Drug Usage, Personality, Attitudinal, and Behavioral Correlates of Driving Behavior

Jamison, Kay Redfield; McGlothlin, William H.

doi:10.1080/00223980.1973.9915599

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…Similar results to those of af Wåhlberg et al (2010) were found by Jamison and McGlothlin (1973) and Donovan, Queisser, Salzberg, and Umlauf (1985), comparing groups of drivers by accidents from state records, and Williams, Henderson, and Mills (1974) for recorded traffic offenders, using standard lie scales. There were no differences between these groups (i.e., no association between the responses to a lie scale and actual traffic behavior).…”

Section: Introductionsupporting

confidence: 77%

Social desirability effects in driver behavior inventories

Wåhlberg

2010

Journal of Safety Research

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Section: Introductionsupporting

confidence: 77%

Social desirability effects in driver behavior inventories

Wåhlberg

2010

Journal of Safety Research

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“…This is in line with previous research in which linkages between standard lie scales and self-reported crashes were observed but no associations were found when measured against independently recorded traffic data (Donovan, Queisser, Salzberg & Umlauf, 1985;Jamison & McGlothlin, 1973;Smith, 1976;Williams, Henderson & Mills, 1974).…”

Section: Selected Social Desirability Scale Studies In Road Safetysupporting

confidence: 92%

“…It has been found at times to be positively correlated with recorded accidents while negative correlations have been observed in relation to selfreported crashes (af . This is in line with previous research in which linkages between standard lie scales and self-reported crashes were observed but no associations were found when measured against independently recorded traffic data (Donovan, Queisser, Salzberg and Umlauf, 1985;Jamison and McGlothlin, 1973;Smith, 1976;Williams, Henderson and Mills, 1974). However it must be noted that the correlations were very low, suggesting that there is no meaningful association between lie scale responding and recorded accidents.…”

Section: Lie Scales and Usage In Road Safetysupporting

confidence: 89%

Common method variance and other sources of bias in road traffic research

Barraclough¹

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Common Method Variance and other sources of bias in road traffic research i KeywordsRoad crashes, traffic offences, common method variance, social desirability, method bias, meta-analysis, self-report data, road safety. AbstractReducing the number of fatalities and injuries due to vehicle crashes is a major focus for road safety researchers throughout the world. Research on driver behaviours, attitudes and associated crashes is often conducted using self-report surveys. When the predictor and criterion variables are drawn from a single source (such as a cross-sectional survey) as is generally the case, it has been generally acknowledged that a range of methodological issues associated with this approach have the potential to reduce the efficacy of predictive measures. Common method variance (CMV) is bias that occurs when both the predictors and the predicted variables are gathered and analysed in the same manner from the same source. CMV effects have the potential to distort findings by artificially strengthening or weakening the observed relationships between items of interest. This is of particular concern to road safety researchers, given the distinct possibility that the small associations often observed within road safety studies may in turn be inflated due to CMV biases. There is a significant need to examine the exact nature and extent of CMV effects within self-reported road safety data and to explore how best to reduce this measurement error.The current body of research was conducted with three primary aims in mind:1. Examine the existence of CMV effects.2. Explore the level of influence that CMV effects have on self-report data, including whether these effects inflate or deflate observed relationships between variable.3. Test research methods to control for the influence of CMV effects on selfreport data.Study 1 involved a meta-analysis that quantified the association between crashes and traffic offences in light of potential moderators. In reference to the exploration for CMV effects, this study examined the extent to which the relationship between self-reported traffic offences and self-reported crashes is stronger than that recorded using only official data sources. Studies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. The impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were examined. After weighting for sample size, an average correlation of r = .18 between crashes and traffic offences was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. In relation to the existence of CMV effects, a within-country analysis found stronger effect sizes in self-reported data after controlling for crash mean ...

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“…Personality traits affect not only the reception of the road signs content, but are also among explanatory variables in the models used for traffic safety measures prediction. This was confirmed by studies from both the early period of automotive development [31][32][33][34], as well as the later ones [35][36][37][38][39][40][41][42][43][44][45][46]. Modern studies also lead to similar conclusions [47][48][49][50][51][52][53][54][55][56].…”

Section: The Study Of Traffic Signs Comprehensionmentioning

confidence: 56%

Study on the Relationship between Drivers’ Personal Characters and Non-Standard Traffic Signs Comprehensibility

Wontorczyk

Gaca

2021

IJERPH

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Drivers’ incorrect perception and interpretation of the road space are among reasons for human errors. Proper road markings are elements improving perception of road space. Their effectiveness relies on traffic participants receiving the provided information correctly. The range of signs used is constantly expanding and unusual situations in traffic require use of non-standard signs or an unusual combination of existing standard signs. The aim of this study was to explore the level of comprehensibility of four different types of non-standard signs. The relationship between the level of comprehensibility of these signs and personality traits of the drivers was also studied. A total of 369 drivers were tested using a questionnaire to analyze the traffic signs comprehensibility and Five Factor Inventory (NEO-FFI). The obtained results indicate that symbolic signs, unlike symbolic and text ones, are much better comprehended by drivers. Men comprehend the significance of non-standard symbolic regulatory signs better than women. Higher level of comprehensibility of symbolic and text regulatory signs is shown by older, better educated drivers and professional drivers. The study found there is a link between personality traits of the driver and the comprehensibility of symbolic regulatory signs.

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Drug Usage, Personality, Attitudinal, and Behavioral Correlates of Driving Behavior

Cited by 9 publications

References 13 publications

Social desirability effects in driver behavior inventories

Social desirability effects in driver behavior inventories

Common method variance and other sources of bias in road traffic research

Study on the Relationship between Drivers’ Personal Characters and Non-Standard Traffic Signs Comprehensibility

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