Statistical Models for Psychology Using R: Thinking with Straight Lines
1st Edition
033525263X
·
9780335252633
© 2025 | Published: May 12, 2025
Are you intimidated by statistics? Fear no longer! Statistics for Psychology Using R provides you with an accessible introduction to statistics using R and encourages you to develop a critical understanding of applied statistics that will prepare y…
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List of figures
List of tables
Introduction
1 Straight lines and the R programming language
1.1 Linear relationships
1.2 The equation of a straight line
1.3 Introduction to R
1.4 Summary
2 Probability and the normal distribution
2.1 Probability space
2.2 The psychology of probabilities
2.3 Probability distributions
2.4 Working with the normal distribution
2.5 Summary
3 Fitting linear models to data
3.1 First, some geometry
3.2 Importing data
3.3 Linear regression
3.4 Which line fits best?
3.5 Example: ‘Tips from the Top’
3.6 Summary
4 Linear models with categorical predictors
4.1 Variables in R
4.2 Linear models for categorical predictors
4.3 The t-test: a linear model in disguise
4.4 More than two categorical levels
4.5 Summary
5 Logarithms, exponentials and data transformations
5.1 Exponentials and logarithms
5.2 Example: gender representation in cinema
5.3 Visualizing skewed data
5.4 Importing, reshaping and cleaning data
5.5 Example: visual search
5.6 Summary
6 The bigger picture: contextualizing statistical methods in psychology
6.1 What do our statistics actually represent?
6.2 Statistical errors and power analysis
6.3 Simulation and sensitivity analysis
6.4 Data visualization
6.5 Summary
7 Linear models with more than one predictor
7.1 Regression with multiple predictors
7.2 Interactions between variables
7.3 Summary
8 Linear models in the real world: overfitting, collinearity, confounding
and sampling biases
8.1 Problems with adding new predictors
8.2 Causal reasoning for beginners
8.3 Summary
9 Repeated measures and multilevel models
9.1 Example: ‘Tips from the Top’ again
9.2 Fixed versus random effects
9.3 More complex random effect structures
9.4 Summary
10 Models for binary dependent variables
10.1 Generalized linear models for binary outcomes
10.2 Working with multiple predictors
10.3 Multilevel logistic regression
10.4 Summary
Epilogue
Glossary of terms
References
List of tables
Introduction
1 Straight lines and the R programming language
1.1 Linear relationships
1.2 The equation of a straight line
1.3 Introduction to R
1.4 Summary
2 Probability and the normal distribution
2.1 Probability space
2.2 The psychology of probabilities
2.3 Probability distributions
2.4 Working with the normal distribution
2.5 Summary
3 Fitting linear models to data
3.1 First, some geometry
3.2 Importing data
3.3 Linear regression
3.4 Which line fits best?
3.5 Example: ‘Tips from the Top’
3.6 Summary
4 Linear models with categorical predictors
4.1 Variables in R
4.2 Linear models for categorical predictors
4.3 The t-test: a linear model in disguise
4.4 More than two categorical levels
4.5 Summary
5 Logarithms, exponentials and data transformations
5.1 Exponentials and logarithms
5.2 Example: gender representation in cinema
5.3 Visualizing skewed data
5.4 Importing, reshaping and cleaning data
5.5 Example: visual search
5.6 Summary
6 The bigger picture: contextualizing statistical methods in psychology
6.1 What do our statistics actually represent?
6.2 Statistical errors and power analysis
6.3 Simulation and sensitivity analysis
6.4 Data visualization
6.5 Summary
7 Linear models with more than one predictor
7.1 Regression with multiple predictors
7.2 Interactions between variables
7.3 Summary
8 Linear models in the real world: overfitting, collinearity, confounding
and sampling biases
8.1 Problems with adding new predictors
8.2 Causal reasoning for beginners
8.3 Summary
9 Repeated measures and multilevel models
9.1 Example: ‘Tips from the Top’ again
9.2 Fixed versus random effects
9.3 More complex random effect structures
9.4 Summary
10 Models for binary dependent variables
10.1 Generalized linear models for binary outcomes
10.2 Working with multiple predictors
10.3 Multilevel logistic regression
10.4 Summary
Epilogue
Glossary of terms
References
Are you intimidated by statistics? Fear no longer! Statistics for Psychology Using R provides you with an accessible introduction to statistics using R and encourages you to develop a critical understanding of applied statistics that will prepare you for the modern demands of psychological research, such as advancing psychological theories, improving research methods or tackling contemporary challenges.
Introducing essential statistical concepts such as t-tests, ANOVA, correlation, and regression within a unified framework based on linear models, this book offers a powerful and intuitive way to analyse data while highlighting the connections between statistical techniques rather than treating them as separate procedures. It will act as a trusted guide for psychology and social science students at undergraduate and postgraduate level, especially, but not exclusively, for those using R. It will also benefit professionals seeking to update their understanding of statistics and enhance their data analysis skills as part of their continuous professional development, especially those looking to apply advanced techniques using R.
Statistics for Psychology Using R is accompanied by an Online Learning Centre (OLC) featuring practical activities, including data analysis exercises that map onto the content covered by the chapters and scenario-based exercises that draw on databases to enable students to put their knowledge into practice.
Key Features:
Matteo Lisi is Lecturer in Psychology at Royal Holloway, University of London, UK. He holds a PhD in Cognitive Science from the University of Padua, Italy. His research employs psycho-physics, eye-tracking and computational modelling to study visual perception and decision-making, focusing on how people process uncertainty in various contexts.
Introducing essential statistical concepts such as t-tests, ANOVA, correlation, and regression within a unified framework based on linear models, this book offers a powerful and intuitive way to analyse data while highlighting the connections between statistical techniques rather than treating them as separate procedures. It will act as a trusted guide for psychology and social science students at undergraduate and postgraduate level, especially, but not exclusively, for those using R. It will also benefit professionals seeking to update their understanding of statistics and enhance their data analysis skills as part of their continuous professional development, especially those looking to apply advanced techniques using R.
Statistics for Psychology Using R is accompanied by an Online Learning Centre (OLC) featuring practical activities, including data analysis exercises that map onto the content covered by the chapters and scenario-based exercises that draw on databases to enable students to put their knowledge into practice.
Key Features:
- Accessible introduction to statistics using R
- Promotes critical understanding of applied statistics
- Focuses on linear models to offer a unified and flexible approach to data analysis
- Shows how practical applications of R can advance psychological theories, improve research methods or tackle contemporary challenges
- Aimed at psychology and social science undergraduate and postgraduate students, as well as professionals seeking statistics and R training as part of their continuous professional development
Matteo Lisi is Lecturer in Psychology at Royal Holloway, University of London, UK. He holds a PhD in Cognitive Science from the University of Padua, Italy. His research employs psycho-physics, eye-tracking and computational modelling to study visual perception and decision-making, focusing on how people process uncertainty in various contexts.