Statistics 209 / HRP 239/ Education 260
                                        Winter 2014

Statistical Methods for Group Comparisons and Causal Inference

previous title: Understanding Statistical Models and their Social Science Applications

David Rogosa
 rag {AT} stat {DOT} stanford {DOT} edu

Lecture: TTh 12:50-2:05, room TBA

2014 course web page at http://www.stanford.edu/~rag/stat209/
                To see full course materials from Winter 2013 go here

Instructor. David Rogosa, Sequoia 224,  rag {AT} stat {DOT} stanford {DOT} edu.
                   Office hours T 2:30-3:15.
TA,    TBA    office hour.   

Registrar's Information
Description
 Critical examination of statistical methods in social science applications, especially for 
 cause and effect determinations. Topics: path analysis, multilevel models, matching and 
 propensity score methods,  analysis of covariance, instrumental variables, compliance, 
 longitudinal data, mediating and moderating variables. Prerequisite: intermediate-level statistical methods
Course Overview
For students who have had intermediate-level instruction in statistical methods including multiple regression, logistic regression, log-linear models.
At the very least, the content of the course should provide some consolidation of previous instruction in statistical methods. The goal is also to instill some introspection and critical analysis for the uses of statistical methods common in social science and medical applications.   The focus of the course is on understanding what useful information statistical modeling can provide in experimental and especially non-experimental social science settings.

Quick Course Outline 
Week 1. Course Introduction;  properties of regression models
Week 2. Experiments vs observational studies;  Neyman-Rubin-Holland formulation
Week 3. Path analysis and causal modeling, multiple regression with pictures
Week 4. Multilevel data. Contextual effects, aggregation bias, random effects models
Week 5. The many uses and forms of analysis of covariance (including regression discontinuity designs)
Week 6. Instrumental variable methods, simultaneous equations, reciprocal effects
Week 7. Compliance and experimental protocols; encouragement designs; intent to treat
Week 8. Matching and propensity score methods
Week 9. Time-1, Time-2 group comparisons for experimental and non-experimental designs:
        including Lord's paradox, predictors of change, Repeated Measures Anova,
        value-added analysis, interrupted time-series
Dead Week overflow and course summary. discussion of case studies