Discovering Statistics Using R and RStudio

Höfundur: Andy Field (Útgáfa: 2)
Discovering Statistics Using R and RStudio

Kaup valmöguleikar

Í þessari algerlega endurskoðuðu annarri útgáfu leiðir Andy Field lesandann í könnunarferð um R og RStudio. Raunveruleg dæmi og kímni eru fléttuð saman við hagnýtar leiðbeiningar sem gæða tölfræði og forritun lífi. Með sínum einkennandi hispurslausa og grípandi stíl fjallar Andy Field um tölfræði á skemmtilegan og sveigjanlegan en jafnframt fræðilega traustan hátt. Bókin:

  • byggir upp skilning á tölfræði á viðráðanlegum hraða og veitir lesandanum sjálfstraust til að efla þekkingu sína á tölfræði og forritun;
  • hvetur til sjálfsprófunar og ígrundunar svo lesandinn geti æft nýja færni;
  • býður upp á stuðning og fjölbreytt úrræði fyrir ólíkar námsaðferðir og hvert það stig sem lesandinn er kominn á, með aðstoð litskrúðugra persóna.

Með spennandi nýju útliti og nýjum persónum er þetta kjörin kennslubók fyrir alla í félags- og atferlisvísindum sem vilja læra tölfræði með R og RStudio.

Nánar um bókina

Útgefandi
SAGE Publications, Ltd. (UK)
ISBN
9781529740165
Print ISBN
9781526461353
Format
ePub
Útgáfa
2
Höfundar
Andy Field
Tungumál
English
Útgefið
2026-02-19
Prent takmörkun á líftíma
100
Prent takmörkun
30
Afritunar takmörkun
30

Kaflar

  • Cover
  • Half Title
  • Acknowledgment
  • Title Page
  • Copyright Page
  • Toc brief
  • Contents
  • Preface
  • How to use this book
  • Thank you
  • Dedication
  • 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 ARGH! ARGH!Studio and Quarto
  • 4.1 What will this chapter tell me?
  • 4.2 Packages used in this chapter
  • 4.3 What is R?
  • 4.4 Getting started
  • 4.5 A tour of RStudio
  • 4.6 Workflow
  • 4.7 Quarto
  • 4.8 Code fundamentals
  • 4.9 Writing code in Quarto
  • 4.10 Workflow for the rest of the book
  • 4.11 Jane and Brian’s story
  • 4.12 What next?
  • 4.13 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 5 Describing and visualizing data
  • 5.1 What will this chapter tell me?
  • 5.2 Getting started
  • 5.3 Describing data
  • 5.4 The art of visualizing data
  • 5.5 Introducing ggplot2
  • 5.6 Histograms
  • 5.7 Frequency polygons
  • 5.8 Boxplots (box-whisker diagrams)
  • 5.9 Plotting means
  • 5.10 Repeated measures designs
  • 5.11 Line plots and mixed designs
  • 5.12 Graphing relationships: the scatterplot
  • 5.13 Brian and Jane’s story
  • 5.14 What next?
  • 5.15 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 Getting started
  • 6.3 Descent into statistics hell
  • 6.4 What is bias?
  • 6.5 Outliers
  • 6.6 Overview of assumptions
  • 6.7 Linearity and additivity
  • 6.8 Spherical errors
  • 6.9 Normally distributed something or other
  • 6.10 Checking for bias and describing data
  • 6.11 Reducing bias with robust methods
  • 6.12 Jane and Brian’s story
  • 6.13 What next?
  • 6.14 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 7 Correlation
  • 7.1 What will this chapter tell me?
  • 7.2 Getting started
  • 7.3 Modelling relationships
  • 7.4 Bivariate correlation
  • 7.5 Partial and semi-partial correlation
  • 7.6 Comparing correlations
  • 7.7 Calculating the effect size
  • 7.8 How to report correlation coefficents
  • 7.9 Jane and Brian’s story
  • 7.10 What next?
  • 7.11 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 8 The linear model (regression)
  • 8.1 What will this chapter tell me?
  • 8.2 Getting started
  • 8.3 The linear model (regression) … again!
  • 8.4 Bias in linear models
  • 8.5 Generalizing the model
  • 8.6 Sample size and the linear model
  • 8.7 Fitting linear models: the general procedure
  • 8.8 Using R to fit a linear model with one predictor
  • 8.9 Interpreting a linear model with one predictor
  • 8.10 The linear model with two or more predictors (multiple regression)
  • 8.11 Using R to fit a linear model with several predictors
  • 8.12 Interpreting a linear model with several predictors
  • 8.13 Robust linear models
  • 8.14 Bayesian regression
  • 8.15 Reporting linear models
  • 8.16 Jane and Brian’s story
  • 8.17 What next?
  • 8.18 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 9 Categorical predictors: Comparing two means
  • 9.1 What will this chapter tell me?
  • 9.2 Getting started
  • 9.3 Looking at differences
  • 9.4 A mischievous example
  • 9.5 Categorical predictors in the linear model
  • 9.6 The t-test
  • 9.7 Assumptions of the t-test
  • 9.8 Comparing two means: general procedure
  • 9.9 Comparing two independent means using R
  • 9.10 Comparing two related means using R
  • 9.11 Reporting comparisons between two means
  • 9.12 Between groups or repeated measures?
  • 9.13 Jane and Brian’s story
  • 9.14 What next?
  • 9.15 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 10 A tale of three Ms: Moderation, mediation and missingness
  • 10.1 What will this chapter tell me?
  • 10.2 Getting started
  • 10.3 Moderation: interactions in the linear model
  • 10.4 Mediation
  • 10.5 Missing data
  • 10.6 Jane and Brian’s story
  • 10.7 What next?
  • 10.8 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 11 GLM 1: Comparing several independent means
  • 11.1 What will this chapter tell me?
  • 11.2 Getting started
  • 11.3 A puppy-tastic example
  • 11.4 Goodbye one-way ANOVA, hello linear model
  • 11.5 Comparing several means with the linear model
  • 11.6 Assumptions when comparing means
  • 11.7 Planned contrasts (contrast coding)
  • 11.8 Post hoc procedures
  • 11.9 Effect sizes when comparing means
  • 11.10 Comparing several means using R
  • 11.11 Trend analysis
  • 11.12 Robust comparisons of several means
  • 11.13 Bayesian comparison of several means
  • 11.14 Reporting results when comparing means
  • 11.15 Jane and Brian’s story
  • 11.16 What next?
  • 11.17 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 12 GLM 2: Comparing means adjusted for other predictors (analysis of covariance)
  • 12.1 What will this chapter tell me?
  • 12.2 Getting started
  • 12.3 Goodbye ANCOVA, hello general linear model
  • 12.4 The F-statistic with multiple predictors
  • 12.5 Adjusted means
  • 12.6 Effect sizes for adjusted means
  • 12.7 Fitting the model using R
  • 12.8 Bayesian analysis with covariates
  • 12.9 Reporting results
  • 12.10 Jane and Brian’s story
  • 12.11 What next?
  • 12.12 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 13 GLM 3: Factorial designs
  • 13.1 What will this chapter tell me?
  • 13.2 Getting started
  • 13.3 Factorial designs
  • 13.4 A goggly example
  • 13.5 Independent factorial designs and the linear model
  • 13.6 Interpreting interaction plots
  • 13.7 Simple effects analysis
  • 13.8 F-statistics in factorial designs
  • 13.9 Model assumptions in factorial designs
  • 13.10 Factorial designs using R
  • 13.11 Interpreting factorial designs
  • 13.12 Robust models of factorial designs
  • 13.13 Bayesian models of factorial designs
  • 13.14 Reporting the results of factorial designs
  • 13.15 Jane and Brian’s story
  • 13.16 What next?
  • 13.17 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 14 GLM 4: Multilevel linear models
  • 14.1 What will this chapter tell me?
  • 14.2 Getting started
  • 14.3 Hierarchical data
  • 14.4 Multilevel linear models
  • 14.5 Practical issues
  • 14.6 Multilevel modelling using R
  • 14.7 How to report a multilevel model
  • 14.8 A message from the octopus of inescapable despair
  • 14.9 Jane and Brian’s story
  • 14.10 What next?
  • 14.11 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 15 GLM 5: Repeated-measures designs
  • 15.1 What will this chapter tell me?
  • 15.2 Getting started
  • 15.3 Emergency! The aliens are coming!
  • 15.4 Growth models
  • 15.5 Repeated-measures experiments
  • 15.6 One-way experimental repeated-measures designs
  • 15.7 A scented factorial repeated-measures design
  • 15.8 Jane and Brian’s story
  • 15.9 What next?
  • 15.10 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 16 GLM 6: Mixed designs
  • 16.1 What will this chapter tell me?
  • 16.2 Getting started
  • 16.3 Mixed designs
  • 16.4 Assumptions in mixed designs
  • 16.5 Growth models for groups
  • 16.6 Mixed experimental designs: a speed-dating example
  • 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 Exploratory factor analysis
  • 17.1 What will this chapter tell me?
  • 17.2 Getting started
  • 17.3 When to use factor analysis
  • 17.4 Factors and components
  • 17.5 Discovering factors
  • 17.6 An anxious example
  • 17.7 How to report factor analysis
  • 17.8 Reliability analysis
  • 17.9 Reliability analysis using R
  • 17.10 How to report reliability analysis
  • 17.11 Jane and Brian’s story
  • 17.12 What next?
  • 17.13 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 18 Categorical outcomes: chi-square and loglinear analysis
  • 18.1 What will this chapter tell me?
  • 18.2 Getting started
  • 18.3 Analysing categorical data
  • 18.4 Associations between two categorical variables
  • 18.5 Associations between several categorical variables: loglinear analysis
  • 18.6 Assumptions when analysing categorical data
  • 18.7 General procedure for analysing categorical outcomes
  • 18.8 Doing chi-square using R
  • 18.9 Loglinear analysis using R
  • 18.10 Reporting the results of loglinear analysis
  • 18.11 Jane and Brian’s story
  • 18.12 What next?
  • 18.13 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • 19 Categorical outcomes: logistic regression
  • 19.1 What will this chapter tell me?
  • 19.2 Getting started
  • 19.3 What is logistic regression?
  • 19.4 Theory of logistic regression
  • 19.5 Sources of bias and common problems
  • 19.6 Logistic regression using R
  • 19.7 Interactions in logistic regression: a sporty example
  • 19.8 Reporting logistic regression
  • 19.9 Jane and Brian’s story
  • 19.10 What next?
  • 19.11 Key terms that I’ve discovered
  • Smart Alex’s Tasks
  • Epilogue
  • Appendix
  • Glossary
  • References
  • Index