Conquer the subject of statistics and be confident in putting the core techniques into practice Understanding Statistics in Psychology, 9th edition is an accessible introduction to the intimidating subject of statistics in psychology for students of all years and abilities. The software-agnostic approach helps you to grasp the fundamentals of statistics and apply these yourself using whichever statistical package you choose.
Conquer the subject of statistics and be confident in putting the core techniques into practice Understanding Statistics in Psychology, 9th edition is an accessible introduction to the intimidating subject of statistics in psychology for students of all years and abilities. The software-agnostic approach helps you to grasp the fundamentals of statistics and apply these yourself using whichever statistical package you choose.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dennis Howitt is a reader in Psychology at Loughborough University, a chartered forensic psychologist and fellow of the British Psychological Society, with a specific interest in the study of mass communications and the application of psychology to social issues. Duncan Cramer is an emeritus professor at Loughborough University with a specific interest in topics such as mental health, personality, personal relationships, organizational commitment, psychotherapy and counselling.
Inhaltsangabe
Preface 1. Why statistics? Part 1 Descriptive statistics 1. Some basics: Variability and measurement 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: Standard unit of measurement in statistics 6. Relationships between two or more variables: Diagrams and tables 7. Correlation coefficients: Pearsons correlation and Spearman's rho 8. Regression: Prediction with precision Part 2 Significance testing 9. Samples from populations 10. Statistical significance for the correlation coefficient: Practical introduction to statistical inference 11. Standard error: Standard deviation of the means of samples 12. Related or paired-samples t-test: Comparing two samples of related/correlated/paired scores 13. Unrelated or independent-samples t-test: Comparing two samples of unrelated/uncorrelated/independent scores 14. What you need to write about your statistical analysis 15. Confidence intervals 16. Effect size in statistical analysis: Do my findings matter? 17. Chi-square: Differences between samples of frequency data 18. Probability 19. One- versus two-tailed or -sided significance testing 20. Ranking tests: Nonparametric statistics Part 3 Introduction to analysis of variance 21. Variance ratio test: F-ratio to compare two variances 22. Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA 23. ANOVA for correlated scores or repeated measures 24. Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one? 25. Multiple comparisons in ANOVA: A priori and post hoc tests 26. Mixed-design ANOVA: Related and unrelated variables together 27. Analysis of covariance (ANCOVA): Controlling for additional variables 28. Multivariate analysis of variance (MANOVA) 29. Discriminant (function) analysis especially in MANOVA 30. Statistics and analysis of experiments Part 4 More advanced correlational statistics 31. Partial correlation: Spurious correlation, third or confounding variables, suppressor variables 32. Factor analysis: Simplifying complex data 33. Multiple regression and multiple correlation 34. Path analysis 35. Analysis of a questionnaire/survey project Part 5 Assorted advanced techniques 36. Meta-analysis: Combining and exploring statistical findings from previous research 37. Reliability in scales and measurement: Consistency and agreement 38. 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: Analysis of complex contingency tables 41. Multinomial logistic regression: Distinguishing between several different categories or groups 42. Binomial logistic regression Part 7 Bringing things together 43. Data mining and Big Data 44. Towards a masterplan Appendices Glossary References Index
Preface 1. Why statistics? Part 1 Descriptive statistics 1. Some basics: Variability and measurement 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: Standard unit of measurement in statistics 6. Relationships between two or more variables: Diagrams and tables 7. Correlation coefficients: Pearsons correlation and Spearman's rho 8. Regression: Prediction with precision Part 2 Significance testing 9. Samples from populations 10. Statistical significance for the correlation coefficient: Practical introduction to statistical inference 11. Standard error: Standard deviation of the means of samples 12. Related or paired-samples t-test: Comparing two samples of related/correlated/paired scores 13. Unrelated or independent-samples t-test: Comparing two samples of unrelated/uncorrelated/independent scores 14. What you need to write about your statistical analysis 15. Confidence intervals 16. Effect size in statistical analysis: Do my findings matter? 17. Chi-square: Differences between samples of frequency data 18. Probability 19. One- versus two-tailed or -sided significance testing 20. Ranking tests: Nonparametric statistics Part 3 Introduction to analysis of variance 21. Variance ratio test: F-ratio to compare two variances 22. Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA 23. ANOVA for correlated scores or repeated measures 24. Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one? 25. Multiple comparisons in ANOVA: A priori and post hoc tests 26. Mixed-design ANOVA: Related and unrelated variables together 27. Analysis of covariance (ANCOVA): Controlling for additional variables 28. Multivariate analysis of variance (MANOVA) 29. Discriminant (function) analysis especially in MANOVA 30. Statistics and analysis of experiments Part 4 More advanced correlational statistics 31. Partial correlation: Spurious correlation, third or confounding variables, suppressor variables 32. Factor analysis: Simplifying complex data 33. Multiple regression and multiple correlation 34. Path analysis 35. Analysis of a questionnaire/survey project Part 5 Assorted advanced techniques 36. Meta-analysis: Combining and exploring statistical findings from previous research 37. Reliability in scales and measurement: Consistency and agreement 38. 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: Analysis of complex contingency tables 41. Multinomial logistic regression: Distinguishing between several different categories or groups 42. Binomial logistic regression Part 7 Bringing things together 43. Data mining and Big Data 44. Towards a masterplan Appendices Glossary References Index
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