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Discovering Statistics Using IBM SPSS Statistics
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Discovering Statistics Using IBM SPSS Statistics

Fifth Edition
Additional resources:


November 2017 | 1 104 pages | SAGE Publications Ltd

Hear about student feedback on the character features that make this book special...

With an exciting new look, new characters to meet, and 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.

What’s brand new:

  • A radical new design with original illustrations and even more colour
  • A maths diagnostic tool to help students establish what areas they need to revise and improve on. 
  • 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
  • New sections on replication, open science and Bayesian thinking
  • Now fully up to date with latest 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. For instructions on how to upload the resources you want, please visit the Instructors' page  or alternatively, contact your local SAGE sales representative.

Please note that ISBN: 9781526445780 comprises the paperback edition of the Fifth Edition and the student version of IBM SPSS Statistics. More information on this version of the software's features can be found here.

 

 
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: 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
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, 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 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

 
How to report 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

 This book turned my hatred of stats and SPSS into love. 

Sharmina August
MSc in Applied Quantitative Methods

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!

Raymond Grogan
Harvard University

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

Hsing-Chi Hwang
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. They provide a good synthesis of each of the chapters and some easy options to review

Rickelle Mathis
Harvard University

I really really love the book, it's the main reason why I'm not curled up in bed with my cats sobbing in fear at the moment. Speaking of cats, I gotta say the correcting cat/misconception mutt framing is very cute, and it almost broke my heart finding out the origin of that orange spiritual feline. I'm having a blast reading about stats, who would've thunk it?

Joao Matos Amara Da Silveria
Harvard University

Great for everybody who is still working with SPSS. Answers a lot of questions and is very informative and well written!

Mrs Myriam Annabelle Baum
Dept. of Psychology, Saarland University
October 25, 2019

Very detailed - so works as a reference book.
I have added it to my recommended reading list.

Dr Jill Bogie
Business, Gordon Institute of Business Science
October 23, 2019

A good book with well structured content, helpful explanations and summaries. Fairly comprehensive reference book for anyone working in the statistics

Miss burcu gumus
Communication Sciences, Dogus University
September 25, 2019

Great, very good and very understandable and all important aspects are taken into account, e.g. interaction effects! Excellent!

Professor Dieter Kulke
Fakultät Angewandte Sozialwissenschaften, Fachhochschule Würzburg-Schweinfurt
August 21, 2019

The book contains the most current knowledge of the statistical methods, which is essential for my Master's degree students. I have used previous editions of this book and it has been very accessible to students.

Professor Annukka Vainio
Agriculture, University of Helsinki
August 2, 2019

Sample Materials & Chapters

Discovering Statistics Chapter 1

Discovering Statistics Chapter 2


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Andy Field

Andy Field is Professor of Child Psychopathology at the University of Sussex. He has published over 80 research papers, 29 book chapters, and 17 books mostly on child emotional development and statistics. He is the founding editor of the Journal of Experimental Psychopathology and has been an associate editor and editorial board member for the British Journal of Mathematical and Statistical Psychology, Cognition and Emotion, Clinical Child and Family Psychology Review and Research Synthesis Methods. His ability to make statistics accessible and fun has been recognized with local and national teaching awards (University of Sussex... More About Author

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