#
Discovering Statistics Using R (Englisch)

von Field, Andy & Miles, Jeremy & Field, Zoe

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Lecturers - request an e-inspection copy of this text or contact your local SAGE representative to discuss your course needs.

Watch Andy Field's introductory video to Discovering Statistics Using R

Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world.

The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you're doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect.

Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

Given this book's accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.

Available with Perusall - an eBook that makes it easier to prepare for class

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Why Is My Evil Lecturer Forcing Me to Learn Statistics?

What will this chapter tell me?

What the hell am I doing here? I don't belong here

Initial observation: finding something that needs explaining

Generating theories and testing them

Data collection 1: what to measure

Data collection 2: how to measure

Analysing data

What have I discovered about statistics?

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Everything You Ever Wanted to Know About Statistics (Well, Sort of)

What will this chapter tell me?

Building statistical models

Populations and samples

Simple statistical modelsGoing beyond the data

Using statistical models to test research questions

What have I discovered about statistics?

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

The R Environment

What will this chapter tell me?

Before you start

Getting started

Using R

Getting data into R

Entering data with R Commander

Using other software to enter and edit data

Saving Data

Manipulating Data

What have I discovered about statistics?

R Packages Used in This Chapter

R Functions Used in This Chapter

Key terms that I've discovered

Smart Alex's Tasks

Further reading

Exploring Data with Graphs

What will this chapter tell me?

The art of presenting data

Packages used in this chapter

Introducing ggplot2

Graphing relationships: the scatterplot

Histograms: a good way to spot obvious problems

Boxplots (box-whisker diagrams)

Density plots

Graphing means

Themes and options

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Exploring Assumptions

What will this chapter tell me?

What are assumptions?

Assumptions of parametric data

Packages used in this chapter

The assumption of normality

Testing whether a distribution is normal

Testing for homogeneity of variance

Correcting problems in the data

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Correlation

What will this chapter tell me?

Looking at relationships

How do we measure relationships?

Data entry for correlation analysis

Bivariate correlation

Partial correlation

Comparing correlations

Calculating the effect size

How to report correlation coefficents

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Regression

What will this chapter tell me?

An Introduction to regression

Packages used in this chapter

General procedure for regression in R

Interpreting a simple regression

Multiple regression: the basics

How accurate is my regression model?

How to do multiple regression using R Commander and R

Testing the accuracy of your regression model

Robust regression: bootstrapping

How to report multiple regression

Categorical predictors and multiple regression

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Logistic Regression

What will this chapter tell me?

Background to logistic regression

What are the principles behind logistic regression?

Assumptions and things that can go wrong

Packages used in this chapter

Binary logistic regression: an example that will make you feel eel

How to report logistic regression

Testing assumptions: another example

Predicting several categories: multinomial logistic regression

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Comparing Two Means

What will this chapter tell me?

Packages us

In statistics, R is the way of the future. The big boys and girls have known this for some time: There are now millions of R users in academia and industry. R is free (as in no cost) and free (as in speech). Andy, Jeremy, and Zoe's book now makes R accessible to the little boys and girls like me and my students. Soon all classes in statistics will be taught in R.

I have been teaching R to psychologists for several years and so I have been waiting for this book for some time. The book is excellent, and it is now the course text for all my statistics classes. I'm pretty sure the book provides all you need to go from statistical novice to working researcher.

Take, for example, the chapter on t-tests. The chapter explains how to compare the means of two groups from scratch. It explains the logic behind the tests, it explains how to do the tests in R with a complete worked example, which papers to read in the unlikely event you do need to go further, and it explains what you need to write in your practical report or paper. But it also goes further, and explains how t-tests and regression are related---and are really the same thing---as part of the general linear model. So this book offers not just the step-by-step guidance needed to complete a particular test, but it also offers the chance to reach the zen state of total statistical understanding.

Prof. Neil Stewart

Warwick University

Field's Discovering Statistics is popular with students for making a sometimes deemed inaccessible topic accessible, in a fun way. In Discovering Statistics Using R, the authors have managed to do this using a statistics package that is known to be powerful, but sometimes deemed just as inaccessible to the uninitiated, all the while staying true to Field's off-kilter approach.

Dr Marcel van Egmond

University of Amsterdam

Probably the wittiest and most amusing of the lot (no, really), this book takes yet another approach: it is 958 pages of R-based stats wisdom (plus online accoutrements)... A thoroughly engaging, expansive, thoughtful and complete guide to modern statistics. Self-deprecating stories lighten the tone, and the undergrad-orientated 'stupid faces' (Brian Haemorrhage, Jane Superbrain, Oliver Twisted, etc.) soon stop feeling like a gimmick, and help to break up the text with useful snippets of stats wisdom. It is very mch a student textbook but it is brilliant... Field et al. is the complete package.

David M. Shuker

AnimJournal of Animal Behaviour

4

## Über den Autor

Andy Field is Professor of Child Psychopathology at the University of Sussex. He has published over 80 research papers, 29 book chapters, and 17 books mostly on child emotional development and statistics.

He is the founding editor of the Journal of Experimental Psychopathology and has been an associate editor and editorial board member for the British Journal of Mathematical and Statistical Psychology, Cognition and Emotion, Clinical Child and Family Psychology Review and Research Synthesis Methods.

His ability to make statistics accessible and fun has been recognized with local and national teaching awards (University of Sussex, 2001, 2015, 2016; the British Psychological Society, 2007), a prestigious UK National Teaching Fellowship (2010), and the British Psychological Society book award (2006). He adores cats (and dogs), and loves to listen to and play very heavy music. He lives in Brighton with his wonderful wife Zoë, his son Zach, his crazy spaniel Ramsey and Fuzzy the cat.

## Inhaltsverzeichnis

Why Is My Evil Lecturer Forcing Me to Learn Statistics?

What will this chapter tell me?

What the hell am I doing here? I don't belong here

Initial observation: finding something that needs explaining

Generating theories and testing them

Data collection 1: what to measure

Data collection 2: how to measure

Analysing data

What have I discovered about statistics?

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Everything You Ever Wanted to Know About Statistics (Well, Sort of)

What will this chapter tell me?

Building statistical models

Populations and samples

Simple statistical models

Going beyond the data

Using statistical models to test research questions

What have I discovered about statistics?

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

The R Environment

What will this chapter tell me?

Before you start

Getting started

Using R

Getting data into R

Entering data with R Commander

Using other software to enter and edit data

Saving Data

Manipulating Data

What have I discovered about statistics?

R Packages Used in This Chapter

R Functions Used in This Chapter

Key terms that I've discovered

Smart Alex's Tasks

Further reading

Exploring Data with Graphs

What will this chapter tell me?

The art of presenting data

Packages used in this chapter

Introducing ggplot2

Graphing relationships: the scatterplot

Histograms: a good way to spot obvious problems

Boxplots (box-whisker diagrams)

Density plots

Graphing means

Themes and options

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Exploring Assumptions

What will this chapter tell me?

What are assumptions?

Assumptions of parametric data

Packages used in this chapter

The assumption of normality

Testing whether a distribution is normal

Testing for homogeneity of variance

Correcting problems in the data

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Correlation

What will this chapter tell me?

Looking at relationships

How do we measure relationships?

Data entry for correlation analysis

Bivariate correlation

Partial correlation

Comparing correlations

Calculating the effect size

How to report correlation coefficents

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Regression

What will this chapter tell me?

An Introduction to regression

Packages used in this chapter

General procedure for regression in R

Interpreting a simple regression

Multiple regression: the basics

How accurate is my regression model?

How to do multiple regression using R Commander and R

Testing the accuracy of your regression model

Robust regression: bootstrapping

How to report multiple regression

Categorical predictors and multiple regression

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Logistic Regression

What will this chapter tell me?

Background to logistic regression

What are the principles behind logistic regression?

Assumptions and things that can go wrong

Packages used in this chapter

Binary logistic regression: an example that will make you feel eel

How to report logistic regression

Testing assumptions: another example

Predicting several categories: multinomial logistic regression

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Comparing Two Means

What will this chapter tell me?

Packages used in this chapter

Looking at differences

The t-test

The independent t-test

The dependent t-test

Between groups or repeated measures?

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Comparing Several Means: ANOVA (GLM 1)

What will this chapter tell me?

The theory behind ANOVA

Assumptions of ANOVA

Planned contrasts

Post hoc procedures

One-way ANOVA using R

Calculating the effect size

Reporting results from one-way independent ANOVA

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Analysis of Covariance, ANCOVA (GLM 2)

What will this chapter tell me?

What is ANCOVA?

Assumptions and issues in ANCOVA

ANCOVA using R

Robust ANCOVA

Calculating the effect size

Reporting results

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Factorial ANOVA (GLM 3)

What will this chapter tell me?

Theory of factorial ANOVA (independant design)

Factorial ANOVA as regression

Two-Way ANOVA: Behind the scenes

Factorial ANOVA using R

Interpreting interaction graphs

Robust factorial ANOVA

Calculating effect sizes

Reporting the results of two-way ANOVA

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Repeated-Measures Designs (GLM 4)

What will this chapter tell me?

Introduction to repeated-measures designs

Theory of one-way repeated-measures ANOVA

One-way repeated measures designs using R

Effect sizes for repeated measures designs

Reporting one-way repeated measures designs

Factorisal repeated measures designs

Effect Sizes for factorial repeated measures designs

Reporting the results from factorial repeated measures designs

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Mixed Designs (GLM 5)

What will this chapter tell me?

Mixed designs

What do men and women look for in a partner?

Entering and exploring your data

Mixed ANOVA

Mixed designs as a GLM

Calculating effect sizes

Reporting the results of mixed ANOVA

Robust analysis for mixed designs

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Non-Parametric Tests

What will this chapter tell me?

When to use non-parametric tests

Packages used in this chapter

Comparing two independent conditions: the Wilcoxon rank-sum test

Comparing two related conditions: the Wilcoxon signed-rank test

Differences between several independent groups: the Kruskal-Wallis test

Differences between several related groups: Friedman's ANOVA

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Multivariate Analysis of Variance (MANOVA)

What will this chapter tell me?

When to use MANOVA

Introduction: similarities and differences to ANOVA

Theory of MANOVA

Practical issues when conducting MANOVA

MANOVA using R

Robust MANOVA

Reporting results from MANOVA

Following up MANOVA with discriminant analysis

Reporting results from discriminant analysis

Some final remarks

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Exploratory Factor Analysis

What will this chapter tell me?

When to use factor analysis

Factors

Research example

Running the analysis with R Commander

Running the analysis with R

Factor scores

How to report factor analysis

Reliability analysis

Reporting reliability analysis

What have I discovered about statistics?

R Packages Used in This Chapter

R Functions Used in This Chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Categorical Data

What will this chapter tell me?

Packages used in this chapter

Analysing categorical data

Theory of Analysing Categorical Data

Assumptions of the chi-square test

Doing the chi-square test using R

Several categorical variables: loglinear analysis

Assumptions in loglinear analysis

Loglinear analysis using R

Following up loglinear analysis

Effect sizes in loglinear analysis

Reporting the results of loglinear analysis

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Multilevel Linear Models

What will this chapter tell me?

Hierarchical data

Theory of multilevel linear models

The multilevel model

Some practical issues

Multilevel modelling on R

Growth models

How to report a multilevel model

What have I discovered about statistics?

R packages used in this chapter

R functions used in this chapter

Key terms that I've discovered

Smart Alex's tasks

Further reading

Interesting real research

Epilogue: Life After Discovering Statistics

Troubleshooting R

Glossary

Appendix

Table of the standard normal distribution

Critical Values of the t-Distribution

Critical Values of the F-Distribution

Critical Values of the chi-square Distribution

References

## Klappentext

Lecturers - request an e-inspection copy of this text or contact your local SAGE representative to discuss your course needs.

Watch Andy Field's introductory video to Discovering Statistics Using R

Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world.

The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you're doing, the emphasis is on *applying* what you learn to playful and real-world examples that should make the experience more fun than you might expect.

Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

Given this book's accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.