Jamovi for Psychologists

This textbook offers a refreshingly clear and digestible introduction to statistical analysis for psychology using the user-friendly jamovi software. The authors provide a concise, practical guide that takes students from the early stages of research design, with a jargon-free explanation of terminology, and walks them through key analyses such as the t-test, ANOVA, correlation, chi-square, and linear regression. The book features written interpretations to help learners identify relevant statistics along the way. With fascinating examples from psychological research, as well as screenshots and activities from jamovi, this text is sure to encourage even the most reluctant statistics student. The comprehensive companion website provides an extra helping hand, with practice datasets and a full suite of tutorial videos to help consolidate understanding.

This is essential reading for psychology students using jamovi for their courses in Research Methods and Statistics or Data Analysis.

A practical guide to jamovi, a user-friendly statistical analysis programmePacked with engaging examples from psychological research throughoutConcise, step-by-step and straightforward, with a layered approach which steadily builds confidence and knowledgeBoasts a range of supporting online materials to consolidate understanding, including screencasts and downloadable practice data

Auteur
Paul Richardson is a Principal Lecturer and the Deputy Head of Psychology at Sheffield Hallam University, UK.Laura Machan is an Associate Lecturer at Sheffield Hallam University, UK.

Contenu

1. Research Design.- 2. Data Preparation, Common Assumptions, and Descriptive Statistics.- 3. P-Values, Effect Sizes and 95% confidence intervals.- 4. Statistical Power.- 5. Reliability and Validity.- 6. Correlations.- 7. Chi Square.- 8. Independent T-Tests.- 9. Paired T-Tests.- 10. Comparing multiple means for Between-subjects designs (One-way ANOVA & Kruskal-Wallis).- 11. Comparing multiple means for Repeated measures designs (one-way ANOVA and Friedman's ANOVA).- 12. Factorial ANOVA (assessing effects of multiple independent variables).- 13. Simple, Multiple, and Hierarchical Linear Regression.