The Analysis of Data

31 110 5.25 577.5 12100 27.5625 32 107 3.5 374.5 11449 12.25 33 118 6.5 767 13924 42.25 34 105 4.0 420 11025 16 35 113 7.0 791 12769 49 36 106 4.5 477 11236 20.25 37 120 4.5 540 14400 20.25 38 128 6.25 800 16384 39.0625 39 125 3.5 437.5 15625 12.25 40 110 7.5 825 12100 56.25 N=40 ∑X=4549 ∑Y=232.5 ∑XY=26634.75 ∑X 2 =524169 ∑Y 2 =1426 a. To obtain data is analyzed to find out the positive relation between students’ motivation and their achievement in learning English. The writer used product pearson moment correlation, in SPSS Statistical Product for Service Solution is used. 1 N ∑XY – ∑X ∑Y √N∑X 2 – ∑X 2 N ∑Y 2 – ∑Y 2 r xy = Coefficient of correlation between X variable and Y variable koefisien korelasi antara variable X dan variable Y X = sum of score in X distribution Jumlah skor dalam distribusi X Y = sum of score in Y distribution Jumlah skor dalam distribusi Y XY = sum of multiplication of X and Y Jumlah perkalian X dan Y X 2 = sum of X quadrate Jumlah kuadrat dari X Y 2 = sum of Y quadrate Jumlah kuadrat dari Y N ∑XY – ∑X ∑Y √[[N∑X 2 – ∑X 2 ] [N ∑Y 2 – ∑Y 2 ] 40 x 26634.75 – 4549 x 232.5 √[40 x 524169 – 4549 2 ] [40 x 1426 – 232.5 2 ] 1065390 – 1057642.5 √[20966760 – 20693401] [57040 – 54056.25] r xy = r xy = r xy = r xy = 1 Drs. Anas Sudijono, Pengantar Statistik Pendidikan Jakarta: PT. Raja Grafindo Persida, 2005 Cet ke-15, P:206. 7747.5 √ 273359 x 2983.75 7747.5 √ 815634916.3 0.271 Table 4.5 Simple Interpretation of Correlation r xy = r xy = r xy = R xy Interpretation 0.00 – 0.20 There is correlation between X variable and Y variable, but it is very weak or very low. So the correlation is rejected. In other words, there is no correlation between X variable and Y variable 0.20 – 0.40 There is a weak or low correlation between X variable and Y variable but it is sure 0.40 – 0.70 There is an enough correlation between X variable and Y variable 0.70 – 0.90 There is a strong or high correlation between X variable and Y variable 0.90 – 1.00 There is a very strong or very high correlation between X variable and Y variable From the calculation Pearson’s Product moment correlation above, the writer got the result from r xy = 0.271 it is between 0.20 - 0.40. According to simple interpretation above, we noticed that the correlation between X variable and Y variable is low. Thus, we can interpret that there is a positive correlation between learning motivation as X variable and learning achievement as Y variable.

C. The Test of Hypothesis

After calculating r xy , the result of r xy is 0.271. The writer determined degree of freedom df to get T t T table. df = N-nr = 40–2 = 38. After looking at the table, df 38 get significance 5 is 0.312 and get significance 1 is 0.403 see appendix. The writer concluded that r xy is smaller than t t or r xy r t = 0.312 0.271 0.403, so that H a is rejected H o is accepted.

D. The Interpretation of Data

From the correlation computation, we can interpret that there is a positive correlation between learning motivation as X variable and learning achievement as Y variable. From the calculation Pearson’s Product moment correlation above, the writer got the result from r xy = 0.271 it is between 0.20 - 0.40. According to simple interpretation above, we noticed that the correlation between X variable and Y variable is low correlation and it is considered there is no significance correlation between x variable learning motivation and y variable students’ achievement in English.