Table 3.10 Score Category
Grade Category
Score
1 Failed
30-39 2
Lack 40-55
3 Good Enough
56-65 4
Good 66-79
5 Very Good
80-100 Arikunto , 2013: 281
To find out the mean of the results of pre-test and post-test, I used the formula:
Where, M : mean
: the sum of the item score N : the number of the students
Tuckman, 1978: 249
3.9.1.1 Normality Test of Pre-test and Post-test Data
This test was used to know whether the data that would be analyzed have normal distribution or not. I used the chi- square formula.
k i
i i
i
E E
O
1 2
Where:
2
= chi square
i
O = observation frequency
i
E = expected frequency k = the numbers of interval class
i = 1,2,3,....,k Sudjana, 2010: 145
Therefore, to make easier in calculation I used SPSS to analyze the result of pre-test and post-test. I used chi-square in SPSS. Brown 2005:121 explains that
in normal distribution have five categories score:
Categories Percent
Very low 2
Low 14
Medium 68
High 14
Very high 2
Table 3.11 Criteria Score of Normal Distribution Data Brown, 2005:121
The steps in analyzing normality test by using SPSS are as follows:
Step 1: 1 Click the Analyze- Descriptive Statistics-Frequently
2 Input the data 3 Click statistics
4 Click percentile 5 Input the value of percentile 2,16,84 and 98
Step 2: 1 Click Transform-Recode into different Variables
2 On the Name box , write Category then click change 3 Click Old and new Values
4 Click range : Based on Category Score before
Lowest through - values 1 then click add
… through … - value 2, click add
… through …- value 3, click add
… through … -value 4, click add
… through highest –value 5, click add Step 3:
1 Normal Table Distribution
Table 3.11 Percentile Score Category 2 Click Analyze-Non Parametric Test-Chi-Square
3 Input Code into Test Variable List 4 Input 1, 5, 25, 5 and 1
Category Percentage
N=37
Very Low 2
1 Low
14 5
Medium 68
25 High
14 5
Very High 2
1 Total
100 37
Percentile Score Category
5 Click OK
Widhiarso, 2008:1-5
Criterion For α= 5 and df= k-1, if P
value
= 0.156 α α =0.05, the data is normally
distributed.
3.9.1.2 Test of Significance
To know whether the means of pre-test and post-test were statistically significant, the t-value should be obtained and consulted with the critical value in t-table. The
writer used the t-value formula:
√ In which,
t = t-value
M
d
= mean from different score between pre-test and post-test x
d
= deviation of each subject d-M
d
∑x
2
d = total deviation square N
= number of subject df
= N-1 Arikunto 2013: 349-350
Criterion There is a significant difference between pre-test and post-test if t t
table
3.9.2 Data Interpretation
