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LATEST UPDATE (17 oct 17.00 Amsterdam Time zone)
Sadly, the venue we had chosen for our Winter Stats Camp in Puerto Rico is unable to host us and we must postpone the event to January of 2019. The meeting space sustained damage that needs repairs. Unfortunately, the demand for repairs on the island is too great and the hotel will not be able to hold meetings until later in the spring when they are hopefully able to get contractors to the venue. We at Stats Camp are committed to the recovery and rebuilding efforts of Puerto Rico and have renewed our contract to conduct a Winter Stats Camp in Puerto Rico in January of 2019. Please mark your calendars! If you made airline reservations, check with your airline; you may qualify for a refund because the cancellation is related to Hurricane Maria.
On a regular basis I teach in StatsCamp organized by Todd Little. After a successful edition in Portugal last spring, we started planning a winter-edition (WinterCamp) on Puerto Rico specifically for people disliking rain, cold or snow. But then came hurricane Maria which caused many causalities and destroyed much of the infrastructure on the island. We were in doubt whether to continue with our plans… Almost immediately we realized the main income source for Puerto Rico are tourists. Therefore, after checking with the venue, we decided to proceed with our plans AND to donate 100% of our proceeds from each registration to relief efforts for Puerto Rico. Everything is fine with the venue and the local population is eager to host us. Join Us for one of the most unique educational experiences of your life.
Intro to Bayesian Statistics – rethinking statistics: An interactive workshop
The use of Bayesian estimation has increased over the years because this estimation framework can handle some commonly encountered problems in orthodox statistics. For example, Bayesian methods can be used for producing more accurate parameter estimates and aiding in situations where only small sample sizes are available. Or, some researchers believe in the Bayesian way of updating knowledge with new data instead of testing the null hypothesis over and over again assuming nothing is going on in the population. This course will introduce participants to the prevailing “best practices” for Bayesian estimation (including structural equation modeling) entailing direct application to the research questions of the participants. During this course, you will be gently introduced into Bayesian statistics using class examples. We highly recommend bringing your own data as well; however, we have plenty of data available for participants to analyze. Using these examples, we will explore the benefits of Bayesian statistics and discuss what is needed to run your first Bayesian model. During the last course, all participants went home with Bayesian results which could directly be used in their dissertations/papers. More…
Growth Modeling With Mplus Seminar – Instructor, Kevin J. Grimm, Ph.D.
Introduction to Data Mining Seminar – Instructor, Gitta Lubke, Ph.D.
Intro to Bayesian Statistics – rethinking statistics: An interactive workshop – Instructor, Rens van de Shoot, Ph.D.
Multilevel Modeling – Instructor, Tasha Beretvas, Ph.D.
R Programming – Instructor, Daniel Bontempo, Ph.D.
SEM Foundations and Extended Applications Seminar – Instructor, Todd D. Little, Ph.D. and Elizabeth Grandfield