Accident Analysis and Prevention 80 (2015) 172–177
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Accident Analysis and Prevention
journal homepage: www.elsevier.com/locate/aap
Assessing dangerous driving behavior during driving inattention:
Psychometric adaptation and validation of the Attention-Related
Driving Errors Scale in China
Weina Qu, Yan Ge * , Qian Zhang, Wenguo Zhao, Kan Zhang
Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 2 December 2014
Received in revised form 8 April 2015
Accepted 13 April 2015
Available online 22 April 2015
Driver inattention is a significant cause of motor vehicle collisions and incidents. The purpose of this
study was to translate the Attention-Related Driving Error Scale (ARDES) into Chinese and to verify its
reliability and validity. A total of 317 drivers completed the Chinese version of the ARDES, the Dula
Dangerous Driving Index (DDDI), the Attention-Related Cognitive Errors Scale (ARCES) and the Mindful
Attention Awareness Scale (MAAS) questionnaires. Specific sociodemographic variables and traffic
violations were also measured. Psychometric results confirm that the ARDES-China has adequate
psychometric properties (Cronbach’s alpha = 0.88) to be a useful tool for evaluating proneness to
attentional errors in the Chinese driving population. First, ARDES-China scores were positively correlated
with both DDDI scores and number of accidents in the prior year; in addition, ARDES-China scores were a
significant predictor of dangerous driving behavior as measured by DDDI. Second, we found that ARDESChina scores were strongly correlated with ARCES scores and negatively correlated with MAAS scores.
Finally, different demographic groups exhibited significant differences in ARDES scores; in particular,
ARDES scores varied with years of driving experience.
ã 2015 Elsevier Ltd. All rights reserved.
Keywords:
Attention-Related Driving Errors Scale
Driver inattention
Dangerous driving
Reliability and validity
1. Introduction
Driver inattention is widely discussed in the literature. After a
detailed analysis of definitions and taxonomies of the phrase
“driver inattention," Regan et al. (2011) concluded that driver
inattention can be defined as insufficient or no attention to
activities critical for safe driving. Driver inattention occurs when,
for example, a driver does not realize that the vehicle in front of
him has slowed down; he then must brake abruptly to avoid a
crash. Driving is a complex behavior that requires multiple tasks
to be performed simultaneously. Driver inattention produces
errors and can cause failures in performance while driving (Hole,
2007). Evidence is increasingly emerging that driver inattention
is the primary cause of motor vehicle collisions and incidents
(Dingus et al., 2006; Klauer et al., 2006). According to the most
recent Chinese Road Traffic Accident Statistics (CRTAS, 2012),
4.727 million traffic accidents occurred in 2012. Inattentive
* Corresponding author at: 16 Lincui Road, Chaoyang District, Beijing 100101,
China. Tel.: +86 10 64836956; fax: +86 10 64836047.
E-mail address: [email protected] (Y. Ge).
http://dx.doi.org/10.1016/j.aap.2015.04.009
0001-4575/ ã 2015 Elsevier Ltd. All rights reserved.
behaviors by drivers (e.g., failure to yield the right of way to
others, driving in the wrong direction) accounted for 89.31% of
these accidents. Considering the extremely negative influence of
driver inattention on driving safety and the special traffic
environment in China (for example, streets are often filled with
pedestrians and bicycles, and traffic signs are often perplexing in
China; Zhang et al., 2006), there is an urgent need to develop an
effective instrument to explore attention-related driving errors in
China.
Driver inattention has an influence on driving safety. Previous
studies have shown that inattention impairs driver performance
and is a significant risk factor for crash involvement (Farmer et al.,
2010; Klauer et al., 2006; Lemercier et al., 2014; Stutts et al., 2001).
According to one study, inattention is involved in between 10% and
33% of all accidents in the United States (Ranney, 2008). Harbluk
et al. (2002) investigated the impact of cognitive distraction on
driver behavior in an on-road experiment. Drivers drove an 8 km
city route while performing three different secondary tasks as
distractors. The experiment found that inattentive drivers checked
their mirrors less often, had reduced eye-scanning behavior, and
tended to brake more abruptly and more strongly. Another study
asked drivers to report their inattention while completing a driving
W. Qu et al. / Accident Analysis and Prevention 80 (2015) 172–177
task in a simulated driving environment. The results indicated that
inattentive driving entails a failure to monitor the environment
and a decrease in the standard deviation (SD) of speed (He et al.,
2011). Many studies have shown a reduction in both lateral and
longitudinal SD when drivers were in an inattentive state (Kubose
et al., 2006; Reimer, 2009), and a reduction in lateral variation
could be considered to reflect a decline in performance (Reimer,
2009). Previous studies measured inattention in a specific scenario
in an on-road or simulated driving environment. However,
inattention occurs more often in actual driving than in experimental situations. Therefore, instead of studying inattention in
specific laboratory scenarios, the questionnaire provides an
alternative method to measure inattention while driving.
The Attention-Related Driving Error Scale (ARDES) is a 19-item
self-reported questionnaire developed by Ledesma et al. (2010) to
assess individual differences in the tendency to make attentional
errors in specific driving contexts (e.g., “On approaching a corner, I
do not realize that a pedestrian is crossing the street”). The original
ARDES was constructed based on the culture and language in
Argentina. Its items specifically refer to non-deliberate errors in
driving behavior resulting from an attentional failure, such as
failing to notice a traffic light due to inattention (Ledesma et al.,
2010). These items were taken from the lapses scale of the Driving
Behavior Questionnaire (DBQ; Reason et al., 1990) and from the
Multidimensional Driving Style Inventory (MDSI; Taubman-BenAri et al., 2004). In the DBQ, some items do not clearly refer to
attention-related errors; for example, the “I plan my route badly, so
that I hit the traffic that I could have avoided” item refers to an
error in trip planning rather than inattentive driving. In the MDSI,
the same applies to the “I misjudge the speed of an oncoming
vehicle when passing” item, which instead reflects an error related
to a lack of expertise. In comparison with these questionnaires, the
ARDES specifically includes items referring only to attentionrelated errors due to attentional failures while driving. In addition,
the ARDES was also constructed to avoid overlapping with other
psychological constructs (such as daydreaming, absorption, or
dissociation). Furthermore, the internal consistency of the original
Argentinean version of the ARDES has been reported to be higher
(Cronbach’s alpha = 0.86) than that of the attentional lapse
subscale of the DBQ (Cronbach’s alpha values ranged from
0.64 to 0.69). An exploratory factor analysis suggested that all
19 items belong to a single factor that accounted for 30% of the total
variance in the proneness to attentional errors while driving
(Ledesma et al., 2010). The ARDES has also been validated in Spain,
and the resulting ARDES-Spain scores have exhibited good internal
consistency (Cronbach’s alpha = 0.88). A factor analysis suggested
that a single factor accounted for 32.70% of the total variance in
ARDES-Spain scores (Roca et al., 2013a,b). Overall, the Cronbach’s
alpha coefficient values and the factor structure of the ARDESSpain and ARDES-Argentina demonstrated that these scales
exhibit good validity and reliability; thus, the ARDES can be
considered as a simple and useful measure of individual differences in attention-related driving errors. Lopez-Ramon et al.
(2011) found that the drivers with higher ARDES scores exhibited a
general slowness in performance and less endogenous preparation
for high-priority warning signs (Lopez-Ramon et al., 2011).
However, because language, culture, traffic regulations and driving
habits vary across countries, Roca et al. (2013a,b) study suggested
that future studies that adapt this questionnaire to other countries
would help to expand the cross-cultural equivalence of the ARDES.
To our knowledge, the ARDES has not previously been validated in
China.
The relationships between the ARDES and a variety of cognitive
and psychological variables have been analyzed to provide further
evidence of the validity of this scale. First, Ledesma et al. (2010)
found significant correlations between ARDES scores and a general
173
tendency to make attentional errors in everyday life, as measured
using the Attention-Related Cognitive Errors Scale (ARCES).
Inattentive driving errors may not only arise from triggering
events but can also be affected by a given psychological state.
Individual differences in cognitive abilities, such as the ability to
maintain attention, exist, and certain psychological traits can lead
to greater error-proneness. Individuals who are prone to inattention in their daily lives may also be more likely to be inattentive
while driving. Second, driving attention errors are related to
individuals’ levels of awareness in the performance of daily life
activities. Some studies have suggested that absent-mindedness is
related to attentional failures in daily life (Herndon, 2008; Wallace
and Vodanovich, 2003; Walsh et al., 2009). Ledesma et al. (2010)
found significant negative correlations between ARDES scores and
a lack of awareness in everyday life (Mindful Attention Awareness
Scale, MAAS).
Driver inattention is also dependent on many intrinsic factors.
These intrinsic variables may include the driver’s age and years of
driving experience (Young et al., 2008). Studies that have examined
the relationship between age and inattention have yielded
inconsistent results. Some studies have shown that older drivers
have a lower attentional error propensity than do younger drivers
(Roca et al., 2013a,b; Roca et al., 2013b; Smallwood et al., 2004).
However, other research failed to find a correlation between age
and inattention (Einstein and McDaniel, 1997; Ledesma et al.,
2010). The results of studies that have investigated the relationship
between years of driving experience and inattention have also
been inconsistent. Klauer et al. (2006) showed that drivers who
had more years of driving experience were more often involved in
inattention-related accidents and near-accidents. One explanation
of this finding could be that as a result of experience, older drivers
may require fewer attentional resources for vehicle control and
may make more inattention errors (Triggs and Regan, 1998).
Another study found that the numbers of inattentive errors did not
vary with the years of driving experience (Ledesma et al., 2010).
The aims of the current study were as follows:
(1) To adapt the Argentinean and Spanish versions of the ARDES to
the culture, language, traffic environment and regulations of
China and thus to provide a Chinese version of the ARDES;
(2) To verify the criterion validity of the ARDES by examining the
relationships between the ARDES, dangerous driving behavior
(as measured by a self-reported questionnaire, the Dula
Dangerous Driving Index, DDDI) and self-reported traffic
accidents and violations;
(3) To further verify the relationship between the ARDES and
experiences in daily life by investigating the relationships
between the ARDES, the ARCES, and the MAAS; and
(4) To investigate the relationships between driver inattention and
sociodemographic characteristics (e.g., age, driving years).
2. Methods
2.1. Participants
A total of 317 participants (215 males and 102 females)
completed the questionnaire voluntarily and anonymously. The
participants were recruited by a research company through
interviewing individual drivers encountered in or around parking
lots or residential areas. The ages of the participants ranged from
20 to 60 years (mean = 38.41, SD = 10.09); 24.61% of the participants
were of age 20–30 years, 60.25% were of age 31–50 years, and
15.14% were of age 51 years or older. The subjects who had
completed high school accounted for 84.22% of the study sample.
All of the participants were licensed drivers with more than one
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W. Qu et al. / Accident Analysis and Prevention 80 (2015) 172–177
year driving experience. The average participant had been driving
for 6.86 years (SD = 5.54) since obtaining a driver’s license.
2.2. Measures
2.2.1. ARDES
The ARDES was developed by Ledesma et al. (2010) to assess
attention-related driving errors. The ARDES includes 19 items
describing driving errors resulting from attention. Participants
were requested to read each item and indicate the frequency with
which they commit that type of error on a 5-point scale that ranged
from never or nearly never (1) to always or nearly always (5). Total
scores ranged from 19 to 95. In our study, a Chinese version of the
ARDES was developed. The English version of the ARDES (Ledesma
et al., 2010) was translated into Chinese using the following
procedure. First, two students in psychology translated the English
version of the ARDES into Chinese. Second, two professors checked
and discussed the translation and created a revised draft of the
questionnaire. Third, we invited three drivers to check the draft to
ensure that the items in the questionnaire were clear and
unambiguous. Fourth, a professional translator who was proficient
in both English and Chinese back-translated the scale into English
to evaluate whether the translation was correct. Finally, we
modified the scale via a group discussion and finalized the scale
based on the feedback of the three experienced drivers who had
been recruited to pre-test the draft translation. These participants
marked the items that they did not initially understand when they
completed the questionnaire.
2.2.2. DDDI
The DDDI is a 28-item self-reporting instrument developed by
Dula and Balard (2003) to measure dangerous driving behaviors.
For this scale, the study participants rate the frequency with which
they engage in each item using a 5-point Likert scale ranging from
1 (never) to 5 (always). Total scores ranged from 28 to 140. The
original scale had three components: risky driving (RD; twelve
items, a = 0.83), negative cognitive/emotional driving (NCED; nine
items, a = 0.85) and aggressive driving (AD; seven items, a = 0.84;
Dula and Balard, 2003). Two items were removed from the RD
component and considered separately to define a drunk driving
factor (DD, a = 0.79) in the Flemish version of the DDDI (Willemsen
et al., 2008). The Chinese version of the DDDI (Qu et al., 2014) was
used in our study. This version of the DDDI was derived from the
DDDI (Dula and Balard, 2003). A total score is calculated for each
subscale, and the overall DDDI score is calculated by summing the
score for each item. Higher mean scores indicate more dangerous
driving behavior. The Chinese version of the DDDI has four
components: RD (ten items, a = 0.78), NCED (nine items, a = 0.80),
AD (seven items, a = 0.78) and DD (two items, a = 0.63).
2.2.3. ARCES
The ARCES was used to assess daily performance failures that
were caused by attention lapses and memory failures (Carriere
et al., 2008; Cheyne et al., 2006; Smilek et al., 2010). It includes 12
items that are scored from 1 (never) to 5 (very often). Total scores
ranged from 12 to 60. The total ARCES score represents the
frequency with which an individual makes cognitive errors. More
attention-related cognitive errors are indicated by higher scores.
The Chinese version of the ARCES translated by Carciofo et al.
(2014) was used in this study; this version has been shown to have
sufficient internal consistency (a = 0.92).
2.2.4. MAAS
The MAAS (Brown and Ryan, 2003), which includes 15 items,
was used to assess individuals’ general levels of awareness and
attention to present events and experiences. All items are
negatively worded (e.g., “I find it difficult to stay focused on
what’s happening in the present”), and the scores for each item
were reversed for analysis. In this study, we used the Chinese
version of the MAAS constructed by Deng et al. (2011). The items of
this scale were answered based on a 6-point scale from nearly
always (1) to nearly never (6). Total scores ranged from 15 to 80.
Higher scores on this scale reflect higher levels of dispositional
mindfulness. In Deng et al. (2011), the scale’s Cronbach’s alpha was
0.85.
2.2.5. Sociodemographic variables
Several sociodemographic variables were measured using
driver self-reports, including age, gender, level of education,
occupation, number of years of driving experience, number of
accidents, and penalty points and fines during the past year. In
China, one receives six penalty points when he or she is caught
driving through a red light. Driver’s licenses are suspended if
12 penalty points are received in one year. The study participants
were also asked to provide information about traffic accidents they
were in and penalty fines they received in the previous year, as well
as information about the following driving violations: speeding,
ignoring traffic signs or markings, driving through red lights, not
yielding the right of way to other drivers in accordance with
regulations, and driving on the wrong side of the road.
2.3. Procedure
The survey was conducted by a research company in Beijing,
China. All of the participants were randomly chosen in and around
Table 1
Descriptive statistics for all variables.
Age
Driving years
Annual mileage (10,000 km)
Accidents
Points
Fines
ARDES
ARCES
MAAS
DDDI
NCED
AD
RD
DD
Mean
SD
Range
Number of items
Cronbach’s alpha
38.41
6.86
1.095
0.35
1.02
113.88
1.76
1.91
4.56
1.88
2.06
1.70
1.92
1.44
10.09
5.54
0.68
0.87
2.30
326.17
0.49
0.53
1.12
0.45
0.54
0.55
0.51
0.62
20–40
1–30
0.05–3.4
0–6
0–12
0–3,300
1–3.47
1–3.5
1.13–6
1–3.18
1–3.78
1–3.43
1–3.5
1–4
19
12
15
28
9
7
10
2
0.90
0.86
0.96
0.89
0.73
0.78
0.73
0.54
W. Qu et al. / Accident Analysis and Prevention 80 (2015) 172–177
parking lots, shopping malls, or residential areas. After the research
assistant introduced the survey, the participant could voluntarily
agree to participate in the study. All of the participants were
informed that their information would be maintained strictly
confidential and used only for scientific research. After each
participant completed a consent form, he or she was given a packet
of questionnaires. The packet contained each of the previously
mentioned surveys. The participants completed the questionnaires
individually and anonymously within a period of approximately
20 min. After completing the survey, each participant received a
gift. This study was approved by the Institutional Review Board of
the Institute of Psychology of the Chinese Academy of Sciences.
175
addition, the Cronbach’s alpha coefficient was 0.90, suggesting that
the ARDES scores exhibited good internal consistency. Each case
was subjected to principle components analysis (PCA). The KaiserMeyer-Olkin (KMO) measure of sampling adequacy was 0.909 and
Bartlett’s test of sphericity was significant (x2 (171) = 1975.92,
p < 0.001), which indicates that the data were suitable for factor
analysis. No rotation was used because only one component was
extracted. Factor analysis indicated that a single factor exceeded
the parallel analysis criterion and accounted for 35.28% of variance.
All the 19 items had positive loadings on this factor, ranging from
0.52 to 0.68 (Table 2).
3.3. ARDES validity
3. Results
3.1. Descriptive statistics for all variables
Table 1 presents the descriptive statistics for all of the measured
variables. All of the scales have acceptable reliability except for DD,
which only had two items. The descriptive statistics for selfreported accidents, points and fines are included. Few drivers
reported that they had been involved in accidents (n = 62), received
penalty points (n = 66) or been fined (n = 78) in the prior year.
3.2. ARDES reliability
Descriptive statistics for each of the 19 items of the ARDESChina are presented in Table 2, which also provides mean values,
corrected item-total correlation values and factor loadings. The
mean values range from 1.61 to 2.04. Each of the items was
averaged into a single score. The higher the score is, the greater the
attentional error propensity will be. The ARDES has a mean score of
1.76. The corrected item-total correlation values range from 0.46 to
0.61, i.e., from moderate to high levels, indicating that the items
feature good discrimination power. The lowest corrected itemtotal correlation values were for item 6 (“On approaching a corner, I
do not realize that a pedestrian is crossing the street.”), while item
13 has the highest value (“I drive through a traffic light that has just
turned red as I was following the car right in front of me.”). In
To obtain further evidence of the validity of the predictive
capacity of the ARDES-China, the association between ARDES and
DDDI scores was analyzed. The results of a correlation analysis
showed that ARDES scores were positively related with ARCES,
DDDI and its subscales, which suggests that the numbers of driving
attentional errors may increase with increasingly dangerous
driving behavior, as measured using the DDDI. The correlations
between ARDES scores and self-reported traffic accidents and
violations were also analyzed using Spearman’s correlation. ARDES
positively correlated with the total number of traffic accidents that
they were involved during the previous year. However, no
significant correlation was found between ARDES and the total
penalty points or fines for traffic citations during the previous year.
The correlation index is also shown in Table 3.
To assess the validity of the ARDES, Pearson’s correlation analysis
was used to examine the associations between ARDES, DDDI and
reported daily attention (see the correlation matrix in Table 3).
The relationships between ARDES-China scores and sociodemographic variables were analyzed. ARDES scores exhibited a
small negative correlation with age (r = 0.15, p < 0.01) and driving
years (r = 0.18, p < 0.01). Age and ARDES score were not
significantly correlated in a partial correlation that controlled
for driving years. However, the correlation between driving years
and ARDES scores remained significant (r = 0.13, p < 0.05) when
controlling for age.
Table 2
Descriptive statistics for the 19-item Attention-Related Driving Errors Scale (n = 317).
Item
Mean Std.
dev.
Range Factor
loading
Corrected item-total
correlation
1. When I head toward a known place, I drive past it for being inattentive.
2. I signal a move, and unintentionally make another (e.g., I turn on the right-turn blinker but turn left
instead).
3. On approaching an intersection, I miss a car coming down the road for being inattentive.
4. Suddenly I notice that I have lost or mistaken my way to a known place.
5. On approaching an intersection, instead of looking at the traffic coming in, I look at the opposite
direction.
6. On approaching a corner, I do not realize that a pedestrian is crossing the street.
7. I do not realize that there is an object or a car behind and unintentionally hit into it.
8. I do not realize that the vehicle right in front of me has slowed down and I have to brake abruptly to
avoid a crash.
9. Another driver honks at me making me realize that the traffic light has turned green.
10. I forget that my lights are on full beam until flashed by another motorist.
11. For a brief moment, I forget where I am heading to.
12. I have to take more turns than necessary to arrive at a place.
13. I drive through a traffic light that has just turned red as I was following the car right in front of me.
14. I try to drive the car forward and do not realize that I have not put it into first gear.
15. I try to use a car device but use another one instead (e.g., I turn on the lights instead of the
windshield wipers).
16. I intend to go to a certain place and suddenly realize that I am heading somewhere else.
17. I realize that I had been inattentive and had not noticed the traffic light.
18. I unintentionally make a wrong turn or drive toward coming traffic.
19. I unintentionally make a mistake in shifting the gear or shift to the wrong gear.
2.04
1.66
0.90
0.77
1–4
1–4
0.55
0.60
0.49
0.54
1.71
1.97
1.78
0.75
0.87
0.82
1–4
1–4
1–5
0.66
0.52
0.65
0.60
0.46
0.59
1.62
1.76
1.79
0.76
0.82
0.84
1–4
1–4
1–4
0.54
0.61
0.62
0.47
0.55
0.55
1.82
1.77
1.64
2.00
1.77
1.66
1.68
0.83
0.85
0.82
0.98
0.80
0.79
0.76
1–5
1–4
1–4
1–5
1–4
1–4
1–5
0.61
0.60
0.64
0.52
0.68
0.56
0.61
0.56
0.54
0.58
0.47
0.61
0.50
0.54
1.61
1.75
1.74
1.73
0.75
0.80
0.82
0.82
1–4
1–5
1–4
1–4
0.57
0.58
0.55
0.57
0.50
0.52
0.48
0.51
176
W. Qu et al. / Accident Analysis and Prevention 80 (2015) 172–177
Table 3
Correlation matrix of variables in ARDES validation study.
Accidents
ARCES
MAAS
DDDI
NCED
AD
RD
DD
ARDES
ARCES
0.21**
0.60**
0.24**
0.63**
0.53**
0.62**
0.48**
0.53**
0.25**
0.56**
0.51**
0.49**
0.45**
0.39**
MAAS
0.16**
0.14**
0.18**
0.11**
0.08**
DDDI
NCED
AD
RD
0.90**
0.80**
0.90**
0.56**
0.57**
0.76**
0.39**
0.55**
0.53**
0.36**
Notes: ARDES: Attention-Related Driving Errors Scale; ARCES: Attention-Related
Cognitive Errors Scale; MAAS: Mindful Attention Awareness Scale; DDDI: Dula
Dangerous Driving Index (total score); NCED: negative cognitive/emotional driving
in DDDI; AD: aggressive driving in DDDI; RD: risky driving in DDDI; DD: drunk
driving in DDDI.
*
p < 0.05.
**
p < 0.01.
To measure the effect of ARCES and ARDES on dangerous driving
behavior, hierarchical multiple regression analyses were conducted while controlling the age and the number of driving years in
the first step. The ARCES score was entered at the second step, and
the ARDES score was entered at the third step. Total DDDI score
was selected as the dependent variable. The results of the
regression are presented in Table 4. The final regression equation
is DDDI = 0.61 + 0.01 (driving years) + 0.09 (annual miles) + 0.25
(ARCES) + 0.42 (ARDES). The results indicate that the ARDES was a
significant predictor of dangerous driving behavior and accidents.
The effects were significant even when the demographic variables
and general attention-related errors were controlled for.
4. Discussion
4.1. Summary of the findings
The primary aim of this paper was to develop an adapted ARDES
for Chinese people based on the culture, language, traffic
environment and regulations in China. In our study, the ARDES
was translated into Chinese, and its reliability and validity were
confirmed. Desirable psychometric qualities were found in the
Chinese version of the ARDES. The predictive capacity of ARDESChina scores was also confirmed in relation to self-reported traffic
violations.
First, psychometric results confirmed that the ARDES-China has
adequate psychometric properties to be a useful tool for evaluating
proneness to attentional errors in the Chinese driving population.
The Chinese ARDES was found to be highly reliable and to have a
stable structure. The internal consistency of the ARDES was
relatively high and was comparable with those of other versions of
the ARDES (Roca et al., 2013a,b). The ARDES-China has a slightly
Table 4
Effect of demographic variables, ARCES and ARDES on DDDI.
b
Variables
Step 1
Age
Driving years
Annual mileage
0.13*
0.01
0.25**
ARCES
0.55**
ARDES
0.45**
Step 2
Step 3
*
**
p < 0.05.
p < 0.01.
R2
DR2
0.07
0.07**
0.36
0.29**
0.47
0.12**
higher Cronbach’s alpha coefficient value than does the ARDESArgentina (0.88 and 0.86, respectively). All the 19 items yielded
high loadings on the first factor, good discrimination indexes, and
high internal consistency. The ARDES-China items measure a
common factor related to individual differences in attentionrelated errors while driving. This finding is in accordance with the
results of previous research conducted using similar instruments;
these prior studies discovered an “inattention factor” that could be
differentiated from other dimensions of driver behavior (Reason
et al., 1990; Taubman-Ben-Ari et al., 2004).
Second, ARDES-China scores were positively correlated with
dangerous driving behavior as measured by not only DDDI but also
the four DDDI subscales of RD, AD, DD and NCED. Regression
analysis revealed that ARDES was a significant predictor of
dangerous driving behavior when controlling for social demographic variables and ARCES. In addition, the numbers of accidents
reported by study participants were used as a criterion to support
the empirical validity of the Chinese ARDES. In particular, ARDESChina score was found to be positively related to the number of
accidents in the prior year. These findings suggest that inattention
during driving is highly correlated with driver behavior and driver
safety. Consistent with the findings of Ledesma et al. (2010) and
Roca et al. (2013a,b), our results show that drivers who reported
being in traffic collisions were more prone to attentional errors
while driving than the drivers who did not report being in
accidents. According to another report that conducted in-depth
analyses of driver inattention using the driving data collected in
the 100-Car Naturalistic Driving Study, 78% of accidents and 65% of
near-accidents involved one or more inattention factors (Dingus
et al., 2006). Our results further support that driver inattention
could be a major cause of dangerous driving behavior.
Third, we also explored the relationships between ARDES scores
and a variety of cognitive and psychological variables. We found
that ARDES-China scores were strongly correlated with a measure
of the frequency of cognitive errors in everyday life. This finding
indicated that the drivers with attention-related driving errors are
more likely to experience attentional failures in everyday life, in
agreement with previous research (Ledesma et al., 2010; Roca
et al., 2013a,b). Furthermore, ARDES-China scores were found to be
strongly correlated with MAAS scores, which strengthens the
hypothesis that this type of driving error is closely linked to
inattention and a lack of awareness in everyday life. This result
further supports previous findings that mindfulness is negatively
correlated with the commission of errors (Cheyne et al., 2006;
Herndon, 2008).
Finally, comparisons of scores across different demographic
groups and dangerous driving behaviors revealed significant
differences and showed that the numbers of attention-related
driving errors varied with age and the years of driving experience.
Our study found that age negatively correlated with the number of
attentional driving errors. However, the effect disappeared when
controlling for driving experience. Moreover, the years of driving
experience were also found to negatively correlate with the
number of attentional driving errors when age was controlled for.
This result is in contrast with the results of a study by Klauer et al.
(2006), which showed that experienced drivers are involved in
accidents more frequently than are novice drivers. However, some
studies have also found that ARDES scores did not vary significantly
with the number of years of driving experience (Ledesma et al.,
2010). It should be noted that age is a variable that is strongly
associated with driving experience. Roca et al. (2013a,b) also found
significant correlation between ARDES-Spain total scores and the
age of the drivers. However, the result was not significant after
controlling for the driving years, which suggests that the negative
correlation between ARDES and age might be explained by
differences in driver experience.
W. Qu et al. / Accident Analysis and Prevention 80 (2015) 172–177
4.2. Limitations
This study has several limitations. One important limitation of
this study is that the data depend on drivers’ self-reports to
measure traffic accidents and illegal behaviors; these reports may
be affected by social pressures. If we were able to obtain the actual
traffic records of traffic offenders (i.e., those convinced of traffic
accidents, ignoring traffic signs or markings), the analysis of the
scale scores for such individuals would be more reliable. Future
studies would be enhanced by integrating self-reported measures
with other methods (such as field observation and simulated
driving). Another concern is whether the sequence in which the
questionnaires are completed is counter-balanced. Finally, the
sample recruitment method yielded a sample that is not
representative of all drivers.
5. Implications
The current study is the first to translate the ARDES into Chinese.
The Chinese version of the ARDES demonstrates a relatively high
internal consistency and good validity for surveying different types
of attentional driving errors. For example, the results of this studycan
be used to design training classes that provide feedback to drivers
about their behaviors that lead to attentional driving errors. Such
programs would improve the drivers’ awareness of attentional
driving errors and help them to modify their driving behavior.
Furthermore, ARDES is useful for evaluating driver attention and
could be used in practical applications after additional testing in onroad studies. Additionally, driver attention might change according
to the traffic situation and familiarity with the road. Further studies
should investigate these effects on inattention driving errors.
Relevant results could then be used to develop specific training
classes to help drivers respond appropriately when encountering
novel driving situations.
Acknowledgments
This study was partially supported by grants from the National
Natural
Science
Foundation
of
China
(Grant
nos.
31100750,31400886 and 91124003) and the Basic Project of
National Science and Technology of China (No. 2009FY110100).
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