40 These variables are said to have a value of cronbach alpha of his
larger 0.60 Nunnally in Ghozali, Imam 2005: 42. Reliability test aims to see the consistency of the measurement tool will be used,
i.e. whether the measuring instrument is accurate, stable, and consistent. The techniques used were cronbach alpha coefficient.
Reliability an instrument is acceptable if it has minimal cronbach alpha coefficient is 0.60, which meant that the instrument could be
used as a reliable data-collecting the results of measurements of the relative coefficient if it is done restart rod for.
2. Classic Assumption Tests
Multiple linear regression model may be referred to as a good model if the model of classical statistical assumptions. A classic
assumption test process can be performed using SPSS 20.0 FOR Windows programs
a. Normality
Normality testing is the testing of the average distribution of the data. This test is the most widely performed testing for
parametric statistical analysis. The use of tests of normality because on the statistical analysis of the parametric assumption that must be
owned by the data is that the data is distributed normally. The intent of the distributed data normally is that the data will follow the
form of a normal distribution. That data concentrates on the average
41 and median. To know the shape of the distribution of the data we can
use graphs of the distribution.
b. Multicollinearity
Multicollinearity is only to indicate the existence of a linear relationship between variables independent in the regression model.
If the free variables correlated perfectly then it can be called the perfect multicollinearity. To find out whether or not there are
multicollinearity in regression model are as follows: 1 Analyzes the correlation matrix of variables. If there is a
correlation between the free variables are quite high generally above 90 then it is indicated the presence of
multicollinearity. 2 According to Ghozali Priest 2005 : 91 , the value of a
common cutoff is used to indicate the presence of relevant is if the tolerance value is 0.10 or equal to the value of the VIF 10.
This test is done to avoid errors in the estimate the ability of the independent variables as predictors of the dependent
variable. Multicollinearity test done by looking at the value of VIF regression if the value of the independent variable VIF
greater than 10 then it can be inferred that these variables have a very stronglinear relationships with other free variables. With
regard to eliminate Multicollinearity is to eliminate the free variables from the regression equation.
