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Rowe, R., Roman, G.D., McKenna, F.P., Barker, E. and Poulter, D.R. (2014)
Measuring
errors
and
violations
on
the
road:
A
bifactor
modeling
approach
to
the
Driver
Behavior
Questionnaire
Richard
Rowe
a,*,
Gabriela
D.
Roman
b,
Frank
P.
McKenna
c,
Edward
Barker
d,
Damian
Poulter
ba
UniversityofSheffield,Sheffield,UK
bUniversityofGreenwich,Greenwich,UK cUniversityofReading,Reading,UK d
InstituteofPsychiatry,King'sCollegeLondon,UK
ARTICLE INFO
Articlehistory:
Received19August2014
Receivedinrevisedform4October2014 Accepted9October2014
Availableonline29October2014
Keywords:
DriverBehaviorQuestionnaire Bifactor
Confirmatoryfactoranalysis Youngdrivers
ABSTRACT
TheDriverBehaviorQuestionnaire(DBQ)isaself-reportmeasureofdrivingbehaviorthathasbeen widelyusedovermorethan20years.Despitethiswealthofevidenceanumberofquestionsremain, includingunderstandingthecorrelationbetweenitsviolationsanderrorssub-components,identifying howthesecomponentsarerelatedtocrashinvolvement,andtestingwhetheraDBQbasedonareduced numberofitemscanbeeffective.Weaddresstheseissuesusingabifactormodelingapproachtodata drawnfromtheUKCohortIIlongitudinalstudyofnovicedrivers.Thisdatasetprovidesobservationson 12,012driverswithDBQdatacollectedat.5,1,2and3yearsafterpassingtheirtest.Abifactormodel, including ageneralfactorontowhichall itemsloaded, andspecific factorsforordinaryviolations, aggressiveviolations,slipsanderrorsfittedthedatabetterthancorrelatedfactorsandsecond-order factorstructures.Amodelbasedononly12itemsreplicatedthisstructureandproducedfactorscores that werehighly correlated with the full model. Theordinary violationsand general factor were significantindependentpredictorsofcrashinvolvementat6monthsafterstartingindependentdriving. Thediscussionconsiderstheroleofthegeneralandspecificfactorsincrashinvolvement.
ã2014TheAuthors.PublishedbyElsevierLtd.ThisisanopenaccessarticleundertheCCBYlicense (http://creativecommons.org/licenses/by/3.0/).
1.Introduction
Roadtrafficcrashescontinuetopresentaseriouspublichealth challenge.AccordingtotheWorldHealthOrganizationtherewere approximately1.24milliondeathsontheroadin2010acrossthe world,equatingtoalmost3400aday,withestimatesofinjuries arisingfromroadtrafficcrashesrisingfromtheeleventhtothe eighthleadingcauseofmortalityfrom2002to2010(Pedenetal.,
2004;WorldHealthOrganisation,2013).Humanbehaviorisakey
factor in crash risk. Based on Reason’s extensive work on the humancontributiontodisasteracrossawiderangeofsituations (Reason, 1990), the Driver Behavior Questionnaire (DBQ) was designed as a self-report measure of the behaviors that may increase risk of crash involvement (Reason et al., 1990). The measure distinguishes unintentional cognitive failures from deliberateviolations of theaccepted principles of safedriving.
Cognitive failures are often further categorized into slips and lapses,“theunwittingdeviationofactionfromintention”(Reason etal.,1990page1315),suchasgettingintothewronglaneata junction, and errors, which involve “the departure of planned actions from some satisfactory path towards a desired goal” (Reasonetal.,1990page1315),suchasmissingagive-waysign. Violationsmayalsobesubcategorized.Ordinaryviolations,suchas speeding and crossing red lights, may be distinguished from violations thatinvolveaggressiontowards otherroadusers,for example,soundingthehorntodisplayaggression(Lawtonetal.,
1997).SinceitspublicationtheDBQhasbeenveryinfluential,with
morethan174papersusingthemeasure(deWinterandDodou, 2010).
Despitethisvolumeofresearch,anumberofquestionsremain abouttheDBQ.Akeyissueforitsutilityistheextenttowhichthe DBQsubscalesrelatetocrashinvolvement.Relevantevidencehas been mixed,with someearly workconcluding violations were goodpredictorsofself-reportedcrasheswhereascognitivefailures werenot (Parkeret al.,1995).However,a recent meta-analysis concluded that there were simple correlations between self-reported crashinvolvement and both cognitive failures (r=.10,
*Correspondingauthorat:DepartmentofPsychology,UniversityofSheffield, WesternBank,SheffieldS102TP,UK.Tel.:+441142226606.
E-mailaddress:r.rowe@sheffield.ac.uk(R.Rowe).
http://dx.doi.org/10.1016/j.aap.2014.10.012
0001-4575/ã2014TheAuthors.PublishedbyElsevierLtd.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/3.0/).
ContentslistsavailableatScienceDirect
Accident
Analysis
and
Prevention
both cognitive failures and violations may be reported more frequentlyby highermileagedrivers. Sharedorcorrelated risk factors may also contribute to the association. For example, hyperactivityis associated with bothrisk-taking anderrors in children’sminorinjuryinvolvement(RoweandMaughan,2009). A similar situation may exist in driving: the inattentive component of hyperactivity may lead to increased rates of cognitive failures and theimpulsivity component maylead to morefrequentviolations.Itisalsolikelythatacomponentofthe correlationisduetosharedmethodvariance,asdiscussedbyde
WinterandDodou(2010)andafWahlbergandDorn(afWahlberg
andDorn,2012).Scaleswhicharecompletedbythesamereporter maybespuriouslycorrelatedforanumberofreasons,including individualdifferencesinresponsestylesuchaslocatinganswers on particular points of a scale consistently across measures (Podsakoffetal.,2003).
Effectively modeling the relationship between cognitive failuresandviolationsmaybekeytounderstandingthenature oftheirrelationshiptocrashinvolvement.Oneapproachhasbeen tospecifyasecond-orderfactorstructure.Forexample,Lajunen
et al. (2004) fitted a second-order model in which a general
violationsfactoraccountedfor theassociationbetween aggres-siveandordinaryviolationfactorsandageneralcognitivefailures factoraccountedfortheassociationofslipsanderrors.Thismodel fitted the data well across large British, Dutch and Finnish samples.Morerecently,Martinussenetal.(2013)fitteda second-order factorstructureusing confirmatory factoranalysis(CFA). Thismodel wasbased ona somewhat different itemsetfrom
Lajunenetal.(2004).Themodelcontainedonlythreefirstorder
factors(errors,lapsesandviolations)aswellasasingle“aberrant behavior” second-order factor onto which all three firstorder factorsloaded.
Secondordermodelsareusefuldatareductiontechniques,but comeatalossofdifferentiationbetweenfactors.Oncethefirst order factors of violations and cognitive failures have been specifiedtorepresentasinglegenericfactorofaberrantbehavior, they are no longer used as independent constructs. Bifactor modellingoffersanalternativeconceptualizationofgeneraland specificfactorswithbothbeingdefinedbydirectloadingsofthe observed items. Specific factors may be correlated with each other,butareassumedtobeindependentfromthegeneralfactor (Chenetal.,2006).Thebifactormodellingapproachhasrecently been applied to understand the relationships between related constructsinanumberofotherdomainsincludinganxietyand depressionwithinnegativeaffectdisorders(Simmsetal.,2008), impulsivityand inattentionwithin hyperactivity (Martel et al.,
2010) and irritabilityand behavioral problems within
opposi-tionalbehaviors(Burkeetal.,2014).Chenetal.(2006)describea numberofadvantagesofthebifactorapproach.Oneadvantageis thatthe bifactormodelexplicitlydemonstratesthestrengthof thespecificfactors.Inasecond-orderfactormodel,ontheother hand,itmaynotbereadilyapparentwhetheraspecificfactoris importantindependentlyfromtherelationshipoftheitemstothe higher order factor. This feature is particularly useful when examining the external correlates of the specific factors independently from the general factor. Therefore a bifactor
usedtheeighthighestloadingitemsonthe3factorsofordinary violations, errorsandlapsesfrom theoriginal 50-itemversion (Parker et al., 1995), and a 27-item version that included 3additionalitemsonaggressiveviolationspreviouslyidentified asdistinguishablefromordinaryviolations(Lawtonetal.,1997). Recently, researchers have shown interest in using further shortenedversionsofthemeasure.Forexample,a4-itemversion oftheordinaryviolationssub-scale(whichusuallyhas8-items) was employed in the Genesis1219 study (Rowe et al., 2013). Furthermore,Martinussenetal.(2013)providedevidenceonthe validityofa9-itemversionoftheDBQusingconfirmatoryfactor analysis. In this model,factors of violations,lapses and errors weremeasuredwiththreeitemseach.
InthepresentstudyweusetheUKDepartmentforTransport’s CohortIIstudyofnovicedriverstoexaminethefitofabifactor modeltoDBQdata.Wecomparethistothefitoffirst-orderand second-orderfactormodels.Wetestwhetherashortversionofthe DBQ based on the best-fitting factor model provides adequate psychometric properties.Finally,weexploretheexternal corre-latesofthebestfittingmodel,includingcontemporaneous self-reportedcrashinvolvement.
2.Method
2.1.Sample
Detailed description of the study design may be found elsewhere (Wells et al., 2008). Briefly, the Cohort II study randomly sampled 128,000 practical test candidates between November 2001 and August 2005. The identified samplewere sent an initial questionnaire 10–16 days after taking the final componentofthedrivingtest.Theinitialresponseratewas33% (42,851 responses). Only 20,512 respondents (49%) that had passedtheirtestwereeligibletocontinueinthestudy.Thestudy design allowed for follow-up questionnaires at 6, 12, 24 and 36 months. However, at thetime of study termination not all participantshadcompleted36monthsdriving.Allparticipants hadbeendrivingfor 1year,but only77%hadbeendrivingfor 2 yearsand 52% hadcompleted thefull follow-upperiod. The currentanalysesarebasedon12,012driverswhoprovideddataat up to four timepoints: 10,064 participated at 6 months (49% responserate),7450at12months(36%),4189at24months(27%) and 2765 at 36 months (26%). The participants tended to be young;59%wereundertheageof20yearsand76%undertheage of 25years.The gendercompositionwas63%womenand37% men. The age distributionis comparable to the population of newly licenseddrivers whilefemaleswereover-represented in thesample.(Wellsetal.,2008).
TheCohortIIstudywascommissionedandfundedbytheUK Department for Transport and conducted by the Transport Research Laboratory, Crowthorne, UK (Transport Research
Laboratory Safety Security and Investigations Division, 2008).
2.2.Measures
Informationaboutdrivingbehaviorwasself-reportedthrough theDriverBehaviorQuestionnaire(Reasonetal., 1990).Theversion usedinouranalysesfollowedthatusedbyLajunenetal.(2004). Thisincludes27items,8ordinaryviolations,errorsandlapses,and 3aggressiveviolations.Responseswereona6-pointLikertscale, rangingfrom1=‘never’to6=‘nearlyallthetime’.
Additionallyweutilizedmeasuresofself-reportedmileageand numberofpublicroadcrashes,bothassessedat6 monthsafter licensure.Mileagewasconstructedasthenumberofmilesdriven inthefirst6monthsofdriving.Themedianreportedmileagewas 2000.Thenumberofcrasheswasself-reportedonascalefrom0to 3(3=threeormorecrashes).Onlycrashesonapublicroadwere includedhere.Eighty-seven percentof thesample reported no crashesduringthisperiod,12.1%reportedonecrash,1.1%reported twocrashesand.2%reportedthreecrashesormore.
2.3.Analyticstrategy
Weemployedconfirmatoryfactoranalysis(CFA)to systemati-cally test four different factor structures. The first structure comprisedasinglefactorofaberrantdriving.Thesecondstructure comprised two factors: violations (combining aggressive and ordinaryviolations)andcognitivefailures(combiningerrorsand slips). The third structure comprised three factors: violations (combiningaggressiveandordinaryviolations),errorsandslips. Finally,the fourth structure comprised four factors: aggressive violations,ordinaryviolations,errorsandslips.Threemodelswere applied to each of these structures (except for the one-factor structure): a simple first-order structure with permitted inter-factorcorrelations, a second-order model and a bifactor model with no residual correlations. Following suggestions given by modelmodificationindices,anadditionalbifactormodelwitha residualcorrelationbetweenaggressive and ordinaryviolations was applied to the structure where ordinary and aggressive violations were separated. This procedure provided a total of 11models,repeatedateachofthefourstudytime-points.
Models were estimated using Mplus v.7.11 (Muthen and
Muthen,1998–2012MuthenandMuthen,1998–2012).Toaccount
for data skewness and missingness, the robust maximum likelihoodestimatorwas used.Values .90 onthecomparative
fitindex(CFI)andtheTucker-Lewisindex(TLI)and0.08onthe root mean square error of approximation (RMSEA) indicated adequatemodelfit(HuandBentler,1999).Additionally,CFIandTLI values.95andRMSEAvalues0.06indicatedexcellentmodelfit (Bentler,1990).
3.Results
3.1.FactorialstructureoftheDBQ
Acrossalltime-points,thefourfactorstructurewassuperiorto theone,twoandthreefactorstructures(Table1).Onlythefour factorbifactormodelreachedadequatefitconsistentlyacrossthe fourtimepoints.Furtherimprovementsinfitwereobservedwhen a residual correlation was permitted between the factors of aggressiveandordinaryviolations.
Table2providestheitemloadingsfromthebestfittingmodel.
Mostoftheitemsshowedasimilarpatternofloadingsacross time-points.All27-itemsloadedsignificantlyontotheGeneralfactor. The three aggressive violations loaded significantly onto the specificAggressiveviolationsfactor.Thetwospeedingitemswere consistentlystrongloadingitemsontheOrdinaryviolationsfactor aswastheracingawayfromtrafficlightsitem.Otheritemsshowed positiveandsignificantloadingsconsistentlyovertimewiththe exceptionofpullingoutofajunctionsofarthatotherdrivershave toletyouout.Thisitemwasnon-significantat6monthsandthe loadings were relatively low, though significant, at other time points.Gettingintothewronglaneandtakingthewrongturning fromroundaboutswerethestrongestloadingitemsontheSlips factor.Theotheritemsalsoloadedsignificantlywiththeexception of hitting something unseen when reversing. This item was significantat6and12monthsandnotatlatertimepointsandall loadingswere.06orbelow.ItemloadingsontheErrorsfactorwere generally weaker than on the other factors. Only three items showedsignificantloadingsatalltimepoints(nearlyhitacyclist ontheinsidewhenturningleft,missedgivewaysign,attemptto overtakeandhadn'tnoticedsignalingright).
3.2.DBQshortversion
Next we tested whether a shortened version of the DBQ provides adequate psychometric properties within a bifactor
Table1
Modelfitforfirst-order,second-orderandbifactormodels,atthefourtime-points.
Model df 6months 12months 24months 36months
x2
CFI TLI RMSEA x2
CFI TLI RMSEA x2
CFI TLI RMSEA x2
CFI TLI RMSEA
N=1 324 7320.25 .76 .73 .05 6850.84 .76 .74 .05 4593.92 .75 .73 .06 3462.66 .73 .71 .06
–
N=2
Simple 323 5235.49 .83 .81 .04 4748.45 .84 .82 .04 2972.29 .84 .83 .05 2205.76 .84 .83 .05 2ndorder 322 5219.29 .83 .81 .04 4733.76 .84 .82 .04 – – – – 2198.93 .84 .83 .05 Bifactor 297 3087.52 .90 .88 .03 – – – – 1903.96 .91 .89 .04 1453.80 .90 .88 .04
–
N=3
Simple 321 4567.21 .85 .84 .04 4139.80 .86 .84 .04 2661.44 .86 .85 .04 1926.22 .86 .85 .04 2ndorder Fitstatisticsarethe
sameasN=3Simple structure
Bifactor 297 3012.39 .91 .89 .03 2898.90 .90 .89 .04 1979.40 .90 .88 .04 1411.88 .91 .89 .04
–
N=4
framework.Thethreeitemswith thehighestloadingsoneach specificfactorwerechosen.Themodelfitwasexcellentateach timepoint,withCFI.98,TLI.96andRMSEA.03.Allitems loaded significantly onto their respective specific factors and onto the General factor (Table 3). The factorloadings of each
itemwere strikingly similar at each time-point.Item loadings weremoderatetostrong inrelation totheOrdinaryviolations and Aggressive violations factors, moderate in relation to the Slips factorand theGeneralfactorandfairlylowinrelationto theErrorsfactor.
E Speedofoncomingvehiclewhenovertaking .49(.46,.52) .53(.50,.57) .53(.50,.56) .55(.51,.60) O Crossedjunctionknowinglightshaveturnedagainstyou .47(.44,.50) .51(.48,.54) .51(.48,.55) .50(.46,.55) E Braketooquicklyonslipperyroadorsteerwronginskid .47(.44,.50) .51(.47,.54) .50(.47,.53) .52(.47,.57) S Getintowronglanewhenapproachingroundabout/junction .46(.43,.48) .46(.43,.48) .48(.45,.51) .49(.45,.54) O Pulloutofjunctionsofarthatdriverhastoletyouout .45(.43,.48) .49(.47,.54) .48(.45,.51) .48(.43,.53) S Realizedhavenorecollectionofroadbeentravelling .42(.40,.45) .43(.40,.45) .44(.40,.47) .45(.40,.50) O Havedisregardedspeedlimitonmotorway .39(.37,.42) .38(.35,.41) .38(.35,.42) .33(.28,.38) S Misreadsignsandtakenwrongturningoffroundabout .39(.37,.42) .39(.36,.41) .42(.39,.46) .48(.43,.52) S Noticedondifferentroadtodestinationwanttogo .38(.35,.41) .39(.36,.42) .40(.36,.43) .42(.38,.47) O Racedawayfromtrafficlightstobeatotherdriver .37(.34,.40) .38(.35,.41) .38(.34,.41) .41(.36,.46) E Whenturninglefthavenearlyhitcyclistoninside .34(.30,.38) .43(.36,.49) .38(.34,.43) .41(.36,.46) O Stayinmotorwaylaneknowwillbeclosed .34(.30,.37) .39(.35,.43) .37(.33,.41) .32(.27,.37) A Becomeangeredbydriverandindicatehostility .34(.31,.37) .38(.35,.41) .32(.28,.36) .32(.26,.37) E Driveawayfromtrafficlightsattoohighagear .33(.30,.36) .37(.34,.40) .39(.36,.43) .37(.32,.41) S Hitsomethingwhenreversingthathadn'tseen .32(.28,.35) .39(.33,.44) .35(.31,.40) .37(.30,.43) S Switchononethingwhenmeanttoswitchonother .31(.29,.34) .35(.32,.38) .39(.35,.42) .36(.31,.40) E Attempttoovertakeandhadn'tnoticedsignalingright .31(.27,.35) .35(.29,.41) .38(.34,.42) .41(.35,.47) S Forgetwhereleftcarincarpark .29(.26,.32) .30(.27,.33) .29(.25,.32) .32(.27,.37) A Becomeangeredbydriverandgivenchase .27(.23,.31) .35(.29,.40) .26(.21,.30) .26(.20,.32) O Overtakeaslowdriveroninside .26(.23,.29) .31(.27,.35) .29(.24,.33) .27(.22,.32) A Soundhorntoindicateannoyance .26(.23,.28) .29(.25,.32) .29(.25,.33) .29(.24,.34)
–
Aggressiveviolations
Becomeangeredbydriverandindicatehostility .72(.68,.74) .71(.68,.74) .75(.71,.79) .69(.64,.74) Soundhorntoindicateannoyance .59(.55,.62) .59(.56,.63) .62(.58,.66) .63(.58,.68) Becomeangeredbydriverandgivenchase .45(.41,.49) .43(.39,.47) .44(.38,.49) .48(.42,.54)
–
Ordinaryviolations
Havedisregardedspeedlimitonmotorway .56(.52,.60) .61(.57,.65) .59(.54,.63) .62(.57,.68) Racedawayfromtrafficlightstobeatotherdriver .54(.50,.58) .55(.50,.59) .56(.51,.60) .55(.50,.60) Disregardspeedlimitonresidentialroad .48(.45,.52) .51(.47,.54) .53(.49,.58) .52(.47,.58) Overtakeaslowdriveroninside .27(.23,.31) .32(.28,.37) .41(.36,.46) .43(.38,.49) Stayinmotorwaylaneknowwillbeclosed .24(.19,.29) .31(.27,.36) .35(.30,.41) .41(.35,.47) Crossedjunctionknowinglightshaveturnedagainstyou .23(.19,.26) .25(.22,.29) .29(.24,.34) .28(.23,.34) Drivesoclosetocarthatwouldn'tbeabletostop .21(.18,.24) .25(.21,.28) .27(.22,.31) .26(.21,.31) Pulloutofjunctionsofarthatdriverhastoletyouout .04[.095]( .01,.08) .05[.021](.01,.10) .12(.07,.17) .13(.07,.20)
–
Slips
Misreadsignsandtakenwrongturningoffroundabout .49(46,.53) .53(.50,.57) .38(.30,.46) .45(.38,.53) Getintowronglanewhenapproachingroundabout/junction .42(.39,.45) .46(.42,.49) .38(.30,.45) .42(.34,.50) Noticedondifferentroadtodestinationwanttogo .33(.29,.36) .32(.28,.36) .38(.30,.45) .37(.28,.45) Forgetwhereleftcarincarpark .28(.25,.32) .31(.26,.35) .35(.27,.43) .32(.23,.41) Switchononethingwhenmeanttoswitchonother .25(.22,.28) .26(.22,.30) .24(.18,.29) .25(.18,.31) Realizedhavenorecollectionofroadbeentravelling .19(.15,.22) .15(.11,.19) .23(.16,.31) .18(.10,.27) Driveawayfromtrafficlightsattoohighagear .14(.10,17) .15(.12,.19) .19(.14,.24) .12(.06,.19) Hitsomethingwhenreversingthathadn'tseen .06[.001](.03,.10) .05[.021](.01,.10) .05[.088]( .01,.11) .06[.098]( .01,.13)
–
Errors
Attempttoovertakeandhadn'tnoticedsignalingright .25(.16,.35) .29(.21,.37) .28(.16,.39) .17[.002](.07,.28) Whenturninglefthavenearlyhitcyclistoninside .22(.11,.33) .26(.15,.37) .40(.26,.54) .35(.21,.49) Missedgivewaysignsandavoidedcollidingwithtraffic .21(.11,31) .21(.12,.31) .23(.14,.31) .43(.29,.57) Failedtonoticepeoplecrossingwhenturnedintosidestreet .15(.07,.22) .06[(.077]( .01,.12) .18(.11,.26) .10[.049](.00,.19) Speedofoncomingvehiclewhenovertaking .13(.06,.20) .10[.001](.04,.15) .08[.042](.00,.16) .10[.113]( .02,.21) Whenqueuingtoturnleftnearlyhitcarinfront .07(.074)( .01,.22) .05[.135]( .02,.11) .04[.246]( .03,.12) .03[.536]( .06,12) Braketooquicklyonslipperyroadorsteerwronginskid .03[.688]( .10,.16) .04[.356]( .04,.11) .09[.031](.01,.17) .21[.00]1(.08,.34) Failedtocheckrear-viewmirrorbeforemaneuvering . 20[.035]( .38,01) .22( .31, .13) .08[.066]( .17,.01) .08[.127]( .02,.17)
a
ItemsareorderedaccordingtofactorloadingatTime1.Valuesinroundparenthesesrepresent95%confidenceintervals.
*
Bivariatecorrelationsbetweenfactorscoresobtainedfromthe 27-itemversionandfromthe12-itemversionrevealedthat,at eachtime-point,theshortversionaccuratelyreproducedthefull versionwithregardtothe Generalfactor(r=.82–.84;p<.001), the Ordinary violations factor (r=.83–.86; p<.001) and the Aggressiveviolationsfactor(r=.93–.95;p<.001).Highagreement in scores were also obtained for the Slips factors (r=.76–.90; p<.001) and moderate agreementwas obtained for theErrors factor(r=.65–.83;p<.001).
Table4showsthecorrelationsbetweenfactorscoresgenerated
fromthebifactormodeloftheshortenedDBQ.Allfactor scores weresignificantlyassociated,withthemajorityshowingsmallto moderatecorrelations.Asexpectedfromthemodelspecification, thecorrelationbetweenAggressiveandOrdinaryviolationswas moresubstantial.Therewerealsomoresubstantialcorrelations
betweenthespecificerrorsandslipsfactorsandbetweentheslips andthegeneralfactor.
3.3.CovariatesandoutcomesoftheshortversionDBQ
To examine the relationship between driver behavior, as conceptualizedin thebifactor modelof theshort versionDBQ, andknowncovariatesandoutcomes,wesavedfactorsscoresfrom the model using the six-month time point. Allvariables were standardized.Nonparametricbivariatecorrelationsindicatedthat, at6monthsafterlicensure,youngerparticipantshadhigherlevels ofgeneralaberrantbehavior(r= .20,p<.001),ordinaryviolations (r= .31,p<.001)andaggressiveviolations(r= .21,p<.001),but slightlylowerlevelsof errors(r=.14,p<.001)and slips(r=.02, p=.04) than older participants. Nonparametric independent-Table3
ModelfitandfactorloadingsforthefourfactorbifactormodelappliedtotheshortversionDBQ.
Scale Time1 Time2 Time3 Time4
x2 192.13 251.41 154.88 107.68
CFI .99 .98 .98 .99
TLI .98 .96 .97 .98
RMSEA .02 .03 .03 .03
–
Generalfactor b b b b
E Missedgivewaysignsandavoidedcollidingwithtraffic .46(.42,.51) .46(.40,.52) .45(.40,.50) .49(.41,.56) S Getintowronglanewhenapproachingroundabout/junction .46(.41,.50) .44(.40,.48) .48(.42,.53) .48(.40,.56) O Disregardspeedlimitonresidentialroad .45(.42,.48) .45(.41,.48) .44(.39,.49) .43(.37,.48) S Misreadsignsandtakenwrongturningoffroundabout .42(.38,.46) .42(.38,.46) .47(.42,.53) .51(.44,.57) S Noticedondifferentroadtodestinationwanttogo .41(.37,.46) .41(.37,.45) .44(.38,.50) .47(.40,.53) O Havedisregardedspeedlimitonmotorway .36(.33,.40) .35(.32,.39) .36(.31,.40) .29(.23,.34) O Racedawayfromtrafficlightstobeatotherdriver .34(.30,.38) .34(.30,.38) .34(.29,.39) .37(.31,.43) A Becomeangeredbydriverandindicatehostility .32(.28,.36) .38(.34,.42) .31(.26,.36) .27(.21,.34) E Whenturninglefthavenearlyhitcyclistoninside .32(.27,.37) .40(.32,.48) .36(.31,.41) .41(.35,.47) E Attempttoovertakeandhadn'tnoticedsignalingright .27(.22,.32) .32(.26,.39) .34(.29,.40) .38(.30,.47) A Becomeangeredbydriverandgivenchase .26(.20,.32) .35(.27,.42) .22(.16,.29) .24(.14,.33) A Soundhorntoindicateannoyance .24(.20,.28) .27(.23,.31) .29(.24,.34) .29(.22,.36)
–
Aggressiveviolations
Becomeangeredbydriverandindicatehostility .72(.68,.75) .70(.67,.74) .74(.70,.79) .72(.67,.77) Soundhorntoindicateannoyance .60(.56,.63) .60(.56,.64) .63(.58,.67) .62(.57,.67) Becomeangeredbydriverandgivenchase .46(.42,.50) .44(.39,.49) .45(.40,.51) .49(.42,.56)
–
Ordinaryviolationsa
Racedawayfromtrafficlightstobeatotherdriver .70(.65,.74) .70(.67,.74) .70(.65,.76) .69(.63,.75) Havedisregardedspeedlimitonmotorway .44(.40,.48) .48(.43,.52) .47(.42,.52) .51(.45,.56) Disregardspeedlimitonresidentialroad .40(.36,.44) .41(.36,.45) .43(.37,.49) .42(.35,.48)
–
Slips
Misreadsignsandtakenwrongturningoffroundabout .49(.43,.55) .55(.49,.62) .39(.27,.50) .50(.39,.61) Getintowronglanewhenapproachingroundabout/junction .47(.40,.53) .49(.42,.56) .49(.37,.61) .45(.32,.58) Noticedondifferentroadtodestinationwanttogo .25(.20,.30) .25(.19,.30) .21(.12,.29) .24(.15,.34)
–
Errors
Attempttoovertakeandhadn'tnoticedsignalingright .34(.21,.47) .30(.20,.40) .31(.20,.41) .22(.11,.32) Whenturninglefthavenearlyhitcyclistoninside .24(.13,.34) .33(.20,.46) .40(.27,.53) .37(.20,.54) Missedgivewaysignsandavoidedcollidingwithtraffic .23(.13,.32) .25(.15,.35) .29(.19,.40) .43(.26,.61) Allpvalues<.001.
aItemsareorderedaccordingtofactorloadingat6 months.Valuesinparenthesesrepresent95%confidenceintervals.A=AggressiveviolationsO=Ordinaryviolations
E=ErrorsS=Slips.
Table4
Non-parametricbivariatecorrelationsbetweenfactorscoresofdriverbehaviourdimensionsfromtheshortversionDBQat6months.
Generalbehavior Aggressiveviolations Ordinaryviolations Errors Slips Generalbehavior –
Aggressiveviolations .10** –
Ordinaryviolations .19**
.68** –
Errors .23**
.13**
.23** –
samples t-tests indicated that men exhibited higher levels of generalaberrantbehavior(Z=7.25,p<.001),ordinaryviolations (Z=21.50,p<.001)andaggressiveviolations(Z=15.74,p<.001), but lowerlevelsof errors (Z=3.68,p<.001)and slips (Z=6.53, p<.05)thanwomen.Mileagewas positivelyrelatedtoordinary violations(r=.24,p<.001),aggressiveviolations(r=.22,p<.001) andgeneralaberrantdriving(r=.12,p<.001),butwasnegatively relatedtoerrors(r= .10,p<.001)andunrelatedtoslips(r<.01, p=.65).
Toinvestigateassociationsbetweendriverbehaviorand self-reportedcrashinvolvementweusedordinallogisticregression,as appropriatetotheorderedcategoricalnatureof thepublicroad crashesoutcomevariable(scoredfromzerotothree).Preliminary testsindicated that theproportional odds assumptionwas not violated,astheparallellinestestrevealedanon-significant chi-square:
x
2(16)=17.95, p=.33. Table 5 shows the results from modelsincluding each DBQ factor score separatelyand from a modelcontainingallfiveDBQfactorscoresasjointpredictors.We foundtherewerenomulticollinearityproblemsinthejointmodel despite the correlations between predictors discussed above. Coefficients are presented in the form of odds-ratio where 1 standard deviation represents a unit change in continuous predictorvariables.Agegenderandmileagewereenteredascovariatesinallmodels. Inthejointpredictormodel,riskforpublicroadcrashesdecreased with age (OR=.83, p<.001; 95% CI=.77–.90)and increasedfor drivers with higher reported mileage (OR=1.06, p=.004; 95% CI=1.02–1.11),but was not significantly heightenedfor menas opposedtowomen1(OR=1.09,p=.17;95%CI=.96–1.25).Asshown
inTable5,withregardtodriverbehavior,theriskforpublicroad
crashes was increased in the presence of heightened levels of generalaberrantbehaviorandordinaryviolationsbothinthesingle andjointpredictormodels.Aggressiveviolationsandslipswere significantlyrelatedtocrashinvolvementinthemodelswherethey weretheonlyDBQpredictors.However,theywerenotsignificant predictorswhentheotherDBQfactorscoreswereincludedinthe jointmodel.Conversely,errorsdidnotsignificantlypredictcrash involvementinasingleDBQpredictormodelandhigherlevelsof errorsofweresignificantlynegativelyrelatedtocrashriskinthe jointpredictormodel, wherethecorrelations betweentheDBQ factorsweretakenintoaccount.
4.Discussion
TheDBQhasbeenusedinmanystudiesofdrivingbehavior, withvariousfactorstructuresbeingproposed. Inthis studywe
Inordertounderstandthenatureofthefactorsextractedinthe bifactormodelderivedfromtheshortversionDBQ,weexamined theircorrelatesfromthefirst6monthsofdriving.Interpretationof theserelationshipsprovideanovelperspectiveontheaspectsof theDBQ that areassociated withrisk forcrashinvolvement in novicedrivers.Wefoundthatordinaryandaggressiveviolations were more common in younger people and in males. These demographic factors are well-documented correlates of crash involvement(Evans,2004).Inaddition,ordinaryviolationswerea significant independent correlate of crash involvement. These resultsconfirmtheimportanceofordinaryviolationsasacorrelate ofcrashinvolvementasindicatedinawiderangeofotherstudies, summarizedindeWinterandDodou's(2010)meta-analysis.Our current results show that this remains the case once ordinary violations are modeled as a specific factor, independent from aggressiveviolations,slipsanderrorsfactorsandfromageneral factorofaberrantdriving.
Aggressive violations were significant correlates of crash involvement but were reduced to non-significance once their relationshipwithotherDBQfactorscoreswasaccountedfor;as expectedthecorrelationwas strongestwithordinaryviolations. Onepossibilityisthataggressiveandordinaryviolationsarepartof asingleviolationsconstructandshouldnotbescoredseparately. However, ourconfirmatory factoranalyses supported modeling aggressiveandordinaryviolationsasseparatethoughcorrelated factors.Anotherinterpretationoftheseresultsisthataggressive violations are only related to crash involvement due to their correlationwithordinaryviolations.Forexample,itmaybethat theaggressivestateunderlyingaggressiveviolationsalsoleadsto ordinaryviolationsanditistheordinaryviolationsthatconferrisk forcrashing.Exploringthispossibilitywillbeimportant,because aggressive states ofmind arestill hypothesizedtobecausalto crash involvement in this model, with the effect mediated by ordinary violations. If this is correct then aggressive violations remain a legitimate target for interventions to reduce crash involvement,eventhoughtheydonotpredictcrashinvolvement independentlyfromordinaryviolations.
In contrast, the specific errors and slips factors showed a differentpatternofcorrelationstothespecificviolationfactors; bothfactorsweremorecommonlyreportedbyfemalesthanmales, andbothweresignificantlymorecommoninoldernovicedrivers, although the correlation coefficient for slips was of a small magnitude. Slips showed a weak positive relationship tocrash involvementwhenmodeledseparatelyfromtheotherDBQscales butwasunrelatedwhenthecorrelationswithotherDBQfactors were accounted for. The specific errors factor was negatively, though non-significantly, related to crash involvement when modeledseparatelyfromtheotherDBQfactors.Thisrelationship became significantonce the correlation withother DBQ factor scoreswastakenintoaccount;driversthatscoredhighlyonthe errorsfactorwerelesslikelytoreportcrashinvolvement.
Atfirstsight,theresultsregardingtheslipsanderrorsspecific factorsmightappearinconsistentwiththefindingsofdeWinter
and Dodou (2010)meta-analysis where a general composite of
cognitivefailurescomprisingbotherrorsandslipswereassociated withincreasedcrashinvolvement. Thereason forthis different
a
Allmodelscontrolforage,genderandmileage.
* p<.05 ** p<.01 ***
p<.001
1
patternofresultsdoesnotlieinanyquirksoftheCohortIIdataset.
de Winter and Dodou (2010) included analyses of the links
betweencognitivefailuresandcrashinvolvementinCohortIIas wellastheirmeta-analysisofresultspublishedelsewhere.These analyses foundthat a composite cognitive failures scale, albeit containingsomewhat differentitems fromthe slips and errors factorsusedhere,werepositivelyrelatedtocrashinvolvementin cross-sectional and longitudinal analyses. It is likely that our resultsdonotshowthesamerelationshipsofslipsanderrorswith crashinvolvementbecauseour bifactorapproach models them independently from violations and a general aberrant driving factor. Once the components of variance shared with these constructs hasbeenremoved,the varianceunique toslips and errorswasnotassociatedwithincreasedriskofcrashinvolvement. The variables available in Cohort II are not well placed to investigate the mechanisms through which the specific errors factorwasnegativelyrelatedtocrashinvolvement.Onepossibility isthatmorecautiouspeoplearelikelytoreportmoreerrors,either becausetheycommitmoreerrorsoraremorelikelytoremember them,but that theircautionin other aspects of theirbehavior reducestheirriskofcrashinvolvement.
Asisoftenfoundintheapplicationofbifactormodelling(Burke
et al., 2014; Martel et al., 2010), our general factor showed
significantloadingsfromallitemsintheanalysis.Therefore,all items loading on the specific factors, which were modeled as independentfactors,alsoloadedontothegeneralfactor.Therewas somevariationinthestrengthofloadingsontothegeneralfactor. The confidence intervals on many of these estimates do not overlap,asshowninTable2,illustratingthatthevariationinthe loadingsarenotsimplyduetosamplingerror.Erroritemsshowed particularlystrongloadingsintheanalysesbasedonthefullDBQ. In the short version DBQ analyses, however, errors were less prominent among the highest loading items. Factor scores generated for the general factor from this model showed the largestcorrelationwiththespecificerrorsfactor. Similartothe specificordinaryandaggressiveviolationfactors,higherscoreson the general factor were associated with younger ages, higher mileage,andweremorecommoninmales.Thegeneralfactorwas alsoanindependentcorrelateofcrashinvolvement,withan odds-ratiosimilartothatofordinaryviolations.Thesefindingsindicate thatthegeneral factor isnot simplymeasuringsharedmethod variancebut identifiesa component ofdrivingthat is indepen-dentlyrelatedtocrashinvolvement.
Asnotedintheapplicationofbifactormodelsinotherdomains, such asattention deficit hyperactivity (Martelet al.,2010), risk factorsforgeneralandspecificfactorsmaydiffer.Withregardtothe DBQ, risk factors for the general factor and specific ordinary violationsfactorareofmostinterest astheseareindependently relatedtocrashinvolvement.Thefewriskfactorsstudiedhere(age, sex,mileage)showedsimilarassociationswiththesefactors.Other potentialrisk factors include drivertraining experiences, socio-economic status, sensation-seeking and antisocial tendencies. Further work is needed to examine whether these factors are similarlyrelatedtothegeneralfactorandtothespecificviolations factor.Itisalsopossiblethatthegeneralfactorandspecificviolations factor may be differentially affected by varied intervention approaches.Forexample,thegeneralfactor,withitsinclusionof drivingskillrelevantitems,maybeamenabletochangethrough interventionsthat improve driving skills.The specific violations factor,incontrast,mayonlybeimprovedbyattitude-or enforce-ment-basedinterventions(althoughseeMcKennaetal.,2006).
Inouranalysesthespecificcognitivefailurescalesdidnotshow characteristics to warrant inclusion in measurement of driving behaviorsthatincreasecrashrisk.However,measurementofthe relevantitemsisjustifiedinordertomeasurethegeneralfactorthat ismadeupfromacombinationofalltheviolationandcognitive
failureitems.Itremainspossiblethatcognitivefailuresdoperform animportantindependentroleinthecrashinvolvementofyoung driversbutthatthisisnotcapturedbythecurrentcognitivefailure items.Morerecentevidenceoncandidatedrivingerrorsthatdo increasecrashriskmayprovideanimpetustodevelopnew self-reportitems.Forexample,mobilephoneuse,whichmayitselfbe betterconceptualizedasaviolation,hasbeenshowntoincreaserisk ofcrash(McEvoyetal.,2007).Cognitivefailuresresultingfromthe distraction ofmobilephoneusearelikelytobethemechanism ofthis effect.Therefore,thetypesoferrorsthatdriversmakewhenusinga mobilephonemayprovidetheopportunitytoidentifycognitive failuresthatdoincreaseriskofcrashingandcanbemeasuredin self-report questionnaires.For example, onestudyidentifiedgreater speedfluctuationsasacharacteristicofdriversusingamobilephone inadriving simulator(Stavrinosetal.,2013).Therefore,anitem measuringspeedfluctuationmightbeeffectiveinmarkingdriversat riskof crash throughcognitivefailure.Alternatively,it ispossiblethat self-report does notallow accessto manyof thekey processes involvedindrivingperformance.Forexample,itisunlikelythat self-reportcouldprovideanaccurateassessmentofhazardperception ability,whererelativelylargedifferencesinskillmightonlyresultin responsetimeimprovingbya fewhundred millisecondsor less (Wettonetal.,2011).In addition,itmightbeinherentlymoredifficult fordriverstoidentifyandrecallaninappropriateplanforagiven situation,comparedtoidentifyingandrememberingwhenaplan wasnotsuccessfullyexecuted(Reasonetal.,1990).Thedifficulty withdetectingerrorsmayindicatethattheyarebettermeasuredby objectivemeanssuchasdrivingsimulations.
Inadditiontothenovelbifactorapproachtounderstandingthe structure of driving behaviors that confer crash risk, we also presentourshortversionoftheDBQasapotentialnewmethodof measuring and scoring the DBQ. A briefer form of the DBQ is desirablefor manysettings,for exampletheevaluationof road safety interventions. As noted in the introduction, other approaches to shortening the DBQ have been explored. The advantageoftheapproachtakenhereisthatitallowsscoringof bothgeneralandspecificfactorsthathavebeenidentifiedinthe modelingpresentedinthispaper,whileusingonly12items.
Theresultspresentedheremustbeinterpretedinthecontextof a number of limitations. First, although the Cohort II study providesauniquelargescalelongitudinalstudyofnovicedrivers,it inevitably suffered from non-participation and attrition which mayhavecoloredtheresults.Theresultsreportedherereplicatea numberofwell-documentedfindingsintheliterature,including identifying the expected correlates of the DBQ. This increases confidence that the novel findings identified here will also be replicable.Second,thesampleonlycontainsnovicedrivers,sowe were unable to investigate whether the results reported here generalizetomoreexperienceddrivers.However,thenovicedriver periodisoneonthemostimportantstagesinwhichtounderstand drivingbehaviorgiventheirelevatedratesofcrashinvolvement. Third,crashinvolvementwasmeasuredusingself-reportonly.It hasbeenarguedthatself-reportofcrashesmaybefallibleandmay beartificiallylinked toself-reportsof drivingbehavior through shared-methodvariance(afWahlbergandDorn,2011).Whilethis positionremainscontroversialandtheremaybesomeadvantages of self-reportdataover officially recordeddata(deWinter and
Dodou,2011), it wouldbe a usefulgoalfor furtherresearchto
replicatetheseresultsusing designswithdifferentmethodsfor recordingcrashessuchasofficialrecords.
Acknowledgements
acrossfivelargecommunitysamplesofchildren.J.AbnormalChildPsychol.doi: http://dx.doi.org/10.1037/a0037898(inpress).
Chen,F.F.,West,S.G.,Sousa,K.H.,2006.Acomparisonofbifactorandsecond-order modelsofqualityoflife.MultivariateBehav.Res.41(2),189–225.doi:http://dx. doi.org/10.1207/s15327906mbr4102_5.
deWinter,J.C.F.,Dodou,D.,2010.TheDriverBehaviourQuestionnaireasapredictor ofaccidents:ameta-analysis.J.Saf.Res.41(6),463–470.doi:http://dx.doi.org/ 10.1016/j.jsr.2010.10.007.
deWinter,J.C.F.,Dodou,D.,2011.ResponsetocommentaryonTheDriverBehaviour Questionnaireasapredictorofaccidents:ameta-analysis.J.Saf.Res.43,85–89. doi:http://dx.doi.org/10.1016/j.jsr.2011.06.013.
Evans,L.,2004.TrafficSafety.ScienceServingSociety,BloomfieldHills,MI.
Hu, L., Bentler, P.M., 1999. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ.Model.:AMultidisciplinaryJ.6(1),1–55.doi:http://dx.doi.org/10.1080/ 10705519909540118.
Lajunen, T.,Parker, D., Summala, H.,2004. TheManchester DriverBehaviour Questionnaire:across-culturalstudy.Accid.Anal.Prev.36(2),231–238.
Lawton,R.,Parker,D.,Manstead,A.S.R.,Stradling,S.G.,1997.Theroleofaffectin predictingsocialbehaviors:thecaseofroadtrafficviolations.J.Appl.Social Psychol.27(14),1258–1276.
Martel,M.M.,vonEye,A.,Nigg,J.T.,2010.RevisitingthelatentstructureofADHD:is therea‘g'factor?J.ChildPsychol.Psychiatry8,905–914.doi:http://dx.doi.org/ 10.1111/j.1469-7610.2010.02232.x.
Martinussen,L.M.,Lajunen,T.,Moller,M.,Ozkan,T.,2013.Shortanduser-friendly: the development and validation of the mini-DBQ. Accid. Anal. Prev. 50, 1259–1265.doi:http://dx.doi.org/10.1016/j.aap.2012.09.030.
McEvoy,S.P.,Stevenson,M.R.,Woodward,M.,2007.Thecontributionofpassengers versusmobile phone useto motor vehicle crashes resulting in hospital
org/10.1037/0021-9010.88.5.879.
Reason,J.T.,1990.HumanError.CambridgeUniversityPress,Cambridge.
Reason, J.T., Manstead, A., Stradling, S., Baxter, J., Campbell, K.,1990. Errors and violations onthe roads –a real distinction.Ergonomics 33(10–11), 1315–1332.
Rowe,R.,Maughan,B.,2009.Theroleofrisk-takinganderrorsinchildren’sliability tounintentionalinjury.Accid.Anal.Prev.41(4),670–675.doi:http://dx.doi.org/ 10.1016/j.aap.2009.03.004.
Rowe,R.,Maughan,B.,Gregory,A.M.,Eley,T.C.,2013.Thedevelopmentofrisky attitudesfrompre-drivingtofully-qualifieddriving.Inj.Prev.19(4),244–249. doi:http://dx.doi.org/10.1136/injuryprev-2012-040551.
Simms,L.J.,Gros,D.F.,Watson,D.,O'Hara,M.W.,2008.Parsingthegeneraland specificcomponentsofdepressionandanxietywithbifactormodeling.Depress. Anxiety25(7),E34–46.doi:http://dx.doi.org/10.1002/da.20432.
Stavrinos,D.,Jones,J.L.,Garner,A.A.,Griffin,R.,Franklin,C.A.,Ball,D.,Fine,2013. Impactofdistracteddrivingonsafetyandtrafficflow.Accid.Anal.Prev.61, 63–70.doi:http://dx.doi.org/10.1016/j.aap.2013.02.003.
Transportresearchlaboratorysafetysecurityandinvestigationsdivision(2008). CohortII:astudyoflearnerandnovicedrivers,2001–2005Retrievedfrom:
http://dx.doi.org/10.5255/UKDA-SN-5985-1.
Wells,P.,Tong,S.,Sexton,B.,Grayson,G.,Jones,E.,2008.CohortII:AStudyofLearner and New Drivers. Volume1–Main Report.Transport Research Laboratory, London.
Wetton,M.A.,Hill,A.,Horswill,M.S.,2011.Thedevelopment andvalidationof ahazardperceptiontestforuseindriverlicensing.Accid.Anal.Prev.43(5), 1759–1770.doi:http://dx.doi.org/10.1016/j.aap.2011.04.007.
