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Discovering Statistics Using IBM SPSS Statistics
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Discovering Statistics Using IBM SPSS Statistics
North American Edition

Fifth Edition
Additional resources:


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, Canvas, and Moodle. Learn more at edge.sagepub.com/field5e

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Video Links
Go behind the scenes and learn more about the man behind the book at Andy’s YouTube channel

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

 
Histograms

 
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?

 
Outliers

 
Overview of assumptions

 
Additivity and linearity

 
Normally distributed something or other

 
Homoscedasticity/homogeneity of variance

 
Independence

 
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

 
Mediation

 
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

Supplements

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

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

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
COLLEGE OF DOCTORAL STUDIES, Grand Canyon University
November 6, 2017
Key features

NEW TO THIS EDITION:

  • 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.

KEY FEATURES:

  • 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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