Current Courses

POLS 395: Introduction to Statistics [syllabus]

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

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 Algorithms
    Lecture 4: The Metropolis-Hastings Algorithm and the Gibbs Sampler
    Lecture 5: Practical MCMC for Estimating Models
    Lecture 6: Bayesian Hierarchical Models and GLMs
    Lecture 7: Fitting Hierarchical Models with BUGS
    Lecture 8: Item Response Theory and the Scaling of Latente Dimensions
    Lecture 9: Model Checking, Validation, and Comparison
    Lecture 10: Missing Data Imputation
    Lecture 11: Multilevel Regression and Poststratification
    Lecture 12: Bayesian Spatial Autoregressive Models

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

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]

Future Courses and Teaching Specializations

Research Design and Epistemology
Game Theory
Behavioral and Evolutionary Game Theory
Core in Public Policy
Policy Analysis
Public Choice
Bureaucratic Politics
Experimental Political Science
Maximum Likelihood/Limited Dependent Variable Models
Computational Statistical Methods
Empirical Implications of Theoretical Models


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Justin Esarey and Elizabeth Barre