Dennis Howitt and Duncan Cramer are with Loughborough University.

• Part 1 Introduction to SPSS
• 1 Brief introduction to statistics
• 2 Basics of SPSS data entry and statistical analysis
• Part 2 Descriptive statistics
• 3 Describing variables: Tables
• 4 Describing variables: Diagrams
• 5 Describing variables numerically: Averages, variation and spread
• 6 Shapes of distributions of scores
• 7 Relationships between two or more variables: Tables
• 8 Relationships between two or more variables: Diagrams
• 9 Correlation coefficients: Pearson's correlation and Spearman's rho
• 10 Regression: Prediction with precision
• Part 3 Significance testing and basic inferential tests
• 11 Related t-test: Comparing two samples of related/correlated/paired scores
• 12 Unrelated t-test: Comparing two groups of unrelated/uncorrelated/independent scores
• 13 Confidence intervals
• 14 Chi-square: Differences between unrelated samples of frequency data
• 15 McNemar's test: Differences between related samples of frequency data
• 16 Ranking tests for two groups: Non-parametric statistics
• 17 Ranking tests for three or more groups: Non-parametric statistics
• Part 4 Analysis of variance
• 18 Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA
• 19 Analysis of variance for one-way correlated scores or repeated measures
• 20 Two-way analysis of variance for unrelated/uncorrelated scores
• 21 Multiple comparison in ANOVA
• 22 Analysis of variance for two-way correlated scores or repeated measures
• 23 Two-way mixed analysis of variance (ANOVA)
• 24 Analysis of covariance (ANCOVA)
• 25 Multivariate analysis of variance (MANOVA)
• Part 5 More advanced statistics
• 26 Partial correlation
• 27 Factor analysis
• 28 Item reliability and inter-rater agreement
• 29 Stepwise multiple regression
• 30 Simultaneous or standard multiple regression
• 31 Simple mediational analysis
• 32 Hierarchical multiple regression
• 33 Log-linear analysis
• 34 Meta-analysis
• Part 6 Data handling procedures
• 35 Missing values
• 36 Recoding values
• 37 Computing a scale score with some values missing
• 38 Computing a new group variable from existing group variables
• 39 Selecting cases
• 40 Reading ASCII or text files into the Data Editor