Understanding Statistics in Psychology

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.

Become confident with the most common statistical techniques so that you can grasp the fundamentals and transition from a student to a professional researcher

Now in its ninth edition, Understanding Statistics in Psychology , by Dennis Howitt and Duncan Cramer continues to provide an accessible introduction to the intimidating subject of statistics in psychology for students of all years and abilities.

Clear explanations and diagrams break down the statistical techniques that are used in modern psychological research and updated examples of real-life studies bring the topic to life by showing you how statistics are used in practice.

The new software-agnostic approach of this edition means that you will gain a solid understanding of statistics which can be applied to whichever statistical package you are using to analyse your data. The modular structure of this text and its small accessible chapters also mean that it is easy to dip in and out of, concentrating on the techniques that are the most relevant for you and your own research projects.

This text does not just focus on how to analyse data but also contains clear and detailed guidance of the whole research process, from how to choose the appropriate tests, to interpreting your findings and successfully writing up your research.

Autorentext

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.

Inhalt

Preface

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: Standard unit of measurement in statistics
7. Relationships between two or more variables: Diagrams and tables
8. Correlation coefficients: Pearsons correlation and Spearman's rho

9. Regression: Prediction with precision Part 2 Significance testing

10. Samples from populations
11. Statistical significance for the correlation coefficient: Practical introduction to statistical inference
12. Standard error: Standard deviation of the means of samples
13. Related or paired-samples t-test: Comparing two samples of related/correlated/paired scores
14. Unrelated or independent-samples t-test: Comparing two samples of unrelated/uncorrelated/independent scores
15. What you need to write about your statistical analysis
16. Confidence intervals
17. Effect size in statistical analysis: Do my findings matter?
18. Chi-square: Differences between samples of frequency data
19. Probability
20. One- versus two-tailed or -sided significance testing

21. Ranking tests: Nonparametric statistics Part 3 Introduction to analysis of variance

22. Variance ratio test: F-ratio to compare two variances
23. Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA
24. ANOVA for correlated scores or repeated measures
25. Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one?
26. Multiple comparisons in ANOVA: A priori and post hoc tests
27. Mixed-design ANOVA: Related and unrelated variables together
28. Analysis of covariance (ANCOVA): Controlling for additional variables
29. Multivariate analysis of variance (MANOVA)
30. Discriminant (function) analysis especially in MANOVA

31. Statistics and analysis of experiments Part 4 More advanced correlational statistics

32. Partial correlation: Spurious correlation, third or confounding variables, suppressor variables
33. Factor analysis: Simplifying complex data
34. Multiple regression and multiple correlation
35. Path analysis

36. Analysis of a questionnaire/survey project Part 5 Assorted advanced techniques

37. Meta-analysis: Combining and exploring statistical findings from previous research
38. Reliability in scales and measurement: Consistency and agreement
39. 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: Analysis of complex contingency tables
42. Multinomial logistic regression: Distinguishing between several different categories or groups

43. Binomial logistic regression Part 7 Bringing things together

44. Data mining and Big Data

45. Towards a masterplan
    Appendices

    Glossary

    References

    Index