TESIS BIOSTATISTIK(2)

CHAPTER 1

INTRODUCTION

1.1 BACKGROUND

According to Malaysian Road Safety Department,3.9% road death increases in 2008

compared to the previous year. Traffic signs represent one of the most common

devices for controlling traffic in that they help regulate, warn, and guide road users. In

spite of their importance, traffic signs are not always clear to the drivers (Tamar &

David 2006).

1.2 METHODOLOGY JUSTIFICATION

i. To increase awareness and driving etiquette on road.

ii. To decrease road accidents risk.

iii. To improve the knowledge regarding road signs among students.

iv. To know the comprehension regarding road signs among students.

1

http://www.jkjr.gov.my/

1.3 CONCEPTUAL FRAMEWORK

1.3.1 Gender factor

We are taking male and female students.

1.3.2 License factor

The male and female students taken are divided between ones having license and the

other without license.

GOVERNMENT GOVERNMENT NON-GOVERNMENTNON-GOVERNMENT

SOURCE ROAD SIGN KNOWLEDGE

CampaignCampaign

Gender factorGender factor

Driving schoolDriving school

License factorLicense factor

Mass Media Mass Media

Surrounding factorSurrounding factor

Area factorArea factor

2

1.3.3 Government

Government took part in holding campaigns and restricted the laws.

1.3.4 Media

Media serve by the television, radio, newspaper, and information computer and

technology (ICT)

1.3.5 Education

The driving school and driving lesson

1.3.6 Surrounding factor

Urban and rural areas are taken into study.

1.4 OBJECTIVE

1.4.1 General Objective

To study the understanding about road signs among the first year FSKB’s students in

UKM, KL session 2008/2009.

1.4.2 Specific Objective

i. To identify the student’s knowledge about the road signs.

ii. To determine the differences in knowledge between gender about the road

signs.

iii. To determine the differences in knowledge about the road signs between

student having and not having license.

iv. To determine the differences in knowledge about the road signs among the

student who living in urban and rural area

3

v. To identify the students’ sources of knowledge about the road sign.

1.5 Hypothesis

i. There are differences between knowledge’s score and comprehension‘s score

regarding the road signs among the first year students of FSKB .(question part B)

ii. There are differences in the score of knowledge between gender, license

availability and area.

iii.There are differences in score of knowledge on road signs between gender.

(question part A – no 1 , part B & C )

iv. There are differences in score of knowledge on road signs between students with

and without driving license.(question part A – no 4, part B & C )

v. There are differences in score of knowledge on road signs between students living

in urban and rural area. (question part A – no 3, part B & C )

iv.There are association between the gender/area/license with sources of student’s

knowledge on the road sign.(question part A-no 7)

4

CHAPTER 2

LITERATURE REVIEW

2.1 DEFINITION

Definition of sign is an indication, an event, an action and a fact that shows that

something exists or may happen that you can find and see. Meanwhile, the definition

of road is a hard surface built for vehicles (Oxford Advanced Learner’s Dictionary 6th

Edition).

The general definition of road signs is a sign near a road giving information or

instruction to driver.

Then, the specific definitions of road signs are used to give information about the

location of either the driver or possible destinations and are considered a subset of

informative sign group (Ross & Alan, 1992).

2.2 TYPES OF ROAD SIGN

Types of road signs are divided into three parts which is first, law road signs such as

no entry, speed zone and stop. Secondly, warning road signs such as dangerous bend

road, slippery road and accident spot. Lastly, direction road signs such as destination

sign board and information sign board (Law T.H, 2004). Every each of these types

should be distinct in its shape and colors (Tama and David, 2006).

2.3 FUNCTIONS OF ROAD SIGN

Functions of road signs are use to arrange traffic, to warn and act as guidance to road

users. Besides, the designs of the road signs which are big, simple and similar are

5

easier for understanding, plus noticeable. This it can give enough time for road user to

be ready for a certain unexpected condition such as sudden animal crossing. The roan

signs are place in plain sight. Furthermore, the road signs are informative in terms of

providing directions. (Marc Green & John Senders, 2004)

6

CHAPTER 3

RESEARCH METHODOLOGY

3.1 BACKGROUND

In order to do this research, we will survey the 1st year students of Faculty of Allied

Health Sciences (FSKB), National University of Malaysia, Kuala Lumpur (UKMKL)

intake 2008/2009. The selected students come from 12 different courses which are

Biomedical Science Department, Audiology and Speech Science Department, Dietetic

and Nutrition Department, Optometry Department, Diagnostic and Radiotherapy

Program, Occupational Therapy Program, Physiotherapy Program, Environmental

Health Program, Forensics Science Program and also Emergency Medicine Program.

From all the 383 students of the 1st year in FSKB, only 192 are selected to be our

respondents. Apart from that, we will later pick later based on the ratio of male to

female from the answered questionnaires. We then will get the population of student

either having or not having license. The license can be either `L` or` P` or even full

license also known as Competent License that are registered under Malaysian Road

Transport Department (JPJ).

3.2 RESEARCH DESIGN

We have selected the best way of designing our research. The cross-sectional study

will be the best and suitable research design for us. Basically, we don`t refer to any

other sources to get the result but we have to do the result based on our questionnaire

to the respondent. Moreover, the result can be analyzed easily using the SPSS system.

We also use open survey type question to gather all the respondent data.

7

3.3 SAMPLING METHOD

3.3.1. Target Population = Students of UKM, Kuala Lumpur.

3.3.2. Sample Population = 1st year students of FSKB intake 2008/2009 in UKMKL.

3.3.3. Sample Size = 192 students.(calculation from sample size of selected

population formula below)

Calculating sample size

n = ____X 2NP (1-P)____

∆2(N-1) + X2P(1-P)

= 192

Where X2 = 3.84, ∆ = 0.05, P = 0.5

But 10% would drop out so,

n* = __192__

(1-0.1)

= 213.3

= 214

Questionnaire

Distribution : 214

Received : 195

Not received: 19

Percentage of unreceived : 9.74%

8

3.4 METHOD OF GETTING THE DATA

i. List name of first year students of FSKB UKM, KL are collected.

ii. Using stratified sampling method in divide the student population according to

gender. Followed by systematic random sampling to distribute the

questionnaire.

iii. Questionnaires consist of multiple choice, text open end and agreement scale

(close end) test types are distribute among the samples.

9

CHAPTER 4

RESEARCH ANALYSIS

4.1 RESEARCH ANALYSIS

The collected data from the completed survey forms are analyzed using the SPSS. The

following are the tests used for this research:

1. Descriptive Statistic.

2. Independent t test

3. Chi square

4. Logistic Regression

4.2 Data analysis

Objective 1 :To identify the student’s knowledge about the road signs.

Hypothesis 1: There are differences between knowledge’s score and comprehension’s

score regarding the road sign among the first year FSKB’s students.

Test: Descriptive Statistics

10

Road signs Comprehension score (%) Knowledge score (%)

1 2 3 4 5 Correct Incorrect

60..0 19.0 5.1 6.7 9.2 90.8 9.2

39.0 31.3 16.9 9.7 3.1 45.6 54.4

65.6 19.5 4.6 2.1 8.2 95.9 4.1

72.8 13.8 2.6 2.1 8.7 92.3 7.7

73.3 16.4 1.0 2.1 7.2 97.4 2.6

56.9 22.1 8.7 6.2 6.2 96.4 3.6

50.8 23.6 11.8 7.2 6.7 90.3 9.7

51.3 30.3 7.7 5.1 5.6 87.7 12.3

31.8 35.4 17.9 10.8 4.1 45.6 54.4

53.3 27.7 8.7 5.1 5.1 89.2 10.8

Table 1.0: Student’s comprehension and knowledge score.

Section B(a)1 vs Section B(b)1

11

There are 64.4% students who answer correctly and really understand about this road

sign and only 7.9% did not know about this road sign but still can answer correctly.

There are only 16.7% said really understand but still answer incorrectly and 22.2%

students who answer incorrectly and did not know about this road sign although this

road sign quite common in used representing hospital.

12



sign and only 2.2% did not know about this road sign but still can answer

correctly.However,44.3% who said really understand but answer incorrectly and only

3.8% students who answer incorrectly and do not understand about this road sign. It is

because There are misunderstands this road sign with bumper sign.


13



There are 37.5% who really understand but did answer incorrectly and 37.5% students

who answer incorrectly and understand about this road sign.


14



But there are 60% who really understand but did answer incorrectly and 6.7% students

who answer incorrectly and not understand about this road sign. Supposedly, student

should know about this road sign because this road sign familiar for us and we can see

this road sign in every parking lot.

15




Unfortunately,80% of students said really understand but still answer incorrectly and

about 20% students who answer incorrectly but understand about this road sign.There

is no students answer incorrectly and don’t know about this road sign.

16



sign and only 6.4% did not know about this road sign but also answer correctly. On

the other hand,a large percentage,that is around 57.1% who said really understand but

answer incorrectly and about 14.3% students who answer incorrectly but a bit

understand about this road sign.

17


52.8

31.6

23.3 26.3

11.415.8

6.3

15.8

6.310.5

0

10

20

30

40

50

60

c orrec t inc orrec t

com

perh

ensi

on (%

)

really unders tand unders tanda bit unders tand little unders tandingdon't know



There are 31.6% students who really understand but also answer incorrectly and about

10.5% students who answer incorrectly did not know about this road sign

18


There are 55% students who answer correctly and really understand about this road

sign and only small value that is 0.7% did not know about this road sign but still can

answer correctly. There are 25% students who really understand and 45% said

understand but answer incorrectly. About 12.5% students who answer incorrectly and

did not know about this road sign.

19


28.134.9

39.3

32.1

19.1 17

912.3

4.5 3.8

0

10

20

30

40

50

c orrec t inc orrec t

com

perh

ensio

n (%

)

really unders tand unders tanda bit unders tand little unders tandingdon't know

There are only 28.1% students who answer correctly also really understand,39.3%

said understand about this road sign and 4.5% did not know about this road sign but

still can answer correctly. However, 34.9% students who said really understand and

32.1% said understand but answer incorrectly and only about 3.8% students who

answer incorrectly and did not know about this road sign. Maybe, students confuse

between this road sign with do not parking road sign.

20



sign and only 4% did not know about this road sign but still can answer correctly.

There are 38.1% students who really understand but also answer incorrectly and about

14.3% students who answer incorrectly and did not know about this road sign. It is

because there misunderstands with the narrow bridge sign.

21

Knowledge’s score and comprehension’s score about road signs are depend on types

of road signs and evaluation of students about that road sign. So, there are differences

between knowledge’s score and comprehension’s score regarding the road sign among

the first year FSKB’s students.

Objective 2: To determine the differences in knowledge between gender about the

road signs.

Hypothesis: There are differences in score of knowledge on road signs between

gender. The score are taken through the answer of question part A – no 1, part Ba &

C.

• HA, μ1≠μ2: There are differences in score of knowledge on road signs between

gender.

Table 2.0: Test of normality for gender factor.

Gender Kolmogorov-Smirnov(a)

Statistic Df Sig.

score Male 0.194 45 0.000

female 0.171 150 0.000

Base on the test of normality Kolmogorov-Smirnov(a) , the significant level of p value

is lower than 0.001. It is significant. Thus, the data is not normally distributed.

Table 2.1: Descriptive table for gender factor.

Gender statistic Std. dev.

score male skewness -2.117 0.354

Kurtosis 7.680 0.695

female skewness -2.618 0.198

22

Kurtosis 12.599 0.394

Through the calculation of skewness, the standard deviation (0.354) multiply by 2 and

the answer (0.708) is in the range of statistic value (-2.117 to +2.117). It shows that,

the distribution of the data is normal for the male. For the female, standard deviation

(0.198) multiply by 2 and the answer (0.396) is also in the range of statistic value (-

2.618 to +2.618). It shows that, the distribution of the data is absolutely normal. It is a

parametric analysis.

To compare the mean score of the two groups which are male and female student,

independent sample t-test is use. Score as the test variable and gender as the grouping

variable.

Table 2.2: Statistical test for gender factor.

Gender n Mean Standard Deviation p-value

Male 45 13.8667 2.24216 0.207

Female 150 14.3533 2.26481

Mean score of knowledge and standard deviation for male is 13.8667 and 2.24216

while for female is 14.3533 and 2.26481.

On the output result, Levene’s test is higher than 0.05. It is assume that the data

variances are relatively equal. Therefore, the upper row of the significant value is use.

Base on it, the significance level of p value on the upper row is higher than 0.05.

Thus, the mean score of knowledge of the two groups are not significantly different.

t=1.267, df=193, p>0.05.

23

Objective 3: To determine the differences in knowledge about the road signs between

student having and not having license.


student with and without driving license. The score are taken through the answer of

question part A – no 4, part Ba & C.

• HA, μ1≠μ2: There are differences in score of knowledge on road signs between

student with and without driving license.

Table 3.0: Test of normality for license factor.

License Kolmogorov-Smirnov(a)

Statistic Df Sig.

score Yes 0.164 134 0.000

No 0.171 61 0.000



Table 3.1: Descriptive table for license factor.

license statistic Std. dev.

score yes skewness -1.968 0.209


24

no skewness -2.060 0.306




the distribution of the data is normal for student with license. For student without

license, standard deviation (0.306) multiply by 2 and the answer (0.612) is also in the

range of statistic value (-2.060 to +2.060). It shows that, the distribution of the data is

absolutely normal. It is a parametric analysis.

To compare the mean score of the two groups which are student with license and

student without license, independent sample t-test is use. Score as the test variable and

gender as the grouping variable.

Table 3.2: Statistical test for license factor.

License n Mean Standard Deviation p-value

Yes 134 14.5746 13.5082 0.012

No 61 1.72717 3.02557

Mean score of knowledge and standard deviation for student with license is 14.5746

and 13.5082 while for student without license is 1.72717 and 3.02557.

On the output result, Levene’s test is lower than 0.05. It is assume that the data

variances are relatively different. Therefore, the lower row of the significant value is

use. Base on it, the significance level of p value on the lower row is lower than 0.05.

Thus, the means score of the knowledge on road sign between student with and

without driving license are different.

25

t=2.569, df=78.344, p<0.05.

Objective 4: To observe the dissimilarities in knowledge about the road signs among

the student who living in urban and rural area.


students living in urban and rural area. The score are taken through the answer of

question part A – no 3, part Ba & C.

HA, µ1≠µ2 : There are differences in score of knowledge on road signs among

the student who living in urban and rural area.

Table 4.0: Test of normality for area of residential factor.

Gender Kolmogorov-Smirnov(a)

Statistic Df Sig.

score Male 0.191 122 0.000

female 0.162 73 0.000



Table 4.1: Descriptive table for area of residential factor.

area statistic Std. dev.

score urban skewness -3.095 0.219

Kurtosis 15.932 0.435

rural skewness -1.498 0.281


26



the distribution of the data is normal for student live in urban. For student live in rural,

standard deviation (0.281) multiply by 2 and the answer (0.562) is also in the range of

statistic value (-1.498 to +1.498). It shows that, the distribution of the data is

absolutely normal. It is a parametric analysis.

To compare the mean score of the two groups which are student who living in urban

area and student who living in rural area, independent sample t-test is use. Score as the

test variable and area as the grouping variable.

Table 4.2: Statistical test for area of residential factor.

Area n Mean Standard Deviation p-value

Urban 122 14.3115 2.24927 0.575

Rural 73 14.1233 2.29701

Mean score of knowledge and standard deviation for student live in urban area is

14.3115 and 2.24927 while for student live in rural area is 14.1233and 2.29701.

On the output result, Levene’s test is higher than 0.05. It is assume that the data

variances are relatively equal. Therefore, the upper row of the significant value is use.

Base on it, the significance level of p value on the upper row is higher than 0.05.

Thus, the mean scores of the knowledge on road sign among student who living in

urban and rural area are no different.

t=0.561, df=193, p>0.05.

27

Objective 5: To identify the sources of student’s knowledge on the road sign.

Hypothesis: There are associations between the gender/area/license with sources of

student’s knowledge on the road sign.

Ha = Sources of student’s knowledge on the road sign dependent on gender.

Variables

Sources of knowledge such as driving school or non-driving school (mass

media, campaign, environment, others)

Gender of student

Chi-square test

Case Processing Summary

CasesValid Missing Total

N Percent N Percent N Percentgender * sources3

166 85.1% 29 14.9% 195 100.0%

Figure 1.0: The association graph of sources within gender.

28

The Association Graph of Sources Within Gender

0

20

40

60

80

Male Female

Gender

Perc

enta

ge (%

)

Driving School

Non-Driving School

Table 5.0: Association of gender and sources of student knowledge.

Sources

Gender

Driving school Non-driving

school

Total P value Pearson Chi-Square value

Male 25(64.1%) 14(35.9%) 39(100.00%)

P = 0.423 X 2 = 0.641

Female 90(70.9%) 37(29.1%) 127(100.00%)

Total 115(69.3%) 51(30.7%) 166(100.00%)

The Pearson Chi-Square is 0.641. The p value is 0.423. We can conclude that is a not

significant association between gender and sources of student’s knowledge on the

road sign, and therefore we do not reject the null hypothesis.

X 2 = 0.641, df = 1, p > 0.05

Hypothesis: HA : Sources of student’s knowledge on the road sign dependent on area

29

Variables



Area of student such as urban and rural

Chi-square test




166 85.1% 29 14.9% 195 100.0%

Figure 2.0: The association graph of sources within area of residential.

The Association of Sources Within Area

01020304050607080

Urban Rural

Area

Perc

enta

ge (%

)

Driving School

Non-Driving School

Table 5.1: Association of residential area and sources of student knowledge.

30

Sources

Area


school


Urban 77(74.0%) 27(32.0%) 104(100.00%)

P = 0.085 X 2 = 2.966

Rural 38(61.3%) 24(38.7%) 62(100.00%)

Total 115(69.3%) 51(30.7%) 166(100.00%)



road sign, and therefore we do not reject the null hypothesis.

X 2 = 2.966, df = 1, p > 0.05

Hypothesis:

HA : Sources of student’s knowledge on the road sign dependent on license.

Variables



License of student

Chi-square test




166 85.1% 29 14.9% 195 100.0%

Figure 3.0: The association graph of sources within license.

31

Table 5.2: Association of license and sources of student knowledge.

Sources

License


school


Yes 99(84.6%) 18(15.4%) 117(100.00%)

P = 0.001 X 2 = 43.813

No 16(32.7%) 33(67.3%) 49(100.00%)

Total 115(69.3%) 51(30.7%) 166(100.00%)



road sign, and therefore license seems to be must factor to contribute sources of

knowledge for student compare to gender and residential area.

X 2 = 43.813, df = 1, p < 0.05

32

Objective 1: To identify the student’s knowledge about the road signs.

Hypothesis: There are different in the score of knowledge between gender, license

availability and area of residential.

The score are taken through the answer of question part A-no 1,3,4, part Ba & C.

Test: Binary logistic regression.

Variable not in Equation

Score Df Sig.

Variable Gender

Area

License

.017

.562

7.417

1

1

1

.895

.453

.006

There are significant value show and it only on the availability of license that is 0.006

(p<0.05) compared to gender, 0.895 and area, 0.453 which is greater than 0.5.

Variable in the Equation

B Sig. Exp(B)

Step 1 Gender

Area

License

.658

-.088

-2.091

.461

.908

.018

1.931

.916

.124

For the data on interaction on mean score of knowledge to the gender, residential area

and availability of license to each student. From the table, there is negative value for

data in column B. the negative value shows the opposite interaction of the second

factor from the first factor to the score of knowledge

Here, interpreted that the mean score of knowledge for the second factor (female

student) is higher (due to positive value of B) 1.931 times (Exp(B) value) from the

first value (male student). Also, the data for this factor showing a non-significant

value, p=0.461

33

For area or residential, it shows that the mean score of knowledge of the second factor

(rural area) is lower (due negative value of B) 0.916 times compared to student from

urban area. The data of significant also shows there is no significant value for this

factor, p=0.908

License showing the score of knowledge for the second factor (not having license) to

be low than the first factor (having license) by 0.124 times less. But, the significant

value show that there is a significant data to be observed, p=0.018

There are different in the score of knowledge on license but there is no different in the

score of knowledge between genders and resident.

34

CHAPTER 5

DISCUSSION

5.1 DISCUSSION

A recent study that evaluated comprehension of traffic sign in four different countries

show that comprehension level varies widely and is apparently related to the extend

that the sign’s design incorporate ergonomic guidelines for good design(Shinar D. et

al 2003). Based on our research, we found that comprehension and knowledge of

students about the road sign is depend on types of the road sign. There are road signs

that show high scores of comprehension and knowledge about the road sign but there

are also road signs that show high score of comprehension but low in score of

knowledge about the road sign and etc. What we can get from this situation is sign

design should be guided by established ergonomics principles to enhance

comprehension, especially for drivers who have not had prior encounters with specific

signs (Tamar B. & Shinar D. 2006).

From the Chi-square test, it shows that the driving school is the main source

in contribute to student knowledge in the road sign compared to mass media,

campaign, environment and others. This is because, from the Kementerian

Penerangan Malaysia, to get the license from driving school each individual need to

pass road law test and usually the test is done by on-line. In this test, every

participant must achieve the standard marks that standardized by Jabatan

Pengangkuatan Jalan, Malaysia. After that, they will expose and apply their

knowledge about the road sign during lesion and test of license. So, the experiences

in the driving school help them to increase their knowledge about the road sign. In

other words, the most factor that influence the score of knowledge is license

compared to other factors that is gender and residential of students.

35

The knowledge about the road signs is very important because from Dr. Haji Mat

Saad Abdul Rahman, Fellow Kanan Syariah Pusat Syariah, Undang-undang dan

Sains Politik, Institute of Islamic Understanding Malaysia (IKIM), presence of road

sign in certain location especially in danger zone is one of important matter to

decrease the fatality rate in road accident.

36

CHAPTER 6

CONCLUSION

6.1 CONCLUSION

License is the most factors that influence the student’s knowledge compared to

gender and residential area. Female students obtain higher knowledge more than

male. Students that live in rural area obtain lower knowledge than urban area.

Student without license obtain lower knowledge less than students with license.

6.2 SUGGESTION

To get the more accurate data, interview is the best way to evaluate student’s

knowledge about road sign to reduce the bias. While developing questionnaire, more

road signs should be added in questionnaire so that our result fulfill the objective in

this research. Furthermore, this questionnaire also can help students to improve their

knowledge about the road sign.

37

BIBLIOGRAPHY

Australia Road. Road safety Audit. Sydney, Australia.1996.

Danish Road Directorate. Manual of Road Safety Audit. Ministry of Transportation.

Copenhagen, Denmark. 1996.

Public Works Department (JKR). Road Safety Audit. Guidelines for the Safety Audit of

Roads Projects in Malaysia, Kuala Lumpur. 1997.

Tamar Ben-Bassat, David Shinar. Ergonomic Guidelines for Traffic Sign Design Increase

Sign Comprehension. Spring. 2006.

http://www.jkr.gov.my [4 Feb 2009}

Kurikulum Pendidikan Pemandu Panduan Pembelajaran. Jabatan Pengangkutan Jalan

Malaysia. Kuala Lumpur. Edisi Ke 2. 2006.

38

APPENDIX

Output SPSS Test: Descriptive Statistics



195 100.0% 0 .0% 195 100.0%Ba1 * Bb1N Percent N Percent N Percent

Valid Missing Total

Cases

Ba1 * Bb1 Crosstabulation

114 33 8 8 14 177

64.4% 18.6% 4.5% 4.5% 7.9% 100.0%

97.4% 89.2% 80.0% 61.5% 77.8% 90.8%

3 4 2 5 4 18

16.7% 22.2% 11.1% 27.8% 22.2% 100.0%

2.6% 10.8% 20.0% 38.5% 22.2% 9.2%

117 37 10 13 18 195

60.0% 19.0% 5.1% 6.7% 9.2% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba1

% within Bb1

Count

% within Ba1

% within Bb1

Count

% within Ba1

% within Bb1

correct

incorrect

Ba1

Total

reallyunderstand understand

a bitunderstand

littleundersta

nding don't know

Bb1

Total

Chi-Square Tests

24.584a 4 .000

20.407 4 .000

19.430 1 .000

195

Pearson Chi-Square

Likelihood Ratio

Linear-by-LinearAssociation

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)

4 cells (40.0%) have expected count less than 5. Theminimum expected count is .92.

a.

39

Ba1incorrectcorrect

Co

un

t

120

100

80

60

40

20

0

Bar Chart

don't knowlittle understandinga bit understandunderstandreally understand

Bb1

40




Valid Missing Total

Cases


29 29 18 11 2 89

32.6% 32.6% 20.2% 12.4% 2.2% 100.0%

38.2% 47.5% 54.5% 57.9% 33.3% 45.6%

47 32 15 8 4 106

44.3% 30.2% 14.2% 7.5% 3.8% 100.0%

61.8% 52.5% 45.5% 42.1% 66.7% 54.4%

76 61 33 19 6 195

39.0% 31.3% 16.9% 9.7% 3.1% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba2

% within Bb2

Count

% within Ba2

% within Bb2

Count

% within Ba2

% within Bb2

correct

incorrect

Ba2

Total


a bitunderstand

littleundersta

nding don't know

Bb2

Total

41

Chi-Square Tests

4.375a 4 .358

4.396 4 .355

2.062 1 .151

195

Pearson Chi-Square

Likelihood Ratio


N of Valid Cases

Value dfAsymp. Sig.

(2-sided)

2 cells (20.0%) have expected count less than 5. Theminimum expected count is 2.74.

a.

Ba2incorrectcorrect

Co

un

t

50

40

30

20

10

0

Bar Chart


Bb2

42




Valid Missing Total

Cases


125 35 8 3 16 187

66.8% 18.7% 4.3% 1.6% 8.6% 100.0%

97.7% 92.1% 88.9% 75.0% 100.0% 95.9%

3 3 1 1 0 8

37.5% 37.5% 12.5% 12.5% .0% 100.0%

2.3% 7.9% 11.1% 25.0% .0% 4.1%

128 38 9 4 16 195

65.6% 19.5% 4.6% 2.1% 8.2% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba3

% within Bb3

Count

% within Ba3

% within Bb3

Count

% within Ba3

% within Bb3

correct

incorrect

Ba3

Total


a bitunderstand

littleundersta

nding don't know

Bb3

Total

Chi-Square Tests

8.644a 4 .071

6.546 4 .162

.611 1 .435

195

Pearson Chi-Square

Likelihood Ratio


N of Valid Cases

Value dfAsymp. Sig.

(2-sided)


a.

43

Ba3incorrectcorrect

Co

un

t

120

100

80

60

40

20

0

Bar Chart


Bb3

44




Valid Missing Total

Cases


133 25 4 2 16 180

73.9% 13.9% 2.2% 1.1% 8.9% 100.0%

93.7% 92.6% 80.0% 50.0% 94.1% 92.3%

9 2 1 2 1 15

60.0% 13.3% 6.7% 13.3% 6.7% 100.0%

6.3% 7.4% 20.0% 50.0% 5.9% 7.7%

142 27 5 4 17 195

72.8% 13.8% 2.6% 2.1% 8.7% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba4

% within Bb4

Count

% within Ba4

% within Bb4

Count

% within Ba4

% within Bb4

correct

incorrect

Ba4

Total


a bitunderstand

littleundersta

nding don't know

Bb4

Total

Chi-Square Tests

11.598a 4 .021

6.277 4 .179

1.230 1 .267

195

Pearson Chi-Square

Likelihood Ratio


N of Valid Cases

Value dfAsymp. Sig.

(2-sided)


a.

45

Ba4incorrectcorrect

Co

un

t

125

100

75

50

25

0

Bar Chart


Bb4

46




Valid Missing Total

Cases


139 31 2 4 14 190

73.2% 16.3% 1.1% 2.1% 7.4% 100.0%

97.2% 96.9% 100.0% 100.0% 100.0% 97.4%

4 1 0 0 0 5

80.0% 20.0% .0% .0% .0% 100.0%

2.8% 3.1% .0% .0% .0% 2.6%

143 32 2 4 14 195

73.3% 16.4% 1.0% 2.1% 7.2% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba5

% within Bb5

Count

% within Ba5

% within Bb5

Count

% within Ba5

% within Bb5

correct

incorrect

Ba5

Total


a bitunderstand

littleundersta

nding don't know

Bb5

Total

Chi-Square Tests

.598a 4 .963

1.107 4 .893

.452 1 .501

195

Pearson Chi-Square

Likelihood Ratio


N of Valid Cases

Value dfAsymp. Sig.

(2-sided)


a.

47

Ba5incorrectcorrect

Co

un

t

125

100

75

50

25

0

Bar Chart


Bb5

48




Valid Missing Total

Cases


107 41 16 12 12 188

56.9% 21.8% 8.5% 6.4% 6.4% 100.0%

96.4% 95.3% 94.1% 100.0% 100.0% 96.4%

4 2 1 0 0 7

57.1% 28.6% 14.3% .0% .0% 100.0%

3.6% 4.7% 5.9% .0% .0% 3.6%

111 43 17 12 12 195

56.9% 22.1% 8.7% 6.2% 6.2% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba6

% within Bb6

Count

% within Ba6

% within Bb6

Count

% within Ba6

% within Bb6

correct

incorrect

Ba6

Total


a bitunderstand

littleundersta

nding don't know

Bb6

Total

Chi-Square Tests

1.292a 4 .863

2.101 4 .717

.327 1 .567

195

Pearson Chi-Square

Likelihood Ratio


N of Valid Cases

Value dfAsymp. Sig.

(2-sided)


a.

49

Ba6incorrectcorrect

Co

un

t

120

100

80

60

40

20

0

Bar Chart


Bb6

50




Valid Missing Total

Cases


93 41 20 11 11 176

52.8% 23.3% 11.4% 6.3% 6.3% 100.0%

93.9% 89.1% 87.0% 78.6% 84.6% 90.3%

6 5 3 3 2 19

31.6% 26.3% 15.8% 15.8% 10.5% 100.0%

6.1% 10.9% 13.0% 21.4% 15.4% 9.7%

99 46 23 14 13 195

50.8% 23.6% 11.8% 7.2% 6.7% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba7

% within Bb7

Count

% within Ba7

% within Bb7

Count

% within Ba7

% within Bb7

correct

incorrect

Ba7

Total


a bitunderstand

littleundersta

nding don't know

Bb7

Total

Chi-Square Tests

4.522a 4 .340

4.152 4 .386

3.746 1 .053

195

Pearson Chi-Square

Likelihood Ratio


N of Valid Cases

Value dfAsymp. Sig.

(2-sided)


a.

51

Ba7incorrectcorrect

Co

un

t

100

80

60

40

20

0

Bar Chart


Bb7

52




Valid Missing Total

Cases


94 48 12 9 8 171

55.0% 28.1% 7.0% 5.3% 4.7% 100.0%

94.0% 81.4% 80.0% 90.0% 72.7% 87.7%

6 11 3 1 3 24

25.0% 45.8% 12.5% 4.2% 12.5% 100.0%

6.0% 18.6% 20.0% 10.0% 27.3% 12.3%

100 59 15 10 11 195

51.3% 30.3% 7.7% 5.1% 5.6% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba8

% within Bb8

Count

% within Ba8

% within Bb8

Count

% within Ba8

% within Bb8

correct

incorrect

Ba8

Total


a bitunderstand

littleundersta

nding don't know

Bb8

Total

Chi-Square Tests

9.035a 4 .060

8.916 4 .063

5.282 1 .022

195

Pearson Chi-Square

Likelihood Ratio


N of Valid Cases

Value dfAsymp. Sig.

(2-sided)


a.

53

Ba8incorrectcorrect

Co

un

t

100

80

60

40

20

0

Bar Chart


Bb8

54




Valid Missing Total

Cases


25 35 17 8 4 89

28.1% 39.3% 19.1% 9.0% 4.5% 100.0%

40.3% 50.7% 48.6% 38.1% 50.0% 45.6%

37 34 18 13 4 106

34.9% 32.1% 17.0% 12.3% 3.8% 100.0%

59.7% 49.3% 51.4% 61.9% 50.0% 54.4%

62 69 35 21 8 195

31.8% 35.4% 17.9% 10.8% 4.1% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba9

% within Bb9

Count

% within Ba9

% within Bb9

Count

% within Ba9

% within Bb9

correct

incorrect

Ba9

Total


a bitunderstand

littleundersta

nding don't know

Bb9

Total

Chi-Square Tests

2.090a 4 .719

2.098 4 .718

.079 1 .778

195

Pearson Chi-Square

Likelihood Ratio


N of Valid Cases

Value dfAsymp. Sig.

(2-sided)


a.

55

Ba9incorrectcorrect

Co

un

t

40

30

20

10

0

Bar Chart


Bb9

56




Valid Missing Total

Cases


96 49 16 6 7 174

55.2% 28.2% 9.2% 3.4% 4.0% 100.0%

92.3% 90.7% 94.1% 60.0% 70.0% 89.2%

8 5 1 4 3 21

38.1% 23.8% 4.8% 19.0% 14.3% 100.0%

7.7% 9.3% 5.9% 40.0% 30.0% 10.8%

104 54 17 10 10 195

53.3% 27.7% 8.7% 5.1% 5.1% 100.0%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within Ba10

% within Bb10

Count

% within Ba10

% within Bb10

Count

% within Ba10

% within Bb10

correct

incorrect

Ba10

Total


a bitunderstand

littleundersta

nding don't know

Bb10

Total

Chi-Square Tests

14.315a 4 .006

10.240 4 .037

8.299 1 .004

195

Pearson Chi-Square

Likelihood Ratio


N of Valid Cases

Value dfAsymp. Sig.

(2-sided)


a.

57

Ba10incorrectcorrect

Co

un

t

100

80

60

40

20

0

Bar Chart


Bb10

58

Output SPSS Test: Independent t-test

GENDER

TEST OF NORMALITY


gender

Cases

Valid Missing Total

N Percent N Percent N Percent

score male 45 100.0% 0 .0% 45 100.0%

female 150 100.0% 0 .0% 150 100.0%

Descriptives

gender Statistic Std. Error

score male Mean 13.8667 .33424

95% Confidence Interval for Mean

Lower Bound 13.1930

Upper Bound 14.5403

5% Trimmed Mean 14.0802

Median 14.0000

Variance 5.027

Std. Deviation 2.24216

Minimum 4.00

Maximum 17.00

Range 13.00

Interquartile Range 2.00

Skewness -2.117 .354

59

Kurtosis 7.680 .695

female Mean 14.3533 .18492


Lower Bound 13.9879

Upper Bound 14.7187


Median 15.0000

Variance 5.129


Minimum .00

Maximum 17.00

Range 17.00



Kurtosis 12.599 .394

Tests of Normality

gender

Kolmogorov-Smirnov(a) Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

score male .194 45 .000 .824 45 .000

female .171 150 .000 .787 150 .000

60

STATISTICAL TEST

Group Statistics

gender N Mean Std. DeviationStd. Error

Mean

score male 45 13.8667 2.24216 .33424

female 150 14.3533 2.26481 .18492

Independent Samples Test

Levene's Test for

Equality of Variances t-test for Equality of Means

F Sig. t df

Sig. (2-tailed)Mean

DifferenceStd. Error Difference

95% Confidence Interval of the Difference

Lower upper

score Equal variances assumed

.000 .996 -1.267 193 .207 -.48667 .38407 -1.24418 .27084

Equal variances not assumed

-1.274 73.038 .207 -.48667 .38199 -1.24796 .27462

62

AREA

TEST OF NORMALITY


area

Cases

Valid Missing Total


score urban 122 100.0% 0 .0% 122 100.0%

rural 73 100.0% 0 .0% 73 100.0%

Descriptives

area Statistic Std. Error

score urban Mean 14.3115 .20364


Lower Bound 13.9083

Upper Bound 14.7146


Median 15.0000

Variance 5.059


Minimum .00

Maximum 17.00

Range 17.00



Kurtosis 15.932 .435

63

rural Mean 14.1233 .26884


Lower Bound 13.5874

Upper Bound 14.6592


Median 14.0000

Variance 5.276


Minimum 4.00

Maximum 17.00

Range 13.00



Kurtosis 4.348 .555

Tests of Normality

area



score urban .191 122 .000 .739 122 .000

rural .162 73 .000 .878 73 .000

64

STATISTICAL TEST

Group Statistic

area N Mean Std. DeviationStd. Error

Mean

score urban 122 14.3115 2.24927 .20364

rural 73 14.1233 2.29701 .26884


Levene's Test for


F Sig. t df

Sig. (2-tailed)Mean



lower upper


.919 .339 .561 193 .575 .18819 .33548 -.47349 .84986


.558 149.113 .578 .18819 .33726 -.47824 .85462

65

LICENSE

TEST OF NORMALITY


lisence

Cases

Valid Missing Total


score Yes 134 100.0% 0 .0% 134 100.0%

No 61 100.0% 0 .0% 61 100.0%

Descriptives

lisence Statistic Std. Error

score yes Mean 14.5746 .14920


Lower Bound 14.2795

Upper Bound 14.8697


Median 15.0000

Variance 2.983


Minimum 4.00

Maximum 17.00

Range 13.00



Kurtosis 9.834 .416

no Mean 13.5082 .38739

95% Confidence Lower Bound 12.7333

66

Interval for Mean

Upper Bound 14.2831


Median 14.0000

Variance 9.154


Minimum .00

Maximum 17.00

Range 17.00



Kurtosis 6.669 .604

Tests of Normality

lisence



score yes .164 134 .000 .845 134 .000

no .171 61 .000 .821 61 .000

67

STATISTICAL TEST.

Group Statistics

lisence N Mean Std. DeviationStd. Error

Mean

score Yes 134 14.5746 1.72717 .14920

No 61 13.5082 3.02557 .38739


Levene's Test for


F Sig. t df Sig. (2-tailed)Mean



Lower Upper


12.214 .001 3.119 193 .002 1.06643 .34195 .39198 1.74088


2.569 78.344 .012 1.06643 .41513 .24004 1.89282

68

Output SPSS Test: Chi Square test

GENDER


166 85.1% 29 14.9% 195 100.0%gender * sources3N Percent N Percent N Percent

Valid Missing Total

Cases

gender * sources3 Crosstabulation

25 14 39

27.0 12.0 39.0

64.1% 35.9% 100.0%

21.7% 27.5% 23.5%

15.1% 8.4% 23.5%

90 37 127

88.0 39.0 127.0

70.9% 29.1% 100.0%

78.3% 72.5% 76.5%

54.2% 22.3% 76.5%

115 51 166

115.0 51.0 166.0

69.3% 30.7% 100.0%

100.0% 100.0% 100.0%

69.3% 30.7% 100.0%

Count

Expected Count

% within gender

% within sources3

% of Total

Count

Expected Count

% within gender

% within sources3

% of Total

Count

Expected Count

% within gender

% within sources3

% of Total

male

female

gender

Total

driving schoolnon-driving

school

sources3

Total

Chi-Square Tests

.641b 1 .423

.363 1 .547

.629 1 .428

.433 .271

.637 1 .425

166

Pearson Chi-Square

Continuity Correctiona

Likelihood Ratio

Fisher's Exact Test


N of Valid Cases

Value dfAsymp. Sig.

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

Exact Sig.(1-sided)

Computed only for a 2x2 tablea.

0 cells (.0%) have expected count less than 5. The minimum expected count is 11.98.

b.

69

Symmetric Measures

-.062 .423

.062 .423

166

Phi

Cramer's V

Nominal byNominal

N of Valid Cases

Value Approx. Sig.

Not assuming the null hypothesis.a.

Using the asymptotic standard error assuming the nullhypothesis.

b.

genderfemalemale

Co

un

t

100

80

60

40

20

0

Bar Chart

non-driving schooldriving school

sources3

70

AREA


166 85.1% 29 14.9% 195 100.0%area * sources3N Percent N Percent N Percent

Valid Missing Total

Cases

area * sources3 Crosstabulation

77 27 104

72.0 32.0 104.0

74.0% 26.0% 100.0%

67.0% 52.9% 62.7%

46.4% 16.3% 62.7%

38 24 62

43.0 19.0 62.0

61.3% 38.7% 100.0%

33.0% 47.1% 37.3%

22.9% 14.5% 37.3%

115 51 166

115.0 51.0 166.0

69.3% 30.7% 100.0%

100.0% 100.0% 100.0%

69.3% 30.7% 100.0%

Count

Expected Count

% within area

% within sources3

% of Total

Count

Expected Count

% within area

% within sources3

% of Total

Count

Expected Count

% within area

% within sources3

% of Total

urban

rural

area

Total


school

sources3

Total

Chi-Square Tests

2.966b 1 .085

2.397 1 .122

2.926 1 .087

.117 .061

2.948 1 .086

166

Pearson Chi-Square


Likelihood Ratio

Fisher's Exact Test


N of Valid Cases

Value dfAsymp. Sig.


Exact Sig.(1-sided)



b.

71

Symmetric Measures

.134 .085

.134 .085

166

Phi

Cramer's V

Nominal byNominal

N of Valid Cases

Value Approx. Sig.



b.

arearuralurban

Co

un

t

80

60

40

20

0

Bar Chart


sources3

72

LICENSE


166 85.1% 29 14.9% 195 100.0%lisence * sources3N Percent N Percent N Percent

Valid Missing Total

Cases

lisence * sources3 Crosstabulation

99 18 117

81.1 35.9 117.0

84.6% 15.4% 100.0%

86.1% 35.3% 70.5%

59.6% 10.8% 70.5%

16 33 49

33.9 15.1 49.0

32.7% 67.3% 100.0%

13.9% 64.7% 29.5%

9.6% 19.9% 29.5%

115 51 166

115.0 51.0 166.0

69.3% 30.7% 100.0%

100.0% 100.0% 100.0%

69.3% 30.7% 100.0%

Count

Expected Count

% within lisence

% within sources3

% of Total

Count

Expected Count

% within lisence

% within sources3

% of Total

Count

Expected Count

% within lisence

% within sources3

% of Total

yes

no

lisence

Total


school

sources3

Total

Chi-Square Tests

43.813b 1 .000

41.405 1 .000

42.432 1 .000

.000 .000

43.549 1 .000

166

Pearson Chi-Square


Likelihood Ratio

Fisher's Exact Test


N of Valid Cases

Value dfAsymp. Sig.


Exact Sig.(1-sided)



b.

73

Symmetric Measures

.514 .000

.514 .000

166

Phi

Cramer's V

Nominal byNominal

N of Valid Cases

Value Approx. Sig.



b.

lisencenoyes

Co

un

t

100

80

60

40

20

0

Bar Chart


sources3

74

Output SPSS Test: Binary logistic regression

LOGISTIC REGRESSION

Dependent Variable Encoding

0

1

Original Value1.00

2.00

Internal Value

Block 0: Beginning Block

Classification Tablea,b

0 8 .0

0 187 100.0

95.9

Observed1.00

2.00

skor2

Overall Percentage

Step 01.00 2.00

skor2 PercentageCorrect

Predicted

Constant is included in the model.a.

The cut value is .500b.

Variables in the Equation

3.152 .361 76.204 1 .000 23.375ConstantStep 0B S.E. Wald df Sig. Exp(B)

Variables not in the Equation

.017 1 .895

.562 1 .453

7.417 1 .006

7.868 3 .049

gender

area

lisence

Variables

Overall Statistics

Step0

Score df Sig.

75

Block 1: Method = Enter

Omnibus Tests of Model Coefficients

7.291 3 .063

7.291 3 .063

7.291 3 .063

Step

Block

Model

Step 1Chi-square df Sig.

Model Summary

59.473a .037 .127Step1

-2 Loglikelihood

Cox & SnellR Square

NagelkerkeR Square

Estimation terminated at iteration number 7 becauseparameter estimates changed by less than .001.

a.

Classification Tablea

0 8 .0

0 187 100.0

95.9

Observed1.00

2.00

skor2

Overall Percentage

Step 11.00 2.00

skor2 PercentageCorrect

Predicted

The cut value is .500a.

Variables in the Equation

.658 .893 .542 1 .461 1.931

-.088 .758 .013 1 .908 .916

-2.091 .885 5.580 1 .018 .124

5.313 2.120 6.280 1 .012 202.868

gender

area

lisence

Constant

Step1

a

B S.E. Wald df Sig. Exp(B)

Variable(s) entered on step 1: gender, area, lisence.a.

76

TESIS BIOSTATISTIK(2)

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