Data Test Analysis DATA ANALYSIS

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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. perpustakaan.uns.ac.id digilib.uns.ac.id commit to user 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 perpustakaan.uns.ac.id digilib.uns.ac.id commit to user 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 perpustakaan.uns.ac.id digilib.uns.ac.id commit to user 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 perpustakaan.uns.ac.id digilib.uns.ac.id commit to user 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 perpustakaan.uns.ac.id digilib.uns.ac.id commit to user

C. Model Assumption Test