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.
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
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