From the table and chart above, it showed that the average score of pre- test was 19.09 and the average of post- test was 78.08. The minimal score in pre- test
was 3; while in post- test was 30. Further, the maximal score in pre- test was 36; while in post- test was 100. The quartile scores Q1, Q2, and Q3 was 13, 20, and
23 for pre- test, and 61.5, 86, and 93 for post- test. Therefore, it also could be seen clearly an improvement between pre- test and post- test on the boxplot chart.
b. Assumption Test
Before the quantitative data on this research were statistically analyzed, the data were assumption tested to determine whether parametric or non-parametric
statistic was used. The parametric statistic had some requirements to fulfill, such as test of data normality, data homogeneity, and data linearity.
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In this part, the researcher shows the result of those tests.
1. Test of Data Normality In the test of data normality, the sample of this research was tested in order to
know whether the sample came from a normally distributed population or not. In testing the normality of data, the researcher used SPSS Statistical Product and
Service Solution to make easier and less time consuming. There were some ways in testing the data normality.
There are three ways in testing the data normality. They are Skewness value, histogram display normal curve, and P-plot normal curve. In this research, the
Skewness value was chosen in testing the data normality by following these steps: a. choose analyze
b. choose descriptive statistic c. choose descriptive
d. choose names of variable and put them on variable column e. choose option
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Budi Susetyo, Statistika untuk Analisis dan Penelitian, Bandung: Refika Aditama, 2010, p. 138
f. choose skewness and kurtosis g. choose continue
h. choose OK After following those steps, the result of testing data normality was as
following:
Table 4.2 Descriptive Statistics for Normality Test
The normality of data can be seen from the value of Skewness. If the value of Skewness is about 0 zero, the data can be categorized as a normal data because a
curve has normal curve.
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From the result of testing of normality data above, the values of Skewness were 0,196 and -0,766. They were about 0 zero. Hence, it could be concluded
that the data came from the normally distributed population. 2. Test of Data Homogeneity
The test of data homogeneity was conducted in order to know whether the data in this research had same variance or not. In other words, whether the data
used in this research had same characteristic or not. In testing the data homogeneity, the researcher also used SPSS Statistical
Product and Service Solution to help the steps easier. The steps were: a. choose analyze
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Budi Susetyo, op.cit, p. 272 N
Minimum Maximum
Mean Std. Deviation
Skewness Kurtosis
Statistic Statistic
Statistic Statistic
Statistic Statistic Std. Error Statistic Std. Error
pretest 37
3 36
19.09 6.967
.196 .388
.251 .759
posttest 37
30 100
78.08 18.024
-.766 .388
-.181 .759
Valid N listwise
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b. choose compare means c. choose one way anova
d. fill dependent list column and factor column e. choose option
f. mark on descriptive and homogeneity of variance test g. choose OK
Then, the result of testing data homogeneity was as following:
Table 4.3
The data was categorized as homogeneous data if the significant value was more than 0.05 and the F-value was less than the F-table. In this research, from the
table above, the significant value was 0.773 and the F-value was 0.667. This data was categorized as homogeneous data because the significant value was more than
0.05 and the F-value was less than the F-table, 2.27. 3. Test of Data Linearity
The data in this research was linearity tested in order to know whether the data were linearly correlated or not. The researcher used SPPS Statistical Product and
Service Solution in calculating the data linearity by following these steps: a. choose analyze
b. choose compare means c. choose means
d. choose dependent and independent variable e. choose option
f. on statistic for first layer, choose test for linearity
ANOVA Table of Homogeneity Test
Pretest Sum of Squares
Df Mean Square
F Sig.
Between Groups 478.519
13 36.809
.667 .773
Within Groups 1268.677
23 55.160
Total 1747.196
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