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mean is 1,3306 or 133,06. The minimum value of DER 0,21 or 21 for PT. Aneka Tambang Persero Tbk year 2009 and the maximum value is
5,26 or 526 for PT. Bumi Resources Tbk year 2011, as well as having a relatively small standard deviation namely 1,10538 or 110,538. It shows
that companies have a relatively small DER because the mean value is quite far from the maximum value.
2. Normality Test
The purpose of the normality test is to determine whether the regression model variables are normally distributed or not. A good
regression model is to have normal or nearly normal distribution. In this research, to detect whether normally distributed data or not, it can be done
with using graph analysis namely histogram graph Normal Probability Plot P-P Plot and statistical analysis namely kolmogorov-smirnov test.
Figure 4.1 Normality Test Result
Source : Secondary Data Output from SPSS 20.0
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From result of figure 4.1 shows that data spread around the diagonal line and follow the direction of the diagonal line. So, it can be
concluded that the regression model meet the normality assumption. Table 4.5 below will show the result of statistical analysis namely
kolmogorov-smirnov test:
Table 4.5 Kolmogorov-Smirnov Test
The result of Kolmogorov-Smirnov test on table 4.5 also shows that the value of Kolmogorov-Smirnov 0.636 with the level of significant
probability 0.814, the value of p 0.05. So the residual data is distributed normally. Therefore, regression model used in this research has met the
normality test assumption.
One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual
N 48
Normal Parameters
a,b
Mean 0E-7
Std. Deviation ,07829805
Most Extreme Differences Absolute
,092 Positive
,083 Negative
-,092 Kolmogorov-Smirnov Z
,636 Asymp. Sig. 2-tailed
,814 a. Test distribution is Normal.
b. Calculated from data.
Source : Secondary Data Output from SPSS 20.0
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3. Classical Assumption Test
a. Multicolinearity Test
The aim from multicolinearity test is to test whether the regression model found a correlation among the independent variables.
A good regression model should there is no correlation among independent variables. In this research, to detect the presence or
absence of multicolinearity can be done by calculating value of variance inflation factor VIF of each independent variable.
Table 4.6 Multicolinearity Test Result
Based on table 4.6 above, the result shows that value of variance
inflation factor VIF of each independent variable is less than 10. So, it can be concluded that there is no multicolinearity.
Coefficients
a
Model Collinearity Statistics
Tolerance VIF
1 Board_Size
,504 1,986
Company_Size ,504
1,986 Profitability
,930 1,076
Leverage ,929
1,076 a. Dependent Variable: CSR_Reporting
Source: Secondary Data Output from SPSS 20.0
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b. Heteroskedastisity Test
The aim from heteroskedastisity test is to test whether the regression model occur the variance inequality of the residual from one
observation to another observation. A good regression model is homokedastisity or there is no heteroskedastisity. In this research,
heteroskedastisity test can be viewed with using the chart Scatterplot between the predicted value of dependent variable ZPRED and
residual SRESID.
Figure 4.2 Heteroskedastisity Test Result
Source : Secondary Data Output from SPSS 20.0
From result of figure 4.2 shows that there is no clear pattern, as well as the dots spread above and below zero 0 on the Y axis. So, it
can be concluded that there is no heteroskedastisity homokedastisity.
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c. Autocorrelation Test
Autocorrelation test aims to test whether a regression model there is a correlation between data in variable. A good regression model
is a regression that is free from autocorrelation. In this research, autocorrelation test is done by using the Durbin Watson DW test.
Table 4.7 Autocorrelation Test Result
Model Summary
b
Model R
R Square Adjusted
R Square Std. Error of
the Estimate Durbin-Watson
1 ,735
a
,540 ,497
,08186 1,243
a. Predictors: Constant, Leverage, Board_Size, Profitability, Company_Size b. Dependent Variable: CSR_Reporting
Source : Secondary Data Output from SPSS 20.0
Based on table 4.7 above, the result shows that value of Durbin-
Watson DW is 1,243 DW = -2 – +2. So, it can be concluded that
there is no autocorrelation.
4. Hypothesis Test
