Self-Reported Impulsivity Predicts Missed Study Appointments: Validating a German Adaptation of the BIS-11

Abstract

Impulsivity influences behavioral tendencies and can contribute to dysfunction both in everyday life and in psychopathology. A common measure of impulsivity is the Barratt Impulsiveness Scale (BIS), a widely-used self-report questionnaire currently in its 11^th version (BIS-11). In this validation study, we integrated several German translations into a unified version and evaluated its factor structure as well as its construct and criterion validity. The original factor structure of motor, attentional and non-planning impulsivity was not supported in our data, as factor analyses yielded poor model fit. Nonetheless, the validity of the BIS-11 sum score was supported by associations with other impulsivity-related questionnaires (UPPS Impulsive Behavior Scale, Brief Self-Control Scale) and differentiation from unrelated constructs (UPPS sensation seeking, trait anxiety). We found no links between BIS-11 scores and behavioral performance in response inhibition and automaticity tasks. Crucially, higher BIS-11 scores predicted participants’ dependability, as indexed by missed study appointments, thus linking the questionnaire to everyday behavior. In sum, although we could not reliably detect the BIS-11’s factor structure in our data, our findings support its use as a valid overall measure of impulsivity that relates meaningfully to everyday functioning.

Keywords

impulsivity dependability questionnaire construct validity criterion validity

Introduction

The concept of impulsivity and its associations to human behavior is a topic of interest both in psychological research and outside the research community. One widely accepted definition describes impulsivity as a “predisposition toward rapid, unplanned actions to internal or external stimuli without regard to the negative consequences of these actions to the impulsive individual or to others” (Moeller et al., 2001). As this definition highlights, impulsivity is linked to decision-making, and has been associated to potentially harmful behaviors such as aggression, delinquency, substance use, gambling, risky sexual behavior, as well as excessive shopping and eating (Huang et al., 2024; Sharma et al., 2014). Impulsivity has been shown to predict psychopathology (Fields et al., 2021; Friedman et al., 2019) and suicidality (Moore et al., 2022), influence the course of substance use disorders (Kozak et al., 2019; Kräplin et al., 2020), and is a key characteristic of various mental disorders, including borderline personality disorder (American Psychiatric Association, 2013).

Despite the widespread use of impulsivity as a construct, there is significant disagreement about what precisely impulsivity entails. Which components of impulsivity are examined differs greatly depending on how it is operationalized, e.g., the specific task or questionnaire. Common self-report scales capture different combinations of impulsivity domains, including cognitive factors, inhibition, risk-taking and affective responses (Carver & White, 1994; Lynam et al., 2006; Patton et al., 1995). The Barratt Impulsiveness Scale (BIS) was originally developed to distinguish impulsivity from anxiety (Barratt, 1959), as well as from impulsivity-related constructs such as sensation seeking, extraversion, and risk tolerance (Barratt, 1985). The current version (BIS-11; Patton et al., 1995) consists of 30 items, with a principal component analysis on data from 733 participants identifying three higher-order factors: attentional impulsiveness (i.e., difficulty to focus on a given task), motor impulsiveness (i.e., acting without forethought) and non-planning impulsiveness (i.e., lack of forward planning). However, while the BIS-11 is frequently used to assess impulsivity, researchers often rely on the good psychometric properties of the BIS-11 sum score (Stanford et al., 2009), as its factor structure has been criticized (Reise et al., 2013; Steinberg et al., 2013; Vasconcelos et al., 2012).

Our study aimed to validate a German version of the BIS-11, developed by our research group, that unified several existing German translations, across several general population samples. We investigated whether our questionnaire reflects the factor structure proposed by Patton (attentional, motor and non-planning impulsivity; Patton et al., 1995) using exploratory and confirmatory factor analysis. Subsequently, to establish construct validity, we compared the BIS-11 with another well-established impulsivity questionnaire, the UPPS (Whiteside et al., 2005), which distinguishes between four dimensions of impulsivity. We assumed positive correlations between the BIS-11 sum score and the subscales urgency, lack of premeditation and lack of perseverance. As the BIS-11 was developed to be distinct from sensation seeking, we expected no association with the UPPS subscale sensation seeking. Additionally, we tested the association to trait self-control, which describes the ability to resist immediate impulses and refrain from initially appealing but undesired behavior (Tangney et al., 2004). Given that impulsivity and trait self-control are negatively correlated and often considered opposing constructs (Duckworth & Kern, 2011; Evenden, 1999), we expected a negative association between the BIS-11 and the Brief Self-Control Scale (Tangney et al., 2004). Furthermore, as early versions of the BIS aimed to distinguish impulsivity and anxiety, we used the trait scale of the State-Trait Inventory for Cognitive and Somatic Anxiety (Ree et al., 2008) to test the BIS-11’s discriminant validity against anxiety. Another common approach in investigating self-reported impulsivity is through behavioral paradigms, often assessing inhibitory control. These investigations mostly focus on response inhibition, i.e., withholding a prepotent response (Stevens et al., 2007) or automaticity, i.e., acting with reduced intent due to learned stimulus-response contingencies (Flaudias & Llorca, 2014). However, findings on the association between laboratory tasks, especially regarding response inhibition, and self-reported impulsivity have been inconsistent (Aichert et al., 2012; Bernoster et al., 2019; Perales et al., 2009; Sach et al., 2018; Torres et al., 2013; Weidacker et al., 2015), and some researchers suggest a dissociation between the two (Cyders & Coskunpinar, 2011; Sharma et al., 2014). We examined the link between the BIS-11 and performance in a Go/Nogo, a Stop-Signal and a Stroop task, expecting a negative association between BIS-11 scores and behavioral task performance. Based on their conceptual overlap, we investigated potential links between behavioral performance and the motor impulsivity scale as well as the BIS-11 sum score.

A crucial aspect of questionnaire evaluation is criterion validity, which assesses whether the measure corresponds with external criteria beyond the test situation (Piedmont, 2014). Based on the associations between impulsivity, psychopathology, and delinquency, the BIS is often validated in patient or delinquent populations (Stanford et al., 2009; Steinberg et al., 2013), while studies in the general population are scarce. As a result, criterion validity based on clinical status or aggressive behavior may not be easily transferable. Here, we propose using missed appointments to participate in a study as a practical, daily-life measure of impulsivity. Short-notice cancellation or failure to attend appointments has previously been linked to impulsivity (Müller et al., 2021) and likely reflects bad planning or choosing a more appealing alternative while ignoring potential negative consequences. In a novel approach, we assessed criterion validity via the association between BIS-11 scores and the number of appointments missed due to cancellation or no-show (except for illness-related reasons).

Methods

Participants

We assessed the BIS-11 in three different general population samples, as detailed in Table 1. All participants gave informed consent and received either financial compensation or course credit for participation. The projects were approved by the ethics committee at the University Hospital Carl Gustav Carus at the Technical University Dresden (TUD) and conducted in accordance with the ethical guidelines of the Declaration of Helsinki. Sample 1 was recruited online via the TUD’s study mailing list. Samples 2 and 3 were recruited in the general area of Dresden via online and local advertisements and the TUD’s study mailing list. Inclusion and exclusion criteria, along with detailed sample descriptions, are given in Supplement 1.

Table 1.

Sample Characteristics

	Sample 1	Sample 2	Sample 3
Sample size	N = 355	N = 252	N = 148
Age	M = 27.09, SD = 8.69 range: 18–63	M = 24.98, SD = 5.11 range: 18–45	M = 24.61, SD = 4.60 range: 18–35
Gender	75% female	50% female	52% female
Education	93% high school or higher	94% high school or higher	92% high school or higher
Mental disorder	N = 110	N = 29	N = 16

Note. Mental disorder: number of participants reporting a lifetime or acute diagnosis of a mental disorder other than the respective exclusion criteria.

Procedure

We used data from three different samples to validate our version of the BIS-11. All self-report questionnaires were completed via the online platform LimeSurvey (LimeSurvey GmbH, n.d.).

Sample 1

An exploratory factor analysis of the BIS-11 was conducted using data from Sample 1, collected online as part of a validation study for the German adaptation of the Creature of Habit Scale (Ersche et al., 2017; Overmeyer et al., 2020).

Sample 2

We used data from Sample 2 to conduct a confirmatory factor analysis and investigate the associations between the BIS-11, other self-report questionnaires, and behavioral response inhibition and automaticity tasks. Participants took part in a research project on cognitive control functions related to impulsivity and compulsivity (https://osf.io/ywnze/). During the first lab session, they completed a computerized neuropsychological test battery (Wolff, Krönke, & Goschke, 2016; Wolff, Krönke, Venz, et al., 2016) via Matlab 2010a (The MathWorks Inc, 2010). The test battery contained nine executive functioning tasks, including a Stroop task assessing automaticity. In a second laboratory session, participants then underwent electroencephalographic (EEG) assessment of cognitive control. We used the behavioral performance from a Go/Nogo and a Stop-signal task as indices of response inhibition. All EEG tasks were presented via Presentation® software (version 19.0, Neurobehavioral Systems, Inc., Berkeley, CA, www.neurobs.com).

Sample 3

In Sample 3, we investigated the association between BIS-11 scores and missed study appointments. Data originates from a project investigating cognitive control functions, impulsivity and compulsivity (https://osf.io/bjpc8/). Participation consisted of a lab session including an EEG task battery and self-report questionnaires.

Measures

BIS-11

As outlined above, the BIS-11 captures impulsivity through its attentional, motor and non-planning components. These subscales are composed of 30 self-report items measuring impulsive behavior on a rating scale ranging from 1 (“almost never”) to 4 (“almost always”).

The BIS-11 has been translated into German several times, making it difficult to compare the different versions. Besides the official translation of the BIS-11 by Preuss et al. (2008), unpublished versions have been created by different research groups. Hartmann et al. (2011) published a version of the BIS-11 to be used for adolescents (translated from Italian; Fossati et al., 2002). Further, the BIS-15 constitutes a short (15 items) version of the questionnaire (Meule et al., 2011). Using mainly a subset of the German items from Preuss et al., Meule et al. could confirm the three-factor structure of the BIS-15 as proposed by Spinelli (2004). The version of the BIS-11 used in this paper was developed by native German speakers and combined questions from these previous German translations and new translations of specific items from the original English BIS-11 (Patton et al., 1995). The final version is provided in Supplement 2.

Additional Questionnaires

UPPS Impulsive Behavior Scale

The UPPS scale (Schmidt et al., 2008; Whiteside & Lynam, 2001) is a self-report questionnaire measuring four domains of impulsivity through 59 items on a 4-point rating scale (1: “strongly agree” to 4: “strongly disagree”). These items are grouped into the subscales of urgency (a tendency to act rashly in response to negative emotion), lack of premeditation (a tendency to act without thinking and considering consequences), lack of perseverance (difficulty in completing tasks), and sensation seeking (a desire for novel and exciting experiences). The subscale “lack of premeditation” demonstrated acceptable internal reliability (Cronbach’s alpha = .74 and McDonald’s omega = .81), whereas all other subscales reached good internal reliability (Cronbach’s alpha and McDonald’s omega >.80) in our sample (see Table 2 for details on internal consistency measures of all questionnaires).

Table 2.

Internal Consistency of the BIS-11 and Additional Questionnaires

	M (SD)	α	ω
BIS-11
Sample 1 (N = 355)
Sum score	59.66 (8.60)	.77	.81
Non-planning	23.21 (4.26)	.63	.71
Motor	21.41 (3.68)	.57	.72
Attentional	15.05 (3.14)	.63	.74
Sample 2 (N = 252)
Sum score	60.58 (8.93)	.81	.85
Non-planning	23.29 (4.25)	.67	.74
Motor	21.71 (3.49)	.58	.69
Attentional	15.57 (3.24)	.68	.79
Sample 3 (N = 148)
Sum score	62.15 (11.68)	.88	.91
Non-planning	24.0 (5.2)	.76	.80
Motor	22.3 (4.3)	.70	.78
Attentional	15.7 (4.0)	.81	.87
Additional questionnaires (sample 2, N = 252)
UPPS urgency	24.28 (5.34)	.84	.87
UPPS lack of premeditation	22.33 (4.27)	.74	.81
UPPS lack of perseverance	19.46 (4.54)	.81	.86
UPPS sensation seeking	32.85 (7.09)	.85	.89
BSCS	41.55 (7.80)	.81	.84
STICSA trait	30.43 (6.25)	.85	.87

Note. α = Cronbach’s alpha, ω = McDonald’s omega. Sum score = BIS-11 sum score. Non-planning = non-planning impulsiveness subscale, motor = motor impulsiveness subscale, attentional = attentional impulsiveness subscale. UPPS = UPPS Impulsive Behavior Scale. BSCS = Brief Self-Control Scale sum score. STICSA trait = State-Trait Inventory for Cognitive and Somatic Anxiety trait subscale score.

Brief Self Control Scale

Trait self-control was assessed using the Brief Self Control Scale (Bertrams & Dickhäuser, 2009; Tangney et al., 2004), a 13-item self-report measure in which participants rate the extent to which various behavioral tendencies apply to them on a 5-point scale (1: “completely inaccurate” to 5: “completely accurate”). The Brief Self Control Scale showed good internal reliability (Cronbach’s alpha = .81 and McDonald’s omega = .84) in our data.

State-Trait Inventory for Cognitive and Somatic Anxiety

The State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA; Overmeyer & Endrass, 2023; Ree et al., 2008) is a self-report measure that distinguishes cognitive and somatic symptoms of anxiety. The 42 items, with responses ranging from 1 to 4, are divided into state and trait scales, both with strong reliability. For our analyses, we employed the trait scale, which assesses the frequency of anxiety symptoms experienced by individuals and showed good internal reliability in our sample (Cronbach’s alpha = .85 and McDonald’s omega = .87).

Behavioral Paradigms

We examined the relationship between participants’ impulsivity as indexed by BIS-11 scores and their performance in tasks assessing automatic behavior (Counting-Stroop task) and response inhibition (Go/Nogo and Stop-Signal tasks).

Counting-Stroop Task

In Stroop tasks, participants are required to respond to one stimulus modality while ignoring another more salient modality that typically elicits a more automatic response (Stroop, 1935). We used a modified version of the task based on Bush et al. (1998). On each trial, participants viewed a stimulus consisting of one to four identical digits (1-4). The digit count and identity were either congruent (1, 22, 333, 4444) or incongruent (111, 2222, 3, 44). Participants were instructed to report the number of digits via key press, ignoring the identity of the digits. The task comprised of 160 trials (50% congruent), where one of eight possible stimulus sets (see Figure 1(A)) was shown for 1,000 ms, followed by an inter-trial-interval with a fixation cross for 750 ms. To assess task performance, we determined the individual inverse efficiency scores (IES; Townsend & Ashby, 1978) for congruent vs. incongruent trials, which integrates response speed (mean reaction time for correct trials; RT) and accuracy (proportion of correct responses):

D e l t a_{I E S} = \frac{R T (i n c o n g r u e n t)}{a c c u r a c y (i n c o n g r u e n t)} - \frac{R T (c o n g r u e n t)}{a c c u r a c y (c o n g r u e n t)} .

Figure 1.

Behavioral tasks. Note. (A) Stimulus sets for the Counting-Stroop task, with congruent (top) and incongruent (bottom) stimuli. (B) Exemplary trials of the Stop-Signal task. Participants were instructed to respond to the Go stimulus (green arrow, second column) but stop their initiated response if it is followed by a Stop stimulus (red arrow, third column in the bottom row). (C) Exemplary trials of the Go/Nogo task. Participants were instructed to respond to the Go stimulus (green square, second column in the top row), but withhold their response to the Nogo stimulus (red square, bottom row)

Higher Delta_IES scores indicate increased behavioral automaticity, reflecting reduced inhibitory control.

Stop-Signal Task

The Stop-Signal-Task (Aron & Poldrack, 2006; Logan, 1994) measures the ability to stop an already initiated response. Participants were instructed to respond as fast as possible to the Go stimulus (a green arrow pointing left or right) by pressing the corresponding key. In 25 % of the trials the arrow turned red shortly after onset, signaling participants to withhold their response (Stop signal; see Figure 1(B)). Initial Stop signal delays (SSD; 100, 150, 200 and 250 ms) were evenly distributed across trials and dynamically adjusted in 50 ms steps based on performance: shortened after successful and lengthened after failed inhibition, targeting a stopping success rate of around 50%. The task continued until participants completed two blocks with a stopping performance between 40% and 60% (valid blocks) or a maximum of four blocks. Each block consisted of 128 trials, with an inter-trial-interval of 400-800 ms. Only valid task blocks were included in the analyses. Participants were excluded if they did not complete at least two valid blocks (n = 5) or due to task recording issues (n = 3), resulting in a final sample of N = 244 participants. Task performance was conceptualized as a race between Go and Stop processes, with the outcome measured by Stop signal reaction time (SSRT). SSRT was calculated by subtracting the mean SSD from the mean reaction time in Go trials (Verbruggen & Logan, 2009). Higher SSRT scores reflect slower and thus poorer response inhibition.

Go/Nogo Task

In the Go/Nogo task (Enriquez-Geppert et al., 2010) participants were instructed to respond as fast and as accurately as possible to a Go stimulus (a green square) and withhold their response to a Nogo stimulus (a red square; see Figure 1(C)). Both stimuli were presented inside a white circle on a black background for up to 500 ms, followed by a variable inter-stimulus interval of 900-1,200 ms (jittered randomly in 50 ms; M = 1,050 ms). The task consisted of 192 Go (75%) and 64 Nogo trials (25%). Data was analyzed from N = 250 participants (after participant exclusion due to poor task compliance [multiple responses; n = 1] and task recording issues [n = 1]. Task performance was assessed with participants’ RT in Go trials and the percentage of correct responses in Nogo trials (Nogo accuracy).

Missed Study Appointments

Missed study appointment were used to assess criterion validity by linking participants’ BIS-11 scores to their everyday behavior and (un-)dependability. In Sample 3, we documented cancellations of appointments and no-shows to partake in a neurocognitive study at the lab, which included financial compensation. Data includes participants who then rescheduled their appointment and eventually completed the study. We distinguished between illness-related and non-illness-related reasons; Only non-illness-related cancellations and no-shows were included in our analyses, resulting in a binary variable (missed appointment = 1; no missed appointment = 0). This applied to ten participants who missed a study appointment at the lab for non-illness-related reasons.

Data Analysis

All analyses were carried out in R, version 4.4.3 (R Core Team, 2023). We first assessed the internal reliability of the BIS-11 using McDonald’s omega (McDonald, 2013) and Cronbach’s alpha (Cronbach, 1951) and conducted an item analysis in Sample 1 using the R packages psych (version 2.4.12) and sjPlot (version 2.8.17; Lüdecke, 2024; Revelle, 2024). Outliers were identified in all samples but retained to preserve analytical power. To explore the dimensionality of the BIS-11, we performed exploratory factor analysis (EFA) with oblique rotation (Promax) and Maximum-Likelihood-Estimation on the data from Sample 1, again using psych (Revelle, 2024). Due to the non-normality of the data, as indicated by Mardia’s test (Mardia, 1970) in MVN (version 5.9; Korkmaz et al., 2014), we used a polychoric correlation matrix (Holgado–Tello et al., 2010). The number of factors or components was determined with minimum average partial procedure, parallel analysis for component extraction, optimal coordinates, acceleration factor (nFactors version 2.4.4.1; Raiche & Magis, 2010) and comparison data (RGenData version 1.0; Ruscio, 2018). These techniques were chosen for their high accuracy rates (Ruscio & Roche, 2012). The final factor solution was chosen based on item loadings >.30, minimal cross-loadings, and having at least three items per factor (Costello & Osborne, 2019; Fabrigar et al., 1999). The factorial structure was then validated via confirmatory factor analysis on Sample 2 data, using diagonally weighted least squares for ordinal data (Li, 2016) in lavaan (version 0.6-19; Rosseel, 2012).

Construct validity was evaluated through correlations between the BIS-11 sum score and other self-report questionnaires in Sample 2. We employed impulsivity-related questionnaires (UPPS urgency, lack of premeditation and lack of perseverance scales as well as the Brief-Self-Control Scale (BSCS; Tangney et al., 2004; Whiteside & Lynam, 2001) to measure the BIS-11’s utility in capturing impulsivity. UPPS sensation seeking and the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA; Ree et al., 2008) were employed to differentiate the BIS-11 from unrelated constructs (sensation seeking and trait anxiety). We further investigated the associations between self-report and behavioral measures of impulsivity: BIS-11 sum scores and motor impulsivity scores, i.e., scores for the respective factor derived by EFA, were correlated with performance in the Stroop, Go/Nogo and Stop-Signal tasks. All relationships were calculated as Pearson correlations using the package stats. We additionally calculated Kendall’s tau (Kendall, 1938) to account for non-normality of the data indicated by Shapiro-Wilk tests (Shapiro & Wilk, 1965; see Supplement 5).

To assess criterion validity, we conducted a logistic regression on the number of missed appointments in relation to the BIS-11 sum score in Sample 3 (using stats; R Core Team, 2023), with further investigation using Odds Ratio (Bland & Altman, 2000).

Results

Internal Reliability

First, we examined the distribution of the BIS-11 items and its sum score, calculating item difficulty and discrimination in Sample 1 (see Supplemental Table S3.1). Although items 3, 4 and 23 yielded item discriminations near zero, their removal would have resulted in only marginally improved Cronbach’s alpha scores and was therefore not performed. Cronbach’s alpha and McDonald’s omega revealed acceptable (alpha = .77, omega = .81) to good (alpha = .88, omega = .91) internal consistency of the BIS-11 sum score in all three samples (see Table 2). We further calculated internal consistency of the BIS-11 subscales as proposed by Patton et al. (1995), which revealed acceptable scores only in Sample 3 (see Table 2). The mean BIS-11 sum score ranged between 60 and 62, which is considered to be average (Stanford et al., 2009).

Factorial Validity

Exploratory Factor Analysis

Parallel analysis, optimal coordinates and minimum average partials procedure extracted four, acceleration factor extracted one and comparison factor extracted five factors (see Supplement 3 for scree plot). However, these factor solutions were unsatisfactory, as the four- and five factor solutions contained factors with less than three items, and the one-factor solution contained factor loadings near zero (see Supplemental Tables S3.2-4). We thus pursued with a Promax-rotated three-factor solution, which yielded less cross-loadings and more coherent factors. As shown in Table 3 and Figure 2, the clustering only partially reflected the original factors attentional, motor and non-planning impulsivity (Patton et al., 1995). Items were distributed differently across the factors, with eleven items being assigned to different factors as compared to the published factor solution and cross-loadings of several items. The retained factors were correlated.

Table 3.

Promax-Rotated Standardized EFA Loadings for the Three-Factor Solution

Item number	Item (original English wording)	Original factor	Factor 1	Factor 3	Factor 2
12^a	I am a careful thinker	NP	.91		-.21
20^a	I am a steady thinker	A	.90		-.23
15^a	I like to think about complex problems	NP	.66	-.49
1^a	I plan tasks carefully	NP	.53
8^a	I am self controlled	NP	.37
14	I say things without thinking	NP	.34	.23
29^a	I like puzzles	NP	.34
10^a	I save regularly	NP	.34		.23
5	I don’t “pay attention.”	A	.33		.27
30^a	I am future-oriented	M	.32
7^a	I plan trips well ahead of time	NP	.31	.22	-.23
19	I act on the spur of the moment	M	.24	.69	-.24
17	I act “on impulse.”	M		.68
21	I change residences	M		.53
16	I change jobs	M		.52
24	I change hobbies	A		.47
2	I do things without thinking	M	.38	.42
22	I buy things on impulse	M		.34
27	I am more interested in the present than the future	NP		.22
13^a	I plan for job security	NP		.21
9^a	I concentrate easily	A	.42	-.22	.66
4	I am happy-go-lucky	M		.25	-.64
26	I often have extraneous thoughts when thinking	A		.22	.60
6	I have “racing” thoughts	A	-.28	.24	.60
28	I am restless at the theater or lectures	A			.51
11	I “squirm” at plays or lectures	A			.47
3	I make-up my mind quickly	M		.39	-.45
25	I spend or charge more than I earn	M		.29	.32
23	I can only think about one thing at a time	M			.32
18	I Get easily bored when solving thought problems	NP	.20		.31

Note. Factor loadings based on a polychoric correlation matrix. Only loadings greater than .2 or less than −.2 are displayed. N = 355.

The highest factor loading per item is shown in boldface. The table presents the English wording of each item and the highest-loading factor in the original BIS-11 (Patton et al., 1995). A = attentional, M = motor, NP = non-planning impulsiveness.

^a = inverted items.

Figure 2.

Path diagram for the BIS-11 confirmatory factor analysis on Sample 2, based on the factor structure from the current exploratory factor analysis.

Confirmatory Factor Analysis

Model fit indices for the current three-factor-model derived from EFA ranged from acceptable for Root Mean Square Error of Approximation (RMSEA_robust = .07) and Chi-square test (X² (402) = 835, p < .001) to insufficient for Comparative Fit Index (CFI_robust = .85) and Tucker Lewis Index (TLI_robust = .84). Standardized factor loadings for the BIS-11 reached significance for all but two items (4 and 23). However, only 10 out of 30 items showed satisfactory factor loadings of λ > .50 (Kahn, 2006). These well-performing items were mainly distributed across the motor and non-planning impulsivity factors, supporting these dimensions in CFA, while the attentional impulsivity factor remained questionable (see Figure 2 for factor loadings).

Further analyses revealed a similarly poor model fit for the original three-factor model proposed by Patton et al. (1995; see Supplemental Table S3.5 for factor loadings), with RMSEA_robust = .08, CFI_robust = .83, TLI_robust = .82 and (X² (402) = 821.10, p < .001). Therefore, neither the factor structure from the current EFA nor the original three-factor model of the BIS-11 could be confirmed. Lastly, we tested a model with only one latent factor. CFA did not yield improved model fit over the three-factor models (RMSEA_robust = .08, CFI_robust = .83, TLI_robust = .82 and X² (405) = 881, p < .001; see Supplemental Table 3.6 for factor loadings of the one-factor model).

Given the unsatisfactory results of CFA in our version of the BIS-11, we further explored a possible short form of the questionnaire, based on the BIS-15 as proposed by Meule et al. (2011), which is detailed in Supplement 4.

Construct Validity

Associations with Other Questionnaires

As predicted, BIS-11 sum scores showed robust associations with the UPPS subscales urgency, lack of premeditation, and lack of perseverance scales, but only a small association with sensation seeking. The BIS-11 was negatively linked with trait self-control in the BSCS. Additionally, BIS-11 sum scores were correlated with trait anxiety indicated by the STICSA to a small degree (see Table 4 for all Pearson correlations between self-report questionnaires). We additionally calculated Kendall’s tau to account for non-normality of the data (see Supplemental Table S5.1), which yielded similar results.

Table 4.

Pearson Correlations of Self-Report Questionnaires in Sample 2

	M (SD)	1	2	3	4	5	6
1 BIS-11	60.58 (8.93)
2 UPPS-u	24.28 (5.34)	.53**
3 UPPS-pr	22.33 (4.27)	.58**	.28**
4 UPPS-pe	19.46 (4.54)	.51**	.39**	.21**
5 UPPS-ss	32.85 (7.09)	.22**	.07	.32**	-.01
6 BSCS	41.55 (7.80)	-.65**	-.67**	-.24**	-.65**	-.06
7 STICSA	30.43 (6.25)	.20**	.40**	-.14*	.28**	.00	-.33**

Note. BIS-11 = BIS-11 sum score. UPPS-u = UPPS urgency subscale. UPPS-pr = UPPS lack of premeditation subscale. UPPS-pe = UPPS lack of perseverance subscale. UPPS-ss = UPPS sensation seeking subscale. BSCS = Brief Self-Control Scale. STICSA = Trait subscale of the State-Trait Inventory for Cognitive and Somatic Anxiety.

N = 252. *p < .05. **p < .01.

Associations with Behavioral Inhibitory Task Performance

Analyses of the behavioral paradigms revealed no significant correlations between behavioral inhibitory performance and BIS-11 scores (see Table 5). We additionally calculated Kendall’s tau to account for non-normality of the data (see Supplemental Table S5.2), which yielded similar results.

Table 5.

Pearson Correlations of BIS-11 Scores With Inhibitory Performance Parameters

		BIS-11 sum score		BIS-11 motor impulsivity
		r	p	r	p
Counting-stroop^a	Delta_IES	-.07	.254	-.09	.137
Go/Nogo^b	Nogo accuracy	-.01	.865	-.03	.676
Go/Nogo^b	RT Go	-.06	.313	-.07	.245
Stop-signal^c	SSRT	-.06	.360	-.08	.218

Note. BIS-11 motor impulsivity = sum score of the motor impulsivity factor derived from exploratory factor analysis of the BIS-11.

^aN = 252.

^bN = 250.

^cN = 244.

Criterion Validity: Missed Study Appointments

In a binomial logistic regression analysis, BIS-11 scores significantly predicted whether a study appointment at the lab was missed. The analysis revealed an odds ratio (OR) of 1.16 (95% confidence interval [1.07, 1.30], p = .002), indicating that for each additional point on the BIS-11, the odds of missing an appointment increased by 16%.

Discussion

This study aimed to validate a German translation of the BIS-11 as a self-report measure of impulsivity across multiple general population samples, examining its internal reliability, factorial structure, and construct validity via other self-report measures. Additionally, we explored its association with behavioral inhibitory task performance and behavior relevant for daily life (missed appointments). While the BIS-11 showed strong internal consistency, we could not confirm the factor structure proposed by Patton et al. (1995). Construct validity of the BIS-11 appears to be given: Its sum score showed moderate positive correlations with UPPS subscales urgency, lack of premeditation and lack of perseverance and negative associations with self-control on the BSCS, while correlations with the UPPS scale sensation seeking and trait anxiety on the STICSA were small. However, we observed a dissociation between self-reported impulsivity and performance on cognitive inhibition tasks. The BIS-11 sum score significantly predicted participants’ missed appointments to partake in a study.

Factor Structure

The exploratory and confirmatory factor analyses did not confirm the original three-factor structure of attentional, motor and non-planning impulsivity proposed by Patton et al. (1995). Instead, the alternative three-factor model with adjusted item distribution showed better, but still suboptimal model fit. Specifically, the attentional impulsivity factor appeared less robust, with several items displaying cross-loadings and low factor loadings. This fits the ambiguity in the development of the BIS as detailed by Stanford et al. (2009), where an assumed cognitive factor of impulsivity could not be defined and later resulted in the attentional impulsivity scale. Despite the relatively small sample sizes used for the factor analyses, our results indicate that we could not detect a three-factor model of impulsivity in our data. These findings are consistent with previous reports that have not observed the factor structure of the BIS-11 (Reise et al., 2013; Steinberg et al., 2013; Vasconcelos et al., 2012).

Validity and Reliability

The BIS-11, especially its sum score, is one of the most-widely used self-report measures in impulsivity research (Stanford et al., 2009). The BIS-11 sum score demonstrated good internal consistency across all samples, suggesting that the overall measure is reliable. In contrast, questionnaires such as the UPPS can be employed to focus on individual facets of impulsivity (Whiteside et al., 2005) but give only limited information on impulsivity as a superordinate trait. Our findings on the factorial structure of the BIS-11 in our data correspond with those of Huang et al. (2024). The authors, after analyzing a multitude of self-report and behavioral measures, propose impulsivity to be a unidimensional construct with a small number of related constructs (sensation seeking, punishment and reward sensitivity and cognitive behavioral factors). The BIS-11 sum score might thus reflect such a “general factor” of impulsivity.

The construct validity of the BIS-11 sum score as a measure of impulsivity was further supported with related impulsivity measures, such as the UPPS urgency, lack of premeditation, and lack of perseverance scales. Small positive correlations were observed between the BIS-11 and trait anxiety as well as UPPS sensation seeking. As expected, we found a negative link between the BIS-11 and the BSCS, which aligns with the conceptualization of impulsivity and self-control as opposing constructs (Duckworth & Kern, 2011; Evenden, 1999). Trait self-control likely involves, amongst others, conscientiousness and planning to avoid temptation (Gao et al., 2021; Milyavskaya et al., 2021), both of which interfere with the quick, unplanned or not well considered action tendencies that characterize impulsivity.

The expected link between self-reported impulsivity and behavior was not observed for the laboratory tasks. BIS-11 scores were not significantly associated with performance on the Go/Nogo, Stop-Signal, or Counting-Stroop tasks, indicating a dissociation between self-reported impulsivity and behavioral response inhibition and automaticity. This finding is consistent with meta-analyses revealing little to no correlation between self-reported impulsivity and cognitive tasks (Cyders & Coskunpinar, 2011; Sharma et al., 2014), despite both being commonly used as operationalizations of impulsivity. One proposed explanation is that self-report measures capture broader, trait-like action tendencies, whereas cognitive control tasks assess momentary, micro-level processes (Sharma et al., 2014). Hedge et al. (2018) further highlight the reliability paradox: Cognitive behavioral tasks are optimized to minimize between-subjects variability to detect within-subject effects, which limits their utility for capturing individual differences, in contrast to self-report scales, which rely on such variability. The lack of convergence between these approaches calls for further research to clarify which facets of impulsivity each method captures and whether they reflect distinct dimensions of impulsive behavior.

One of the key strengths of our study was the demonstration of criterion validity through the association between BIS-11 sum scores and daily-life dependability, measured by non-illness related missed study appointments at the lab, which may relate to core characteristics of impulsivity (Moeller et al., 2001). The significant positive relationship between higher impulsivity scores and increased likelihood of missing appointments to participate in a study provides external validation of the BIS-11 as a measure of everyday impulsive behavior. Here, potentially poor planning, resulting in competing activities, could be due to a lack of forethought. A person’s dependability in terms of appointments presumably applies not only to their participation in a study but also other aspects of their life. Missing appointments to engage in more appealing activities likely involves disregard for potential negative consequences, such as inconveniencies for the other involved parties, negative effects on their relationship, the individual’s reputation or missed opportunities. The BIS-11 is thus linked to our participants’ impulsivity-related behavioral tendencies. Crucially, this is the first time, to our knowledge, that self-reported impulsivity has been associated with a very simple but direct measure of participants’ everyday behavior. More precisely, we were able to capture an externally observable action. Although it needs replication, this assessment appears to give useful insights without the detour of self-report, possibly avoiding social desirability. This is especially important in the context of impulsivity, as participants may under-report typically impulsive behaviors such as substance use and risky behaviors when asked directly in a questionnaire.

Limitations and Conclusion

Limitations of this validation study include the relatively small and homogeneous samples for factor analyses (Tabachnik & Fidell, 2012). Larger and more diverse samples may help clarify the generalizability of these findings across different populations. Additionally, one could question several items’ utility due to their discrimination scores near zero and low factor loadings in the CFA (λ < .20; 3, 4, and 23). Further, several items loaded on all three factors in the EFA (6, 7, 9, 19), questioning their use for differentiating different aspects of impulsivity. Both the three-as well as the one-factor solution showed poor model fit. However, the sum score remains supported by its relation to other self-report measures. While we demonstrated strong criterion validity through missed appointments, additional behaviors in participants’ daily life should be examined to strengthen its ecological validity.

In conclusion, this study provides a comprehensive evaluation of a German version of the BIS-11, demonstrating that the sum score has satisfactory reliability and validity, as indexed by its associations to other related constructs as well as an external measure of participants’ behavior. The BIS-11 remains a valuable tool for assessing impulsivity, but further refinement and investigation into its subscales and external correlates are needed.

Supplemental Material

Supplemental Material - Self-Reported Impulsivity Predicts Missed Study Appointments: Validating a German Adaptation of the BIS-11

Supplemental Material for Self-Reported Impulsivity Predicts Missed Study Appointments: Validating a German Adaptation of the BIS-11 by Kerstin Dück, Rebecca Overmeyer, Lena Eger, Tanja Endrass in Personality Science.

Footnotes

Acknowledgments

We express our gratitude to Dr. Diana Armbruster for her insight on the experimental design of this study.

ORCID iDs

Kerstin Dück

Rebecca Overmeyer

Tanja Endrass

Ethical Considerations

This analysis used data from three different projects. All were approved by the ethics committee at the University Hospital Carl Gustav Carus, TUD (EK 310082018, EK 372092017 and EK 161042019).

Consent to Participate

All participants gave written informed consent to participate.

Consent for Publication

Not applicable.

Author Contributions

Kerstin Dück: Conceptualization, Data Curation, Formal Analysis, Methodology, Resources, Software, Validation, Visualization, Writing – Original Draft.

Rebecca Overmeyer: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Software, Supervision, Validation, Writing – Review & Editing.

Lena Eger: Conceptualization, Data Curation, Formal Analysis, Writing – Original Draft.

Tanja Endrass: Conceptualization, Funding Acquisition, Methodology, Project Administration, Resources, Software, Supervision, Validation, Writing – Review & Editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number SFB 940, project C6 (awarded to Tanja Endrass), and a grant awarded to Rebecca Overmeyer (MK201903, centralized funds of the Faculty of Psychology, Technische Universität Dresden).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The R code and datasets used for this analysis are available under .

Transparency,Openness,and Reproducibility

This study was not preregistered. Our analyses on the factorial structure and validity of the BIS-11 are thus exploratory. The R code and datasets are available under https://osf.io/6vzk4/. As detailed in the participants section, data from Sample 1 was also used in a validation study of the Creature of Habit Scale (Ersche et al., 2017; Overmeyer et al., 2020), while data for Samples 2 and 3 stems from research projects on the electrophysiological basis of cognitive functions (https://osf.io/ywnze/ and ).

Supplemental Material

Supplemental material for this article is available online.

Notes

Not applicable.

References

Aichert

D. S.

Wöstmann

N. M.

Costa

Macare

Wenig

J. R.

Möller

H.-J.

Rubia

Ettinger

(2012). Associations between trait impulsivity and prepotent response inhibition. Journal of Clinical and Experimental Neuropsychology, 34(10), 1016–1032. https://doi.org/10.1080/13803395.2012.706261

American Psychiatric Association . (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Association Publishing. https://doi.org/10.1176/appi.books.9780890425596

Aron

A. R.

Poldrack

R. A.

(2006). Cortical and subcortical contributions to stop signal response inhibition: Role of the subthalamic nucleus. Journal of Neuroscience, 26(9), 2424–2433. https://doi.org/10.1523/jneurosci.4682-05.2006

Barratt

(1959). Anxiety and impulsiveness related to psychomotor efficiency. Perceptual and Motor Skills, 9(3), 191–198. https://doi.org/10.2466/pms.9.3.191-198

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.

Bernoster

Groot

K. D.

Wieser

M. J.

Thurik

Franken

I. H. A.

(2019). Birds of a feather flock together: Evidence of prominent correlations within but not between self-report, behavioral, and electrophysiological measures of impulsivity. Biological Psychology, 145, 112–123. https://doi.org/10.1016/j.biopsycho.2019.04.008

Bertrams

Dickhäuser

(2009). Messung dispositioneller Selbstkontroll-Kapazität. Diagnostica, 55(1), 2–10. DOI:10.1026/0012-1924.55.1.2

Bland

J. M.

Altman

D. G.

(2000). The odds ratio. BMJ, 320(7247), 1468. https://doi.org/10.1136/bmj.320.7247.1468

Bush

Whalen

P. J.

Rosen

B. R.

Jenike

M. A.

McInerney

S. C.

Rauch

S. L.

(1998). The counting Stroop: An interference task specialized for functional neuroimaging—validation study with functional MRI. Human Brain Mapping, 6(4), 270–282. https://doi.org/10.1002/(sici)1097-0193(1998)6:4<270::aid-hbm6>3.0.co;2-0

10.

Carver

C. S.

White

T. L.

(1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. Journal of Personality and Social Psychology, 67(2), 319–333. https://doi.org/10.1037/0022-3514.67.2.319

11.

Costello

A. B.

Osborne

(2019). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research and Evaluation, 10(7), 1–7. https://doi.org/10.7275/jyj1-4868

12.

Cronbach

L. J.

(1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/bf02310555

13.

Cyders

M. A.

Coskunpinar

(2011). Measurement of constructs using self-report and behavioral lab tasks: Is there overlap in nomothetic span and construct representation for impulsivity? Clinical Psychology Review, 31(6), 965–982. https://doi.org/10.1016/j.cpr.2011.06.001

14.

Duckworth

A. L.

Kern

M. L.

(2011). A meta-analysis of the convergent validity of self-control measures. Journal of Research in Personality, 45(3), 259–268. https://doi.org/10.1016/j.jrp.2011.02.004

15.

Enriquez-Geppert

Konrad

Pantev

Huster

R. J.

(2010). Conflict and inhibition differentially affect the N200/P300 complex in a combined Go/Nogo and stop-signal task. NeuroImage, 51(2), 877–887. https://doi.org/10.1016/j.neuroimage.2010.02.043

16.

Ersche

K. D.

Lim

T.-V.

Ward

L. H. E.

Robbins

T. W.

Stochl

(2017). Creature of habit: A self-report measure of habitual routines and automatic tendencies in everyday life. Personality and Individual Differences, 116, 73–85. https://doi.org/10.1016/j.paid.2017.04.024

17.

Evenden

J. L.

(1999). Varieties of impulsivity. Psychopharmacology, 146(4), 348–361. https://doi.org/10.1007/pl00005481

18.

Fabrigar

L. R.

Wegener

D. T.

MacCallum

R. C.

Strahan

E. J.

(1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989x.4.3.272

19.

Fields

S. A.

Schueler

Arthur

K. M.

Harris

(2021). The role of impulsivity in major depression: A systematic review. Current Behavioral Neuroscience Reports, 8(2), 38–50. https://doi.org/10.1007/s40473-021-00231-y

20.

Flaudias

Llorca

P.-M.

(2014). A brief review of three manipulations of the Stroop task focusing on the automaticity of semantic access. Psychologica Belgica, 54(2), 199–221. https://doi.org/10.5334/pb.am

21.

Fossati

Barratt

E. S.

Acquarini

Ceglie

A. D.

(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(2), 621–635. https://doi.org/10.2466/pms.2002.95.2.621

22.

Friedman

N. P.

Hatoum

A. S.

Gustavson

D. E.

Corley

R. P.

Hewitt

J. K.

Young

S. E.

(2019). Executive functions and impulsivity are genetically distinct and independently predict psychopathology: Results from two adult twin studies. Clinical Psychological Science, 8(3), 519–538. https://doi.org/10.1177/2167702619898814

23.

Gao

Zhang

Zhou

Feng

(2021). The effect of conscientiousness on procrastination: The interaction between the self-control and motivation neural pathways. Human Brain Mapping, 42(6), 1829–1844. https://doi.org/10.1002/hbm.25333

24.

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(2), 353–368. https://doi.org/10.2466/08.09.10.pms.112.2.353-368

25.

Hedge

Powell

Sumner

(2018). The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences. Behavior Research Methods, 50(3), 1166–1186. https://doi.org/10.3758/s13428-017-0935-1

26.

Holgado–Tello

F. P.

Chacón–Moscoso

Barbero–García

Vila–Abad

(2010). Polychoric versus pearson correlations in exploratory and confirmatory factor analysis of ordinal variables. Quality and Quantity, 44(1), 153–166. https://doi.org/10.1007/s11135

27.

Huang

Luan

Chen

Hertwig

(2024). Impulsivity is a stable, measurable, and predictive psychological trait. Proceedings of the National Academy of Sciences, 121(24), 1–12. https://doi.org/10.1073/pnas.2321758121

28.

Kahn

J. H.

(2006). Factor analysis in counseling psychology research, training, and practice: Principles, advances, and applications. The Counseling Psychologist, 34(5), 684–718. https://doi.org/10.1177/0011000006286347

29.

Kendall

M. G.

(1938). A new measure of rank correlation. Biometrika, 30(1–2), 81–93. https://doi.org/10.1093/biomet/30.1-2.81

30.

Korkmaz

Goksuluk

Zararsiz

(2014). MVN: An R package for assessing multivariate normality. The R Journal, 6(2), 151–162. https://doi.org/10.32614/rj-2014-031. https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf

31.

Kozak

Lucatch

A. M.

Lowe

D. J. E.

Balodis

I. M.

MacKillop

George

T. P.

(2019). The neurobiology of impulsivity and substance use disorders: Implications for treatment. Annals of the New York Academy of Sciences, 1451(1), 71–91. https://doi.org/10.1111/nyas.13977

32.

Kräplin

Höfler

Pooseh

Wolff

Krönke

K.-M.

Goschke

Bühringer

Smolka

M. N.

(2020). Impulsive decision-making predicts the course of substance-related and addictive disorders. Psychopharmacology, 237(9), 2709–2724. https://doi.org/10.1007/s00213-020-05567-z

33.

C.-H.

(2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48(3), 936–949. https://doi.org/10.3758/s13428-015-0619-7

34.

LimeSurvey GmbH. (n.d.) .. LimeSurvey: An open source survey tool. LimeSurvey GmbH. https://www.limesurvey.org.

35.

Logan

G. D.

(1994). On the ability to inhibit thought and action: A users’ guide to the stop signal paradigm. In D

Carr

T. H.

(Eds.), Inhibitory processes in attention, memory, and language (pp. 189–239). Academic Press.

36.

Lüdecke

(2024). sjPlot: Data visualization for statistics in social science. R package version 2.8.16. https://CRAN.R-project.org/package=sjPlot

37.

Lynam

D. R.

Smith

G. T.

Whiteside

S. P.

Cyders

M. A.

(2006). The UPPS-P: Assessing five personality pathways to impulsive behavior (Vol. 10). Purdue University.

38.

Mardia

K. V.

(1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530. https://doi.org/10.1093/biomet/57.3.519

39.

McDonald

R. P.

(2013). Test theory: A unified treatment. Psychology Press. https://doi.org/10.4324/9781410601087

40.

Meule

Vögele

Kübler

(2011). Psychometrische evaluation der Deutschen Barratt Impulsiveness Scale – Kurzversion (BIS-15). Diagnostica, 57(3), 126–133. https://doi.org/10.1026/0012-1924/a000042

41.

Milyavskaya

Saunders

Inzlicht

(2021). Self‐control in daily life: Prevalence and effectiveness of diverse self‐control strategies. Journal of Personality, 89(4), 634–651. https://doi.org/10.1111/jopy.12604

42.

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(11), 1783–1793. https://doi.org/10.1176/appi.ajp.158.11.1783

43.

Moore

F. R.

Doughty

Neumann

McClelland

Allott

O’Connor

R. C.

(2022). Impulsivity, aggression, and suicidality relationship in adults: A systematic review and meta-analysis. eClinicalMedicine, 45, 101307. https://doi.org/10.1016/j.eclinm.2022.101307

44.

Müller

S. M.

Stolze

Brand

(2021). Predictors of social-zapping behavior: Dark triad, impulsivity, and procrastination facets contribute to the tendency toward last-minute cancellations. Personality and Individual Differences, 168, 110334. https://doi.org/10.1016/j.paid.2020.110334

45.

Overmeyer

Endrass

(2023). Cognitive symptoms link anxiety and depression within a validation of the German state-trait inventory for cognitive and somatic anxiety (STICSA). Clinical Psychology in Europe, 5(2), 1–21. https://doi.org/10.32872/cpe.9753

46.

Overmeyer

Fürtjes

Ersche

K. D.

Ehrlich

Endrass

(2020). Self-regulation is negatively associated with habit tendencies: A validation of the German creature of habit scale. Personality and Individual Differences, 163, 110029. https://doi.org/10.1016/j.paid.2020.110029

47.

Patton

J. H.

Stanford

M. S.

Barratt

E. S.

(1995). Factor structure of the Barratt impulsiveness scale. Journal of Clinical Psychology, 51(6), 768–774. https://doi.org/10.1002/1097-4679(199511)51:6<768::aid-jclp2270510607>3.0.co;2-1

48.

Perales

J. C.

Verdejo-García

Moya

Lozano

Ó.

Pérez-García

(2009). Bright and dark sides of impulsivity: Performance of women with high and low trait impulsivity on neuropsychological tasks. Journal of Clinical and Experimental Neuropsychology, 31(8), 927–944. https://doi.org/10.1080/13803390902758793

49.

Piedmont

R. L.

(2014). Criterion validity (p. 1348). Springer. https://doi.org/10.1007/978-94-007-0753-5_618

50.

Preuss

U. W.

Rujescu

Giegling

Watzke

Koller

Zetzsche

Meisenzahl

E. M.

Soyka

Möller

H. J.

(2008). Psychometrische evaluation der deutschsprachigen version der Barratt-Impulsiveness-Skala. Nervenarzt, Der, 79(3), 305–319. https://doi.org/10.1007/s00115-007-2360-7

51.

R Core Team . (2023). R: A language and environment for statistical computing (Version 4.4.3) [Computer software]. R Foundation for Statistical Computing. https://www.r-project.org

52.

Raiche

Magis

(2010). nFactors: An R package for parallel analysis and non graphical solutions to the Cattell scree test. R Package Version, 2(3).

53.

Ree

M. J.

French

MacLeod

Locke

(2008). Distinguishing cognitive and somatic dimensions of state and trait anxiety: Development and validation of the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA). Behavioural and Cognitive Psychotherapy, 36(3), 313–332. https://doi.org/10.1017/s1352465808004232

54.

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(2), 631–642. https://doi.org/10.1037/a0032161

55.

Revelle

(2024). Psych: Procedures for psychological, psychometric, and personality research: Northwestern University. https://CRAN.R-project.org/package=psych

56.

Rosseel

(2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

57.

Ruscio

(2018). RGenData: Generates multivariate non normal data and determines how many factors to retain. https://CRAN.R-project.org/package=RGenData

58.

Ruscio

Roche

(2012). Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. Psychological Assessment, 24(2), 282–292. https://doi.org/10.1037/a0025697

59.

Sach

Enge

Strobel

Fleischhauer

(2018). MPQ control (versus impulsivity) and need for cognition – relationship to behavioral inhibition and corresponding ERPs in a Go/No-Go task. Personality and Individual Differences, 121, 200–205. https://doi.org/10.1016/j.paid.2017.04.005

60.

Schmidt

R. E.

Gay

d’Acremont

van der Linden

(2008). A German Adaptation of the UPPS Impulsive Behavior Scale: Psychometric Properties and Factor Structure. Swiss Journal of Psychology / Schweizerische Zeitschrift für Psychologie / Revue Suisse de Psychologie, 67(2), 107–112. DOI:10.1024/1421-0185.67.2.107

61.

Shapiro

S. S.

Wilk

M. B.

(1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3–4), 591–611. https://doi.org/10.2307/2333709

62.

Sharma

Markon

K. E.

Clark

L. A.

(2014). Toward a theory of distinct types of “impulsive” behaviors: A meta-analysis of self-report and behavioral measures. Psychological Bulletin, 140(2), 374–408. https://doi.org/10.1037/a0034418

63.

Spinella

(2004). Neurobehavioral correlates of impulsivity: Evidence of prefrontal involvement. International Journal of Neuroscience, 114(1), 95–104. https://doi.org/10.1080/00207450490249347

64.

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(5), 385–395. https://doi.org/10.1016/j.paid.2009.04.008

65.

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(1), 216–226. https://doi.org/10.1037/a0030550

66.

Stevens

M. C.

Kiehl

K. A.

Pearlson

G. D.

Calhoun

V. D.

(2007). Functional neural networks underlying response inhibition in adolescents and adults. Behavioural Brain Research, 181(1), 12–22. https://doi.org/10.1016/j.bbr.2007.03.023

67.

Stroop

J. R.

(1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology: General, 18(6), 643–662. https://doi.org/10.1037/h0054651

68.

Tabachnik

Fidell

(2012). Using multivariate statistics (6th ed.). Pearson.

69.

Tangney

J. P.

Baumeister

R. F.

Boone

A. L.

(2004). High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72(2), 271–324. https://doi.org/10.1111/j.0022-3506.2004.00263.x

70.

The MathWorks Inc . (2010). MATLAB (Version R2010a). [Computer software]. https://www.mathworks.com

71.

Torres

Catena

Megías

Maldonado

Cándido

Verdejo-García

Perales

J. C.

(2013). Emotional and non-emotional pathways to impulsive behavior and addiction. Frontiers in Human Neuroscience, 7, 43. https://doi.org/10.3389/fnhum.2013.00043

72.

Townsend

J. T.

Ashby

F. G.

(1978). Methods of modeling capacity in simple processing systems. In Castellan

Restle

(Eds.), Cognitive theory (Vol. 3, pp. 200–239). Erlbaum.

73.

Vasconcelos

A. G.

Malloy-Diniz

Correa

(2012). Systematic review of psychometric properties of barratt impulsiveness scale version 11 (BIS-11). Clinical Neuropsychiatry, 9(2), 61–74.

74.

Verbruggen

Logan

G. D.

(2009). Models of response inhibition in the stop-signal and stop-change paradigms. Neuroscience & Biobehavioral Reviews, 33(5), 647–661. https://doi.org/10.1016/j.neubiorev.2008.08.014

75.

Weidacker

Whiteford

Boy

Johnston

S. J.

(2015). Response inhibition in the parametric Go/No-Go task and its relation to impulsivity and subclinical psychopathy. Quarterly Journal of Experimental Psychology, 70(3), 473–487. https://doi.org/10.1080/17470218.2015.1135350

76.

Whiteside

S. P.

Lynam

D. R.

(2001). The five factor model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences, 30(4), 669–689. https://doi.org/10.1016/s0191-8869(00)00064-7

77.

Whiteside

S. P.

Lynam

D. R.

Miller

J. D.

Reynolds

S. K.

(2005). Validation of the UPPS impulsive behaviour scale: A four‐factor model of impulsivity. European Journal of Personality, 19(7), 559–574. https://doi.org/10.1002/per.556

78.

Wolff

Krönke

K.-M.

Goschke

(2016a). Trait self-control is predicted by how reward associations modulate Stroop interference. Psychological Research, 80(6), 944–951. https://doi.org/10.1007/s00426-015-0707-4

79.

Wolff

Krönke

K.-M.

Venz

Kräplin

Bühringer

Smolka

M. N.

Goschke

(2016b). Action versus state orientation moderates the impact of executive functioning on real-life self-control. Journal of Experimental Psychology: General, 145(12), 1635–1653. https://doi.org/10.1037/xge0000229

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