Using multinomial logistic models to predict adolescent behavioral risk

This study was interested in profiling adolescents who were at greatest behavioral risks. Multinomial logistic regression was applied to a real world data set. Results showed that gender, intention to drop from the school, family structure, self-esteem, and emotional risk were effective predictors. The model was validated by (a) significant test of the overall model, (b) tests of regression coefficients, (c) goodness-of-fit measures, & (d) validation of predicted probabilities. Three methodological issues were highlighted in the discussion: (1) the use of odds ratio, (2) the Hosmer and Lemeshow test extended to multinomial logistic models, and (3) the missing data problem