Statistical Models for Psychology Using R: Thinking with Straight Lines
1st Edition
0335252648
·
9780335252640
© 2025 | Published: July 18, 2025
“This is the first accessible resource to linear models and R coding for Psychology students! Clarke and Lisi have mastered the art of explaining complex concepts and statistical analyses in an easy-to-understand manner and a seamless pathway.”Ch…
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- Access the eBook anytime, anywhere: online or offline
- Create notes, flashcards and make annotations while you study
- Full searchable content: quickly find the answers you are looking for
- 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
- 1 Straight lines and the R programming language