= 60 Middle Group
= M - 1SD up to M + 1SD = 50
– 10 up to 50 + 10 = 40 up to 60
Lowest Group = M - 1SD
= 50 – 10
= 40 Based on these empulations, it can be made frequency distribution
tendency of St udents’ Learning Motivation in Accounting Learning as
follows:
Table 15. Tendency Distribution on
Students’ Learning Motivation in
Accounting Learning Y No
Class Interval
Frequency F
Tendency Category
1 60
49 72.1
High 2
40 – 60
19 27.9
Fair 3
40 Low
Total 68
100 Source : Primary Data Processed
From the table above can be seen that in the tendency of Students’ Learning Motivation in Accounting Learning Y there are 49 students
72.1 in the high category, 19 students 27.9 in the fair category, and no students 0 in the low category.
The tendency of Students’ Learning Motivation in Accounting Learning variable presented in Pie Chart as follows:
Figure 7. Pie Chart
of Tendency on Students’ Learning Motivation Variables
B. Prerequisites Test Analysis
1. Normality
Normality test is used to test the correctness of the data whether it is
normally distributed or not. The results of normality testing are as follows: Table 16. The Result of Normality Test
Variable Z Score
K-S Asymp. Sig.
2-tailed Notes
Conclusion Moving Class
Implementation 0.502
0.963 p 0.05
Normal Accounting
Classrooms Facilities
0.926 0.357
p 0.05 Normal
Students’ Learning Motivation in
Accounting Learning
0.780 0.577
p 0.05 Normal
Source : Primary Data Processed
172.1 27.9
Pie Chart of Tendency on Students Learning Motivation
High Fair
Low
Based on the table above, is obtained Kolmogorov-Smirnov Z score for Moving Class Implementation variable is 0.502 with asymp sig 0.963. While
the Kolmogorov-Smirnov Z score for the variable of Accounting Classrooms Facilities is 0.926 with asymp sig 0.357. Kolmogorov-Smirnov Z score for
Students’ Learning Motivation in Accounting Leraning is 0.780 with asymp sig 0.577. Therefore, the score of these three variables asymp sig greater than
0.05, it can be concluded that the data of Moving Class Implementation Variable, Accounting Classrooms
Facilities and Students’ Learning Motivation in Accounting Learning were distributed normally.
2. Linearity
Linearity test is intended to determine whether the independent variables and the dependent variable have a linear relationship or not. To confirm the
linear characteristics between these two types of variables can be achieved by using regression line. Linearity test in this research used F-test. The
independent variable and dependent variable is linear if F
emp
F
table
with the significance level 5. Based on the data analysis using Deviation From
Linearity on ANOVA table from the output of SPSS Statistics 17.0 for Windows, the coefficient F
emp
F
table
for X
1
with Y variable is 1.121 2.04; and then X
2
with Y variabel is 1.915 2.01.
Based on the analysis, it can be concluded that the relation between X
1
with Y variable and X
2
with Y variable are linear. The result of linearity testing as follows:
Table 17. The Result of Linearity Test No
Variable DF
F
emp
F
table
Sig. Conclusion
1 X1
Y 50 : 16
1.121 2.124
0.362 Linear
2 X2
Y 47 : 19
0.915 2.006
0.569 Linear
Source : Primary Data Processed Conclusion:
1. Test for linearity to the variable of Moving Class Implementation X
1
on Students’ Motivation Learning Y. The result shows that the coefficient
of F
emp
1.121 is less than F
table
48:18 2.124 and the significance value is 0.365 greater than 0.05. Therefore, the relationship between Moving Class
Implementation X
1
variable with Students’ Motivation Learning in
Accounting Learning Y is linear. 2. Test for linearity to the variable of Accounting Classrooms Facilities X
2
on Students’ Motivation Learning Y. The result shows that the
coefficient F
emp
0.915 is less than F
table
47:19 2.006 and the significance value is 0.569 greater than 0.05. Therefore, the relationship between
Accounting Classrooms Facilities X
2
variable with Students’ Motivation
Learning in Accounting Learning Y is linear.
3. Multicollinearity
Multicollinearity was intended to test the linear relationship between the independent variable with the other independent variables. Based on the
analysis of the data obtained the following results:
Table 18. The Result of Multicollinearity Test
No Variable Tolerance
VIF Conclusion
1 Moving Class Implementation 0.853
1.173 There is no
multico- llinearity
2 Accounting Classrooms
Facilities 0.853
1.173 Source : Primary Data Processed
Tolerance limit score is 0.1 and the VIF limit is 10. It shows that if the tolerance score is below 0.1 or VIF is above 10, there is a multicollinearity
disruption. The VIF score of Moving Class Implementation and Accounting Classrooms Facilities is 1.173, so it can be concluded that there is no
multicollinearity.
4. Heteroscedasticity
Heteroscedasticity is intended to determine whether there are variations in the residual absolute, equal or not for all observations. Heteroscedasticity
testing is done with Park Test. Based on the analysis of the data obtained the following results:
