Predictors of Adolescents’ Expected Active Political Participation: A Random Forest Analysis of Civic Knowledge, Attitudes, and Engagement

This study used explainable Random Forest models to identify predictors of adolescents’ expected active political participation using ICCS 2022 data from 1487 students in Schleswig-Holstein, Germany. Three predictor specifications were examined to distinguish prediction based on variables conceptually close to the outcome from prediction based on more general civic characteristics. The Full Random Forest achieved the highest predictive accuracy (R2 = 0.426), with expected legal and electoral participation as the strongest predictors. Removing proximal participation measures reduced predictive accuracy but revealed a stable set of predictors. In the most restrictive model, citizenship self-efficacy, beliefs about missing conventional citizenship, and civic knowledge consistently ranked highest under both permutation importance and SHAP. Civic knowledge showed substantial predictive importance but a consistently negative association with the outcome, supported by the correlation (r = −0.139), SHAP dependence, and ALE analyses. Random Forest’s predictive advantage over benchmark models was modest and specification-dependent.