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Discovering Statistics Using IBM SPSS Statistics

Discovering Statistics Using IBM SPSS Statistics
North American Edition

Fifth Edition
Experience with SAGE edge

November 2017 | 816 pages | SAGE Publications Ltd
With an exciting new look, math diagnostic tool, and a research roadmap to navigate projects, this new edition of Andy Field’s award-winning text offers a unique combination of humor and step-by-step instruction to make learning statistics compelling and accessible to even the most anxious of students. The Fifth Edition takes students from initial theory to regression, factor analysis, and multilevel modeling, fully incorporating IBM SPSS Statistics© version 25 and fascinating examples throughout.

SAGE edge offers a robust online environment featuring an impressive array of free tools and resources for review, study, and further exploration, keeping both instructors and students on the cutting edge of teaching and learning. Course cartridges available for Blackboard and Moodle. Learn more at

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Andy Field is the award winning author of An Adventure in Statistics: The Reality Enigma and is the recipient of the UK National Teaching Fellowship (2010), British Psychological Society book award (2006), and has been recognized with local and national teaching awards (University of Sussex, 2015, 2016).
Chapter 1. Why is My Evil Lecturer Forcing Me to Learn Statistics?
What the hell am I doing here? I don't belong here  
The Research Process  
Initial observation: finding something that needs explaining  
Generating and testing theories and hypotheses  
Collecting data: measurement  
Collecting data: research design  
Analysing data  
Reporting data  
Chapter 2. The Spine of Statistiscs
What will this chapter tell me?  
What is the SPINE of statistics?  
Statistical models  
Populations and samples  
P is for parameters  
E is for estimating parameters  
S is for standard error  
I is for (confidence) interval  
N is for null hypothesis significance testing  
Reporting significance tests  
Chapter 3. The Phoenix of Statistics
Problems with NHST  
NHST as part of wider problems with science  
A phoenix from the EMBERS  
Sense, and how to use it  
Pre-registering research and open science  
Effect size  
Bayesian approaches  
Reporting effect sizes and Bayes factors  
Chapter 4. The IBM SPSS Statistics Environment
Versions of IBM SPSS Statistics  
Windows, Mac OS, and Linux  
Getting started  
The data editor  
Entering data into IBM SPSS Statistics  
Importing data  
The SPSS viewer  
Exporting SPSS output  
The syntax editor  
Saving files  
Opening files  
Extending IBM SPSS Statistics  
Chapter 5. Exploring Data With Graphs
The art of presenting data  
The SPSS Chart Builder  
Boxplots (box-whisker diagrams)  
Graphing means: bar charts and error bars  
Line charts  
Graphing relationships: the scatterplot  
Editing graphs  
Chapter 6. The Beast of Bias
What is bias?  
Overview of assumptions  
Additivity and linearity  
Normally distributed something or other  
Homoscedasticity/homogeneity of variance  
Spotting outliers  
Spotting normality  
Spotting linearity and heteroscedasticity/heterogeneity of variance  
Reducing bias  
Chapter 7. Non-Parametric Models
When to use non-parametric tests  
General procedure of non-parametric tests in SPSS  
Comparing two independent conditions: the Wilcoxon rank-sum test and Mann-Whitney test  
Comparing two related conditions: the Wilcoxon signed-rank test  
Differences between several independent groups: the Kruskal-Wallis test  
Differences between several related groups: Friedman's ANOVA  
Chapter 8. Correlation
Modeling relationships  
Data entry for correlation analysis  
Bivariate correlation  
Partial and semi-partial correlation  
Comparaing correlations  
Calculating the effect size  
How to report correlation coefficents  
Chapter 9. Linear Model (Regression)
An introduction to the linear model (regression)  
Bias linear models?  
Generalizing the model  
Sample size and the linear model  
Fitting linear models: the general procedure  
Using IBM SPPS Statistics to fit a linear model with one predictor  
Interpreting a linear model with one predictor  
Interpreting a linear model with two or more predictors (multiple regression)  
Using IBM SPSS Statistics to fit a linear model with several predictors  
Interpreting a linear model with several predictors  
Robust regression  
Bayesian regression  
Reporting linear models  
Chapter 10. Comparing Two Means
Looking for differences  
An example: are invisible people mischievous?  
Categorical predictors in the linear model  
The t-test  
Assumptions of the t-test  
Comparaing two means: general procedure  
Comparing two independent means using IBM SPSS Statistics  
Comparing two related means using IBM SPSS Statistics  
Reporting comparisons between two means  
Between groups or repeated measures?  
Chapter 11. Moderation, Mediation and Multicategory Predictors
The PROCESS tool  
Moderation: interactions in the linear model  
Categorical predictors in regression  
Chapter 12. GLM 1: Comparing Several Independent Means
Using a linear model to compare several means  
Assumptions when comparing means  
Planned contrasts (contrast coding)  
Post hoc procedures  
Comparing several means using IBM SPSS Statistics  
Output from one-way independent ANOVA  
Robust comparisons of several means  
Bayesian comparisons of several means  
Calculating the effect size  
Reporting results from one-way independent ANOVA  
12.15 Smart Alex's tasks  
Chapter 13. GLM 2: Comparing Means Adjusted For Other Predictors (Analysis of Covariance)
What is ANCOVA?  
ANCOVA and the general linear model  
Assumptions and issues in ANCOVA  
Conducting ANCOVA using IBM SPSS Statistics  
Interpreting ANCOVA  
Testing the assumption of homogeneity of regression slopes  
Robust ANCOVA  
Bayesian analysis with covariates  
Calculating the effect size  
Reporting results  
Chapter 14. GLM 3: Factorial Designs
Factorial designs  
Independent factorial designs and the linear model  
Model assumptions in factorial designs  
Factorial designs using IBM SPSS Statistics  
Output from factorial designs  
Interpreting interaction graphs  
Robust models of factorial designs  
Bayesian models of factorial designs  
Calculating effect sizes  
Reporting results of two-way ANOVA  
Chapter 15. GLM 4: Repeated-Measures Designs
Introduction to repeated-measures designs  
A grubby example  
Repeated-measures and the linear model  
The ANOVA approach to repeated-measures designs  
The F-statistics for repeated-measures designs  
Assumptions in repeated-measures designs  
One-way repeated-measures designs  
Chapter 16. GLM 5: Mixed Designs
Mixed designs  
Assumptions in mixed designs  
A speed-dating example  
Mixed designs using IBM SPSS Statistics  
Output for mixed factorial designs  
Calculating effect sizes  
Reporting the results of mixed designes  
Chapter 17. Multivariate Analysis of Variance (MANOVA)
Introducing MANOVA  
Introducing matrices  
The theory behind MANOVA  
Practical issues when conducting MANOVA  
MANOVA using IBM SPSS Statistics  
Interpreting MANOVA  
Reporting results from MANOVA  
Following up MANOVA with discriminant analysis  
Interpreting discriminant analysis  
Reporting results from discriminant analysis  
The final interpretation  
Chapter 18. Exploratory Factor Analysis
When to use factor analysis  
Factors and components  
Discovering factors  
An anxious example  
Factor analysis uisng IBM SPSS Statistics  
Interpreting factor analysis  
How to report factor analysis  
Reliability analysis  
Reliability analysis using IBM SPSS Statistics  
Interpreting reliability analysis  
How to report reliability analysis  
Chapter 19. categorical Outcomes: Chi-Square and Loglinear Analysis
Analysing categorical data  
Associations between two categorical variables  
Associations between several categorical variables: loglinear analysis  
Assumptions when analysisng categorical data  
General procedure for analysing categorical outcomes  
Doing chi-square uisng IBM SPSS Statistics  
Interpreting the chi-square test  
Loglinear analysis using IBM SPSS Statistics  
Interpreting loglinear analysis  
Reporting the results of loglinear analysis  
Chapter 20. Categorical Outcomes: Logistic Regression
What is logitsic regression?  
Theory of logistic regression  
Sources of bias and common problems  
Binary logistic regression  
Interpreting logistic regression  
Reporting logistic regression  
Testing assumptions: another example  
Predicting several categories: multinominal logistic regression  
Reporting multinominal logistic regression  
Chapter 21. Multilevel Linear Models
Hierarchical data  
Theory of multilevel linear models  
The multilevel model  
Some practical issues  
Multilevel modeling using IBM SPSS Statistics  
Growth models  
How to report a multilevel model  
A message from the octopus of inescapable despair  
Chapter 22. Epilouge


Companion Website

Companion Website
Instructors: The following online resources are included FREE with this text. For a brief demo, contact your sales representative today.

Instructor Teaching Site

SAGE EDGE FOR INSTRUCTORS supports your teaching by making it easy to integrate quality content and create a rich learning environment for students and includes:

  • Assessment tools that foster review, practice, and critical thinking, and offer a more complete way to measure student engagement, including:
    • Test banks designed to support several subjects including Health, Business, Nursing, Education, and Sports written in ExamView test generation, so you can easily set assignments and exams
    • Links to a wealth of student quizzes to support student self-study
    • Instructions on how to use and integrate the comprehensive assessments and resources provided
  • Video resources crafted by Andy Field himself demystify tricky concepts introduced throughout the book
  • EXCLUSIVE, influential SAGE journal and reference content
  • Editable, chapter-specific PowerPoint® slides that offer flexibility when creating multimedia lectures so you don’t have to start from scratch but you can customize to your exact needs
  • All tables and figures from the textbook
  • Course cartridges available for Blackboard and Moodle

Student Study Site

SAGE EDGE FOR STUDENTS enhances learning, it’s easy to use, and offers:

  • Study skills and tips materials on preparing for exams, time management, reading research and presenting data
  • eFlashcards that strengthen understanding of key terms and concepts, and make it easy to maximize student study time, anywhere, anytime
  • eQuizzes and a Math diagnostics tool that allow students to assess how much they’ve learned and where they need to focus their attention
  • Chapter summaries with learning objectives that reinforce the most important material
  • Chapter-specific study questions that allow students to engage with the material
  • Video tutorials created by Andy Field explain the key concepts introduced throughout the book and are supported by videos from SAGE’s award winning video products
  • Exclusive access to influential SAGE journal and reference content that ties important research and scholarship to chapter concepts to strengthen learning

I've had previous editions of this text and as always, I am very impressed with Dr. Fields' humorous approach to statistics. He provides instruction on conducting analyses in the most current version of SPSS and has included Bayesian statistics in the new edition. Students receive comprehensive statistical instruction and many online resources are also provided. This is the best statistics textbook!

Dr Nancy Bridier
November 6, 2017

Excellent, well organized text. Unique, in my experience, the text and substantial student support website, provide supplemental materials for students who may be struggling and advanced materials for those who are ready. This facilitates course design and teaching in classes where a mix of preparation backgrounds is the norm.

Dr William Kittredge
College Of Business, Bellevue University
January 22, 2018
Key features


  • Full integration of IBM SPSS Statistics© version 25 helps take students from introductory through very advanced statistical concepts.
  • A new chapter on the open science movement discusses issues such as p-hacking, HARK-ing, researcher degrees of freedom, and pre-registration of research, and provides an introduction to Bayesian statistics.
  • New sections on R demonstrate how to use the R plugin to get Bayes factors and shows students how to do robust tests using R.
  • An exciting new character, Misconceptions Mutt, poses common misconceptions about statistics, only to have them dispelled by Correcting Cat.
  • The general linear model theme, now expanded, focuses on the commonalities between models traditionally labelled as regression, ANOVA, ANCOVA, t-tests etc.
  • Updated throughout, this edition includes even clearer, more engaging presentations, completely redrawn figures, and new SPSS Statistics screen shots.
  • FREE SAGE edge digital resources expand pedagogical support with SAGE video, case studies, datasets, and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills.


  • Light-hearted, humorous examples reflect topics that play on the minds of the average student (sex, drugs, rock and roll, celebrity, etc.) to make learning statistics accessible and even enjoyable.
  • Comprehensive coverage takes students from learning the basics of doing research to mastering multilevel modeling.
  • Data sets associated with this book are available on the companion website. 
  • SPSS tips offer hints and pitfalls related to SPSS.
  • Self-test questions range from simple questions that allow students to gauge what they’ve just learned to questions that ask students to apply techniques from previous chapters to a new context. 
  • Guides to reporting offers practice for writing the statistical analysis. 
  • Real research examples in every chapter from published research on fascinating topics provide students with "real data" to play with. 

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