2013 Schedule

Four-Week Courses

First Session, June 24-July 19

See our guide for selecting Summer Program courses during the first 4-week session (PDF).













  • Blalock Lectures: Advanced Topics in Social Research

Second Session, July 22-August 16

See our guide for selecting Summer Program courses during the second 4-week session (PDF).


9am-10am, 5pm-6pm











Three- to Five-Day Workshops

Unless otherwise indicated, workshops are held in Ann Arbor Michigan, on the University of Michigan campus. Workshops usually meet daily from 9:00 am to 5:00 pm.

Note: We are still creating our full schedule of short workshops. So, be sure to check back frequently for updated information on the course list, dates, and locations.

May 27-31 Introduction to Regression Analysis (Montreal, QC)
June 3-5 Analyzing Developmental Trajectories (Amherst, MA)
June 10-14 Hierarchical Linear Models I: Introduction (Amherst, MA)
June 10-14 Longitudinal Data Analysis, including Categorical Outcomes
June 10-14 Panel Data Analysis Using Stata
June 17-18 Programming Estimation Commands in Stata and Mata
June 17-19 Designing, Conducting, and Analyzing Field Experiments (New York, NY)
June 17-21 Network Analysis: An Introduction
June 17-21 Structural Equation Models and Latent Variables: An Introduction (Chapel Hill, NC)
June 17-21 The Workflow of Data Analysis using Stata (Amherst, MA)
June 24-26 Mixed Methods: Approaches for Combining Qualitative and Quantitative Research Strategies (Chapel Hill, NC)
June 24-28 Network Analysis: A Second Course
June 24-28 Introduction to Spatial Regression Analysis (Chapel Hill, NC)
June 24-28 Missing Data: An Introduction to the Analysis of Incomplete Datasets (Bloomington, IN)
July 1-3 Integrated Data Analysis for Addiction Research
July 8-12 Applied Multilevel Models for Longitudinal Data (Boulder, CO)
July 8-12 Models for Categorical Outcomes Using Stata: Specification, Estimation, and Interpretation
July 9-12 Hierarchical Linear Models II: Special Topics
July 15-19 Analysis of Large-Scale Networks
July 15-19 Applied Multilevel Models for Cross-Sectional Data (Boulder, CO)
July 15-19 Doing Bayesian Data Analysis: An Introduction (Bloomington, IN)
July 15-19 Social Network Analysis: An Introduction (Berkeley, CA)
July 15-19 Time Series Analysis: An Introduction for Social Scientists
July 22-26 Dynamic Models for Policy, Economics, and Society: Time Series Methods
July 22-26 Nursing Research: Survey Design and Analysis Using Mixed Methods
July 29-31 Coordinated Data Analysis: Maximizing Early Care and Education Data
July 29-August 2 Curating and Managing Research Data for Re-Use
July 29-August 2 Family Connections Across Generations and Nations: Jamaica, Guyana, and the United States
August 5-8 Assessing and Mitigating Disclosure Risk: Essentials for Social Science
August 5-9 Causal Inference in the Social Sciences: Matching, Propensity Scores, and Other Strategies (Berkeley, CA)
August 5-9 Latent Growth Curve Models (LGCM): A Structural Equation Modeling Approach
August 12-14 Growth Mixture Models: A Structural Equation Modeling Approach
August 12-14 Structural Equation Modeling with Stata (Berkeley, CA)
August 12-16 Analyzing Social Networks: An Introduction (Chapel Hill, NC)
August 12-16 Health Disparities Research and Vulnerable Populations: Exploring ICPSR Data Sources
August 12-16 Spatial Regression for Contagion, Diffusion, and Interdependent Processes
August 19-21 Analyzing Multilevel and Mixed Models Using Stata

Note: The ICPSR Summer Program in Quantitative Methods of Social Research makes every effort to provide an up-to-date course schedule, as well as fully accurate course descriptions. Occasionally, however, unforeseen circumstances may require changes in course content, instructors, timing, or location. Fortunately, such events are very rare. But when they do occur, we reserve the right to make any changes that are necessary to maintain the Program. We will post corrected information to the Summer Program website and inform participants who are affected by such changes as quickly as possible.

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