The JASP Blog

This is a guest post by Tom Faulkenberry (Tarleton State University). Click here to access the supplementary materials. Amid the COVID-19 pandemic, universities have needed to quickly adjust their traditional methods of instruction to allow for maximum flexibility. This means that professors have also had to think critically about how they can best deliver instruction in new formats. Ever the…

To facilitate the transition to Bayesian inference we recently created the “Learn Bayes module in JASP” (with support from a grant from the APS Fund for Teaching and Public Understanding of Psychological Science). The goal of this module is to illustrate all steps of Bayesian parameter estimation and testing. In the previous blogpost we outlined the Binomial Estimation analysis, which…

This is a guest post by Calvin Deans-Browne (UCL) and Henrik Singmann (UCL). Click here to access the supplemental materials. The Setup This article contains an introduction to the different indices measured in the signal detection theory (SDT) framework, a case study to put them into context, and how to compute them in JASP. An Introduction to SDT Indices Signal…

JASP 0.14 brings robust Bayesian meta-analysis (RoBMA). This extension of Bayesian meta-analysis allows researchers to adjust for publication bias when conducting model-averaged meta-analysis. RoBMA applies a set of twelve models simultaneously, some assuming publication bias and some assuming no publication bias. The inference will then be based most strongly on the models that predicted the data best. In other words,…

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…