Students’ Achievement of Extensive Reading

To provide an additional vivid description of the data distribution of Extensive Reading achievement, the histogram of frequency distribution is presented in figure 4.1 as follows: Figure 4.2 The Histogram of Data of Extensive Reading Achievement From the figure 4.2 above, the total number of students is 45 students. The students who got score 14, 70, 71, 72, 73 and 75 are one student for each, students who got score 76 score are 3 students, score 77 are 2 students, score 80 are 3 students, score 81 are 5 students, score 83 are 1 students, score 84 are 5 students, score 85 are 11 students, score 86 are 2 students, score 87 are 3 students, score 88 are 2 students and score 89 and 90 are one students for each. Apparently, Achievement of Extensive Reading Achievement of Extensive Reading from the symmetrical bell-shaped curve, it shows that the data of IQ is normally distributed. Furthermore, the statistics score of Extensive Reading achievement were counted using Frequencies of Descriptive Statistics in SPSS program in the 24.0 version. The purpose of counting the statistics score is to know the mean, median, mode, maximum and minimum score, and sum. The data is described as follows: Table 4.5 The Statistics Score of Extensive Reading Achievement Statistics Extensive Reading Achievement N Valid 45 Missing Mean 80.62 Median 84.00 Mode 85 Std. Deviation 11.302 Variance 127.740 Range 76 Minimum 14 Maximum 90 Sum 3628 From the descriptive statistics above, the respondents of this study are 45 students. The mean of Extensive Reading score is 80.62, which means that it is the average score obtained by the students. The median score of the Extensive Reading is 84.00. Then, the mode of score is 85, which means that most students obtained 85 in Extensive Reading achievement test. Then, the lowest score of writing is 14 while the highest is 90. Therefore, the range score between the highest and the lowest score is 76. In addition, the standard deviation of Extensive Reading score is 11.302 with variance 27.740. In Department of English Education, the score is characterized as follows: 3 80 – 100 = A 70 – 79 = B 60 – 69 = C 50 – 59 = D – 50 = E An “A” is characterized as an excellent score. It is the highest score and the students who obtained it, passed the test excellently. Then, “B” is characterized as a good score. Ne xt, “C” is characterized as a medium score, or the test takers passed but it is recommended to retake the test or have a remedial test. The last, “D” and “E” are characterized as a bad score or means that the test takers failed to pass the test.

B. Data Analysis

1. Analysis of the Linearity of Tests

The writer analyzed the linearity of the tests by using SPSS version 24.0. The linearity of the tests was checked in order to see whether the regression of relationship between two variables in linear. The result of analyzing the linearity of the tests is presented in ANOVA table as follows: Table 4.6 The Linearity Test Result of the Data 3 Pedoman Akademik Universitas Islam Negeri UIN Syarif Hidayatullah Jakarta 20122013, Jakarta: Biro Akademik dan Kemahasiswaan UIN Jakarta, 2012, p.39. ANOVA Table Sum of Squares Df Mean Square F Sig. Reading Achieve ment IQ Between Groups Combined 1194.108 10 119.411 .917 .529 Linearity 5.201 1 5.201 .040 .843 Deviation from Linearity 1188.907 9 132.101

1.01 5

.448 From the table above, the significance value of deviation from linearity was 0.448, in which the data distribution was good linear because the significance of deviation from linearity was bigger than 0.05 0.448 0.05. In addition, based on the F count = 1.015, and F.005 which counted using the formula in Microsoft Excel as follow: F table = F.INV.RTprobability,deg_freedom1,deg_freedom2 with the the df number in the table above showed that df 9.34, in which found that F table = 2.17. Because F count F table 1.015 2.17, in which the data was significant regression. Overall, the data revealed the Intelligence Quotient IQ and Extensive Reading achievement have linear regression.

2. Analysis of the Correlation Coefficient

After analyzing the linearity of the data, to measure the correlation coefficient, Pearson Moment Formula was used. The formula was used because the data distribution is linear. Before doing the calculation, the data is described as below: Table 4.7 The Data Analysis of IQ X and Extensive Reading Achievement Y Participants X Y XY X2 Y2 Student 1 116 83 9628 13456 6889 Student 2 81 76 6156 6561 5776 Student 3 109 81 8829 11881 6561 Student 4 113 84 9492 12769 7056 Student 5 113 76 8588 12769 5776 Student 6 100 87 8700 10000 7569 Student 7 113 81 9153 12769 6561 Student 8 116 14 1624 13456 196 Student 9 109 81 8829 11881 6561 Within Groups 4426.470 34 130.190 Total 5620.578 44 Participants Y X XY Y2 X2 Student 10 131 76 9956 17161 5776 Student 11 119 85 10115 14161 7225 Student 12 121 80 9680 14641 6400 Student 13 109 87 9483 11881 7569 Student 14 116 85 9860 13456 7225 Student 15 109 89 9701 11881 7921 Student 16 113 73 8249 12769 5329 Student 17 121 87 10527 14641 7569 Student 18 113 85 9605 12769 7225 Student 19 113 77 8701 12769 5929 Student 20 121 84 10164 14641 7056 Student 21 106 90 9540 11236 8100 Student 22 113 71 8023 12769 5041 Student 23 113 85 9605 12769 7225 Student 24 121 86 10406 14641 7396 Student 25 116 85 9860 13456 7225 Student 26 133 84 11172 17689 7056 Student 27 109 85 9265 11881 7225 Student 28 91 70 6370 8281 4900 Student 29 131 80 10480 17161 6400 Student 30 116 80 9280 13456 6400 Student 31 116 72 8352 13456 5184 Student 32 116 75 8700 13456 5625 Student 33 121 88 10648 14641 7744 Student 34 116 81 9396 13456 6561 Student 35 106 81 8586 11236 6561 Student 36 113 85 9605 12769 7225 Student 36 121 84 10164 14641 7056 Student 38 119 85 10115 14161 7225