Accident Analysis and Prevention 49 (2012) 385–391
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Accident Analysis and
Prevention
j o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /a a
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Confirmatory factor analysis of the Behaviour of Young Novice Drivers Scale (BYNDS)
B.Scott-Parker a ,c ,∗,B.Watson a ,c ,M.J.King a ,c ,M.K.Hyde b ,c
a
Centre for Accident Rearch and Road Safety –Queensland,Queensland University of Technology,K Block,130Victoria Park Road,Kelvin Grove,Queensland 4059,Australia b
School of Psychology and Counlling,Queensland University of Technology,O Block,B Wing Victoria Park Road,Kelvin Grove,Queensland 4059,Australia c
Institute of Health and Biomedical Innovation,Queensland University of Technology,Queensland,Australia
a r t i c l e
i n f o
Article history:
Received 6September 2011
Received in revid form 10February 2012Accepted 26February 2012
Keywords:Young drivers Novice drivers Risky driving
Confirmatory factor analysis BYNDS
a b s t r a c t
Purpo:The greatly incread risk of being killed or injured in a car crash for the young novice driver has been recognid in the road safety and injury prevention literature for decades.Risky driving behaviour has consistently been found to contribute to traffic crashes.Rearchers have devid a number of instru-ments to measure this risky driving behaviour.One tool developed specifi
cally to measure the risky behaviour of young novice drivers is the Behaviour of Young Novice Drivers Scale (BYNDS)(Scott-Parker et al.,2010).The BYNDS consists of 44items comprising five subscales for transient violations,fixed vio-lations,misjudgement,risky driving exposure,and driving in respon to their mood.The factor structure of the BYNDS has not been examined since its development in a matched sample of 476novice drivers aged 17–25years.
Method:The current rearch attempted to refine the BYNDS and explore its relationship with the lf-reported crash and offence involvement and driving intentions of 390drivers aged 17–25years (M =18.23,SD =1.58)in Queensland,Australia,during their first 6months of independent driving with a Provisional (intermediate)driver’s licence.A confirmatory factor analysis was undertaken examining the fit of the originally propod BYNDS measurement model.
Results:The model was not a good fit to the data.A number of iterations removed items with low factor loadings,resulting in a 36-item revid BYNDS which was a good fit to the data.The revid BYNDS was highly internally consistent.Crashes were associated with fixed violations,risky driving exposure,and misjudgement;offences were moderately associated with risky driving exposure and transient violations;and road-rule compliance intentions were highly associated with transient violations.左边腰疼
Conclusions:Applications of the BYNDS in other young novice driver populations will further explore the factor structure of both the original and revid BYNDS.The relationships between BYNDS subscales and lf-reported risky behaviour and attitudes can also inform countermeasure development,such as targeting young novice driver non-compliance through enforcement and education initiatives.
© 2012 Elvier Ltd. All rights rerved.
1.Introduction
1.1.Young novice drivers
The road safety literature has documented the overreprenta-tion of young novice drivers in fatalities and injuries arising from car crashes around the world for decades.Drivers aged 17–24years comprid 13.4%of licend drivers in the Australian state of Queensland in 2010but they contributed 20.0%of the road toll.In
∗Corresponding author at:Centre for Accident Rearch and Road Safety –Queensland,Queensland University of Technology,K Block,130Victoria Park Road,Kelvin Grove,Queensland 4059,Australia.Tel.:+61731387727;fax:+61731381111.
E-mail address:b.scott-parker@qut.edu.au (B.Scott-Parker).
that same year,drivers with a Provisional licence reprented 5.3%of the licend driving population but they contributed 8.6%of the state’s fatalities,and 25.3%of road urs who were fatally injured died as a result of a crash involving a young driver (Department of Transport and Main Roads (DTMR),2011).
Fundamental to reducing the risky behaviour of young novice drivers through targeted interventions such as graduated driver licensing (GDL 1)programs is the measurement of the risky
1
In the enhanced-GDL program in Queensland,young novice drivers progress from a Learner to a Provisional 1(P1)licence after successfully completing a practical driving asssment.A P1driver’s licence must be held for a minimum of 1year.P1drivers are prohibited from carrying more than one young pasnger (excluding immediate family members)(Queensland Transport,2007).
0001-4575/$–e front matter © 2012 Elvier Ltd. All rights rerved.doi:10.1016/j.aap.2012.02.021
386 B.Scott-Parker et al./Accident Analysis and Prevention49 (2012) 385–391
behaviour of the young novice driver.A multitude of a control,Lam et al.,2003;naturalistic obrvations, Ronbloom et al.,2007;logbook analys,Harrison,2004;crash insurance reports,Cooper et al.,1995)and a variety of purpo-built and general Speeding Perception Inventory, Gabany et al.,1997;Driver Behaviour Questionnaire,Lawton et al., 1997)have been ud to measure the risky behaviour of young novice drivers.Few measurement scales,however,are designed specifically to explore young novice driver risky the DBQ was developed from and for u in adult drivers of all ages).
Whilst lf-report has been criticid as being methodologi-cally unsound as it may be vulnerable to bias such as impression management which can compromi the accuracy of the data, it can be challenging for rearchers to identify novice driver risky behaviour–such as driving whilst fatigued and missing an exit or turn whilst driving–without lf-report measures. Therefore it is vital that reliable,comprehensive and valid tools specifically designed to measure the risky behaviour of young novice drivers be ud to inform countermeasure development and evaluation.
1.2.The Behaviour of Young Novice Drivers Scale(BYNDS)
The Behaviour of Young Novice Drivers Scale(BYNDS)was developed by Scott-Parker et al.(2010)with the aim of providing a reliable and valid instrument to measure the risky behaviour of young novice drivers specifically.In their study,761tertiary stu-dents aged17–25years(M=19years,SD=1.59,mode=18years) with a Provisional driver’s licence were recruited from Queens-land’s major tertiary institutions via a broadcast email.Participants completed63risky driving items derived from the literature relat-ing to young driver crash risk and GDL restrictions as part of a larger online survey.Participants also lf-reported their offence and crash involvement as a Provisional driver,and their intentions to comply with the road rules,including GDL restrictions,within the next year.An exploratory factor analysis of the respons of 238males and238females matched for age and tertiary institu-tion using principal components extraction with oblique promax rotation identifiedfive factors.The items within each factor were summed and compridfive subscales.The subscales were then summed to create a composite BYNDS score.The BYNDS was highly internally consistent(Cronbach’s alpha=.95).
Table1lists the subscales and their corresponding items.As can be en,the transient violations subscale measures driving behaviours that are able to be performed multiple times during the journey;thefixed violations subscale measures items that are more stable in nature across the journe
y;the misjudgement sub-scale reflects driver errors;the risky exposure subscale measures the young novice driver’s exposure to risky driving times;and the driver mood subscale measures the driver’s emotive respon to driving. Driver mood,transient andfixed violations were weakly asso-ciated with lf-reported crash involvement,fixed and transient violations were moderately associated with lf-reported offence involvement,and transient violations were highly associated with intentions to comply with road rules.
1.3.Study aims
The BYNDS was developed using a state-wide sample of tertiary students.In addition,the very high internal consistency indicates there may be some redundancy within the BYNDS’subscale(s). Therefore it is timely that the BYNDS be applied in a cond young novice driver sample,and the factor structure of the BYNDS be examined.The study had two aims:(a)to examine the BYNDS, lf-reported crashes,offences,and intentions characteristics of a Table1
The items within the subscales of the Behaviour of Young Novice Drivers(BYNDS) and their mean and standard deviation.
Items M SD Transient violations
You drove over the speed limit in areas where it was unlikely
there was a radar or speed camera
1.870.93
写班主任的作文
You went10–20km/h over the speed 72km/h in a
60km/h zone,112km/h in a100km/h zone)
1.690.87 You deliberately sped when overtaking 1.91 1.02 You sped at night on roads that were not well lit 1.370.67 You went up to10km/h over the speed 65km/h in a
60km/h zone,105km/h in a100km/h zone)
2.220.94 You went more than20km/h over the speed
60km/h in a40km/h zone,120km/h in a100km/h zone)
1.280.58
You raced out of an interction when the light went green 1.770.92 You travelled in the right lane on multi-lane highways 2.09 1.03 You sped up when the lights went yellow 2.050.91 You went too fast around a corner 1.730.72 You did an illegal U-turn 1.330.61 You overtook someone on the left 1.560.81 You spoke on a mobile that you held in your hands 1.350.67 Fixed violations
Your pasngers did not wear atbelts 1.040.31 You drove after taking an illicit drug such as marijuana or
ecstasy
1.030.22
You carried more pasngers than could legallyfit in your car 1.060.30 You did not always wear your atbelt 1.030.27 You drove without a valid licence as becau you had not
applied for one yet or it had been suspended
1.010.15
You did not wear a atbelt if it was only for a short trip 1.030.19 If there was no red light camera,you drove through
围棋人机大战interctions on a red light
1.040.27
You carried more pasngers than there were atbelts for in
your car
1.040.23 You drove when you thought you may have been over the legal
alcohol limit天魔神谭
1.120.36 You drove a high-powered vehicle 1.090.39 Misjudgements
茶悟人生哲理句子You misjudged the speed when you were exiting a main road 1.270.50 You misjudged the speed of an oncoming vehicle 1.340.53 You misjudged the gap when you were turning right 1.510.48 You misjudged the stopping distance you needed 1.450.63 You turned right into the path of another vehicle 1.140.38 You misjudged the gap when you were overtaking another
vehicle
1.150.41 You misd your exit or turn 1.960.79 You entered the road in front of another vehicle 1.390.57 You didn’t always indicate when you were changing lanes 1.420.79 Risky exposure
You drove on the weekend 3.860.97 You drove in the rain 3.180.73 You drove at peak times in the morning and afternoon 3.07 1.05 You drove at night 3.400.99 You drove at dusk or dawn 2.77 1.05 You carried your friends as pasngers at night 2.24 1.00 You drove when you knew you were tired 2.200.90 Your car was full of your friends as pasngers 1.960.97 You went for a drive with your mates giving you directions to
where they wanted to go
2.25 1.07
Driver mood
Your driving was affected by negative emotions like anger or
frustration
1.770.84 You allowed your driving style to be influenced by what mood
you were in
1.780.82 You drove faster if you were in a bad mood 1.700.90 Source:Adapted from Scott-Parker et al.(2010).
The mean and standard deviations were calculated using the raw data in PASW18.0.
cond young novice driver population in Queensland,Australia; and(b)to undertake a confirmatory factor analysis of the BYNDS with the goal of developing a parsimonious,internally consis-tent revid version which is consistent with lf-reported risky behaviour of the young novice drivers.
B.Scott-Parker et al./Accident Analysis and Prevention49 (2012) 385–391387
2.Methods
2.1.Participants
Three hundred and ninety(113males,29.0%male)drivers aged 17–25years(M=18.23,SD=1.58,Mode=17,Median=18)com-pleted a30-min online survey.All drivers had held a Provisional 1(P1)driver’s licence for6months.The participants reprented the Queensland po
pulation according to access to goods,rvices and social interactions(Commonwealth Department of Health and Aged Care,2010);to illustrate,60.0%of the state’s population lived in inner city areas in2006(Australian Bureau of Statistics,2010), and61.8%of the participants resided in inner city areas,and2.0% of the state’s population and2.2%of the study participants resided in remote areas.
2.2.Measures
Participants reported their age and gender and completed the 44-item BYNDS(Scott-Parker et al.,2010)(1=never,5=almost always).Participants also responded to items asking if they had been in a car crash and been detected by Police for committing a driving offence as a driver with a Provisional licence(yes,no);and if they were likely to bend any road rules,including GDL provisions, over the next year(1=definitely will not,7=definitely will).
2.3.Procedure and design
Every Learner driver in Queensland who pasd their practical driving asssment and therefore progresd from a Learner to a Provisional1(P1)driver’s licence in the period April through June 2010was invited to participate in a longitudinal rearch project exploring the behaviours and attitudes of novice drivers(due to Pri-vacy restrictions,the invitation was issued by DTMR on behalf o
f the rearch team).The drivers completed thefirst survey exploring their behaviours as Learner drivers at the time of recruitment.A reminder letter providing the hyperlink for the online survey was posted to9393drivers who were eligible to participate(again,this letter was issued by DTMR on behalf of the rearch team).Six months later,the Learner participants completed their cond sur-vey exploring their behaviours and attitudes whilst they were P1 drivers.Two reminders which contained the online survey hyper-link were nt to the email address provided in thefirst(Learner) survey.The online survey tool was created in KeySurvey Enter-pri Online Survey Software.Only eligible novice drivers received the survey hyperlink,and the survey site is curely maintained by the Authors’rearch institution.The behaviours and attitudes reported in the cond survey were ud in the current analys.
2.4.Statistical analys
Measures of internal consistency utilid Cronbach’s alpha (˛).Bivariate correlations between continuous variables utilid the non-parametric Spearman’s correlation coefficient(r s).Bivari-ate correlations between continuous and dichotomous variables utilid the non-parametric Kendall’s tau-b( )correlations.Con-firmatory factor analysis(CFA)was undertaken to examine thefit of the BYNDS model of Scott-Parker et al.(2010).All analys were conducted using AMOS version18and
PASW version18.0.
3.Results
3.1.Psychometric properties of the original BYNDS subscales and scale
Table1also reports the means and standard deviations for the individual items within the original BYNDS.On average,the young novice drivers reported high levels of exposure to risk,such as driv-ing on the weekend and at night,moderate levels of risky driving as evidenced by lf-reported speeding and driving whilst affected by their mood and emotions,and driving errors such as missing exits and turns,and lower levels of risky behaviours such as driving without their atbelts.
Table2reports the mean,standard deviation,and˛for each of the subscales and the composite BYNDS for the P1drivers.The participants reported a large amount of risky driving exposure(evi-denced by the average score per item=2.80,on a5-point scale), a moderate amount of transient violations(1.73)and driving in respon to mood(1.78),and some misjudgement(1.36)andfixed rule violations(1.06).The composite BYNDS and thefive subscales were highly internally consistent(Table2).
Table2also shows the correlations amongst(sub)scales,lf-reported crashes,offences and driving intentions.Thirty-ven (9.6%)participants reported being involved in a car crash(8.9% of females,10.7%of males),and46(11.8%)participants reported being detected for an offence(9.5%of females,17.7%of males),as a driver with a Provisional licence.Most participants intended to follow the road rules in the next year(n=248,64.8%),with74par-ticipants(19.3%)unsure if they were going to follow the rules or not and the remaining61participants(15.9%)intending to break the road rules in the next year.The bivariate correlations amongst the subscales,the composite BYNDS,crashes,offences and intentions were all statistically significant.The transient violations subscale was most strongly associated with driving intentions.Driving at risky times and transient violations subscales were most strongly associated with lf-reported offence detection.Fixed violations and driving at risky times subscales were most strongly associated with lf-reported crash involvement.
张廷玉
3.2.Confirmatory factor analysis
Prior to confirmatory factor analysis,the individual BYNDS items and the subscales were assd for normality.The items exhibited considerable non-normality as measured by skew and kurtosis(for example,“Your pasngers didn’t wear atbelts:skew=9.64,kur-tosis=102.56).This was not an unex
pectedfinding,as most drivers generally follow the road rules,including GDL restrictions.There are implications for the CFA however,as non-normal data will result in an inaccurate asssment offit(particularly the chi-square test),therefore the model may erroneously be rejected(Anderson and Gerbing,1988).Transformation of each item did not ame-liorate the violation of the normality assumption.In addition to the univariate non-normality,the data also were found to exhibit multivariate non-normality(kurtosis>7,West et al.,1995).There-fore it was decided that the confirmatory factor analysis would operationali the raw data and utili the Bollen–Stine bootstrap method using2000bootstrap samples to adjust for non-normality (Bollen and Stine,1992)with maximum likelihood estimation. Seven univariate outliers who reported commonly driving in a very risky way were identified prior to the CFA and the were removed from further analys to facilitate statisticalfidelity.In addition,the covariances were examined and were found to be consistent with the factor structure of the exploratory factor analysis conducted in the development of the original BYNDS(Scott-Parker et al.,2010).
The CFA required an asssment of good modelfit which was determined by a non-significant Bollen–Stine chi-square( 2).In addition,the Joreskog–Sorbom goodness offit index(GFI≥.95 indicative of good modelfit for a normally distributed sam-ple),Bentler’s Comparative Fit Index(CFI≥.9
5),the Steiger–Lind Root Mean Square Error of Approximation(RMSEA≤.08)includ-ing90%confidence intervals(Kline,2011),the Tucker–Lewis Index (TLI≥.95),and Akaike’s Information Criteria(AIC)were examined and ud for the purpos of model comparison and improved
388 B.Scott-Parker et al./Accident Analysis and Prevention49 (2012) 385–391
Table2
学生时间规划表Psychometric properties of and the bivariate correlations between the original Behaviour of Young Novice Drivers(BYNDS)(sub)Scales,lf-reported crashes,offences,and anticipated driving behaviour.
Measure n M SD˛Skew Kurtosis Correlations with(sub)scales
I II III IV V BYNDS
I Transient violations1322.567.40.89 1.20 1.24 1.00
II Fixed violations1010.58 1.75.75 6.2552.38.37*** 1.00
III Misjudgement912.22 2.85.73 1.46 2.88.43***.20*** 1.00
IV Risky exposure925.19 5.23.81.14.19.50***.27***.38*** 1.00
V Driver mood3 5.34 2.33.87 1.11 1.19.52***.24***.39***.40*** 1.00
BYNDS composite4475.8815.04.92.96 1.19.87***.42***.63***.79***.65*** 1.00 Crash––––––.10*.22***.14**.18***.12*.17*** Offence––––––.19***.17***.14**.19***.13**.21*** Intentions1 2.66 1.66–.74−.43.48***.28***.19***.25***.37***.44*** Bivariate correlations between continuous variables utilid the non-parametric Spearman’s correlation coefficient(r s).Bivariate correlations between continuous and dichotomous variables utilid the non-parametric Kendall’s tau-b( )correlations.–,not applicable.Means,standard deviations,Cronbach’s alpha,skew,kurtosis,and correlations were calculated using the raw data in PASW18.0.
*p<.05.
**p<.01.
中国无线电协会***p<.001.
Table3
Goodness-of-fit indices and the items removed iteratively from the Original Behaviour of Young Novice Drivers(BYNDS)Scale.
Item Bollen–Stine GFI CFI TLI RMSEA(95%CI)AIC
2p
Full model(n=44items)2340.41.006.80.77.76.06(.06to.07)2536.41 Step1:Removedfive items with loading<.301703.58.04.83.83.81.05(.05to.069)1879.58 You went for a drive with your mates giving you directions to where they wanted to go
You drove when you thought you may have been over the legal alcohol limit
You drove a high-powered vehicle
Your pasngers didn’t wear atbelts
You didn’t always indicate when you were changing lanes
Step2:Removed one item with lowest factor loading1540.50.02.84.84.83.06(.05to.06)1712.50 You didn’t wear a atbelt if it was only for a short trip
Step3:Removed one item with lowest factor loading1418.13.04.85.85.84.06(.05to.06)1586.13 You went too fast around a corner
Step4:Removed one item with lowest factor loading1285.74.06.86.87.86.05(.05to.06)1449.74 Your car was full of your friends as pasngers
modelfit during the iterative CFA process.Modification indices were not ud to guide improvement of modelfit,rather examina-tion of the individual item loadings informed the iterative removal of items.The original BYNDS model was not a goodfit to the data. Table3summaris the items that were removed and the corre-sponding goodness-of-fit indices.After iterative removal of eight items,thefinal model was a goodfit to the data.
In an attempt to improve model parsimony,the item“You trav-elled in the right lane on multi-lane highways”was removed from the transient violations subscale.Whilst this behaviour is illegal(pun-ishable by a AUD$60fine and two licence demerit points,DTMR, 2010),this item was considered to be the item least likely to con-tribute to the young novice driver being involved in a crash within this subscale.The model was not a goodfit to the data,and it was decided that the revid36-item BYNDS would be retained at this time.Fig.1illustrates this model.
3.3.The revid BYNDS and its subscales
The psychometric properties of the revid BYNDS composite scale and its subscales are summarid in Table4,which reports the mean,standard deviation,and˛of the revid(sub)scales, as well as the bivariate correlations between the revid BYNDS (sub)scales and the original BYNDS(sub)scales,lf-reported crashes and offences,and driving intentions.As shown,the revid BYNDS(sub)scales were internally consistent,and as expected they were also strongly correlated with the corresponding original(sub) scales.
The revid BYNDS transient violations subscale was weakly associated with lf-reported crashes,moderately associated with lf-reported offences,and strongly associated with driving inten-tions.The revidfixed violations subscale was moderately associated with crashes,offences,and intentions.The revid mis-judgement,risky exposure,and driver mood scales were weakly associated with crashes and offences,and moderately associated with intentions.The revid BYNDS composite scale was weakly associated with crashes,moderately associated with offences,and strongly associated with driving intentions.2
4.Discussion
The BYNDS emerged from a need for a tool designed specifically for measuring the lf-reported risky behaviour of the young novice driver.In addition to the requirement of road safety rearchers for instruments that are reliable and valid,and given that multi-ple measures are typically incorporated in programs of rearch, parsimonious tools are fundamental.The items within the origi-nal,and therefore the revid,BYNDS were drawn from the road
2Logistic regression analys were conducted to explore the relationship between the BYNDS subscales and lf-reported crash involvement and violations.The sub-scales explained approximately12.2%of variance(Nagelkerke R2)in lf-reported crash involvement and16.6%of variance(Nagelkerke R2)in lf-reported viola-tions.Risky driving exposure was a significant predictor of both crashes(p=.004) and offences(p=.009);andfixed violations was a significant predictor of crashes (p=.045).Caution should be exercid in the interpretation of thefindings,how-ever,as the very small sample sizes preclude definitive conclusions.
B.Scott-Parker et al./Accident Analysis and Prevention49 (2012) 385–391389 The Revid Behaviour of Young Novice Drivers Scale (B YNDS) Model
Fig.1.The revid Behaviour of Young Novice Drivers Scale(BYNDS)model.
safety literature and GDL restrictions.The original scale and sub-scales exhibited very high internal consistency(Scott-Parker et al., 2010),and thefindings were repeated in the current rearch, notwithstanding the non-normality of the data.
Further,behavioural measures apart from traditional road safety outcomes of crashes and offences are required.This is particularly the ca as not every road rule transgression is detected by regula-tory authorities,such as the Police.Crashes are also comparatively
Table4
Psychometric properties of the revid Behaviour of Young Novice Drivers(BYNDS)(sub)Scales,and the correlation of the revid(sub)Scales with the original BYNDS (sub)scales,crashes,offences,and anticipated driving behaviour.
Scale Revid BYNDS Correlations
n M SD˛Skew Kurt.I II III IV V BYNDS Crash Off.Intent.
I Transient violations1220.807.00.88 1.17 1.14.99***.38***.41***.50***.51***.86***.10*.19***.62***
II Fixed violations6 6.27 1.10.72 6.0944.07.33***.64***.12*.18**.15**.32***.24***.20***.28***
III Misjudgement810.96 2.59.73 1.36 2.28.40***.19***.96***.33***.35***.58***.11*.12**.20***
IV Risky exposure720.79 4.36.78.15.33.46***.25***.31***.95***.37***.71***.17***.17***.30***
V Driver mood3 5.34 2.33.88 1.11 1.19.53***.27***.38***.40*** 1.00.63***.12*.13***.39*** BYNDS composite3664.1513.09.90.86.74.89***.42***.60***.75***.65***.99***.16***.21***.58***
Bivariate correlations between continuous variables utilid the non-parametric Spearman’s correlation coefficient(r s).Bivariate correlations between continuous and dichotomous variables utilid the non-parametric Kendall’s tau-b( )correlations.Kurt.,kurtosis;Off.,offence;Intent.,intentions.Means,standard deviations,Cronbach’s alpha,skew,kurtosis,and correlations were calculated using the raw data in PASW18.0.
*p<.05.
**p<.01.
***p<.001.
