Advanced statistical techniques: structural equation modeling (SEM), multilevel modeling (MLM), drift diffusion modeling (DDM), Monte Carlo simulations, quantitative analytics, common factor analyses (CFA, EFA), principle components analysis (PCA)

Statistical environments: R, Python (anaconda), SPSS

Other skills: power analyses for sample size planning, data visualization, geospatial data analysis, basic machine learning techniques (e.g., random forests), Git, meta-analysis, Shiny app development