With an exciting new look, new characters to meet, and its unique combination of humour and step-by-step instruction, this award-winning book is the statistics lifesaver for everyone. From initial theory through to regression, factor analysis and multilevel modelling, Andy Field animates statistics and SPSS software with his famously bizarre examples and activities. What's brand new: A radical new design with original illustrations and even more colour A maths diagnostic tool to help students establish what areas they need to revise and improve on. A revamped online resource that uses video, case studies, datasets, testbanks and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills New sections on replication, open science and Bayesian thinking Now fully up to date with latest versions of IBM SPSS Statistics (c). All the online resources above (video, case studies, datasets, testbanks) can be easily integrated into your institution's virtual learning environment or learning management system. This allows you to customize and curate content for use in module preparation, delivery and assessment. Please note that ISBN: 9781526445780 comprises the paperback edition of the Fifth Edition and the student version of IBM SPSS Statistics.
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 Reporting Data Chapter 2: The SPINE of statistics 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, NHST Reporting significance tests Chapter 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 Preregistering research and open science Effect sizes Bayesian approaches Reporting effect sizes and Bayes factors Chapter 4: The IBM SPSS Statistics environment Versions of IBM SPSS Statistics Windows, MacOS 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 Statistics Chapter 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 graphs Chapter 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 Bias Chapter 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 ANOVA Chapter 8: Correlation Modelling relationships Data entry for correlation analysis Bivariate correlation Partial and semi-partial correlation Comparing correlations Calculating the effect size How to report correlation coefficents Chapter 9: The Linear Model (Regression) An Introduction to the linear model (regression) Bias in linear models? Generalizing the model Sample size in regression Fitting linear models: the general procedure Using SPSS Statistics to fit a linear model with one predictor Interpreting a linear model with one predictor The linear model with two of more predictors (multiple regression) Using SPSS Statistics to fit a linear model with several predictors Interpreting a linear model with several predictors Robust regression Bayesian regression Reporting linear models Chapter 10: Comparing two means Looking at differences An example: are invisible people mischievous? Categorical predictors in the linear model The t-test Assumptions of the t-test Comparing two means: general procedure Comparing two independent means using SPSS Statistics Comparing two related means using 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 regression Chapter 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 SPSS Statistics Output from one-way independent ANOVA Robust comparisons of several means Bayesian comparison of several means Calculating the effect size Reporting results from one-way independent ANOVA Chapter 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 SPSS Statistics Interpreting ANCOVA Testing the assumption of homogeneity of regression slopes Robust ANCOVA Bayesian analysis with covariates Calculating the effect size Reporting results Chapter 14: GLM 3: Factorial designs Factorial designs Independent factorial designs and the linear model Model assumptions in factorial designs Factorial designs using SPSS Statistics Output from factorial designs Interpreting interaction graphs Robust models of factorial designs Bayesian models of factorial designs Calculating effect sizes Reporting the results of two-way ANOVA Chapter 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-statistic for repeated-measures designs Assumptions in repeated-measures designs One-way repeated-measures designs using SPSS Output for one-way repeated-measures designs Robust tests of one-way repeated-measures designs Effect sizes for one-way repeated-measures designs Reporting one-way repeated-measures designs A boozy example: a factorial repeated-measures design Factorial repeated-measures designs using SPSS Statistics Interpreting factorial repeated-measures designs Effect Sizes for factorial repeated-measures designs Reporting the results from factorial repeated-measures designs Chapter 16: GLM 5: Mixed designs Mixed designs Assumptions in mixed designs A speed dating example Mixed designs using SPSS Statistics Output for mixed factorial designs Calculating effect sizes Reporting the results of mixed designs Chapter 17: Multivariate analysis of variance (MANOVA) Introducing MANOVA Introducing matrices The theory behind MANOVA MANOVA using SPSS Statistics Interpreting MANOVA Reporting results from MANOVA Following up MANOVA with discriminant analysis Interpreting discriminant analysis Reporting results from discriminant analysis The final interpretation Chapter 18: Exploratory factor analysis When to use factor analysis Factors and Components Discovering factors An anxious example Factor analysis using SPSS statistics Interpreting factor analysis How to report factor analysis Reliability analysis Reliability analysis using SPSS Statistics Interpreting Reliability analysis How to report reliability analysis Chapter 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 analysing categorical data General procedure for analysing categorical outcomes Doing chi-square using SPSS Statistics Interpreting the chi-square test Loglinear analysis using SPSS Statistics Interpreting loglinear analysis Reporting the results of loglinear analysis Chapter 20: Categorical outcomes: logistic regression What is logistic 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: multinomial logistic regression Chapter 21: Multilevel linear models Hierarchical data Theory of multilevel linear models The multilevel model Some practical issues Multilevel modelling using SPSS Statistics Growth models How to report a multilevel model A message from the octopus of inescapable despair Chapter 22: Epilogue