Normality The Analysis of The Data and Hypotheses Testing 1. Item Test Analysis

of 35 = 0.224. It can be conclude that the pre-test of controlled class is normal, too. Table 4.2 Normality of Pre-test Using Lilliefors kelompok Kolmogorov-Smirnov a Shapiro-Wilk Statistic df Sig. Statistic df Sig. Nilai KWL Plus .136 35 .102 .948 35 .096 Jigsaw .130 35 .142 .965 35 .315 a. Lilliefors Significance Correction Besides using the normality test calculation of One-Sample Kolmogorov- Smirnov Test the writer also using Lilliefors test. It shows that the normality is significant too. It is shown the absolute difference D in Lilliefors table of KWL Plus Experiment Class data is 0.136 and the absolute different D in Lilliefors table of Jigsaw Controlled Class data is 0.130. Both absolute different D of Experiment class data and the absolute different D of Controlled Class data are much less than the calculation Kolmogorov-Smirnov table with critical points of 35 = 0.224. Table 4.3 Normality of Post-test Using One-Sample Kolmogorov-Smirnov Test KWL Plus Jigsaw N 35 35 Normal Parameters a,b Mean 72.1714 68.6857 Std. Deviation 9.99260 10.78062 Most Extreme Differences Absolute .106 .132 Positive .090 .125 Negative -.106 -.132 Kolmogorov-Smirnov Z .629 .780 Asymp. Sig. 2-tailed .824 .576 a. Test distribution is Normal. b. Calculated from data. As in the pre-test, the normality calculation of post-test above used One- Sample Kolmogorov-Smirnov Test too. From the table above, it is shown the absolute difference D of KWL Plus Experiment Class is 0.106. It is much less than the calculation in Kolmogorov-Smirnov table with the closest Kolmogorov- Smirnov critical points of 35 = 0.224. It is means the post-test of experiment class is normal. While the absolute difference D of Jigsaw Controlled Class data is 0.132. It is also much less than Kolmogorov-Smirnov table with the closest Kolmogorov-Smirnov critical points of 35 = 0.224. It is also means that the post- test of controlled class is normal distribution. Table 4.4 Normality of Post-test Using Lilliefors Kolmogorov-Smirnov a Shapiro-Wilk Statistic df Sig. Statistic df Sig. KWL Plus Class Experiment .155 35 .033 .949 35 .103 Jigsaw Class Controlled .140 35 .081 .931 35 .029 a. Lilliefors Significance Correction The writer using the Lillifors test for normality of post-test, it shows that the normality is significant too. It is shown the absolute D in the Lillifors table of KWL Plus Experiment class data is 0.155 and the absolute difference D of Jigsaw Controlled Class data is 0.140. Both absolute difference D of experiment and controlled class are much less than the calculation in Kolmogorov- Smirnov table with critical points of 35 at degrees significance 0.05 = 0.224. Hence, it can be conclude that the data is normal distribution.

b. Homogeneity

The calculation of homogeneity of pre-test and post-test was counted by using SPSS Statistic 18, too. The test would be homogenous if the calculation of test result is higher than the Kolmogorov-Smirnov table with the critical points of 35 = 0.224. Table 4.5 Pre-Test of Homogeneity of Variance Levene Statistic df1 df2 Sig. Nilai Based on Mean .062 1 68 .804 Based on Median .098 1 68 .755 Based on Median and with adjusted df .098 1 63.937 .755 Based on trimmed mean .095 1 68 .758 From the calculation table of homogeneity test of pre-test above, it can be conclude that all of degrees of significance are 0.804, 0.755, 0.755, and 0.758. These are higher than 0.05. So, it shows that both of the groups are homogenous. Table 4.6 Post-test of Homogeneity of Variance Levene Statistic df1 df2 Sig. Nilai Based on Mean 1.161 1 68 .285 Based on Median .900 1 68 .346 Based on Median and with adjusted df .900 1 67.973 .346 Based on trimmed mean 1.166 1 68 .284 From the calculation table of homogeneity test of pre-test above, it can be conclude that all of degrees of significance are 0.285, 0,346, 0.346, and 0.284. These are higher than 0.05. So, it shows that both of the groups are homogenous.

3. Analysis of Data

Hypothesis testing is the last step to find out the answer of hypothesis. From the hypothesis test, it will be found whether Know Want Learn Plus more effective to students than jigsaw. To analyze the hypothesis the writer used T-test formula as bellow: � = � 1 − � 2 �� � � −� 2 And the statistic calculation can be seen as follows: a. Determining Mean of Variable X Variable 1 � � = ∑� � 1 = 18.34 b. Determining Mean of Variable Y Variable 2 � � = ∑� � 2 = 13.6 c. Determining Standard Deviation of Variable X score ��� = � ∑� 2 � 1 = � 1683.886 35 = √48.1110286 = 6.9362114 d. Determining Standard of Deviation of Variable Y score ��� = � ∑� 2 � 2 = � 1190.4 35 = √34.0114286 = 5.83193181 e. Determining Errors Standard of Mean Variable X ���� = �� � �� 1 −1 = 6.9362114 √35−1 = 1.18955044 f. Determining Error Standard of Mean Variable Y ��� � = �� � �� 2 −1 = 5.83193181 √35−1 = 1.00016805 g. Determining Error Standard of the Difference between Mean Variable X and Mean Variable Y ��� 1 − � 2 = ���� 1 2 + ��� 2 2 = √1.18955044 2 + 1.00016805 2 = 1.5541449 h. Determining to � = � 1 −� 2 �� ��−�2 = 18.34−13.6 1.5541449 = 3.04990867 i. Determining Degree of Freedom �� = [� 1 + � 2 ] − 2 = [ 35 + 35] − 2 = 70 – 2 = 68

4. Hypotheses Testing

The research was held to answer the question whether Know Want Learn Plus technique more effective than jigsaw technique on students’ ability in reading analytical exposition text on eleventh grade students of SMAN 8 Tangerang Selatan. In order to provide answer for the question above, the Alternative Hypothesis � � and Null Hypothesis � were proposed as follows: a. � � Alternative Hypothesis: “The use of Know Want Learn Plus is more effective in teaching reading of analytical exposition text than the use of jigsaw in teaching reading of analytical exposition text.”