The JASP Blog

JASP 0.14 has been released and is now available on our download page. JASP 0.14 contains the following new features and improvements: Publication bias-adjusted meta-analysis. JASP version 0.14 adds frequentist selection models as well as Robust Bayesian meta-analysis to correct for publication bias. Head over to our tutorial videos and a tutorial paper to learn how to use these analyses.…

JASP 0.13.1 fixes several small issues. Specifically: Previously, our new “save as powerpoint” option for figures did not work on MacOS. Now it does. For some time, JASP was unstable on MacOS with Intel Iris GPU. This has now been fixed. Windows users with a “foreign” character in their username experienced problems. We have resolved this. A bug concerning changing…

JASP 0.13 has been released and is now available on our download page. JASP 0.13 contains the following new features and improvements: Linear Mixed Models and Generalized Linear Mixed Models. JASP version 0.13 adds the possibility to analyze both classical and Bayesian linear mixed models and generalized linear mixed models. This analysis is now available on the ribbon, in between…

As statistics instructors, students, and researchers around the world discover JASP, more of them a seeking guidance on how to use it. When your software is being developed at “breakneck speed” it is sometimes difficult to keep up with training. The JASP team has added a series of training videos for common frequentist statistical tests using the newest JASP Version…

In a previous post, “Hell is other people’s data”, we introduced a first attempt to document the 50 data sets that constitute the JASP Data Library. At the time, we only had a preface and two chapters. We are happy to report that all 50 data sets have now been documented. Each of the 50 chapters follows the JASP data…

This post demonstrates the Equivalence Testing Module, new in JASP 0.12. In traditional hypothesis testing, both frequentist and Bayesian, the null hypothesis is often specified as a point (i.e., there is no effect whatsoever in the population). Consequently, in very large samples, small but practically meaningless deviations from the point-null will lead to its rejection. In order to take into…

At Utrecht University JASP is increasingly used by lecturers in statistics courses and by thesis students and researchers for the (Bayesian) evaluation of their data. To contribute to the further development of JASP and to facilitate its use at Utrecht University a pilot project has started executed by scientists from Utrecht University. Core features of this project are: the addition…

Mark Goss-Sampson’s popular manual “Statistical analysis with JASP: A guide for students” has now been followed up by a new guide that focuses on Bayesian inference. The guide first introduces the JASP interface and data handling procedures, and then outlines the Bayesian terminology together with an explanation of the different plots that are typically produced by the analyses. Next, the…

JASP 0.12 brings Bayesian meta-analysis! Based on the metaBMA package (Heck, Gronau & Wagenmakers, 2019), JASP now includes Bayesian model-averaged meta-analysis so you no longer have to make an all-or-none choice between fixed and random effects models. Additionally, a constrained random effects approach is implemented which answers the question whether every study shows an effect in the same, expected direction.…

In June, 2019, three JASP team members (Alexander Etz, Julia Haaf, and Johnny van Doorn) taught a webinar on Bayesian inference for The Society for Personality and Social Psychology. The webinar provides a gentle introduction to Bayesian statistics and demonstrates how to perform Bayesian analyses using JASP. The video is structured as follows: Bayesian basics Bayesian hypothesis testing and the…