Descriptive Statistics Quantitative Research Findings
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1 Test of Data Normality
I used Kolmogorov-Simirnov test from SPSS 21 to gain the normality of data distribution. If the significance value was more than 0,05 than the data had
normal distribution. If the significance value was less than 0,05, then the data did not have normal distribution. If the data had normal distribution than the statistical
analysis used was Pearson’s Product Moment Correlation for two variables correlation and Multiple Correlation for three variables correlation. The result of
the data normality test is shown in Table 4.3. Table 4.3
Normality Test for S tudents’ Learning Style x
1
, English Speaking Ability x
2
, and Academic Performance y
One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual
N 30
Normal Parameters
a,b
Mean .0000000
Std. Deviation 5.34510361
Most Extreme Differences Absolute
.118 Positive
.111 Negative
-.118 Kolmogorov-Smirnov Z
.648 Asymp. Sig. 2-tailed
.795 a. Test distribution is Normal.
b. Calculated from data.
In Table 4.3, the significance values of Kolmogorov-Smirnov Z is 0,65 and
Asymp. Sig. 2-tailed is 0,79. Both values are more than 0,05 meaning that data scores have normal distribution meaning that the data scores of the research
variables are able to be processed statistically using associative correlation of Pearson Product Moment Correlation for two variables and Multiple Correlation
for three variables in SPSS 21.
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2 Test of Data Linearity
The linearity test was used to find out if the independent and dependent variables in the research had significant linear correlation or not as the condition
of conducting correlation statistics which was employed in this research. If the result of significance value was less than 0,05 then there was linear correlation
between dependent and independent variables. To describe whether the correlation was linear or not, two ways of interpretation were provided. The first if
the significance value was less than 0,05, then the correlation was linear. On the other hand, if the significance value was more than 0,05, then the correlation was
not linear. The second one was by comparing the F-observed to the F-Table. If the F-observed was less than F-Table, then the correlation was linear. On the other
hand, if the F-observed was less than F-Table, then the correlation was not linear. The first one is the linearity test between
students’ learning style x
1
and academic performance y. The result of the calculation is shown in Table 4.4.
From Table 4.4, the significance value is 0,42 which means that the value is less than 0,05 so that the correlation is linear. The F-observed is 1,091. To find the F-
Table, I looked at the Distribution Table F Value for 5 margins of error. The deviation from the linearity in Table 4.4 means the degrees of freedom for
nominator in F-Table. Within the groups in Table 4.4 means the degrees of freedom for denominator. From Table 4.4, the deviation from the linearity is 10
and the deviation within the groups is 18. Based on this df, the F-Table shows 2,41 for the value. Then F-observed which shows 1,091 is less than F-Table. Then
the correlation is linear.
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Table 4.4 Linearity Test between S
tudents’ Learning Style x
1
and Academic Performance y
ANOVA Table
Sum of Squares Df
Mean Square
F Sig.
academi performance
learning styles
Between Groups
Combined 1844.033
11 167.639 1.621 .175
Linearity 716.301
1 716.301 6.928 .017
Deviation from Linearity
1127.732 10
112.773 1.091 .418 Within Groups
1861.167 18
103.398 Total
3705.200 29
The second one is the linearity test between the variables of English speaking ability x
2
and academic performance y. From Table 4.5, the significance value is 0,42 which is less than 0,05 so that the correlation is linear.
The F-observed is 1,172. To find the F-Table, I looked at the Distribution Table F Value for 5 margins of error. The deviation from linearity in Table 4.5 means
the degrees of freedom for nominator in F-Table. Within the groups in Table 4.5 means the degrees of freedom for denominator. From Table 4.5, the deviation
from linearity is 19 and the deviation within groups is 9. Based on this df, the F- Table shows 2,9 for the value. Then F-observed which shows 1,172 is less than F-
Table. Then the correlation is linear. Table 4.5
Linearity Test between English Speaking Ability x
2
and Academic Performance y
ANOVA Table
Sum of Squares df
Mean Square
F Sig.
academi performance
speaking ability
Between Groups
Combined 3466.533
20 173.327
6.536 .003 Linearity
2875.788 1
2875.788 108.445 .000 Deviation from
Linearity 590.746
19 31.092
1.172 .420 Within Groups
238.667 9
26.519 Total
3705.200 29
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Since the independent and dependent variables of the research have linear correlations, then the condition of applying correlation statistics is fulfilled.