Teaching The Problem of Points with JASP

The field of statistics and probability theory was born around 1654, in a famous correspondence between Blaise Pascal and Pierre de Fermat. These two French mathematicians concerned themselves with a problem in gambling: suppose two players A and B are engaged in a match – for concreteness, suppose they are repeatedly tossing a fair coin. Whenever the coin lands heads,…

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Meta-Analysis of Prediction Model Performance

It is highly recommended to evaluate the performance of prediction models across different study populations, settings, or locations since good performance is essential for proper decision making regarding patients’ health (Debray et al., 2015). When multiple estimates of prediction model performance are available (e.g. from the published literature), meta-analysis may help to obtain a summary estimate and investigate the presence…

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Using JASP to Adjust for Publication Bias with WAAP-WLS

We wish to reanalyze the BGC vaccine dataset that was used as evidence of a positive preventive effect of the BGC vaccine against tuberculosis. As in our previous blog post about PET-PEESE, we want to assess the effect of publication bias –a preferential publishing of statistically significant studies (Rosenthal & Gaito, 1964)– on the meta-analytic effect size estimate. In contrast…

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Using JASP to Adjust for Publication Bias with PET-PEESE

Source: shutterstock.com/szczygiel The analyses in this blog post are based on an example from Bartoš et al. (2021). Publication bias is a serious problem for meta-analyses that can lead to inflated effect size estimates. In order to adjust for and assess the effect of publication bias –a preferential publishing of statistically significant studies (Rosenthal & Gaito, 1964)– on the meta-analytic…

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Time Series Forecasting: Introduction to the Prophet Module in JASP

We are happy to present JASP’s first procedure for time series analysis! Version 0.15 includes the Prophet module which contains the homonymous analysis developed by Facebook’s Taylor and Letham (2018). Its core feature is a model that allows flexible time series forecasting on different scales. You want time series visualization? Changepoint estimation? Does your time series data have strong seasonalities?…

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How to Install JASP on Your Chromebook

All Chromebooks that came out in 2019 or later explicitly support Linux and therefore allow JASP to be installed. Certain older models might also have this capability. To find out if your Chromebook supports Linux, go to “Settings” and look for the “Linux (Beta)” option. It should look something like this: If you do not see this entry then your…

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Learn Bayes with Binomial Estimation in JASP

When students are first confronted with Bayesian statistics they have to become familiar with key concepts that differ fundamentally from those that they were taught in frequentist courses. To assist 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…

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A Hack for Editing JASP Graphs

From JASP 0.13 onwards, it is possible to save JASP graphs “as pptx”, courtesy of the R package “officer”. The resulting .pptx file can then be easily edited in Powerpoint or its open-source cousin Impress. Hence, “save as pptx” offers a new opportunity to edit JASP graphs. Obviously this is a temporary patch and not the ultimate solution; full graph…

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Improved Annotations in JASP 0.12, Demonstrated with a Bayesian Meta-Analysis of Kristal et al., 2020

The goal of this JASP blog post is threefold: To demonstrate the improved ability to annotate analyses. For annotations, JASP 0.12 now uses Quill. As stated on https://quilljs.com/, “Quill is a free, open source WYSIWYG editor built for the modern web. With its modular architecture and expressive API, it is completely customizable to fit any need.“ To demonstrate the ease…

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JAGS Meets JASP

Data from the reproducibility project. The x-axis shows the effect size of the original studies, the y-axis shows the effect size of the replications. The color indicates whether a replication was significant (purple) or not (black). A linear regression line is fit for each group. Figure from JASP. If there is one thing that caused widespread adoption of Bayesian inference,…

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