Upcoming Workshops, Spring 2025
Binary Logistic Regression in Stata, Monday, May 12, 2025 from 1 to 4 p.m. PT via Zoom
The analysis of binary outcomes is widespread across scientific fields. This workshop introduces the basics of the most commonly used statistical model for binary outcomes, logistic regression. Some basic background on probability and odds will be provided, as well as a brief review of the model itself. This workshop then covers how to run a logistic regression in Stata, as well as estimating predictions and marginal effects, graphing model predictions, and assessing model fit. A little background in Stata is assumed.
The workshop notes are here: https://stats.oarc.ucla.edu/stata/seminars/logistic-regression-in-stata-newer/
Register here: https://ucla.zoom.us/meeting/register/v5CaiPFJThGZVM41SL-LAA
Using the SPSS Mixed Command, Monday, May 19, 2025 from 1 to 4 p.m. PT via Zoom
The purpose of this workshop is to show the use of the mixed command in SPSS. Although it has many uses, the mixed command is most commonly used for running linear mixed effects models (i.e., models that have both fixed and random effects). Such models are often called multilevel models. Because the purpose of this workshop is to show the use of the mixed command, rather than to teach about multilevel models in general, many topics important to multilevel modeling will be mentioned but not discussed in detail. References will be provided so that those interested in these topics can find additional information.
The workshop notes are here: https://stats.oarc.ucla.edu/spss/seminars/spss-mixed-command/
Register here: https://ucla.zoom.us/meeting/register/Hg0d9DCRRV6XXmMWlWnRJw
Introduction to Power Simulations in R, Monday, June 2, 2025 from 1 to 4 p.m. PT via Zoom
This workshop introduces power analysis and simulations to assess power and estimate sample sizes needed for sufficient power. We will begin with simple analyses such as the t-test and will work our way up to various regression models. We will provide guidance in R to program simple Monte Carlo simulations to assess power. Some background in programming loops would be helpful but is not necessary.
The workshop notes are here: forthcoming
Register here: https://ucla.zoom.us/meeting/register/OSBBbhASQj6pxODNu8Rd5w
Introduction to directed acyclic graphs (DAGs) for causal inference in R, Monday, June 9, 2025 from 1 to 4 p.m. PT via Zoom
Directed Acyclic Graphs (DAGs) have emerged as an important tool in causal modeling to understand the relationships among variables. DAGs can inform what variables should be included or excluded in a statistical model intended to minimize bias in the estimation of a causal effect. This workshop discusses the basics of DAGs used for causal inference and outlines simple rules one can follow to know which variables to include in a causal statistical model. The R package daggity will be discussed to visualize DAGs.
The workshop notes are here: forthcoming
Register here: https://ucla.zoom.us/meeting/register/84uoBVOpSr2rVsDCbM2bQA
Past Classes and Workshops Available Online
- Introduction to Stata 16
- Introduction to Stata 18
- 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
- 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
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