Data Analyze Technique and Hypotheses Test

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E. Data Analyze Technique and Hypotheses Test

1. Data Test Technique a. Validity test Validity is the extent to which the data collected truly reflect the phenomenon being studied. For the sake of the clarity, Sekaran 2000 can group validity test under three broad headings: content validity, criterion-related validity, and construct validity. This research use construct validity test because this approach is more objectives, simple and it use in many research. Construct validity testifies to how well the results obtained from the use of the measure fit the theories around which the test is designed Sekaran, 2000: 208. Any biases could also be detected if the respondents had tended to respond similarly to all items or stuck to only certain points on the scale Sekaran, 2000: 208. To test whether latent constructs are unidimensional or indicators measurement constructs are valid. First, we must see whether indicators are statistically significant or not. Second, we must see convergent validity value or loading factor value for each indicator. Some established research use 0,70 for good validity value. While convergent validity 0,50-0,60 still acceptable for earlier research Ghozali, 2008: 132. b. Reliability Test The reliability of a measure indicates the extent to which the measure is without bias error free and hence offers consistent perpustakaan.uns.ac.id digilib.uns.ac.id commit to user measurement across time and across the various items in the instrument Sekaran, 2000: 204. According to Ticehurst and Veal 2000 in Kripanont 2007: 128, reliability is the extent to which research findings would be the same if the research were to be repeated at a later date, or with a different sample of subjects. A construct or variable is said reliable, if the Cronbach’s alpha value is 0,70 Ghozali, 2008: 137. According to Sekaran 2006 in Bhilawa 2010: 33, reliability less than 0.6 is considered to be poor, those in the 0.7 is acceptable, and those over 0.8 is good. The closer the reliability coefficient gets to 1.0 is the better. c. Normality Data Assumption SEM requires normal distribution of data. If data distributes abnormal, maybe it will influence data analysis resulting to high bias data. In this research, normality test is counted by using computerized program, AMOS 18. The postulate used in this research to examine data normality is the critical ratio cr value. The data distribution is normal if cr skewness value or kurtosis cr value is between -2,58 and +2,58 Wijaya, 2009: 134. Curran et al. in Bhilawa 2010: 34 divides normality data level into three parts, they are: • normal, if z statistic value critical ratio or c.r. skewness 2 and c.r. kurtosis value is 7, • moderately non-normal, if c.r skewness is between 2 to 3 and c.r kurtosis value is between 7 to 21, perpustakaan.uns.ac.id digilib.uns.ac.id commit to user • extremely non-normal, if c.r. skewness is 3 and c.r. kurtosis is 21. d. Outlier Evaluation Outlier is the observation that appears with extremely values, which have a unique different characteristic from other observation, and it appears on extreme value, whether it on one variable or combination variables Hair et al. in Bhilawa, 2010: 33. Outlier can be handled with erasing one or some data which far from the certain spot center. Test to multivariate outliers is done using Mahalanobis Distance criteria at the level p0,001. Mahalanobis Distance evaluated using χ 2 at free degree as big as variables sum, which is used in research Ferdinand in Bhilawa, 2010: 33. This outlier evaluation is done with computer’s software, AMOS 18. 2. Model Assumption Test This research uses Structural Equation Modeling SEM multivariate analyzing to examine hypotheses using AMOS 18 software. SEM is a statistical model that provides approximate calculation of the strength of the hypothesis on the relationship between variables in a theoretical model, either directly or through intervening variables Maruyama in Wijaya, 2009: 1. SEM refers to the relationship between endogenous variables and exogenous variables, which is the variable can not be observed or calculated directly unobserved variables or latent variables Pedhazur in Wijaya, 2009: 2. perpustakaan.uns.ac.id digilib.uns.ac.id commit to user AMOS 18 used to examine whether the estimated model has goodness of fit and has causality relation as hypothesized. The test consists of: a. Goodness of Fit Measurement Structural model categorized as “good fit” if it fulfills these conditions below. 1 Chi-Square χ 2 Measurement Statistic CMIN This analysis is purposing to develop and examine a model which appropriate with the data. Chis square is so sensitive to very small sample as well as to very large sample. Thus, this examination needs to complete with another examine the instrument Ghozali, 2008: 130. CMIN shows the likelihood ratio chi-square statistic for each fitted model tested against the saturate model. If the p value for each model is greater than 0.05, this means that the data do not depart significantly from the model. Furthermore, if at each step up the hierarchy from the unconstrained model to the measurement residuals model, the increase in chi-square is never much larger than the increase in degrees of freedom a non-significant chi-square, p value greater than 0.05, the model up the hierarchy is preferable otherwise, the model up the hierarchy is worse a significant chi-square, p value less than 0.05 Arbuckle in Kripanont, 2007: 147. perpustakaan.uns.ac.id digilib.uns.ac.id commit to user 2 Minimum Probability Value Level P value is the probability of getting as large a discrepancy as occurred with the present sample under appropriate distributional assumptions and assuming a correctly specified model. So P is a “p value” for testing the hypothesis that the model fits perfectly in the population. Therefore, this is a method to select the model by testing the hypothesis to eliminate any models that are inconsistent with the available data Kripanont, 2007: 192. The minimum probability value level that needs is 0,1 or 0,2, but for probability level about 0,05 is still able. Hair et al. in Bhilawa, 2010: 36. 3 Normed Chi-Square CMINDF This index is chi square value divided with degree of freedom. According to Wheaton et al. 1977, ratio value ≤ 5 is a reasonable measurement. Other researchers such as Byrne 1988 suggest to this value ratio 2 is a fit measurement Ghozali, 2008: 67. CMINDF χ2 df is the minimum discrepancy divided by its degrees of freedom; the ratio should be close to 1 for correct models. Although Arbuckle 2005 claimed that it is not clear how far from 1 we should let the ratio get before concluding that a model is unsatisfactory. In contrast, Byrne 2006 suggested that ratio should not exceed 3 before it cannot be accepted. Since the chi-square statistic χ2 is sensitive to sample size it is necessary to look at others that also support goodness of fit Kripanont, 2007: 193. perpustakaan.uns.ac.id digilib.uns.ac.id commit to user 4 Measures Based on the Population Discrepancy The Root Mean Square of Approximation RMSEA indicates expected goodness of fit if the model estimated in population. Recommended RMSEA acceptant value is ≤ 0,08 Wijaya, 2009: 7. According to Ghozali 2008: 67, RMSEA value between 0,05 to 0,08 is acceptable. 5 Goodness of Fit Index GFI GFI is a goodness- of- fit index for ML Maximum likelihood and ULS Unweighted Least Squares estimation Kripanont, 2007: 193. GFI is used to calculate the weighted proportion of the variance in the sample covariance matrix described by the covariance matrix in estimated population Wijaya, 2009: 8. Recommended acceptant level by GFI is ≥ 0,90 Ghozali, 2008: 67. 6 Adjusted Goodness of Fit Index AGFI AGFI is GFI development, adjusted with degree of freedom that is available to test whether the model accepted. Recommended value is 0,90 Ghozali, 2008: 67. Wijaya 2009: 8 also recommends AGFI value for at least equals or greater than 0,90. 7 Tucker Lewis Index TLI TLI is an incremental fit index alternative that compares a tested model against a baseline model Wijaya, 2009: 8. TLI is a index fit measure that less influenced by sample size. Recommended acceptance value by TLI is ≥ 0,90 Ghozali, 2008: 68. perpustakaan.uns.ac.id digilib.uns.ac.id commit to user 8 Comparative Fit Index CFI CFI is also known as Bentler Comparative Index. CFI is incremental fit index which also compares the tested model with null model Wijaya, 2009: 8. This index is quite good for measuring the goodness of fit because it is not influenced by sample size. Recommended value by CFI is ≥ 0,90 Wijaya, 2009: 9. 9 Normed Fit Index NFI NFI is a comparison measurement between proposed model and null model. NFI value is various starting from 0 no fit at all to 1 perfect fit. In parallel with TLI, NFI does not have an absolute standard value, but generally it recommends for equals or more than 0,90 Ghozali, 2008: 68. Table III.2 Goodness of Fit Indices Fit Indices Cut Off Value Source Chi-Square Approaches Wijaya, 2009 Probability level ≥ 0.05 Wijaya, 2009 CMINDF ≤ 2 Ghozali, 2008 RMSEA 0.05 Ghozali, 2008 GFI 0-1 Ghozali, 2008 AGFI Approaches 1 Ghozali, 2008 TLI ≥ 0.90 Ghozali, 2008 Wijaya, 2009 CFI ≥ 0.95 Bentler and Bonnet, 1995 NFI Approaches 1 Ghozali, 2008 Wijaya, 2009 Source: Wijaya 2009, Ghozali 2008, Huang et al. 2006 perpustakaan.uns.ac.id digilib.uns.ac.id commit to user Figure III.1 TAM with Perceived Mobility Value PMV and Perceived Enjoyment PE 3CHAPTER III Source: Data processing 2011 perpustakaan.uns.ac.id digilib.uns.ac.id commit to user

CHAPTER IV DATA ANALYSIS