66
B. Analysis
1. Descriptive Statistics
Descriptive statistic provide an overview of the minimum value, maximum value, average value mean and standard deviation of the
data used in the research.
O u
t p
u Source: Output SPSS 22.0
Variable changes in the board of commissioners BOC indicates the minimum value is 0 and the maximum is 1. This is because the
variable BOC is a dummy variable with analysis categories 0 and 1. The mean value of variable BOC is 0,450. It is clear that only about
45 of total sample from 120 companies that make the turn or change the board of commissioners.
Variable changes in board of directors BOD shows the minimum value is 0 and the maximum is 1. This is because the variable BOD is
also a dummy variable with analysis category 0 and 1. The mean value of the variable BOD is 0,650. It shows that on average only about 65
Table 4.2
Descriptive Statistics
BOC BOD
Com_Indd Lev
PYAO N
Valid 120
120 120
120 120
Missing Mean
.450 .650
39.9716 .409359
.142 Std. Deviation
.4996 .4790
10.40472 .1661897
.3502 Minimum
.0 .0
22.22 .0599
.0 Maximum
1.0 1.0
75.00 .7684
1.0
67 of the sample companies from total 120 companies that make the turn
or change board of directors. Variable independent commissioner Ind_Comm shows the
minimum value is 22,22 and the maximum value is 75,00. This means that the sample of companies, the percentage of the number of
independent directors at least is equal to 22,22 of total number of commissioners and at most 75 of total number of commissioners.
Mean of variable independent commissioner is 39,9716. This explains that the average proportion of independent directors on the companys
sample was 39,9716. Variable leverage Lev has a minimum value 0,7684 and
maximum value 0,0599 and 0,409359 mean. Leverage variable is proxied by total liabilities to total assets.
Variable previous year audit opinion PYAO is a dummy variable with category analysis 0 for non-going concern opinion and 1 for
going concern opinion. PYAO has a minimum value is 0 and a maximum value is 1 and mean also 0,142. This shows the average of
PYAO 14,2 of the sample companies from total 120 companies get going concern audit opinion.
68
2. Logistic Regression
In this research, the hypothesis was tested using logistic regression. Logistic regression was used to examine the probability of occurrence
of the dependent variable can be explained by the independent variable. This analysis techniques no longer require data normality test
on the independent variable Ghozali, 2013. Testing is done with a significant level of 5 0,05 Stanislaus, 2006: 236 in Amilin and
Indrawan 2008: 80.
a. Overall Model Fit Test
This test is performed to determine whether the model was fit to the data, either before or after the independent variables
included in the regression model solikah, 2010: 102. Testing of overall model fit is done by comparing values between -2 Log
Likelihood at the start Block Number = 0 with -2 Log Lokelihood end Block Number = 1. Hypotheses to assess that model fit are:
H : Model hypothesized fit to the data
H
a
: The model does not fit with the data hypothesized Based on this hypothesis, H
must be accepted and H
a
must be rejected so the model will be fit with data. Likelihood L from
model is the probability that show the model can describe the input data.
Kategori