Descriptive Statistic Data Descriptive

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2. Multicollinearity

Multicollinearity test is used to test the existing of perfect relationship or near-perfect relationship between the independent variables the regression model. The multicollinearity test results are shown in the following table: Table 4.5 The Result Of Correlation between Independent Variable BOD CA FO FDM BOD 1.000000 0.483204 0.200482 0.301886 CA 0.483204 1.000000 0.009040 -0.091258 FO 0.200482 0.009040 1.000000 0.110585 FDM 0.301886 -0.091258 0.110585 1.000000 Source: Output Eviews 8.0 Based on the table 4.5, it is known that the value of each variables BOD, CA, FO and FMD toward the other veriables is less than 0,8. According to Ghozali 2013:83, it can be concluded that the multicollinearity does not occur on board of director, size of committee, family ownership and family managerdirector variables .

3. Autocorrelation

Autocorrelation test is used to detect the internal correlation among the groups of a series observation arrange in a series of place and time. The basic of decision making in this test are based on Durbin-Watson Test, which can be seen in the table below: 69 Table 4.6 The Result Of Durbin-Watson Statistic dw Count dU Table Value 4 – dU dL Table Value 4 - dL 1.737531 1.7716 2.2284 1.5070 2.493 Source: Output Eviews 8.0 Based on Ghozali 2013:138, the value of dw count is placed between dU table and 4 – dU dU d 4 – dU. Therefore, it can be concluded that the data is conceive of autocorrelation problem. Nevertheless, according to Gujarati 2004:475, if one research is using Generalized Least Square GLS model, thus, the output does not have an autocorrelation problem. The regression model that have used in this research is using GLS model, thus it can be concluded that the autocorrelation problem is solved.

4. Heteroscedasticity

Heteroscedasticity test is used to indicate in a regression model whether there is variance inequality of residual in one other observation or not. The way to indicate the heterocedasticity can be done by white test. The result of white heteroscedasticity test can be seen in the following table : 70 Table 4.7 The Result Of Heteroscedasticity : White Test F-statistic 2.256020 Prob. F4,75 0.0710 ObsR-squared 8.591898 Prob. Chi-Square4 0.0722 Scaled explained SS 6.915882 Prob. Chi-Square4 0.1404 Source: Output Eviews 8.0 Besed on Ghazali 2013:105, white heteroscedasticity test that provided in the table 4.4 known that the p-value 0.0722 is higher than α 0,05, it means that the data in this research does not conceive heteroscedasticity according to.

b. Model Selection in Panel Data Regression

As mention in chapter 3, there are three models in panel data regression, they are : Pooled Least Square PLS, Fixed Effect Model FEM, and Random Effect Model REM. Before determine which one is the best model for the research, there are some of test that have to be done. The first is chow test. Chow test use to determine whether PLS or FEM model that will uses to processing the data. If the result of chow test indicates that the FEM id better than PLS, the nest stage will be Hausman test. Hausman test used to determine whether FEM or REM that will be used to processing the data. 1. Chow Test The aim of chow test is to determine whether PLS of FEM model that will be used to processing the data. The requirement to reject H is, if the F chow is F table or if the probability of F chow is α 0,05. 71 The formula is looking for F table is as follows: DF1 = k -1 DF2 = n - k where : k: is the number of all variables n: is the number of sample Table 4.8 The Result of Chow Test Cross-saction F F Chow F Table Probability F Probability α Decision 10.98 2.49 0.0000 0.05 FEM Source: Output Eviews 8.0 Based on the chow test result that have processed in the table 4.8, it is known that the value of Cross-saction F is higher that F table that is 10.98 2.48 and the probability of F chow in Cross- saction F is lower than α that is 0.0000 0.05, thus the H0 PLS model is rejected, Ghazali 2013:269. Thus, the tentative conclusion is FEM model is used. After finished the chow test, the next stage will be the Hausman test, to determine whether the FEM or REM model that will be used to precess the data.