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1 FREE Night Included, Click Here to Book Your Room!

Venue Information: Embassy Suites By Hilton 1000 Woodward Pl NE, Albuquerque, NM 87102

Cancellation Policy:

If you cancel your registration at least two weeks prior to the day the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). If you cancel within the 14 days prior to the course you may transfer the full amount towards any future course or receive a full refund (minus a processing fee of $295).

In the unlikely event that Stats Camp Foundation must cancel a course, our team will do our best to inform you as soon as possible with more information. Students then have the option of receiving a full refund of the course fee or a full credit towards another future course. In no event shall Stats Camp Foundation be held responsible for any incidental or consequential damages that you may incur because of the cancellation. Course disclaimer. All courses include 365 days of asynchronous access after event.

*You must register prior to the event start, attendance is NOT mandatory. However; CE credits are only available if you attend live for the duration of the course. (Includes: On-Demand, Livestream and In-Person courses, all Stats Camp Retreats are in-person attendance only.)


what students are saying

Thank you to Dr. Todd Little and his team for facilitating such a great statistical methods training workshop in Albuquerque. I absolutely got what I needed and was energized in my work when I returned to Cleveland. This was a wonderful experience that I will be sure to share with my colleagues here at CWRU. Now I am looking forward to mediation this summer in Albuquerque! Who knew I would be excited for more stats!
Best Statistics Training Course Online

Leigh-Ann Sweeney, Ph.D., Lecturer and Health Service Researcher

I participated in the SEM: Foundations and Extended Applications course earlier this summer. It was such a wonderful statistics training opportunity that has, already, found lots of applications in various grant proposals and analyses. As I’ve said to several colleagues since, Stats Camp has a special ability to take something that prior to my arrival seemed overwhelming and complicated and make it seem so do-able.
Best Statistics Training Course Online

Amy K. Syvertsen, Ph.D., Applied Developmental Scientist

I can honestly say that for the first time in years I have been able to focus on myself and my research. I feel physically and mentally healthier than ever and I am excited about the cutting-edge knowledge and resources I am gaining in the rapidly evolving field of latent variable modeling. I am going to make it a goal to prioritize Statscamp for myself and graduate students on a yearly basis. Definitely recommend!
Best Statistics Training Course Online

Sarah D. Lynne-Landsman, Ph.D., Family, Youth & Community Sciences

The Stats Camp instructors have an unmistakable dedication to research methods and data analysis of the very highest quality-but are remarkably balanced in their very obvious efforts to connect with others on a professional and personal level as very likable and real people. I really enjoyed the networking opportunities that the breakout sessions provided and will be returning for another Summer Camp soon!
Best Statistics Training Course Online

Chen Zhang, Ph.D., Faculty University of Memphis

The highlight of the summer break was attending ‘Stats Camp,’ an educational camp in Albuquerque New Mexico that provides advanced-level training in Statistics. The 5-day class I took (‘SEM with Mplus’) passed by quickly, as an information-packed series of lectures, hands-on examples, personal consultations, and jokes. Classes were small, lectures succinct/direct, and training content based on each individual student requests.
Best Statistics Training Course Online

Anna Yu Lee, PhD, MPH, MA, Counselor/Therapist

The Stats Camp instructor’s clear and practical presentation of material that was once intimidating to me has uncovered a powerful analytic tool. I feel comfortable that I’ve learned the correct application of SEM Foundations and Extended Applications from experts in the field. At the same time, I was introduced to cutting edge statistics techniques and I understand the advantages of their practical use and application.
Best Statistics Training Course Online

Jenny Tehan, Ph.D., Department of Psychology University of Akron

The Stats Camp expert instructors were clear, concise, and helpful in addressing questions. Before attending, I was concerned that I would have trouble truly understanding all of the concepts and material in such a short time, but the instruction was fantastic and not overwhelming. I particularly found it advantageous to stay on-site at the Embassy Suites so I could participate in all of the after hours networking events.
Best Statistics Training Course Online

Spiros Tzivelekis, Ph.D., GHE Director - Amgen

I was impressed with the amount of material Dr. Todd Little and team were able to cover in Summer Camp. The instructors moved at a pace appropriate for the participants, adapted the materials as we went along to accommodate this pace, and still offered individual consultations. I am confident that I can take everything I learned, from the basic to the advanced topics & employ them independently in my own research.
Best Statistics Training Course Online

Katie Paschall, Ph.D., Senior Research Scientist at Child Trends

I took both Foundations of SEM and Longitudinal SEM last summer and I have to say this has been one of the most useful learning experiences of my life. The courses were excellent! Everything was explained in a clear fashion with plenty of time for questions and practice. Learning was fun and the teaching happened at multiple levels, such that everyone would have a lot of knowledge gain regardless of their level of expertise.
Best Statistics Training Course Online

Rodica Damian, Ph.D., Associate Professor University of California, Davis

I can strongly recommend the Stats Camp Summer course in Longitudinal Mixture Modeling. It is as systematic and comprehensive as the very best of the research methods courses I have participated in since I moved from business to academics 12 years ago. It has been an important tool box for pulling apart differing effects in subpopulations for the coming generation of social and behavioral science researchers, starting now!
Best Statistics Training Course Online

Alan R. Johnson, Ph.D., Senior Research Fellow at NORD University, Bodø, Norway

The Summer Stats Camp training institute provides a wonderful opportunity for researchers and data analysts to learn the basic foundations of SEM as well as some advanced applications and research opportunities that SEM can facilitate. Dr. Little provides personable “hands-on”instruction in a relaxed and enjoyable environment. I highly recommend this summer institute to faculty and graduate students alike!
Best Statistics Training Course Online

Paul Schrodt, Ph.D., Professor and Director of Graduate Studies

Stats Camp is the place for a meeting of the minds and an opportunity to sit down and sort through complex ideas with people who can guide you, side-by-side, through the sticking points so you can get back into the flow. It’s amazing that something like this exists. Dr. Little has given the world a tremendous gift with Stats Camp. I walked in knowing nothing substantial and walked out delighted and better skilled.
Best Statistics Training Course Online

Benjamin Theisen, Ph.D., Business Psychology Consulting Group

Stats Camp was the most useful statistical training I’ve ever had. The instructors are down to earth and practical in their teaching style and the classroom environment was relaxed and non-threatening, which is necessary for such a potentially daunting topic. In particular, the one-on-one private consultation with my own data was invaluable, I highly recommend signing up for the Summer Stats Camp in Albuquerque!
Best Statistics Training Course Online

Kris Carlson, Ph.D., Sandia National Laboratories

IN PERSON – 5-day Bayesian Data Analysis Statistics Course

Seminar Overview:

This is a comprehensive five day Bayesian statistics course. This is our most popular training event each year where students and professionals have the opportunity to learn directly from a vetted statistics instructor. The live lectures and one-on-one Q & A provide a rare chance to bring your own personal research and let us help you break through any roadblocks standing in the way of progressing your work. The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis statistics.

Seminar Topics:

  • 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, count regression, robust regression, regularization and polynomials, and missing data treatments in Bayesian inference.

Seminar Description:

Many fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian Data Analysis Solutions. 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. This seminar shows you how to do Bayesian data analysis, hands on.

Instructor: Mauricio Garnier-Villarreal, Ph.D.

Bayesian Data Analysis Course for Researchers

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.

Instructor: Esteban Montenegro, Ph.D.

Bayesian Statistics Course

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.

APA Continuing Education Credits:

Bayesian Data Analysis Training Course

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.

Bayesian Statistics Course 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.
  • Implement Bayes’ rule
  • Utilize Markov Chain Monte Carlo in the brms package for R to evaluate Bayesian models.
  • Apply the appropriate prior distribution to a Bayesian analysis.
  • Conduct generalized linear models using Bayesian estimation, including simple linear regression, multiple regression, and robust regression.
  • Conduct a T-test using Bayesian estimation.
  • Conduct a Bayesian ANOVA.
  • Evaluate Logistic and Count regression models using Bayesian estimation.
  • Evaluate Multilevel linear regression models using Bayesian estimation.
  • Conduct a Bayesian Multilevel growth curve analysis.
  • Understand how issues of missing data are addressed in the Bayesian framework.
  • Explain the difference between frequentist and Bayesian statistics
  • Critically evaluate applications of Bayesian analysis in scientific studies.
  • Analyze data using Bayesian techniques in R.
  • Specify, estimate, evaluate, and compare different Bayesian models to fit possible hypothesis.

Participants are encouraged 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.

Bayesian Data Analysis Solutions Prerequisites:


  • Basic proficiency in multiple linear regression, the generalized linear model.
  • Intermediate proficiency with at least one statistical software package (e.g., SPSS, Stata, SAS, R, etc.).

Not required but advantageous:

  • At least limited experience (e.g., graduate-level course) with multilevel models
  • Intermediate proficiency in R, or syntax base software

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Bayesian Statistics
  • Data analysis using R

Software and Computer Support:

It is important to bring a notebook computer to the seminar, so you can run the programs and see how their output corresponds with the presentation material.  Please install the software before arriving at the seminar. We’ll be estimating the examples in R language using the packages rstan and brms. The latter will be the main package in this course. You can read more about brms package: https://mc-stan.org/users/interfaces/brms.

Seminar Audience:

The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis.

Seminar Files

Below are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. 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.

Instructor will provide password on first day of seminar:
Click Here to Access Bayesian Data Analysis Seminar Files

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 Data Analysis
Monday June 5, 2023
9:00–10:45 Bayesian reasoning generally
10:45–11:00 Rest Break
11:00–12:30 Perfidious p values and the con game of confidence intervals
12:30–1:30 Rest Break
1:30–3:15 Bayes’ rule, grid approximation, and R; Simple examples to train intuition
3:15–3:30 Rest Break
3:30–5:00 Markov Chain Monte Carlo
Tuesday June 6, 2023
9:00–10:45 Brief introduction to R and Rstudio
10:45–11:00 Rest Break
11:00–12:30 Know your distributions. Application of priors
12:30–1:30 Rest Break
1:30–3:15 General linear model. Multiple regression, and robust regression
3:15–3:30 Rest Break
3:30–5:00 General linear model. Robust regression
Wednesday June 7, 2023
9:00–10:45 Model comparison
10:45–11:00 Rest Break
11:00–12:30 General linear model. T-test and ANOVA
12:30–1:30 Rest Break
1:30–3:15 Regularization and polynomials in linear regression
3:15–3:30 Rest Break
3:30–5:00 Generalized linear Model. Logistic regression
Thursday June 8, 2023
9:00–10:45 Generalized linear Model: Count regression
10:45–11:00 Rest Break
11:00–12:30 Multilevel linear regression
12:30–1:30 Rest Break
1:30–3:15 Multilevel linear regression
3:15–3:30 Rest Break
3:30–5:00 Multilevel growth curve/repeated measures
Friday June 9, 2023
9:00–10:45 Introduction to missing data
10:45–11:00 Rest Break
11:00–12:30 Missing data in Bayesian inference
12:30–1:30 Rest Break
1:30~3:30 Individual Consultations

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