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.
Andy Field is a Reader in Psychology at The University of Sussex. He adores cats, listens to and plays loud music and enjoys teaching statistics. His ability to make statistics accessible and fun has been recognised with teaching awards from the University of Sussex (2001) and the British Psychological Society (2006). Prof. dr. Eric-Jan Wagenmakers is a mathematical psychologist and a dedicated Bayesian at the University of Amsterdam, Netherlands. His lab heads the development of the JASP open-source software program for statistical analyses.
1. Why is my evil lecturer forcing me to learn statistics? 2. The SPINE of statistics 3. The phoenix of statistics 4. The JASP environment 5. Visualizing data 6. The beast of bias 7. Correlation 8. The linear model (regression) 9. Categorical predictors: Comparing two means 10. Moderation and mediation 11. GLM 1: Comparing several independent means 12. GLM 2: Comparing means adjusted for other predictors (analysis of covariance) 13. GLM 3: Factorial designs 14. GLM 4: Repeated-measures designs 15. Non-parametric models 16. Categorical outcomes: chi-square 17. Categorical outcomes: logistic regression
