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newspapers, the internet and other literature concerning the object studied.
E. Data Analysis Methods
Analysis of the data in this study using SPSS application assistance 20. Tests were performed using SPSS 20 aid applications are as follows:
1. Test Validity
Validity is the level of research instruments to express the data in accordance with the matter to be disclosed. In other words, the validity
indicates the extent to which a measuring instrument that can be used to measure what should be measured. Validity test is used to determine the
feasibility of the items in a list of questions to define a variable. The list of questions generally support a group of specific variables. A questionnaire
as valid if there are similarities between the data collected by the data actually happened on the object under study. Sugiyono, 2004: 172.
In determining whether or not an item that will be used, usually to test the significance of the correlation coefficient in the minimum limit of
correlation of 0.30, meaning that an item is considered valid if the total
score is greater than 0.30 Priyatno, 2010: 90. 2. Test Reliability
Reliability test is a tool to measure a questionnaire which is an indicator of the variables or constructs. A questionnaire said to be reliable
or reliable if someone answers on the statement is consistent or stable over time Ghozali, 2011: 47. Reliability measurements performed with
Cronbach Alpha statistical test. A variable is said to be reliable if the
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value of Cronbach Alpha 0.60 Nunnally in Ghozali, 2011: 48.
3. Classical Assumption Test
a. TestMulticolinearity This test is used to test whether there is any correlation between the
independent variable or the independent variable Ghozali, 2011: 105. Another opinion says that multicollinearity in the regression model to test
whether there is formed a high or perfect correlation between the independent variables of the model has revealed multicollinearity
symptoms Suliyanto, 2011: 81. Multicolinierity test is done by looking at R
2
and the value of t statistics If R
2
is high and the F test rejects the null hypothesis, but the value of t statistics are very small or even not having independent
variables significantly so that it shows any symptoms of multicollinearity Suliyanto, 2011: 81. Other methods multicolinierity test is to analyze
the correlation matrix between the dependent variable and calculating the value of Tolerance and Variane Inflation Factor VIF. Low tolerance
value equal to the value of high VIF because VIF = 1 tolerance. Cutoff value that is commonly used to indicate the presence of multicollinearity
is the tolerance value ≤ 0.10, or equal to the value of VIF 10.
b. Test Heteroscedasticity Heteroskesdasticity test aims to test whether the regression model
occurred inequality variant of the residuals of the observations to other observations remained, is called homocedasticity. Two methods used in
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this study to see heteroscedasticity is to look at the graph plot between the predicted value of the dependent variable ZPRED with residual
SRESID Ghozali, 2011: 139. Heteroscedasticity Test whether there is a variant of variables in the regression model is not the same constant
called heteroscedasticity, or variants of variables in the regression models have the same value constant called homocedasticity Suliyanto, 2011:
95. c. Normality Test
Normality test aims to test whether the regression model, or residual confounding variable has a normal distribution. Good data and fit
for use in research is one that has a normal distribution. Normality of data can be viewed in several ways, including by looking at the normal curve
p-plot. A variable is said to be normal if the distribution of the image data points are spread around the diagonal line, and the spread of the data
points in the direction to follow a diagonal line. Ghozali, 2011:161. 4. Multiple Linear Regression Analysis
In general, this analysis is used to examine the effect of some independent variable variable X to the dependent variable Y. In the
multiple regression independent variable variable X are taken into account its effect on the dependent variable Y, there are more than one.
Regression Suilyanto, 2011: 53 is the dependent variable is affected by two or more independent variables so that the functional relationship
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between the dependent variable Y with independent variables X1, X2, ... .Xn.
In this study, the independent variable is consumer unsatisfaction, advertising, pricing, word of mouth and brand image X. The dependent
variable in this research is brand switching Y so that multiple regression equation is:
Y = a + b1X1 + b2X2 + b3X3 + B4X4 +B5X5 e
Explanation: Y = The dependent variable displacement brand brand switching
a = Intercept constant b1 = Regression coefficient for the dependent variable X1
b2 = Coefficient of regression for the dependent variable X2 b3 = Coefficient of regression for the dependent variable X3
b4 = Coefficient of regression for the dependent variable X4 b5 = Coefficient of regression for the dependent variable X5
X1 = Customer unsatisfaction X2 = Advertising
X3 = Price X4 = Word of Mouth
X5 = Brand image e = Error.
a. Simultaneous testing of Regression Coefficients Test F F test is done to look at the distribution of variants caused by
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regression and variance caused by residual danPada basically used to indicate whether all independent or independent variables have
influence together to dependent variable bound Ghozali, 2011: 98. This can be done by the following criteria Ghozali, 2011:
98: 1 Determining Hypothesis Formulation
a Ho: b1, b2, b3 = 0. That is, there are no positive effects of each independent variable X simultaneously the
dependent variable Y. b
Ho: b1, b2, b3 ≠ 0. That is, there are no positive effects of each independent variable X simultaneously the
dependent variable Y. 2 Determining the degree of probability of 95 or the 0.05 one-way
One-tail. 3 Determine the criteria for decision-making
When the F count F table, then Ho is rejected and Ha accepted. It means that independent variables simultaneously
affect the dependent variable. b. Test Coefficient of Determination
The coefficient of determination R2 was essentially used to measure how far the regression models ability to explain variation
in the dependent variable Ghozali, 2011: 97. R2 small value means the ability of independent variables in explaining the
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dependent variable is very limited. However, many researchers recommend using adjusted R2 values when evaluating where the
best regression model Ghozali, 2011: 97. c. Tests on Partial Regression t test
The t-test was conducted to test each independent variable X to the dependent variable Y, which is conducted to determine
how much each variable consumer unsatisfaction, the characteristics of the product category, and variety seeking
influence on brand switching brand switching. Test steps are as follows Ghozali, 2011: 98:
1 Determining Hypothesis Formulation a Ho: bi = 0. That is, there is no influence of each
independent variable X partially on the dependent variable Y.
b Ho: bi ≠ 0. That is, there is the influence of each
independent variable X partially on the dependent variable Y.
2 Determining the degree of probability of 95 or the 0.05 one-way One-tail.
3 Determine the criteria for decision-making a Quick look: if the value of t 2 in absolute value, then Ho is
rejected and Ha accepted. b If t t table, then Ho is rejected and Ha accepted. That is
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partially independent variables affect the dependent variable.
F. Variable Operational Research
Kategori