Steven Heeringa, Institute for Social Research, University of Michigan
One-Day Short Course, 25 May 2014, 9:00am - 4:30pm (lunch break 12:00 - 1:30) Room FG 139, near the Medical Sciences building, at the University of Toronto
In order to extract maximum information at minimum cost, survey designs are typically more complex than simple random samples. Clustered and stratified sample designs are common. But how do you analyze the resulting data - in particular, how do you determine margins of error and make inferences that take into account the complex sample design features? This workshop discusses methods for the analysis of complex sample survey data, including estimation of descriptive parameters, analysis of categorical data, and linear and logistic regression modeling. The workshop is intended for anyone analyzing survey data collected from complex samples, and assumes a background in applied statistical analysis. The course is largely based on selected chapters from the book Applied Survey Data Analysis (Chapman and Hall, April 2010) by Steve Heeringa, Brady West, and Pat Berglund, published by Chapman and Hall. Statistical procedures will be illustrated through example analyses that focus on physical and mental health data from major surveys such as the U.S. National Health and Nutrition Examination Survey (NHANES), the National Comorbidity Survey-Replication (NCS-R) and the Health and Retirement Survey (HRS). The workshop will be lecture-based, but participants may bring their own laptop computers with software for the analysis of survey data installed to follow along with the examples.