Descriptive Statistics Quantitative Research Findings

84 1 Test of Data Normality I used Kolmogorov-Simirnov test from SPSS 21 to gain the normality of data distribution. If the significance value was more than 0,05 than the data had normal distribution. If the significance value was less than 0,05, then the data did not have normal distribution. If the data had normal distribution than the statistical analysis used was Pearson’s Product Moment Correlation for two variables correlation and Multiple Correlation for three variables correlation. The result of the data normality test is shown in Table 4.3. Table 4.3 Normality Test for S tudents’ Learning Style x 1 , English Speaking Ability x 2 , and Academic Performance y One-Sample Kolmogorov-Smirnov Test Unstandardized Residual N 30 Normal Parameters a,b Mean .0000000 Std. Deviation 5.34510361 Most Extreme Differences Absolute .118 Positive .111 Negative -.118 Kolmogorov-Smirnov Z .648 Asymp. Sig. 2-tailed .795 a. Test distribution is Normal. b. Calculated from data. In Table 4.3, the significance values of Kolmogorov-Smirnov Z is 0,65 and Asymp. Sig. 2-tailed is 0,79. Both values are more than 0,05 meaning that data scores have normal distribution meaning that the data scores of the research variables are able to be processed statistically using associative correlation of Pearson Product Moment Correlation for two variables and Multiple Correlation for three variables in SPSS 21. 85 2 Test of Data Linearity The linearity test was used to find out if the independent and dependent variables in the research had significant linear correlation or not as the condition of conducting correlation statistics which was employed in this research. If the result of significance value was less than 0,05 then there was linear correlation between dependent and independent variables. To describe whether the correlation was linear or not, two ways of interpretation were provided. The first if the significance value was less than 0,05, then the correlation was linear. On the other hand, if the significance value was more than 0,05, then the correlation was not linear. The second one was by comparing the F-observed to the F-Table. If the F-observed was less than F-Table, then the correlation was linear. On the other hand, if the F-observed was less than F-Table, then the correlation was not linear. The first one is the linearity test between students’ learning style x 1 and academic performance y. The result of the calculation is shown in Table 4.4. From Table 4.4, the significance value is 0,42 which means that the value is less than 0,05 so that the correlation is linear. The F-observed is 1,091. To find the F- Table, I looked at the Distribution Table F Value for 5 margins of error. The deviation from the linearity in Table 4.4 means the degrees of freedom for nominator in F-Table. Within the groups in Table 4.4 means the degrees of freedom for denominator. From Table 4.4, the deviation from the linearity is 10 and the deviation within the groups is 18. Based on this df, the F-Table shows 2,41 for the value. Then F-observed which shows 1,091 is less than F-Table. Then the correlation is linear. 86 Table 4.4 Linearity Test between S tudents’ Learning Style x 1 and Academic Performance y ANOVA Table Sum of Squares Df Mean Square F Sig. academi performance learning styles Between Groups Combined 1844.033 11 167.639 1.621 .175 Linearity 716.301 1 716.301 6.928 .017 Deviation from Linearity 1127.732 10 112.773 1.091 .418 Within Groups 1861.167 18 103.398 Total 3705.200 29 The second one is the linearity test between the variables of English speaking ability x 2 and academic performance y. From Table 4.5, the significance value is 0,42 which is less than 0,05 so that the correlation is linear. The F-observed is 1,172. To find the F-Table, I looked at the Distribution Table F Value for 5 margins of error. The deviation from linearity in Table 4.5 means the degrees of freedom for nominator in F-Table. Within the groups in Table 4.5 means the degrees of freedom for denominator. From Table 4.5, the deviation from linearity is 19 and the deviation within groups is 9. Based on this df, the F- Table shows 2,9 for the value. Then F-observed which shows 1,172 is less than F- Table. Then the correlation is linear. Table 4.5 Linearity Test between English Speaking Ability x 2 and Academic Performance y ANOVA Table Sum of Squares df Mean Square F Sig. academi performance speaking ability Between Groups Combined 3466.533 20 173.327 6.536 .003 Linearity 2875.788 1 2875.788 108.445 .000 Deviation from Linearity 590.746 19 31.092 1.172 .420 Within Groups 238.667 9 26.519 Total 3705.200 29 87 Since the independent and dependent variables of the research have linear correlations, then the condition of applying correlation statistics is fulfilled.

c. Correlation and Hypothesis Testing

This section will find out the correlation between dependent and independent variables using quantitative findings based on the degree of correlation coefficients. Using the correlation coefficient findings, then the hypothesis which were already formulated were able to be tested in order to reject or to accept the hypothesis. 1 Correlation and Hypothesis Testing between Students’ Learning Style x 1 and Academic Performance y To find out the correlation between students’ learning style x 1 and academic performance y, I applied Pearson Produce Moment Correlation in SPSS 21. The range of correlation coefficient was divided into positive correlation which was shown by the correlation coefficient of +1.00, no correlation which was shown by the correlation coefficient of 0.00, and negative correlation which was shown by the correlation of -1.00. The correlation coefficient between students’ learning style x 1 and y is shown in Table 4.6 . In Table 4.6, the correlation coefficient between s tudents’ learning style x 1 and academic performance y is 0,44 meaning positive correlation because it is more than 0,00. The degree of correlation is medium because the value is in the interval of 0,40 – 0,59. The interval guidance of the correlation degree is shown in Table 4.7. Table 4.6 Correlation between S tudents’ Learning Style x 1 and Academic Performance y Correlations LS AP LS Pearson Correlation 1 .440 Sig. 2-tailed .015 N 30 30 AP Pearson Correlation .440 1 Sig. 2-tailed .015 N 30 30 . Correlation is significant at the 0.05 level 2-tailed.