Data Analysis Method RESEARCH METHOD

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3.6 Data Analysis Method

Data analysis became one of the important steps and very crucial in research activity. The purpose of this data analysis was to test the hyphotheses by using Spearman Rank Order Correlation formula. Sudijono 2000:218 notes that Spearman Rank Order correlation technique is effective to be used if the research samples are less than 30. It was suitable to use this formula in this present research because the research’ respondents were only 25 students. This formula was used to describe the strenght of the relationship between two variables. To test the hyphothesis whether there was a positive correlation between attitude towards learning English and reading comprehension achievement, the researcher used the Statistical Computation Spearman Rank Order Correlation formula as follows: In which: = Rank Order Correlation Index 6 1 = Constant number D = Difference rank of difference between variable X Attitude towards learning English and Variable Y Reading Comprehension Achievement, So D = R 1 – R 2 N = Number of cases Sudijono, 2000:219 There were two main data in the research, attitude towards learning English x and reading comprehension achievement y. In analyzing the hypotheses, the alternative hyphotheses H a formulated “There is significant correlation between attitude towards learning English and reading comprehension achievement of grade XI Bahasa 1 students at SMA Muhammadiyah 3 Jember in the 20152016 academic year” should be changed into nul hyphotheses H o to avoid bias. The nul hyphotheses H o formulation is “There is no correlation between attitude towards learning English and reading comprehension achievement of grade XI Bahasa 1 students at SMA Muhammadiyah 3 Jember in the 20152016 academic year”. In this research the researcher used statistical 36 formula Spearman Rank Order Correlation whether or not there is correlation among variables. After revealing the Rank Order Correlation coefficient, the researcher used table critic see Appendix M to know whether or not the null hypotheses H o accepted or rejected. Furthermore, when the result of computation obtained, it needed further interpretation by categorizing the relationship between variables whether or not was in strong relationship. The table 3.5 explained the interpretation of the coefficient correlation between the two variables after the results of computation obtained. Table 3.5 The Interpretation of Coefficient Correlation Size of Correlation Strenght of Relationship 0.00 to 0.19 No relationship or weak relationship likely to be statistically significant only in large samples of 1,000 or more 0.20 to 0.34 Slight relationship detecable in samples of 100 or more 0.35 to 0.64 Moderately strong relationship 0.65 to 0.84 Strong relationship 0.85 to 1.00 Very strong realationship Lodico et al., 2006:233 53

CHAPTER V. CONCLUSION AND SUGGESTION