POL 280: Research Methods [syllabus] Day One / Introduction to R [lecture] [R script] Basic Descriptive Statistics with R [lecture] [R script] More Descriptive Statistics in R [lecture] [R script and data] Sampling and Probability Distributions [lecture] [R script and data]
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
MetropolisHastings 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 910: 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
