To make sure the result of the calculation above, the writer used SPSS program. The using of SPSS is to know whether the calculation that
the writer did manually was correct and to make sure that there is no mismatching calculation between scores that the researcher counted. The
result of SPSS was described such as follow:
Table 4.5 Pearson Product Moment Table
Reading Anxiety
Reading Pearson Correlation 1
-.761 Sig. 2-tailed
.000 N
119 119
Anxiety Pearson Correlation -.761
1 Sig. 2-tailed
.000 N
119 119
. Correlation is significant at the 0.01 level 2-tailed.
The results of those two calculations manual calculation and SPSS calculation are same, in which show the value of r
xy or
r
o
= -0.761. It means that there is no mismatch in the process of calculating the data by
doing manually or using SPSS program. d.
Analysis of Determinant Coefficient In addition, to know the percentage contribution of X variable to Y
variable, it can be found from coefficient of determination through this formula:
R = r
2
x 100
In which: R
= value of determinant coefficient r
2
= value of the squared correlation coefficient
R = -0.761
2
x 100 = 0.579 x 100
R = 57.9
It means that students’ anxiety contributes students’ English reading skill as much 57.9 and the remain about 42.1 was given by
other variables, for example stu dents’ motivation in learning English,
students’ physical situation—health or sick, their concentration in the class, background knowledge, etc.
2. Testing Hypothesis
Based on the calculation above, it shows that the correlation value is r
o
= -0.761 and df= 117. The writer found df with the formula: In which:
df = Degree of freedom N = Total number of respondents
df = N -2
= 119 – 2 = 117
In the table of significance shows if df value is 117, the table of significance 5 is -0.197.
-0.761
-0.197 0.197 0.761
Figure 4.1 Hypothesis Testing of Pearson Product Moment
In this section, when the r
o
result is negative, the hypothesis was interpreted using curve above.
1
As we can see in the figure 4.1, it shows
1
Jonathan Sarwono,
“Korelasi”, Buku Lepas, pp. 62-64, retrieved from http:www.jonathansarwono.infokorelasikorelasi.htm, at October, 26
th
2015, 11:45 PM.
Ho
is Rejected
where the area of Ho null hypothesis is accepted. In this case, because of
the result of r
o
is negative, so the hypothesis testing was done from the left side curve. The curve above showed that the r
o
is in the area where Ho is rejected and automatically Ha is accepted. Consequently, there are a
correlation between students’ anxiety and their English reading skill. Furthermore, it was also proved by the significant value and the hypothesis
showed like this:
a. If r
≥ r
table
in significant degree of 0.05, Ho null hypothesis is accepted. It means that there is no significant correlation between
students’ anxiety and their English reading skill of the tenth year student at SMK Negeri 3 Kota Tangerang.
b. If r
r
table
in significant degree of 0.05, Ho null hypothesis is rejected. It means that there is significant correlation between students’
anxiety and their their English reading skill of the tenth year at SMK Negeri 3 Kota Tangerang.
Based on the score of r
o
= -0.761, it indicated that the score of r
o
r
t
, in which -0.761 -0.197. It means that Ho null hypothesis is rejected. In other word, there is significant correlation between students’
anxiety and their achievement in learning English of the tenth year at SMK Negeri 3 Kota Tangerang.
In addition, the result of coefficient correlation was also compared to t
table
in order to find the significance of variables by calculating r
o
which is tested by significant test formula:
t
test
=
In which: t
test
r n
= t value = the result of correlation coefficient
= number of sample
t
test
=
=
=
=
t
test
= -12.64 It can be seen from that calculation of t value, the result of t
test
is - 12.65 and by df is 117, the t
table
in significant level of 5 is 1.980.
-12.65
-1.980 1.980 12.65
Figure 4.2 Hypothesis Testing of Ttest
Similar with the hypothesis testing of pearson product moment, this significant test also described using curve in Figure 4.2 because the
result of t
test
is negative. Consequently, the null hypothesis Ho is rejected due to the t
test
is in the area where the Ho is rejected. In other words, Ha is accepted and it means that there is significant correlation between
students’ anxiety and their English reading skill at SMK Negeri Kota Tangerang. In addition, if the hypothesis testing was proved by significant
value, the hypothesis should be like this:
Ho
is Rejected
a. If t
test
t
table
, the null hypothesis is rejected and there is significant correlation between two variables.
b. If t
test
t
table
, the null hypothesis is accepted and there is no significant correlation between two variables.
Along with the calculation above, t
test
is lower than t
table
-12.64 - 1.980. Hence the null hypothesis Ho is rejected. In other words, there is
significant correlation between students’ anxiety and their English reading skill at SMK Negeri Kota Tangerang.
The calculation above shows that the correlation of students’ anxiety variable X and their achievement variable Y in learning
English is significant with the high correlation value. The correlation value is -0.761. It can be seen on the table below:
Table 4.6 The Interpretation of Coefficient Correlation of ‘r’ Value
2
Coefficient Interpretation
0.00 - 0.199 There is a correlation between variable X and
variable Y, but the correlation is very low. It is considered that there is no significant
correlation in this rating. 0.20 - 0.399
There is a correlation between variable X and variable Y, but it is low.
0.40 - 0.599 There is a correlation between variable X and
variable Y. The value is moderate. 0.60 - 0.799
There is a high correlation between variable X and variable Y.
0.80 - 1.00 There is a very high correlation between
variable X and variable Y.
2
Sugiyono, Statistika untuk Penelitian, Bandung: Alfabeta, 2014, p. 231.
