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1. Descriptive Statistics
Descriptive statistics are used to provide a description of the data that is visible from the average, standard deviation, variance,
sum, range, kurtosis, maximum, minimum and skewness Ghozali, 2013; 19. Mean used to estimate the average size of the estimated
population of the sample. Standard deviation is used to assess the dispersion of the sample average. Minimum and Maximum used to
view the minimum and maximum values of the population. This needs to be done to see the overall picture of the samples were
collected and are eligible for the research sample.
2. Logistic Regression
Hypothesis testing is done by using a multivariate logistic regression. Logistic regression is used when the dependent variable
is a non-metric with two categories and the independent variables are one or more metric and non-metric Ghozali, 2013: 333. The
dependent variable in this research is a going concern audit opinion expressed by a dummy variable, where category 1 for the company
going concern and category 0 for companies non-going concern.
3. Overall Model Fit test
The first analysis done is to assess the overall fit model to the data. Hypotheses to assess model fit is:
H : hypothesized model fit to the data
H
a
: hypothesized model that does not fit with the data
57 From this hypothesis it is clear that we would not reject the
hypothesis that no model fit the data. Statistics used by the data likelihood function. Likelihood L of the model is the probability
that the hypothesized model that describes the input data. To test the null and alternative hypotheses, L transformed into -2LogL
Ghozali 2013: 340. Decrease likelihood -2LL show regression model better, or in other words the hypothesized model fits the
data.
4. Coefficient of Determinationn Nagelkerke R Square
Cox and Snells R Square is a measure that seeks to imitate the size of R2 on a multiple regression based on estimation techniques
likelihood with a maximum value of less than one so difficult to interpretation as the value of R2 in multiple regression Nagelkers
R Square is a modification from coefficient cox and snell to ensure that its value varies from 0 zero to 1 one. This was done by
dividing the value of Cox and Snells R2 to the maximum value. Nagelkerkes value R2 can be interpretation as R2 in multiple
regression. A small value means the ability of independent variables explains the variation in the dependent variable is very
limited. A value close to the mean of independent variables provide almost all the information needed to predict the variation of the
dependent variable.
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