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have the value of Cronbachs Alpha is greater than 0.60. So it can be summed up all the variables used in this study is reliable
Table 4.4 The Result of Reliability Test
Customer Satisfaction Y
Cronbachs Alpha
Cronbachs Alpha Based on
Standardized Items
N of Items
,642 ,641
3
Based on the table 4.4 above SPSS output, note that all variables have the value of Cronbachs Alpha is greater than 0.60. So it can be
summed up all the variables used in this study is reliable.
2. Classic Asumption
a. Normality Test
Normality test aims to test whether the regression model, the dependent variable customer satisfaction and independent variables
service quality X
1
and promotion X
2
both have a normal distribution or not. If the distribution of the residual values cannot be considered to be
normally distributed, then it is said there are problems with the normality assumption.
According Ghozali 2006: 149, the principle of normality can be detected by looking at the spread of the data dots on the diagonal axis of
the graph probability plots or by looking at the histogram of the residual.
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Basis for decision making as follows: 1 Detection of the histogram, if the normal curve in the graph
follow a bell shape, then the data are normally distributed. 2 While the detection of the normal probability plot on the graph, if
the data dots spread around the diagonal line, and follow the direction of the diagonal line, then the regression model to meet the assumption of
normality. If the spread of the data points do not follow the direction of the diagonal, then the regression model did not meet the assumption of
normality. 3 This statistical test that can be used to test the normality of the
residuals is a statistical test of non - parametric Kolmogorov-Smirnov KS Ghozali, 2006: 151. Basis for decision making, when the value of the
Kolmogorov Smirnov significance greater than 0.05, it can be said to be normally distributed data. If the value of the significance of the KS test is
smaller than 0.05, it can be said the data was not normally distributed.
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Figure 4.1 Normal P-Plot of Regression Standardized Residual
Dependent Variable : Customer Satisfaction
Source :Processed primary Data by SPSS 21
Based on the figure above this research has done normality data distribution test. From the p-p plots above diagram above, it can be seen
that the plots are distributed along the diagonal Lin. Thus it can be concluded that the data used in this research has a normal distribution.
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Figure 4.2 Histogram
Dependent Variable : Customer Satisfaction
Source: Processed primary Data by SPSS 21
Based on the chart above, the Histogram Graphic shows normal distribution. So that regression model requires normality assumes.
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Table 4.5
One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual
N 100
Normal Parameters
a,b
Mean ,0000000
Std. Deviation 1,05012444
Most Extreme Differences Absolute
,113 Positive
,113 Negative
-,073 Kolmogorov-Smirnov Z
1,133 Asymp. Sig. 2-tailed
,154 a. Test distribution is Normal.
b. Calculated from data.
Source: Processed primary Data by SPSS 21
Based on the table above the value of the Kolmogorov Smirnov significance greater than 0.05, it can be said to be normally distributed data.
b. Multicollinearity Test
According to Ghozali 2006:95, multicollinearity test aims to test whether the regression model found a correlation among the independent
variables service quality X
1
, promotion X
2
. Good regression model should not happen correlation between the independent variables service
quality X
1
, promotion X
2
, if among the independent variables correlated with each other, then these variables are not orthogonal.
To detect the presence or absence of multicollinearity among the independent variables in regression model, it can be seen from Tolerance
and VIF value. Cutoff value commonly used to indicate whether there is
60
multicolliniearity or not is t olerance value ≤0.10 or equal to VIF value ≥10.
Table 4.6
Coefficients
a
Model Unstandardized
Coefficients Standardized
Coefficients t
Sig. Collinearity Statistics
B Std. Error
Beta Tolerance
VIF 1
Constant ,380
1,078 ,352
,726 Service Quality
,071 ,018
,335 3,882
,000 ,719
1,391 Promotion
,374 ,071
,456 5,294
,000 ,719
1,391 a. Dependent Variable: Customer Satisfaction
Source: Processed primary Data by SPSS 21 Based on the table
above the value of Tolerance is ≤0.10 and the value of VIF is ≥10. It can be said there is no Multicollinearity among
independent variables in regression model.
c. Heteroscedasticity Test
Heteroscedasticity test aims to test whether the regression model of the residual variance occurs inequality an observation to other observations.
If the variance of the residuals one observations to other observations stable, it is called different homoskedastisitas and if it is different called
heteroscedasticity. Good regression a model is that happened homoskedastisitas or did not happen heteroscedasticity Santoso: 2012:
238. 1 Looking at the scatterplot graph, if forming certain patterns, such
as dots form a certain pattern regularly wavy, widened then narrowed, then Heteroscedasticity indicates has occurred. If there is no clear pattern,
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and the points spread above and below the 0 on the Y axis, then there is no heteroscedasticity Santoso, 2012: 240.
2 Glejser Test is done with the regressed absolute value of residuals against the independent variables service quality X
1
and promotion X
2
. Guidelines from Glejser test is looking at the significance level of each independent variable service quality X
1
and promotion X
2
on the dependent variable customer satisfaction. If the significance level yield number 0.05, it can be said regression model does not contain
any Heteroscedasticity. Ghozali, 2006: 129.
Figure 4.3 Scatterplot
Dependent Variable : Customer Satisfaction
62
Source: Processed primary Data by SPSS 21 Based on the figure 4.3 there is no clear pattern and the points
spread above and below the 0 on the Y axis, then it can be said there is no heteroscedasticity.
Table 4.7 Heteroscedasticity Test
Coefficients
a
Model Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error
Beta 1
Constant -,021
,620 -,034
,973 Service Quality
,002 ,011
,027 ,230
,819 Promotion
,054 ,041
,156 1,322
,189 a. Dependent Variable: res_2
Source: Processed primary Data by SPSS 21 From the figure above the significance level is 0.05, it can be said
regression model does not contain any heteroscedasticity.
3. Descriptive Analysis
