Upcoming Workshops, Fall 2025
Applied Survey Data Analysis Using SAS, Monday, November 10, 2025 from 1 to 4 p.m. PT via Zoom
This workshop will cover both the use of descriptive and inferential statistics with complex survey data in SAS. We will also discuss regression commands, the analysis of subpopulations, and some graphical methods that can be used with weighted data.
The workshop notes are here.
Register here: https://ucla.zoom.us/meeting/register/knDT7SbKRDSWFuyqeAsMzQ#/registration
Output Tables in R, Monday, November 17, 2025 from 1 to 4 p.m. PT via Zoom
R is a powerful programming language specifically designed for statistical analysis. In this workshop, we will explore some of the tools and packages available in R to craft visually appealing tables that effectively communicate your results. Whether you’re using R Markdown and seeking to generate tables in your HTML file or aiming to create polished and publication-ready tables in your report, these packages can greatly enhance the clarity of your work.
The workshop notes are here.
Register here: https://ucla.zoom.us/meeting/register/nyL1Z4ObSwW9joqYnFpKXw#/registration
Covariate Selection in Regression: A Crash Course on Good and Bad Controls, Monday, December 8, 2025 from 1 to 4 p.m. PT via Zoom
Choosing which covariates to control for in regression models is a well-known challenge, and clear guidance on this issue is often lacking. Causal diagrams, specifically directed acyclic graphs (DAGs), offer a powerful framework for addressing this challenge. This workshop discusses the paper “A Crash Course in Good and Bad Controls” (Cinelli, Forney, & Pearl, 2022), which introduces the concepts of “good” and “bad” controls in statistical regression analysis and clarifies when adding a variable improves or undermines the accuracy of causal effect estimates.
Through a series of simulated examples, we will demonstrate how different variable structures can lead to bias reduction (good controls), bias amplification (bad controls), or neutral effects, and discuss how some neutral controls can nonetheless influence the precision of estimates. The workshop’s primary goal is to show how DAGs can be used systematically to identify appropriate control variables, enabling valid causal interpretations of regression models.
While the material is primarily conceptual, all simulations and DAG visualizations will be demonstrated in R, with additional discussion on how AI tools can be used to support this workflow.
The workshop notes will be posted soon.
Register here: https://ucla.zoom.us/meeting/register/Mhtn81ybQQO43Ai0rysH8Q#/registration
Past Classes and Workshops Available Online
- Introduction to Stata 16
- Introduction to Stata 19.5
- Stata Data Management
- Regression with Stata
- Logistic Regression with Stata (newer)
- Logistic Regression with Stata (older)
- Beyond Binary Logistic Regression with Stata
- Multiple Imputation in Stata 15
- Introduction to Survey Data Analysis
- Applied Survey Data Analysis
- Advanced Topics in Survey Data Analysis
- Survival Analysis Using Stata
- Introduction to Meta-analysis using Stata
- Introduction to Programming in Stata
- Decomposing, Probing, and Plotting Interactions in Stata
- Introduction to SAS 9.4 using SAS OnDemand (new)
- Introduction to SAS 9.4
- Programming Basics in SAS 9.4
- Analyzing and Visualizing Interactions in SAS 9.4
- Regression with SAS
- Logistic Regression in SAS
- Repeated Measures Analysis in SAS
- Applied Survey Data Analysis using SAS 9.4
- Multiple Imputation in SAS 9.4
- Survival Analysis Using SAS
- Using Arrays in SAS
- Introduction to SAS Macro Language
- Introduction to SPSS (point-and-click, using SPSS version 29)
- Introduction to Regression with SPSS
- Introduction to Mediation Models with the PROCESS macro in SPSS
- Graphing Interactions Using the PROCESS Macro in SPSS
- A Practical Introduction to Factor Analysis
- Principal Components (PCA) and Exploratory Factor Analysis (EFA) with SPSS
- Introduction to SPSS Syntax, Part1 (using SPSS version 21)
- Introduction to SPSS Syntax, Part 2 (using SPSS version 21)
- SPSS Syntax to the Next Level
- Applied Survey Data Analysis Using SPSS 29
- Repeated Measures Analysis in SPSS
- Using the SPSS Mixed Command
- Graphics using SPSS
Mplus and Latent Variable Analysis
- Introduction to Mplus
- Building Your Mplus Skills
- A Practical Introduction to Factor Analysis
- Introduction to Mediation Analysis using Mplus
- Introduction to R
- R Data Management
- R Markdown Basics
- Introduction to ggplot2
- Introduction to Linear Regression in R
- Introduction to Regression in R
- Decomposing, Probing and Plotting Interactions in R
- Analysis and Visualization of Interactions in R
- Survey Data Analysis with R
- Survey Data Analysis with R 4.5.1
- Introduction to Survival Analysis in R
- Repeated Measures Analysis in R
- Latent Growth Models (LGM) and Measurement Invariance with R in lavaan
- Introduction to Structural Equation Modeling (SEM) in R with lavaan
- Confirmatory Factor Analysis with in R with lavaan
- Missing Data in R
- Introduction to R Programming
- Introduction to Generalized Linear Regression Model in R
- Beyond Logistic regression in R
- Zero-inflated and Hurdle models for Count Data in R
- Output Tables in R
- Effect Size Measures for Linear Multilevel Models in R
- Introduction to Power Simulations in R
- Introduction to DAGs for Causal Inference in R
- Machine Learning for Scientific Research in R, Part 1
Longitudinal Data Analysis
- Longitudinal Research: Present Status and Future Prospects by Judith Singer & John Willett
- Analyzing Longitudinal Data using Multilevel Modeling
Power Analysis
- Deciphering Interactions in Logistic Regression
- Regression Models with Count Data
- Statistical Writing
Other