ICPSR Summer Program in Quantitative Methods of Social Research
Posted: 3/5/2018 (Conference)
The ICPSR Summer Program provides in-depth, hands-on training in statistical techniques and research methodologies used across the social, behavioral, and medical sciences. We strive to fulfill the needs of researchers throughout their careers by offering instruction on a broad range of topics, from introductory statistics to advanced quantitative methods and cutting-edge techniques.
From May through August 2018, the Summer Program will offer more than 80 courses in cities across the US, Canada, and Europe. Registration is now open. For more information, visit icpsr.umich.edu/sumprog or contact email@example.com or (734) 763-7400.
Held in Ann Arbor (MI), the Summer Program’s Four-week Sessions provide an immersive learning experience—think “summer camp for social scientists”! Participants in our First (June 25 – July 20) and Second (July 23 – August 17) Sessions can choose from more than 35 courses, including regression, Bayesian analysis, longitudinal analysis, game theory, MLE, SEM, causal inference, multilevel models, race/ethnicity and quantitative methods, and more. New courses in 2018 include social choice theory, as well as two-week workshops on meta-analysis and network analysis.
Scholarships are available for students in sociology, public policy, education, and other disciplines.
For researchers needing to learn a specific methodological technique in just a few days, the Summer Program offers more than 40 short workshops in 7 cities. New locations in 2018 include Houston and St. Gallen (Switzerland). Workshops of interest include:
- Network Analysis: Statistical Approaches (May 21-25, Chapel Hill)
- Process Tracing in Qualitative and Mixed Methods Research (May 30-June 1, Ann Arbor)
- Qualitative Comparative Analysis (June 4-6, Ann Arbor)
- Regression Analysis for Spatial Data (June 11-15, Boulder)
- Applied Multilevel Models for Longitudinal and Clustered Data (June 25-29, Boulder)
Location: University of Michigan