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Introduction Acknowledgements Key Differences between SPSS 16 and earlier versions Guided tour of the book Guided tour of the accompanying CD Part 1 Introduction to SPSS 1. Basics of SPSS data entry and statistical analysis Part 2 Descriptive statistics 2. Describing variables: Tables and diagrams 3. Describing variables numerically: Averages, variation and spread 4. Shapes of distributions of scores 5. Standard deviation: The standard unit of measurement in statistics 6. Relationships between two or more variables: Diagrams and tables 7. Correlation coefficients: Pearson's correlation and Spearman's rho 8. Regression: Prediction with precision Part 3 Significance testing and basic inferential tests 9. Standard error 10. The t- test: Comparing two samples of correlated/related scores 11. The t- test: Comparing two groups of unrelated/uncorrelated scores 12. Confidence intervals 13. Chi-square: Differences between samples of frequency data 14. Ranking tests for two groups: Non-parametric statistics 15. Ranking tests for three or more groups: Non-parametric statistics Part 4 Analysis of variance 16. The variance ratio test: Using the F- ratio to compare two variances 17. Analysis of variance (ANOVA): Introduction to the one-way unrelated or uncorrelated ANOVA 18. Analysis of variance for correlated scores or repeated measures 19. Two-way analysis of variance for unrelated/uncorrelated scores 20. Multiple comparisons in ANOVA 21. Two-way mixed analysis of variance (ANOVA) 22. Analysis of covariance (ANCOVA) 23. Multivariate analysis of variance (MANOVA) 24. Discriminant function analysis (for MANOVA) Part 5 More advanced correlational statistics 25. Partial correlation 26. Factor analysis 27. Item reliability and inter-rater agreement 28. Stepwise multiple regression 29. Hierarchical multiple regression Part 6 Advanced qualitative or nominal techniques 30. Log-linear analysis 31. Multinomial logistic regression 32. Binomial logistic regression Part 7 Data handling procedures 33. Reading ASCII or text files into the Data Editor 34. Missing values 35. Recoding values 36. Computing new variables with no values missing 37. Computing new variables with some values missing 38. Selecting cases 39. Samples and populations: Generating a random sample 40. Inputting a correlation matrix 41. Checking accuracy of data input Appendix: Other statistics in SPSS Glossary Index ...
