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Discovering Statistics Using IBM SPSS Statistics

Discovering Statistics Using IBM SPSS Statistics
North American Edition

Fifth Edition
Additional resources:

February 2018 | 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, Canvas, and Moodle.

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


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 been using Discovering... with SPSS... for a couple of years & have just updated to the 5th edition. I LOVE chapter 3, and have actually been covering a lot of that material in my class, so it's nice to have it explicit in the text as well.

Jennifer Gutbezahl, Harvard University

Jennifer Gutbezahl
Harvard University

"By the way, has anyone had a chance to look at the text? I love it! It's the first text I've come across that has been written in such a captivating way. There's humor, tons of information, and awesome resources both within and on the companion website. Kudos to Prof. Field!"

Anonymous Student, Harvard University

"I never thought I would find a statistics textbook amusing but somehow our text pulls it off.  I also appreciated the online supplementary tools provided by the publisher.  If you haven't seen them yet, you should check them out.  They provide a good synthesis of each of the chapters and some easy options to review."

Anonymous Student, Harvard University

"I get started on the text and can't agree more with you on how the book is. I also appreciate how the author made the text interesting to read, but the content is rich enough to provide readers good knowledge on how to draw insights from stats and data. Also, it provides a lot of practical guides for reporting results and findings for research paper. Can't wait to take a deeper dive into the text!"

Anonymous Student, Harvard University
Students of Jennifer Gutbezahl
Harvard University

Very helpful for using SPSS, a perfect aid

Dr Michael Sheppard
LMS Administration, Texas Chiropractic College
March 21, 2023

Excellent book on statistical methods of analysis in research

Professor Irina Lyublinskaya
Mathematics/Science/Tech Dept, Teachers College
July 28, 2021

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
February 8, 2018

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
February 14, 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. 

Sample Materials & Chapters

Chapter 1

Chapter 2

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ISBN: 9781526440310