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Quantitative and Statistical Methods:
Faculty: Michael Seltzer, Phil Ender

These students are typically interested in learning advanced statistical methods with the goal of further developing and applying them in real-world situations: for example, in education, public policy, sociology, psychology, and biomedicine.

After their first year – in which they gain a broad education in research methods – students in this track can pursue coursework both in the department of education and in other campus departments such as statistics, biostatistics, sociology and psychology. This work leads to expertise in such areas as hierarchical linear modeling, structural equation modeling, and latent variable modeling. (Follow this link for a list of Applied Statistics courses offered outside the department.)

While building this expertise, students are involved in research projects that also develop their knowledge of methods and the ability to apply them in innovative ways. Within the school of education, there are opportunities for students to participate as methodologists in research projects, to both help with the design of studies and in the analysis of data.  Some research projects that students in the Advanced Statistical Methods track have worked on during their time in the program involve the use of innovative longitudinal methods to model change in peer victimization in middle school students, the use of mixture models to explore the experiences of first-year teachers,  and the development of new methods for Bayesian analysis.

It is through the dissertation that advanced statistics students further develop their expertise and make a contribution to their fields of interest.

Here are some representative dissertation projects:

Katherine Masyn, Discrete-Time Survival Mixture Analysis for Single and Recurrent Events Using Latent Variables.  Advisor: Bengt Muthen

K.C. Choi, Latent variable regression in a three-level hierarchical modeling framework:  A fully Bayesian approach.  Advisor:  Mike Seltzer