Job stress evaluation using response surface data mining

While job stress evaluation methods reported in the recent literature represent significant advances in the types of questionnaires currently available, there are limitations to their usefulness as analytic tools. We present four steps to address these limitations. The first step is the creation of an integrated job stress questionnaire (IJSQ) that incorporates physical, psychosocial, and environmental factors. The second step combines data mining (DM) with response surface methodology (RSM), to deal with specific situations by creating a new methodology called response surface data mining (RSDM). The third step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest. The fourth and final step is a case study using the IJSQ and RSDM. The case study demonstrates that the proposed RSDM can effectively find significant physical, psychosocial, and environmental risk factors by reducing the dimensionality. In addition, the process provides detailed statistical inferences. The evaluation and alleviation of job stress is critically connected to job performance in the workplace. In order to improve the job satisfaction of industrial workers, it is essential to identify the causes of job stress. IJSQ and RSDM can be used to analyze a number of the causes of job stress such as physical, psychosocial, and environmental factors in industrial settings.