Statistical Models for Psychology Using R: Thinking with Straight Lines

1st Edition
0335252648 · 9780335252640
“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… Read More
180 Day
Available for purchase 2025/07/18
£22.99
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  • 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