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Driving Violations

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Driving Violations Powered By Docstoc
					“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:



                      QuickTime™ and a
            TIFF (Uncompressed) decompressor
               are needed to see this picture.
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:



                       QuickTime™ and a
             TIFF (Uncompressed) decompressor
                are needed to see this picture.
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!