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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.
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Table 4.3 Iteration History 0
Source: Output SPSS 22.0 Based on the results of the processing of SPSS 22.0, in
Table 4.4 shows that the initial value of -2 Log Likelihood Iteration History table 0 amounted as 86,455. Mathematically, the
numbers are significant at alpha 5 and means that the null hypothesis H
is rejected. This means that only constants not fit to the data before independent variable being inserted into the
regression model Ghozali, 2013: 340. The next step is to comparing the value of -2 Log
Likelihood early Iteration History table 0 with -2 Log Likelihood end Iteration History table 1. On the Iteration History table 0, the
value -2 Log Likelihood preliminary 86,455. After the independent variables included in the regression model, the value -2 Log
Likelihood in Table 4.5 History Iteration 1 is 47,771.
Iteration History
a,b,c
Iteration -2 Log likelihood
Coefficients Constant
Step 0 1 89.833
-1.533 2
86.525 -1.950
3 86.455
-2.022 4
86.455 -2.024
5 86.455
-2.024 a. Constant is included in the model.
b. Initial -2 Log Likelihood: 86.455 c. Estimation terminated at iteration number 5 because parameter
estimates changed by less than .001.
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Table 4.4 Iteration History 1
Iteration History
a,b,c,d
Iteration -2 Log
likelihood Coefficients
Constant
BOC BOD Com
Indd Lev
PYAO Step 1 1
62.095 -1.573 .137 -.050
-.004 -.387
2.458 2
50.173 -2.000 .328 -.118
-.013 -.876
3.362 3
47.975 -1.950 .529 -.175
-.025 -1.296 3.895
4 47.774
-1.769 .625 -.185 -.034 -1.442
4.087 5
47.771 -1.720 .640 -.182
-.037 -1.457 4.111
6 47.771
-1.718 .640 -.182 -.037 -1.457
4.112 7
47.771 -1.718 .640 -.182
-.037 -1.457 4.112
a. Method: Enter b. Constant is included in the model.
c. Initial -2 Log Likelihood: 86.455 d. Estimation terminated at iteration number 7 because parameter estimates
changed by less than .001.
Source: Output SPSS 22.0 Based on the results of the output, a decline in values
between -2 Log Likelihood initial and -2 Log Likelihood end is 38,684. The decline can be interpreted that the addition of
independent variables in the regression model improve the model. So H
accepted, in other words, the model fit to the data.
b. Coefficient of Determination
Coefficient of determination used to describe how much the independent variables are able to explain the variability of the
dependent variable Solikah, 2010: 106. The coefficient of determination in the logistic regression showed with Nagelkerke R
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