6 23
32 7
39 40
8 43
32 9
46 44
10 48
46 11
54 42
12 58
44 13
59 58
14 60
52
Total Mean
Median Mode
636 45.50
44 44
The table 4.3 represents score of auditory learners. It shows that the total reading score of ETIS of English Education students is 636. Then, the mean of the
score is 45.50. Further, the median of the visual learners score is 44. In the same way with the median, the mode of the reading score is 44. The data point that the
highest reading score of auditory learners is 76 and the lowest reading score is 28.
Table 4.4 Reading Comprehension Scores of Kinesthetic Learners
Kinesthetic Learning Style No
Students Reading Comprehension Score
1 1
68 2
4 42
3 6
54 4
7 40
5 11
42 6
12 54
7 13
60 8
15 42
9 16
52 10
19 38
11 20
28 12
22 60
13 26
34 14
27 48
15 29
50 16
30 52
17 31
36 18
32 26
19 33
58 20
34 30
21 35
34 22
36 30
23 37
40 24
40 60
25 42
48 26
44 44
27 45
46 28
50 42
29 52
58 30
53 48
31 56
50
Total Mean
Median Mode
1414 45.62
46 42
On the table 4.4, the highest reading score of kinesthetic learners is 68 and the lowest reading score is 26. The total score of reading of ETIS from English
Education students is 1414. Then, the mean of the score is 45.62. Further, the median of the visual learners score is 46. Additionally, the mode of the reading
score is 42. As the result, from three tabl
es of learning styles’ group and reading comprehension above, the data reveals that highest score of reading
comprehension is 76 and it is obtained by auditory learner group. For the lowest score is 26, it comes from kinesthetic learners. Besides that, the highest total score
and mean among three groups of learning styles come from kinesthetic learner group, 1414 and M = 45.62.
Additionally, the total frequency of students’ reading comprehension score from the entire groups is also analyzed by using
SPSS. The data reports that the mean score of students’ reading comprehension is
45. 37. The median score is 44 and then the mode score of the data score is 42. The detail of reading comprehension mean, median and modus score can be seen
on the appendix.
B. Data Analysis
1. The Assumption Test
This part presents the data analysis from the result of assumption tests, normality and homogeneity test. Assumption test is important to be conducted in
parametric study because both of tests are shown whether the population of the study has been distributed normal and homogenous or not.
a. Normality Test
The test used to know whether the data in this study normally distributed or not. To analyze the normality test was used Kolmogorov-Smirnov test with
ɑ = 0.05. It was used Kolmogorov-Smirnov test due to the sample of the study is 50. And the data was analyzed by using SPSS 20.0. The detail of
the results is reported on Appendix. Nevertheless, the short result of normality test shown below:
Table 4.5 Tests of Normality
Kolmogorov-Smirnov
a
Shapiro-Wilk Statistic
df Sig.
Statistic df
Sig. Score
,083 60
,200 ,976
60 ,278
. This is a lower bound of the true significance. a. Lilliefors Significance Correction
From the result above, it can be seen that the data are categorized normally distributed because the value of significant is higher than 0.05. The
significant value is 0.83, Sig. = 0.83 0.05, hence it can be conclude that all the data normal distributed. As supported by Syofian Siregar statement about
the degree of normality test: Hypothesis:
H ˳ : Population are normally distributed
H ₁: Population are not normally distributed randomly distributed
Criteria of normality test are: H
˳ is rejected if probability of significant value 0.05 H
˳ is accepted if probability of significant value 0.05
1
Additionally, to visualize the normality of data, it was provided a curve P-plots. It was clearly seen that the data were normally distributed, as asserted
by Budi Susetyo that the data were normally distributed or close to normally distributed if the picture of dots on P-plots curve spread around the diagonal
line and the spreading of the dots has the same direction with the diagonal line.
2
Then, Budi Susetyo also stated that to visualize the data are normally distributed or not. It can be used histogram and if the curve on the histogram
1
Syofian Siregar, Statistika Deskriptif untuk Penelitian, Jakarta: Rajawali Press, 2011, p. 256.
2
Budi Susetyo, Statistika untuk Analisis Data Penelitian, Bandung: Refika Aditama, 2010, p. 275.
shaped like a bell, it is clearly stated that the data are normally distributed. See Appendix
b. Homogeneity Test
After tested the normality, the homogeneity test was also analyzed. The test was conducted for recognizing that the variances of data are homogenous
or not. Homogenous means the data have the same characteristics. To analyze the homogeneity test, this study used Levene statistics technique. It also
calculated by using SPSS 20.0. The result of homogeneity test can be seen as follow:
Table 4.6 Test of Homogeneity of Variances
ReadingScore Levene Statistic
df1 df2
Sig. ,134
2 57
,875
Consequently, from the result of homogeneity presented above, we can reveal that the variance of the data is homogenous. Budi Susetyo said that
data are homogenous if the significance value is greater than the alpha value 0.05. Therefore, it can be stated that H
˳ is accepted and the variance of data are homogenous with the sig. 0.875 which clearly seen that it higher than
0.05. Hypothesis:
H ˳ : The variances of the data are homogenous
H ₁: The variances of the data are not homogenous
The criteria of homogeneity test: H
˳ is rejected if significant sig. value 0.05 H
˳ is accepted if significant sig. value 0.05
