Upcoming Workshops, Summer 2025
Introduction to Stata, Monday, August 11, 2025 from 1 to 4 p.m. PT via Zoom
This workshop introduces the functionality of Stata with a focus on data analysis for scientific research. We will discuss the basic Stata interface and do-files, as well as explore commands to import data, create and modify variables, summarize variables, combine data sets and perform basic statistical analyses. No experience with Stata is assumed.
The workshop notes will be posted soon.
Register here: https://ucla.zoom.us/meeting/register/GoleXEPuQyiviWQuSNxXeg#/registration
Machine Learning for Scientific Research Part 1, Monday, August 18, 2025 from 1 to 4 p.m. PT via Zoom
While machine learning methods have traditionally been used for prediction in fields like industry and technology, they are increasingly being adopted in scientific research for exploration and inference. This two-part workshop introduces key machine learning techniques used for scientific applications. Part 1 discusses core concepts such as prediction accuracy, the bias-variance tradeoff, and cross-validation. Part 2 introduces commonly used methods for scientific research, including ridge regression, LASSO, principal components analysis (PCA), regression trees, and random forests. Practical examples and demonstrations will be provided using R, and we will discuss how these tools can complement traditional statistical approaches in scientific research. No prior experience with machine learning is required, though familiarity with basic regression is recommended.
The workshop notes will be posted soon.
Register here: https://ucla.zoom.us/meeting/register/6jsOVcVAS2mVcCULU7dn8g#/registration
Machine Learning for Scientific Research Part 2, Monday, August 25, 2025 from 1 to 4 p.m. PT via Zoom
While machine learning methods have traditionally been used for prediction in fields like industry and technology, they are increasingly being adopted in scientific research for exploration and inference. This two-part workshop introduces key machine learning techniques used for scientific applications. Part 1 discusses core concepts such as prediction accuracy, the bias-variance tradeoff, and cross-validation. Part 2 introduces commonly used methods for scientific research, including ridge regression, LASSO, principal components analysis (PCA), regression trees, and random forests. Practical examples and demonstrations will be provided using R, and we will discuss how these tools can complement traditional statistical approaches in scientific research. No prior experience with machine learning is required, though familiarity with basic regression is recommended.
The workshop notes will be posted soon.
Register here: https://ucla.zoom.us/meeting/register/WdGYi5C6Q7ixSy_nH6h52Q#/registration
Survey Data Analysis with R, Monday, September 15, 2025 from 1 to 4 p.m. PT via Zoom
This workshop will show how descriptive analyses, both numerical and graphical, can be done with continuous and categorical variables. Subpopulation analysis will be discussed, and then examples of OLS regression and logistic regression will be considered.
The workshop notes will be posted soon.
Register here: https://ucla.zoom.us/meeting/register/IJ-3bjGqS5i6CANjV4fcbg#/registration
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
- Introduction to Power Simulations in R
- Introduction to DAGs for Causal Inference 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