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Exploring the effects of attitudinal and perception characteristics on drinking and driving non-compliant behaviour
Accident Analysis and Prevention 60 (2013) 316–323
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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
Exploring the effects of attitudinal and perception characteristics on drinking and driving non-compliant behaviour Ioannis Politis a,∗ , Socrates Basbas b , Panagiotis Papaioannou a a b
Department of Civil Engineering, Aristole University of Thessaloniki, PC 54124, Greece Department of Rural and Surveying Engineering, Aristole University of Thessaloniki, PC 54124, Greece
a r t i c l e
i n f o
Article history: Received 15 October 2012 Received in revised form 21 March 2013 Accepted 28 March 2013 Keywords: Drinking and driving Confirmatory factor analysis Structural equation modelling Behavioural interpretation LISREL
a b s t r a c t The objective of this paper is to examine a number of factors (observed and latent) that might have a causal effect on drinking and driving (D&D) behaviour. Face-to-face surveys were conducted among patrons at bars and cafeterias and 305 valid questionnaires were filled. A confirmatory factor analysis was performed so as to identify the latent constructs and a mixed structural equation model was developed. From the analysis it came up that non-compliant behaviour of D&D is limited at older ages, also associated with high levels of income and car availability. Though men are consuming more alcohol, women seem to be more prone in driving under the influence (DUI) of alcohol. Furthermore, it was found that people who strongly support the examined interventions in the study (e.g. better enforcement, more traffic safety campaigns, stricter penalties) are more unlikely to drive after drinking compare to those who have some objections. Finally, it was not found any statistically significant relation between individuals’ level of awareness and D&D behaviour. © 2013 Elsevier Ltd. All rights reserved.
1. Introduction Drinking and driving (D&D) is according to World Health Organization (WHO), one of the most important causes for road accidents worldwide, especially those occurring at night hours (Murray and Lopez, 1996). Unfortunately, this non-compliant behaviour is primarily adopted by young people, with the majority of them being novice drivers; in previous decades, a lot of funds have been spent in order to raise the awareness of people in road safety related issues, such as preventing driving after alcohol consumption, though without great success. Drinking and driving – D&D (also referred as Driving Under the Influence of Alcohol – DUI) is a significant public health concern in modern communities worldwide. Due to its great social impact, D&D behaviour has been analyzed and explored by a number of research disciplines, such as Medicine, Social Sciences, Psychology and Engineering. The literature review that follows, attempts to briefly present the research outcomes, primarily from the Psychology discipline side. As mentioned before, numerous studies have given special focus on young people (college and university students) as well as on novice drivers. In a paper exploring the primary psychosocial factors that predict the problem of drinking in college students, Ham and Hope (2003) examined a number of factors
∗ Corresponding author. Tel.: +30 2310995775. E-mail address: [email protected] (I. Politis). 0001-4575/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.aap.2013.03.033
including demographic variables, personality, drinking history, alcohol expectancies, drinking motives, stress and coping, activity involvement, and peer and family influence. They found that the variables associated with college drinking seem to vary at levels dealing with one’s personality and coping mechanisms, one’s thought processes about drinking, and the environment. Liourta and Empelen (2008), explored the importance of selfregulatory and goal-conflicting processes in the avoidance of D&D among young students in Greece. They found that intention is a factor for non-compliant behaviour. Furthermore, the study showed that goal conflict, behavioural willingness, alcohol limitation planning and alcohol limitation self-efficacy could explain the intention–behaviour gap distinguishing between intenders who reported avoidance of drunk driving or those who had not avoided drunk driving. Finally, LaBrie et al. (2011) developed a logistic regression model, showing that sex status, fraternity or sorority affiliation, family history of alcohol abuse, medium or heavy drinking (as compared to light drinking), are some of the factors that increase the likelihood of D&D among college students. The application of well known models and theories derived directly from the disciplinary of Psychology is a common approach when personal habitual characteristics of the driver are examined. Moan and Rise (2011) for example, used an extended version of theory of planned behaviour (TPB) in order to predict intentions regarding D&D. The results showed that the TPB variables explained 10% of the variance in intentions in the sample as a whole. Perceived behavioural control was the strongest predictor of intentions, followed by descriptive norm, attitude, and moral norm. The role of
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attitude, social norm, drinking habits and intention of individuals’ in exogenous D&D behaviour through the TPB, was also tested by Åberg and Haglund (2001) in Sweden. Also, Chan et al. (2010) used the TPB to explore the role of invulnerability and the intention to D&D non-compliant behaviour. The total explained variances in the intention to drink and drive reached 79% and subjective norms, attitudes, and perceived behavioural control of risky behaviours were consistently found to be positively correlated with each other. Finally, Nolan et al. (1994) used a cluster analysis to identify 5 personality types (Impulsive-Extravert, Normal, Neurotic-Introvert, Neurotic-Hostile, and Unassertive-Conformist) that differed predictably on demographic variables, drinking behaviour, and driving records. A common approach in D&D behavioural analysis is the evaluation of alcohol prevention and intervention programmes and the impact/influence that have at the targeted population. Voas et al. (2000, 2003) for example, found a significant relationship between three major alcohol safety laws and the downward trend in alcohol-related fatal crashes in the United States. The cause and effect relationship between the minimum legal drinking age (MLDA) law and reductions in highway crashes was also confirmed by McCarthy et al. (2005). A lifestyle management class (LMC) programme evaluated by Kim and William (2004) showed decreases in driving after drinking relative to control participants. Changes in heavy drinking varied as a function of treatment condition, readiness to change, and gender. Finally, Boluarte et al. (2011), used the data from 2008 Flash Eurobarometer survey conducted across the 27 EU member states and found that most alcohol policies tested, had no significant influence on risk perceptions. Only the blood alcohol concentration (BAC) limit for driving and health warnings on advertisements and/or alcoholic beverage containers showed a significant effect, increasing risk perceptions among adolescents. Similar outcomes regarding the effects of BAC were obtained from the analysis of Wagenaar et al. (2007), where the effects of legal BAC limits on fatal crash involvement of 28 states in U.S. from 1976 to 2002 were addressed. Further, a meta-analysis of 0.08 Blood Concentrations laws in 19 jurisdictions in the U.S. showed that almost 950 lives would have been saved throughout 2000, in case of a national implementation of such limits to all 50 states (Tippetts et al., 2005). Finally, in a large sample of 1.5 million people that were killed in fatal accidents in U.S. between 1994 and 2008, Phillips and Brewer (2011) proved that 0.01 BAC is associated with significantly more dangerous accidents than 0.00 BAC. Taking into consideration the existing knowledge regarding drinking and driving, briefly presented above, the objective of this paper is to extend this knowledge so as to identify which of the personal characteristics and beliefs of the individuals, actuate on drinking and driving behaviour and how. More specific, the paper is trying to investigate in a complementary and integrative manner some of the gaps in the existing knowledge which have been identified at the literature review presented above and can be summarized into the following (research) questions: (a) Which are the specific personal and habitual/behavioural characteristics of a person that affect D&D behaviour? (b) In which way the targeted road safety policies can prevent D&D behaviour? (e.g. how effective lectures at schools can be in D&D prevention or how better enforcement can reduce the examined phenomenon?) (c) What is the role of individuals’ awareness for issues related to road safety and alcohol consumption in D&D behaviour? For the purpose of the research, 305 valid questionnaires have been completely filled through a revealed preference (RP) survey. The survey was conducted among patrons at bars, restaurants and cafeterias at the city of Serres, a midsized city of 80.000
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inhabitants, located in northern Greece, with a high number of university students. A confirmatory factor analysis (CFA) is initially performed in order to validate the proposed measurement model. Afterwards, the structural equation modelling (SEM) technique is applied in order to set up a multivariable model that identifies which of the parameters examined, play a key role on individuals’ drinking and driving non-compliant behaviour. Both the CFA and the SEM model construction were set up in LISREL v8.8 student edition, which restricts these analyses to a maximum number of 15 variables (Jöreskog and Sörbom Dag, 1996). 2. Undertaken research This section describes the steps that were taken in order to address the research questions previously presented. Additionally, initial results regarding the bivariate correlation of the variables used in the study are shown and commented. Finally, the variance–covariance matrix is given for replication of the study. 2.1. Questionnaire survey For the purposes of this research, a direct face-to-face survey was employed. Although an onsite survey method is more costly than other methods (e.g. website or telephone surveys), it provides numerous benefits such as high response rate, reliability of the given answers and absence of missing values (Lavrakas, 2008). Undergraduate students were involved in data collection which took place from December 1 to December 17, 2010. The questionnaires were distributed in 11 randomly selected cafeterias and bars, between 23.00 p.m. and 2.00 a.m. From the 330 collected questionnaires, 305 were finally considered as valid and reliable for analysis. The questionnaire itself consisted of 24 questions allocated in 4 sections. Since the patrons visit the bars and cafeterias for recreational purpose, an effort was given to provide them with comprehensible, easy-to-understand and easy-to-answer questions. In addition, the interviewers were well trained so as to identify the patrons who were not in position to understand and answer the questionnaire correctly, avoiding with that way additional bias in the sample. The confirmatory factor analysis (CFA) as well as the structural equation model (SEM) were developed either by utilizing the original responses of the participants or by creating new variables (recoding) by combining the responses to different questions. At the end, only 14 variables were used for the analysis and only these are presented and commented at the following sections. 2.2. Measurement scale and correlations This paragraph gives a brief description of the variables used and of the scale these were measured and coded for the statistical analysis. Furthermore, it presents the correlation and (co)variance matrices, which are useful tools for qualitative outcomes and replication of the study respectively. Table 1 shows the 14 variables used in this paper, together with a short description and presentation of the measurement scale. Since LISREL treats a variable as ordinal in case of less than 15 categories, all variables used in this research are considered as ordinal with the exception of AGE and CARS MEM, which were considered as continuous. The 5-point Likert scale, corresponds to answers varying from Totally Disagree (coded as “1”) up to Totally Agree (coded as “5”), with code “3” stating the Neutral/Don’t Know selection. Finally, MAX PENA and TOTAL DE define the level of awareness of the respondents in two issues related with D&D and
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Table 1 Coding, description and measurement scale of variables. Variable code
Description
Measurement scale
EXITS PE
Visit of bars per week
AGE INCOME
Age of respondent Income of respondent (thousand euros)
CARS MEM
Available cars per household Gender of respondent Better enforcement will reduce the DUI of alcohol Stricter penalties in Road Traffic Code for D&D Campaigns will reduce D&D phenomenon Road safety lectures at schools will reduce accidents Penalty foreseen for 2 glasses of alcohol consumption Road accident deaths in Greece in 2008 Last time notice (exposed to) a road safety campaign
1: 1 time, 2: 2 times, 3: 3 times, 4: >3 times Original response 1: <15, 2: 16–25, 3: 26–35, 4: 36–45, 5: 46–55, 6: >56 Number of cars per persons per household 1: man, 2: woman 5-Point Likert scale
GENDER BETTER E STRICTER MORE CAM SCHOOL L
MAX PENA
TOTAL DE EXPOS CA
TOTAL DR
5-Point Likert scale 5-Point Likert scale
3. Methodology
5-Point Likert scale
The first step of building a structural equation model is to specify the relationship between the observed indicators and the latent constructs. In this paper we perform a confirmatory factor analysis (CFA), a theory driven approach, instead of the respective data driven method of Exploratory Factor Analysis (EFA) (Brown, 2006). The parameters for CFA and SEM were estimated through the maximum likelihood estimation (MLE) method.
1: correct answer, 2: wrong answers 1: correct answer, 2: wrong answers 1: 1 month, 2: 6 months, 3: 1 year, 4: never Original response
Total number of drinks per visit to bars Driving car to return home
DRIVING
Regarding the option of car as travel mode to return home, the research shows that this behaviour is adopted primarily by young men (r = −0.34 and r = 0.32, p < 0.001) with high income (r = −0.43, p < 0.001). Finally, those from households with high car possession rate are also in favour of not choosing alternative (safest) modes to return back to home (r = −0.28, p < 0.001). Following the advice of Schumacher and Lomax (2004) regarding the reporting of SEM research, we present in Table 3 the variance–covariance matrix. The covariance matrix may be used in lieu of the raw data in calculating a number of multivariate statistical models, such as confirmatory and exploratory factor analysis, path analysis, or other general linear models, including the special cases of multiple regression, analysis of variance, and repeated measures analysis of variance.
3.1. Confirmatory factor analysis (CFA)
1: yes, 2: no
As the path diagram of Fig. 1 shows, the final CFA is a three factor model, with three latent constructs ( 1 . . . 3 ), 12 observed indicators (1 . . .12 ) with the respective measurement errors (ı1 . . .ı12 ) and 13 factor loadings (ij , i = 1 to 3 and j = 1 up to 5). The first latent variable, describes the personal background of the individual (Perso Ba) and is measured by 5 observed variables; the age of the responder, the gender, the car availability per household, the income and the number of visits to bars and cafeterias per week. The second latent variable is entitled “values” and measures the opinion of the responders to 4 policy interventions that could reduce the D&D non-compliant behaviour. Finally, the last latent variable (Prob Awa) represents the level of awareness of the individual in road safety issues and DUI of alcohol impacts. More specific, the last latent variable defines (measures) the awareness of individuals by examining how well are informed about specific issues such as how many road accident deaths were observed in Greece in 2008 and which is the penalty foreseen by the Greek law for the consumption of 2 glasses of alcohol. All observed indicators for this latent variable were interval scale and the interval was set
road safety. Both variables corresponds to questions with predefined answers were only one of them was correct (and coded as “1”). All mistaken selections were coded as “2”. Table 2 presents the correlation matrix for the 14 variables of the study from which useful bivariate outcomes can be delivered (the numbers at the first row corresponds to the 14 variables of the first column). From Table 2 it can concluded that people who use to consume a lot of alcohol are patrons who regularly visit bars and cafeterias during the week (r = 0.25, p < 0.001), are not aware about the BAC limits and alcohol consumption relation (r = 0.13, p < 0.05) and are negative to any intervention proposed in the research such as better enforcement (r = −0.14, p < 0.05), road safety lectures at schools (r = −0.16, p < 0.01), adoption of stricter penalties (r = −0.20, p < 0.001) and introduction of campaigns (r = −0.18, p < 0.01). As expected, young people visit bars and cafeterias more often than older ones (r = 0.36, p < 0.001) who believe on the other hand that targeted campaigns can reduce the D&D phenomenon (r = 0.15, p < 0.01). Table 2 Correlations and two tailed probabilities (N = 305).
TOTAL DR EXITS PE EXPOS CA BETTER E SCHOOL L STRICTER MORE CAM GENDER AGE INCOME MAX PENA TOTAL DE DRIVING CARS MEM a b c
1
2
3
4
5
6
7
8
9
10
11
12
13
14
– 0.25a 0.04 −0.14c −0.16b −0.20a −0.18b −0.17b −0.09 0.10 −0.13c −0.08 −0.04 −0.04
– −0.07 −0.10 −0.02 −0.10 −0.11 −0.08 −0.36a −0.01 −0.05 −0.07 0.17b −0.07
– −0.09 −0.23a 0.11 −0.09 0.03 0.03 0.01 0.06 0.16b −0.08 −0.05
– 0.28a 0.26a 0.19a 0.15b 0.03 0.12c 0.05 0.11 −0.17b 0
– 0.17b 0.41a 0.08 0.14c −0.08 −0.10 −0.23a −0.02 0
– 0.09 0.08 0.02 −0.02 0.06 0.06 0.09 −0.10
– 0.05 0.15b 0.15b −0.13c −0.02 −0.05 −0.01
– −0.35a −0.27a 0.04 −0.03 0.32a −0.13c
– 0.31a −0.02 −0.08 −0.34a 0.04
– −0.23a −0.03 −0.43a 0.18b
– 0.07 0.20 0.03
– 0.04 0.01
– −0.28a
–
p < 0.001. p < 0.01. p < 0.05, two tailed.
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Table 3 Variance–covariance matrix of the sample population (N = 305).
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1.18 0.25 0.07 −0.27 −0.67 −0.36 −0.48 −0.18 −0.76 0.18 −0.14 −0.09 −0.04 −0.01
0.86 −0.11 −0.16 −0.08 −0.15 −0.24 −0.08 −2.46 −0.02 −0.04 −0.06 0.16 −0.02
2.82 −0.27 −1.47 0.29 −0.39 0.06 0.36 0.04 0.10 0.27 −0.14 −0.03
3.18 1.89 0.77 0.82 0.26 0.44 0.34 0.09 0.19 −0.31 0.00
14.51 1.04 3.79 0.30 3.85 −0.51 −0.39 −0.89 −0.08 0.00
2.66 0.36 0.13 0.25 −0.05 0.10 0.10 0.14 −0.05
5.99 0.12 2.80 0.60 −0.33 −0.05 −0.12 −0.01
1.00 −2.65 −0.44 0.04 −0.03 0.32 −0.04
56.04 3.73 −0.11 −0.63 −2.51 0.09
2.59 −0.36 −0.05 −0.69 0.08
1.00 0.07 0.20 0.01
1.00 0.04 0.00
1.00 −0.08
0.08
in such a way that it was impossible for a middle-informed individual to give wrong answer. The last indicator explores the exposure rates of the responder to a road safety campaign (which was the last time they have seen/listened to a traffic safety campaign in TV/radio). The overall statistical indices used in CFA suggest that the model fits the data well. The chi-square statistic is too small (2 = 43, df = 45) to reject the null hypothesis of good fit (p = 0.56). Additionally, the root mean square error of approximation (RMSEA) indicates a perfect fit with a 90% confidence interval returning a value of 0.04. Finally, as far as the overall fit indices are concerned, most of them are satisfying the cutting off criteria, such as the goodness of fit index (GFI = 0.98), adjusted goodness of fit index (AGFI = 0.96), standardized root mean square residual (SRMR = 0.044) and normed fit index (NFI = 0.82).
Table 4 presents the standardized estimates, the t-values, the standard error as well as the correlation coefficient (R2 ) between the three latent and the twelve observed variables. It can be concluded that the latent variables of Personal Background and Values are measured in a satisfactory level by the proposed observed variables, however for the last one, that of Problem Awareness, the description seems not to be fulfilled well (very low R2 ). For this latent variable, the observed indicator variables with low R2 are the MAX PENA and TOTAL DE, two variables that were recoded as mentioned in the previous paragraphs as correct and wrong answer and perhaps this is the cause for the low explanation of the latent construct The observed variables with the higher relation (explanation) to the latent variables are the age (R2 = 0.44) for the Personal Background, the belief that school lectures will improve the safety levels (R2 = 0.50) for the Values and
Fig. 1. Path diagram of unstandardized factor loadings for the CFA as calculated by LISREL.
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Table 4 Results of confirmatory factor analysis. Constructs and variables Personal background ( 1 ) Visits per week (1 ) Age (2 ) Income (3 ) Cars per household (4 ) Gender (5 ) Values ( 2 ) Better enforcement (6 ) Stricter penalties (7 ) More campaigns (8 ) School lectures (9 ) Problem awareness ( 3 ) Maximum penalty (10 ) Total deaths in accidents (11 ) Last Exposed to Campaign (12 )
Standardized estimate
t-value
S.E.
R2
0.480 0.663 0.346 0.221 0.400
3.64a −2.79b −3.45a −2.04c –
0.69 9.04 0.65 0.16 –
0.23 0.44 0.12 0.05 0.16
0.387 0.187 0.520 0.707
4.87a 2.34c 5.75a 4.64a
0.10 0.11 0.01 0.12
0.15 0.04 0.27 0.50
0.155 0.239 0.436
1.65 2.48c 3.16b
0.04 0.03 0.20
0.02 0.06 0.19
2 = 43.00, df = 45, p-value = 0.56, RMSEA = 0.00, GFI = 0.98, AGFI = 0.96, SRMR = 0.044, NFI = 0.82, CN = 494.63. a p < 0.001. b p < 0.01. c p < 0.05, two tailed.
the period unexposed to a road safety campaign (R2 = 0.19) for the Problem Awareness. 3.2. The structural equation model (SEM) Structural equation models are made up of two components: the first (called latent variable model) describes the relationship between endogenous and exogenous latent variables, and permits the evaluation of both direction and strength of the causal effects among these variables; the second (called measurement model) describes the relationship between latent and observed variables. The basic equation of the latent variable model is the following (Bollen, 1989): = B + +
(1)
in which (eta) is an (mx1) vector of the endogenous variables, (xi) is an (nx1) vector of the exogenous latent variables, and (zeta) is an (mx1) vector of random variables. The elements of the B (beta) and (gamma) matrices are the structural coefficients of the model; the B matrix is an (mxm) coefficient matrix of the latent endogenous variables; the matrix is an (mxn) coefficient matrix for the latent exogenous variables. The basic equations for the measurement model are the following: x = x + ı
(2)
for the exogenous variables, and y = y + ε
(TOTAL DR) and the usage of car as a travel mode to return home (DRIVING). Another key issue that should be highlighted is the interrelation and co-linearity of the indicators (e.g. the income and the age). Although in traditional regression models, the interrelation of the independent variables can lead to wrong outcomes and for this reason is avoided when strong co-linearity exists, in SEM the relation (variance) between two variables is stated by a two headed arrow. For example, the variables age, income and number of cars per household are proved to be highly correlated, as it is showed in the next figure. Fig. 2 shows the path diagram of the proposed model together with the unstandardized (regression) weights for each structural coefficient. A detailed description of the results is presented next.
(3)
for the endogenous variables, in which x and ı (delta) are column q-vectors related to the observed exogenous variables and errors, respectively; x (lamda) is a (qxn) structural coefficient matrix for the effects of the latent exogenous variables on the observed variables; p and e (epsilon) are column p-vectors related to the observed endogenous variables and errors, respectively; y is a (pxm) structural coefficient matrix for the effects of the latent exogenous variables on the observed ones. In this section, we present a structural equation model analysis for drinking and driving non-compliant behaviour. Based on the CFA commented in the previous section, we examine the three exogenous latent variables as factors that could reveal the D&D behaviour. The endogenous D&D latent behaviour, coded as Compl beh, is determined by two respective observed variables, the total number of drinks consumed per visit to bars and cafeterias
3.3. Results and interpretation The first criterion that should be taken into account, when a SEM is validated, is the overall statistical indices (Schumacher and Lomax, 2004). The statistical analysis of the overall model indicated that 2 was 107.54, with 65 degrees of freedom (p < 0.001). The standardized root mean square of residual (SRMR) was 0.061, the goodness of fit index (GFI) was 0.95 and the adjusted goodness of fit index (AGFI) was 0.92. These results indicate that the model is reasonably consistent with the data and does not require specification or modification. In terms of statistical interpretation, the above mentioned indices show that the true population is deemed consistent with the implied theoretical model being tested (or the sample covariance matrix S is sufficiently reproduced by the implied theoretical model). In simply words, the convergence indicates that the proposed model fits the data well and is reliable for qualitative and quantitative outcomes. The second criterion in judging the statistical significance and substantive meaning of a theoretical model is the examination of statistical significance of individual parameters estimates for the paths of the model. Table 5 presents this information from which it can concluded that most of the parameters examined, fulfil the cutting of criterion for t-value (greater than 1.96 for confidential level of 95%). Almost all the observed indicators of the two latent variables Values and Personal Background are significant at 0.001 level of significance. From the analysis, it can also be concluded that these two latent variables can have a significant impact in D&D non-compliant behaviour, at 0.05 and 0.001 level of significance respectively. The third latent variable of Problem Awareness is not considered as significant in affecting D&D behaviour. Finally, the
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Fig. 2. Structural equation model for drinking and driving non-compliant behaviour.
only unconstrained variable, that of total drinks per visit to bars and cafeterias, interprets the D&D behaviour at 0.05 level of significance. Last but not least, the third criterion considers the magnitude and the direction of the parameter estimates. The interpretation of the results that is presented next, simultaneously investigates the fulfilment of this criterion. Taking into account the standardized estimates presented in Table 4, both for the observed and the latent variables, the following outcomes can be derived. The number of exits to bars and cafeterias per week as expected, is positively affecting the D&D behaviour. Assuming that the frequency of visiting bars is directly related to the frequency of
drinking, the above mentioned outcome is in consistency with the study of Birdshall et al. (2012) who found that drinking 9 or more times in the past month doubled the odds of impaired driving. Following the outcomes of numerous relevant studies presented in the introductory part of this paper, the analysis shows that age seems to be one of the most crucial factors for D&D behaviour (the higher regression estimate). The negative sign confirms the findings of Vegega and Klitzner (1989) and the outcomes of NHTSA study (NHTSA, 1997) that DUI of alcohol is reduced in older ages. Furthermore, as it was discussed previously, income and car availability are strongly related with the age and therefore it cannot be concluded that people with higher income or more available cars in
Table 5 Results of structural equation model. Paths EXITS PER AGE INCOME CARS MEM GENDER BETTER E STRICTER MORE CAM SCHOOL L MAX PENA TOTAL DE EXPOS CA Compl Be Compl Be Compl Be TOTAL DR DRIVING
← ← ← ← ← ← ← ← ← ← ← ← ← ← ← ← ←
–, Factor loading constrained to one. a p < 0.001. b p < 0.01. c p < 0.05. d p < 0.1 two tailed.
Perso Ba Perso Ba Perso Ba Perso Ba Perso Ba Values Values Values Values Prob Awa Prob Awa Prob Awa Perso Ba Values Prob Awa Compl Be Compl Be
Standardized estimates
t-value
S.E.
0.44 −0.68 −0.35 −0.29 0.38 0.41 0.18 0.51 0.52 0.13 0.23 0.42 0.19 −0.08 −0.05 0.89 2.08
4.59a −5.50a −4.19a −3.33a – 5.18a 2.32c 5.95a 4.54a 1.46 2.43c 3.15b – −0.94 −1.71d 2.55c –
0.53 4.93 0.57 0.13 – 0.10 0.11 0.09 0.12 0.04 0.03 0.20 – 0.05 0.04 0.55 –
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their households are more compliant than others, as the negative sign in Table 4 reveals. Apart from age, another factor that is frequently examined in D&D behavioural studies is the gender. Although men consume more alcohol, the analysis shows that women seem to be more prone to the DUI of alcohol phenomenon. This outcome was also found from the studies of Zador (1991) and Waller and Blow (1995) and was explained by the fact that women metabolize alcohol differently from men, causing them to reach higher BAC’s at the same doses (Howat et al., 1991). In the case of this research, where the non-compliant behaviour is measured by the number of drinks consumed and the mode used to return home, it can be concluded that compared to men, women have less possibility to leave their car and return home with another travel mode, when they are drunk. The four variables that measure the Values of the responder have all the same positive impact on D&D behaviour. The strongest the support of the individual is to the proposed actions, the lower the frequency of drinking and driving is in real life. People, who do not drink and drive, believe that the most effective intervention to stop the D&D phenomenon is attending special lectures at schools as well as awareness increase through road safety campaigns. On the other hand they do not believe that stricter penalties will reduce the phenomenon. The potential of campaigns in reducing D&D behaviour is also commented in a similar study of Grube and Wallack (1994). Regarding the last endogenous variable, the Awareness of the Problem, the analysis shows that the most significant indicator is individuals’ exposure to road safety campaigns. However, the latent endogenous variable does not identify the latent exogenous variable of D&D behaviour in a satisfactory manner (in terms of statistical significance). The poor explanation power on D&D behaviour is also evident from the unaccountable signs of the parameters estimates.
4. Conclusions Drinking and driving represents non-compliant behaviour with social and financial impacts not easily measured. The examination of the factors that can interpret and finally change this non-compliant behaviour is a vital task for modern communities. Accidents related with high alcohol consumption have as monadic cause the human factor, in contrary to the road accidents in general, where important contributors are also considered the road environment and the vehicle itself. Human factor related accidents can be reduced either by prevention actions or through enforcement related interventions. Obviously, the latter cannot relief the phenomenon on its own in a constant way. The preventing actions can be a variety of initiatives covering issues related to the education and training of kids at schools, the information and the awareness of drivers, integrated actions for change of current behaviour, establishment of a strict, fair and efficiency legal framework etc. Within the framework of this paper, a focus was given to identify some attitudinal and behavioural characteristics that may have an effect on drinking and driving phenomenon. Based on the three questions that were addressed at the introductory part of the paper, the analysis confirmed numerous outcomes that previous studies had also reached in the past. For example, the analysis shows that elements related with the personal characteristics of the individual, such as age, gender and frequency of visiting bars per week, are the most important contributors for the drinking and driving behaviour. Furthermore, from the analysis on individuals’ opinion on some policy interventions that can reduce drinking and driving, it turns out that patrons who are not used to drive after drinking
are in favour of the proposed interventions; only the proposal for stricter penalties foreseen for drinking and driving was not supported by the total of the interviewers. The willingness to accept a suitable policy towards drinking and driving, should be taken into consideration when major national interventions are being planned at higher, strategic levels of decision making. Finally, the research found no relation between the drinking and driving phenomenon and the knowledge of fatality rates due to road accidents in Greece when examining the awareness level of the problem The relief of major problems caused merely by human behaviour, such as drinking and driving, demands in depth analysis of the parameters attributing to this non-compliant behaviour. As it was found in this paper, some of these parameters can be related either with personal beliefs or individuals’ personal characteristics. A detailed examination of the targeted population in terms of its habitual characteristics, its preferences and tastes as well as the willingness to accept or not a proposed action is an important process for integrated, well designed and effective road safety interventions. Finally, it should highlighted that the qualitative and quantitative outcomes of the study are localized due to the limited sample of 11 bars and cafeterias, and therefore any conclusions drawn cannot be generalized for the whole region or country. A stratified sample survey is needed in order to particularize the proposed actions at a national level.
References Åberg L., Haglund, M., 2001. Young people, drinking habits, transportation and peer relations. 14th ICTCT Workshop Road Characteristics with emphasis on lifestyles, quality of life and safety, Caserta, Italy, Proceedings in Electronic format. Birdshall, W.C., Reed, B.G., Hug, S.S., Wheeler, L., Rush, S., 2012. Alcohol-impaired driving: average quantity consumed and frequency of drinking do matter. Traffic Injury Prevention 13 (1), 24–30. Bollen, K.A., 1989. Structural Equations with Latent Variables. Wiley, New York. Boluarte, T.A., Mossialos, E., Rudisill, C., 2011. The impact of alcohol policies across Europe on young adults’ perceptions of alcohol risks. CESinfo Economic Studies 57 (4), 763–788. Brown, T.A., 2006. Confirmatory Factor Analysis for Applied Research. Guilford Press, New York. Chan, D.C.N., Wu, A.M.S., Hung, E.P.W., 2010. Invulnerability and the intention to drink and drive: an application of the theory of planned behaviour. Accident Analysis and Prevention 42, 1549–1555. Grube, J.G., Wallack, L., 1994. Television beer advertising and drinking knowledge, beliefs, and intentions among schoolchildren. American Journal of Public Health 84 (2), 254–259. Ham, L.S., Hope, D.A., 2003. College students and problematic drinking. Clinical Psychology Review 23, 719–759. Howat, P., Sleet, D., Smith, I., 1991. Alcohol and driving: is the 0.05% blood alcohol concentration limit justified? Drug and Alcohol Review 10 (2), 151–166. Jöreskog, K.G., Sörbom, D., 1996. LISREL 8 user’s reference guide. Scientific Software International, Inc., Lincolnwood, IL. Kim, F., William, C., 2004. Prevention of heavy drinking and associated negative consequences among mandated and voluntary college students. Journal of Consulting and Clinical Psychology 72 (6), 1038–1049. LaBrie, J.W., Kenney, S.R., Mirza, T., Lac, A., 2011. Identifying factors that increase the likelihood of driving after drinking among college students. Accident Analysis and Prevention 43, 1371–1377. Lavrakas, P., 2008. Encyclopedia of Survey Research Methods. Sage Publications Inc. Liourta, E., Empelen, P., 2008. The importance of self-regulatory and goal-conflicting processes in the avoidance of drunk driving among Greek young drivers. Accident Analysis & Prevention 40, 1191–1199. McCarthy, D.M., Pedersen, S.L., Leuty, M.E., 2005. Negative consequences and cognitions about drinking and driving. Journal of Studies on Alcohol 66 (4), 567–570. Moan, I.S., Rise, J., 2011. Predicting intentions not to drink and drive using an extended version of the theory of planned behaviour. Accident Analysis and Prevention 43, 1378–1384. Murray, C.J., Lopez, A.D., 1996. The Global Burden of Disease. Harvard School of Public Health, Boston, MA. National Highway Traffic Safety Administration – NHTSA, 1997. 1995 Youth Fatal Crash and Alcohol Facts. Department of Transportation, Washington, DC. Nolan, Y., Johnson, J.A., Pincus, A.L., 1994. Personality and drunk driving: identification of DUI types using the Hogan Personality Inventory. Psychological Assessment 6 (1), 33–40. Phillips, D.P., Brewer, K.M., 2011. The relationship between serious injury and blood alcohol concentration (BAC) in fatal motor vehicle accidents: BAC = 0.01% is
I. Politis et al. / Accident Analysis and Prevention 60 (2013) 316–323 associated with significantly more dangerous accidents than BAC = 0.00%. Addiction 106 (9), 1614–1622. Schumacher, R.E., Lomax, R.G., 2004. A beginners guide to Structural Equation Modelling, second ed. Lawrence Erlbaum Associates, Inc., Mahwah, NJ. Tippetts, A.S., Voas, R.B., Fell, J., Nichols, J.L., 2005. A meta-analysis of.08 BAC laws in 19 jurisdictions in the United States. Accident Analysis & Prevention 37, 149–161. Vegega, M.E., Klitzner, M.D., 1989. Drinking and driving among youth: a study of situational risk factors. Health Education Quarterly 16 (3), 373–388. Voas, R.B., Tippetts, A.S., Fell, J., 2000. The relationship of alcohol safety laws to drinking drivers in fatal crashes. Accident Analysis & Prevention 32, 483–492.
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Voas, R.B., Tippetts, A.S., Fell, J., 2003. Assessing the effectiveness of minimum legal drinking age and zero tolerance laws in the United States. Accident Analysis & Prevention 35, 579–587. Wagenaar, A.C., Maldonado-Molina, M.M., Ma, L., Tobler, A.L., Komro, A.L., 2007. Effects of legal BAC limits on fatal crash involvement: analyses of 28 states from 1976 through 2002. Journal of Safety Research 38, 493–499. Waller, P.F., Blow, F.C., 1995. Women, alcohol, and driving. In: Recent Developments in Alcoholism: Vol. 12. Alcoholism and Women. Plenum Press, New York. Zador, P.L., 1991. Alcohol-related relative risk of fatal driver injuries in relation to driver age and sex. Journal of Studies on Alcohol 52 (4), 302–310.
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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
Exploring the effects of attitudinal and perception characteristics on drinking and driving non-compliant behaviour Ioannis Politis a,∗ , Socrates Basbas b , Panagiotis Papaioannou a a b
Department of Civil Engineering, Aristole University of Thessaloniki, PC 54124, Greece Department of Rural and Surveying Engineering, Aristole University of Thessaloniki, PC 54124, Greece
a r t i c l e
i n f o
Article history: Received 15 October 2012 Received in revised form 21 March 2013 Accepted 28 March 2013 Keywords: Drinking and driving Confirmatory factor analysis Structural equation modelling Behavioural interpretation LISREL
a b s t r a c t The objective of this paper is to examine a number of factors (observed and latent) that might have a causal effect on drinking and driving (D&D) behaviour. Face-to-face surveys were conducted among patrons at bars and cafeterias and 305 valid questionnaires were filled. A confirmatory factor analysis was performed so as to identify the latent constructs and a mixed structural equation model was developed. From the analysis it came up that non-compliant behaviour of D&D is limited at older ages, also associated with high levels of income and car availability. Though men are consuming more alcohol, women seem to be more prone in driving under the influence (DUI) of alcohol. Furthermore, it was found that people who strongly support the examined interventions in the study (e.g. better enforcement, more traffic safety campaigns, stricter penalties) are more unlikely to drive after drinking compare to those who have some objections. Finally, it was not found any statistically significant relation between individuals’ level of awareness and D&D behaviour. © 2013 Elsevier Ltd. All rights reserved.
1. Introduction Drinking and driving (D&D) is according to World Health Organization (WHO), one of the most important causes for road accidents worldwide, especially those occurring at night hours (Murray and Lopez, 1996). Unfortunately, this non-compliant behaviour is primarily adopted by young people, with the majority of them being novice drivers; in previous decades, a lot of funds have been spent in order to raise the awareness of people in road safety related issues, such as preventing driving after alcohol consumption, though without great success. Drinking and driving – D&D (also referred as Driving Under the Influence of Alcohol – DUI) is a significant public health concern in modern communities worldwide. Due to its great social impact, D&D behaviour has been analyzed and explored by a number of research disciplines, such as Medicine, Social Sciences, Psychology and Engineering. The literature review that follows, attempts to briefly present the research outcomes, primarily from the Psychology discipline side. As mentioned before, numerous studies have given special focus on young people (college and university students) as well as on novice drivers. In a paper exploring the primary psychosocial factors that predict the problem of drinking in college students, Ham and Hope (2003) examined a number of factors
∗ Corresponding author. Tel.: +30 2310995775. E-mail address: [email protected] (I. Politis). 0001-4575/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.aap.2013.03.033
including demographic variables, personality, drinking history, alcohol expectancies, drinking motives, stress and coping, activity involvement, and peer and family influence. They found that the variables associated with college drinking seem to vary at levels dealing with one’s personality and coping mechanisms, one’s thought processes about drinking, and the environment. Liourta and Empelen (2008), explored the importance of selfregulatory and goal-conflicting processes in the avoidance of D&D among young students in Greece. They found that intention is a factor for non-compliant behaviour. Furthermore, the study showed that goal conflict, behavioural willingness, alcohol limitation planning and alcohol limitation self-efficacy could explain the intention–behaviour gap distinguishing between intenders who reported avoidance of drunk driving or those who had not avoided drunk driving. Finally, LaBrie et al. (2011) developed a logistic regression model, showing that sex status, fraternity or sorority affiliation, family history of alcohol abuse, medium or heavy drinking (as compared to light drinking), are some of the factors that increase the likelihood of D&D among college students. The application of well known models and theories derived directly from the disciplinary of Psychology is a common approach when personal habitual characteristics of the driver are examined. Moan and Rise (2011) for example, used an extended version of theory of planned behaviour (TPB) in order to predict intentions regarding D&D. The results showed that the TPB variables explained 10% of the variance in intentions in the sample as a whole. Perceived behavioural control was the strongest predictor of intentions, followed by descriptive norm, attitude, and moral norm. The role of
I. Politis et al. / Accident Analysis and Prevention 60 (2013) 316–323
attitude, social norm, drinking habits and intention of individuals’ in exogenous D&D behaviour through the TPB, was also tested by Åberg and Haglund (2001) in Sweden. Also, Chan et al. (2010) used the TPB to explore the role of invulnerability and the intention to D&D non-compliant behaviour. The total explained variances in the intention to drink and drive reached 79% and subjective norms, attitudes, and perceived behavioural control of risky behaviours were consistently found to be positively correlated with each other. Finally, Nolan et al. (1994) used a cluster analysis to identify 5 personality types (Impulsive-Extravert, Normal, Neurotic-Introvert, Neurotic-Hostile, and Unassertive-Conformist) that differed predictably on demographic variables, drinking behaviour, and driving records. A common approach in D&D behavioural analysis is the evaluation of alcohol prevention and intervention programmes and the impact/influence that have at the targeted population. Voas et al. (2000, 2003) for example, found a significant relationship between three major alcohol safety laws and the downward trend in alcohol-related fatal crashes in the United States. The cause and effect relationship between the minimum legal drinking age (MLDA) law and reductions in highway crashes was also confirmed by McCarthy et al. (2005). A lifestyle management class (LMC) programme evaluated by Kim and William (2004) showed decreases in driving after drinking relative to control participants. Changes in heavy drinking varied as a function of treatment condition, readiness to change, and gender. Finally, Boluarte et al. (2011), used the data from 2008 Flash Eurobarometer survey conducted across the 27 EU member states and found that most alcohol policies tested, had no significant influence on risk perceptions. Only the blood alcohol concentration (BAC) limit for driving and health warnings on advertisements and/or alcoholic beverage containers showed a significant effect, increasing risk perceptions among adolescents. Similar outcomes regarding the effects of BAC were obtained from the analysis of Wagenaar et al. (2007), where the effects of legal BAC limits on fatal crash involvement of 28 states in U.S. from 1976 to 2002 were addressed. Further, a meta-analysis of 0.08 Blood Concentrations laws in 19 jurisdictions in the U.S. showed that almost 950 lives would have been saved throughout 2000, in case of a national implementation of such limits to all 50 states (Tippetts et al., 2005). Finally, in a large sample of 1.5 million people that were killed in fatal accidents in U.S. between 1994 and 2008, Phillips and Brewer (2011) proved that 0.01 BAC is associated with significantly more dangerous accidents than 0.00 BAC. Taking into consideration the existing knowledge regarding drinking and driving, briefly presented above, the objective of this paper is to extend this knowledge so as to identify which of the personal characteristics and beliefs of the individuals, actuate on drinking and driving behaviour and how. More specific, the paper is trying to investigate in a complementary and integrative manner some of the gaps in the existing knowledge which have been identified at the literature review presented above and can be summarized into the following (research) questions: (a) Which are the specific personal and habitual/behavioural characteristics of a person that affect D&D behaviour? (b) In which way the targeted road safety policies can prevent D&D behaviour? (e.g. how effective lectures at schools can be in D&D prevention or how better enforcement can reduce the examined phenomenon?) (c) What is the role of individuals’ awareness for issues related to road safety and alcohol consumption in D&D behaviour? For the purpose of the research, 305 valid questionnaires have been completely filled through a revealed preference (RP) survey. The survey was conducted among patrons at bars, restaurants and cafeterias at the city of Serres, a midsized city of 80.000
317
inhabitants, located in northern Greece, with a high number of university students. A confirmatory factor analysis (CFA) is initially performed in order to validate the proposed measurement model. Afterwards, the structural equation modelling (SEM) technique is applied in order to set up a multivariable model that identifies which of the parameters examined, play a key role on individuals’ drinking and driving non-compliant behaviour. Both the CFA and the SEM model construction were set up in LISREL v8.8 student edition, which restricts these analyses to a maximum number of 15 variables (Jöreskog and Sörbom Dag, 1996). 2. Undertaken research This section describes the steps that were taken in order to address the research questions previously presented. Additionally, initial results regarding the bivariate correlation of the variables used in the study are shown and commented. Finally, the variance–covariance matrix is given for replication of the study. 2.1. Questionnaire survey For the purposes of this research, a direct face-to-face survey was employed. Although an onsite survey method is more costly than other methods (e.g. website or telephone surveys), it provides numerous benefits such as high response rate, reliability of the given answers and absence of missing values (Lavrakas, 2008). Undergraduate students were involved in data collection which took place from December 1 to December 17, 2010. The questionnaires were distributed in 11 randomly selected cafeterias and bars, between 23.00 p.m. and 2.00 a.m. From the 330 collected questionnaires, 305 were finally considered as valid and reliable for analysis. The questionnaire itself consisted of 24 questions allocated in 4 sections. Since the patrons visit the bars and cafeterias for recreational purpose, an effort was given to provide them with comprehensible, easy-to-understand and easy-to-answer questions. In addition, the interviewers were well trained so as to identify the patrons who were not in position to understand and answer the questionnaire correctly, avoiding with that way additional bias in the sample. The confirmatory factor analysis (CFA) as well as the structural equation model (SEM) were developed either by utilizing the original responses of the participants or by creating new variables (recoding) by combining the responses to different questions. At the end, only 14 variables were used for the analysis and only these are presented and commented at the following sections. 2.2. Measurement scale and correlations This paragraph gives a brief description of the variables used and of the scale these were measured and coded for the statistical analysis. Furthermore, it presents the correlation and (co)variance matrices, which are useful tools for qualitative outcomes and replication of the study respectively. Table 1 shows the 14 variables used in this paper, together with a short description and presentation of the measurement scale. Since LISREL treats a variable as ordinal in case of less than 15 categories, all variables used in this research are considered as ordinal with the exception of AGE and CARS MEM, which were considered as continuous. The 5-point Likert scale, corresponds to answers varying from Totally Disagree (coded as “1”) up to Totally Agree (coded as “5”), with code “3” stating the Neutral/Don’t Know selection. Finally, MAX PENA and TOTAL DE define the level of awareness of the respondents in two issues related with D&D and
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Table 1 Coding, description and measurement scale of variables. Variable code
Description
Measurement scale
EXITS PE
Visit of bars per week
AGE INCOME
Age of respondent Income of respondent (thousand euros)
CARS MEM
Available cars per household Gender of respondent Better enforcement will reduce the DUI of alcohol Stricter penalties in Road Traffic Code for D&D Campaigns will reduce D&D phenomenon Road safety lectures at schools will reduce accidents Penalty foreseen for 2 glasses of alcohol consumption Road accident deaths in Greece in 2008 Last time notice (exposed to) a road safety campaign
1: 1 time, 2: 2 times, 3: 3 times, 4: >3 times Original response 1: <15, 2: 16–25, 3: 26–35, 4: 36–45, 5: 46–55, 6: >56 Number of cars per persons per household 1: man, 2: woman 5-Point Likert scale
GENDER BETTER E STRICTER MORE CAM SCHOOL L
MAX PENA
TOTAL DE EXPOS CA
TOTAL DR
5-Point Likert scale 5-Point Likert scale
3. Methodology
5-Point Likert scale
The first step of building a structural equation model is to specify the relationship between the observed indicators and the latent constructs. In this paper we perform a confirmatory factor analysis (CFA), a theory driven approach, instead of the respective data driven method of Exploratory Factor Analysis (EFA) (Brown, 2006). The parameters for CFA and SEM were estimated through the maximum likelihood estimation (MLE) method.
1: correct answer, 2: wrong answers 1: correct answer, 2: wrong answers 1: 1 month, 2: 6 months, 3: 1 year, 4: never Original response
Total number of drinks per visit to bars Driving car to return home
DRIVING
Regarding the option of car as travel mode to return home, the research shows that this behaviour is adopted primarily by young men (r = −0.34 and r = 0.32, p < 0.001) with high income (r = −0.43, p < 0.001). Finally, those from households with high car possession rate are also in favour of not choosing alternative (safest) modes to return back to home (r = −0.28, p < 0.001). Following the advice of Schumacher and Lomax (2004) regarding the reporting of SEM research, we present in Table 3 the variance–covariance matrix. The covariance matrix may be used in lieu of the raw data in calculating a number of multivariate statistical models, such as confirmatory and exploratory factor analysis, path analysis, or other general linear models, including the special cases of multiple regression, analysis of variance, and repeated measures analysis of variance.
3.1. Confirmatory factor analysis (CFA)
1: yes, 2: no
As the path diagram of Fig. 1 shows, the final CFA is a three factor model, with three latent constructs ( 1 . . . 3 ), 12 observed indicators (1 . . .12 ) with the respective measurement errors (ı1 . . .ı12 ) and 13 factor loadings (ij , i = 1 to 3 and j = 1 up to 5). The first latent variable, describes the personal background of the individual (Perso Ba) and is measured by 5 observed variables; the age of the responder, the gender, the car availability per household, the income and the number of visits to bars and cafeterias per week. The second latent variable is entitled “values” and measures the opinion of the responders to 4 policy interventions that could reduce the D&D non-compliant behaviour. Finally, the last latent variable (Prob Awa) represents the level of awareness of the individual in road safety issues and DUI of alcohol impacts. More specific, the last latent variable defines (measures) the awareness of individuals by examining how well are informed about specific issues such as how many road accident deaths were observed in Greece in 2008 and which is the penalty foreseen by the Greek law for the consumption of 2 glasses of alcohol. All observed indicators for this latent variable were interval scale and the interval was set
road safety. Both variables corresponds to questions with predefined answers were only one of them was correct (and coded as “1”). All mistaken selections were coded as “2”. Table 2 presents the correlation matrix for the 14 variables of the study from which useful bivariate outcomes can be delivered (the numbers at the first row corresponds to the 14 variables of the first column). From Table 2 it can concluded that people who use to consume a lot of alcohol are patrons who regularly visit bars and cafeterias during the week (r = 0.25, p < 0.001), are not aware about the BAC limits and alcohol consumption relation (r = 0.13, p < 0.05) and are negative to any intervention proposed in the research such as better enforcement (r = −0.14, p < 0.05), road safety lectures at schools (r = −0.16, p < 0.01), adoption of stricter penalties (r = −0.20, p < 0.001) and introduction of campaigns (r = −0.18, p < 0.01). As expected, young people visit bars and cafeterias more often than older ones (r = 0.36, p < 0.001) who believe on the other hand that targeted campaigns can reduce the D&D phenomenon (r = 0.15, p < 0.01). Table 2 Correlations and two tailed probabilities (N = 305).
TOTAL DR EXITS PE EXPOS CA BETTER E SCHOOL L STRICTER MORE CAM GENDER AGE INCOME MAX PENA TOTAL DE DRIVING CARS MEM a b c
1
2
3
4
5
6
7
8
9
10
11
12
13
14
– 0.25a 0.04 −0.14c −0.16b −0.20a −0.18b −0.17b −0.09 0.10 −0.13c −0.08 −0.04 −0.04
– −0.07 −0.10 −0.02 −0.10 −0.11 −0.08 −0.36a −0.01 −0.05 −0.07 0.17b −0.07
– −0.09 −0.23a 0.11 −0.09 0.03 0.03 0.01 0.06 0.16b −0.08 −0.05
– 0.28a 0.26a 0.19a 0.15b 0.03 0.12c 0.05 0.11 −0.17b 0
– 0.17b 0.41a 0.08 0.14c −0.08 −0.10 −0.23a −0.02 0
– 0.09 0.08 0.02 −0.02 0.06 0.06 0.09 −0.10
– 0.05 0.15b 0.15b −0.13c −0.02 −0.05 −0.01
– −0.35a −0.27a 0.04 −0.03 0.32a −0.13c
– 0.31a −0.02 −0.08 −0.34a 0.04
– −0.23a −0.03 −0.43a 0.18b
– 0.07 0.20 0.03
– 0.04 0.01
– −0.28a
–
p < 0.001. p < 0.01. p < 0.05, two tailed.
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Table 3 Variance–covariance matrix of the sample population (N = 305).
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1.18 0.25 0.07 −0.27 −0.67 −0.36 −0.48 −0.18 −0.76 0.18 −0.14 −0.09 −0.04 −0.01
0.86 −0.11 −0.16 −0.08 −0.15 −0.24 −0.08 −2.46 −0.02 −0.04 −0.06 0.16 −0.02
2.82 −0.27 −1.47 0.29 −0.39 0.06 0.36 0.04 0.10 0.27 −0.14 −0.03
3.18 1.89 0.77 0.82 0.26 0.44 0.34 0.09 0.19 −0.31 0.00
14.51 1.04 3.79 0.30 3.85 −0.51 −0.39 −0.89 −0.08 0.00
2.66 0.36 0.13 0.25 −0.05 0.10 0.10 0.14 −0.05
5.99 0.12 2.80 0.60 −0.33 −0.05 −0.12 −0.01
1.00 −2.65 −0.44 0.04 −0.03 0.32 −0.04
56.04 3.73 −0.11 −0.63 −2.51 0.09
2.59 −0.36 −0.05 −0.69 0.08
1.00 0.07 0.20 0.01
1.00 0.04 0.00
1.00 −0.08
0.08
in such a way that it was impossible for a middle-informed individual to give wrong answer. The last indicator explores the exposure rates of the responder to a road safety campaign (which was the last time they have seen/listened to a traffic safety campaign in TV/radio). The overall statistical indices used in CFA suggest that the model fits the data well. The chi-square statistic is too small (2 = 43, df = 45) to reject the null hypothesis of good fit (p = 0.56). Additionally, the root mean square error of approximation (RMSEA) indicates a perfect fit with a 90% confidence interval returning a value of 0.04. Finally, as far as the overall fit indices are concerned, most of them are satisfying the cutting off criteria, such as the goodness of fit index (GFI = 0.98), adjusted goodness of fit index (AGFI = 0.96), standardized root mean square residual (SRMR = 0.044) and normed fit index (NFI = 0.82).
Table 4 presents the standardized estimates, the t-values, the standard error as well as the correlation coefficient (R2 ) between the three latent and the twelve observed variables. It can be concluded that the latent variables of Personal Background and Values are measured in a satisfactory level by the proposed observed variables, however for the last one, that of Problem Awareness, the description seems not to be fulfilled well (very low R2 ). For this latent variable, the observed indicator variables with low R2 are the MAX PENA and TOTAL DE, two variables that were recoded as mentioned in the previous paragraphs as correct and wrong answer and perhaps this is the cause for the low explanation of the latent construct The observed variables with the higher relation (explanation) to the latent variables are the age (R2 = 0.44) for the Personal Background, the belief that school lectures will improve the safety levels (R2 = 0.50) for the Values and
Fig. 1. Path diagram of unstandardized factor loadings for the CFA as calculated by LISREL.
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Table 4 Results of confirmatory factor analysis. Constructs and variables Personal background ( 1 ) Visits per week (1 ) Age (2 ) Income (3 ) Cars per household (4 ) Gender (5 ) Values ( 2 ) Better enforcement (6 ) Stricter penalties (7 ) More campaigns (8 ) School lectures (9 ) Problem awareness ( 3 ) Maximum penalty (10 ) Total deaths in accidents (11 ) Last Exposed to Campaign (12 )
Standardized estimate
t-value
S.E.
R2
0.480 0.663 0.346 0.221 0.400
3.64a −2.79b −3.45a −2.04c –
0.69 9.04 0.65 0.16 –
0.23 0.44 0.12 0.05 0.16
0.387 0.187 0.520 0.707
4.87a 2.34c 5.75a 4.64a
0.10 0.11 0.01 0.12
0.15 0.04 0.27 0.50
0.155 0.239 0.436
1.65 2.48c 3.16b
0.04 0.03 0.20
0.02 0.06 0.19
2 = 43.00, df = 45, p-value = 0.56, RMSEA = 0.00, GFI = 0.98, AGFI = 0.96, SRMR = 0.044, NFI = 0.82, CN = 494.63. a p < 0.001. b p < 0.01. c p < 0.05, two tailed.
the period unexposed to a road safety campaign (R2 = 0.19) for the Problem Awareness. 3.2. The structural equation model (SEM) Structural equation models are made up of two components: the first (called latent variable model) describes the relationship between endogenous and exogenous latent variables, and permits the evaluation of both direction and strength of the causal effects among these variables; the second (called measurement model) describes the relationship between latent and observed variables. The basic equation of the latent variable model is the following (Bollen, 1989): = B + +
(1)
in which (eta) is an (mx1) vector of the endogenous variables, (xi) is an (nx1) vector of the exogenous latent variables, and (zeta) is an (mx1) vector of random variables. The elements of the B (beta) and (gamma) matrices are the structural coefficients of the model; the B matrix is an (mxm) coefficient matrix of the latent endogenous variables; the matrix is an (mxn) coefficient matrix for the latent exogenous variables. The basic equations for the measurement model are the following: x = x + ı
(2)
for the exogenous variables, and y = y + ε
(TOTAL DR) and the usage of car as a travel mode to return home (DRIVING). Another key issue that should be highlighted is the interrelation and co-linearity of the indicators (e.g. the income and the age). Although in traditional regression models, the interrelation of the independent variables can lead to wrong outcomes and for this reason is avoided when strong co-linearity exists, in SEM the relation (variance) between two variables is stated by a two headed arrow. For example, the variables age, income and number of cars per household are proved to be highly correlated, as it is showed in the next figure. Fig. 2 shows the path diagram of the proposed model together with the unstandardized (regression) weights for each structural coefficient. A detailed description of the results is presented next.
(3)
for the endogenous variables, in which x and ı (delta) are column q-vectors related to the observed exogenous variables and errors, respectively; x (lamda) is a (qxn) structural coefficient matrix for the effects of the latent exogenous variables on the observed variables; p and e (epsilon) are column p-vectors related to the observed endogenous variables and errors, respectively; y is a (pxm) structural coefficient matrix for the effects of the latent exogenous variables on the observed ones. In this section, we present a structural equation model analysis for drinking and driving non-compliant behaviour. Based on the CFA commented in the previous section, we examine the three exogenous latent variables as factors that could reveal the D&D behaviour. The endogenous D&D latent behaviour, coded as Compl beh, is determined by two respective observed variables, the total number of drinks consumed per visit to bars and cafeterias
3.3. Results and interpretation The first criterion that should be taken into account, when a SEM is validated, is the overall statistical indices (Schumacher and Lomax, 2004). The statistical analysis of the overall model indicated that 2 was 107.54, with 65 degrees of freedom (p < 0.001). The standardized root mean square of residual (SRMR) was 0.061, the goodness of fit index (GFI) was 0.95 and the adjusted goodness of fit index (AGFI) was 0.92. These results indicate that the model is reasonably consistent with the data and does not require specification or modification. In terms of statistical interpretation, the above mentioned indices show that the true population is deemed consistent with the implied theoretical model being tested (or the sample covariance matrix S is sufficiently reproduced by the implied theoretical model). In simply words, the convergence indicates that the proposed model fits the data well and is reliable for qualitative and quantitative outcomes. The second criterion in judging the statistical significance and substantive meaning of a theoretical model is the examination of statistical significance of individual parameters estimates for the paths of the model. Table 5 presents this information from which it can concluded that most of the parameters examined, fulfil the cutting of criterion for t-value (greater than 1.96 for confidential level of 95%). Almost all the observed indicators of the two latent variables Values and Personal Background are significant at 0.001 level of significance. From the analysis, it can also be concluded that these two latent variables can have a significant impact in D&D non-compliant behaviour, at 0.05 and 0.001 level of significance respectively. The third latent variable of Problem Awareness is not considered as significant in affecting D&D behaviour. Finally, the
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Fig. 2. Structural equation model for drinking and driving non-compliant behaviour.
only unconstrained variable, that of total drinks per visit to bars and cafeterias, interprets the D&D behaviour at 0.05 level of significance. Last but not least, the third criterion considers the magnitude and the direction of the parameter estimates. The interpretation of the results that is presented next, simultaneously investigates the fulfilment of this criterion. Taking into account the standardized estimates presented in Table 4, both for the observed and the latent variables, the following outcomes can be derived. The number of exits to bars and cafeterias per week as expected, is positively affecting the D&D behaviour. Assuming that the frequency of visiting bars is directly related to the frequency of
drinking, the above mentioned outcome is in consistency with the study of Birdshall et al. (2012) who found that drinking 9 or more times in the past month doubled the odds of impaired driving. Following the outcomes of numerous relevant studies presented in the introductory part of this paper, the analysis shows that age seems to be one of the most crucial factors for D&D behaviour (the higher regression estimate). The negative sign confirms the findings of Vegega and Klitzner (1989) and the outcomes of NHTSA study (NHTSA, 1997) that DUI of alcohol is reduced in older ages. Furthermore, as it was discussed previously, income and car availability are strongly related with the age and therefore it cannot be concluded that people with higher income or more available cars in
Table 5 Results of structural equation model. Paths EXITS PER AGE INCOME CARS MEM GENDER BETTER E STRICTER MORE CAM SCHOOL L MAX PENA TOTAL DE EXPOS CA Compl Be Compl Be Compl Be TOTAL DR DRIVING
← ← ← ← ← ← ← ← ← ← ← ← ← ← ← ← ←
–, Factor loading constrained to one. a p < 0.001. b p < 0.01. c p < 0.05. d p < 0.1 two tailed.
Perso Ba Perso Ba Perso Ba Perso Ba Perso Ba Values Values Values Values Prob Awa Prob Awa Prob Awa Perso Ba Values Prob Awa Compl Be Compl Be
Standardized estimates
t-value
S.E.
0.44 −0.68 −0.35 −0.29 0.38 0.41 0.18 0.51 0.52 0.13 0.23 0.42 0.19 −0.08 −0.05 0.89 2.08
4.59a −5.50a −4.19a −3.33a – 5.18a 2.32c 5.95a 4.54a 1.46 2.43c 3.15b – −0.94 −1.71d 2.55c –
0.53 4.93 0.57 0.13 – 0.10 0.11 0.09 0.12 0.04 0.03 0.20 – 0.05 0.04 0.55 –
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their households are more compliant than others, as the negative sign in Table 4 reveals. Apart from age, another factor that is frequently examined in D&D behavioural studies is the gender. Although men consume more alcohol, the analysis shows that women seem to be more prone to the DUI of alcohol phenomenon. This outcome was also found from the studies of Zador (1991) and Waller and Blow (1995) and was explained by the fact that women metabolize alcohol differently from men, causing them to reach higher BAC’s at the same doses (Howat et al., 1991). In the case of this research, where the non-compliant behaviour is measured by the number of drinks consumed and the mode used to return home, it can be concluded that compared to men, women have less possibility to leave their car and return home with another travel mode, when they are drunk. The four variables that measure the Values of the responder have all the same positive impact on D&D behaviour. The strongest the support of the individual is to the proposed actions, the lower the frequency of drinking and driving is in real life. People, who do not drink and drive, believe that the most effective intervention to stop the D&D phenomenon is attending special lectures at schools as well as awareness increase through road safety campaigns. On the other hand they do not believe that stricter penalties will reduce the phenomenon. The potential of campaigns in reducing D&D behaviour is also commented in a similar study of Grube and Wallack (1994). Regarding the last endogenous variable, the Awareness of the Problem, the analysis shows that the most significant indicator is individuals’ exposure to road safety campaigns. However, the latent endogenous variable does not identify the latent exogenous variable of D&D behaviour in a satisfactory manner (in terms of statistical significance). The poor explanation power on D&D behaviour is also evident from the unaccountable signs of the parameters estimates.
4. Conclusions Drinking and driving represents non-compliant behaviour with social and financial impacts not easily measured. The examination of the factors that can interpret and finally change this non-compliant behaviour is a vital task for modern communities. Accidents related with high alcohol consumption have as monadic cause the human factor, in contrary to the road accidents in general, where important contributors are also considered the road environment and the vehicle itself. Human factor related accidents can be reduced either by prevention actions or through enforcement related interventions. Obviously, the latter cannot relief the phenomenon on its own in a constant way. The preventing actions can be a variety of initiatives covering issues related to the education and training of kids at schools, the information and the awareness of drivers, integrated actions for change of current behaviour, establishment of a strict, fair and efficiency legal framework etc. Within the framework of this paper, a focus was given to identify some attitudinal and behavioural characteristics that may have an effect on drinking and driving phenomenon. Based on the three questions that were addressed at the introductory part of the paper, the analysis confirmed numerous outcomes that previous studies had also reached in the past. For example, the analysis shows that elements related with the personal characteristics of the individual, such as age, gender and frequency of visiting bars per week, are the most important contributors for the drinking and driving behaviour. Furthermore, from the analysis on individuals’ opinion on some policy interventions that can reduce drinking and driving, it turns out that patrons who are not used to drive after drinking
are in favour of the proposed interventions; only the proposal for stricter penalties foreseen for drinking and driving was not supported by the total of the interviewers. The willingness to accept a suitable policy towards drinking and driving, should be taken into consideration when major national interventions are being planned at higher, strategic levels of decision making. Finally, the research found no relation between the drinking and driving phenomenon and the knowledge of fatality rates due to road accidents in Greece when examining the awareness level of the problem The relief of major problems caused merely by human behaviour, such as drinking and driving, demands in depth analysis of the parameters attributing to this non-compliant behaviour. As it was found in this paper, some of these parameters can be related either with personal beliefs or individuals’ personal characteristics. A detailed examination of the targeted population in terms of its habitual characteristics, its preferences and tastes as well as the willingness to accept or not a proposed action is an important process for integrated, well designed and effective road safety interventions. Finally, it should highlighted that the qualitative and quantitative outcomes of the study are localized due to the limited sample of 11 bars and cafeterias, and therefore any conclusions drawn cannot be generalized for the whole region or country. A stratified sample survey is needed in order to particularize the proposed actions at a national level.
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