**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 **