Data of Students The Correlation between Students’ Motivation in Reading and

28 109 220.80 11881 48752.64 24067.2 29 91 173.18 8281 29990.09 15759.06 30 101 214.45 10201 45987.11 21659.05 ∑ 3092 6675.182741 319658 1522829 691866.4 91 153.1560694 8281 23456.78 14702.98 114 288 12996 82944 30078.98 Based on the result of the score above, it can be described as follows: N : 30 ∑X : 3092 ∑Y : 6675.18 ∑X 2 : 319658 ∑Y 2 : 1522829 ∑XY : 691866.4 The highest and the lowest score or the two variables are as follows: a. The Highest Scores: 1 X : 114 2 Y : 288 3 X 2 : 12996 4 Y 2 : 82944 5 XY : 30078.98 b. The Lowest Scores: 1 X : 91 2 Y : 153.15 3 X 2 : 8281 4 Y 2 : 23456.78 5 XY : 14702.98

B. The Data Analysis

After the calculation of whole the data from variable x, and variable y, the next step is to statistical data analysis in order to insert the information from the Table into the raw score formula Product Moment to find the correlation index, as follows: r xy = √ r xy = √ r xy = √ r xy = 32 9 √ 292 2 0 3 r xy = 32 9 √329 3 3 r xy = 32 9 2 9 r xy = 0.64 The Last step is Determining Degree of Freedom df df = N – nr = 30 – 2 = 28 df = 28 the values for df 28 are 5 and 1 At the degree of significance 5 = 0,304 At the Degree of significance 1 = 0,393

C. The Test of Hypothesis

To prove the result of hypothesis, the writer calculates the obtained data by u sing Pearson’s coefficient of correlation or “Product Moment” as follows: 1. Formulation alternative hypothesis Ha: there is a significance correlation between variable X and variable Y. 2. Formulation the null hypothesis Ho: there is not significance correlation between variable X and variable Y. From the formulation above, the writer followed some assumption as bellow: 1. If the result of calculation r o is lower than r t r table r o r t , the null hypothesis Ho is accepted, and the alternative hypothesis Ha is rejected. 2. If the result of calculation r o is bigger than r t r table r o r t , the null hypothesis Ho is rejected, and alternative hypothesis Ha is accepted. Based on the description of calculation above, the result of this research is r o is bigger than r t r table r o r t, so the null hypothesis ho is rejected, and alternative hypothesis Ha is accepted.

D. The Interpretation of the Data

After the writer preceded the formula, as it has been found out about the result of the correlation, the next step is to give the interpretation of “r” score r xy . 1. From the data of students’ motivation in reading score and their reading speed score, it appeared that the correlation index between variable X and variable Y is 0.64. it means there is a strong enough correlation between the two variables. To give the simple interpretation toward a correlation index “r” Product Moment r xy can be seen by the table of the Interpretation of Product Moment Score. Table 4.1 Interpretation of Product Moment Score Coefficient of Correlation “ r ” Interpretation 0.00 —0.20 The Correlation is Neglected 0.20 —0.40 The Correlation is Weak 0.40 —0.70 The Correlation is Strong Enough 0.70 —0.90 The Correlation is Strong 0.90 —1.00 The Correlation is Very Strong Looking at the score r xy = 0.64 that the score is between 0.40 – 0.70 which is the correlation between the two variable is strong enough or it means there is a correlation between variable X and variable Y. 2. The writer used the interpretation with table of value “r” : df = N – nr = 30 – 2 = 28. Looking at the table of significance of 5 in r table = 0.304, and 1 = 0.393 because r xy on the table of significance of 5 is bigger than r table 0.64 0.304, so on the table degree of significance of 5 the null hypothesis Ho is rejected but the alternative hypothesis Ha is accepted. So, it means on the degree of significance 5 there is a strong enough correlation between variable X and variable Y. Then, because on the degree of significance 1 r xy is bigger than r table 0.64 0.393, so on the degree of significance 1 the null hypothesis Ho is rejected but the alternative hypothesis Ha is accepted. So it means on the degree of significance 5 there is a strong enough correlation between variable X and variable Y. From the calculation of estimation above, it concludes that there is a strong enough correlation between students’ motivation in reading and their reading speed, and the hypothesis of the research is accepted. It means that both variables are correlated. E. The Heterogeneity and Normality Test of the Data 1. Heterogenity Test of the Data One important assumption of the classical linear regression model is that the disorder  I that obtained from the population of regression is homoskedastik and all disturbances have the same variance. Heteroscedasticity is one of the classical linear regression assumption violations, in example where the variance of the interference is no longer constant. Usually heteroskedasticity problems often occur in cross-sectional data than in time series data. To see homokedastisitas or heterogeneity of the data, the hypothesis must be concluded as follows: Ho : There is no heterokedastisitas H1 : There is heterokedastisitas If the probability value of Sig 0.05 Ho is accepted. If the probability of Sig 0.05 Ho is rejected. To see the result of heterokedastisitas, the writer use SPSS data analysis to find out the result, the result and the explanations are belows: Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 Constant -187.008 92.947 -2.012 .054 Motivation 3.973 .900 .640 4.413 .000 a. Dependent Variable: Speed Reading Based on the tabe above table, it can be seen that the value of Sig variable 0.54 0.05, therefore Ho is accepthed. It means that there is no heterokedastisitas between the two variable, and the data given is homogen. In conclusion, there is a homogeniety on the data.