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United States schools and students suffer from problems associated with student behavioral disorders. There is a need for innovate statistical methods to analyze data to which will help inform the development of new strategies to deal with the issues associated with behavioral problems. The three papers in this dissertation focus on explicating certain mixture models which have shown promise in analyzing behavioral data. An important interest in mixture modeling is the investigation of what types of individuals belong to each latent class by relating classes to auxiliary variables. The first paper presents results from real data examples and simulations to show how various factors, such as sample size, can impact the estimates and standard errors of auxiliary variable effects and testing mean equality across classes. Based on the results of the examples and simulations, suggestions are made about how to select …