What it does
Statistics Without Maths for Psychology is conceived, written and designed to help students enjoy statistics as well as enable them to understand statistical concepts . This, the 3rd edition, continues the good work of previous editions and remains a valuable tool for overcoming students' fear of the subject .
How it is done
Statistics Without Maths for Psychology provides an accessible description of the key statistical concepts and techniques needed by psychology students. These concepts are explained clearly and without reference to underlying formulae. The authors take the approach that statistics is best taught by 'doing' and they provide a wealth of exercises, activities and multiple choice questions to enable students to practice their knowledge and skills. The use of SPSS for Windows (SPSSFW) in conducting statistical analyses is explained in detail and is fully integrated throughout the text. The book also provides comprehensive guidance on interpreting and reporting the output from SPSSFW.
Some of what is new
- New, more accessible, text design including use of two colours to enhance the clarity of figures and diagrams.
- Added discussion of important topics, e.g. dichotomising continuous variables.
- Restructured topics to enhance understanding and clarity of concepts being explained.
- Fully updated examples from psychological literature.
- Extra multiple choice questions to enable students to further test their own learning.
- Updated explanations to ensure compatibility with SPSSFW versions 10, 11, 11.5 and 12.
- Introductions to some of the useful help facilities available in SPSSFW.
- Companion website where all SPSSFW data files are available to download, and additional multiple choice questions.
Statistics Without Maths for Psychology is the perfect book to help you survive the jump.
Christine P. Dancey is Professor of Psychology at the University of East London.
John Reidy is a Senior Lecturer in the Department of Psychology at Sheffield Hallam University.
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1. Variables and research design
2. Descriptive statistics
3. Probability, sampling and distributions
4. Hypothesis testing and statistical significance
5. Correlational analysis: Pearson's r
6. Analyses of differences between two conditions: the t-test
7. Issues of significance
8. Measures of association
9. Analysis of differences between three or more conditions: one-factor ANOVA
10. Analysis of variance with more than one IV
11. Regression analysis
12. Introduction to factor analysis
13. Analysis of three or more groups partialling out effects of a covariate
14. Introduction to multivariate analysis of variance (MANOVA)
15. Non-parametric statistics