Bayesian Data Analysis Training Seminar

Applied Latent Class Analysis Seminar

An introductory 3-day course about Bayesian Data Analysis, focus on the differences to the frequentist approach, and application of several models.

Many fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian data analysis. Bayesian analysis provides rich information about the relative credibilities of all candidate parameter values for any descriptive model of the data, without reference to p-values. Bayesian analysis applies flexibly and seamlessly to complex hierarchical models and realistic data structures, including small samples, large samples, unbalanced designs, missing data, censored data, outliers, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic models. (More about why to go Bayesian is described below.) This course shows you how to do Bayesian data analysis, hands on, with free software called R.

Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts, relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.

Fall Camp: September 14 – 16, 2017
Brea, CA – Embassy Suites North Orange County

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Topics include:

  • The rich information provided by Bayesian analysis and how it differs from traditional (Frequentist) statistical analysis
  • The concepts of Bayesian reasoning along with the easy math and intuitions for Bayes’ rule
  • The concepts and hands-on use of modern algorithms (“Markov chain Monte Carlo”) that achieve Bayesian analysis for realistic applications
  • How to use the free software R for Bayesian analysis.
  • An extensive array of applications, including comparison of two groups, ANOVA-like designs, linear regression, logistic regression, multilevel regression, and growth models, etc.
Payment Options

$1,095 Faculty/Professional or $945 Student/Post-Doc

Learning Objectives

This 3-day statistics training institute on Psychometrics will enable participants to:

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

  • Explain the difference between frequentist and bayesian statistics
  • Critically evaluate applications of Bayesian analysis in scientific studies.
  • Analyze data using R
  • Specify, estimate, evaluate, and compare different Bayesian models to fit possible hypothesis.

Participants are encourage to bring data of interest to work with during the week, there will be time to work on it with help of the instructor. Work on datasets of interest will magnify the learning and impact of the course.

Participants will also complete the course with a foundation for future learning about Bayesian statistics and knowledge about available resources to guide such endeavors.


ThursdaySeptember 14, 2017
9:00-10:45Bayesian reasoning generally
10:45-11:00Snack and refreshment break
11:00-12:30Perfidious pp values and the con game of confidence intervals
12:30-1:30Lunch break
1:30-3:00Bayes’ rule, and R; Simple examples to train intuition
3:00-3:15Snack and refreshment break
3:15-5:00Markov Chain Monte Carlo; R programs for Bayesian analysis
FridaySeptember 15, 2017
9:00-10:45Know your distributions. Application of priors
10:45-11:00Snack and refreshment break
11:00-12:30General linear model. Multiple regression, and robust regression
12:30-1:30Lunch break
1:30-3:00Model comparison
3:00-3:15Snack and refreshment break
3:15-5:00General linear model. T-test and ANOVA
SaturdaySeptember 16, 2017
9:00-10:45Generalized linear Model. Logistic regression
10:45-11:00Snack and refreshment break
11:00-12:30Multilevel linear regression
12:30-1:30Lunch break
1:30-3:00Multilevel growth curve/repeated measures
3:00-3:15Snack and refreshment break
3:15-5:00Catch up and Individual Consultations

Instructor: Mauricio Garnier-Villarreal Ph.D.

Baysian Data Analysis Training Course

Dr. Mauricio Garnier-Villarreal is a Research Assistant Professor at Marquette University. His Ph.D focus of 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 the generalized linear model, mixed-effects and SEM models. Contributes to the development of the blavaan R package (, use to apply SEM models from the Bayesian framework. He has experience not only working in the test and development of methods, but also in the application of these in data; in close collaborations with colleagues from the fields of special education, cognitive decline in aging, healthy aging, nursing, exercise science. His teaching at Marquette focus in intermediate and advance statistics for health science, counseling psychology, and psychology graduate students, focusing in the application in substantive areas. He has been involved in the Stats Camp since 2011.

Software and Computer Support

Participants need to bring a laptop computer with Wi-Fi capabilities.

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.

Instruction will be provided for the methods using the most current version of the open source platform R (, the recommended editor would be RStudio ( These programs are free to download and install in any operating system (Windows, Mac, Linux)

Course Files

Below are links to course files for those who enrolled in the course. Please download these files onto your computer on the first day of the course. The files are password protected to respect the intellectual property rights of the instructors. By using your login information, you agree not to share your login information or the content protected by it.

Course files and downloads will be posted after the first day of class.

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Course Resources
  • Breakfast (Embassy guests), Lunches, & Snacks Daily