Background
1.1 Background
Road accident has been identified as one of the major contributor to national deaths in so many countries. The accidents have been found to be caused by various reasons such as reckless driving, unroadworthy vehicles, driving while under the influence of alcohol and poor states of roads to mention but a few (Sidhu & Sodi, 2013). The accidents have not only claimed the life of the occupants of the vehicles but also those who walk as pedestrians along the roads. Many interventions have been put by governments through relevant departments to ensure the rate of road carnage has been brought down significantly (Onokala, 2015) and (Smith, 2009). This has been in an effort to help the traffic police. In some countries, apart from the traffic police, departments such as department of transport and transport safety authorities have been instituted to ensure road safety. Apart from using institutions, guides such as road signs have been used (Bolade, 2013). These have been erected everywhere along the road so as to guide drivers on the state of the road and the environment they are driving in. Road safety campaigns have also been taken to the masses to scale down road accidents (Stansfield & McGreevy, 2008). The other element that has contributed to the number of accidents on the roads is the use of motorcycles. This is very common in cities and urban areas where passengers use the motorcycles during peak hours to avoid being trapped in traffic snarl (Robertson, 2012) and (Ross, 2011). As a result of greed to make more money, the motorcycle riders take advantage of these situations and speed t in between the vehicles so as to make numerous trips thus causing accidents.
1.2 Statement of the problem
It is natural to conclude that accidents are not planned and that they occur randomly. On the same breathe, it can be said that males and females get involved in the accident without any pattern because it is a random occurrence. People of all ages also get involved in the accidents without any pattern. This report sought to establish whether there is significant difference in the number of females and males that die in road accident in the states of America in 2012. The report also wants to establish whether there is a difference of the same among various age groups. To be able to accomplish this task, the report sourced data from motor vehicle safety website while others were collected.
1.3 Justification of the use of sample data
Since this report was dealing in deaths due to road accidents, data regarding the same was collected. A sample data was used since this was an accurate representation of the entire population. It had the characteristics that the report was focused on analyzing. These were motor vehicle occupants’ death rates in the United States. The parameters under investigation in this report are occupants’ death rate by age and occupants’ death rate by gender. It will not be easy to categorize the variables in this data as either dependent or independent. This will only be possible in the context of various tests that will be conducted in the sections to follow.
Statement of the problem
1.4 Research objectives
- To establish the rate of accidents in different states of the United States.
- To identify the common causes of road accidents.
- To find out whether there is a difference in the number of deaths as a result of accidents by gender.
- To find out whether there is a difference in the number of deaths as a result of road accidents by age groups.
1.5 Research questions
- Is the rate of accidents in the different states the same?
- Does the number of deaths as a result of road accident differ by gender?
- Does the number of deaths as a result of road accident differ by age?
1.6 Scope of the research
This report focuses on road accidents caused by motor vehicles. Therefore, the population of the report is occupants of the motor vehicles by gender and age who die as a result of road accidents.
1.7 Significance of the research
As there has been a gap in information about motor vehicle occupant death rate by gender and age, the results of this study will come in handy in supplying this kind of information to the relevant authority for necessary interventions. The results of this study will also add up to the existing literature about road accidents in the states thus enriching the existing knowledge about road accidents. This research is obviously having its weaknesses and strong points. The weaknesses will provoke criticism hence further research by interested parties. This will also go a long way in providing information that has been lacking when it comes to road carnage in the states. Lastly, this research will be a source of important information to students who are pursuing education in road safety.
1.8 Limitations
The main limitation of the study was that cases of accidents suffered under-reporting since it depended on traffic police accuracy. They might have been overstated or understated.
2.1 Research design
This research employed the use of survey to identify and collect accident information from the relevant authorities in different states. This design was thought appropriate by the research since there was need to collect data over a relatively large geographical area.
2.2 Target population
The target population was the number of deaths which due to accidents. The other population which only served to supply the study with peripheral information was the traffic officers from different states in the U.S. However, the research will heavily rely on secondary data.
2.3 Sample design
Traffic officers from traffic headquarters from 53 states were approached to supply the study with relevant secondary data. They were also asked questions regarding road accident sin their states just to be able to shed more insight on the state of rad accidents in their areas of jurisdictions.
2.4 Data collection
This research study relied on secondary data rather than primary data. The data was sampled from accident reports from all states and sampled by the research team.
2.5 Data analysis
The research data was analyzed by employing both descriptive and inferential statistics. Descriptive statistics was employed to establish measures of central tendencies such as mean, median and mode. Measures of dispersion such as standard deviation range and variance was also obtained. Inferential statistics sought to help the report be able to make inferences and conclusion about tests. Pearson correlation was employed to establish the strength and direction of correlation between the deaths of female and males. Regression analysis was also used to investigate whether there is a linear relationship between both gender deaths due to road accidents.
2.5.1 Reliability and validity tests
Before the data was subjected to in depth analysis, reliability and validity tests were conducted. The best reliable data is always measured against Cronbach’s value. The value of the Cronbach for the data was as indicated below;
Justification of the use of sample data
Table of reliability results
|
Reliability Statistics |
|
|
Cronbach’s Alpha |
N of Items |
|
.971 |
6 |
Table 1
The table above shows Cronbach’s value for reliability test. It can be observed that the value is 0.971. This is an indication that there is sufficient internal consistency. Normally, a Cronbach’s value of over 0.7 means adequate internal consistency of the data.
2.5.2 Validity test
A validity test was carried out to establish validity of the data being used. This was done to identify which variables can be removed from the data and which ones can be retained. The validity test results are as shown in the table below;
Table of validity test results
|
Item-Total Statistics |
||||
|
Scale Mean if Item Deleted |
Scale Variance if Item Deleted |
Corrected Item-Total Correlation |
Cronbach’s Alpha if Item Deleted |
|
|
Male_2012 |
40.0915 |
335.780 |
.951 |
.968 |
|
Male_2014 |
40.5064 |
339.057 |
.978 |
.962 |
|
Female_2012 |
45.9851 |
450.083 |
.962 |
.970 |
|
Female_2014 |
46.1213 |
459.605 |
.901 |
.974 |
|
AllAges_2012 |
43.0574 |
389.736 |
.982 |
.958 |
|
AllAges_2014 |
43.3553 |
396.078 |
.984 |
.958 |
Table 2
As can be observed from the table above, the item corrected totals are all high. They are above 0.7. This means that no item is fit to be deleted from the list of variables.
3.1 DESCRIPTIVE STATISTICS
3.1.1 Descriptive statistics for death rates of females and males in 2012
|
Statistics |
|||
|
Male_2012 |
Female_2012 |
||
|
N |
Valid |
51 |
48 |
|
Missing |
1 |
4 |
|
|
Mean |
11.3549 |
5.8521 |
|
|
Median |
10.2000 |
5.4000 |
|
|
Mode |
5.90a |
4.70a |
|
|
Std. Deviation |
5.61827 |
2.63156 |
|
|
Variance |
31.565 |
6.925 |
|
|
Skewness |
.976 |
.741 |
|
|
Std. Error of Skewness |
.333 |
.343 |
|
|
Kurtosis |
.759 |
.014 |
|
|
Std. Error of Kurtosis |
.656 |
.674 |
|
|
Minimum |
4.10 |
1.70 |
|
|
Maximum |
29.30 |
12.90 |
|
|
a. Multiple modes exist. The smallest value is shown |
Table 3
It can be observed from table 3 of descriptive statistics that the mean rate of motor vehicle- occupant death rate for the males was far much higher than those of their female counterparts. The rate was 11.35 and 5.85 respectively. The modal occupant death rate for the male was 5.9 and 4.7 for the females. The highest occupant death ever experienced in 2012 was 29.3 and 12.9 for the males and females respectively. The lowest occupant death ever experienced in 2012 was 4.1 and 1.7 for the males and females respectively.
3.1.2 Descriptive statistics for death rates of females and males in 2012
|
Statistics |
|||
|
Male_2014 |
Female_2014 |
||
|
N |
Valid |
49 |
47 |
|
Missing |
3 |
5 |
|
|
Mean |
11.0878 |
5.7021 |
|
|
Median |
9.7000 |
5.0000 |
|
|
Mode |
6.00 |
4.00 |
|
|
Std. Deviation |
5.47546 |
2.57120 |
|
|
Variance |
29.981 |
6.611 |
|
|
Skewness |
.889 |
.920 |
|
|
Std. Error of Skewness |
.340 |
.347 |
|
|
Kurtosis |
.430 |
1.455 |
|
|
Std. Error of Kurtosis |
.668 |
.681 |
|
|
Minimum |
3.80 |
1.50 |
|
|
Maximum |
27.70 |
14.30 |
Table 4
It can be observed from table 4 of descriptive statistics that the mean rate of motor vehicle- occupant death rate for the males was far much higher than those of their female counterparts. The rate was 11.08 and 5.7 respectively. The modal occupant death rate for the male was 6.0 and 4.0 for the females. The highest occupant death ever experienced in 2012 was 27.7 and 14.3 for the males and females respectively. The lowest occupant death ever experienced in 2012 was 3.8 and 1.5 for the males and females respectively.
3.1.3 Descriptive statistics for death rates for all ages in 2012 and 2014
|
Statistics |
|||
|
AllAges_2012 |
AllAges_2014 |
||
|
N |
Valid |
51 |
51 |
|
Missing |
1 |
1 |
|
|
Mean |
8.5157 |
8.1039 |
|
|
Median |
7.4000 |
6.8000 |
|
|
Mode |
5.10a |
6.00 |
|
|
Std. Deviation |
4.05608 |
4.00000 |
|
|
Variance |
16.452 |
16.000 |
|
|
Skewness |
.877 |
.912 |
|
|
Std. Error of Skewness |
.333 |
.333 |
|
|
Kurtosis |
.273 |
.799 |
|
|
Std. Error of Kurtosis |
.656 |
.656 |
|
|
Minimum |
2.90 |
2.30 |
|
|
Maximum |
20.20 |
21.00 |
|
|
a. Multiple modes exist. The smallest value is shown |
Table 5
It can be observed from table 5 of descriptive statistics that the mean rate of motor vehicle- occupant death rate for all ages in 2012 and 2014. The mean rate in 2012 was 8.5 and 8.1 in 2012 and 2014 respectively. The modal occupant death rate for 2012 was 5.1 and 6.0 in 2014. The highest occupant death for all ages ever experienced in 2012 was 20.2 and 21 in 2014. The lowest occupant death for all ages ever experienced in 2012 was 2.9 and 2.3 in 2014.
3.2 TEST FOR DIFFERENCE IN MEANS
3.2.1 Test whether there is significant difference in the mean occupant death rate between the males and females in 2012
A t-test was employed in this test since we were comparing difference in means between two variables. The hypothesis is as below;
Research objectives
Hypothesis
H0: There is no significant difference in the occupant death rate between the males and females in 2012.
Versus
H1: There is a significant difference in the occupant death rate between the males and females in 2012.
The results for the t-test as in the table below;
|
Paired Samples Test |
|||||||||
|
Paired Differences |
t |
df |
Sig. (2-tailed) |
||||||
|
Mean |
Std. Deviation |
Std. Error Mean |
95% Confidence Interval of the Difference |
||||||
|
Lower |
Upper |
||||||||
|
Pair 1 |
Male_2012 – Female_2012 |
5.84792 |
3.37462 |
.48708 |
4.86803 |
6.82781 |
12.006 |
47 |
.000 |
Table 6
The result of the t-test indicates a p-value (0.00) which is less than the level of significance (0.05). Due to this result, we are directed to accept the alternative. The report therefore concludes that there is a significant difference in the occupant death rate between the males and females in 2012.
3.2.2 Test whether there is significant difference in the mean occupant death rate between the males and females in 2014
A t-test was employed in this test since we were comparing difference in means between two variables. The hypothesis is as below;
Hypothesis
H0: There is no significant difference in the occupant death rate between the males and females in 2014.
Versus
H1: There is a significant difference in the occupant death rate between the males and females in 2014.
The results for the t-test as in the table below;
|
Paired Samples Test |
|||||||||
|
Paired Differences |
t |
df |
Sig. (2-tailed) |
||||||
|
Mean |
Std. Deviation |
Std. Error Mean |
95% Confidence Interval of the Difference |
||||||
|
Lower |
Upper |
||||||||
|
Pair 1 |
Male_2014 – Female_2014 |
5.61489 |
3.32520 |
.48503 |
4.63858 |
6.59121 |
11.576 |
46 |
.000 |
Table 7
The result of the t-test indicates a p-value (0.00) which is less than the level of significance 0.05). Due to this result, we are directed to accept the alternative. The report therefore concludes that there is a significant difference in the occupant death rate between the males and females in 2014.
3.2.3 Test whether there is significant difference in the overall mean occupant death rate between 2012 and 2014.
A t-test was employed in this test since we were comparing difference in means between two variables. The hypothesis is as below;
Hypothesis
H0: There is no significant difference in the occupant death rate between 2012 and 2014.
Versus
H1: There is a significant difference in the occupant death rate between 2012 and 2014.
The results for the t-test as in the table below;
|
Paired Samples Test |
|||||||||
|
Paired Differences |
t |
df |
Sig. (2-tailed) |
||||||
|
Mean |
Std. Deviation |
Std. Error Mean |
95% Confidence Interval of the Difference |
||||||
|
Lower |
Upper |
||||||||
|
Pair 1 |
AllAges_2012 – AllAges_2014 |
.41176 |
1.32328 |
.18530 |
.03959 |
.78394 |
2.222 |
50 |
.031 |
Table 8
The result of the t-test indicates a p-value (0.03) which is less than the level of significance 0.05). Due to this result, we are directed to accept the alternative. The report therefore concludes that there is a significant difference in the occupant death rate between 2012 and 2014.
3.3 Correlation Analysis
3.3.1 Test for correlation of death rate between 2012 and 2014
Results table
|
Correlations |
|||
|
AllAges_2012 |
AllAges_2014 |
||
|
AllAges_2012 |
Pearson Correlation |
1 |
.946** |
|
Sig. (2-tailed) |
.000 |
||
|
N |
51 |
51 |
|
|
AllAges_2014 |
Pearson Correlation |
.946** |
1 |
|
Sig. (2-tailed) |
.000 |
||
|
N |
51 |
51 |
|
|
**. Correlation is significant at the 0.01 level (2-tailed). |
Table 9
The correlation results above shows a Pearson coefficient of 0.96. This indicates that there is a strong positive correlation between death rates in 2012 and 2014.
3.3.2 Test for correlation of death rate between males in 2012 and females in 2012
Results table
|
Correlations |
|||
|
Male_2012 |
Female_2012 |
||
|
Male_2012 |
Pearson Correlation |
1 |
.915** |
|
Sig. (2-tailed) |
.000 |
||
|
N |
51 |
48 |
|
|
Female_2012 |
Pearson Correlation |
.915** |
1 |
|
Sig. (2-tailed) |
.000 |
||
|
N |
48 |
48 |
|
|
**. Correlation is significant at the 0.01 level (2-tailed). |
Table 10
The correlation results above shows a Pearson coefficient of 0.92. This indicates that there is a strong positive correlation between males and females in 2012.
3.3.3 Test for correlation of death rate between males in 2014 and females in 2014
Results table
|
Correlations |
|||
|
Female_2014 |
Male_2014 |
||
|
Female_2014 |
Pearson Correlation |
1 |
.904** |
|
Sig. (2-tailed) |
.000 |
||
|
N |
47 |
47 |
|
|
Male_2014 |
Pearson Correlation |
.904** |
1 |
|
Sig. (2-tailed) |
.000 |
||
|
N |
47 |
49 |
|
|
**. Correlation is significant at the 0.01 level (2-tailed). |
Table 11
The correlation results above shows a Pearson coefficient of 0.9. This indicates that there is a strong positive correlation between males and females in 2014.
3.4 Regression Analysis
Regression analysis was also conducted to establish whether there is a linear relationship between variables. Prior to regression analysis, assumptions for the linear relationship were confirmed. The data was assumed to be normally distributed since the data points were more than 30 and the fact that it was extracted from a normally distributed population.
3.4.1 Regression analysis between male death rates and female death rates in 2012
|
Model Summary |
|||||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|||
|
1 |
.915a |
.838 |
.834 |
2.28408 |
|||
|
a. Predictors: (Constant), Female_2012 |
|||||||
|
ANOVAa |
|||||||
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
||
|
1 |
Regression |
1240.737 |
1 |
1240.737 |
237.825 |
.000b |
|
|
Residual |
239.983 |
46 |
5.217 |
||||
|
Total |
1480.720 |
47 |
|||||
|
a. Dependent Variable: Male_2012 |
|||||||
|
b. Predictors: (Constant), Female_2012 |
|||||||
|
Coefficientsa |
|||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
|||
|
B |
Std. Error |
Beta |
|||||
|
1 |
(Constant) |
.274 |
.811 |
.338 |
.737 |
||
|
Female_2012 |
1.952 |
.127 |
.915 |
15.422 |
.000 |
||
|
a. Dependent Variable: Male_2012 |
Tables 12
Figure 1
From the regression analysis above, it can be observed that that there is a linear relationship between the death rates of females and males in 2012. To add on, it can also be observed that the value of R-squared is 0.83. This is an indication that 83% of the female deaths due to road accidents are explained by male deaths.
3.4.2 Regression analysis between death rates in 2012 and 2014
|
Model Summary |
||||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
||
|
1 |
.946a |
.895 |
.893 |
1.32660 |
||
|
a. Predictors: (Constant), AllAges_2014 |
||||||
|
ANOVAa |
||||||
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
736.353 |
1 |
736.353 |
418.411 |
.000b |
|
Residual |
86.234 |
49 |
1.760 |
|||
|
Total |
822.587 |
50 |
||||
|
a. Dependent Variable: AllAges_2012 |
||||||
|
b. Predictors: (Constant), AllAges_2014 |
|
Coefficientsa |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
.741 |
.423 |
1.751 |
.086 |
|
|
AllAges_2014 |
.959 |
.047 |
.946 |
20.455 |
.000 |
|
|
a. Dependent Variable: AllAges_2012 |
Tables 13
From the regression analysis above, it can be observed that that there is a linear relationship between the death rates of females and males in 2012. To add on, it can also be observed that the value of R-squared is 0.895. This is an indication that 89.5 % of the female deaths due to road accidents are explained by male deaths.
Conclusion
The results of this research found that the hypothesis of the research that there was no significant difference in motor vehicle occupant death rates between the males and females in 2012. The same case played out in 2014. When it came to overall motor vehicle occupant death rate, it was also found that there was no significant difference between 2012 and 2014. This is an indication that the accidents that occur in the U.S just occur randomly and do not follow any particular pattern. A strong correlation also exists between motor vehicle occupant death rates of males and females in 2012 and 2014.
References
Onakomaiya, S.O. (2011) General Trend of Safety and Accident Records in Nigerian Transport Sector. In: Bolade T. and Ogunsanya, A. (2013) Accident Control and Safety Measures in Transit ration in Nigeria, Ibadan University Press, P. 11.
Onokala, P.C. (2015) The Effect of Landuse on Road Traffic Accident in Benin-City, Nigeria. Journal of Transport Studies, Vol. 1, No. 1, Pp. 34-44.
Robertson, L.S. (2012) Injury Epidemiology. Oxford University Press, New York.
Ross, A. Baguley, C. Hills, V. Mchonald, M. and Silcock, d. (2011) Towards Safer Roads in Developing Countries: A guide for Planners and Engineers. Crowthorne, U.K.; Transport search laboratories.
Sidhu, D.S.; Sodi, G.S. and Banerjee, A.K. (2013) Mortality Profile in Trauma Victims.
Journal of the Indian Medical Association, 1:16-18.
Smith, G.S. and Barss, P. (2009) Unintentional Injuries in Developing Countries: The Epidemiology of a neglected problem. Epidemiology Review. 13, 228-266.
Stansfield, S.K.; Smith, J.S. and McGreevy, W.P. (2008) Injury in Diseases Priorities in Developing Countries
Taket, A. (2016) Accident Mortality in Children, Adolescence and Young Adults. World Health Statistics Quarterly, 39:232-256
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