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• Preface
• How to use this book
• Thank you
• Symbols used in this book
• A brief maths overview
• 1 Why is my evil lecturer forcing me to learn statistics?
• 1.1 What the hell am I doing here? I don’t belong here
• 1.2 The research process
• 1.3 Initial observation: finding something that needs explaining
• 1.4 Generating and testing theories and hypotheses
• 1.5 Collecting data: measurement
• 1.6 Collecting data: research design
• 1.7 Analysing data
• 1.8 Reporting data
• 1.9 Jane and Brian’s story
• 1.10 What next?
• 1.11 key terms that I’ve discovered
• Smart Alex’s tasks
• 2 The SPINE of statistics
• 2.1 What will this chapter tell me?
• 2.2 What is the SPINE of statistics?
• 2.3 Statistical models
• 2.4 Populations and samples
• 2.5 The linear model
• 2.6 P is for parameters
• 2.7 E is for estimating parameters
• 2.8 S is for standard error
• 2.9 I is for (confidence) interval
• 2.10 N is for null hypothesis significance testing
• 2.11 Reporting significance tests
• 2.12 Jane and Brian’s story
• 2.13 What next?
• 2.14 Key terms that I’ve discovered
• Smart Alex’s tasks
• 3 The phoenix of statistics
• 3.1 What will this chapter tell me?
• 3.2 Problems with NHST
• 3.3 NHST as part of wider problems with science
• 3.4 A phoenix from the EMBERS
• 3.5 Sense, and how to use it
• 3.6 Preregistering research and open science
• 3.7 Effect sizes
• 3.8 Bayesian approaches
• 3.9 Reporting effect sizes and Bayes factors
• 3.10 Jane and Brian’s story
• 3.11 What next?
• 3.12 Key terms that I’ve discovered
• Smart Alex’s tasks
• 4 The IBM SPSS Statistics environment
• 4.1 What will this chapter tell me?
• 4.2 Versions of IBM SPSS Statistics
• 4.3 Windows, Mac OS and Linux
• 4.4 Getting started
• 4.5 The data editor
• 4.6 Entering data into IBM SPSS Statistics
• 4.7 SPSS syntax
• 4.8 The SPSS viewer
• 4.9 Exporting SPSS output
• 4.10 Saving files and restore points
• 4.11 Opening files and restore points
• 4.12 A few useful options
• 4.13 Extending IBM SPSS Statistics
• 4.14 Jane and Brian’s story
• 4.15 What next?
• 4.16 Key terms that I’ve discovered
• Smart Alex’s tasks
• 5 Visualizing data
• 5.1 What will this chapter tell me?
• 5.2 The art of visualizing data
• 5.3 The SPSS Chart Builder
• 5.4 Histograms
• 5.5 Boxplots (box–whisker diagrams)
• 5.6 Visualizing means: bar charts and error bars
• 5.7 Line charts
• 5.8 Visualizing relationships: the scatterplot
• 5.9 Editing plots
• 5.10 Brian and Jane’s story
• 5.11 What next?
• 5.12 Key terms that I’ve discovered
• Smart Alex’s tasks
• 6 The beast of bias
• 6.1 What will this chapter tell me?
• 6.2 Descent into statistics hell
• 6.3 What is bias?
• 6.4 Outliers
• 6.5 Overview of assumptions
• 6.6 Linearity and additivity
• 6.7 Spherical errors
• 6.8 Normally distributed something or other
• 6.9 Checking for bias and describing data
• 6.10 Reducing bias with robust methods
• 6.11 A final note
• 6.12 Jane and Brian’s story
• 6.13 What next?
• 6.14 Key terms that I’ve discovered
• Smart Alex’s tasks
• 7 Non-parametric models
• 7.1 What will this chapter tell me?
• 7.2 When to use non-parametric tests
• 7.3 General procedure of non-parametric tests using SPSS
• 7.4 Comparing two independent conditions: the Wilcoxon rank-sum test and Mann–Whitney test
• 7.5 Comparing two related conditions: the Wilcoxon signed-rank test
• 7.6 Differences between several independent groups: the Kruskal–Wallis test
• 7.7 Differences between several related groups: Friedman’s ANOVA
• 7.8 Jane and Brian’s story
• 7.9 What next?
• 7.10 Key terms that I’ve discovered
• Smart Alex’s tasks
• 8 Correlation
• 8.1 What will this chapter tell me?
• 8.2 Modelling relationships
• 8.3 Data entry for correlation analysis
• 8.4 Bivariate correlation
• 8.5 Partial and semi-partial correlation
• 8.6 Comparing correlations
• 8.7 Calculating the effect size
• 8.8 How to report correlation coefficients
• 8.9 Jane and Brian’s story
• 8.10 What next?
• 8.11 Key terms that I’ve discovered
• Smart Alex’s tasks
• 9 The linear model (regression)
• 9.1 What will this chapter tell me?
• 9.2 The linear model (regression) … again!
• 9.3 Bias in linear models
• 9.4 Generalizing the model
• 9.5 Sample size and the linear model
• 9.6 Fitting linear models: the general procedure
• 9.7 Using SPSS to fit a linear model with one predictor
• 9.8 Interpreting a linear model with one predictor
• 9.9 The linear model with two or more predictors (multiple regression)
• 9.10 Using SPSS to fit a linear model with several predictors
• 9.11 Interpreting a linear model with several predictors
• 9.12 Robust regression
• 9.13 Bayesian regression
• 9.14 Reporting linear models
• 9.15 Jane and Brian’s story
• 9.16 What next?
• 9.17 Key terms that I’ve discovered
• Smart Alex’s tasks
• 10 Categorical predictors: Comparing two means
• 10.1 What will this chapter tell me?
• 10.2 Looking at differences
• 10.3 A mischievous example
• 10.4 Categorical predictors in the linear model
• 10.5 The t-test
• 10.6 Assumptions of the t-test
• 10.7 Comparing two means: general procedure
• 10.8 Comparing two independent means using SPSS
• 10.9 Comparing two related means using SPSS
• 10.10 Reporting comparisons between two means
• 10.11 Between groups or repeated measures?
• 10.12 Jane and Brian’s story
• 10.13 What next?
• 10.14 Key terms that I’ve discovered
• Smart Alex’s tasks
• 11 Moderation and mediation
• 11.1 What will this chapter tell me?
• 11.2 The PROCESS tool
• 11.3 Moderation: interactions in the linear model
• 11.4 Mediation
• 11.5 Jane and Brian’s story
• 11.6 What next?
• 11.7 Key terms that I’ve discovered
• Smart Alex’s tasks
• 12 GLM 1: Comparing several independent means
• 12.1 What will this chapter tell me?
• 12.2 A puppy-tastic example
• 12.3 Compare several means with the linear model
• 12.4 Assumptions when comparing means
• 12.5 Planned contrasts (contrast coding)
• 12.6 Post hoc procedures
• 12.7 Effect sizes when comparing means
• 12.8 Comparing several means using SPSS
• 12.9 Output from one-way independent ANOVA
• 12.10 Robust comparisons of several means
• 12.11 Bayesian comparison of several means
• 12.12 Reporting results from one-way independent ANOVA
• 12.13 Jane and Brian’s story
• 12.14 What next?
• 12.15 Key terms that I’ve discovered
• Smart Alex’s tasks
• 13 GLM 2: Comparing means adjusted for other predictors (analysis of covariance)
• 13.1 What will this chapter tell me?
• 13.2 What is ANCOVA?
• 13.3 The general linear model with covariates
• 13.4 Effect size for ANCOVA
• 13.5 Assumptions and issues in ANCOVA designs
• 13.6 Conducting ANCOVA using SPSS
• 13.7 Interpreting ANCOVA
• 13.8 The non-parallel slopes model and the assumption of homogeneity of regression slopes
• 13.9 Robust ANCOVA
• 13.10 Bayesian analysis with covariates
• 13.11 Reporting results
• 13.12 Jane and Brian’s story
• 13.13 What next?
• 13.14 Key terms that I’ve discovered
• Smart Alex’s tasks
• 14 GLM 3: Factorial designs
• 14.1 What will this chapter tell me?
• 14.2 Factorial designs
• 14.3 A goggly example
• 14.4 Independent factorial designs and the linear model
• 14.5 Interpreting interaction plots
• 14.6 Simple effects analysis
• 14.7 F-statistics in factorial designs
• 14.8 Model assumptions in factorial designs
• 14.9 Factorial designs using SPSS
• 14.10 Output from factorial designs
• 14.11 Robust models of factorial designs
• 14.12 Bayesian models of factorial designs
• 14.13 More effect sizes
• 14.14 Reporting the results of factorial designs
• 14.15 Jane and Brian’s story
• 14.16 What next?
• 14.17 Key terms that I’ve discovered
• Smart Alex’s tasks
• 15 GLM 4: Repeated-measures designs
• 15.1 What will this chapter tell me?
• 15.2 Introduction to repeated-measures designs
• 15.3 Emergency! The aliens are coming!
• 15.4 Repeated measures and the linear model
• 15.5 The ANOVA approach to repeated-measures designs
• 15.6 The F-statistic for repeated-measures designs
• 15.7 Assumptions in repeated-measures designs
• 15.8 One-way repeated-measures designs using SPSS
• 15.9 Output for one-way repeated-measures designs
• 15.10 Robust tests of one-way repeated-measures designs
• 15.11 Effect sizes for one-way repeated-measures designs
• 15.12 Reporting one-way repeated-measures designs
• 15.13 A scented factorial repeated-measures design
• 15.14 Factorial repeated-measures designs using SPSS
• 15.15 Interpreting factorial repeated-measures designs
• 15.16 Reporting the results from factorial repeated-measures designs
• 15.17 Jane and Brian’s story
• 15.18 What next?
• 15.19 Key terms that I’ve discovered
• Smart Alex’s tasks
• 16 GLM 5: Mixed designs
• 16.1 What will this chapter tell me?
• 16.2 Mixed designs
• 16.3 Assumptions in mixed designs
• 16.4 A speed-dating example
• 16.5 Mixed designs using SPSS
• 16.6 Output for mixed factorial designs
• 16.7 Reporting the results of mixed designs
• 16.8 Jane and Brian’s story
• 16.9 What next?
• 16.10 Key terms that I’ve discovered
• Smart Alex’s tasks
• 17 Multivariate analysis of variance (MANOVA)
• 17.1 What will this chapter tell me?
• 17.2 Introducing MANOVA
• 17.3 The theory behind MANOVA
• 17.4 Practical issues when conducting MANOVA
• 17.5 MANOVA using SPSS
• 17.6 Interpreting MANOVA
• 17.7 Reporting results from MANOVA
• 17.8 Following up MANOVA with discriminant analysis
• 17.9 Interpreting discriminant analysis
• 17.10 Reporting results from discriminant analysis
• 17.11 The final interpretation
• 17.12 Jane and Brian’s story
• 17.13 What next?
• 17.14 Key terms that I’ve discovered
• Smart Alex’s tasks
• 18 Exploratory factor analysis
• 18.1 What will this chapter tell me?
• 18.2 When to use factor analysis
• 18.3 Factors and components
• 18.4 Discovering factors
• 18.5 An anxious example
• 18.6 Factor analysis using SPSS
• 18.7 Interpreting factor analysis
• 18.8 How to report factor analysis
• 18.9 Reliability analysis
• 18.10 Reliability analysis using SPSS
• 18.11 Interpreting reliability analysis
• 18.12 How to report reliability analysis
• 18.13 Jane and Brian’s story
• 18.14 What next?
• 18.15 Key terms that I’ve discovered
• Smart Alex’s tasks
• 19 Categorical outcomes: chi-square and loglinear analysis
• 19.1 What will this chapter tell me?
• 19.2 Analysing categorical data
• 19.3 Associations between two categorical variables
• 19.4 Associations between several categorical variables: loglinear analysis
• 19.5 Assumptions when analysing categorical data
• 19.6 General procedure for analysing categorical outcomes
• 19.7 Doing chi-square using SPSS
• 19.8 Interpreting the chi-square test
• 19.9 Loglinear analysis using SPSS
• 19.10 Interpreting loglinear analysis
• 19.11 Reporting the results of loglinear analysis
• 19.12 Jane and Brian’s story
• 19.13 What next?
• 19.14 Key terms that I’ve discovered
• Smart Alex’s tasks
• 20 Categorical outcomes: logistic regression
• 20.1 What will this chapter tell me?
• 20.2 What is logistic regression?
• 20.3 Theory of logistic regression
• 20.4 Sources of bias and common problems
• 20.5 Binary logistic regression
• 20.6 Interpreting logistic regression
• 20.7 Interactions in logistic regression: a sporty example
• 20.8 Reporting logistic regression
• 20.9 Jane and Brian’s story
• 20.10 What next?
• 20.11 Key terms that I’ve discovered
• Smart Alex’s tasks
• 21 Multilevel linear models
• 21.1 What will this chapter tell me?
• 21.2 Hierarchical data
• 21.3 Multilevel linear models
• 21.4 Practical issues
• 21.5 Multilevel modelling using SPSS
• 21.6 How to report a multilevel model
• 21.7 A message from the octopus of inescapable despair
• 21.8 Jane and Brian’s story
• 21.9 What next?
• 21.10 Key terms that I’ve discovered
• Smart Alex’s tasks
• Epilogue
• Appendix
• Glossary
• References
• Index