2015
DOI: 10.1016/j.aap.2015.04.016
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Impact of alcohol checks and social norm on driving under the influence of alcohol (DUI)

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Cited by 29 publications
(17 citation statements)
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“…In terms of police roadside alcohol checks, Meesmann et al (2015) has stated they may be more likely to target male drivers (as well as younger drivers and those who drive at night). At the same time, the ESRA2 survey held in 2018 and 2019 revealed that male respondents (a) consider it more likely to be checked by the police for using alcohol as a car driver on a typical journey (25.6%) than women do (19.5%), (b) were more likely to have been subjected to a Breathalyzer test at least once in the last year while driving a car on a typical journey (23.5%) than women were (13.0%) (Goldenbeld et al, 2022).…”
Section: Discussionmentioning
confidence: 99%
“…In terms of police roadside alcohol checks, Meesmann et al (2015) has stated they may be more likely to target male drivers (as well as younger drivers and those who drive at night). At the same time, the ESRA2 survey held in 2018 and 2019 revealed that male respondents (a) consider it more likely to be checked by the police for using alcohol as a car driver on a typical journey (25.6%) than women do (19.5%), (b) were more likely to have been subjected to a Breathalyzer test at least once in the last year while driving a car on a typical journey (23.5%) than women were (13.0%) (Goldenbeld et al, 2022).…”
Section: Discussionmentioning
confidence: 99%
“…Through data-linking, individual survey data and national characteristics (e.g. economic development, legislation) can be combined in a common statically model (example of multilevel analysis combining national data with individual SARTE4 survey data: Meesmann, Martensen, & Dupont, 2015). Therefore, the individual data of the next ESRA survey (ESRA2), will be supplemented by national contextual information/data, to facilitate more in-depth analyses and interpretation of the available data.…”
Section: Discussionmentioning
confidence: 99%
“…Multilevel modeling takes into consideration within-cluster correlations and has been used extensively in a variety of disciplines such as econometrics, education, psychology, and epidemiology ( 24 , 25 , 32 – 34 ) to properly account for hierarchical structures. It captures the potential between-cluster heterogeneity because of unobserved factors, which has made practical implications in several applications ( 20 , 23 , 35 ). Furthermore, it breaks down the variance in the outcome variable and provides insight into the proportion of each level of the hierarchy in the outcome variance, which is not available from traditional methods ( 20 , 26 ).…”
Section: Literature Reviewmentioning
confidence: 99%