The Manchester Driver Behaviour Questionnaire: a cross-cultural study

Lajunen, Timo; Parker, Dianne; Summala, Heikki

doi:10.1016/s0001-4575(02)00152-5

Cited by 350 publications

(231 citation statements)

References 17 publications

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“…The 28-item questionnaire (Lawton et al, 1997) and its Finnish translation (Lajunen et al, 2004) served as the basis of the current study. The item related to driving under the influence of alcohol was removed as recommended by Lajunen et al (2004).…”

Section: Measures and Model Specificationmentioning

confidence: 99%

“…Previous studies have investigated the cross-cultural stability of the DBQ factor structures and the four-factor solution has been found to be more or less stable across countries (Lajunen, Parker, & Summala, 2004;Özkan, Lajunen, Chliaoutakis, Parker, & Summala, 2006). In these studies, the factor structures were compared by examining the factor loading matrices and calculating various indices of approximate factor similarity, such as identity, additivity, proportionality and correlation coefficients (van de Vijver & Leung, 1997).…”

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confidence: 99%

“…In these studies, the factor structures were compared by examining the factor loading matrices and calculating various indices of approximate factor similarity, such as identity, additivity, proportionality and correlation coefficients (van de Vijver & Leung, 1997). The values of these indices ranged from 0.85 to 0.98 when comparing Finnish, Dutch and British data (Lajunen et al, 2004).…”

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confidence: 99%

“…In addition, competing factor models (the two-, three-and four-factor solutions) were not compared in the studies of Lajunen et al (2004) and Özkan et al (2006). The meta-analysis by De Winter & Dodou (2010) argued for the two-factor solution while the studies by Lajunen et al (2004) and Özkan et al (2006) stated that the four-factor model offers a good fit across countries and traffic cultures.…”

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confidence: 99%

“…The meta-analysis by De Winter & Dodou (2010) argued for the two-factor solution while the studies by Lajunen et al (2004) and Özkan et al (2006) stated that the four-factor model offers a good fit across countries and traffic cultures. Then again, the original study by Reason et al (1990) and, for instance, the more recent study Davey, Wishart, Freeman, & Watson (2007) concluded that the three-factor (or three-component) solution fits the data best.…”

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confidence: 99%

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

Mattsson

O’Brien

Lajunen

et al. 2015

Accident Analysis & Prevention

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Käyttäytymistieteiden laitosTässä pro gradu -työssä tarkastellaan liikennepsykologiassa ehkä eniten käytetyn kyselyinstrumentin, Driver Behaviour Questionnaire DBQ:n mittausinvarianssia suomalaisten ja irlantilaisten nuorten kuljettajien (18-25 v.) otoksissa. DBQ on kehitetty 1990-luvun alussa pääkomponenttianalyysin perusteella. Kysely perustui alun perin laajalti testattuun kognitiivisen ergonomian teoriaan (Generic Error Modeling System, GEMS), mutta sittemmin kyselyn osioiden joukko ja sen faktori-tai pääkomponenttirakenne ovat määräytyneet aineistolähtöisesti. Tämä on johtanut siihen, että kyselystä on olemassa runsaasti erilaisia versioita, joiden osiomäärä vaihtelee yhdeksästä yli sataan ja faktorimäärä yhdestä seitsemään. Kyselyn perusteella lasketaan yleisesti summa-tai keskiarvomuuttujia, joita vertaillaan vastaajien osajoukoissa. Mahdollisesti yleisimmin kyselystä käytetään 28 osion versiota, jonka ajatellaan mittaavan kahta, kolmea tai neljää latenttia muuttujaa. Tässä tutkimuksessa tarkastellaan konfirmatorisen faktorianalyysin ja erityisesti mittausinvarianssianalyysin keinoin, mikä esitetyistä faktorirakenteista sopii kuvaamaan suomalaisten ja irlantilaisten nuorten kuljettajien vastauksia parhaiten.Mittausinvarianssianalyysi perustuu ajatukseen siitä, että aineistoon sovitetaan sarja malleja, joissa rajoitetaan koko ajan kasvava joukko mallin parametreja identtisiksi vertailtavien otosten välillä. Mikäli rajoitettu malli ei sovi tilastollisessa mielessä rajoittamatonta mallia huonommin aineistoon, voidaan rajoitettua mallia soveltaa kaikkiin (tässä: molempiin) vertailtaviin aineistoihin. Aineistoon sovitettavat mallit ovat järjestyksessä: 1) mittausmalli, jossa ainoastaan faktoreiden määrä on rajoitettu, 2) heikon invarianssin malli, jossa faktorilataukset on rajoitettu samoiksi, 3) vahvan invarianssin malli, jossa lisäksi osioiden vakiotermit on rajoitettu samoiksi ja 4) tiukan invarianssin malli, jossa lisäksi osioiden virhetermit on rajoitettu samoiksi. Lisäksi sovelletaan osittaisen invarianssin malleja, joissa vain osa kyseisessä invarianssitestauksen vaiheessa asetettavista rajoituksista pidetään voimassa. Mallien tilastollisen vertailun lisäksi niiden sopivuutta aineistoon arvioidaan erilaisten kuvailevien tunnuslukujen ja graafisten esitysten avulla.Keskeisenä tuloksena esitetään, että vertailluista malleista neljän faktorin malli sopii parhaiten molempiin aineistoihin, vaikka mallia onkin muokattava aineistolähtöisesti, jotta riittävä yhteensopivuus aineiston kanssa saadaan varmistettua.Tarkempi tarkastelu osoittaa, että neljästä faktorista kaksi ovat luonteeltaan erilaisia kahdessa otoksessa, sillä vain irlantilaisessa otoksessa kaikki osiot latautuvat odotusten mukaisille faktoreille. Toisaalta kahden muun faktorin analyysi osoittaa, että niille latautuvat osiot tulkitaan olennaisesti samalla tavoin kahdessa otoksessa ja heikon invarianssin oletus voidaan tehdä. Lisäksi voidaan tehdä osittaisen vahvan invarianssin oletus yhden faktorin tapauksessa, vaikka tällöinkin suurin osa faktor...

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Section: Measures and Model Specificationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

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

Mattsson

O’Brien

Lajunen

et al. 2015

Accident Analysis & Prevention

Self Cite

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Psychometric examination and validation of the aggressive driving scale (ADS)

Zhang

Houston

2016

Aggressive Behavior

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Aggressive driving behavior is an important cause of traffic accidents. Based on the recent view that aggressive driving is one way that trait aggression manifests itself, a growing research area has focused on the development of scales to assess aggressive driving. The aggressive driving scale (ADS) analyzed in the present study consists of 24 items. A sample of 276 participants was analyzed to obtain the factor structure and reliability of the ADS and 67 of them participated in the behavioral experiment in order to examine the construct and predictive validity of the scale. Results indicated a 3-factor structure (interference with other drivers, violations/risk taking, and anger/aggression expression behavior) with high item loadings. The ADS had high internal consistency and test-retest reliability. Construct validity of the ADS was established as the ADS subscale scores correlated significantly with trait measures of anger and aggression. Predictive validity of the ADS was verified as most items were significantly correlated with behavioral measures derived from a driving simulator. The ADS was a significant predictor of behavioral measures both in the simulated environment (i.e., frequency of driving off the road, red light running behavior, frequency of colliding with a vehicle, frequency and distance of over speeding, frequency and distance of central crossing) and reported real world situations (i.e., annual moving violations and accidents). These results suggest that the ADS is a reliable and valid tool in evaluating aggressive driving behavior as the current study provides behavioral support for the effectiveness of the ADS in measuring aggressive driving behavior. Aggr. Behav. 42:313-323, 2016. © 2016 Wiley Periodicals, Inc.

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Aggressive driving: A consumption experience

Ruvio

Shoham

2011

Psychology and Marketing

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In this paper, the literature on aggressive driving tendencies as a consumption experience is reviewed. Two studies test a comprehensive model that includes personality, attitudinal, and value antecedents to such behavior. The research supports the role of most of the antecedents. However, personality and attitudinal constructs were found to be stronger predictors of aggressive driving than values. Furthermore, the paper demonstrates that aggressive driving tendencies increase incidences of law breaking. Suggestions for future research and practical implications are discussed.

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The Manchester Driver Behaviour Questionnaire: a cross-cultural study

Cited by 350 publications

References 17 publications

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

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

Psychometric examination and validation of the aggressive driving scale (ADS)

Aggressive driving: A consumption experience

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