ICPSR Summer Program
Evgenia Bystrov and Anna Nemirovskaya tell about their participation in the ICPSR Summer Program in Quantitative Methods of Social Research!
The Second Four-Week Session of the ICPSR Summer Program in Quantitative Methods of Social Research took place in Ann Arbor, Michigan between July 16 and August 10, 2012. Anna Nemirovskaya and Evgenia Bystrov, associate researchers of the LCSR, share their experiences of the program.
Anna: I participated in courses on Applied Multilevel Models and Simultaneous Equation Models, and Evgenia participated in Advanced Maximum Likelihood Estimation. Both of us took Latent Class Analysis as well – a short three day lecture-series on advanced topics in social research. In addition, we audited another very interesting course on Categorical Data Analysis. Shawna Smith, the professor, who offered this course was extremely popular, and the class was always full of students despite the unbelievably low temperature in the classroom in the heat of July and beginning of August. I noticed that the audience was international and diverse. In the Multilevel Models class the audience was mixed: researchers from all over the world, with various research backgrounds and various scholarly positions all attended, while in other courses the public seemed more homogenous. It was interesting to see how various research approaches and methods attracted different groups of people.
Evgenia: A fascinating fact is that both Anna and I took part in Essex Summer School last year and here we are again, in another summer program on another continent. We met old acquaintances here and made new ones. I can recommend this program to those interested in deepening their knowledge of cutting-edge statistical tools, methodological procedures and the most recently developed software for social researchers. Since the level and content of the courses offered varied tremendously, it was most convenient and useful that students could switch courses during the program. Of course, I should mention that Ann Arbor is a very peaceful and stimulating learning environment. This university town with its green spaces, coffee-shops and recreational facilities allows one to truly enjoy the process of improving statistical skills.