Correlation Analysis Determination Coefficient

52

CHAPTER IV FINDING AND DISCUSSION

This chapter deals with the data analysis as well as interpretation. It encompasses the discussion of the correlation analysis aimed at figuring out the correlation coefficient, computation of the index of the determination, the obtained regression equation, and their interpretations.

4.1 Correlation Analysis

After getting the scores of the students mastery of past tense and their achievement in writing recount as presented in appendix 5, the data were statistically computed to find the correlation between the two variables. As stated in the previous chapter, the r-value was computed using Pearson Product moment formula. The following table presents the result of the computation of correlation coefficient. Correlations 1 .724 . .000 50 50 .724 1 .000 . 50 50 Pearson Correlation Sig. 2-tailed N Pearson Correlation Sig. 2-tailed N X Y X Y Correlation is significant at the 0.01 level 2 t il d . From the data presented in the table, it is found out that the obtained correlation coefficient is 0.724. For α = 5 and df = 50-2 = 48, the critical value of r product moment is 0.284. Because r-value is higher than the critical value of r product 53 moment, the correlation coefficient is significant. This means that the null hypothesis is rejected and it can be stated that there is a positive correlation between students’ mastery of past tense and their achievement in writing recount. This positive correlation is applied especially for the eleventh graders of SMA I Weleri in the Academic Year of 20062007.

4.2 Determination Coefficient

Interpreting the strength of the relationship between the two variables through the correlation coefficient is not sufficient. It is, therefore, necessary to compute the determination index r 2 R 2 in the table below. It is labeled determination index because 100R 2 of the variation within the dependent variable Y can be accounted for by the relationship with the independent variable X if linear regression Y on X exists. In this case, determination index shows us the percentage of variation among the achievement in writing recount that can be attributed to the relationship between the two variables. Model Summary .724 a .525 .515 6.01433 Model 1 R R Square Adjusted R Square Std. Error of the Estimate Predictors: Constant, X a. From the computation above, the obtained determination coefficient R square is 0.525. This means that more or less 52.5 of the variation in writing scores is accounted for by the relationship with the past tense scores, while the rest 100- 52.48 = 47.52 was probably due to other factors such as the students’ motivation, their interest in learning writing, their health or frame of mind on the 54 day they took the test, the frequency of practice, their writing habit, etc. This indicates that of the relationship existing between students’ mastery of past tense and their achievement in writing recount, as much as 52.5 can be explained by the correlation between the two variables, while the remaining 47.5 can be attributed to other factors mentioned above.

4.3 Regression Equation