Normality Test The Analysis of Data

Table 4.8 Homogeneity Pre-test and Post-test Result between Experimental Class and Control Class Pre-Test of Homogeneity of Variance Levene Statistic df1 df2 Sig. Value Based on Mean .477 1 58 .493 Based on Median .377 1 58 .542 Based on Median and with adjusted df .377 1 56.768 .542 Based on trimmed mean .459 1 58 .501 The table 4.8 shows that the significance of pre-test result between experimental class and control class are 0.493, 0.542, 0.542, and 0.501. Therefore, it can be concluded that the data of pre-test are homogeneous because all of the data result 0.05. Table 4.9 Post-Test of Homogeneity of Variance Levene Statistic df1 df2 Sig. Value Based on Mean .400 1 58 .530 Based on Median .309 1 58 .581 Based on Median and with adjusted df .309 1 52.144 .581 Based on trimmed mean .319 1 58 .575 The table 4.9 shows that the significance of post-test result between experimental class and control class are 0.530, 0.581, 0.581, and 0.575. Therefore, it can be concluded that the data of post-test are homogeneous because all of the data result are higher than 0.05. So, it shows that both of the groups are homogeneous. Based on the pre-requisite test statistical analysis, found that the data normally distributed and homogeneous. Therefore, next, the data was analyzed by using T-test formula. This technique is useful to prove statistically whether there is any significant difference between students’ vocabulary mastery achievement in experimental and control class. The experimental class was X variable and the control class was Y variable. Before analyzing the data, the following table is the recapitulation of the data which describes the comparison between the experimental class and control class: Table 4.10 The Comparison Score between Experimental Class and Control Class Students’ Number Experimental Class X Control Class Y x X – MX y Y – MY x 2 y 2 1 25 5 6.9 -4.8 47.61 23.04 2 10 5 -8.1 -4.8 65.61 23.04 3 30 25 11.9 15.2 141.61 231.04 4 15 -3.1 -9.8 9.61 96.04 5 15 15 -3.1 5.2 9.61 27.04 6 15 -3.1 -9.8 9.61 96.04 7 25 10 6.9 0.2 47.61 0.04 8 15 10 -3.1 0.2 9.61 0.04 9 25 6.9 -9.8 47.61 96.04 10 30 10 11.9 0.2 141.61 0.04 11 20 10 1.9 0.2 3.61 0.04 12 15 30 -3.1 20.2 9.61 408.04 13 20 25 1.9 15.2 3.61 231.04 14 15 10 -3.1 0.2 9.61 0.04 15 10 5 -8.1 -4.8 65.61 23.04 16 20 -5 1.9 -14.8 3.61 219.04 17 15 15 -3.1 5.2 9.61 27.04 18 5 -5 -13.1 -14.8 171.61 219.04 19 10 -13.1 0.2 171.61 0.04 20 20 1.9 -9.8 3.61 96.04 21 20 15 1.9 5.2 3.61 27.04 22 25 15 6.9 5.2 47.61 27.04 23 25 25 6.9 15.2 47.61 231.04 24 20 1.9 -9.8 3.61 96.04 25 20 10 1.9 0.2 3.61 0.04 26 15 10 -3.1 0.2 9.61 0.04 27 10 15 -8.1 5.2 65.61 27.04 28 25 6.9 -9.8 47.61 96.04 29 30 10 11.9 0.2 141.61 0.04 30 10 20 -8.1 10.2 65.61 104.04 Total 545 295 7 1 1418.3 2424.2 In order to get the calculation of T-test, there are several steps to be taken; determining Mean, Standard Deviation and Standard Error from each variable. The following steps describe as follows: 1. Determining Mean of Variable X: ∑ 2. Determining Mean of Variable Y: ∑ 3. Determining Deviation Standard of Score of Variable X: √ ∑ √ √ 4. Determining Deviation Standard of Score of Variable Y: √ ∑ √ √ 5. Determining Standard Error of Mean of Variable X: √ √ √