Jamovi for Psychologists
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14 327 Ft
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Product details:
- Edition number 1st ed. 2021
- Publisher Bloomsbury Publishing (UK)
- Date of Publication 21 March 2021
- Number of Volumes Paperback
- ISBN 9781352011852
- Binding Paperback
- No. of pages312 pages
- Size 232x154x22 mm
- Weight 472 g
- Language English 138
Categories
Long description:
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.
Table of Contents:
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.