samples of research. The students in class XI A was selected to be the experimental group, and class XI B was taken as the control group.
3. Post-test Result
Post-test was administered on August 27
th
, 2014 to 50 samples. After gathering the data of post-test scores, similar statistical analysis as pre-test
was also accomplished. Beside the calculation on normality, homogeneity, and independent t-test , the effect size was also employed to discover at what
value style writer computer software affects student’s writing score.
Normal Distribution Test
First step taken was quantifying the normality test by utilizing Kolmogorov-Smirnov test. The hypotheses proposed were the null and
alternative hypothesis.
Table 4.5 Tests of Normality
Kolmogorov-Smirnov Shapiro-Wilk
Statistic Df
Sig. Statistic
df Sig.
VIIIA VIIIB
.159 .149
25 25
.102 .160
.936 .936
25 25
.122 .117
In the test, the level of significance was set up at 0.05. as presented in table 4.5., the asymp.sig of post-test scores is 0.160 and experimental group
is 0.102. Both of the data are higher than the level of significance 0.05, or 0.1600.05 and 0.1020.05. it suggests that the null hypothesis is not rejected
and alternative hypothesis is rejected. The data of control and experimental group are normally distributed.
Homogeneity of Variance
Second, the homogeneity test was based on the hypothesis posed in thisanalysis. The result of calculation is presented on the table below.
Table 4.6 Test of Homogeneity of Variance
Levene Statistic df1
df2 Sig.
POSTEST Based on Mean Based on Median
Based on Median and with adjusted df
Based on Trimmed Mean .108
.115 .115
.101 1
1 1
1 48
48 47.9923
48 .743
.736 .736
.751
The level of significance of this test was established at 0.05. Moreover, table 4.6 above shows that the asymp.sig is 0.743 that is greater than 0.05
0.7430.05. it indicates that the null hypothesis is not rejected and alternative hypothesis is rejected. It means that there is no difference of
variance scores between the control and the experimental group.
Independent t-test
The answer of the first research problem would be shown from the result of the calculation of independent t-test on post-test data. This test
established null hypothesis and alternative hypothesis as the tentative statement. The null hypothesis announces that there is no significant
difference between the mean of control and experimental group’s scores. Moreover, the alternative hypothesis reveals the means of score between the
two groups that are significantly different. The table below is the result of the statistical calculation.
Tabel 4.7 Independent Samples Test
Levene’s Test for Equality of
Variances t-test for Equality of Means
F Sig.
t Df
Sig 2-
tailed Mean
Difference Std. Error
Difference 95 Confidence
Interval of the Difference
Lower Upper
POSTEST Equal
Variances assumed
Equal Variances not
Assumed .108
.743 2.753
2.753 48
47.573 .008
.008 1.60000
1.60000 .58126
.43102 .43129
.43102 2.76871
2.76898
This test is established the level of significance in 0.05 and df = 48. Meanwhile, table 4.7 above informs that the significance value is lower than
0.05, 0.0080.05. regarding to this finding, it discovers that the null hypothesis is rejected, , but alternative hypothesis is not rejected. It affirms
that there is a difference in mean of post-test scores between the experimental and control groups.
In accordance with the result of normality, homogeneity, and independet t-test on post-test scores above, it is noticeable that after the
treatments, the scores of writing argumentative text in experimental group were improved. Therefore, a significant difference appeared between the
means scores of experimental and control groups. In other words, style writer computer software improved students’ ability in revising argumentative text
result. In order to find out whether style writer computer software affected
students’ ability in revising argumentative text, the calculation of effect size
was conducted. The calculation was performed manually by using the following formula developed by Coolidge.
1
The t refers to the t value obtained from the independent t-test calculation on post-test data. Afterward,
the df is the amount of samples minus by 2 df = N-2 √
Derived from table 4.7, t value is 2.753 and df is 48. Hence, after completing the computation, it is found that r value is 0.369. converted to the
effect size table see table 3.1, the obtained value shows medium effect size.
4. The Paired t-test Analysis on Experimental Group Scores
A paired t-test was conducted to discover the differences in experimental group score before and after the students were given the
treatments. The calculation of paired t-test was used to analyze the score of the experimental and control groups.
Table 4.8 Paired Samples Statistics Pre-Test and Post-Test Experimental
Group
Mean N
Std. Deviation Std. Error Mean
Pair 1 PRETEST POSTEST
12.0400 13.6400
25 25
2.38886 1.95533
.47777 .39107
1
Frederick L. Coolidge, Statistic A Gentle Introduction. London: Sage Publications Ltd., 2000 p. 105
