Exploring the facets of overall job satisfaction through a novel ensemble learning

The aim of this work is to understand the relationship between the overall Job Satisfaction and the facet Job Satisfaction, using a comprehensive dataset of Italian Social Cooperatives workers. On this issue, recent works explored how ensemble learning like Random Forest and TreeBoost can be used to assess the importance of potential predictors in the Job Satisfaction. Taking a similar way, in this study we use a tailored data mining approach for hierarchical data, namely a new algorithm called CRAGGING, shedding some light about the drivers of Job Satisfaction. To this end we use a variable importance measure and then we grow a synthetic model to relate the overall Job Satisfaction with corresponding facets. In doing this we obtain a simple model with unambiguous results.