Public Opinion of Traffic Cameras in the New Orleans Area

UNIVERSITY OF NEW ORLEANS

Public Opinion on Traffic Cameras

Soc. 2707 Statistics Project

Anna Keelin Billue

Anna Keelin Billue

Table of Contents

Introduction: The Research Question……………………………2

The Survey Instrument…………………………………………………3-4

Data Entry (Data View and Variable View)…………………….5

Data Analysis………………………………………………………………..6-19

Hypotheses………………………………………………………………….21-43

Conclusion……………………………………………………………….....44-47

Completed Surveys……………………………………………………...Back of binder.

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Introduction: The Research QuestionThe research demonstrated and explained in this project serves as a measure of

opinions and attitudes towards traffic-control cameras that monitor speeding and red-light

infractions. When one speeds through or runs a red light in an intersection monitored by

these traffic-control cameras, they will receive a traffic citation by mail. The level of

fairness, cost effectiveness, and safety the traffic cameras provide is still fairly disputed.

I will ask the following questions concerning perceptions and relationships towards

traffic cameras:

1. Are those who received a ticket or tickets more likely to oppose the traffic cameras in general?

2. Are those who received a ticket or tickets more likely to perceive the traffic cameras of a violation of their privacy or the privacy of others?

3. Are those who received a ticket or tickets more likely to perceive the traffic cameras as catalysts for accidents?

4. Are those who received a ticket or tickets more likely to feel that traffic cameras do not help reduce speeding, red light running, and improve traffic safety?

5. Are those who received tickets aware of how traffic cameras operate?6. Are those who received tickets aware of the location of traffic cameras in the

area they received the ticket or tickets?7. Were those who received a ticket or tickets likely to contest the ticket in

court?8. Are those who received a ticket or tickets more likely to oppose the

privatization of the traffic cameras?9. Is number of tickets received and perceived seriousness of speeding related

in the population?10. Is number of tickets received and perceived seriousness of red light running

related in the population?

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The Survey Instrument

1. Have you received a speeding/red light ticket from a traffic camera?

a. Yes

b. No

2. How many tickets from traffic cameras have you received (Fill in the blank)? ____________

3. Were you aware of the traffic camera in your area or areas that you received your ticket or tickets?

a. Yes, I was aware.

b. I was aware of the cameras in some area I received tickets but not others.

c. No, I was not aware.

d. Did not receive ticket.

4. Are you aware of how the traffic cameras operate in the technical sense?

a. Yes.

b. No

5. If you received a ticket, did you try to contest your ticket in court (Do not answer if you did not receive a ticket)?

a. Yes.

b. No

6. Do you feel like the traffic cameras are a violation of your privacy or the privacy of others?

a. Yes

b. No

7. Do you support the New Orleans’s choice to use a private firm to manage the traffic cameras and the distribution of tickets?

a. Support

b. Oppose

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(Survey continued on page 4.)

8. How serious of a problem do you find speeding to be?

a. Very serious

b. Serious

c. Minor

d. Very minor

9. How serious of a problem do you find running red lights to be?

a. Very serious

b. Serious

c. Minor

d. Very Minor

10. Do you believe traffic cameras help reduce speeding, red light running, and improve traffic safety?

a. Yes

b. No

11. Do you think traffic cameras potentially cause accidents?

a. Yes

b. No

12. In general, do you support or oppose the use of traffic cameras to enforce speeding and red lights?

a. Support

b. Oppose

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Data Entry (Data Set and Variable Set)

Variables

Survey Data

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Data Analysis1. Have you received a speeding/red light ticket from a traffic camera?

Table 1

Table 1, above, represents the frequency distribution from the data collected from the

question “Have you received a speeding/red light ticket from a traffic camera?” There were a total

of 20 respondents with no missing values. The majority of the respondents (60%) indicated they

had received a traffic ticket due to a traffic camera violation by answering “Yes” on the survey. 40%

indicated they had not received a ticket by answering “No” on the survey. This question is a

nominal variable. Thus, cumulative frequency was not included.

Table 2

Table 2, above, represents the data’s appropriate measure of central tendency. The variable

is nominal and independent (for the purposes of this survey). Thus, the appropriate measure of

central tendency is mode. The mode for this survey question is 0, indicating that most people

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answered “Yes” when asked if they received a ticket. The appropriate measure of disbursement is

IQV. However, IQV is not necessary to test per the project instructions.

2. How many tickets have you received from traffic cameras?

Table 3


question “How many tickets have you received from traffic cameras?” There were a total of 20

respondents with no missing values, and because this is data is classified as interval ratio,

cumulative percent is included. 40%, the plurality, of the population has not received a ticket. 25%

of the population had received two tickets. The cumulative frequency shows that the majority of

people had one ticket or less, and it also shows that 90% of the population received 3 tickets or less.

Table 4

Table 4, above, represents the data’s appropriate measures of central tendency and

measures of disbursement. This is an interval ratio variable. Thus, the appropriate measures of

central tendency are mean, median, and mode. The mean for this question was 1.50. This means

that the average number of tickets received is 1.50. The median for this question was 1.50, as well.

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This means that 1.50 tickets were the center of the distribution where 50% of the tickets were

more and 50% of the cases were less. The mode for this survey question is 0, indicating that most

people answered “Yes” when asked if they received a ticket.

The appropriate measures of disbursement for Question 2 are standard deviation, variance,

and range. The variance of the number of tickets received was 2.368. This means that the answers

have a variability of 2.368 from the mean. By taking the square root of the variance, the standard

deviation is calculated into original units that are more easily comparable to the mean. The

standard deviation for this question is 1.539 units. This means essentially the same thing as

variance, but it is expressed in units that are not squared. The range is the distance between the

highest amount of tickets and lowest amount of tickets. In this question, the number of tickets

received range from a low of 0 to a high of 5.

For this question and for my significance test, I grouped the interval ratio variables to

compare them with ordinal tests (see Figure 1):

Figure 1

How many tickets did you receive?

10 50.0 50.0 50.08 40.0 40.0 90.02 10.0 10.0 100.0

20 100.0 100.0

.001.002.00Total

ValidFrequency Percent Valid Percent

CumulativePercent

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3. Were you aware of the traffic camera in the area or areas you received your ticket or tickets?

Table 5

Table 5, above, represents the frequency distribution from the data collected from the question

“Were you aware of the traffic camera in the area or areas you received your ticket or tickets?”

There were a total of 20 respondents with no missing values. The majority of the respondents

(80%) indicated they had not received a ticket, and thus, they could not answer the question. 30%

of respondents (who had received more than one ticket) were aware of the cameras in some areas

they received tickets in, but they were not aware of the cameras in other areas they received tickets

in. 10% of those who received tickets were aware of traffic cameras in the area where they were

cited for speeding or redlight running. This means 40% of the population was at least aware of the

presence of traffic cameras when they acted outside of the speeding limit or traffic lights. This

question is a nominal variable. Thus, cumulative frequency was not included.

Table 6

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Table 6 represents the data’s appropriate measure of central tendency. The variable is

nominal and dependent. Thus, the appropriate measure of central tendency is mode. The mode for

this survey question is 3, indicating that most people answered “Did not receive ticket” when asked

if they were aware of the traffic cameras in the area they received their ticket. The appropriate

measure of disbursement is IQV. However, IQV is not necessary to test per the project instructions.

4. Are you aware of how traffic cameras operate in a technical sense?

Table 7


question “Are you aware of how traffic cameras operate in a technical sense?” There were a total of

20 respondents with no missing values. The majority of the respondents (55%) indicated they were

aware of how traffic cameras operated in a technical sense by answering “Yes” on the survey. 45%

indicated they did not understand how the cameras worked by answering “No” on the survey. This


Table 8

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this survey question is 0, indicating that most people answered “Yes” when asked if they were

aware of how traffic cameras operated in a technical sense. The appropriate measure of

disbursement is IQV. However, IQV is not necessary to test per the project instructions.

5. If you received a ticket, did you try to contest it in court?

Table 9


question “If you received a ticket, did you try to contest it in court?” There were a total of 20

respondents with 30% of the values missing. The 30% of values missing can be accounted for

because this question was a filter question in which those who had not received tickets did not

answer the question. The majority of the respondents (70%) indicated they did not try to contest

their speeding ticket from traffic cameras in court by answering “No” on the survey. None of the

respondents who received tickets answered “Yes” they had contested their ticket in court. This


Table 10

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this survey question is 1, indicating that most people answered “No” when asked if they had

contested their ticket in court. There is a missing value of 6 respondents, but as mentioned above,

the missing values can be accounted for because this question was a filter question in which those

who had not received tickets did not answer the question. The appropriate measure of

disbursement is IQV. However, IQV is not necessary to test per the project instructions.

6. Do you feel like traffic cameras are a violation of your privacy or the privacy of others?

Table 11


question “Do you feel like traffic cameras are in violation of your privacy or the privacy of others?”

There were a total of 20 respondents with no missing values. The majority of the respondents

(65%) indicated they felt that traffic cameras are a violation of their privacy or the privacy of others

by answering “Yes” on the survey. Only 35% of respondents did not feel that traffic cameras were a

violation of privacy. This question is a nominal variable. Cumulative frequency was not included.

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Table 12

Table 12, above, represents the data’s appropriate measure of central tendency. The

variable is nominal and dependent. Thus, the appropriate measure of central tendency is mode. The

mode for this survey question is 0, indicating that most people answered “Yes” when asked if they

felt that traffic cameras were in violation of their privacy or the privacy of others. The appropriate

measure of disbursement is IQV. However, IQV is not necessary to test per the project instructions.

7. Do you support New Orleans’ decision to use a private firm to manage the traffic cameras and the distribution of tickets?

Table 13


question “Do you support New Orleans’ decision to use a private firm to manage the traffic cameras

and the distribution of tickets?” There were a total of 20 respondents with no missing values. The

majority of the respondents (90%) indicated they opposed New Orleans’ decision to use a private

firm to manage the traffic cameras the distribution of tickets by answering “Oppose” on the survey.

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10% indicated did supported New Orleans’ decision to use a third party ticketing company. This


Table 14



mode for this survey question is 1, indicating that most people answered “Oppose” when asked if

they supported or opposed a third party ticket vendor. The appropriate measure of disbursement is

IQV. However, IQV is not necessary to test per the project instructions.

8. How serious of a problem do you find speeding to be?

Table 15


question “How serious of a problem do you find speeding to be?” There were a total of 20

respondents with no missing values, and because this is data is classified as ordinal, cumulative

percent is included. 50%, the majority, of the population felt that speeding was a “minor” problem.

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Only 5% of the population felt like speeding was a very minor problem, and only 10% felt like

speeding was a very serious problem. The cumulative frequency shows that 95% of people felt like

speeding is either “minor”, “serious”, or “very serious”.

Table 16


measures of disbursement. This is an ordinal variable. Thus, the appropriate measures of central

tendency are median and mode. The median answer for this question was 2.00. This means that 2

tickets were the center of the distribution where 50% of the answers were more and 50% were

less. The mode for this survey question is 2, indicating that most people answered “minor” when

asked how serious of a problem they felt speeding was.

The appropriate measure of disbursement for Question 8 is range. The range is the distance

between very minor and very serious. In this question, the seriousness of feelings against speeding

range from a very minor to very serious.

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9. How serious of a problem do you find running red lights to be?

Table 17


question “How serious of a problem do you find running red lights to be?” There were a total of 20

respondents with no missing values, and because this is data is classified as ordinal, cumulative

percent is included. 35%, the plurality, of the population felt that red light running was a “minor”

problem. Only 5% of the population felt like red light running was a very minor problem, and only

10% felt like speeding was a very serious problem. The cumulative frequency shows that 60% of

people feel like speeding is a “serious” or “very serious” problem.

Table 18


measures of disbursement. This is an ordinal variable. Thus, the appropriate measures of central

tendency are median and mode. The median answer for this question was 1.00. This means that 1

ticket was the center of the distribution where 50% of the answers were more and 50% of the cases

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were less. The mode for this survey question is 2, indicating that most people answered “minor”

when asked how serious of a problem they felt red light running was.

The appropriate measure of disbursement for Question 9 is range. The range is the distance

between very minor and very serious. In this question, the seriousness of feelings against red light

running range from a very minor to very serious.

10. Do you believe traffic cameras help improve traffic safety?

Table 19


question “Do you believe traffic cameras help improve traffic safety?” There were a total of 20

respondents with no missing values. The majority of the respondents (65%) indicated they

believed traffic cameras helped improve traffic safety by answering “Yes” on the survey. 35%

indicated they did not believe traffic cameras improved traffic safety by answering “No” on the

survey. This question is a nominal variable. Thus, cumulative frequency was not included.

Table 20

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mode for this survey question is 0, indicating that most people answered “Yes” when asked if they

believed traffic cameras help improve safety. The appropriate measure of disbursement is IQV.

However, IQV is not necessary to test per the project instructions.

11. Do you think traffic cameras potentially cause accidents?

Table 21


question “Do you think traffic cameras potentially cause accidents?” There were a total of 20

respondents with no missing values. Respondents were clearly divided on their opinion. 50% of

respondents though traffic cameras could potentially cause accidents. The other 50% did not think

the cameras were capable of causing accidents. This question is a nominal variable. Thus,

cumulative frequency was not included.

Table 22

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variable is nominal and dependent. Thus, the appropriate measure of central tendency is mode.

This survey is bimodal because 50% of the respondents answered “Yes”, and 50% of the

respondents answered “No.” The appropriate measure of disbursement is IQV. However, IQV is not

necessary to test per the project instructions.

12. In general, do you support or oppose the use of traffic cameras to enforce speeding and red lights?

Table 23


question “In general, do you support or oppose the use of traffic cameras to enforce speeding and

red lights?” There were a total of 20 respondents with no missing values. The majority of

respondents (75%) opposed the use of traffic cameras to enforce speeding and red lights. 25% of

the population supported the use of traffic cameras to enforce speeding and red lights. This


Table 24

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mode for this survey question is 1, indicating that most people answered “No” when asked if they

supported or opposed traffic cameras to enforce speeding and red lights. The appropriate measure

of disbursement is IQV. However, IQV is not necessary to test per the project instructions.

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HypothesesHypothesis 1: Are those who received a ticket or tickets more likely to oppose the

traffic cameras in general?

Figure 2

Case Processing Summary

20 100.0% 0 .0% 20 100.0%

Do you support oroppose the use of trafficcameras to enforcespeeding and red lights?* Have you received aspeedin/red light ticketfrom a traffic camera?

N Percent N Percent N PercentValid Missing Total

Cases

*No missing values in Figure 2.

Figure 3

Do you support or oppose the use of traffic cameras to enforce speeding and red lights? * Have youreceived a speedin/red light ticket from a traffic camera? Crosstabulation

2 3 53.0 2.0 5.0

16.7% 37.5% 25.0%

10 5 159.0 6.0 15.0

83.3% 62.5% 75.0%

12 8 2012.0 8.0 20.0

100.0% 100.0% 100.0%

CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?

Support

Oppose

Do you support or opposethe use of traffic camerasto enforce speeding andred lights?

Total

Yes No

Have you received aspeedin/red light ticketfrom a traffic camera?

Total

(Figure 4 and test continued on next page.)

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Figure 4

Chi-Square Tests

1.111b 1 .292.278 1 .598

1.095 1 .295.347 .296

1.056 1 .304

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Pearson Chi-SquareContinuity Correctiona

Likelihood RatioFisher's Exact TestLinear-by-LinearAssociationN of Valid Cases

Value dfAsymp. Sig.

(2-sided)Exact Sig.(2-sided)

Exact Sig.(1-sided)

Computed only for a 2x2 tablea.

2 cells (50.0%) have expected count less than 5. The minimum expected count is 2.00.

b.

*Cells have an expected count of less than 5. So the significance test will be skewed.

1. Assumptions

Independent Random Sample

Nominal x and y

2. Hypothesis

H : Receiving a ticket and support of traffic cameras aren’t related in the population.₀

H : Receiving a ticket and support of traffic cameras are related in the population.₁

3. Type of Test

(independence)χ₂

1 tail test

= .05α

degrees of freedom= 1

4. Calculations

= 1.111χ₂

5. Decision about the Null

critical value= 3.841

Fail to reject the null hypothesis.

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6. Decision about the Alternative

Fail to support the alternative

Receiving a ticket and support of traffic cameras are not related in the population.

The majority of both groups (those who did or didn’t receive a ticket) generally

opposed traffic cameras in general.

Hypothesis 2: Are those who received a ticket or tickets more likely to perceive the

traffic cameras of a violation of their privacy or the privacy of others?

Figure 5


20 100.0% 0 .0% 20 100.0%

Do you feel like thetraffic cameras are inviolation of your privacyor the privacy of others?* Have you received aspeedin/red light ticketfrom a traffic camera?


Cases

*No missing value in Figure 5. This is okay.

Figure 6

Do you feel like the traffic cameras are in violation of your privacy or the privacy of others? *Have you received a speedin/red light ticket from a traffic camera? Crosstabulation

8 5 137.8 5.2 13.0

66.7% 62.5% 65.0%

4 3 74.2 2.8 7.0

33.3% 37.5% 35.0%

12 8 2012.0 8.0 20.0

100.0% 100.0% 100.0%


Yes

No

Do you feel like thetraffic cameras are inviolation of your privacyor the privacy of others?

Total

Yes No


Total

(Figure 7 and test continued on next page.)

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Figure 7

Chi-Square Tests

.037b 1 .848

.000 1 1.000

.037 1 .8481.000 .608

.035 1 .852

20



Value dfAsymp. Sig.


Exact Sig.(1-sided)



b.


1. Assumptions


Nominal x and y

2. Hypothesis

H : Receiving a ticket and perception of traffic cameras as a violation of privacy ₀

aren’t related in the population.

H : Receiving a ticket and perception of traffic cameras as a violation of privacy are ₁

related in the population.

3. Type of Test

(independence)χ₂

1 tail test

= .05α


4. Calculations

= 0.037χ₂

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Receiving a ticket and perceiving traffic cameras as violations of privacy are not

related in the population. Both those who had and hadn’t received tickets almost

equally felt like traffic cameras were a violation of the positive.

Hypothesis 3: Are those who received a ticket or tickets more likely to perceive the

traffic cameras as catalysts for accidents?

Figure 8


20 100.0% 0 .0% 20 100.0%

Do you think trafficcameras potentiallycause car accidents? *Have you received aspeedin/red light ticketfrom a traffic camera?


Cases

*No missing values in Figure 8. This is okay.

(Figures 9-10 and tests continued on next page.)

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Figure 9

Do you think traffic cameras potentially cause car accidents? * Have you received aspeedin/red light ticket from a traffic camera? Crosstabulation

8 2 106.0 4.0 10.0

66.7% 25.0% 50.0%

4 6 106.0 4.0 10.0

33.3% 75.0% 50.0%

12 8 2012.0 8.0 20.0

100.0% 100.0% 100.0%


Yes

No

Do you think trafficcameras potentiallycause car accidents?

Total

Yes No


Total

Figure 10

Chi-Square Tests

3.333b 1 .0681.875 1 .1713.452 1 .063

.170 .085

3.167 1 .075

20



Value dfAsymp. Sig.


Exact Sig.(1-sided)



b.


1. Assumptions


Nominal x and y

2. Hypothesis

H : Receiving a ticket and perception of traffic cameras as a catalyst for accidents ₀

aren’t related in the population. (see H on next page).₁

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H : Receiving a ticket and perception of traffic cameras as a catalyst for accidents ₁

are related in the population.

3. Type of Test

(independence)χ₂

1 tail test

= .05α


4. Calculations

= 3.333χ₂


critical value=3.841




Receiving a ticket and perception of traffic cameras as a catalyst for accidents are

not related in the population. However, those who had received tickets

overwhelmingly felt that traffic cameras were catalysts for accidents (66.7%), and

75% of those who had not received tickets did not feel that traffic cameras were

catalysts for accidents.

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Hypothesis 4: Are those who received a ticket or tickets more likely to feel that traffic

cameras do not help reduce speeding, red light running, and improve traffic safety?

Figure 11


20 100.0% 0 .0% 20 100.0%

Do you believe trafficcameras help improvetraffic safety? * Haveyou received aspeedin/red light ticketfrom a traffic camera?


Cases


Figure 12

Do you believe traffic cameras help improve traffic safety? * Have you received a speedin/redlight ticket from a traffic camera? Crosstabulation

8 5 137.8 5.2 13.0

66.7% 62.5% 65.0%

4 3 74.2 2.8 7.0

33.3% 37.5% 35.0%

12 8 2012.0 8.0 20.0

100.0% 100.0% 100.0%


Yes

No

Do you believe trafficcameras help improvetraffic safety?

Total

Yes No


Total

(Figure 13 and tests on next page.)

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Figure 13

Chi-Square Tests

.037b 1 .848

.000 1 1.000

.037 1 .8481.000 .608

.035 1 .852

20



Value dfAsymp. Sig.


Exact Sig.(1-sided)



b.


1. Assumptions


Nominal x and y

2. Hypothesis

H : Receiving a ticket and perception of traffic cameras improving traffic safety ₀

aren’t related in the population.

H : Receiving a ticket and perception of traffic cameras improving traffic safety are ₁


3. Type of Test

(independence)χ₂

1 tail test

= .05α


4. Calculations

= 0.037χ₂

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Receiving a ticket and perception of traffic cameras of promoters of traffic safety are

not related in the population.

Hypothesis 5: Are those who received tickets more aware of how traffic cameras

operate?

Figure 14


20 100.0% 0 .0% 20 100.0%

Are you aware of how thetraffic cameras operatein the technical sense? *Have you received aspeedin/red light ticketfrom a traffic camera?


Cases


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Figure 15

Are you aware of how the traffic cameras operate in the technical sense? * Have you received aspeedin/red light ticket from a traffic camera? Crosstabulation

6 5 116.6 4.4 11.0

50.0% 62.5% 55.0%

6 3 95.4 3.6 9.0

50.0% 37.5% 45.0%

12 8 2012.0 8.0 20.0

100.0% 100.0% 100.0%


Yes

No

Are you aware of how thetraffic cameras operatein the technical sense?

Total

Yes No


Total

Figure 16

Chi-Square Tests

.303b 1 .582

.008 1 .927

.305 1 .581.670 .465

.288 1 .592

20



Value dfAsymp. Sig.


Exact Sig.(1-sided)



b.


1. Assumptions


Nominal x and y

2. Hypothesis

H : Receiving a ticket and awareness of how traffic cameras operate aren’t related in₀

the population.

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H : Receiving a ticket and awareness of how traffic cameras operate are related in ₁

the population.

3. Type of Test

(independence)χ₂

1 tail test

= .05α


4. Calculations

= 0.303χ₂





Fail to support the alternative.

Receiving a ticket and support of traffic cameras and knowedge of how traffic

cameras operate are not related in the population.

Hypothesis 6: Are those who received tickets aware of the location of traffic cameras

in the area they received the ticket or tickets?

Figure 17


20 100.0% 0 .0% 20 100.0%

Were you aware of thetraffic camera in the areaor areas that you receivedyour ticket or tickets? *Have you received aspeedin/red light ticketfrom a traffic camera?


Cases

*No missing values. This is okay.(Figures 18-19 and tests continued on next page.)

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Figure 18

Were you aware of the traffic camera in the area or areas that you received your ticket or tickets? * Haveyou received a speedin/red light ticket from a traffic camera? Crosstabulation

2 0 21.2 .8 2.0

16.7% .0% 10.0%

6 0 63.6 2.4 6.0

50.0% .0% 30.0%

4 0 42.4 1.6 4.0

33.3% .0% 20.0%

0 8 84.8 3.2 8.0

.0% 100.0% 40.0%

12 8 2012.0 8.0 20.0

100.0% 100.0% 100.0%

CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?

Yes, I was aware.

I was aware of thecameras in somearea I receivedtickets but not others.

No, I was not aware.

Did not receive ticket.

Were you aware ofthe traffic camera inthe area or areasthat you receivedyour ticket or tickets?

Total

Yes No


Total

Figure 19

Chi-Square Tests

20.000a 3 .00026.920 3 .000

14.061 1 .000

20

Pearson Chi-SquareLikelihood RatioLinear-by-LinearAssociationN of Valid Cases

Value dfAsymp. Sig.

(2-sided)

8 cells (100.0%) have expected count less than 5. Theminimum expected count is .80.

a.


1. Assumptions


Nominal x and y

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2. Hypothesis

H : Receiving a ticket and awareness of traffic cameras in the area aren’t related in ₀

the population.

H : Receiving a ticket and awareness of how traffic cameras in the area are related ₁

in the population.

3. Type of Test

(independence)χ₂

1 tail test

= .05α


4. Calculations

= 0.303χ₂






Receiving a ticket and awareness of traffic cameras in the area of citations are not

related in the population. Most people who said they received tickets were aware of

traffic cameras in some area but not others.

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Hypothesis 7: Were those who received a ticket or tickets likely to contest the ticket in

court?

Figure 20


14 70.0% 6 30.0% 20 100.0%

If you received a ticket,did you try to contestyour ticket in court? *Have you received aspeedin/red light ticketfrom a traffic camera?


Cases

*30% of the values are missing because this test was used a filter question for comparison. So, this is okay.

Figure 21

If you received a ticket, did you try to contest your ticket in court? * Have you received aspeedin/red light ticket from a traffic camera? Crosstabulation

12 2 1412.0 2.0 14.0

100.0% 100.0% 100.0%

12 2 1412.0 2.0 14.0

100.0% 100.0% 100.0%

CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?CountExpected Count% within Have youreceived a speedin/redlight ticket from a trafficcamera?

NoIf you received a ticket,did you try to contestyour ticket in court?

Total

Yes No


Total

Figure 22

Chi-Square Tests

.a

14Pearson Chi-SquareN of Valid Cases

Value

No statistics are computed because Ifyou received a ticket, did you try tocontest your ticket in court? is a constant.

a.

*This was a filter question in which none of the respondents answered “Yes.” Thus 100%

did not contest the ticket. Not contesting a ticket in court is a constant (see Figure 22).

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Anna Keelin Billue

Hypothesis 8: Are those who received a ticket or tickets more likely to oppose the

privatization of the traffic cameras?

Figure 23


20 100.0% 0 .0% 20 100.0%

Do you support NewOrleans's choice to use aprivate firm to manage thetraffic cameras and thedistribution of tickets? *Have you received aspeedin/red light ticketfrom a traffic camera?


Cases

*No missing values. This is okay.

Figure 24

Do you support New Orleans's choice to use a private firm to manage the traffic cameras and thedistribution of tickets? * Have you received a speedin/red light ticket from a traffic camera?

Crosstabulation

1 1 21.2 .8 2.0

8.3% 12.5% 10.0%

11 7 1810.8 7.2 18.0

91.7% 87.5% 90.0%

12 8 2012.0 8.0 20.0

100.0% 100.0% 100.0%


Support

Oppose

Do you support NewOrleans's choice to use aprivate firm to manage thetraffic cameras and thedistribution of tickets?

Total

Yes No


Total

Figure 25

Chi-Square Tests

.093b 1 .761

.000 1 1.000

.091 1 .7631.000 .653

.088 1 .767

20



Value dfAsymp. Sig.


Exact Sig.(1-sided)


2 cells (50.0%) have expected count less than 5. The minimum expected count is .80.

b.


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Anna Keelin Billue

1. Assumptions


Nominal x and y

2. Hypothesis

H : Receiving a ticket and awareness of traffic cameras in the area aren’t related in ₀

the population.

H : Receiving a ticket and awareness of how traffic cameras in the area are related ₁

in the population.

3. Type of Test

(independence)χ₂

1 tail test

= .05α


4. Calculations

= 0.930χ₂





Fail to reject the alternative.

Receiving a ticket and support of the privatization of traffic cameras are not related

in the population. However, both those who received tickets and those who didn’t

seemed to overwhelmingly oppose the privatization of ticket companies.

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Anna Keelin Billue

Hypothesis 9: Is number of tickets received and perceived seriousness of speeding related in the population?

For the purpose of this test, I grouped the interval ratio variables to compare them with ordinal tests in SPSS. However, I still used my interval ratio variable with one of my ordinal variables. I used Gamma for this test.

Figure 26


20 100.0% 0 .0% 20 100.0%

How serious of a problemdo you find speeding tobe? * How many ticketsdid you receive?


Cases

*No missing values in Figure 26. All is okay.

Figure 27

How serious of a problem do you find speeding to be? * How many tickets did you receive? Crosstabulation

1 1 0 2

10.0% 12.5% .0% 10.0%

3 4 0 7

30.0% 50.0% .0% 35.0%

5 3 2 10

50.0% 37.5% 100.0% 50.0%

1 0 0 1

10.0% .0% .0% 5.0%

10 8 2 20

100.0% 100.0% 100.0% 100.0%

Count% within How manytickets did you receive?Count% within How manytickets did you receive?Count% within How manytickets did you receive?Count% within How manytickets did you receive?Count% within How manytickets did you receive?

very serious

serious

minor

very minor

How serious of aproblem do youfind speeding tobe?

Total

.00 1.00 2.00How many tickets did you receive?

Total

Figure 28

Chi-Square Tests

3.786a 6 .7064.808 6 .569

.000 1 1.000

20


Value dfAsymp. Sig.

(2-sided)


a.

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Anna Keelin Billue

Figure 29

Symmetric Measures

-.056 .338 -.165 .86920

GammaOrdinal by OrdinalN of Valid Cases

ValueAsymp.

Std. Errora Approx. Tb Approx. Sig.

Not assuming the null hypothesis.a.

Using the asymptotic standard error assuming the null hypothesis.b.

* Figure 29 shows that Gamma says -5.6% of the variance in the respondents’ perception of seriousness of speeding can be explained by the amount of speeding tickets they have.

Figure 30

Directional Measures

-.033 .204 -.165 .869

-.034 .211 -.165 .869

-.033 .197 -.165 .869

SymmetricHow serious of a problemdo you find speeding tobe? DependentHow many tickets did youreceive? Dependent

Somers' dOrdinal by OrdinalValue

Asymp.Std. Errora Approx. Tb Approx. Sig.



1. Assumptions

Random sampling

Ordinal X and Y (X is a grouped interval ratio).

Normal sampling distribution

2. Hypothesis

H : y=0₀

H : y≠0₁

3. Type of Test

t (Y)

2 tail test

= .05α

4. Calculations

t= -0.165

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Anna Keelin Billue


.869> .05



Fail to support the alternative.

The number of tickets received and perception of seriousness of speeding are not


Hypothesis 10: Is number of tickets received and perceived seriousness of speeding related in the population?

For the purpose of this test, I grouped the interval ratio variables to compare them with ordinal tests in SPSS. However, I still used my interval ratio variable with one of my ordinal variables. I used Gamma for this test.

Figure 31


20 100.0% 0 .0% 20 100.0%

How serious of a problemdo you find running redlights to be? * How manytickets did you receive?


Cases

*No missing values in Figure 31. This is good.

(Figures 32-35 and tests are on the next few pages.)

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Anna Keelin Billue

Figure 32

How serious of a problem do you find running red lights to be? * How many tickets did you receive?Crosstabulation

3 3 0 6

30.0% 37.5% .0% 30.0%

2 3 1 6

20.0% 37.5% 50.0% 30.0%

4 2 1 7

40.0% 25.0% 50.0% 35.0%

1 0 0 1

10.0% .0% .0% 5.0%

10 8 2 20

100.0% 100.0% 100.0% 100.0%

Count% within How manytickets did you receive?Count% within How manytickets did you receive?Count% within How manytickets did you receive?Count% within How manytickets did you receive?Count% within How manytickets did you receive?

Very Serious

Serious

Minor

Very Minor

How serious of aproblem do youfind running redlights to be?

Total

.00 1.00 2.00How many tickets did you receive?

Total

Figure 33

Chi-Square Tests

2.929a 6 .8183.900 6 .690

.083 1 .773

20


Value dfAsymp. Sig.

(2-sided)


a.

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Anna Keelin Billue

Figure 34

Directional Measures

-.078 .196 -.402 .688

-.086 .217 -.402 .688

-.072 .178 -.402 .688

SymmetricHow serious of a problemdo you find running redlights to be? DependentHow many tickets did youreceive? Dependent

Somers' dOrdinal by OrdinalValue

Asymp.Std. Errora Approx. Tb Approx. Sig.



Figure 35

Symmetric Measures

-.122 .304 -.402 .68820

GammaOrdinal by OrdinalN of Valid Cases

ValueAsymp.

Std. Errora Approx. Tb Approx. Sig.



* Figure 29 shows that Gamma says -12.2% of the variance in the respondents’ perception of seriousness of redlight running can be explained by the amount of speeding tickets they have.

1. Assumptions

Random sampling

Ordinal X and Y (X is a grouped interval ratio).

Normal sampling distribution

2. Hypothesis

H : y=0₀

H : y≠0₁

(Test continued on next page)

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Anna Keelin Billue

3. Type of Test

t (Y)

2 tail test

= .05α

4. Calculations

t= -0.402


.688>.05




Receiving a ticket and perception of seriousness of red light running are not

statistically significantly related.

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Anna Keelin Billue

Conclusion

My goal was to measure general public opinion and relationships between those who had or

had not received tickets based on a violation captured by a traffic camera. I wanted to find out if

receiving a ticket affected their feelings towards the traffic cameras or not.

My first hypothesis (Figures 2-4) was if people who received tickets were more likely to

oppose or support traffic cameras. My hypothesis failed, but I still feel like, in general, the majority

of people do not support traffic cameras. The percentages express this same feeling. Those who

have received citations because of violations caught by a traffic camera are especially likely to

oppose traffic cameras.

My second hypothesis (Figures 5-7) asked if people who received tickets or not perceived

traffic cameras a violation. This hypothesis was also rejected and did not find a statistically

significant relationship. I wanted to measure feelings of privacy violation as a sub-measure of

opposition to traffic cameras. Also, I was interested to know what other people thought.

My third hypothesis (Figures 8-10) measured if people felt traffic cameras could potentially

cause accidents. Once again, my hypothesis failed and did not find a statistically significant

relationship. However, the percentages revealed that those who had received a ticket felt, as a

majority, that traffic cameras could be catalysts for accidents. This indicated to me that those who

had received tickets had experienced or thought of a potentially dangerous situation that made

them think about the fairness of the traffic camera operations. Thus, those who had received tickets

were more likely to feel weary of the actual overall improvement in safety that can be attributed to

traffic cameras.

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Anna Keelin Billue

My fourth hypothesis (Figures 11-13) questioned if people felt like traffic cameras

improved safety. This hypothesis was to test my theory expressed in the last hypothesis. However,

this relationship was not statistically significant.

In my fifth hypothesis (Figures 14-16), I questioned if people who received tickets were

aware of how traffic cameras operated. I wanted to see if those who had received tickets or not had

a better understanding of the operational processes of the cameras. This relationship was not

statistically significant.

In my sixth hypothesis (Figures 17-19), I measured awareness of traffic cameras in areas

were tickets were received. This relationship also came out statistically insignificant, but most

people who received tickets said they were aware of traffic cameras in some areas they received a

ticket but not others.

In my seventh hypothesis (Figures 20-22), I had a strange result, and I did not really

understand what to do. I wanted to know if the people who received tickets had contested their

tickets in court. 30% of the values were missing, which can indicate instability, but it was expected

because the question is a filter question on my survey. However, the respondents that did answer

all answered “No.” SPSS said that the question and answers were then considered constant.

However, I believe if I had taken a bigger sample, there would have been more ticket contestants.

Since the sample was so small, I am not entirely surprised. Since no data was provided, I could not

complete the hypothesis test, and I am left to use the percentages and say “Most people do not

contest.”

My eighth hypothesis (Figures 23-25) was to question if people who had or had not

received a ticket or tickets felt like the privatization of traffic cameras through a third party was

wrong or right. Like the other tests, this test came out not statistically significant. However, over

85% of both groups (those who had received a ticket or tickets and those who had no tickets) felt

45

Anna Keelin Billue

the privatization was very wrong. So, although the hypothesis test indicated there was no

relationship, I feel like there must be. People strongly oppose the privatization behind the traffic

cameras.

My ninth hypothesis (Figures 26-30) measured if the number of tickets received affected

perception of seriousness of speeding. I wanted to measure this to see if, as the number of tickets a

person had increased their feeling that speeding was not that big of a problem decreased. However,

this relationship was also not statistically significant. The results were inconclusive.

My tenth hypothesis (Figures 31-35) measured if the number of tickets received affected

perception of seriousness of red light running. I wanted to measure this to see if, as the number of

tickets a person had increased their feeling that redlight running was not that big of a problem

decreased. However, this relationship was also not statistically significant. The percentages showed

that those who received more tickets were more likely to feel that red light running was either

“serious” or “minor”. Those who had received fewer or no tickets were more prone to rate their

feelings towards red light running at the extreme ends of the survey: “very serious” or “very minor.

Although none of my hypotheses revealed statistically significant relationships, I feel like

the percentage differences reveal a few relationships. For instance, people pretty strongly oppose

the privatization of a third party ticket vendor. However, between those who received tickets and

those who didn’t, there often were not too many differences. Thus, I conclude that statistical

analysis helped me as far as calculating the percentages, but the actual significance tests were not

that helpful. I would have had to do at least some statistical analysis to find my answers for this

project. So, I found statistical analysis pretty helpful in helping me measure feelings towards traffic

cameras.

This project was very interesting, and I am interested in my results. However, now that I

have completed, I realized there were a few errors and sort of useless questions in my survey. I did

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Anna Keelin Billue

not really think about how the questions would all connect or how to draw conclusions out of all of

my answers while writing my survey. This made my hypotheses difficult to connect and measure.

However, now that I have done this entire project, I realize how to make surveys a little clearer and

easier.

47

Documents

Public Opinion of Traffic Cameras in the New Orleans Area