ABSTRACT
This chapter will focus on two methods that are similar to multiple regression-logistic regression and multilevel modeling (aka hierarchical linear modeling)—but that require specialized analysis with something other than the linear regression procedures in general statistics programs. These are methods you are likely to encounter in your reading, so it is useful to have a conceptual understanding of them. Logistic regression is useful when the dependent variable you are interested in is dichotomous (or categorical with more than two categories). Multilevel modeling takes into account the often-nested or clustered nature of our data, such as children within schools, or individuals within families.
