University of Michigan – This graduate-level course takes a broad look at quantitative data collection and analysis techniques used across the social sciences. Each week, we explore a specific technique (or a related set of techniques) to understand how the technique is used in practice, the assumptions that underlie its use, and the types of questions for which the technique may yield insights. The class explores methods as varied as missing data imputation, network analysis, structural equation modeling, and non-parametric bootstrapping.
- It’s not my consensus: Motivated reasoning and the sources of scientific illiteracy
- Negativity and Positivity Biases in Economic News Coverage: Traditional vs. Social Media
- Perceptions of health risks of cigarette smoking: A new measure reveals widespread misunderstanding
- Motivated Reasoning in Perceived Credibility of Public Opinion Polls
- Real-World Use and Self-Reported Health Outcomes of a Patient-Designed Do-it-Yourself Mobile Technology System for Diabetes: Lessons for Mobile Health