Data Analysis Method

D. Data Analysis Method

Data analysis method consist of three aspects, there are: descriptive analysis, statistical test, and structural model test. Each topic will be explained below.

1. Descriptive Analysis Descriptive analysis of sample is aimed to know the profile of respondents that is used as background factor in this study. It is used to generalize the result on population contact. To apply this study into different background factor, it is needed to look on the demographic factor that can influence the model.

Statistical test are first step for interpreting data. It is contained of two tests, validity test and reliability test. The explanations of each point are stated as follow.

a. Validity Test Validity test is aimed to know the accuracy and precision of measurement tool in measuring the variable. In validity test each indicators are examined related to the relationship. So the indicators that have small loading factor, which cannot explain the construct, are eliminated from data analysis. In this study, validity test uses confirmatory factor analysis processed by SPSS for Windows Version 16.0, when each question item require loading factor higher than 0,40. Validity test is analyzed by comparing the factor loading value in component matrix. The bigger value of component question items, the bigger correlation of total score constructs. This decision is based on significant rate > 0.40 (see Hair et. al., 1998).

b. Reliability Test In reliability test, consistency of indicators are tested, so the higher correlation the higher consistency of the indicants. It is considered as a relevant procedure to measure the instrument research. Reliability is measured by Cronbach Alpha that reliable at range higher than 0.7 b. Reliability Test In reliability test, consistency of indicators are tested, so the higher correlation the higher consistency of the indicants. It is considered as a relevant procedure to measure the instrument research. Reliability is measured by Cronbach Alpha that reliable at range higher than 0.7

3. Structural Equation Modeling Analysis Structural Equation Model Analysis is aimed to estimate the multiple regression equation separately, but each has ties simultaneously or concurrently. In this analysis it is possible there is more than one dependent variable, and this variable becomes possible independent variables for the other dependent variables.

In principle, the structural model aims to test the causative relationship between variables, so if one of the variables changed, it will be changed in other variables as well. In this study, data will be processed using Analysis of Moment Structure software or AMOS version 18.

In this study, the statistical approach that is used to test the structural model is Structural Equation Model (SEM), considered by previous study measurement (Smith et. al., 2009). This approach will be used to test the structural model to different groups simultaneously. The difference between groups can be evaluated based on the goodness-of-fit model suggested on the following criteria:

a. Chi-Square: The purpose of this analysis is to develop and test a model that fits the data. X 2 data test with low value and generate a greater level of a. Chi-Square: The purpose of this analysis is to develop and test a model that fits the data. X 2 data test with low value and generate a greater level of

b. Goodness of Fit Index (GFI) This index reflects the level of overall model fit, calculated from the residual squares of the model that predicted compared to actual data. The value result that approaching 1 implies that the model tested had goodness of fit. The recommended value is GFI ≥ 0.90. The greater the value of GFI, the better fit owned by the model.

c. Adjusted Goodness of Fit Index (AGFI) This index is a development of GFI that adjusted for the ratio of the degree of freedom model proposed by degree of freedom from the null model (single construct model with all indicators of construct measurement.) The recommended value is AGFI ≥ 0.90. The greater the value of AGFI, the better fit owned by the model.

d. Root Mean Square Residual (RMR) RMR is an index that describes as the average squared differences between the residuals of the sample covariances and the residuals of the estimated covariances. RMR values ≤ 0.05 indicate a good index to d. Root Mean Square Residual (RMR) RMR is an index that describes as the average squared differences between the residuals of the sample covariances and the residuals of the estimated covariances. RMR values ≤ 0.05 indicate a good index to

f. Trucker Lewis Index (TLI) TLI is an incremental fit index that compares the tested model with the null model. Recommended acceptance of the value is the value of TLI ≥ 0.95.

g. Incremental Fit Index (IFI) IFI is an incremental fit index. The size of this index is in range from 0 to 1, and values result that approaching 1 indicates the model has a good level of fitness models. The recommended value of receives is IFI ≥

h. Comparative Fit Index (CFI) CFI is also an incremental fit index. The size of this index is in range from 0 to 1, and values result that approaching 1 indicates the model has a good level of fitness models. This index is highly recommended to

be used for this index is relatively insensitive to sample size and less be used for this index is relatively insensitive to sample size and less

i. Normed chi square (CMIN/DF) Cmin/df is a measure of the value of chi-square divided by degree of freedom. This index is a parsimonious fit index that measures the relationship between goodness-of-fit model and the amounts estimated coefficients that are expected to reach the level of fitness. Value result that recommended receiving the suitability model is Cmin/df < 2.0.

The summary of Goodness-of-fit framework criteria is described on Table III.1.

Table III.1 Goodness-of-Fit Model Criteria Criteria

Control of Value

X 2 Chi Square