## Upcoming Workshops, Winter 2023

**Introduction to Meta-analysis in Stata**, Monday, February 27 from 1 to 4 p.m. PST via Zoom

Meta-analysis is the synthesis of results from previous studies. It is used to increase power, obtain a better estimate of an effect size, and sometimes to resolve conflicting conclusions in the literature. In this workshop, we will discuss how the data for a meta-analysis are collected and organized, as well as how such data are analyzed and graphed. We will also discuss some of the limitations meta-analysis and what should be included in a meta-analysis for publication.

Register here: **https://ucla.zoom.us/meeting/register/tJUpcO-vpzsqH93pnL7-ZfVKhYwxcbaX6j5f**

The workshop notes are here.

**Missing Data in R**, Monday, March 6 from 1 to 4 p.m. PST via Zoom

The purpose of this workshop is to discuss techniques and introduce some useful packages in R for handling missing data. In particular, we will focus on multiple imputation and how to perform it using the R package, mice: “Multivariate Imputation by Chained Equations”. As prerequisite to this seminar, we suggest participants have basic knowledge in R and if they do not have prior training in R, a seminar providing an introduction R can be found here: https://stats.idre.ucla.edu/r/seminars/intro/.

**Register here: https://ucla.zoom.us/meeting/register/tJMod-urpjwsGt0IpKni-92vrKDJsIyKclhO**

The workshop notes will be posted soon.

**Decomposing and Visualizing Interactions in R**, Monday, March 13 from 1 to 4 p.m. PST via Zoom

In regression, we are often interested in an interaction, which is the modification or moderation of the effect of an independent variable by another. Understanding interactions involves interpreting the regression coefficients, estimating and testing simple effects and their differences, and visualizing the interaction. This workshop will teach you how to do all of these thing in R using base R, as well as the emmeans and ggplot2 packages. Some prior knowledge of linear regression and experience with R is recommended but not necessary.

#### Register here: https://ucla.zoom.us/meeting/register/tJwkfumspzsqHtKbf6iQV7U-xADsn3h3BhT1

The workshop notes are here.

## Past Classes and Workshops Available Online

- Introduction to Stata 16
- Stata Data Management
- Regression with Stata
- Logistic Regression with Stata
- 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
- 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 and syntax)
- 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
- Repeated Measures Analysis in SPSS
- Using the SPSS Mixed Command
- Graphics using SPSS

**Mplus and Latent Variable Analysis**

- Introduction to R
- R Data Management
- R Markdown Basics
- Introduction to ggplot2
- Introduction to Regression in R
- Decomposing, Probing and Plotting 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

**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**