Reliability Test The Effect of Auditor Experience and Professional Judgment Towards Quality of Audit Evidence Collected(Study about CPA Firm in South Jakarta)
59
Table 4.10 Kolmogorov-Smirnov Test
Source: Processed primary data by spss 20.0
The result of Kolmogorov-Smirnov test on table
ÇÈ É Ê
lso shows that the value of Kolmogorov-Smirnov
Ë ÈÇÉÌ
with the level of significant probability
Ë ÈÍ
Ì Î Ï
the value of p
Ë È Ë
ÐÈ
So the residual data is distributed normally
È
Therefore
Ï
regression model used in this research has met the normality test assumption
È
b
È Ñ
ulticollinearity Test The aim from
Ñ
ulticolinearity Test is to test whether the regression model found a correlation among the independent variables
È
A good regression model should there is no correlation among independent variables
È Ò
n this research
Ï
to detect the presence or absence of
Ñ
ulticolinearity can be
One-Sample Kolmogorov-Smirnov Test
Unstandardize d Residual
N 50
Mean 0E-7
Normal Parameters
a,b
Std. Deviation 4.18400133
Absolute .069
Positive .063
Most Extreme Differences
Negative -.069
Kolmogorov-Smirnov Z .487
Asymp. Sig. 2-tailed .972
a. Test distribution is Normal. b. Calculated from data.
60
done by calculating value of variance inflation factor
Ó
V
Ô
F
Õ
of each independent variable
Ö
Table 4.11 Multicollinearity Test
Source: Processed primary data by spss 20.0
Based on table
× Ö
Ø
above
Ù
the result shows that there is no value of variance inflation factor
Ó
V
Ô
F
Õ
of each independent variable which is more than
Ú Ö
Û
or less than
ÛÚ Ö
So
Ù
it can be concluded that there is no
Ü
ulticolinearity
Ö
c
Ö Ý Þ
teroscedasticity Test
Coefficients
a
Unstandardized Coefficients
Standard ized
Coefficie nts
Collinearity Statistics
Model
B Std.
Error Beta
T Sig.
Tolera nce
VIF Constant
21.041 4.704
4.473 .000
Auditor Experiece
2.270 .516
.534 4.395
.000 .979 1.022
1 Professional
Judgment of Auditor
.255 .109
.284 2.343
.023 .979 1.022
a. Dependent Variable: Quality of Audit Evidence Collected
61
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
ß
A good regression model is homocedastisity or there is no heteroscedastisity
ß à
n this research
á
heteroscedastisity test can be viewed with using the chart Scatter plot between the predicted value of dependent
variable
â
ZPRED
ã
and residual
â
SRESID.
Figure 4.2 Heteroscedasticity Test
S
ource: Processed primary data by spss 20.0
From the scatterplot graph above it can be seen there is no clear pattern and the points spread above and below the
ä
on the Y axis
á
it can be concluded not happen heteroscedasticity in regression models
ß