Josh has academic degrees in both Statistics and Computer Science, with deep experience in statistical methodology and use of statistical software. His areas of interest include causal inference, propensity score matching, multiple regression diagnosis, structural equation modeling, software implementation and general computing concerns.
Brady has extensive experience with regression models for clustered and longitudinal data, and he is the lead author of the book Linear Mixed Models: A Practical Guide Using Statistical Software. He is also a co-author of the book Applied Survey Data Analysis. He has extensive expertise in the design and analysis of sample surveys, data management and regression analysis.
Shyamala has many years of experience with study design and statistical analysis, with a focus on time series analysis, design of experiments, and regression modeling. She has worked in a variety of disciplines, especially in the social sciences.
Michael has modeling and consulting experience across varied domains, including social and physical sciences, business, and humanities. He has particular expertise in mixed models, additive models, latent variable models, text analysis, structural equations modeling, machine learning techniques, Bayesian inference, reproducible research practices, data visualization, and efficient programming practices.
For more about Michael, visit https://m-clark.github.io/.