“Gender and age-related differences in attitudes toward traffic laws and

traffic violations”

Seminar #34

Karen Jakubowski

Introduction: The study examined gender and age-

related differences in drivers’ normative motives for compliance with traffic laws and in gain-loss considerations related to driving. Normative behaviors: result from internalization

of the law and the perceived legitimacy of the authorities enforcing the law.

Subjects: Respondents were students at a Northern

Israeli university and at a college for adult education 43 male and 47 female respondents between the

ages of 18 and 24 49 male and 42 female students aged 30-62

Study design: Completed a 20-30 min. questionnaire

measuring several normative motives for: compliance with traffic laws perceived gains and danger involved in the

commission of traffic violations Frequency of committing various driving

violations

Introduction: Studies examining demographic factors

relating to dangerous driving show that gender is significant in predicting involvement in accidents The rate of men’s involvement in fatal road

accidents is twice as high as women’s. A woman’s chance of getting injured in a traffic

accident is 25% lower than that of a man’s.

Introduction: Involvement in accidents has a distinct

gender-related component: Men are involved more often in accidents caused

by speeding and driving under the influence of alcohol.

Women are involved more often in accidents caused by judgment errors.

Introduction: Age is a second demographic variable

frequently found to be related to risky driving: Younger drivers violate the law more often, are

more involved in crashes, and suffer more fatal road accidents.

Introduction: There is also an interactive effect of gender and

age on driving behavior: Young male drivers are considered a high-risk group in

regard to: accident involvement risky driving violation of traffic laws even parking illegally in spaces reserved for people with

disabilities?!

Study design: The study involved variables in three general

categories that evaluated respondents’ feelings on the following issues: Q1, Q2, Q3 measured “Normative motives for compliance

with traffic laws” Q4, Q5 measured “Gain-loss considerations relating to

traffic-law violations” Q6 measured “Self-reported commission of traffic

violations”

Quantitative Explanatory Variable #1 (Q1): A sense of obligation to obey traffic laws was measured with

five statements: “It is ok to violate traffic laws sometimes as long as the

driver is careful” “There is no harm in exceeding the speed limit

sometimes” “A good driver can allow himself/herself to exceed the

speed limit” “When driving at night, it is all right sometimes to drive

while the traffic light is red as long as the driver makes sure that there is no crossing vehicle”

“A driver should obey all traffic laws, regardless of whether they seem logical or not”

Q1: The answers were given on a 5-point scale,

ranging from 1 = “absolutely wrong” 5 = “absolutely correct”

How would you answer some of those questions?

Quantitative Explanatory Variable #2 (Q2): The evaluation of the content of traffic laws was

conducted with a list of 10 adjectives, including: Logical Annoying Reassuring Old fashioned

A 5-point scale was used, with 1 = “to a very small extent” and 5 = “to a very large extent”

Quantitative Explanatory Variable #3 (Q3): The perceived importance of traffic laws relative to other

laws was measured with a list of laws in ten areas, including: Taxation Environmental issues Freedom of speech

A 5-point scale was once again used, ranging from: 1 = “the laws are equally important” to 5 = “traffic laws are definitely more important”

Attitudes toward traffic laws: Perception of the danger involved in the

commission of traffic violations has been described as affective driving behavior.

In the area of driving: Losses = the danger of a road accident resulting

from the commission of violations or the risk of apprehension

Gains = pleasure and convenience from driving

Quantitative Explanatory Variable #4 (Q4): Gains relating to traffic violations were

measured with a list of eight motives related to the commission of a traffic violation, including: “arriving quickly” “feeling in control/challenged” “getting ahead of other drivers” “adding interest to driving”

Quantitative Explanatory Variable #5 (Q5): The perceived danger involved in

committing driving violations was assessed by answers given to 12 items, including: “failing to comply with a ‘stop’ sign” “ not fastening the safety belt” “driving under the influence of alcohol”

A 10-point scale was used, with 1 = “not dangerous at all” to 10 = “very dangerous”

Q5: The respondents were then asked to

indicate whether each potential gain would increase their tendency to commit a driving violation.

Answers were given on a 5-point scale: 1 = “to a very small extent” 5 = “to a very large extent”

Quantitative Response Variable #6 (Q6): The frequency of committing driving violations

was measured with the same 12 items mentioned previously, which also included: “failing to give the right-of-way to other vehicles” “turning at high speed”

The respondents indicated the frequency of committing each violation on a 5-point scale, from

1 = “never” to 5 = “frequently”

Discussion of data: The data obtained from the study was

reported in 5 tables I will show you 2 that report correlations

Table 2: Pearson correlations among the research variables by gender and age:

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Explanation of Table 2: Table 2 describes the relationship between 5

different quantitative variables: Sense of obligation to obey the law (Q1) Evaluation of traffic laws (Q2) Importance of traffic laws (Q3) Perceived danger involved in the commission of violations

(Q4) Perceived gains involved in the commission of violations

(Q5)

Explanation of Table 2: Below the table, we notice:

*p<0.05 **p<0.01

Why not reject at just p<0.05? Rejecting at p<0.01 provides even stronger evidence

against Ho The researchers found it very convincing to denote those

values that had a p-value less than 0.01

Explanation of Table 2: Some values in the table are denoted by a single

asterisk * or a double asterisk ** Only those values that reject Ho (gender and age are not

influential) are denoted by their p-value Using the asterisks provides a more clear, concise way of

showing the degree of evidence without stating the p-value for each individual value in the table.

Therefore, all other values (that are not denoted by a * or ** ) have p-values that failed to reject the null hypothesis. These values are not significant!

Explanation of Table 2: Importance of correlation:

Correlation closer to 1 <---> smaller p-value For comparable sample sizes, stronger correlations

tend to lead to smaller p-values because the test statistic is

T = b1 / SEb1 (where the denominator SEb1 is a function of the spread s about the regression line)

So r closer to 1 goes with a smaller s, which makes SEb1 smaller, which makes t larger, which makes the p-value smaller!

Explanation of Table 2: What do the values mean?

Example 1: the first value in the table (0.33) represents a fairly strong positive correlation between young female driver’s sense of obligation to obey the law and thus the danger they see in the commission of violations.

Example 2: The value -0.23 ( in the 5th column) represents a fairly strong negative correlation between the negative evaluation men have of traffic laws and thus the perceived gains they find in the commission of violations.

Table 5: Correlations of variables Q1-Q5 with Q6, separated by gender and age categories:

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Explanation of Table 5: Table 5 shows the relationship between the 5 quantitative

explanatory variables with the single quantitative response variable, number of violations committed. This is done taking gender and age into account.

Out of the 5 relationships Qi <---> Q6 (where Qi represents Q1-Q5) the first 4 are naturally negative: Violations go down as:

Sense of obligation goes up People’s evaluation of the laws go up Perceived importance of the law goes up Perceived danger in committing violations goes up

Only the last variable Q5 is positive because violations go up as perceived gains go up.

Explanation of Table 5: Table 5 also demonstrated the following:

Negative evaluation of traffic laws is more strongly related to the commission of traffic violations among younger drivers than older.

Perceived gains involved in the commission of violations are more strongly related to the commission of violations among older drivers than younger.

Discussion: Women express a more positive evaluation

of the content of traffic laws and have a stronger sense of obligation to comply with traffic laws than do men. For example, women are less likely to exceed

the speed limit even if they are convinced it would be safe for them to do so.

Discussion: A possible explanation for gender differences in

normative motivations and driving behavior is gender differences in socialization processes: Upbringing of boys often characterized by an emphasis on

independence; they are expected to express anger, take risks/compete, and therefore commit more driving violations

Girls encouraged to be dependent and obedient; therefore expected not to take risks and more compliant with the law.

Results: Younger drivers and male drivers express a

lower level normative motivation to comply with traffic laws than do female and older drivers.

Discussion: Age differences:

Younger drivers perceive traffic laws as less important than older drivers

Discussion: Gender differences:

Men tend to overestimate their driving ability (more than women)

Men are likely to feel more confident in complying selectively with traffic laws and determining according to the situation whether a law is relevant.

Summary: High-risk drivers are characterized by both:

an underestimation of the dangers involved in the commission of traffic violations

a low level of normative motivation for compliance with traffic laws.

Flaws with study design? The only demographic variables studied were age

and gender. The sample was taken from students in college, an could

be biased on the basis of education.

The study does not take into account potential confounding variables such as

Socio-economic situation Marital status

Flaws with study design? Misleading questionnaire?

Questions and/or answers may have forced subjects to answer in ways that did not accurately reflect their driving habits

Location of study? Driving conditions vary greatly from one city to

another, so we cannot conclusively state if these results can be generalized to other cities.

The End!