Introduction to Statistics in Psychology is a comprehensive, modern guide to understanding and using statistics in psychological research. This edition has been significantly revised to incorporate the essential SPSS steps you need for carrying out statistical analysis.

Contents:

1. Why statistics?

Part 1: Descriptive statistics

2. Some basics: Variability and measurement

3. Describing variables: Tables and diagrams

4. Describing variables numerically: Averages, variation and spread

5. Shapes of Distributions of Scores

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

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

8. Correlation coefficients: Pearson correlation and Spearman’s rho

9. Regression: Prediction with precision

Part 2: Significance testing

10. Samples and populations: Generalising and inferring

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

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

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

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

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

16. Probability

17. Reporting significance levels succinctly

18. One-tailed versus two-tailed significance testing

19. Ranking tests: Nonparametric statistics

Part 3: Introduction to analysis of variance

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

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

22. Analysis of variance for correlated scores or repeated measures

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

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

25. Mixed-design ANOVA: Related and unrelated variables together

26. Analysis of covariance (ANCOVA): Controlling for additional variables

27. Multivariate Analysis of Variance (MANOVA)

28. Discriminant (Function) analysis especially in MANOVA

29. Statistics and the analysis of experiments

Part 4: More advanced correlational statistics

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

31. Factor analysis: Simplifying complex data

32. Multiple regression and multiple correlation

33. Path analysis

34. The analysis of a questionnaire/survey project

Part 5: Assorted advanced techniques

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

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

37. Reliability in scales and measurement: Consistency and agreement

38. Confidence intervals

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

40. Statistical power analysis: getting the sample size right

Part 6: Advanced qualitative or nominal techniques

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

42. Multinomial logistic regression: Distinguishing between several different categories or groups

43. 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