Current Courses

POL 280: Research Methods [syllabus]

POL 286: Advanced Quantitative Methods for Social Research [syllabus]

POL 243: Corruption [syllabus]
              Independent Study Variant [syllabus]

POL 300-C: Data-Driven Advocacy [syllabus]

ICPSR Summer Course: Bayesian Modeling I [syllabus]

Past Courses

Florida State University

PUP 3002: Introduction to Public Policy [syllabus]

Graduate Workshop in zTree [slides]

Emory University

POLS 208: Political Science Methods [syllabus]

POLS 341: The Presidency [syllabus]

POLS 514: Advanced Game Theory (PhD) [syllabus]

POLS 515: Applied Game Theory (PhD) [syllabus]

POLS 509: The Linear Model (PhD) [syllabus]
    Lecture 2a: The Role of Assumptions in Statistical Analysis [lecture] [R script] [notes]
    Lecture 2b: Developing Regression through Error Minimization [lecture] [R script] [notes]
    Lecture 3: The Geometry of OLS [webcast lecture] [R script] [notebook]
    Lecture 4: Properties of OLS [webcast lecture] [R script] [notebook]
    Lecture 5: Hypothesis Testing in the Linear Model [webcast lecture] [R script] [notebook]
    Lecture 6: Complex Hypotheses and Interaction [webcast lecture] [R script] [notebook]
    Lecture 7: OLS Assumptions: Problems and Solutions [webcast lecture] [
R script] [notebook]
    Lecture 8: Measurement Error and Endogeneity [webcast lecture] [
R script] [notebook]
    Lecture 9: Panel Data [webcast lecture] [
R script, Stata do file, and data] [notebook]
    Lecture 10: Hierarchical Linear Models [webcast lecture] [
R script, Stata do file, and data] [notebook]

Rice University

POLS 395: Introduction to Statistics [syllabus]

POLS 500: Social Scientific Thinking I (PhD) [syllabus]

GLBL 503: Introduction to Statistics (MA) [syllabus]

POLS 505: Advanced MLE: Analyzing Categorical and Longitudinal Data [syllabus]
    Lecture 1: Generalized Linear Models [computer files]
    Lecture 2: Instrumental Variable Models [computer files]
    Lecture 3: Generalized Method of Moments [computer files]
    Lecture 4: Specification Testing [computer files]
    Lecture 5: Multinomial Models [computer files]
    Lecture 6: Models for Censored and Truncated Data [computer files]
    Lecture 7: Models for Duration/Survival Data [computer files]
    Lecture 8: Models for Count Data [computer files]
    Lecture 9: Identifying and Modeling Stationary Time Series Data [computer files]
    Lecture 10: Panel Data I: Pooled Models with SE corrections [computer files]
    Lecture 11: Panel Data II: Fixed and Random Effects Models [computer files]
    Lecture 12: Panel Data III: Dynamic Panel GMM Models [computer files]

POLS 506: Bayesian Statistics (PhD) [syllabus]
    Lecture 0: Introduction to R [webcast lecture] [R script]
    Lecture 1: Basic Concepts of Bayesian Inference [webcast lecture][R script][notebook]
    Lecture 2: Simple Bayesian Models [webcast lecture] [R script] [notebook]
    Lecture 3: Basic Monte Carlo Procedures and Sampling [webcast lecture] [R script] [notebook]
    Lecture 4: The Metropolis-Hastings Algorithm/Gibbs Sampler [webcast lecture] [R script] [notebook]
    Lecture 5: Practical MCMC for Estimating Models [webcast lecture] [scripts and data]
          Bonus Lecture: Model Fitting with JAGS [webcast lecture] [scripts and data]
    Lecture 6: Bayesian Hierarchical Models and GLMs [webcast lecture] [scripts and data] [notebook]
    Lecture 7: Fitting Hierarchical Models with BUGS [webcast lecture] [scripts and data] [notebook]
    Lecture 8: Missing Data Imputation [webcast lecture] [scripts and data] [notebook]
    Lecture 9-10: Parameter Expansion & Model Comparison [webcast lecture] [scripts and data] [notebook]
    Lecture 11: IRT and the Scaling of Latent Dimensions [webcast lecture] [scripts and data] [notebook]
    Lecture 12: Multilevel Regression and Poststratification [webcast lecture] [scripts and data] [notebook]

POLS 507: Nonparametric Models and Machine Learning (PhD) [syllabus]
    Lecture 1: Introduction to Nonparametric Statistics [webcast lecture] [R script] [notebook]
    Lecture 2: Nonparametric Uncertainty Est. and Bootstrapping [webcast lecture] [R script] [notebook]
    Lecture 3: Ensemble Models and Bayesian Model Averaging [webcast lecture] [R script] [notebook]
    Lecture 4: Kernel Regularized Least Squares [webcast lecture] [R script] [notebook]
    Lecture 5: "Causal Inference" and Matching [webcast lecture] [R script] [notebook]
    Lecture 6: Instrumental Variable Models [webcast lecture] [R script] [notebook]
    Lecture 7: Bayesian Networks and Causality [webcast lecture] [R script] [notebook]
    Lecture 8: Assessing Fit in Discrete Choice Models [webcast lecture] [R script] [notebook]
    Lecture 9: Identifying and Measuring Latent Variables [webcast lecture] [R script] [notebook]
    Lecture 10: Neural Networks [webcast lecture] [R script] [notebook]
    Lecture 11: Classification and Regression Trees [webcast lecture] [R script] [notebook]
    Lecture 12: Support Vector Machines


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(c) Justin Esarey and Elizabeth Barre