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B. Data Test Analysis
1. Normality Test
Normality data can be evaluated with skewness critical ratio value criteria at -2.58c.r2.58 at 0.01 significant level. Data conclude has a
normal distribution if skewness critical ratio value is between the absolute value of ±2.58. The result of normality data is seen as table IV.4. From the
skewness critical ratio value is seen that all indicators have normal distribution except for PU5 and PMV1. Analysis to non-normal distributed
data can bias the interpretation because the result of Chi-square value analysis the lean increases so probability level decreases.
Data that used in this study represents as the real condition which is obtained from primary data based on the very various answers of the
respondents so it was difficult to get perfect normal data distribution. According to Hair et al. in Bhilawa 2010: 52 large sample size leans
decreasing the analysis distortion from non-normality data that will be analyzed. Further more, Maximum Likelihood Estimates MLE used in this
study is not too robust to non-normal data, so we can still do the next analysis.
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Table IV.4 Normality Test
Variable Min Max Skew c.r. kurtosis c.r.
BI1 2.000 4.000
-.049 -.227
.207 .479
BI2 2.000 4.000 .226 1.048
1.224 2.838 BI3 2.000
4.000 .014
.063 -.130
-.302 BI4 2.000
4.000 .121
.560 1.428
3.311 PE1 2.000
4.000 .215
.999 -.412
-.956 PE2
2.000 4.000 .448 2.076 .257
.597 PE3 2.000
4.000 .038
.175 .213
.495 ATT1 2.000
4.000 -.072
-.334 -.393
-.912 ATT2 2.000
4.000 .156
.722 .620
1.438 ATT3 2.000
4.000 .111
.513 .244
.567 PEOU1 2.000
4.000 .023
.105 .304
.706 PEOU2 2.000
4.000 -.023
-.105 .304
.706 PEOU3 2.000
4.000 .054
.249 1.604
3.718 PEOU4 2.000
4.000 -.164
-.761 .393
.911 PU6 2.000
4.000 -.107
-.496 -.695
-1.610 PU5
2.000 4.000 .573 2.659 -1.134 -2.630 PU4 2.000
4.000 .215
.997 -1.513
-3.507 PU3 2.000
4.000 .029
.134 .057
.133 PU2 2.000
4.000 .002
.009 .071
.165 PU1
2.000 4.000 .256 1.189 -.830 -1.924
PMV4 2.000 4.000 .505 2.343 -1.232 -2.856
PMV3 2.000 4.000
-.031 -.144
-.888 -2.058
PMV2 2.000 4.000
-.081 -.374
-.525 -1.218
PMV1 3.000 4.000 .564 2.614 -1.682 -3.900
Multivariate 72.966 11.729
2. Outlier evaluation
Outlier is an observation condition from data, which has unique characteristic that looks so different from other observations and exists in
extreme value, whether it for a single variable or combination variables. Detection to multivariate outliers tested with Mahalanobis distance. Criteria
Source: Primary data processing 2011
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that use are based on Chi-squares at degree of freedom 24 is total variables at a significant level p0.001. The value of Mahalanobis distance is 51,17,
result from Chiinv formulas 0,001, 24, where 24 is total indicator variables at significancy level p0,001. It means all cases that have Mahalanobis
distance value higher from 51,17 is outliers multivariate.
Table IV.5 Outliers Data
From table IV.5, there are two cases observation number 93 and 44 categorized as the outlier, but those cases no need to take out. That because in
research analysis if there is no specific reason to take out the outlier case so that case should be taking on the research Ferdinand, 2006 in Bhilawa, 2010:
55 3.
Validity Test Valid instrument is measurement tools used to get valid data and use
to measure what things would be measured Sugiyono in Bhilawa 2010: 47. Because of the construct that will be tested is adapted from the prior research
which has success to identify factors that build the construct so this research will use Confirmatory Factor Analysis Ghozali, 2008: 121.
Value of a factor loading on a standardized estimates model is defined as construct the validity test. General standard for factor analysis is lambda
Observation number
Mahalanobis d-squared
p1 p2
93 53.515 0.000
0.063 44 52.594
0.001 0.003
Source: Primary data processing 2011
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value or factor loading value is more than 0.4 Ferdinand in Bhilawa, 2010: 47. The result of a factor loading calculation can be seen at table IV.6
below. As we can see that all constructs are valid so the study can be continued to the next test analysis.
Table IV.6 Validity Test
Variable Item Factor
Loading Validity
Perceived mobility Value PMV PMV1
0.536 Valid
PMV3 0.651
Valid PMV2
0.597 Valid
PMV4 0.68
Valid Perceived Usefulness PU
PU1 0.612
Valid PU2
0.637 Valid
PU3 0.686
Valid PU4
0.649 Valid
PU5 0.639
Valid PU6
0.536 Valid
Perceived Easy of Use PEOU PEOU4
0.576 Valid
PEOU3 0.764
Valid PEOU2
0.862 Valid
PEOU1 0.768
Valid Attitude ATT
ATT3 0.796
Valid ATT2
0.817 Valid
ATT1 0.74
Valid Perceived Enjoyment PE
PE3 0.668
Valid PE2
0.805 Valid
PE1 0.85
Valid Behavioural Intention BI
BI4 0.558
Valid BI3
0.854 Valid
BI2 0.683
Valid BI1
0.705 Valid
Source: Primary data processing 2011
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4. Reliability Test
Reliability test in this study is purposed to know how far the measurement result still consistent whether it measuring twice or more with
the same symptom and measurement tools. A researcher measured the reliability using Cronbach’s Alpha value from each variable item. A construct
variable is reliable if it has Cronbach’s Alpha Value 0.60 Nunnaly in Ghozali, 2008. Reliability test did to each questionnaire item which has
passed the validity test. From the calculation using SPSS version 19, the result is as seen as table IV.7.
From table IV.7 above, it concludes that Perceived Enjoyment PE, Perceived Usefulness PU, Perceived Ease of Use PEOU, Attitude Toward
Using ATT, Behavioral Intention BI has good reliability because the value is above 0.8. Not same for the Perceived Mobility Value, it has Cronbach’s
Alpha value between 0.60-0.79 0.707 so the reliability is accepted.
Table IV.7 Reliability Test
Variable Item Cronbachs
Alpha Reliability
PE PE
1
- PE
3
0.814 Good Reliability
PMV PMV
1
-PMV
4
0.707 Accepted Reliability
PU PU
1
-PU
6
0.809 Good Reliability
PEOU PEOU
1
-PEOU
4
0.824 Good Reliability
ATT ATT
1
-ATT
3
0.841 Good Reliability
BI BI
1
-BI
4
0.813 Good
Reliability
Key: PMV
= Perceived mobility value PU = Perceived usefulness PEOU = Perceived ease of use ATT
= Attitude BI = Behavioral intention
PE = Perceived enjoyment
Source: Primary data processing 2011
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C. Model Assumption Test