40
For α = 5 and number of subject 15, the r
11
is 0.646 and r
table
0.514. because r
11
is higher than r
table
r
11
r
table
, then the instrument is reliable. see the whole computation on appendix 6
4.2 Result
4.2.1 Mean of experimental Group
First, I calculated students’ score of experimental group to compute the mean of experimental group using the formula as stated by Arikunto 2002: 236.
The scores distribution of the experimental group can be seen in appendix 7. The mean score computation of experimental group was calculated as follows:
8182 .
11 33
. 390
= =
= ∑
= Nx
X Mx
The mean score of experimental group was 11.8182.
4.2.2 Mean of control Group
Second, I computed the mean of control group. Nevertheless, I had to calculate the students’ scores of the control group can be seen in appendix 7. The
Mean computation of the control group was calculated as follows:
06061 .
5 33
167 =
= =
∑ =
Ny y
My
41
Nc Xc
Xc ∑
= The mean score of control group was 5.06061. After calculating the Mean
of the control group and experimental group, I calculated the deviation of each group.
The computation of the deviation of the experimental group:
1153 09
. 4609
5762 33
390 5762
2 2
2 2
= −
= −
= ∑
− ∑
= ∑
Nx x
X x
The deviation of the experimental group is 1152.91
The computation of the deviation of the control group:
88 .
1339 121
. 845
2185 33
167 2185
2 2
2 2
= −
= −
= ∑
− ∑
= ∑
Ny y
y y
The deviation value of the control group is 1339.88
4.2.3 Difference between Two Means
I computed the difference between two means using the following formula according to Arikunto 2002: 264:
Ne Xe
Xe ∑
= The Means of the experimental group on Post-test
42
6061 .
65 33
2165 =
= ∑
= Nc
Xc Xc
303 .
72 33
. 2386
= =
∑ =
Ne Xe
Xe
Whereas, the Means of the control group on post- test
From the calculation, the Mean of the experimental was 72.30 and the Mean of the control group was 65.61 so the Means of the two groups were not
different from each other. The Mean of the control group was lower than the Mean of the experimental group. However, I could not conclude that the
difference between the two Means was significant. Therefore, to determine whether the difference between the two means was statistically significant, I
applied the t-test formula. The formula is as follow:
⎥ ⎥
⎦ ⎤
⎢ ⎢
⎣ ⎡
Ν +
Ν ⎥
⎦ ⎤
⎢ ⎣
⎡ −
Ν +
Ν Σ
+ Σ
− =
y x
Y X
2 2
Y X
1 1
2 x
M M
t
1
y
43
39825 .
4 54
. 1
76 .
6 36059
. 2
76 .
6 33
2 64
2493 76
. 6
33 1
33 1
2 33
33 1340
1153 06
. 5
82 .
11
= =
= ⎭
⎬ ⎫
⎩ ⎨
⎧ ⎭
⎬ ⎫
⎩ ⎨
⎧ =
⎭ ⎬
⎫ ⎩
⎨ ⎧ +
⎭ ⎬
⎫ ⎩
⎨ ⎧
− +
+ −
= t
To interpret the t obtained it should be consulted with the critical value in the t-table, I used the 5 0.05 level of significance. If the t-value is higher than
t-table means that there is significant difference between the two means. Contrary, if the t-value is lower than t-table means that there is no significant difference
between two means. While t-table at Nx + Ny – 2 = 33 + 33 – 2 = 64 is 2.00 it means that t-
calculation is higher than t-table. The number of subjects in this study for experimental and control groups
were 66 with degrees of freedom df = 64, that was Nx + Ny – 2 = 64. At the 5 0.05 alpha level of significance, t-value that was obtained is 4.399 and t-table
was 2.00 so the t-value is higher than t-table means that there is significant difference between two means.
44
4.3 Test of Significance
