Eric-Jan Wagenmakers – författare

Unlock the world of statistics with Discovering Statistics using JASP, a comprehensive guide that brings the power of JASP software into the classroom. Building on the legacy of the acclaimed DSUSS series, this book distils complex statistical concepts into engaging, step-by-step content designed for undergraduate courses. Students will gain practical skills in data analysis without needing to learn coding, thanks to JASP’s intuitive point-and-click interface.

This first edition also offers:

Global relevance: Features international examples and case studies, making it ideal for diverse classroom settings.A student-focused approach: An abridged version tailored to undergraduate needs, with accessible summaries and practical solutions.Cutting-edge tools: Leverages free JASP software, supported by world-renowned experts and the University of Amsterdam.Alignment with open science: Encourages reproducibility and transparency in research practices.

Perfect for undergraduates and lecturers alike, this book is the ultimate resource for mastering statistics with JASP. The wealth of online resources can be easily integrated into your institution's virtual learning environment or learning management system. This allows you to customise and curate content for use in module preparation, delivery and assessment.

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Two recent innovations, the emergence of formal cognitive models and the addition of cognitive neuroscience data to the traditional behavioral data, have resulted in the birth of a new, interdisciplinary field of study: model-based cognitive neuroscience.