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With its unique combination of humour and step-by-step instruction, this award-winning book is the statistics lifesaver for everyone. From initial theory through to regression, factor analysis and multilevel modelling, Andy Field animates statistics and SPSS software with his famously bizarre examples and activities.

Features:
•Flexible coverage to support students across disciplines and degree programmes
•Can support classroom or lab learning and assessment
•Analysis of real data with opportunities to practice statistical skills
•Highlights common misconceptions and errors
•A revamped online resource that uses video, case studies, datasets, testbanks and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills
•Covers the range of versions of IBM SPSS Statistics©.

All the online resources above (video, case studies, datasets, testbanks) can be easily integrated into your institution's virtual learning environment or learning management system. This allows you to customize and curate content for use in module preparation, delivery and assessment.


 
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

 
Reporting Data

 
 
Chapter 2: The SPINE of statistics
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, NHST

 
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

 
Preregistering research and open science

 
Effect sizes

 
Bayesian approaches

 
Reporting effect sizes and Bayes factors

 
 
Chapter 4: The IBM SPSS Statistics environment
Versions of IBM SPSS Statistics

 
Windows, MacOS 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: Data Visualisation
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
Modelling relationships

 
Data entry for correlation analysis

 
Bivariate correlation

 
Partial and semi-partial correlation

 
Comparing correlations

 
Calculating the effect size

 
How to report correlation coefficents

 
 
Chapter 9: The Linear Model (Regression)
An Introduction to the linear model (regression)

 
Bias in linear models?

 
Generalizing the model

 
Sample size in regression

 
Fitting linear models: the general procedure

 
Using SPSS Statistics to fit a linear model with one predictor

 
Interpreting a linear model with one predictor

 
The linear model with two of more predictors (multiple regression)

 
Using 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 at differences

 
An example: are invisible people mischievous?

 
Categorical predictors in the linear model

 
The t-test

 
Assumptions of the t-test

 
Comparing two means: general procedure

 
Comparing two independent means using SPSS Statistics

 
Comparing two related means using SPSS Statistics

 
Reporting comparisons between two means

 
Between groups or repeated measures?

 
 
Chapter 11: Moderation and Mediation
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 SPSS Statistics

 
Output from one-way independent ANOVA

 
Robust comparisons of several means

 
Bayesian comparison of several means

 
Calculating the effect size

 
Reporting results from one-way independent ANOVA

 
 
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 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 SPSS Statistics

 
Output from factorial designs

 
Interpreting interaction graphs

 
Robust models of factorial designs

 
Bayesian models of factorial designs

 
Calculating effect sizes

 
Reporting the 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-statistic for repeated-measures designs

 
Assumptions in repeated-measures designs

 
One-way repeated-measures designs using SPSS

 
Output for one-way repeated-measures designs

 
Robust tests of one-way repeated-measures designs

 
Effect sizes for one-way repeated-measures designs

 
Reporting one-way repeated-measures designs

 
A boozy example: a factorial repeated-measures design

 
Factorial repeated-measures designs using SPSS Statistics

 
Interpreting factorial repeated-measures designs

 
Effect Sizes for factorial repeated-measures designs

 
Reporting the results from factorial repeated-measures designs

 
 
Chapter 16: GLM 5: Mixed designs
Mixed designs

 
Assumptions in mixed designs

 
A speed dating example

 
Mixed designs using SPSS Statistics

 
Output for mixed factorial designs

 
Calculating effect sizes

 
Reporting the results of mixed designs

 
 
Chapter 17: Multivariate analysis of variance (MANOVA)
Introducing MANOVA

 
Introducing matrices

 
The theory behind MANOVA

 
MANOVA using 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 using SPSS statistics

 
Interpreting factor analysis

 
Interpreting factor analysis

 
Reliability analysis

 
Reliability analysis using 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 analysing categorical data

 
General procedure for analysing categorical outcomes

 
Doing chi-square using SPSS Statistics

 
Interpreting the chi-square test

 
Loglinear analysis using SPSS Statistics

 
Interpreting loglinear analysis

 
Reporting the results of loglinear analysis

 
 
Chapter 20: Categorical outcomes: logistic regression
What is logistic 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: multinomial logistic regression

 
 
Chapter 21: Multilevel linear models
Hierarchical data

 
Theory of multilevel linear models

 
The multilevel model

 
Some practical issues

 
Multilevel modelling using SPSS Statistics

 
Growth models

 
How to report a multilevel model

 
A message from the octopus of inescapable despair

 
 
Chapter 22: Epilogue

Supplements

Datasets & more

Hosted by the author, this website contains additional materials including: 

  • Solutions to Smart Alex’s tasks.
  • The data files and SPSS workbooks for each chapter.
  • Errata
  • Solutions to Labcoat Leni tasks
  • Oditi’s Lantern
  • Oliver Twisted’s pot of statistical gruel.
  • Solutions to self-test tasks within chapters

This is surely the most comprehensive SPSS offering on the market, and it offers delivery in an entertaining way which means that it offers a unique and valuable reading experience that cannot be obtained via new competing methods including AI.

I think this book also ties in well with 'An Adventure in Statistics' by the same author, which offers a more thorough discussion of the basics, again with the same engaging style, but without the need for any statistics software.

I teach both R and SPSS, for ease of delivery it would be good if both this book and 'Discovering Statistics using R' were in sync. I guess I am encouraging Andy Field to continue, to publish a new edition of the R book as well!

Dr Ben Derrick
Department of Mathematics & Statistics, University of The West of England
May 2, 2024

SPSS step by step procedures.

Dr Jean Amost Cadet
Nursing, Andrews University-Michigan
April 29, 2024

Used a previous version, great text.

Dr James Lacey
LMS Administration, Endicott College
April 30, 2024

After many years doing research and teaching on research methods, even if the examples in the book are not just in the field of health sciences, I have finally found a book that contains both theoretical details of statistics along with how to practically perform tests on SPSS. Something that attracted my attention was the use of colourful images and figures and even the colourful text.
This book really recommended to anyone who is going to work with SPSS and also understand why and how to use it. Thanks to the author!

Professor Mojtaba Vaismoradi
Nursing, Nord University
April 12, 2024

Lovely text with illustrations to aid those who are using statistics in their research project. I would recommend this as it is a visually friendly text with plenty of examples. Each chapter begins with "what will this chapter tell me" to give a quick summary of what will be covered. There are also self-tests at the end of each chapter to test knowledge.

Miss Lee Lui
Faculty of Health, Social Care and Education, Kingston University
May 10, 2024

I didn't receive the book

Dr Aram Ghaemmaghami
School of Criminology and Criminal Justice, Portsmouth University
May 29, 2024

Summarizing:
- The chapters do not follow the usual sequence from simpler to more complex in Statistics textbooks.
- Most chapters dedicate very little to explain how to use SPSS for each type of problem, from the data entry to the interpretation of results.
- The attempt to use colloquial conversation for explanations is more a distraction than a facilitator of understanding.
-The use of some cartoon-like images through the chapters is distracting; it does not add to the comprehension of concepts.

Dr Fabio Chacon
LMS Administration, Bowie State University
March 7, 2024

Best book for biostatistics on a MSc level available. This rework is again a step up, while the level was already so good.
We don't have a 'mandatory book' list anymore, otherwise it would be on it. We have it on the reading list with strong references to the chapters within our study manual as well.

Mr Roland Reezigt
Health, Hanze University of Applied Sciences
March 27, 2024

Great textbook, very student friendly, highly recommended

Dr Joseph Vella
Dept of Media and Communication, University of Malta
May 29, 2024
Key features
What's new in this edition: 

• Updated to comply with the latest version of IBM SPSS Statistics (version 29) 
• Updated and expanded theory on the general linear model
• More emphasis on Partial eta-square in general linear model chapters
• Updated examples and features throughout to make them more diverse and inclusive

For instructors

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