Introduction to Statistics in Psychology

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