Understanding Statistics in Psychology, 9th edition

Published by Pearson (November 14, 2024) © 2025

  • Dennis Howitt University of Loughborough
  • Duncan Cramer University of Loughborough

Understanding Statistics in Psychology

Please select an option to purchase

In this eTextbook — More ways to learn

  • More support. Get AI help with personalised summaries, instant explanations, and quick translations.
  • More flexible. Start learning right away, on any device.
  • More interactive. Learn through audio, video, and practice.
  • More memorable. Study better with notes, highlights, and flashcards.
Requires a Course ID, a link from your instructor or an LMS link (Blackboard™, Canvas™, Moodle or D2L®)
Products list
£57.99
Free delivery

Details

  • A print edition

Title overview

The most comprehensive guide to statistics in psychology, now in a practical, digital eBook format

A text blending academic and practitioner material, this all-in-one eTextbook offers leading textbook content and a range of digital tools, with integrated resources designed to support your learning.

With eTextbooks, you can:

  • Customise how you study and add your own notes, bookmarks, and highlights directly in your eTextbook
  • Organise your studying and time and read online or offline, anytime, anywhere
  • Optimise your study with learning content you can access via your computer, mobile, and Android apps
  • Study for your course using an environmentally and accessible friendly learning resource.

Find out more about the benefits of Pearson eTextbooks.

Table of contents

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

Part 2 Significance testing

  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

Part 3 Introduction to analysis of variance

  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

Part 4 More advanced correlational statistics

  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

Part 5 Assorted advanced techniques

  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

Part 6 Advanced qualitative or nominal techniques

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

Part 7 Bringing things together

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

Appendices

Glossary

References

Index

Need help?Get in touch