Contents
- I. Background
- 2.1 Introduction to psychological measurement
- 2.2 Scales of measurement
- 2.3 Assessing the reliability of a measurement
- 2.4 The “role” of variables: predictors and outcomes
- 2.5 Experimental and non-experimental research
- 2.6 Assessing the validity of a study
- 2.7 Confounds, artifacts and other threats to validity
- 2.8 Summary
- II. An Introduction to R
- 3.1 Installing R
- 3.2 Typing commands at the R console
- 3.3 Doing simple calculations with R
- 3.4 Storing a number as a variable
- 3.5 Using functions to do calculations
- 3.6 Letting RStudio help you with your commands
- 3.7 Storing many numbers as a vector
- 3.8 Storing text data
- 3.9 Storing “true or false” data
- 3.10 Indexing vectors
- 3.11 Quitting R
- 3.12 Summary
- III. Working with data
- 5.1 Measures of central tendency
- 5.2 Measures of variability
- 5.3 Skew and kurtosis
- 5.4 Getting an overall summary of a variable
- 5.5 Descriptive statistics separately for each group
- 5.6 Standard scores
- 5.7 Correlations
- 5.8 Handling missing values
- 5.9 Summary
- 5.10 Epilogue: Good descriptive statistics are descriptive!
- 7.1 Tabulating and cross-tabulating data
- 7.2 Transforming and recoding a variable
- 7.3 A few more mathematical functions and operations
- 7.4 Extracting a subset of a vector
- 7.5 Extracting a subset of a data frame
- 7.6 Sorting, flipping and merging data
- 7.7 Reshaping a data frame
- 7.8 Working with text
- 7.9 Reading unusual data files
- 7.10 Coercing data from one class to another
- 7.11 Other useful data structures
- 7.12 Miscellaneous topics
- 7.13 Summary
- IV. Statistical Theory
- 11.1 A menagerie of hypotheses
- 11.2 Two types of errors
- 11.3 Test statistics and sampling distributions
- 11.4 Making decisions
- 11.5 The \(p\) value of a test
- 11.6 Reporting the results of a hypothesis test
- 11.7 Running the hypothesis test in practice
- 11.8 Effect size, sample size and power
- 11.9 Some issues to consider
- 11.10 Summary
- V. Statistical tools
- 12.1 The \(\chi^2\) goodness-of-fit test
- 12.2 The \(\chi^2\) test of independence (or association)
- 12.3 The continuity correction
- 12.4 Effect size
- 12.5 Assumptions of the test(s)
- 12.6 The most typical way to do chi-square tests in R
- 12.7 The Fisher exact test
- 12.8 The McNemar test
- 12.9 What’s the difference between McNemar and independence?
- 12.10 Summary
- 13.1 The one-sample \(z\)-test
- 13.2 The one-sample \(t\)-test
- 13.3 The independent samples \(t\)-test (Student test)
- 13.4 The independent samples \(t\)-test (Welch test)
- 13.5 The paired-samples \(t\)-test
- 13.6 One sided tests
- 13.7 Using the t.test() function
- 13.8 Effect size
- 13.9 Checking the normality of a sample
- 13.10 Testing non-normal data with Wilcoxon tests
- 13.11 Summary
- 14.1 An illustrative data set
- 14.2 How ANOVA works
- 14.3 Running an ANOVA in R
- 14.4 Effect size
- 14.5 Multiple comparisons and post hoc tests
- 14.6 Assumptions of one-way ANOVA
- 14.7 Checking the homogeneity of variance assumption
- 14.8 Removing the homogeneity of variance assumption
- 14.9 Checking the normality assumption
- 14.10 Removing the normality assumption
- 14.11 On the relationship between ANOVA and the Student \(t\) test
- 14.12 Summary
- 15.1 What is a linear regression model?
- 15.2 Estimating a linear regression model
- 15.3 Multiple linear regression
- 15.4 Quantifying the fit of the regression model
- 15.5 Hypothesis tests for regression models
- 15.6 Testing the significance of a correlation
- 15.7 Regarding regression coefficients
- 15.8 Assumptions of regression
- 15.9 Model checking
- 15.10 Model selection
- 15.11 Summary
- 16.1 Factorial ANOVA 1: balanced designs, no interactions
- 16.2 Factorial ANOVA 2: balanced designs, interactions allowed
- 16.3 Effect size, estimated means, and confidence intervals
- 16.4 Assumption checking
- 16.5 The \(F\) test as a model comparison
- 16.6 ANOVA as a linear model
- 16.7 Different ways to specify contrasts
- 16.8 Post hoc tests
- 16.9 The method of planned comparisons
- 16.10 Factorial ANOVA 3: unbalanced designs
- 16.11 Summary
- VI. Endings, alternatives and prospects