Understanding Statistics in Psychology with SPSS

Dennis Howitt and Duncan Cramer are based at Loughborough University.

Chapter 1 Why statistics?
Chapter 2 Some basics: Variability and measurement
Chapter 3 Describing variables: Tables and diagrams
Chapter 4 Describing variables numerically: Averages, variation and spread
Chapter 5 Shapes of distributions of scores
Chapter 6 Standard deviation and z-scores: Standard unit of measurement in statistics
Chapter 7 Relationships between two or more variables: Diagrams and tables
Chapter 8 Correlation coefficients: Pearson’s correlation and Spearman’s rho
Chapter 9 Regression: Prediction with precision
Chapter 10 Samples from populations
Chapter 11 Statistical significance for the correlation coefficient: A practical introduction to statistical inference
Chapter 12 Standard error: Standard deviation of the means of samples
Chapter 13 Related t-test: Comparing two samples of related/correlated/paired scores
Chapter 14 Unrelated t-test: Comparing two samples of unrelated/uncorrelated/ independent scores
Chapter 15 What you need to write about your statistical analysis
Chapter 16 Confidence intervals
Chapter 17 Effect size in statistical analysis: Do my findings matter?
Chapter 18 Chi-square: Differences between samples of frequency data
Chapter 19 Probability
Chapter 20 One-tailed versus two-tailed significance testing
Chapter 21 Ranking tests: Nonparametric statistics
Chapter 22 Variance ratio test: F-ratio to compare two variances
Chapter 23 Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA
Chapter 24 ANOVA for correlated scores or repeated measures
Chapter 25 Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one?
Chapter 26 Multiple comparisons with in ANOVA: A priori and post hoc tests
Chapter 27 Mixed-design ANOVA: Related and unrelated variables together
Chapter 28 Analysis of covariance (ANCOVA): Controlling for additional variables
Chapter 29 Multivariate analysis of variance (MANOVA)
Chapter 30 Discriminant (function) analysis – especially in MANOVA
Chapter 31 Statistics and analysis of experiments
Chapter 32 Partial correlation: Spurious correlation, third or confounding variables, suppressor variables
Chapter 33 Factor analysis: Simplifying complex data
Chapter 34 Multiple regression and multiple correlation
Chapter 35 Path analysis
Chapter 36 Meta-analysis: Combining and exploring statistical findings from previous research
Chapter 37 Reliability in scales and measurement: Consistency and agreement
Chapter 38 Influence of moderator variables on relationships between two variables
Chapter 39 Statistical power analysis: Getting the sample size right
Chapter 40 Log-linear methods: Analysis of complex contingency tables
Chapter 41 Multinomial logistic regression: Distinguishing between several different categories or groups
Chapter 42 Binomial logistic regression
Chapter 43 Data mining and big data