CHAPTER IV RESEARCH FINDING AND DISCUSSION
A. Research Finding
2. Linearity Test
Based on the score interpretation speaking ability test, which was adopted from RPS Speaking for Effective Communication (UIN Sultan Maulana Hasanuddin Banten, 2021), it can be concluded that students of the third semester of UIN Sultan Maulana Hasanuddin Banten have middle-level speaking abilities reflected from their score in Table 4.9. This conclusion was found by the presence of 11 participants who obtained a score of B. In this case, there were still 8 (14%) participants who did not pass the speaking ability test. However, 50 (86%) participants passed the speaking ability test. This shows that students of the third semester of UIN Sultan Maulana Hasanuddin Banten are able to speak English well and have good speaking abilities.
a. Linearity Regression of Variable X1 toward Y
The first partial linearity test was conducted between students' verbal- linguistic intelligence (X1) and their speaking ability (Y). The steps involved in performing a linearity test on certain variables are detailed in the tables below:
Table 4. 10. The Contribution of Independent Variable (X1) Model Summary
Model R R Square
Adjusted R Square
Std. Error of the Estimate
1 .717a .513 .505 7.85980
a. Predictors: (Constant), Verbal_Linguistic_Intelligence
According to data in Table 4.10 above, the R-value is 0,717, which indicates that the relationship between the variables is strong. In percentage notation, the R square (0,513 x 100%= 51,3%) indicates that the independent variable contributed 51,3% contribution. And the remaining 48,7% is contributed by other variables.
Table 4. 11. Linearity Test between X1 and Y Variables (ANOVAa) ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 3651.298 1 3651.298 59.105 .000b
Residual 3459.478 56 61.776
Total 7110.776 57
a. Dependent Variable: Speaking_Ability
b. Predictors: (Constant), Verbal_Linguistic_Intelligence
The level of significance is set at 0.05 percent. There is no linearity if the sig value is more than 0,05. However, there is linearity if the sig value is less than 0.05. Table 4.11 shows that the significant value of verbal-linguistic intelligence is 0,00, indicating that linearity is met.
Table 4. 12. Coefficientsa of Correlation between X1 and Y Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1 (Constant) 21.807 7.339 2.971 .004
Verbal-Linguistic Intelligence .710 .092 .717 7.688 .000 a. Dependent Variable: Speaking Ability
In table 4.12, in column B the value of Constant (a) is 21,807. while the value of verbal-linguistic intelligence (b) is 0,732. so that the equation/regression model can be written as follows: Y = a+bX or 21,807 + 0,710X
Based on the results above, it can be concluded that the positive constant value of 21,807 indicates the positive influence of the independent variable (verbal-linguistic intelligence). Therefore, if the independent variable increases or affects one unit, the speaking ability variable will increase or be fulfilled.
Furthermore, the X regression coefficient of 0,710 states that if verbal- linguistic intelligence (X) increases by one unit, speaking ability (Y) will increase by 0,710.
b. Linearity Regression of Variable X2 toward Y
The second partial linearity test was conducted between students' self- efficacy (X2) and their speaking ability (Y). The steps involved in performing a linearity test on certain variables are detailed in the tables below:
Table 4. 13. The Contribution of Independent Variable (X2) Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .496a .246 .232 9.78572
a. Predictors: (Constant), Self_Efficacy
According to data in Table 4.13 above, the R-value is 0,496, which indicates that the relationship between the variables is strong. In percentage notation, the R square (0,246 x 100%= 24,6%) indicates that the independent variable contributed 24,6% contribution. And the remaining 75,4% is contributed by other variables.
Table 4. 14. Linearity Test between X2 and Y Varibles (ANOVAa) ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1 Regression 1748.203 1 1748.203 18.256 .000b
Residual 5362.573 56 95.760
Total 7110.776 57
a. Dependent Variable: Speaking_Ability b. Predictors: (Constant), Self_Efficacy
The level of significance is set at 0.05 percent. If the sig value is more than 0,05, there is no linearity; nevertheless, there is linearity if the sig value is less than 0,05. As seen in Table 4.14, self-efficacy has a significance value of 0,00
< 0,05; so, linearity is met.
Table 4. 15. Coefficientsa Correlation between X2, and Y Variables Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -2.057 18.704 -.110 .913 Self_Efficacy .964 .226 .496 4.273 .000 a. Dependent Variable: Speaking_Ability
In table 4.15, in column B the value of Constant (a) is 2,057. while the value of verbal-linguistic intelligence (b) is 0,964. so that the
equation/regression model can be written as follows: Y = a+bX or 2,057 + 0,964X
Based on the results above, it can be concluded that the positive constant value of 2,057 indicates the positive influence of the independent variable (self-efficacy). Therefore, if the independent variable increases or affects one unit, the speaking ability variable will increase or be fulfilled. Furthermore, the X regression coefficient of 0,964 states that if self-efficacy (X2) increases by one unit, speaking ability (Y) will increase by 0,964.
c. Linearity Regression of Independent Variable (X1 and X2) toward Dependent Variable (Y)
The multiple linearity test was used to determine the relationship between students' verbal-linguistic intelligence (X1), self-efficacy (X2), and speaking ability (Y). These variables were used in the computation of the multiple linearity test, which is detailed in the tables below:
Table 4. 16. The Contribution of Independent Variables (X1 and X2) Model Summary
Model R R Square
Adjusted R Square
Std. Error of the Estimate
1 .726a .527 .510 7.81828
a. Predictors: (Constant), Verbal_Linguistic_Intelligence, Self_Efficacy
Based on table 4.16. It demonstrated that the R-value is 0,726, which indicates a significant relationship between the variables. The R square is translated into percentage form (0,527 x 100% = 52,7%), indicating that the independent variable contributes 52,7%. And the remaining 47,3 percent is accounted for by other factors.
Table 4. 17. Linearity Test between X1, X2 and Y Variables (ANOVAa) ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 3748.873 2 1874.436 30.665 .000b
Residual 3361.903 55 61.126
Total 7110.776 57
a. Dependent Variable: Speaking_Ability
b. Predictors: (Constant), Self_Efficacy, Verbal_Linguistic_Intelligence
The level of significance is set at 0.05 percent. If the sig value is more than 0,05, there is no linearity; nevertheless, there is linearity if the sig value is less than 0,05. As seen in Table 4.17, the significant value of verbal-linguistic intelligence and self-efficacy is 0,00; hence, linearity is satisfied, and the line will tend to be straight.
Table 4. 18. Coefficientsa Correlation between X1, X2 and Y Variables Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 5.254 14.998 .350 .727
Verbal-Linguistic Intelligence
.632 .111 .638 5.721 .000
Self_Efficacy .274 .217 .141 1.263 .212 a. Dependent Variable: Speaking_Ability
The result indicated that the constant a = 5,254, the coefficient b = 0,632, and the coefficient c = 0,274. The linear regression equation for verbal- linguistic intelligence and self-efficacy concerning speaking ability is thus Y=5,254+0,632X1+0.274X2. This linear regression predicts that increasing one point in verbal-linguistic intelligence would enhance speaking ability by up to 0.632 while increasing one point in self-efficacy will increase speaking
ability by up to 0.274.