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IN PERSON – 5-day Statistics Short Course

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Seminar Overview:

You have a good to great understanding of structural equation modeling (SEM), maybe you have been doing traditional SEM for years, but you’ve began a project where your observations are not normally distributed, you have a small sample size, or even worse – your model is not converging! What do you do now? Bayesian SEM is your next step!

Advanced SEM: Bayesian SEM will cover models that may be too complicated or impossible to estimate in the traditional SEM framework. This seminar highlights the philosophical and practical advantages of the Bayesian approach to SEM.

Material and examples will be provided using R, but if you prefer to work with another program, we will assist you to apply these methods in your program of choice. You can also request topics to be included during the weeklong seminar that may be developed into its own module! In addition, personal consultation time will be available to help you propel your research forward.

A perfect follow up for SEM Foundations or Bayesian Data Analysis!

 

Seminar Topics:

  • Coming Soon…

Seminar Description:

You have a good to great understanding of structural equation modeling (SEM), maybe you have been doing traditional SEM for years, but you’ve began a project where your observations are not normally distributed, you have a small sample size, or even worse – your model is not converging! What do you do now? Bayesian SEM is your next step!

Advanced SEM: Bayesian SEM will cover models that may be too complicated or impossible to estimate in the traditional SEM framework. This seminar highlights the philosophical and practical advantages of the Bayesian approach to SEM.

Instructor: Mauricio Garnier-Villarreal, Ph.D.

Mauricio is a full time assistant professor at Vrije Universiteit Amsterdam. His Ph.D focused on Quantitative Psychology at the University of Kansas completed in the summer of 2016. His research focus is on the application of Bayesian methods to complex structure data for longitudinal analysis, from both mixed-effects and SEM models. He has experience not only working in the test and development of methods, but also in the application of these in data; in different fields like special education, cognitive decline in aging, healthy aging (orcid.org/0000-0002-2951-6647). He has been involved in the Stats Camp since 2011.

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Instructor: Esteban Montenegro, Ph.D.

I’m a researcher at the UC Davis Alzheimer’s Disease Center- East Bay. I conduct data analysis using advanced statistical methods such as latent variable modeling. I have 6 years of experience programming in R, and I love learning about Linux and statistical new tools.I’m always open to new projects and ideas, I collaborate with several teams around the world on topics related to healthy aging, Alzheimer Disease and other health related topics.

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APA Continuing Education Credits:

This course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.

Seminar Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Describe the fundamental properties of Bayesian reasoning and Bayes’ rule.
  • Differentiate between direct probability and indirect probability.
  • Utilize Markov Chain Monte Carlo estimation using the Gibbs and Hamiltonian samplers.
  • Analyze Bayesian models in several software packages, including JAGS, STAN, and blavaan.
  • Identify the properties of key distributions used in Bayesian analysis.
  • Conduct a confirmatory factor analysis using the Bayesian framework.
  • Implement prior estimations in conducting CFA modeling.
  • Evaluate model fit and compare CFA models.
  • Model multiple group CFAs using the Bayesian framework.
  • Evaluate latent regression models, latent interactions, quadratic effects, and mediation in a Bayesian framework.
  • Use Bayesian methods to evaluate non-normal continuous data.
  • Conduct SEM for categorical data using Bayesian Item Response Theory.
  • Account for missing data.
  • Evaluate longitudinal models in a Bayesian framework.

 

Seminar Prerequisites:

Coming Soon…

Software and Computer Support:

Coming Soon…

Seminar Audience:

Q: Does this seminar focus on JAGS or STAN?

A: I do cover Stan. The seminar doesn’t focus on writing either JAGS or Stan code, since this is more complicated.

The seminar software focus on the use of blavaan (user friendly), which runs the analysis for you in either JAGS or Stan (your choice). For special cases of models, we go over how to edit the code (JAGS or Stan) to run models that blavaan doesnt include yet.

If there is the interest from a student to go over more detail Stan code we can do that, there is time to adjust the seminar to the students needs, or work on that during consultation.

Seminar Files

Instructor will provide password on first day of seminar.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Summer Stats Camp 2023: Bayesian SEM
Monday June 12, 2023
9:00–10:45 Welcome and Introductions.
10:45–11:00 Rest Break
11:00–12:30 Bayesian reasoning and Baye’s rule : Direct probability vs indirect probability
12:30–1:30 Rest Break
1:30–3:15 Markov Chain Monte Carlo Estimation: Gibbs and Hamiltonian samplers
3:15–3:30 Rest Break
3:30–5:00 Introduction to R: blavaan, JAGS, STAN
Tuesday June 13, 2023
9:00–10:45 Know your distributions
10:45–11:00 Rest Break
11:00–12:30 Confirmatory Factor Analysis
12:30–1:30 Rest Break
1:30–3:15 Priors: relevance, choice, strengths. CFA with cross-loadings
3:15–3:30 Rest Break
3:30–5:00 Individual Consultations
Wednesday June 14, 2023
9:00–10:45 Model fit and model comparison
10:45–11:00 Rest Break
11:00–12:30 Multiple-group CFA
12:30–1:30 Rest Break
1:30–3:15 Latent regression models: latent interactions, and mediation
3:15–3:30 Rest Break
3:30–5:00 Individual Consultations.
Thursday June 15, 2023
9:00–10:45 SEM for categorical data: Item Response Theory
10:45–11:00 Rest Break
11:00–12:30 Missing data handling
12:30–1:30 Rest Break
1:30–3:15 Longitudinal CFA (Measurement Invariance)
3:15–3:30 Rest Break
3:30–5:00 Individual Consultations.
Friday June 16, 2023
9:00–10:45 Latent Growth Curve models
10:45–11:00 Rest Break
11:00–12:30 Latent Change Scores
12:30–1:30 Rest Break
1:30~3:30 Individual Consultations.

 

 

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