2018 Syllabi and Reading Lists
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- Bayesian Modeling for the Social Sciences I: Introduction and Application
- Ryan Bakker and Johannes Karreth - Game Theory I: Introduction
- Scott Ainsworth -
Maximum Likelihood Estimation I: Generalized Linear Models
- Dean Lacy -
Meta-Analysis - Introduction and Application
- Colin Lewis-Beck -
Multilevel Models I: Introduction and Application
- Mark Manning -
Multivariate Statistical Methods: Advanced Topics
- Bob Henson -
Network Analysis I: Introduction
- Ann McCranie -
Race, Ethnicity, and Quantitative Methodology
- Kerem Ozan Kalkan and Jamil Scott -
Rational Choice Theories of Politics and Society
- James Johnson -
Regression Analysis I: Introduction
- Michelle Dion -
Regression Analysis II: Linear Models
- Tim McDaniel -
Regression Analysis III: Advanced Methods
- David Armstrong -
Scaling and Dimensional Analysis
- Adam Enders -
Social Choice Theory
- Maggie Penn -
Statistics and Data Analysis I: Introduction
- Niccole Pamphilis -
Time Series Analysis I: Introduction
- Sara Mitchell and Clayton Webb - Mathematics for Social Scientists, I
- Stephen Bringardner - Mathematics for Social Scientists, II
- Howard Thompson - Mathematics for Social Scientists, III
- Don Eckford - Introduction to Computing 1st Session
- Michael Hawthorne - Introduction to the R Statistical Computing Environment
- Kerem Ozan Kalkan
Workshops
Lectures
-
Bayesian Modeling for the Social Sciences II: Advanced Topics
- Elizabeth Menninga and Daniel Stegmueller -
Categorical Data Analysis
- Shawna Smith -
Causal Inference for the Social Sciences
- Jake Bowers, Ben Hansen, and Tom Leavitt -
Empirical Modeling of Social Science Theory: Advanced Topics
- Robert Franzese -
Game Theory II: Advanced Topics
- James Morrow -
Longitudinal Analysis
- Michael Berbaum -
Maximum Likelihood Estimation II: Advanced Topics
- David Darmofal and Eline de Rooij -
Multilevel Models II: Advanced Topics
- John Poe -
Network Analysis II: Advanced Topics
- Bruce Desmarais -
Regression Analysis II: Linear Models
- Brian Pollins -
Simultaneous Equation Models
- Sandy Marquart-Pyatt -
Spatial Econometrics
- Robert Franzese -
Statistics and Data Analysis II: Intermediate
- Shane Singh -
Structural Equation Models With Latent Variables
- Doug Baer -
Time Series Analysis II: Advanced Topics
- Paul Kellstedi and Mark Pickup - Introduction to the R Statistical Computing Environment
- John Fox - Introductory - Review Lectures on Matrix Algebra
- Tim McDaniel
Workshops
Lectures
- Analyzing Intensive Longitudinal Data: A Guide to Diary, Experience Sampling, and Ecological Momentary Assessment Methods
- Niall Bolger and Jean-Philippe Laurenceau - Analyzying Longitudinal and Multilevel Data With R and Stan
- John Fox and Georges Monette - Applied Multilevel Models for Longitudinal and Clustered Data
- Ryan Walters - Bayesian Multilevel Models
- Ryan Bakker -
Data Curation for Rehabilitation Research and Related Clinical Trials
- James Graham, Lynette Hoelter, Shane Redman, and Alison Stroud -
Dynamical Systems Analysis
- Jonathan Butner and Brian Baucom -
Exploring and Analyzing Monitoring the Future Data - A Primer
- Pat Berglund, Deb Kloska, and Austin McKitrick -
Field Experiments
- Eline de Rooij, Florian Foos, and Alexander Coppock -
Group-based Trajectory Modeling for the Medical and Social Sciences
- Tom Loughran, Daniel S. Nagin -
Growth Mixture Models - A Structural Equation Modeling Approach
- Shawn Bauldry -
Handling Missing Data - Using Multiple Imputation in Stata
- Rose Medeiros -
Hierarchical Linear Models - Introduction
- Aline Sayers and Holly Laws -
How to Identify and Remediate Disclosure Risk
- John Marcotte - Introduction to Mixed Methods Research
- Kathleen M.T. Collins - Introduction to R
- Ryan Kennedy -
Item Response Theory - Methods for the Analysis of Discrete Survey Response Data
- Kimberly Colvin - Latent Class Analysis in Social Science Research
- Tenko Raykov -
Latent Growth Curve Models (LGCM): A Structural Equation Modeling Approach
- Ken Bollen -
Linear Regression Analysis in the Social Sciences
- Jim Granato and Sunny Wong -
Longitudinal Data Analysis, Including Categorical Outcomes
- Don Hedeker -
Machine Learning for the Analysis of Text as Data
- Brice Acree -
Machine Learning: Applications and Opportunities in the Social Sciences
- Christopher Hare -
Machine Learning - Uncovering Hidden Structure in Data
- Christopher Hare -
Maximum Likelihood Estimation for Generalised Linear Models
- Niccole Pamphilis -
Modeling Categorical Outcomes - Advanced Methods for Models with Binary, Ordinal, and Nominal Outcomes
- Scott Long - Modern Causal Inference: Experiments, Matching, and Beyond
- Douglas Steinley - Multi-level Modeling
- Mark Tranmer -
Multilevel and Mixed Models Using Stata
- Rose Medeiros -
Network Analysis: Statistical Approaches
- John Skvoretz -
Optimal Methods and Strategies for Reproducible Research
- Scott Long -
Panel Study of Income Dynamics (PSID) Data User Workshop
- Paula Fomby, Noura Insolera, et al -
Process Tracing in Qualitative and Mixed Methods Research
- Derek Beach -
Providing Social Science Data Services - Strategies for Design and Operation
- Bobray Bordelon, Jane Fry, and Ron Nakao -
Qualitative Comparative Analysis (QCA)
- Ingo Rohling - Qualitative Research Methods
- Paul Mihas -
R: Learning by Example
- David Armstrong -
Regression Analysis for Spatial Data
- Elizabeth Root -
Regression Discontinuity Designs
- Rocio Titunik and Sebastian Calonico - Spatial Econometrics
- Robert Franzese -
Structural Equation Models and Latent Variables: An Introduction
- Ken Bollen - Survey Data Analysis Using Stata
- William Rising -
The Population Assessment of Tobacco and Health (PATH) Study Data User Workshop - Biomarker Restricted-Use Files
- Katy Edwards