Manufacturing workers constantly experience tremendous job stress. However, this group has been rarely assessed in terms of this topic, with few studies having evaluated the research methods used to study job stress and workers' well-being. To address this gap, this study comprehensively analyzed relevant 32 studies that used a variety of study designs, measurement tools, and data collection, sampling, and statistical methods. Cross-sectional and longitudinal were the most common study designs used. Job stress was assessed using existing stress scales and self-reported work conditions. Workers' well-being was assessed using multiple indicators mainly related to physical and mental health. Data were collected by collaborating with local organizations and manufacturers using self- or group-administered questionnaires. Sampling techniques included simple random, cluster, stratified, convenience, and purposive sampling. The three key statistical methods were multiple regression, structural equation modeling, and logistic regression. This study may help to guide future research on the strengths and weaknesses of these research methods and in selecting the one most suited to assessing the management structures of specific manufacturing industries and of the needs of the workers concerned.