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Table 3.3
Result of Validity and Reliability Test for promotion
Item-Total Statistics
Scale Mean if Item
Deleted Scale
Variance if Item Deleted
Corrected Item-Total
Correlation Cronbachs
Alpha if Item Deleted
PR1 34,4000
6,571 ,310
,800 PR2
34,3800 6,240
,438 ,783
PR3 34,3800
6,077 ,507
,773 PR4
34,3800 6,077
,507 ,773
PR5 34,3600
5,868 ,590
,761 PR6
34,3800 6,077
,507 ,773
PR7 34,4800
6,214 ,500
,774 PR8
34,5200 6,214
,533 ,771
PR9 34,6400
6,562 ,521
,776
Source : Processed primary Data by SPSS 20
Based on table above 9 question about Promotion it can be seen that all The questions are valid, because the score of α 0.30, it means the
data is valid and have positive correlation or the score corrected item total correlation is 0.30, it means the data is valid.
Measurement result is reliable if the coefficient α 0.60 Imam Ghozali 2005: 42 from table above the value of Cronbach alpha more
than 0.60, it means 9 questions of promotion have the reliability value is good and feasible.
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Table 3.4
Result of Validity and Reliability Test for customer satisfaction
Item-Total Statistics
Scale Mean if Item
Deleted Scale
Variance if Item Deleted
Corrected Item-Total
Correlation Cronbachs
Alpha if Item Deleted
CS1 34,1600
6,137 ,542
,763 CS2
34,1400 5,919
,615 ,751
CS3 34,1200
6,189 ,544
,763 CS4
34,1400 6,694
,452 ,776
CS5 34,1800
6,926 ,381
,784 CS6
34,1400 6,776
,417 ,780
CS7 33,8200
6,273 ,543
,763 CS8
34,1600 6,668
,480 ,772
CS9 34,1800
7,049 ,327
,791
Source : Processed primary Data by SPSS 20
Based on table above 9 question about Customer Satisfaction it can be seen that all The questions are valid, because the score of α 0.30,
it means thedata is valid and have positive correlation or the score corrected item total correlation is 0.30, it means the data is valid.
Measurement result is reliable if the coefficient α 0.60 Imam Ghozali 2005: 42 from table above the value of Cronbach alpha more
than 0.60, it means 9 questions of customer satisfaction have the reliability value is good and feasible.
a. Reliability Test
According to Imam Ghozali 2005: 42 reliablity measurement can be done in 2 ways:
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1. Measure or measurement repeated: here someone will be given the
same question s at different times, and then see if the remains consistent with the answers.
2. One shot or one time measurement: here measurement only
running once and then the results were compared with another question or measure the correlation between answers to questions.
SPSS provide facilities to measure the reliability with statistical uni Cronbach alpha α. A construct or variable is said cronbach reliable
if the value of alpha 0.60.
As a gauge that shows consistency of the measuring instrument to measure the same phenomenon in other occasions. To look of reliability
value, the variable cronbach alpha has a value greater than 0.60 Imam Ghozoli, 2005: 42.
Table 3.5
1. Result of Reliability Test Variables
Cronbach’s Alpha
Cronbach’s Alpha Item- Corrected
Result
Service QualitY X1 0.835
0.60 Reliable
Product Quality X2 0.873
0.60 Reliable
Promotion X3 0.796
0.60 Reliable
Customer Satisfaction Y 0.792
0.60 Reliable
Source : Processed primary Data by SPSS 20
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The reliability test result of service quality, product quality, promotion, and customer satisfaction as seen in the table above the
Cronbach’s Alpha are 0.835 Service quality, 0.873 Product quality, 0.796 Promotion and 0.792 Customer satisfaction which are more
than 0.60. Thus it can be concluded that the all respondent’s answers on the understanding on them can said as reliable.
C. Classic assumption Test
1. Normality Test
Normality test is aimed to know data distribution in the variables can be used in the research . A good data which can be used in the
research, which have 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.
Figure 2.3
Source : Processed primary Data by SPSS 20
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Based on the figure above this research has done normality data disrtibution test. From the p-p plots above diagram above , it can
bee 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 2.4
Source : Processed primary Data by SPSS 20
Based on the chart above the Histogram Graphic shows normal distribution . So that regression model reqiures normality assumes.
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2. Multicollinearity Test
According to the Wibowo, 2012: 87 one way to detect multicollinearity is to use a test tool called Variance inflation factor
VIF. If the value of VIF 10 it shows on the model there are no symptoms of multicollinearity. From the table below , it can be
conclude every variable does not have multicollinearity effect because that VIF 10.
Table 3.6
Test of Multicollinearity
Coefficients
a
Model Unstandardized
Coefficients Standardized
Coefficients t
Sig. Collinearity
Statistics B
Std. Error Beta
Tolerance VIF
1 Constant
,194 ,136
1,430 ,156
XI ,574
,059 ,581 9,683
,000 ,224
4,4 57
X2 ,222
,041 ,247 5,455
,000 ,393
2,5 44
X3 ,144
,050 ,204 2,896
,005 ,162
6,1 70
a. Dependent Variable: Y
Source
: Processed primary Data by SPSS 20 Analaysis the data Tolerance value shows there is no independent
variable which has tolerance value less then 0.10 that means there is no correlation among indepedent variables, In other hand VIF
shows similar things that there is no one indepedent variable has VIF value more than 10, thus it can be concluded that there is no
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multicollinearity among indepedent variables in regression model and feasible to use.
3. Heteroscedasticity Test
According To Wibowo, 2012: 93 if the result has a significance probability value alpha values 0.05 , then the model is not
experiencing heteroscedasticity. Based on the table below, when significant probability value of variable greater than 0.05
Table 3.7 Test of Heteroscedasticity
Coefficients
a
Model Unstandardized
Coefficients Standardized
Coefficients t
Sig. Collinearity
Statistics B
Std. Error Beta
Tolerance VIF
1 Constant -1,022E-015
,136 ,000
1,000 XI
,000 ,059
,000 ,000 1,000
,224 4,457 X2
,000 ,041
,000 ,000 1,000
,393 2,544 X3
,000 ,050
,000 ,000 1,000
,162 6,170 a.
Dependent Variable: Y
Source
: Processed primary Data by SPSS 20
4. Autocorrelation Test
A model can be expressed is not the case if the probability value autocorrelationsymptoms Durbin Waston 0.05 Wibowo, 2012:
106. In this table below Durbin Watson symptoms 1.966 0.05, so
that model does not have autocorrelation effect.
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Table 3.8 Test of Autocorrelation
Model Summary
b
Mode l
R R Square
Adjusted R Square
Std. Error of the Estimate
Durbin- Watson
1 ,960
a
,922 ,920
,22499 1,966
a. Predictors: Constant, x3, x2, x1 b. Dependent Variable: y
Source : Processed primary Data by SPSS 20
D. Mutiple regression Analysis
Table : 3.9 Coefficient Determination
Model Summary
b
Mode l
R R Square
Adjusted R Square
Std. Error of the Estimate
1 ,961
a
,924 ,922
2,00404 a. Predictors: Constant, x3, x2, x1
b. Dependent Variable: y
Source : Processed primary Data by SPSS 20
From the coefficient determination’s view from the table above
Adjust R square is 0.922 or 92.2 it means all indepedent variable like service quality, product quality, and promotion, toward
customer satisfaction have significant influence about 92.2. thus the residual coefficient, around 7.8 it will expalin by the other
factors that is not calculated in this research.
