Reliability Test The Effect of Auditor Experience and Professional Judgment Towards Quality of Audit Evidence Collected(Study about CPA Firm in South Jakarta)

59 Table 4.10 Kolmogorov-Smirnov Test Source: Processed primary data by spss 20.0 The result of Kolmogorov-Smirnov test on table ÇÈ É Ê lso shows that the value of Kolmogorov-Smirnov Ë ÈÇÉÌ with the level of significant probability Ë ÈÍ Ì Î Ï the value of p Ë È Ë ÐÈ So the residual data is distributed normally È Therefore Ï regression model used in this research has met the normality test assumption È b È Ñ ulticollinearity Test The aim from Ñ ulticolinearity Test is to test whether the regression model found a correlation among the independent variables È A good regression model should there is no correlation among independent variables È Ò n this research Ï to detect the presence or absence of Ñ ulticolinearity can be One-Sample Kolmogorov-Smirnov Test Unstandardize d Residual N 50 Mean 0E-7 Normal Parameters a,b Std. Deviation 4.18400133 Absolute .069 Positive .063 Most Extreme Differences Negative -.069 Kolmogorov-Smirnov Z .487 Asymp. Sig. 2-tailed .972 a. Test distribution is Normal. b. Calculated from data. 60 done by calculating value of variance inflation factor Ó V Ô F Õ of each independent variable Ö Table 4.11 Multicollinearity Test Source: Processed primary data by spss 20.0 Based on table × Ö Ø above Ù the result shows that there is no value of variance inflation factor Ó V Ô F Õ of each independent variable which is more than Ú Ö Û or less than ÛÚ Ö So Ù it can be concluded that there is no Ü ulticolinearity Ö c Ö Ý Þ teroscedasticity Test Coefficients a Unstandardized Coefficients Standard ized Coefficie nts Collinearity Statistics Model B Std. Error Beta T Sig. Tolera nce VIF Constant 21.041 4.704 4.473 .000 Auditor Experiece 2.270 .516 .534 4.395 .000 .979 1.022 1 Professional Judgment of Auditor .255 .109 .284 2.343 .023 .979 1.022 a. Dependent Variable: Quality of Audit Evidence Collected 61 The aim from heteroscedastisity test is to test whether the regression model occur the variance inequality of the residual from one observation to another observation ß A good regression model is homocedastisity or there is no heteroscedastisity ß à n this research á heteroscedastisity test can be viewed with using the chart Scatter plot between the predicted value of dependent variable â ZPRED ã and residual â SRESID. Figure 4.2 Heteroscedasticity Test S ource: Processed primary data by spss 20.0 From the scatterplot graph above it can be seen there is no clear pattern and the points spread above and below the ä on the Y axis á it can be concluded not happen heteroscedasticity in regression models ß

4. Hypothesis Testing

a ß Coefficient of Determination