Literature DB >> 25058842

Development of a short form of the driving anger expression inventory.

Amanda N Stephens1, Mark J M Sullman2.   

Abstract

The present study developed a revised version of the driving anger expression inventory (25-items) and a short (15-item) version using data from 551 drivers. Split half factor analyses on both versions confirmed the original four factors; personal physical aggressive expression, use of a vehicle to express anger, verbal aggressive expression and adaptive/constructive expression. The two DAX versions were strongly correlated, demonstrating the suitability of both forms of the scale and the aggressive forms of expression were higher for drivers who reported initiating road rage interactions. Total aggressive expression was also higher for drivers who reported recent crash-related conditions, such as: loss of concentration, losing control of their vehicle, moving violations, near-misses and major crashes. The revised DAX and DAX-short provide shorter versions of the 49-item DAX that can more easily be combined with other questionnaires and require smaller sample sizes to analyse. Further research is required to validate these tools among different samples and populations.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Keywords:  Aggressive driving; Anger expression; DAS; DAX; Driving anger

Mesh:

Year:  2014        PMID: 25058842     DOI: 10.1016/j.aap.2014.06.021

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  3 in total

1.  Differences in Driving Anger among Professional Drivers: A Cross-Cultural Study.

Authors:  Milanko Damjanović; Spasoje Mićić; Boško Matović; Dragan Jovanović; Aleksandar Bulajić
Journal:  Int J Environ Res Public Health       Date:  2022-03-31       Impact factor: 3.390

2.  Self-reported changes in aggressive driving within the past five years, and during COVID-19.

Authors:  Amanda N Stephens; Steven Trawley; Justin Ispanovic; Sophie Lowrie
Journal:  PLoS One       Date:  2022-08-01       Impact factor: 3.752

3.  Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data.

Authors:  Peter Barraclough; Anders Af Wåhlberg; James Freeman; Barry Watson; Angela Watson
Journal:  PLoS One       Date:  2016-04-29       Impact factor: 3.240

  3 in total

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