Part One Descriptive Statistics.
- Chapter 1 Why you need statistics: types of data
- Chapter 2 Describing variables: Tables and diagrams
- Chapter 3 Describing variables numerically: averages, variation and spread
- Chapter 4 Shapes of distributions of scores
- Chapter 5 - Standard deviation, z-scores and standard error: the standard unit of measurement in statistics
- Chapter 6 Relationships between two or more variables: diagrams and tables
- Chapter 7 Correlation coefficients: Pearson correlation and Spearmans rho
- Chapter 8 Regression and standard error
Part Two: Comparing Two or More Variables and the Analysis of Variance.
- Chapter 9 - The analysis of a questionnaire/survey project
- Chapter 10 The related t-test: Comparing two samples of correlated/related scores
- Chapter 11 the unrelated t-test: comparing two samples of unrelated/uncorrelated scores
- Chapter 12 Chi-square: Differences between samples of frequency data
Part Three: Introduction to Analysis of Variance
- Chapter 13 Analysis of variance (ANOVA): introduction to one-way unrelated or uncorrelated ANOVA
- Chapter 14 Two way analysis of variance for unrelated/uncorrelated scores: two studies for the price of one?
- Chapter 15 Analysis of covariance (ANCOVA): controlling for additional variables
- Chapter 16 Multivariate analysis of variance (MANOVA)
Part Four: More advanced correlational statistics and techniques
- Chapter 17 - Partial correlation: spurious correlation, third or confounding variables (control variables), suppressor variables
- Chapter 18 Factor analysis: simplifying complex data
- Chapter 19 Multiple regression and multiple correlation
- Chapter 20 Multinomial logistic regression: Distinguishing between several different categories or groups
- Chapter 21 - Bionomial logistic regression
- Chapter 22 - Log-linear methods: The analysis of complex contingency tables