CONTENTS

Preface
How to use this book
Acknowledgements
Dedication
Symbols used in this book
Some maths revision
1 Why is my evil lecturer forcing me to learn statistics?
1.1. What will this chapter tell me? ①
1.2. What the hell am I doing here? I don’t belong here ①
1.2.1. The research process ①
1.3. Initial observation: finding something that needs explaining ①
1.4. Generating theories and testing them ①
1.5. Collect data to test your theory ①
1.5.1. Variables ①
1.5.2. Measurement error ①
1.5.3. Validity and reliability ①
1.5.4. Correlational research methods ①
1.5.5. Experimental research methods ①
1.5.6. Randomization ①
1.6. Analysing data ①
1.6.1. Frequency distributions ①
1.6.2. The centre of a distribution ①
1.6.3. The dispersion in a distribution ①
1.6.4. Using a frequency distribution to go beyond the data ①
1.6.5. Fitting statistical models to the data ①
1.7. Reporting data ①
1.7.1. Dissemination of research ①
1.7.2. Knowing how to report data ①
1.7.3. Some initial guiding principles ①
1.8. Brian’s attempt to woo Jane ①
1.9. What next? ①
1.10. Key terms that I’ve discovered
1.11. Smart Alex’s tasks
1.12. Further reading
2 Everything you never wanted to know about statistics
2.1. What will this chapter tell me? ①
2.2. Building statistical models ①
2.3. Populations and samples ①
2.4. Statistical models ①
2.4.1. The mean as a statistical model ①
2.4.2. Assessing the fit of a model: sums of squares and variance revisited ①
2.4.3. Estimating parameters ①
2.5. Going beyond the data ①
2.5.1. The standard error ①
2.5.2. Confidence intervals ②
2.6. Using statistical models to test research questions ①
2.6.1. Null hypothesis significance testing ①
2.6.2. Problems with NHST ②
2.7. Modern approaches to theory testing ②
2.7.1. Effect sizes ②
2.7.2. Meta-analysis ②
2.8. Reporting statistical models ②
2.9. Brian’s attempt to woo Jane ①
2.10. What next? ①
2.11. Key terms that I’ve discovered
2.12. Smart Alex’s tasks
2.13. Further reading
3 The IBM SPSS Statistics environment
3.1. What will this chapter tell me? ①
3.2. Versions of IBM SPSS Statistics ①
3.3. Windows versus MacOS ①
3.4. Getting started ①
3.5. The data editor ①
3.5.1. Entering data into the data editor ①
3.5.2. The variable view ①
3.5.3. Missing values ①
3.6. Importing data ①
3.7. The SPSS viewer ①
3.8. Exporting SPSS output ①
3.9. The syntax editor ③
3.10. Saving files ①
3.11. Retrieving a file ①
3.12. Brian’s attempt to woo Jane ①
3.13. What next? ①
3.14. Key terms that I’ve discovered
3.15. Smart Alex’s tasks
3.16. Further reading
4 Exploring data with graphs
4.1. What will this chapter tell me? ①
4.2. The art of presenting data ①
4.2.1. What makes a good graph? ①
4.2.2. Lies, damned lies, and … erm … graphs ①
4.3. The SPSS chart builder ①
4.4. Histograms ①
4.5. Boxplots (box–whisker diagrams) ①
4.6. Graphing means: bar charts and error bars ①
4.6.1. Simple bar charts for independent means ①
4.6.2. Clustered bar charts for independent means ①
4.6.3. Simple bar charts for related means ①
4.6.4. Clustered bar charts for related means ①
4.6.5. Clustered bar charts for ‘mixed’ designs ①
4.7. Line charts ①
4.8. Graphing relationships: the scatterplot ①
4.8.1. Simple scatterplot ①
4.8.2. Grouped scatterplot ①
4.8.3. Simple and grouped 3-D scatterplots ①
4.8.4. Matrix scatterplot ①
4.8.5. Simple dot plot or density plot ①
4.8.6. Drop-line graph ①
4.9. Editing graphs ①
4.10. Brian’s attempt to woo Jane ①
4.11. What next? ①
4.12. Key terms that I’ve discovered
4.13. Smart Alex’s tasks
4.14. Further reading
5 The beast of bias
5.1. What will this chapter tell me? ①
5.2. What is bias? ①
5.2.1. Assumptions ①
5.2.2. Outliers ①
5.2.3. Additivity and linearity ①
5.2.4. Normally distributed something or other ①
5.2.5. Homoscedasticity/homogeneity of variance ②
5.2.6. Independence ②
5.3 Spotting bias ②
5.3.1. Spotting outliers ②
5.3.2. Spotting normality ①
5.3.3. Spotting linearity and heteroscedasticity/heterogeneity of variance ②
5.4. Reducing bias ②
5.4.1. Trimming the data ②
5.4.2. Winsorizing ①
5.4.3. Robust methods ③
5.4.4. Transforming data ②
5.5. Brian’s attempt to woo Jane ①
5.6. What next? ①
5.7. Key terms that I’ve discovered
5.8. Smart Alex’s tasks
5.9. Further reading
6 Non-parametric models
6.1. What will this chapter tell me? ①
6.2. When to use non-parametric tests ①
6.3. General procedure of non-parametric tests in SPSS ①
6.4. Comparing two independent conditions: the Wilcoxon rank-sum test and Mann–Whitney test ①
6.4.1. Theory ②
6.4.2. Inputting data and provisional analysis ①
6.4.3. The Mann–Whitney test using SPSS ①
6.4.4. Output from the Mann–Whitney test ①
6.4.5. Calculating an effect size ②
6.4.6. Writing the results ①
6.5. Comparing two related conditions: the Wilcoxon signed-rank test ①
6.5.1. Theory of the Wilcoxon signed-rank test ②
6.5.2. Running the analysis ①
6.5.3. Output for the ecstasy group ①
6.5.4. Output for the alcohol group ①
6.5.5. Calculating an effect size ②
6.5.6. Writing the results ①
6.6. Differences between several independent groups: the Kruskal–Wallis test ①
6.6.1. Theory of the Kruskal–Wallis test ②
6.6.2. Follow-up analysis ②
6.6.3. Inputting data and provisional analysis ①
6.6.4. Doing the Kruskal–Wallis test in SPSS ①
6.6.5. Output from the Kruskal–Wallis test ①
6.6.6. Testing for trends: the Jonckheere–Terpstra test ②
6.6.7. Calculating an effect size ②
6.6.8. Writing and interpreting the results ①
6.7. Differences between several related groups: Friedman’s ANOVA ①
6.7.1. Theory of Friedman’s ANOVA ②
6.7.2. Inputting data and provisional analysis ①
6.7.3. Doing Friedman’s ANOVA in SPSS ①
6.7.4. Output from Friedman’s ANOVA ①
6.7.5. Following-up Friedman’s ANOVA ②
6.7.6. Calculating an effect size ②
6.7.7. Writing and interpreting the results ①
6.8. Brian’s attempt to woo Jane ①
6.9. What next? ①
6.10. Key terms that I’ve discovered
6.11. Smart Alex’s tasks
6.12. Further reading
7 Correlation
7.1. What will this chapter tell me? ①
7.2. Modelling relationships ①
7.2.1. A detour into the murky world of covariance ①
7.2.2. Standardization and the correlation coefficient ①
7.2.3. The significance of the correlation coefficient ③
7.2.4. Confidence intervals for r ③
7.2.5. A word of warning about interpretation: causality ①
7.3. Data entry for correlation analysis using SPSS ①
7.4. Bivariate correlation ①
7.4.1. General procedure for running correlations in SPSS ①
7.4.2. Pearson’s correlation coefficient ①
7.4.3. Spearman’s correlation coefficient ①
7.4.4. Kendall’s tau (non-parametric) ①
7.4.5. Biserial and point-biserial correlations ③
7.5. Partial correlation ②
7.5.1. The theory behind part and partial correlation ③
7.5.2. Partial correlation in SPSS ③
7.5.3. Semi-partial (or part) correlations ②
7.6. Comparing correlations ③
7.6.1. Comparing independent rs ③
7.6.2. Comparing dependent rs ③
7.7. Calculating the effect size ①
7.8. How to report correlation coefficients ①
7.9. Brian’s attempt to woo Jane ①
7.10. What next? ①
7.11. Key terms that I’ve discovered
7.12. Smart Alex’s tasks
7.13. Further reading
8 Regression
8.1. What will this chapter tell me? ①
8.2. An introduction to regression ①
8.2.1. The simple linear model ①
8.2.2. The linear model with several predictors ②
8.2.3. Estimating the model ②
8.2.4. Assessing the goodness of fit, sums of squares, R and R2 ①
8.2.5. Assessing individual predictors ①
8.3. Bias in regression models? ②
8.3.1. Is the model biased by unusual cases? ②
8.3.2. Generalizing the model ②
8.3.3. Sample size in regression ③
8.4. Regression using SPSS: One Predictor ①
8.4.1. Regression: the general procedure ①
8.4.2. Running a simple regression using SPSS ①
8.4.3. Interpreting a simple regression ①
8.4.4. Using the model ①
8.5. Multiple regression ②
8.5.1. Methods of regression ②
8.5.2. Comparing models ②
8.5.3. Multicollinearity ②
8.6. Regression with several predictors using SPSS ②
8.6.1. Main options ②
8.6.2. Statistics ②
8.6.3. Regression plots ②
8.6.4. Saving regression diagnostics ②
8.6.5. Further options ②
8.6.6. Robust regression ②
8.7. Interpreting multiple regression ②
8.7.1. Descriptives ②
8.7.2. Summary of model ②
8.7.3. Model parameters ②
8.7.4. Excluded variables ②
8.7.5. Assessing multicollinearity ②
8.7.6. Bias in the model: casewise diagnostics ②
8.7.7. Bias in the model: assumptions ②
8.8. What if I violate an assumption? Robust regression ②
8.9. How to report multiple regression ②
8.10. Brian’s attempt to woo Jane ①
8.11. What next? ①
8.12. Key terms that I’ve discovered
8.13. Smart Alex’s tasks
8.14. Further reading
9 Comparing two means
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10 Moderation, mediation and more regression
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11 Comparing several means: ANOVA (GLM 1)
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12 Analysis of covariance, ANCOVA (GLM 2)
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13 Factorial ANOVA (GLM 3)
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14 Repeated-measures designs (GLM 4)
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