Preface

1. Why 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: Pearson’s correlation and Spearman's rho
8. Regression: Prediction with precision

1. Samples from populations
2. Statistical significance for the correlation coefficient: Practical introduction to statistical inference
3. Standard error: Standard deviation of the means of samples
4. Related or paired-samples t-test: Comparing two samples of related/correlated/paired scores
5. Unrelated or independent-samples t-test: Comparing two samples of unrelated/uncorrelated/independent scores
6. What you need to write about your statistical analysis
7. Confidence intervals
8. Effect size in statistical analysis: Do my findings matter?
9. Chi-square: Differences between samples of frequency data
10. Probability
11. One- versus two-tailed or -sided significance testing
12. Ranking tests: Nonparametric statistics

1. Variance ratio test: F-ratio to compare two variances
2. Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA
3. ANOVA for correlated scores or repeated measures
4. Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one?
5. Multiple comparisons in ANOVA: A priori and post hoc tests
6. Mixed-design ANOVA: Related and unrelated variables together
7. Analysis of covariance (ANCOVA): Controlling for additional variables
8. Multivariate analysis of variance (MANOVA)
9. Discriminant (function) analysis – especially in MANOVA
10. Statistics and analysis of experiments

1. Partial correlation: Spurious correlation, third or confounding variables, suppressor variables
2. Factor analysis: Simplifying complex data
3. Multiple regression and multiple correlation
4. Path analysis
5. Analysis of a questionnaire/survey project

1. Meta-analysis: Combining and exploring statistical findings from previous research
2. Reliability in scales and measurement: Consistency and agreement
3. Influence of moderator variables on relationships between two variables
4. Statistical power analysis: Getting the sample size right

1. Log-linear methods: Analysis of complex contingency tables
2. Multinomial logistic regression: Distinguishing between several different categories or groups
3. Binomial logistic regression

1. Data mining and Big Data
2. Towards a masterplan

Appendices

Glossary

References

Index

Understanding Statistics in Psychology, 9e by Dennis Howitt and Duncan Cramer provides a trusted, accessible, and complete introduction to statistics in psychology.

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 then be applied using any statistical package to analyse your data. The modular structure means that it is easy for you to dip in and out of this text, concentrating on the techniques that are the most relevant for you and your own research projects.

This text does not only show you how to analyse data, but also contains clear and detailed guidance of the whole research process. From choosing the appropriate test to interpreting your findings and successfully writing up reports, this text helps you build the skills you need to transition from a student to a researcher.

About the authors:

Dennis Howitt is a reader in psychology at Loughborough University, a chartered forensic psychologist and a 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.

Pearson, the world's learning company.

Relevant, real-life understanding of concepts

• Has up to date examples which demonstrate to students how statistical tests have been used in real research.
• New chapter Analysis of a questionnaire/survey project provides an extended example of a student navigating their own research project and encourages students to learn from their experience

Puts theory into practice

• Illustrations and diagrams throughout the book enliven the text and help students understand more complex concepts.
• New chapter Towards a masterplan explains to students how to write up their analysis according to the most recent reporting standards
• Explaining Statistics sections, take students through statistical tests step-by-step

User-friendly structure

• Modular structure and small chapters are easily accessible for students to dip in and out of.
• Clear Overview sections at the beginning of each chapter explicitly highlight the key areas that will be explored.
• End-of-chapter Key Points provide a useful means of revising and checking understanding.

• Updated, software-agnostic approach to the text helps students get to grips with using statistics rather than showing them simply which buttons to press in software programs.
• New chapter Analysis of a questionnaire/survey project gives students a real-world example navigating a research project to put concepts into context for students.
• New chapter Towards a masterplan provides students with a roadmap on how to write up their analysis with current reporting standards to succeed in their own research projects.
• Updated, global examples throughout the text reflect the variety of statistics used in current research publications.