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 zscores: 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 ttest: Comparing two samples of correlated/related scores
14. The ttest: Comparing two samples of unrelated/uncorrelated scores
15. Chisquare: Differences between samples of frequency data
16. Probability
17. Reporting significance levels succinctly
18. Onetailed versus twotailed significance testing
19. Ranking tests: Nonparametric statistics
Part 3: Introduction to analysis of variance
20. The variance ratio test: The Fratio to compare two variances
21. Analysis of variance (ANOVA): Introduction to the oneway unrelated or uncorrelated ANOVA
22. Analysis of variance for correlated scores or repeated measures
23. Twoway 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. Mixeddesign 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. Metaanalysis: 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. LogLinear 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 ttest
Appendix F: Table of significance for Chisquare
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 MannWhitney Utest
Appendix J: Table of significance values for the Fdistribution
Appendix K: Table of significant values oft when making multiple ttests
Glossary
References
Index
