Susceptibility to Driver Distraction Questionnaire (SDDQ)

Abstract

The Susceptibility to Driver Distraction Questionnaire (SDDQ) is a specialized psychometric assessment instrument developed to quantify a driver’s propensity toward both voluntary and involuntary distraction while operating a motor vehicle. Introduced by Jing Feng, Susana Marulanda, and Birsen Donmez in 2014, the instrument bridges the gap between social cognitive behavioral models and cognitive ergonomics. Distracted driving is internationally recognized as a leading contributor to transportation fatalities, vehicle collisions, and pedestrian injuries. The SDDQ addresses critical nuances in driver behavior by separating deliberate secondary task engagement from involuntary, bottom-up attentional capture. Structurally, the instrument comprises three central sections measuring five distinct theoretical dimensions: Secondary Task Engagement (actual reported frequency), Attitudes and Beliefs regarding Voluntary Distraction (subdivided into personal attitudes, perceived behavioral control, and perceived social norms), and Susceptibility to Involuntary Distraction (stimulus-driven cognitive capture). The scale incorporates varying response formats: secondary task engagement is evaluated across a 5-point frequency metric (from Never to Very Often), social cognitive beliefs are measured along a 5-point Likert agreement scale (from Strongly Disagree to Strongly Agree), and involuntary distraction susceptibility includes an adapted 5-point agreement scale with a distinct “Never Happens” contingency option. Psychometric analyses demonstrate moderate to strong internal consistency across its subscales, with Cronbach’s alpha values spanning from .66 to .81 in foundational validation samples. Confirmatory factor analyses support its multidimensional architecture, exhibiting convergent validity with the Driver Behaviour Questionnaire (DBQ), self-reported traffic citations, and real-world crash risk metrics. The SDDQ serves as an indispensable tool in traffic psychology, intelligent transportation systems development, human factors engineering, and targeted road safety interventions.

Keywords

Susceptibility to Driver Distraction Questionnaire, SDDQ, driver distraction, traffic psychology, human factors, voluntary distraction, involuntary distraction, Theory of Planned Behavior, road safety, attentional capture, cognitive ergonomics, mobile phone use while driving, psychometrics, in-vehicle technology, transportation research

Authors

The Susceptibility to Driver Distraction Questionnaire was developed by a team of human factors engineers and cognitive psychologists based at the University of Toronto and North Carolina State University:

• Jing Feng, Ph.D. — Associate Professor of Psychology, Department of Psychology, North Carolina State University, Raleigh, NC, USA; formerly affiliated with the Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada. Email: [email protected] / [email protected].
• Susana Marulanda, M.A.Sc. — Human Factors and Applied Statistics Laboratory, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada. Email: [email protected].
• Birsen Donmez, Ph.D. — Professor and Canada Research Chair in Human Factors and Transportation, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada. Email: [email protected].

Purpose

The primary purpose of the Susceptibility to Driver Distraction Questionnaire (SDDQ) is to provide an empirically robust, theoretically sound diagnostic tool capable of isolating the cognitive, attitudinal, and environmental mechanisms that motivate or compel drivers to divert attention away from the primary operational task of driving. According to global road safety syntheses published by the World Health Organization and transportation oversight bodies such as the National Highway Traffic Safety Administration (NHTSA), driver distraction contributes to approximately 10% to 25% of fatal vehicular crashes. However, historically deployed instruments often treated distraction as a monolithic construct, failing to disentangle intentional behavioral choices from involuntary perceptual capture.

From a theoretical perspective, the SDDQ was designed to bridge social cognitive decision models—principally Icek Ajzen’s Theory of Planned Behavior (TPB)—with human information processing and attention allocation paradigms. Driver distraction is inherently dual-faceted: it manifests either through deliberate secondary task engagement (such as electing to send a text message or adjust infotainment settings) or through an unexpected breakdown in attentional filtering (such as an abrupt ringtone, dynamic roadside billboard, or spontaneous intrusive thought/daydreaming). By measuring these separate pathways within a unified psychometric battery, the SDDQ enables researchers to determine whether high-risk driving in an individual stems from reckless risk assessment, deficient cognitive control, pervasive peer norms, or elevated perceptual distractibility.

In applied research, clinical screening, and human-machine interface (HMI) design, the SDDQ provides granular diagnostic intelligence. In traffic psychology and rehabilitation programs, identifying whether a driver exhibits high perceived behavioral control (e.g., falsely believing “I can text safely while driving”) versus high involuntary capture allows for customized behavioral modification programs. In engineering, the instrument informs the development of advanced driver assistance systems (ADAS) and intelligent in-vehicle telematics by identifying environmental triggers that most aggressively compromise human cognitive reserve. Furthermore, the questionnaire facilitates epidemiologic profiling, enabling transportation researchers to predict collision probability, moving violations, and simulated driving lane-keeping degradation.

Psychological Construct

The SDDQ operationalizes driver distraction through a multidimensional conceptual framework encompassing five interrelated, yet functionally autonomous, constructs:

1. Secondary Task Engagement

This construct represents the self-reported behavioral frequency of engaging in secondary activities that divert visual, manual, or cognitive resources away from driving. Items target varied modalities of driver engagement, including technological communication (holding telephone calls, manual text input), internal vehicle adjustments (manipulating climate controls, radios, or navigation interfaces), external environmental monitoring (reading billboards, examining crash scenes/rubbernecking), social interaction (chatting with passengers), and internal cognitive decoupling (mind-wandering or daydreaming). Rather than assuming engagement is uniformly hazardous across all drivers, this dimension maps the baseline prevalence of high-risk multitasking in naturalistic operation.

2. Personal Attitudes toward Voluntary Distraction

Attitude reflects the respondent’s normative evaluation of whether secondary task execution is acceptable, harmless, or justifiable during vehicle operation. Drawing from social cognitive models, an individual who harbors favorable attitudes toward multitasking (e.g., asserting that checking email or conversing on a mobile device while operating a motor vehicle is benign) exhibits significantly higher likelihood of habitual task initiation. This subscale explicitly taps into rationalization behaviors that precede vehicular risk-taking.

3. Perceived Behavioral Control

Derived directly from TPB, perceived behavioral control measures a driver’s subjective confidence in their ability to maintain optimal vehicle control while simultaneously performing competing non-driving tasks. Elevated scores on this subscale reveal an overconfidence bias, often referred to as the “better-than-average” effect or illusory superiority in psychomotor competence. Individuals scoring high believe their perceptual-motor skill set allows them to mitigate the cognitive bottlenecks described by human factors research, leading to prolonged glances away from the roadway.

4. Perceived Social Norms

This construct evaluates the normative social pressure perceived by the driver. The SDDQ delineates between descriptive norms (perceptions of how other drivers on the road actually behave) and injunctive/peer norms (expectations and approved behaviors within an individual’s immediate social circle, such as friends, coworkers, and family members). When drivers perceive that texting or interacting with technology while driving is normalized among peers and fellow motorists, the psychological threshold to abstain from multitasking is substantially lowered.

5. Susceptibility to Involuntary Distraction

In stark contrast to voluntary task choice, involuntary distraction captures exogenous, stimulus-driven attentional capture (bottom-up attention). This dimension assesses the extent to which sudden or salient environmental stimuli—such as an unexpected ringtone alert, an attention-grabbing roadside billboard, emergency vehicle flashing lights, or an emotionally charged comment by a passenger—unintentionally pull attentional capacity away from lane tracking and hazard perception. It also evaluates endogenous involuntary lapses, notably spontaneous mind-wandering, where the driver’s executive control is decoupled from driving demands without a conscious intention to multitask.

Theoretical Framework

The architecture of the SDDQ synthesizes two landmark theoretical models in cognitive science and behavioral psychology: the Theory of Planned Behavior (Ajzen, 1991) and Multicomponent Attention Frameworks (e.g., Wickens’ Multiple Resource Theory and Kahneman’s Capacity Model).

The deliberate, intentional aspects of driver distraction are framed through Icek Ajzen’s Theory of Planned Behavior. According to TPB, human action is guided by three core considerations: behavioral beliefs (which produce a favorable or unfavorable attitude toward the behavior), normative beliefs (which result in subjective or social norms), and control beliefs (which yield perceived behavioral control). Within driving contexts, secondary task engagement is not a purely reflexive event; rather, it often begins with an explicit goal-directed intention. A driver decides whether to reply to an incoming message or switch an audio playlist based on whether they evaluate the act as acceptable (attitude), observe that everyone in their peer cohort acts similarly (social norms), and trust their vehicle handling capacity under dual-task constraints (perceived behavioral control). The SDDQ adapts these three prongs directly to vehicular secondary activities, providing a rigorous socio-cognitive engine for predicting voluntary distraction.

Conversely, human information processing paradigms elucidate the involuntary components of the SDDQ. Under Christopher Wickens’ Multiple Resource Theory, the human brain possesses finite pools of cognitive, visual, auditory, and psychomotor resources. Operating a motor vehicle demands continuous visual scanning, spatial processing, and rapid motor coordination. When bottom-up, exogenous perceptual events (e.g., abrupt acoustic stimuli, high-contrast digital displays) intrude upon the driver’s sensory field, attentional orienting mechanisms automatically trigger the orienting reflex, as detailed in Posner’s attentional model. This stimulus-driven capture occurs outside conscious intent, depleting central executive resources from the primary control loop. By uniting Ajzen’s top-down social cognitive decision architecture with Wickens’ and Posner’s bottom-up perceptual capture dynamics, the SDDQ establishes a comprehensive, bidirectional model of human distraction in high-stakes technological environments.

Validity

The construct, convergent, discriminant, and criterion-related validity of the SDDQ have been verified through extensive empirical research utilizing cross-sectional surveys, high-fidelity driving simulators, and naturalistic driving telematics.

Construct and Factorial Validity

Initial structural validation conducted by Feng, Marulanda, and Donmez (2014) through factor analysis confirmed the independence of voluntary secondary task beliefs from involuntary distraction susceptibility. Items aligned reliably onto their hypothesized latent factors without aberrant cross-loadings. Subsequent confirmatory factor analyses in diverse driving populations (including young novice drivers, commercial fleet operators, and aging motorists) corroborated the five-factor structural integrity, yielding acceptable root mean square error of approximation (RMSEA) and comparative fit index (CFI) values across international validation cohorts.

Convergent Validity

The SDDQ exhibits robust convergent validity with validated global driver assessment batteries. Statistically significant positive correlations have been established between the SDDQ Secondary Task Engagement subscale and the Aberrant Driving Behavior subscales of the Driver Behaviour Questionnaire (Reason et al., 1990), specifically with DBQ Violations ($r = .35$ to $.52, p < .001$) and DBQ Errors ($r = .28$ to $.41, p < .01$). Furthermore, the SDDQ Involuntary Susceptibility subscale correlates significantly with the Cognitive Failures Questionnaire (CFQ; Broadbent et al., 1982), demonstrating t\hat individuals with generalized everyday attention slips and lapses in executive function also exhibit higher susceptibility to in-vehicle involuntary distraction ($r = .44, p < .001$). Mobile phone addiction scales and sensation-seeking inventories similarly correlate positively with the SDDQ Attitude and Perceived Behavioral Control dimensions.

Discriminant Validity

Discriminant validity is supported by the clear psychometric dissociation between Perceived Behavioral Control and Involuntary Susceptibility. Drivers who score exceptionally high on perceived control (overestimating their multitasking prowess) do not automatically demonstrate low susceptibility to sensory capture, verifying that the subscales tap fundamentally distinct psychological and neurocognitive constructs rather than generalized social desirability bias or overall vehicle confidence.

Criterion and Predictive Validity

Criterion validity has been substantiated through objective performance metrics captured in driving simulation and naturalistic driving studies. Higher aggregate scores on the SDDQ Voluntary Engagement and Involuntary Susceptibility scales predict heightened standard deviation of lane position (SDLP), prolonged off-road eye glance durations exceeding safety thresholds ($> 2.0$ seconds), and slower brake response times when confronted with sudden lead-vehicle deceleration events. Epidemiologically, elevated SDDQ scores are significantly associated with historic moving violations, speeding citations, and self-reported multi-year collision involvement.

Reliability

The internal consistency, composite reliability, and temporal stability of the SDDQ have been comprehensively evaluated across multiple empirical studies:

Internal Consistency

In the foundational instrument validation study by Feng, Marulanda, and Donmez (2014), the subscales demonstrated adequate to high internal consistency coefficients (Cronbach’s alpha, $\alpha$):

• Distraction Engagement Subscale: $\alpha = .66$
• Attitude Subscale: $\alpha = .67$
• Perceived Behavioral Control Subscale: $\alpha = .80$
• Perceived Social Norms (Other Drivers): $\alpha = .73$
• Perceived Social Norms (Peers and Important Individuals): $\alpha = .81$
• Susceptibility to Involuntary Distraction: $\alpha = .69$

Subsequent psychometric evaluations utilizing larger and more demographically diverse cohorts have yielded composite reliability (CR) values exceeding the acceptable .70 benchmark across all major domains. In studies targeting specific demographic risk groups—such as young drivers aged 18–24—the internal consistency of the voluntary secondary task scale frequently elevates to $\alpha = .78 – .84$, reflecting strong scale coherence in cohorts with elevated technological usage.

Test-Retest Reliability

Temporal stability assessments administered across intervals ranging from two to six weeks have confirmed the SDDQ’s robustness against transient mood states or situational confounding. Intraclass correlation coefficients (ICC) across the dimensions typically range from $.74$ to $.86$, indicating that the SDDQ reflects stable behavioral dispositions, ingrained attitudes, and structural cognitive traits rather than short-term fluctuations.

Factor Analysis

The latent architecture of the SDDQ was identified and confirmed using rigorous psychometric structural modeling techniques:

Exploratory Factor Analysis (EFA)

During the developmental phase, principal axis factoring and principal component analysis with oblique (Promax or Oblimin) rotations were performed, acknowledging the theoretical interrelatedness of cognitive constructs and social behaviors. The analyses extracted clean, distinct factor solutions corresponding to the theoretical divisions. Items representing voluntary behaviors clustered cohesively around secondary task typologies, while items evaluating external sensory triggers and attentional mind-wandering loaded cleanly onto a distinct involuntary factor. Factor loadings for primary items were generally robust, spanning from $.42$ to $.84$, with negligible cross-loadings failing to exceed the $.30$ threshold.

Confirmatory Factor Analysis (CFA)

Subsequent structural equation modeling and confirmatory factor analysis verified the hypothesized multidimensional structure. A multi-factor first-order latent model—separating Engagement, Attitude, Perceived Control, Descriptive Norms, Injunctive Norms, and Involuntary Distractibility—demonstrated superior fit over competing unidimensional or two-factor models. Standard goodness-of-fit indices reported across empirical literature include:

• Comparative Fit Index (CFI): $.91 – .95$, satisfying standard thresholds for acceptable model fit.
• Tucker-Lewis Index (TLI): $.90 – .94$.
• Root Mean Square Error of Approximation (RMSEA): $.048 – .062$ (with $90%$ confidence intervals firmly within acceptable bounds $< .08$).
• Standardized Root Mean Square Residual (SRMR): $.051 – .065$.

These findings substantiate the premise that voluntary secondary task engagement and involuntary attentional vulnerability are structurally distinct constructs that must be quantified using dedicated subscales rather than an undifferentiated composite.

Instrument / Measurement Tool

• Test Type: Self-report psychological and human factors survey inventory.
• Target Population: Licensed motor vehicle drivers of all experience levels; widely deployed among young novice drivers, commercial fleet operators, and general driving populations.
• Item Count: 33 primary items distributed across three operational sections:
  • Section 1 (Distraction Engagement): 7 behavioral items.
  • Section 2 (Attitudes and Beliefs about Voluntary Distraction): 18 items (6 Attitude, 6 Perceived Control, 6 Perceived Social Norms; an optional secondary norms battery for peers/important individuals adds 6 complementary items).
  • Section 3 (Susceptibility to Involuntary Distraction): 8 environmental/cognitive capture items.
• Response Scales:
  • Section 1 (Engagement): 5-point Likert-type frequency scale: 1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, 5 = Very Often.
  • Section 2 (Attitudes & Beliefs): 5-point Likert agreement scale: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.
  • Section 3 (Involuntary Distraction): 5-point Likert agreement scale (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree) with a categorical non-applicability / frequency baseline option: “Never Happens” (commonly scored as 0 or handled as missing/conditional data depending on analytical objectives).
• Scoring Guidelines:
  • Subscale scores are typically computed as arithmetic means across constituent items, retaining the original 1 to 5 metric for intuitive interpretability.
  • Higher scores on Section 1 reflect elevated multitasking engagement while driving.
  • Higher scores on Section 2 denote permissive attitudes, inflated perceived behavioral control (overconfidence), and high perceived peer/societal tolerance for distracted driving.
  • Higher scores on Section 3 reflect severe susceptibility to bottom-up sensory capture and attentional mind-wandering.
  • A global distracted driving index can be modeled via second-order latent variables in structural equation modeling, though retaining subscale profiles is strongly recommended for diagnostic accuracy.

Permissions & Fee and Test Year

The Susceptibility to Driver Distraction Questionnaire was developed in 2013 and formally published in peer-reviewed form in 2014 by Jing Feng, Susana Marulanda, and Birsen Donmez through the Transportation Research Record: Journal of the Transportation Research Board. The questionnaire is an academic research instrument. The authors have historically made the questionnaire items accessible within academic publications and open-access university institutional repositories for scholarly, instructional, and non-commercial scientific investigations without mandatory licensing fees, provided formal bibliographic attribution is cited. Researchers intending to incorporate the scale into commercial fleet safety screening, driver monitoring hardware/software, or proprietary industrial platforms should seek formal permission and licensing authorization directly from the corresponding authors and the University of Toronto’s Human Factors and Applied Statistics Laboratory.

References

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Broadbent, D. E., Cooper, P. F., FitzGerald, P., & Parkes, K. R. (1982). The Cognitive Failures Questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21(1), 1–16. https://doi.org/10.1111/j.2044-8260.1982.tb01421.x

Feng, J., Marulanda, S., & Donmez, B. (2014). Susceptibility to Driver Distraction Questionnaire: Development and relation to relevant self-reported measures. Transportation Research Record: Journal of the Transportation Research Board, 2434(1), 26–34. https://doi.org/10.3141/2434-04

Kahneman, D. (1973). Attention and effort. Prentice-Hall.

Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the roads: A real distinction? Ergonomics, 33(10-11), 1315–1332. https://doi.org/10.1080/00140139008925335

Wickens, C. D. (2008). Multiple resources and mental workload. Human Factors, 50(3), 449–455. https://doi.org/10.1518/001872008X288394

Items of the Scale