Bayesian Repeated-Measures ANOVA: An Updated Methodology Implemented in JASP

This post is a teaser for van den Bergh, D., Wagenmakers, E., & Aust, F. (2022). Bayesian Repeated-Measures ANOVA: An Updated Methodology Implemented in JASP. Preprint available on PsyArXiv: https://psyarxiv.com/fb8zn/ In JASP 0.16.3 we changed the default Bayesian repeated-measures ANOVA. It is important to understand this change as it may affect the results, bringing them more in line with the…

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Generalized Linear Models (GLM) in JASP

It took a while, but finally, the frequentist Generalized Linear Model (GLM) has become available in JASP, as part of the Regression module! In this blog post, we give you a quick introduction to the idea behind GLM and the full functionality of this new JASP sub-module. We also show you how you can conduct a binomial regression analysis using…

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Introducing JASP 0.16.3: Quality Control, GLMs, Bayesian State Space Models, Improvements to Bayesian ANOVA, and More

We are happy to announce that JASP 0.16.3 has been released and is now available on our download page. JASP 0.16.3 contains the following new features and improvements: The new module Quality Control has been added which you can use to investigate if a manufactured product adheres to a defined set of quality criteria. The newly added module for Bayesian…

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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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How to Predict with Machine Learning Models in JASP: Classification

This blog post will demonstrate how a machine learning model trained in JASP can be used to generate predictions for new data. The procedure we follow is standardized for all the supervised machine learning analyses in JASP, so the demonstration here generalizes to all of them. Please note that we use the latest version of JASP (version 0.16.2). For our…

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Introducing JASP 0.16.2: Performance Improvements, Apple Silicon, and Bug Fixes

JASP has been updated! This is a maintenance release that contains the following improvements: Performance improvements on both Mac and Windows resulting from upgrades to our development framework (Qt 6.2, R 4.1.3) There is a new build for Apple Silicon (e.g., M1 iMacs and Macbooks) that makes use of Apple’s new chipset resulting in a significant speed up. The installation…

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Latent Growth Curve Modeling (LGCM) in JASP

‘How can we model the form of change in an outcome as time passes by?’, ‘Which statistical technique helps us to describe individual growth trajectory’s over time?’, ‘Can individual differences in an initial state and in change over time be analyzed?’ These questions are of importance to researchers who examine developmental, longitudinal, or consecutive measurements across multiple occasions. What solves…

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Introducing JASP 0.16.1: French Translation, Cochrane Meta-Analysis, Decision Trees, and More.

We are happy to announce that JASP 0.16.1 has been released and is now available on our download page. JASP 0.16.1 contains the following new features and improvements: JASP is now available in French (incomplete). The Machine Learning module has been expanded so that you can now perform (1) Support Vector Machine regression and classification analyses, and (2) Decision Tree…

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Multiple Indicators Multiple Causes (MIMIC) Model in JASP

Researchers often have questions about inter-relationships between observed variables (indicators) and latent variables (factors). The Multiple Indicators and Multiple Causes (MIMIC) model is one of the models to quench the thirst for such questions! The latest JASP release provides MIMIC models as part of the SEM module. This tutorial introduces the idea of MIMIC models and, with a simple example,…

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Measurement Invariance Testing Using the Structural Equation Modeling (SEM) Module in JASP

Many research questions in the social and behavioral sciences rely on between-group comparisons of scores on scales from questionnaires. But how do we know that the questionnaire measures the same thing across different groups? Such comparisons require measurement invariance to be appropriate. Multi-group-Modeling, an analytical approach that belongs to the class of Structural Equation Modeling (SEM), provides the toolbox that…

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New in JASP 0.16: Zoltan Dienes’ General Bayesian Tests

There are multiple statistical tests concerning a single parameter of interest such as t-test or a binomial test of proportions. Those tests can be performed with summary statistics that completely describe the observed data. In this new analysis, we extend the “Summary Statistics” module with a “General Bayesian Tests” analysis that allows us to evaluate the evidence in favor of…

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