Does accurate self-awareness increase traffic safety behaviour

Does accurate self-‐awareness increase

traffic safety behaviour?

Author: Erik Sommarström

Supervisor: Jan Andersson

Linköpings Universitet 729A64 Erik Sommarström


Abstract

This simulator study aims to examine how self-awareness affects traffic safety

behaviour. Self-awareness (SA) is, in this study, the ability to accurately know one’s

strengths and weaknesses. This was tested using 27 participants in the ages between

65 and 75. SA is measured using the DSI-questionnaire (Warner et al., 2013) and

comparing specific DSI-items to a suitable counterpart in the simulator. The

difference between the self-assessment and the actual performance is then calculated

to become a measure for SA. This measure of SA could then be compared to six

different calculated variables for traffic safety behaviour, which also were taken from

actual performance in the simulator scenario. The main test used a multivariate

ANOVA to test for differences between SA and traffic safety. No difference in SA

could be found between over and under-estimators of SA and participants with good

SA. Given this result it is hypothesized that self-awareness might only affect learning

how to drive and the null-results could therefor be a result of the chosen test group,

which were experienced older drivers.


1 Table of Contents

2 Does accurate self-‐awareness increase traffic safety behaviour ........................... 1

2.1 Self-‐awareness ............................................................................................................ 1

2.2 Self-‐awareness and traffic safety behaviour ............................................................... 2

2.3 Operationalization ...................................................................................................... 4

3 Method ................................................................................................................... 6

3.1 Participants ................................................................................................................. 6

3.2 Questionnaires ............................................................................................................ 6

3.3 Simulator ..................................................................................................................... 7

3.4 Procedure ................................................................................................................... 7

3.4.1 Scenario 1 ............................................................................................................. 7

3.4.2 Scenario 2 ............................................................................................................. 8

3.5 Analysis ....................................................................................................................... 8

3.5.1 Design .................................................................................................................. 8

3.5.2 Measures ............................................................................................................. 9

3.5.3 Simulator measures ............................................................................................. 9

3.5.4 Self-‐awareness and traffic safety behaviour measures ..................................... 10

3.5.5 Statistical tests ................................................................................................... 12

4 Results .................................................................................................................. 13

5 Discussion ............................................................................................................. 15

5.1 Result discussion ....................................................................................................... 15

5.2 Methodological discussion ........................................................................................ 16

5.3 Concluding remarks .................................................................................................. 18

6 References ............................................................................................................ 19


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2 Does accurate self-‐awareness increase traffic safety

behaviour

Every time you get in your car you take a risk of ending up in an accident where you

are involved. But is there a difference between drivers that end up in accidents and

drivers that do not? The importance of a driver having a good understanding of traffic

safety behaviour and its cofounding factors is an important matter for both the

driver’s safe usage of the car but also for the other drivers in the traffic context.

Traffic safety behaviour is dependent on several factors both external (e.g. weather or

technical problems with the car) and internal (e.g. reaction time or alertness) but also

on how well the driver can plan and cooperate with other drivers (Trafikverket.se,

2014). This paper will however focus on metacognition; knowledge about ones own

knowledge and what implications it might have on driving ability (cf. Brown, 1978).

But more specifically on self-awareness and how it affects traffic safety behaviour.

What is meant by self-awareness in this article is the ability to know ones own

weaknesses and limitations and act accordingly (Bandura & Cervone, 1983;

Lundqvist & Alinder, 2007). Metacognitive skills have been shown to be very

important for reaching expert level in a skill (Kolb, 1984; Mezirow, 1990). And thus

it should be equally important for reaching a safe driving skill level; not only in driver

education but also in the continuous experience the driver receives whilst driving

(Hattaka et al., 2002). This study aims to see if there are relations between self-

awareness and traffic safety behaviour. Traffic safety behaviour, in this paper, refers

to avoiding accidents and dangerous situations and having good marginal for avoiding

them.

2.1 Self-‐awareness

There are several studies that show that drivers often over-estimate their driving

ability, when asked to compare themselves to average drivers (Amado et al., 2014;

Groeger & Grande, 1996; Stapleton, Connolly & O’neil, 2012; Svenson, 1981). This

suggests that many drivers drive beyond their actual ability. However, since the term

“average driver” might be seen as negative this might affect the drivers’ rating of

themselves, these results might therefor be unreliable (Groeger & Grande, 1996).


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Studies made where drivers have been rated by an expert instructor whilst driving and

after having to rate themselves the comparison between the two ratings have shown

that drivers who over estimate their driving skills are more likely to fail a driving test

(Lundqvist & Alinder, 2007; Mallon, 2006). To test self-awareness in this present

study the Driver Skill Inventory (DSI) questionnaire will be used. The DSI consists of

eleven items targeting perceptual motor skills and nine items targeting safety skills in

traffic (Warner et al., 2013). This self-rating will then be compared to events in a

simulator scenario corresponding to items in the DSI. This comparison will assess a

measure of self-awareness. The importance of self-awareness for traffic safety

behaviour is also argued in the Goal Driver Education matrix (GDE-matrix, Hattaka

et al., 2002). The GDE-matrix points out that there are three important factors, or

goals, that a safe driver has to have. These goals are “knowledge and skill” (e.g.

motoric and cognitive capabilities necessary for driving, knowledge about traffic

legislation), “Risk-increasing factors” (e.g. Knowledge about potential risks in traffic)

and “self-evaluation” (e.g. learning from mistakes) Hattaka et al., 2002; (Peräaho,

Keskinen & Hatakka, 2003). Self-awareness in this article refers to how accurate ones

self-evaluation is.

2.2 Self-‐awareness and traffic safety behaviour

A driver with great “Knowledge and skill” is necessarily not a better or safer driver by

default. Adequate cognitive and motoric functions are not enough to be a safe driver

since this could lead to the driver increasing task difficulty (Hattaka et al, 2002;

Evans, 1991; Näätänen & Sumala, 1974). With higher technical skill it is more likely

that the driver would take more chances of, for example, overtaking in heavy traffic

or/and focusing on more secondary tasks, which would lead to more risk for the

driver, instead of less risk (Evans, 1991). This would also be in line with the risk

homeostasis theory, which states that every person has a risk target level that they try

to level with (Hoyes, Stanton & Taylor, 1996). This would however still affect a

driver with good self-awareness; this is only to point out that increasing technical skill

would not affect traffic safety behaviour in general. Although more experience of

driving before acquiring a license has shown a decrease in traffic accidents involving

novice drivers. It is argued that this is not because the novice has an increased

technical ability but rather that the driver becomes more aware of the risks of driving


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and learns to handle situations that could lead to accidents (Gregersen et al, 2000;

Hattaka et al, 2002). This would also be in line with the GDE-matrix, which includes

“Risk-increasing factors” as one of the three factors of driver education.

In the GDE-matrix self-evaluation is an important aspect of driving because it

regulates the other factors of driving education and it is also the main factor that is

important to continue becoming a better driver after acquiring the driver’s license. It

is shown that metacognitive skills are important for achieving an expert level of a

skill. A driver needs to know the limits of his or hers skill in order to improve them

(Hattaka et al., 2002). Also since car driving is essentially a self-paced action, where

the driver solely makes decisions on risk increasing factors such as speed and distance

to next vehicle. Good self-awareness would effectively lead to avoidance of risky

situations and accidents since the driver would know how fast he or she can drive and

still be safe (Bailey, 2009; Hatakka et al., 2002; Näätänen & Sumala, 1974).

Performance could loosely be divided in to three categories. These would be the three

levels of performance, strategic, tactical and operational according to Michon (1979).

The strategic level would be how the driver plans the trip before driving. Tactical

performance regards the planning of actions, which are executed at the operational

level. Hence the tactical level requires knowledge and awareness of ones own ability

on the operational level (Lundqvist & Alinder, 2007; Michon, 1979). If this is correct,

different accidents could be divided in to these three categories even though some

accidents are the result of a combination of several levels. If a pedestrian would

suddenly walk out onto the road an accident can be avoided with adequate reaction

time, which would correspond to the operational level. However, the driver might

have been able to slow down the car and be ready to break if the driver suspects that

someone would suddenly walk out onto the road, this would correspond to the tactical

level. Here the categories become quite fuzzy since it is difficult to place the accident

into a specific category. Thus it should be reasonable to assume that some accidents

can be caused more or less by inadequate self-awareness but perhaps not solely

because of it. Accidents on the strategic level would refer to bad planning of the

journey, such as driving at night or having to drive faster because of a time constraint.

Thus a line must be drawn on which accidents to focus on and also realize which


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accidents are caused by inadequate self-awareness and which are caused by

inadequate reaction time or other factors.

The Swedish statistics of accidents from 2013 (Transportstyrelsen.se, 2014) list the

most usual car accident types and their frequency. Five of the most frequent car

accidents are accidents with pedestrians or bike/moped, accidents where two cars

meet, accidents where one car drives in to another car from the rear and accidents

where a single car crashes. An analysis of the reason for the accidents from this

papers point of view would be that accidents where a single car crashes or when a car

drives in to the rear of another car would be caused by lacking self-awareness. For

example, if the driver has to little space to the car in front or that the driver drives to

fast and looses control of the vehicle. The accidents where you meet a car or hit a

pedestrian or bike/moped could be caused by both lacking self-awareness and

inadequate reaction time. In some cases the driver may be able to plan ahead to avoid

the accident but in some a car/bike/moped might suddenly loose control and drive into

the wrong lane in which case good reaction time would be needed more than good

self-awareness.

2.3 Operationalization

As mentioned earlier this study will measure self-awareness using selected DSI-items

(Warner et al., 2013) to measure a driver’s estimation of their driving ability and

comparing those with their actual ability in a simulator. For example, one DSI-item is;

“Conforming to the speed limits?”. The participant answers if this is a weak or a

strong ability on a scale from one to five, one being definitely weak and five being

definitely strong. In the simulator this exact question will be tested with an event or

stretch in the scenario and then compared to the self-assessment from the DSI. This

will give an estimation of how much the drivers own idea of his or hers ability differs

from ability in the simulator. This is similar to other studies where drivers have had to

rate themselves after a drive with an instructor and the instructor also rates their drive.

The self-assessment and the instructor’s assessment would then be compared to each

other (Lundqvist & Alinder, 2007; Mallon, 2006). This will be repeated for a five of

the DSI-items that are possible to measure in the scenario. The keen reader might

remember that the DSI was split in two parts - perceptual motor skills and safety


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skills. Theoretically the items that tests perceptual motor skills should be related and

vice versa, because of this the five different self-awareness measures were split into

two groups – perceptual motor skills and safety skills.

As a measure of traffic safety behaviour two different measures will be used. First of

the participants will answer a questionnaire about their history of traffic safety

behaviour (e.g. incidents, accidents, violations). Using Svenssons (1998) research

these answers will give each participant a value of traffic safety. Second, traffic safety

behaviour will be measured in a simulator. Since there is no research that specifically

states how traffic safety behaviour should be measured this will be done using six

different events in the scenario. For each event it was decided what was a safe

behaviour in the given situation. For example, merging in traffic was deemed safe if

the participant held a high time to collision (TTC) to the cars in the front and behind.

Time to collision measures the time in seconds to when both cars will collide. The

calculation needs to account for both the cars speed and trajectory and calculates the

time to the point they will collide. Hence if two cars are driving along side each other

and their trajectory never intersects the TTC will be infinite but if one car changes its

course so that the trajectories intersect there will be a TTC measure in seconds. Six

different events were used to capture different aspects of safe driving behaviour.

Using the self-awareness measure and comparing this to traffic safety behaviour in

the simulator it should be possible to see if self-awareness is related to a traffic safe

behaviour. The hypothesis in this study is that drivers with good self-awareness will

exhibit safer traffic behaviour.


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3 Method

3.1 Participants

27 participants completed the questionnaires and the two scenarios in the simulator.

The sample consisted of 45.8% women and 54.2% men. Participants were between 55

and 75 years with a mean age of 62.3 (SD = 5.11). Participants that did not finish the

simulator scenario or any of the questionnaires were excluded from the data. The

requirements for a participant to be contacted were that their age should be between

55 and 75. They should have a normal field of vision and also be driving at least 1500

kilometres per year. These requirements were used because the sample group were

made to correspond with a test group from another study. Participants were contacted

via mail through the Swedish vehicle registry. From a list of possible participants a

randomized sample of participants was selected. All participants lived in the

Linköping area in Sweden. The participants all received 500 SEK for participating.

3.2 Questionnaires

The driver skill inventory (DSI) was used to rate self-awareness (Lundqvist &

Alinder, 2007). The DSI consists of eleven items relating to perceptual control skills,

such as car control and nine items relating to safety skills. The participant answers

each question with the participant’s weakest and strongest sides in mind. Each item is

constituted by a question and a five-point scale where 1 is “definitely weak” and 5 is

“definitely strong”.

The participants also answered the Multidimensional locus of control-questionnaire

(T-loc) about their risk taking and their perspective on contextual factors affecting

potentially dangerous situations (i.e. what factors in traffic are responsible for

accidents) (Özkan & Lajunen, 2005). This questionnaire consisted of seventeen items,

which were rated on a five-point scale, 1 being “not at all possible” and 5 being

“definitely possible”.

After driving the simulator the participants answered a questionnaire with questions

regarding driving experience of the simulator and their traffic experience. Participants

also answered a questionnaire regarding their involvement in traffic accidents in the


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last three years. This questionnaire was however rejected from the analysis since it

was noticed that almost none of the participants answered more than zero accidents on

the questions. One of the questions that related to near-incidents was also interpreted

differently by many participants and therefor couldn’t be analysed for within group.

3.3 Simulator

The simulator that was used in the study is the “Simulator III” at VTI in Linköping. It

is a motion-based simulator that can simulate lateral and longitudinal forces. The

simulator uses a vibration table under the chassis to simulate contact with the road

and provide a more realistic driving experience. The graphics are PC-based and uses

six projectors to create a 120-degree frontal view and three smaller screens for the

rear-view mirrors. The simulator can be used with either manual or automatic

gearbox. In this study the automatic was used.

3.4 Procedure

When contacting participants via mail they were given the DSI and the T-loc

questionnaire. Participants answered these at home and then handed them in to the

researcher before driving the simulator. The test took approximately 90 minutes and

consisted of driving two simulator scenarios. After the scenarios were finished the

participants answered one questionnaire about accident-involvement and one

questionnaire about the simulator in general.

Before driving the scenarios participants were given seven minutes of practice in the

simulator. During this time participants could ask the researcher questions, which they

were told not to do during the test scenarios. Participants then drove the first scenario

of two.

3.4.1 Scenario 1

The purpose of this scenario was to test the participant’s driving ability and driving

safety skills. The scenario consisted of a two-lane rural road, a four-lane highway and

finally driving in an urban environment. During each stretch the participants were

faced with potentially dangerous events. For example merging in heavy traffic or


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having to emergency-break before “hard-to-see” pedestrians’ walking/running out

onto the road. These events were scattered throughout the different settings and

environments of the scenario. The scenario lasted for 50 minutes. Once the

participants had completed the scenario, they stopped the car and got ready for

scenario 2.

3.4.2 Scenario 2

The purpose of this scenario was to test the participant’s field of vision. Participants

fitted themselves with two clickers, one on each index finger. The participants had

received instructions on how to use and attach the clickers before starting the first

scenario. During the scenario, if the simulator screen showed a blue/white road sign

the participant was instructed to click the left index finger clicker. If the screen

showed a red/yellow sign they were to click the right index finger clicker.

The scenario lasted for 7 minutes. This data could then be analysed according to

signal detection theory to see the ratio between true hits/misses and false hits/misses

(Solso, 1988). For a further explanation of a similar test see Jenssen (2003).

After the participants were finished driving they filled in a questionnaire about the

simulator and also a questionnaire about their accident involvement the last three

years.

3.5 Analysis

3.5.1 Design

This study uses a within-group design where the independent variable is grouped into

three groups. The independent variable is self-awareness, which is grouped into three

parts: over-estimator, under-estimator and good self-awareness. The dependent

variable is traffic safety behaviour. The different groups of the independent variable

are then compared to the dependent variable in an ANOVA. The statistical tests will

be explained in more detail later.


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3.5.2 Measures

The control variables that were measured were kilometres driven per year, accident

involvement during the last three years, years of having a driver’s license, age and

gender.

3.5.3 Simulator measures

To measure how a participant has performed in the simulator each event in the

scenario needs different measures. The reason for using different measures and not a

single on is that each unique measure gives different aspects in the driving behaviour

of the participant. The measures used in the study are the following: Time to collision

(TTC), Time head way (THW), Lane-keeping, Speed-keeping and reaction time.

These will be explained in more detail below.

• TTC, as mentioned earlier, measures the time until the participant’s car and

another car will collide, given the speed and trajectory of both vehicles. The

minimum TTC a participant reached was the TTC-measure for that event.

• THW measures the time until the next vehicle if the vehicle in front would

suddenly stop, this does not take trajectory or speed of the other vehicle into

account. As with the TTC-measure the THW also only uses the minimum

value for an event. TTC can be said to measure cooperation in traffic and

THW measures the safe behaviour of the individual in the traffic context.

• Lane-keeping measures the car’s lateral positioning on the road, in this study

the standard deviation of Lane-keeping is used which gives a measure of how

well the participant has kept the car steady.

• Speed-keeping in this study measures the ratio between how many times the

participant drives within the speed-limit and not. Within the speed-limit is

classed as 2.5 km/h above or below the speed limit.


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• Reaction time is measured in milliseconds between the time it takes for a

participant to react to an object after it becomes visible (i.e. pedestrian

walking out from behind a bus).

Lane-keeping and Reaction time will have an inverse value compared to the others

since all values need to be the higher the better or vice versa to be able to compare to

each other. This does not affect variance at all.

3.5.4 Self-‐awareness and traffic safety behaviour measures

The independent variable was self-awareness (SA) and the dependent variable was

traffic safety behaviour (TS). To measure SA specific DSI items were compared with

the participants’ actual performance in the simulator. For example, one of the items in

the DSI is “Conforming to the speed limits” where the participants answered a

number between one and five (one being definitely bad and five being definitely

good). SA was then calculated using the residual values from the regression line

between a specific DSI item and its simulator counterpart. This method of using

residuals is illustrated with the graph below. The regression line is the optimal SA

compared to the normal distribution of all the participants and the difference between

the regression line and the participants’ actual answer and performance is the self-

awareness measure.

Figure 1 – The regression line is the optimal SA. If a participant answers a four on the DSI and shows a speed deviation of 1.2 the true SA for the participant would be 0.8475, the difference between the actual and the optimal SA (i.e. the residual). It should be noted that this is only an example and not actual data.


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Five variables for SA were created from DSI items 1 (i.e.“Fluent driving”), 5 (i.e.

“Predicting traffic situations ahead”), 7 (i.e.”Fluent lane-changing in heavy traffic”),

11 (i.e. “Keeping a sufficient following distance”) and 16 (i.e. “Conforming to the

speed limits”). These items were compared to suitable simulator measures that

reflected on the nature of the item. The residuals were calculated for each DSI-item.

These five SA-measures were then unified using the categories of the DSI, which

reduced self-awareness to two variables; “Traffic safety skills” (DSI 1, 5 and 7) and

“Perceptual motor skills” (DSI 11 and 16). In the table below the different simulator

measures used for each DSI item is presented.

DSI item Simulator measure

DSI 1 - Fluent driving (Traffic safety

skills)

TTC, Lane-keeping, Speed keeping

DSI 5 - Predicting traffic situations ahead

(Traffic safety skills)

Reaction time to breaking before a

pedestrian walking/running out onto the

road.

DSI 7 - Fluent lane-changing in heavy

traffic (Traffic safety skills)

TTC

DSI 11 - Keeping a sufficient following

distance (Perceptual motor skills)

THW

DSI 16 - Conforming to the speed limits

(Perceptual motor skills)

Speed keeping

Table 1 – A table over what measures was used for each used DSI item

The TS-variable was measured using specific events in the scenario. For each event

different measures were chosen depending on what was deemed as safe traffic

behaviour in that specific event. For example, one event consisted of keeping a good

following distance to the car in front; in this case distance to the car ahead was

measured in seconds with regard to the participants own speed (i.e. Time head way:

THW). For other events in the scenario measurements such as speed keeping, lane

keeping and TTC were measured depending on what was relevant and traffic safe to


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that event. The different traffic safety measures summed up to six different TS

variables with different measures between them.

3.5.5 Statistical tests

To test for normality both Shapiro-Wilk and Kolmogorov-Smirnov were used. For the

main statistical test a multivariate ANOVA was used to test for effects between the

SA-measures and the TS-measures. Pearson’s correlation coefficient was used to test

the correlation between the SA-measures. A Linear regression was used to test how

well the SA-measures could predict the TS-measures.


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4 Results

Below are the descriptive data for the SA measures presented. It should be noted that

both of the measures are normally distributed with a non-significant Shapiro-Wilk and

Kolmogorov-Smirnov score. The closer to zero a SA-value is the better SA the

participant has.

SA-Measure N Min. Value Max. Value Mean (SD)

Perceptual

motor skills

20 -1.02 1.81 0.063 (0.68)

Safety skills 24 -0.94 1.43 0.006 (0.575) Table 2 – Descriptive data of the self-awareness measures.

There was no significant correlation between the two measures for SA, p = .427.

Because of this, each measure of SA was analysed independently from each other.

Below are the descriptive data for the TS measures. These are also tested for

normality of distribution. The measures that are not normally distributed are marked

with a *. After a logarithmic transformation of all the data TS 3 and TS 6 were still

not normally distributed but TS 1 was. Seeing as the logarithmic transformation didn’t

do much change to the TS-variables they were instead rejected from the analysis. The

higher TS-value is the safer a participant is.

Traffic Safety

measure no.

N Min. Value Max. Value Mean (SD)

TS 1* 27 2.579 12.467 6.458 (2.38)

TS 2 26 1.41 6.14 3.8 (1.53)

TS 3* 27 3.143 74.301 12.927 (14.233)

TS 4 27 0.512 3.691 1.691 (0.854)

TS 5 23 2.57 11.21 5.918 (2.52)

TS 6* 27 -0.10 0.85 0.112 (0.237) Table 3 – Descriptive data for traffic safety behaviour measures.

Before the analysis the SA measures were split into three groups depending on their

mean-value and standard deviation: Over-estimation, Good SA and Under-estimation.


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“Under-estimation” was defined as the mean plus half the standard deviation of SA.

“Over-estimation” was defined as the mean minus the half the standard deviation of

SA. “Good SA” was the values in between these extremes. This was done since the

variables for SA ranged from, for example -1.02 to +1.8 where the perfect SA would

be zero or close to zero.

One multivariate ANOVA were made as an exploratory analysis to see if there were

any effects between the two grouped SA variables and the three normally distributed

TS variables. There were no significant effects between the groupings of both the SA

variables and the TS variables when doing a pairwise comparison using Bonferroni

correction, p > .1. There was however one significant effect between SA in safety

skills and the second TS variable, F(5,9) = 5.86, p < 0.5, 𝜔= .57. Although this effect

is under much doubt given the null-results of all the other effect but also that there is a

possibility that the variables tested may share some variance. This effect is therefor

rejected.

In order to see if good or bad SA could predict traffic safety an absolute value of each

the SA variables were made. This means that a negative value will become positive

and it will make a more exact comparison possible. Although this takes away possible

effect of over and under estimation of SA a more exact test can be used instead, which

in effect utilize all of the variance. However, the effect of under and over estimators

has already been ruled out by the ANOVA. To see how well self-awareness predicts

traffic safety behaviour regression analysis was used. A regression analysis was made

for each of the SA-variables and each of the TS-variables. They did however reveal

no significant effects at all p> 0.1.


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5 Discussion

The hypothesis of this study was that participants with good self-awareness would

exhibit better traffic safety behaviour. From the analysis the null-hypothesis could not

be disproven. However, this does not mean the hypothesis has been disproven since

there could still be a number of reasons for why this is. The overall statistical power

for the tests was very low and therefor a complete rejection of the hypothesis is

impossible. The first part of the discussion will mainly circle around the reliability of

the measurements used in this study. The second part will discuss results and what

these results would mean if they would be valid and alsox propositions for future

research and proposed changes to this study. The discussion will finish up with a brief

conclusion.

5.1 Result discussion

A reason for the null-results might be that self-awareness does not directly affect

traffic safety behaviour as suggested in the hypothesis. It might instead affect traffic

safety indirectly through other cognitive functions, such as learning. A point that is

raised in the introduction is the importance of metacognition for learning new skills

fast and become good at it. It might be that self-awareness is only important when

learning from accidents and how well you learn from feedback. If you receive perfect

feedback on your abilities this would give you a better idea on what you would need

to change in order to improve your ability. If this were the case it would not be

strange that these results point towards the null-hypothesis since self-awareness might

only affect learning the ability to drive. This is actually compared with the GDE-

matrix in the introduction where self-evaluation is pointed out as an important factor

for learning. Self-awareness would, in the context of the GDE-matrix, be the accuracy

of self-awareness.

The difference in self-awareness between participants could in this case be a result of

the method used to calculate the measure. The residuals from the regression will

assume normality since the regression line will take into account every case in the

variance. If the participants are all very self-aware drivers the residuals will show a

difference between participants even though there might not be a big difference in

reality. In light of this the measurement used would be very crude and might only

give a fair result if the group tested is younger and not as experienced. This could in


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that case be tested in a study where the participant has to learn a new skill and then

measure the participants’ self-awareness in comparison to the performance of the

newly learnt skill.

In an article by Kruger and Dunning (1999) it is stated that a possible way to remedy

incompetence is by teaching the incompetent person in the subject where they have

inadequate knowledge. This was shown through a series of trials where participants

got to assess their own skills their performance, which was then compared to their

actual performance. It could be seen that good performers underestimated their ability

while bad performers often over-estimated themselves. The effect of the under-

estimators was credited to the false-consensus bias that if they perform well their

peers should perform equally well. The over-estimators were explained by the fact

that they might not know how incompetent they actually are since they do not have

this knowledge. This could not be replicated in this study but it might be, as noted

earlier, an effect of the participants’ level of experience with driving.

In the introduction self-awareness is connected with the risk homeostasis theory. The

theory that you always keep the same level of risk no matter what safety functions

there are (e.g. seat-belt, airbags, ABS). If self-awareness would affect the level of risk

for a person or any other trait this might not be reflected in the simulator scenario. It

might very well be that the simulator scenario does not challenge the driver’s ability

and that such effects are never noticed in the data because of this. This would go

against the connection between traffic safety and levels of performance. It is claimed

earlier that self-awareness would be connected with accidents made on the tactical

level of performance, where the driver can plan the execution of the operational level

of performance. The simulator scenario does reflect on both types of accidents but it

might not reflect on them in the correct way or measured in the correct way for that

matter.

5.2 Methodological discussion

As is often found, more participants are needed to replicate a result. In the case of this

study the statistical power becomes weak since the independent variable is grouped

into three sub-groups. If a replication study were to be made there should however be

a number of changes made. Mainly the simulator scenario should be much more


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specific as to what it tests and therefor it could be much shorter. A bigger difference

that would be made is to have a questionnaire before the driving test that asks about

general driving ability, as it was in this study, but also have a questionnaire after that

asks how the simulator driving went. Even though previous research has shown that

these self-assessments’ would be very similar (Kruger & Dunning, 1999). This

questionnaire would of course have the same questions so that they could be

compared. In this study this might be a big problem, since driving in a car simulator is

a different task than driving a real car, even if it seems like a similar task.

The outcome of the analysis could be the result of bad reliability in the study. Data

from a simulator study needs a lot of work before it can be analysed. This can result

in a lot of reliability problems where the researcher thinks that a measure measures

what is sought after when in reality it does not. The traffic safety measures are made

from hypothesis’ that certain behaviour is more traffic safe than other behaviour. In

this study the minimum value of time to collision (TTC) and the minimum value of

time head way (THW) was measured. This was in line with the assumption that a

lower THW and TTC would be a safer behaviour than a high TTC and THW. This

assumption might well still be true but in the specific events of this study it might not

have been the most correct solution. This could be validated using other measures of

traffic safety behaviour or having an outside driving instructor to assess the traffic

safety of a specific participant.

For the self-awareness measures there might be a similar problem with reliability.

Partly because it is difficult to know if the DSI-item was compared to the correct

simulator measure since this is also built upon assumptions that the measure is

measuring the correct thing, for example speed keeping in an adequate way.

Moreover it might also be a problem with participants interpreting the scale of the

subjective measure in the same way; a three for one participant might be very high

compared to what another participant thinks. It was shown in the analysis that the two

measures for self-awareness were not correlated. This might seem like a problem but

it should also be emphasized that according to the research done on the DSI

questionnaire these measure different categories of weak versus strong driving, the

non-correlated self-awareness measures might only be a reflection of different kinds

of self-awareness. It should also be noted that the residuals of the regression would


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naturally be normally distributed. This might in effect give a perfect self-awareness

score to a participant that might not be perfectly self-aware. This would however not

affect the within-group comparisons’ since it would have the same variance.

5.3 Concluding remarks

The analysis of the data gave results pointing towards the null-hypothesis, that people

with good self-awareness do not exhibit safer traffic behaviour. Given this unexpected

result it was hypothesized that self-awareness might not affect traffic safety at all but

rather it might affect learning how to become a safe driver. This would also imply that

the null-result might depend on the older age of the drivers. If the study would be

replicated using a more diverse sample with both young and old drivers this might

affect the result. However, since the statistical power of the result is not enough a

rejection of the hypothesis is not possible.


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