Teaching

Current Courses

POLS 395: Introduction to Statistics [syllabus]

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

POLS 506: Bayesian Statistics (PhD) [syllabus]
    Lecture 1: Introduction to R/Conducting a Monte Carlo Assessment [R script]
    Lecture 2: Basic Bayesian Models [webcast lecture][R script][notebook]
    Lecture 3: Computational Sampling Tools [webcast lecture] [R script] [notebook]
    Lecture 4: Practical MCMC for Estimating Models [webcast lecture] [R/BUGS script]
    Lecture 4a: MCMC with JAGS [webcast lecture] [R/JAGS script]
    Lecture 5: Hierarchical Linear Models [webcast lecture] [R/BUGS script] [notebook]
    Lecture 6: Item Response Theory [webcast lecture] [R/BUGS script] [notebook]
    Lecture 7: Model Checking and Validation [R script] [notebook]
    Lecture 8: Missing Data Imputation [R/BUGS script] [notebook]
    Lecture 9: Multilevel Regression with Poststratification
    Lecture 10: Applications of Bayesian Statistics in Political Science
    Lecture 11: Bayesian Spatial Modeling

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