I primarily teach graduate level statistics courses, which is both challenging and enjoyable. The materials tend to be very dense and sometimes difficult to grasp. Accordingly, much of my teaching style capitalizes on slowly building materials, grounding ideas in application, and relating these techniques to the research of the students and faculty attending the course. For example, I have found that mastering and then regularly revisiting the basic principles, or building blocks, for statistics can provide a foundation for understanding very complicated ideas. Using these, I can often teach techniques that would otherwise be beyond the grasp of the students. My graduate courses on structural equation modeling and longitudinal data analysis along with my undergraduate courses on introductory statistics and advanced statistics use this approach to communicate the underlying theory and math of advanced materials rather than only learning how to make an application provide output. I supplement this with regular assignments, readings, and handouts so that the students are practiced in their use and interpretation with knowledge of where to go for refreshers and more advanced ideas.
Probably my strongest asset as a teacher is the enthusiasm with which I teach these materials. I have long been fascinated with the elegance underlying the math and theory behind statistics that enables our field to thrive. In covering contemporary statistical research, I try to communicate an understanding of this elegance, and why we should care about how it works. When teaching undergraduate students, this enthusiasm comes out in making students think of themselves as researchers. They learn statistics through class participation where we conduct surveys, build sampling distributions of M&Ms, and have discussions about the grey areas of research.
My biggest challenge is that the graduate courses I teach are among the few that go beyond the first year of graduate quantitative training in our department. As a result, I am aware that I am often the main conduit for teaching graduate students about the most advanced statistical techniques applicable to their area of research, and for keeping the department as a whole up-to-date on current advances. There is no easy way to do this. My graduate level courses incorporate a combination of classic ideas and cutting edge research in statistics and methods. I have also had success in a new course design where students help build workshops for the department.
I also teach one substantive course on dynamic systems theory in social psychology (technically, this course is also quantitative). The aims of the course are to develop an understanding of the terminology and tenants of a systems approach and examine its role in a broad range of social psychological phenomena. Some of these areas are established dynamics topics while others are topics that have dynamical implications and applications. My own personal goal for this class is to foster new ideas in the graduate students that might result in future collaborative projects. The primary comment from students who took it last time was that it completely changed the way they looked at phenomena.
Courses I Teach
Quantitative Methods I
This is a classic style Regression Analysis course infusing regression graphical techniques with past and current trends.
Structural Equation Modeling
This is a mid-level to advanced course on Structural Equation Modeling taught from a regression logic integrated with Matrix Algebra.
Analysis of Temporal Data
Uses multilevel modeling and structural equation modeling to examine data dependency as a function of time.
Selected Topics in Quantitative Psychology
In this course, the students help build workshops and stand alone issues in quantitative psychology.
Dynamical Systems in Social Psychology
This course integrates math theory with psychology to generate new theoretical and quantitative approaches to psychological phenomena.