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Discovering Statistics Using IBM SPSS Statistics

Discovering Statistics Using IBM SPSS Statistics

Sixth Edition
Additional resources:

February 2024 | 1 144 pages | SAGE Publications Ltd
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.

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


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
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


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


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

Andy Field

Andy Field is Professor of Quantitative Methods at the University of Sussex. He has published widely (100+ research papers, 29 book chapters, and 17 books in various editions) in the areas of child anxiety and psychological methods and statistics. His current research interests focus on barriers to learning mathematics and statistics.He is internationally known as a statistics educator. He has written several widely used statistics textbooks including Discovering Statistics Using IBM SPSS Statistics (winner of the 2007 British Psychological Society book award), Discovering Statistics Using R, and An Adventure... More About Author

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