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the sustainability report SR from GRI Index. The research will examine 30 sustainability reports and Annual Report databases from 5 companies listed in
Indonesia Stock Exchange in the period of 2009 to 2014
.
3.4. Analysis Method
The method of analysis data in this research is using statistical calculations; the name of application is SPSS Statistical Product and Service
Solutions. The emphasis will be put on the frequency of sustainability report disclosure in sustainability report. The content analysis comprises 30
sustainability reports and annual reports which coded, analyzed and scored. The limitation of the content analysis is to identify the disclosure of social information
under each theme. Each type of information will be scored by using numbers. Zero numbers
for no disclosure through the GRI G3 in sustainability reports, 1 for element that disclosure as guided by GRI G3 indicators. The variables in this research will be
tested through the method of descriptive statistical and hypothesis testing, followed as:
3.4.1. Descriptive Method
Descriptive statistical testing in this research basically is a process transformation research data in a form of tabulation in order that can be easier to
understand and interprets. Tabulation in generally is used by researcher to obtain information about characteristics of primary variable in research.
The measurement applied in this descriptive statistical testing depends on the type of scale of measurement. The descriptive statistical testing obtains a
picture or describes data that can be seen from median, mean, mode, standard
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deviation, variance, maximum and minimum,
3.4.2 Classical Assumption
1. Normality Test
According to Hair et al. 2006 cited in Adinugraha et al 2007, the purpose of the normality test is to determine whether the regression model
variables are normally distributed or not. The normality test conducted to determine whether the inferential statistics to be used is a parametric or non-
parametric statistics. There are two ways to test, i.e. the graph analysis and statistical tests Ghozali 2011. Researcher chooses two tools to test whether the
data is normally distributed or not. a.
Graph Analysis When using graph analysis, normality test can be done by looking at the
spread of the data dots on the diagonal axis of the graph or by looking at the histogram from the residual.
1 If the dots spread around the diagonal line and follow the direction of the
diagonal line, the regression model meets the normality assumption. 2
If the dots spread away from diagonal lines and or do not follow the direction of the diagonal line, the regression model does not meet the
normality assumption. b.
Statistical Test Kolmogorov-Smirnov Z 1-Sample KS uses for making decision regarding
the normality test. 1
If the value Asymp. Sig. 2-tailed less than 0.05, it means that the data are not normally distributed.
2 bIf the value Asymp. Sig. 2-tailed of more than 0.05, it means that the
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data are normally distributed.
2. Multicollinearity Test
Multicollinearity test aims to test whether the regression model found a correlation between the independent variables Ghozali 2011. A good regression
model should not happened correlation between the independent variables. To detect the presence or absence of multicollinearity in the regression model can be
seen from the value of tolerance and the variance inflation factor opponent VIF. Multicollinearity views from the tolerance value 0.10 or VIF 10. Both of these
measurements indicate each independent variable, which is explained by the other independent variables.
3. Heteroscedasticity Test
Heteroscedasticity test aims to test if there is variance difference from residual of one observation to an other observations occurred Santoso, 2010.
Furthermore, if the variance remains constant, it is called homoscedasticity and if it is changing or different, it is called heteroscedasticity Santoso, 2010. A good
regression model is homoscedasticity or there is no heteroscedasticity. In this study, heteroscedasticity test can be viewed by using the Scatter
plot graph between the standardized predicted variable ZPRED and studentized residual SRESID. Y-axis becomes the axis that has been predicted and the X-
axis is the residual Y predicted-Y actual. Decision-making can be made by this consideration:
a. If there is a specific pattern, like dots, which form well-ordered pattern
waving, spreading then narrowing, it indicates that heteroscedasticity occurs. b.
If there are no well-ordered pattern and the dots spread above and below 0 in
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Y-axis, heteroscedasticity does not prevail.
4. Autocorrelation
Autocorrelation test aims to find if there is correlation in linear regression model between disturbances in t period with previous period t-1 Santoso,
2010. A good regression model is a regression that is free from autocorrelation. Autocorrelation can be determined using the Durbin
–Watson. Durbin– Watson statistic is a test statistic used to detect the presence of autocorrelation a
relationship between values separated from each other by a given time lag in the residuals prediction errors from a regression analysis. It is named
after James Durbin and Geoffrey Watson. To get a conclusion from the test, it need to compare the displayed statistic
with lower and upper bounds in a table. If D upper bound, no correlation exists; if D lower bound, positive correlation exists; if D is in between the two bounds,
the test is inconclusive
Positive Autocorrelation Detection:
Negative Autocorrelation Detection:
Source: Imam Ghozali, 2011:111
If d dL there is positive autocorrelation If d dU there is no positive autocorrelation,
If dLddU the test does not convince or inconclusive.
If 4 - d dL there is negative autocorrelation If 4 - d dU there is no negative autocorrelation,
If dL4-ddU the test does not convince or inconclusive.
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3.4.3 Multiple Regression Analysis
Multiple regression analysis used to test the effect of two or more independent variables toward the dependent variable Ghozali, 2011. Regression
analysis divided into two kinds, simple regression analysis if there is only one independent variable and multiple regression analysis if there is more than one
independent variable. Multiple regression analysis can be measured partially indicated by coefficient of partial regression jointly indicated by coefficient of
multiple determination or R
2
. Independent variable in this research is Good Corporate Governance
components which elaborate into size of board of commissioners, proportion of independent commissioners and size of audit committee. Besides, dependent
variable is sustainability report which appropriates with GRI G3 Indicators.
Structural equation model that proposed as an empirical model is as
follows:
Y
1
= β + β
1
X
1
+ β
2
X
2
+ β
3
X
3
+ ε
Where
Y1 Sustainability Report
X1
Size of Board of Commissioners
X2 Proportion of Independent Commissioners
X3
Size of Audit Committee
β1
Regression Variable Size of Board of Commissioners
β2
Regression Variable Proportion of Independent Commissioners
β3
Regression Variable Size of Audit Committee
ε
Error
