Image from Syndetics
Discovering statistics using R / Andy Field, Jeremy Miles and Zoe Field.
By: Field, Andy P [author]
.
Contributor(s): Miles, Jeremy [author] | Field, Zoë [author].
Material type: 
BookPublisher: London : Sage Publications, 2012Copyright date: ©2012Description: 957 pages : illustrations ; 25 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781446200469 (paperback).Subject(s): Statistics -- Data processing | R (Computer program language)DDC classification: 519.5
Contents:
Why is my evil lecturer forcing me to learn statistics -- Everything you ever wanted to know about statistics (well, sort of) -- The R environment -- Exploring data with graphs -- Exploring assumptions -- Correlation -- Regression -- Logistic regression -- Comparing two means -- Comparing several means : ANOVA -- Analysis of covariance, ANCOVA -- Factorial ANOVA -- Repeated measures designs -- Mixed designs -- Non-parametric tests -- Multivariate analysis of variance -- Exploratory factor analysis -- Categorical data -- Multilevel linear models.
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
| General Lending | MTU Bishopstown Library Lending | 519.5 (Browse shelf(Opens below)) | Checked out | 16/04/2024 | 00197754 |
Total holds: 0
Enhanced descriptions from Syndetics:
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.
includes bibliographical references and index.
Why is my evil lecturer forcing me to learn statistics -- Everything you ever wanted to know about statistics (well, sort of) -- The R environment -- Exploring data with graphs -- Exploring assumptions -- Correlation -- Regression -- Logistic regression -- Comparing two means -- Comparing several means : ANOVA -- Analysis of covariance, ANCOVA -- Factorial ANOVA -- Repeated measures designs -- Mixed designs -- Non-parametric tests -- Multivariate analysis of variance -- Exploratory factor analysis -- Categorical data -- Multilevel linear models.
Table of contents provided by Syndetics
- 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 Stats Quiz
- 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 (between-groups)
- 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