Statistical Hypothesis Test of Hypothesis

1. Intelligence Quotient IQ

These are the score of IQ gotten from the IQ test conducted by the PLP institution. Table 4.1 The Result of IQ Test Students’ Number Intelligence Quotient X Classification Student 1 116 High Average Student 2 81 Low Average Student 3 109 Average Student 4 113 High Average Student 5 113 High Average Student 6 100 Average Student 7 113 High Average Student 8 116 High Average Student 9 109 Average Student 10 131 Superior Student 11 119 High Average Student 12 121 Superior Student 13 109 Average Student 14 116 High Average Student 15 109 Average Student 16 113 High Average Student 17 121 Superior Student 18 113 High Average Student 19 113 High Average Student 20 121 Superior Student 21 106 Average Student 22 113 High Average Student 23 113 High Average Student 24 121 Superior Students’ Number Intelligence Quotient X Classification Student 25 116 High Average Student 26 133 Superior Student 27 109 Average Student 28 91 Average Student 29 131 Superior Student 30 116 High Average Student 31 116 High Average Student 32 116 High Average Student 33 121 Superior Student 34 116 High Average Student 35 106 Average Student 36 113 High Average Student 36 121 Superior Student 38 119 High Average Student 39 109 Average Student 40 119 High Average Student 41 106 Average Student 42 100 Average Student 43 109 Average Student 44 100 Average Student 45 119 High Average In addition, to describe the result of IQ test —CFIT test, the classification of IQ which created by Stanford-Binet was used in this study. In other words, the CFIT itself is the IQ test which is referred to the Standford-Binet theory. Therefore, the table below describes the descriptive classifications of Intelligence Quotient: 2 2 Lester D. Crow and Alice Crow, Educational Psychology, New York: American Book Company, 1958, p. 156. Table 4.2 Classification of Intelligence Quotient IQ Classification IQ Near genius or genius 140 and above Very superior 130 – 139 Superior 120 – 129 Above average 110 – 119 Normal or average 90 – 109 Below average 80 – 89 Dull or borderline 70 – 79 Feeble-minded: moron 50 – 69 Imbecile, idiot 49 and below Adopted from Standford-Binet theory To provide an additional vivid description of the data distribution of IQ, the histogram of frequency distribution is presented in figure 4.1 as follows: Figure 4.1 The Histogram of Data of Intelligence Quotient IQ From the figure 4.1 above, the total number of students are 45 students. The students who got score 81 and 91 are 1 student for each, students who got score 100 and 106 score are 3 students, score 109 are 7 students, score 113 are 9 students, score 116 are 8 students, score 119 are 4 students, score 121 are 6 students, score 131 are 2 students and score 133 is 1 students. Apparently, from the symmetrical bell-shaped curve, it shows that the data of IQ is normally distributed. Intelligence Quotient IQ Intelligence Quotient IQ Then, to count the statistics score of IQ, the writer used 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 4.3 The Statistics Score of Intelligence Quotient IQ From the descriptive statistics above, the respondents of this study is 45 students. The mean of IQ score is 133.22, which means that it is the average students could be classified as above average IQ. The median score of IQ is 113.00. Then, the mode of score is 113, which means that most students have 113 score in IQ test. The lowest score of IQ is 81 while the highest is 133, so the range score is 52. In addition, the standard deviation score is 9.349 and the variance is 89.086. Based on description above, it can be concluded that the seventh semester students of Department of English Education have the high average IQ score. It can be seen that most of students obtained score 113, in which score 113 is classified as high average IQ. Overall, they can be said as smart students. Statistics Intelligence Quotient N Valid 45 Missing Mean 113.22 Median 113.00 Mode 113 Std. Deviation 9.439 Variance 89.086 Range 52 Minimum 81 Maximum 133 Sum 5095

2. Students’ Achievement of Extensive Reading

As Y variable dependent va riable, students’ achievement of Extensive Reading was taken from documentation of Extensive Reading score list from the lecturer of Extensive Reading course. The description is shown in table 4.1. Table 4.4 The Scores of Extensive Reading Students’ Number Extensive Reading Score Y Student 1 83 Student 2 76 Student 3 81 Student 4 84 Student 5 76 Student 6 87 Student 7 81 Student 8 14 Student 9 81 Student 10 76 Student 11 85 Student 12 80 Student 13 87 Student 14 85 Student 15 89 Student 16 73 Student 17 87 Student 18 85 Student 19 77 Student 20 84 Student 21 90 Student 22 71 Student 23 85 Students’ Number Extensive Reading Score Y Student 24 86 Student 25 85 Student 26 84 Student 27 85 Student 28 70 Student 29 80 Student 30 80 Student 31 72 Student 32 75 Student 33 88 Student 34 81 Student 35 81 Student 36 85 Student 36 84 Student 38 85 Student 39 88 Student 40 85 Student 41 85 Student 42 77 Student 43 86 Student 44 84 Student 45 85 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 Participants Y X XY Y2 X2 Student 39 109 88 9592 11881 7744 Student 40 119 85 10115 14161 7225 Student 41 106 85 9010 11236 7225 Student 42 100 77 7700 10000 5929 Student 43 109 86 9374 11881 7396 Student 44 100 84 8400 10000 7056 Student 45 119 85 10115 14161 7225 N=45 ΣX = 5095 ΣY = 3628 ΣXY = 410913 ΣX 2 = 580787 ΣY 2 = 298118 After getting the result above, the calculation of the data to Pearson Product Moment Formula is presented as follows: Formula: Calculation: N = 45 ΣX = 5095 ΣY = 3268 ΣX 2 = 580787 ΣY 2 = 298118 ΣX 2 = 25959025 ΣY 2 = 13162384 ΣXY = 410913 To make sure the result of the calculation above, the Pearson Product Moment in SPSS statistics program version 24.0 was used to know whether the calculation that has been calculated manually is correct or not and to make sure that there is no mismatching calculation between score that the writer counted. The results of those calculations; manual calculation and calculation using SPSS statistics program version 24.0 are equal, in which the value of r xy or r o are 0.0304. It means that there is no mismatch in the process of calculating the data by calculating manually or using the SPSS formula. Then, the calculation of Pearson Product Moment is described as follows: Table 4.8 Pearson Product Moment Table for the Intelligence Quotient IQ and Achievement of Extensive Reading Correlations IQ Extensive Reading Achievement IQ Pearson Correlation 1 .030 Sig. 2-tailed .843 N 45 45 Extensive Reading Achievement Pearson Correlation .030 1 Sig. 2-tailed .843 N 45 45 The results of those calculations; manual calculation and calculation using SPSS statistics program version 24.00 were equal, in which the value of r xy or r o for IQ and Extensive Reading achievement was 0.030. It means that there was no mismatch in the process of calculating the data by calculating manually or using the SPSS statistics program version 24.00.

3. Analysis of Determination Coefficient

The contribution o f the independent variable x, students’ achievement of Extensive Reading towards the dependent variable y, Intelligence Quotient IQ, are investigated through the determination coefficient r 2 . The result of r 2 can be found through this formula: 4 R = r 2 x 100 = 0.030 2 x 100 = 0.0009 x 100 = 0.09 Note: R : score of determinant coefficient r 2 : score of the squared correlation coefficient Based on the result of determination coefficient, the students’ IQ contribute to students’ achievement of Extensive Reading up to 0.09. The remains 99.91 were given by other variables, for example reader’s knowledge, motivation, reason, strategies, skills, stable characteristics, and physical characteristics. 5

C. Test of Hypothesis

To test the hypotheses, the correlation coefficient from the calculation r xy is compared to correlation coefficient from Product Moment table r t . In the term of the statistics hypotheses, these can be portrayed as follows: 1. If r o r t = H a is accepted. There is a relationship between the Intelligence Quotient IQ and students’ achievement of Extensive Reading. 4 Riduwan and Akdon, Rumus dan Data dalam Analisis Statistika, Bandung: Alfabeta, 2013, p.125. 5 J. Charles Alderson, Assessing Reading, Edinburgh: Cambridge University Press, 2001, p. 33. 2. If r o r t = H a is rejected. There is a no relationship between the Intelligence Quotient IQ and students’ achievement of Extensive Reading. To find r xy or r o , the degree of freedom must be determined with the formula: d f = N –nr = 45 – 2 = 43 Note : d f : degree of freedom n : number of cases respondents nr : number of variables In the table of significance see appendix 5, it is shown that the r t of a two tailed test in the significance of 5 and d f of 43 is found to be 0.301. Based on the score of r o 0.030, it is indicated that the score of r o is lower than r t , in which 0.030 0.301. It means that H a is rejected; or in other words there is no relationship between the Intelligence Quotient IQ and students’ achievement of Extensive Reading. Moreover, the result of t count is compared to t table in order to find the significance of variables. The formula of getting t count is presented as follows: Description of the formula: t count = t value r = 0.030 n = 45 Calculation: The formulation of test: a. If t o t table , it means that the null hypothesis is rejected and there is significant relationship between the two variables. b. If t o t table , the null hypothesis is accepted and there is no significant relationship between the two variables. From the table of significance see appendix 6, it is obtained that t table of 5 and d f = 43 is 2.01669. It indicates that t o is lower than t table , in which 0.1968 2.01669. Therefore, the null hypothesis H o is accepted. In other words, there is no significant relationship between the Intelligence Quotient IQ and students’ achievement of Extensive Reading. According to the result of the calculation of Pearson Product Moment above, the score of correlation coefficient r o is 0.030. To interpret the gravity of 0.030, the table of “r” product moment shows that the correlation value is on the very weaklow level, in which between 0.00 – 0-19. The very weak or low correlation means that the relationship tends to the negative relationship. The table of “r” interpretation was adopted from J.P. Guilford’s theory. 6 Table 4.9 The Interpretation of Correlation Coefficient Interpretation 0.00 – 0.20 Slight: almost negligible relationship. 6 J.P. Guilford, Fundamental Statistics in Psychology and Education, New York: Mc-Graw Hill Book Company Inc., 1950, p. 145. Interpretation 0.20 – 0.40 Low correlation; definite but small relationship. 0.40 – 0.70 Moderate correlation; substantial relationship. 0.70 – 0.90 High correlation; marked relationship. 0.90 – 1.00 Very high correlation; very dependable relationship. Adopted from Guilford, 1956.

D. Discussion

Based on the data description of Extensive Reading achievement, it is found that the seventh semester students of Department of English Education commonly have high reading achievement, which is indicated by the result of the average score found is 80.62 and the mode score is 85.00. Therefore, they have passed the Extensive Reading course successfully. They may also have high reading comprehension. Meanwhile, from the data description of the Intelligence Quotient IQ which has measured by Culture Fair Intelligence Test CFIT, it is found that seventh semester students of Department of English Education commonly in high average level, which is indicated by the average score is 113.22 and the mode score is 113. They tend to have high intelligence that can be said they are smart. Then, there are nine students who are in superior level or can be said that they are genius. Therefore, they have had one of good condition to learn and develop reading skill then gain high reading achievement. In addition, the finding reveals that there is no significant relationship between the Intelligence Quotient IQ and students’ achievement of Extensive Reading. It is shown that the score of correlation coefficient r xy is smaller than the score r table r t . In this case, the correlation coefficient is 0.030 and it was compared with r t at the level of significance 0.05 obtained respectively 0.301, in which r = 0.030 r t = 0.310. Similarly, based on the calculation of t count above, the score of t count is smaller than the score of t table at the level significance 0.05, in which t count = 0.1968 t table = 2.01669. Since the r o and t count is smaller than r t and t table, it means that the alternative hypothesis H a is rejected and null hypothesis H o is accepted. In other words, there is no significant relationship between the Intelligence Quotient IQ and students’ achievement of Extensive Reading at the seventh semester students of Department of English Education, Faculty of Educational Sciences, Syarif Hidayatullah Jakarta State Islamic University in the 2016 – 2017 academic year. Therefore, students who have high IQ do not always have high reading achievement. As Sprinthall states that the IQ scores are not infallible predictors in every case, since the highest possible correlation is 1.00. There is children whose IQ scores is lower than other children’s but whose grades are higher, because a number of nonintellectual factors such as physical illness, emotional upset, and lack of motivation also influence scholastic success. 7 Additionally, based on the determination of coefficient r 2 = 0.0009 obtained, Intelligence Quotient IQ was considered to have contribution of 0.09 towards students’ achievement of Extensive Reading. In other words, the achievement of Extensive Reading of the seventh semester students of Department of English Education, Faculty of Educational Sciences, Syarif Hidayatullah Jakarta State Islamic University in the 2016 – 2017 academic year is 0.09 influenced by their IQ and there were 99,91 as the remains. The remains indicate that there is other factors which influence the achievement of Extensive Reading. To sum up, the data interpretation shows a finding that the Intelligence Quotient IQ and students’ achievement of Extensive Reading were not correlated each other. The IQ gave contribution r 0.09 to the achievement of Extensive Reading. The relationship between the IQ and achievement of Extensive Reading had not significant value. It means that the IQ did not indicate and predict the students’ achievement of Extensive Reading.

E. Limitations

In conducting this study, there were some challenges which lead this study to have some limitation. First, the instrument used for te st the students’ IQ should 7 Norman A. Sprinthall and Richard C. Sprinthall, Educational Psychology, New York: McGraw Hill Inc., 1994, p. 437. be made and distributed by the professional psychologists or an institution. The writer could not arrange and distribute a set of IQ test by herself. Then, the writer thought to use IQ test online, but the research advisor did not recommend it. Later, the writer tried to ask some psychologists and institutions. As a result, the writer cooperate with Psychology Service Centre of UIN Jakarta —usually called Pusat Layanan Psikologi PLP to hold IQ test for the students. Second, the number of sample in this research was smaller than the writer had expected. The writer got a little difficulties to gather samples of research in order to have the IQ test. Actually, the samples of research need to have the IQ test in one-time and also come to the PLP institution. It was rather difficult to adjust their schedule in order to have the IQ test itself. In this case, the writer need to contact them one by one. As a result, the writer could schedule the IQ test in one-time. However, in the day of implementation of IQ test, there were some students who canceled to be the sample of research because of some reasons. There were students who were sick and also having some urgent things that obstruct them to come to the PLP institution.