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Discovering Statistics Using R

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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
Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more .

Rezension
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

Portrait

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.

Zitat
"This work should be in the library of every institution where statistics is taught. It contains much more content than what is required for a beginning or advanced undergraduate course, but instructors for such courses would do well to consider this book; it is priced comparably to books which contain only basic material, and students who are fascinated by the subject may find the additional material a real bonus. The book would also be very good for self-study. Overall, an excellent resource."
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  • Artikelbild-0
  • 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

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Beschreibung

Produktdetails

Einband Taschenbuch
Seitenzahl 992
Erscheinungsdatum 01.04.2012
Sprache Englisch
ISBN 978-1-4462-0046-9
Verlag Sage Publications
Maße (L/B/H) 26.4/19.5/5.3 cm
Gewicht 2313 g
Abbildungen schwarzweisse Abbildungen, Tabellen, Diagramme
Buch (Taschenbuch, Englisch)
Buch (Taschenbuch, Englisch)
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Eines der besten Statistikbücher für R, die es gibt
von einer Kundin/einem Kunden aus Potsdam am 22.11.2016

Ich verwende dieses Buch, um Studierenden an einer Universität Statistik beizubringen. Es macht wirklich viel Spaß mit diesem Buch zu arbeiten und findet auf einem guten Niveau statt. Wenn meine Kollegen mit Statistikfragen zu mir kommen, schaue ich meist direkt ins Buch und zeige ihnen den Text. Ich kann es jedem empfehlen, der... Ich verwende dieses Buch, um Studierenden an einer Universität Statistik beizubringen. Es macht wirklich viel Spaß mit diesem Buch zu arbeiten und findet auf einem guten Niveau statt. Wenn meine Kollegen mit Statistikfragen zu mir kommen, schaue ich meist direkt ins Buch und zeige ihnen den Text. Ich kann es jedem empfehlen, der einen Rumdumblick von statistischem Wissen benötigt - das steht alles hier drin.