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    Discovering statistics using R / Andy Field, Jeremy Miles, Zoë Field.

    • Title:Discovering statistics using R / Andy Field, Jeremy Miles, Zoë Field.
    •    
    • Author/Creator:Field, Andy P.
    • Other Contributors/Collections:Miles, Jeremy, 1968-
      Field, Zoë.
    • Published/Created:London ; Thousand Oaks, Calif. : Sage, 2012.
    • Holdings

       
    • Library of Congress Subjects:Social sciences--Statistical methods--Computer programs.
      R (Computer program language)
      Statistics--Computer programs.
    • Description:xxxiv, 957 pages : illustrations ; 27 cm
    • Summary:"Hot on the heels of the award-winning and best selling Discovering Statistics Using SPSS Third Edition, Andy Field has teamed up with Jeremy Miles (co-author of Discovering Statistics Using SAS) to write Discovering Statistics Using R. Keeping the uniquely humorous and self-depreciating 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 the freeware R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioral sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next the importance of exploring and graphing data will be discovered, before moving onto statistical tests that are the foundations of the rest of the book (for e.g. correlation and regression). Readers will then stride confidently into intermediate level analyses such as ANOVA, before ending their journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help the reader gain the necessary conceptual understanding of what they're doing, the emphasis is on applying what's learned to playful and real-world examples that should make the experience more fun than expected."--Publisher's website.
    • Notes:Includes bibliographical references (pages 941-947) and index.
    • ISBN:9781446200469
      1446200469
      1446200450
      9781446200452 (hbk.)
    • Contents:Machine generated contents note: 1. Why is my evil lecturer forcing me to learn statistics?
      1.1. What will this chapter tell me?
      1.2. What the hell am I doing here? I don't belong here
      1.3. Initial observation: finding something that needs explaining
      1.4. Generating theories and testing them
      1.5. Data collection 1: what to measure
      1.5.1. Variables
      1.5.2. Measurement error
      1.5.3. Validity and reliability
      1.6. Data collection 2: how to measure
      1.6.1. Correlational research methods
      1.6.2. Experimental research methods
      1.6.3. Randomization
      1.7. Analysing data
      1.7.1. Frequency distributions
      1.7.2. centre of a distribution
      1.7.3. dispersion in a distribution
      1.7.4. Using a frequency distribution to go beyond the data
      1.7.5. Fitting statistical models to the data
      What have I discovered about statistics?
      Key terms that I've discovered
      Smart Alex's tasks
      Further reading
      Interesting real research
      2. Everything you ever wanted to know about statistics (well, sort of)
      2.1. What will this chapter tell me?
      2.2. Building statistical models
      2.3. Populations and samples
      2.4. Simple statistical models
      2.4.1. mean: a very simple statistical model
      2.4.2. Assessing the fit of the mean: sums of squares, variance and standard deviations
      2.4.3. Expressing the mean as a model
      2.5. Going beyond the data
      2.5.1. standard error
      2.5.2. Confidence intervals
      2.6. Using statistical models to test research questions
      2.6.1. Test statistics
      2.6.2. One- and two-tailed tests
      2.6.3. Type I and Type II errors
      2.6.4. Effect sizes
      2.6.5. Statistical power
      What have I discovered about statistics?
      Key terms that I've discovered
      Smart Alex's tasks
      Further reading
      Interesting real research
      3. R environment
      3.1. What will this chapter tell me?
      3.2. Before you start
      3.2.1. R-chitecture
      3.2.2. Pros and cons of R
      3.2.3. Downloading and installing R
      3.2.4. Versions of R
      3.3. Getting started
      3.3.1. main windows in R
      3.3.2. Menus in R
      3.4. Using R
      3.4.1. Commands, objects and functions
      3.4.2. Using scripts
      3.4.3. R workspace
      3.4.4. Setting a working directory
      3.4.5. Installing packages
      3.4.6. Getting help
      3.5. Getting data into R
      3.5.1. Creating variables
      3.5.2. Creating dataframes
      3.5.3. Calculating new variables from exisiting ones
      3.5.4. Organizing your data
      3.5.5. Missing values
      3.6. Entering data with R Commander
      3.6.1. Creating variables and entering data with R Commander
      3.6.2. Creating coding variables with R Commander
      3.7. Using other software to enter and edit data
      3.7.1. Importing data
      3.7.2. Importing SPSS data files directly
      3.7.3. Importing data with R Commander
      3.7.4. Things that can go wrong
      3.8. Saving data
      3.9. Manipulating data
      3.9.1. Selecting parts of a dataframe
      3.9.2. Selecting data with the subset() function
      3.9.3. Dataframes and matrices
      3.9.4. Reshaping 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
      4. Exploring data with graphs
      4.1. What will this chapter tell me?
      4.2. art of presenting data
      4.2.1. Why do we need graphs
      4.2.2. What makes a good graph?
      4.2.3. Lies, damned lies, and ... erm ... graphs
      4.3. Packages used in this chapter
      4.4. Introducing ggplot2
      4.4.1. anatomy of a plot
      4.3.2. Geometric objects (geoms)
      4.4.3. Aesthetics
      4.4.4. anatomy of the ggplot() function
      4.4.5. Stats and geoms
      4.4.6. Avoiding overplotting
      4.4.7. Saving graphs
      4.4.8. Putting it all together: a quick tutorial
      4.5. Graphing relationships: the scatterplot
      4.5.1. Simple scatterplot
      4.5.2. Adding a funky line
      4.5.3. Grouped scatterplot
      4.6. Histograms: a good way to spot obvious problems
      4.7. Boxplots (box
      -whisker diagrams)
      4.8. Density plots
      4.9. Graphing means
      4.9.1. Bar charts and error bars
      4.9.2. Line graphs
      4.10. 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
      5. Exploring assumptions
      5.1. What will this chapter tell me?
      5.2. What are assumptions?
      5.3. Assumptions of parametric data
      5.4. Packages used in this chapter
      5.5. assumption of normality
      5.5.1. Oh no, it's that pesky frequency distribution again: checking normality visually
      5.5.2. Quantifying normality with numbers
      5.5.3. Exploring groups of data
      5.6. Testing whether a distribution is normal
      5.6.1. Doing the Shapiro
      -Wilk test in R
      5.6.2. Reporting the Shapiro
      -Wilk test
      5.7. Testing for homogeneity of variance
      5.7.1. Levene's test
      5.7.2. Reporting Levene's test
      5.7.3. Hartley's Fmax: the variance ratio
      5.8. Correcting problems in the data
      5.8.1. Dealing with outliers
      5.8.2. Dealing with non-normality and unequal variances
      5.8.3. Transforming the data using R
      5.8.4. When it all goes horribly wrong
      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
      6. Correlation
      6.1. What will this chapter tell me?
      6.2. Looking at relationships
      6.3. How do we measure relationships?
      6.3.1. detour into the murky world of covariance
      6.3.2. Standardization and the correlation coefficient
      6.3.3. significance of the correlation coefficient
      6.3.4. Confidence intervals for r
      6.3.5. word of warning about interpretation: causality
      6.4. Data entry for correlation analysis
      6.5. Bivariate correlation
      6.5.1. Packages for correlation analysis in R
      6.5.2. General procedure for correlations using R Commander
      6.5.3. General procedure for correlations using R
      6.5.4. Pearson's correlation coefficient
      6.5.5. Spearman's correlation coefficient
      6.5.6. Kendall's tau (non-parametric)
      6.5.7. Bootstrapping correlations
      6.5.8. Biserial and point-biserial correlations
      6.6. Partial correlation
      6.6.1. theory behind part and partial correlation
      6.6.2. Partial correlation using R
      6.6.3. Semi-partial (or part) correlations
      6.7. Comparing correlations
      6.7.1. Comparing independent rs
      6.7.2. Comparing dependent rs
      6.8. Calculating the effect size
      6.9. How to report correlation coefficents
      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
      7. Regression
      7.1. What will this chapter tell me?
      7.2. introduction to regression
      7.2.1. Some important information about straight lines
      7.2.2. method of least squares
      7.2.3. Assessing the goodness of fit: sums of squares, R and R2
      7.2.4. Assessing individual predictors
      7.3. Packages used in this chapter
      7.4. General procedure for regression in R
      7.4.1. Doing simple regression using R Commander
      7.4.2. Regression in R
      7.5. Interpreting a simple regression
      7.5.1. Overall fit of the object model
      7.5.2. Model parameters
      7.5.3. Using the model
      7.6. Multiple regression: the basics
      7.6.1. example of a multiple regression model
      7.6.2. Sums of squares, R and R2
      7.6.3. Parsimony-adjusted measures of fit
      7.6.4. Methods of regression
      7.7. How accurate is my regression model?
      7.7.1. Assessing the regression model I: diagnostics
      7.7.2. Assessing the regression model II: generalization
      7.8. How to do multiple regression using R Commander and R
      7.8.1. Some things to think about before the analysis
      7.8.2. Multiple regression: running the basic model
      7.8.3. Interpreting the basic multiple regression
      7.8.4. Comparing models
      7.9. Testing the accuracy of your regression model
      7.9.1. Diagnostic tests using R Commander
      7.9.2. Outliers and influential cases
      7.9.3. Assessing the assumption of independence
      7.9.4. Assessing the assumption of no multicollinearity
      7.9.5. Checking assumptions about the residuals
      7.9.6. What if I violate an assumption?
      7.10. Robust regression: bootstrapping
      7.11. How to report multiple regression
      7.12. Categorical predictors and multiple regression
      7.12.1. Dummy coding
      7.12.2. Regression with dummy variables
      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
      8. Logistic regression
      8.1. What will this chapter tell me?
      8.2. Background to logistic regression
      8.3. What are the principles behind logistic regression?
      8.3.1. Assessing the model: the log-likelihood statistic
      8.3.2. Assessing the model: the deviance statistic
      8.3.3. Assessing the model: R and R2
      8.3.4. Assessing the model: information criteria
      8.3.5. Assessing the contribution of predictors: the z-statistic
      8.3.6. odds ratio
      Contents note continued: 8.3.7. Methods of logistic regression
      8.4. Assumptions and things that can go wrong
      8.4.1. Assumptions
      8.4.2. Incomplete information from the predictors
      8.4.3. Complete separation
      8.5. Packages used in this chapter
      8.6. Binary logistic regression: an example that will make you feel eel
      8.6.1. Preparing the data
      8.6.2. main logistic regression analysis
      8.6.3. Basic logistic regression analysis using R
      8.6.4. Interpreting a basic logistic regression
      8.6.5. Model 1: Intervention only
      8.6.6. Model 2: Intervention and Duration as predictors
      8.6.7. Casewise diagnostics in logistic regression
      8.6.8. Calculating the effect size
      8.7. How to report logistic regression
      8.8. Testing assumptions: another example
      8.8.1. Testing for multicollinearity
      8.8.2. Testing for linearity of the logit
      8.9. Predicting several categories: multinomial logistic regression
      8.9.1. Running multinomial logistic regression in R
      8.9.2. Interpreting the multinomial logistic regression output
      8.9.3. Reporting the 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
      9. Comparing two means
      9.1. What will this chapter tell me?
      9.2. Packages used in this chapter
      9.3. Looking at differences
      9.3.1. problem with error bar graphs of repeated-measures designs
      9.3.2. Step 1: calculate the mean for each participant
      9.3.3. Step 2: calculate the grand mean
      9.3.4. Step 3: calculate the adjustment factor
      9.3.5. Step 4: create adjusted values for each variable
      9.4. t-test
      9.4.1. Rationale for the t-test
      9.4.2. t-test as a general linear model
      9.4.3. Assumptions of the t-test
      9.5. independent t-test
      9.5.1. independent t-test equation explained
      9.5.2. Doing the independent t-test
      9.6. dependent t-test
      9.6.1. Sampling distributions and the standard error
      9.6.2. dependent t-test equation explained
      9.6.3. Dependent t-tests using R
      9.7. 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
      10. Comparing several means: ANOVA (GLM 1)
      10.1. What will this chapter tell me?
      10.2. theory behind ANOVA
      10.2.1. Inflated error rates
      10.2.2. Interpreting F
      10.2.3. ANOVA as regression
      10.2.4. Logic of the F-ratio
      10.2.5. Total sum of squares (SST)
      10.2.6. Model sum of squares (SSM)
      10.2.7. Residual sum of squares (SSR)
      10.2.8. Mean squares
      10.2.9. F-ratio
      10.3. Assumptions of ANOVA
      10.3.1. Homogeneity of variance
      10.3.2. Is ANOVA robust?
      10.4. Planned contrasts
      10.4.1. Choosing which contrasts to do
      10.4.2. Defining contrasts using weights
      10.4.3. Non-orthogonal comparisons
      10.4.4. Standard contrasts
      10.4.5. Polynomial contrasts: trend analysis
      10.5. Post hoc procedures
      10.5.1. Post hoc procedures and Type I (α) and Type II error rates
      10.5.2. Post hoc procedures and violations of test assumptions
      10.5.3. Summary of post hoc procedures
      10.6. One-way ANOVA using R
      10.6.1. Packages for one-way ANOVA in R
      10.6.2. General procedure for one-way ANOVA
      10.6.3. Entering data
      10.6.4. One-way ANOVA using R Commander
      10.6.5. Exploring the data
      10.6.6. main analysis
      10.6.7. Planned contrasts using R
      10.6.8. Post hoc tests using R
      10.7. Calculating the effect size
      10.8. 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
      11. Analysis of covariance, ANCOVA (GLM 2)
      11.1. What will this chapter tell me?
      11.2. What is ANCOVA?
      11.3. Assumptions and issues in ANCOVA
      11.3.1. Independence of the covariate and treatment effect
      11.3.2. Homogeneity of regression slopes
      11.4. ANCOVA using R
      11.4.1. Packages for ANCOVA in R
      11.4.2. General procedure for ANCOVA
      11.4.3. Entering data
      11.4.4. ANCOVA using R Commander
      11.4.5. Exploring the data
      11.4.6. Are the predictor variable and covariate independent?
      11.4.7. Fitting an ANCOVA model
      11.4.8. Interpreting the main ANCOVA model
      11.4.9. Planned contrasts in ANCOVA
      11.4.10. Interpreting the covariate
      11.4.11. Post hoc tests in ANCOVA
      11.4.12. Plots in ANCOVA
      11.4.13. Some final remarks
      11.4.14. Testing for homogeneity of regression slopes
      11.5. Robust ANCOVA
      11.6. Calculating the effect size
      11.7. 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
      12. Factorial ANOVA (GLM 3)
      12.1. What will this chapter tell me?
      12.2. Theory of factorial ANOVA (independent design)
      12.2.1. Factorial designs
      12.3. Factorial ANOVA as regression
      12.3.1. example with two independent variables
      12.3.2. Extending the regression model
      12.4. Two-way ANOVA: behind the scenes
      12.4.1. Total sums of squares (SST)
      12.4.2. model sum of squares (SSM)
      12.4.3. residual sum of squares (SSR)
      12.4.4. F-ratios
      12.5. Factorial ANOVA using R
      12.5.1. Packages for factorial ANOVA in R
      12.5.2. General procedure for factorial ANOVA
      12.5.3. Factorial ANOVA using R Commander
      12.5.4. Entering the data
      12.5.5. Exploring the data
      12.5.6. Choosing contrasts
      12.5.7. Fitting a factorial ANOVA model
      12.5.8. Interpreting factorial ANOVA
      12.5.9. Interpreting contrasts
      12.5.10. Simple effects analysis
      12.5.11. Post hoc analysis
      12.5.12. Overall conclusions
      12.5.13. Plots in factorial ANOVA
      12.6. Interpreting interaction graphs
      12.7. Robust factorial ANOVA
      12.8. Calculating effect sizes
      12.9. 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
      13. Repeated
      -measures designs (GLM 4)
      13.1. What will this chapter tell me?
      13.2. Introduction to repeated-measures designs
      13.2.1. assumption of sphericity
      13.2.2. How is sphericity measured?
      13.2.3. Assessing the severity of departures from sphericity
      13.2.4. What is the effect of violating the assumption of sphericity?
      13.2.5. What do you do if you violate sphericity?
      13.3. Theory of one-way repeated-measures ANOVA
      13.3.1. total sum of squares (SST)
      13.3.2. within-participant sum of squares (SSW)
      13.3.3. model sum of squares (SSM)
      13.3.4. residual sum of squares (SSR)
      13.3.5. mean squares
      13.3.6. F-ratio
      13.3.7. between-participant sum of squares
      13.4. One-way repeated-measures designs using R
      13.4.1. Packages for repeated measures designs in R
      13.4.2. General procedure for repeated-measures designs
      13.4.3. Repeated-measures ANOVA using R Commander
      13.4.4. Entering the data
      13.4.5. Exploring the data
      13.4.6. Choosing contrasts
      13.4.7. Analysing repeated measures: two ways to skin a .dat
      13.4.8. Robust one-way repeated-measures ANOVA
      13.5. Effect sizes for repeated-measures designs
      13.6. Reporting one-way repeated-measures designs
      13.7. Factorial repeated-measures designs
      13.7.1. Entering the data
      13.7.2. Exploring the data
      13.7.3. Setting contrasts
      13.7.4. Factorial repeated-measures ANOVA
      13.7.5. Factorial repeated-measures designs as a GLM
      13.7.6. Robust factorial repeated-measures ANOVA
      13.8. Effect sizes for factorial repeated-measures designs
      13.9. 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
      14. Mixed designs (GLM 5)
      14.1. What will this chapter tell me?
      14.2. Mixed designs
      14.3. What do men and women look for in a partner?
      14.4. Entering and exploring your data
      14.4.1. Packages for mixed designs in R
      14.4.2. General procedure for mixed designs
      14.4.3. Entering the data
      14.4.4. Exploring the data
      14.5. Mixed ANOVA
      14.6. Mixed designs as a GLM
      14.6.1. Setting contrasts
      14.6.2. Building the model
      14.6.3. main effect of gender
      14.6.4. main effect of looks
      14.6.5. main effect of personality
      14.6.6. interaction between gender and looks
      14.6.7. interaction between gender and personality
      14.6.8. interaction between looks and personality
      14.6.9. interaction between looks, personality and gender
      14.6.10. Conclusions
      14.7. Calculating effect sizes
      14.8. Reporting the results of mixed ANOVA
      14.9. Robust analysis for mixed designs
      What have I discovered about statistics?
      R packages used in this chapter
      Contents note continued: R functions used in this chapter
      Key terms that I've discovered
      Smart Alex's tasks
      Further reading
      Interesting real research
      15. Non-parametric tests
      15.1. What will this chapter tell me?
      15.2. When to use non-parametric tests
      15.3. Packages used in this chapter
      15.4. Comparing two independent conditions: the Wilcoxon rank-sum test
      15.4.1. Theory of the Wilcoxon rank-sum test
      15.4.2. Inputting data and provisional analysis
      15.4.3. Running the analysis using R Commander
      15.4.4. Running the analysis using R
      15.4.5. Output from the Wilcoxon rank-sum test
      15.4.6. Calculating an effect size
      15.4.7. Writing the results
      15.5. Comparing two related conditions: the Wilcoxon signed-rank test
      15.5.1. Theory of the Wilcoxon signed-rank test
      15.5.2. Running the analysis with R Commander
      15.5.3. Running the analysis using R
      15.5.4. Wilcoxon signed-rank test output
      15.5.5. Calculating an effect size
      15.5.6. Writing the results
      15.6. Differences between several independent groups: the Kruskal
      -Wallis test
      15.6.1. Theory of the Kruskal
      -Wallis test
      15.6.2. Inputting data and provisional analysis
      15.6.3. Doing the Kruskal
      -Wallis test using R Commander
      15.6.4. Doing the Kruskal
      -Wallis test using R
      15.6.5. Output from the Kruskal
      -Wallis test
      15.6.6. Post hoc tests for the Kruskal
      -Wallis test
      15.6.7. Testing for trends: the Jonckheere
      -Terpstra test
      15.6.8. Calculating an effect size
      15.6.9. Writing and interpreting the results
      15.7. Differences between several related groups: Friedman's ANOVA
      15.7.1. Theory of Friedman's ANOVA
      15.7.2. Inputting data and provisional analysis
      15.7.3. Doing Friedman's ANOVA in R Commander
      15.7.4. Friedman's ANOVA using R
      15.7.5. Output from Friedman's ANOVA
      15.7.6. Post hoc tests for Friedman's ANOVA
      15.7.7. Calculating an effect size
      15.7.8. Writing and interpreting the 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
      16. Multivariate analysis of variance (MANOVA)
      16.1. What will this chapter tell me?
      16.2. When to use MANOVA
      16.3. Introduction: similarities to and differences from ANOVA
      16.3.1. Words of warning
      16.3.2. example for this chapter
      16.4. Theory of MANOVA
      16.4.1. Introduction to matrices
      16.4.2. Some important matrices and their functions
      16.4.3. Calculating MANOVA by hand: a worked example
      16.4.4. Principle of the MANOVA test statistic
      16.5. Practical issues when conducting MANOVA
      16.5.1. Assumptions and how to check them
      16.5.2. Choosing a test statistic
      16.5.3. Follow-up analysis
      16.6. MANOVA using R
      16.6.1. Packages for factorial ANOVA in R
      16.6.2. General procedure for MANOVA
      16.6.3. MANOVA using R Commander
      16.6.4. Entering the data
      16.6.5. Exploring the data
      16.6.6. Setting contrasts
      16.6.7. MANOVA model
      16.6.8. Follow-up analysis: univariate test statistics
      16.6.9. Contrasts
      16.7. Robust MANOVA
      16.8. Reporting results from MANOVA
      16.9. Following up MANOVA with discriminant analysis
      16.10. Reporting results from discriminant analysis
      16.11. Some final remarks
      16.11.1. final interpretation
      16.11.2. Univariate ANOVA or discriminant 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
      17. Exploratory factor analysis
      17.1. What will this chapter tell me?
      17.2. When to use factor analysis
      17.3. Factors
      17.3.1. Graphical representation of factors
      17.3.2. Mathematical representation of factors
      17.3.3. Factor scores
      17.3.4. Choosing a method
      17.3.5. Communality
      17.3.6. Factor analysis vs. principal components analysis
      17.3.7. Theory behind principal components analysis
      17.3.8. Factor extraction: eigenvalues and the scree plot
      17.3.9. Improving interpretation: factor rotation
      17.4. Research example
      17.4.1. Sample size
      17.4.2. Correlations between variables
      17.4.3. distribution of data
      17.5. Running the analysis with R Commander
      17.6. Running the analysis with R
      17.6.1. Packages used in this chapter
      17.6.2. Initial preparation and analysis
      17.6.3. Factor extraction using R
      17.6.4. Rotation
      17.6.5. Factor scores
      17.6.6. Summary
      17.7. How to report factor analysis
      17.8. Reliability analysis
      17.8.1. Measures of reliability
      17.8.2. Interpreting Cronbach's α (some cautionary tales ...)
      17.8.3. Reliability analysis with R Commander
      17.8.4. Reliability analysis using R
      17.8.5. Interpreting the output
      17.9. 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
      18. Categorical data
      18.1. What will this chapter tell me?
      18.2. Packages used in this chapter
      18.3. Analysing categorical data
      18.4. Theory of analysing categorical data
      18.4.1. Pearson's chi-square test
      18.4.2. Fisher's exact test
      18.4.3. likelihood ratio
      18.4.4. Yates's correction
      18.5. Assumptions of the chi-square test
      18.6. Doing the chi-square test using R
      18.6.1. Entering data: raw scores
      18.6.2. Entering data: the contingency table
      18.6.3. Running the analysis with R Commander
      18.6.4. Running the analysis using R
      18.6.5. Output from the Cross Table() function
      18.6.6. Breaking down a significant chi-square test with standardized residuals
      18.6.7. Calculating an effect size
      18.6.8. Reporting the results of chi-square
      18.7. Several categorical variables: loglinear analysis
      18.7.1. Chi-square as regression
      18.7.2. Loglinear analysis
      18.8. Assumptions in loglinear analysis
      18.9. Loglinear analysis using R
      18.9.1. Initial considerations
      18.9.2. Loglinear analysis as a chi-square test
      18.9.3. Output from loglinear analysis as a chi-square test
      18.9.4. Loglinear analysis
      18.10. Following up loglinear analysis
      18.11. Effect sizes in loglinear analysis
      18.12. 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
      19. Multilevel linear models
      19.1. What will this chapter tell me?
      19.2. Hierarchical data
      19.2.1. intraclass correlation
      19.2.2. Benefits of multilevel models
      19.3. Theory of multilevel linear models
      19.3.1. example
      19.3.2. Fixed and random coefficients
      19.4. multilevel model
      19.4.1. Assessing the fit and comparing multilevel models
      19.4.2. Types of covariance structures
      19.5. Some practical issues
      19.5.1. Assumptions
      19.5.2. Sample size and power
      19.5.3. Centring variables
      19.6. Multilevel modelling in R
      19.6.1. Packages for multilevel modelling in R
      19.6.2. Entering the data
      19.6.3. Picturing the data
      19.6.4. Ignoring the data structure: ANOVA
      19.6.5. Ignoring the data structure: ANCOVA
      19.6.6. Assessing the need for a multilevel model
      19.6.7. Adding in fixed effects
      19.6.8. Introducing random slopes
      19.6.9. Adding an interaction term to the model
      19.7. Growth models
      19.7.1. Growth curves (polynomials)
      19.7.2. example: the honeymoon period
      19.7.3. Restructuring the data
      19.7.4. Setting up the basic model
      19.7.5. Adding in time as a fixed effect
      19.7.6. Introducing random slopes
      19.7.7. Modelling the covariance structure
      19.7.8. Comparing models
      19.7.9. Adding higher-order polynomials
      19.7.10. Further analysis
      19.8. 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
      A.1. Table of the standard normal distribution
      A.2. Critical values of the t-distribution
      A.3. Critical values of the F-distribution
      A.4. Critical values of the chi-square distribution
      References
      Index
      Functions in R.
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