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Social Statistic (In the form of report based on Data Analysis)
Technical Report · January 2011
DOI: 10.13140/2.1.2268.0009
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Razieh Tadayon Nabavi
University of Science and Culture
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Social Statistic
(In the form of report based on Data Analysis)
By
Razieh Tadayon Nabavi
List of tables and figures
Table 1 1 SAT04 ........................................................................................................................................... 5
Table 1 2 summery of table 1-1 .................................................................................................................... 6
Table 1 3 cases out-of-rang ........................................................................................................................... 6
Table 1-4 missing number ............................................................................................................................ 6
Table 2 1 demographic profile ...................................................................................................................... 7
Table 2 2 activity profile ............................................................................................................................... 8
Table 2 3 readership of newspaper ............................................................................................................... 9
Table 2 4 usage of two products ................................................................................................................. 10
Table 3 1 Tot_sat_grp ................................................................................................................................. 12
Table 3 2Tot_sat_grp1 ................................................................................................................................ 13
Table 4 1race ............................................................................................................................................... 14
Table 4 2race1 ............................................................................................................................................. 15
Table 4 3Tot_sat_grp1 * race1 Cross tabulation ........................................................................................ 16
Table 4 4Chi-square Tests .......................................................................................................................... 16
Table 4 5nst * race1 Crosstabulation .......................................................................................................... 18
Table 4 6Chi-square Tests .......................................................................................................................... 18
Table 4 7race1* thestar Crosstabulation ..................................................................................................... 20
Table 4 8Chi-square Tests .......................................................................................................................... 20
Table 4 9race1 * umsia Crosstabulation ..................................................................................................... 22
Table 4 10Chi-square Tests ........................................................................................................................ 22
Table 4 11race1 * bharian Crosstabulation ................................................................................................. 24
Table 4 12 Chi-square Tests ....................................................................................................................... 24
Table 4 13 badmint * race1 Crosstabulation ............................................................................................... 26
Table 4 14 Chi-square Tests ....................................................................................................................... 26
2
Table 4 15 bowling * race1 Crosstabulation ............................................................................................... 28
Table 4 16 Chi-square Tests ....................................................................................................................... 28
Table 4 17 disco * race1 Crosstabulation ................................................................................................... 30
Table 4 18 Chi-square Tests ....................................................................................................................... 30
Table 4 19 fishing * race1 Crosstabulation ................................................................................................. 32
Table 4 20 Chi-square Tests ....................................................................................................................... 32
Table 4 21 turfclub * race1 Crosstabulation ............................................................................................... 34
Table 4 22 Chi-square Tests ....................................................................................................................... 34
Table 4 23 tennis * race1 Crosstabulation .................................................................................................. 36
Table 4 24 Chi-square Tests ....................................................................................................................... 36
Table 5 1 Descriptive Statistics ................................................................................................................... 40
Table 5 2 Independent samples Test ........................................................................................................... 41
Table 6 1 Religious inclination ANOVA .................................................................................................... 43
Table 6 2 Religious inclination Post Hoc Tests Multiple Comparisons; Scheffe test................................. 44
Table 6 3 Religious inclination Post Hoc Tests Multiple Comparisons; Scheffe test................................. 45
Table 6 4 Moral standards ANOVA ........................................................................................................... 46
Table 6 5 Moral standards Post Hoc Tests Multiple Comparisons; Scheffe test ....................................... 47
Table 6 6 Moral standards Post Hoc Tests Multiple Comparisons; Scheffe test ........................................ 48
Table 7 1 Correlation .................................................................................................................................. 50
Table 7 2 summary of correlation table ...................................................................................................... 50
Table 8 1 Descriptive Statistics ................................................................................................................... 52
Table 8 2 Correlations ................................................................................................................................. 53
Table 8 3 Variables Entered/Removedb ..................................................................................................... 53
Table 8 4 Model Summary ........................................................................................................................ 53
Table 8 5 ANOVAb .................................................................................................................................... 54
3
Table 8 6 Coefficientsa ............................................................................................................................... 54
Table 9 1 Reliability Statistics .................................................................................................................... 55
Table 9 2 Inter-Item Correlation Matrix ..................................................................................................... 56
Table 9 3 Item-Total Statistics .................................................................................................................... 57
Table 9 4 Reliability Statistics .................................................................................................................... 58
Table 9 5 Inter-Item Correlation Matrix ..................................................................................................... 59
Table 9 6 Item-Total Statistics .................................................................................................................... 60
Table 9 7 Item-Total Statistics .................................................................................................................... 61
Table 9 8 Item-Total Statistics .................................................................................................................... 62
Table 9 9 Item-Total Statistics .................................................................................................................... 62
Table 9 10 Item-Total Statistics .................................................................................................................. 63
Table 10 1 KMO and Bartlett's Test ........................................................................................................... 64
Table 10 2 Communalities .......................................................................................................................... 65
Table 10 3 Total Variance Explained ......................................................................................................... 66
Figure 10 1 component number .................................................................................................................. 67
Table 10 4 Rotated Component Matrixa ..................................................................................................... 68
4
Clean the Data.
For cleaning the data, first of all the data should be screen. In this view, I used SPSS software to
run Analyze and Frequencies to check any out-of-range values. On this regards, in output results,
the researcher controlled it against raw data. For example, according to the table 1-1, after
running Frequencies and checked the output, I found that SAT04 had an extra value 9 in 28
cases. So, this value 9 considered as out of the range, I treated it as missing number. The
following was data with out-of-range value and need to be clean; I had just one variable with
out-of-range among all data;
1. Job (sat04) had a value 9 which is out-of-range.
Very Dissatisfied
Table 1 1 SAT04
Frequency
Percent Valid Percent
4
1.6
2.1
Cumulative Percent
2.1
Dissatisfied
8
3.2
4.3
6.4
Somewhat Dissatisfied
26
10.4
13.9
20.3
Somewhat Satisfied
48
19.2
25.7
46.0
Satisfied
57
22.8
30.5
76.5
Very Satisfied
16
6.4
8.6
85.0
9
28
11.2
15.0
100.0
Total
187
74.8
100.0
63
25.2
-
Missing System
5
-
The following table 1-2 is the summary of table 1-1. It shows the total valid, missing and also out of
range data.
Table 1 2 summery of table 1-1
SAT04
N
N
N
Total
187
63
28
250
Valid
Missing System
Out-of-Range (9)
The following table 1-3 demonstrates, 28 cases as out-of-rang with value 9 for variable SAT04
from the raw data:
Table 1 3 cases out-of-rang
Cases in Out-of-Range (9)
Variable
SAT04
19-166-169-171- 173-174-176-178-179-182-184-189-195-200-206-209-211213-214-216-218-219-222-224-229-235-240-249
I treated SAT04 as missing number in the table1-4;
Table 1-4 missing number
SAT04
N
N
N
Total
159
91
250
Valid
Missing System
Out-of-Range (9)
After identified all out-of-range values, the researcher must correct the out-of-range values by
defined them as missing values. After the correction action, the researcher runs Frequencies
again to recheck the data.
6
Discuss the profile of the respondents with respect to the following items:

Demographic profile

Activity profile (e.g. bowling, fishing, etc.)

Readership of newspaper

Usage of two products, i.e., carbonated soft drinks and laundry detergents
A. Demographic profile encompasses sex, age, income, marital, and race. Table 2-1 shows
demographic profile of the respondents;
Table 2 1 demographic profile
Frequency
sex
Male
Female
Marital
Single
Married without children
Married with children
Divorced
Race
Malay
Chinese
Indian
Others
Income
Less than $400
$400 to $749
$750 to$999
$1000 to $1499
$1500 to $2499
$2500 to $3499
$3500 to $4999
$5000 and above
Not applicable
Age
16-21
22-28
29-40
7
Percentage
131
119
52.4
47.6
166
24
58
2
66.4
9.6
23.2
.8
80
123
38
9
32.0
49.2
15.2
3.6
22
53
13
29
21
5
2
5
100
8.8
21.2
5.2
11.6
8.4
2.0
8
2.0
40.0
100
71
79
40.0
28.4
31.6
B. Activity profile encompasses badminton, bowling, disco, fishing, go to turf club, and tennis.
Table 2-2 shows activity profile of the respondents;
Table 2 2 activity profile
Frequency
Percentage
Badminton
Not interested at all
Would like but have not done it yet
44
31
17.6
12.4
Sometimes
Often
Bowling
134
41
53.6
16.4
Not interested at all
Would like but have not done it yet
Sometimes
Often
Disco
87
68
77
18
34.8
27.2
30.8
7.2
Not interested at all
Would like but have not done it yet
106
44
42.4
17.6
Sometimes
Often
Fishing
93
7
37.2
2.8
Not interested at all
Would like but have not done it yet
Sometimes
Often
Go to turf club
76
79
92
3
30.4
31.6
36.8
1.2
Not interested at all
Would like but have not done it yet
193
41
77.2
16.4
Sometimes
Often
Tennis
12
4
4.8
1.6
Not interested at all
89
35.6
Would like but have not done it yet
Sometimes
95
60
38.0
24.0
Often
6
2.4
8
C. Readership of newspaper encompasses four types of newspaper: New Straits Times, The
Star, Utusan Malaysia, and Berita Harian. Table 2-3 shows readership of newspaper profile of
the respondents;
Table 2 3 readership of newspaper
Frequency
Percentage
New Straits Times
Yes
No
149
101
59.6
40.4
The Star
Yes
No
154
96
61.6
38.4
Utusan Malaysia
Yes
No
89
161
35.6
64.4
Berita Harian
Yes
No
87
163
34.8
65.2
9
D. Usage of two products, i.e., carbonated soft drinks and laundry detergents. Table 2-4 shows
usage of two products profile of the respondents;
Table 2 4 usage of two products
Frequency
Percentage
Carbonated Soft Drinks Cola (bottle,cans)
Coca-Cola
Pepsi-Cola
126
49
50.4
19.6
Others
Do not consume
Laundry Detergents
27
48
10.8
19.2
Breeze
Fab
Ekonomi Handalan
Trojan
Others
Do not consume
71
88
24
27
32
8
28.4
35.2
9.6
10.8
12.8
3.2
Refer to the table 2-1; there are 131 male respondents and 119 female respondents. There are
total 250 respondents. Majority of them are single (66.4%), Chinese (49.2%), age below 21(1621) 40% and (28.4%) are between 22-28. Just (2%) earn $5000 above and $2500 to $3499, most
of them (21.2%) earn $400 to $749.
Refer to the table 2-2; majority of the respondents are not interested go to turf club (77.2%).
Respondents are not interested at all to go to turf club might due to the tendency of low income
and young age. However (53.6%) play badminton sometimes and (16.4%) often. This is because
(68.4%) of the respondents are below 28 years old. (42.4%) of the respondents are not interested
go to the disco just (2.8%) of them often go disco. Fishing was one of the respondents‟ favorite
activities since (36.8%) of them fishing sometimes often by (7.2%) of the respondents. Tennis
10
was one of the another respondents‟ favorite activities since (38.0%) of them would like to do it
but have not done it yet and (2.4%) of them do it often.
Refer to the table 2-3; more than (59.6%) of the respondents read New Straits Times and The
Star. Respondents read more Utusan Malaysia (35.6%) than Berita Harian (34.8%). The high
tendency of readership in The Star newspaper might due to the fact that (49.2%) of the
respondents are Chinese and they can read English.
Refer to the table 2-4; majority of the respondents consume soft drinks with the brand of CocaCola with (50.4%) mine while, (19.6%) consume Pepsi-Cola. Another product which was used
by respondents was detergent that majority of them used two kinds of brand Fab (35.2%) and
Breeze (28.4%).
11
Divide the respondents into three groups depending on their level of satisfaction in life:
High, Medium and Low. Take only the High and Low groups for further analysis in Q4.
The satisfaction in life construct was measured by using the ten items of the questionnaire. Those
were from SAT01 to SAT10. I transformed the data from interval to ordinal (record). I have
created totsat variable which is interval in nature. Then I need to divide satisfaction level into
three groups: High, Medium, and Low. By dividing it into three groups will convert the data to
ordinal scale.
I created a new variable called tot_sat_grp to run the ordinal data. Then I used the percentile
function within the frequencies. The total score for totsat was from 13-54. The score for group 1
(Low satisfaction) was 13-38, the score for group 2 (Medium satisfaction) was 39-45, and the
score for group 3 (High satisfaction) was 46-54.
Table 3-1 was the statistic table;
Table 3 1 Tot_sat_grp
Frequency
Percent
1
Low satisfaction
51
20.4
2
Medium satisfaction
56
22.4
3
High satisfaction
47
18.8
12
However, this question only ask for High and Low group for further analysis in next question.
So, I created a new variable called tot_sat_grp1 and drop the medium group in this variable. I
defined the medium group as missing value. Table 3-2 was the new table;
Table 3 2Tot_sat_grp1
Frequency
Percent
1
Low satisfaction
51
20.4
3
High satisfaction
47
18.8
In conclusion, 20.4% (51) of the respondents had low level of satisfaction in life and 18.8% (47)
of the respondents had high level of satisfaction in life.
13
Cross tabulate ethnic groups (combine the “Indian” and “Others” into one group) with:
a. Satisfaction in life groups
b. Readership of newspaper
c. Activities involved (combine “would like” and “not interested” groups to form a
new group called “never”)
Dose the three ethnic groups differ in their involvement in a, b and c?
In the questionnaire Indian race and others has defined as 3 and 4 respectively. So, in this
question, I need to combine “Indian” and “Others” into one group. I also need to combine
“Indian” and “Others” due to these two groups were too small to run Chi-square. Based on table
4-1 I defined 1 = Malay, 2 = Chinese, 3 = Indian and 4 = Others.
Table 4 1race
Frequency
Percent
1
Malay
80
32.0
2
Chinese
123
49.2
3
Indian
38
15.2
4
Others
9
3.6
250
100.0
Total
Then I created a new variable called race1 (table 4-2). I clicked on transform then selected
recorded into same variable. After move race 1 to the numeric valuable, click on old and new
values. Then I put 4 in the old value box and 3 in new value box. Two groups (3&4) were
combining. After I had done the process of combining, I changed also “Others” value into
“Indian”.
14
Table 4 2race1
Frequency
Percent
1
Malay
80
32.0
2
Chinese
123
49.2
47
18.8
250
100.0
3&4 (3)
Indian&Others
(Indian)
Total
15
A. Satisfaction in life groups;
For this part I ran a cross tabulation on satisfaction in life groups. Table 4-3 was the output
result;
Table 4 3Tot_sat_grp1 * race1 Cross tabulation
Race1
1.00
Malay Chinese Indian
Total
% within Tot_sat_grp1
27.5%
54.9%
17.6%
100.0%
% within race1
48.3%
53.8%
52.9%
52.0%
% of Total
14.3%
28.6%
9.2%
52.0%
3.00
% within Tot_sat_grp1
31.9%
51.1%
17.0%
100.0%
High satisfaction
% within race1
51.7%
46.2%
47.1%
48.0%
% of Total
15.3%
24.5%
8.2%
48.0%
% within Tot_sat_grp1
29.6%
53.1%
17.3%
100.0%
% within race1
100.0% 100.0% 100.0% 100.0%
% of Total
29.6%
Low satisfaction
Tot_sat_grp1
Total
Table 4 4Chi-square Tests
value
df
53.1%
17.3%
Asymp.sig.(2-sided)
Pearson Chi-Square
.238
a
2
.888
Likelihood Ratio
.238
2
.888
Linear-by-Linear Association
.138
1
.710
N of Valid Cases
98
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 8.15.
16
100.0%
First I examined the Chi-square test for tot_sat_grp1 (table 4-4). The P = 0.888 which was
greater than α = 0.05, so we can conclude that our result is not significant and do not reject null
hypothesis. There was not significance different in level of satisfaction in life among three ethnic
groups.
Next, I examined the tot_sat_grp1 * race1 Cross tabulation (table 4-3). I interpret the percentage
in the direction of independent variable (race) across dependent variable (satisfaction in life).
From the output, I can interpret that Malay tend to be more satisfy in life (51.7%) as compare to
Indian (47.1%) and Chinese (46.2%). However, Indian tend to be more satisfied in life (47.1%)
than Chinese (46.2%).
17
B. Readership of newspaper; there are four types of newspaper, New Strait Times, The Star,
Utusan Malaysia, and Berita Harian.
i) The following table (4-5) was output for readership of the New Strait Times;
Table 4 5nst * race1 Crosstabulation
Race1
Yes
nst
No
Total
Pearson Chi-Square
Malay Chinese Indian
Total
% within nst
26.8%
47.0%
26.2%
100.0%
% within race1
50.0%
56.9%
83.0%
59.6%
% of Total
16.0%
28.0%
15.6%
59.6%
% within nst
39.6%
52.5%
7.9%
100.0%
% within race1
50.0%
43.1%
17.0%
40.4%
% of Total
16.0%
21.2%
3.2%
40.4%
% within nst
32.0%
49.2%
18.8%
100.0%
% within race1
100.0% 100.0% 100.0% 100.0%
% of Total
32.0%
Table 4 6Chi-square Tests
value
df
a
14.100
2
49.2%
18.8%
Asymp.sig.(2-sided)
.001
Likelihood Ratio
15.354
2
.000
Linear-by-Linear Association
11.754
1
.001
N of Valid Cases
250
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 18.99.
18
100.0%
First I examined the Chi-square test for nst table (4-6). The P = 0.001 which was smaller than α
= 0.05, statistically significant. Reject null hypothesis. There was significance different in New
Strait Times readership among three ethnic groups.
Next, I examined the nst * race1 Cross tabulation (table 4-5). I interpret the percentage in the
direction of independent variable (race) across dependent variable (nst). From the output, I can
interpret which Indian tend to read more New Strait Times (83.0%) as compare to Chinese
(56.9%) and Malay (50.0%). Mine while, Chinese tend to read more New Strait Times (56.9%)
than Malay (50.0%).
19
ii) The following table (4-7) was the output for readership of the The Star
Table 4 7race1* thestar Crosstabulation
thestar
Malay
Race1
Chinese
Indian
Total
% within race1
Yes
28.8%
No
71.3%
Total
100.0%
% within thestar
14.9%
59.4%
32.0%
% of Total
9.2%
22.8%
32.0%
% within race1
75.6%
24.4%
100.0%
% within thestar
60.4%
31.3%
49.2%
% of Total
37.2%
12.0%
49.2%
% within race1
80.9%
19.1%
100.0%
% within thestar
24.7%
9.4%
18.8%
% of Total
15.2%
3.6%
18.8%
% within race1
61.6%
38.4%
100.0%
% within thestar
100.0%
100.0%
100.0%
% of Total
61.6%
38.4%
100.0%
Table 4 8Chi-square Tests
value
df
Asymp.sig.(2-sided)
Pearson Chi-Square
54.066a
2
.000
Likelihood Ratio
54.441
2
.000
Linear-by-Linear Association
42.849
1
.000
N of Valid Cases
250
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 18.05.
20
First I examined the Chi-square test for thestar table (4-8). The P = 0.000 which was smaller than
α = 0.05, statistically significant. Reject null hypothesis. There was significance different in The
Star readership among three ethnic groups.
Next, I examined the race1* thestar Cross tabulation (table 4-7). I interpret the percentage in the
direction of independent variable (race) across dependent variable (The Star). From the output, I
can interpret which Indian tend to read more The Star (80.9%) as compare to Chinese (75.6%)
and Malay (28.8%). Mine while, Chinese tend to read more The Star (75.6%) than Malay
(28.8%).
21
iii) The following table (4-9) was the output for readership of the Utusan Malaysia;
Table 4 9race1 * umsia Crosstabulation
umsia
Malay
Chinese
Race 1
Indian
Total
Yes
No
Total
% within race1
73.8%
26.3%
100.0%
% within umsia
66.3%
13.0%
32.0%
% of Total
23.6%
8.4%
32.0%
% within race1
12.2%
87.8%
100.0%
% within umsia
16.9%
67.1%
49.2%
% of Total
6.0%
43.2%
49.2%
% within race1
31.9%
68.1%
100.0%
% within umsia
16.9%
19.9%
18.8%
% of Total
6.0%
12.8%
18.8%
% within race1
35.6%
64.4%
100.0%
% within umsia
100.0%
100.0%
100.0%
% of Total
35.6%
64.4%
100.0%
Table 4 10Chi-square Tests
value
df
Asymp.sig.(2-sided)
Pearson Chi-Square
80.453a
2
.000
Likelihood Ratio
83.355
2
.000
Linear-by-Linear Association
36.846
1
.000
N of Valid Cases
250
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 16.73.
22
First I examined the Chi-square test for umsia table (4-10). The P = 0.000 which was smaller
than α = 0.05, statistically significant. Reject null hypothesis. There was significance different in
the Utusan Malaysia readership among three ethnic groups.
Next, I examined the race1* umsia Cross tabulation (table 4-9). I interpret the percentage in the
direction of independent variable (race) across dependent variable (umsia). From the output, I
can interpret which Malay tend to read more Utusan Malaysia (73.8%) as compare to Chinese
(12.2%) and Indian (31.9%). Mine while, Indian tend to read more Utusan Malaysia (31.9%)
than Chinese (12.2%).
23
iv) The following table (4-11) was the output for readership of the Berita Harian;
Table 4 11race1 * bharian Crosstabulation
bharian
Yes
No
Total
% within race1
55.0%
45.0%
100.0%
% within bharian
50.6%
22.1%
32.0%
% of Total
17.6%
14.4%
32.0%
% within race1
21.1%
78.9%
100.0%
% within bharian
29.9%
59.5%
49.2%
% of Total
10.4%
38.8%
49.2%
% within race1
36.2%
63.8%
100.0%
% within bharian
19.5%
18.4%
18.8%
% of Total
6.8%
12.0%
18.8%
% within race1
34.8%
65.2%
100.0%
% within bharian
100.0%
100.0%
100.0%
% of Total
34.8%
65.2%
100.0%
Malay
Chinese
Race1
Indian
Total
Table 4 12 Chi-square Tests
value
df
Asymp.sig.(2-sided)
Pearson Chi-Square
24.544a
2
.000
Likelihood Ratio
24.602
2
.000
Linear-by-Linear Association
8.617
1
.003
N of Valid Cases
250
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 16.36.
24
First I examined the Chi-square test for bharian table (4-12). The P = 0.000 which was smaller
than α = 0.05, statistically significant. Reject null hypothesis. There was significance different in
the Berita Harian readership among three ethnic groups.
Next, I examined the race1* bharian Cross tabulation (table 4-11). I interpret the percentage in
the direction of independent variable (race) across dependent variable (bharian). From the output,
I can interpret which Malay tend to read more Berita Harian (55.0%) as compare to Chinese
(21.1%) and Indian (36.2%). Mine while, Indian tend to read more Berita Harian (36.2%) than
Chinese (21.2%).
C. Activities involved
(combine “would like” and “not interested” groups to form a new group called “never”);
In this part I need to combine “would like” and “not interested” groups to form a new group
called “never”. From the transform then record into same variable, pick out all the activities
(badminton, bowling, disco, fishing, go to turf club, and tennis). Over the numeric variable box
then click on old and new values. In this option of old value I selected value, type 2 (would like)
in its below box and in the new value I selected value, type 1 (not interested) then go for add
option. Now I changed „„would like” and “not interested at all” to form a new group “never”.
I defined 1 = never, 2 = sometimes, and 3 = often. Following tables were the output results;
25
i) The following table (4-13) was the output for Badminton Activity;
Table 4 13 badmint * race1 Crosstabulation
Race1
Malay
Chinese
Indian
Total
% within badmint
36.0%
38.7%
25.3%
100.0%
% within race1
33.8%
23.6%
40.4%
30.0%
% of Total
10.8%
11.6%
7.6%
30.0%
% within badmint
35.1%
56.0%
9.0%
100.0%
% within race1
58.8%
61.0%
25.5%
53.6%
% of Total
18.8%
30.0%
4.8%
53.6%
% within badmint
14.6%
46.3%
39.0%
100.0%
% within race1
7.5%
15.4%
34.0%
16.4%
% of Total
2.4%
7.6%
6.4%
16.4%
% within badmint
32.0%
49.2%
18.8%
100.0%
% within race1
100.0%
100.0%
100.0%
100.0%
% of Total
32.0%
49.2%
18.8%
100.0%
Never
Sometimes
badminton
Often
Total
Table 4 14 Chi-square Tests
value
df
Asymp.sig.(2-sided)
Pearson Chi-Square
25.174a
4
.000
Likelihood Ratio
25.592
4
.000
Linear-by-Linear Association
3.328
1
.068
N of Valid Cases
250
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 7.71.
26
First I examined the Chi-square test for badminton table (4-14). The P = 0.000 which was
smaller than α = 0.05, statistically significant. Reject null hypothesis. There was significance
different in the Badminton Activity among three ethnic groups.
Next, I examined the badmint race1* Cross tabulation (table 4-13). I interpret the percentage in
the direction of independent variable (race) across dependent variable (badmint). From the
output, I can interpret that Indian tend to play more badminton often (34.0%) as compare to
Chinese (15.4%) and Malay (7.5%). However, Chinese tend to play more badminton sometimes
(61.0%) as compare to Malay (58.8%) and Indian (25.5%).
27
ii) The following table (4-15) was the output for Bowling Activity;
Table 4 15 bowling * race1 Crosstabulation
Race1
Malay
Chinese
Indian
Total
% within bowling
40.6%
40.6%
18.7%
100.0%
% within race1
78.8%
51.2%
61.7%
62.0%
% of Total
25.2%
25.2%
11.6%
62.0%
% within bowling
20.8%
62.3%
16.9%
100.0%
% within race1
20.0%
39.0%
27.7%
30.8%
% of Total
6.4%
19.2%
5.2%
30.8%
% within bowling
5.6%
66.7%
27.8%
100.0%
% within race1
1.3%
9.8%
10.6%
7.2%
% of Total
.4%
4.8%
2.0%
7.2%
% within bowling
32.0%
49.2%
18.8%
100.0%
% within race1
100.0%
100.0%
100.0%
100.0%
% of Total
32.0%
49.2%
18.8%
100.0%
Never
Sometimes
Bowling
Often
Total
Table 4 16 Chi-square Tests
value
df
Asymp.sig.(2-sided)
Pearson Chi-Square
17.629a
4
.001
Likelihood Ratio
19.586
4
.001
Linear-by-Linear Association
8.224
1
.004
N of Valid Cases
250
a. 1 cells (11.1%) have expected count less than 5. The minimum expected count is 3.38.
28
First I examined the Chi-square test for bowling table (4-16). The P = 0.001 which was smaller
than α = 0.05, statistically significant. Reject null hypothesis. There was significance different in
the Bowling Activity among three ethnic groups.
Next, I examined the bowling race1* Cross tabulation (table 4-15). I interpret the percentage in
the direction of independent variable (race) across dependent variable (bowling). From the
output, I can interpret that Indian tend to play more bowling often (10.6%) as compare to
Chinese (9.8%) and Malay (1.3%). However, Chinese tend to play more bowling sometimes
(39.0%) as compare to Indian (27.7%) and Malay (20.0%). On the other hand, Malay never play
bowling (78.8%) as compare Indian (61.7%) and Chinese (51.2%).
29
iii)The following table (4-17) was the output for Disco Activity;
Table 4 17 disco * race1 Crosstabulation
Race1
Malay
Chinese
Indian
Total
% within disco
38.0%
42.0%
20.0%
100.0%
% within race1
71.3%
51.2%
63.8%
60.0%
% of Total
22.8%
25.2%
12.0%
60.0%
% within disco
18.3%
63.4%
18.3%
100.0%
% within race1
21.3%
48.0%
36.2%
37.2%
% of Total
6.8%
23.6%
6.8%
37.2%
% within disco
85.7%
14.3%
.0%
100.0%
% within race1
7.5%
.8%
.0%
2.8%
% of Total
2.4%
.4%
.0%
2.8%
% within disco
32.0%
49.2%
18.8%
100.0%
% within race1
100.0%
100.0%
100.0%
100.0%
% of Total
32.0%
49.2%
18.8%
100.0%
Never
Sometimes
Disco
Often
Total
Table 4 18 Chi-square Tests
value
df
Asymp.sig.(2-sided)
Pearson Chi-Square
22.063a
4
.000
Likelihood Ratio
22.722
4
.000
Linear-by-Linear Association
.122
1
.727
N of Valid Cases
250
a. 3 cells (33.3%) have expected count less than 5. The minimum expected count is 1.32.
30
First I examined the Chi-square test for disco table (4-18). The P = 0.000 which was smaller than
α = 0.05, statistically significant. Reject null hypothesis. There was significance different in the
Disco Activity among three ethnic groups.
Next, I examined the disco race1* Cross tabulation (4-17). I interpret the percentage in the
direction of independent variable (race) across dependent variable (disco). From the output, I can
interpret that Chinese tend to go more disco often (0.8%) as compare to Malay (7.5%) and Indian
(0.0%). However, Chinese tend to go more disco sometimes (48.0%) as compare to Indian
(36.2%) and Malay (21.3%). On the other hand, Malay never go to disco (71.3%) as compare
Indian (63.8%) and Chinese (51.2%).
31
iv) The following table (4-19) was the output for Fishing Activity;
Table 4 19 fishing * race1 Crosstabulation
Race1
Malay
Chinese
Indian
Total
% within fishing
29.7%
53.5%
16.8%
100.0%
% within race1
57.5%
67.5%
55.3%
62.0%
% of Total
18.4%
33.2%
10.4%
62.0%
% within fishing
33.7%
43.5%
22.8%
100.0%
% within race1
38.8%
32.5%
44.7%
36.8%
% of Total
12.4%
16.0%
8.4%
36.8%
% within fishing
100.0%
.0%
.0%
100.0%
% within race1
3.8%
.0%
.0%
1.2%
% of Total
1.2%
.0%
.0%
1.2%
% within fishing
32.0%
49.2%
18.8%
100.0%
% within race1
100.0%
100.0%
100.0%
100.0%
% of Total
32.0%
49.2%
18.8%
100.0%
Never
Sometimes
fishing
Often
Total
Table 4 20 Chi-square Tests
value
df
Asymp.sig.(2-sided)
Pearson Chi-Square
9.058a
4
.060
Likelihood Ratio
9.495
4
.050
Linear-by-Linear Association
.291
1
.590
N of Valid Cases
250
a. 3 cells (33.3%) have expected count less than 5. The minimum expected count is .56.
32
First I examined the Chi-square test for fishing table (4-20). The P = 0.060 which was greater
than α = 0.05, so we can conclude that our result is not significant and do not reject null
hypothesis. There was not significance different in the Fishing Activity among three ethnic
groups.
Next, I examined the fishing race1* Cross tabulation (4-19). I interpret the percentage in the
direction of independent variable (race) across dependent variable (fishing). From the output, I
can interpret that Malay tend to do more fishing often (3.8%) as compare to Chinese (0.0%) and
Indian (0.0%). However, Indian tend to do more fishing sometimes (44.7%) as compare to Malay
(38.8%) and Chinese (32.5%). On the other hand, Chinese never do to fishing (67.5%) as
compare Malay (57.5%) and Indian (55.5%).
33
v) The following table (4-21) was the output for Turf club Activity;
Table 4 21 turfclub * race1 Crosstabulation
Race1
Malay
Chinese
Indian
Total
% within turfclub
32.9%
47.4%
19.7%
100.0%
% within race1
96.3%
90.2%
97.9%
93.6%
% of Total
30.8%
44.4%
18.4%
93.6%
% within turfclub
25.0%
66.7%
8.3%
100.0%
% within race1
3.8%
6.5%
2.1%
4.8%
% of Total
1.2%
3.2%
.4%
4.8%
% within turfclub
.0%
100.0%
.0%
100.0%
% within race1
.0%
3.3%
.0%
1.6%
% of Total
.0%
1.6%
.0%
1.6%
% within turfclub
32.0%
49.2%
18.8%
100.0%
% within race1
100.0%
100.0%
100.0%
100.0%
% of Total
32.0%
49.2%
18.8%
100.0%
Never
Sometimes
Turfclub
Often
Total
Table 4 22 Chi-square Tests
value
df
Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear Association
N of Valid Cases
Asymp.sig.(2-sided)
6.057a
4
.195
7.726
4
.102
.031
1
.859
250
a. 5 cells (55.6%) have expected count less than 5. The minimum expected count is .75.
34
First I examined the Chi-square test for turf club table (4-22). The P = 0.195 which was greater
than α = 0.05, so we can conclude that our result is not significant and do not reject null
hypothesis. There was not significance different in the Turf club Activity among three ethnic
groups.
Next, I examined the turfclub race1* Cross tabulation (4-21). I interpret the percentage in the
direction of independent variable (race) across dependent variable (turfclub). From the output, I
can interpret that Chinese tend to go more turf club often (3.3%) as compare to Malay (0.0%)
and Indian (0.0%). Also, Chinese tend to go more turf club sometimes (6.5%) as compare to
Malay (3.8%) and Indian (2.1%). On the other hand, Indian never go to turf club (97.9%) as
compare Malay (96.3%) and Indian (90.2%).
35
vi) The following table (4-23) was the output for Tennis Activity;
Table 4 23 tennis * race1 Crosstabulation
Race1
Malay
Chinese
Indian
Total
% within tennis
31.0%
52.2%
16.8%
100.0%
% within race1
71.3%
78.0%
66.0%
73.6%
% of Total
22.8%
38.4%
12.4%
73.6%
% within tennis
33.3%
40.0%
26.7%
100.0%
% within race1
25.0%
19.5%
34.0%
24.0%
8.0%
9.6%
6.4%
24.0%
% within tennis
50.0%
50.0%
.0%
100.0%
% within race1
3.8%
2.4%
.0%
2.4%
% of Total
1.2%
1.2%
.0%
2.4%
% within tennis
32.0%
49.2%
18.8%
100.0%
% within race1
100.0%
100.0%
100.0%
100.0%
32.0%
49.2%
18.8%
100.0%
Never
Sometimes
Tennis
% of Total
Often
Total
% of Total
Table 4 24 Chi-square Tests
value
df
Asymp.sig.(2-sided)
Pearson Chi-Square
5.541a
4
.236
Likelihood Ratio
6.428
4
.169
Linear-by-Linear Association
.008
1
.929
N of Valid Cases
250
a.3 cells (33.3%) have expected count less than 5. The minimum expected count is 1.13.
36
First I examined the Chi-square test for tennis table (4-24). The P = 0.236 which was greater than
α = 0.05, so we can conclude that our result is not significant and do not reject null hypothesis.
There was not significance different in the Tennis Activity among three ethnic groups.
Next, I examined the tennis race1* Cross tabulation (4-23). I interpret the percentage in the
direction of independent variable (race) across dependent variable (tennis). From the output, I
can interpret that Malay tend to play more tennis often (3.8%) as compare to Chinese (2.4%) and
Indian (0.0%). However, Indian tend to play more tennis sometimes (34.0%) as compare to
Malay (25.0%) and Chinese (19.5%). On the other hand, Chinese never play tennis (78.0%) as
compare Malay (71.3%) and Indian (66.0%).
Conclusion;
Malay tend to be more satisfied in life (51.7%) as compare to Indian (47.1%) and Chinese
(46.2%).
Malay read Utusan Malaysia (73.8%) and Berita Harian (55.0%) more regularly as compare to
others. Chinese read New Strait Times (56.9%) more regularly as compare with other race.
Indian read The star (80.9%) more regularly as compare with other race.
Malay tend to do fishing often (3.8%) and play tennis (3.8%) more regularly as compare to
others. They also never go more to disco (71.3%) as compare others. Chinese tend to play
badminton sometimes (61.0%) and play bowling (39.0%) more regularly as compare to others.
They also tend to go more turf club sometimes (6.5%) as compare to others. Chinese never play
37
tennis more (78.0%) as compare to others. Indian tend to do fishing sometimes (44.7%) more
regularly as compare to others. They also tend to play tennis sometimes (34.0%) and badminton
often (34.0%) more regularly as compare to others. Indian never go to turf club (97.9%) as
compare to others.
Dose the three ethnic groups differ in their involvement in a, b and c?
As a conclusion, I would like to say that the three ethnic groups were differ in their involvement
in a, b, and c.
38
Perform some statistical tests to see sex group differences with respect to all the items in the
satisfaction in life scale.
In this question, I want to examine differences between male and female toward the level of
satisfaction in life (table 5-1). Independent T-test was used to test for equality of variances
(homoscedasticity) and equality of the mean score. I used t-test due to the dependent variables
(totasat) were interval in nature and the independent variables (male and female) were nominal
data.
An independent sample t-test analysis was used to compare the values of dependent variables the
male group and female group, and to identify whether there were significant differences between
the two groups. Because the dependent variables (totasat) were interval in nature and the
independent variables (male and female) were nominal data. Data were tested for normal
distribution with the Kolmogorov- Smirnov test and for homogeneity of variances with Levene‟s
test.
39
Table 5 1 Descriptive Statistics
Items
Sex
SAT01 (Money)
SAT02 (Friends)
SAT03 (Love affair/romance)
SAT04 (Job if you are working)
SAT05 (Study)
SAT06 (Relation with parents)
SAT07 (Leisure)
SAT08 (Your physical appearance)
SAT09 (Sexual life or relation with the opposite sex)
SAT10 (Material comfort)
40
N
mean
Male
131
3.59
Female
119
3.59
Male
131
4.47
Female
119
4.53
Male
131
4.11
Female
119
3.62
Male
95
4.14
Female
64
4.34
Male
131
3.74
Female
119
3.75
Male
131
4.76
Female
119
4.93
Male
131
4.25
Female
119
4.13
Male
131
4.37
Female
119
4.05
Male
126
4.10
Female
113
3.89
Male
131
4.11
Female
117
4.15
Table 5 2 Independent samples Test
Levene's Test for
Independent t – test
Equality of
Variances
F
Sig.
t
Sig.(2-tailed)
1.020
.314
SAT01 Equal variances assumed
-.003
.998
Equal variances not assumed
-.003
.998
SAT02 Equal variances assumed
Equal variances not assumed
.862
.354
-.488
-.488
.626
.627
SAT03 Equal variances assumed
Equal variances not assumed
4.039
.046
2.663
2.648
.008
SAT04 Equal variances assumed
Equal variances not assumed
1.023
.313
-1.118
-1.160
.265
.248
SAT05 Equal variances assumed
Equal variances not assumed
.204
.652
-.048
-.048
.961
.962
SAT06 Equal variances assumed
Equal variances not assumed
.353
.553
-1.171
-1.168
.243
.244
SAT07 Equal variances assumed
Equal variances not assumed
4.314
.039
.946
.935
.345
.351
SAT08 Equal variances assumed
Equal variances not assumed
.489
.485
2.494
2.491
.013
.013
SAT09 Equal variances assumed
Equal variances not assumed
.516
.473
1.194
1.188
.234
.236
SAT10 Equal variances assumed
Equal variances not assumed
1.581
.210
-.230
-.229
.818
.819
.009
Error! Reference source not found., shows the Levene‟s test values for the assumption of
equality of variances and also results independent t test for all items between male and female
groups. The results of Levene‟s test for SAT01, SAT02, SAT04, SAT05, SAT06, SAT08,
SAT09 and SAT10 were not significant (i.e., p > 0.05). Thus, the Equal variances assumed t-test
statistic can be used for evaluating the null hypothesis of equality of means. However the results
41
of Levene‟s test for SAT03 and SAT07 were significant (i.e., p < 0.05) and the assumption that
the samples variances are equal is rejected and the Equal variances not assumed t-test statistic
should be used.
Based on the table 5-2, the results from the independent t- test analyses indicate that there is
significant difference between male and female groups for SAT03 (p=0.009) and SAT08
(p=0.013), while there is no significant difference between male and female groups for other
items (p>0.05).
42
Perform some statistical tests to see ethnic group differences (take only Malay, Chinese and
Indian) with respect to all the items in the religious inclination and moral standards scales.
In this question the moral standards construct were measured by using eight items. The items
were A04, A14, A15, A31, A33, A38, A42 and A43. The religious inclination construct were
measured using a three items scale. The items were A08, A19 and A30.
Table 6 1 Religious inclination ANOVA
Dependent variable
F
A08 (I don‟t like to have an unlucky number formy house
or my car registration number)
6.047
Between Groups
.003
A19 (Relying on a bomoh or geomancy (fengsui) in
choosing my residence is wise)
Between Groups
6.538
.002
24.328
.000
Sig.
A30 (I believe religion is an important part of my life)
Between Groups
A one-way between-groups analysis of variance was conducted to explore the impact of ethnic
on of the religious inclination construct, as measured by A08, A09 and A30. Subjects were
divided into three groups according to their race (Malay; n=249, Indian; n=249,Chinese; n=249).
There was a statistically significant difference at the p<.05 level in A08 [F (2, 247) =6.047,
p=.003], A19 [F (2, 247) =6.538, p=.002 and A30 [F (2, 247) =24.328, p=.000] for the three race
Groups (Table 6-1). Therefore, at least one pair of races differs significantly in these terms.
Likewise, this research had only one categorical factor (ethnic group) and two metric dependent
variables (the religious inclination and moral standards scale) that is why one-way ANOVA was
conducted. Next question is, which pairs differ significantly from each other?
43
Table 6 2 Religious inclination Post Hoc Tests Multiple Comparisons; Scheffe test
Dependent variable
(I)race
(J) race
Mean difference
sig
(I-J)
Malay
Chinese
-.723*
.012
Indian
.033
.994
Malay
.723*
.012
Indian
.756*
.032
Malay
-.033
.994
Chinese
-.756*
.032
Chinese
-.553*
.009
Indian
.039
.986
Malay
.553*
.009
Indian
.592*
.022
Malay
-.039
.986
Chinese
-.592*
.022
Chinese
1.226*
.000
Indian
.884*
.001
Malay
-1.226*
.000
Indian
-.342
.271
Malay
-.884*
.001
.342
.271
A08 (I don‟t like to have an
unlucky number for my house or
Chinese
my car registration number)
Indian
Malay
A19
(Relying on a bomoh or
Chinese
geomancy (fengsui) in choosing
my residence is wise)
Indian
Malay
A30
(I believe religion is an
Chinese
important part of my life)
Indian
Chinese
44
The Summary of : Table 6-2 is shown in the following table.
Table 6 3 Religious inclination Post Hoc Tests Multiple Comparisons; Scheffe test
Dependent variable
A08 (I don‟t like to have an
unlucky number for my house or
my car registration number)
Chinese
Mean difference
(I-J)
-.723*
.012
Indian
.033
.994
Indian
Chinese
-.756*
.032
Malay
Chinese
-.553*
.009
Indian
.039
.986
Indian
Chinese
-.592*
.022
Malay
Chinese
1.226*
.000
Indian
.884*
.001
Chinese
.342
.271
(I)race
(J) race
Malay
A19 (Relying on a bomoh or
geomancy (fengsui) in choosing
my residence is wise)
A30 (I believe religion is an
important part of my life)
Indian
sig
Based on table (6-3) Post-hoc comparisons using the scheffe test indicated that the mean score of
A08 for Chinese group was significantly different from Malay group (MD=-.723, P= .012) and
Indian group (MD= -.756, P= 0.032). From the Scheffe Multiple Comparison, also the mean
score of A19 for Chinese group was significantly different from Malay group (MD= -.0553, P=
.009) and Indian group (MD= -.592, P= 0.022). Finally, the scheffe test indicated that the mean
score of A30 for Malay group was significantly different from Chinese group (MD=1.226, P=
0.000) and Indian group (MD= 0.884, P= 0.001).
45
Table 6 4 Moral standards ANOVA
Dependent variable
F
Sig.
A04 (I would rather work smart than work hard)
Between Groups
1.665
.191
A14 (Most people are trust worthy and honest)
Between Groups
.005
.995
A15 (I believe that the end justifies the means)
Between Groups
1.234
.293
2.660
.072
6.089
.003
A38 (I listen to the advice of my elders)
Between Groups
1.310
.272
A42 (A woman‟s life is fulfilled only if she can provide a happy
home for her family)
Between Groups
6.089
.003
A43 (It is wrong to have sex before marriage)
Between Groups
6.575
.002
A31 (I believe that filial piety is very much alive in our society)
Between Groups
A33 (Respect for authority is important in our society)
Between Groups
A one-way between-groups analysis of variance was conducted to explore the impact of ethnic
on of the moral standards construct, as measured by A04, A14, A15, A31, A33, A38, A42, and
A43. Subjects were divided into eight groups according to their race Malay, Chinese, and Indian.
There was a statistically significant difference at the p<.05 level in A33 [F (2, 247) =6.089,
p=.003], A42 [F (2, 247) =6.089, p=.003 and A43 [F (2, 247) =6.575, p=.002] for the three race
Groups (Table 6-3). Therefore, at least one pair of races differs significantly in these terms.
Likewise, this research had only one categorical factor (ethnic group) and two metric dependent
variables (the religious inclination and moral standards scale) that is why one-way ANOVA was
conducted. Next question is, which pairs differ significantly from each other?
46
Table 6 5 Moral standards Post Hoc Tests Multiple Comparisons; Scheffe test
Dependent variable
(I)race
Malay
A04 (I would rather work smart than work
hard)
Chinese
Indian
Malay
A14(Most people are trust worthy and honest)
Chinese
Indian
Malay
A15 (I believe that the end justifies the means)
Chinese
Indian
Malay
A31 (I believe that filial piety is very much
alive in our society)
Chinese
Indian
Malay
A33 (Respect for authority is important in our
society)
Chinese
Indian
Malay
A38 (I listen to the advice of my elders)
Chinese
Indian
Malay
A42 (A woman’s life is fulfilled only if she can
provide a happy home for her family)
Chinese
Indian
Malay
A43 (It is wrong to have sex before marriage)
Chinese
Indian
47
(J) race
Chinese
Indian
Malay
Indian
Malay
Chinese
Chinese
Indian
Malay
Indian
Malay
Chinese
Chinese
Indian
Malay
Indian
Malay
Chinese
Chinese
Indian
Malay
Indian
Malay
Chinese
Chinese
Indian
Malay
Indian
Malay
Chinese
Chinese
Indian
Malay
Indian
Malay
Chinese
Chinese
Indian
Malay
Indian
Malay
Chinese
Chinese
Indian
Malay
Indian
Malay
Chinese
Mean difference
(I-J)
sig
-.309
.027
.309
.336
-.027
-.336
.009
-.010
-.009
-.019
.010
.019
-.103
-.385
.103
-.282
.385
.282
.327
.396
-.327
.069
-.396
-.069
.135
-.493*
-.135
-.628*
.493*
.628*
.219
.172
-.219
-.047
-.172
.047
.714*
.664
-.714*
-.050
-.664
.050
.736*
-.042
-.736*
-.778*
.042
.778*
.301
.994
.301
.369
.994
.369
.999
.999
.999
.996
.999
.996
.869
.301
.869
.476
.301
.476
.129
.160
.129
.938
.160
.938
.670
.041
.670
.003
.041
.003
.280
.623
.280
.960
.623
.960
.004
.053
.004
.981
.053
.981
.008
.990
.008
.023
.990
.023
The Summary of table 6-5 is shown in the following table.
Table 6 6 Moral standards Post Hoc Tests Multiple Comparisons; Scheffe test
Mean
Dependent variable
(I)race
(J) race
difference
(I-J)
Malay
.670
Indian
-.493
.041
Chinese
Indian
-.628*
.003
Malay
Chinese
.714*
.004
Indian
.664
.053
Chinese
Indian
-.050
.981
Malay
Chinese
.736*
.008
Indian
-.042
.990
Indian
-.778*
.023
A42 (A woman‟s life is fulfilled only if
she can provide a happy home for her
.135
*
A33 (Respect for authority is important
in our society)
Chinese
sig
family)
A43 (It is wrong to have sex before
marriage)
Chinese
Based on table (6-6) Post-hoc comparisons using the scheffe test indicated that the mean score of
A33 for Indian group was significantly different from Malay group (MD=-.493, P= .041) and
Chinese group (MD= -.628, P= 0.003). From the Scheffe Multiple Comparison, also the mean
score of A42 for Chinese group was significantly different from Malay group (MD= .714, P=
.004). Finally, the scheffe test indicated that the mean score of A43 for Malay group was
significantly different from Chinese group (MD=.736, P= 0.008) and Indian group (MD= -.778,
P= 0.023).
48
Perform a correlation analysis on the following constructs/variables: satisfaction in life,
moral standards, religious inclination, and age. Discuss the results.
A correlation tells us how and to what extent two variables are linearly related. In correlation
analysis, all variable must be metric. A new variable (tot moral) which was the composite of:
A04 (I would rather work smart than work hard); A14 (Most people are trust worthy and honest);
A15 (I believe the end justifies the means); A31 (I believe that filial piety is very much alive in
our society); A33 (Respect for authority is important in our society); A38 (I listen to the advice
of my elders); A42 (A woman‟s life is fulfilled only if she can provide a happy home for her
family); A43 (It is wrong to have sex before marriage) was created. Also, another variable (tot
religious) which was composite of: A08 (I don‟t like to have an unlucky number for my house
or my car registration number); A19 (Relying on a bomoh or geomancy (fengsui) in choosing my
residence is wise); A30 (I believe religion is an important part of my life) was created. Likewise,
the variable of (tota sat) which was composite of: SAT01 (Money), SAT02 (Friends), SAT03
(Love affair/romance), SAT04 (Job (if you are working)), SAT05 (study), SAT06 (Relation with
parents), SAT07 (Leisure (entertainment)), SAT08 (Your physical appearance), SAT09 (Sexual
life or relation with the opposite sex), and SAT10 (Material comfort) was created.
Refer to the following table; we have 4 variables and 2 different correlation values to report in
this analysis.
49
Table 7 1 Correlation
tota_sat tot_moral tot_religious
Pearson Correlation
tota_sat
1
Sig. (2-tailed)
tot_moral
tot_religious
age
age
.209**
.036
.128
.010
.654
.113
N
154
152
154
154
Pearson Correlation
.209**
1
-.020
.056
Sig. (2-tailed)
.010
.756
.376
N
152
248
248
248
Pearson Correlation
.036
-.020
1
.061
Sig. (2-tailed)
.654
.756
N
154
248
250
250
Pearson Correlation
.128
.056
.061
1
Sig. (2-tailed)
.113
.376
.334
N
154
248
250
.334
250
*.Correlation is significant at the 0.01 level (2-tailed).
Table 7 2 summary of correlation table
tot_moral
tota_sat
Pearson Correlation
.209**
Sig. (2-tailed)
.010
N
152
50
Correlation
P-value
Based on table (7-2) the relationship between satisfaction in life (as measured by the tota_sat)
and moral standard (as measured by the tot_moral) was investigated using Pearson correlation
coefficient. Preliminary analyses were performed to ensure no violation of the assumptions of
normality, linearity and homoscedasticity. There was a low, positive correlation between the two
variables [r=.209, n=152, p<.01], with high levels of satisfaction in life associated with high
levels of moral standard.
According P-value = .01 the null hypothesis rejected and there were significance relationship
between satisfaction in life and moral standard. In conclusion, ethical people are more satisfy in
their life.
On the other hand, there were not relationships between other variables. Due to of P-value
which was > α, so null hypothesis did not reject. They were as follow;
Tota sat vs. tot religious ( p = 0.654 ), tota sat vs. age ( p = 0.113 ), tot moral vs. tot religious ( p
= 0.756 ), tot moral vs. age ( p = 0.376 ), tot religious vs. age ( p = 0.334 ).
51
Perform a regression analysis, Using the following variables:

Dependent variable = satisfaction in life

Independent variable = Moral standards, Religious inclination and Race.
All variables must be metric to run multiple regressions. Dummy variables were used for
respectively race variable from nominal scale to ratio scale.
Categorical
D1
D2
1. Malay
1
0
= Race _d1
D = K-1
2. Chinese
0
1
= Race_d2
2 = 3-1
3. Indian
0
0
K-1 = 2
Indian group regarded as reference group.
Then I ran direct method regression. In this method, all independent variables were included
simultaneously. The following tables were the output results of direct method regression:
Table 8 1 Descriptive Statistics
Mean
Std. Deviation
N
Tota_sat
41.9145
6.59715
152
race_d1
.3355
.47374
152
race_d2
.4803
.50126
152
tot_religious
10.1382
2.43879
152
tot_moral
28.6776
3.73032
152
52
Pearson
Correlation
Sig. (1-tailed)
Table 8 2 Correlations
Tota_sat
race_d1
1.000
.077
Tota_sat
race_d2
-.110
tot_religious
.044
tot_moral
.209
race_d1
.077
1.000
-.683
.132
.152
race_d2
-.110
-.683
1.000
.054
-.235
tot_religious
.044
.132
.054
1.000
.003
tot_moral
.209
.152
-.235
.003
1.000
Tota_sat
.
.173
.089
.295
.005
race_d1
.173
.
.000
.053
.031
race_d2
.089
.000
.
.256
.002
tot_religious
.295
.053
.256
.
.483
tot_moral
.005
.031
.002
.483
.
Table 8 3 Variables Entered/Removedb
Model
1
Variables Entered
tot_moral,
tot_religious, race_d1,
race_d2a
a. All requested variables entered.
b. Dependent Variable: Tota_sat
Variables Removed
.
Method
Enter
Table 8 4 Model Summary
Model
1
R
.223a
R Square
.050
Adjusted R Square
.024
Std. Error of the Estimate
6.51773
a. Predictors: (Constant), tot_moral, tot_religious, race_d1, race_d2
From the table (8-4) Model Summary, the adjusted R2 = 0.024. The strength of association in
multiple regressions is measured by the coefficient of multiple determination R2. R2 is adjusted
for the number of independent variable and sample size to account for diminishing returns.
53
Table 8 5 ANOVAb
Model
1 Regression
Residual
Sum of Squares
327.201
df
4
Mean Square
81.800
6244.687
147
42.481
6571.888
151
F
1.926
Sig.
.109a
Total
a. Predictors: (Constant), tot_moral, tot_religious, race_d1, race_d2
b. Dependent Variable: Tota_sat
Examine the ANOVA (table 8-5)for test of significant. P = 0.109. Do not reject H0. Insignificant.
Model
Table 8 6 Coefficientsa
Unstandardized Coefficients
Standardized
Coefficients
B
Std.Error
Beta
t
Sig.
6.392
.000
1 (Constant)
31.293
4.896
race_d1
-.109
1.576
-.008
-.069
.945
race_d2
-.949
1.504
-.072
-.631
.529
tot_religious
.130
.224
.048
.582
.561
tot_moral
.341
.146
.193
2.333
.021
a. Dependent Variable: Tota_sat
Based on table (8-6) Coefficients was used as a test for significance.
Tot moral was the most significance since its P < α.
Y = 31.293 + -0.109raced1 + -0.949raced2 + 0.130tot religious + 0.341tot moral
The finding shows that satisfaction in life is very much depend on moral standard. There was not
different between Malay and Chinese (race_d1 & race_d2) about tend to satisfy in life.
54
Assess the reliability of the satisfaction in life and moral standard scales. Can the reliability
of the scale be improved?
For the first step, I ran the reliability Analysis to check the satisfaction in life scales. The
following table (9-1) was the output;
Cronbach's Alpha
.779
Table 9 1 Reliability Statistics
Cronbach's Alpha Based on
Standardized Items
.794
N of Items
10
Cronbach‟s Alpha = 0.779 was reliable because was more than 0.6.
The correlation matrix demonstrating the simple correlation between all possible pairs of
variables including in the analysis. It constructed from the data obtained to understand
satisfaction in life is shown in the following table;
55
Table 9 2 Inter-Item Correlation Matrix
Variabl
e
SAT01
SAT02
SAT03
SAT04
SAT05
SAT06
SAT07
SAT08
SAT09
SAT10
SAT0
1
1.000
.419
.042
.530
.449
.194
.335
.376
.135
.502
SAT0
2
.419
1.000
.003
.592
.556
.402
.185
.312
-.009
.240
SAT0
3
.042
.003
1.000
.109
.116
.149
.057
.071
.328
.233
SAT0
4
.530
.592
.109
1.000
.551
.323
.319
.382
.085
.337
SAT0
5
.449
.556
.116
.551
1.000
.331
.217
.381
-.038
.411
SAT0
6
.194
.402
.149
.323
.331
1.000
.389
.455
.057
.290
SAT0
7
.335
.185
.057
.319
.217
.389
1.000
.344
.377
.334
SAT0
8
.376
.312
.071
.382
.381
.455
.344
1.000
.034
.408
SAT0
9
.135
-.009
.328
.085
-.038
.057
.377
.034
1.000
.190
SAT1
0
.502
.240
.233
.337
.411
.290
.334
.408
.190
1.000
SAT01 = Money
SAT02 = Friends
SAT03 = Love affair/romance
SAT04 = Job (if you are working)
SAT05 = Study
SAT06 = Relation with parents
SAT07 = Leisure (entertainment)
SAT08 = Your physical appearance
SAT09 = Sexual life or relation with the opposite sex
56
SAT10 = Material comfort
Table 9 3 Item-Total Statistics
Variable
Corrected ItemTotal Correlation
Squared Multiple
Correlation
Cronbach's Alpha
if Item Deleted
SAT01
.549
.450
.745
SAT02
.500
.484
.756
SAT03
.206
.198
.798
SAT04
.604
.504
.739
SAT05
.543
.462
.746
SAT06
.462
.372
.758
SAT07
.475
.374
.757
SAT08
.504
.350
.754
SAT09
.215
.283
.792
SAT10
.564
.389
.746
From the table (9-3), Corrected Item-Total Correlation describes the most important in the scale.
If we delete an item, alpha increase means that the item is not important. With delete the item,
alpha become reliable. So, among all satisfaction in life scale, deleting SAT03 (Love
affair/romance) will not change the Cronbach‟s Alpha = 0.779. Therefore, I can interpret that
SAT03 was the least important scale.
If we delete an item, alpha decrease means that item was very important. Delete the item, alpha
become less reliable. So, among all satisfaction in life scale, deleting SAT04 (Job) will decrease
the Cronbach‟s Alpha from 0.779 to 0.739. So, I can say that SAT04 was the most important
scale.
57
For the second step, I ran the reliability Analysis to check the moral standard scale. The
following table (9-4) was the output;
Cronbach's Alpha
.264
Table 9 4 Reliability Statistics
Cronbach's Alpha Based on
Standardized Items
.328
N of Items
8
Cronbach‟s Alpha = 0.264 not reliable because was less than 0.6.
The correlation matrix demonstrating the simple correlation between all possible pairs of
variables including in the analysis. It constructed from the data obtained to understand moral
standard is shown in the following table;
58
Table 9 5 Inter-Item Correlation Matrix
A04
A14
A15
A31
A33
A38
A42
A43
A04
1.000
.117
-.026
.037
.010
-.102
-.218
.004
A14
.117
1.000
.165
.072
.011
.115
-.020
.028
A15
-.026
.165
1.000
-.104
.037
.153
.060
-.174
A31
.037
.072
-.104
1.000
.275
.090
.062
.074
A33
.010
.011
.037
.275
1.000
.259
.194
.134
A38
-.102
.115
.153
.090
.259
1.000
.317
-.005
A42
-.218
-.020
.060
.062
.194
.317
1.000
.045
A43
.004
.028
-.174
.074
.134
-.005
.045
1.000
A04 = I would rather work smart than work hard
A14 = Most people are trust worthy and honest
A15 = I believe the end justifies the means
A31 = I believe that filial piety is very much alive in our society
A33 = Respect for authority is important in our society
A38 = I listen to the advice of my elders
A42 = A woman‟s life is fulfilled only if she can provide a happy home for her family
A43 = It is wrong to have sex before marriage.
59
Table 9 6 Item-Total Statistics
A04
-.066
Squared
Multiple
Correlation
.068
A14
.157
.065
.209
A15
.006
.095
.297
A31
.154
.098
.212
A33
.308
.161
.132
A38
.274
.172
.163
A42
.111
.153
.233
A43
.025
.055
.302
Variable
Corrected ItemTotal Correlation
Cronbach's
Alpha if Item
Deleted
.341
From the table (9-6), Corrected Item-Total Correlation describes the most important in the scale.
If we delete an item, alpha increase means that the item is not important. With delete the item,
alpha become reliable. So, in this table I had to drop A04 (I would rather work smart than work
hard) to improve the alpha from 0.264 to 0.341. However, Cronbach‟s Alpha was still lower than
0.6. Repeated the process of search item to delete until alpha had been improved to the level of
reliable.
60
Table 9 7 Item-Total Statistics
A14
Corrected ItemTotal Correlation
.118
Squared Multiple
Correlation
.051
Cronbach's Alpha
if Item Deleted
.323
A15
.015
.094
.386
A31
.146
.098
.308
A33
.318
.159
.220
A38
.327
.169
.229
A42
.207
.119
.264
A43
.024
.055
.407
Variable
So, based on table (9-7) the next to delete was A43 (It is wrong to have sex before marriage).
Dropped it will further improve alpha to 0.407. Cronbach‟s Alpha improves still unreliable.
Continue searching.
61
Variable
A14
Table 9 8 Item-Total Statistics
Corrected ItemSquared Multiple
Total Correlation
Correlation
.119
.047
Cronbach's Alpha
if Item Deleted
.406
A15
.108
.064
.423
A31
.126
.097
.401
A33
.286
.144
.312
A38
.378
.168
.274
A42
.213
.117
.354
So, based on table (9-8) the next to delete was A15 (I believe the end justifies the means).
Dropped it will further improve alpha to 0.423. Cronbach‟s Alpha improve still unreliable.
Continue searching.
Variable
A14
Table 9 9 Item-Total Statistics
Corrected Item-Total
Squared Multiple
Correlation
Correlation
.059
.020
Cronbach's Alpha if
Item Deleted
.476
A31
.196
.081
.381
A33
.314
.144
.299
A38
.355
.152
.285
A42
.217
.113
.379
So, based on table (9-9) the next to delete was A14 (Most people are trust worthy and honest).
Dropped it will further improve alpha to 0.476.
62
Variable
Table 9 10 Item-Total Statistics
Corrected ItemSquared Multiple
Total Correlation
Correlation
Cronbach's Alpha
if Item Deleted
A31
.186
.076
.481
A33
.352
.143
.338
A38
.343
.139
.360
A42
.263
.111
.442
Table (9-10) shows that no more deleting of any scales can improve the alpha, so I had to stop
deleting and accept Cronbach‟s Alpha = 0.476.
In conclusion, the satisfaction in life Cronbach‟s Alpha = 0.779 was reliable because was more
than 0.6. The moral standard scale Cronbach‟s Alpha = 0.476 was not as reliable as the
satisfaction in life scale.
Yes, the reliability of a scale can be improved by deleting the items that its presentation in the
analysis will increase the Cronbach‟s Alpha.
63
Factor analyzes the satisfaction in life scale. Are there dimensions in the scale?
Table 10 1 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity
Approx. Chi-Square
.778
454.267
45
df
.000
Sig.
The test of Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is an index that used to
examine the appropriateness of factor analysis. The high values (between 0.5 and 1.0)
demonstrate factor analysis is appropriate. Also, the value less than 0.5 imply that factor analysis
may not be appropriate.
Bartlett's Test of Sphericity is a test that used to examine the hypothesis that the variables are
uncorrelated in the population. On the other hand, the population correlation matrix is an identity
matrix; each variable correlates perfectly with itself (r = 1) but has no correlation with the other
variables (r = 0).
So, based on the table (10-1), KMO = 0.778. It was good means was sufficient and appropriate
for factor analysis.
Bartlett's Test of Sphericity = 0.000 was significant.
64
Table 10 2 Communalities
variable
Initial
Extraction
1.000
.508
1.000
.614
1.000
.395
1.000
.615
1.000
.612
1.000
.368
1.000
.479
1.000
.441
1.000
.684
1.000
Extraction Method: Principal Component Analysis.
.486
SAT01
SAT02
SAT03
SAT04
SAT05
SAT06
SAT07
SAT08
SAT09
SAT10
Communality is the amount of variance a variable shares with all the other variable being
considered. The higher more important and the lower meat delectable.
So, based on table (10-2) SAT09 (Sexual life or relation with the opposite sex) was the most
important variable while, SAT06 (Relation with parents) was the least important.
65
Table 10 3 Total Variance Explained
Component
t
Total
Initial Eigenvalues
% of
Variance Cumulative %
Extraction Sums of Squared
Rotation Sums of Squared
Loadings
Loadings
% of
% of
Total Variance Cumulative% Total Variance Cumulative %
1
3.730
37.301
37.301
3.730
37.301
37.301
3.499
34.990
34.990
2
1.473
14.726
52.026
1.473
14.726
52.026
1.704
17.036
52.026
3
.977
9.774
61.801
4
.916
9.157
70.958
5
.813
8.128
79.086
6
.517
5.171
84.257
7
.458
4.581
88.838
8
.444
4.445
93.283
9
.358
3.580
96.862
10
.314
3.138
100.000
Extraction Method: Principal Component Analysis.
The eigenvalues for the factors are in decreasing order of magnitude as we go from factor 1 to
factor 10. The eigenvalues shows the total variance attributed to that factor. Just factor with
eigenvalues greater than 1.0 are retained; the other factors are not included in the model.
Factors with variance less than 1.0 are no better than a single variable, because, due to
standardization, each individual variable has a variance of 1.0.
66
Figure 10 1 component number
A screen plot is a plot of the eigenvalues against the number of factors in order of extraction. The
share of the plot is used to determine the number factors.
67
Table 10 4 Rotated Component Matrixa
variable
Component
SAT01
1
.691
2
.174
SAT02
.770
-.147
SAT03
.017
.628
SAT04
.782
.059
SAT05
.781
-.044
SAT06
.568
.214
SAT07
.408
.560
SAT08
.641
.174
SAT09
-.049
.826
SAT10
.553
.424
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.
Unrotated factor matrix is difficult to interpret because the factors are correlated with many
variables. Through rotation, the factor matrix is transformed into a simpler one that is easier to
interpret.
So, from the table (10-4), there were 7 variables (Money, friends, job, study, relation with
parents, your physical appearance and marital comfort) correlated with component 1. I might
classify this group as social and materialistic.
68
Also, there were 3 variables (love affair/romance, leisure (entertainment), sexual life or relation
with the opposite sex) correlated with component 2. I might classify this group as sexual
satisfaction.
In summary, from the table (10-4) there were;
Factor 1
Money
(SAT01)
Friends
(SAT02)
Job
(SAT04)
Study
(SAT05)
Relation with parents
(SAT06)
Your physical appearance (SAT08)
Marital comfort
Factor 2
(SAT10)
Love affair/romance
(SAT03)
Leisure (entertainment)
(SAT07)
Sexual life or relation with the opposite sex
(SAT09)
69
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