Mohammad Rifki, Elok Damayanti
4. Result and Discussion
1. Validity test
Validity test is a measure that shows the levels of validity and validity of an instrument. An instrument is said to be valid if it can be used to measure what it is supposed to measure. Research results are valid if there are similarities between the data collected and the data that actually happened to the object under study
partially simultaneously
Work environent
(X1)
Work discipline (X2)
Employee performance
(Y)
Table 1. Validity test
Item r Hitung r Table Information
X1.1 0.787 0.361 Valid
X1.2 0.892 0. 361 Valid
X1.3 0.873 0. 361 Valid
X1.4 0.864 0. 361 Valid
X1.5 0.797 0. 361 Valid
X2.1 0.901 0. 361 Valid
X2.2 0.815 0. 361 Valid
X2.3 0.856 0. 361 Valid
X2.4 0.926 0. 361 Valid
Y1.1 0.931 0. 361 Valid
Y1.2 0.885 0. 361 Valid
Y1.3 0.968 0. 361 Valid
Source: Primary Data Processed
From Table 1 above, it can be seen that the value of sig. r question items are smaller than 0.05 (α = 0.05) and r count > r table which means that each variable item is valid, so it can be concluded that these items can be used to measure research variables.
2. Reliability test
The reliability testing technique is to use the Spearman Brown reliability coefficient value. The decision- making criteria is if the Guttman Split-Half Coefficient value is more than 0.80.
The results of the variable reliability test are presented in table 2:
Tabel 2. Reliability test
No. Variable Reliability Coefficient Information
1 Work Environment (X1) 0.810 Reliabel
2 Work Discipline (X2) 0.923 Reliabel
3 Employee Performance (Y) 0.882 Reliabel
From Table 2 it is known that the value of the Guttman Split-Half Coefficient for all variables is greater than 0.80. From what has been determined previously, all variables used for research are reliable.
3. Normality Test
This test is conducted to determine whether the residual value is normally spread or not. The test procedure was carried out using the Kolmogorov-Smirnov test, with the following conditions:
The hypothesis used is H0: normal spread residuals and H1: not normally distributed residuals. If the value is sig. (p-value)> 0.05 then H0 is accepted, which means that normality is met. The results of the normality test in this study can be seen in
Tabel 3. Normality Test One-Sample Kolmogorov-Smirnov Test e table:
Unstandardized Residual
N 30
Normal Parametersa,b Mean .0000000
Std. Deviation .75522806 Most Extreme Differences Absolute .211
Positive .211
Negative -.096
Kolmogorov-Smirnov Z 1.153
Asymp. Sig. (2-tailed) .140
a. Test distribution is Normal.
b. Calculated from data.
From the calculation results obtained sig. value of 0.140 (can be seen in Table 1) or greater than 0.05. Then the provisions of H0 are accepted, namely that the assumption of normality is met.
Figure 2. P-P Plot
Based on the P-P Plot test, it was found that the data points had spread following the diagonal line, so it was said that the residuals had spread in a normal distribution.
4. Multicollinearity Test
The multicollinearity test is conducted to determine that there is no very strong relationship or no perfect linear relationship or it can also be said that the among independent variables are not related. The way to test is to compare the tolerance value that is obtained from multiple regression calculations. If the tolerance value is> 0.10, so that multicollinearity does not occur.
Table 4. Multicollinearity Test Coefficientsa Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) .373 1.308 .285 .778
work environment (X1)
.304 .140 .455 2.177 .038 .191 5.237
work discipline (X2) .356 .166 .448 2.145 .041 .191 5.237
a. Dependent Variable: EmployeePerformance (Y)
Table 5. Multicollinearity Test Variabel bebas Collinearity Statistics
Tolerance VIF
X1 0.191 5.237
X2 0.191 5.237
Based on Table 5, the following are the test results of each independent variable:
1) Tolerance for Work Environment is 0.191 2) Tolerance for Work Discipline is 0.191
The test results show that the overall tolerance value is > 0.10 so it can be concluded that there is no multicollinearity between the independent variables. Multicollinearity test can also be done by comparing the VIF (Variance Inflation Factor) value with the number 10. If the VIF value is < 10, then there is no multicollinearity.
The following are the test results of each independent variable:
1) 5.1 VIF for Work Environment is 5.237 2) 5.2 VIF for Work Discipline is 5.237
From the test results, it can be concluded that there is no multicollinearity between the independent variables. Thus the assumption test of the absence of multicollinearity can be fulfilled.
5. Heteroscedasticity test
The heteroscedasticity test aims to test whether in the regression model there is an inequality of variance and residual from one observation to another. The Glejser test is used by regressing between the independent variables and their residual observational value. If the significance probability is above the 5% confidence level or the significance is more than 0.05 then the regression model does not contain heteroscedasticity.
Heteroscedasticity test in this study is detected by using the Glejser test that is strengthened with a scatterplot obtained as follows:
6. Glesjer test
Table 6 Heteroscedasticity test
Variable Sig. criteria
work environment (X1) 0.922 P > 0.05 work discipline (X2) 0.990 P > 0.05
The results of the heteroscedasticity test show that the significance value of each variable is greater than 0.05.
This shows that there is no heteroscedasticity.
The test procedure is also carried out with the scatter plot test.
The results of the heteroscedasticity test can be seen in Figure 3
Figure 3. Heteroskedasticities test
From the test results, it was found that the scatterplot display diagram spreads and does not form a certain pattern, so there is no heteroscedasticity. So it can be concluded that the residuals have homogeneous variance (constant) or in other words there are no symptoms of heteroscedasticity.
1) Regression Equations
This regression analysis is used to calculate the amount of influence among the independent variables, namely leadership (X1) and work motivation (X2) to the dependent variable, namely employee performance (Y).
The regression equation is used to find out the form of the relationship between the independent variable and the dependent variable. By using the help of SPSS for Windowsver 18.00, the regression model is obtained as in the table:
Table 7. Regression Results Equations Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .373 1.308 .285 .778
work environment (X1) work discipline (X2)
.304 .140 .455 2.177 .038
.356 .166 .448 2.145 .041
a. Dependent Variable: Employee Performance (Y)
Based on Table 4.8.1 the regression equation is obtained as follows:
Y = 0.373 + 0.303 X1 + 0.356 X2
From the above equation can be interpreted as follows:
a. The constant of 0.373 means that if the work environment and work discipline are constant, the employee's performance is 0.373.
b. b1 = 0.303, meaning that Employee Performance will increase by 0.303 units for each additional unit of X1 (Work Environment). So, if the work environment has increased by 1 unit, then Employee Performance will increase by 0.303 units assuming the other variables are considered constant.
c. b2 = 0.356, Employee Performance will increase by 0.356 units for every additional unit of X2 (Work Discipline), So if work discipline increases by 1 unit, then Employee Performance will increase by 0.356 units assuming the other variables are held constant.
2) Koefisien Determinasi (R2)
To determine the contribution of the independent variables (Work Environment (X1) and Work Discipline (X2)) to the dependent variable of Employee Performance (Y), the value of R2 is used. The value of R2 is as in Table 8. below:
Table 8. Correlation and Determination Coefficients Model Summary
Model
R R Square
Adjusted R Square
Std. Error of the Estimate
dimension 1 .880a .775 .758 .783
The coefficient of determination is used to calculate the magnitude of the influence or contribution of the independent variable to the dependent variable. The result of adjusted R (coefficient of determination) is 0.772.
This means that 75.8% of Employee Performance Variables will be influenced by the independent variables, namely the Work Environment (X1) and Work Discipline (X2). While the remaining 24.2% of Employee Performance variables will be influenced by other variables that are not discussed in this study.
In addition to the coefficient of determination, there is also a correlation coefficient that shows the magnitude of the relationship between the independent variables, namely the Work Environment and Work Discipline with Employee Performance Variables, the R value (correlation coefficient) is 0.880, this correlation value indicates that the relationship between the independent variables, namely the Work Environment (X1) and Work Discipline (X2) with Employee Performance (Y) is included in the strong category because it is in the range of 0.6 – 0.8.
7. Simultaneous Test
T test is used to determine whether each independent variable partially has a significant effect on the dependent variable. It can also be said if t count > t table or t count < t table then the result is significant and means H0 is rejected and H1 is accepted. Meanwhile, if t count < t table or t count > t table then the result is not significant and means H0 is accepted and H1 is rejected. The results of the t-test can be seen in Table 9
Table 9 T testing
Dependent Variable Independent Variable t count t Table Information
Employee Performance (Y) X1 2.117 2.051 Significant
X2 2.145 2.051 Significant
Based on Table 9 the following results are obtained:
1) First Hypothesis Testing
The work environment has a significant partial effect on employee performance.
The t test between X1 (Work Environment) and Y (Employee Performance) shows t count = 2.117.
While the t table is 2.051. Because t count > t table, which is 2.117 > 2.051 or sig t value (0.038) <α = 0.05, the effect of X1 (Work Environment) on Employee Performance is significant. This means that H0 is rejected and Ha is accepted so that it can be concluded that employee performance can be significantly influenced by the work environment or by improving the work environment, employee performance will increase significantly.
2) Second Hypothesis Testing
Work discipline partially significant effect on employee performance.
The t-test between X2 (Work Discipline) and Y (Employee Performance) shows t count = 2.145. While the t table is 2.051. Because t count > t table which is 2.145 > 2.051 or sig t value (0.041) <α = 0.05, the effect of X2 (Work Discipline) on Employee Performance is significant. This means that H0 is rejected and Ha is accepted so that it can be concluded that employee performance can be significantly affected by work discipline or by increasing work discipline, employee performance will increase significantly.
8. Hipotesis I F Testing
F test or model testing is used to simultaneously test the significance of the effect of the independent variables (X) on the dependent variable (Y). If the result is significant, then H0 is rejected and Ha is accepted.
Meanwhile, if the results are not significant, then H0 is accepted and Ha is rejected. It can also be said as follows:
H0 is rejected if F count > F table H0 is accepted if F count < F table Tabel 10 F testing
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 56.926 2 28.463 46.461 .000a
Residual 16.541 27 .613
Total 73.467 29
Based on Table 10 the calculated F value is 46,461. While the F table is equal to 3.34. Because F count >
F table, which is 46,461 > 3.34 or sig F value (0.000) <α = 0.05, the regression analysis model is significant. This means that this means that H0 is rejected and H1 is accepted so that it can be concluded that the work environment and work discipline have a significant effect on employee performance simultaneously.