B. A NALYSIS AND D ISCUSSION
1. Descriptive Statistics Analysis
Descriptive statistic analysis done by comparing minimum, maximum and mean values of each variable. Descriptive statistic analysis on table 4.2 is a descriptive analysis for variable used in this study which is Multiple Directorship, Political Connection, Earnings Management.
Table 4.2
Descriptive Statistics Analyses in Period 2013-2018
N Minimum Maximu
m
Mean Std.
Deviation MUL
POL EM Valid N (listwise)
42 42 42 42
.00000 .00000 .00000
1.00000 1.00000 .21314
.8571429 .3095238 .0183107
.35416680 .46790114 .05354630
Source: Modified and Adopted from the Descriptive Summary Results in SPSS 23 by author
With the mean value of 0.8571429, it shows that 85% of the board in an average Manufacturing Company on LQ45 Index filled with multiple directors. This means that if particular company is composed of 10 (ten) directors and commissioners, about eight of them is holding directorship or commissioner position in at least one other company.
In the case of political connection, it shows a high proportion of manufacturing companies are politically connected. While this results is supportive of the argument made by Ding et al. (2014) cited in Apriliani
57 (2015) about how companies in developing countries have a higher tendency to be politically connected, this result should be used with caution since at a given year, the companies can be politically connected or otherwise depending on who the directors are, thus making the above results inconclusive to describe the overall picture of Indonesian companies in terms of political connections.
2. Classical Assumption Test a. Normality Test
Normality test aims to test whether in regression model, independent variable, dependent variable, or both has normal distribution or not. Good regression model has normal distribution of data or close to normal (Ghozali, 2012).
1. Normality Test with Histogram
The Result of normality test can be seen in the Figure 4.1 Figure 4.1
Normality Test with Histogram
58
Source: Captured from the Result in SPSS 23 by author
As seen in histogram graph, the graph is symmetric which means not swerve to right or left. Based on the histogram graph above, the data is normally distributed.
2. Normal Probability Plot Test
Probability Plot test or (P-Plot test) is another way to measure the normality.
Figure 4.2
Normality Test with Probability plot
Source: Captured from the Result in SPSS 23 by author
Normal distribution will form a diagonal straight line and the plot of residual data will be compared with the diagonal line (Ghozali, 2013). As seen in Figure 4.2, the dots are not spread and stuck in the area of diagonal line and the dots follow the direction of the diagonal line. A straight diagonal line in a normal probability plot indicating normally distributed data.
3. Normality Test in Statistics
59 As mentioned in Chapter 3, one of the main assumptions in Linear Regression is normality. The result of Klomogrov-Smirnov shows that Asymp. Sig (2-tailed) result is 0.162 which is more than 0.05. From the result, it concludes that the distribution of data in this research is normal.
Table 4.3
Klomogrov-Smirnov Test Result
Unstanderdized Residual N
Normal Parametersa.b
Mean
Std. Deviation Most Extreme Differences Absolute Positive Negative Test Statistic
Asymp. Sig. (2-tailed)
42 .000000 .04769317 .117 .117 -.070 .117 .162 a. Test distributon is normal
b. Calculated from data
c. Liliefors Significance Correction
Source: Modified and Adopted from the Descriptive Summary Results in SPSS 23 by Author
b. Multicollinearity Test Results
This test aims to examine whether regression model found correlation between independent variable or not. Multicollonearity test done by using Tolerance value or Variance Inflation Factor (VIF).To know the existence or absence of multicollonearity by looking a tolerance value or Variance Inflation Factor (VIF). General Cut Off value use to show the existence of multicollonearity is tolerance value ≥ 0,10 or equal to VIF value ≤ 10. If tolerance value is under 0.10 or VIF value above 10
60 then there is a multicollonearity. Multicollonearity test result is on the table below:
Table 4.4: Multicolinearity Test Coefficients*
Model
Collinearity Statistics
Tolerance VIF
(Constant) MUL
POL
.628 .628
1.592 1.592 a. Dependent Variable: EM
Source: Modified and Adopted from the Descriptive Summary Results in SPSS 23 by author
As it can be seen from Table 4.4, the results show that both independent variables are free from multicolinearity problems since the Tolerance is > 0,10 and VIF < 10.
c. Heteroscedasticity Test Results
Heteroscedasticity test aims to test whether in regression model there is an inequality variance of the residual of one observation to others.
If variance of residual one observation to another observation fixed or same, then it is called homocedasticity and if different it is called heteroscedasticity. Good regression model is homoscedasticity or does not occur heteroscedasticity (Ghozali, 2012). Heteroscedasticity test result is on the table below:
61 Table 4.5: Heteroscedasticity Test with Park Test
Coefficientsa Model
Unstandardized Coefficients
Standardized
Coefficients t Sig.
B
Std.
Error Beta
1 (Constant) MUL POL
-4.204 .656 -.176
.885 .838 .658
.242 -.083
-4.748 .782 -.267
.000 .445 .793 a. Dependent Variable: LN_RES2
Source: Adopted and Captured from the Result in SPSS 23 by author
As it can be seen at figure 4.3, the signification value between independent variable and absolute residual is > 0,05 in Sig. Value. It then concluded that data in this research have similar variants in regression function or homosdasticity or heteroscedasticity does not occur.
d. Autocorrelation Test Results
Autocorrelation test is used to determine and detect the presence of autocorrelation. The autocorrelation test aims to test whether in the linear regression model there is a correlation between confounding error in period t and period t-1 (previous year). A good model is a regression model that is free from autocorrelation (Ghozali, 2012). Autocorrelation test is on the table below:
62 Table 4.6: Autocorrelation Test
Runs Test
Unstandardized Residual
Test Valuea -.00497
Cases < Test Value 21
Cases >= Test Value 21
Total Cases 42
Number of Runs 22
Z .000
1.000 Asymp. Sig. (2-tailed)
a. Median
Source: Modified and Adopted from the Descriptive Summary Results in SPSS 23 by Author
From the table 4.6, Runs test is used for autocorrelation test and the results show the Asymp. Sig. (2 tailed) is >0.05, it means hypothesis 0 is rejected. Therefore, the used data is random so that no autocorrelations occurs to the tested data.
3. Test of Hypothesis
The Hypothesis test in this research uses the multiple regression models. It is conducted with Simultaneous Test (F-Test), Coefficient Determination Test (R2) and partial regression test (T-test).
a. Simultaneous Test Results
F test is used to find out whether the independent variable is simultaneously can affect the dependent variable (Ghozali, 2015).If the F probability is < 0.05, Ha is accepted and rejects Ho, whereas if probability F > 0.05 then Ho is accepted and rejects Ha. F test can be seen in the Table 4.7 below:
63 Table 4.7
Simultaneous Test (F-test)
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1 Regression .024 2 .012 5.080 .011b
Residual .093 39 .002
Total .118 41
a. Dependent Variable: EM
b. Predictors: (Constant), POL, MUL
Source: Modified and Adopted from the Descriptive Summary Results in SPSS 23 by author
As it can be seen from the table 4.7, F value is 5.080 and Sig.
Value is 0.011. It shows that significant value < alpha (α=0.05). It then concluded that there is significant effect simultaneously between multiple directorship and political connection towards earnings management.
b. Determination Coefficient (R2) Test Results
The coefficient of determination (R²) essentially measures how far the model‘s ability to explain the variation of dependent variable. The value of coefficient of determination is between zero and one. Small R² value means the ability of independent variables in explaining dependent variable variation is limited (Ghozali, 2012).
Table 4.8
Determination Coefficient Test (R2)
Model Summaryb
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .455a .207 .166 .04890078
a. Predictors: (Constant), POL, MUL b. Dependent Variable: EM
Source: Modified and Adopted from the Descriptive Summary Results in SPSS 23 by author
64 As it can be seen from table 4.8, the R2 value is 0.166 or 16%.
This means 16% of earnings management variation can be explained by the variation of independent variable which is Multiple Directorship and Political Connections. While the remainder is 84% (100% - 16%) explained by other variable that not include in regression model, such as information asymmetry, audit adjustment, and etc.
c. Partial Test (T-test) Results
Individual parameter significance test is (t statistics test) used to see partially effect of independent variable on dependent variable. To interpret coefficient of independent variable can used unstandardized coefficients or standardized coefficients. This study used standardized coefficients so there is no the constant. The benefit of using standardized beta is that able to eliminate the different of size unit on independent variable (Ghozali, 2012). As we can see in the table below
Table 4.9 Partial Test (T-test)
Source: Modified and Adopted from the Descriptive Summary Results in SPSS 23 by author
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta 1 (Constant)
MUL POL
-.070 .084 .051
.029 .027 .021
.557 .448
-2.432 3.095 2.491
.020 .004 .017 a. Dependent Variable: EM
65 Based on the results from table 4.9, it shows that multiple directorships (MUL) has the significant value of 0.004 (<0.05). It means H1 is accepted and multiple directorships effects earnings management. This output is supportive by (Core et al., 1999; Fich and Shivdasani, 2006).
H1 = Multiple Directorship influences earnings management is accepted
T-test result for political connection (POL) has the significant value of 0.017 (<0.05). It means H2 is accepted and political connection influence earnings management. This output is supportive by (Boubakri et al, 2012).
H2 = Political Connection influences earnings management is accepted
Referring to the result from table 4.9, it concludes the multiple linear regression is as follows:
DACCit = -0.070 - 0.084 MULit - 0.051 POLit
From the linear regression shows that constant value is -0.070, it proposed those independent variables which are multiple directorships and political connection are constant, then earnings management is -0.070 or -7%.
The MUL coefficient value is 0.084, it shows the negative result where the multiple directorship occur in 1% so that will be decrease the earnings management (EM) of 0.084 times in the period 2013-2018, assuming other variables in the fixed regression equation.
The POL coefficient value is 0.051, it shows the negative result where the multiple directorship occur in 1% so that will be decrease the
66 earnings management (EM) of 0.051 times in the period 2013-2018, assuming other variables in the fixed regression equation.