Applying elastic-net regression to identify the best models predicting changes in civic purpose during the emerging adulthood

Abstract

IntroductionChanges in civic purpose during the emerging adulthood has been a significant research topic since it is closely associated with active civic engagement later in human lives. While standard regression methods have been used in previous studies to predict civic purpose development, they have limitations that may not always lead to best prediction models. We aimed to address these limitations by utilizing elastic-net multinomial logistic regression, which favors models with the least number of necessary predictors, in exploration of predictors for civic purpose development in a data-driven manner. MethodsWe analyzed data from the longitudinal Civic Purpose Project while focusing on the model that best predicted civic purpose from Wave 1 (12th grade before high school graduation) to Wave 2 (two years after Wave 1). The reanalyzed data included responses from 476 participants (60.29% females, 39.08% males) who were recruited from Californian high schools in the United States and completed the survey at both Waves. The elastic-net regression was performed 5000 times for predicting three dependent variables, Wave 2 political purpose, community service purpose, and expressive activity purpose, with Wave 1 predictors. We identified which predictors were selected as the constituents of the best regression models during the elastic-net regression process. ResultsResults showed that civic purpose, moral and political identity, and external supports (e.g., parental and peer involvement, school civic opportunities, etc.) in Wave 1 significantly predicted civic purpose in Wave 2. Several predictors were excluded from the regression models during the elastic-net regression process. ConclusionWe found that the elastic-net regression was able to present the more regularized model for prediction. Implications for promoting civic purpose are discussed as well as utilizing the elastic-net regression method.