62. 98
58 9604
3364 5684
63.
96 60
9216 3600
5760
64.
95 62
9025 2704
5890
65.
93 50
8649 2500
4650
∑ 6955 ∑ 3690
∑ 748251 ∑ 213284
∑398406
B. Data Analysis
After the calculation of whole data from variable X and variable Y, the next step is to insert the data from the table into the Pearson’s Product moment
formula to find the correlation index as follows:
= .
∑ −
∑ ∑
N.
∑ −
∑
N.
∑
Y
− ∑
= 65.398406
−
6955. 3690 65.748251
−
6955 65.213284
−
3690
=
.
=
.
=
√
= = 0.909
The last step is determining Degree of freedom df df = N – nr = 65 – 2 = 63
df = 63 see table of “r” values of degree of significance 5 and 1 At the degree of significance 5 = 0.250
At the degree of significance 1 = 0.325 5 = ro : rt = 0.91 : 0.250
1 = ro : rt = 0.91 : 0.325
C. The Test of Hypothesis
To prove the result of hypothesis, the writer calculates the obtained data by using Pearson’s coefficient of correlation or “Product Moment” as follows:
1. Formulation the alternative hypothesis Ha: there is a significant correlation between variable X and variable Y.
2. Formulation the null hypothesis Hо: there is not a significant
correlation between variable X and variable Y. From the formulation above, the writer followed some assumptions as
below: 1. If the result of calculation r
o
is lower than
r
t
r table r
o
r
t
, the null hypothesis Hо is accepted, and alternative hypothesis Ha is
rejected. 2. If the result of calculation r
o
is bigger than r
t
r table r
o
r
t
, the null hypothesis Hо is rejected, and alternative hypothesis Ha is
accepted. Based on the description of calculation above, the result of this research is
r
o
is bigger than r
t
r table r
o
r
t
, so the null hypothesis Hо is rejected, and
alternative hypothesis Ha is accepted.
D. Data Interpretation
After the writer proceeded the formula, as it had been found out about the result of correlation, the next step is to give the interpretation of “r” score r
xy
. 1.
From the data of students’ IQ score and their English score, it appeared that the correlation index between variable X and variable Y is 0.909. It means
there is positive correlation between two variables. To give simple interpretation toward the correlation index “r” Product moment r
xy
can be done by following table:
Table 4.4 Interpretation of Product Moment Score
“r” Score of Product Moment rxy
Interpretation
0.0
– 0.20
0.20 – 0.40 0.40 – 0.70
0.70 – 0.90 0.90 – 1.00
There is no correlation Very low There is low correlation Low
There is medium correlation Enough There is strong correlation High
There is very strong correlation Very high
Looking at the score r
xy
= 0.909 that the score approximately between 0.90 – 1.00 is very strong correlation or high correlation or it means there is significant
correlation between variable X and variable Y. 2.
The writer used the interpretation with table of value “r” : df = N – nr = 65 – 2 = 63. Looking at the table of significance of 5 in
r
table
= 0.250, and 1 = 0.325 because r
xy
on the table of significance of 5 is bigger than
r
table
0.909 0.2
50, so on the table degree of significance of 5 the null hypothesis Hо is rejected but the alternative hypothesis Ha is accepted. So, it means on the
degree of significance 5 there is a significant correlation between variable X and variable Y. Then, because on the degree of significance 1 r
xy
is bigger than
r
table
0.909 0.325 so on the degree of significance 1 the null hypothesis Hо is rejected but the alternative hypothesis Ha is accepted. So,
it means on the degree of significance 5 there is a significant correlation between variable X and variable Y.
From the calculation of estimation above, it concludes that there is very strong correlation between students’ Intelligence Quotient and their achievement
in learning English, and hypothesis of the research is accepted. It means that between both variables have correlations.
E. Discussion
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