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Table 4.9 Result of Reliability Test
Variables Cronbachs
Alpha Number of
Items
Explanation
Organizational Structure 0,755
4 Reliable
Organizational Procedure 0,793
9 Reliable
Organizational Strategy 0,786
7 Reliable
Organizational Culture 0,779
7 Reliable
Taxpayer Compliance 0,749
6 Reliable
Source: Primary Data Output from SPSS 20 Table 4.9 shows the value of cronbachs alpha. The value of alpha
in organizational structure is 0.755, in organizational procedure is 0.793, in organizational strategy is 0.786, in organizational culture is 0.779, and
in taxpayer compliance is 0.749. Thus, it can be concluded that all variables are reliable because the value of cronbachs alpha is greater than
0.60. Every question used will be able to obtain consistent data. It means
that the question will provide relatively the same answer even though it was tasted many times.
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3. Result of Classic Assumption
a. Result of Normality Test
The aim of normality test is to know the distribution of data used in the research. A good data should be normally distributed. One of the
ways to detect whether the data are normally distributed data or not can be done by using graph analysis namely Normal Probability Plot P-P Plot.
The data is normally distributed when the dots spread around the diagonal line and follow the direction of the diagonal line. The result of normality
test can be seen in figure 4.1
Figure 4.1 Result of Normality Data Test
Source: Primary Data Output from SPSS 20 From the P-P Plots diagram above, it can be seen that the dots
spread around the diagonal line and follow the direction of a diagonal
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spread. Thus, it can be concluded that the distribution of the data close to normal or have met the assumptions of normality. This result also tells
that the regression model is qualified to apply due to the assumption of normality test Ghozali 2009: 149.
b. Result of Multicollinearity Test
The aim from multicolinearity test is to test whether the regression model found a correlation among the independent variables. In this
research, detection of the presence or absence in multicolinearity can be done by calculating value of variance inflation factor VIF of each
independent variable.
Table 4.10 Result of Multicollinearity Test
Coefficients
a
Model Collinearity Statistics
Tolerance VIF
1 Constant
ST .526
1.902 PO
.389 2.568
SO .568
1.760 BO
.532 1.879
a.
Dependent Variable: KWP
Source: Primary Data Output from SPSS 20
Based on table 4.10 above, the result shows that there is no value
of variance inflation factor VIF of each independent variable which is less than 0.1 or more than 10. So, it can be concluded that there is no
multicolinearity.
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c. Result of Heteroscedasticity Test
The aim from heteroscedastisity test is to test whether the regression model occur the variance inequality of the residual from one
observation to another observation. A good regression model is homocedastisity or there is no heteroscedastisity. The results of
heteroscedastisity by using scatter plot can be seen in the following figure 4.2
Figure 4.2 Result of Heteroscedastic Test
Source: Primary Data Output from SPSS 20 Based on figure 4.2 above, it can be seen that the dots spread
above and below zero 0 on the Y axis. So, it indicates that there is no heteroscedasticity or homocedasticity.
