Validity and Reliability Test Classical Assumptions Test

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2. Secondary Data

According to Cooper and Schindler 2006: 89 secondary data is the result of studies done by others and for different purpose than the one for which the data are being reviewed. Secondary data is data obtained indirectly or through another party, or a historical report prepared in the archives, published or not. Secondary data use: book, magazines and the internet. Malhotra ,2009:124

D. Analysis Method

1. Validity and Reliability Test

a. Validity According to Malhotra 2009: 316 validity is the extent to which observed scale scores reflect true differences among objects on the characteristic being measured, rather than systematic or random errors. According to Duwi Priyatno 2010:90 in determining the worth absence of an item to be used, usually performed significance test of correlation coefficient at 0.30 limitations minimal correlation, meaning that an item is considered valid if the total score is greater than 0.30. Validity and reliability tests conducted by distributing questionnaires to 100 respondents around Uin Syarif Hidayatullah Jakarta, in which questionnaire contains 15 questions statements that must be answered by the respondents and then the data using software Product Statistics SPSS 20 for Windows. 37 b. Reliability Reliability refers to the extent to which a scale produces consistent results if repeated measurements are made. Therefore, reliability can be defined as the extent, to which measures are free from random error Maholtra, 2009:315 According to Ghazali 2006:46 reliability measurements can be done in 2 ways: 1 Measure or measurements repeated: here someone will be given the same questions at different times, and then see if he remains consistent with the answers. 2 One shot or one-time measurement: here measurement only once and then the results were compared with another question or measure the correlation between answers to questions. This research will use one time measurement that using Cronbach alpha test α. A variable is said to provide reliable if the Cronbach alpha values 0.60. Ghozali, 2006:46

2. Classical Assumptions Test

a. Multicollinearity Test According to Imam Ghozali 2006:95 multicollinearity test aimed to test whether regression model is founded correlation among independent variables. To detect the presence or least multicollinearity in the regression model is as follows: 38 1 The value of R 2 is generated by an empirical regression estimates are very high, but individually variable, independent variables are many that do not affect the dependent variable. 2 Analyzing the correlation matrix of variables-the independent variable. If there is a correlation between independent variables is quite high usually above 0.90, then this is an indication of multicollinearity. If below 0.90, the absence of multicollinearity. 3 Multicollinearity also can be seen from the value of tolerance and Variance Inflation Factor VIF. Both these measures indicate each independent variable which is explained by other independent variables. Tolerance measures the independent variables were selected that are not explained by other independent variables. Low tolerance value equal to a high VIF value because VIF = 1Tolerance. Value commonly used to indicate the presence multicollinearity is tolerance value 0.10 and the value of VIF 10. Each investigator must determine the level of colinearity which it still can be tolerated. b. Heteroscedasticity Test According to Imam Ghozali 2006:125 heteroscedasticity test aimed to test the regression model. In the regression model, there are differences on variants from one observation to others. If variants from residual constant, so it called heterokesdastisity. A good regression model if there is no heterokesdastisity. 39 According to Duwi Priyatno 2012:158 glejser test is done by regressing between the independent variable with residual absolute value. If the value of significance between independent variable with absolute residuals more than 0.05, so there is no problems heteroscedasticity. Step analyses in SPSS are as follows: 1. Find the value of unstandardized residuals: click Analyze Regression Linie 2. Classify the dependent and independent table 3. Click Save unstandardized Ok 4. Search for absolute residual values: click Transform Compute Variable 5. ABS_RES click on the Target Variable and enter the Numeric Expression unstandardized residuals at the start with the words ABS 6. Regressing the independent variable with the absolute value of residuals: click Analyze Regression Linear 7. Enter ABS_RES of Dependent table and enter the variable X1, X2, X3 in the Independent table OK c. Normality Test According to Imam Ghozali 2006:147 normality test is a test of the normality of data distribution. Normality test is a test of the most widely performed by parametric statistical analysis. The use of normality tests because there is a parametric statistical analysis, the assumptions that must be owned by data is that the data are normally distributed. The 40 purpose normally distributed data is that data will follow a normal distribution form. There are two ways to detect whether or not residual normal distribution, those are with graph analysis and statistical tests. One of the easiest ways to see the normality of residuals is to look at a histogram graph comparing observational data with the distribution of near-normal distribution. Normal distribution will create a straight line diagonal and plotting residual data will be compared with the diagonal line. If the residual data distribution is normal, then the line that describes the actual data will follow the diagonal line.

E. Multiple Linier Regression