Director of Code Horizons
Stephen Vaisey, Ph.D. is Professor of Sociology and Political Science and the Director of the Worldview Lab at Duke University. He specializes in the use of survey data to measure cultural differences and cultural change.
Vaisey completed his Ph.D. at the University of North Carolina at Chapel Hill in 2008. He was an assistant professor at the University of California, Berkeley before moving to Duke University in 2011. He has published papers in the American Journal of Sociology, the American Sociological Review, Social Forces, and other top journals.
His substantive research focuses on the measurement, origins, and consequences of moral and political differences. He has also published on the benefits and limitations of using panel data for causal inference.
Vaisey has taught applied statistics courses at the graduate level for over a decade. He takes great pleasure in helping people gain an intuitive and practical understanding of statistical methods and has received outstanding reviews for his teaching.
You can visit his university webpage here.
Multilevel and Mixed Models Using Stata
Wednesday, May 10 –
Friday, May 12, 2023
This seminar provides an intensive introduction to multilevel models, a class of regression models for data that have a hierarchical (or nested) structure. Common examples of such data structures are students nested within schools or classrooms, patients nested within hospitals,...View Details
Longitudinal Data Analysis Using R
Tuesday, June 13 –
Friday, June 16, 2023
The most common type of longitudinal data is panel data or repeated measures data, consisting of measurements of predictor and response variables at two or more points in time for many individuals (or other units). Panel data enable two major advances over cross-sectional...View Details
Matching and Weighting for Causal Inference with R
Tuesday, July 25 –
Friday, July 28, 2023
This course offers an in-depth introduction to matching and weighting methods using R. Matching and weighting are quasi-experimental techniques for estimating causal effects from observational data using the potential outcomes or counterfactual framework. They are often (but not always) based...View Details
Multilevel and Mixed Models Using R
Tuesday, August 8 –
Friday, August 11, 2023
Multilevel models are a class of regression models for data that have a hierarchical (or nested) structure. Common examples of such data structures are students nested within schools or classrooms, patients nested within hospitals, or survey respondents nested within countries.View Details
Longitudinal Data Analysis Using Stata
In this course, we will focus on the following approaches for using panel data: Mixed models (including latent growth curves) Two period difference-in-differences Fixed-effects models (one-way and two-way) Between-within models Dynamic panel modelsView Details
Statistics with Stata*
This seminar will provide a comprehensive introduction to Stata software, covering Stata’s data management, graphics, data analysis, and statistical modeling capabilities. You will develop facility with Stata’s data manipulation commands, its wide array of graphical tools for exploring data, and...View Details
Treatment Effects Analysis
This course offers an in-depth survey of a family of techniques known as treatment-effects estimators. Treatment-effects analysis is a quasi-experimental technique for estimating causal effects from observational data using the potential outcomes or counterfactual framework. These techniques — which include...View Details
Treatment Effects Analysis
This seminar focuses on matching and weighting cross-sectional observational data to obtain better causal estimates of treatment effects. The most common technique for estimating such effects is propensity score matching. We will cover this technique but you’ll soon see that it is just the tip of...View Details