**Stats Camp at UCLA seminars, October 5-7, 9 a.m. to 5 p.m.
**

Stats Camp is presenting five statistics training seminars (https://www.statscamp.org/university-camp) in partnership with UCLA IDRE Statistical Consulting. Stats Camp seminars are intensive but accessible workshops that teach best practices in modern advanced statistical analysis. Participants can also bring their own data and engage in private consultations with the instructor for up to an hour after class each day. Classes will all be virtual over Zoom, and an email will be provided prior to the event start date with Zoom meeting group logins. Additionally, all classes will be recorded and will be available for streaming for one year for participants.

Register with a valid UCLA email address and receive a 25% discount on registration fees!

** ****Craft of SEM, Instructors: Todd D. Little, Ph.D. & Elizabeth Grandfield, Ph.D. (**https://www.statscamp.org/university-camp/ucla-craft-of-sem-seminar)

Well, this is it! Based on over thirty years of experience in crafting SEM models for thousands of research projects, we will show you what you need to know for crafting a structural equation modeling (SEM) that truly tests your research questions! Most campers report their prior training was insufficient and/or outdated. We will introduce you to the current techniques and advances in SEM as well as guide you through the steps to ‘craft’ an exquisite SEM model.

**Multilevel Modeling Using R, Instructor: Alexander M. Schoemann, Ph.D.** (https://www.statscamp.org/university-camp/ucla-multilevel-modeling-using-r-seminar)

This seminar is designed to introduce theoretical and applied understandings of multilevel modeling. The fundamentals of multilevel modeling are taught by extending knowledge of regression analyses to designs involving a nested data structure. Nested data structures include, for example, students within classrooms, professionals within corporations, patients within hospitals, or repeated observations from the same person. In each of these cases and many more, the data are hierarchically arranged and may require methods beyond multiple regression or analysis of variance. These methods fall under the heading of multilevel modeling, which is also sometimes referred to as mixed modeling, hierarchical linear modeling, or random coefficients modeling.

**Psychometrics, Instructor: Matthew A. Diemer, Ph.D. & Michael B. Frisby, M.S.** (https://www.statscamp.org/university-camp/ucla-psychometrics-seminar)

Psychometrics is the science of how we measure things, such as the psychological attributes of people. These psychological attributes include abilities, aptitudes, achievement, attitudes, interests, personality traits, cognitive functioning, and mental health. Psychometrics, theoretically-informed and precise measurement of such latent phenomena, is an essential component of many of the things we hold dear. Scientific advances (e.g., can I make a claim that I am measuring what I purport to measure?), educational placement decisions (e.g., should a child be placed into a gifted program?), statistical power (e.g., is my measure precise enough to suggest that X predicts Y?), and other key considerations are all affected by psychometrics. This seminar emphasizes the conceptual understanding of and the application of psychometric principles.

**Programming in R, Instructor: Audrey J. Leroux, Ph.D.** (https://www.statscamp.org/university-camp/ucla-programming-in-r-seminar)

R is a freely available, open source software platform that is growing in both popularity and capacity. Participants will learn the logic of programming along with the tools and best practices for cleaning, manipulating, and graphically displaying data for analysis. The first part of the seminar introduces the logic of R’s primary data structures and how to work with functions. The second part of the seminar covers tools and best practices for cleaning, manipulating, visualizing, and analyzing data. It also introduces the concept of reproducibility as a fundamental tenet of high-quality data analysis.

**Mediation and Moderation, Instructor: Mwarumba Mwavita** (https://www.statscamp.org/university-camp/ucla-mediation-and-moderation-seminar)

Great! You have an idea you’re interested in, now what? You may even have a theory that X will predict Y, but the more important question, the question that we really what to know is, why does X predict Y, or when does X predict Y. Modeling these mechanisms of change are where Mediation and/or Moderation become your methodological hero!

## Workshops for Summer, 2020

**Multiple Imputation in Stata, **Monday, August 3 from 1 to 4 p.m. via Zoom

Description: 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 in Stata. The Stata code for this seminar is developed using Stata 15.

Attendance is restricted to researchers from the University of California. The information for the Zoom meeting will be sent the day before the workshop.

The notes for the workshop are here.

Sign up here .

**Decomposing, Probing, and Plotting Interactions in Stata**, Monday, August 10 from 1 to 4 p.m. via Zoom

Description: In regression, we are often interested in an interaction, which is the modification of the effect of an independent variable by another. Interactions can be decomposed and probed in three ways, by 1) testing each effect against zero, 2) testing differences between effects, and 3) plotting. This seminar will demonstrate how to do all three analyses in Stata for the following linear models with interactions: continuous-by-continuous, categorical-by-continuous, and categorical-by-categorical. Recommended: Prior knowledge of linear regression and a computer with Stata installed.

Attendance is restricted to researchers from the University of California. The information for the Zoom meeting will be sent the day before the workshop.

The notes for the workshop are here: Decomposing, Probing, and Plotting Interactions in Stata.

Sign up here .

**Applied Survey Data Analysis with R**, Monday, August 17 from 1 to 4 p.m. via Zoom

Description: This workshop will show how descriptive analyses, both numerical and graphical, can be done with continuous and categorical variables. Subpopulation analysis will be discussed, and then examples of OLS regression and logistic regression will be considered.

Attendance is restricted to researchers from the University of California. The information for the Zoom meeting will be sent the day before the workshop.

The notes for the workshop are will be posted soon.

Sign up 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

- 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

**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**