Lecture slides, example Stata and R code, and other resources available via the links below

Workshops (1 or 2 day)

 - Data & Model Visualization

       - Data Management and Other Fundamentals for Efficient and Reproducible Research

- Interaction Effects: Advanced Topics

       - Missing Data: Purdue AMAP        

       - Stata Programming—Tools and Tricks for the Applied Analyst:

       - Survey Design

       - Survey Experiments

- Teaching Workshops & Methods Courses

Statistical/Code Horizons Courses (3-4 days)

- Categorical Data Analysis: Statistical Horizons Seminar (next offering July 2023)

- Data Visualization Using Stata: Code Horizons Seminar (next offering August 2023)

- Missing Data Using Stata: Statistical Horizons Seminar (coming soon)

- Programming with Stata: Code Horizons Seminar (coming soon)

Graduate Courses

Categorical Data Analysis: Purdue SOC 681

• Topics covered include (by section #): (01) Introduction and overview of linear regression, (02) Nonlinear effects in linear regression, (03) Models for count dependent variables, (04) Models for binary dependent variables, (05) Zero-inflated count models, (06) Hypothesis testing, model fit, and model diagnostics, (07) Interaction effects and cross-model comparisons of effects, (08) Models for nominal dependent variables, (09) Models for ordinal dependent variables, (10) Bonus hodgepodge: three-way interactions, robust SEs, fractional response models, visualizing distributions, visualizing intersectional effects, etc.

Data Visualization: Purdue SOC 681

• Topics covered include (by section #): (01) Introduction, (02) Univariate distributions, (03) Transformations, (04) Stata tools, (05) Plotting parts of a whole, (06) Balance plots, (07) Bivariate relationships, (08) Plotting change over time, (09) General advice for effective graphs, (10) Maps, (11) Model results, (12) Plots of predictions, (13) Marginal effects, (14) Interaction effects, (15) Diagnosing and modeling nonlinearities, (16) Model diagnostics, (17) Miscellaneous advanced topics

 Experimental Design: Purdue SOC 609 

• Topics covered include (by section #): (01) Causality, (02) Validity and design, (03) Lab experiments, (04) Survey experiments, (05) Sampling, (06) Social desirability, (07) Factorial and conjoint experiments, (08) Audit and field experiments,  (09) Power analysis, (10) Analyzing experimental data, (11) Writing about experiments

Latent Variable Modeling: Purdue SOC 609

• Topics covered include (by section #): (01) Introduction to latent variables, (02) Summated scales, (03) Principal components analysis (PCA) and exploratory factor analysis (EFA), (04) Confirmatory factor analysis (CFA), (05) Item response theory (IRT) and nonlinear factor analysis (NLFA), (06) Latent class analysis (LCA) and latent profile analysis (LPA), (07) Structural equation modeling (SEM), (08) Measurement invariance (MI) and differential item functioning (DIF)

Social Psychology: Purdue SOC 609

• Topics covered include (by section #): (01) Theory building, (02) Symbolic interactionism, (03) Identity theory, (04) Social identity theory, (05) Group position model, (06) Stereotypes, (07) Cognition and Bias, (08 - 09) Status characteristics theory, (10) Stigma and labeling, (11) Health and illness, (12) Exchange theory, (13) Emotions, (14 - 15) Affect control theory, (16) Cooperation

Undergraduate Courses

Social Psychology: Purdue SOC 340

• Topics covered include: Theory, Methods, Symbolic Interactionism, The Self, Social Roles, Identity  Theory, Social Identity  Theory, Group Position Model, Small Groups, Status, Status Beliefs, Status Construction, Status Interventions, Well-Being, Stereotypes, Cognition, Bias, Attitude Change, Emotions, Affect, Cooperation

Miscellaneous 

• Discrete Choice Analysis: What Our Choices Reveal about Preferences, Tradeoffs, and Alternatives (Statistical Horizons blog post)

• Making Predictions that Match Causal Questions

• Organizing Your Research Workflow: Tips & Tricks

• ASA Data Visualization Workshop: Flexible Approaches for Assessing and Modeling Nonlinearities

• Linear Regression / Introduction to Quantitative Data Analysis: Indiana University