control variables and model 2 includes the Relation variable. Only difference between two model
is existence of relation variable. Result for lrtest which compare two model shows Relation
variable has effect on Attitude variable and it is statistically significant (Prob > Chi2= 0.0031).
Table 3: Ordinary Logistic Regression Result
Model 1
Model 2
Coefficient
Std. Error
Coefficient
Std. Error
Relation
-0.401*** (0.12)
Age 0.0155**
(0.0061)
0.0117*
(0.0062)
Size 0.220**
(0.11)
0.206*
(0.11)
Wage level
-0.106
(0.13)
-0.0672
(0.13)
Manufacture
Industry 0.115 (0.51)
0.174
(0.51)
Construction Industry
0.404 (0.76) 0.638
(0.78)
Service
Industry
(Reference: The others)
-0.181 (0.5) -0.154
(0.51)
Irregular Member
Yes
-1.159*** (0.41) -1.186***
(0.41)
Irregular Member
No
(Reference: no irregular
workers in workplace)
-0.518** (0.24) -0.588**
(0.24)
Observations 318
318
R-squared 0.0276
0.0394
Note: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
This table shows coefficient value in logistic regression method, the coefficient given by all
logistic method describes independent variable’s effect on the log odds that attitude for irregular
workers. Interpretation is that for a unit increase in relation variable (more bad relation with
employer), the probability to be friendly to irregular workers increases 1.5(e
β=-0.401
)
times, holding
other variables constant. It means if the more union thinks ‘employer supports union and treats
8
Convention