Predictive Modeling of Change in Depressive Disorder and Counts of Depressive Symptoms in Urban Youths

A longitudinal multivariate model examined data from a sample of 2,415 adolescents residing in 10 cities to see if both depressive symptoms and diagnosis of depressive disorder change over time in association with the same salient events. The model posits that risk variables (e.g., stressful circumstances and traumatic events), protective variables (e.g., social relationships), and a Risk × Protective interaction statistically predict change in depression, whether operationalized as a disorder or as a symptom count. Multiple regression, controlling for the univariate effects of sex and race, is used to analyze the model for depressive symptoms, whereas logistic regression (which, unlike multiple regression, is appropriate for a nominal dependent variable) is used to analyze the model for depressive disorder. The two forms of analyses show that risk variables and protector variables explain change in depression, both as a symptom count and as a disorder. However, the multiple regression, being more sensiti...