## Upcoming Workshops, Summer 2022

**SPSS Syntax to the Next Level**, Tuesday, August 23 from 1 to 4 p.m. PDT via Zoom

This workshop will focus mostly on data management in SPSS in a wide variety of settings. Examples will include the creation of new variables using different commands, the use of scratch variables, loops and the output management system (OMS).

#### Register here: https://ucla.zoom.us/meeting/register/tJEkcOqgrDMiG9TzxoV-ztFTu06zxU8LVdwt

The workshop notes will be posted soon.

**Beyond Binary Logistic Regression in R**, Tuesday, August 30 from 1 to 4 p.m. PDT via Zoom

This workshop covers the statistical analysis of categorical outcomes with more than two categories, specifically ordinal logistic and multinomial logistic regression in R.

#### Register here: https://ucla.zoom.us/meeting/register/tJYtce6upz0sHtBBLmktmWI3YcsoIqFyMFcA

The workshop notes will be posted soon.

**Statistical Significance, False Positives, and Flexibility in Data Analysis (Researcher Degrees of Freedom)**, Tuesday, September 6 from 1 to 4 p.m. PDT via Zoom

This workshop is a discussion of the important article “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant” by Simmons, Nelson and Simonsohn (2012), which discusses how seemingly benign decisions during data collection and analysis, or *researcher degrees of freedom*, make false positives much more likely. Their simulations show how a combination of a few of these decisions can increase the false-positive rate to above 50%. When researcher degrees of freedom are not disclosed in the reporting of the study, reported p-values are not reflective of the flexibility required to achieve those results and will tend to be overconfident. Solutions for both researchers and journal reviewers will be proposed.

#### Register here: https://ucla.zoom.us/meeting/register/tJ0sfumqqTwuE9TxlXJ5Eim1GfBnLt8hX6Y1

The workshop notes will be posted soon.

## 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)
- Repeated Measures Analysis in SPSS
- Using the SPSS Mixed Command
- Graphics using SPSS

**Mplus and Latent Variable Analysis**

- 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
- 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
- Introduction to Survival 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**