PREDICTOR VARIABLES OF IMMIGRANT ROOTING: A MODEL USING MACHINE LEARNING

Rooting is essential for the social and cultural integration of immigrants in the host country. This research aims to use machine learning techniques to explore predictor variables of the rooting process of South American immigrants in Spain. The study follows a cross-sectional design, with a sample of 634 immigrants. The main results indicate that low perceived prejudice, contact with Spaniards, and stable employment are important predictors of rooting. Future studies need to explore more deeply into the quality of contact and the work experience of immigrants. The discussion is focuses on the relationship between theory and results, with suggestions for improving the prediction of the rooting with machine learning. The results of this study are valuable for public policies aimed at immigrant integration.