Investigating the factorial invariance of the 28-item DBQ across genders and age groups: an Exploratory Structural Equation Modeling study

Factor structure and measurement invariance of the Chinese version of the COVID-19 Phobia Scale in depressive symptoms sample during COVID-19 closure: An exploratory structural equation modeling approach

The COVID-19 Phobia Scale is an instrument for measuring the phobia of coronavirus. It has a stable four-factor structure and good reliability and validity in other countries and regions. In order to expand related research, this study aims to test the reliability and validity of the COVID-19 Phobia Scale in Chinese adolescents with depressive symptoms. The C19P-SC was translated into Chinese by the method of forward and back translation and tested in 1933 Chinese adolescents with depressive symptoms. Confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) were used to test and compare the four-factor model of the C19P-SC. Then we tested the measurement invariance of the C19P-SC across gender and time. Finally, the reliability was measured with the McDonald's omega coefficients. Consistent with previous studies, the C19P-SC showed a stable four-factor structure. The results showed that ESEM was better than CFA and more reasonable. In addition, the results of multi-group ESEM showed that the C19P-SC met the strict invariance at male and female and partial longitudinal strict invariance. The Mcdonald's omega coefficients of the C19P-SC total scale and each subscale reached the expected acceptable level. In short, the reliability and validity index of C19P-SC has reached an acceptable level, and the measurement invariance of different genders and different time points was established, but the cross-factor phenomenon of individual items was abnormal, and a further revision and testing are still needed.

Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran

INTRODUCTION

Digital billboards (DBs) are a competing factor for attracting drivers' attention; evidence shows that DBs may cause crashes and vehicle conflicts because they catch drivers' attention. Because of the complexity of a system that includes road conditions, driver features, and environmental factors, it is simply not possible to identify relationships between these factors. Thus, the present study was conducted to provide a well-organized procedure to analyze the effects of DBs on drivers' behavior and measure factors responsible for drivers' distraction in Babol, Iran, as a case study.

METHOD

Corresponding data were collected through a Naturalistic Driving Study (NDS) of 78 participants when facing DBs (1,326 samples). These data were analyzed by applying structural equation modeling (SEM) to concurrently recognize relationships between endogenous and exogenous variables. Human, environmental, and road factors were determined as exogenous latent variables in a model to evaluate their influences on drivers' distraction as an endogenous variable.

RESULTS

The results showed that road, environmental, and human factors reciprocally interact with drivers' distraction, although the estimated coefficient of human factors was more of a factor than that of the other groups. Furthermore, younger drivers, beginner drivers, and male drivers (as human factors); night and unclear weather like a rainy day (as environmental factors); and installing DBs at complicated traffic positions like near-intersections (as road factors) were determined to be the main factors that increase the possibility of drivers' distraction. Finally, model assessment was suggested using the goodness-of-fit indices.

Validating Driver Behavior and Attitude Measure for Older Italian Drivers and Investigating Their Link to Rare Collision Events

The present study aimed to: (a) validate the factor structures of three scales assessing driving behavior, attitudes toward traffic safety (ATTS) and self-regulation in driving, in a sample of Italian older adults, through confirmatory factor analyses and (b) to determine the effectiveness of these measures in predicting the likelihood and the frequency of collision involvements in the following year. A 28-item driver behavior questionnaire (DBQ), a 16-item ATTS, a 21-item extended driving mobility questionnaire (DMQ-A) were administered to 369 active Italian drivers, aged between 60 and 91 years. Results showed a four-factor structure for the DBQ, a five-factor structure for the ATTS and a two-factor structure for the Extended DMQ-A, as the best fitting models. Hurdle model analysis of count data with extra-zeros showed that all factors of DBQ predicted the likelihood of road collisions. Risky behavior, except for aggressive violations, self-regulation and attitudes toward traffic rules were associated with the frequency of collision involvement. The aforementioned three scales seemed to be a useful and concise suite of instruments assessing risky as well as protective factors of driving behavior in elderly.

Network models of driver behavior

The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

The Driver Behaviour Questionnaire for older drivers: Do errors, violations and lapses change over time?

The aim of the current study was to examine how self-reported aberrant driving behaviours change across a three time-points in a group of older drivers. Two hundred and twenty-seven older drivers (males = 69.6%) from the Candrive/Ozcandrive longitudinal study completed the Driving Behaviour Questionnaire (DBQ) each yearacross three time-points (i.e., Year 1, Year 2, Year 3). At the third time-point, older drivers ranged in age from 77 to 96 years (M = 81.74 years; SD = 3.44 years). A longitudinal confirmatory factor analysis showed that a modified 21-item, 3-factor (errors, lapses and violations) DBQ was invariant across the time period, suggesting that the structure of the questionnaire was stable across each time-point. Further, multiple domain latent growth analysis on the resultant factors for errors, lapses and violations showed that the frequency of errors remained similar across the three-year period, while violations and lapses showed very marginal decreases in frequency. These changes were independent of the absolute number of these behaviours; Drivers with higher violations or lapses in Year one, showed similar decreases in frequency as those who self-reported lower frequencies of the behaviours. These results suggest that the DBQ is a reliable tool to measure older drivers' self-reported aberrant driving behaviours, and that these behaviours do not show much change across time. Future research should validate the self-reported responses from the DBQ with more objective measures such as those collected through naturalistic driving study (NDS) methodology or on-road driving tasks.

Change detection in urban and rural driving scenes: Effects of target type and safety relevance on change blindness

The ability to detect changes is crucial for safe driving. Previous research has demonstrated that drivers often experience change blindness, which refers to failed or delayed change detection. The current study explored how susceptibility to change blindness varies as a function of the driving environment, type of object changed, and safety relevance of the change. Twenty-six fully-licenced drivers completed a driving-related change detection task. Changes occurred to seven target objects (road signs, cars, motorcycles, traffic lights, pedestrians, animals, or roadside trees) across two environments (urban or rural). The contextual safety relevance of the change was systematically manipulated within each object category, ranging from high safety relevance (i.e., requiring a response by the driver) to low safety relevance (i.e., requiring no response). When viewing rural scenes, compared with urban scenes, participants were significantly faster and more accurate at detecting changes, and were less susceptible to "looked-but-failed-to-see" errors. Interestingly, safety relevance of the change differentially affected performance in urban and rural environments. In urban scenes, participants were more efficient at detecting changes with higher safety relevance, whereas in rural scenes the effect of safety relevance has marginal to no effect on change detection. Finally, even after accounting for safety relevance, change blindness varied significantly between target types. Overall the results suggest that drivers are less susceptible to change blindness for objects that are likely to change or move (e.g., traffic lights vs. road signs), and for moving objects that pose greater danger (e.g., wild animals vs. pedestrians).

Measuring family satisfaction with inpatient rehabilitation care

PURPOSE

The purpose of this paper is to investigate the psychometric characteristics of the Questionnaire of Family members' Satisfaction about the Rehabilitation (QFSR), a new questionnaire assessing the satisfaction of patients' families with the in-hospital rehabilitation service, i.e., the organizational procedure, medical treatment, relationship with nurses/other health workers, and outcome.

DESIGN/METHODOLOGY/APPROACH

The QFSR (13 items) was administered to 1,226 (F=60.4 percent; mean age=57.4, SD 15 years) family members of patients admitted to two units for inpatient rehabilitation, i.e., cardiovascular and neuromotor.

FINDINGS

Confirmatory factor analysis (CFA) confirmed the theoretical four-factor structure of the questionnaire in a subsample of 308 respondents randomly selected from the sample (SB χ² (61)=57.4, p=0.61; RMSEA=0.0; 90 percent CI [0.0, 0.031], CFI=1.00). The remaining 708 respondents (393 relatives of cardiovascular unit inpatients and 315 relatives of neuromotor unit inpatients) were used to test measurement invariance between the groups of family members with patients in the two units. The configurial, scalar, and strict factorial invariance provided a good fit to the data.

ORIGINALITY/VALUE

The QFSR, specifically developed to measure the satisfaction of family members of patients undergoing rehabilitation, appears to be a promising brief questionnaire that can provide important indications for continuous improvement in the delivery of healthcare.

Validation of the Driver Behaviour Questionnaire in a representative sample of drivers in Australia

The Driver Behaviour Questionnaire (DBQ) is a widely used measure of driving behaviours that may increase a driver's risk of crash involvement. However, there are several different versions of the DBQ varying in terms of number of items and factor structure. The aim of the current research was to assess the construct validity of the popular 28-item four-factor DBQ solution in a representative sample of drivers in Australia. A further aim was to test the factorial invariance of the measure across gender, age and also between fleet and non-fleet drivers using multigroup confirmatory factor analyses. Data on a range of attitudes towards road safety were collected using an online survey. A stratified sampling procedure was undertaken to ensure the age, gender and location distributions of participants were representative of the Australian population. A total of 2771 responses were obtained from fully licensed motor vehicle drivers (male: 46%). Confirmatory factor analysis supported the 28-item four-factor DBQ in the Australian sample. The DBQ was also found to be gender-invariant and strong partial measurement invariance was found for drivers aged from 26 to 64, but not for younger (17-25) or older (65-75) drivers. Modifications to the DBQ suggest how the DBQ can be improved for use in these two age groups.

Novice drivers' individual trajectories of driver behavior over the first three years of driving

Identifying the changes in driving behavior that underlie the decrease in crash risk over the first few months of driving is key to efforts to reduce injury and fatality risk in novice drivers. This study represented a secondary data analysis of 1148 drivers who participated in the UK Cohort II study. The Driver Behavior Questionnaire was completed at 6 months and 1, 2 and 3 years after licensure. Linear latent growth models indicated significant increases across development in all four dimensions of aberrant driving behavior under scrutiny: aggressive violations, ordinary violations, errors and slips. Unconditional and conditional latent growth class analyses showed that the observed heterogeneity in individual trajectories was explained by the presence of multiple homogeneous groups of drivers, each exhibiting specific trajectories of aberrant driver behavior. Initial levels of aberrant driver behavior were important in identifying sub-groups of drivers. All classes showed positive slopes; there was no evidence of a group of drivers whose aberrant behavior decreased over time that might explain the decrease in crash involvement observed over this period. Male gender and younger age predicted membership of trajectories with higher levels of aberrant behavior. These findings highlight the importance of early intervention for improving road safety. We discuss the implications of our findings for understanding the behavioral underpinnings of the decrease in crash involvement observed in the early months of driving.

Dimensions of driving anger and their relationships with aberrant driving

The purpose of this study was to investigate the relationship between driving anger and aberrant driving behaviours. An internet-based questionnaire survey was administered to a sample of Chinese drivers, with driving anger measured by a 14-item short Driving Anger Scale (DAS) and the aberrant driving behaviours measured by a 23-item Driver Behaviour Questionnaire (DBQ). The results of Confirmatory Factor Analysis demonstrated that the three-factor model (hostile gesture, arrival-blocking and safety-blocking) of the DAS fitted the driving anger data well. The Exploratory Factor Analysis on DBQ data differentiated four types of aberrant driving, viz. emotional violation, error, deliberate violation and maintaining progress violation. For the anger-aberration relation, it was found that only "arrival-blocking" anger was a significant positive predictor for all four types of aberrant driving behaviours. The "safety-blocking" anger revealed a negative impact on deliberate violations, a finding different from previously established positive anger-aberration relation. These results suggest that drivers with different patterns of driving anger would show different behavioural tendencies and as a result intervention strategies may be differentially effective for drivers of different profiles.

Measurement invariance of the Driver Behavior Questionnaire across samples of young drivers from Finland and Ireland

This article investigates the factor structure of the 27-item Driver Behavior Questionnaire (DBQ) in two samples of young drivers (18-25 years of age); one from Finland and the other from Ireland. We compare the two-, three-, and four-factor solutions using Confirmatory Factor Analysis (CFA) and show that the four-factor model (with the latent variables rule violations, aggressive violations, slips and lapses) fits the data from the two countries best. Next, we compare the fit of this model across samples by the means of a measurement invariance analysis in the CFA framework. The analysis shows that the four-factor model fails to fit both samples equally well. This is mainly because the socially-oriented latent variables (rule violations and aggressive violations) are different in nature in the two samples. The cognitively-oriented latent variables (slips and lapses) are, however, similar across countries and the mean values of slips can be compared using latent variable models. However, the common practice of calculating sum scores to represent the four latent DBQ variables and comparing them across subgroups of respondents is unfounded, at least when comparing young respondents from Finland and Ireland.

A quarter of a century of the DBQ: some supplementary notes on its validity with regard to accidents

This article synthesises the latest information on the relationship between the Driver Behaviour Questionnaire (DBQ) and accidents. We show by means of computer simulation that correlations with accidents are necessarily small because accidents are rare events. An updated meta-analysis on the zero-order correlations between the DBQ and self-reported accidents yielded an overall r of .13 (fixed-effect and random-effects models) for violations (57,480 participants; 67 samples) and .09 (fixed-effect and random-effects models) for errors (66,028 participants; 56 samples). An analysis of a previously published DBQ dataset (975 participants) showed that by aggregating across four measurement occasions, the correlation coefficient with self-reported accidents increased from .14 to .24 for violations and from .11 to .19 for errors. Our meta-analysis also showed that DBQ violations (r = .24; 6353 participants; 20 samples) but not DBQ errors (r = - .08; 1086 participants; 16 samples) correlated with recorded vehicle speed. Practitioner Summary: The DBQ is probably the most widely used self-report questionnaire in driver behaviour research. This study shows that DBQ violations and errors correlate moderately with self-reported traffic accidents.

Personality domains and traits that predict self-reported aberrant driving behaviours in a southeastern US university sample

Personality traits are meaningful predictors of many significant life outcomes, including mortality. Several studies have investigated the relationship between specific personality traits and driving behaviours, e.g., aggression and speeding, in an attempt to identify traits associated with elevated crash risk. These studies, while valuable, are limited in that they examine only a narrow range of personality constructs and thus do not necessarily reveal which traits in constellation best predict aberrant driving behaviours. The primary aim of this study was to use a comprehensive measure of personality to investigate which personality traits are most predictive of four types of aberrant driving behaviour (Aggressive Violations, Ordinary Violations, Errors, Lapses) as indicated by the Manchester Driver Behaviour Questionnaire (DBQ). We recruited 285 young adults (67% female) from a university in the southeastern US. They completed self-report questionnaires including the DBQ and the Personality Inventory for DSM-5, which indexes 5 broad personality domains (Antagonism, Detachment, Disinhibition, Negative Affectivity, Psychoticism) and 25 specific trait facets. Confirmatory factor analysis showed adequate evidence for the DBQ internal structure. Structural regression analyses revealed that the personality domains of Antagonism and Negative Affectivity best predicted both Aggressive Violations and Ordinary Violations, whereas the best predictors of both Errors and Lapses were Negative Affectivity, Disinhibition and to a lesser extent Antagonism. A more nuanced analysis of trait facets revealed that Hostility was the best predictor of Aggressive Violations; Risk-taking and Hostility of Ordinary Violations; Irresponsibility, Separation Insecurity and Attention Seeking of Errors; and Perseveration and Irresponsibility of Lapses.

Exploratory Structural Equation Modeling of Personality Data

The current article compares the use of exploratory structural equation modeling (ESEM) as an alternative to confirmatory factor analytic (CFA) models in personality research. We compare model fit, factor distinctiveness, and criterion associations of factors derived from ESEM and CFA models. In Sample 1 ( n = 336) participants completed the NEO-FFI, the Trait Emotional Intelligence Questionnaire–Short Form, and the Creative Domains Questionnaire. In Sample 2 ( n = 425) participants completed the Big Five Inventory and the depression and anxiety scales of the General Health Questionnaire. ESEM models provided better fit than CFA models, but ESEM solutions did not uniformly meet cutoff criteria for model fit. Factor scores derived from ESEM and CFA models correlated highly (.91 to .99), suggesting the additional factor loadings within the ESEM model add little in defining latent factor content. Lastly, criterion associations of each personality factor in CFA and ESEM models were near identical in both inventories. We provide an example of how ESEM and CFA might be used together in improving personality assessment.

On testing factorial invariance: A reply to J.C.F. de Winter

Since its publication, the Driver Behavior Questionnaire (DBQ) has been used for comparing subgroups of respondents on the constructs formed through factor analyzing the questionnaire items. However, not enough attention has been paid to ascertaining that the instrument actually measures the same constructs in the same way in all respondent groups. I recently published an article (Mattsson, 2012) that aimed to do this for the Finnish 28-item version of the DBQ using the stage-wise factorial invariance approach in the Exploratory Structural Equation Modeling (ESEM) context. de Winter (2013) commented on the publication, arguing that the results were artifacts due to measurement error that too many factors were extracted and that too strict criteria for invariance were applied. In this contribution, I reply to each criticism and suggest methodological approaches for ensuring the measurement invariance of self-report instruments such as the DBQ.

A comparison of static and dynamic hazard perception tests

Hazard perception is a vital component to safe driving and hazard perception tests (HPTs) are being used with greater frequency for driver training, assessment and licensure. In this study, we compared a dynamic HPT (Scialfa et al., 2011), which presents short video scenes to observers and a static HPT (Scialfa et al., 2012), which uses still images. Both tests require the observer to indicate the presence of a traffic conflict that would lead to a collision between the "camera" vehicle and another road user or fixed object. Young adult drivers (n=56) completed both forms of the HPT, along with a modified version of the Driver Behavior Questionnaire (Reason et al., 1990) and a measure of simple reaction time. Self-reported collision and moving violation data were also collected. As in previous work, both static and dynamic HPTs had good reliability. The correlation between composite static and dynamic scores was approximately .40, but was reduced to approximately .25 when simple reaction time was controlled. Both HPTs predicted lapses and errors on the Driver Behavior Questionnaire, but neither predicted self-reported collisions or moving violations. Discussion focuses on the differences in visual cues available in dynamic and static scenes and how these differences could influence decisions about potential hazards.

Small sample sizes, overextraction, and unrealistic expectations: A commentary on M. Mattsson

In a recent article about the Manchester Driver Behaviour Questionnaire (DBQ), Mattsson (2012) concluded that the factor structure was not invariant across subgroups of respondents. This commentary contests this conclusion.