## Workshops for Winter, 2021

**R Markdown Basics, Monday, February 1 from 1 to 4 p.m. PST via Zoom **

sign up here Zoom access information will be sent the day before the workshop.

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.

**Retiring Statistical Significance: Interpreting and Reporting P-Values and Confidence Intervals Without Significance Testing, Monday, February 8 from 1 to 4 p.m. PST via Zoom **

sign up here Zoom access information will be sent the day before the workshop.

The misuse of null hypothesis significance testing (NHST) and p-value thresholds, together with selective reporting of statistically significant results, have produced an inundation of overstated conclusions and unreplicable results in scientific research. Many of these abuses come from misunderstandings of how to interpret p-values, confidence intervals, the words “statistical significance”, and the uncertainty in a statistical result. The misuse is so widespread that the American Statistical Association is calling for the retirement of NHST and the words “statistical significance”. In this workshop, you’ll learn current recommendations of how to interpret and report the results of statistical analyses to reflect the uncertainty in the findings and to make expectations of future replication more realistic. Hopefully, the adoption of these methods will refocus the aims of research to find scientifically significant rather than statistically significant results.

**Introduction to Structural Equation Modeling in R with lavaan, Monday, February 22 from 1 to 4 p.m. PST via Zoom **

sign up here Zoom access information will be sent the day before the workshop.

This workshop will introduce basic concepts of structural equation modeling (SEM) motivated by a hypothetical study on student achievement. The emphasis will be on understanding the output of lavaan and less on clinical interpretations or mathematical derivation. The matrix formulation and path diagram will be introduced for a path analysis, measurement and structural regression model. Concepts include identification, model fit, and modification indices. Suggested prerequisites: a review of the webpage Confirmatory Factor Analysis (CFA) in R with lavaan (https://stats.idre.ucla.edu/r/seminars/rcfa), familiarity with linear regression and basic matrix notation.

**Introduction to Mediation Analysis Using the SPSS PROCESS Macro, Monday, March 1 from 1 to 4 p.m. PST via Zoom **

sign up here Zoom access information will be sent the day before the workshop.

This workshop will introduce the basics of conducting mediation analyses using the PROCESS Macro for SPSS, which was by written by Andrew F. Hayes. As this is an introductory workshop, most examples will consider models in which all variables are continuous. Discussion topics will include the use of bootstrapping confidence intervals, the calculation of effect sizes, and tips for reporting the results of the mediation analysis.

**Multiple Imputation in R, Monday, March 8 from 1 to 4 p.m. PST via Zoom **

sign up here Zoom access information will be sent the day before the workshop.

The purpose of this workshop is to discuss commonly used techniques for handling missing data and common issues that could arise when these techniques are used. In particular, we will focus on the one of the most popular methods, multiple imputation, and how to perform it using the package *mice* in R. We will also briefly go over some other useful packages in R for handling missing data. As prerequisite to this workshop, we suggest participants have basic knowledge in R, and if they do not have prior training in R, the Introduction to R workshop can be found here: https://stats.idre.ucla.edu/r/seminars/intro/ .

## 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
- (NEW) 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 (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**

- (NEW) Confirmatory Factor Analysis with in R with lavaan
- Decomposing, Probing and Plotting Interactions in R
- Introduction to R
- R Markdown Basics
- Introduction to ggplot2
- R Data Management
- Repeated Measures Analysis in R
- Introduction to Regression in R
- Survey Data Analysis with 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**