66
2. Reliability Test
The reliability of instruments means the instrument that used for many times to measure the same object will make a same data of the
result. From the table 4.6 the score for Cronbach’s Alpha is 0.568. Certain instruments could be said as reliable if gain coefficient above
0.60, therefore can be summarize the alpha result from all questionnaires is enough reliable. This shows that the whole items of
questionnaires could be use for further research and if the same question is distributed, the answer to it will not be far difference with
the previous answers.
Table 4.6 Reliability Statistic
The reliablity test result of reform administration tax, emotional intelligence and awareness variable are shown in the table 4.6. As seen in
the table, the Cronbach’s Alpha are 0.747 Rat, 0.763 EI, 0.741
EI X2
11 0.000
VALID 12
0.000 VALID
13 0.000
VALID 14
0.002 VALID
15 0.042
VALID
Variables Cronbachs
Alpha Cronbach;s
Alpha Item- Corrected
Explanations
Rat X
1
0.747 0.568
Reliable Ei X
2
0.763 0.568
Reliable Awareness Y
0.741 0.568
Reliable
67 Awareness which are more than 0,60. Thus, it can be concluded that the
respondent’s answer on the understanding on them can said as reliable.
3. Classic assumption
a. Normality test
Figure 4.1 Normality Test Result
Source: Primary Data Output from SPSS 17
68 Normality data test is aimed to know data distribution in the
variables that is used in the research. A good data to be used in the reasearch are the data, which has a normal distribution. One of the way
to see whether the data in this research are normal or not, is by seeing P-P Plot graph. When the plots in the graph are distributed along the
diagonal line, it can be said that the data has a normal distribution. This research has done normality data distribution test. The result
acquired from SPSS v.17 statistic software. From the P-P Plots diagram above, it can be seen that the plots are distributed along the diagonal
line. Thus, it can be concluded that the data used in this research has a normal distribution.
Figure 4.2 Histogram
Source: Primary Data Out put from SPSS 17.0
69 Based on above Histogram Graphic Show normal distribution
pattern, so regression model require normality assumes. b. Autocorrelation
Table 4.7 Autocorrelation Statistics
Model Summary
b
Model R
R Square Adjusted R
Square Std. Error of the
Estimate Durbin-Watson
1 .812
a
.659 .650
3.36904 1.697
Source: Primary Data Output from SPSS 17.0 Examining autocorrelation in a research is aimed to recognize
whether there is any correlation between intruder variables e
t
or not, in certain period with the previous intruder variable e
t – 1
. To see the correlation between residuals, we examines Durbin Watson test. The
simpler way to examines autocorrelation between one variable and others is by seeing Durbin Watson’s number. A good model is model which the
data has no correlation between one residual and the other residuals. Since this research has the number of samples n 80, the
significance level α 0.05, and predictor variables k 2, we can find the upper and lower level of Durbin Watson for this research. From the table
of Durbin Watson, this research has Durbin Watson upper level d
u
for 1.688 and Durbin Watson lower level d
l
for 1.586. This research has done the autocorrelation test through SPSS v.17. The table 4.7 shows that
the number of Durbin Watson in this research is 1.697.
70 Since the Durbin Watson formula is du d 4 - du = 1.688
1.697 2.312, thus, Durbin-Watson 1.697 du 1.688 4-1.688 4-du, it can be concluded that there is not occur a correlation between residuals
in this research. Or, in other way, the model in this research has passed the autocorrelation test.
c. Heteroscedastic Test
Figure 4.3 Heteroscedastic
Source: Primary Data Output from SPSS 17.0
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