure of the Barratt Impulsiveness Scale (BIS-11): Assessment of impulsivity in a large population of euthymic bipolar patients. Journal of Affective Disorders, 253, 203–209. https://doi.org/10.1016/j.jad.2019.04.060

31.

Leverso

Bielby

Hoelter

L. F.

(2015). Back on the streets: Maturation and risk factors for recidivism among serious juvenile offenders. Journal of Adolescence, 41, 67–75. https://doi.org/10.1016/j.adolescence.2015.02.008

32.

C. S. R.

Chen

S. H.

(2007). Obsessive-compulsiveness and impulsivity in a non-clinical population of adolescent males and females. Psychiatry Research, 149, 129–138. https://doi.org/10.1016/j.psychres.2006.05.001

33.

Loyola

(2011). Validez y confiabilidad de la escala de impulsividad de Barratt versión 11 (BIS-11) en mujeres encarceladas [Validity and reliability of the Barratt version 11 impulsivity scale (BIS-11) in incarcerated women] (Doctoral thesis). Universidad Católica de Perú.

34.

Lynam

D. R.

Miller

J. D.

(2004). Personality pathways to impulsive behavior and their relations to deviance: Results from three samples. Journal of Quantitative Criminology, 20, 319–341. https://doi.org/10.1007/s10940-004-5867-0

35.

MacCallum

R. C.

Browne

M. W.

Sugawara

H. M.

(1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130–149. https://doi.org/10.1037//1082-989X.1.2.130

36.

Martínez-Loredo

Fernández-Hermida

J. R.

Fernández-Artamendi

Carballo

J. L.

García-Rodríguez

(2015). Spanish adaptation and validation of the Barratt Impulsiveness Scale for early adolescents (BIS-11-A). International Journal of Clinical and Health Psychology, 15, 274–282. https://doi.org/10.1016/j.ijchp.2015.07.002

37.

Meade

A. W.

Lautenschlager

G. J.

(2004). A Monte-Carlo study of confirmatory factor analytic tests of measurement equivalence/invariance. Structural Equation Modeling, 11, 60–72. https://doi.org/10.1207/S15328007SEM1101_5

38.

Moeller

F. G.

Barratt

E. S.

Dougherty

D. M.

Schmitz

J. M.

Swann

A. C.

(2001). Psychiatric aspects of impulsivity. American Journal of Psychiatry, 158, 1783–1793. https://doi.org/10.1176/appi.ajp.158.11.1783

39.

Morean

M. E.

DeMartini

K. S.

Leeman

R. F.

Pearlson

G. D.

Anticevic

Krishnan-Sarin

Krystal

J. H.

O’Malley

S. S.

(2014). Psychometrically improved, abbreviated versions of three classic measures of impulsivity and self-control. Psychological Assessment, 26, 1003–1020. https://doi.org/10.1037/pas0000003

40.

Muthén

(1998–2015). Mplus user’s guide (7th ed.).

41.

Nelson

R. J.

Trainor

B. C.

(2007). Neural mechanisms of aggression. Nature Reviews Neuroscience, 8, 536–546. https://doi.org/10.1038/nrn2174

42.

Oquendo

M. A.

Baca-García

Graver

Morales

Montalban

Mann

J. J.

(2001). Spanish adaptation of the Barratt Impulsiveness Scale (BIS-11). European Journal of Psychiatry, 15, 147–155. https://doi.org/10.1016/S0034-7450(14)60239-0

43.

Patton

J. H.

Stanford

M. S.

Barratt

E. S.

(1995). Factor structure of the Barratt Impulsiveness Scale. Journal of Clinical Psychology, 51, 768–774. https://doi.org/10.1002/1097-4679(199511)51:6<768::AID-JCLP2270510607>3.0.CO;2-1

44.

Pechorro

Ayala-Nunes

Nunes

Maia

Gonçalves

R. A.

(2017). The Barratt Impulsiveness Scale-11 among a Portuguese sample of female juvenile delinquents and community youths. International Journal of Forensic Mental Health, 16, 46–57. https://doi.org/10.1080/14999013.2016.1235627

45.

Pechorro

Maroco

Ray

J. V.

Gonçalves

R. A.

(2015). Psychometric properties of the Barratt Impulsiveness Scale version 11 among a Portuguese sample of incarcerated juvenile offenders. Psychology, Crime & Law, 21, 854–870. https://doi.org/10.1080/1068316X.2015.1054386

46.

Preuss

U. W.

Rujescu

Giegling

Watzke

Koller

Zetzsche

Meisenzahl

E. M.

Soyka

Möller

H. J.

(2008). Psychometric evaluation of the German version of the Barratt Impulsiveness Scale. Nervenarzt, 79, 305–319. https://doi.org/10.1007/s00115-007-2360-7

47.

Redondo

Canto

A. L.

Graña

J. L.

Muñoz-Rivas

M. J.

O’Leary

K. D.

(2019). Treatment-induced changes in undercontrolled and overcontrolled anger subtypes of perpetrators of intimate partner violence and 5-year recidivism. Criminal Justice and Behavior, 46, 1700–1718. https://doi.org/10.1177/0093854819879201

48.

Reise

S. P.

Moore

T. M.

Sabb

F. W.

Brown

A. K.

London

E. D.

(2013). The Barratt Impulsiveness Scale-11: Reassessment of its structure in a community sample. Psychological Assessment, 25, 631–642. https://doi.org/10.1037/a0032161

49.

Rodriguez

J. M. A.

Fernandez

M. E. P.

Gomez

J. L. G.

(2002). Adaptación psicométrica de la version Española del Cuestionario de Agresión [Validation of the Spanish version of the Aggression Questionnaire]. Psicothema, 14, 476–482.

50.

Ruiz

M. A.

Skeem

J. L.

Poythress

N. G.

Douglas

K. S.

Lilienfeld

S. O.

(2010). Structure and correlates of the Barratt Impulsiveness Scale (BIS-11) in offenders: Implications for psychopathy and externalizing pathology. International Journal of Forensic Mental Health, 9, 237–244. https://doi.org/10.1080/14999013.2010.517258

51.

Smith

Waterman

Ward

(2006). Driving aggression in forensic and non-forensic populations: Relationships to self-reported levels of aggression, anger and impulsivity. British Journal of Psychology, 97, 387–403. https://doi.org/10.1348/000712605X79111

52.

Someya

Sakado

Seki

Kojima

Reist

Tang

S. W.

Takahashi

(2001). The Japanese version of the Barratt Impulsiveness Scale, 11th version (BIS-11): Its reliability and validity. Psychiatry and Clinical Neurosciences, 55, 111–114. https://doi.org/10.1046/j.1440-1819.2001.00796.x

53.

Spinella

(2007). Normative data and a short form of the Barratt Impulsiveness Scale. International Journal of Neuroscience, 117, 359–368. https://doi.org/10.1080/00207450600588881

54.

Stanford

M. S.

Houston

R. J.

Mathias

C. W.

Villemarette-Pittman

N. R.

Helfritz

L. E.

Conklin

S. M.

(2003). Characterizing aggressive behavior. Assessment, 10, 183–190. https://doi.org/10.1177/1073191103252064

55.

Stanford

M. S.

Mathias

C. W.

Dougherty

D. M.

Lake

S. L.

Anderson

N. E.

Patton

J. H.

(2009). Fifty years of the Barratt Impulsiveness Scale: An update and review. Personality and Individual Differences, 47, 385–395. https://doi.org/10.1016/j.paid.2009.04.008

56.

Steinberg

Sharp

Stanford

M. S.

Tharp

A. T.

(2013). New tricks for an old measure: The development of the Barratt Impulsiveness Scale-Brief (BIS-Brief). Psychological Assessment, 25, 216–226. https://doi.org/10.1037/a0030550

57.

Swann

A. C.

Bjork

J. M.

Moeller

Dougherty

D. M.

(2002). Two models of impulsivity: Relationship to personality traits and psychopathology. Biological Psychiatry, 51, 988–994. https://doi.org/10.1016/S0006-3223(01)01357-9

58.

Swogger

M. T.

Walsh

Houston

R. J.

Cashman-Brown

Conner

K. R.

(2010). Psychopathy and Axis I psychiatric disorders among criminal offenders: Relationships to impulsive and premeditated aggression. Aggressive Behavior, 36, 45–53. https://doi.org/10.1002/ab.20330

59.

Vandenberg

R. J.

Lance

C. E.

(2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4–70. https://doi.org/10.1177/109442810031002

60.

Vasconcelos

A. G.

Malloy-Diniz

Correa

(2012). Systematic review of psychometric properties of Barratt Impulsiveness Scale version 11 (BIS-11). Clinical Neuropsychiatry, 9, 61–74.

61.

Vasconcelos

A. G.

Teodoro

M. L. M.

Malloy-Diniz

Correa

(2015). Componentes da Impulsividade avaliados pela versao brasileira da Barratt Impulsiveness Scale (BIS-11) [Impulsivity components measured by the Brazilian version of the Barratt Impulsiveness Scale (BIS-11)]. Psicologia: Reflexao e Critica, 28, 96–105. https://doi.org/10.1590/1678-7153.201528111

62.

Yang

H. Q.

Yao

S. Q.

Zhu

X. Z.

(2007). The Chinese version of the Barratt Impulsiveness Scale 11th version (BIS-11) in college students: Its reliability and validity. Chinese Mental Health Journal, 4, 223–225.

63.

Zhou

Xiao

Lui

(2006). Reliability and validity of Chinese version of Barratt Impulsiveness Scale-11. Chinese Journal of Clinical Psychology, 4, 343–344.