Introduction to Statistics in Psychology

Introduction to Statistics in Psychology

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Description

Introduction to Statistics in Psychology, 5th edition is the comprehensive, straightforward and essential student guide to understanding statistics and carrying out statistical analyses in psychology.

Revised and updated throughout, the text offers clear and detailed coverage of the statistical methods used by psychologists.

Key aspects of the process are highlighted throughout the text in special sections:



'Research Design Issues' offers advice and guidance on important topics and concepts.
'Examples from the published literature' highlight how tests have been applied in interesting classic and more recent research.
'Calculation' presents and separates out the mathematical principles underlying the analyses.
'Quick Steps Guide' - this new feature offers a practical, quick guide on how to carry out a statistical analysis in SPSS Statistics and helps students understand the links between the different tests.

This book is supported by a companion website featuring a range of resources to help students in their studies. Self test questions, additional datasets and plus handy quick guides to carrying out tests can all be found at www.pearsoned.co.uk/howitt



"Howitt and Cramer's "Introduction to Statistics in Psychology" maintains its lead as the best textbook in what is the most difficult area of study for most psychology students. Indeed, it is more than an 'introduction' - its' clear and comprehensive coverage of statistical analysis from basic descriptive and bivariate techniques to multivariate methods will be a priceless resource for students throughout all years of study." Ronnie Wilson, University of Ulster
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Product details

  • Paperback | 672 pages
  • 196 x 265 x 30mm | 1,590g
  • Prentice-Hall
  • Harlow, United Kingdom
  • English
  • 5th edition
  • 027373430X
  • 9780273734307
  • 157,605

Table of contents

Guided tour

Introduction

List of figures

List of tables

List of boxes

Acknowledgements

Part 1: Descriptive statistics

1. Why you need statistics: Types of data

2. Describing variables: Tables and diagrams

3. Describing variables numerically: Averages, variation and spread

4. Shapes of Distributions of Scores

5. Standard deviation and z-scores: The standard unit of measurement in statistics

6. Relationships between two or more variables: Diagrams and tables

7. Correlation coefficients: Pearson correlation and Spearman's rho

8. Regression: Prediction with precision

Part 2: Significance testing

9. Samples and populations: Generalising and inferring

10. Statistical significance for the correlation coefficient: A practical introduction to statistical inference

11. Standard error: The standard deviation of the means of samples

12. The t-test: Comparing two samples of correlated/related/paired scores

13. The t-test: Comparing two samples of unrelated/uncorrelated scores

14. Chi-square: Differences between samples of frequency data

15. Probability

16. Reporting significance levels succinctly

17. One-tailed versus two-tailed significance testing

18. Ranking tests: Nonparametric statistics

Part 3: Introduction to analysis of variance

19. The variance ratio test: The F-ratio to compare two variances

20. Analysis of variance (ANOVA): Introduction to the one-way unrelated or uncorrelated ANOVA

21. Analysis of variance for correlated scores or repeated measures

22. Two-way analysis of variance for unrelated/uncorrelated scores: Two studies for the price of one?

23. Multiple comparisons in ANOVA: Just where do the differences lie?

24. Mixed-design ANOVA: Related and Unrelated Variables together

25. Analysis of covariance: Controlling for additional variables

26. Multivariate Analysis of Variance (MANOVA)

27. Discriminant (Function) analysis especially in MANOVA

28. Statistics and the analysis of experiments

Part 4: More advanced correlational statistics

29. Partial correlation: Spurious correlation, third or confounding variables, suppressor variables

30. Factor analysis: Simplifying complex data

31. Multiple regression and multiple correlation

32. Path analysis

33. The analysis of a questionnaire/survey project

Part 5: Assorted advanced techniques

34. The size of effects in statistical analysis: Do my findings matter?

35. Meta-analysis: Combining and exploring statistical findings from previous research

36. Reliability in scales and measurement: Consistency and agreement

37. Confidence intervals

38. The influence of moderator variables on relationships between two variables

39. Statistical power analysis: getting the sample size right

Part 6: Advanced qualitative or nominal techniques

40. Log-Linear Methods: The analysis of complex contingency tables

41. Multinomial logistic regression: Distinguishing between several different categories

42. Binomial Logistic Regression



Appendices

Appendix A: Testing for excessively skewed distributions

Appendix B1: Large sample formulae for the nonparametric tests

Appendix B2: Nonparametric tests for three or more groups

Appendix C: Extended table of significance for the Pearson correlation coefficient

Appendix D: Table of significance for the Spearman correlation coefficient

Appendix E: Extended table of significance for the t-test

Appendix F: Table of significance for Chi-square

Appendix G: Extended table of significance for the sign test

Appendix H: Table of significance for the Wilcoxon Matched Pairs Test

Appendix I: Table of significance for the Mann-Whitney U-test

Appendix J: Table of significance values for the F-distribution

Appendix K: Table of significant values oft when making multiple t-tests

Glossary

References

Index
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