KONTRIBUSI STATUS SOSIAL EKONOMI,LINGKUNGAN KERJA DAN TINGKAT PENDIDIKANTERHADAP KINERJA GURU SD(Studi di SD Negeri Segugus Wijaya Kusuma KecamatanSukodono Kabupaten Sragen) - YULIANA, YANI (2011)
The purpose of the implementation of the research on Cluster Wijaya Kusuma Sukodono Sragen district are: 1) to determine whether there is significant influence between socio-economic status, the performance of teachers. 2) to determine whether there is significant relationship between level of education on teacher performance 3) to determine whether there is significant influence between the work environment on the performance guru.4) to determine whether there is significant influence between socio-economic status, education level and working environment of teachers' performance. In line with its objectives, this research uses descriptive quantitative method. Data collected through queisioner.Data shaped instrument were analyzed using multiple linear regression analysis method and the coefficient of determination which is processed by using SPSS serial 15. The results of research and discussion, it can be concluded: 1. There is a significant contribution of social status (X1), Work environment (X2) and education level (X3) on performance (Y) 2. Results of analysis showed that the R2 is 0.129, this shows that the percentage of donations that have been given Socioeconomic Status, Work Environment and Level of Education on Teacher Performance is 12.9%, while the remaining amount of 87.1% can be explained by other factors outside research, however, if viewed from a 1%, then the ? the F value 3.207 which is significant at 0.029 or model can be stated quite good. 3. Having analyzed the classical assumption test, the test for normality distribution of data conclusively variable Y that is derived from the normal population, test the linearity of all independent variables in this study, linear regression model multicollinearity test in this study there was no multicollinearity problems and to test heteroscedasticity regression model did not contain the heteroskedasticity regression model and there are no problems of autocorrelation. Thus, these data can be applied to regression.