Significance Values of Multi-Group Analyses.

IntroductionThe phenomenon of dropout in higher education needs the acknowledging of its multi-domain complexity. In the post-pandemic era, exhaustion may be a relevant feature affecting students. This cross-sectional study aimed primarily to test a predictive model of five domains of variables (background, academic, social, psychological, and economic) on dropout intention, in a relation mediated by academic exhaustion. Secondarily, it aimed to assess the structural invariance of this model across working status (working vs. non-working students) and residence status (living away from family’s residence vs. living in family residence). If these groups are differently affected by dropout determinants, specific dropout prevention measures should be implemented.MethodA stratified sample of 1402 Portuguese university students aged between 19 and 45 years (M = 22.87, SD = 3.64), selected through a convenience quota method, was assessed for background, academic, social, psychological, and economic variables using self-report instruments. Structural equation modelling was used.ResultsThe predictive model explained 51% of the variance in dropout intention. Academic exhaustion was the stronger predictor (β = 0.523, p DiscussionThis study shows the relevance of students’ academic exhaustion experiences as a pathway through which different types of factors exert their influence on students´ dropout intentions. The invariance of the predictive model of dropout intention across different groups points the robustness of the model and the relevance of the integrated variables. The results emphasize the importance of student´s individual factors (e.g., academic exhaustion, lack of fit with the course) in dropout decisions, also stressing the role of academic institutions and of the education system in addressing this phenomenon, concerning academic workload, vocational orientation, social environment, and financing.