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 Table of Contents  
ORIGINAL ARTICLE
Year : 2020  |  Volume : 27  |  Issue : 2  |  Page : 93-100

Adaptation of the driver behaviour questionnaire and behavioural risk factors for traffic violation arrest and self-reported crash involvement among Nigerian drivers


1 Department of Surgery, Ekiti State University, Ado-Ekiti, Nigeria
2 Department of Surgery, University of Nigeria Teaching Hospital, Enugu, Enugu State, Nigeria
3 Department of Surgery, Federal Medical Centre, Makurdi, Benue State, Nigeria
4 Department of Surgery, Benue State University Teaching Hospital, Makurdi, Benue State, Nigeria

Date of Submission01-Nov-2019
Date of Decision08-Jan-2020
Date of Acceptance21-Jan-2020
Date of Web Publication11-Apr-2020

Correspondence Address:
Prof. Kehinde Sunday Oluwadiya
Ekiti State University, Ado-Ekiti
Nigeria
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/npmj.npmj_172_19

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  Abstract 


Context: Few studies have been conducted to investigate the driving behaviour of drivers in Africa. Aims: This study aims to determine the behavioural risk factors for road crashes among Nigerian drivers. Settings and Design: This is a case–control study. Cases were drivers who were booked for traffic violation or who had been involved in road crashes in the past, while the controls were drivers with no such histories. Subjects and Methods: Both the cases and controls were administered the Driver Behaviour Questionnaire (DBQ). Principal component analysis with varimax rotation was run to examine the factor structure of the scale. Cronbach's alpha was used for assessing the internal consistency of the DBQ, and logistic regression was used to determine risk factors for crash involvement. Results: Six hundred active drivers consisting of 300 cases and 300 controls were selected. The mean scores of all DBQ items, except one, were significantly higher among booked drivers compared to those who had never been booked. Consistent with many previous studies, factor analysis identified three factors in the DBQ (aggressive violation, ordinary violation and error). However, the factors were constructed differently with most ordinary violation items in the original DBQ loading as aggressive violation in the present study. Eight variables were predictive of being booked for traffic offences while only five variables were predictive of self-reported crash involvement. Conclusions: The most important variable associated with previous crash involvement was alcohol use. A major policy implication of this is the need for better attention to anti-drunk driving measures.

Keywords: Driver Behaviour Questionnaire, drivers, Nigeria, road safety


How to cite this article:
Oluwadiya KS, Popoola SO, Onyemaechi NO, Kortor JN, Denen-Akaa P. Adaptation of the driver behaviour questionnaire and behavioural risk factors for traffic violation arrest and self-reported crash involvement among Nigerian drivers. Niger Postgrad Med J 2020;27:93-100

How to cite this URL:
Oluwadiya KS, Popoola SO, Onyemaechi NO, Kortor JN, Denen-Akaa P. Adaptation of the driver behaviour questionnaire and behavioural risk factors for traffic violation arrest and self-reported crash involvement among Nigerian drivers. Niger Postgrad Med J [serial online] 2020 [cited 2020 Jun 2];27:93-100. Available from: http://www.npmj.org/text.asp?2020/27/2/93/282313




  Introduction Top


Currently, road traffic injuries (RTI) is ranked as the 11th most common cause of death and the ninth leading cause of disability-adjusted life years lost worldwide. By 2020, without intervention or improvement, RTI is expected to rise to become the 3rd leading cause of death.[1] Most of these increases are expected to be in low-middle income countries (LMIC). To prevent this rise, preventive measures need to be put in place. Such interventions can only be successful if it is based on the identification of risk factors that are involved in RTIs. Traditionally, the causes of road crashes are divided into three: human (driver), environment and vehicle factors. It has been estimated that driver-related risk factors are responsible for most road crashes.[1],[2],[3] Specifically, more than half of road crashes are caused by human factors, while another 30%–40% occur as a result of the combination of human and/or the vehicle and/or the environment. The driver-related risk factors can be further divided into driver behaviour and driver performance with the latter having a greater effect on crash fatalities.[4] Driver behaviour is responsible for the chosen speed, manoeuvring on the road as well as reactions to perceived risks.[5]

Providing countermeasures to identified risk-taking behaviours should serve to reduce the incidence of RTI.[1] Some of these measures have been applied with some success in high-income countries (HIC). However, the situation is different in most LMIC countries where RTIs are rapidly becoming epidemic. However, while many of the countermeasures that worked in HIC countries are expected to work also in the LMICs, many will not.[6],[7],[8] There is a need for more researches to identify risk factors and modify proven countermeasures to reflect the local needs.[9],[10],[11]

The present study was designed with such a goal in mind. It is a case–control study which seeks to ascertain whether there were certain behaviours or risk factors for road crashes that were more prevalent among drivers that were booked by the Federal Road Safety Commission (FRSC) marshals compared with non-booked drivers. More specifically, the study aims to examine the factor structure of the Driver's Behaviour Questionnaire (DBQ) in a sample of Nigerian drivers, determine the usefulness of the DBQ in predicting traffic violation and involvement in crashes and to determine factors that are predictive of convictions for traffic violation and involvement in crashes.

Findings from the study will assist policymakers in designing appropriate countermeasures to road crashes and traffic violations.


  Subjects and Methods Top


Study setting

In Nigeria, the government agency responsible for formulating policies and actions aimed at reducing RTI is the FRSC.[12],[13] Nigeria has 36 States and a Federal Capital Territory. The field operations of FRSC is divided into 12 zonal commands, with each zone made up of a number of states designated as sector commands. Each sector command has one or more lower commands called unit commands.[13] The present study took place in the Benue State sector command with its headquarters in Makurdi, the state capital. The activities of the command are carried out by the regular staff of the commission referred to as regular marshals, and volunteers called special marshals. Both corps marshals and special marshals carry out field operations and are vested with powers to stop, interrogate and issue traffic tickets to traffic offenders. The traffic ticket, officially refer to as “notice of offence form” stipulates the traffic violation for which the driver was booked and its punishment. When a driver is stopped for traffic violation, his or her documents are confiscated, and he or she is given a traffic ticket and directed to the sector command headquarters within a stipulated timeframe, where he or she is made to listen to talks about and watch documentaries on road safety.

Selection of cases and controls

The study, which was conducted in 2014, was a case–controlled study where cases were drivers who were stopped by FRSC officers for perceived traffic violations, were booked and directed to the Benue State FRSC sector command headquarters at Makurdi. At the command headquarters, a consecutive sampling methodology was employed in which each booked driver was approached, the rationale for the study explained to him or her and his or her willingness to take part in the study sought. The drivers who were willing to take part in the study questionnaire were administered the questionnaire by trained personnel with identifiable tags in one of the offices at the headquarters. Their previous traffic violation conviction status notwithstanding, all freshly booked drivers were eligible for selection as cases, the only exclusion criterion was if the driver was not willing to be included in the study.

Controls were selected using the roadside survey method used in a previous study from Kenya.[14] Controls were drivers who were routinely stopped for traffic inspection and who had no history of previous traffic violation arrests. The routine for selecting and administering questionnaires to the control subjects was as follows: When the corps marshals arrived at the patrol spot, the first vehicle driving from either direction would be stopped. After conducting a routine check, the rationale for the study would be explained to the driver and he or she would be asked whether he/she would be willing to take part in the study. The study questionnaire was administered by trained personnel with identifiable tags who were with the corps marshals. To ensure that no driver was made to wait, the next driver was only selected after the initial interview had been completed and this would be the first driver coming in the opposite direction.

Identical questionnaires were administered to both cases and controls; however, the interviewers skipped the questions on previous arrest (s) for traffic violations for controls. For both groups, the interviews were anonymous (no names or other identifying information collected), and the interviews were conducted in private settings where no other people could listen to the interviews. The interviews were conducted in both English and the local language. When the interviews were conducted in the local language, the interviewer would read from a previously translated version of the questionnaire, but the responses were filled on the English language version. All drivers with no previous booking for traffic violations were eligible for selection as controls, the only exclusion criterion was if the driver was not willing to be included in the study.

The Driver Behaviour Questionnaire

The questionnaire was based on the DBQ. The DBQ was originally developed by Reason et al. to examine self-reported driving behaviours.[15] The behaviours which the scale addressed were errors and violations. Errors were defined 'as the failure of planned actions to achieve their intended consequences' while violations were defined as 'deliberate (though not necessarily reprehensible) deviations from those practices believed necessary to maintain the safe operation of a potentially hazardous system'. Thus, the major difference between the two is the 'issue of deliberate versus accidental behaviour'.[16] Errors are further divided into slips and lapses, which is the unwitting deviation of actions from intentions; and mistakes; defined as the departure of planned actions from some satisfactory goals.[15]

The original DBQ was developed in the United Kingdom, and it consisted of three factors: violations, dangerous errors and lapses. The DBQ has been used in road safety researches in many countries and cultures including: Australia,[16] China,[17] the Scandinavian countries[18],[19] and Persian peninsula.[20] Some of these studies have confirmed the original three factor structure,[18] while others had come up with two,[21] four,[20] five[22] or even six factor structure.[23] In addition, these studies have also shown variations from the original DBQ as far as the items making up the individual factors are concerned. For example, aggressive driving factor was found to contain many of the items that were previously associated with Highway Code violations in a sample of Australian fleet drivers.[16] Similarly, in Qatar, it was found that the error factor had 10 items, but two of the items were normally associated with lapses and one with ordinary violation.[20]

There are many reasons for the different factor structure of the DBQ in different countries and cultures. The driving style is dependent on both intrinsic factors such as age and sex, as well as extrinsic factors such as socioeconomic and cultural conditions.[24] Young drivers tend to commit violations more frequently than older drivers, and men tend to do it more frequently than women.[15],[24] Social norms and other road users may also account for the difference in the level of road safety observed in the Western/Northern European and Southern European countries.[24]

For this study, the 19-item DBQ that was successfully deployed in six countries in Europe and Asia by Özkan et al. was used.[24] This DBQ contained three factors: errors (eight items), ordinary violations (eight items) and aggressive violations (three items). We modified it to reflect the peculiarities of the driving environment in Nigeria. The modifications were based on review of crash literature from Nigeria, pilot interviews with drivers and rewording by an English language lecturer in the university. Participants were asked to indicate how often they committed each of the 19 behaviours in the previous year on a six-point Likert scale (0 = very unlikely, 5 = very likely).

Other sections of the questionnaire contained questions about demographic measures (age, sex, education, occupation and religion); driving experience (duration of driving in years, crash involvement in the past, possession of license, whether or not he or she ever received a formal training and general alcohol use) and traffic violation arrests (number of times, how long ago and the nature of the offence).

Statistical analysis

Chi-square and Student's t-test were run to study whether there were significant differences between cases and controls on demographic, traffic variables and the DBQ item scores. Principal component analysis (PCA) with varimax rotation was run to examine the factor structure of the scale. Eingenvalue, Cattell Scree plot as well as parallel analysis were used to arrive at the final factor structure. Cronbach's alpha reliability coefficients were also calculated for assessing the internal consistency of the DBQ and its factors. Further analysis was by estimation of the odds ratio (confidence Interval: 95%) using logistic regression. The dependent variable was Booked/Never Booked. Statistical analysis was performed using PASW Statistics for Windows, Version 18.0. (Chicago: SPSS Inc). Parallel analysis was done with the Monte Carlo PCA for Parallel Analysis software (Watkin, 2000).

Ethical approval was obtained from the Benue State University Teaching Hospital Ethical Committee.


  Results Top


A total of 600 drivers participated in the study consisting of 300 cases and 300 controls. The controls were the remaining 300 drivers who had no history of traffic violations and were never booked for any offences whatsoever by the FRSC (by the time we got 300 controls, 317 had been approached, which means that 17 declined to be interviewed. No case demurred). There were 496 (82.7%) males and 104 (17.3%) females. The mean age of the drivers was 39.4 years (range 19–65 years). While majority of the participants were located in Benue and its adjourning states, almost all the states of Nigeria were represented in the sample. On the average, participants had been driving for 8.3 years (range 1–45 years). Only 191 (31.8%) of the 600 participants were formally trained before driving on the road. One hundred and sixty-seven (27.8%) had been involved in a road crash before, and 301 (50.2%) take alcohol.

[Table 1] compares the frequency of some traffic and demographic variables between cases and controls. The less education a participant had, the more likely was he/she to have been booked for traffic violations. However, contrary to expectations, booked drivers (cases) were significantly older (average: 2.3 years older) than controls.
Table 1: Demographic characteristics of the participants separately for cases (booked drivers) and controls (never-booked drivers)

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[Table 2] shows the means and the standard deviations of the DBQ items for cases and controls. Cases scored significantly higher on all DBQ items except for 'Sound your horn to indicate your annoyance to another road user' (an aggressive violation item) where controls scored higher; incidentally, this is also the highest scoring item of the DBQ. 'Trying to turn left onto a main road, you pay such close attention to the main stream of traffic that you nearly hit the car in front' (an error item) was the least scoring item.
Table 2: Means and standard deviations of Driver Behaviour Questionnaire items for cases (booked) and controls (never booked)

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Factor structure of the Driver Behaviour Questionnaire

Initially, six factors had eigenvalues over 1.0 in the samples. However, the Scree plot suggested just one factor while parallel analysis suggested a three-factor solution.[25] We decided to go with the three factor structure because specifying too few factors and specifying too many factors can lead to errors that will affect the result. In addition, parallel analysis is believed to be one of the most accurate methods.[25] The three-component solution explained a total of 43.7% of the variance, with component 1 contributing 29.9%, component 2 contributing 7.3% and component 3 contributing 6.4%.

As presented in [Table 3], the component 1 included 8 items. It contained six of the ordinary violation items of the original DBQ. However, it also included an error item 'Underestimate the speed on an oncoming vehicle when overtaking' and an aggressive violation item 'Become angered by a certain type of driver and indicate your hostility by whatever means you can'. Due to the predominance of ordinary violation items in this component, one would have been tempted to label this component ordinary violation, but all eight items were indicative of someone who was in a hurry and was so impatient that he or she was prepared to force his or her way through the traffic by any means necessary. Thus, such a person was prepared to force his or her way through the traffic, drive very close to the vehicle at the front, or even overtake on the passenger's side to gain an advantage; and when he/she felt thwarted, he/she showed his/her anger by whatever means he/she could. We therefore labelled this component aggressive violation.
Table 3: Factor structure of the Driver Behaviour Questionnaire

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The component 2 contained seven items consisting of four error items, two ordinary violation items and one aggressive violation item. This was the most difficult component to pin with a label, but we decided to label this component ordinary violation because some of the error items e.g., 'On turning left, nearly hit a motorcyclist who has come up on your inside' and 'Brake too quickly on a slippery road, for example after rainfall' may be considered in some circumstances to be violations. In fact, the item 'Fails to trafficate (signal) while turning into a street' can be interpreted as a violation, because the Highway Code mandates that drivers should signal to show their intention to turn into another street, or change directions. Similarly, 'Overspeed on residential road' could easily be interpreted as a violation.

The component 3 contained four items; three of which were error items. The fourth item was an aggressive violation item, 'Sound your horn to indicate your annoyance to another road user'. Because of the predominance of error items in this component (75% of the components), it was labelled 'errors'. Five of the factors were loaded on more than one factor.

Reliability of the scale

The internal consistency of the DBQ scale was examined through calculating Cronbach's alpha reliability coefficients. The overall reliability score of the scale was 0.864. The individual score for the factors were 0.802 for aggressive violation, 0.800 for ordinary violation and 0.498 for errors.

Prediction of traffic violation [Table 4]
Table 4: Adjusted logistic regression for predictors of traffic offence

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Binary logistic regression was performed to assess the impact of a number of variables on the likelihood that drivers would be booked for committing traffic violation offences. The model contained booking status as the dependent variable and twelve independent variables (the three DBQ factors and nine other variables). The Hosmer and Lemeshow Goodness of Fit test indicated a good fit; meaning that the model was able to correctly differentiate between drivers who were booked for traffic offences and those who were not. Overall, between 30.8% and 41.1% of the variance in the dependent variable (booking status) was explained by the model. As [Table 4] shows, eight of the independent variables were significant predictors of booking status. These included the three DBQ factors as well as age, sex, educational status, driving experience (years) and working vehicle speedometer. Aggressive violation has the highest impact; those who scored high on this have close to three times the odds of being booked for traffic offences than those who score low. Perhaps, surprisingly, educational status had a negative impact on the model. According to the model, the more educated respondents had an increased odd of previous traffic violations.

Prediction of previous involvement in road crash

This was similar to the previous procedure except for the fact that the dependent factor is now self-reported previous crash involvement. Consequently, the independent variables in the model were 11 (we did not include booking status for previous traffic violation in the model because it was the basis for categorising the participants into cases and controls). As in the previous procedure, the Hosmer and Lemeshow Goodness of Fit test also indicated a good fit; meaning that the model was able to correctly differentiate between drivers who reported and did not report previous crash involvement. Overall, just 14.6%–21.1% of the variance in the dependent variable (previous crash involvement) was explained by the model. As [Table 5] shows, five of the independent variables were significant predictors of self-reported previous crash involvement. Only two of the three DBQ factors were significant (error was not significant). Age, experience and alcohol intake were the other significant predictors. Alcohol has the highest impact; those who admitted to imbibing alcohol had twice the odds of being booked for traffic offenses than those who did not. Aggressive violation has the second highest impact.
Table 5: Logistic regression for the predictors of self-reported crash involvement

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  Discussion Top


The DBQ has become one of the most important measurement tools for studying motorists' behaviours.[16],[26] Although we can find no evidence of any published research on its use in Africa, the DBQ has been used in all the other major continents of the world where it has exhibited good cross-cultural validity.[16] One of the aims of this study was to look at the applicability of the DBQ to the understanding of the driving behaviours of Nigerian drivers. Consistent with studies from other countries, the scores of the DBQ in this study is generally low.[16],[24] However, the mean DBQ item scores in our sample were generally higher than the scores obtained in the UK and other Western/Northern European countries.[24] Furthermore, the factor with the highest mean score was aggressive violation, which is in keeping with the scoring of the DBQ factors among drivers from the so-called 'dangerous' Southern European/Middle Eastern countries. In contrast, drivers from 'safe' Western/Northern European countries scored highest on ordinary violations.[24] According to Shinar and Reason, both social and cultural context tend to influence the driving style.[15],[27] Aggressive driving in particular is highly influenced by cultural context and is believed to have an interpersonal aggressive component.[24] Driving in Nigeria, which can be likened to driving in the said “dangerous” Southern European/Middle Eastern countries, can be chaotic. The poor road infrastructures, traffic congestions, presence of pedestrians, animals, motorcycles, tricycles and automobiles on roads made narrower by roadside kiosks often lead to aggressive road behaviours, interpersonal conflicts and poor adherence to traffic rules and regulations.[10],[24],[28]

Factor structure of the Driver Behaviour Questionnaire

Both aggressive violation and ordinary violation appeared to have good internal consistency as their reliability scores were quite high and were at levels comparable to those from previous studies.[16],[24] This provides good evidence that the DBQ items making up the two factors in the present sample are measuring the same construct. It is also indicative of how questionnaire items can be perceived and interpreted differently in different countries. So, items that are unrelated in one country may be firmly connected in another country or setting. For example, the three aggressive factor items from the original DBQ ('Become angered by a certain type of driver and indicate your hostility by whatever means you can', 'Become angered by another driver and give chase with the intention of abusing him verbally or by making gestures' and 'Sound your horn to indicate your annoyance to another road user') were distributed one-apiece to the three factors in the present sample. Thus, 'Sound your horn to indicate your annoyance to another road user' which is an aggressive violation item with a high score in the Scandinavian countries becomes an error item in the present sample. This confirmed the findings in a previous study from Nigeria, which showed that motorcyclists on Nigerian roads used their horns for 'conversations'.[29] For example, taxi drivers use their horns to 'tell' prospective commuters standing by the roadside that they are available to take passengers. On the other hand, error, the third factor, scored low on the reliability scale. This is probably due to the small number of items in the factor; it had four while the others had seven and eight items. One of the most important causes of low reliability coefficients in a scale is small number of items in the scale.[24]

While the factor structure of the DBQ in our sample supported the original division into aggressive violation, ordinary violation and error, the makeup of the factors in terms of the number of items, as well as the items contained therein, differ remarkably from the original scale. In the present sample, ordinary violation items loaded mostly on aggressive violation. In the 'safe' driving environment of Western/Northern Europe, strong law enforcement and driver-friendly infrastructure ensures that aggressive violation, which according to Berkowitz (1993) is associated with an intention to injure another person physically or psychologically is either undeveloped or suppressed. Aggression are of two types: emotional aggression and instrumental aggression.[30] Emotional aggression is aimed at hurting another person when angered or disturbed while instrumental aggression is for gaining psychological or material advantage over others.[30],[31] Thus, it can be seen how most of the items in this factor, which in the safe driving environments of the Scandinavian countries may be perceived as 'non-harming' and therefore could be construed as ordinary violations could, in the 'dangerous' Nigerian driving context be seen as means of gaining an advantage over other road users, and for that reason, be construed as aggressive violations.

The second item, ordinary violation, was the most difficult factor to interpret among the three factors. It is a hodgepodge of four original DBQ error items, two original DBQ ordinary violation items and one original DBQ aggressive violation items. Based on the consideration of the meaning of the items in the Nigerian driving context, we decided to label it ordinary violation (Actually, we were tempted to label it error, but because the third factor had proportionally more error items (3/4), we were inclined to leave the tag for the other factor.). The difficulty with tagging it notwithstanding, the factor's Cronbach's alpha score of 0.8 showed that the internal consistency of the factor is high, and the items making-up the factor are measuring the same construct. As earlier mentioned, two of the factors 'Overspeed on residential road' and 'Fails to trafficate (signal) while turning into a street' could easily be construed as ordinary violations because there are specific injunctions against them in the Nigerian Highway Code.

The third factor, error was the easiest to name because three out of the four items in it were original DBQ error items. However, the factor had poor internal consistency as evidenced by a Cronbach's alpha score of 0.498. As mentioned earlier in this section, this is probably due to the small number of items in the factor.

Prediction of booking for offences for traffic violation

The three DBQ factors as well as five other driving and demographic variables predicted traffic offences in the present sample. The three factors were the strongest predictors, and aggressive violation proved to be the strongest predictor of them all. This result greatly contrast with the findings by Davey et al. among a sample of fleet drivers in Australia.[16] They found none of the DBQ factors predictive of traffic offences in their sample. In fact, only the number of kilometres driven proved to be a significant predictor. The difference in result may be because this study is a case-controlled study in which previous traffic violation is a selection criterion, while theirs was a cross-sectional cohort study with relatively fewer numbers of offences reported. The findings here suggest that drivers' behaviour is a very powerful component of traffic violation on Nigerian road. Given that human factors have been estimated to be the main or partial cause of at least four out of every five road crashes,[4] and the present study, which focused mainly on behavioural and some other human aspect of road usage in Nigeria found behavioural factors to be the most powerful component in the study; it suggests that behavioural factors are very important contributors to traffic violation in Nigeria. It is therefore essential for policy makers and other stakeholders to take this into consideration in their decision making. Because, drivers whose vehicles had no functioning speedometers had higher odds of traffic violations, we also advocate that traffic officers enforce the correct working of speedometers in vehicles on Nigerian roads.

Prediction of self-reported crash involvement

There are some major differences between the performances of the DBQ in predicting self-reported crash involvement compared with predicting traffic offences. First, the DBQ is not as robust in predicting crash involvement as it is in predicting traffic offences. The variance explained by the model in the crash involvement regression is 14.3%–20.5% versus 31.9%–42.5% for traffic offence regression. Second, only two of the three DBQ factors were predictive of self-reported crash involvement; whereas, all three factors were predictive of traffic offence. Thirdly, three other factors, age, alcohol and driving experience were predictive of crash involvement while four additional factors were predictive of traffic offences [Table 5]. Finally, alcohol intake was the single most important predictor of self-reported crash involvement. It should be emphasised that this is not driving under the influence per se, but an admission that the respondent imbibes alcohol. Therefore, curbing the use of alcohol, whether by social marketing strategies or strengthened law enforcement against drunk driving or both, may be an important step in reducing road traffic crashes on Nigerian roads.

The finding that age was a negative predictor of self-reported involvement in road crashes is in agreement with a number of previous studies.[4],[32] It suggests that younger drivers were more likely to experience more crashes than older drivers. However, a further look at the B statistics revealed that this is a very weak predictor compared to the other significant predictors. The effect may therefore not be very strong in practical terms.

Limitations of the study

While this study provided a Nigerian version of the DBQ, there are some limitations that may affect the generalisability of its findings in Nigeria and may need to be taken into account in planning future researches on the subject in Nigeria. First, the study was conducted in Benue State which is just one of the 37 states including the Federal capital Territory in Nigeria. However, this may have been mitigated by the fact that Makurdi, the study centre is a popular transit node for traffics between the southern and northern parts of the country. Second, the behaviours reported in the study were based on self-reports and not on actual or observed behaviours. The accuracy of such reported behaviours may be limited by social desirability. However, previous studies have shown a strong link between reported driver behaviours and observed driver behaviours.[33] Third, reported involvement in previous traffic violations may be skewed by recall bias as well as social desirability. Finally, some drivers who deserved booking may have escaped booking by speeding off. The possibility of this happening was reduced by holding the exercise in bad portions of the road.


  Conclusions Top


The present study has offered useful insight into how the tendency of Nigerian drivers to commit traffic violations or be involved in road crashes may be affected by their behaviours. As the first study of its type from Nigeria, and probably from sub-Saharan Africa, the study provides further evidence on the adaptability of the DBQ to diverse cultures and countries, and a template for future studies on the subject. It confirmed the three-factor structure of the DBQ established by Reason et al. and many other authors. However, there are distinct differences in the composition of the factors in the present sample compared to the other studies. Finally, the study established the DBQ factors as the most important predictors of being booked for traffic violations and alcohol intake as the most important predictor of self-reported traffic crash involvement among Nigerian drivers.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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