Which two types of studies might the health department conduct to predict high-risk students for drug use?

The selection of correlation and multiple regression as the appropriate types of studies to conduct for predicting high-risk students for drug use is based on their capacity to analyze relationships and identify predictors among variables.

Correlational studies enable researchers to examine the relationships between variables, such as demographic factors, academic performance, or social influences, and their association with drug use. By identifying how these variables correlate with one another, health departments can ascertain patterns and potential risk factors that may contribute to higher rates of drug use among students.

Multiple regression analysis allows for a more nuanced approach by enabling the examination of multiple independent variables at once, while determining their individual contribution to the dependent variable—here, drug use. This technique helps in understanding the extent to which each factor contributes to the likelihood of high-risk behaviors, while controlling for the influence of other variables. By employing this statistical method, researchers can make predictions about which students are more likely to engage in drug use based on identified risk factors.

Together, these two approaches offer a robust framework for exploring complex interactions and establishing predictive models that can help health departments target interventions more effectively in high-risk populations.