## Workshops for Winter, 2020

**R Markdown, Thursday, January 23 from 9 a.m. to 12 noon in the Visualization Portal, Math Sciences 5628**

Description: R Markdown files integrate text, Markdown, and R code into dynamic documents that weave together plain text, formatted text, and the output of the R code. The resulting dynamic reports can be produced in many formats, including HTML documents, HTML slideshows, LaTeX pdf, Beamer slideshows, MS Word doc, books, scientific articles, and websites. This seminar covers basic coding and conventions of the 3 frameworks upon which R Markdown depends: Markdown for formatting text, knitr for R code chunks, and YAML for rendering the document. The seminar does not assume any previous experience with R Markdown, but attendees who wish to participate in seminar demonstrations should come with RStudio and R Markdown installed on their computers.

If you do not have a laptop to bring, please borrow one from the library before coming to the workshop.

**Students should bring their own laptop computers with R 3.6.1 (****https://cran.r-project.org/**** ) installed. **

**Installation of R-Studio (****https://www.rstudio.com/products/rstudio/download/**** ) is also strongly recommended.** workshop notes sign-up link

NOTE: This event is only available in-person at UCLA. There will not be online access to this event. Please only register for the event if you are able to attend in-person.

**Regression Models with Count Data, Tuesday, February 18 from 1 to 4 p.m. in the Visualization Portal, Math Sciences 5628**

Description: Regression models for data with a count outcome is part of the family of generalized linear models. This workshop is designed to give an overview on regression model with count data. The workshop includes a broad range of analyses available for count regression models such as Poisson regression, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial. The main statistical package in the workshop is R, but code to implement count regression models in other statistical packages such as Stata, SAS and SPSS will be provided. sign-up link

NOTE: This event is only available in-person at UCLA. There will not be online access to this event. Please only register for the event if you are able to attend in-person.

**Introduction to Survey Data Analysis using Stata, Thursday, February 20 from 9 a.m. to 12 noon in the Visualization Portal, Math Sciences 5628
**

Description: The workshop will introduce the basic concepts and elements necessary to analyze data collected via a complex sampling design. The workshop will use Stata to conduct descriptive analyses and create graphs. Examples of subpopulation analyses will be given, as well as examples of linear and logistic regression models. sign-up link

NOTE: This event is only available in-person at UCLA. There will not be online access to this event. Please only register for the event if you are able to attend in-person.

**Introduction to Confirmatory Factor Analysis using R with laavan , Monday, February 24 from 1 to 4 p.m. in the Visualization Portal, Math Sciences 5628**

Description: This workshop will cover basic concepts of confirmatory factor analysis by introducing the CFA model and looking at examples of a one-factor, two-factor and second-order CFA. Concepts such as model identification, standardized solutions, and model fit statistics such as the chi-square statistic, CFI, TLI and RMSEA will be covered. The focus is on learning the CFA model and how to implement and interpret the output in R’s lavaan package. sign-up link

**Advanced Topics in Survey Data Analysis, Thursday, February 27 from 9 a.m. to 12 noon in the Visualization Portal, Math Sciences 5628**

Description:This workshop will cover advanced topics in the analysis of complex survey data. Familiarity with common sampling plans and how to analyze data from them is necessary to understand the topics that will be discussed. It is strongly suggested that attendees who do not have such familiarity attend the Introduction to Survey Data Analysis workshop before attending this workshop.

The workshop presents several advanced topics in dealing with complex survey data, including imputation, generating and using propensity scores, multilevel modeling and latent variable models. The workshop will discuss the use of several different statistical software packages (including SAS, Stata and Mplus), but no single software package will be used for all examples. sign-up link

## 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
- Survival Analysis Using Stata
- Introduction to Meta-analysis using Stata
- Introduction to Programming 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 (Version 23)
- 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)
- Repeated Measures Analysis in SPSS
- Using the SPSS Mixed Command
- Graphics using SPSS

**Mplus and Latent Variable Analysis**

- Introduction to R
- R Markdown Basics
- Introduction to ggplot2
- R Data Management
- Repeated Measures Analysis in R
- Introduction to Regression in R
- Decomposing, Probing and Plotting Interactions 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**