Overview

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.

Full Product Details

Author:   Dennis Howitt ,  Duncan Cramer
Publisher:   Pearson Education Limited
Imprint:   Pearson Education Limited
Edition:   9th edition
ISBN:  

9781292465180

ISBN 10:   1292465182
Publication Date:   12 November 2024
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available  
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

Preface Why statistics? Part 1 Descriptive statistics Some basics: Variability and measurement Describing variables: Tables and diagrams Describing variables numerically: Averages, variation and spread Shapes of distributions of scores Standard deviation and z-scores: Standard unit of measurement in statistics Relationships between two or more variables: Diagrams and tables Correlation coefficients: Pearson’s correlation and Spearman's rho Regression: Prediction with precision Part 2 Significance testing Samples from populations Statistical significance for the correlation coefficient: Practical introduction to statistical inference Standard error: Standard deviation of the means of samples Related or paired-samples t-test: Comparing two samples of related/correlated/paired scores Unrelated or independent-samples t-test: Comparing two samples of unrelated/uncorrelated/independent scores What you need to write about your statistical analysis Confidence intervals Effect size in statistical analysis: Do my findings matter? Chi-square: Differences between samples of frequency data Probability One- versus two-tailed or -sided significance testing Ranking tests: Nonparametric statistics Part 3 Introduction to analysis of variance Variance ratio test: F-ratio to compare two variances Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA ANOVA for correlated scores or repeated measures Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one? Multiple comparisons in ANOVA: A priori and post hoc tests Mixed-design ANOVA: Related and unrelated variables together Analysis of covariance (ANCOVA): Controlling for additional variables Multivariate analysis of variance (MANOVA) Discriminant (function) analysis – especially in MANOVA Statistics and analysis of experiments Part 4 More advanced correlational statistics Partial correlation: Spurious correlation, third or confounding variables, suppressor variables Factor analysis: Simplifying complex data Multiple regression and multiple correlation Path analysis Analysis of a questionnaire/survey project Part 5 Assorted advanced techniques Meta-analysis: Combining and exploring statistical findings from previous research Reliability in scales and measurement: Consistency and agreement Influence of moderator variables on relationships between two variables Statistical power analysis: Getting the sample size right Part 6 Advanced qualitative or nominal techniques Log-linear methods: Analysis of complex contingency tables Multinomial logistic regression: Distinguishing between several different categories or groups Binomial logistic regression Part 7 Bringing things together Data mining and Big Data Towards a masterplan Appendices Glossary References Index

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Author Information

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.

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