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Each chapter of this book covers specific topics in statistical analysis, such as robust alternatives to t-tests or how to develop a questionnaire. They also address particular questions on these topics, which are commonly asked by HCI researchers when planning or completing the analysis of their data. The book presents the current best practice in statistics, drawing on the state-of-the-art literature that is rarely presented in HCI. This is achieved by providing strong arguments that support good statistical analysis without relying on mathematical explanations. It additionally offers some philosophical underpinnings for statistics, so that readers can see how statistics fit with experimental design and the fundamental goal of discovering new HCI knowledge.Read more
- · Readers do not have to struggle with mathematics in order to understand the arguments around how to do better statistics · Provides the underpinning philosophy of statistics in science · Allows readers to quickly find the answers they seek within the concise chapters
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- Publication planned for: March 2019
- format: Paperback
- isbn: 9781108710596
- dimensions: 228 x 152 mm
- contains: 29 b/w illus. 7 tables
- availability: Not yet published - available from March 2019
Table of Contents
Part I. Why we use statistics
1. How statistics support science
2. Testing the null
3. Constraining Bayes
4. Effects: what tests test
Part II. How to use statistics
5. Planning your statistical analysis
6. A cautionary tail: why you should not do a one-tailed test
7. Is this normal?
8. Sorting out outliers
9. Power and two Types of error
10. Using nonparametric tests
11. A robust t-test
12. The ANOVA family and friends
13. Exploring, over-testing and fishing
14. When is a correlation not a correlation?
15. What makes a good Likert item?
16. The Meaning of Factors
17. Unreliable reliability: the problem of Cronbach's alpha
18. Tests for questionnaires
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