Data Quality Test Classic Assumption

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D. Data Quality Test

1. Validity Test Validity is a characteristic of measurement concerned with the extent that a test measures what the researcher actually wishes tool reflect true differences among participants drawn from a population Cooper, 2006:765. According to Ghozali 2005:45 the validity of this research is used to measure the legality of a questionnaire. Test validity used to measure the legal valid or invalid of a questionnaire. A questionnaire is said valid if the questions on the questionnaire are able to reveal something that will be on the questionnaire measure. The total score on test validity said valid if the number of scores 0.30 Sugiono, 2007:178. 2. Reliability Test Reliability is a characteristic of measurement concerned with accuracy, precision, and consistency; a necessary but not sufficient condition for validity if the measure is not reliable, it cannot be valid. Reliability is concerned with estimates of the degree to which a measurement is free a random or unstable error Donald Cooper, 2006:352. 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, 2006: 273. 40

E. Classic Assumption

1. Normality Test Normality test aims to test whether the regression model, the dependent variable customer satisfaction and independent variables service quality X 1 , and promotion X 2 both have a normal distribution or not. If the distribution of the residual values cannot be considered to be normally distributed, then it is said there are problems with the normality assumption. According Ghozali 2006: 149, the principle of normality can be detected by looking at the spread of the data dots on the diagonal axis of the graph probability plots or by looking at the histogram of the residual. Basis for decision making as follows: a. Detection of the histogram, if the normal curves in the graph follow a bell shape, then the data are normally distributed. b. While the detection of the normal probability plot on the graph, if the data dots spread around the diagonal line, and follow the direction of the diagonal line, then the regression model to meet the assumption of normality. If the spread of the data points do not follow the direction of the diagonal, then the regression model did not meet the assumption of normality. This statistical test that can be used to test the normality of the residuals is a statistical test of non - parametric Kolmogorov-Smirnov KS Ghozali, 2006: 151. 41 Basis for decision making, when the value of the Kolmogorov Smirnov significance greater than 0.05, it can be said to be normally distributed data. If the value of the significance of the KS test is smaller than 0.05, it can be said the data was not normally distributed. 2. Multicollinearity Test According to Ghozali 2006:95, multicollinearity test aims to test whether the regression model found a correlation among the independent variables service quality X 1 , promotion X 2 . Good regression model should not happen correlation between the independent variables service quality and promotion, If among the independent variables correlated with each other, then these variables are not orthogonal. To detect the presence or absence of multicollinearity among the independent variables in regression model, it can be seen from Tolerance and VIF value. Cutoff value commonly used to indicate whether there is multicollinearity or not is Tolerance value ≤0.10 or equal to VIF value ≥10. 3. Heteroscedascity Test Heteroscedasticity test aims to test whether the regression model of the residual variance occurs inequality an observation to other observations. If the variance of the residuals one observations to other observations stable, it is called different homoskedastisitas and if it is different called heteroscedasticity. Good regression models is that happened homoskedastisitas or did not happen heteroscedasticity Santoso: 2012: 42 238. a. Looking at the scatterplot graph, if forming certain patterns, such as dots form a certain pattern regularly wavy, widened then narrowed, then heteroscedasticity indicates has occurred. If there is no clear pattern, and the points spread above and below the 0 on the Y axis, then there is no heteroscedasticity Santoso, 2012: 240. b. Glejser Test Glejser test is done with the regressed absolute value of residuals against the independent variables service quality X1, and promotion X2. Guideline from glejser test are looking at the significance level of each independent variable service quality X1, and promotion X2 on the dependent variable customer satisfaction. If the significance level yield number 0.05, it can be said regression model does not contain any heteroscedasticity. Ghozali, 2006: 129.

F. Hypothesis Test