Substantive and Statistical Implications of Distributional Assumptions in Mixture Modeling: An Empirical Example

This study examined the substantive and analytic results of fitting a normal and non-normal (Student t) finite mixture model to social network variables drawn from a smoking cessation trial. Results revealed that the best-fitting normal and Student-t models were substantially different. These results have important implications for applied data-analysis practices.