Foundations of Advanced Statistics

This course lays the foundation of graduate-level statistics concepts for all Ph.D. students in the CLPS Department. The course covers the basic multivariate techniques currently used in social and cognitive sciences: multiple regression, factorial and mixed between/within ANOVA, MANOVA, and linear approaches to categorical data analysis. The course does not cover mixed-effects (multi-level) analysis, covariance structure analysis (PCA, FA, SEM), or Bayesian statistics. The minimum prerequisite statistics knowledge assumed for the course will be undergraduate statistics, as covered in CLPS 0900 or comparable courses. Thus, the course assumes knowledge of such basics as point estimates, confidence intervals, normal distributions, hypothesis testing, t tests, univariate linear regression, and one-way ANOVA. Students will have assigned readings, attend lectures, complete weekly homeworks, and write two take-home exams.