We currently offer workshops on Multilevel Modeling, Structural Equation Modeling, Structural Equation Models for Longitudinal Data, Mixture Models and Cluster Analysis, and Network Analysis. We also provide individually tailored instruction to groups with specific data analytic needs.

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We provide consulting services on each phase of the research process, from study design to the application and interpretation of quantitative methods. We offer several modes of consulting to suit a variety of needs.

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We seek to provide you with the information you need to be a knowledgeable user of quantitative methods, including updates on ongoing developments in the field, discussion of common data analytic concerns, and tutorials on commonly used techniques.

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What’s the difference between finite mixture models and cluster analysis?

Researchers are often interested in identifying subgroups within their data to better understand heterogeneity within the population under study. This task has been the traditional domain of cluster analysis, but over the past decade or so finite mixture models have become an increasingly preferred alternative analytic technique. Sometimes referred to as "model-based clustering" the finite mixture model differs in important ways from more classical methods of cluster analysis, such as the K-Means algorithm. In this video, Dan provides an intuitive description of the underlying assumptions and purposes of the finite mixture model as contrasted with K-means clustering. He describes several important differences between finite mixture models and other cluster analysis techniques that might motivate applied researchers to select one approach over another. If you are interested in learning more about these techniques, including their implementation in popular software programs, you may wish to consider enrolling in our 5-day summer workshop on Cluster Analysis and Mixture Modeling.