## Workshops for Summer Quarter, 2018

**R Graphics: Introduction to ggplot2**, Thursday, July 12 from 9 a.m. to 12 noon in 5628 Math Sciences (Visualization Portal)

Description: This workshop teaches the “grammar” of graphics that underlies the ggplot2 package, allowing the user to build eye-catching, publication-quality graphics easily and intuitively, layer-by-layer. The workshop focuses on producing statistical graphics throughout the data analysis process, including exploratory graphs, graphs of model effects and diagnostic graphs to assess model assumptions. The notes for the workshop can be found here; you can sign up here.

NOTE: All researchers are welcome to attend this workshop. However, there will be NO online component. Please sign up only if you can attend in person.

**Applied Survey Data Analysis in Stata 15**, Thursday, July 19 from 9 a.m. to 12 noon in 5628 Math Sciences (Visualization Portal)

Description: This workshop will cover both descriptive and inferential statistics with complex survey data. We will also discuss some graphical methods that can be used with weighted data. The notes for the workshop can be found here; you can sign up here.

NOTE: All researchers are welcome to attend this workshop. However, there will be NO online component. Please sign up only if you can attend in person.

**Principal Components and Exploratory Factor Analysis with SPSS**, Thursday, July 26 from 9 a.m. to 12 noon in 5628 Math Sciences (Visualization Portal)

Description: This seminar will give a practical overview of both principal components analysis (PCA) and exploratory factor analysis (EFA) using SPSS. We will begin with variance partitioning and explain how it determines the use of a PCA or EFA model. For the PCA portion of the seminar, we will introduce topics such as eigenvalues and eigenvectors, communalities, sum of squared loadings, total variance explained, and choosing the number of components to extract. For the EFA portion, we will discuss factor extraction, estimation methods, factor rotation, and generating factor scores for subsequent analyses. The seminar will focus on how to run a PCA and EFA in SPSS and thoroughly interpret output, using the hypothetical SPSS Anxiety Questionnaire as a motivating example. The notes for the workshop can be found here; you can sign up here.

NOTE: All researchers are welcome to attend this workshop. However, there will be NO online component. Please sign up only if you can attend in person.

## Past Classes and Workshops Available Online

- Introduction to Stata 13/14
- Stata Data Management
- Regression with Stata
- Logistic Regression with Stata
- Beyond Binary Logistic Regression with Stata
- Multiple Imputation in Stata 14
- Introduction to Survey Data Analysis
- Applied Survey Data Analysis
- Survey Data Analysis
- Survival 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

- A Practical Introduction to Factor Analysis
- Introduction to SPSS version 22 (point-and-click and syntax)
- Introduction to SPSS Syntax, Part1 (using SPSS version 21)
- Introduction to SPSS Syntax, Part 2 (using SPSS version 21)
- Regression with SPSS
- Introduction to Regression with SPSS (Version 23)
- Repeated Measures Analysis in SPSS
- Using the SPSS Mixed Command
- Graphics using SPSS

**Mplus and Latent Variable Analysis**

- Introduction to R
- Short Introduction to R
- Introduction to ggplot2
- R Data Management
- Repeated Measures Analysis 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**