Unrivalled in the way it makes the teaching of statistics through the use of IBM SPSS statistics compelling and accessible to even the most anxious of students, the only statistics textbook you and your students will ever need just got better!

Chapter 1. Why is My Evil Lecturer Forcing Me to Learn Statistics? What the hell am I doing here? I don¿t belong here The Research Process Initial observation: finding something that needs explaining Generating and testing theories and hypotheses Collecting data: measurement Collecting data: research design Analysing data Reporting dataChapter 2. The Spine of Statistiscs What will this chapter tell me? What is the SPINE of statistics? Statistical models Populations and samples P is for parameters E is for estimating parameters S is for standard error I is for (confidence) interval N is for null hypothesis significance testing Reporting significance testsChapter 3. The Phoenix of Statistics Problems with NHST NHST as part of wider problems with science A phoenix from the EMBERS Sense, and how to use it Pre-registering research and open science Effect size Bayesian approaches Reporting effect sizes and Bayes factorsChapter 4. The IBM SPSS Statistics Environment Versions of IBM SPSS Statistics Windows, Mac OS, and Linux Getting started The data editor Entering data into IBM SPSS Statistics Importing data The SPSS viewer Exporting SPSS output The syntax editor Saving files Opening files Extending IBM SPSS StatisticsChapter 5. Exploring Data With Graphs The art of presenting data The SPSS Chart Builder Histograms Boxplots (box-whisker diagrams) Graphing means: bar charts and error bars Line charts Graphing relationships: the scatterplot Editing graphsChapter 6. The Beast of Bias What is bias? Outliers Overview of assumptions Additivity and linearity Normally distributed something or other Homoscedasticity/homogeneity of variance Independence Spotting outliers Spotting normality Spotting linearity and heteroscedasticity/heterogeneity of variance Reducing biasChapter 7. Non-Parametric Models When to use non-parametric tests General procedure of non-parametric tests in SPSS Comparing two independent conditions: the Wilcoxon rank-sum test and Mann-Whitney test Comparing two related conditions: the Wilcoxon signed-rank test Differences between several independent groups: the Kruskal-Wallis test Differences between several related groups: Friedman¿s ANOVAChapter 8. Correlation Modeling relationships Data entry for correlation analysis Bivariate correlation Partial and semi-partial correlation Comparaing correlations Calculating the effect size How to report correlation coefficentsChapter 9. Linear Model (Regression) An introduction to the linear model (regression) Bias linear models? Generalizing the model Sample size and the linear model Fitting linear models: the general procedure Using IBM SPPS Statistics to fit a linear model with one predictor Interpreting a linear model with one predictor Interpreting a linear model with two or more predictors (multiple regression) Using IBM SPSS Statistics to fit a linear model with several predictors Interpreting a linear model with several predictors Robust regression Bayesian regression Reporting linear modelsChapter 10. Comparing Two Means Looking for differences An example: are invisible people mischievous? Categorical predictors in the linear model The t-test Assumptions of the t-test Comparaing two means: general procedure Comparing two independent means using IBM SPSS Statistics Comparing two related means using IBM SPSS Statistics Reporting comparisons between two means Between groups or repeated measures?Chapter 11. Moderation, Mediation and Multicategory Predictors The PROCESS tool Moderation: interactions in the linear model Mediation Categorical predictors in regressionChapter 12. GLM 1: Comparing Several Independent Means Using a linear model to compare several means Assumptions when comparing means Planned contrasts (contrast coding) Post hoc procedures Comparing several means using IBM SPSS Statistics Output from one-way independent ANOVA Robust comparisons of several means Bayesian comparisons of several means Calculating the effect size Reporting results from one-way independent ANOVA 12.15 Smart Alex¿s tasksChapter 13. GLM 2: Comparing Means Adjusted For Other Predictors (Analysis of Covariance) What is ANCOVA? ANCOVA and the general linear model Assumptions and issues in ANCOVA Conducting ANCOVA using IBM SPSS Statistics Interpreting ANCOVA Testing the assumption of homogeneity of regression slopes Robust ANCOVA Bayesian analysis with covariates Calculating the effect size Reporting resultsChapter 14. GLM 3: Factorial Designs Factorial designs Independent factorial designs and the linear model Model assumptions in factorial designs Factorial designs using IBM SPSS Statistics Output from factorial designs Interpreting interaction graphs Robust models of factorial designs Bayesian models of factorial designs Calculating effect sizes Reporting results of two-way ANOVAChapter 15. GLM 4: Repeated-Measures Designs Introduction to repeated-measures designs A grubby example Repeated-measures and the linear model The ANOVA approach to repeated-measures designs The F-statistics for repeated-measures designs Assumptions in repeated-measures designs One-way repeated-measures designsChapter 16. GLM 5: Mixed Designs Mixed designs Assumptions in mixed designs A speed-dating example Mixed designs using IBM SPSS Statistics Output for mixed factorial designs Calculating effect sizes Reporting the results of mixed designesChapter 17. Multivariate Analysis of Variance (MANOVA) Introducing MANOVA Introducing matrices The theory behind MANOVA Practical issues when conducting MANOVA MANOVA using IBM SPSS Statistics Interpreting MANOVA Reporting results from MANOVA Following up MANOVA with discriminant analysis Interpreting discriminant analysis Reporting results from discriminant analysis The final interpretationChapter 18. Exploratory Factor Analysis When to use factor analysis Factors and components Discovering factors An anxious example Factor analysis uisng IBM SPSS Statistics Interpreting factor analysis How to report factor analysis Reliability analysis Reliability analysis using IBM SPSS Statistics Interpreting reliability analysis How to report reliability analysisChapter 19. categorical Outcomes: Chi-Square and Loglinear Analysis Analysing categorical data Associations between two categorical variables Associations between several categorical variables: loglinear analysis Assumptions when analysisng categorical data General procedure for analysing categorical outcomes Doing chi-square uisng IBM SPSS Statistics Interpreting the chi-square test Loglinear analysis using IBM SPSS Statistics Interpreting loglinear analysis Reporting the results of loglinear analysisChapter 20. Categorical Outcomes: Logistic Regression What is logitsic regression? Theory of logistic regression Sources of bias and common problems Binary logistic regression Interpreting logistic regression Reporting logistic regression Testing assumptions: another example Predicting several categories: multinominal logistic regression Reporting multinominal logistic regressionChapter 21. Multilevel Linear Models Hierarchical data Theory of multilevel linear models The multilevel model Some practical issues Multilevel modeling using IBM SPSS Statistics Growth models How to report a multilevel model A message from the octopus of inescapable despairChapter 22. Epilouge