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Factor Analysis CFA test used to test whether the construct have unidimentionality or the indicators can confirm the construct or variable.
Table 5 Validity Result
Variables Kaiser-Meyer-
Olkin Measure of Sampling
Adequacy Initial
Eigen values
Cumula tive
Significant Status
Need for achievement NA
.723 63.701
.000 valid
Work attitude WA
.772 39.174
.000 valid
Budgetary participation BP .757
59.526 .000
valid
Job performance JP
.821 47.638
.000 valid
The result of CFA test, Kaiser-Meyer-Oklin Measure of Sampling Adequacy KMO MSA to each variable is more than 0.50 this is fulfilling
assumption of factor analysis and reliable and also assess the Bartletts Test of Sphericity BTS by chi-square is significant of hence inferential that factor
analysis can be continued. In conclusion the data of these research questionnaires were reliable and
valid to be measured. If we have met the valid and reliable data, so we can continue the next step of this research which is measuring the hypotheses of this
research.
D. Classic Assumption Analysis
1. Result of multicolinearity test
The goal of multicollinierity test is to test whether the regression model found the correlation between the independent variables. The good regression
model must not have correlation between the independent variables. If the
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independent variables have correlation, so the variables are not orthogonal. The result of multicolinearity test is summarized as follow:
1 The first regression model the dependent variable: budgetary participation
Table 6 Multicollinearity test result
Model Unstandardized
Coefficients Standar
dized Coeffici
ents t
Sig. Collinearity
Statistics
B Std.
Error Beta
Toler ance
VIF Constant
13.627 4.948
2.754 .008
Need for achievement .378
.123 .340
3.078 .003
.982 1.018
Work attitude .086
.047 .201
1.823 .073
.982 1.018
Dependent variable: Budgetary Participation 2
The second regression model the dependent variable: job performance
Table 7 Multicollinearity test result
Model Unstandardized
Coefficients
Standar dized
Coeffic
ients t
Sig. Collinearity
Statistics
B Std.
Error Beta
Toler ance
VIF Constant
27.825 7.105
3.916 .000
Need for achievement .577
.179 .342
3.232 .002
.864 1.158
Work attitude .014
.066 .021
.210 .835
.937 1.067
Budgetary participation
.543 .164
.359 3.312
.001 .826
1.211 Dependent variable: Job Performance
The result multicollinearity test from the independent variables examinee of both regression model above, the correlation among independent variables is
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not exceed boundary 95 Appendix III. Hence it can be told there is no multicolinearity. Result of calculation assess the tolerance seen that there is no
independent variable owning value tolerance is less than 0.10 it means that there is no correlation between variable independent which more than 95. The
calculation result of Variation Inflation Factor VIF from four independent variables of examinee, there is no VIF value which more than 10 Appendix III,
hence it can be conclude that there are no multicollinearity between variable independents in regression model.
2. Result of heteroskedasticity test
