Testing Research Results

3. Testing Research Results

a. Instrument Test

1) Validity Test From the questionnaire or questions on an initial study of the 4 variables and 16 indicators

obtained a number of 26 questions that will serve as the initial research material to test the validity of the questionnaire research material conducted to 30 respondents were then tested using SPSS version 17 with the results of the remaining 20 questions were declared valid and can be used for materials research.

2) Reliability Test Having to test the validity and then proceed with the reliability test using SPSS version 17

with the results of all the variables and items have numbers above 0.239, then all the variables declared unreliable or reliable.

b. Classical Test Assumptions

1) Data Normality Test Having to test the validity and reliability of the data it obtained 20 valid and reliable questionnaire

question which is then submitted to the respondent or muzakki remain in Lazismu Al Manar as many as 19 people as research material.

From the research or the answers of the respondents, then processed and tested to determine the normality of the data whether it has a normal distribution using SPSS software version following the normality test results using graphs P-Plot of Regression Standardized Residual presented in Figure 1.

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Figure 1 Graph Normal P-Plot of Regression Standardized Residual

Source: SPSS Versi 17 Computation

From Figure 1 it can be seen that the spread of points around the line and follow the diagonal line of the residuals in the regression model is expressed normally distributed.

2) Multicollinearity Test Multicolliniearity test is used to test whether the model found a correlation between the

independent variables. Good regression model should be no correlation between the independent variables.

Testing method used is by looking at the value of the Variance Inflation Factor (VIF) and tolerance to regression models. If the VIF value is less than 10 and tolerance more than 0,1 then the regression model is free of multicollinearity.

Table 2 Multicollinearity Results Test

Coefficientsa

Collinearity Model

Sig. Statistics

Tolerance VIF (Constant)

B Std. Error

Beta

.710 Capability (X1)

.081 .740 1.352 Benevolence (X2)

.024 .435 2.299 Integrity (X3)

a. Dependent Variable: Loyalty (Y) Source: SPSS Version 17 Computation

From Table is known that the VIF is less than 10, the variable X 1 at 0,740; X 2 at 0,435; and X 3 at 0,410 and tolerance value more than 0,1 while the variable X 1 at 1,352; X 2 at 2,299; and X 3

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3) Autocorrelation Test Autocorrelation test is used to test whether there is a correlation regression model residuals in

period t with residual in the previous period (t-1). Good regression models is the absence of autocorrelation problem. Testing method used is the Durbin-Watson test (DW test).

Decision-making on the Durbin-Watson test as follows:

1) DW value below -2 means there is positive autocorrelation

2) DW value between -2 to +2, means there is no autocorrelation

3) DW value above +2 means there is negative autocorrelation.

Table 3 Autocorrelation Results Test

Model Summaryb

Model R

R Square

Adjusted R

Std. Error of the

Durbin-

Watson 1 .901a

Square

Estimate

1.963 a. Predictors: (Constant), Integritas (X3), Kemampuan (X1), Kebajikan (X2) b. Dependent Variable: Loyalitas (Y)

Source: SPSS Version 17 Computation

Durbin Watson value of output regression can be seen in Table 3 Model Summary. It is known that the Durbin Watson value of 1,963; then the regression model is stated there is no autocorrelation.

4) Heteroscedasticity Test Heteroscedasticity test is used to test whether the regression model variants of residual

inequality occurs in one observation to another observation. Regression models were either not happen heteroscedasticity.

Heteroscedasticity test with scatterplot method is to look at the points at scatterplot regression. If the points spread with no clear pattern above and below the Y-axis then there is no problem of heteroscedasticity.

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Figure 2 Heteroscedasticity Test Results Variable Data

Source: SPSS Version 17

From heteroscedasticity test results can be seen in the output image of scatterplot regression. It can

be seen points spread with no clear pattern above and below the 0 on the Y-axis can be concluded that there is no problem of heteroscedasticity in the regression model.

c. Regression Testing

Statistical tests of the results are as follows:

Table 4 Regression Test Results

Coefficientsa

Collinearity Statistics Model

B Std.

Tolerance

VIF (Constant)

.740 1.352 Benevolence (X2)

Capability (X1)

.435 2.299 Integrity (X3)

.410 2.436 a. Dependent Variable: Loyalty (Y)

Sumber: Perhitungan SPSS Versi 17.00

1) Regression Analysis Based on Table 4 known multiple regression equation is:

Y =-0.774 + 0, 276 X 1 + 0, 467X 2 + 0,392X 3 (1)

where: Y = Muzakki Loyalty (dependent variable)

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X 1 = Capability (an independent variable)

X 2 = Benevolence (an independent variable)

X 3 = Integrity (an independent variable) Explanation of the equation as follows:

a) The constant of -2.226; meaning that if the capability (X 1 ), Benevolence (X 2 ), and Integrity (X 3 ) has the value 0, then the Muzakki Loyalty value of -2,226; means that loyalty very dependent on three independent variables for the muzakki loyalty in Lazismu Al Manar.

b) Capability variable regression coefficient (X 1 ) at 0,177; meaning that if the capability (X 1 ) increased one unit, then the Muzakki Loyalty (Y) will increase by 0,177 units assuming an independent variable other remains valuable.

c) Benevolence variable regression coefficient (X 2 ) at 0,239; meaning if benevolence has increased one unit, then the Muzakki Loyalty (Y) will increase by 0,239 units assuming an independent variable other remains valuable.

d) Integrity variable regression coefficient (X 3 ) at 0,349; meaning that if Integrity has increase one unit, then the Muzakki Loyalty (Y) will increase by 0,349 units assuming an independent variable other remains valuable.

2) Determination Coefficient Analysis Correlation and determination analysis used to determine the percentage contribution of the

effect of an independent variable together against the dependent variable. From the results of the statistical test results are as follows:

Table 5 Determination Coefficient Results

Model Summaryb

Model R

R Square

Adjusted R

Std. Error of

Durbin-

Watson 1 .901a

Square

the Estimate

1.963 a. Predictors: (Constant), Integriy (X3), Capability (X1), Benevolence (X2) b. Dependent Variable: Loyaly (Y)

Source: SPSS Version 17 Computation

From the analysis above determination can be seen in the output Model Summary of the results of multiple linear regression analysis, for the three independent variables used Adjusted R square as the coefficient of determination. Adjusted R Square is R Square value that has been adjusted. Based on output obtained Adjusted R square at 0,774 or 77,4%. It showed that the percentage

contribution of independent variable influences namely capability (X 1 ), benevolence (X 2 ), and Integrity (X 3 ) on Muzakki Loyalty variable (Y) was 77,4%. Or variations of the independent variables used in the model is able to explain 77,4% of variation in the dependent variable,

meaning that the three independent variables is large enough to influence the dependent variable. While the remaining 22,6% is influenced by other variables not included in this research model.

d. Hypothesis Test

Hypothesis testing in this research using two regression test namely the simultaneous regression coefficient test or F test and the partial regression coefficients or t test.

1) Simultaneous Linear Regression Coefficients Test (F Test) Subtema: Islamic Jurisprudence in Resolving Contemporary Problems | 317

From statistical tests performed on the variables in this research obtained the following results:

Table 6 Simultaneous Test Results (F Test)

ANOVAb

Model

F Sig. Regression

Sum of Squares

df Mean Square

21.570 .000a Residual

a. Predictors: (Constant), Integrity (X3), Capability (X1), Benevolence (X2) b. Dependent Variable: Loyality (Y)

Source: SPSS Version 17 Computation

From the table 6 it is known that simultaneous testing (F test) obtained value F count at 21,570 and

F table at 3,127 so it can be formulated as follows: 21,570 > 3,127 H 0 is rejected. It can be concluded that the variable of capability (X 1 ), benevolence (X 2 ), and Integrity (X 3 ),

simultaneously have a significant influence on muzakki loyalty variable (Y).

2) Partial Regression Coefficients Test (t Test) The purpose of this t-test was used to determine the influence of each variable of capability

(X 1 ), benevolence (X 2 ), and Integrity (X 3 ) partially to the Muzakki Loyalty Lazismu Al Manar Tasikmalaya.

Table 7 Partial Test Results (t Test)

Coefficientsa Unstandardized

Collinearity Model

Sig. Tolerance

Statistics B

VIF (Constant)

Std. Error

Beta

.710 Capability (X1)

.081 .740 1.352 Benevolence (X2)

.024 .435 2.299 Integrity (X3)

.046 .410 2.436 a. Dependent Variable: Loyalitas (Y)

Source: SPSS Version 17 Computation

From Table 7 the influence of each independent and dependent variables can be described as follows:

a) Influence of capability variable (X 1 ) to muzakki loyalty Lazismu Al Manar Tasikmalaya (Y) refers to a Table 7 that can be described as a partial test (t test) obtained t count of 1,868 and t table 1,729 and the significance of the t count of 0,081. So it can be formulated:

i.t count 1,868 > t table 1,729 (H 0 is rejected)

ii.t count sig 0,081 > 0,05 (not significant) It can be concluded that the capability variable (X 1 ) have positive influence but not

significant to Muzakki Loyalty variable (Y).

b) Influence of benevolence variable (X 2 ) to muzakki loyalty Lazismu Al Manar Tasikmalaya variable (Y) refers to Table 6 that can be described as a partial test (t test) obtained t count of 2,507 t and t table 1,729 and the significance of the t count of 0,024. So it can be formulated:

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It can be concluded that the benevolence variables (X 2 ) significantly positive influence on muzakki loyalty variable (Y).

c) Influence of integrity variable (X 3 ) to muzakki loyalty Lazismu Al Manar Tasikmalaya variable (Y) refers to Table 6 that can be described as a partial test (t test) obtained t count of 2,176 t and t table 1,729 and the significance t count of 0,046. So it can be formulated:

i.t count 2,176 > t table 1,729 (H 0 is rejected) ii.t count sig 0,046 < 0,05 (H 0 is rejected)

It can be concluded that the integrity variables (X 3 ) is significantly positive influence on muzakki loyalty variable (Y).